Student ID: 2098288 - Swinburne · useful advice, and encourageme nts. They have been great mentors throughout my PhD journey. Their exceptional professionalism and sincere concern

“BUILDING INFORMATION MODELLING ADOPTION IN FACILITIES MANAGEMENT SECTOR”

Mustafa Abdullah Hilal Hilal Student ID: 2098288

Submitted in total fulfilment of the requirements for Doctor of Philosophy in Civil and

Construction Engineering

Department of Civil and Construction Engineering

Faculty of Science Engineering and Technology

Swinburne University of Technology

[2020]

i

LIST OF PUBLICATIONS AND CONFERENCES

Published journal papers:

1. Hilal, M., Maqsood, T. and Abdekhodaee, A. (2019), "A scientometric analysis of BIM

studies in facilities management", International Journal of Building Pathology and

Adaptation, Vol. 37 No. 2, pp. 122-139.

2. Hilal, M., Maqsood, T. and Abdekhodaee, A. (2019), "A hybrid conceptual model for

BIM in FM", Construction Innovation, Vol. 19 No. 4, pp. 531-549.

Submitted and under review journal papers:

1. HILAL, M., MAQSOOD, T. & ABDEKHODAEE, A. Barriers for Building

Information Modelling adoption in Facilities Management. International Journal of

Building Pathology and Adaptation, submitted on 2-May 2019.

2. HILAL, M., MAQSOOD, T. & ABDEKHODAEE, A. A hybrid conceptual model for

BIM adoption in facilities management. Construction Innovation: Information,

Process, Management, submitted on 7-November 2019.

Published conference papers:

1. HILAL, M. A. & MAQSOOD, T. Toward improving BIM acceptance in facilities

management: A hybrid conceptual model integrating TTF and UTAUT. The Ninth

International Conference on Construction in the 21st Century (CITC-9), Dubai, United

Arab Emirates, 5-7 March 2017, 2017.

2. HILAL, M., ABDEKHODAEE, A. & MAQSOOD, T. 2017. Bibliometric Analysis of

Building Information Modelling (BIM) in the Construction Industry. 22nd International

Conference on Advancement of Construction Management and Real Estate(CRIOCM

2017), 20-23 November 2017, Swinburne University of Technology in Melbourne ,

Australia.

ii

3. HILAL, M., MAQSOOD, T. & ABDEKHODAEE, A. 2018. SCIENTOMETRIC

ANALYSIS OF BUILDING INFORMATION MODELLING (BIM) IN FACILITY

MANAGEMENT (FM). The Tenth International Conference on Construction in the

21st Century (CITC-10), July 2nd-4th, 2018, Colombo, Sri Lanka.

4. HILAL, M., MAQSOOD, T. & ABDEKHODAEE, A. 2019. A Hybrid Conceptual

Model for BIM Adoption in Facilities Management: A Descriptive Analysis for the

Collected Data. 11th International Conference (CITC-11), September 9-11, 2019,

London, UK.

5. HILAL, M., MAQSOOD, T. & ABDEKHODAEE, A. 2019. Impeding Factors of

Building Information Modelling adoption in Facilities Management. The Association of

Researchers in Construction Management (ARCOM), 4th of July, 2019, Melbourne,

Australia.

iii

ABSTRACT

Current studies demonstrate the benefits of building information modelling (BIM) for the

overall project life-cycle; however, adoption is still low in many countries and remains a

significant focus of BIM practice and research. BIM has also been used to streamline the

provision of information for facilities management (FM) practices and systems, but the use of

BIM in the FM phase of the project life-cycle is minimal in comparison to the design and

construction phases.

This PhD research is exploratory and descriptive in nature. It studies the nature of building

information Modelling (BIM) adoption within the facilities management (FM) organizations in

Australia. The study investigates BIM adoption from the perspective of users’ acceptance and

technology tasks fit. The primary research objective is to understand the nature of BIM adoption

and the potential barriers at the individual and organizational level. Then, identifying the key

factors that influence BIM adoption in FM. It explores key BIM adoption processes within

leading FM organizations using a quantitative research approach.

The aim of this research is to assist FM practitioners to better understand, plan and monitor

BIM adoption issues during the adoption and implementation stages. The study includes the

development of a conceptual model for BIM adoption in FM based on integrating the tasks

technology fit (TTF) and the unified theory of acceptance and use of technology (UTAUT).

Then the conceptual model has been examined, and the research hypotheses have been tested

based on the outcome of the research survey and data analysis.

Data collection stage has been implemented through two steps. First, one by one, and face to

face meetings with professionals in BIM-FM have been conducted to explain the nature of the

research, the nature of the questionnaire survey, and whether they were interested in

participating in the survey. Second, the interested professionals were provided an access link to

the online questionnaire website to answer the related questions for the research survey. The

expected outcome of the research was an empirically tested conceptual model that help FM

practitioners to improve their understanding of BIM adoption in FM.

The findings show that besides UTAUT factors (technology perceptions) such as performance

expectancy, TTF factors also have a significant effect on FM adoption for BIM. Thus, when

studying the factors that affect BIM adoption in FM, attention should be paid to the effect of

the best task technology fit, and not be concerned only with technology perceptions based such

iv

as UTAUT, Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT)

theories. Further, the causal relationship between both theories including TTF and technology

perceptions needs more attention. For instance, and in the current study, it has been found there

is a significant relationship between performance expectancy factor and TTF factor. This result

supports the previous research as it emphasized that the integrated models of UTAUT and TTF

provide more explanation on user adoption compared with the individual UTAUT and TTF

models.

v

ACKNOWLEDGEMENTS

First, my appreciation goes to the Iraqi Ministry of Higher Education for granting me a

scholarship for doctoral studies in Australia.

Also, I would also like to express my deepest gratitude to my supervisors, Dr Amir

Abdekhodaee, Associate Prof Tayyab Maqsood and Dr. Michael Fahey for their motivation,

useful advice, and encouragements. They have been great mentors throughout my PhD journey.

Their exceptional professionalism and sincere concern regarding my academic progress is

greatly appreciated.

The support of many peoples and departments, whose directly or indirectly contributed to this

dissertation and specifically the Faculty of Science Engineering and Technology, Swinburne

University of Technology, Australia.

Finally, I express my deepest appreciation to my family for their patience and support they have

given me. Especially to my father, mother, Faten my wife and my wonderful children for their

courage to be a big part of this journey. For my children, Maryam, Omar and Amenah, this PhD

is dedicated especially to all of you. I hope that the experience you get throughout this journey

will make you better people.

vi

STATEMENT OF ORIGINAL AUTHORSHIP

I, Mustafa Abdullah Hilal, declare that this thesis entitled:

“Building Information Modelling Adoption in Facilities Management Sector”

Contains no material which has been accepted for the award of any other degree

or diploma, except where due reference is made in the text of the thesis,

Contains, to the best of my knowledge, no material previously published or

written by another person except where due reference is made in the text of thesis,

Discloses the relative contributions of the respective workers or authors, where

the work is based on a joint research or publications.

Signature:

Name: Mustafa Abdullah Hilal Hilal

Date: 17th of February 2020

Civil and Construction Engineering Department

Faculty of Science, Engineering and Technology

Swinburne University of Technology

Melbourne, Australia

vii

TABLE OF CONTENTS

List of Publications and Conferences ....................................................................................................... i

Abstract .................................................................................................................................................. iii

Acknowledgements .................................................................................................................................. v

Statement of Original Authorship .......................................................................................................... vi

Table of Contents .................................................................................................................................. vii

List of Figures .......................................................................................................................................... x

List of Tables ......................................................................................................................................... xii

List of Abbreviations ............................................................................................................................ xiv

Chapter 1: Introduction .................................................................................................... 1

Introduction .............................................................................................................................................. 1

1.1 Research background ..................................................................................................................... 1

1.2 Rationale for the research .............................................................................................................. 5

1.3 Research objective ......................................................................................................................... 6

1.4 Research scope ............................................................................................................................... 7

1.5 Research method ............................................................................................................................ 7 Conceptual model development ........................................................................................... 8 Quantitative analysis/model assessment .............................................................................. 8 Research discussions and conclusions ................................................................................. 8

1.6 Thesis structure .............................................................................................................................. 9

1.7 The summary................................................................................................................................ 10

Chapter 2: BIM adoption in FM* ................................................................................... 11

Introduction ............................................................................................................................................ 11

2.1 The construction industry ............................................................................................................. 13

2.2 Facilities management .................................................................................................................. 14 FM definitions ................................................................................................................... 14 Facility Management Associations .................................................................................... 17 The range of FM roles and knowledge .............................................................................. 19 The levels of FM ................................................................................................................ 20 Sourcing strategies in facilities management ..................................................................... 21 Facility management practitioners ..................................................................................... 22 Information valuable for FM during project life-cycle ...................................................... 23

2.3 Building information modelling ................................................................................................... 24 Building information modelling (BIM) definitions ........................................................... 24 Building information modelling benefits and usages ........................................................ 25 BIM and level of development (LOD)............................................................................... 26 Building information modelling and nD ............................................................................ 28

viii

BIM rate of adoption.......................................................................................................... 30 Barriers to BIM adoption ................................................................................................... 30 Bibliometric analysis of BIM in the construction industry ................................................ 32 Scientometric analysis of BIM in FM ................................................................................ 41

2.4 Technology acceptance model and related theories ..................................................................... 58

2.5 BIM adoption in AEC/FM ........................................................................................................... 65 Background ........................................................................................................................ 65 Details on BIM adoption in AEC/FM ................................................................................ 71

2.6 Gap in literature ........................................................................................................................... 76

2.7 The summary................................................................................................................................ 76

Chapter 3: The Model Development .............................................................................. 78

3.1 Development of the Conceptual Model ....................................................................................... 78

3.2 Hypothesis development based on the research model ................................................................ 82 UTAUT constructs ............................................................................................................. 82 TTF constructs ................................................................................................................... 86

3.3 The summary................................................................................................................................ 88

Chapter 4: Research Methodology and Design ............................................................. 90

Introduction ............................................................................................................................................ 90

4.1 The meaning of the word research ............................................................................................... 90

4.2 Research paradigms ..................................................................................................................... 91

4.3 Research methodologies and methods ......................................................................................... 93

4.4 Selecting the research process for this research ........................................................................... 94

4.5 Research design and justification ................................................................................................. 94

4.6 Literature review and compilation of knowledge ........................................................................ 98

4.7 The model development ............................................................................................................... 98 Quantitative analysis/model assessment .......................................................................... 100 Questionnaire development ............................................................................................. 100 Sample size ...................................................................................................................... 103 Data analysis .................................................................................................................... 103

4.8 The impeding factors for BIM in FM ........................................................................................ 104

4.9 Ethics .......................................................................................................................................... 105

4.10 The summary.............................................................................................................................. 105

Chapter 5: Data Analysis and Results* ........................................................................ 107

Introduction .......................................................................................................................................... 107

5.1 Non-response bias test ............................................................................................................... 109

5.2 Data cleaning and screening ...................................................................................................... 110 Missing data and imputation ............................................................................................ 110 Suspicious response Patterns ........................................................................................... 112 Normality test of data ...................................................................................................... 112 Outlier .............................................................................................................................. 113

5.3 Common method Bias ................................................................................................................ 114

5.4 Multicollinearity Analysis .......................................................................................................... 114

ix

5.5 Respondents’ features and demographic profiles....................................................................... 116

5.6 Descriptive Statistics .................................................................................................................. 117

5.7 Structural equation modelling (SEM) ........................................................................................ 121 Measurement model ......................................................................................................... 121 Structural model ............................................................................................................... 127

5.8 The summary.............................................................................................................................. 136

Chapter 6: The Impeding Factors of Building Information Modelling adoption in Facilities Management* ....................................................................................................... 137

Introduction .......................................................................................................................................... 137

6.1 BIM adoption barriers in FM ..................................................................................................... 137

6.2 The questionnaire survey ........................................................................................................... 142

6.3 The impeding factors for BIM in FM ........................................................................................ 143

6.4 The results of the analysis .......................................................................................................... 144 General characteristics of respondents............................................................................. 144 Descriptive analysis and Exploratory Factor Analysis of the data .................................. 146

6.5 The summary.............................................................................................................................. 151

Chapter 7: Conclusions .................................................................................................. 153

Introduction .......................................................................................................................................... 153

7.1 The conceptual model development and the research hypotheses ............................................. 154

7.2 The impeding’s factors ............................................................................................................... 158

7.3 Theoretical and practical implications ....................................................................................... 161

7.4 Limitations ................................................................................................................................. 162

7.5 Future research ........................................................................................................................... 163

7.6 The summary.............................................................................................................................. 163

Chapter 8: Appendix ...................................................................................................... 165

Bibliography ......................................................................................................................... 177

x

LIST OF FIGURES

Figure 2.1: Map of Chapter 2- Literature Review .................................................................... 12

Figure 2.2: Facility Management components (Ikediashi, 2014) ............................................. 15

Figure 2.3: Integrated FM in Practice (Kincaid, 1994) ........................................................... 20

Figure 2.4: Qualification of FM practitioners (Hays Facility Management, 2006) ................. 22

Figure 2.5: Project Life-cycle BIM (Image source: http://buildipedia.com/aec-pros/design-news/the-daily-life-of-building-information-Modelling-bim) ................................... 25

Figure 2.6: Building Information Modelling and nD (adopted from bimporn.tumblr.com) .... 28

Figure 2.7: Number of BIM publications in each area of knowledge ...................................... 33

Figure 2.8: Countries where BIM research has been published ............................................... 34

Figure 2.9: Top Journals that have published research on BIM ............................................... 35

Figure 2.10: Impact Factor of Automation in Construction ..................................................... 35

Figure 2.11: Citation of Automation in Construction .............................................................. 36

Figure 2.12: Top 15 authors who published in BIM ................................................................ 36

Figure 2.13: Publication year ................................................................................................... 37

Figure 2.14: Number of BIM publications in each area of knowledge .................................... 38

Figure 2.15: Journals that have published research on BIM .................................................... 38

Figure 2.16: Top 15 authors who published on BIM ............................................................... 39

Figure 2.17: Countries where BIM research has been published ............................................. 40

Figure 2.18: Publication year ................................................................................................... 40

Figure 2.19: Classification of the BIM 450 articles according to project’s phases ................. 43

Figure 2.20: Year of BIM-FM research publications ............................................................... 47

Figure 2.21: Main research areas in BIM-FM field and their relatedness ............................... 48

Figure 2.22: Main research areas after removing the similarities ............................................ 48

Figure 2.23: Network of prominent sources for publications in BIM-FM ............................... 50

Figure 2.24: Collaboration network of organizations in BIM-FM research ............................ 53

Figure 2.25: Zoomed cluster .................................................................................................... 54

Figure 2.26: Collaboration network of countries in BIM-FM research ................................... 55

Figure 2.27: Zoomed cluster .................................................................................................... 56

Figure 2.28: Technology Acceptance Model (TAM) (Davis Jr, 1986) .................................... 58

Figure 2.29: Technology Acceptance Model 2 (TAM2) (Venkatesh and Davis, 2000) .......... 59

Figure 2.30: Technology Acceptance Model 3 (TAM3) (Venkatesh and Bala, 2008) ............ 60

Figure 2.31: UTAUT model by Venkatesh et al. (2003) ......................................................... 61

xi

Figure 2.32: Task Technology Fit Model (TTF) by Goodhue and Thompson (1995) ............. 61

Figure 3.1: Conceptualization of the Model ............................................................................. 81

Figure 3.2: Conceptualization of the Model ............................................................................. 88

Figure 4.1: Research onion (Saunders et al., 2007). ................................................................ 91

Figure 4.2: Research stages represented by layers of a research onion (Saunders et al., 2007). ......................................................................................................................... 94

Figure 4.3: The research phases. .............................................................................................. 97

Figure 4.4: Conceptualization of the model. ............................................................................ 99

Figure 5.1: Path model 1 (PLS Algorithm) including outer loading of each item, path coefficients and R2 values for endogenous variables. .............................................. 129

Figure 5.2: Path model 1 (Bootstrapping). ............................................................................. 130

https://liveswinburneeduau-my.sharepoint.com/personal/mhilal_swin_edu_au/Documents/Thesis%20submission%20to%20HDR%20-%20The%20rquired%20documents/20200212-%20Corrections%20inprogress/2020-PhDThesis-MustafaHilal%20-FINAL.docx#_Toc32829485

xii

LIST OF TABLES

Table 1.1: BIM use during the project’s life-cycle phases (Eadie et al., 2013) ......................... 3

Table 2.1: BIM use during the construction stages (adopted from (Eadie et al., 2013)) ......... 14

Table 2.2: Valuable Information for FM for Each Phase (Alvarez-Romero, 2014) ............... 23

Table 2.3: BIM Usage for the Project Life-cycle ..................................................................... 26

Table 2.4: BIM and LOD ......................................................................................................... 26

Table 2.5: Main research areas (co-occurrence of keywords analysis) .................................... 49

Table 2.6: Top BIM-FM sources outlets .................................................................................. 51

Table 2.7: Collaboration network of authors in BIM-FM research ......................................... 52

Table 2.8: Technology acceptance theories in different studies .............................................. 62

Table 2.9: Summary of related studies in the domain .............................................................. 66

Table 3.1: Factors definition of the proposed model ............................................................... 81

Table 3.2: Research Hypotheses .............................................................................................. 87

Table 4.1: Fundamental beliefs of research paradigms in social sciences (Rahmani, 2016) ... 92

Table 4.2: Comparison between quantitative and qualitative approach (Firestone, 1987). ..... 93

Table 4.3 Panel judgment feedback ....................................................................................... 101

Table 5.1: Results for Non-response bias ............................................................................... 109

Table 5.2: Result of Normality Test ....................................................................................... 113

Table 5.3: Result of outlier test .............................................................................................. 114

Table 5.4: Result of CMV ...................................................................................................... 114

Table 5.5: Collinearity Assessment based on VIF ................................................................. 115

Table 5.6: Multicollinearity test based on correlation coefficients ........................................ 115

Table 5.7: Responses rate ....................................................................................................... 116

Table 5.8: Frequency distribution of demographic characteristics ........................................ 117

Table 5.9: Descriptive statistics related to user adoption of BIM .......................................... 118

Table 5.10: Descriptive statistics related to Performance Expectancy .................................. 118

Table 5.11: Descriptive statistics related to Effort Expectancy ............................................. 119

Table 5.12: Descriptive statistics related to social influence ................................................. 119

Table 5.13: Descriptive statistics related to facilitating conditions ....................................... 119

Table 5.14: Descriptive statistics related to TTF ................................................................... 120

Table 5.15: Descriptive statistics related to technology characteristics ................................. 120

Table 5.16: Descriptive statistics related to task characteristics ............................................ 120

Table 5.17: The result of convergent validity ........................................................................ 123

xiii

Table 5.18: Correlation of latent variables and discriminant validity (Fornell-Larcker) ....... 125

Table 5.19: Correlation of latent constructs and discriminant validity (HTMT method) ...... 125

Table 5.20: Loading and cross loading of constructs for discriminant validity assessment .. 126

Table 5.21: List of hypotheses and relative paths .................................................................. 127

Table 5.22: List of hypotheses and relative paths .................................................................. 131

Table 5.23: Results of the coefficient of determination (R2) ................................................. 132

Table 5.24: Results of effect size f² for three endogenous variables ...................................... 133

Table 5.25: Results of predictive relevance (Q 2) .................................................................. 134

Table 5.26: Test of Indirect Effects using Bootstrapping ...................................................... 134

Table 5.27: Total (Direct and Indirect) effects variables on user adoption of BIM ............... 135

Table 5.28: List of Hypotheses and Relative Paths ................................................................ 135

Table 6.1: Technology and Process Barriers .......................................................................... 143

Table 6.2: Organizational Barriers ......................................................................................... 144

Table 6.3: The general characteristics of the respondents ..................................................... 145

Table 6.4: Descriptive Statistics of the impeding factors of BIM in FM ............................... 147

Table 6.5: Correlation Matrix ................................................................................................. 149

Table 6.6: Rotated component matrix .................................................................................... 149

Table 6.7: The Total Variance Explained .............................................................................. 150

xiv

LIST OF ABBREVIATIONS

AECO architecture, engineering, construction, and owner/operated

AVE average variance extracted

BBB bridging BIM and building

BEA bureau of economic analysis

BIFM British institute of facilities management

BIM building information modelling

CE cost estimate

CFA confirmatory factor analysis

CMV common method variance

CPS cyber throughout the physical systems

CR composite reliability

CRM customer relationship management

DI diffusion of innovations

EFA expletory factor analysis

EIR employer’s information requirements

EM expectation maximization

FB familiarity with bank

FC facilitating condition

FM facility management

FMA facility management association

GDP gross domestic product

ICT information communications technology

IDT innovation diffusion theory

IFC industry foundation classes

xv

IFMA international facility management association

IT information technology

KMO Kaiser-Meyer-Olkin

LOD level of development

LSA latent semantic analysis

MCAR missing completely at random

MI multiple imputation

NBIMS national building information modelling standards

NIST national institute of standards and technology

PEU perceived ease of use

PIS project information statement

PLS partial least squares

PPP public private partnership

PU perceived usefulness

QTO quantity take-off

SA structural assurance

SEM structural equation modelling

TAC task characteristics

TAM technology acceptance model

TFM total facilities management

TPB theory of planned behavior

TRA theory of reasoned action

TTF tasks technology fit

UTAUT unified theory of acceptance and use of technology

VIF variance inflation factors

WBS work breakdown structure

xvi

WOS web of science

Chapter 1: Introduction 1

Chapter 1: Introduction

Introduction

This PhD research studies the nature of building information Modelling (BIM)

adoption within the facilities management organizations in Australia. It is exploratory

and descriptive in nature. The study investigates BIM adoption from the perspective of

users’ acceptance and tasks technology fit. The primary research objective is to

understand the nature of BIM adoption at the individual and organizational level by

identifying key factors influencing BIM adoption in facility management (FM). It

explores key BIM adoption processes within leading FM organizations using a

quantitative research approach. Also, this research applies the light on the main barriers

that influence BIM adoption in FM. The aim of the research is to assist FM organizations

to better understand, plan and monitor BIM adoption issues during adoption process.

The expected outcome of the research is an empirically tested conceptual model that

may help FM practitioners to improve their understanding of BIM adoption in FM.

In this chapter, an overview of this thesis is presented by describing the main themes

related to the research background, objectives, scope and methods. Section 1.1

introduces the research background and the demand for undertaking this research.

Section 1.2 presents the rationale of the research. Section 1.3 discusses the research

objectives. Section 1.4 deals with the scope of the study. In section 1.5, the research

method along with analytical and statistical techniques that adopted in the study are

discussed. Section 1.6 details the structure of the thesis. Finally, Section 1.7 presents a

summary of this chapter.

1.1 Research background

Practitioners have proved facility management as an information based profession.

It consists of a wide range of activities and requires an enormous amount of information.

Specifically, it requires a large quantity of readily available and relevant information for

various stakeholders. Therefore, efficient access and provision of information are

2 Chapter 1: Introduction

needed. According to Mendez (2006), construction project delivery systems have

enormous communication gaps, especially between the constructors and

operators/owners. A traditional hand-over of a project by a constructor to an

operator/owner most often leads to losing a significant part of information and provides

incompatible information for the FM systems later on (Lee et al., 2012). This happens

because most of the current systems and practices for the FM such as 2D drawings and

manual discrete processes are still obsolete or lack information. Consequently, the

operator/owner must spend more time and money to distil and re-populate the required

FM information.

In terms of time and cost, the construction industry has suffered for decades from

wastages and inefficiencies that lead to large cost and time overrun. The majority of

these issues occur during the operation and management of the facility. Since the FM

phase is the longest phase in the project life-cycle, it presents the greatest deal of the

facility life-cycle cost. According to Lewis et al. (2010), FM represents about 60 to 85

% of the total life-cycle cost, whereas both the design and construction phases account

for about 5 to 10 %. In the same context, according to (Teicholz, 2004), less than 15 %

of the total cost of ownership is spent on design and construction while over 75 % is

spent on the operation and maintenance phase. Thus, enhancing and optimizing the

management and operation of the facility is a crucial issue.

Based on a survey and interview responses gathered by National Institute of Standards

and Technology (NIST), the cost of interoperability inefficiencies in the U.S

construction sector reached $15.8 billion in 2004. The study showed that the operators

and owners shared approximately two-thirds of the estimated costs. These inefficiencies

include the re-creation and re-entry of information and task repetition (GCR, 2004).

Building Information Modelling (BIM) is beneficial to streamlining the provision of the

information for the facility management practices and systems (Alvarez-Romero, 2014).

Becerik-Gerber et al. (2012) showed that using BIM has extended to benefit the

operation and maintenance of the facilities in these aspects: locating building

components, facilitating real-time data access, visualization and marketing, checking

maintainability, space management, planning and feasibility studies for noncapital


construction, emergency management, controlling and monitoring energy, personnel

training and development. Teicholz (2013) realized that the key benefit of using BIM in

FM is that the data can be captured from BIM with no re-entry of the data again. These

data include space information, equipment types, zones, systems, finishes, etc. A study

by Eadie et al. (2013) showed an interesting finding regarding the financial ranking of

BIM benefits of different stakeholders involved in the construction project life-cycle.

The results were: rank 1 for Clients, rank 2 for Facilities Managers, rank 3 for Software

Vendors, rank 4 for Principal Contractors, rank 5 for Building Users/Occupants, rank 6

for Consultants, rank 7 for Specialist Contractors and finally, rank 8 for Suppliers.

However, and despite these benefits, BIM adoption is still relatively low in many

countries (Xu et al., 2014).

Eadie et al. (2013) conducted an extensive survey on how often BIM is used in each of

the project’s life-cycle phases. The results were as shown in Table 1.1 below.

Table 1.1: BIM use during the project’s life-cycle phases (Eadie et al., 2013)

Project Phase Usage

Feasibility 26.92 %

Design 54.88 %

Preconstruction 51.90 %

Construction 34.67 %

Operation & management 8.82 %

Hence, the adoption of BIM remains a significant concern of BIM practice and research

(Lee et al., 2015). It has been proven that technology adoption results from user

acceptance of using that technology (Ammenwerth et al., 2006). Specifically, the

technology acceptance theories such as TAM, Unified Theory of User Acceptance and

Use of Technology (UTAUT), IDT, Theory of Planned Behavior (TPB) and others, have

ability to model how users come to adopt and use a new technology. Several research


have been carried out to measure the user adoption and usage of new technologies in

different areas such as e-mail systems, word processing, personal computing,

spreadsheet software, e-training, etc. Some of the studies have extended the constructs

of those acceptance theories to enhance our understanding of the usage and adoption of

technology, and to be compatible with different contexts including the construction

industry (Cao et al., 2014, Davies and Harty, 2013, Son et al., 2012).

Accordingly, the application of these theories can be applied whenever there is a new

technology, which could be adopted by the users. For example, Lee and Yu (2013)

identified the main factors that affect the acceptance of BIM in South Korean

construction organizations . Their study was based on extension of the TAM constructs

to analyse the effect of extrinsic and intrinsic motivation factors on individual and

organizational acceptance of BIM. Davies and Harty (2013) developed scales to

measure beliefs related to BIM implementation by using a questionnaire survey

targeting employees of large construction organizations in the UK. Their conceptual

model was based on UTAUT. After a complex statistical analysis, the scales and

constructs revealed acceptable measurement properties.

Based on the institutional theory, Cao et al. (2014) examined how three types of

isomorphic pressures, including coercive, normative and mimetic pressure, impact BIM

implementation activities in the construction projects. Xu et al. (2014) proposed an

extensive model based on TAM and IDT to examine the key factors that affect the

implementation of BIM in China. Son et al. (2015) empirically examined the technical,

individual, organizational and social factors affecting the adoption of BIM in Korean

design organizations. The finding highly supported the modified TAM in predicting the

intention of architects’ adoption of BIM and provided an insight into the role

management that controls a successful adoption of BIM among the Korean design firms.

Lee et al. (2012) presented conceptual acceptance model for BIM in FM based on TAM2

and TPB. However, this conceptual acceptance model has not been checked for validity

and reliability. Thus, the authors suggested a completion of these gaps in the future (Lee

et al., 2012). Overall, the literature shows a lack of BIM usage, specifically in the FM


field, leading to the following question: why with the proven benefits of BIM, the

adoption is still only minimal?

The detailed related literature review, research problem , rationale of the developed

model, are comprehensively discussed in the author’s papers entitled ; “A scientometric

analysis of BIM studies in facilities management” (Hilal et al., 2019k) , “A hybrid

conceptual model for BIM in FM” (Hilal et al., 2019a), “Toward improving BIM

acceptance in facilities management: A hybrid conceptual model integrating TTF and

UTAUT” (HILAL & MAQSOOD, 2017), “Bibliometric Analysis of Building

Information Modelling (BIM) in the Construction Industry” (Hilal et al., 2017), “

SCIENTOMETRIC ANALYSIS OF BUILDING INFORMATION MODELLING

(BIM) IN FACILITY MANAGEMENT (FM)” (Hilal et al., 2018), and “ A Hybrid

Conceptual Model for BIM Adoption in Facilities Management: A Descriptive Analysis

for the Collected Data” (Hilal et al., 2019), included in Chapter 2 and Chapter 3.

1.2 Rationale for the research

Despite the above-mentioned contributions to the construction industry, the field

of technology adoption research, and specifically BIM adoption, is considered in its

infancy within the FM context in Australia. More research targeting BIM adoption

within FM context is required.

In the FM field, there is still a great deal of BIM adoption issues, which remain vague,

specifically from social aspects individually and organizationally. In addition, the

concept of successful BIM adoption leads to an improved organizational performance

and it should be explored to provide empirical proof to aid such an assumption. This

research aims to bridge the above knowledge gap by introducing new concepts of BIM

adoption in FM using the integration of the UTAUT and TTF. Thus, it is essential to

answer the following question; “why does the integration of variables from UTAUT and

TTF help to explain the BIM adoption in FM?”. First, the UTAUT model identifies

variables that impact technology adoption. These variables are facilitating conditions,

effort expectancy, performance expectancy and social influence, which have a direct

and indirect influence on behavioral intention and use behavior to adopt the technology


(Venkatesh et al., 2003). Also, the successful integration of the UTAUT and TTF in IT

field such as the studies conducted by (Afshan and Sharif, 2016, Faria, 2013, Oliveira

et al., 2014, Pai and Tu, 2011, Park et al., 2015, Tai and Ku, 2014, Vongjaturapat et al.,

2015, Zhou et al., 2010), encourages to ask the following question; “what is the

difference between BIM adoption (as a new technology/process) in FM and the adoption

of the new technology in the IT sector?”. The need for including the TTF is based on

the following inquiry; “why does user come to accept and use new technology if this

technology does not fit the job task requirements? “. This new concept has not been

explored in the field of FM yet. Thus, from this research outcome, it is expected to

consolidate the perceptions of BIM adoption in FM so it helps the related stakeholders

to gain the benefits of implementing BIM in their jobs.

Overall, the primary research question addressed in this study is:

How to push FM practitioners for widespread and informed BIM adoption in their

practices ? or in other word, how to consolidate the perceptions of BIM adoption in FM

so it helps stakeholders benefit from implementing BIM ?

Based on these considerations, four research questions have been formulated in this

research:

RQ1. What are the key factors that influence BIM adoption in the FM context?

RQ2. What are the relationships among UTAUT and TTF constructs that

constitute the underlying BIM adoption in FM context?

RQ3. Does the integration of UTAUT and TTF lead to predicting the adoption of

BIM in FM?

RQ4. What are influential levels of the key barriers for BIM in FM?

1.3 Research objective

Based on the knowledge gaps and research demands identified in the previous

section, this research is being conducted with the objectives below:


• to investigate and identify the key factors that influence the widespread adoption

of BIM in the FM which prepare the foundation for the conceptual model,

• to develop and validate the conceptual model for BIM adoption in FM within

Australian context,

• to examine the relationships among the model’s constructs and predict the level

of BIM adoption in FM.

• to investigate the influential level of the barriers’ factors for BIM in FM,

1.4 Research scope

The research is conducted within the following scope:

• the research is limited to the context of FM organization and firms in Australia,

• the research approach is cross-sectional rather than longitudinal,

• the research focuses on the factors that have been supposed to stimulate the

adoption process and identified the impeding factors within FM sector only.

1.5 Research method

The nature of this research is considered relevant to the social science field of

research. The current study has adopted a quantitative research approach because the

nature of the study dealt with the model-conceptualizing, testing of the hypotheses, and

finding the relation between the model’s constructs, which more suits the quantitative

research approach. Unlike qualitative method, quantitative method outcomes can be

generalized using the statistical analysis tests. This is what has been done in this

research. On the other word, quantitative method considers the advantage of using

statistical methods in terms of generalisability that can benefit this study. Our primary

research methodology was to first review the literature to determine:

• how users come to accept new technologies (technology acceptance theories);

• what leads to adoption of BIM in the construction industry and in FM; and

• how to identify gaps as the starting point for developing a conceptual

framework for greater adoption of BIM in FM.


Using the results from the literature review, the researcher then formulated conceptual

framework for BIM adoption in FM. A quantitative survey has been performed to

evaluate and refine the relationships and structure in the conceptual model and to

determine how well the model represents and describes them. After this stage, a SEM

analysis has been conducted to further approve the validity of the conceptual model.

Then, the results from the research were discussed and the conclusions were drawn.

Last, future research strategies were proposed to develop and extend the findings of the

current research. Following are the key research stages summarization:

Conceptual model development

The literature review was the key approach to formulate the research questions

based on the research gap. To answer the research questions, a conceptual model has

been developed based on the extensive related literature review. The developed model

was the foundation in establishing a set of hypotheses, which represent the relationships

of the model factors. The detailed model, including related literature review, rationale

of the model, and findings, is comprehensively discussed in the authors’ paper entitled

“A hybrid conceptual model for BIM in FM” (Hilal et al., 2019a), included in Chapter

3.

Quantitative analysis/model assessment

The purpose of the quantitative analysis was to empirically assess and refine the

developed model by conducting quantitative research method, which used a

questionnaire survey targeting the FM organizations. The questionnaire has been

developed based on the defined factors and items that encapsulated the conceptual

model. The data obtained from the FM organizations and practitioner has been used to

conduct a set of statistical analyses such as Exploratory Factor Analysis (EFA) and

Structural Equation Modelling (SEM) to evaluate the model and assess the significance

of the relationships among the model components, which led to further refinement.

Research discussions and conclusions

The results from the research have been discussed and the conclusions have been presented. It

summarized the main research outcomes, contributions and the implications for FM. Also, it

came with the limitations of the research and presented future research recommendations.


1.6 Thesis structure

This thesis consists of seven chapters as follows:

Chapter 1: introduces an outline for the whole thesis starting from the research

background and knowledge gap. Then, the research objectives and scope are presented.

After that, an overview of the research methods is presented. Finally, an outline of the

thesis structure is given.

Chapter 2: consists of an extensive literature review that relates to the technology

adoption theories in various fields and focusing on the construction industry and facility

management. In addition, the literature introduces the application of BIM and how it can

be spread to the whole project life-cycle including the facility management and

operation phase. This chapter also explores gaps that still exist in the body of knowledge

related to the aforementioned area.

Chapter 3: presents the proposed conceptual model based on the review of the literature

and the research gaps that lead to the research questions. This is followed by the

formulation of research hypotheses that relate to the causal relationships between the

model components.

Chapter 4: explains the research method used in this study. Specifically, the quantitative

analysis approach.

Chapter 5: presents the results of the analysis performed based on the collected data from

the questionnaire survey using different statistical analysis such as EFA and SEM to

validate the research model and test the research hypothesis.

Chapter 6: presents the impeding factors of building information modelling adoption in

facilities management based on the survey results.

Chapter 7: summarizes the main research outcomes, contributions and the implications

for FM. It also comes with the limitations of the research and presents future research

recommendations.


1.7 The summary

This chapter introduces this thesis “Building Information Modelling Adoption in

facilities management Sector”. The chapter briefly describes the main themes that relate

to the whole thesis. Section 1.1 introduces the research background and the demand for

undertaking this research. Section 1.2 presents the rationale of doing this research with

justifications. Section 1.3 discusses the research objectives. Section 1.4 presents the

scope of the study while section 1.5 shows the research methods and the reasons of

adopting quantitative research method and illustrates analytical and statistical

techniques adopted in the study such as EFA and SEM. Section 1.6 details the structure

of the thesis. Finally, Section 1.7 presents a summary of this chapter.

Chapter 2: BIM adoption in FM* 11

Chapter 2: BIM adoption in FM*

Introduction

This chapter sheds light on four main themes. First, the chapter describes the

construction industry characteristics. Second, it presents the facility management

definitions, concepts, components, and development. Then it discusses the BIM as a

new sophisticated approach in the construction industry and how it can be adopted. This

is followed by presenting the current technology acceptance theories and how they can

measure the adoption of new technology in certain field (BIM in FM in our case). After

that, an extensive review of relevant studies is presented to find the knowledge gaps in

the domain and to formulate the research questions. The literature shows that even with

great benefits that can be provided by BIM to FM, the adoption is still very slow. In

addition, there is a lack of studies exploring the BIM adoption factors in FM in

quantitative approach. Based on this gap in literature, the conceptual model of this study

is proposed in Chapter 3 to answer the research questions posed in Chapter One. The

model integrates UTAUT and TTF to measure the key factors that influence the adoption

of BIM in FM. In addition, the justifications of this new trend of integration are

discussed. Figure 2.1 represents a concept map of Chapter 2 that help to guide the

readers.

*Some of content given in this chapter are based on the material published in:

Paper 1: HILAL, M., ABDEKHODAEE, A. & MAQSOOD, T. 2017. Bibliometric Analysis of Building Information

Modelling (BIM) in the Construction Industry. 22nd International Conference on Advancement of Construction Management and Real Estate(CRIOCM 2017), 20-23 November 2017, Swinburne University of Technology in Melbourne , Australia.

Paper 2 : HILAL, M., MAQSOOD, T. & ABDEKHODAEE, A. 2019. A scientometric analysis of BIM studies in facilities management. International Journal of Building Pathology and Adaptation, 37, 122-139.

12 Chapter 2: BIM adoption in FM*

Figure 2.1: Map of Chapter 2- Literature Review

Construction Industry

Building Information

Modelling (BIM)

Facilities Management

Technology Acceptance

Theories

FM definitions

Facility Management Associations

The range of FM roles and knowledge

The levels of FM

Sourcing Strategies in Facilities Management

Facility Management practitioners

Information Valuable for FM during Project Life

BIM Definitions

BIM benefits and Usages

BIM and Level of Development (LOD)

Bibliometric Analysis of BIM in the Construction

BIM and nD Modelling

BIM Rate of Adoption

Barriers to BIM Adoption

Technology Acceptance Model

Unified Theory of User Acceptance / Use of Technology

Technology Task Fit

BIM Adoption in AEC/FM

Gap in Literature


2.1 The construction industry

The construction industry has distinct and unique characteristics among many

other industries. It involves different stockholders at different phases (feasibility, design,

construction, and operation and maintenance), that make it complex and fragmented in

many aspects. Also, it is exposed to a high level of uncertainty and risks compared to

many other industries.

According to Rogers (2013), the construction industry produces the built environment.

The built environment is infrastructure, amenities and physical space in which most

human existence and activities happens. It includes infrastructures, such as rail, sewage,

roads, electrical and water grids that support the use of hospitals, housing, schools,

shops, and other buildings. More recent definitions include more perspectives such as

carbon emission control and waste management.

The construction industry forms a significant percentage of the global and national

economies. For instance, according to the Australian Bureau of Statistics, the

construction industry is the fourth largest contributor to Gross Domestic Product (GDP)

in the Australian economy and has a major contribution to economic growth. It

accounted for 6.8 % of GDP between 2008-2009. In addition, according to the Bureau

of Economic Analysis (BEA) in the U.S, the economy in the U.S has changed

considerably. However, construction industry has maintained its importance. For

example, in the first quarter of 2016, construction was the largest contributor to the U.S.

economy’s growth with a contribution of 1.1 %. Construction industry contributed 0.36

% to the inflation-adjusted, or real, GDP growth. The industry’s growth in real value

added increased to 9.0 %, after increasing to 7.6 % in the previous quarter. Enhancing

this industry is crucial in terms of global and national economic development. This

requires an update of the current traditional practices in the industry that cause low

productivity, time overrun and lack of communication among project stockholders.

Research has revealed that the construction industry suffers from the low adoption of

new Information Communications Technology (ICT) compared to other industries

(Love et al., 2004). However, there is a great difference among the project phases in

term of that adoption. For example, the design phase is in the top of the pyramid in


adopting new ICT, while the construction phase comes after that, and the operation and

maintenance phase is at the bottom of the list. Eadie et al. (2013) studied how often BIM

(as a new innovation) is used in each of projects life-cycle phase through an extensive

survey. The results are shown in Table 2.1 below.

Table 2.1: BIM use during the construction stages (adopted from (Eadie et al., 2013))

Construction

Project Phase

Feasibility Design Preconstruction Construction Operation and

Management

Usage 26.92 % 54.88 % 51.90 % 34.67 % 8.82 %

This current research focuses on facilities management. Hence, more details on facility

management are presented in following section.

2.2 Facilities management

FM definitions

Until now, there is no clear and specific definition for FM. That is why many

definitions define FM from different perspectives. International Facility Management

Association (IFMA) in 2014 defined FM as a “Profession that encompasses multiple

disciplines to ensure functionality of the built environment by integrating people, place,

process and technology”. In addition, FM has been described as “a hybrid management

discipline that combines people, property and process management expertise to provide

vital services in support of the organization” (Shiem-Shin Then, 1999). Another

definition for FM is “operating a group of assets over the whole technical life-cycle

guaranteeing a suitable return and ensuring defined service and security standards”

(Schneider et al., 2006). (Barrett and Baldry, 2003) defined FM as “An integrated

approach to maintaining, improving and adapting the buildings of an organization in

order to create an environment that strongly supports the primary objectives of that

organization.”

According to Ikediashi (2014), most of these definitions related to the fact that FM is

about integrating processes, people, and places as illustrated in Figure 2.2.


Figure 2.2: Facility Management components (Ikediashi, 2014)

Generally, the operation and maintenance phase in building projects is considered the

longest phase in the building life-cycle. Also, it shares about 60-85 % of the total life-

cycle cost, whereas both the design and construction phases account for about 5-10 %

(Lewis et al., 2010). In the same context, Teicholz (2004) stated that less than 15 % of

the total cost of ownership is spent on design and construction while over 75 % is spent

on operation and maintenance phase.

FM department plays a supportive role in achieving the design intent (objectives) for

the physical space. Buildings that are more complex need to introduce strategic and

tactical long term and short terms FM functions. In addition, other functions such as the

management of information technology and human resources are needed (Alvarez-

Romero, 2014).


FM departments have evolved over the years of practice. They vary from one

organization to another. The reason is that they are developed to respond to each

particular organization needs. Barrett and Baldry (2009) proposed the following

classification regarding the structure of the FM process:

• office Manager: in this model, FM is not the main function within the

organization. Instead, a person manages the FM tasks as a part of his general

duties.

• single Site: Suitable for organizations located at one site only and are large

enough to have a separate FM department.

• localized Site: Applicable to organizations that own buildings on more than one

site, but within the same area.

• multiple Sites: Suitable for large organizations that spread across several

locations but within the same country.

• international Sites: Suitable for large international organizations that operate in

different countries and deal with various laws and languages.

Alvarez-Romero (2014) argued that besides these models that focus on the size and

location of the buildings, there is also a classification of the FM department organization

in terms of the type of facility such as industrial, education, health, residential, etc., or

by the nature of the organizations such as private sector or public sector.

On the other hand, and more specific, it is important to shed the light on the data format

that support FM systems and software like CAFM, CMMS, etc. The Construction

Operations Building Information Exchange (COBIE) project was established 2006. The

aim of COBIE is to identify the information exchange required by facility operators,

maintainers and asset managers of data available during the design and construction of

the facility lifecycle. COBIE is an industry foundation classes (IFC) data format that

supporting direct software information exchange and is a spreadsheet which can be used

to pick-up COBIE data for both capital projects and small renovation. That means

COBIE data can be captured during design and construction phases by entering data as

the same are created (East and Brodt, 2007).


Actually, FM activities need more specific data exchange tools. In this regard, Yu et al.

(2000) developed FM classes (FMC) that refers to a collection of object class definitions

for representing the information used in carrying out FM activities, and for supporting

the sharing and exchange of FM information among FM applications within an

integrated environment.

FMC is strongly linked to the IFC, and they reference IFC components directly where

possible and extend IFC components in areas where the IFC are unable to support FM

activities. Also, it is recommended to be implemented in synchronism with IFC to

support data exchange among FM applications and between AEC and FM systems.

Recently, the emergence of BIM has played a significant role in storing, managing and

providing of the facility information and data in a way that streamlines the handover of

the project to the owner/operator. This potential benefits could be explored more and

more to update the traditional FM processes.

Facility Management Associations

There are many facility management organizations and professional associations with

strategic programs designed to support the standing of the facility management industry

in the world. Below are the most famous FM organizations:

British Institute of Facilities Management

The British Institute of Facilities Management (BIFM) is a professional body for

facilities management (FM). Founded in 1993, it promotes excellence in facilities

management to benefit practitioners, economy, and society. Supporting and

representing over 17,000 members around the world, including individual FM

professionals and organizations through qualifications and training (BIFM, 2017).

International Facility Management Association

Founded in 1980, IFMA is the world’s largest and most widely recognized international

association for facility management professionals, supporting 24,000 members in 104

countries. IFMA manages over 78 billion square feet of property and annually purchases

over trillion billion in products and services. It produces World Workplace, the world’s

most extensive series of facility management conferences and expositions, and Facility


Fusion, a more intimate gathering of FM professionals with powerful networking and a

fusion of education, leadership training, best practices, and an exposition. Also, it

conducts research that strengthens the knowledge and skills of the FM professionals

while advancing the FM profession while providing a wide range of educational courses

from entry-level programs to those for highly experienced facility managers. It produces

industry-leading publications, including magazines, newsletters, and blogs.

European Facility Management Network

EuroFM is a European FM platform organization that brings educators, researchers, and

practitioners in the field of facility management together. EuroFM, as the FM network

association, has its members in 23 countries in Europe from research institutes to

universities, service providers, and national FM related associations. The aim is to bring

forward the FM profession and to come to a better mutual understanding by learning

and sharing FM knowledge. EuroFM is a member association. As an organization, it

harbours different types of membership. These vary within the three pillars of EuroFM,

namely: Research, Education, Practice, and Corporates (EuroFM, 2017).

Global Facility Management Association

Global FM (Global Facility Management Association) is a worldwide federation of

member-centric organizations committed to providing leadership in the facility

management profession. As a single, united, entity promoting facility management,

Global FM is a conduit for furthering the knowledge and understanding of facility

management and sharing best practices, resulting in added value to the individual

members of each member organization. Global FM mission promotes the strategic value

and progress of the facilities management profession by leveraging the Global FM

member associations’ strengths, knowledge, and experience. Its objectives can be

summarized as follows (Global FM, 2017):

Supporting countries that wish to form FM related organizations,

where one is not yet established

Encouraging greater collaboration between FM-communities


Recognizing excellence in FM

Facility Management Australia

The Facility Management Association (FMA) is the peak national industry body for

facilities management, representing and supporting professionals and organizations

responsible for the operational management of Australia’s built environments.

Established in 1988, the primary focus of the FMA is to ensure the needs of

professionals and organizations working and dealing with facilities management are

understood and considered in government and business policy formulation and decision-

making. FMA provides a range of services to members, including advocacy and industry

standards development, research, networking and information based events and

seminars, education and professional development opportunities, and support for special

interest groups (FMA, 2017).

The range of FM roles and knowledge

According to Kincaid (1994), FM involves not only the practice of managing services

in an organization. To make it work efficiently, it should integrate both the knowledge

of the facility and the knowledge of the management. Figure 2.3. shows the

aforementioned concept in details (Kincaid, 1994):


Figure 2.3: Integrated FM in Practice (Kincaid, 1994)

The levels of FM

FM covers a wide range of activities including the operation and maintenance of the

facilities, maintaining records and equipment lists of facilities, and their maintenance

timeline at operational level. The whole FM process operates at three levels. The first is

the strategic level, where main planning decisions are considered. The second is the

tactical level, where short term plans takes place, and the last is the operational level,

which deals with day-to-day routine tasks (Alvarez-Romero, 2014).

According to Price (2003), FM industry can be categorized into three types: facility

managers, specialist consultants and service providers. Facility managers are in charge


of particular facilities either for several organizations or one organization and function

mostly at a strategic level. Specialist consultants function largely at a tactical level and

in charge of providing targeted expertise in different areas such as structural,

architectural, services fit-out, and landscape design, project management, environmental

assessment, cost management, energy planning, due diligence, and dispute resolution.

Service providers function largely at an operational level, and dealing with wide rage of

tasks like insurers, cleaning contractors, furniture suppliers, construction, security, fleet

management catering.

Sourcing strategies in facilities management

The increasing need for facilities management services emphasizes the need for

organizations to take an informed decision whether to outsource or use in-house staff

(Ikediashi, 2014). According to Ancarani and Capaldo (2005), there are several

strategies available for FM. These include:

• In-house; where a service is provided by a dedicated resource directly employed

by the organization even though the monitoring and control of performance are

conducted under the terms of conventional employer/employee relationship.

• Outsourcing; where a service is commissioned from an external supply

organization usually under the terms of a formal contractual arrangement based

upon terms derived from a service level agreement.

• Public private partnership (PPP); where a partnership or a strategic alliance is

formed between the organization and service provider based on sharing of the

responsibility for the delivery and performance of the service, including sharing

the profits arising from any efficiency gains and cost savings.

• Total facilities management (TFM); where a whole range of services are bundled

together and externalized to a single supplier which becomes responsible for the

delivery, monitoring, control, and attainment of stated performance objectives in

the contract.


Facility management practitioners

Facility management practitioners, to engage as a strategic partner within the

organizational structure, should have an appropriate level of knowledge base and skills

to achieve the role at a high level.

According to a survey undertaken by the Facility Management Association of Australia,

over 83 % of practitioners within the facility management industry were male with 63.3

% aged over 46 years. Also, 61 % had over ten years industry experience with nearly 60

% of practitioners earning an average salary package of over $100,000 AUD, with the

top 5 % of earning over $250,000 AUD (FMA, 2012). The academic background survey

results showed that 49.5 % of practitioners held a diploma in a related discipline, with

20 % undertaking further education (FMA, 2012).

Another survey undertaken by Hays Facility Management and Facility Management

Association of Australia in 2006, revealed that of the 89 % of participants who

responded to this question held a degree qualification with 68 % being in a related

discipline as shown in Figure 2.4.

Figure 2.4: Qualification of FM practitioners (Hays Facility Management, 2006)


Information valuable for FM during project life-cycle

Alvarez-Romero (2014) stated that during the construction phase, the contractor should

hand-over the owner all the necessary information related to the facilities. The owner

and the operator should spend a great time and cost to re-arrange and re-form that large

amount of information to make it compatible with the FM system and processes. Thus,

it is very important to point out the valuable information for the FM from each facility

phase. Table 2.2 summarizes the detailed information done by Alvarez-Romero (2014)

in this regard:

Table 2.2: Valuable Information for FM for Each Phase (Alvarez-Romero, 2014)

Facility

Phase

Definition Valuable Information for FM

Planning and

programming

The study and analysis of information requirements and needs of the user of the facility

Condition, efficiency, capacity and operating cost

of the facilities

Design Translates the program requirements into a comprehensive description of the facility in terms of geometry and functional and operational characteristics and presented in graphic and text form through drawings and specifications

Systems of the facility are defined

Construction Transforms the design into a physical facility

and makes it a reality

As-built documents

Closeout and

commissioning

The final stage that serves as a transition

between completion of construction and

initialization of operation

Building and production systems are started up,

occupancy certificate is issued, operation and

maintenance training is conducted, record plans

(as-built) are submitted, operation and

maintenance manual are submitted, an as-built

schedule is generated and a final inspection is

performed

Operation and

maintenance

Longest phase and contributes the largest part to

the life-cycle cost of the facility to ensure the

functionality of the facility

Equipment lists, warranties, spare part lists,

preventive maintenance schedules, etc.

Disposal Demolition or transfer of the asset Demolition information requirements include the

types and quantities of material to be removed,

salvage values of equipment, and structural

information to define the demolition strategy. If

the facility is to be sold, information of the current

condition of the facility, the remaining life of

building systems and land value are required


The current section sheds light on the FM concepts to understand how this sector can be

improved and what is the best innovation that can be adopted to achieve this target.

Hence, the following section will present an extensive information about building

information Modelling (BIM) and how this paradigm shift in the construction industry

can be adopted in the whole project life-cycle, including the FM.

2.3 Building information modelling

Building information modelling (BIM) definitions

In the last few years, building information Modelling (BIM) has reshaped the

architecture, engineering, and construction industry. BIM includes both a process and

technology. The process part enables high level of cooperation and promotes integration

of the functions among stakeholders on the construction projects. But the technology

component helps project members to visualize the construction operation of the whole

project in a simulated environment to recognize any potential design, construction and

operational conflict (Azhar et al., 2012). There are many definitions of BIM. The

National Building Information Modelling Standards (NBIMS) committee of USA

defines BIM as; “a digital representation of physical and functional characteristics of a

facility. A BIM is a shared knowledge resource for information about a facility forming

a reliable basis for decisions during its life-cycle; defined as existing from earliest

conception to demolition. A basic premise of BIM is collaboration by different

stakeholders at different phases of the life-cycle of a facility to insert, extract, update or

modify information in the BIM to support and reflect the roles of that stakeholder”

(NBIMS, 2007). Further, Succar (2010) defined BIM as “a set of interacting policies,

processes and technologies generating a methodology to manage the essential building

design and project data in digital format throughout the building’s life-cycle”. See

Figure 2.5.


Figure 2.5: Project Life-cycle BIM (Image source: http://buildipedia.com/aec-pros/design-news/the-daily-life-of-building-information-Modelling-bim)

Building information modelling benefits and usages

Considerable literature has been published on potential and actual benefits by adopting

BIM in the construction industry. Latiffi et al. (2015), studied possible benefits of

implementing BIM on real projects in Malaysia. Their study revealed these benefits:

• Avoidance of delays in construction and construction cost overruns

• Contribution to a better quality of the end product

• Minimization of waste

• Clash detection before the start of construction

• Resolution of fabricator issue

• 3D visualization of project design

Azhar et al. (2008) stated that BIM has been considered as an innovative approach to

projects management. Also, they concluded that when the adoption of BIM increases,

the expected collaboration among project teams should increase, which will to improve

the profitability, decrease costs, and enhance the time management.


Baldwin (2012) , classified the usage of BIM for the project life-cycle as shown in Table

2.3.

Table 2.3: BIM Usage for the Project Life-cycle

BIM and level of development (LOD)

The definitions of each LOD from LOD 100 – LOD 500, given in AIA Draft Document G202 – 2012, are summarized in Table 2.4.

Table 2.4: BIM and LOD


BIM Forum has released LOD Specifications in 2016. Interpretation of each level is as

follows;

LOD 100: elements are not geometric representations. Examples are information

attached to other model elements or symbols, showing the existence of a component but

not its shape, size, or precise location. Any information derived from LOD 100 elements

must be considered approximate.

LOD 200: elements are generic placeholders. They may be recognizable as the

components they represent, or they may be volumes for space reservation. Any

information derived from LOD 200 elements must be considered approximate.

LOD 300: The quantity, size, shape, location, and orientation of the element as designed

can be measured directly from the model without referring to non-modelled information

such as notes or dimension call-outs. The project origin is defined and the element is

located accurately regarding the project origin.

LOD 350: Parts necessary for coordination of the element with nearby or attached

elements are modelled. These parts will include such items as supports and connections.

The quantity, size, shape, location, and orientation of the element as designed can be

measured directly from the model without referring to non-modelled information such

as notes or dimension call-outs.

LOD 400: element is modelled at sufficient detail and accuracy for the fabrication of

the represented component. The quantity, size, shape, location, and orientation of the

element as designed can be measured directly from the model without referring to non-

modelled information such as notes or dimension call-outs.

LOD 500: relates to field verification and is not an indication of progression to a higher

level of model element geometry or non-graphic information; this specification does not

define or illustrate it.


Building information modelling and nD

Many areas in the construction industry can benefit from BIM implementation. One of

them is the nD building information Modelling. The idea was started form a conversion

of a 2D drawing to a 3D drawing. The element attributes can be added to the 3D model

to make it more real. A 4D model is formed by adding one more dimension to the 3D

model, which is time. With this merit, the project can be virtualized at any time and

tested within different scenarios. And a 5D model can be formed by including the cost

estimation to the 4D model. Thus, the model is open to inclusion of more dimensions.

That is why it is called nD BIM. See Figure 2.6.

Figure 2.6: Building Information Modelling and nD (adopted from bimporn.tumblr.com)

Time and cost are the most dominant issues in the construction industry. The traditional

methods to deal with these two key factors are obsolete, time-consuming and inefficient.

The new BIM environment offers significant opportunity to cover this shortage.

However, until now, there is no research has dealt with time and cost aspects with a

hundred percent efficiency. Every research tried to solve the limitation of the previous

studies. The problem arises from the difficulty to include and process all the information

and details that the estimator and planner need inside the building model, while the

information processing is difficult to reveal and remains a 'black box'. Automation of


details that affect the cost and time calculations is a very complicated issue. Sattineni

and Macdonald (2014) stated that the software that deals with 5D-BIM needs a high

level of expertise in construction processes and in technological expertise. Also, they

stressed there no single BIM software available to enable and represent all the

construction operations.

Shen et al. (2013) presented a new trend for cost estimation which represents the

development of the cost model estimation, directs the value of contextual information

and the requirement of extension of pricing information using Industry Foundation

Classes (IFC). The authors concluded that the IFC standard can facilitate cost

estimation. Liua et al. (2014) claimed that all the previous studies in this area have been

developed only for one specific factor of construction management, for instance,

scheduling or cost estimating. They revealed that most of these researches are still

restricted to the product element level but not to the construction operation level. The

authors filled this gap by presenting a BIM-based integrated framework for detailed

schedule planning and cost estimating. The idea was to combine the BIM 3D model with

the construction process information model which restored from RS means based on

work breakdown structure (WBS) to facilitate scheduling and cost estimation. The

researchers applied the new framework on a small project as a case study. The results

show an accepted level of accuracy. However, there are limitations such as the manual

development of the WBS and incompatibility between BIM-based quantity take-off and

estimation in some items such as temporary facilities, grout quantity, and others.

Aram et al. (2014) developed a framework for a knowledge-based system to perform a

model based quantity take-off (QTO) and cost estimate (CE). The presented framework

consists of four layers: domain, reasoning, task, and interface. The structure of this

framework looks like an expert system. They emphasized that QTO software needs to

achieve three conditions to reach a successful performance as following:

• Architectural and structural design models should be readily suitable for

quantity take-off and cost estimation

• The information needs to be quantitative


• Designers' models should contain complete information needed for these

tasks

They defined a way of retrieving the minimum set of model information required for

cost estimation and quantity take-off focusing on precast concrete as an example to test

their concepts. The proposed KBS adjusts design models to make them suitable for

quantity take-off and cost estimation without the need to redesign the model. However,

they described their model as an efficient and semi-automated model.

Briefly, the new technology and process of building information Modelling have many

benefits and applications in the construction industry. One of the most important

application is enabling of nD modelling, especially 4D for time, and 5D for cost.

All the previous and current researches are trying to enhance the ability of BIM model

to include sufficient information using different methodologies and frameworks. No

perfect method has been established yet, thus the door is still open for more research.

BIM rate of adoption

Numerous studies have revealed the potential and actual benefits of BIM usage in terms

of productivity, cost, and time. However, there has been relatively slow adoption of BIM

in the construction industry (Azhar et al., 2008, Xu et al., 2014).

Lindblad (2013) stated that the construction industry is slow in adopting new technology

such as BIM, which will have a great effect on work processes and culture in the

projects. However, for successful BIM, there is a certain need to change work processes.

The fragmented nature of the construction industry is a big problem because this change

cannot be made by a single player, but it affects all the involved parties. BIM adoption

is based on collaboration, integration, and innovation, considering large cultural and

legal changes in the industry (Lindblad, 2013).

Barriers to BIM adoption

It has been demonstrated that enormous benefits can be gained by BIM adoption in the

construction industry. However, other research has considered barriers in BIM adoption.


Among barriers linked to different aspects such as process, technology, people, and

policy are (Lee et al., 2015, Lindblad, 2013) ;

• Unclear and invalidated benefits of BIM in ongoing practices

• Lack of familiarity with adopting this new technology

• Interoperability issues

• Lack of supporting education and training for BIM

• Lack of supporting resources (software, hardware) to use BIM tools

• Lack of effective collaboration between project stakeholders for Modelling and

model utilization

• Unclear roles and responsibilities for loading data into a model or databases and

maintaining the model

• Lack of sufficient legal framework for integrating owners’ view on design and

construction.

• Different views on BIM by project stockholders

• Poor match with the user’s needs

• Changing work processes

• Risks and challenges with using a single model

Khosrowshahi and Arayici (2012) conducted an extensive survey targeting UK

construction organizations and they found these barriers, ordered from most important

to less one;

• firms are not familiar enough with BIM use

• reluctance to initiate new workflows or train staff

• benefits from BIM implementation do not outweigh the costs to implement it

• benefits are not tangible enough to warrant its use

• BIM does not offer enough of a financial gain to warrant its use

• lack of capital to invest in using hardware and software

• BIM is too risky from a liability standpoint to warrant its use

• resistance to culture change

• no demand for BIM use


Bibliometric analysis of BIM in the construction industry

As a part of the literature review for the current thesis, the author used a bibliometric

analysis technique in this part of thesis through the Web of Science and Scopus

databases. The purpose of that was to define the best journals in BIM publication, top

Journal Impact factor, and citations, countries actively involved in BIM research, and

the top authors who published on BIM. This analysis aims to guide the researchers

interested in BIM to select the most relevant BIM research in this domain. Preparing

this information was helpful to define BIM-FM topics later. This aspect of work has been

published in (Hilal et al., 2017)

Method

A bibliometric analysis is a statistical analysis method for the published articles in a

certain topic (Rey-Martí et al., 2016). This kind of analysis leads to useful information

for researchers seeking to examine scientific activities. A bibliometric analysis

represents a guide to the status of research into a certain domain.

The presented analysis, adopted the methodology from Rey-Martí et al. (2016). The

online database Web of Science (WOS) and Scopus database, which consist of scientific

research in all disciplines, were used.

The keywords that have been used in this analysis were “building information

Modelling”, and the results were limited to the journal article only. The analysis

presented the top-ranked results instead of them all to avoid confusion resulting from a

large unwanted output.

The key units of the bibliometric analysis

In this part of literature review of the current thesis, a bibliometric analysis was

conducted in 2017 using WOS database and Scopus database on BIM research for the

period between (2003-2016). The bibliometric key units of the analysis used were:

• Language of publication on BIM research

• Areas of knowledge

• Countries, where authors have published research on BIM

• Journals, in which authors have published research on BIM

• Top Journal Impact Factor and citations


• Authors who have published on BIM

• Comparison between WOS and Scopus output.

Bibliometric analysis using WOS

Language in BIM publications: The bibliometric analysis results revealed that some

publications have been published in languages other than English. From the total of 450

journal articles, English language publications have a share of 438 journal articles.

Research area : BIM has been studied from the perspective of engineering, construction

management, architecture, business, etc. The bibliometric analysis has revealed that the

top three areas in WOS contain 347 documents in engineering, 192 in construction

building technology, and 90 in computer science. These figures reveal a clear difference

between the number of documents in engineering and the number in other areas. This

finding implies that BIM is a highly relevant topic in the field of engineering. Figure 2.7

shows the top five most relevant research areas relevant to BIM. It can be seen clearly

from Figure 2.7 that the total number of publications exceeds 450 documents. The

reason is that some documents can be classified in more than one research area

simultaneously.

Figure 2.7: Number of BIM publications in each area of knowledge

Countries: Figure 2.8 shows the countries involved in publishing BIM research between

2003-2016. The top country to publish BIM research is the USA with 172 publications.

South Korea ranks second with 84 publications. China got the third rank with 61

347

192

90

28

20

0 50 100 150 200 250 300 350 400

Engineeing

Construction Building Technology

Computer Science

Architecture

Education Educational Rsearch


publications. Figure 2.8 also shows that the remaining 31 countries account for 60

articles altogether.

Figure 2.8: Countries where BIM research has been published

Top journals in BIM: It is important for the researchers to know about the top journals

that publish on BIM research. Figure 2.9 shows the journals that have published most

research papers on BIM. Automation in Construction takes the lead with 108 published

documents on BIM. Journal of Computing in Civil Engineering got the second rank with

32 publications, while Advanced Engineering Information ranked third with 30

publications.

17284

613130

262321

1611111088

55

60

0 20 40 60 80 100 120 140 160 180 200

USASouth Korea

ChinaGermany

TaiwanAustralia

CanadaIsrael

EnglandFINLAND

EGYPTTURKEY

PORTUGALMALAYSIA

SWEDENSPAIN

The rest 31 countires


Figure 2.9: Top Journals that have published research on BIM

Top journal impact factor and citations: the Automation in Construction was the most

published journal in BIM. This journal was selected to conduct more significant

analysis. The impact factor and the total citation of this journal were analysed through

WOS between 2002-2015 as shown in Figure 2.10 and Figure 2.11, considering that the

data is not available yet for the year 2016. The analysis showed that the impact factor

increased consistently with the time except for three jumps in 2006, 2008 and 2009,

respectively. On the other hand, the total number of citations increased rapidly during

the same period.

Figure 2.10: Impact Factor of Automation in Construction

108

32

30

21

16

15

11

11

9

9

8

6

6

5

5

0 20 40 60 80 100 120

Automation in Construction

Journal of Computing in Civil Engineering

Advanced Engineering Information

Journal of Construction Engineering and Management

Journal of Management in Engineering

Journal of Professional Issues in Engineering Education and Practice

Journal of Information Tchnology in Construction

ENERGY AND BUILDINGS

KSCE JOURNAL OF CIVIL ENGINEERING

JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT ASCE

Journal of Asian Architechture and Building Engineering

Journal of Civil Engineering and Management

CANADIAN JOURNAL OF CIVIL ENGINEERING

JOURNAL OF BUILDING ENGINEERING

INTERNATIONAL JOURNAL OF PROJECT MANAGEMENT


Figure 2.11: Citation of Automation in Construction

Authors: In terms of authors who have published on BIM between 2003-2016, the

bibliometric analysis shows that Sacks, R. has published 21 research papers, the highest

number of publications on BIM in that period ( see Figure 2.12). Eastman got the second

rank with 15 publications. Lee, G. and Teizer, J. got the third rank with 13 publications

each. The rest of the top 15 authors are listed in Figure 2.12.

Figure 2.12: Top 15 authors who published in BIM

Publication years: The search criteria have been set to include any year between 2003-

21

15

13

13

10

10

9

8

8

7

7

7

6

6

6

0 5 10 15 20 25

Sacks, R

Eastman, C.M

Lee, G

Teizer, J

Issa, R.R.A

Kim, H

Lee, S

Kim, I

Kim, J

CHENG, JCP

Lee, JK

Wang, XY

FISCHER, M

HSIEH, SH

LI, H


2016. Figure 2.13 shows the number of publications each year. In the year 2016, the

number of publications was the highest with 129 publications. The figures show the

increase in the number of publications on BIM with time, especially between 2013-

2016.

Figure 2.13: Publication year

Bibliometric analysis using Scopus

The Scopus indexed 904 journal articles for the period 2003-2106. These figures show

the analysis results for Scopus:

Research area: BIM has been studied from the perspective of engineering, computer

science, business, etc. The bibliometric analysis has revealed that the top three areas in

Scopus contain 693 documents in engineering, 237 in computer science, and 156 in

business and management. This finding implies that BIM is a highly relevant topic in

the field of engineering. Figure 2.14 shows the top five most relevant research areas

relevant to BIM.

129

104

78

52

27

27

14

7

3

3

3

2

0

0

0 20 40 60 80 100 120 140

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003


Figure 2.14: Number of BIM publications in each area of knowledge

Top journals in BIM: it is important for the researchers to know about the top journals

that publish on BIM research. Figure 2.15 shows the journals that have published most

research papers on BIM. Automation in Construction has the highest publication rate of

133 journal articles. Journal of Information Technology in Construction got the second

rank with 38 publications, while Journal of Construction Engineering and Management

ranked third with 33 publications.

Figure 2.15: Journals that have published research on BIM

Authors: Scopus has shown the top authors who published on BIM between 2003-2016.

The bibliometric analysis shows that Wang, X. has published 22 journal articles, the

highest number of publications on BIM in that period (see Figure 2.16). Sacks, R. got

the second rank with 19 publications. Issa, R.R.A and got the third rank with 14

693

237

156

92

71

0 100 200 300 400 500 600 700 800

Engineeing

Computer Science

Business, Management and Acco.

Social Sciences

Environmental Science

133

38

33

31

25

21

21

15

14

13

13

12

12

12

12

0 20 40 60 80 100 120 140

Automation in ConstructionJournal of Information Tchnology in Construction

Journal of Construction Engineering and ManagementAdvanced Engineering Information

Journal of Computing in Civil EngineeringConstruction Innovation

Electronic Journal of Information Tchnology in ConstructionJournal of Management in Engineering

Journal of Professional Issues in Engineering Education and PracticeAchitectural Engineering and Designe Management

Jurnal TeknologiAchitectural Design

Construction Management and EconomicsEnergy and Buildings

Practice Periodical on Structural Design and Construction


publications. The rest of the top 15 authors are listed in Figure 2.16.

Figure 2.16: Top 15 authors who published on BIM

Countries: Figure 2.17 shows the countries involved in publishing BIM research

between 2003-2016. The top country to publish BIM research is USA at 248. China

ranks second with 115 publications while England got the third rank with 100

publications. The Figure also shows the other rest countries.

22

19

14

13

12

11

11

11

10

8

8

8

8

8

8

0 5 10 15 20 25

Wang, X

Sacks, R

Issa, R.R.A

Teizer J

Lee, G

Cheng, J

Eastman, C.M

Wang, J

Irizarry, J

Becerik-Gerber, B

Chong, H

Kim, I

Lee, J

Love, P

Zhang, S


Figure 2.17: Countries where BIM research has been published

Publication years: Figure 2.18 Shows the number of publications each year. In the year

2016, the number of publications is the highest with 229 publications. The figures show

the increase in the number of publications in BIM with time, especially between 2011-

2016.

Figure 2.18: Publication year

248115

110107

844544

3939

312625

1915131199998888

0 50 100 150 200 250 300

USAChina

EnglandSouth Korea

AustraliaTaiwan

MalaysiaCanada

Honk KongGermany

UndefinedFinland

IsraelNetherlands

EgyptTurkey

ItalyJapan

PolandSweden

IndiaPortugalSlovenia

Spain

229

207

169

101

61

54

36

21

12

6

8

0

0

0

0 50 100 150 200 250

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003


A comparison between WOS and Scopus in terms of the research area, publication year,

country and authors shows that Scopus has indexed double journal articles on BIM in

this period. However, the popularity of the WOS is due to the searching features

embedded in it, which makes it more user-friendly for the researchers.

The bibliometric analysis in this presented thesis discovered that BIM research topics

were consistently growing. There were 450 BIM journal articles gathered from the WOS

database between the years 2003-2016 and 904 in Scopus. The bibliometric analysis

results showed that the leading country of BIM research is the USA in both databases.

The journal that has published most of the BIM research is Automation in Construction.

The analysis in this study is helpful as a general guide to researchers and doctoral

students carrying out research on BIM.

Scientometric analysis of BIM in FM

As a part of the literature review for the current thesis, the researcher used a

scientometric analysis technique for the WOS databases. The purpose of that was to

present a critical statistical analysis of BIM research in FM by adopting a scientometric

analysis technique based on the outcome of the previous bibliometric analysis study

mentioned in the last sub-section 2.3.7. This aims to classify and categorize those

publications found in the previous bibliometric analysis by providing visual maps

analysis of BIM-FM articles in a simple, easy, and readable way. Quantitative approach

has been employed using science mapping techniques, specifically scientometric

analysis, to examine BIM-FM articles using WOS database for the period between

(2000- April 2018).

The findings guide the researchers interested in BIM-FM topic by providing visual maps

analysis through analysing the co-occurrence of keywords, and co-citation analysis

(references, sources, authors), and co-authorship analysis (author, organization,

countries). Then, the generated maps were analysed to distil useful information. Those

findings help the researchers to understand which authors and journals to consider when


dealing with BIM-FM topics. Finally, several gaps in this domain were identified based

on those findings of scientometric analysis.

The results of the analysis depended on the database extracted from WOS, and therefore

it carries any of WOS’s limitations such as how much it covers of the published studies.

Another limitation was that the study was based on an exploration of "what", rather than

“how” and “why” questions.

Over the last few years, the emergence of building information Modelling (BIM) has

successfully achieved a paradigm shift in Architectural, Engineering, Construction and

facility management (AEC/FM) sectors. This led to publish many articles and papers in

these sectors. To statistically classify and categorize these publications, various

bibliometric and scientometric analysis research studies have been conducted. The

existing research sheds light on BIM in the construction industry, focusing on the design

and construction phases. The literature review has shown lack of bibliometric and

scientometric analysis studies for BIM in FM specifically. This thesis addressed this

lack and established the first scientometric analysis study for BIM in FM. This aspect of

work has been published in (Hilal et al., 2019). These sections present the scientometric analysis

stages:

The current study foundation

FM suffers from low BIM adoption as found by (Eadie et al., 2013). To some extent,

this finding also supports the finding of the author of the current thesis as discussed in

the previous section. It has been found in previous section a similar finding in terms of

lack of studies of BIM adoption and use in FM, so FM is still suffering from low ICT-

adoption and specifically, BIM adoption. As mentioned in the previous section, the

author employed a bibliometric analysis technique to statistically analyse 450 journal

articles on BIM research published between (2003-2016) in WOS database. The current

section has extended the outcome of the previous section and adopted a scientometric

analysis, which resulted in a significant finding. The author of this thesis has re-

classified those 450 journal articles according to the projects’ main phases, namely the


design, construction, and facility management, as illustrated in Figure 2.19. This

classification helps to get a better understanding of the current level of adoption and the

degree of importance of BIM in each of project’s phases. In addition, it helps to identify

the state of art of BIM adoption and the potential gaps that should be bridged.

The filtration of those 450 articles has led to quantifying the share of each phase from

those articles, and presents a holistic picture on using BIM in a project life-cycle, as

shown in Figure 2.19.

Figure 2.19: Classification of the BIM 450 articles according to project’s phases

Figure 2.19 shows a lack of research in the operation and facilities management phase.

The highest number of research occurs in the design and construction phases while the

FM share of research is minimal. It can be concluded, with proven benefits of BIM


adoption during the whole project life-cycle, that more BIM research is needed,

particularly during the FM phase.

Based on the above conclusion from the previous study, the current section focuses on

BIM studies in FM specifically, by employing a comprehensive scientometric analysis

for the period between (2000- Apr 2018) to critically review the existing literature and

to identify the potential research gaps.

Previous literature has demonstrated the benefits of BIM during the whole project life-

cycle. However, there is a lack of BIM studies in FM specifically. This analysis aims to

bridge this gap and to provide a better understanding of this area of research. The

findings guide the researchers interested in BIM-FM topic by providing visual maps

analysis through analysing the co-occurrence of keywords, co-citation analysis

(references, sources, authors), and co-authorship analysis (author, organization,

countries). Those findings help the researchers to understand which authors and journals

to consider when dealing with BIM-FM topics. Finally, several knowledge gaps in this

domain are identified easier based on the results of the scientometric analysis.

Scientometric analysis definition

Science mapping represents how fields, specialties, disciplines, and individual authors

or publications are correlated (Small, 1999). It has proven benefits in dealing with

comprehensive bodies of literature visually and statistically. According to Cobo et al.

(2011a), science mapping has certain features in depicting systematic patterns in a

massive amount of literature and bibliographical units.

Scientometric analysis, bibliometric analysis, and informatics analysis can be

categorized under science mapping studies. In this study, a scientometric analysis has

been adopted. There are different scientometric analysis software packages such as

Bibexcel, Pajek CiteSpace, Saint, VOSviewer, etc. In this study, VOSviewer has been

used due to its comprehensive capabilities, simplicity, and compatibly with the WOS

database. The developers of VOSviewer (van Eck and Waltman, 2009) stated that

“Unlike most computer programs that are used for bibliometric mapping, VOSviewer


pays special attention to the graphical representation of bibliometric maps. The

functionality of VOSviewer is especially useful for displaying large bibliometric maps

in an easy-to-interpret way”. Also, VOSviewer is compatible with a large number of

operating system platforms and hardware, freely available, and can be opened from the

internet directly.

According to (Cobo et al., 2011b), after the creation of the map, VOSviewer offers its

examination by four views:

• Label view: each element is represented by a circle and label. The Circles

that have the same colour belong to the same clusters. This colour is

similar to the corresponding cluster’s colour in the cluster view.

• Density view: each item is displayed by a label in the same way as in the

label view. Each point in the map has a colour that depends on the density

of items at that point, which depends both on the weights items and on the

number of neighbouring items.

• Cluster density view: this view is available only if a certain item has been

previously assigned to a cluster. The cluster density view is the same as

the ordinary density view except that the density of items is presented

separately for each cluster of items.

• Scatter view: in this view, any item is indicated by a small circle, in which

no label is displayed.

Tool selection

Scientometric analysis has different tools such as VOSviewer, BibExcel, CiteSpace,

CoPalRed, Sci2, VantagePoint, and Gephi (Cobo et al., 2011a). VOSviewer has been

adopted in this research for the aforementioned reasons.

Data acquisition

VOSviewer allows users to download bibliographic records directly from the WOS,

Scopus, Google Scholar, and PubMed. From these options, WOS was selected for its

reliable searching features and the availability of most sources. The search keywords in

WOS was “Facility Management” OR “Facilities Management” OR “Asset


Management” OR “Assets Management”, to retrieve the bibliometric data associated

with published studies on FM in general. The search had timeframe limitation with the

date range set between January 2000 and April 2018. Searching attempts using these

keywords were conducted on the title of published studies only. This produced 3346

documents. After that, the results were refined based on limiting the search by applying

another keyword, specifically “BIM”, to filter only articles published in BIM-FM, which

is the objective of this study. On 10th of April 2018, 68 articles were identified, for

which all bibliometric data were extracted and downloaded from WOS, forming the

database used in this study.

Data analysis

A scientometric analysis was adopted for this study in two stages. The first stage

included the creation of networks through analysing the co-occurrence of keywords, and

co-citation analysis (references, sources, authors), and co-authorship analysis (author,

organization, countries). In the second stage, the generated maps in stage one were

analysed to distil useful information. Price and Gürsey (1975) stated these measures

showed “the conceptual, intellectual, or social evolution of the research field,

discovering patterns, trends, seasonality, and outliers”.

Findings and discussions

These sections present the research findings. It is worth mentioning that VOSviewer

software shows all letters in lowercase, which can be confusing for readers, as they are

not used to deal with this status. For instance, BIM will be shown as bim, and so on.

Timeline trends of BIM-FM research: The search criteria have been set to include any

year between Jan 2000 and Apr 2018. Figure 2.20 shows the number of publications

each year. In the year 2016, the number of publications was the highest with 15. The

figures show an increase in the number of publications in BIM-FM during the time with

a minor fluctuation in the last two years. No, research has been done between 2000 and

2008.


Figure 2.20: Year of BIM-FM research publications

The first study on BIM-FM within the dataset was published in 2009 which a title ‘Life-

cycle Management of Facilities Components Using Radio Frequency Identification and

Building Information Model’ by Motamedi, Ali; Hammad, Amin, Journal of

Information Technology in Construction.

Through the publication year analysis, the poverty of research in the BIM-FM area can

be seen clearly.

Research areas (co-occurrence of keywords analysis): A network comprised of 24

nodes and 130 links was created, illustrating the main areas of research identified in

BIM-FM research (see Figure 2.21).


Figure 2.21: Main research areas in BIM-FM field and their relatedness

To remove the similarities among the nodes of the networks above, another analysis has

been conducted to extract the most weighted nodes resulting in the following Figure

2.22.

Figure 2.22: Main research areas after removing the similarities


Calculating network measures can be done by extracting certain information from the

network itself through VOSviewer software. Degree centrality represents a

measurement of the centrality of a node in a network using several connections, which

indicates the effect of a node on other nodes.

According to Cobo et al. (2011a), " a modified version of degree centrality, weighted

degree in the network, takes into account the average mean of the sum of the weights of

the links on all the nodes in the graph". In addition, they stated that “Involving the

weight of links into calculating degrees will reveal the focal points or the level of

involvement of nodes in a given network”.

Table 2.5 shows the results of an analysis of the networks throughout VOSviewer

software output. The main research areas have been ranked according to the relative

importance as shown this Table 2.5.

Table 2.5: Main research areas (co-occurrence of keywords analysis)

Several findings can be extracted from Table 2.5 and Figure 2.22 which, reflect gaps

and issues within BIM-FM literature:

1- There is a special focus on research areas such as bim, facilities management,

and system.

2- As an unexpected finding, less important research areas focus on Industry

Foundation Classes (IFC) and interoperability. This reflects a lack of literature

and attention by the researcher to these significant areas located on the bottom of


the ranking. Hence, more research is required for the IFC and interoperability

topics, which are the core of the connection between BIM and FM.

3- As illustrated in Figure 2.22, bim, facilities management, maintenance,

framework, integration, IFC, asset management, and interoperability are

linked as one largest cluster in the network. These areas are positioned as central

areas of research in BIM-FM and this might be attributed to the importance of

BIM literature and the potential benefits of BIM in FM. In addition, Figure 2.22

shows the lack of research studies that integrate BIM and FM, which leads to a

serious gap within the existing literature on FM. This would lead to the following

conclusion: the integration of FM processes with BIM implementation

requirements is still considered a barrier to more extensive implementation. This

finding supports recent research results regarding the interoperability issues

between BIM and FM systems (Pärn et al., 2017, Yalcinkaya and Singh, 2014).

Top research outlets (direct citation analysis of outlets): Direct citation analysis of

outlets is very important for the interested researcher. Dealing with highly cited sources

can be one of the best ways to reach accurate and reliable information in a certain

domain. Main cited sources in BIM-FM field and their relatedness is visualized through

VOSviewer after the exportation of the related database from WOS. Figure 2.23 shows

the main cited sources in BIM-FM field and their relatedness.

Figure 2.23: Network of prominent sources for publications in BIM-FM


Calculating network measures can be done by extracting certain information from the

network itself, as illustrated in Figure 2.23. Table 2.6 shows the top BIM-FM sources

outlets.

As shown in Table 2.6, the Journal of Automation in Construction ranked in the first

order with the highest number of citations and can be the most dominant source in BIM-

FM research.

As shown in Table 2.6, the flow of information starts from Automation in Construction

with a Total Link Strength of 107.18, which is well above any other sources in the Table.

In addition, the analysis shows that Journal of Construction Engineering and

Management ASCE ranked second with Total Link Strength of 44.30, while the Journal

of Advanced Engineering Informatics ranked third with a Total Link Strength of 44.21.

Further, the strongest collaboration is between Automation in Construction and

Advanced Engineering Informatics with a link strength of 28.43. The second strongest

collaboration is between Automation in Construction and the Journal of Construction

Engineering and Management ASCE with A Link strength of 27.86.

Table 2.6: Top BIM-FM sources outlets

Source (Brief name/ Full Name) Citation Degree Centrality

/Links

Weighted Degree

Centrality/

Relative Importance

automat constr / Automation in Construction 234 6 107.18 1 j constr eng m asce /Journal of Construction Engineering and Management ASCE

52 6 44.30 2

adv eng inform / Advanced Engineering Informatics 57 6 44.21 3 facilities / Facilities 45 6 33.40 4 building inform mode / BIM Journal 27 6 23.77 5 j comput civil eng / Journal of Computing in Civil Engineering 23 6 21.94 6 itcon / Journal of Information Technology in Construction 24 6 20.73 7

Co-authorship analysis: this includes these issues:


Authors: A Collaboration network analysis of authors in BIM-FM research has been

conducted, as shown in Table 2.7. The results of the analysis showed that only Wang,

Xy, and Wang, J have two authoring documents in the BIM-FM area with 397 citations.

Thabet, Wy, and Wetzel, Em have the strongest collaboration in BIM-FM research

authorship with 3 authoring documents for each.

Table 2.7: Collaboration network of authors in BIM-FM research

Author Citation Degree Centrality

/Links

Weighted Degree

Centrality/ Wang, xy 397 1 2 Wang, j 397 1 2 Dawood, N. 26 2 2 Kang, TW 37 0 0 Kassem.M 26 2 2 Lockly, s 20 4 2 Mathews, j 47 2 2 Love, ped 47 2 2 Thabet, wy 20 1 3 Wetzel, em 20 1 3 Nicolle, c 21 0 0 Motamedi, a 49 1 2 Hammad, a 49 1 2

Organizations: A Collaboration network analysis of organizations was created.

According to Cobo et al. (2011a), cited (Ding, 2011), this organizations analysis

“benefits the field, particularly in terms of providing input into research partnership

policy making”.

Figure 2.24 shows that most organizations in the network had no collaboration links

among other organizations of the network, such as Concordia University, Curtin

University, etc.


Figure 2.24: Collaboration network of organizations in BIM-FM research

As shown in Figure 2.24, the red cluster consists of several organizations. This cluster

can be zoomed in to get more information, as illustrated in Figure 2.25.


Figure 2.25: Zoomed cluster

Countries: To shed light on the most influential countries and to visualize the

collaboration among them, a network analysis was conducted using VOSviewer. The

number of documents and the number of citations for each country was utilized as a

criterion to identify influence within this network, as shown in Figure 2.26. Nodes sizes'

are based on the influence of each country within the criterion.


Figure 2.26: Collaboration network of countries in BIM-FM research

Some countries in the network had no collaboration links among other countries of the

network such as Taiwan, South Korea, Canada, and France.

As shown in the previous Figure, the red dominated USA cluster consists of several

countries. This cluster can be zoomed in to get more information about those other

countries as illustrated in Figure 2.27.


Figure 2.27: Zoomed cluster

The USA ranked at the top with 9 documents published in BIM-FM, while England

ranked as second with 7 documents. China positions with five documents. All the other

countries are on the bottom of the list with less than 5 documents each according to the

circle size in the Figure. Smaller circles mean fewer documents

Summarization of the scientometric analysis

This section summarized the main findings of the scientometric analysis. This study

applies scientometric analysis method to statistically analyse the literature database on

BIM-FM research using WOS dataset.

Based on a strong quantitative approach that uses the science mapping tool, namely

VOSviews, wide scientometric analysis networks have been drawn to deal with the


research in BIM-FM to identify the potential gaps and extract useful information that

might be important to the researcher in BIM-FM domain.

Below are the main findings with some discussions and clarifications;

• According to the scope of this study within WOS database, first research papers

on BIM-FM topic were published in 2009. This shows that integrating FM and

BIM was late in comparison with the other project phases such as design and

construction. After 2009, the publication rate increased slightly. This result is

supported by (Eadie et al., 2013) research of (Eadie et al., 2013). In his study, he

found that using BIM during the project life-cycle is focussed on design,

preconstruction, and construction phases mainly, with minimal implementation

in operation and management phase. More research is needed to bridge this gap.

• More research is required for IFC and interoperability topics, which are the core

of the integration between BIM and FM.

• The journal of Automation in Construction is the best source of BIM-FM studies.

The results of the analysis showed that only Wang, Xy, and Wang, J have two

authoring documents in the BIM-FM area with 397 citations each. USA ranked

at the top of published documents in BIM-FM. All the others were far from this

limit. Some countries in the network had no collaboration links among remaining

counters of the network such as Taiwan, South Korea, Canada, and France. This

should be noticed by these countries to adjust their research policies, as they are

far from the dominant network of collaboration in BIM-FM.

• Some organizations, as shown in the network analysis, had no collaboration links

among remaining organizations of the network such as Concordia University,

Curtin University, etc. This should be noticed by these organizations to adjust

their research policies, as they are far from the dominant network of collaboration

in BIM-FM.


Overall, section (2.3 Building Information Modelling) explores wide aspects of BIM.

However, the question that already comes to the mind is that with the great benefits of

BIM in the construction and FM sectors, how can the factors that influence the

acceptance and adoption of BIM be measured? To answer this question, the following

section discusses the technology acceptance theories which have been considered as

efficient methods to achieve this target.

2.4 Technology acceptance model and related theories

Technology Acceptance Theories are information systems theories that model how users

come to accept and use new technologies. One of the most famous ones is the TAM.

TAM was developed by Davis in 1986 as a requirement of his PhD research (Davis Jr,

1986). It explains the usage of computer and the acceptance and approval of information

technology and study of people's reactions to them. TAM has been derived from the

theory of reasoned action –TRA- and is considered one of the most popular and

commonly accepted models (Basri 2012). Although there is several technology

acceptance theories such as; The theory of reasoned action (TRA), TPB, task technology

fit (TTF), Diffusion of Innovations (DI) and Unified Theory of Acceptance and Use

(UTAUT), there is a consensus that TAM is at the top of the hierarchy (Basri, 2012).

The first version of TAM can be represented by Figure 2.28 (Davis Jr, 1986) .

Figure 2.28: Technology Acceptance Model (TAM) (Davis Jr, 1986)

According to the Figure 2.28, external variables affect the attitude toward the use

indirectly, which eventually leads to actual system use by influencing perceived

usefulness and perceived ease of use (Lee and Yu, 2013) .


Lee et al. (2013) stated that “TAM assumes that an individual’s behavioral intention to

use a system is determined by two beliefs: perceived usefulness, defined as the extent to

which a person believes that using the system will enhance his or her job performance,

and perceived ease of use, defined as the extent to which a person believes that using

the system will be free of effort”. TAM assumes that the effects of external variables

(e.g. system characteristics, development process, training) on the intention to use are

mediated by perceived usefulness and perceived ease of use”. However, TAM has been

developed during the last three decades by researchers to cover different situations.

Subjective norms can make a significant effect on intention in a compulsory

environment system use, but not in the voluntary settings (Lee et al., 2012). Thus, the

updated TAM called TAM2 differs from the original TAM by the included subjective

norms as an additional predictor of intention with mandatory system use condition

(Venkatesh and Davis, 2000). Figure 2.29 and illustrates the TAM2.

Figure 2.29: Technology Acceptance Model 2 (TAM2) (Venkatesh and Davis, 2000)

Another research conducted on TAM led to developing TAM3 as shown in Figure 2.30

(Venkatesh and Bala, 2008).


Figure 2.30: Technology Acceptance Model 3 (TAM3) (Venkatesh and Bala, 2008)

TAM can be an efficient measurement tool to investigate acceptance and/or rejection of

the new technology adoption by the individuals in the organization.

The UTAUT model has been developed by Venkatesh et al. in 2003, based on the

extension version of the Technology Acceptance Model and other acceptance theories

such as integrated model of technology acceptance, the motivational model, the IDT,

the theory of reasoned action, the PC utilization model, planned behavior, and the social

cognitive theory. It is a collection of various acceptance theories and is therefore called

the unified theory. The model concluded that facilitating conditions, effort expectancy,

performance expectancy, and social influence have a direct and indirect influence on

behavioral intention and use behavior. Also, those relations are mediated by gender, age,

experience, and voluntariness of use (Venkatesh et al., 2003). Figure 2.31 illustrates the

UTAUT model.


Figure 2.31: UTAUT model by Venkatesh et al. (2003)

Task Technology Fit Model (TTF) is another acceptance technology theory that came

with a new concept and determinant through its constructs. As many research studies

have been conducted mainly to explain user adoption of new technology from

perceptions like perceived ease of use, perceived usefulness, and subjective norm, etc.

However, the adoption of certain technology by the users is not determined only by their

perception, but also by the aspect of whether that technology fits the tasks requirements.

Thus, if the technology does not fit the task, then there is no reason for the user to adopt

it (Zhou et al., 2010). Owing to this, the task technology fit is the crucial determinant of

the new technology adoption. Figure 2.32 illustrates the components of the Task

Technology Fit Model (Goodhue and Thompson, 1995).

Figure 2.32: Task Technology Fit Model (TTF) by Goodhue and Thompson (1995)

As mentioned, technology acceptance theories are widely used to measure the users’

adoption and acceptance of innovations and technology. Thus, the IT field accounts for

the highest amount of studies. The researcher has summarized some related studies that

adopted the technology acceptance theories to measure certain innovation in particular

fields. Table 2.8 shows some findings is this aspect.


Table 2.8: Technology acceptance theories in different studies

Technology adoption/implementation/ acceptance in different IT fields /Quantitative Approach No. Article Title Method Focus Finding Source 1 Integrating TTF and UTAUT to

explain mobile banking user adoption

survey / mobile banking user / Sample size of 250

The research model based on UTAUT and TTF constructs

-performance expectancy, task technology fit, social influence, and facilitating conditions have significant effects on user adoption - significant effect of task technology fit on performance expectancy

(Zhou et al., 2010)

2 The acceptance and use of customer relationship management (CRM) systems: An empirical study of distribution service industry in Taiwan

Survey/ customer relationship management (CRM) systems users / Taiwan /Sample size of 271

The research model based on UTAUT and TTF constructs to explore the factors affecting the acceptance and use of CRM systems

- No positive effect of performance expectancy on behavioral intention. - effort expectancy has shown positive effect on user behavior - social expectancy shown positively effects on user behavior - facilitating condition has positive influence on actual usage - task technology fit positively affects behavioral intention

(Pai and Tu, 2011)

3 Consumer acceptance of a revolutionary technology-driven product: The role of adoption in the industrial design development

Survey/ customer marketing company /Sample size of 275

examine factors that affect consumer intentions to use revolutionary technology-driven product(RTP) by integrating UTAUT and TTF

The model extends UTAUT and TTF theories by showing that UTAUT variables mediate between the variables of TTF and adoption intentions. The results shows the need for nonlinear industrial development processes involving consumers

(Park et al., 2015)

4 Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM

survey / mobile banking user / Sample size of 194

The research model based on UTAUT , TTF and ITM

Facilitating conditions and behavioral intentions positively influence mobile banking adoption. Initial trust, performance expectancy, technology characteristics, and task technology fit have total influence on behavioral intention

(Oliveira et al., 2014)


5 Acceptance of mobile banking framework in Pakistan

survey / mobile banking user / Sample size of 198/higher education student

The research model based on UTAUT , TTF and ITM

There was significant contribution of task characteristics (TAC) and Technology characteristics (TEC) in facilitating task technology fit. Initial trust is also found to be facilitated by structural assurance (SA) and familiarity with bank (FB). The statistical results also support the significant association of task technology fit, initial trust (IT) and facilitating condition (FC) with intention to adopt mobile banking

(Afshan and Sharif, 2016)

6 Toward an understanding of construction professionals' acceptance of mobile computing devices in South Korea: An extension of the technology acceptance model

survey / construction organization / South Korea / Sample size of 144 construction professionals

The research model based on extended TAM constructs to understanding of construction professionals' acceptance of mobile computing devices

This study provides insight into the role management in the acceptance of mobile computing devices among professionals in the construction industry

(Son et al., 2012)

7 Investigating the determinants of construction professionals' acceptance of web-based training: An extension of the technology acceptance model

survey / construction organization / South Korea / Sample size of 408 construction professionals

The research model based on extended TAM constructs to understanding of construction professionals' acceptance of web-based training

the conceptual model successfully predict for how construction professionals come to accept web-based training

(Park et al., 2012)

8 Developing ERP systems success model for the construction industry

survey / construction industries /Sample size of 281 participants

The research model based on extended TAM and information system success model

identify success factors for enterprise resource planning systems implementation such as project related variables and user related variables

(Chung et al., 2009)

9 An extension of the technology acceptance model in an ERP implementation environment

survey /Sample size of 409 participants

The research model based on extended TAM

- Empirical and theoretical prove for the usage of managerial interventions such as training and communication that affect the acceptance of technology, considered that perceived ease of use and usefulness contribute to behavioral intention to adopt the technology

(Amoako-Gyampah and Salam, 2004)

10 Acceptance of Automated Road Transport Systems (ARTS):an adaptation of the UTAUT model

survey / Europe/ Sample size of 349participants

The research model based on UTAUT constructs

Factors such as performance expectancy, effort expectancy and social influence are all useful predictors of behavioral intentions to use Automated Road Transport

(Madigan et al., 2016)


Systems, with performance expectancy having the highest impact

11 An internet shopping user adoption model using an integrated TTF and UTAUT: Evidence from Iranian consumers

survey / Iran/ Sample size of 392 participants

The research model based on UTAUT and TTF

- Except for effort expectation there was positive relationship between other variables (task technology fit, performance expectation, social impact and facilitating conditions) and internet shopping. -Significant influence of task features and technology features on task technology fit

(Bozorgkhou, 2015)

12 Integrating Technology Acceptance Model and Motivational Model towards Intention to Adopt Accounting Information System

survey / Sample size of 348 participants

The research model based on TAM and Motivational Model

-integration perception and motivation increases user’s attitude towards acceptance of IS - planning for information system adoption in the organizational context is very crucial factor that should be considered along with the attitude of the decision makers and users towards system adoption.

(Abduljalil and Zainuddin)


The successful outcome of the above-mentioned studies has encouraged the researchers

in the construction industry domain to adopt the same approach and measuring the

stakeholder belief and perception regarding the adoption of technology innovation such

as BIM. The following section presents the BIM in the construction industry and FM.

2.5 BIM adoption in AEC/FM

Background

This section presents an extensive literature review on the adoption of technology in

AEC/FM and other fields. However, the main focus is on the adoption of BIM in the

construction industry, specifically for FM, through the usage of the technology

acceptance theories and case studies. The purpose of that is to identify the gaps in the

body of knowledge related to the adoption and usage of BIM in FM. This will help to

identify the influence factors affecting the adoption of BIM in FM and answer the

research questions. The researcher summarizes the most important studies that have

been done in this domain, as shown in Table 2.9. The classification is according to three

categories: first, quantitative studies related to BIM adoption in construction industries.

Second, qualitative studies related to BIM adoption construction industries, specifically

in FM. Last, quantitative studies related to technology adoption in different IT fields.

This Table will be a guide for the researcher in this field and more information will be

extracted from it to be discussed in more details in the following sections.


Table 2.9: Summary of related studies in the domain

BIM adoption/implementation/ acceptance in construction industries /Quantitative Approach

No. Article title Method Focus Finding Source 1 Measurement and exploration of

individual beliefs about the consequences of building information modelling use

survey/construction contracting organization / United Kingdom/ Sample size of 762 construction employees

The research model based on UTAUT constructs

-The usage of BIM was broadly favourable. Enhancing job performance by using BIM were highly related to expectations that BIM use was compatible with existing approaches of working. -There is a complex construct such as social influence which may be a multidimensional construct

(Davies and Harty, 2013)

2 BIM Acceptance Model in Construction Organizations

survey/construction organization / South Korea / Sample size of 114 construction employees

The research model based on extended TAM constructs

Identifying the factors that affect BIM acceptance from an organizational and individual perspective and analyses correlations between them

(Lee et al., 2015)

3 The Adoption of Building Information Modelling in the Design Organization: An Empirical Study of Architects in Korean Design Firms

survey / Design organization / South Korea / Sample size of 162 architects


Understanding of factors supporting the adoption of BIM in Korean design organizations. The results support the extended TAM in estimating the intention to adopt BIM. The significant effect of top management support, compatibility, computer self-efficacy, and subjective norm on behavioral intention through perceived usefulness and perceived ease of use.

(Son et al., 2014)

4 Effects of Intrinsic and Extrinsic Motivation Factors on BIM Acceptance

survey / construction organization / South Korea / Sample size of 114 construction employees


It is found that BIM can be adopted only when users have the intention to use BIM to perform their job (individual acceptance) and the organization the users engaged in has the intention to implement BIM (organizational acceptance)

(Lee and Yu, 2013)

5 An Extension of the Technology Acceptance Model for BIM-based FM

Conceptualization only without validation

Based on TAM2+ TPB constructs

proposed Foundation conceptual model for further future validation

(Lee et al., 2012)

6 Comparative Study of BIM Acceptance between Korea and the United States

survey/construction organization / South Korea and USA / Sample size of 164 construction employees


The comparison between the results from the USA and Korea could serve as a reference for developing a framework model fit for Korea construction industry

(Lee and Yu, 2016a)


7 Discriminant Model of BIM Acceptance Readiness in a Construction Organization

survey / construction organization/ Sample size of 164 construction employees


The proposed model helps to better understand the key factors influencing BIM adoption in the construction organization and how to improve the establishment of efficient and effective improvement strategies for the acceptance of BIM

(Lee and Yu, 2016b)

8 Addressing individual perceptions: An application of the unified theory of acceptance and use of technology to building information modelling

survey / construction organization/ Sample size of 84 industry stockholders

The research model based on the extension of UTAUT constructs

These findings showed the need to develop policies, strategies, and incentive in to enhance the acceptance of BIM in the construction industry

(Howard et al., 2017)

9 Users-orientated evaluation of building information model in the Chinese construction industry


The research model based on TAM and IDT

The organizational, attitude and technological factors indirectly influence the actual use of BIM through perceived ease of use (PEU) and perceived usefulness (PU).

(Xu et al., 2014)

10 Identifying and contextualising the motivations for BIM implementation in construction projects: An empirical study in China


The research model based on Institutional Theory and IDT

The result of the regression analysis suggests that BIM implementation factors relate mainly to project characteristics and organizational ownership type

(Cao et al., 2016)

11 What drives the adoption of building information Modelling in design organizations? An empirical investigation of the antecedents affecting architects' behavioral intentions

survey / Design organization / South Korea / Sample size of 162 architects


This result provides a framework for better understanding of adoption behavior within the context of design organization in South Korea and increases the chances for successful usage of BIM

(Son et al., 2015)

12 Assessment of BIM Acceptance Degree of Korean AEC Participants

survey / construction organization / South Korea / Sample size of 303 participants

The research model based on TAM and IDT

The proposed conceptual model contributes to Developing more effective and practical constructs for BIM adoption

(Kim et al., 2016)

13 Key factors for the BIM adoption by architects: a China study

survey/architects organization / China / Sample size of 181 architects

New constructs Motivation, technical defects of BIM, and BIM capability are the statistically significant factors affecting architects’ BIM adoption, whereas management support and knowledge structure are not.

(Ding et al., 2015)

14 Impacts of Isomorphic Pressures on BIM Adoption in Construction Projects

survey / construction projects / China / Sample size of 92 architects

Institutional approach The findings also revealed how different sorts of institutional forces can be better adapted to facilitate the implementation of BIM in the construction industry

(Cao et al., 2014)


15 Using TAM to Explore Vocational Students’ Willingness to Adopt a Web-based BIM Cost Estimating System

survey / vocational school/ China / Sample size of 38 students

The research model based on TAM constructs

The proposed conceptual model showed that the Web-based BIM cost estimating system was very useful for learning cost estimation

(Wu et al., 2014)

16 Identifying the consideration factors for successful BIM projects

survey / Sample size of 61 international BIM expert

critical success factors for BIM, criteria for determining BIM software, BIM function, and BIM pilot projects

- information sharing, master BIM model team/manager, and leadership of the senior management are the most important critical success factors - the previous successes of BIM cases that used the relevant software and which supported the function of interest are the most important for selecting BIM software - criteria such as the project manager’s willingness in adoption BIM, and whether BIM was requested by the client should be considered in determining BIM pilot projects

(Won and Lee, 2010)

17 Survey: Common Knowledge in BIM for Facility Maintenance

survey / Sample size of 63 Focus on knowledge sharing and accumulation for BIM-FM during the project life-cycle

The survey analysis showed that maintainability issues should be considered during the facility design phase, which will lead to building BIM-FM knowledge sharing database.

(Liu and Issa, 2015)

18 BIM implementation throughout the UK construction project life-cycle: An analysis

survey / Sample size of 92 of BIM users

Identifying key information related to implementing of BIM throughout the UK construction project life-cycle

-benefit most financially from BIM followed by Facilities Managers. -Identification of Key Performance Indicators being used for BIM is provided and findings indicate a lack of industry expertise and training, providing an opportunity for education providers.

(Eadie et al., 2013)

19 BIM use and requirement among building owners

Survey/ 20 members of Construction Owner’s Association of America

How make owner adopt and use BIM

- the maturity of building owners in terms of BIM implementation is still in its first steps - even owners with BIM knowledge are not utilizing their BIMs in post-construction for FM. - results indicated similar barrier outlined by the literature including: lack of interoperability , misunderstanding of information hand-over requirements, and a general lack of software knowledge required to utilize BIM deliverables

(Mayo et al., 2012)

BIM adoption/implementation/ acceptance in construction industries /Qualitative Approach No. Article Title Method Focus Finding Source


20 Exploring the Adoption of Building Information Modelling (BIM) in the Malaysian Construction Industry: A Qualitative Approach

Qualitative approach / interviews / 8 experts

Explore the process and the level of BIM adoption in the construction industry in Malaysian

- BIM level in Malaysia is between 0-1 - no national BIM standard and guideline in Malaysia - companies should develop their own strategies such as developing a new BIM unit, new education and training program, new management style and new responsibilities and roles

(Bin Zakaria et al., 2013)

21 Factors Affecting the Current Diffusion of BIM: A Qualitative Study of Online Professional Network

Qualitative approach / analyse 45 discussion threads using NVivo 10 software/threads were retrieved from online BIM specific discussion groups

Factors influencing the Current Diffusion of BIM within the AEC

the difficulty in changing the already used workflow, lake of understanding of BIM, and implementing BIM for short term before gaining the long term benefits.

(Panuwatwanich and Peansupap, 2013)

22 BIM for facility management: a review and a case study investigating the value and challenges

case study approach/ conducted on an existing asset / 32 non-residential buildings in Northumbria University’s city campus

The value and the challenges of BIM adoption in Facility management

The showed evidence of the value of BIM in enhancing the effectiveness of FM work orders and accuracy of geometric information data.

(Kelly et al., 2013)

23 Determinants of Building Information Modelling (BIM) acceptance for supplier integration: A conceptual model

A conceptual model based on extended UTAUT

Investigating the affecting factors of BIM acceptance for supplier integration

Supply Chain firms’ disposition towards BIM is the main factors of BIM usage and adoption

(Mahamadu et al., 2014)

24 BIM in facilities management applications: a case study of a large university complex

case study of Northumbria University’s city campus

empirically explore the value and challenges of BIM in FM

BIM benefits in FM comes from improvement to current obsolete processes of information hand-over; the accuracy of FM data, the accessibility of FM data and efficiency increase in work order execution. The main challenges are the lack of methods that shows the tangible benefits of BIM in FM, the lack knowledge of adoption requirement including BIM for FM, the interoperability between FM and BIM technologies, the presence of disparate operational systems managing the same building, and the lack of BIM experience in the FM industry

(Kassem et al., 2015)

25 Effective digital collaboration in the construction industry – A case study of BIM deployment in a hospital construction project

case study approach/ hospital construction project / interviews / 8 experts

BIM deployment in a hospital construction project

Identified a set of key factors enabling digital collaboration such as change agents, new roles and responsibilities, a cloud computing infrastructure, BIM contracts, and a BIM learning environment.

(Merschbrock and Munkvo


ld, 2015)

26 Effective Facility Management and Operations Via a BIM-Based Integrated Information System

The principal of grounded theory/case project ( UNITEC)

-summarize the status quo BIM in FM and operation to identify prevailing issues - devise technical solutions based on an exemplar case

- lack of guidelines and efficient technologies for capturing BIM models of existing facilities - coping with non-consistent terminologies and taxonomies - identifying which information and what levels of detail are desired by the FM&O teams

(Parsanezhad and Dimyadi, 2013)

27 A benefits realization management building information Modelling framework for asset owners

A conceptual framework based on literature review

BIM for asset owners Conceptual framework that helps the asset owners to ensure great value from investing in BIM

(Love et al., 2014)

28 Utilizing Building Information Modelling for Facilities Management

Mixed methodology; quantitative ( survey of 42 participants) and qualitative ( interviews )

Exploring the perceptions of using BIM in the FM and highlighting real-world issues that FM have faced while implementing BIM

-BIM has the great benefits for FM professionals through data acquisition. But, lack of guidelines and best practice lead to unclear how BIM should be used for FM, - Lack of evidence of BIM for facility management cost savings is a barrier for adoption.

(Williams et al., 2014)


Details on BIM adoption in AEC/FM

Overall, the research suggests that the construction industry and FM have benefited from

BIM adoption so it reshapes the industry itself.

For example, Becerik-Gerber et al. (2012) show how the use of BIM has been extended to

include the operation and maintenance phase in terms of locating building components,

facilitating real-time data access, visualization and marketing, checking maintainability, space

management, planning and feasibility studies for noncapital construction, emergency

management, controlling and monitoring energy, and personnel training and development.

Thus, a project life-cycle integration in terms of project phases has become more reliable and

requires a new way of project delivery. Brooks and Lucas (2014) identified key factors for

success in streamlining BIM use in post-construction and show how BIM can benefit the

contractor. Their study aimed to bridge the gap between the owner and contractor and resulted

in developing a framework that helps the contractor in the hand-over process in the post-

construction stage. Similarly, Kassem et al. (2015) conducted a case study to explore the

benefits and challenges of BIM in facilities management at Northumbria University campus.

The results showed that BIM benefits to facilities management arise from an enhancement of

current paper-based hand-over processes, improve the level of accuracy of required data, and

facilitate the accessibility of data and efficiency in work order procedures. They also noted

these challenges:

• The lack of methodologies that demonstrate the tangible benefits of BIM in FM.

• The limited knowledge of implementation requirements, including BIM for facilities

management modelling requirements and interoperability between BIM and facilities

management technologies.

• The presence of disparate operational systems managing the same building.

• The shortage of BIM skills in the facilities management industry.

Regarding facilities management activities, Arayici et al. (2012) showed how BIM can support

the efficient and effective conduct of facilities management activities using the university

building in MediaCityUK as a case-study. These benefits were summarized:

• Walkthroughs generated from the BIM model offer virtual tours to visually assess key

considerations during relocation.


• Automated quantification and scheduling capability help in setting cost and time targets

such as the development and confirmation of the budget.

• Accurate quantification and scheduling attribute provide detailed information on

number and types of furniture to be moved and another cost-intensive decision-making

considerations.

Volk et al. (2014) reviewed over 180 publications on BIM. They concluded there is a carcity of

research in BIM implementation for existing buildings. They emphasized this scarcity is due

to:

• The effort of conversion from as-built building data into BIM objects

• Information updating in BIM

• Dealing with uncertain data, relations, and objects in BIM in existing buildings.

Their study has raised the attention to the existing building, which forms the most percentage

of the construction projects sector, and this may help to enhance the FM sector by implementing

BIM. Yalcinkaya and Singh (2014) have reviewed 87 papers in BIM for FM using different

database sources. They found that there is a great value and potential benefits of BIM in FM.

Those benefits include; automatisation of the whole project life-cycle information, including

the operation and maintenance phase and optimization of project’s cost and time using real-

time access for non-graphical and data graphical. Also, they revealed challenges related to the

adoption of BIM in FM such as;

• Interoperability between FM systems and BIM.

• Unclear implementation of BIM in FM through early project’s stages.

• Importof as-built information of the facilities to BIM model.

• Lack of information exchange frameworks like COBie to solve data transfer

issues.

Ilter and Ergen (2015) have reviewed 24 papers related to BIM application for building

refurbishment and maintenance. Their work revealed that the research focussed on these areas;

building a survey and as-built BIM, energy modelling and managing, integration of

maintenance information and knowledge, and interoperability and data exchange. Ashworth et

al. (2019) studied the critical success factors for facility management Employer’s Information

Requirements (EIR) for BIM. They conducted an extensive case study and concluded that the


EIR is a useful collaboration tool to gather stakeholders in the early planning phase to

understand the information needs of the client. This refers to the significance of the

collaboration among the project’s life-cycle stockholders to improve the final outcome

represented by the FM and operation. This supports(Hsieh et al., 2019) the study of (Hsieh et

al., 2019), which emphasized that as BIM technology is becoming more mature, there is a great

need for the interoperability and interaction between BIM tools and different FM systems.

Gao and Pishdad-Bozorgi (2019) emphasized that adoption of BIM-FM systems is hindered by

several factors such as the interoperability between BIM and FM context, understanding of the

implied FM principles for BIM adoption, and cost-value issues. The author suggested that a

possible starting point to address the interoperability issue in the BIM-FM context is the

adoption of the NIST Cyber throughout the Physical Systems (CPS) model. Also, he concluded

that more studies, including surveys, are needed to understand the principles for BIM adoption

in FM. Dixit et al. (2019) addressed 16 issues based on a literature review of 54 studies under

the four categories of BIM execution and information management, cost-based and legal,

technological, and contractual issues. The survey results of FM professionals with 57 complete

responses revealed that the key issue is the lack of FM professionals’ engagement in early

project phases when BIM is developing. Barbarosoglu and Arditi (2019) proposed a

maintainability checking system algorithm which can be specified for all building elements,

and it can be compatible with BIM tool such as Revit. They emphasized that BIM can reduce

the gap between the design and facilities management without increasing the load of designers.

This can be done by allowing designers to design so it improves the FM issues at the design

phase itself.

Yalcinkaya and Singh (2015) stressed that the previous BIM reviews were typically qualitative

and subjective, prone to bias, and included a few reviewed publications. They argued that his

research brought some insight by labeling and synthesizing much BIM studies published

between 2004 to 2014. About a thousand of academic papers’ abstracts were analysed using

Latent Semantic Analysis (LSA) technique. Through their findings, they identified twelve

principal research areas, and different specific research subjects related to each principal area.

This principal and specific outcome indicated the trends and patterns of BIM research. Chen et

al. (2015) developed an integrated conceptual framework analysing 75 related BIM studies and

structuring future research proposal for the missing area. Their proposed conceptual model for

bridging BIM and building (BBB) highlighted the necessity of synchronizing data and


information between BIM and real-life building activities. They stressed that the integrated

conceptual framework can facilitate future research on BBB.

Pärn et al. (2017) pointed out the scarcity of research that studies BIM for facilities management

in the architecture, engineering, construction, and owner/operated (AECO) sector. They

reviewed the published papers on the most recent studies and standards development, which

affect the application of BIM in FM. Their findings revealed that real challenges facing the

facilities management include; more attention of long term strategic aspirations, enhancement

of data interoperability/integration issues, enhancement of performance measurement,

augmented knowledge management, develop the level of competence for the FM practitioners.

Also, they proposed more case studies to observe and report the current practice to develop this

sector.

Hosseini et al. (2018) summarized some of the most influential BIM studies. Specifically, they

identified the intellectual shortages in BIM studies and skewed distribution of these studies

output across BIM-related themes. They dealt with BIM studies in different project life-cycle

phases, which led to diversity to the reader and lack of specific details on each phase. Pezeshki

and Ivari (2018) surveyed, reviewed, and classified BIM literature development between

(2000–2016) to analyse how different BIM methods have been developed. The selected articles

have been classified into ten BIM applications categories such as economic system, medical

system, education system, traffic control, electrical and electronics system, manufacturing and

system modelling, image processing and feature extraction, BIM enhancements and social

sciences, and forecasting and predictions. A brief future outline has been mentioned for each

category. Also, they revealed three main types of future development trends for BIM

methodologies, article types and domains as following;

• BIM methodologies are developed toward expertise orientation

• Different social science methodologies could be implemented using BIM as

another kind of expert methodology

• The ability to continually change and the learning capability are the driving

power.

Miettinen et al. (2018) identified the gap between BIM adoption in design and FM. Premises

Centre of the City of Helsinki key professional experts of FM were interviewed to discuss the

information tools being used in their centre, and the needs and impediments of BIM adoption


in the FM. They emphasized that the challenges in the BIM adoption are in which ways the data

and information in BIM models could be integrated with FM systems.

BIM helps support functions of facilities management by its analysis tools, visualization

capabilities, and provision of initial information to facilities management systems. Although

many experts and researchers agree about the potential benefits of BIM in FM, there is still

considerable uncertainty about how to use BIM efficiently and to what extent BIM can help

solve facilities management problems. Hence, the adoption of BIM remains a significant

concern of BIM practice and research (Lee and Yu, 2013). According to Lee et al. (2012), "One

of the key measures of implementation success is achieving the intended level of use of

information technology (IT)". IT adoption is reflected in user acceptance (Ammenwerth et al.,

2006). Hence, Technology Acceptance Models have significant benefits in this context. Several

studies measured user acceptance and use of innovations in IT fields. Some have extended TAM

constructs to enhance our understanding of the use and acceptance of new IT and to be

compatible with different contexts, including the construction industry.

Lee and Yu (2013) argued that many of the new BIM-related research findings confirm the

development of an application technology based on BIM and require implementing BIM. They

pointed out the lack of research on BIM implementation methods or research that identifies the

factors impeding BIM implementation. They found that many of these factors cover only the

technological aspects of BIM improvement methods. In addition, studies on the correlation

between BIM implementation and influential factors are lacking. They developed a conceptual

BIM acceptance model based on TAM and related theories. Their model identified the main

factors that affect the acceptance of BIM in South Korean construction organizations and

analysed the effects of extrinsic and intrinsic motivation factors on individual and

organizational acceptance.

Davies and Harty (2013) developed scales to measure beliefs related to BIM implementation

from a questionnaire survey of 762 employees of large construction organizations in the UK.

Their results supported most of the research hypotheses of the proposed conceptual model. They

concluded that “Expectations that BIM would enhance job performance were strongly related

to expectations that BIM use was compatible with preferred and existing ways of working”.

Cao et al. (2014), Lee et al. (2012) presented a BIM framework in facilities management based

on TAM2 and TPB. However, the model was theoretical, and it was not checked for validity

and reliability. Hence, they recommended addressing these gaps in future research.


Son et al. (2014) examined the technical, individual, organizational, and social factors affecting

the adoption of BIM in Korean design organizations using TAM. Their findings strongly

support a modified TAM in predicting the intention of architects' adoption of BIM and provide

insights regarding the role of management in controlling the successful adoption of BIM among

Korean design firms.

2.6 Gap in literature

The literature to date strongly suggests significant benefits of BIM adoption in the

overall project life-cycle, including facilities management and how technology

acceptance theories play the main role in measuring the adoption of innovation and

technology in different areas. However, studies also highlight the lack of actual BIM

adoption in facilities management, which raises the following question, “Why, with the

proven benefits of BIM in the project life-cycle, is adoption still minimal for facilities

management?”. Within the field of facilities management, there are still many BIM

adoption issues, which remain vague, specifically the social aspects, individually and

organizationally. Thus, successful BIM adoption, which leads to improved performance

of the organization, should be further explored.

To fill this knowledge gap, the researcher conceptualized a BIM-FM model based on

integrating UTAUT and TTF. An expected outcome from this research is to consolidate

perceptions of BIM adoption in facilities management so it helps stakeholders gain the

benefits of implementing BIM. It was named a hybrid model because the model

integrated two of the well-known technology acceptance theories of the UTAUT and

TTF. This model helps to measure facilities management practitioners’ perceptions

regarding BIM adoption. The model was based on validated and reliable variables and

items. The model provided the rationale for the constructs (factors) relying on the

theoretical background on TTF and UTAUT.

2.7 The summary

In this chapter, an extensive literature review has been done relevant to three main

themes, namely FM, BIM, and technology acceptance theories. The purpose was to

explore the benefits of BIM adoption in the construction industry in general and in FM


in particular. The literature has shown the benefit of using BIM in the whole projects

life-cycle and with an acceptable range of adoption in design and construction.

However, the BIM adoption in FM is still very low compared to other phases. In

addition, there is a lack of studies on BIM adoption in FM. That led to finding the

research gap, deriving the research questions, and formulating the research objectives.

The aim was to investigate and identify the key factors affecting the adoption of BIM in

FM.

In the next chapters of this research, the conceptual model has been developed based on

the outcome of the current chapter. Also, the research hypotheses have been drawn from

the proposed model.

78 Chapter 3: The Model Development

Chapter 3: The Model Development

Introduction: This chapter introduces the development of the conceptual model related to this

research, as well as the research questions, which has formed the basis of the developed

conceptual model in order to address the identified knowledge gap. Also, it elaborates

the operationalisation of the conceptual model constructs, which is considered a

significant step before the data collection and analysis stages.

In addition, the chapter discusses the key factors proposed to have an influence on BIM

adoption in facility management based on relevant literature review. This proposed

model has been a basis for formulating the research hypotheses for this research. Briefly,

the chapter starts by exploring the conceptual foundation of the proposed model

framework. Then the research hypotheses are derived based on the proposed model.

Finally, details on the model constructs and relationship among them are discussed.

3.1 Development of the Conceptual Model

The literature review in the previous chapter strongly suggests significant benefits

of BIM adoption in the overall project life-cycle, including facilities management and

how technology acceptance theories play the main role in measuring the adoption of

innovation and technology in different areas. However, studies also highlight the lack

of actual BIM adoption in facilities management, which raises the following question,

“Why, with the proven benefits of BIM in the project life-cycle, is adoption still minimal

for facilities management?”. Within the field of facilities management, there are still

many BIM adoption issues, which remain vague, specifically the social aspects,

individually and organizationally. Thus, successful BIM adoption, which leads to an

improved performance of the organization, should be further explored.

To fill this knowledge gap, a BIM-FM model based on integrating UTAUT and TTF

has been conceptualized. An expected outcome from this research is to consolidate

Chapter 3: The Model Development 79

perceptions of BIM adoption in facilities management in a way that helps stakeholders

gain the benefits of implementing BIM. In this research, a hybrid conceptual model has

been proposed. It was named a hybrid model because the model has integrated two of

the well-known technology acceptance theories the UTAUT and TTF. This model helps

to measure facilities management practitioners’ perceptions regarding BIM adoption.

The current section below discusses step by step the development of the conceptual

model.

As mentioned, technology acceptance theories such as TAM, TTF, UTAUT, etc., model

how users come to accept and use new technology and innovations. Many studies have

shown a successful use of those theories to explain individual perceptions regarding the

adoption of new technology in their work. Thus, technology acceptance theories have

been adopted in this study to measure the influencing factors of BIM adoption for

facilities management. Concepts of BIM adoption are introduced in facilities

management via integrating UTAUT and TTF to address the question “How the

integration of UTAUT and TTF help explain user perceptions regarding BIM adoption

for facilities management?”.

First, the UTAUT model identifies variables with an impact on technology adoption

such as facilitating conditions, effort expectancy, performance expectancy, and social

influence, which have direct and indirect influence on behavioral intention to adopt the

technology.

Second, UTAUT is based on an extended version of the technology acceptance model

and other acceptances theories, making it a robust foundation for exploring a different

range of technology adoption topics. In addition, the relations among their constructs

are mediated by gender, age, experience, and voluntariness of use.

Third, including TTF addresses questions of how users come to accept and use new

technology if this technology does not fit the job task requirements. Adoption of

technology by the users is not determined by their perceptions alone but by whether or

not that technology fits the task requirements. Hence, task technology fit is a crucial


determinant of new technology adoption

Fourth, the successful integration of the UTA UT and TTF in the IT field, for example

(Zhou et al., 2010, Pai and Tu, 2011, Faria, 2013, Oliveira et al., 2014, Tai and Ku, 2014,

Vongjaturapat et al., 2015, Park et al., 2015, Afshan and Sharif, 2016), helps us

understand possible links between the adoption of a new technology in IT and BIM

adoption in facilities management. Research has shown the successful applicability of

technology acceptance theories in the construction industry as much as their

applicability in the IT fields (Lee et al., 2012, Son et al., 2012, Davies and Harty, 2013,

Lee and Yu, 2013, Mahamadu et al., 2014, Son et al., 2014, Wu et al., 2014, Cao et al.,

2014, Xu et al., 2014, Lee et al., 2015, DAWOOD and CHAN, 2015, Ding et al., 2015,

Son et al., 2015, Lee and Yu, 2016a, Cao et al., 2016 Lee, 2016 #102, Howard et al.,

2017). However, these concepts have yet to be explored in facilities management, and

one aim of this research is to consolidate the perceptions of BIM adoption in facilities

management so it helps stakeholders benefit from implementing BIM.

Based on these considerations, three research questions have been formulated in this

chapter, and another research question was presented in chapter 6. Those three research

questions are as following:

• what are the key factors that influence BIM adoption in the facilities management

context?

• what are the relationships among UTAUT and TTF constructs that constitute the

underlying BIM adoption in a facilities management context?

• does the integration of UTAUT and TTF lead to predicting the adoption of BIM

in facilities management?

These questions are explored through the model described in the following section.

The conceptualization of the proposed model in Figure 3.1 is a hybrid integration of a

UTAUT-based rationale for model parameters and variables, and the Task Technology

Fit Model. External and internal factors were identified to consider:

(1) External factors such as performance expectancy, effort expectancy, social

influence, facilitating conditions, task characteristics, and technical


characteristics. These variables are hypothesized to have direct and indirect

influence on the internal and targeted factors of the model.

(2) Internal factors include task technology fit.

(3) Targeted factors include user adoption of BIM for facilities management.

Figure 3.1: Conceptualization of the Model

The comprehensive literature review was the key component in this conceptualization

of the proposed model. Synthesis and comparison technique have been conducted to

generate the model considering the suitable modification and wording aspects to the

constructs’ measurement items to be compatible with BIM-FM context. Table 3.1 shows

the sources of the model factors (constructs) of the current research.

Table 3.1: Factors definition of the proposed model

Factor/Construct Definition Source

Performance

Expectancy

“The degree to which an individual believes that using the

system will help him/her to attain gains in job performance.”

(Venkatesh et al., 2003)

Effort Expectancy “The degree of ease associated with the use of the system” (Venkatesh et al., 2003)

Social Influence

“The degree to which an individual perceives that important

others believe he/she should use the new system.”


External Factors

Targeted

Factor

TTF

External Factors Internal Factor

Task Characteristics

Technology Characteristics

Task Technology Fit

Performance Expectancy

Effort Expectancy

Social Influence

Facilitating Conditions

BIM adoption in FM

UTAUT


Facilitating Conditions “The degree to which an individual believes that an

organizational and technical infrastructure exists to support the

use of the system.”


Task Technology Fit

“Task technology fit is the rational perspective of what new

technology can do to optimize a job. It is affected by the nature

of the task and practicality of the technology to complete the

task.”

(Oliveira et al., 2014)

Technology

Characteristics

Main determinant of the task technology fit theory that

considered the technology characteristics aspect

(Goodhue and Thompson, 1995)

Task Characteristics Main determinant of the task technology fit theory that

considered the task characteristics aspect

(Goodhue and Thompson, 1995)

3.2 Hypothesis development based on the research model

Research has identified the factors that might have an influence on the adoption

and acceptance of technology through several robust conceptual models. Reliable

technology acceptance and adoption theories like TAM, UTAUT, IDT, TTF, etc. have

contributed to the body of knowledge significantly and in many fields. The constructs

of those models have been tested in different domains and achieved a high level of

accuracy. In this research, a hybrid model integrating TTF and UTAUT has been

conceptualized to consolidate factors influencing the acceptance of BIM in the FM

sector. Hence, this study depends on reliable constructs and items to develop BIM

conceptual model in FM. Those constructs of the proposed model will be discussed in

more details to derive the research hypothesis of this study.

UTAUT constructs

Performance expectancy

Performance expectancy can be defined as the degree to which an individual

believes that using the system will help the user to attain gains in job performance

(Venkatesh et al., 2003). It reflects FM perception of the expected performance

improvement when using BIM in managing FM tasks and practices such as facilitating

real-time data access, locating building components, checking maintainability,

visualization and space management. Research have revealed that the Performance

expectancy is a key factor for a user to adopt, use or even has behavior intention to use

certain technology (Zhou et al., 2010, Pai and Tu, 2011, Oliveira et al., 2014,


Bozorgkhou, 2015, Afshan and Sharif, 2016, Madigan et al., 2016, Howard et al., 2017).

Performance expectancy corresponds to Perceived Usefulness in TAM. Perceived

Usefulness is defined as the individual believes that using certain technology will make

positive contributions and enhancements in job performance (Davis, 1989). The benefits

and value of BIM have been demonstrated in many studies.

Further, knowing how to measure the factor (construct) can help to better

understanding of that factor. According to (Lee et al., 2015), perceived usefulness

(performance expectancy) can be measured through these scales items:

• interoperability among stakeholders is improved when BIM is used

• using BIM allows comprehensive management of life-cycle information

(design-construction- operation & maintenance)

• decision-making time is reduced when BIM is used

• BIM utilization may expand the range of collaboration with other organizations

• work task-handling time can be reduced when using BIM

• task accuracy can be improved when utilizing BIM

• fast response is possible on any changes when using BIM

Thus, the following hypothesis has been derived from the research model;

H1: Performance expectancy has a positive influence on BIM adoption in FM.

Effort expectancy

Effort expectancy is defined as a degree of easiness associated with the use of a

system”(Venkatesh et al., 2003). It reflects how difficult it is to adopt BIM approach in

the tasks of FM professionals. When FM professionals feel that BIM requires little

effort, and it is easy to use, high acceptance toward BIM adoption will be expected.

Effort expectancy corresponds Perceived Ease of Use in TAM. Perceived Ease of Use

is defined as the individual believes that using certain technology will be free of effort

(Davis, 1989). Effort expectancy is one of the key determinants in UTUAU to predict

adoption, usage or even has behavior intention to use certain technology (Zhou et al.,

2010, Pai and Tu, 2011, Oliveira et al., 2014, Bozorgkhou, 2015, Afshan and Sharif,


2016, Madigan et al., 2016, Howard et al., 2017). In fact, the easier the technology is,

the more it will get adopted and accepted by users.

Perceived ease of use (effort expectancy) can be measured through these scales items

(Lee et al., 2015)};

• It is easy to learn how to cooperate with BIM

• If we adopt BIM, it is easy to exchange information among stakeholders

• The guideline for collaboration with BIM is defined so we could follow easily

The following hypothesis has been derived from the research model;

H2: Effort expectancy positively influences the adoption of BIM in FM.

Social influence

(Venkatesh et al., 2003) stated that social influence is the degree to which an

individual perceives that it is important for others to believe that he/she should use the

new system. Social influence corresponds to the subjective norm in TAM2 and TAM3.

It represents the effect of external factors such as the opinions of relatives and friends

on FM professional’s behavior. Their opinions will affect FM adoption and usage of

BIM. Fishbein and Ajzen (1975) defined the subjective norm as "a person's perception

that most people who are important to him think he should or should not perform the

behavior in question". Research has revealed that social influence is a key factor for

adoption, usage or even intention to use certain technology (Zhou et al., 2010, Pai and

Tu, 2011, Oliveira et al., 2014, Bozorgkhou, 2015, Afshan and Sharif, 2016, Madigan

et al., 2016, Howard et al., 2017). Social influence can be described as a kind of prestige

that encourages the society to follow. This concept is true in most aspects of life and is

sometimes called prestige.

According to (Bozorgkhou, 2015), the evidence suggests that social influence is

effective on two levels. The first level is before using technology. Many users are

encouraged to use technology services by friends in other companies that used that

technology. The second level is after using it, in which social influence can be

considered as a bonus. As users achieve higher performance due to using new


technology, they expect to get good feedback from the social environment.

According to (Davies and Harty, 2013), social influence can be measured using these

scales:

• I would use BIM because of the high proportion of co-workers who use BIM.

• The senior management of my organizational unit strongly supports the use of

BIM.

• Thus, the hypothesis will be as following;

H3: Social influence has a positive influence on the adoption of BIM in FM.

Facilitating conditions

Facilitating conditions is defined by (Venkatesh et al., 2003) as “The degree to

which an individual believes that an organizational and technical infrastructure exists to

support the use of the system". BIM as a new approach requires FM professionals to

have specific skills such as dealing with BIM tools and feeding BIM with consistent

information and data. In addition, the top management team needs to bear the costs of

applying this approach and all the required changes. If users do not have these necessary

operational skills and financial resources, they will not adopt BIM. Research has

revealed the importance of facilitating conditions as key determinant in UTUAU model

to predict user adoption (Zhou et al., 2010, Pai and Tu, 2011, Oliveira et al., 2014,

Bozorgkhou, 2015, Afshan and Sharif, 2016, Madigan et al., 2016, Howard et al., 2017).

According to (Davies and Harty, 2013), facilitating conditions can be measured using

these scales;

• Guidance would be available to me for the selection of BIM tools.

• Specialized instruction concerning BIM will be made available to me.

• A specific person (or group) is available for assistance with BIM difficulties.

In fact, the user is encouraged by his/her organization when he receives support related

to new technology. This leads to the following hypothesis;

H4: Facilitating conditions has a positive influence on the adoption of BIM in FM.


TTF constructs

Task technology fit

Task technology fit is ‘‘the rational perspective of what a new technology can do

to optimize a job. It is affected by the nature of the task and practicality of the technology

to complete the task" (Oliveira et al., 2014). A good fit of task technology promotes FM

practitioners’ for more adoption and acceptance of BIM. In contrast, a poor fit decreases

that adoption. For example, although BIM has approved benefits in the whole project

life-cycle, if FM practitioners do not require BIM benefits because of incompatibility

between FM systems and BIM requirements, they will select their own traditional

approach rather than BIM. Previous research has revealed the importance of task

technology fit as key determinant in TTF model to predict user adoption of technology

(Zhou et al., 2010, Pai and Tu, 2011, Oliveira et al., 2014, Bozorgkhou, 2015, Afshan

and Sharif, 2016, Madigan et al., 2016, Howard et al., 2017). In addition, some of them

showed the importance of task technology fit to positively influence performance

expectancy as well. Many researchers have argued that there is no reason for the users

to adopt certain technology if this technology does not fit their job tasks. Thus, the

following hypotheses have been placed:

H5: Task technology fit positively influences the adoption of BIM in FM.

H6: Task technology fit positively influences performance expectancy.

Technology characteristics

Technology characteristics can be considered the main determinant of the task

technology fit theory (Goodhue and Thompson, 1995). Research has revealed the

importance of technology characteristics as key determinant in TTF model TTF

prediction (Zhou et al., 2010, Pai and Tu, 2011, Oliveira et al., 2014, Bozorgkhou, 2015,

Afshan and Sharif, 2016, Madigan et al., 2016, Howard et al., 2017). Also, according to

(Pai and Tu, 2011), if technology character is increased, task technology fit will be

enhanced. Thus, it can be hypothesized that:

H7: Technology characteristics positively influences the TTF


Task characteristics

Task characteristics is the main determinant of the task technology fit theory that

considers the task characteristics aspect (Goodhue and Thompson, 1995). According to

(Pai and Tu, 2011), " Task character means to explore whether the influence of using

technology included characters of non-routine and dependency". Research has revealed

the importance of task characteristics as key determinant in TTF model to predict TTF

(Zhou et al., 2010, Pai and Tu, 2011, Oliveira et al., 2014, Bozorgkhou, 2015, Afshan

and Sharif, 2016, Madigan et al., 2016, Howard et al., 2017). Thus, it can be

hypothesized that:

H8: Task characteristics positively influences the TTF

Overall, eight research hypotheses representing the relationships among the proposed

conceptual model constructs were developed to address the research questions. Further,

Table 3.2 presents the research hypotheses along with the related references. Figure 3.2

illustrates the developed conceptual model with the related hypotheses reflecting the

relationships among the constructs.

Table 3.2: Research Hypotheses

Hypothesis Definition Source

H1

Performance expectancy has a positive

influence on BIM adoption in FM

(Howard et al., 2017, Afshan and Sharif, 2016, Bozorgkhou, 2015, Pai

and Tu, 2011, Zhou et al., 2010, Madigan et al., 2016, Oliveira et al.,

2014)

H2

Effort expectancy positively influences

the adoption of BIM in FM

(Oliveira et al., 2014, Madigan et al., 2016 , Howard et al., 2017, Zhou

et al., 2010, Afshan and Sharif, 2016, Bozorgkhou, 2015, Pai and Tu,

2011 )

H3

Social influence has a positive influence

on the adoption of BIM in FM

(Madigan et al., 2016, Howard et al., 2017, Afshan and Sharif, 2016,

Bozorgkhou, 2015, Pai and Tu, 2011, Zhou et al., 2010, Oliveira et al.,

2014)

H4

Facilitating conditions has a positive

influence on the adoption of BIM in FM

(Pai and Tu, 2011 , Oliveira et al., 2014, Madigan et al., 2016 ,

Howard et al., 2017, Zhou et al., 2010, Afshan and Sharif, 2016,

Bozorgkhou, 2015 )

H5

Task Technology Fit positively

influences the adoption of BIM in FM

(Bozorgkhou, 2015, Pai and Tu, 2011 , Oliveira et al., 2014, Madigan

et al., 2016 , Howard et al., 2017, Zhou et al., 2010, Afshan and

Sharif, 2016 )


H6

Task Technology Fit positively

influences performance expectancy

(Afshan and Sharif, 2016, Bozorgkhou, 2015, Pai and Tu, 2011 ,

Oliveira et al., 2014, Madigan et al., 2016 , Howard et al., 2017, Zhou

et al., 2010 )

H7

Technology characteristics positively

influence the TTF

(Zhou et al., 2010, Afshan and Sharif, 2016, Bozorgkhou, 2015, Pai

and Tu, 2011 , Oliveira et al., 2014, Madigan et al., 2016 , Howard et

al., 2017 )

H8

Task characteristics positively influence

the TTF

(Howard et al., 2017, Afshan and Sharif, 2016, Bozorgkhou, 2015, Pai

and Tu, 2011, Zhou et al., 2010, Madigan et al., 2016, Oliveira et al.,

2014)

3.3 The summary

A conceptual BIM acceptance framework has been proposed in this chapter based on

reliable literature review. The model aims to identify the key factors affecting the

adoption of BIM in FM. The research hypotheses are derived and discussed. The

proposed model consists of eight factors (variables). The external variables of the model

are performance expectancy, effort expectancy, social influence, facilitating conditions,

task characteristics, and technical characteristics. These variables supposed to have a

direct and indirect influence on the internal and targeted variable of the model. The

internal variable includes task technology fit, while the targeted variables include user

adoption of BIM in FM.

Task Technology Fit

External Factors

Targeted

Factor

TTF


UTAUT




Effort Expectancy

Social Influence


BIM adoption in FM

H1

H2

H3

H4

H6

H7

H8

Figure 3.2: Conceptualization of the Model

H5


In the chapter 5 of this research, the measurement model has been tested for the

reliability and validity, while the structural model has been examined by SEM to test

the model relations and hypotheses after conducting an extensive survey targeting the

FM practitioners in Australia.

90 Chapter 4: Research Methodology and Design

Chapter 4: Research Methodology and Design

Introduction

This chapter presents the research methodology and design adopted to answer the

research questions. It begins with an explanation of the meaning of the term research, it

continues with research paradigms, and it is followed by details on the research approach

and strategy. Then, a selection of an appropriate methodology for this research is

discussed. After that, a research outline is presented, followed by a clarification on data

collection and analysis. Finally, a quick review of reliability, validity, and ethics

agreement is presented.

4.1 The meaning of the word research

Research can be defined as “The process of finding solutions to a problem after a

thorough study and analysis of the situational factors” (Sekaran and Bougie, 2016).

Further, Creswell (2008) defined research as “A process of steps used to collect and

analyse information to increase our understanding of a topic or issue. At a general level,

the research consists of three steps; pose a question, collect data to answer the question,

and present an answer to the question”.

According to Johnson and Duberley (2000), research is often based on assumptions

philosophically grounded and related to a human’s view or perception of reality. The

reality can be called ontology and epistemology in the research literature.

Further, Scotland (2012) stated that “Ontology is the study of being. Ontological

assumptions are concerned with what constitutes reality, in other words, what is.

Researchers need to take a position regarding their perceptions of how things really are

and how things really work. Epistemology is concerned with the nature and forms of

knowledge. Epistemological assumptions are concerned with how knowledge can be

created, acquired and communicated, in other words, what it means to know”.

Chapter 4: Research Methodology and Design 91

4.2 Research paradigms

The study conducted by Scotland (2012) revealed that a research paradigm

includes components such as epistemology, ontology, methodology, and, methods.

Further, Scotland (2012) stated that ”Every paradigm is based upon its own ontological

and epistemological assumptions. Since all assumptions are conjecture, the

philosophical underpinnings of each paradigm can never be empirically proven or

disproven. Different paradigms inherently contain differing ontological and

epistemological views; therefore, they have differing assumptions of reality and

knowledge which underpin their particular research approach. This is reflected in their

methodology and methods”.

The methodology is the strategy that justifies the reason for selection and use of specific

methods while methods are the particular procedures and techniques that used for data

collection and analysis (Scotland, 2012).

Understanding the research paradigm can be difficult and complicated due to different

perspectives by different specialists and researchers in this aspect. Thus, “research

onion” proposed by (Saunders et al., 2007) simplifies the whole picture as shown in

Figure 4.1.

Figure 4.1: Research onion (Saunders et al., 2007).


From the outside towards the inside, the first shell of the onion represents the research

philosophies such as positivism, realism, interpretivism, and pragmatism. The second

shell represents the research approaches, which include deduction and induction.

According to Sekaran and Bougie (2016), deductive approach is a process to reach a

reasonable conclusion by a logical generalization of a known fact while induction is a

process where the researcher observes certain phenomenon to reach a conclusion.

The third shell comprises of research strategies or research methods as termed in some

textbooks, such as experiments, surveys, case studies, action research, grounded

theories, ethnography, and archival research. Further, the second to last shell contains

the time horizon, which is divided into a cross-sectional horizon that happens in a limited

period, and a longitudinal one, which happens a couple of times. Further, the second to

last shell contains the time horizon, dividing the observational studies into cross-

sectional and longitudinal ones. Cross-sectional studies compare different population

groups at a single point in time while longitudinal studies involve observations of the

same subjects over an extended period. Finally, the core of the onion represents the data

collection and data analysis method. This onion shell is very useful for a classification

of research according to its elements.

Rahmani (2016) formulated a matrix that shows the relation between the fundamental

beliefs and the research philosophies based on multiple resources (Hallebone and Priest,

2008, Guba and Lincoln, 1994, Lewis, Saunders and Thornhill, 2009). See Table 4.1.

Table 4.1: Fundamental beliefs of research paradigms in social sciences (Rahmani, 2016)

Fundamental beliefs

Paradigm

Positivism

(Realism)

Post-positivism

(Critical Realism)

Interpretevism

(Constructivism)

Pragmatism

Ontology

What is the nature of

reality

External, objective and

independent of social

actors

Objective, exists in a

certain context of

relevant law or dynamic

of social structures

(critical realist)

Socially constructed,

subjective, constructed

by social actors and

people’s perceptions

External, multiple views

chosen to achieve the

best answer to the

research question


Epistemology

What constitutes

knowledge

Only observable

phenomena can provide

credible data, facts.

Focus on causality and

law-like generalizations

reducing phenomena to

simplest elements

Only observable

phenomena can provide

credible data, facts.

Focus on explaining

within a context or

contexts

Subjective meanings and

social phenomena.

Focus on the details of

the situation, the reality

behind these details,

subjective meanings and

motivating actions

Either or both observable

phenomena and

subjective meanings can

provide acceptable

knowledge dependent

upon the research

question

Axiology

The role of values in

research and the

researcher’s stance

Value-free and etic.

Research is undertaken

in a value-free way, the

researcher is

independent of the data

and maintains an

objective stance

Value-laden and etic.

Research is value-laden;

the researcher is biased

by world views, cultural

experiences, and

upbringing

Value-bond and emic.

Research is value-bond,

the researcher is part of

what is being researched,

cannot be separated and

so will be subjective

Value-bond and etic-

emic.

Values play a large role

in interpreting the

results, the researcher

adopting both objective

and subjective points of

view

Methodology

The model behind the

research process

Quantitative Quantitative or

Qualitative

Qualitative Quantitative and

Qualitative

4.3 Research methodologies and methods

As stated above, research methodology is the strategy that justifies the reason of

the selection and use of specific methods while research methods are the particular

procedures and techniques used for data collection and analysis (Scotland, 2012).

Firestone (1987) revealed in his research four main point to compare between

quantitative and qualitative approach cited from (Taylor & Bogdan, 1984; Cronbach,

1975; Goodenough, 1971). See Table 4.2.

Table 4.2: Comparison between quantitative and qualitative approach (Firestone,

1987).

Quantitative Qualitative

Assumptions about the

world

Based on a positivist philosophy which has

assumed that there are social facts with an

objective reality apart from the beliefs of

individuals

Rooted in a phenomenological paradigm which holds

that reality is socially constructed through individual or

collective definitions of the situation

Purpose Seeks to explain the causes of changes in

social facts primarily through objective

measurement and quantitative analysis

More concerned with understanding the social

phenomenon from the actors perspective through

participating in the life of those actors

Approach Typically employs experimental or

correlational designs to reduce error, bias,

and other noise

Ethnography which helps the reader understand the

definitions of the situation of those studied


Research role The researcher avoids bias The researcher becomes immersed in the phenomenon

of interest

4.4 Selecting the research process for this research

This study uses the concept of research onion developed by Saunders et al.

(2007) to plan the research, as illustrated in Figure 4.2.

Figure 4.2: Research stages represented by layers of a research onion (Saunders et al.,

2007).

4.5 Research design and justification

The nature of the presented research is considered relevant to social science

research. Particularly, it belongs to the field of construction management and built

environment. The study attempts to answer a research question by testing the proposed

conceptual model through evaluating the relationships among its constructs and

hypotheses testing.

Generally, as explained, the research approach can be classified as quantitative or

qualitative. Quantitative research deals with conceptualization, constructs measuring,

and analysing data related to the real-world by conducting means of numerical data and


representing explicitly defined constructs/variables (Mitchell and Bernauer, 1998). This

approach is based on a positivist philosophy, which assumes there is objective reality

obtained from social facts besides the beliefs of individuals. It employs statistical

methods to compare a significant sample of observations, which makes the findings

possibly generalized to the whole population (Firestone, 1987, Mitchell and Bernauer,

1998).

According to (Creswell, 2003), a positivist approach is used to investigate and establish

explanatory relations, in which the effects are determined by causes, so the obtained

knowledge is based on a specific observation of reality.

In contrast, qualitative research relies on well-defined variables that capture the values

through textual data instead of numerical data from a few specified case studies to

analyse them through specific techniques. Consequently, this approach is considered

more relevant to social constructionism approach (Mitchell and Bernauer, 1998).

According to (Panuwatwanich, 2008), constructs sometimes require methods of social

constructionism to provide a better understanding. In contrast, social constructionism

studies sometimes need objectivity to justify and generalize the results of the research.

Overall, many researchers who work in organizations argue that applying mix methods

to some degree provides more perspectives on the phenomena under investigation

(Easterby-Smith and Thorpe, 2002).

The current study has adopted a quantitative research approach because the nature of the

study dealt with the model-conceptualizing, testing of the hypotheses, and finding the

relation between the model’s constructs, which more suits the quantitative research

approach. Unlike qualitative method, quantitative method outcomes can be generalized

using the statistical analysis tests. This is what has been done in this research. On the

other word, quantitative method considers the advantage of using statistical methods in

terms of generalisability that can benefit this study.

A questionnaire survey approach has been conducted to evaluate and refine the

relationships and structure in the conceptual model and to determine how well the model


represents and describes them. After this stage, SEM analysis has been applied to

approve the validity of the empirically conceptualized model. Then, the results from the

research were discussed and the conclusions were drawn.

The following sections explain each phase of the current research represented in Figure

4.3.


Figure 4.3: The research phases.

Literature Review

Compilation of

Knowledge

Initial Survey Instruments

Experts Revision

Modified Conceptual BIM Adoption Model in FM

Final Survey Instruments

Final Survey

Final Conceptual BIM Adoption Model in FM

Questionnaire Survey Stages Conceptual

BIM Adoption Model in FM

Knowledge Gap


4.6 Literature review and compilation of knowledge

This stage gathers the fundamental knowledge related to the adoption of BIM in the

AEC/FM phases. A comprehensive review and a critical analysis were conducted on the

vast related literature in this domain.

Specifically, this stage involves exploring and examining both the general and

construction-specific materials pertinent to these topics:

• Building Information Modelling concepts

• Facilities Management concepts

• Technology Acceptance Theories

• Technology adoption concepts and related terminologies

• Factors influencing the adoption of technology in construction industries

• Adoption of BIM in FM context.

Literature review and knowledge compilation led to a detailed understanding of the

fields relevant to the presented study and of the theoretical frameworks that have been

established. In addition, after the knowledge gap identification during the review

process, the need for a conceptual model to address such shortages became evident.

4.7 The model development

The literature review was the key approach to formulate the research questions based on

the research gap. To answer the research questions, a conceptual model was developed

based on an extensive related literature review. The developed model helped to establish

a set of hypotheses which represented the relationships of the model factors to uncover

the BIM adoption in FM. The integrated model of TTF and UTAUT consists of eight

constructs, which hypothesized to have a direct and indirect relationship between them

to predict the user adoption of BIM as illustrated in Figure 4.4.


Figure 4.4: Conceptualization of the model.

Eight hypotheses were derived to measure the perceptions of adoption as following:

H1: Performance Expectancy has a positive influence on BIM adoption in FM

H2: Effort Expectancy positive influences the adoption of BIM in FM

H3: Social Influence has a positive influence on the adoption of BIM in FM

H4: Facilitating Conditions has a positive influence on the adoption of BIM in

FM.

H5: Task Technology Fit positively influences the adoption of BIM in FM

H6: Task Technology Fit positively influences Performance Expectancy

H7: Technology Characteristics positively influences the TTF

H8: Task Characteristics positively influences the TTF

Also, operationalisation of the model constructs has been conducted to ensure reliability

and accuracy. Then, the conceptual model with the associated hypotheses have been

assessed and validated through quantitative analysis method. These sections details on

this.

Task Technology Fit

External Factors

Targeted

Factor

TTF


UTAUT




Effort Expectancy

Social Influence


BIM adoption in FM

H1

H2

H3

H4

H6

H7

H8

H5


Quantitative analysis/model assessment

The purpose of the quantitative analysis was to empirically assess and refine the

developed model by conducting quantitative research method which using a

questionnaire survey that targeting BIM-FM practitioners. The questionnaire was

developed based on the defined factors and items that encapsulated the proposed model.

The data obtained from the FM organizations has been used to perform a set of statistical

analysis such as EFA and SEM to evaluate the model and assess the significance of the

relationships among the model component which lead to further refinement.

Questionnaire development

A Questionnaire is a way to elicit knowledge from experts. (Dillman, 2000) suggested

a procedure for developing and administering the questionnaire in order to gain accurate

and usable data as follows:

• using appropriate language;

• making the questionnaire appear short and easy;

• making the questionnaire interesting and easy to complete by carefully designing

the layout, structuring the order of the questions, and using graphics to create visual

navigational guides;

• using an introductory letter to establish the significance of the study, to show

positive regard, and to thank the respondents in advance;

• establishing trust by providing pre-incentives as a token of appreciation and

using post-incentives as a way of giving tangible rewards;

• making it convenient for the respondents to return the completed questionnaire

by providing a pre-paid reply envelope and creating a web-based version of the

questionnaire as an alternative means of completing the questionnaire.

Further, some of the useful questionnaire questions from previous related studies were

also adopted in this research, thus giving the developed questionnaire more strength and

reliability.

The draft questionnaire has been tested to ensure the understanding of the questions by

the respondents. According to (Panuwatwanich, 2008), there are two ways to check the


questionnaire before conducting the survey, panel judgment, and a pilot study. He stated

that “a panel judgment ensures the content validity of the questionnaire, while a pilot

study identifies any ambiguities in the questionnaire, as well as enabling the researchers

to see if the collected data behave as expected”. This research only used a panel

judgment method. This is because during the pilot test method, a large number of

respondents can be involved, which makes the remaining sample inadequate when

conducting the main survey (Cavana, 2001).

Ethic clearance from SUHREC for the study number SHR Project 2017/131 was

obtained in August 2017. Then, a panel judgment procedure was conducted between

August and November, 2017. The objectives of the panel judgment procedure were to

explore whether the questions and the instructions of the questionnaire survey were clear

and understandable. Also, to make sure that the questions conveyed consistent meaning

for all respondents. Two of the experts in BIM-FM area were chosen. The experts have

been requested to judge the questionnaire regarding of the format, length and any

language/ terminology issues.

They emphasized that the questionnaire was simple, easy and well designed, except

some changes that would help to make the questionnaire more understandable as shown

in the Table 4.3.

Table 4.3 Panel judgment feedback Expert ID Note on questionnaire Expert No. 1 Consent form reads well. They following feedback related to each section in the questionnaire :

Section 1:

• I think the questions need to be more personalised, e.g. what is your gender? What is your level of education?

• Q 4: the response category "staff" is vague. I think this should be more specific. Also include the category "Other" would be helpful for the respondents in the case their title doesn't match with one of the answer (this is to reduce their burden of trying to determine how their title could best fit one of the answers)

• Q 5: Job experience in what industry? Should be more specific - in FM or in construction overall?

• Q 6: BIM usage in FM? Or in design? Be more specific.

Section 2:


• I assume your sampling frame will include those people who have used BIM in FM? If so that's fine. If not then you need to ensure they have sufficient knowledge to answer this section accurately

• Q 6: Unclear - too much of a high level language used. Please simplify it. • Q 7: Training of BIM? • Q 15: What's the meaning of "Owner's view"? Just the owner's opinion or the actual influence in

the design and construction?

Section 3:

• 3.4 SI4: "the organization" should read "my organization" • 3.7 TEC1 - TEC3 the term "services" is unclear. Maybe it's better to include some examples.

This would assist the respondents to easily recall their experience.

Expert No. 2 Section 1:

• Consider adding “Certificate or Associates Degree” & “Apprenticeship / Licensure”. It is plausible that some of your participants may have limited or no higher education.

• Be clear what you are asking here. If you just want to know how long they have been working, then this reads fine. If you want to know how long they have been working in AEC industry, the question should be more specific.

• Is this question related to the individual’s experience or the company’s involvement with BIM?

Section 2:

• Consider using terms in lieu of numbers. If (1) is strongly disagree, it is safe to assume that (2) is disagree. The question is, is (3) “neutral” or “no opinion” or “not applicable”. In addition, based on the setup of this response, the researchers need to be comfortable with the possibility of each respondent answering all with the same “level of agreement.” In other words, if you are trying to decide which is most important and least important, you may need to consider a different approach as a respondent could select all fives or all ones.

Section 3:

• Be sure to clarify that these are FM based. CAD staff use BIM to optimize cost and time but not

necessarily for FM related tasks.

• PE2 and PE3 are written as if the user does use BIM and is making a decision of the impact. PE1

and PE4, ask a non-user to forecast how BIM may impact them. These are two separate users

and the questions that are asked, should be a function of the responses given in 3.1. This

comment is also relevant for Section 3.3.

The final questionnaire has been formed after applying most of the feedback from the

panel judgment as shown in Appendix A.

Many ways can be used for questionnaire delivering like e-mail, post and using social

media like Linked In. The researcher emailed the invitation letters and project

information statement (PIS) to all identified survey participants. The consent to

participate was implied and the interested participants have participated in the online

survey.


The participants were asked to rate the answers according to a Likert scale from 1 to 7,

where 1 stands for “mostly disagree” and 7 for “mostly agree”. After getting the required

sample size, the data was analysed according to specific steps.

Sample size

The target number of participants for this study was around 120 participants related to

the BIM-FM area. The justification for targeting these participants is that they were

expectedly involved in using building information Modelling (BIM) in their facilities

management organizations. So, getting information from them regard using BIM was

considered a best way to test and validate the proposed model. The appropriate sample

size in this context should be at least 4-5 times the number of items used in the study

(Hair et al., 2006a). Hence, the number of items of this research is 28. That means a

sample size of 120 was acceptable. Further, Partial Least squares (PLS)-SEM has been

used to analyse the collected data for this research. PLS-SEM is extensively used in

social science and management research (Hair et al., 2012, Sosik et al., 2009, Tenenhaus

et al., 2005) as a nonparametric method to analyse the ordinal data (Marcoulides and

Saunders, 2006). PLS-SEM is used when the research objective focuses on prediction

and explaining the variance of key target constructs by different explanatory constructs.

PLS-SEM makes fewer distributional assumptions than covariance-based SEM

(Tenenhaus et al., 2005). For example, it does not propose a probability model for the

measurement of latent variables. Instead, latent variables are approximated using linear

composites of observed variables. For these reasons, it avoids problems of non-

convergence that may occur with covariance-based SEM when the sample size is small

such as the status of the current research (Hair et al., 2013)

Data analysis

Regarding data analysis, SPSS and Smart-PLS have been used for that purpose. The

proposed conceptual model for this study consisted complicated data of dependent and

independent variables. Thus, using multivariate statistics should be the best choice

(Panuwatwanich, 2008). Specifically, the analysis is going through main three steps;


• First; descriptive data statistics

• Second; validity of the measurement model

• Third; validity of structural model

The collected data was analysed with the statistical software (SPSS) version 23 for

descriptive statistics and some other statistical tests. SmartPLS Version 3 was applied

for Partial Least squares (PLS)-SEM. PLS-SEM is extensively used in social science

and management research (Hair et al., 2012, Sosik et al., 2009, Tenenhaus et al., 2005)

as a nonparametric method to analyse the ordinal data (Marcoulides and Saunders,

2006). PLS-SEM is used when the research objective focuses on prediction and

explaining the variance of key target constructs by different explanatory constructs.

PLS-SEM makes fewer distributional assumptions than covariance-based CB-SEM

(Tenenhaus et al., 2005). For example, it does not propose a probability model for the

measurement of latent variables. Instead, latent variables are approximated using linear

composites of observed variables. For these reasons, it avoids problems of non-

convergence that may occur with covariance-based SEM when the sample size is small

(Hair et al., 2013). The PLS-SEM approach involves a measurement model and a

structural model. In the measurement model, convergent and discriminant validity were

examined. Structural models were used to test the hypothesized relationships in this

research. Chapter 5 presents the detailed results of preliminary analysis, demographic

variables of respondents and followed by descriptive analysis for research variables.

Also, results for the measurement and structural models are then presented for the

research hypotheses proposed.

4.8 The impeding factors for BIM in FM

Beside the conceptual model, an extensive review of impeding factors was performed

to generate a list of impeding factors. The impeding factors for BIM in FM used in this

research are listed in Appendix A section 2. The initial list contained more than 25

factors, which were then reduced to 17 factors as shown in Appendix A section 2. The

impeding factors that have been developed by Becerik-Gerber et al. (2012) were


adopted, because they included the most common factors mentioned in other studies in

different ways such as (Kassem et al., 2015, Volk et al., 2014, Yalcinkaya and Singh,

2014, Hsieh et al., 2019, Gao and Pishdad-Bozorgi, 2019, Dixit et al., 2019, Pärn et al.,

2017, Miettinen et al., 2018, Kiviniemi and Codinhoto, 2014). However, those factors

have not been examined in quantitative manner, nor in Austrian context which made the

research gap for the current study. EFA was used to find the underlying group of factors

for these identifies barriers.

4.9 Ethics

The need of ethics in research is to ensure there is no harm to the participants and

the environment when conducting the research and gathering the data. Different

countries apply different standard in dealing with ethics in reach. However, the core idea

is there is no or negligible harm. Thus, for conducting this research, the ethical standards

have been followed. In addition, respondents’ privacy participating in this research have

been kept in all times during and after the research.

The researcher declare that the presented research was conducted according to the

ethical code of practice in research of Swinburne University of Technology. Before

starting the data collection, an application for ethics approval to carry out the data

collection was released. Consent was sought before the start of the data collection to

ensure participants’ willingness to participate in the research project. A copy of the

consent form was given to the participants with the option to withdraw from the data

collection phase if desired. As mentioned before, prior to the data collection stage,

human research ethics clearance was obtained from the Human Research Ethics

Committee of Swinburne University of Technology (SUHREC). Ethics clearance from

SUHREC for the study number SHR Project 2017/131 was obtained in August 2017.

4.10 The summary

The current chapter presents the research method, research approach, research

design, and the relevant statistical techniques adopted in this study. The research

approach started with a literature review and knowledge compilation that led to

identifying the knowledge gaps and formulating the research questions. Then, the


conceptual model was proposed to answer the research questions based on validated

related constructs. This study employs a quantitative method involving a questionnaire

survey of FM organizations. The quantitative data analysis employed descriptive

analyses, multivariate analyses and EFA by utilizing SPSS and SEM by utilizing Smart-

PLS. The aim of this stage is to assess and refine the proposed conceptual model to

produce an updated empirical model that best depicts the relationships among the model

constructs.

Chapter 5: Data Analysis and Results* 107

Chapter 5: Data Analysis and Results*

Introduction

This chapter includes the results of descriptive statistics obtained from data

analysis and also the results from inferential statistics based on research hypotheses. The

collected data was analysed with the statistical software (SPSS) version 23 for

descriptive statistics and some other statistical tests. SmartPLS Version 3 was applied

for Partial Least squares (PLS)-SEM. PLS-SEM is extensively used in social science and management research (Hair et al.,

2012, Sosik et al., 2009, Tenenhaus et al., 2005) as a nonparametric method to analyse

the ordinal data (Marcoulides and Saunders, 2006). PLS-SEM is used when the research

objective focuses on prediction and explaining the variance of key target constructs by

different explanatory constructs. PLS-SEM makes fewer distributional assumptions

than covariance-based SEM (Tenenhaus et al., 2005). For example, it does not propose

a probability model for the measurement of latent variables. Instead, latent variables are

approximated using linear composites of observed variables. For these reasons, it avoids

problems of non-convergence that may occur with covariance-based SEM when the

sample size is small (Hair et al., 2013). The PLS-SEM approach involves a

measurement model and a structural model. In the measurement model, convergent and

discriminant validity were examined. Structural models were used to test the

hypothesized relationships in this research. This section starts with the results of

preliminary analysis, demographic variables of respondents and followed by descriptive

analysis for research variables. Results for the measurement and structural models are

then presented for the research hypotheses proposed.

*Some of content given in this chapter are based on the material published or submitted and under review in:

Paper 1: Hilal, M., Maqsood, T. and Abdekhodaee, A. (2019), "A hybrid conceptual model for BIM in FM", Construction

Innovation, Vol. 19 No. 4, pp. 531-549.

Paper 2: HILAL, M., MAQSOOD, T. & ABDEKHODAEE, A. A hybrid conceptual model for BIM adoption in facilities

management. Construction Innovation: Information, Process, Management, submitted on 7-November 2019.

108 Chapter 5: Data Analysis and Results*

The questionnaire survey

In this chapter, a questionnaire was designed to examine the key factors that

influence the adoption of BIM in FM within Austrian context. The data collection

through a questionnaire survey was carried out between November 2017 to December

2018 in Australia. Prior to data collection stage, human research ethics clearance was

first obtained from the Human Research Ethics Committee of Swinburne University of

Technology (SUHREC). Ethic clearance from SUHREC for the study number SHR

Project 2017/131 was obtained in August 2017. Then, an expert judgment procedure

was conducted between August and November, 2017. The objectives of the expert

judgment procedure were to explore whether the questions and the instructions of the

questionnaire survey were clear and understandable. Also, to make sure that the

questions conveyed consistent meaning for all respondents.

Two of the experts in BIM-FM area were chosen to this issue. The experts have

been requested to give their general judgment of the questionnaire regarding of the

format, length and any language/ terminology issues. They emphasized that the

questionnaire was simple, easy and well designed, except some changes that would help

to make the questionnaire more understandable. After doing all the required corrections,

the questionnaire was designed through Opinio and published online in November,

2017. The researcher targeted every event and conference related to BIM-FM in

Australia, where the study was. The strategy was to ask every interested expert during

and after the end of the event, and explain the purpose of this research, goals and the

possible contribution by doing an online survey. By getting the participant’s acceptance

to participate in this online survey, the researcher would send them the online link so

they can do the survey at the same time by using the researchers’ platform devices or

the participants own mobile devices. This strategy was very successful and achieved

high rate of participations during about four months only. 134 participants accepted to

participate in the survey. However, only 121 have completed the survey.

The questionnaire related to this chapter as shown in Appendix A section 1 and 3,

consists of two parts: (1) general information regarding the respondents; and (2) the key

factors affecting the adoption of BIM in FM. The respondents were aske d to evaluate


the importance of the listed impeding factors using a 1–7 point Likert scale, where (1)

represents strongly disagree and (7) represents strongly agree.

5.1 Non-response bias test

The non-response bias is concerned with the subject that systematic differences

could happen between respondents that contributed to the survey and non- respondents.

The assessment of the non- response bias is based on the proposal of Armstrong/Overton

(1997) that non-respondents are like to those respondents that contributed very late in

the survey. Therefore, they recommend comparing the response behavior between early

and late respondents to test for the existence of a non- response bias. Early responses

are defined in this study as responses received before sending the first reminder (30 days

from the first mailing), whereas late responses are those received after that date.

Considering this principle, 17 responses were considered as early responses and 104

responses were considered as late responses. Hence, the sample is split into two parts,

based on the date of response. Results for both groups of early and late respondents are

shown in Table 5.1.

The constructs Likert-scaled indicators of early and late respondents are then tested for

significant differences applying a Mann-Whitney U test. The null hypothesis that early

and late respondents are not statistically different for all items. According to the results

of the Mann-Whitney test, it was found a very satisfactory result and it can, therefore,

be concluded that there are no significant differences between early and late responses,

in short, a non- response bias does not exist in this study.

Table 5.1: Results for Non-response bias Item Mann-Whitney U Z P-value

TTF1 779.5 -0.804 0.421

TTF2 877.5 -0.05 0.960

TTF3 774 -0.856 0.392

TEC1 642.5 -1.873 0.061

TEC2 862.5 -0.167 0.868

TEC3 806 -0.599 0.549

TAC1 710.5 -1.331 0.183


TAC2 689.5 -1.502 0.133

TAC3 817 -0.514 0.607

USE1 849 -0.266 0.790

USE2 749 -1.038 0.299

USE3 865 -0.145 0.885

PE1 748 -1.053 0.292

PE2 758 -0.966 0.334

PE3 732 -1.169 0.243

PE4 677.5 -1.579 0.114

EE1 840.5 -0.338 0.735

EE2 848 -0.278 0.781

EE3 796.5 -0.679 0.497

EE4 700.5 -1.428 0.153

SI1 806.5 -0.595 0.552

SI2 814.5 -0.534 0.593

SI3 738 -1.123 0.261

SI4 692.5 -1.467 0.142

FC1 714 -1.306 0.192

FC2 724 -1.224 0.221

FC3 795.5 -0.675 0.500

FC4 623.5 -1.995 0.046

5.2 Data cleaning and screening

The data cleaning stage is very important to apply SEM, the measurement model

stage tries to detect the error component of the data and remove it from the analysis. As

a result, the research design phase of any project must be carefully planned and executed

so the answers to questions are as valid and reliable as possible.

When empirical data are collected using questionnaires, usually data collection issues

must be addressed after the data are collected. The main issues that need to be inspected

include missing data, outliers and data distribution. We briefly address each of these on

the following pages. The reader is referred to more comprehensive discussions of these

issues (Hair Jr et al., 2010).

Missing data and imputation

Missing data refers to not available information for a subject (or case) in the


questionnaire (Hair Jr et al., 2015). Missing data cause by the respondent’s denial or

forget to answer one or more questions. Therefore, the questionnaire has been including

a guideline in order to decrease the missing data or answering twice for one item. To

check the error, the researcher needs to look for values those falls out the range of right

values for the items (Pallant, 2013). Furthermore, there is an important need to check

the errors before starting the analysis because these errors can change it. In order to

check the errors this study has done frequencies for each item. Therefore, now the data

are screened and cleaned to do the analysis. Cases with over 20 percent of data missing

were not appropriate for the analysis, as the questionnaire was likely to distort the

analytical statistics and cause misinterpretation of the results. This was particularly

critical in the justification of SEM (Cohen et al., 2014, Cunningham, 2008).

Experts have not reached a consensus regarding the percentage of missing data that

becomes problematic. Schafer (1999a) recommended 5% as the cut-off. However,

Bennett (2001) suggested that when over 10% of data is missing, statistical analyses are

likely to be biased; and others have used 20% (Peng et al., 2006). Missing data are a rule

rather than an exception in quantitative research. Enders (2003) stated that a missing

rate of 15% to 20% was common in educational and psychological studies. As a result,

after detailed selection, out of 134 respondents 13 questionnaires, which had more than

20 percent of data missing, were discarded from the sample pool. A statistical

comparison between cases with more than 20% missing data with cases with less than

20% missing data was done and results indicated that these two groups for all items were

not statistically different. See Appendix B. Based on frequency distribution, missing

observation for all items was defined and the results showed that 23 items out of 28

items had at least one missing observations. There were only 95 missing data points

from 3,388 that needed to be handled before the model measurement. This represented

2.8 % of the total. While the completed data represented 97.2 %.

Further analysis was established to check the missing value pattern. There are three

general types of missing patterns:

• Missing at Random (MAR)


• Missing Completely at Random (MCAR):

• Not missing at random (NMAR)

Missing Completely at Random (MCAR) can be tested by comparing the actual pattern

of missing data with randomly distributed missing data (Little and Rubin, 2019). Little’s

MCAR test showed Chi-Square = 820.9, DF = 765, Sig. = .079. Multiple Imputation

(MI) combines the strength of a maximum likelihood approach with the Expectation

Maximization (EM) and creates five to ten data sets in which raw data are generated that

can be applied to replace the missing data (Schafer, 1999b). The data from the imputed

data set is then pooled and all statistical parameters are estimated. MI is based on

generating a maximum likelihood-based covariance matrix and vector of means, similar

to the EM algorithm. MI is particularly flexible for an extensive diversity of linear and

nonlinear models. It has been mentioned that MI outperforms listwise and pairwise in

most cases (Allison, 2001, Schafer, 1997).

In this research, MI was performed using five data set using SPSS based on the

automatic method which scans the data and uses the monotone method if the data show

a monotone pattern of missing values; otherwise, fully conditional specification is used.

Suspicious response Patterns

Before analysing their data, researchers should also inspect response patterns. In

this regard, researchers are looking for a pattern often defined as straight lining. The

straight lining is when a respondent marks the same answer for a high proportion of the

questions. A visual inspection of the responses or the analysis of descriptive statistics

(e.g., mean, variance, and distribution of the responses per respondent) allows

identifying suspicious response patterns. However, in this study, there were no cases for

having suspicious response patterns.

Normality test of data

Examination of normality of the data is an essential assumption before using

multivariate data analysis methods including regression analysis and SEM. When a


normality assumption is violated, an alternative method should be hired (Henseler et al.,

2009). In this study, the normality of data was evaluated as a basic assumption through

skewness and kurtosis and results for all variables in the model are shown in Table 5.2.

Byrne (2010) stated that if the skewness value is between -2 to +2, and the kurtosis value

is between -7 to +7, the data are considered normal. The skewness ranged from 0.026

to 0.614 and the kurtosis ranged from -0.645 to 0.361 that revealed all variables are

normally distributed.

Table 5.2: Result of Normality Test Variable Skewness Std. Error Kurtosis Std. Error

TTF 0.297 0.22 0.361 0.437

TEC 0.614 0.22 -0.167 0.437

TAC 0.083 0.22 0.262 0.437

USE 0.293 0.22 0.045 0.437

PE 0.026 0.22 -1.221 0.437

EE 0.582 0.22 -0.645 0.437

SI 0.134 0.22 -0.234 0.437

FC 0.490 0.22 0.442 0.437

EE: Effort Expectancy, FC: Facilitating Conditions, Performance Expectancy, SI:

Social Influence, TAC: Task Characteristics, TEC: Technology Characteristics,

TTF: Task Technology Fit, USE: User Adoption of BIM

Outlier

The outlier is an observation that is greatly different from other observations due

to its high or low scores (Hair et al., 2006b). Therefore, researchers affirm that outliers

can have an impact on normality (Kline, 2015). Outliers can happen when the standard

score (Z-score) is larger than ±4 (Tabachnick et al., 2007). According to the outlier test

for this, the range (Min-Max) of Z-score for all research constructs were -3.09 to 2.22.

The result showed that all Z-score were in an acceptable range. Table 5.3 shows the

outlier results of all research variables in this study.


Table 5.3: Result of outlier test

Minimum Maximum

Z-score (TTF.M) -2.73 2.04

Z-score (TEC.M) -2.15 2.22

Z-score (TAC.M) -2.87 1.79

Z-score (USE.M) -2.59 2.08

Z-score (PE.M) -1.97 1.46

Z-score (EE.M) -1.82 2.17

Z-score (SI.M) -3.09 1.81

Z-score (FC.M) -2.44 2.43

5.3 Common method Bias

Since this study was done as a cross-sectional study (single data collection

method), a test was done to inspect the possible presence for common method variance.

Common Method Variance (CMV) is the simulated "variance that is stated to the

measurement method rather than to the constructs the measures are assumed to

represent" or equivalently as "systematic error variance shared among variables

measured with and introduced as a function of the same method and/or source". This

can be assessed by applying a single-factor analysis for models that recommend

measuring multiple constructs (Podsakoff and Organ, 1986). In this study Harman’s

(1976) single-factor test was used to measure the CMV. The first factor accounts for

41.215 % of the overall variance, which shows that CMV likely does not affect the

results since it was less than 50% (Podsakoff and Organ, 1986). See Table 5.4.

Table 5.4: Result of CMV Extraction Sums of Squared Loadings

Total % of Variance Cumulative %

11.54 41.215 41.215

5.4 Multicollinearity Analysis

Multicollinearity in sets of predictor variables can be dealt with using multiple


regression analysis. In general, it is recommended that two variables with a bivariate

correlation with r=0.7 or higher must not be used in the same analysis (Meyers et al.,

2006, Tabachnick et al., 2007). Variance Inflation Factors (VIFs) also are used to

measure the influence of multicollinearity. While a VIF value of 10 is reflected

challenging .Two endogenous variables with more than one predictors were include in

this research and as shown in Table 5.5, for the first endogenous variables (task

technology fit) the highest VIF was 1.178 and for second endogenous variables (User

Adoption of BIM) the highest VIF was 2.526 which were far below the cut-off of 10,

and also below the conservative cut-off of 2.5.

Table 5.5: Collinearity Assessment based on VIF

TTF USE

EE 2.526

FC 1.981

PE 1.816

SI 2.484

TAC 1.178

TEC 1.178

TTF 1.857

EE: Effort Expectancy, FC: Facilitating Conditions, Performance Expectancy, SI:

Social Influence, TAC: Task Characteristics, TEC: Technology Characteristics,

TTF: Task Technology Fit, USE: User Adoption of BIM

As shown below in Table 5.6, the correlation coefficients among all independent

variables in this research were assessed, they were all less than 0.7 indicating that there

is no multicollinearity. The scatter plot also indicated a linear relationship between

independent variables and dependent variables (See Appendix C).

Table 5.6: Multicollinearity test based on correlation coefficients

TTF TEC TAC PE EE SI FC

TTF 1

TEC 0.679 1

TAC 0.330 0.392 1


PE 0.390 0.589 0.210 1

EE 0.618 0.618 0.279 0.640 1

SI 0.604 0.565 0.253 0.545 0.646 1

FC 0.613 0.580 0.299 0.401 0.593 0.648 1

EE: Effort Expectancy, FC: Facilitating Conditions, Performance Expectancy, SI: Social Influence, TAC: Task

Characteristics, TEC: Technology Characteristics, TTF: Task Technology Fit, USE: User Adoption of BIM

5.5 Respondents’ features and demographic profiles

The rate of responses for this study was as shown in Table 5.7:

Table 5.7: Responses rate

Total invitations sent Total

collected

Dropped

(Unrelated)

Used

questionnaires

Response rate

536 134 13 121 22.5%

As shown in the Table 5.7 above, 536 invitations were sent and 134 participants

accepted to participate in the survey. However, only 121 have completed the survey.

Table 5.8 shows the respondent's distribution for all demographic variables. Based on

the results of the respondent's gender showed that the majority of respondents were male

(71.1%). Based on the results of educational most of the respondents had postgraduate

(51.2%) followed by others certificate or associates degree/licensure (26.4%). Results

for age showed that the respondents aged between 30 to 39 years had the highest

frequency (40.5%) followed by respondents aged under 30 years (33.1%) and the lowest

frequency was observed for respondents aged over 50 years (9.1%). For job title, the

highest percentage belonged to other (46.3%) followed by technical staff (21.5%) and

managers (20.7%). The results for “Job experience in AECO” revealed that the highest

frequency belonged to “1 to 3 years” (43.8%) followed by “10 and over” (30.6%) and

the lowest frequency was observed for” 4 to 6 year “(11.6%). The results for “Years of

using BIM” indicated that the highest frequency belonged to “1 to 3 years” (52.9%)

followed by “4 to 6 years” (19.8%) and the lowest frequency was observed for” 7 to 9

year “ (9.9%).

Generally, the participants in this research provided reliable and useful information as


they were well informed and within the targeted community.

Table 5.8: Frequency distribution of demographic characteristics Variable Level Frequency Percent

Gender Female 35 28.9

Male 86 71.1

Education

Undergraduate 27 22.3

Postgraduate 62 51.2

Others certificate or associate degree /

licensure

32 26.4

Age

Under 30 40 33.1

30-39 49 40.5

40-49 21 17.4

50 and over 11 9.1

Job title

Assistant Manager 8 6.6

General Manager 6 5

Manager 25 20.7

Technician Staff 26 21.5

Others 56 46.3

Job experience in

AECO

1-3 53 43.8

4-6 14 11.6

7-9 17 14

10 and over 37 30.6

Years of using

BIM

1-3 64 52.9

4-6 24 19.8

7-9 12 9.9

10 and over 21 17.4

5.6 Descriptive Statistics

This section presents the descriptive statistics of the constructs in the model

evaluation. Descriptive statistics were employed in the initial stage of the data analysis

process, i.e. by computing all the constructs in the study; effort expectancy, facilitating

conditions, performance expectancy, social influence, task characteristics, technology

characteristics, task technology fit and user adoption of BIM. SPSS was used to

calculate these constructs based on the respondents’ feedback regarding each construct.

The respondents’ feedback for user adoption of BIM was measured based on three items,

based on the 7-point Likert scale ranging from “strongly disagree” to “strongly agree”.


As shown in Table 5.9 regarding user adoption of BIM, the statement “I often use BIM

to optimize the time” had the highest mean score with (M=4.6, SD=1.55), while in this

statement "I often use BIM to manage my FM tasks" had the lowest mean score with

(M=3.95, SD=1.6). The overall mean for this question was M=4.33, SD=1.29) which

was more than the median of scale (4) and shows an almost moderate level for this

construct.

Table 5.9: Descriptive statistics related to user adoption of BIM No. Items Mean SD

USE1 I often use BIM to manage my FM tasks 3.95 1.60

USE2 I often use BIM to optimize the cost 4.43 1.44

USE3 I often use BIM to optimize the time 4.60 1.55

Total

4.33 1.29

Performance expectancy was measured based on four items included with 4 items. Table

5.10 provides the overall mean of performance expectancy was M=5.3 which was more

than the median of scale (4) and indicated an almost high level for this variable.

Table 5.10: Descriptive statistics related to Performance Expectancy

No. Items Mean SD

PE1 I would find BIM useful in my job. 5.51 1.31

PE2 Working with BIM enables me to accomplish tasks more quickly. 5.29 1.38

PE3 Working with BIM increases my productivity. 5.42 1.28

PE4 If I work with BIM, I will increase my chances of getting a raise. 4.97 1.55

Total 5.30 1.17

Effort expectancy was measured by four items. As seen in Table 5.11, the overall mean

of effort expectancy was M=4.83 which was close to the median of scale (4), which

revealed an almost moderate level for this construct.


Table 5.11: Descriptive statistics related to Effort Expectancy No. Items Mean SD

EE1 My interaction with BIM would be clear and understandable. 4.86 1.18

EE2 It would be easy for me to become skilled at working with BIM. 4.78 1.27

EE3 I would find BIM easy to use. 4.74 1.26

EE4 Leaning to operate BIM is easy for me. 4.92 1.28

Total 4.83 1.00

Social influence was measured by four items. Table 5.12 shows that the overall mean of

social influence was M=4.79 which was close to the median of scale (4), which revealed

an almost moderate level for this construct.

Table 5.12: Descriptive statistics related to social influence

No. Items mean SD

SI1 People who influence my behavior think I should use BIM 4.64 1.55

SI2 People who are important to me think that I should use BIM 4.79 1.43

SI3 The senior management of this business has been helpful in the use of BIM 4.72 1.46

SI4 In general, my organization has supported the use of BIM 5.00 1.46

Total

4.79 1.22

Facilitating conditions was measured by four items. Table 5.13 provides the overall

mean of facilitating conditions was M=4.38 which was close to the median of scale (4),

which revealed an almost moderate level for this construct.

Table 5.13: Descriptive statistics related to facilitating conditions No. Items Mean SD

FC1 I have the resources necessary to work with BIM. 4.63 1.48

FC2 I have the knowledge necessary to work with BIM. 4.88 1.36

FC3 BIM is not compatible with the work tools I use. 3.52 1.68

FC4 A specific person (or group) is available for assistance with BIM difficulties. 4.48 1.57

Total

4.38 1.08


TTF was measured by three items and Table 5.14 provides the descriptive statistics on

the ratings related to the TTF. The overall mean of TTF was M=4.43 which was almost

close to the median of scale (4), which revealed a moderate level for this construct.

Table 5.14: Descriptive statistics related to TTF No. Items Mean SD

TTF1 In helping complete my FM tasks, the functions of BIM are enough. 4.32 1.42

TTF2 In helping complete my FM tasks, the functions of BIM are appropriate. 4.50 1.43

TTF3 In general, the functions of BIM fully meet my task context. 4.47 1.34

Total

4.43 1.26

Technology Characteristics was measured by three items and Table 5.15 provides the

descriptive statistics on the ratings related to the technology characteristics. The overall

mean of technology characteristics was M=4.63 which was close to the median of scale

(4), which revealed almost a moderate level for this construct.

Table 5.15: Descriptive statistics related to technology characteristics No. Items Mean SD

TEC1 BIM provides ubiquitous services. 4.43 1.27

TEC2 BIM provides real-time services. 4.62 1.22

TEC3 BIM provides reliable services. 4.84 1.27

Total

4.63 1.07

Task Characteristics was measured by three items and Table 5.16 provides the

descriptive statistics on the ratings related to the task characteristics. The overall mean

of task characteristics was M=4.70 which was more than the median of scale (4), which

revealed above the moderate level for this construct.

Table 5.16: Descriptive statistics related to task characteristics No. Items Mean SD


TAC1 I need to manage FM tasks efficiently 4.71 1.39

TAC2 I need to export accurate and actual information to FM systems 4.84 1.37

TAC3 I need to acquire FM information in real-time 4.54 1.52

Total

4.70 1.29

5.7 Structural equation modelling (SEM)

SEM identifies latent factors and allows regression among such factors. It

comprises a set of relationships between one or more independent variables and one or

more dependent variables. SEM can be used to answer research questions about the

indirect or direct observation of variables. SEM can also be used to support the validity

a proposed causal process and/or model (De Carvalho and Chima, 2014). The SEM

process involves two steps: validation of the measurement model and fitting the

structural model (Lomax and Schumacker, 2004).

Measurement model

The measurement model involves specifying how the latent variables are measured

in terms of the observed variables, and in CB-SEM defines the measurement properties

of the observed variables. That is, the relations between a set of observed variables, such

as ratings or questionnaire items, and the unobserved variables or constructs they were

intended to measure. In CB-SEM, the measurement model includes a probability model

to take account of measurement error in the observed variables. There is no probability

model to explicitly account for measurement error in PLS-SEM (Bentler and Huang,

2014). Thus, it is having been recommended that the measurement model is assessed

using various ad-hoc approaches (Hair et al., 2019). We outline and follow these

approaches below.

Convergent validity. Convergent validity is considered as a part of construct validity.

Convergent validity is done to validate the measurement model. Convergent validity

supports the measure of Average Variance Extracted (AVE) to measure the percentage

of explained variance by items relative to measurement errors. The convergent validity

of the constructs can be evaluated by examining the average variance extracted (AVE).

Rendering to the PLS-SEM analysis, the minimum desirable level of average variance


extracted (AVE) is 0.5 (Fornell and Larcker, 1981, Gefen et al., 2000, Hair Jr et al.,

2017a). High outer loadings on a construct specifies that the related item of each construct have

strong relationship with the construct, and this characteristic is also typically called

indicator reliability that can be measured through outer loadings. As an overall rule of

thumb, the (standardized) outer loadings must be 0.708 or higher (Hair Jr et al., 2017a).

Indicators with very low outer loadings (below 0.40) must, though, always be omitted

from the scale (Hair et al., 2011). Generally, indicators with outer loadings between

0.40 and 0.70 should be considered for removal from the scale only when deleting the

indicator leads to a substantial increase in the composite reliability and AVE (Henseler

et al., 2009).In this study the AVE ranged between 0.649 to 0.809 which indicated

adequate convergent validity for all constructs.

Table 5.17 below shows the outer loadings of all items for all variables in initial and

modified measurement model after deleting FC3. According to the initial model results

all outer loadings except one item “FC3” related to facilitating condition construct.

Thus, it was deleted from initial measurement model due to low loading factor which

were less than 0.5, that confirmed their low contribution to related constructs.

The common method for measuring the internal consistency is Cronbach's alpha, which

offers an estimate of the reliability based on the inter-correlations of the indicator

variables, but it is sensitive to the number of items in the scale and lead to underestimate

the internal consistency reliability (Cortina, 1993). Therefore, it is recommended to use

different indices of internal consistency reliability, which is referred to as composite

reliability (CR). This type of reliability considers the different outer loadings of the

indicator variables and is calculated using the following formula:


Where li represents the factor loadings for each item i and ei is the error term for each

item i (Brunner and SÜβ, 2005).

To further assess the reliability of reflective constructs, Cronbach’s alpha and CR

measures are similarly assessed by using PLS. The Cronbach’s alpha and CR of all

reflective constructs are described in Table 5.17. Cronbach’s alpha values ranged from

0.793 and 0.883, which are acceptable for exploratory research (Nunnally, 1978). CR

larger than 0.7 is acceptable (Hair Jr et al., 2017b), then, the CR following the

improvement of the reliability of the questionnaire is likely via removing the statements

increasing error. As shown in Table 5.17, CR for each construct is above the 0.7

threshold (Segars, 1997).

Table 5.17: The result of convergent validity

Construct Item

Outer loading Cronbach's

Alpha

Composite

Reliability AVE Initial

model

Modified

Model

EE EE1 0.75 0.75 0.819 0.881 0.649

EE2 0.801 0.801

EE3 0.818 0.818

EE4 0.852 0.852

FC FC1 0.829 0.848 0.837 0.902 0.755

FC2 0.884 0.909

FC3 0.285 deleted

FC4 0.835 0.848

PE PE1 0.815 0.815 0.87 0.911 0.721

PE2 0.903 0.903

PE3 0.916 0.916

PE4 0.752 0.752

SI SI1 0.782 0.782 0.85 0.899 0.689

SI2 0.876 0.876

SI3 0.809 0.809

SI4 0.85 0.85

TAC TAC1 0.909 0.909 0.883 0.927 0.809

TAC2 0.909 0.909

TAC3 0.88 0.88

TEC TEC1 0.816 0.816 0.813 0.889 0.729

TEC2 0.877 0.877

TEC3 0.867 0.867


TTF TTF1 0.89 0.89 0.879 0.925 0.805

TTF2 0.909 0.909

TTF3 0.893 0.893

USE USE1 0.732 0.729 0.793 0.88 0.712

USE2 0.929 0.93

USE3 0.858 0.86

EE: Effort Expectancy, FC: Facilitating Conditions, Performance Expectancy, SI: Social Influence, TAC:

Task Characteristics, TEC: Technology Characteristics, TTF: Task Technology Fit, USE: User Adoption

of BIM

Discriminant validity. Discriminant validity means that a latent variable is able to

account for more variance in the observed variables associated with it than a)

measurement error or similar external, unmeasured influences; or b) other constructs

within the conceptual framework. If this is not the case, then the validity of the

individual indicators and of the construct is questionable (Fornell and Larcker, 1981).

Discriminant validity can be measured by three different methods as follow:

• Fornell-Larcker’s (1981) criterion

• HTMT (Hetrotrait-Monotrait ratio of correlations) criterion

• Cross Loading criterion

To evaluate the discriminant validity the square root of the AVE of each latent variable

can be compared with the correlations of a construct with all other constructs. According

to Fornell and Larcker (1981) rule, the square root of AVE must be larger than the

correlations among the latent variables. This result supports that the measurement model

has the discriminant validity (Chin, 1998).

As shown in Table 5.18 correlation of latent variables and discriminant validity (Fornell-

Larcker), the squared correlations between the factors were smaller than the

corresponding AVE estimates. This result indicates that the constructs were more

strongly related to their respective indicators compare to the other constructs in model.

The result indicating the measure has adequately discriminant validity.

However, several methodology scholars have disapproved the Fornell Larker’s (1981)

criterion for distinguishing discriminant validity. As such Henseler et al. (2015)


recommended another method to assess discriminant validity, and that is through

Heterotrait-Monotrait ratio of criterion (HTMT). HTMT is a new method to assess

discriminant validity in PLS-SEM, and it estimates what would be the exact correlation

between two constructs if they were impeccably measured (i.e., if they are perfectly

reliable with no error). For the purpose of this study, HTMT was also done for the model

to measure discriminant validity.

Hair Jr et al. (2010) recommended that the HTMT value must be in the range of 0.85 to

0.90, meaning that the two constructs were distinct. Table 5.19 reveals the HTMT values

for all of the constructs in this research. Thus, the constructs showed sufficient

discriminant validity.

Table 5.18: Correlation of latent variables and discriminant validity (Fornell-Larcker)

EE FC PE SI TAC TEC TTF USE

EE 0.806

FC 0.567 0.869

PE 0.644 0.453 0.849

SI 0.647 0.672 0.547 0.83

TAC 0.277 0.159 0.212 0.247 0.9

TEC 0.619 0.506 0.593 0.562 0.388 0.854

TTF 0.607 0.537 0.39 0.602 0.323 0.673 0.897

USE 0.595 0.54 0.512 0.579 0.266 0.653 0.593 0.844



of BIM

Table 5.19: Correlation of latent constructs and discriminant validity (HTMT method)


EE

FC 0.685

PE 0.759 0.527

SI 0.778 0.792 0.630

TAC 0.326 0.194 0.236 0.298


TEC 0.757 0.608 0.698 0.678 0.458

TTF 0.714 0.626 0.437 0.696 0.367 0.794

USE 0.737 0.651 0.600 0.695 0.322 0.816 0.713



of BIM

The third method; cross loading criterion were also used in this study to estimate

discriminant validity. In the cross-loadings discriminant test, indicator’s outer loading

with its construct should be higher than any other correlations (cross-loadings) with

other constructs to indicate a discriminant validity. Logically, the indicator must to be

highly correlated with its construct rathar than its corellation with any other construct in

the model to meet the discriminant validity.

In other words, the item loading of their individual construct should be more than the

loading on other construct. The results in Table 5.20 demonstrated that all the indicators’

loadings of assigned latent construct are higher than the cross loading on other constructs

(by row and by column). The result indicated a good degree of unidimensionality for

each construct.

Table 5.20: Loading and cross loading of constructs for discriminant validity assessment


EE1 0.75 0.50 0.62 0.56 0.29 0.51 0.36 0.49

EE2 0.80 0.39 0.48 0.51 0.25 0.54 0.53 0.50

EE3 0.82 0.47 0.42 0.47 0.17 0.47 0.52 0.44

EE4 0.85 0.47 0.54 0.53 0.18 0.47 0.54 0.49

FC1 0.47 0.85 0.39 0.64 0.07 0.38 0.47 0.46

FC2 0.53 0.91 0.45 0.53 0.15 0.48 0.45 0.49

FC4 0.47 0.85 0.34 0.59 0.20 0.46 0.48 0.46

PE1 0.52 0.36 0.82 0.43 0.12 0.46 0.23 0.33

PE2 0.51 0.46 0.90 0.47 0.20 0.55 0.35 0.47

PE3 0.59 0.41 0.92 0.45 0.19 0.56 0.36 0.50

PE4 0.56 0.30 0.75 0.50 0.19 0.43 0.35 0.41

SI1 0.50 0.48 0.39 0.78 0.25 0.45 0.46 0.41

SI2 0.59 0.57 0.55 0.88 0.26 0.52 0.55 0.51

SI3 0.60 0.52 0.40 0.81 0.26 0.49 0.54 0.44

SI4 0.47 0.64 0.46 0.85 0.08 0.42 0.46 0.55


TAC1 0.23 0.25 0.26 0.28 0.91 0.36 0.32 0.29

TAC2 0.29 0.17 0.21 0.21 0.91 0.35 0.26 0.21

TAC3 0.24 0.01 0.10 0.16 0.88 0.34 0.30 0.22

TEC1 0.51 0.32 0.43 0.42 0.33 0.82 0.54 0.50

TEC2 0.56 0.46 0.51 0.51 0.34 0.88 0.58 0.60

TEC3 0.52 0.51 0.57 0.50 0.33 0.87 0.61 0.57

TTF1 0.57 0.49 0.33 0.49 0.29 0.60 0.89 0.59

TTF2 0.51 0.47 0.34 0.50 0.37 0.59 0.91 0.45

TTF3 0.55 0.48 0.37 0.63 0.21 0.62 0.89 0.55

USE1 0.44 0.28 0.31 0.40 0.34 0.52 0.53 0.73

USE2 0.54 0.56 0.46 0.55 0.32 0.59 0.56 0.93

USE3 0.53 0.50 0.52 0.51 0.03 0.54 0.43 0.86

Structural model

Path analysis is a statistical method based on jointly estimated regression models.

It is often used in social science and management to inspect complex relationships

simultaneously (Tabachnick et al., 2007). The structural model is used evaluate the

relationships among the research constructs. Evaluation of the structural model focuses

the size, direction, precision and significance of the hypothesized parameter estimates,

(Hair et al., 2006b). In particular, we look for evidence for or against the proposed study

relationships shown in Table 5.21.

Table 5.21: List of hypotheses and relative paths

Hypothesis Path

H1: Performance Expectancy has a positive influence on BIM adoption in FM PE-->USE

H2: Effort Expectancy positive influences the adoption of BIM in FM EE-->USE

H3: Social Influence has a positive influence on the adoption of BIM in FM SI-->USE

H4: Facilitating Conditions has a positive influence on the adoption of BIM in FM FC-->USE

H5: Task Technology Fit positively influences the adoption of BIM in FM TTF-->USE


H6: Task Technology Fit positively influences Performance Expectancy TTF-->PE

H7: Technology Characteristics positively influences the TTF TEC-->TTF

H8: Task Characteristics positively influences the TTF TAC-->TTF

H9: Performance Expectancy mediate the relationship between task technology fit

and the adoption of BIM in FM TTF--> PE-->USE



of BIM

PLS-SEM was used to evaluate the research hypotheses which are shown in Figure 5.1

along with their coefficient estimates.


Figure 5.1: Path model 1 (PLS Algorithm) including outer loading of each item, path

coefficients and R2 values for endogenous variables.


Figure 5.2: Path model 1 (Bootstrapping).

Bootstrapping approach was used to evaluate the significance of the proposed research

hypotheses for the model. Bootstrapping comprises the random re-sampling of the

original dataset to generate new samples of the same size as the dataset. Hair Jr et al.

(2016) advised that the resample number in the bootstrap ought to be 5000+ to be able

to generate stable estimates. A t-value for each path relationship should be greater than

the critical value in order to consider that this path is statistically significant at a specific

error probability (p-value). PLS-SEM does not assume that the data is normally

distributed, which suggests that parametric significance tests cannot be applied to test

whether coefficients (outer weights, outer loadings and path coefficients) are significant.


Instead, PLS-SEM relies on a nonparametric bootstrap procedure. This technique tests

not only the reliability of the dataset but also evaluating the statistical significance of

these coefficients and subsequently the error of the estimated path coefficients (Chin,

1998). As shown in Figures 5.1and 5.2 the standardized path coefficients (β) and p-

values, the significance of the paths, and the R2 for each endogenous construct were

tested. The result of boot strapping method has been shown in Table 5.22, where it

demonstrates p-values for each path.

According to these results the effect of effort expectancy on user adoption of BIM was

positive and significant (β =0.155, p=0.001). Similarly, facilitating conditions had a

positive and significant (β=0.142, p=0.006) on user adoption of BIM. Results of

bootstrapping also indicated that performance expectancy had a positive and significant

(β=0.172, p<0.001) impact on user adoption of BIM. Bootstrapping results also showed

that the effect of social influence on user adoption of BIM was statistically significant

(β=0.119, p=0.019). The effect of task technology fit on user adoption of BIM was

positive and significant (β =0.285, p<0.001). According to these results the effect of task

characteristics on task technology fit was positive and significant (β =0.0.073, p=0.01).

Similarly, technology characteristics had a positive, strong and significant (β=0.645,

p=0.006) on task technology fit. According to these results the effect of task technology

fit on performance expectancy was positive and significant (β =0.390, p<0.001).

Table 5.22: List of hypotheses and relative paths

path β SE T-value p-values

EE -> USE 0.155 0.049 3.159 0.001

FC -> USE 0.142 0.056 2.521 0.006

PE -> USE 0.172 0.044 3.955 <0.001

SI -> USE 0.119 0.057 2.08 0.019

TAC -> TTF 0.073 0.039 1.875 0.03

TEC -> TTF 0.645 0.028 23.15 <0.001

TTF -> PE 0.390 0.029 13.223 <0.001


TTF -> USE 0.285 0.038 7.444 <0.001



of BIM

Coefficient of determination (R2). The R2 value shows the amount of variance in

dependent variables that is explained by the independent variables. Thus, a larger R2

value increases the predictive ability of the structural model. In this study, Smart-PLS

algorithm function is used to obtain the R2 values. As shown in Table 5.23, the adjusted

R2 for performance expectancy in this model was 0.151 that indicated 15.1% of

performance expectancy could be explained by one endogenous latent variable “task

technology fit”. The adjusted R2 for task technology fit in the model was 0.456, which

means 45.6% of changes in the task technology fit among respondents can be explained

by task characteristics and technology characteristics. The adjusted R2 for user adoption of BIM in this model was 0.491 that indicated 49.1%

of user adoption of BIM could be explained by five predictors including effort

expectancy, facilitating conditions, performance expectancy, social influence and task

technology fit.

Table 5.23: Results of the coefficient of determination (R2)

Endogenous Latent Variable R2 Adj R2

PE 0.152 0.151

TTF 0.458 0.456

USE 0.494 0.491

Performance Expectancy, TTF: Task Technology Fit, USE: User Adoption of BIM

Effect size f². The change in the R² value while a particular independent construct is

eliminated from the model can be used to evaluate whether the omitted construct has a

basic influence on the dependent constructs. This measure indicates the f² or effect size.

The calculation of the effect size is as below:


Recommended guidelines for assessing effect size are: f² ≥ 0.02, f2≥ 0.15 and f2 ≥ 0.35,

respectively representing small, medium and large effects size of the exogenous

construct (Cohen, 1998). According to Table 5.24, the result of f2 indicated that the effect

size of TTF on performance expectancy indicates a moderate effect size with f2=0.179.

For TTF the highest effect size belonged to technology characteristics with f²=0.652

which was largest effect size followed by task characteristics f²=0.008 with small effect

size. Regarding the main dependent variable in the model "user adoption of BIM" the

highest effect size belonged to task technology fit with f²=0.086 which was between

small and medium effect size followed by performance expectancy f²=0.032, facilitating

conditions f²=0.020 with small effect size.

Table 5.24: Results of effect size f² for three endogenous variables

Exogenous variable Endogenous variables

PE TTF USE

EE 0.019

FC 0.020

PE 0.032

SI 0.011

TTF 0.179 0.086

TAC 0.008

TEC 0.652



of BIM

Predictive relevance Q² of structural Model. An important aspect of a structural model

is its ability to determine the predictive relevance of the model. Blindfolding procedure

was used to launch cross-validated redundancy measures for each construct (Akter et

al., 2011). As shown in Table 5.25, the results revealed that the Q2 values of

performance expectancy with value (0.100) , Q2 values of task technology fit with value


(0.349) and user adoption of BIM ( 0.335) demonstrated predictive relevance for data

points of the indicators in reflective measurement models of these two endogenous

construct in the model since all of the Q2 values were greater than zero (Hair et al.,

2011).

Table 5.25: Results of predictive relevance (Q 2) Endogenous Latent Variable SSO SSE Q² (=1-SSE/SSO)

PE 2,904.00 2,612.65 0.100

TTF 2,178.00 1,417.90 0.349

USE 2,178.00 1,449.45 0.335

PE: Performance Expectancy, TTF: Task Technology Fit, USE: User Adoption of BIM

Test of mediation. Mediation analysis is used to understand a known relationship by

exploring the underlying mechanism or process by which one variable effects another

variable through a mediator variable (Cohen et al., 2003). Mediation happens when a

third variable intervenes between two other related constructs. Precisely, a change in the

exogenous construct (independent variable) causes a change in the mediator variable,

which, in turn, results in a change in the endogenous construct (dependent variable) in

the path model. Thus, a mediator variable governs the nature) of the relationship

between two constructs (Hair Jr et al., 2016). Mediation analysis of performance expectancy on relationship between task technology

fit the adoption of BIM was done based on (Hair Jr et al., 2016), and Table 5.26

represents the indirect effect of task technology fit on user adoption of BIM using

bootstrapping. According to the results, the indirect effects of task technology fit on user

adoption of BIM (βab=0.067, p<0.001) was significant through performance expectancy.

Table 5.26: Test of Indirect Effects using Bootstrapping

Path ab SE t-value p-value

TTF -> USE 0.067 0.017 3.885 <0.001



The results of the total, direct and indirect effect; are presented in Table 5.27 below.

According to these results, it was found that both direct and indirect effects of TTF on

user adoption of BIM were statistically significant (p<0.05) therefore it may be declared

that in this study performance expectancy partially mediates the relationship between

TTF on user adoption of BIM.

Table 5.27: Total (Direct and Indirect) effects variables on user adoption of BIM

Path Total effect Direct effect Indirect effect Results

TTF -> PE->USE 0.352

(p<0.001)

0.285

(p<0.001)

0.067

(p<0.001)

Partially

Mediation


Table 5.28 below shows the research hypotheses and relative paths. It can be seen clearly

that all research hypotheses are supported, and p-values are significant.

Table 5.28: List of Hypotheses and Relative Paths Hypothesis Path β p-value Results

H1: Performance Expectancy has a positive influence on

BIM adoption in FM PE-->USE 0.172 <0.001 Supported

H2: Effort Expectancy positive influences the adoption

of BIM in FM EE-->USE 0.155 0.001 Supported

H3: Social Influence has a positive influence on the

adoption of BIM in FM SI-->USE 0.119 0.019 Supported

H4: Facilitating Conditions has a positive influence on

the adoption of BIM in FM FC-->USE 0.142 0.006 Supported

H5: Task Technology Fit positively influences the

adoption of BIM in FM TTF-->USE 0.352 <0.001 Supported

H6: Task Technology Fit positively influences

Performance Expectancy TTF-->PE 0.390 <0.001 Supported

H7: Technology Characteristics positively influences

the TTF TEC-->TTF 0.645 <0.001 Supported

H8: Task Characteristics positively influences the TTF TAC-->TTF 0.073 0.03 Supported

H9: Performance Expectancy mediate the relationship

between task technology fit and the adoption of BIM in

FM

TTF--> PE-->USE 0.067 <0.001 Supported


5.8 The summary

This chapter presented and explained the steps for preparing the empirical data and

performing the empirical analyses that used in the current research. The process started

with the analysis of the responses rate of the sample involved in the survey. After that,

the descriptive analysis of the respondents was discussed. Then, the missing value

analysis and some other tests were presented. Lastly, the data were cleaned and prepared

for the second phase of analyses. In the second phase, PLS-SEM approach was used

which involves a measurement model and a structural model. In the measurement

model, convergent and discriminant validity were examined. PLS-SEM were used to

test the hypothesized relationships in this research. Results for the measurement and

structural models were then presented for the research hypotheses proposed. The main

purpose of this chapter was to achieve a validation and confirmation of the measurement

model which is considered very important to perform a structural equation model. All

the results from this chapter have been discussed in Chapter 7 and all conclusions have

been drawn.

Chapter 6: The Impeding Factors of Building Information Modelling adoption in Facilities Management* 137

Chapter 6: The Impeding Factors of Building Information Modelling adoption in Facilities Management*

Introduction This chapter includes the results of descriptive statistics obtained from data

analysis. The collected data was analysed with the statistical software (SPSS) version

23 for descriptive statistics and some other statistical tests. This chapter starts with the

results of preliminary analysis, demographic variables of respondents and followed by

descriptive analysis for research variables. 17 barriers factors for BIM in FM are

identified. Results for the EFA are then presented to find the underlying group of factors

for these identifies barriers.

6.1 BIM adoption barriers in FM

Recent studies have shown that the construction industry and FM have

significantly benefited from BIM adoption. But still there are many challenges in this

aspect. For example, Kassem et al. (2015) performed a case study to explore the benefits

and challenges of BIM in FM at Northumbria University campus. The results revealed

that BIM benefits to FM based mainly on enhancement of current paper-based hand-

over processes, improve the level of accuracy of required data, and facilitate the

accessibility of data and efficiency in work order procedures.

*Some of content given in this chapter are based on the material published or submitted and under review in:

- HILAL, M., MAQSOOD, T. & ABDEKHODAEE, A. Barriers for Building Information Modelling adoption in Facilities

Management. International Journal of Building Pathology and Adaptation, submitted on 2-May 2019.

-HILAL, M., MAQSOOD, T. & ABDEKHODAEE, A. 2019. A Hybrid Conceptual Model for BIM Adoption in Facilities

Management: A Descriptive Analysis for the Collected Data. 11th International Conference (CITC-11), September 9-11, 2019,

London, UK.

-HILAL, M., MAQSOOD, T. & ABDEKHODAEE, A. 2019. Impeding Factors of Building Information Modelling adoption

in Facilities Management. The Association of Researchers in Construction Management (ARCOM), 4th of July, 2019,

Melbourne, Australia.

138 Chapter 6: The Impeding Factors of Building Information Modelling adoption in Facilities Management*

They also noted these challenges include:

• The shortage of BIM skills in the facilities management industry.

• The un known tangible benefits of BIM in FM.

• The interoperability between BIM and facilities management technologies.

• The presence of disparate operational systems managing the same building.

Volk et al. (2014) reviewed over 180 publications on BIM. They revealed that there is a

limited of studies in BIM implementation for existing buildings. They emphasized this

scarcity is due to:

• Information updating in BIM

• The effort of conversion from as-built building data into BIM objects

• Dealing with uncertain data, relations and objects in BIM in existing buildings. Their study has highlighted the attention to the existing building which form the most

percentage of the construction projects sector, and this may help to enhance the FM

sector by implementing BIM.

Yalcinkaya and Singh (2014) have reviewed 87 papers in BIM-FM using different data

base sources. They presented the challenges that face the adoption of BIM in FM such

as;

• Lack of information exchange frameworks like COBie to solve data transfer

issues.

• Interoperability between FM systems and BIM

• Unclear implementation of BIM in FM through early project’s stages

• Importing of as-built information of the facilities to BIM model.

Ashworth et al. (2019) identified the critical success factors for facility management

EIR for BIM. They performed an extensive case study and figured out that the EIR as a

useful collaboration tool to gather stakeholders in early planning phase to understand


the client information needs. This refers to the significance of the collaboration among

the project’s life-cycle stockholders to improve the final outcome representing by the

FM and operation. This supports (Hsieh et al., 2019) study as they emphasized that as

BIM technology is becoming more mature, there is a great need for the interoperability

and interaction between BIM tools and different FM systems. Gao and Pishdad-Bozorgi

(2019) emphasized that adoption of BIM-FM systems is still hindered by several factors

such as the interoperability between BIM and FM context, understanding the implied

FM principles for BIM adoption, and cost-value issues. The author suggested possible

starting point to address the interoperability issue in the BIM-FM context is by adopting

the NIST CPS model. Also, he concluded that more studies including surveys are needed

to understand the principles for BIM adoption in FM. Dixit et al. (2019) addressed 16

issues based on literature review of 54 research under the four categories of BIM

execution and information management, cost-based and legal, technological and

contractual issues. The survey results’ of FM professionals with 57 complete responses revealed that the

most key issue is the lack of FM professionals’ engagement in early project phases when

BIM is developing. Barbarosoglu and Arditi (2019) proposed a maintainability checking

system algorithm which can be specified for all building elements, and it can be

compatible with BIM tool such as Revit. They emphasized that BIM can reduce the gap

between the design and facilities management without increasing the load of designers.

This can be done by allowing designers to design in a way that improves the FM issues

at the design phase itself. Pärn et al. (2017) emphasized the scarcity of research that

study BIM for facilities management in the AECO sector. They reviewed the published

studies on the most recent studies and standards development which affect the

application of BIM in FM. Their findings revealed that real challenges facing the

facilities management include; more attention of long term strategic aspirations,

enhancement of data interoperability/integration issues, enhancement of performance

measurement, augmented knowledge management, develop the level of competence for

the FM practitioners. Also, they proposed more case studies to observe and report the

current practice in order to develop this sector.


Hosseini et al. (2018) have identified the most influence BIM research. They highlighted

the intellectual shortages in BIM studies and skewed distribution of these studies output

across BIM-related themes. Although of their contribution, some diversity to the reader

and lack of specific details on each phase has been noticed.

Miettinen et al. (2018) identified the gap between BIM adoption in design and in FM.

Premises Centre of the City of Helsinki key professional experts of FM were interviewed

to discuss the information tools being used in their centre, and the needs and

impediments of BIM adoption in the FM. The emphasized that the challenges in the

BIM adoption are in which ways the relevant data and information included in BIM

models could be integrated with FM systems. Becerik-Gerber et al. (2012) has identified several technological and organizational

challenges for BIM in FM as following:

1) Technology and process related challenges:

• Unclear roles and responsibilities for loading data into the model or databases

and maintaining the model;

• Diversity in BIM and FM software tools, and interoperability issues;

• Lack of effective collaboration between project stakeholders for modelling and

model utilization;

• Necessity yet difficulty in software vendor’s involvement, including

fragmentation among different vendors, competition, and lack of common

interests

2) Organizational challenges:

• Cultural barriers toward adopting new technology;

• Organization wide resistance: need for investment in infrastructure, training,

and new software tools;

• Undefined fee structures for additional scope;


• Lack of sufficient legal framework for integrating owners’ view in design and

construction;

• Lack of real-world cases and proof of positive return of investment.

In addition, one of the most challenges in implementing BIM in the FM practices is that

most of organizations have their own FM systems and software platforms to manage the

FM information. New buildings are small portion of the already existing buildings, and

this situation raises many questions related to challenging the change (Kiviniemi and

Codinhoto, 2014).

Briefly, BIM helps support functions of facilities management by its analysis tools,

visualization capabilities and provision of initial information to facilities management

systems. Although many experts and researchers agree on the potential benefits of BIM

in FM, there is still considerable uncertainty about how to use BIM efficiently and to

what extent BIM can help solve facilities management problems (Lee et al., 2012).

Hence, the impeding factors of BIM-FM remains a significant concern of BIM practice

and research. Overall, studies in the literature primarily focus on BIM adoption especially in

qualitative manner by using specific case studies. Yalcinkaya and Singh (2015) have

emphasized that the previous BIM review research were typically qualitative and

subjective, prone to bias, and included a few reviewed publications. Previous studies

indicate there are various challenges of BIM adoption in FM and robustly classify those

challenges’ factors. However, these studies fail to provide a comprehensive study that

based on quantitative analysis approach which help to generalize the outcome of the

research results throughout the statistical analysis basis. Also, these challenges of BIM

in FM never been tested in Australian context. This chapter bridges this gap and adopts a quantitative analysis approach to solve this

issue and to provide a better understanding by conducting a comprehensive

questionnaire survey that targeting BIM-FM professionals in Australia. A better

understanding of impeding factors of BIM adoption in FM is necessary to plan


appropriate strategies for BIM, especially in countries where BIM is fairly new to the

FM context.

6.2 The questionnaire survey

A questionnaire was designed to quantify the effects of various impeding factors

for BIM in FM. The data collection through a questionnaire survey was carried out

between November 2017 to December 2018 in Australia. Before data collection stage,

human research ethics clearance was first obtained from the Human Research Ethics

Committee of Swinburne University of Technology (SUHREC). Ethic clearance from

SUHREC for the study number SHR Project 2017/131 was obtained in August 2017.

Then, an expert judgment procedure was conducted between August and November,

2017. The objectives of the expert judgment procedure was to explore whether the

questions and the instructions of the questionnaire survey were clear and

understandable.

Also, to make sure that the questions conveyed consistent meaning for all respondents.

Two of the experts in BIM-FM area were chosen. The experts have been requested to

give their general judgment of the questionnaire regarding of the format, length and any

language/ terminology issues. They emphasized that the questionnaire was simple, easy

and well designed, except some changes that would help to make the questionnaire more

understandable. After doing all the required corrections, the questionnaire was designed

through Opinio and published online in November, 2017. The researcher targeted every

event and conference related to BIM-FM in Australia, where the study was. The strategy

was to ask every interested expert during and after the event, and explain the purpose of

this research, goals and the possible contribution by doing an online survey. By getting

the participant’s acceptance to participate in this online survey, the researcher would

send them the online link so they can do the survey at the same time by using the

researchers’ platform devices or the participants own mobile devices. This strategy was

very successful and achieved high rate of participations during about four months only.

Accordingly, the 134 participants were interviewed in total. However, only 91 have

completed this interview that related to the barriers factors.


The questionnaire related to this chapter (shown in Appendix A section 1 and 2), consists

of two parts: (1) general information regarding the respondents; and (2) the impeding

factors for BIM in FM.

The respondents were asked to evaluate the importance of the listed impeding factors

using a 1–7 point Likert scale, where (1) represents strongly disagree and (7) represents

strongly agree.

6.3 The impeding factors for BIM in FM

An extensive review of impeding factors was performed to generate a list of

impeding factors. The impeding factors for BIM in FM used in this research are listed

in Appendix A section 2. The initial list contained more than 25 factors, which were

then reduced to 17 factors as shown in Appendix A section 2. The impeding factors that

have been developed by Becerik-Gerber et al. (2012) were adopted, because they

included the most common factors mentioned in other studies in different ways such as

(Kassem et al., 2015, Volk et al., 2014, Yalcinkaya and Singh, 2014, Hsieh et al., 2019,

Gao and Pishdad-Bozorgi, 2019, Dixit et al., 2019, Pärn et al., 2017, Miettinen et al.,

2018, Kiviniemi and Codinhoto, 2014). However, those factors have not been examined

in quantitative manner, nor in Austrian context which made the research gap for the

current study.

A team of two highly experienced BIM consultants and two university professors was

establish to give their opinion on the impeding factors list. The final list was adopted as

shown in Table (6.1) and Table (6.2) below, which included in the questionnaire survey.

Table 6.1: Technology and Process Barriers

ID Barriers Factors Level of agreement

1 2 3 4 5 6 7

Q7 Unclear roles and responsibilities for loading data into the model

or databases and maintaining the model

Q8 Diversity in BIM and FM software tools, and interoperability

issues

Q9 Lack of effective collaboration between project stakeholders for

modelling and model utilization


Q10 Necessity yet difficulty in software vendor’s involvement,

including fragmentation among different vendors, competition,

and lack of common interests

Q11 Entrenched traditional practices and lack of best practice

Q12 Lack of FM team participation in the design phase, which means

their ability to influence data requirements and specifications are

limited.

Q13 Timeliness training before hand-over stage, as FM team is

largely unaware of what is contained in as-built model until the

hand-over stage

Q14 Unknown FM data requirements

Q15 Inappropriate technologies and reluctance to use open standards

for information exchange

Q16 IT skills shortages

Table 6.2: Organizational Barriers ID Barriers Factors

Level of agreement

1 2 3 4 5 6 7

Q17 Cultural barriers toward adopting new technology

Q18 Organization wide resistance: need for investment in

infrastructure, training, and new software tools;

Q19 Undefined fee structures for additional scope;

Q20 Lack of sufficient legal framework for integrating owners’ view

and the actual influence in the design and construction;

Q21 Lack of real-world cases and proof of positive return of

investment.

Q22 Maturity of BIM standards and frameworks

Q23 Uncertainties in client-side life-cycle management strategies

6.4 The results of the analysis

General characteristics of respondents

The objective of this descriptive analysis is to describe the general information regarding

the responses of participants who were actually engaging in the survey of this research,

and the characteristics of them. It provides a comprehensive information and a better

understanding of the survey data, including information about: the gender and the age

of the participants; the level of education of the respondents; the Job experience; and the


degree of implementation of BIM practices in the company. Table 6.3, which was

developed by the authors, shows general characteristics of the respondents.

Although women make up a good proportion of the community, their participation in

the survey was 29.9 % only, and 70.1 % for the men. Age of respondents was categorized

into four clusters. The first cluster was under 30 years, and that was 35.1%, the second

cluster was 30-39 years which made 38.1%, the third cluster was 40-49 years and that

made 17.9 %, and the last age cluster was 50 years and over which made 9%.

Regarding the level of education of the respondents, the first level was is undergraduate

that made(23.9%), and the second level was postgraduate that made (50.0%), others

level was certificate or associates degree / licensure that made (26.1%) from respondents

were the part of research study.

The largest percentage of Job experience was (1-3) years that made 45.5%, while the

category (4-6) made 11.2% which was the lowest category. The category (7-9) was

13.4% and the category (10 and over) was 29.9%.

Regarding the company using BIM, the largest percentage was (1-3) years that made

50.7%, while the category (4-6) made 20.9% the lowest category.

The category (7-9) made 9.7% and finally, the category (10 and over) made 18.7%.

Generally, the participants in this research provided reliable and useful information

because the participants were well informed and within the targeted community.

Table 6.3: The general characteristics of the respondents

Variable Category Frequency Percentage %

Gender Male 94 70.1

Female

40 29.9

Age/ Years Under 30 47 35.0

30-39 51 38.1

40-49 24 17.9

50 and over 12 9.0


Education Undergraduate 32 23.9

Postgraduate 67 50.0

Others 35 26.1

Job Experience

/ Years

1-3 61 45.5

4-6 15 11.2

7-9 18 13.4

10and over

40 29.9

Company using

BIM

1-3 68 50.7

4-6 28 20.9

7-9 13 9.7

10and over 25 18.7

Descriptive analysis and Exploratory Factor Analysis of the data

Table 6.4 provides descriptive statistics of the impeding factors of BIM in FM

based on the 91 responses after removing the incomplete responses. The findings

suggest that unknown FM data requirements, Lack of FM team participation in the

design phase, timeliness training before hand-over stage, Lack of effective collaboration

between project stakeholders for modelling and model utilization, entrenched

traditional practices and lack of best practice, and organization wide resistance are the

most significant barrier of BIM adoption in FM, whereas undefined fee structures for

additional scope, unclear roles and responsibilities for loading data into the model or

databases and maintaining the model, maturity of BIM standards and frameworks, lack

of sufficient legal framework, cultural barriers toward adopting new technology, and

Inappropriate technologies and reluctance to use open standards for information

exchange have been considered less significant. These findings are highly similar to a

number of previous studies which have been done mostly in qualitative manner such as

(Becerik-Gerber et al., 2012) and other studies like; (Kassem et al., 2015, Volk et al.,

2014, Yalcinkaya and Singh, 2014, Hsieh et al., 2019, Gao and Pishdad-Bozorgi, 2019,

Dixit et al., 2019, Pärn et al., 2017, Miettinen et al., 2018, Kiviniemi and Codinhoto,

2014).


Table 6.4: Descriptive Statistics of the impeding factors of BIM in FM

ID Impeding factors

Mean Std.

Deviation

Variance

Q14 Unknown FM data requirements 5.13 1.376 1.892

Q12 Lack of FM team participation in the design phase, which means

their ability to influence data requirements and specifications

are limited.

5.08

1.351 1.825

Q13 Timeliness training before hand-over stage, as FM team is

largely unaware of what is contained in as-built model until the

hand-over stage

5.02

1.299 1.688

Q9 Lack of effective collaboration between project stakeholders for

modelling and model utilization

4.98

1.453 2.111

Q11 Entrenched traditional practices and lack of best practice 4.98

1.414 2.000

Q8 Diversity in BIM and FM software tools, and interoperability

issues

4.97

1.309 1.715

Q18 Organization wide resistance: need for investment in

infrastructure, training, and new software tools;

4.97

1.336 1.785

Q10 Necessity yet difficulty in software vendor’s involvement,

including fragmentation among different vendors, competition,

and lack of common interests

4.91

1.435 2.059

Q23 Uncertainties in client-side life-cycle management strategies 4.90 1.422 2.023

Q21 Lack of real-world cases and proof of positive return of

investment.

4.86

1.480 2.190

Q16 IT skills shortages 4.83

1.432 2.051

Q19 Undefined fee structures for additional scope; 4.79 1.403 1.967

Q7 Unclear roles and responsibilities for loading data into the

model or databases and maintaining the model

4.78

1.497 2.240

Q22 Maturity of BIM standards and frameworks 4.74 1.497 2.241

Q20 Lack of sufficient legal framework for integrating owners’ view

and the actual influence in the design and construction;

4.69

1.427 2.038

Q17 Cultural barriers toward adopting new technology 4.65

1.559 2.431

Q15 Inappropriate technologies and reluctance to use open standards

for information exchange

4.59

1.476 2.177

In the next step, EFA was employed to discover the underlying groups of the those

identified impeding factors for BIM in FM. Factor analysis is a set of methods that can

test how underlying constructs affect the responses on a set of measured variables

(DeCoster, 1998).


EFA and CFA are considered the primary two types of factor analysis. Ozorhon and

Karahan (2016) Stated “Exploratory factor analysis attempts to discover the nature of

the constructs influencing a set of responses, whereas CFA tests whether a specified set

of constructs is influencing responses in a predicted way”.

In this study, EFA was used to examine the multiple dimensions of impeding factors for

BIM adoption in FM. First, Kaiser-Meyer-Olkin (KMO) adequacy test and the Barlett’s

test of sphericity are used to check whether factor analysis could be applied or not.

KMO test is used to compare the difference between the values of the observed

correlation coefficients and the partial correlation coefficients. According to Field

(2005), KMO values lower that 0.5 are not allowed and values between 0.5 and 0.7 are

medium, while values between 0.7 and 0.8 are good and values between 0.8 and 0.9 are

great, and last, values above 0.9 are excellent. Bartlett’s measure tests the null

hypothesis that the original correlation matrix is an identity matrix (Field, 2005).

In this study, the KMO value was 0.871 which indicates that the data set is quit suitable

for factor analysis. Also, the chi-square value in Bartlett’s test was very large (778.459),

and the associated significance level was small (p = 0.000). Thus, it can be concluded

that factor analysis can be used appropriately for this study. Factor analysis was

performed using SPSS software. SPSS output files can be in the form of total variance

explained, correlation matrix, screen plot, rotated component matrix, component matrix,

component, component plot in rotated space, and transformation matrix. The correlation

matrix can be shown in Table 6.5, where the pattern of the relationship between the

variables can be observed based on this matrix.

The total variance shown in the correlation matrix below, can be interpreted by several

common factors. The purpose is to have best number of factors that would maintain for

enough percentage of the total variance. The components which explain a quit low

portion of the total variance would not be worth maintaining. To calculate the rotated

component matrix as shown found in Table 6.6, Varimax rotation method was used. The

cumulative percentage of variance value is 61.32% as shown in Table 6.7, resulted in


three components, which is good as suggested in the literature (Field, 2005). If a forth

component was added, only 5% more of the data would have been explained. Based on

the rotated component matrix, there are three common factor groups of 17 impeding

factors, and all of the variables are split into three meaningful groups.

Table 6.5: Correlation Matrix

Table 6.6: Rotated component matrix Component

1 2 3

Q7 .795 .168 .166

Q8 .709 .212 .305

Q9 .662 .086 .447

Q10 .624 .379 .244

Q11 .518 .496 .207

Q17 .647 .367 .095

Q18 .572 .471 .095

Q12 .364 .214 .674

Q13 .425 .463 .539

Q14 .423 .072 .644

Q15 .332 .347 .617

Q21 -.031 .189 .741

Q16 .370 .507 .142

Q19 .287 .807 .135

Q20 .130 .813 .220

Q22 .138 .577 .442

Q23 .338 .571 .429


Table 6.7: The Total Variance Explained

The first common factors group is related to technology and availability of resources

issues; this common factors group can be labeled as technical-related factors. The

second common factors group is much related to people and, and this common factors

group can be labeled as top management -related factors. The third one can be labeled

as policy -related factors.

The technical-related factors gather 47.75% of the total variances in the correlation

matrix. The components are ; Unclear roles and responsibilities for loading data into

the model or databases and maintaining the model, Diversity in BIM and FM software

tools, and interoperability issues, Lack of effective collaboration between project

stakeholders for modelling and model utilization, Necessity yet difficulty in software

vendor’s involvement, including fragmentation among different vendors, competition,

and lack of common interests, Entrenched traditional practices and lack of best practice,

Cultural barriers toward adopting new technology, and Organization wide resistance.

The top management -related factors cause 6.84% of the total variances in the

correlation matrix. This factor consists of ; IT skills shortages, Undefined fee structures

for additional scope, Lack of sufficient legal framework for integrating owners’ view

and the actual influence in the design and construction, Maturity of BIM standards and

frameworks, and Uncertainties in client-side life-cycle management strategies.

The policy related factors account for 6.732% of the total variances in the correlation

matrix. Lack of FM team participation in the design phase, which means their ability to


influence data requirements and specifications are limited , Timeliness training before

hand-over stage, as FM team is largely unaware of what is contained in as-built model

until the hand-over stage, Unknown FM data requirements, Inappropriate technologies

and reluctance to use open standards for information exchange , and Lack of real-world

cases and proof of positive return of investment are variables of this principal factor.

The average of the mean values for each variable to obtain a mean value for each group

are calculated. Policy related factors with mean =4.94 is the most essential barrier for a

BIM in FM. Followed by technical-related factors with mean of 4.89, and top

management related factors with mean of 4.79is the less essential barrier.

The results suggest that policy related factors are the primary barrier factors, whereas

technical-related factors have a moderate effect. Top management related factors have

been importance slightly less than the rest group factors.

6.5 The summary

This chapter presented the results of descriptive statistics obtained from data

analysis regarding the barriers factors for BIM in FM. The collected data was analysed

descriptive statistics and some other statistical tests. The chapter started with the results

of preliminary analysis, demographic variables of respondents and followed by

descriptive analysis for identified barriers factors. 17 barriers factors for BIM in FM

were identified. Then, results for the EFA were presented to find the underlying group

of those identifies barriers. All the results from this chapter have been discussed in

Chapter 7 and all conclusions have been drawn.

Chapter 7: Conclusions 153

Chapter 7: Conclusions

Introduction The focus of this research has been concerned with the investigation and

examination of the factors that influence the adoption of BIM in FM within Australian

context. The research attempted to develop a better understanding of the foundations

necessary to improve BIM adoption in the whole project life-cycle and specifically for

FM. The ultimate objectives of the research were: first, to investigate and identify the

key factors that influence the widespread adoption of BIM in the FM which prepare the

foundation for the conceptual model; second, to develop and validate the conceptual

model for BIM adoption in FM within Austrian context; third, to examine the

relationships among the model’s constructs and predict the level of BIM adoption in

FM; Finally, to investigate the influential level of the barriers’ factors for BIM in FM.

To address the research questions, the research was set out in three parts over seven

chapters:

First part “conceptualization” was presented in Chapter 1 and Chapter 2. The main

purpose of part one, was to discuss the concepts used in this research, particularly to set

the boundaries of the concepts used in the research. It gave a detailed view of FM, BIM

and technology acceptance theories. The barriers of BIM adoption in FM were

considered, and the influencing factors were presented. Part one also introduced the

research problem by providing and outlining the adoption issues. The purpose of the

study was highlighted.

Second part “research design” consists of Chapter 3 and Chapter 4. It addressed the main

research design and methodology undertaken for this study. The justification of the

research approach and methodology were discussed. Following this, the research design

procedures were considered. The research design explained the procedures regarding

obtaining a reliable data, upon which the research questions and hypotheses could be

154 Chapter 7: Conclusions

investigated properly. The questionnaire development and data collection strategy were

discussed in the following section. Using the SEM was also justified.

Third part “data analysis and findings” was presented over four chapters. In Chapter 5,

the responses to the survey were first assessed for their adequacy, and 121 usable

responses were accepted. After cleaning and confirming the statistical assumption of the

data, the two-stage SEM was tested on the data. In the first stage, measurement model

was evaluated. The developed models were then analysed to ensure the

multicollinearity, reliability, convergent validity and discriminant validity of the

structural model. Then, the hypotheses about the research questions and the research

objectives were tested. Then Chapter 7 concluded the study by discussing the

implications of the research, the limitations and also the recommendations for future

studies.

Four research questions have been formulated in this research as following:

RQ1. What are the key factors that influence BIM adoption in the FM context?

RQ2. What are the relationships among UTAUT and TTF constructs that

constitute the underlying BIM adoption in FM context?

RQ3. Does the integration of UTAUT and TTF lead to predicting the adoption of

BIM in FM?

RQ4. What are influential levels of the key barriers for BIM in FM?

The above research questions have been addressed in these sections.

7.1 The conceptual model development and the research hypotheses

The first research question (RQ1) focussed on the key factors that influence BIM

adoption in the FM. The conceptualization of the proposed model was a hybrid

integration of a UTAUT-based rationale for model parameters and variables and the

TTF. External and internal factors were identified to consider:


• External factors such as performance expectancy, effort expectancy, social

influence, facilitating conditions, task characteristics and technology

characteristics: these variables are hypothesized to have direct and indirect

influences on the internal and targeted factors of the model.

• Internal factors include task technology fit.

• Targeted factors include user adoption of BIM for FM.

The comprehensive literature review was the key component in this conceptualization

of the proposed model. Synthesis, criticize and comparison techniques have been

conducted to generate the model considering the suitable modification and wording

aspects to the constructs’ measurement items to be compatible with BIM-FM context.

These factors have been shown to have an influence on BIM adoption in facilities

management. Technology acceptance theories (TAM, TTF, UTAUT, etc.) model how

users come to accept and use new technology and innovations. Many studies have shown

the successful use of those theories to explain individual perceptions regarding the

adoption of new technology in their work. Thus, technology acceptance theories have

been adopted in this study to measure the influencing factors of BIM adoption for

facilities management. The researcher also introduced the concepts of BIM adoption in

facilities management via integrating UTAUT and TTF to address how the integration

of UTAUT and TTF help explain user perceptions regarding BIM adoption for facilities

management? First, the UTAUT model identifies variables with an impact on

technology adoption such as facilitating conditions, effort expectancy, performance

expectancy, and social influence, which have direct and indirect influences on

behavioral intention to adopt the technology.

Second, UTAUT is based on an extended version of the technology acceptance model

and other acceptances theories, making it a robust foundation for exploring a different

range of technology adoption topics.

Third, including TTF addresses of how users come to accept and use new technology if

this technology does not fit the job task requirements. Adoption of a technology by the

users is not determined by their perceptions alone but by whether or not that technology


fits the task requirements. Hence, task technology fit is a crucial determinant of new

technology adoption.

Fourth, the successful integration of the UTAUT and TTF in the IT field, for example

(Zhou et al., 2010, Pai and Tu, 2011, Faria, 2013, Oliveira et al., 2014, Tai and Ku, 2014,

Vongjaturapat et al., 2015, Park et al., 2015, Afshan and Sharif, 2016), helps us

understand possible links between the adoption of a new technology in IT and BIM

adoption in facilities management. Research has shown the successful applicability of

technology acceptance theories in the construction industry as much as their

applicability in the IT fields (Lee et al., 2012, Son et al., 2012, Davies and Harty, 2013,

Lee and Yu, 2013, Mahamadu et al., 2014, Son et al., 2014, Wu et al., 2014, Cao et al.,

2014, Xu et al., 2014, Lee et al., 2015, DAWOOD and CHAN, 2015, Ding et al., 2015,

Son et al., 2015, Lee and Yu, 2016a, Cao et al., 2016 Lee, 2016 #102, Howard et al.,

2017). However, these concepts have yet to be explored in the context of facilities

management, and one aim of this research was to consolidate the perceptions of BIM

adoption in facilities management so it helps stakeholders benefit from implementing

BIM.

The second research question (RQ2) focussed on the relationships among UTAUT and

TTF constructs that constitute the underlying BIM adoption in FM context. An online

questionnaire survey has been performed to test the integrated model of TTF and

UTAUT that explains FM perceptions regarding the adoption of BIM. The results show

that all hypotheses of TTF are supported by the data using PLS-SEM. Both task

characteristics and technology characteristics positively affect the TTF, which further

determines the FM adoption of BIM. However, technology characteristics has a much

higher effect on TTF. These results support the previous research’s findings (Junglas

and Watson, 2008, Lee et al., 2007, Lin and Huang, 2008, Zhou et al., 2010). When FM

promote their practices and adopt BIM, they need to consider the fit between FM tasks’

requirements and BIM functions and applications. BIM is probably more appropriate

for those FM organizations that always keep their FM systems upgraded to maintain a

high level of interoperability with the latest BIM applications. In contrast, traditional

FM organizations with low modernized and less upgraded systems get lower benefits


from BIM adoption in their practices. Accordingly, FM professionals need to perform

market analysis and try to find the required criteria of different FM organization groups.

Then, they can make a careful selection among the available BIM software and services

to achieve a suitable TTF. This will promote user adoption and usage behavior.

For the UTAUT model factors, performance expectancy, effort expectancy, social

influence, and facilitating conditions all have significant effects on user adoption. These

findings are similar to those of previous research (Carlsson et al., 2006, Park et al., 2007,

Zhou et al., 2010). Among UTAUT model factors affecting FM adoption of BIM,

performance expectancy has a relatively high effect. Therefore, when FM organizations

decide to adopt BIM, they need to consider FM expectations toward BIM benefits and

functions. FM can improve its services based on FM practitioners’ suggestions in order

to improve their performance expectations. Also, FM organizations need to run

extensive training sessions to enhance their employees’ knowledge about BIM

applications, functions, and skills in using BIM. Then, FM employees’ perceptions

regarding facilitating conditions can be boosted.

Social influence also has a significant effect and worth more attention. Traditional FM

organizations can take advantage of earlier adopters of BIM, whose reviews and

opinions may generate positive influence regarding encouragement on subsequent

adoption behavior (Zhou et al., 2010). Disseminating such recommendations and

obtaining the pioneer FM organizations endorsements will help to boost FM adoption.

Effort expectancy has a direct and significant effect on BIM adoption by FM. FM should

consider the negative effects of difficulties regarding the entry of the data and

information into BIM and should find usable and easy ways to adopt BIM.

The third research question (RQ3) focussed on whether integrating UTAUT and TTF

lead to predicting the adoption of BIM in FM? The results revealed that correlation

between TTF and UTAUT constructs were existed and significant. The TTF has an

obvious positive effect on performance expectancy. In addition, performance

expectancy mediates the TTF and BIM adoption by FM. This result supports the

previous research as it emphasized that the integrated models of UTAUT and TTF


provide more explanation on user adoption compared with the individual UTAUT and

TTF models (Zhou et al., 2010). Thus, good TTF is an important way to enhance FM

performance expectancy. If FM gets BIM tools and applications that are a misfit with

their requirements, they will get the impression these services to be of little usefulness

and form a slight performance expectancy. For instance, BIM in FM can be applied and

used for facilitating real-time data access, locating building components, checking

maintainability, visualization and space management. Most FM employees consider

these applications as beneficial for their work practices. However, some users may be

arguing these applications may affect their current traditional approach and put them to

face the challenge of change. For these users, BIM applications are unfit with their

requirements and may even result in negative perceptions toward BIM adoption. Hence,

FM organizations should first get users' permission before BIM adoption. Running

training sessions and learning the best practice of BIM adoption will help to enhance

FM perceptions.

7.2 The impeding’s factors

The fourth research question (RQ4) focussed on the key barriers for BIM adoption in

FM. One objective of this study was to identify the impeding factors of BIM adoption

in FM, quantify their importance levels, and determine the underlying factors that

contribute to better understanding of BIM adoption in FM in Austrian context. Analysis

of impeding factors of BIM adoption in FM was done in two steps: (1) basic statistics

to discover the most important items among a list of 17 variables, and (2) factor analysis

to represent a list of 17 variables in fewer number of factors and name those based on

their common features. Findings of both steps were discussed considering the three

factors obtained in factor analysis as explained.

Based on the findings of data analysis of 91 responses, Unknown FM data requirements

got the highest mean value with 5.13 as shown in Table 3, and this refers clearly on how

large the gap between all the project phases and the FM phase is. In fact, in most design

and the construction practice, they are unaware about the project requirements during

the operation and management phase of the project. This leads to the ignorance of the


most FM details during developing BIM by the design and construction team.

Consequently, it is normal that the Lack of FM team participation in the design phase

barrier got the second importance rank with 5.08 mean value. This shows the need to

engage FM team in the early stage of the design phase, so they can collaborate and

provide the FM data and information to the design team in a way the can be compatible

with the BIM requirements. This concept called design for FM which leads to decrease

the conflict between BIM and FM to the minimum.

Timeliness training before hand-over stage got 5.02 mean value and placed in the third

rank of the importance barriers. This is fair as FM team is often highly unaware of what

is contained in BIM until the hand-over stage finished. Accordingly, FM team spends

huge time and efforts to arrange and provide the necessary information to the FM system

and practices. This leads to cost and time overrun. Lack of effective collaboration

between project stakeholders for modelling and model utilization barrier scored 4.98

mean value in 4th rank. Recent research has revealed the importance of the collaboration

among all project stakeholders to build up the BIM repository with proper information

and data to use them effectively later during the whole project life-cycle. Entrenched

traditional practices and lack of best practice has similar importance value to the

previous barrier. People face the change in different levels. Change may put some

pressure on the stakeholders to practice new approaches which may have some

difficulties and uncertainties.

Diversity in BIM and FM software tools, and interoperability issues is very close to the

previous two barriers. Selection of the best fit software tools is a very crucial issue.

Research has shown the negative consequences when using different incompatible

platforms. Organization wide resistance: need for investment in infrastructure, training,

and new software tools shows high level of importance as well. This concept has been

discussed above. Necessity yet difficulty in software vendor’s involvement is another

barrier which got 4.91 mean value. In fact, missing of software vendor’s involvement

causes possibilities of misunderstanding the project requirements well, which may lead

to improper decisions. Uncertainties in client-side life-cycle management strategies

another important barrier with 4.90 mean value. Clint priorities may change occasionally


depending on many aspects from the client-side and causes some barriers to BIM

adoption strategies. 4.86 mean value is for Lack of real-world cases and proof of positive

return of investment. The usual question for any owner and investor is whether the

certain tool worth its investment cost or not? At the end, the owner and investors need a

real and tangible profit.

Another barrier, namely IT skills shortages shows acceptable level of importance with

4.83 score. FM current systems and practices suffers from low IT adoption in compare

to the first project phase. The common sense is that the FM team is still considered a

quite far from the first project phases team concerns and targets. Successes project

should have integrity among all the project stakeholders and during all project life-cycle.

This is considered the most significant issue for the successful BIM adoption, because

BIM is a data repository for the whole project lifetime.

These barriers Undefined fee structures for additional scope, Unclear roles and

responsibilities for loading data into the model or databases and maintaining the model,

Maturity of BIM standards and frameworks, Lack of sufficient legal framework for

integrating owners’ view and the actual influence in the design and construction,

Cultural barriers toward adopting new technology, and Inappropriate technologies and

reluctance to use open standards for information exchange are on the bottom of the

importance level as shown in Table 3. However, their mean values ranging from 4.79 to

4.59. This means that even the lowest mean value is still importance and above the

average of (1-7) Likert Scale which is 3.50. Overall, all the studied barriers are

acceptable and show a good level of importance. Overall, it is expected with the outcome

of this study that FM professionals and organizations can better understanding the

barriers behind the BIM adoption in FM.

Also, EFA has been performed for further investigation regards the underling group

factors for those 17 impeding factors. The results revealed three group factors, which

point out the importance of technical, top management and policy related factors

necessary to lay the foundation of the barrier factors for BIM adoption in FM.


The results suggest that policy related factors are the primary barrier factors, whereas

technical-related factors have a moderate effect. Top management related factors have

been importance slightly less than the rest group factors.

The findings of this study can be helpful for guiding senior managers of FM departments

and organizations and BIM consultants for a better adoption process. The FM

organizations that intend to adopt BIM are advised first to invest interoperability

between FM systems and BIM requirements. They should remember that benefits will

be realized in the long term adoption to achieve the return on their investments.

7.3 Theoretical and practical implications

From a theoretical side, this study integrates TTF and UTAUT to explain FM

perceptions regarding BIM adoption. The findings show that besides UTAUT factors

(technology perceptions) such as performance expectancy, TTF factors also have a

significant effect on FM adoption for BIM. Thus, when studying the factors that affect

BIM adoption in FM, attention should be paid to the effect of the best task technology

fit, and not be concerned only with technology perceptions based such as UTAUT, TAM

and IDT theories. Further, the causal relationship between both theories including TTF

and technology perceptions needs more attention. For instance, and in the current study,

it has been found there is a significant relationship between performance expectancy

factor and TTF factor. This result supports the previous research as it emphasized that

the integrated models of UTAUT and TTF provide more explanation on user adoption

compared with the individual UTAUT and TTF models (Zhou et al., 2010). From a practical side, the finding showed that both TTF and performance expectancy

has significant effects on FM adoption of BIM. Also, it has been found that TTF has a

significant effect on performance expectancy. Accordingly, BIM software providers

need to improve the TTF to be compatible with various levels of BIM adoption. They

can study the market and provide different services to satisfy all levels. Thus, BIM

providers can provide different services to meet different FM originations group’s task

demands to improve user adoption of BIM. Also, BIM providers can improve FM

technology perceptions such as performance expectancy. This can be achieved by


presenting a user-friendly interface that will reduce effort expectancy and raising

performance expectancy.

7.4 Limitations

Single context: This research only focuses on a single context – the facilities

management organizations and partitioners in Australia context. Thus, the data obtained

in this study may not be generalize the findings to other different countries. However,

this study may provide a point of starting that facilitates more work in this area.

Limited respondents: As this research only gathered 121 usable responses, this study

could not cross-validate the results. Usually with large data that contains over 300

responses, the data can be split into two groups, in which the first group is used to

analysis and develop the model, and the other group can be used to cross-validate the

results and the model developed. The survey-based approach in this research also

contributed to the limitation. The survey type of research has been criticized for its lower

response rate. Although the research design is considered good method for improving

the response rate, a high level of non-responses is not inevitable.

Limitation of the study approach chosen in this study is self-reporting bias. Although

the questionnaire process in a quantitative approach attempted to reduce the problem

possibility, the possibility for bias to be occurred is always happen. Future studies might

consider combining a qualitative approach with a quantitative approach, as the

integration might help to reduce the shortage of both types of approaches.

Although the significant of the theoretical and practical outcomes, this paper heavily

depends on the PLS-SEM results. Thus, it’s important to consider some limitations as

following:

• Measurement error is not explicitly modelled in PLS-SEM and the constructs

are only approximations of latent variables. Hence, results may differ from CB-

SEM.


• The sample size is small. Hence, not all coefficients were precisely estimated. It

was impossible, owing partly to sample size, to consider the effects of

covariates such as education and age. Such measurement invariance ought to

be examined in future work.

• The properties of PLS-SEM are not as well understood as CB-SEM and, thus,

replication of these results will give more insight.

7.5 Future research

Following the results of this research, future studies should cross-validate this

study by collecting new data. While the data acquisition might be limited and not easily

available, collecting this type of data successfully may result in noble achievement that

may offer significant implications for policy makers and practitioners.

Future research could test and compare the BIM-FM adoption in two or more relatively

different countries. Interesting findings for future investigation could be the relative

organizational benefits of different countries, in terms of BIM-FM adoption. Also, the

research needs to investigate whether the same outcomes could be concluded in the other

countries.

Future research may combine both TTF and UTAUT models to test FM perceptions

regarding potential technology adoption such as augmented reality. It has been

concluded that integrating UTAUT and TTF models will produce more robust insights

compared with each individual model.

7.6 The summary

A conceptual BIM acceptance framework has been developed based on a reliable

literature review. The model aims at presenting the key factors that influence the

adoption of BIM in FM. The research hypotheses are derived and discussed. The

proposed model consists of eight factors (variable). The external variables of the model

are performance expectancy, effort expectancy, social influence, facilitating conditions,

task characteristics and technology characteristics. These variables have a direct and


significant influence on the internal and targeted variables of the model. The internal

variable includes TTF, while the targeted variables include user adoption of BIM in FM.

The measurement model has been evaluated for reliability and validity, while the

structural model has been examined by SEM to test the models’ constructs relations and

hypotheses testing based on an extensive questionnaire survey data targeting the BIM-

FM practitioners. The findings show an acceptable level of the measurement model

validation, and research hypotheses testing. All the research hypotheses were supported

and significant.

Chapter 8: Appendix 165

Chapter 8: Appendix

APPENDIX A – QUESTIONNAIRE Building Information Modelling Adoption in Facility Management Principal Investigators Main student investigator: Mustafa Abdullah Hilal, Faculty of Science, Engineering and Technology /School of Engineering/ Department of Civil and Construction Engineering /Swinburne University of Technology, Australia (Tel: +61 432366872; E-mail: [email protected]) Chief Investigator & Research Supervisor: Dr Amir Abdekhodaee, Faculty of Science, Engineering and Technology/ School of Engineering/ Department of Mechanical Engineering and Product Design Engineering / Swinburne University of Technology, Australia (Tel: +61 392145263; E-mail: [email protected]) Co-investigator/ Associate Supervisor: : A/Prof. Tayyab Maqsood, College of Design and Social Context / School of Property, Construction and Project Management / RMIT University, Australia (Tel: +61 3 9925 3916; E-mail: [email protected]) Research Project Overview Maintenance and operation of built facilities requires a significant amount of readily available and relevant information for various stakeholders. At the end of the construction phase, the contractor submits to the owner/operator an enormous amount of building information such as as-built drawings, operation manuals, maintenance manuals, warranties, etc. This information is used by Facilities Management (FM) staff in order to maintain and operate assets within the facility. Often, the information submitted to the Owner/Operator during project closeout is unstructured and disorganized. This requires FM staff to hard enter O&M data into existing FM systems or store the unstructured information until needed for O&M purposes. Consequently, retrieval of relevant O&M information needed for a FM task is inefficient and costly. Building Information Modelling (BIM) can help to inform communication and streamline the provision of the necessary information for the facility management systems. Recent research has revealed the significance of BIM in the facilities management (FM) as much as in design and construction sectors (Citations required). However, the adoption of BIM in FM is only minimal. A study showed that the use of BIM has also extended to benefit the operation and maintenance of the facilities in the following aspects ; Locating Building Components, Facilitating Real-Time Data Access, Visualization and Marketing, Checking Maintainability, Space Management, Planning and Feasibility Studies for Noncapital Construction, Emergency Management, Controlling and Monitoring Energy, Personnel Training and Development . However, most of the FM are still manage their facilities using obsolete and time-consuming methods such as paper-based drawing, spreadsheets and manual re-creation of information. Thus, BIM acceptance and adoption in FM is still one of main issues in this field. Using Technology Acceptance Theories such as Technology Acceptance Model (TAM), Task Technology Fit (TTF) and Unified Theory of Acceptance and Use of Technology (UTAUT) can help to measure how FM practitioners come to accept and adopt a new technology. Accordingly, this research aims at identifying the key factors that influence the widespread adoption and usage of BIM in facility management and developing a hybrid conceptual model that integrating TTF and UTAUT for enhancing BIM in this sector. The methods that are being adopted in this ongoing research include a comprehensive literature review, interviews and a survey. The conceptual model has already been developed by the research team and published. Perceptions and information from FM practitioners will be collected through a questionnaire survey and semi-structured interviews for developing and validating the model. To achieve the research aim, the following objectives are targeted:

• Investigate the challenges of implementing BIM in FM. • Identify the key factors that influence the acceptance and usage of BIM in facilities management. • Examine the relationships between these factors using Structural Equation Models. • Developing a conceptual BIM acceptance model in FM.

Participation Requirements We request you to complete our questionnaire survey by relating to your recent experience and/or perceptions on using building information modelling in facilities management. Kindly, you are expected to submit your responses to this online survey within two weeks after you receive our invitation. The justification for targeting your participation is that you are expectedly involved in using Building Information Modelling (BIM) in your facilities management organizations. Therefore, getting information from you regard using BIM is considered a best way to develop the proposed model for this research. A downloadable soft copy of the questionnaire is available at: http://................................ If you prefer, alternative ways of submitting your survey responses you may either: e-mail to: [email protected] hardcopy post to: School of Engineering/ Department of Mechanical Engineering and Product Design Engineering (H38), Swinburne

mailto:[email protected]

166 Chapter 8: Appendix

University of Technology, P O Box 218, HAWTHORN VIC 3122, Australia The online survey will take approximately 20 minutes to be completed. We really appreciate your participation and contributions in our academic research. Data Treatment All data gathered and analysed during the research project will be securely stored in the supervisor office, and used for academic purpose. At the conclusion of the project, the data will be securely kept for 5 years in softcopy format. After 5 years of post-publication, all stored data will be completely deleted. Further information about the project—who to contact If you would like further information about the project and the project outcomes, you may contact: Dr Amir Abdekhodaee, Faculty of Science, Engineering and Technology/School of Engineering/ Department of Mechanical Engineering and Product Design Engineering / Swinburne University of Technology, Australia (Tel: +61 392145263; E-mail: [email protected]) P O Box 218, HAWTHORN VIC 3122, Australia Concerns/complaints about the project – who to contact This project has been approved by, or on behalf of, Swinburne’s Human Research Ethics Committee (SUHREC) in line with the National Statement on Ethical Conduct in Human Research. If you have any concerns or complaints about the conduct of this project, you can contact: Research Ethics Officer, Swinburne Research (H68), Swinburne University of Technology, P O Box 218, HAWTHORN VIC 3122. Tel (03) 9214 3845/+61 3 9214 3845 or [email protected] The participant’s consent declaration 1. I hereby consent to participate in the survey and agree to provide responses for the questionnaire. 2. I understand that: a. my participation is voluntary; b. my participation is anonymous unless I prefer otherwise; c. any information gathered as the result of my participating in this project will be (i) collected and retained for the purpose of this academic research (ii) accessed and analysed by the researcher(s) of this academic research (iii) treated as confidential information and (iv) securely stored for this academic research. By clicking the “start” button, you accept the conditions above and agree to participate in this project. *For participants who choose to do the survey not through online system, by moving on to the question sections and kindly answering the questions, you accept the conditions above and agree to participate in this project. Section 1. Respondent background Information 1. What is your gender?

o Male o Female o Others

2. 1. What is your level of education? o Undergraduate o Postgraduate o Others certificate or associates degree / licensure

3. What is your age (years)? o Under 30 o 30-39 o 40-49 o 50 and over

4. What is your Job Title: o Technician Staff o Assistant Manager o Manager o Deputy General Manager o General Manager o Others

5. How long you have job experience in AECO (years)? o 1-3 o 4-6


o 7-9 o 10 and over

6. For how long your company using BIM (years)? o 1-3

o 4-6

o 7-9

o 10 and over

Section2. below are the common barriers of implementing BIM in FM. Based on your experience, please select the most appropriate 7-

points Likert-type scales regarding each item that measure the related barrier type, where (1) represents strongly disagree and (7) represents strongly agree

A- Technology and Process Barriers

No. Barriers item Level of agreement

1 2 3 4 5 6 7

1 Unclear roles and responsibilities for loading data into the model or databases and maintaining the model

2 Diversity in BIM and FM software tools, and interoperability issues

3 Lack of effective collaboration between project stakeholders for modelling and model utilization

4

Necessity yet difficulty in software vendor’s involvement, including fragmentation among different vendors, competition, and lack of common interests

5 Entrenched traditional practices and lack of best practice

6 Lack of FM team participation in the design phase, which means their ability to influence data requirements and specifications are limited.

7 Timeliness training before hand-over stage, as FM team is largely unaware of what is contained in as-built model until the hand-over stage

8 Unknown FM data requirements

9 Inappropriate technologies and reluctance to use open standards for information exchange

10 IT skills shortages

B- Organizational Barriers

No. Barriers item Level of agreement

1 2 3 4 5 6 7

1 Cultural barriers toward adopting new technology

2 Organization wide resistance: need for investment in infrastructure, training, and new software tools;

3 Undefined fee structures for additional scope;

4 Lack of sufficient legal framework for integrating owners’ view and the actual influence in the design and construction;

5 Lack of real-world cases and proof of positive return of investment.

6 Maturity of BIM standards and frameworks

7 Uncertainties in client-side life-cycle management strategies


Section 3. Following in bold letter are the common key factors affecting the adoption of BIM in the facilities management, which

represent the components of the proposed conceptual model of this study. Each factor has a definition and its own items that measure the related factor. Based on your experience, please select the most appropriate Likert-type scales regarding each item that measure the related factor, where (1) represents strongly disagree and (7) represents strongly agree.

3.1 User Adoption of BIM: it means user actual use of BIM in FM. Please provide your perception/experience based answers on a 7-

points Likert-type scales. (Strongly disagree 1 --------------------------------- 7 Strongly agree).

No.

Items Level of agreement

1 2 3 4 5 6 7

USE1 I often use BIM to manage my FM tasks

USE2 I often use BIM to optimize the cost

USE3 I often use BIM to optimize the time

3.2 Performance Expectancy: is “The degree to which an individual believes that using the system will help him/her to attain gains

in job performance”. Please provide your perception/experience based answers on a 7-points Likert-type scales. (Strongly disagree 1 --------------------------------- 7 Strongly agree).

No.


1 2 3 4 5 6 7

PE1 I would find BIM useful in my job.

PE2 Working with BIM enables me to accomplish tasks more quickly.

PE3 Working with BIM increases my productivity.

PE4 If I work with BIM, I will increase my chances of getting a raise.

3.3 Effort Expectancy: is “The degree of ease associated with the use of system”. Please provide your perception/experience based

answers on a 7-points Likert-type scales. (Strongly disagree 1 --------------------------------- 7 Strongly agree).

No. Items

Level of agreement

1 2 3 4 5 6 7

EE1 My interaction with BIM would be clear and understandable.

EE2 It would be easy for me to become skilled at working with BIM.

EE3 I would find BIM easy to use.

EE4 Leaning to operate BIM is easy for me.

3.4 Social Influence : is “The degree to which an individual perceives that important others believe he/she should use the new system”.

Please provide your perception/experience based answers on a 7-points Likert-type scales. (Strongly disagree 1 --------------------------------- 7 Strongly agree).

No. Items

Level of agreement

1 2 3 4 5 6 7

SI1 People who influence my behavior think I should use BIM.

SI2 People who are important to me think that I should use BIM.

SI3 The senior management of this business has been helpful in the use of BIM.

SI4 In general, my organization has supported the use of BIM.

3.5 Facilitating Conditions : is “The degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system”. Please provide your perception/experience based answers on a 7-points Likert-type scales. (Strongly disagree 1 --------------------------------- 7 Strongly agree).


No. Items

Level of agreement

1 2 3 4 5 6 7

FC1 I have the resources necessary to work with BIM.

FC2 I have the knowledge necessary to work with BIM.

FC3 BIM is not compatible with the work tools I use.

FC4 A specific person (or group) is available for assistance with BIM difficulties.

3.6 Task Technology Fit: is “Task technology fit is the rational perspective of what a new technology can do to optimize a job. It is

affected by the nature of the task and practicality of the technology to complete the task”. Please provide your perception/experience based answers on a 7-points Likert-type scales. (Strongly disagree 1 --------------------------------- 7 Strongly agree).

No Items

Level of agreement

1 2 3 4 5 6 7

TTF1 In helping complete my FM tasks, the functions of BIM are enough.

TTF2 In helping complete my FM tasks, the functions of BIM are appropriate.

TTF3 In general, the functions of BIM fully meet my task context.

3.7 Technology Characteristics: is the main determinate of the task technology fit theory that considered the technology

characteristics aspect. Please provide your perception/experience based answers on a 7-points Likert-type scales. (Strongly disagree 1 --------------------------------- 7 Strongly agree).

No


1 2 3 4 5 6 7

TEC1 BIM provides ubiquitous services.

TEC2 BIM provides real-time services.

TEC3 BIM provides reliable services.

3.8 Task Characteristics: is the main determinate of the task technology fit theory that considered the task characteristics aspect.

Please provide your perception/experience based answers on a 7-points Likert-type scales. (Strongly disagree 1 --------------------------------- 7 Strongly agree).

No


1 2 3 4 5 6 7

TAC1 I need to manage FM tasks efficiently

TAC2 I need to export accurate and actual information to FM systems

TAC3 I need to acquire FM information in real-time.

Thank you very much for your kind participation, it is really appreciated


FSET/ SHR Project 2017/131 – Ethics Clearance To: Dr Amir Abdekhodaee, FSET SHR Project 2017/131 – Building Information Modelling Adoption in Facility Management Sector Dr Amir Abdekhodaee, Mustafa Abdullah Hilal (Student) – FSET/A/Prof Tayyab Maqsood – RMIT Approved duration: 21-08-2017 to 13-02-2019 [Adjusted] I refer to the ethical review of the above project by a Subcommittee (SHESC2) of Swinburne's Human Research Ethics Committee (SUHREC). Your responses to the review as emailed on 12 July and 7 and 8 August 2017 were put to the Subcommittee delegates for consideration. I am pleased to advise that, as submitted to date, ethics clearance has been given for the above project to proceed in line with standard on-going ethics clearance conditions outlined below.

- The approved duration is 21-08-2017 to 13-02-2019 unless an extension request is subsequently approved.

- All human research activity undertaken under Swinburne auspices must conform to Swinburne and external regulatory standards, including the National Statement on Ethical Conduct in Human Research and with respect to secure data use, retention and disposal.

- The named Swinburne Chief Investigator/Supervisor remains responsible for any personnel appointed to or associated with the

project being made aware of ethics clearance conditions, including research and consent procedures or instruments approved. Any change in chief investigator/supervisor, and addition or removal of other personnel/students from the project, requires timely notification and SUHREC endorsement.

- The above project has been approved as submitted for ethical review by or on behalf of SUHREC. Amendments to approved

procedures or instruments ordinarily require prior ethical appraisal/clearance. SUHREC must be notified immediately or as soon as possible thereafter of (a) any serious or unexpected adverse effects on participants and any redress measures; (b) proposed changes in protocols; and (c) unforeseen events which might affect continued ethical acceptability of the project.

- At a minimum, an annual report on the progress of the project is required as well as at the conclusion (or abandonment) of the

project. Information on project monitoring and variations/additions, self-audits and progress reports can be found on the Research Internet pages.

- A duly authorised external or internal audit of the project may be undertaken at any time.

The delegates would like it noted though that the download link is still required in Appendix B and that the public documents contain a mix of American and Australian English. Please contact the Research Ethics Office if you have any queries about on-going ethics clearance, citing the Swinburne project number. A copy of this e-mail should be retained as part of project record-keeping. Best wishes for the project. Yours sincerely, Sally Fried Secretary, SHESC2

http://www.swinburne.edu.au/research/ethics/integrity-ethics/human-research-ethics/monitoring-reporting-and-changes-after-approval/


Appendix B. Comparison between cases with more than 20% missing data with cases

with less than 20% missing data

Item Mann-

Whitney U Wilcoxon W Z Asymp.

Sig. (2-tailed)

TTF1 215 225 -0.08 0.936

TTF2 206 6311 -0.224 0.823

TTF3 384.5 412.5 -0.39 0.696

TEC1 410 438 -0.034 0.973

TEC2 524 7784 -0.154 0.878

TEC3 527.5 582.5 -0.614 0.539

TAC1 91.5 6196.5 -1.363 0.173

TAC2 260 6930 -0.377 0.706

TAC3 156 6597 -1.088 0.276

USE1 158 173 -1.65 0.099

USE2 311 332 -0.517 0.605

USE3 251 272 -1.231 0.218

PE1 448.5 7588.5 -1.794 0.073

PE2 377.5 7758.5 -2.029 0.042

PE3 388.5 7528.5 -1.879 0.06

PE4 307 7210 -1.141 0.254

EE1 678.5 756.5 -0.388 0.698

EE2 610 676 -0.429 0.668

EE3 567.5 633.5 -0.839 0.401

EE4 490 545 -1.04 0.298

SI1 308.5 7689.5 -1.238 0.216

SI2 412.5 7552.5 -0.649 0.517

SI3 408.5 7429.5 -0.656 0.512

SI4 456.5 7596.5 -1.253 0.21

FC1 391 7651 -0.903 0.367

FC2 349.5 394.5 -1.808 0.071

FC3 374.5 402.5 -0.491 0.623

FC4 360 7146 -0.516 0.606


Appendix C. Scatter plots




Appendix D. FSET/ SHR Project 2017/131 – Ethics Clearance To: Dr Amir Abdekhodaee, FSET SHR Project 2017/131 – Building Information Modelling Adoption in Facility Management Sector Dr Amir Abdekhodaee, Mustafa Abdullah Hilal (Student) – FSET/A/Prof Tayyab Maqsood – RMIT Approved duration: 21-08-2017 to 13-02-2019 [Adjusted] I refer to the ethical review of the above project by a Subcommittee (SHESC2) of Swinburne's Human Research Ethics Committee (SUHREC). Your responses to the review as emailed on 12 July and 7 and 8 August 2017 were put to the Subcommittee delegates for consideration. I am pleased to advise that, as submitted to date, ethics clearance has been given for the above project to proceed in line with standard on-going ethics clearance conditions outlined below.

- The approved duration is 21-08-2017 to 13-02-2019 unless an extension request is subsequently approved.

- All human research activity undertaken under Swinburne auspices must conform to Swinburne and external regulatory standards, including the National Statement on Ethical Conduct in Human Research and with respect to secure data use, retention and disposal.

- The named Swinburne Chief Investigator/Supervisor remains responsible for any personnel appointed to or associated with the

project being made aware of ethics clearance conditions, including research and consent procedures or instruments approved. Any change in chief investigator/supervisor, and addition or removal of other personnel/students from the project, requires timely notification and SUHREC endorsement.

- The above project has been approved as submitted for ethical review by or on behalf of SUHREC. Amendments to approved

procedures or instruments ordinarily require prior ethical appraisal/clearance. SUHREC must be notified immediately or as soon as possible thereafter of (a) any serious or unexpected adverse effects on participants and any redress measures; (b) proposed changes in protocols; and (c) unforeseen events which might affect continued ethical acceptability of the project.

- At a minimum, an annual report on the progress of the project is required as well as at the conclusion (or abandonment) of the

project. Information on project monitoring and variations/additions, self-audits and progress reports can be found on the Research Internet pages.

- A duly authorised external or internal audit of the project may be undertaken at any time.

The delegates would like it noted though that the download link is still required in Appendix B and that the public documents contain a mix of American and Australian English. Please contact the Research Ethics Office if you have any queries about on-going ethics clearance, citing the Swinburne project number. A copy of this e-mail should be retained as part of project record-keeping. Best wishes for the project. Yours sincerely, Sally Fried Secretary, SHESC2

http://www.swinburne.edu.au/research/ethics/integrity-ethics/human-research-ethics/monitoring-reporting-and-changes-after-approval/

Bibliography 177

Bibliography

Bibliography

Bibliography 179

ABDULJALIL, K. M. & ZAINUDDIN, Y. 2015. Integrating Technology Acceptance Model and Motivational Model towards Intention to Adopt Accounting Information System.

AFSHAN, S. & SHARIF, A. 2016. Acceptance of mobile banking framework in Pakistan. Telematics and Informatics, 33, 370-387.

AKTER, S., D'AMBRA, J. & RAY, P. 2011. An evaluation of PLS based complex models: the roles of power analysis, predictive relevance and GoF index.

ALLISON, P. D. 2001. Missing data, Sage publications. ALVAREZ-ROMERO, S. O. 2014. Use of Building Information Modeling technology in the

integration of the handover process and facilities management. Worcester Polytechnic Institute.

AMMENWERTH, E., ILLER, C. & MAHLER, C. 2006. IT-adoption and the interaction of task, technology and individuals: a fit framework and a case study. BMC Med Inform Decis Mak, 6, 3.

AMOAKO-GYAMPAH, K. & SALAM, A. F. 2004. An extension of the technology acceptance model in an ERP implementation environment. Information & Management, 41, 731-745.

ANCARANI, A. & CAPALDO, G. 2005. Supporting decision-making process in facilities management services procurement: A methodological approach. Journal of Purchasing and Supply Management, 11, 232-241.

ARAM, S., EASTMAN, C. & SACKS, R. A Knowledge-based framework for quantity takeoff and cost estimation in the AEC industry using BIM. The 31st International Symposium on Automation and Robotics in Construction and Mining, 2014. 458-436.

ARAYICI, Y., ONYENOBI, T. & EGBU, C. 2012. Building information modelling (BIM) for facilities management (FM): The MediaCity case study approach. International Journal of 3D Information Modelling, 1, 55-73.

ASHWORTH, S., TUCKER, M. & DRUHMANN, C. K. 2019. Critical success factors for facility management employer’s information requirements (EIR) for BIM. Facilities, 37, 103-118.

AZHAR, S., KHALFAN, M. & MAQSOOD, T. 2012. Building information modelling (BIM): now and beyond. Australasian Journal of Construction Economics and Building, 12, 15-28.

AZHAR, S., NADEEM, A., MOK, J. Y. & LEUNG, B. H. Building Information Modeling (BIM): A new paradigm for visual interactive modeling and simulation for construction projects. Proc., First International Conference on Construction in Developing Countries, 2008. 435-446.

BALDWIN, M. 2012. BIM implementation & execution plans. BIM Journal, , Vol. 3 pp. 73-76.

BARBAROSOGLU, B. V. & ARDITI, D. 2019. A System for Early Detection of Maintainability Issues Using BIM. Advances in Informatics and Computing in Civil and Construction Engineering. Springer.

BARRETT, P. & BALDRY, D. 2003. Total facilities management towards best practice. Malden: Blackwell Science Inc.

BARRETT, P. & BALDRY, D. 2009. Facilities management: Towards best practice, John Wiley & Sons.

BECERIK-GERBER, B., JAZIZADEH, F., LI, N. & CALIS, G. 2012. Application Areas and Data Requirements for BIM-Enabled Facilities Management. Journal of Construction Engineering and Management, 138, 431-442.

BENNETT, D. A. 2001. How can I deal with missing data in my study? Australian and New Zealand journal of public health, 25, 464-469.

180 Bibliography

BENTLER, P. M. & HUANG, W. 2014. On components, latent variables, PLS and simple methods: Reactions to Rigdon's rethinking of PLS. Long Range Planning, 47, 138-145.

BIN ZAKARIA, Z., MOHAMED ALI, N., TARMIZI HARON, A., MARSHALL-PONTING, A. & ABD HAMID, Z. 2013. Exploring the adoption of Building Information Modelling (BIM) in the Malaysian construction industry: A qualitative approach. International Journal of Research in Engineering and Technology, 2, 384-395.

BOZORGKHOU, N. 2015. An Internet shopping user adoption model using an integrated TTF and UTAUT: Evidence from Iranian consumers. Management Science Letters, 5, 199-204.

BROOKS, T. J. & LUCAS, J. D. 2014. A Study to Support BIM Turnover to Facility Managers for Use after Construction. Computing in Civil and Building Engineering (2014), 243-250.

BRUNNER, M. & SÜΒ, H.-M. 2005. Analyzing the reliability of multidimensional measures: An example from intelligence research. Educational and Psychological Measurement, 65, 227-240.

BYRNE, B. M. 2010. Structural equation modeling with AMOS: Basic concepts, applications, and programming.

CAO, D., LI, H. & WANG, G. 2014. Impacts of isomorphic pressures on BIM adoption in construction projects. Journal of Construction Engineering and Management.

CAO, D., LI, H., WANG, G. & HUANG, T. 2016. Identifying and contextualising the motivations for BIM implementation in construction projects: An empirical study in China. International Journal of Project Management.

CARLSSON, C., CARLSSON, J., HYVONEN, K., PUHAKAINEN, J. & WALDEN, P. Adoption of mobile devices/services-searching for answers with the UTAUT. Proceedings of the 39th annual Hawaii international conference on system sciences (HICSS'06), 2006. IEEE, 132a-132a.

CAVANA, R. Y. A. 2001. Applied business research : qualitative and quantitative methods, Milton, Qld. : John Wiley & Sons Australia, 2001.

CHEN, K., LU, W., PENG, Y., ROWLINSON, S. & HUANG, G. Q. 2015. Bridging BIM and building: From a literature review to an integrated conceptual framework. International journal of project management, 33, 1405-1416.

CHIN, W. W. 1998. The partial least squares approach to structural equation modeling. Modern methods for business research, 295, 295-336.

CHUNG, B., SKIBNIEWSKI, M. J. & KWAK, Y. H. 2009. Developing ERP systems success model for the construction industry. journal of construction engineering and management, 135, 207-216.

COBO, M. J., LÓPEZ-HERRERA, A. G., HERRERA-VIEDMA, E. & HERRERA, F. 2011a. Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science & Technology, 62, 1382-1402.

COBO, M. J., LÓPEZ‐HERRERA, A. G., HERRERA‐VIEDMA, E. & HERRERA, F. 2011b. Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62, 1382-1402.

COHEN, J. 1998. Statistical Power Analysis for the Behavioural Sciences, xxi. Hillsdale, NJ: L Erlbaum associates.

COHEN, J., COHEN, P., WEST, S. & AIKEN, L. 2003. Applied multiple regression/correlation analysis for the behavioral sciences. Mahwah, New Jersey: Laurence Erlbaum Associates. Inc., Publishers.

COHEN, P., WEST, S. G. & AIKEN, L. S. 2014. Applied multiple regression/correlation analysis for the behavioral sciences, Psychology Press.

Bibliography 181

CORTINA, J. M. 1993. What is coefficient alpha? An examination of theory and applications. Journal of applied psychology, 78, 98.

CRESWELL, J. 2008. Educational research planning, conducting, and evaluating qualitative and inquiry and research. New Jersey: Pearson International Edition.

CRESWELL, J. W. 2003. Research design: Qualitative, quantitative, and mixed methods design. Sage, London.

CUNNINGHAM, E. 2008. A practical guide to structural equation modelling using Amos. Melbourne: Statsline.

DAVIES, R. & HARTY, C. 2013. Measurement and exploration of individual beliefs about the consequences of building information modelling use. Construction Management and Economics, 31, 1110-1127.

DAVIS, F. D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.

DAVIS JR, F. D. 1986. A technology acceptance model for empirically testing new end-user information systems: Theory and results. Massachusetts Institute of Technology.

DAWOOD, G. & CHAN, K.-Y. 2015. THE EFFECT OF MTM AND THE TAM ON INNOVATION DIFFUSION: A CASE IN THE CONSTRUCTION INDUSTRY.

DE CARVALHO, J. & CHIMA, F. O. 2014. Applications of structural equation modeling in social sciences research. American International Journal of Contemporary Research, 4, 6-11.

DECOSTER, J. 1998. Overview of factor analysis. DILLMAN, D. A. 2000. Mail and internet surveys: The tailored design method, Wiley New

York. DING, Z., ZUO, J., WU, J. & WANG, J. 2015. Key factors for the BIM adoption by architects:

a China study. Engineering, Construction and Architectural Management, 22, 732-748. DIXIT, M. K., VENKATRAJ, V., OSTADALIMAKHMALBAF, M., PARIAFSAI, F. &

LAVY, S. 2019. Integration of facility management and building information modeling (BIM) A review of key issues and challenges. Facilities.

EADIE, R., BROWNE, M., ODEYINKA, H., MCKEOWN, C. & MCNIFF, S. 2013. BIM implementation throughout the UK construction project lifecycle: An analysis. Automation in Construction, 36, 145-151.

EAST, W. E. & BRODT, W. J. J. O. B. I. M. 2007. BIM for construction handover. 2007, 28-35.

EASTERBY-SMITH, M. T. & THORPE, R. 2002. R. and Lowe, A.(2002). Management research: An introduction, 2, 342.

ENDERS, C. K. 2003. Using the expectation maximization algorithm to estimate coefficient alpha for scales with item-level missing data. Psychological methods, 8, 322.

FARIA, M. G. 2013. Mobilie Banking adoption: a novel in the portuguese context. FIELD, A. 2005. Discovering statistics using SPSS 2nd Ed., Sage, London ed.: CA: Sage

Publications Inc. FIRESTONE, W. A. 1987. Meaning in method: The rhetoric of quantitative and qualitative

research. Educational researcher, 16, 16-21. FORNELL, C. & LARCKER, D. F. 1981. Evaluating structural equation models with

unobservable variables and measurement error. Journal of marketing research, 18, 39-50.

GAO, X. & PISHDAD-BOZORGI, P. 2019. BIM-enabled facilities operation and maintenance: A review. Advanced Engineering Informatics, 39, 227-247.

GCR, N. 2004. Cost analysis of inadequate interoperability in the US capital facilities industry. National Institute of Standards and Technology (NIST).

182 Bibliography

GEFEN, D., STRAUB, D. & BOUDREAU, M.-C. 2000. Structural equation modeling and regression: Guidelines for research practice. Communications of the association for information systems, 4, 7.

GOODHUE, D. L. & THOMPSON, R. L. 1995. Task-technology fit and individual performance. MIS quarterly, 213-236.

HAIR, J. F., BLACK, W. C., BABIN, B. J., ANDERSON, R. E. & TATHAM, R. L. 2006a. Multivariate data analysis, Pearson Prentice Hall Upper Saddle River, NJ.

HAIR, J. F., BLACK, W. C., BABIN, B. J., ANDERSON, R. E. & TATHAM, R. L. 2006b. Multivariate data analysis 6th Edition. Pearson Prentice Hall. New Jersey. humans: Critique and reformulation. Journal of Abnormal Psychology, 87, 49-74.

HAIR, J. F., RINGLE, C. M. & SARSTEDT, M. 2011. PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19, 139-152.

HAIR, J. F., RINGLE, C. M. & SARSTEDT, M. 2013. Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long range planning, 46, 1-12.

HAIR, J. F., RISHER, J. J., SARSTEDT, M. & RINGLE, C. M. 2019. When to use and how to report the results of PLS-SEM. European Business Review, 31, 2-24.

HAIR, J. F., SARSTEDT, M., RINGLE, C. M. & MENA, J. A. 2012. An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the academy of marketing science, 40, 414-433.

HAIR JR, J. F., BLACK, W. C., BABIN, B. J. & ANDERSON, R. E. 2010. Multivariate data analysis. vectors, 7th Editio. Pearson Prentice Hall.

HAIR JR, J. F., HULT, G. T. M., RINGLE, C. & SARSTEDT, M. 2016. A primer on partial least squares structural equation modeling (PLS-SEM), Sage publications.

HAIR JR, J. F., MATTHEWS, L. M., MATTHEWS, R. L. & SARSTEDT, M. 2017a. PLS-SEM or CB-SEM: updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1, 107-123.

HAIR JR, J. F., SARSTEDT, M., RINGLE, C. M. & GUDERGAN, S. P. 2017b. Advanced issues in partial least squares structural equation modeling, Sage Publications.

HAIR JR, J. F., WOLFINBARGER, M., MONEY, A. H., SAMOUEL, P. & PAGE, M. J. 2015. Essentials of business research methods, Routledge.

HENSELER, J., RINGLE, C. M. & SARSTEDT, M. 2015. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the academy of marketing science, 43, 115-135.

HENSELER, J., RINGLE, C. M. & SINKOVICS, R. R. 2009. The use of partial least squares path modeling in international marketing. New challenges to international marketing. Emerald Group Publishing Limited.

HILAL, M., ABDEKHODAEE, A. & MAQSOOD, T. 2017. Bibliometric Analysis of Building Information Modelling (BIM) in the Construction Industry. 22nd International Conference on Advancement of Construction Management and Real Estate(CRIOCM 2017), 20-23 November 2017, Swinburne University of Technology in Melbourne , Australia.

HILAL, M., MAQSOOD, T. & ABDEKHODAEE, A. 2019. A scientometric analysis of BIM studies in facilities management. International Journal of Building Pathology and Adaptation, 37, 122-139.

HOSSEINI, M. R., MAGHREBI, M., AKBARNEZHAD, A., MARTEK, I. & ARASHPOUR, M. 2018. Analysis of Citation Networks in Building Information Modeling Research. Journal of Construction Engineering and Management, 144, 04018064.

Bibliography 183

HOWARD, R., RESTREPO, L. & CHANG, C.-Y. 2017. Addressing individual perceptions: An application of the unified theory of acceptance and use of technology to building information modelling. International Journal of Project Management, 35, 107-120.

HSIEH, C.-C., LIU, C.-Y., WU, P.-Y., JENG, A.-P., WANG, R.-G. & CHOU, C.-C. 2019. Building information modeling services reuse for facility management for semiconductor fabrication plants. Automation in Construction, 102, 270-287.

IKEDIASHI, D. I. 2014. A framework for outsourcing facilities management services in Nigeria’s public hospitals. Heriot-Watt University.

ILTER, D. & ERGEN, E. 2015. BIM for building refurbishment and maintenance: current status and research directions. Structural Survey, 33, 228-256.

JOHNSON, P. & DUBERLEY, J. 2000. Understanding Management Research : An Introduction to Epistemology, London, SAGE Publications Ltd.

JUNGLAS, I. A. & WATSON, R. T. 2008. Location-based services. Communications of the ACM, 51, 65.

KASSEM, M., KELLY, G., DAWOOD, N., SERGINSON, M., LOCKLEY, S., KUMARASWAMY, M. & LOVE, P. 2015. BIM in facilities management applications: a case study of a large university complex. Built Environment Project and Asset Management, 5.

KELLY, G., SERGINSON, M., LOCKLEY, S., DAWOOD, N. & KASSEM, M. BIM for facility management: a review and a case study investigating the value and challenges. Proceedings of the 13th International Conference on Construction Applications of Virtual Reality, 2013. 30-31.

KHOSROWSHAHI, F. & ARAYICI, Y. 2012. Roadmap for implementation of BIM in the UK construction industry. Engineering, Construction and Architectural Management, 19, 610-635.

KIM, S., PARK, C. H. & CHIN, S. 2016. Assessment of BIM acceptance degree of Korean AEC participants. KSCE Journal of Civil Engineering, 20, 1163-1177.

KINCAID, D. 1994. Integrated facility management. Facilities, 12, 20-23. KIVINIEMI, A. & CODINHOTO, R. 2014. Challenges in the implementation of BIM for

FM—Case Manchester Town Hall complex. Computing in Civil and Building Engineering, 2014, 665-672.

KLINE, R. B. 2015. Principles and practice of structural equation modeling, Guilford publications.

LATIFFI, A. A., MOHD, S. & BRAHIM, J. 2015. Application of Building Information Modeling (BIM) in the Malaysian Construction Industry: A Story of the First Government Project.

LEE, C.-C., CHENG, H. K. & CHENG, H.-H. 2007. An empirical study of mobile commerce in insurance industry: Task–technology fit and individual differences. Decision Support Systems, 43, 95-110.

LEE, S.-K., AN, H.-K. & YU, J.-H. An Extension of the Technology Acceptance Model for BIM-based FM. Construction Research Congress 2012@ sConstruction Challenges in a Flat World, 2012. ASCE, 602-611.

LEE, S. & YU, J. 2016a. Comparative Study of BIM Acceptance between Korea and the United States. Journal of Construction Engineering and Management, 142.

LEE, S. & YU, J. 2016b. Discriminant model of BIM acceptance readiness in a construction organization. KSCE Journal of Civil Engineering, 1-10.

LEE, S., YU, J. & JEONG, D. 2015. BIM acceptance model in construction organizations. Journal of Management in Engineering.

LEE, S. K. & YU, J.-H. 2013. Effects of Intrinsic and Extrinsic Motivation Factors on BIM Acceptance. Journal of the Korea Institute of Building Construction, 13, 242-252.

184 Bibliography

LEWIS, A., RILEY, D. & ELMUALIM, A. 2010. Defining high performance buildings for operations and maintenance. International Journal of Facility Management, 1.

LIN, T.-C. & HUANG, C.-C. 2008. Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit. Information & Management, 45, 410-417.

LINDBLAD, H. 2013. Study of the implementation process of BIM in Construction Projects. LITTLE, R. J. & RUBIN, D. B. 2019. Statistical analysis with missing data, John Wiley &

Sons. LIU, R. & ISSA, R. R. 2015. Survey: Common knowledge in BIM for facility maintenance.

Journal of Performance of Constructed Facilities, 30, 04015033. LIUA, H., LUA, M. & AL-HUSSEINA, M. BIM-based Integrated Framework for Detailed

Cost Estimation and Schedule Planning of Construction Projects. The 31st International Symposium on Automation and Robotics in Construction and Mining, 2014.

LOMAX, R. G. & SCHUMACKER, R. E. 2004. A beginner's guide to structural equation modeling, psychology press.

LOVE, P. E., IRANI, Z. & EDWARDS, D. J. 2004. Industry-centric benchmarking of information technology benefits, costs and risks for small-to-medium sized enterprises in construction. Automation in construction, 13, 507-524.

LOVE, P. E., MATTHEWS, J., SIMPSON, I., HILL, A. & OLATUNJI, O. A. 2014. A benefits realization management building information modeling framework for asset owners. Automation in construction, 37, 1-10.

MADIGAN, R., LOUW, T., DZIENNUS, M., GRAINDORGE, T., ORTEGA, E., GRAINDORGE, M. & MERAT, N. 2016. Acceptance of automated road transport systems (ARTS): an adaptation of the UTAUT model. Transportation Research Procedia, 14, 2217-2226.

MAHAMADU, A.-M., MAHDJOUBI, L. & BOOTH, C. A. Determinants of Building Information Modelling (BIM) acceptance for supplier integration: A conceptual model. Proceedings 30th Annual ARCOM Conference, 2014. 723-732.

MARCOULIDES, G. A. & SAUNDERS, C. 2006. Editor's comments: PLS: a silver bullet? MIS quarterly, iii-ix.

MAYO, G., GIEL, B. & ISSA, R. 2012. BIM use and requirements among building owners. Computing in civil engineering (2012).

MENDEZ, R. O. 2006. The building information model in facilities management. MERSCHBROCK, C. & MUNKVOLD, B. E. 2015. Effective digital collaboration in the

construction industry–A case study of BIM deployment in a hospital construction project. Computers in Industry, 73, 1-7.

MEYERS, L. S., GAMST, G. & GUARINO, A. 2006. Applied multivariate research: Design and interpretation Sage Publications. Inc. Thousand Oaks, CA.

MIETTINEN, R., KEROSUO, H., METSÄLÄ, T. & PAAVOLA, S. 2018. Bridging the lifecycle. Journal of Facilities Management.

MITCHELL, R. & BERNAUER, T. 1998. Empirical research on international environmental policy: designing qualitative case studies. The Journal of Environment & Development, 7, 4-31.

NUNNALLY, J. 1978. C.(1978). Psychometric theory, 2. OLIVEIRA, T., FARIA, M., THOMAS, M. A. & POPOVIČ, A. 2014. Extending the

understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International Journal of Information Management, 34, 689-703.

OZORHON, B. & KARAHAN, U. 2016. Critical success factors of building information modeling implementation. Journal of Management in Engineering, 33, 04016054.

Bibliography 185

PAI, J.-C. & TU, F.-M. 2011. The acceptance and use of customer relationship management (CRM) systems: An empirical study of distribution service industry in Taiwan. Expert Systems with Applications, 38, 579-584.

PALLANT, J. 2013. SPSS survival manual, McGraw-Hill Education (UK). PANUWATWANICH, K. 2008. Modelling the innovation diffusion process in Australian

architectural and engineering design organisations. Griffith University, Queensland, Australia.

PANUWATWANICH, K. & PEANSUPAP, V. Factors affecting the current diffusion of BIM: a qualitative study of online professional network. Creative Construction Conference, Budapest, Hungary, 2013. 6-9.

PARK, J., GUNN, F., LEE, Y. & SHIM, S. 2015. Consumer acceptance of a revolutionary technology-driven product: The role of adoption in the industrial design development. Journal of Retailing and Consumer Services, 26, 115-124.

PARK, J., YANG, S. & LEHTO, X. 2007. Adoption of mobile technologies for Chinese consumers. Journal of Electronic Commerce Research, 8.

PARK, Y., SON, H. & KIM, C. 2012. Investigating the determinants of construction professionals' acceptance of web-based training: An extension of the technology acceptance model. Automation in Construction, 22, 377-386.

PÄRN, E., EDWARDS, D. & SING, M. 2017. The building information modelling trajectory in facilities management: A review. Automation in Construction, 75, 45-55.

PARSANEZHAD, P. & DIMYADI, J. 2013. Effective facility management and operations via a BIM-based integrated information system.

PENG, C.-Y. J., HARWELL, M., LIOU, S.-M. & EHMAN, L. H. 2006. Advances in missing data methods and implications for educational research. Real data analysis, 3178.

PEZESHKI, Z. & IVARI, S. A. S. 2018. Applications of BIM: a brief review and future outline. Archives of Computational Methods in Engineering, 25, 273-312.

PODSAKOFF, P. M. & ORGAN, D. W. 1986. Self-reports in organizational research: Problems and prospects. Journal of management, 12, 531-544.

PRICE, D. D. S. & GÜRSEY, S. 1975. Studies in Scientometrics I Transience and Continuance in Scientific Authorship. Ciência da Informação, 4.

PRICE, I. 2003. Facility management as an emerging discipline. RAHMANI, F. 2016. Conceptual models for selecting and managing Early Contractor

Involvement (ECI) delivery system by clients. RMIT University. REY-MARTÍ, A., RIBEIRO-SORIANO, D. & PALACIOS-MARQUÉS, D. 2016. A

bibliometric analysis of social entrepreneurship. Journal of Business Research, 69, 1651-1655.

ROGERS, J. P. 2013. The strategic adoption of building information modelling by Malaysian engineering consulting services firms.

SATTINENI, A. & MACDONALD, J. A. 5D-BIM: A case study of an implementation strategy in the construction industy. ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction, 2014. IAARC Publications, 1.

SAUNDERS, M., LEWIS, P. & THORNHILL, A. 2007. Research methods for business students, 4th ed. Reference and Research Book News, 22.

SCHAFER, J. L. 1997. Analysis of incomplete multivariate data, Chapman and Hall/CRC. SCHAFER, J. L. 1999a. Multiple imputation: a primer. Statistical methods in medical research,

8, 3-15. SCHAFER, J. L. 1999b. NORM: Multiple imputation of incomplete multivariate data under a

normal model. Computer software]. University Park, PA: Pennsylvania State University, Department of Statistics.

186 Bibliography

SCHNEIDER, J., GAUL, A. J., NEUMANN, C., HOGRÄFER, J., WELLßOW, W., SCHWAN, M. & SCHNETTLER, A. 2006. Asset management techniques. International Journal of Electrical Power & Energy Systems, 28, 643-654.

SCOTLAND, J. 2012. Exploring the philosophical underpinnings of research: Relating ontology and epistemology to the methodology and methods of the scientific, interpretive, and critical research paradigms. English Language Teaching, 5, 9.

SEGARS, A. H. 1997. Assessing the unidimensionality of measurement: A paradigm and illustration within the context of information systems research. Omega, 25, 107-121.

SEKARAN, U. & BOUGIE, R. J. 2016. Research methods for business: A skill building approach, John Wiley & Sons.

SHEN, X., LIU, K. & TANG, L. C. 2013. Cost Estimation in Building Information Model. SHIEM-SHIN THEN, D. 1999. An integrated resource management view of facilities

management. Facilities, 17, 462-469. SMALL, H. 1999. Visualizing Science by Citation Mapping. Journal of the American Society

for Information Science, 50, 799-813. SON, H., LEE, S., HWANG, N. & KIM, C. 2014. The Adoption of Building Information

Modeling in the Design Organization: An Empirical Study of Architects in Korean Design Firms. Proceedings of the 31st ISARC, 194-201.

SON, H., LEE, S. & KIM, C. 2015. What drives the adoption of building information modeling in design organizations? An empirical investigation of the antecedents affecting architects' behavioral intentions. Automation in Construction, 49, Part A, 92-99.

SON, H., PARK, Y., KIM, C. & CHOU, J.-S. 2012. Toward an understanding of construction professionals' acceptance of mobile computing devices in South Korea: An extension of the technology acceptance model. Automation in Construction, 28, 82-90.

SOSIK, J. J., KAHAI, S. S. & PIOVOSO, M. J. 2009. Silver bullet or voodoo statistics? A primer for using the partial least squares data analytic technique in group and organization research. Group & Organization Management, 34, 5-36.

SUCCAR, B. The five components of BIM performance measurement. Proceedings of CIB World Congress, Salford, 2010.

TABACHNICK, B. G., FIDELL, L. S. & ULLMAN, J. B. 2007. Using multivariate statistics, Pearson Boston, MA.

TAI, Y.-M. & KU, Y.-C. 2014. Will Insurance Brokers Use Mobile Insurance Service Platform: An Integration of UTAUT and TTF.

TEICHOLZ, E. 2004. Bridging the AEC/FM technology gap. Journal of Facilities Management, 2.

TEICHOLZ, P. 2013. BIM for facility managers, John Wiley & Sons. TENENHAUS, M., VINZI, V. E., CHATELIN, Y.-M. & LAURO, C. 2005. PLS path

modeling. Computational statistics & data analysis, 48, 159-205. VAN ECK, N. & WALTMAN, L. 2009. Software survey: VOSviewer, a computer program

for bibliometric mapping. Scientometrics, 84, 523-538. VENKATESH, V. & BALA, H. 2008. Technology acceptance model 3 and a research agenda

on interventions. Decision sciences, 39, 273-315. VENKATESH, V. & DAVIS, F. D. 2000. A theoretical extension of the technology acceptance

model: Four longitudinal field studies. Management science, 46, 186-204. VENKATESH, V., MORRIS, M. G., DAVIS, G. B. & DAVIS, F. D. 2003. User acceptance of

information technology: Toward a unified view. MIS quarterly, 425-478. VOLK, R., STENGEL, J. & SCHULTMANN, F. 2014. Building Information Modeling (BIM)

for existing buildings—Literature review and future needs. Automation in construction, 38, 109-127.

Bibliography 187

VONGJATURAPAT, S., CHAVEESUK, S., CHOTIKAKAMTHORN, N. & TONGKHAMBANCHONG, S. 2015. Analysis of Factor Influencing the Tablet Acceptance for Library Information Services: A Combination of UTAUT and TTF Model. Journal of Information & Knowledge Management, 14, 1550023.

WILLIAMS, R., SHAYESTEH, H. & MARJANOVIC-HALBURD, L. 2014. Utilising building information modeling for facilities management. International Journal of Facility Management, 5.

WON, J. & LEE, G. Identifying the consideration factors for successful BIM projects. Proceedings of the International Conference on Computing in Civil and Building Engineering, Nottingham, 2010. 143-148.

WU, Y. W., HSU, I. T. & LIN, H. Y. 2014. Using TAM to Explore Vocational Students’ Willingness to Adopt a Web-Based BIM Cost Estimating System. Advanced Materials Research, 1079-1080, 1098-1102.

XU, H., FENG, J. & LI, S. 2014. Users-orientated evaluation of building information model in the Chinese construction industry. Automation in Construction, 39, 32-46.

YALCINKAYA, M. & SINGH, V. Building information modeling (BIM) for facilities management–literature review and future needs. IFIP International Conference on Product Lifecycle Management, 2014. Springer, 1-10.

YALCINKAYA, M. & SINGH, V. 2015. Patterns and trends in building information modeling (BIM) research: a latent semantic analysis. Automation in Construction, 59, 68-80.

YU, K., FROESE, T. & GROBLER, F. J. A. I. C. 2000. A development framework for data models for computer-integrated facilities management. 9, 145-167.

ZHOU, T., LU, Y. & WANG, B. 2010. Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in human behavior, 26, 760-767.