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

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

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13 August, 2018: Vol. 1, Issue 1

Influence of Project Team 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

The study sought to determine the influence of project team factors on implementation of information technology projects by

commercial banks in Kenya. Discussion on the importance of people involved in projects has become of growing interest to

authors over the last 20 years or so. Numerous studies have been carried out on critical factors that influence successful delivery

of projects, and these studies have diverse contributions on what constitutes success in 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 input to the project management system that lead 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).

Amberg and Wiener (2006) stated that cooperation, coordination and integration are success factors for projects involving

multiple buyers and suppliers. Baccarini and Collins (2003) did an empirical study based on a survey of 150 Australian Project

Management Institute members working in different industries such as construction, information technology, telecommunications

and others. A total of 15 percent of the respondents in this survey were from information technology industry and they identified

15 critical success factors for project success. Among them project understanding and competent project team are identified as

predominant factors for project success. One important thing noticed in this study was that it did not have significant variations

from those observed from different industries. Benjamin (2006) identified project management methodology as critical success

factor for software projects based on an empirical study. However, the responses received in his empirical study were less than

ten.

Abstract

Most information technology projects in commercial banks in Kenya experience myriad challenges from their inception all

the way to completion. The level of internal communication and resultant amity amongst project team members ensures that

projects are delivered on schedule, within budget and scope. Project teams are therefore important stakeholders in information

technology projects. Over time, managing project teams has evolved from the traditional authoritative style to the

contemporary collaborative approach. Project teams‘ commitment, expertise and experience greatly influence how the former

deliver on projects at hand. The study investigated the influence of project team factors on implementation of information

technology projects by commercial banks in Kenya. Mixed research design was used for the study. The target population

comprised all licensed and operational commercial banks as at 31st December 2016 with a headcount of 31,187 members

comprising Management staff, Supervisory and Clerical staff. A sample size of 195 was derived using a model by Nasiurma

(2000). Questionnaires were used to collect primary data and were administered by use of drop off and pick later method to

sampled respondents. Qualitative data was processed using content analysis. Quantitative data in this research was analyzed by

use of descriptive and inferential statistics using SPSS. The study found that project team factors have a positive and

significant influence on effective implementation of information technology projects by commercial banks in Kenya.

Key words: Technology Projects, Information Technology, Commercial Banks

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According to Amade, Ogbonna and Kaduru (2010), commitment of contracting firms, project staff‘s skills, collective

responsibility among project stakeholders, project management tools and techniques, accuracy of project cost estimates, supplier

commitment to project specifications, project financing, environmental factors, accuracy of designs and accuracy of project

schedules contribute about 55.2% of successful project implementation in Nigeria. In the study by Amade et al., (2011) which was

conducted in the construction industry of Nigeria, 10 critical success factors were identified. These factors are project mission, top

management support, project plan, client consultant, personnel, technical tasks, client acceptance, monitoring and feedback,

communication and troubleshooting.

In Kenya, similar studies on critical success factors for implementation of projects have been carried out with a wide range of

factors being identified. In his research, Mwai (2012) concluded that project success is a matter of perception and a project will

most likely be perceived as successful if it meets its technical performance specifications and also meets the project mission and

high level of satisfaction among key people on the project. There is also a general agreement that although schedule and budget

performance are considered as inadequate measures of project success, they are still important components of the overall construct

(Mwai 2012). Wambugu (2012) identified strategy, project team capacity, project communication, monitoring and evaluation and

client consultation as factors influencing success of Constituency Development Fund (CDF) projects in Nyeri County. Kabutu

(2013) posited that top management support, technology, training and competence, organizational resources and funds

management were the success factors of offshore software development and implementation projects in public organizations.

2. Problem Statement

Management of information technology (IT) projects is a challenging task occasioning many projects failing to achieve their

intended objectives (Latendresse & Chen, 2003). Many organizations do not critically examine the causes for ineffective project

implementation and this prevents them from learning from their mistakes (Howell, Windahl & Seidel, 2010). Ineffective

implementation can be classified as partial failure in the sense of not delivering all of the anticipated benefits or in extreme cases,

outright failure or abandonment of the project (Finch, 2003). Although ineffective implementation of IT projects has been widely

recognized as the most pressing problem facing the IT profession, there is still no clear and accepted definition of effective IT

project implementation (Howell et al., 2010).

In 2016, Kenya‘s banking sector witnessed reduced activities in respect to their core banking systems compared to prior years.

Most players continued to leverage their existing information technology (IT) platforms in the provision of quality banking

services that are efficient and have wider scope. Robust IT platforms have enabled financial institutions to respond to demands of

the growing banking population by offering electronic based banking services such as mobile and internet banking. A few banks

have deployed mobile phone platforms to grant short term loans to customers and this has gone a long way in promoting financial

inclusion (Central Bank of Kenya, 2016). Further, CBK‘s 2016 Annual Supervision Report indicates that robust IT platforms have

enabled banks to roll out agency banking services where customers are able to carry out banking services such as deposits and

withdrawals from third parties contracted by individual banks. The robust IT platforms in the sector are supported by stable and

efficient core banking systems.

Commercial banks‘ business strategies are mainly driven by the capabilities of these 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). Kenya‘s

banking sector faces implementation challenges for IT projects despite the remarkable growth of the IT sector in the country

coupled with opportunities created by government strategies in the sector. 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 did not correspond to the users‘ role 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 & Mutai, 2014; Ikua and Namusonge,

2013 however, mainly concentrated on factors influencing the growth of IT projects in Kenya. This left a knowledge gap in the

key area of ascertaining and classifying specific determinants for effective implementation of IT projects among commercial

banks. This study therefore sought to establish the influence of project team factors on implementation of IT projects.

3. Research Objective


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

technology projects by commercial banks in Kenya.

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 Nairobi, primarily because policy decisions take place from the top. The study focused on project

team factors as operationalized by internal communication, team commitment, team composition and team expertise and

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

stakeholder satisfaction.

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 Theory of Constraints

The theory of constraints (TOC) is primarily concerned with managing uncertainty to minimize resource constraints for

multiple projects. The TOC can be used to evaluate obstacles, limitations and similar problems in a project and develop a

breakthrough solution (Rand, 2000). The TOC is used for developing resources‘ timelines, for example resources‘ availability

allows project scheduling to the extent that activities using identical resources are scheduled in series (Steyn, 2002).

Leach (2010) argues that more detailed planning or more sophisticated computer programs cannot correct the constraint-based

problems (over time, within budget and under-scope). It can be argued that project reality has dynamic variation due to uncertain

estimates, dependent events and often scarce resources. Therefore, organizations should focus on project timelines and identify the

core constraints that prevent project execution from performing better rather than breaking the process down and improving the

efficiency of each step (Bevilacqua, Ciarapica & Giacchetta, 2009). TOC incorporates a sequence of progressive steps for

improving the current situation.

The objective is to identify the weakest link in the project management plan which is itself regarded as a constraint, exploit the

constraint, subordinate all else to the strategy to manage the constraint, elevate the constraint and if previous steps fail, go back to

step one (Rand, 2000). Application of TOC needs supportive organizational policy and resource availability to enhance the project

timelines (Leach, 2010). TOC time management technique also referred to as critical chain scheduling (CCS) has been extended

to allocate resources to project-based organizations that share common resources (Steyn, 2002). This maximizes the number of

projects in the organization while maintaining the principles for reducing the duration of individual projects.

Herroelen and Leus (2001) consider CCS to be an effective project management strategy which can be deployed to avoid

project delays caused by Parkinson‘s Law which is an adage stating that work expands to fill the time available for its completion,

whilst protecting for Murphy‘s Law which alludes to uncertainty involved in the work. These views are also supported by Steyn

(2002) and Rand (2000) who highlight that safety reserves are often overestimated in traditional project management approaches

which results in a tendency for project team members to procrastinate. TOC therefore explains project team factors since the

theory is important in management of key constraints that can result in reduced delays and increase the likelihood of delivering

projects on-time, within budget and to scope and quality specifications.

Independent variable Dependent variable

Figure 1: Conceptual framework

Project Team Factors

Internal communication

Team commitment

Team expertise & experience

Team composition

Implementation of IT projects

Budget

Time

Scope

Stakeholders‘ satisfaction


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6. Discussion of Variables

6.1 Project Team Factors

Project team factors are specifically theorized to have a positive impact on effective implementation of information technology

projects. The success of IT projects greatly depends on the team‘s communication, team empowerment, expertise and experience,

commitment and composition (Warren & Hutchinson, 2000). Although these factors specifically relate to employees or project

teams, some factors such as empowerment, team‘s composition, size and geographic distribution are also frequently influenced by

the parent organization and the broader corporate culture that is inherited from it (Howell et al., 2010). The foregoing factors and

organizational boundaries may all affect the team‘s ability to communicate.

Similarly, team communication, commitment, expertise or skill, experience and empowerment determine a teams‘ ability to

quickly comprehend and respond to project risk, thereby improving the chances of effective project implementation (Cheung,

Chan & Kajewski, 2012). Effective implementation of projects is realized under circumstances where there are small teams which

are self-organizing, autonomous, composed of best skilled expertise and experienced, highly collaborative and committed project

team members (Turner, 2006). Project teams‘ commitment is the willingness by a team to devote energy and loyalty to a project

as expressed in three forms: affective, continuance and normative (Jaros, 2010).

Correspondingly, Wan and Wang (2010) found significant positive relationships between team commitment and IT project

management success. This implies that committed project team members more often do not have intentions to quit, which saves

the project the costs of recruiting and orienting new members. Similarly, costs of supervision are mitigated if the project team

members are committed to their project tasks. Effective implementation is realized if the team is highly committed by way of

working full time, knowledgeable, representative and empowered (Zwikael, 2008).

Internal project communication is defined as the practices that increase information exchange and cohesion among

development team members. It enhances the levels of information sharing and collaboration between project team members which

decreases the amount of team conflict and keeps the team stable. Similarly, Yetton et al., (2000) demonstrated that project team

conflict leads to instability in the team and thus result in a project being delayed and exceeding budget. This is because IT projects

are knowledge-intensive and human-intensive activities that require collaboration between team members with diverse skills and

specialties.

Team‘s general or specific expertise includes the ability to work with uncertain objectives, ability to work with top

management, ability to work effectively as a team, ability to understand human implications of a new system and ability to carry

out tasks effectively in technical terms (Jiang & Klein, 2000). These are interpersonal, team or technical skills that can be

determined early during the formation of the project team. Although these skills can be addressed from a number of viewpoints,

generally, management can communicate early the basic project parameters and guidelines to the project team to allow for skill

matching (Turner et al., 2009).

The building of team‘s skills can also be conducted by project managers throughout the life cycle which enhances project

success (Dwivedi, 2008). Information technology projects need highly skilled and senior staff at the beginning of the project

especially during the project definition phase, and then junior or lower-skilled staff can do the assigned work by following pre-

established plans (Fortune & White, 2006). Boehm and Turner (2004) suggested a principle of using fewer and better people for

IT projects. If project teams are composed of people with corresponding and required skills, the project is likely to be completed

on time, budget, scope and quality.

6.2 Implementation of Information Technology Projects

When it comes to implementation of IT projects, organizations use 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


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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 software 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 did not complete 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).

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 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 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.

6.6 Research Gaps

There is so much literature about determinants of implementation of 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


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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).

Renz (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 grouped determinants of 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

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


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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.

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;


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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 Team Factors and Implementation of IT Projects

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

commercial banks in Kenya.

Respondents were asked to give their opinions on whether project teams embraced internal communication during the life

cycle of IT projects and a whopping majority of 82% agreed that indeed internal communication was embraced. Further, most of

the respondents representing 89.9% agreed that their project teams were committed to the course of delivering and actualizing on-

going and new IT projects. On whether project teams were adequately skilled in development of new information technology

projects, 85.6% of the respondents concurred while 12.2% were ambivalent and 2.2% disagreed. Majority of respondents

constituting 89.1% agreed that project teams in their banks had technical skills and experience to deliver on new IT projects,

10.1% neither agreed nor disagreed and a marginal 0.1% out rightly disagreed.

On whether project teams were empowered to carry out their functions without undue interference from stakeholders, 61.8%

agreed that indeed project teams were empowered. However, a sizeable number of respondents made up of 30.9% could neither

confirm nor disagree with the sentiments. Still on the same subject, 7.2% disagreed and felt that there was always an element of

interference. A question was posed on whether project teams in respective banks were composed of talented and multi-

disciplinary members to which 82% of the respondents were in agreement. However, it is worth noting that 13.7% of the

respondents neither agreed nor disagreed. Also, 4.3% were outright in their disagreement about the composition of the project

teams. Overall, responses to the question on team composition was skewed towards general agreement that indeed teams had

talented and multi-disciplinary members as attested by a mean score of 3.99 which according to the mean scoring tool, it falls

under the agree range.

Table 2 Aspects of Project Team Factors

8.5 Regression Analysis

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

Team Project factors Mean Standard

Deviation Internal communication among members is encouraged 3.94 0.689 Team fully committed to actualize IT projects 4.15 0.578 Team adequately skilled in development of new IT projects 4.09 0.675 Team has technical know-how and experience to deliver

new IT projects 4.20 0.641

Team empowered to perform without undue interference 3.69 0.858 Team composed of talented and multi-disciplinary staff 3.99 0.727


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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 Team Factors and ε = Error term

Table 3 Model Summary Project Team Factors and Implementation of IT projects

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

.38018

Project team factors explain 13.6% of successful implementation of information technology projects by commercial banks in

Kenya as represented by the R2. This therefore means that other factors not studied in this research contribute 86.4% of successful

implementation of information technology projects. The adjusted R square (R2) was .130 meaning project team factors explained

13% of successful implementation of IT projects and the rest could be explained by other factors not included in the model. As

shown in Table 4, the R value of .369 implied a relatively moderate positive correlation between project team factors and

implementation of IT projects and the standard error of .38018 indicated the deviation from the line of best fit.

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

Model Sum of Squares Df Mean Square F Sig.

Regression 3.102 1 3.102 21.461 .000b

1 Residual 19.657 136 .145

Total 22.758 137

a. Dependent Variable: Implementation of IT projects

b. Predictors: (Constant), Project Team Factors

The significance value is 0.000 which is less that 0.05 thus the model is statistically significant in predicting how project team

factors influence successful implementation of information technology projects by commercial banks in Kenya. The F calculated

is 21.461 at p <.005 level of significance implying that the overall model was significant.

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) 2.245 .287 7.833 .000

Project -

team factors .329 .071 .369 4.633 .000

a. Dependent Variable: Implementation of IT projects

The regression equation was represented as

1 .369a .136 .130

a.

Predictors: (Constant), Project Team Factors


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Y = 2.245 + .329X1

Where;

Y = Implementation of IT projects and

X1 = Project team factors

The findings confirmed that project team factors positively influenced implementation of IT projects (β1 = .329, t = 4.633, p-

value < 0.001). Results from regression analysis showed that project team factors had a moderate influence on implementation of

IT projects. The model showed that effectiveness in implementation of IT projects would increase by .329 for every unit increase

in project team factors index. Pearson product moment correlation coefficient for project team factors and implementation of IT

projects (r = .369, p-value < 0.001) was significant at 0.05 level of significance.

9. Summary of Findings

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

projects by commercial banks in Kenya. The study findings were established from 139 respondents. Of those respondents (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 lastly 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 team factors are specifically about issues of project teams that are generally theorized to contribute positively to the

impact of successful implementation of IT projects. These factors include team‘s communication, expertise and experience,

commitment and composition. In a nutshell, these team factors determine a team‘s ability to quickly comprehend and respond to

issues concerning the IT projects being worked on.

This study found out that project team factors had a moderate and positive influence on implementation of IT projects. These

results confirm that committed project team members have an inner feeling of loyalty and responsibility to the project hence a

working environment where less supervision is required resulting in cost savings. Similarly, committed team members oftentimes

do not harbour intentions of quitting and this saves the bank costs of recruitment and orientation of new members in terms of both

time and money.

10. Conclusions and Recommendations

10.1 Conclusions

This study concludes that project team factors as determinants of implementation of projects are significant and positively

influence implementation of IT projects. In this study, project team factors returned a high contribution to effective

implementation of IT projects by commercial banks in Kenya. Project team factors especially internal communication makes or

breaks the project organization. There ought to be structured communication amongst the project team members and other

stakeholders to the project. Committed project teams, balanced composition with the right expertise and experience are the gem

that brings success in implementation of IT projects by commercial banks.

10.2 Recommendations

Project teams form the nexus around which the whole implementation process of IT projects in commercial banks revolve.

Banks must ensure that internal communication amongst project team members and other project stakeholders is continuous and

deliberate to ensure that positive energy is spread throughout the project environment. Banks should have committed staff enlisted

as project team members because such staff exhibit loyalty and ownership to IT projects. Also, such staff members bring stability

to the implementation process as they are looked upon by other bank staff. It is also important for banks to encourage

professionalism in their staff cadres by appreciating and rewarding expertise and experience. Project teams must be properly

constituted with equitable distribution across demographics. Meritocracy in project teams must therefore be institutionalized by

commercial banks for better results in implementing IT projects.

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


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

technology projects by commercial banks in Kenya.

References

1. Alexandrova, M., & Ivanova, L. (2012). Critical success factors of project management: empirical evidence

from projects supported by EU programmes. Paper presented at 9th International ASECU Conference on Systematic

Economic Crisis: Current Issues and Perspectives, Skopje, Macedonia.

2. Amade,B., Ogbonna, A.C., & Kaduru, C.C. (2012). Determinants of successful project implementation in Nigeria.

International Journal of Management Sciences and Business Research, 1 (6), 1-16.

3. Amberg, M. and Wiener, M. (2006), Analysis of critical success factors for offshore software development projects

– a German perspective, Proceedings of ISOneWorld 2006, Las Vegas, NV.

4. Baccarini, D., & Collins, A. (2003). Project Success—A Survey. Journal of Construction Research, 5 (2): 211-231.

5. Beer, M., & Nohria, N., (2000). Breaking the code of change. Boston, MA: Harvard Business School Press.

6. Benjamin, R. (2006), Project Success as a Function of Project Management Methodology: An Emergent Systems

Approach, MBA Thesis, University of Hull, Hull.

7. Bevilacqua, M., Ciarapica, F. and Giacchetta, G. (2009), Critical chain and risk analysis applied to high-risk industry

maintenance: a case study, International Journal of Project Management, 27 (4), 419-432.

8. Boehm, B. and Turner, R. (2004), Balancing Agility and Discipline: A Guide for the Perplexed, Boston, MA,

Addison-Wesley.

9. Castillo, J. (2009). The effects of the LCC boom on the urban tourism fabric: The viewpoint of tourism managers.

Tourism Management, 32 (5), 1085-1095.

10. Central Bank of Kenya, (2016). Directory of Commercial Banks and Mortgage,

website:http://www.centralbank.go.ke/downloads/bsd/CommercialBanks Directrory-31 December 2016.pdf.

11. Cheung E., Chan, A. P.C., & Kajewski, S. (2012). Factors Contributing to Successful Public Private Partnership

Projects: Comparing Hong Kong with Australia and the United Kingdom. Journal of Facilities Management, 10

(1), 45–58.

12. Cooke-Davies, T.J., (2002). The ―real‖ success factors on projects. Int. J. Proj. Manag. 20 (3), 185–190.

13. Dwivedi, Y. (2008), An analysis of e-government research published in Transforming Government: People, Process

and Policy (TGPPP), Transforming Government: People, Process and Policy, 3 (1) 7-15.

14. Finch, P. (2003). Applying the Slevin-Pinto Project Implementation Profile to an Information Systems Project.

Project Management Journal, 34(3), 32-39.

15. Fortune, J., White, D., (2006). Framing of project critical success factors by a systems model. International Journal

of. Proect Management, 24 (1), 53–65.

16. Glicken, M. D. (2008). Social research: A simple guide. Boston, MA: Allyn and Bacon.

17. Hastie, S., & Wojewoda, S. (2015). Standish Group 2015 Chaos Report - Q&A with Jennifer Lynch. Info Q, 1-24.

18. Herroelen, W. and Leus, R. (2001), On the merits and pitfalls of critical chain scheduling, Journal of Operations

Management, 19 (5), 559-577.

19. Hillson D. A., (2003). Effective opportunity management for projects: Exploiting positive risk. New York, US.

Published by Marcel Dekker, ISBN 0-8247-4808- 5.

20. Hoegl, M., Gemünden, H.G., (2001). Teamwork quality and the success of innovative projects: a Theoretical

Concept and Empirical Evidence. Organ. Sci. 12 (4), 435–449.

21. Howell, D., Windahl, C. and Seidel, R. (2010), A project contingency framework based on uncertainty and its

consequences, International Journal of Project Management, 28 (3), 256-264.

22. Ika, L., (2009). Project success as a topic in project management journals. Project Management Journal, 40 (4), 6–

19.

23. Ikua, D. M. & Namusonge, G. S., (2013). Factors Affecting Growth of Information Communication Technology

Firms in Nairobi, Kenya. International Journal of Academic Research in Business and Social Sciences, 3 (7), 353.

24. Jaros, S., (2010). Commitment to Organizational Change: A Critical Review, Journal of Change Management, 10

(1), 79 - 108

25. Jiang.J., & Klein, G. (2000). Software Development Risks to Project Effectiveness. Journal of Systems and

Software, 52, 3-10.

26. Jugdev, K. and Muller, R. (2005), A Retrospective Look at Our Evolving Understanding of Project Success, Project

Management Journal, 36(4),19-31.

27. Jun, L., Qiuzhen, W. and Qingguo, M. (2011), The Effects of Project Uncertainty and Risk Management on

Development Project Performance: A Vendor Perspective, International Journal of Project Management, 29 (7),

923-933.


www.ijarke.com


28. Kabutu, P.M. (2013). Offshore software developments and implementation projects in public organizations: A case

study of Kenya Power & Lighting Company. International Journal of Sciences and Entrepreneurship, 1(6), 147-

155.

29. Kenya Bankers Association (2014). KBA Statement on the Banking Industry’s Migration to the EMV Standard.

Retrieved from http://www.kba.co.ke/home/92-latest-news/277-kba-statement-on-thebanking- industry‘s-migration-

to-the-emv-standard

30. Kombo.K.D & Tromp.L.A, (2009). Proposal and Thesis Writing, Don Bosco Printing Press, Kenya.

31. Kotler, P. and Armstrong, G. (2006). Principles of Marketing, (9th Ed.), London: Prentice Hall.

32. Kothari, C. (2004). Research Methodology: Methods & Techniques, (2nd Edition). New Delhi, India. New Age

International Publishers.

33. KPMG (2015), Project Management Survey Report 2013, KPMG, Wellington, available at: Management-Survey-

2015.pdf

34. Krishnaswamy, K., Sivakumar, A., Mathirajan, M. (2006). Management Research Methodology. Integration of

Principles, Methods and Techniques. New Delhi: Dorling Kindersley.

35. Latendresse, P. and Chen, J.C.H. (2003), The Information Age and Why IT Projects Must Not Fail, paper presented

at the 2003 Southwest Decision Sciences Institute Conference SWDSI2003), 221-5.

36. Lavrakas P. (2008). Encyclopedia of Survey Research Methods, Vol. 1 & 2. Los Angeles, United States of America.

Sage Publications.

37. Leach, L. (2010), Critical Chain Project Management, Norwood, MA. Artech House Inc.

38. Morris, P.W.G., (2012). A Brief History of Project Management. In: Morris, P.W.G., Pinto, J.K., Söderlund, J.

(Eds.), The Oxford Handbook of Project Management. Oxford, UK. Oxford University Press.

39. Morrison, M. & Louis, C. (2007). Research Methods in Education, 6th Edition. New York, United States of

America, Routledge.

40. Müller, R., Judgev, K., (2012). Critical Success Factors in Projects: Pinto, Slevin, and Prescott — the elucidation of

project success. International Journal of Project Management, 5 (4), 757–775.

41. Mugenda, A., & Mugenda, O. (2003). Research Methods; Qualitative and Quantitative Approaches. Nairobi, Kenya:

African Center for Technology Studies, (ACTS).

42. Mwai, M.M., (2012). Factors Influencing Project Performance of IT Projects in Kenya: A Case Study of Selected

Firms in Nairobi. Retrieved from http://irlibrary.ku.ac.ke/

43. Nasiurma, D. K. (2000). Survey Sampling: Theory and methods. Nairobi, Kenya: University of Nairobi.

44. Ngugi, K. & Mutai, G. (2014). Determinants Influencing Growth of Mobile Telephony in Kenya: A case of

Safaricom Ltd. International Journal of Social Sciences and Entrepreneurship, 1 (10), 218-230.

45. Njuguna, J., (2008). Organizational Learning, Competitive Advantage and Firm Performance. An Empirical Study

of Kenyan Small and Medium sized Enterprises in the Manufacturing Sector. Jomo Kenyatta University of

Agriculture and Technology. PhD Thesis.

46. O'Sheedy, D., Xu, J. & Sankaran, S., (2010), Preliminary results of a study of agile project management

techniques for an SME environment', International Journal of Arts and Sciences, 3 (7), 278-291.

47. Prabhakar, G. P., (2008), What is project success: A literature review. International Journal of Business and

Management, 3 (9), 3-10.

48. Ramesh, B., Mohan, K. and Cao, L. (2012), Ambidexterity in Agile Distributed Development: an Empirical

investigation, Information System Research, 23 (2), 323-339.

49. Rand, G. (2000), Critical Chain: The Theory of Constraints Applied in Project Management, International Journal

of Project Management, 18 (3), 173- 177.

50. Ruparelia N. B. (2010). Software development lifecycle models, ACM SIGSOFT Software Engineering, 35(3), 8-13.

51. Sewe, F. (2010). Factors Affecting the Strategic Growth of Information Communication Technology (ICT) in Kenya:

A Case Study of ICT Providers in Kenya. Available at SSRN 2101171.

52. Sekaran, U. and Bougie, R. (2011). Research Methods for Business: A Skill Building Approach, (5th Ed.), Delhi,

Aggarwal Printing Press.

53. Smith, G. (2004), Project leadership: why project management alone doesn‘t work, Hospital Material Management

Quarterly, 21 (1), 88-92.

54. Sommer R. and Sommer B., (2001), A Practical Guide to Behavioral Research: Tools and Techniques, (5th Ed.),

Oxford University Press.

55. Steyn, H. (2002), Project Management Application of The Theory of Constraints Beyond Critical Chain Scheduling,

International Journal of Project Management, 20 (1), 75-80.

56. Turner, J.R., Müller, R., (2006). Choosing Appropriate Project Managers: Matching their Leadership Style to the

Type of Project. Newtown Square, PA., Project Management Institute.

57. Turner, J.R., (2007). Towards a Theory of Project Management: The Nature of the Project Governance and Project

Management. International Journal of Project Management, 24 (2), 93-95.

58. Tulasi, C.H.L. and Rao, A.R., (2012), Review on theory of constraints, International Journal of Advances in

Engineering and Technology, 3 (1), 334-344.

59. Trochim, William (2006). The Research Methods Knowledge Base, 2nd Ed., Cincinnati: Atomic Dog Publishing.

http://www.kba.co.ke/home/92-latest-news/277-kba-statement-on-thebanking-


www.ijarke.com


60. Warren, L. and Hutchinson, W.E. (2000), Success factors for high-technology SMEs: a case study from Australia,

Journal of Small Business Management, 38 (3), 289-321.

61. Wambugu, J.M. (2012). The factors influencing the success of Constituency Development Funds (CDF)

Projects in Nyeri County, Central Province, Kenya. Retrieved from http://ir-

library.ku.ac.ke/handle/123456789/3547.

62. Wan, J. and Wang, R. (2010), Empirical research on critical success factors of agile software process improvement,

Software Engineering and Applications, 3 (12), 1131-1140.

63. Wells, H., (2012), How effective are project management methodologies: an Explorative Evaluation of their benefits

in practice. Project Management Journal, 43 (6), 43–58.

64. Winter, M., Smith, C., Morris, P., & Cicmil, S. (2006). Directions for Future Research in Project Management: The

main findings of a UK Government Funded Research Network, International Journal of Project Management, 24

(8), 638–649.

65. Yetton, P., Martin, A., Sharma, R. and Johnston, K. (2000), A Model of Information Systems Development Project

performance, Information Systems Journal, 10 (4), 263-269.

66. Zwikael, O. (2008), Top management Involvement in Project Management – Exclusive Support Practices for

Different Project Scenarios, International Journal of Managing Projects in Business, 1(3), 5.

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http://ir-/

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