INTERNATIONAL JOURNAL OF ACADEMICS & RESEARCH …...core banking systems and other integrated systems. The most common core banking systems include Flexcube, ModelBank (T24) and iMAL

INTERNATIONAL JOURNALS OF ACADEMICS & RESEARCH ISSN: 2617-4391 IJARKE Science & Technology Journal

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26 August, 2018: Vol. 1, Issue 1

Influence of Project Customer Factors on Implementation of Information

Technology Projects by Commercial Banks in Kenya

Patrick Mukhongo, JKUAT, Kenya

Dr. Esther Waiganjo, JKUAT, Kenya

Dr. Agnes Njeru, JKUAT, Kenya

1. Introduction

Project customer factors including user support, involvement and participation, client training and experience have been

considered to be important in playing a positive role in achieving effective project implementation (Holgersson & Söderström,

2015). During the implementation process of IT projects, users are typically involved in early phases of development for

requirements elicitation and feedback (Abelein & Paech, 2015) in what is referred to as requirements engineering. The goal of

requirements engineering (RE) is to ensure that a right and good product is defined and developed from the stakeholders' point of

view. Clients are often seen as the most important stakeholders as they pay for the system. But due to the increasing number of

project failures because of user dissatisfaction (Wagner & Piccoli, 2007), involving users throughout the development lifecycle of

IT projects was intuitively considered to achieve user buy-in, approval and hence effective implementation of IT projects

(Holgersson et al., 2015). However, as Yardley, Morrison, Bradbury and Muller, (2015) point out, clients' primary goal is

generally to have IT projects that support users in their tasks. Thus, users and clients should be considered to be important as it is

they who finally use IT projects.

However, research literature has previously yielded inconsistent results (Holgersson et al., 2015; Cavaye, 1995; Hwang &

Thorn, 1999; He & King, 2008; Kujala, 2003). The causes of these inconsistencies identified in the literature are said to be

methodological problems ( Holgersson et al., 2015), confounding effects of the terms user involvement and user participation

(Cavaye, 1995; Barki & Hartwick, 1989) and contingency factors (McKeen, Guimaraes & Whetherbe, 1992). Firstly, users can

play many different roles within organizations. Secondly, IT projects‘ development lifecycle has many phases and activities that

depend on various dynamic factors such as methodologies used, application domains where projects will be situated, and

technological changes (Cavaye, 1995). Thirdly, the term involvement has often been used inconsistently in previous studies. This

inconsistency has led to ambiguity which makes the meaning and usage of this word to remain unclear.

Abstract

Ineffective implementation of information technology projects by commercial banks in Kenya has been of major concern

to various stakeholders. Ineffectiveness arises when such projects do not meet time, cost and quality criteria in the course of

their lifecycle. Project customer factors in this study included user support; user participation; client training and education;

and client experience. The objective of this study was to investigate the influence of project customer factors on

implementation of information technology projects by commercial banks in Kenya. Using data derived from 139 out of 195

sampled members of staff drawn from commercial banks that were licensed and operational as at 31st December 2016, this

study dissects how project customer factors contribute to project implementation and the extent of the contribution based on

regression models. By combining qualitative and statistical analysis, the study examines how project customer factors

influence the development of information technology projects. The analysis also shows that involving users and clients as the

source of information to be used in implementation of projects is related to effective implementation of projects. The study

found that project customer factors have a positive and significant influence on effective implementation of information

technology projects by commercial banks in Kenya.

Key words: Technology, Customer factors, Projects, Information Technology, Commercial Banks

INTERNATIONAL JOURNALS OF ACADEMICS & RESEARCH (IJARKE Science & Technology Journal)


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To define or precisely measure the effectiveness of implementation of IT projects is also not an insignificant task. The most

preferred criteria for measuring effective implementation in the literature are user acceptance and user satisfaction of the system,

which are often used synonymously. There are also other factors used for measuring implementation success including system

quality, information quality, information use, individual impact and organizational impact (DeLone & McLean, 1992). In this

study our aim is to present a review on project customer factors and their influence on implementation of IT projects.

There is also a distinction between project success criteria which is measured in accordance with meeting project objectives

and the project success factors which are inputs to the project management system that leads directly or indirectly to project

success (Cooke-Davies, 2002; Prabhakar, 2008). One should also distinguish between project success which can be measured only

after project completion and also the project implementation and performance which can be measured at any stage of the project

(Cooke-Davies, 2002).

For organizations to realize effective implementation of IT projects, they would have to embrace new and innovative ideas

(Swanigan, 2017). To further concretize the aforementioned trend, clients are duty bound to adopt projectization as a working

business model. IT projects are known to be capital intensive and therefore users and clients must be fully involved throughout the

lifecycle of projects. The study sought to determine the influence of project customer factors on implementation of information


2. Problem Statement

There is continued rise and prominence in use of project management in organizations with projects being seen as critical to

economic development in both the private and public sectors. The explanation for the expansion of project-based work is because

of the new challenging environment and opportunities brought about by technological developments, the changing boundaries and

frontiers of knowledge, dynamic market conditions, changes in regulatory and environmental factors, and the drive towards

shorter cycles of product development, pronounced customer involvement and the increased scope and complexity of inter-

organizational relationships (Silvius, 2017). Commercial banks‘ business strategies are mainly driven by the capabilities of their

core banking systems and other integrated systems. The most common core banking systems include Flexcube, ModelBank (T24)

and iMAL. Integrated systems include Real Time Gross Settlement (RTGS), Automated Clearing House and Kenya Interbank

Transaction Switch (KITS).

According to Central Bank of Kenya (2016), the regulator commissioned external auditors to conduct IT audits on commercial

banks and mortgage finance companies. The auditors found out that some banks delayed in rolling out industry-wide systems,

there were inconsistencies in segregation of duties; inadequate business continuity plans; lack of IT security awareness trainings;

existence of manual system interfaces in some banks where un-encrypted and editable files were extracted from one system and

uploaded to other systems and also users‘ rights not corresponding to the users‘ roles and responsibilities. According to Kenya

Bankers Association (2014), commercial banks failed to meet the March 31st 2014 deadline on the switch from PIN and stripe to

chip based ATM cards project and were facing major challenges in the implementation phase of the project.

A new bond trading system implemented by the Central Bank of Kenya in early 2012 slowed down activities in the bond market

with trading declining by almost half in one particular week just after the new system implementation had been hailed as

successful (Central Bank of Kenya, 2012). Previous studies in Kenya by Sewe, 2010; Ngugi and Mutai, 2014; Ikua and

Namusonge, 2013 however, mainly concentrated on factors influencing the growth of IT projects in Kenya. There is paucity of

knowledge in the key area of ascertaining and classifying the specific influence of project customer factors on implementation of

IT projects among commercial banks. This study therefore sought to establish the influence of project customer factors on

implementation of IT projects by commercial banks in Kenya.

3. Research Objective

The overall objective of this study was to establish the influence of project customer factors on implementation of information


4. Scope of the Study

This study covered thirty nine (39) commercial banks licensed by the Central Bank of Kenya. The commercial banks that

formed the units of analysis were those in operation as at 31st December 2016. The study focused on Head offices of the

commercial banks, all based in the city of Nairobi primarily because policy decisions take place from the top. The study focused

on project customer factors as operationalized by user support, user participation, client experience and client training and

education. Implementation of IT projects was measured by projects being completed within budget and scope, on time and

stakeholder satisfaction.


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5. Literature Review

5.1 Theoretical Review

A theory is a systematic explanation of the relationship among phenomena. Theories provide a generalized explanation to an

occurrence. Therefore, a researcher should be conversant with those theories that are applicable to his area of research (Kombo &

Tromp, 2009; Smith, 2004). According to Trochim (2006) and Turner (2007), a theoretical framework guides research,

determining what variables to measure and what statistical relationships to look for in the context of the problems under study.

5.2 Resource Advantage Theory

Barney‘s (2001) resource advantage theory explains differences in performance between organizations as being the result of

the unique combination of resources possessed by each organization. The theory is a subset of the broader Resource Based View

framework of the firm literature. RBV has developed to become one of the key paradigms used within strategic management

research to explain the source of sustained advantage over competitors (Barney, 2001; Das & Teng, 2000; Kraaijenbrink, Spender

& Groen, 2010). Rand (2000) provides a definition, stating that ―the RBV focuses on the use and deployment of resources, the

development of resource-based core competencies and the eventual competitive advantage that results from this process.‖

The RBV framework is commonly adopted to explain how organizations (or project teams) can develop and sustain a

competitive advantage through the application of the heterogeneous resource base (Davis & Cobb, 2010). There are differences in

the literature with regard to which resource characteristics are considered relevant. However, in summary, resources are a source

of competitive advantage if they are valuable, scarce, inimitable, non-substitutable, durable, appropriate and organizationally

focused (Barney, 2001; Jugdev, 2004; Jugdev & Mathur, 2013).

The unique combination of resources available to a temporary organization that is the project can be a source of competitive

advantage or disadvantage for a project‘s successful completion (Barney, 2001). Accordingly, the success of a project is at least

partially dependent upon the project users and clients having access to key resources that provide a competitive advantage over

other projects within the organization, and more broadly across industry and market. Therefore the key concept underpinning

RAT is the scarcity of resources in the project environment and their impact upon a project‘s completion. Project customer factors

are consequently explained by the resource advantage theory.

Independent variable Dependent variable

Figure 1: Conceptual framework

6. Discussion of Variables

6.1 Project Customer Factors

Project customer factors are user support and participation, level of client training and education and client experience. User

support and participation consist of the behaviours and activities of the customer in relation to product development (Jun et al.,

2011). Previous literature reveals that user participation significantly increases the likelihood of effective implementation of

information technology projects. Empirical studies by Chow and Cao (2008), Misra, Kumar and Kumar (2009) and Sheffield and

Lemétayer (2013) have provided data to support significant and positive relationship between user participation and support and

effective implementation of information technology projects.

Similarly, Jun et al., (2011) also demonstrated that resolving potential conflicts early arising from greater user participation

plays a vital role in the perceived system satisfaction of IT project developers and users. Further, clients who have an acceptable

level of basic education or training in information technology can easily explain their requirements and needs in a clear form. In

the same breadth, customers who have basic knowledge about their business domain accurately identify their requirements which

save time, costs and contribute to process and product quality (Murad & Cavana, 2012).

6.2 Implementation of Information Technology Projects

Project Customer Factors

User support

User participation

Client training & education

Client experience

Implementation of IT projects

Budget

Time

Scope

Stakeholders‘ satisfaction


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During implementation of IT projects, organizations benchmark against a variety of factors to determine whether or not a

project has been implemented effectively. Some determine effective implementation based on the satisfaction of their

stakeholders, on-time delivery, budget, delivery of benefits, quality, acceptable return on investments (ROI) and other auxiliary

factors (Winter, Smith, Morris & Cicmil, 2006). Leading practice companies determine whether a project has had effective

implementation based on whether it achieves benefits that are in line with strategic objectives and establish mechanisms to track

progress along the way.

While many projects achieve effective implementation outcomes, it is also a reality that some projects only achieve sub-

optimal implementation results. The latter results are linked to internal project issues like missed deadlines and insufficient

resources (Winter et al., 2006). In fact, the top three reasons for ineffective implementation of projects are bad estimates and

missed deadlines, scope changes and insufficient resources which are all internal project factors (Hillson, 2003). The classification

of project implementation is to a degree subjective (Ika, 2009).

Müller and Judgev (2012) describe effective project implementation as predominantly in the eyes of the beholder meaning one

stakeholder may consider a project to have been implemented effectively, whereas another stakeholder would consider it having

been done below par. A requisite criterion defining implementation characteristics used to judge between below par and effective

project implementation constitute the dependent variable. Project implementation is a multidimensional construct where project

stakeholders can select a number of project implementation criteria which they believe are important to pass judgment (Morris,

2012). For each project, not only should implementation criteria be defined from the beginning of the project, but the relevant

implementation factors also need to be identified and incorporated in a timely manner across the project life cycle (Ramesh,

Mohan & Cao, 2012).

6.4 Empirical Literature Review

The understanding of effective project implementation criteria has evolved from the simplistic triple constraint concept, known

as the iron triangle (time, scope and cost) to something that encompasses many more success criteria (Judgev & Müller, 2005;

Müller & Judgev, 2012; Shenhar & Dvir, 2007). Measurement models for effective project implementation that are applicable for

different types of projects or different aspects of project success were developed by among others, Shenhar et al., (2007), Turner

and Müller (2006) and Hoegl and Gemuenden (2001). The Chaos Report 2015 by Standish Group studied 50,000 projects around

the world. The results summarize that 29% of the projects were successful, whereas 52% of the projects were challenged and 19%

of the projects belonged to failed category. The study indicates that there is still work to be done around achieving successful

outcomes from IT project development (Hastie & Wojewoda, 2015).

The results of ‗2015 Project Management Insight‘ conducted by Amplitude Research among different industry sectors in the

US indicated that one third (1/3) of the projects were not completed on time and also exceeded their approved budget. They

concluded that the statistics showed some notable shortcomings and there is significant room for improvement when it comes to

achieving effective project implementation. The Global Construction Survey by KPMG (2015) also confirmed that project

sponsors continue to experience project failure. Survey on private organizations showed that 53% suffered one or more

underperforming projects in the previous year whereas for energy and natural resources and public sector respondents the figures

were 71% and 90% respectively. At the same time, the actual success rate of projects does not meet desired levels. When asked

about how many of the projects were delivered on time, with expected quality and realized benefits, only 8% of the respondents

stated that most of their projects fulfilled these criteria. Approximately 31% estimated that 50-75% of their projects achieved these

criteria, while the majority of the respondents completed only less than half of their projects as planned (KPMG, 2015).

Alexandrova and Ivanova (2012), attempted to study the critical success factors of project management in Bulgaria.

Questionnaires were distributed to 132 project managers and project team members of projects supported by EU programs. There

was 98% response rate (129 respondents out of 132). One of the conclusions of this study was that technical competence of the

project manager is a critical factor for effective project implementation.

6.5 Critique of Literature Review

The Chaos Report 2015 by Standish Group studied 50,000 projects around the world. The results summarize that 29% of the

projects were successful, whereas 52% of the projects were challenged and 19% of the projects belonged to failed category. The

study indicates that there is still work to be done around achieving successful outcomes from software development (Hastie &

Wojewoda, 2015).


US indicated that one third (1/3) of the projects did not complete on time and also exceeded their approved budget. Pinto (2014)

did a survey of 418 PMI members in finding the critical success factors in project implementation. Based on the extensive


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literature review of 17 research papers, Wong and Tein (2004) identified 23 critical success factors for Enterprise Resource

Planning (ERP) projects implementation.

Pinto (2014) suggested to project managers that they should concentrate on multi factor model for critical project success

factors and they should also identify the relative importance among the factors. These studies were general and therefore not

specific to the banking sector. Most of the studies done on critical determinants of effective project implementation have been

conducted in developed countries and as such there are very few studies on the subject carried out locally and especially those

focusing on the banking sector in Kenya. The Chaos Report 2015 by Standish Group studied 50,000 projects around the world.

The results summarize that 29% of the projects were successful, whereas 52% of the projects were challenged and 19% of the

projects belonged to failed category. The study indicates that there is still work to be done around achieving successful outcomes

from software development (Hastie & Wojewoda, 2015).


US indicated that one third (1/3) of the projects did not complete on time and also exceeded their approved budget. Pinto (2014)

did a survey of 418 PMI members in finding the critical success factors in project implementation. Based on the extensive

literature review of 17 research papers, Wong and Tein (2004) identified 23 critical success factors for Enterprise Resource

Planning (ERP) projects implementation.

Pinto (2014) suggested to project managers that they should concentrate on multi factor model for critical project success

factors and they should also identify the relative importance among the factors. These studies were general and therefore not

specific to the banking sector. Most of the studies done on project customer factors as critical determinants of effective project

implementation have been conducted in developed countries and as such there is paucity of studies on the subject carried out

locally and especially those focusing on the banking sector in Kenya.

6.6 Research Gaps

There is so much literature about project customer factors and information technology projects and their application in

different industries, however, the same cannot be said to be fully applicable in the banking sector. Literature on information

technology projects showed that they are implemented better under complex and uncertain environments (Smith, 2004). In

addition, the literature review shows a general use of project management approach by organizations without specific reference to

the specific project management approaches and as such there is a problem of matching project types with specific management

approach (O'Sheedy, Xu & Sankaran, 2010). Literature is silent on what form of challenges organizations especially banks are

likely to face if they do not adopt standardized classification of determinants of effective implementation of IT projects given that

it is a sensitive industry across economies (Beer & Nohria, 2000).

Chao and Qing (2008) noted that every project environment has its own unique factors that influence effective project

implementation throughout the project life cycle which is why most of the literature uses the concepts of organizational theory as

a lens to examine project management phenomena. Last but not the least, the literature review indicates that IT projects have

characteristics ranging from complex to simple depending on the expertise needed to respond to the project needs (Ruparelia,

2010). However, it was noted that there are few studies on such projects and that explains why categorizing individual

determinants appropriately by identifying their requisite thematic relationships and approach to implementation of projects is key

(Wells, 2012). In view of the foregoing literature, this research aims at understanding the influence of grouped project customer

factors on implementation of information technology projects by commercial banks in Kenya.

7. Research Design

This study adopted a mixed research approach that sought to determine the relationship between the independent and

dependent variables. Descriptive survey design was used as well as correlational research design. The overall aim of descriptive

research design is to discover new meaning, describe what exists, determine the frequency with which something occurs and

categorize information (Sekaran & Bougie, 2011). Correlational research design describes and assesses the magnitude and degree

of an existing relationship between two or more continuous quantitative variables with interval or ratio types of measurements or

discrete variables with ordinal or nominal type of measurements (Lavrakas, 2008)

7.1 Target population

Target population refers to the entire group of individuals or objects to which researchers are interested in generalizing their

conclusions (Castillo, 2009). The target population comprised Management staff (10,327), Supervisory staff (6,345) and Clerical

staff (14,515) totaling 31,187 as at 31st December 2016. The main reason for choosing the aforementioned staff was because they


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were the frequent system users and therefore well versed with the nuances of business and information technology systems in

commercial banks.

7.2 Sample and Sampling Technique

The term sample refers to a segment of the population selected for research to represent the population as a whole (Kotler &

Armstrong, 2006). The study used proportionate stratified random sampling where the subjects were selected in such a way that

the existing sub-groups in the population were more or less reproduced in the sample (Mugenda & Mugenda, 2003). The sample

size was determined using a model by Nasiurma (2000) as shown;-

n = (Ncv 2) / (cv

2 + (N-1) e

2)

Where:

n = Sample size

N = Population

cv = Coefficient of variation (take 0.7)

e = Tolerance at desired level of confidence, take 0.05 at 95% confidence level.

The substituted values in determining the sample size from the target population was;

n = 31,187*0.72/ (0.7

2 + (31,187-1) 0.05

2)

n = 15,281.63/ (0.49 + (31,186) 0.0025)

n = 15,281.63/ 78.45

n = 195

7.3 Data Collection Instruments

The study used questionnaires to obtain data for analysis to support or refute hypotheses and to confirm the evidence obtained

from the qualitative and quantitative data analysis. Questionnaire is a popular method of collecting data because researchers can

gather information fairly easily and the responses are easily coded (Sommer & Sommer, 2001). A questionnaire is a research

instrument that gathers data over a large sample and its objective is to translate the research objectives into specific questions, and

answers for each question provide the data for hypothesis testing. The questionnaire contained both closed and open ended

questions. The closed ended questions were aimed at giving precise information which minimized information bias and facilitate

easier data analysis, while the open ended questions gave respondents the freedom to express themselves.

7.4 Data Collection Procedure

This study used drop off and pick up method to administer the questionnaires to the sampled respondents. According to

Glicken (2008), use of Drop Off and Pick Up (DOPU) method results in significantly high response rates. DOPU technique is also

preferred as it is economical and saves time.

7.5 Data Analysis and Presentation

According to Njuguna (2008), data analysis has three basic objectives: getting a feel of the data, test the goodness of the data

and test the hypotheses developed for the research. In this study, both qualitative as well as quantitative methods of data analysis

were used to analyze the research variables. Data was edited, coded, classified and summarized into categories. A Likert scale was

adopted to provide a measure for qualitative data. For qualitative data, code categories were based on the research question and

were entered into a computer with developed pattern codes to group the summaries of data into a smaller number of sets, themes

or constructs, and using Statistical Package for Social Sciences (SPSS), the researcher analyzed the frequencies of the themes;

usually the frequency of appearance of a particular idea is obtained as a measure of content (Krishnaswamy, Sivakumar &

Mathirajan, 2006).

The quantitative data in the research was analyzed by use of descriptive and inferential statistics by use of (SPSS). Descriptive

statistics such as mean, frequency, standard deviation and percentages were used to profile sample characteristics and major

patterns emerging from the data. Further, correlation analysis was used to establish the relationship between the dependent and

independent variables. Results from quantitative data were presented in form of tables and figures.


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8. Data Analysis and Interpretation of Findings

8.1 Response Rate

The researcher distributed one hundred and ninety five (195) questionnaires out of which one hundred and thirty nine (139)

were fully filled which represented 71% of the total questionnaires distributed. According to Kothari (2004), 50% response rate is

considered average, 60% to 70% is considered adequate while anything above 70% is considered to be an excellent response rate.

Morrison and Louis (2007) indicated that for a social science study, anything above 60% response rate is adequate for making

significant conclusions and therefore 71% sufficed for this study.

8.2 General Information

As part of the general information, the respondents were asked to indicate their age bracket, gender and their functional

designations in respective banks. The information was part of the general information about respondents working in commercial

banks. The information is organized starting with age bracket, gender then functional classification. From the findings, majority

of the respondents were aged between 20 and 40 years constituting 78.4%. Respondents below 20 years of age were 0.7% whereas

those above 40 years constituted 20.9%. The statistics are a confirmation that majority of bank workers are youthful with those

above 40 years being continually eased out either by natural attrition or by being incentivized to take voluntary retirement. The

descriptive statistics of the study indicated that 95 respondents were male representing 68.8% while 44 respondents were female

representing 31.2%. Functional positions held by the respondents in their respective workplaces were also sought and there was a

near even distribution of respondents amongst data inputters (22.5%), authorizers (26.1%), operations managers (26.8%) and IT

managers (19.6%). This could be attributed to their routine involvement in active implementation of IT projects unlike business

relationship managers (5.1%) whose role was mostly business and IT relationship management, advisory and user acceptance

testing.

8.3 Implementation of IT projects

The question requested the respondents to rate the extent to which the stated aspects of project management were used to

measure successful implementation of IT projects in their respective banks. The following findings were obtained;

Table 1 Extent to which certain aspects of project management are used to measure successful implementation of

IT projects

According to the findings, respondents indicated with a mean of 3.36 and a standard deviation of 0.873 that in measuring

successful implementation of IT projects, projects being delivered on time, within budget and as per scope were moderately used

as a measure in their bank. Additionally, respondents indicated with a mean of 2.21 and a standard deviation of 0.739 that

delivered IT projects satisfied all stakeholders was lowly used as a measure of successful implementation of IT projects in their

bank. The respondents also indicated with a mean of 3.89 and a standard deviation of 0.714 that the overall quality of IT projects

being acceptable was moderately used as a measure of successful implementation of IT projects in their bank. The respondents

also indicated that ease of use of industry-wide IT projects was moderately used as a measure of successful implementation of IT

projects in their bank with a mean of 3.86 and a standard deviation of 0.727.

8.4. Project Customer Factors and Implementation of IT Projects

The study sought to find out the influence of project customer factors on implementation of information technology projects

by commercial banks in Kenya.

Majority of respondents made up of 77.7% agreed that system users were usually involved in new IT project initiatives out of

which 15.1% strongly agreed. 68.9% of the respondents agreed that staff in their banks had requisite experience in dealing with

Aspect Mean Standard

Deviation Projects delivered on time and within budget and scope 3.36 0.873 Delivered IT systems satisfy all stakeholders 2.21 0.739 Overall quality of IT projects is acceptable 3.89 0.714 Various industry-wide IT projects projects in my banks are easy to

use 3.86 0.727


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industry-wide IT projects. However, a respectable 30.2% held a contrary opinion regarding experience of bank staff on IT projects

with 21.6% being indifferent while 8.6% disagreed. This showed that some banks may indeed be having experienced staff

deployed for IT projects while other banks may be lacking such talent. To ascertain the extent to which system users participated

in activities geared towards realizing new IT projects, 65.5% of the respondents were in agreement that users in their banks were

involved while 23.7% were indifferent.

The indifferent ones may point to case where communication for involvement may be minimal or non-existent altogether.

10.8% of the respondents disagreed indicating that users are not involved and this could be a pointer that a vast majority of banks

do not allow nor encourage free participation of their staff in the actualization process of IT projects. The level of staff training

and education on new industry-wide IT projects was also interrogated. Whereas 60.8% were in agreement that indeed training and

education was carried out, 20.1% neither agreed nor disagreed with 11.5% disagreeing. The statistics point to a situation where

trainings may not have been structured hence this situation made a third of the respondents to hold a contrarian view. Separately,

64.8% of the respondents agreed that system users in their banks held consultations with other stakeholders on new IT projects

with 77.7% of the respondents agreeing that staff had requisite experience in using industry-wide IT projects.

Table 2 Aspects of Project Team Factors

8.5 Regression Analysis

Bivariate regression analysis was used to establish the relationship between the dependent and the independent variables. The

bivariate regression model was;

Y = β0 + β1X1 + ε

Where:

Y = Implementation of IT projects;

β0 = Constant term;

β1 = Beta coefficient;

X1 = Project Customer Factors and

ε = Error term

Table 3: Model Summary Project Customer Factors and Implementation of IT projects

Model R R Square Adjusted R Square Std. Error of the Estimate

.38164

In establishing the influence of project customer factors (X1) on implementation of IT projects (Y), the regression model was

found to be significant (F(1, 136) = 20.256, p – value < 0.001) indicating that project customer factors were valid predictors in the

model. The coefficient of determination (R2) value of .130 implied that project customer factors independently explained 13%

variation in effective implementation of IT projects. The adjusted (R2) explained 12.3% and so the remainder could be explained

Project Customer factors Mean Standard

Deviation System users normally involved in new IT project

initiatives. 3.82 0.689

Staff have requisite end user experience on industry

IT projects. 3.75 0.578

System users participate in activities for actualizing


Bank staff trained and educated on new industry-wide


System users hold consultations with relevant IT project

stakeholders 3.69 0.858

Bank staff have required expertise in using industry-wide

IT projects 3.83 0.727

1 .360a .130 .123

a.

Predictors: (Constant), Project Customer Factors


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by other factors not included in the model. The R value of .360 indicated a moderate positive correlation between project

customer factors and implementation of IT projects. The standard error of .38164 showed the deviation from the line of best fit as

captured in Table 3.

Table 4 ANOVA Results for the Relationship between Project Customer Factors and Implementation of IT Projects

Model Sum of Squares Df Mean Square F Sig.

Regression 2.950 1 2.950 20.256 .000b

1 Residual 19.808 136 .146

Total 22.758 137

a. Dependent Variable: Implementation of IT projects

b. Predictors: (Constant), Project Customer Factors

In establishing the influence of project customer factors (X1) on implementation of IT projects (Y), the regression model was

found to be significant (F(1, 136) = 20.256, p – value < 0.001) indicating that project customer factors were valid predictors in the

model.

Table 5 Regression results for the relationship between project team factors and implementation of IT projects

Model Unstandardized Coefficients Standardized Coefficients t Sig.

B Std. Error Beta

1 (Constant) 2.580 .221 11.655 .000

Project -

team factors .263 .058 .360 4.501 .000

a. Dependent Variable: Implementation of IT projects

The regression equation was represented as

Y = 2.580 + .263X1

Where;

Y = Implementation of IT projects and

X1 = Project customer factors

The beta coefficient for project customer factors was significant (β2 = .263, t = 4.501, p-value < 0.001) implying that for every

single unit increase in the index of project customer factors, there is an improvement index of .263 in effectiveness of IT project

implementation as shown in Table 5.

9. Summary of Findings

The purpose of this study was to determine the influence of project customer factors on implementation of information

technology projects by commercial banks in Kenya. The study findings were established from 139 respondents out of whom 0.7%

were below 20 years; 33.1% were aged between 21 and 30 years; 45.3% were aged between 31 and 40 years then 20.9% were

aged above 40 years. Additionally, 95 of the respondents (68.3%) were male whereas 44 respondents representing 31.7% were

female. Moreover, 31 of the respondents (22.5%) were data inputters, 36 respondents (26.1%) were authorizers, 37 respondents

(26.8%) were operations managers, 27 respondents (19.6%) were IT Managers and 8 respondents representing 5.1% were IT &

Business Relationship Managers.

Project customer factors are an amalgamation of critical characteristics of users and clients in the project organization and

include users‘ mutual support, involvement, experience and participation, client education, training and experience. The criticality

of these characteristics in adding value to the implementation process of IT projects was confirmed by findings in this study and

also by earlier studies conducted.


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The study found that project customer factors positively influenced implementation of IT projects. Project customer factors

had a significant influence on implementation of IT projects by commercial banks in Kenya. This confirms the important role that

project customer factors play in the implementation process of IT projects. The multifaceted characteristics of users can be

enhanced over time in order to become substantive players in commercial banks‘ IT projects. Users‘ attitudes ought to be

supportive and inviting to ensure successful implementation of IT projects.

10. Conclusions and Recommendations

10.1 Conclusions

Based on the findings of this study, project customer factors were found to be significant and had a positive influence having

recorded a significant score contributing to implementation of IT projects by commercial banks in Kenya. The study concludes

that user support and involvement are critical in realizing the envisaged results of successfully implementing IT projects, since

acceptance testing before eventual roll-out are facilitated by users interacting with the new systems. This would imply that various

commercial banks entrusted their users to fully test the new IT projects before roll-out. From the research findings, it was apparent

that training and education did not receive due attention from commercial banks as most users and other members in commercial

banks indicated not to have had capacity building trainings. Also, a sizeable percentage of bank staff did not have requisite

experience on industry-wide IT projects and these hampered seamless roll-out of new projects.

10.2 Recommendations

Banks should engage staff members with the right mental attitude and whose interpersonal skills would incline them towards

supporting bank initiatives including IT projects. Staff must be encouraged to be actively involved in on-going IT projects and that

would improve their experience on such projects. In the same breath, structured training and education must be offered equitably

to staff members. Banks should have incentive schemes such that when staff members deliver on certain projects within the

constraints of time, budget and scope, then they are rewarded and commended for a job well done. Such small actions would

motivate users and other bank staff so much so that when new industry-wide IT projects are due to be rolled out, it would just be a

matter of aligning structures and letting the implementation process begin.

The study was limited to Head offices of commercial banks in Kenya. This study recommends further studies covering non-

bank financial institutions since together they constitute the banking sector. Also, this study only focused on project team factors

which constitute a small fraction of overall determinants of implementation of information technology projects. Further

comprehensive studies are recommended to capture the effect of elaborate determinants of implementation of information


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INTERNATIONAL JOURNAL OF ACADEMICS & RESEARCH …...core banking systems and other integrated systems. The most common core banking systems include Flexcube, ModelBank (T24) and iMAL