A Partial Least Square Structural Equation Modeling …tjfeonline.com/admin/archive/1418.03.20161458277767.pdf · Rev. Téc. Ing. Univ. Zulia. Vol. 38, Nº 3, 143 - 155, 2015 A Partial

Rev. Téc. Ing. Univ. Zulia. Vol. 38, Nº 3, 143 - 155, 2015

A Partial Least Square Structural Equation Modeling (PLS SEM) Preliminary Analysis on Organizational Internal and External Factors Influencing Effective Construction Risk Management among Nigerian

Construction Industries.

A.Q. Adeleke¹; A.Y. Bahaudin² and A. M. Kamaruddeen³ School of Technology Management and Logistics, Universiti Utara Malaysia, Malaysia.

Corresponding Author: A.Q. Adeleke Email: [email protected]

Abstract An increased demand has been placed on construction projects to be more accountable to their clients with measure to the company services and outputs. This demand is because of innumerable antecedent factors repelling risk management in construction projects. However, finishing the projects is not only the concern of the industries but also managing the risks involved with the projects. The impacts of effective risk management on the performance of these industries are as well important. The objective of this study is threefold: (i) to propose an inclusive research model which comprises of the antecedent factors proposed in the model to improve effective risk management in Nigeria construction industries (ii) to serve as a validation process for the developed instrument of the on-going research with the identified constructs of the study (iii) to show the preliminary analysis and the results. Data were collected from forty respondents using seventy-five items instrument. PLS-SEM measurement model was used to assess the reliability and validity of the instruments in this study. The result shows that the instruments are reliable and the pilot study indicated a strong evidence of rational validity. The research is significant because it explores the implementation of some organizational factors which will influence construction risks and will also improve effective risk management in Nigeria construction projects, and the validation of the instrument which requires further exploration of the constructs. Keywords: Effective construction risk management, Measurement Model, Instrument, Preliminary data analysis and result, Reliability and Validity assessment. 1. INTRODUCTION

Risk management is the term designated to the formalized process of harmonizing the risks and opportunities which a decision may results to and this includes proper action to produce an adequate balance in between the two. The way this process is frequently performed, is by using an experience and intuition, which is specific to the individual (Abu Hassan, Ali, Onyeizu and Yusof, 2012).

Furthermore, risk management is commonly recognized as one of the greatest important capability and procedures areas under the umbrella of project management (Artto, 2001; Tadayon et al., 2012). As the fact still remain that each construction project is dynamic and unique, construction operation comprises of several uncertainties, varies techniques, multiple intricacies, and divergent environments. Thus, identifying and managing the possible risk factors, which can importantly differ from one project to another contingent on various conditions, which plays a vital role in improving the performance and achieving the successful delivery of the project (Jarkas, Haupt and Haupt, 2015).

Hertz and Thomas (1983) defined risk as a “form of situations involving many unexpected, unknown,

often unpredictable and frequently undesirable factors.” Perry and Hayes (1985) perceived risk as “a condition

or an uncertain event that, if it arises, has a negative or positive effect on a project objective.” Jaffari (2001), in

addition, stated risk as “the exposure to loss, gain, or the probability of occurrence of loss/gain multiplied by its respective magnitude”, while Abbasi et al., (2005) described risk by “the possibility of injury, loss, destruction

or disadvantage”. Despite the massive breath of literatures on risk, following Berk and Kartal (20120), the researcher

further propose the following definition of risk as “the probability of occurrence of any unexpected or ignored event that can hinder the achievement of project objectives or performance.”

Furthermore, it has been affirmed by various literatures such as (Geraldi, Lee-Kelley and Kutsch, 2010; Karim Jallow et al., 2014; Moe and Pathranarakul, 2006; Doloi, 2009; Abu Hassan et al., 2012; Simpkins, 2009; Ho and Pike, 1992; Kangari and Riggs, 1989; Israelsson and Hansson, 2009; Scupola, 2003; Lewis et al., 2003; Jabnoun and Sedrani, 2005) that certain internal organizational factors (effective communication, team competency and skills and active leadership) and external organizational factors (political, organizational cultural, technology and economic factors) have a relationship with effective construction risks management with moderating effects of rules and regulations as it was used by (Ismail, 2001; Gibb, 2011; Niu, 2008; Iroegbu, 2005; Alaghbari et al., 2007). Yet, the influence of Organizational factors on effective construction risk management among the construction companies operating in Abuja and Lagos State Nigeria have not received considerable attention. Therefore, to elucidate the inconclusive assertions on the relationship between the internal and external organizational factors to the effective construction risks management, a comprehensive

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framework is needed which will integrate these factors via the moderating roles of rules and regulations in the Nigeria construction point of view.

2 RESEARCH MODEL

Previous literature reviewed on the concept of effective risk management in construction projects, has shown a coherent need for a model that will incorporates the antecedent factors (internal and external organizational factors and construction risks), through the moderating role of rules and regulations from the Nigerian government perspectives in a single study.

However, in this study effective construction risks management is the dependent (criterion) variable while the independent (predictor) variables are the antecedents of internal organizational factors which are also known as intangible organizational resources in this study such as; (effective communication, team competency and skill, active leadership) conceptualized following (Geraldi et al., 2010; Karim Jallow et al., 2014). While external organizational factors which are regarded as factors that are beyond the organizational control such as; (political, organizational culture, technology, economic factors), following (Doloi, 2009; Abu Hassan et al., 2012). Rules and regulations as the moderator on the relationship between internal and external organizational factors and effective construction risks management, following (Ismail, 2001; Gibb, 2011; Niu, 2008). However, it is a general belief and regarded in risk management literatures that certain factors influenced effective risks management during the construction process. The proposed model for this study is depicted in Figure 1.

Figure-1.Research Model 2.1 Effective communication

In most cases, effective communication can be seen as hidden element for success. The disposition of the research warrant this variable and to see its relationship with dependent variable as stated in the research theoretical framework. Open, reliable and frequent communication is pivotal for successful project with less risk. However, effective communication is vital for any organization and project team. It is required that authentic and clear information is shared at right time, right place and to right person during the construction project. Flow of information, top down/downward and bottom up/upward communication is an important aspect of project to think about. It reduces conflicts, improve decision making and effect on team member performance to their project manager, so lack of all these attributes will influence or affect effective construction risk management in the organization (Doloi, 2009). The empirical investigation of (Geraldi et al., 2010; Karim Jallow et al., 2014, Moe & Pathranarakul, 2006; Abu Hassan et al., 2012), also confirms that effective communication positively influenced effective construction risk management. 2.2 Team competency and skills

Team competency and skills is an important variable to be considered, because it provides knowledgeable and technical human resource which is required for contractors, project managers and team members to achieve the project goals (Greenberg & Baron, 2008). Team competency and skills can be seen in

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terms of skills, knowledge and attitude. Team dynamics are also connected with team competency; that is what type of characteristic team have and what are the characteristics required for the project execution, thus, any organization that lacks team competency and skills, will definitely affect effective construction risk management. Moe & Pathranarakul (2006) and Geraldi et al., (2010) highlighted a positive relationship between team competency and skills with effective construction risk management.

2.3 Active leadership

Most of the previous studies focus on leadership styles, behavior and strategies. Successful project required different kind of leadership from the normal routine project work. In construction project, there are needs for active leaders which can take serious actions on run time in order to avoid making situation worse. Active leader is one of the most important independent variable proposed in the theoretical framework. Project leader’s priority is to run project in emergency situation as it will be run in normal condition (Barber & Wan,

2005). Thus, any organization without active leadership will certainly affect effective construction risk management. The empirical investigation of Simpkins (2009) and Greenberg & Baron (2008) concluded that active leadership positively influenced effective construction risk management. 2.4 Political factors Jaafari (2001), stated that the influence of environmental variables such as safety, community perception, and legal acceptability, political and social impacts on project is mostly high. It was further explain by the author that political factors includes, discriminatory legislative, covering tax regimes, riots, strikes, civil unrest, wars, terrorism, invasions and religious turmoil will positively influence effective construction risk management in an organization. The study of Abu Hassan et al., (2012) affirmed a positive influenced of political factors on effective construction risk management. 2.5 Organizational culture

Hofstede et al., (1990) and Schein (2004) perceived organizational culture as the elementary assumptions, values, beliefs and models of behavior, practices, rituals, heroes, symbols, technology and artefacts. In addition, Hartog and Verburg (2004) indicated that organizational culture is a strong tool that is associated with “behavior and attitude” of contractors, project managers and team members during execution of project which significantly influenced effective construction risk management positively.

2.6 Technology factors

According to Akanni, Oke & Akpomiemie (2014), technology is the views of an environment which must be considered in developing countries strategic plans. Oladapo & Olotuah, (2007) asserted that a suitable and proper construction technology can be measured by the presence of plant and equipment that are made locally, magnitude of local material resources and the level of utilization of the local construction resources, and the skilled manpower resources. The studies of Walker (2000), Sommerville and Craig (2006) revealed that technology as an external factor significantly influenced effective construction risk management positively.

2.7 Economic factors

The economic and financial aspect of an organization depend on the level of universal economic activity, as well as the available resources to execute the work, which includes the economic competition of several level around the appointment of all parties involved in building projects (Obalola, 2006). The empirical investigation of Odeh and Battaineh (2002) and Abu Hassan et al., (2012) also confirms that economic as an external factor positively influenced effective construction risk management.

2.8 Effective construction risk management (MG, MT,DS, FI, LE)

The construction industry, compare to other industry, is risky. However, construction project are comprehended to have more underlying risks due to many contracting parties involved such as contractors, subcontractors, clients, designers and engineers. There is uniqueness in construction projects because they are built only once. The parties also include irregular project team which are accumulated from different companies, countries and cultures. Moreover, the complexity and size of construction projects are growing higher which add to the risks. This is because of the social, cultural, political and economic situations where the project is to be contracted (El-Sayegh, 2008).

Risk in construction has been the mark of attention by most of the construction parties because of cost and time overruns that are connected with construction project.

The word “risk” has been defined in several ways. While (Porter, 1981; Healy, 1982; Perry & Hayes,

1985) have perceived risk as an experience to economic loss or gain growing from participation in the construction process; Moavenzadeh & Rosow (1999) and Mason (1973) have viewed this as an experience to only loss. Bufaied (1987) and Bothroyed & Emmett (1998) defined risk that are related to construction as a

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condition through which the process of construction project leads to uncertainty in the last cost, time and quality of the project. In this study, construction risk is defined as the probability of occurrence of some uncertainty, that hinder the achievement of project objectives, which it can be from management, materials, design, finance and labour and equipment risks.

According to Sambasivan and Soon (2007), twenty-eight major construction risks factors which leads to delay due to improper effective construction risk management with their effects on the construction projects in Malaysia were identified such as inadequate finance and payments for completed project; lack of materials; labour supply; failure and equipment availability; poor communication between parties; and misapprehension during the construction stage were the leading factors. Consistent with study of Aibinu and Odeyinka (2006) that identified forty-four risk factors that leads to delay due to deficiency of effective construction risk management among construction projects in Nigeria, the study revealed major risk factors such as; management, material, finance and design risk factors. Frimpong, Oluwoye and Crawford (2003) and Sweis et al., (2008) affirmed a positive relationship between internal and external organizational factors and effective construction risk management, Consistent with the study of Ahmed et al., (2002) in USA which revealed a positive relationship between internal, external organizational factors and effective construction risk management. 2.9 Moderating Role of Rules and regulations (RG)

Rules and regulations are enacted to control the risks occurrence on construction project caused by management, material, design, finance and labour and equipment. Therefore, construction industry are mandated to operate under the requirements of rules and regulations (Flanagan & Norman, 1993; Ismail, 2001; Iroegbu, 2005 and Gibb, 2011). Previous researcher’s results have shown that rules and regulations that are focused on the construction industry have a set of positive significance on construction projects and performance of the construction industry (Ismail, 2005; Aniekwu, 1995; and Niu, 2008). In the presence of an immense attention of clients, stakeholder’s pressure and the top management allegiance, a suitable rules and regulations is the best

approach to reduce risks occurrence on construction projects. Rules and regulations strengthens the implementation of internal and external organizational factors by providing standard requirements for organizational conformances. Thus, there is need for rules and regulations compliances to strengthen the dedication of construction industry to minimize risks occurrence on projects.

In the same vein, Ismail (2001) revealed that in Malaysia context, rules and regulations on housing stated that, there must be a replacement for the traditional building practices by an industrialized building system (IBS), which on the long run might save labour, cost, confer quality and durability and time of construction in Malaysian construction companies as cited by (Alaghbari et al., 2007). Similarly, the study of Iroegbu (2005) revealed that government rules and regulations influenced construction projects in Nigeria, such as the importations of the construction materials and taxes. Below are the hypothesis between the moderating variable and the two constructs of the predictor with the criterion variable.

3. METHODOLOGY

This study is a cross-sectional research design. Which means, data were collected at a single-point-in-time using structured questionnaire (Kumar, Abdul Talib & Ramayah, 2013; Sekaran & Bougie, 2013; Zikmund, Babin, Car & Griffin, 2012). However, proportionate stratified random sampling techniques was also employed in the on-going research. The research approach is quantitative, which is a research approach that is mostly adopted in social sciences (Sekaran, Robert & Brain, 2001).Considering this study as the pilot test of an on-going research which was conducted in Abuja and Lagos Nigeria on 19th, June 2015, among the contract manager, executive director, marketing manager, project manager and engineer. According to Malhotra (2008), a pilot study mostly necessitates a range of (15-50) respondents. Therefore, a total number of fifty (50) questionnaires were personally distributed with the return rate of forty (40) which is suitable for the pilot study analysis.

3.1. Instrument Design

Asika (1991) affirmed questionnaire as one of the appropriate survey instrument for research. To make sure all the variables in this research framework are all measured, items for this study were adapted from various sources in order to create item pool and content validity which include previous research findings on the construct of this study (internal and external organizational factors, construction risks and government rules and regulations. These items were adapted and modified from preceding literatures (Kumaraswamy & Chan, 1998; Jaafari, 2001; Kamaruddeen et al., 2012; Sun & Meng, 2009; Aibinu & Odeyinka, 2006; Mezher & Tawil, 1998) with the purpose of creating the validity of the construct including (a) create contact prior to the main study between the researcher and the organizations (b) ascertain the reliability of the constructs and (c) anticipate the likely challenges that may arise before the actual data collection of the study. Similarly, the study adopted the use of five-point likert scale rating from 0.1 = ‘very low,’ 0.3 = ‘low’, 0.5 = ‘medium’, 0.7 = ‘high’,

0.9 = ‘very high’, to measure the feedback to the questionnaires.

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A rating scale helps the researcher in computing the standard deviation and the mean feedback on variables and also the mid-point of the scale (Sekaran, 2003; Sekaran and Bougie, 2009). Previous literatures like Krosnick and Fabrigar (1991) argued that any scale between 5-7 points, have a propensity of a high reliably and validly measure items compare to a shorter or a longer rating. However, Dawis (1987) and Garland (1991) proposed that the choice of the measurement scale mostly depends on the taste of the researcher since there is no single superlative method of constructing a scale. An appropriate method for one research problem might appropriate for another. It was further argued by Krosnick and Fabrigar (1991) that the conduct established by respondents is either to satisfy or optimize the survey. Thus, this study adopts the use of a five-point likert scale in order to avoid the respondents from selecting an unbiased point which may reduce the quality of the questionnaire.

More so, all the constructs/variables in this study are multidimensional. The detail of the constructs and their analogous dimensions are depicted in Table 1

Table 1: Source of Measurement S/N Constructs Dimensions Source Remarks 1 Internal factors Effective communication

Team competency and skills Active leadership

Kumaraswamy & Chan (1998) Adapted

2 External factors

Political factor Organizational culture Technology factor Economic factor

Jaafari (2001) Kamaruddeen et al., (2012) Sun &Meng (2009) Sun & Meng (2009)

Adapted

3 Government policy Rules and regulations Mezher & Tawil (1998) Adapted

4 Effective construction risks management

Management Material Design Finance Labour and equipment

Aibinu & Odeyinka (2006) Adapted

4 PRELIMINARY DATA ANALYSIS AND RESULTS

Partial Least Square (PLS) (SmartPLS version 2.0) was used in analyzing the data for this preliminary study. Partial Least Square Structural Equation Modelling (SEM) is a popular technique used in data analysis, mainly due to its capacity to accommodate relatively small sample size (Goodhue, Lewis and Thompson, 2006, Chin, 1998) compare to other co-variance based Structural Equation Modelling (LISTREL, AMOS). Therefore, with the small sample for this study, PLS SEM is considered appropriate.

4.1 Reliability and Validity Assessment

The organizations serves as the unit of analysis for this study, therefore, respondents were Local, National and Multi-national construction companies (building construction and civil engineering sections) operating in Abuja and Lagos Nigeria. While trying to determine the measurement accuracy, PLS-Graph was used to determine the reliability and validity of the items in this study. Reliability handles the consistency of instrument across time and across various items in the scale, while Validity refers to how accurately a construct reflects what it is meant to measure. More so, some other criteria can be used to ascertain the validity of a construct. Part of it are: content validity, convergent and discriminant validity which were all used in this study. Content validity was carried out from both the academics and practitioners before the main research instrument was designed. Precisely, four experts was chosen from the faculty of Civil engineering, University of Lagos Nigeria. While another one was also chosen from construction industry as the practitioners. Their feedbacks and suggestions were incorporated into the final design of the research instrument.

According to Tore (2005), convergence and discriminant validity, seek to create an agreement in between a theory and a specific measuring instrument by examining whether the measuring scales measures what it supposed to or represent the true attribute. Consequently, factor loadings, composite reliability and average variance extracted (AVE) are parameters used in measuring convergence validity (Hair et al., 2010). However, once there is an established relationship between all the items ostensibly measuring a construct, convergent validity has already been established (Bollen and Lennox, 1991).

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Factor loadings and cross loadings are used to discover if there is any problem with an item. The Table 2.0 depicts the loadings and cross loadings of indicators in

their corresponding constructs. A measurement scale is regarded to have exhibit convergent validity once all the indicators/ items load above 0.5 on their related constructs and no other item loads higher on other constructs more than the construct it supposed to measure (Hair et al., 2010; Barclay et al., 1995). In this preliminary analysis, all the items loaded effectively on their corresponding constructs such that they all loaded above 0.5 as the recommended threshold measures (Hair et al., 2010). As displayed in Table 2, all the items loaded on their mother constructs from a lower range of 0.5278 to an upper range of 0.9244.

Table 2: Factor Loadings and Cross Loadings

AL DS EC EN FI LE MG MT OC PL RG TC TG

AL1 0.7043 0.1293 0.3772 0.2424 -

0.2187 -

0.0232 0.2114 0.0158 0.1544 0.1364 -0.012 0.2024 0.1804

AL2 0.8283 -

0.0687 0.2827 0.3329 0.0272 0.3558 0.3131 0.4002 0.1591 0.2758 0.1379 0.2948 0.4346

AL3 0.7855 0.0602 0.2793 0.4901 0.1546 0.3654 0.3977 0.3701 0.1108 0.2257 0.3561 0.295 0.475

AL4 0.5849 -

0.1753 0.2382 0.163 0.0312

-0.1529

0.0471 0.092 0.1993 0.1722 0.1345 0.1177 0.2942

DS1 -0.214 0.7521 -0.13 0.2473 0.2612 0.2086 0.4864 0.198 0.149 0.244 0.1153 0.1403 0.274 DS4 0.0887 0.8412 0.1228 0.2516 0.2708 0.4352 0.5624 0.4145 0.2933 0.3949 0.0766 0.3814 0.4772 DS5 0.0837 0.5731 0.1281 0.2834 0.54 0.4622 0.3928 0.1467 -0.046 0.0914 0.2338 0.3994 0.1991

DS6 0.0015 0.8171 -

0.1341 0.246 0.1055 0.5614 0.6236 0.387 0.2334 0.2551 0.1633 0.2308 0.5022

EC1 0.2782 0.0322 0.6747 0.0159 -

0.1235 0.0564 0.2334 -0.023 0.2966 0.1353 0.176 0.3933 0.0259

EC2 0.4544 0.0714 0.7365 0.14 -

0.0793 0.1666 0.2788 0.1681 0.1936 -0.05 0.2002 0.2583 0.3865

EC3 0.3255 -

0.0686 0.7508 0.1152 0.2158 0.1111 0.1552 0.1592 0.0995 0.0327 0.4403 0.4096 0.2265

EC4 0.2855 -

0.0244 0.8628 0.025 0.0567 0.0778 0.1344 0.0614 0.2488 0.2323 0.3457 0.4321 0.2128

EC5 0.1016 -

0.0189 0.5984 -0.135 0.0162 -

0.0081 0.2002 0.042 0.4242 0.1797 0.0611 0.5399 -0.076

EN3 0.2991 0.2595 0.1716 0.8756 0.299 0.3594 0.4179 0.0082 0.1025 0.3607 0.3781 0.3904 0.301

EN4 0.4644 0.3436 -

0.0556 0.9174 0.2063 0.49 0.4619 0.3472 0.0554 0.4029 0.3428 0.3757 0.578

FI1 -0.085 0.3633 -0.1029

0.152 0.8532 0.4117 0.2739 0.2587 0.0793 0.1119 0.4005 0.1822 0.2838

FI2 0.0213 0.0488 0.0654 0.2657 0.7812 0.2161 0.0381 -0.013 -0.033 0.1026 0.5152 0.1313 0.1437

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FI3 0.1727 0.3163 0.1261 0.3388 0.8268 0.3125 0.2062 0.0595 0.0541 0.1304 0.3327 0.2504 0.1998 FI4 -0.062 0.3536 0.0435 0.1879 0.7749 0.5916 0.3569 0.3566 0.2918 0.2011 0.105 0.2931 0.2855 LE2 0.1333 0.4344 0.0321 0.3203 0.4866 0.7362 0.4967 0.5179 0.1232 0.1586 0.4452 0.2914 0.6163 LE3 0.3819 0.2692 0.0541 0.3865 0.3133 0.7144 0.4035 0.4799 0.282 0.3058 0.5338 0.3068 0.5368 LE5 0.1343 0.5125 0.1519 0.4067 0.4562 0.7948 0.6251 0.5159 0.3436 0.2693 0.1979 0.4586 0.3599 LE7 0.0498 0.4606 0.0959 0.3258 0.272 0.7535 0.4301 0.3857 0.2393 0.1118 0.1343 0.2181 0.3376 MG10 0.0634 0.3401 0.2171 0.057 0.1127 0.2984 0.6073 0.4118 0.3833 -0.03 -0.033 0.274 0.2138 MG3 0.2587 0.4914 0.3033 0.4415 -0.047 0.335 0.6775 0.321 0.1449 0.2464 0.2253 0.4849 0.4141 MG5 0.2785 0.6444 0.1876 0.3619 0.3116 0.6747 0.8624 0.5397 0.2941 0.23 0.3655 0.4747 0.6123 MG7 0.283 0.4249 0.1176 0.2411 0.2218 0.403 0.6492 0.4196 0.2738 0.1864 0.1466 0.5052 0.4999 MG8 0.2426 0.5597 0.0986 0.4192 0.4579 0.5708 0.6996 0.3635 0.2587 0.3358 0.0355 0.3753 0.5078 MG9 0.3648 0.4988 0.292 0.5509 0.1835 0.4822 0.8114 0.4548 0.259 0.3205 0.3904 0.6074 0.5202 MT1 0.1087 0.2247 0.1713 0.0684 0.0973 0.4608 0.343 0.7165 0.2671 0.0177 0.1475 0.2545 0.3286 MT2 0.3598 0.3905 0.0427 0.2489 0.2822 0.5588 0.5653 0.8843 0.246 0.2023 0.2496 0.2725 0.6235

OC1 -0.041 0.1021 0.2804 -0.024 -

0.0197 0.1893 0.1758 -0.127 0.6505 0.3633 0.03 0.367 0.1009

OC2 0.2875 0.2241 0.2625 0.1232 0.2142 0.3103 0.3653 0.473 0.8938 0.3696 0.0559 0.2713 0.4586

PL1 -0.026 0.3041 -

0.1322 -0.028

-0.0916

0.0379 0.1516 -0.004 0.0957 0.5324 0.089 -0.003 0.1779

PL2 0.3023 0.2131 0.0343 0.3683 0.2364 0.2857 0.1942 0.0224 0.2398 0.8073 0.1454 0.2821 0.2652 PL3 0.2701 0.3883 0.1998 0.5014 0.1828 0.3478 0.3956 0.3344 0.4645 0.8925 0.3093 0.4914 0.5732 PL4 0.2082 0.1495 0.199 0.286 0.1147 0.0978 0.1597 0.0135 0.4842 0.7985 0.1208 0.376 0.3252 RG2 -0.02 0.0356 0.2311 0.0999 0.1343 0.1206 0.2056 0.0245 0.0742 -0.012 0.5679 0.2199 0.0365 RG3 0.3562 0.2269 0.3644 0.4324 0.3517 0.4373 0.3353 0.3201 0.1184 0.3422 0.9244 0.3683 0.549 RG4 0.0915 0.0644 0.3512 0.2189 0.1108 0.3333 0.153 0.345 -0.017 0.1412 0.7433 0.2511 0.3354

RG5 -0.069 0.16 -

0.0948 0.2789 0.4911 0.1996

-0.0151

-0.196 -0.09 -0.01 0.5278 0.0007 0.1732

TC2 0.0106 0.1372 0.462 0.2115 0.1244 0.0565 0.2479 -0.092 0.244 0.3183 0.2195 0.6865 0.0626 TC3 0.1415 0.2424 0.1892 0.3289 0.275 0.4227 0.4704 0.2853 0.0585 0.1194 0.2417 0.7018 0.2126 TC5 0.455 0.4263 0.5177 0.3956 0.2404 0.4592 0.6343 0.4478 0.4493 0.4481 0.289 0.8454 0.4787 TG1 0.1812 0.5248 0.2381 0.3954 0.2577 0.5331 0.6703 0.5183 0.3661 0.2515 0.2204 0.4187 0.6859 TG2 0.2945 0.2507 0.0355 0.2794 0.0894 0.443 0.4136 0.4835 0.2204 0.2753 0.3289 0.0522 0.7628 TG3 0.5261 0.3051 0.2037 0.4023 0.2875 0.3552 0.3491 0.3489 0.2852 0.4696 0.442 0.327 0.7126

**Note. The bold indicators/items identify the items that belong to the column’s construct

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The convergent validity for this research was measured using the AVE- Average Variance Extracted. According to Fornell and Larcker (1981), for a construct to display convergent validity, the Average Variance Extracted (AVE) must be at least 0.5. Indicating that the variance described by the construct is greater than the measurement error. In other words, the AVE describes the average variance shared among a particular construct and its measures. However, according to Couchman and Fulop (2006), an AVE for a construct, is expected to be greater than the variance shared among that particular construct and other constructs in a particular model.

Consequently, all AVE interpretations in Table 3.0 are above 0.4 with 0.5020 as the lowest. This indicates that convergent validity in all the measures is acceptable. And with this acceptable result depicting an adequate item loadings, composite reliability, and AVE coefficients for the individual items, there is decent grounds to prove that the indicators/items truly represent their latent constructs, thus giving another grounds of convergent validity.

Table 3: Convergence and Reliability Analysis

Constructs Dimensions Items Loadings

Composite Reliability

AVE

Effective communication EC1 0.6747 0.8489 0.5328

EC2 0.7365

EC3 0.7508

EC4 0.8628

EC5 0.5984 Team competency and skills TC2 0.6865 0.7906 0.5595

TC3 0.7018

TC5 0.8454

Active leadership AL1 0.7043 0.8193 0.5353

AL2 0.8283

AL3 0.7855

AL4 0.5849

Political factor PL1 0.5324 0.8492 0.5923

PL2 0.8073

PL3 0.8925

PL4 0.7985 Organizational culture OC1 0.6505 0.754 0.611

OC2 0.8938

Technology factor TG1 0.6859 0.7644 0.5200

TG2 0.7628

TG3 0.7126

Economic factor EN3 0.8756 0.8914 0.8042

EN4 0.9174

Management MG10 0.6073 0.8665 0.5235

MG3 0.6775

MG5 0.8624

MG7 0.6492

MG8 0.6996

MG9 0.8114

Material MT1 0.7165 0.7843 0.6477

MT2 0.8843

Design DS1 0.7521 0.8372 0.5673

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DS4 0.8412

DS5 0.5731

DS6 0.8171

Finance FI1 0.8532 0.8837 0.6556

FI2 0.7812

FI3 0.8268

FI4 0.7749 Labour and equipment LE2 0.7362 0.8372 0.5629

LE3 0.7144

LE5 0.7948

LE6 0.7535

LE7 0.7362 Rules and regulations RG1 0.5644 0.7931 0.5020

RG2 0.5679

RG3 0.9244

RG4 0.7433

RG5 0.5278

Furthermore, discriminant validity (as depicted in Table 4.0) was duly created as the indicators/items loaded very well on their corresponding constructs compared to other constructs. The discriminant validity creates that measures which are not allowed to be related are, in literal fact, not related. To compute the discriminant validity, the square root of the AVE for each construct was used. Which means, the square roots of AVE coefficients are used in replacing the correlation matrix along the diagonal line (Fornell, & Larcker, 1981). Ordinarily, the squared AVE (for example, the diagonal coefficients) are required to be much greater than the off-diagonal coefficients in the matching columns and rows (Hair et al., 2006).

The diagonal coefficients in Table 4.0 depicts the square roots of AVE for all the constructs signifying a higher square roots of AVE for economic factor with (0.90), and lower for rules and regulations with (0.71). Therefore, an indication of discriminant validity has been provided since all the AVE square roots for all the constructs along the diagonals are higher compared to the corresponding off-diagonal coefficients of both in columns and rows.

Table 4: Discriminant Validity

AL

DS

EC EN

FI

LE

MG

MT

OC

PL

RG

TC

TG

Active leadership 0.7

3

Design 0.0

0 0.75 Effective

communication 0.4

0 0.00 0.7

3

Economic factor 0.4

3 0.34 0.0

5 0.9

0

Finance 0.0

0 0.38 0.0

3 0.2

8 0.8

1 Labour and

equipment 0.2

2 0.57 0.1

2 0.4

8 0.5

2 0.7

5

Management 0.3

5 0.70 0.2

7 0.4

9 0.3

1 0.6

6 0.7

2

Material 0.3

2 0.40 0.1

2 0.2

2 0.2

5 0.6

4 0.5

8 0.8

0 Organizational

culture 0.2

1 0.22 0.3

4 0.0

9 0.1

6 0.3

3 0.3

7 0.3

1 0.7

8 Political factor 0.2 0.34 0.1 0.4 0.1 0.2 0.3 0.1 0.4 0.7

151


8 4 3 8 8 1 6 6 7 Rules and regulations

0.21 0.19

0.35

0.40

0.37

0.43

0.28

0.26

0.06

0.24

0.71

Team competency and skills

0.32 0.39

0.55

0.43

0.28

0.43

0.63

0.32

0.38

0.43

0.34

0.75

Technological factor

0.48 0.50

0.23

0.50

0.30

0.61

0.66

0.62

0.41

0.47

0.47

0.38 0.72

**Note. The diagonal values in bold signify the average variance extracted (AVE) while the other entries signify the squared correlations. 4. DISCUSSION

This study has highlighted the possible reason behind high risk occurrence on Nigerian construction projects and poor risk management in the industries, this could be because previous researcher failed to consider certain internal and external organization factors with rules and regulations as moderator on the relationship. It has therefore presented a model that integrates the internal and external organizational factors, effective construction risk management with rules and regulations in a single model. This research is one among others that considers the effect of rules and regulations on the relationship between internal and external organizational factors and effective construction risk management. This proposed model is developed through a thorough matrix and review of literatures to provide a deep understanding to academicians and practitioners on the internal and external organizational factors with effective construction risk management and moderated by rules and regulations to enable policy maker enhance construction projects and manage risk effectively through government rules and regulations.

The preliminary analysis result of this study revealed that the variables as depicted in Table 3.1 above are appropriate to be used in the main data collection for the purpose of the analysis in this research. Furthermore, reliability analysis is expected to be performed on the main data collected after conducting factor analysis on the main study based on a larger sample size.

5. CONCLUSION

This study is limited to the organizational stewardship aspect of risk management practices in the construction industry. Therefore, future researchers are charged to investigate the social and economic aspects of risk management practices and empirically validate the proposed model in this study. However, part of the aims of this preliminary study is to validate the research indicators and establish their respective reliability, which has been achieved.

Furthermore, the results displayed in Tables 4.1, 4.2 and 4.3 respectively indicated that the indicators for all the thirteen constructs are properly measuring their respective constructs seeing their statistical significance and parameter estimates (Chow & Chan, 2008).

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