CHAPTER FOUR RESEARCH FINDINGS AND DISCUSSION 4.1 Introduction The general objective of this research was to analyze the factors that affect the successful implementation of information technology projects by commercial banks in Ethiopia. This was guided by the specific objectives which were to examine the influence of project procurement management, project scope management, project management methodology and executive/top management commitment on implementation of information technology projects and also establish the moderating effect of project risk on the factors of implementation of information technology projects by commercial banks in Ethiopia. This chapter outlines the response rate, assesses the reliability and validity of the research instrument, it indicates the demographics of the respondents, it details both descriptive and inferential statistics and also shows the research findings and discussions. The researcher distributed two hundred and five (205) questionnaires out of which one hundred and ninety (190) were filled which represented 92% of the total questionnaires distributed. According to Kothari (2014), 50% response rate is considered average, 60% to 70% is considered adequate while anything above 70% is considered to be an excellent response rate. Bell (2014) indicated that for a social science study, anything above 60% response rate is adequate for making significant conclusions. Attaining 92% was therefore an excellent response rate which could be attributed to the data collection procedure employed whereby the researcher personally administered the questionnaires through drop and pick method and made follow up calls to clarify on any queries as well as to prompt the respondents to fill the questionnaires. The personal involvement of the researcher therefore contributed to the high response rate. Fig 4. 1: Response Rate Source: SPSS output of the survey, 2021 4.2. Demographics Information 4.2.1 Gender distribution The descriptive statistics of the study indicated that 130 respondents were male representing 68.4% while 60 respondents were female representing 31.6%. This is an indication that the study involves more males. However, since both genders are represented in the sample, the study will not suffer from gender biases. This implies that more women are now emerging as user support staff and liaison officers with technical skills necessary for effective implementation of IT projects (Eason, 2014). The gender distribution is shown in Table 4.1; Gender Frequency Percentage 130 68.4 60 31.6 190 100 Male Female Total Table 4.1: Gender of respondents Source: SPSS output of the survey,2021 Fig 4.2: Gender of the respondents Source: SPSS output of the survey,2021 4.2.3 Age distribution Table 4.2 indicates that majority of the respondents were aged between 20 and 30 years constituting 41.6% of total respondents. Respondents between 30 and 40 years constituting 26.8%. Respondents between 40 and 50 years constituting 18.9% of total respondents whereas those above 50 years constituted 12.6%. The statistics are a confirmation that majority of bank workers are youthful with those above 50 years being continually eased out either by natural attrition or by being incentivized to take voluntary early retirement. Younger staff are perceived to have better technical competencies and pursue change initiatives that come with implementation of new IT projects (Kwahk & Kim, 2008). Age 20-30 31-40 41-50 Frequency Percentage Above 50 79 51 36 24 190 41.6 28.6 18.9 12.6 100 Total Table 4.2: Distribution of age in years Source: SPSS output of the survey,2021 4.1.3 Education Level of the Respondents As shown by Table 4.3, from the research findings, majority of the respondents (58.4%) hold bachelor’s degree, certificate level with 0.5%, whereas 41.1% of the respondents indicated their level of education as masters and above. This indicates that the respondents were educated well enough to understand the questions and thus would give credible results. Education Frequency Certificate 1 Degree 111 Master 78 190 Total Table 4.3: Educational level of the respondents Percentage .5 58.4 41.1 100.0 Source: SPSS output of the survey,2021 4.1.4. Employee’s Job Experience As shown by Table 4.4, 40% have served the organization for 6 to 10 years, 24.7% of the respondents indicated that they have served the company for less than five years, 25.8 % indicated to have served in the organization for eleven to fifteen years, 6.3 % indicated to have served in the organization for sixteen to twenty years, whereas 3.2% of the respondents indicated to have served for greater than twenty-one years. This implies that the majority of the respondents in selected Ethiopian commercial banks, had worked for a considerable period of time and therefore they were in a position to give credible information relating to this study. Experience Frequency ≤5 47 6-10 76 11-15 49 16-20 12 21 and above 6 190 Total Table 4.4: Employee’s job experience Percent 24.7 40.0 25.8 6.3 3.2 100.0 Source: SPSS output of the survey,2021 4.2.4 Respondent’s Department Table 4.3 shows the functional positions held by the respondents in their respective workplaces. There was a near even distribution of respondents amongst IT experts (22.1%), procurement management teams (16.8%), business team (6.8%), finance team (16.8%), project management office (11.6%), HR expert (9.5%) and R&D (16.3%). This is due to their routine involvement in active implementation of IT projects unlike Business Relationship Managers whose role was mostly interfacing IT and business relationship management, advisory and user acceptance testing. The wide array of participants constitutes a critical mass that understands the overall needs of their institutions and can guide in achieving success of IT projects (Jain & Metkewar, 2016). Department IT Procurement management Business team Finance Project office HR expert R&D Total Frequency Percentage 42 32 22.1 16.8 13 6.8 32 22 18 31 190 16.8 11.6 9.5 16.3 100.0 Table 4.5: Functional departments of the respondents Source: SPSS output of the survey,2021 4.2.6. Respondent’s Position As it can be seen from the Table 4.6, 38.5 % of the respondents are experts (staff), 19.2% of the respondents were officers, 11.5 % of the respondents were (chef officers, Supervisors and domain experts) and 7.7% of the respondents were supervisors since these are the peoples who actually involved in ERP project implementation, they are expected to give the reality in the current status of ERP project. So, we can conclude that the responses were worthwhile since all the relevant respondents are involved in this study. Position pJunior officer eSenior officer rChief officer V Director aManager lProject imanager dstaff Total Frequency 94 21 13 8 13 13 percentage 28 190 49.5 11.1 6.8 4.2 6.8 6.8 14.7 100.0 Table 4.5: Positions of the respondents Source: SPSS output of the survey,2021 4.3 Reliability and Validity of the Research Instrument 4.3.1 Reliability Analysis Cronbach’s coefficient alpha was used to determine the internal reliability of the questionnaire employed in this study. The alpha values range between 0 and 1.0 whereby while 1.0 indicates perfect reliability, the value 0.70 is deemed to be the lower level of acceptability (Tavakol et al., 2014). The reliability values for each of the variables are presented in Table 4.4 where it is evident that Cronbach’s alpha values for each of the variables were well above the lower limit of acceptability of 0.70. The findings indicated that executive commitment had a coefficient of 0.730, user involvement had a coefficient of 0.777, project team capability had a coefficient of 0.717, project management approach had a coefficient of 0.773, project risk had a coefficient of 0.707 and implementation of information technology projects garnered a coefficient of 0.756. These results therefore indicated that the questionnaire used in this study had a high level of reliability. No Variable 1 2 PPM 3 PSM PMM 4 5 6 NO of Items 9 9 Alpha Value .827 .805 Very good Very good 9 .787 Good EC PR ITPI 8 6 9 .901 .814 .914 Excellent Very good Excellent Total Cronbach's alpha 50 .899 Very good Table 3 4: Reliability Statistics/Cronbach's Alpha Source: Respondents Survey Test Result, 2021. 4.3.2 Validity Validity is the degree to which the results obtained from the analysis of the data actually represents the phenomenon under study (Mugenda et al., 2012). Face validity was carried out through relevant literature review and peer review, which included use of accepted methods and standards that were adopted in other relevant studies. To ensure content and construct validity, the preliminary questionnaire was pre-tested with a sample of respondents drawn from the relevant cadres of staff who were well versed with information technology projects, but who would eventually not be part of the sample. Also, survey items were extracted from existing project management theory and use of tested instruments where available. A 95% response rate was realized in the pilot data collection and feedback received was incorporated in the final questionnaire hence improving it and was found to be adequate for final data collection. 4.4 Descriptive Statistics The purpose of this study was to analyze the factors of implementation of information technology projects by commercial banks in Ethiopia. This section tries to assess the relationship between each of the independent variables (project procurement management, project scope management, project management methodology and executive/top management commitment) to the dependent variable of implementation of IT projects. Frequencies and descriptive statistics are presented first, followed by qualitative analysis then inferential statistics. Questionnaire responses were based on a Likert scale which was aptly coded with numerical values for ease of data analysis. The values assigned were 1 = strongly disagree, 2 = disagree, 3 = neither agree nor disagree, 4 = agree, then 5 = strongly agree. 4.4.1 Project procurement management and Implementation of IT projects Procurement management is a way to more efficiently and productively handle the process of sourcing, requisitioning, ordering, expediting, inspecting and reconciliating of procurement for successful implementation of projects. Project procurement management is one of the factors that expected to be a major factor to examine the implementation of IT projects. Nine statements were presented for respondents to rate them on a Likert scale. The following table presents the responses in each statement and the average reaction of respondents in all of the statements. The average result is found by computing the responses in each statement. N St. Deviation Mean Strongly Agree Agree Statements Neutral Strongly disagree Disagree Table 4. 7 Responses Related to Project procurement management N 1.1 The procedure for obtaining the goods or services required is well defined. 20 % 66 12 38.4 10 34.7 6.3 17 76 16 72 8.9 40 8.4 37.9 4.7 17 68 25 8.9 35.8 13.2 36.3 5.8 10 79 5.3 41.6 19.5 28.4 5.3 17 76 23 8.9 40 12.1 34.7 4.2 16 76 27 8.4 40 14.2 33.7 3.7 N 29 60 26 % 15.3 31.6 13.7 34.7 4.7 N 17 47 % 1.3 Payment to the supplier is made once the contract's N payment criteria have been met and the work has progressed. % 1.4 Inspection and audit are performed to ensure that N the seller's work processes or deliverables are in conformity. % 1.5 For project procurements, a procurement strategy N is produced that specifies what will be procured, the specifications required, and the acquisition timeframe. % 1.6 The make or buy analysis is used to decide if a N project's work should be completed by the project team or purchased from a third party. % 1.7 Market research is conducted and used to inform procurement decisions. 19 10.5 1.2 In the procurement policy, there is a well-defined N formal evaluation review process. 73 37 43 69 54 66 64 66 73 9 11 10 8 7 9 10 2.8789 1.18236 2.8947 1.14987 190 190 2.9421 1.14634 190 2.8684 1.05337 190 2.8526 1.12187 190 2.8421 1.09656 2.8211 1.19946 3.0632 1.09651 190 190 190 1.8 By mutual consent and negotiation, the buyer and seller can alter the procurement contract at any moment. % 1.9 A uniform procurement contract close-out process N is used on all projects. % * Source: SPSS output of the survey, 2021 8.9 31 16.3 24.7 22.6 38.4 5.3 51 40 65 3 26.8 21.1 34.2 1.6 2.7789 1.13323 190 As we can see from table 4.7, all of the respondents were disagreeing responses for the nine different questions in relation to project procurement management. The respondents were asked about the procedure for obtaining the goods or services required is well defined to implement the projects 38.4% of the respondents with the mean average result of 2.8 were disagree, when asked in the procurement policy, there is a well-defined formal evaluation review process 40% of the respondents with the mean average result of 2.9 were disagree, when asked Payment to the supplier is made once the contract's payment criteria have been met and the work has progressed, 36.3% of the respondents with the mean average result of 2.94 were agree, when asked inspection and audit are performed to ensure that the seller's work processes or deliverables are in conformity 41.6% of the respondents with the mean average result of 2.86 were disagree, when asked for project procurements, a procurement strategy is produced that specifies what will be procured, the specifications required, and the acquisition timeframe 40% of the respondents with the mean average result of 2.85 were disagree, when asked the make or buy analysis is used to decide if a project's work should be completed by the project team or purchased from a third party 40% of the respondents with the mean average result of 2.84 were disagree, when asked market research is conducted and used to inform procurement decisions, 34.7% of the respondents with the mean average result of 2.82 were agree, when asked by mutual consent and negotiation, the buyer and seller can alter the procurement contract at any moment, 38.4% of the respondents with the mean average result of 3.06 were agree, when finally the respondent asked that a uniform procurement contract close-out process is used on all projects 34.2% of the respondents with the mean average result of 2.86 agree. Even though the project procurement management is important in implementation of IT projects, the average responses of the respondent show disagree level of agreement, this implies that the majority of the respondent indifference on project procurement management. This means that the selected commercial banks are facing project procurement challenges during implementation process. Thus, the selected commercial banks should pay more attention to project procurement management in order to realize the benefits after the implementation of the IT project. 4.4.2 Project scope management and Implementation of IT projects Project scope management is a process that helps in determining and documenting the list of all the project goals, tasks, deliverables, deadlines, and budgets as a part of the planning process. The same way as it is done to project scope management nine statements that can measure project management issues were posed to the respondents. Almost all statements provided to each respondent being responded as a disagree level of agreement, but it was expected that this variable as being a major factor in examining the implementation of IT projects. The results of the respondents’ responses were compiled and presented in the following table. The table presents the responses in each statement and the average reaction of respondents in all of the statements. N St. Deviation Mean Strongly Agree Agree Statements Neutral Strongly disagree Disagree Table 4. 8 Responses Related to Project scope management N 2.1There were tools and procedures for establishing 14 scope that were well-defined. % 2.2 The objectives are set in accordance with the N customer's requirements. % 2.3 In order to manage scope, the necessary tools and procedures were used. 2.4 The projects were managed using the typical project management process of initiating, planning, executing, monitoring, and evaluating and closing. 81 17 67 11 2.8947 1.14063 190 7.4 42.6 8.9 35.3 5.8 26 74 13.7 38.9 11.1 31.6 4.7 N % 26 13.7 72 18 61 13 37.9 9.5 32.1 6.8 2.8053 1.22106 190 N 28 51 2.9789 1.24279 190 % 14.7 26.8 11.1 40.5 2.5 Stakeholder conflict over scope was avoided using N several ways. % 35 67 35 45 8 35.3 18.4 23.7 4.2 2.6000 1.15836 190 N % N % 21 11 5.8 9 4.7 2.8895 1.18786 13.2 71 17 37.4 8.9 69 26 36.2 13.7 29 15.3 23 40 30 74 17 21.1 15.8 38.9 8.9 77 34 54 2 3.0526 1.25457 190 2.6579 1.05098 190 12.1 40.5 17.9 28.4 1.1 2.6 The primary operations that must be completed and the intended end result are clearly specified. 2.7 The project had clear goals 2.8 There is a custom of confronting difficulties with a N skewed scope. % 2.9 The PMBOK (project management body of N knowledge) and CPM (project management methodology) were used in this project (critical path method) % * Source: SPSS output of the survey, 2021 18.4 11.1 25 21 21 60 77 70 36.8 61 32.1 9 13 2.7474 1.17710 190 6.8 2.7895 1.16741 190 190 The respondents were asked about if there were tools and procedures for establishing scope that were well-defined 42.6% of the respondents with the mean average result of 2.89 were disagree, when asked if the objectives are set in accordance with the customer's requirements 38.9% of the respondents with the mean average result of 2.74 were disagree, when asked in order to manage scope, the necessary tools and procedures were used 37.9% of the respondents with the mean average result of 2.80 were disagree, when asked if the projects were managed using the typical project management process of initiating, planning, executing, monitoring, and evaluating and closing 40.5% of the respondents with the mean average result of 2.97 were agree, when asked if Stakeholder conflict over scope was avoided using several ways 35.3% of the respondents with the mean average result of 2.60 were disagree, when asked if the primary operations that must be completed and the intended end result are clearly specified 37.4% of the respondents with the mean average result of 2.88 were disagree, when asked if the project had clear goals 36.2% of the respondents with the mean average result of 2.78 were disagree, when asked if there is a custom of confronting difficulties with a skewed scope 38.9% of the respondents with the mean average result of 3.05 were agree when finally the respondent asked that the PMBOK and CPM were used in this project 40.5% of the respondents with the mean average result of 2.65 disagree. Even though the execution of project scope management was important in implementation of IT projects, the average responses of the respondents were disagreed level of agreement, this implies that majority of the respondent’s indifference with project scope management. Thus, the selected banks should pay more attention to project PSM in order to realize the benefits after the implementation of the IT projects. 4.4.3 Project management methodology and Implementation of IT projects Nine questions were presented to the respondents to assess their level of agreement towards the project management methodology. The following table presents the summarized result of the questions. N St. Deviation Mean Strongly Agree Agree Statements Neutral Strongly disagree Disagree Table 4. 8 Responses Related to Project management methodology N 3.1 When it comes to project implementation, the bank 26 frequently uses project management technique. % 3.2 The bank employs a project management N technique that it created in-house. % 3.3 The bank uses a defined project management technique to tackle projects. N % 63 27 62 12 2.8474 1.20091 190 13.7 33.2 14.2 32.6 6.3 23 74 12.1 38.9 16.8 27.9 4.2 26 13.7 78 32 50 4 41.1 16.8 26.3 2.1 2.6211 1.08065 190 3.4 The bank employs a bespoke project management N technique at all times. % 24 67 36 55 8 35.3 18.9 28.9 4.2 2.7684 1.12643 190 N % 24 71 22 65 8 37.4 11.6 34.2 4.2 2.8000 1.16474 190 N 3.6 Throughout the lifecycle of a project, the bank uses agile project management technique. % 3.7 In most cases, the bank follows a well-documented N traditional project management technique. % 3.8 Project managers are expected to adapt the project N management approach to the unique characteristics of each project and the surrounding environment. % 3.9 The tools and approaches advocated in your bank's N IT strategic emphasis are employed to complete IT initiatives % * Source: SPSS output of the survey, 2021 33 8 4.2 20 10.5 2.5842 1.16871 14 7.4 78 22 49 41.1 11.6 25.8 63 27 66 33.2 14.2 34.7 13 48 29 3.2316 1.18592 190 6.8 41 25.3 22.1 31.6 15.3 74 9 49 17 2.6158 1.31526 190 21.6 38.9 4.7 25.8 8.9 3.5 Processes linked with agile project management approach are always embraced by the bank. 12.6 12.6 17.4 32 40 53 60 8 190 2.7316 1.12054 3.0789 1.18146 190 190 As we can see from table 4.3, the respondents were asked about when it comes to project implementation, the bank frequently uses project management technique, 33.2% disagreed, when asked if the bank employs a project management technique that it created in-house 38.9% disagreed, when asked if the bank uses a defined project management technique to tackle projects, 41.1% disagreed, when asked if the bank employs a bespoke project management technique at all times, 35.3 disagreed, when asked if Processes were linked with agile project management approach are always embraced by the bank, 37.4 % disagreed, when asked if throughout the lifecycle of a project, the bank uses agile project management technique, 41.1% disagreed, when asked if in most cases, the bank follows a well-documented traditional project management technique, 34.7% agreed, when asked if project managers are expected to adapt the project management approach to the unique characteristics of each project and the surrounding environment 31.6% agreed, when finally the respondent were asked the tools and approaches advocated in your bank's IT strategic emphasis are employed to complete IT initiatives 38.9% disagreed. From the response, a number of respondents seemed unsure of the project management methodologies used in their respective banks since there are many methodologies and the respondents were not in a position to comfortably delineate the particular methodologies that were in use. Table 4. 8 Responses Related to Executive/Top management commitment and implementation of IT projects For the aim of this research, the executive commitment refers to the provision of necessary financial and other resources, establishing policies and procedures, delegating implementation authority, taking risk and responsibilities during implementation of IT projects. The following table presents the responses in each statement and the average reaction of respondents in all of the statements. N St. Deviation Mean Strongly Agree Agree Statements Neutral Strongly disagree Disagree Table 4. 8 Responses Related to Executive commitment N 4.1 Upper-level management of the bank routinely 38 backs projects and provides resources. % 4.2 When it comes to participation in industry N 71 11 57 13 20 37.4 5.8 30 6.8 34 73 12 17.9 38.4 4.7 32.6 6.3 9 62 2.6632 1.28162 2.7105 1.26633 190 190 regulator efforts, the bank has a flexible corporate culture. % 4.3 When new projects need to be implemented, the bank always welcomes change management. 4.4 The executive management of the organization creates policies and processes to monitor project implementation. 4.5 The bank's planning, monitoring, and controlling methods have consistently ensured project efficiency and effectiveness. 4.6 The bank's leadership has a reputation for being willing to take calculated risks while implementing projects. 4.7 For projects, the bank regularly provides a defined vision, goal, and objectives. N % 40 21.1 70 20 46 14 36.8 10.5 24.2 7.4 2.6000 1.26324 190 N 33 74 2.6105 1.18003 190 % 17.4 38.9 14.2 24.2 5.3 N 32 73 2.6895 1.23588 190 % 16.8 38.4 11.1 26.3 7.4 N 29 80 % N % 15.3 42.1 15.3 23.7 4.2 81 25 56 6 42.6 13.2 29.5 3.2 22 11.6 27 21 29 46 50 45 10 14 7 2.5842 1.11780 2.7000 1.10769 190 190 4.8 The team members are rewarded by the executive N management. % * Source: SPSS output of the survey, 2021 44 23.2 74 16 45 11 38.9 8.4 23.7 5.8 2.5000 1.24191 190 The respondents were asked if upper-level management of the bank routinely backs projects and provides resources, 37.4% of the respondents with the mean average result of 2.66 were disagree, when asked when it comes to participation in industry regulator efforts, the bank has a flexible corporate culture 38.4% of the respondents with the mean average result of 2.71 were disagree, when asked when new projects need to be implemented, the bank always welcomes change management 36.8% of the respondents with the mean average result of 2.6 were disagree, when asked the executive management of the organization creates policies and processes to monitor project implementation 38.9% of the respondents with the mean average result of 2.61 disagree, when asked the bank's planning, monitoring, and controlling methods have consistently ensured project efficiency and effectiveness 38.4% of the respondents with the mean average result of 2.68 were disagree, when asked the bank's leadership has a reputation for being willing to take calculated risks while implementing projects 42.1% of the respondents with the mean average result of 2.58 were disagree, when asked for projects, the bank regularly provides a defined vision, goal, and objectives 42.6% of the respondents with the mean average result of 2.7 were disagree when finally the respondent asked that the team members are rewarded by the executive management 38.6% of the respondents with the mean average result of 2.5 were disagree. Even though the executive commitment is important in implementation of IT projects, the average responses of the respondent show disagreed level of agreement, this implies that the majority of the respondent indifference on top executive commitment. This means that the executive management has not played an instrumental role in the implementation process. Thus, the organization should pay more attention to executive commitment in order to realize the benefits after the implementation of IT projects. 4.4.5 Project risk and Implementation of IT projects The study sought to determine the moderating effect of project risk on the determinants of implementation of information technology projects by commercial banks in Ethiopia. The following table presents the responses in each statement and the average reaction of respondents in all of the statements. N St. Deviation Mean Strongly Agree Agree Statements Neutral Strongly disagree Disagree Table 4. 8 Responses Related to project risk N 5.1 Technical complexity has a significant impact on 13 project implementation % 5.2 The relative project size has a significant impact N on project implementation. % 5.3 The urgency with which a project must be completed has a significant impact on project implementation. 5.4 The degree to which a project is vital has a significant impact on its execution. 30 22 103 22 6.8 15.8 11.6 54.2 11.6 8 36 4.2 18.9 8.4 57.4 11.1 N 8 37 % 4.2 19.5 7.9 60.5 7.9 N % 14 16 15 109 21 115 15 3.4789 1.10174 3.5211 1.05263 190 190 3.4842 1.02728 190 3.3895 1.15282 190 7.4 41 12 103 20 21.6 6.3 54.2 10.5 5.5 Project implementation is frequently hampered by N specification revisions. % 8 4.2 35 20 117 10 18.4 10.5 61.6 5.3 3.4526 .98956 190 5.6 Project implementation is frequently hampered by N technological uncertainty. % 15 7.9 29 17 117 13 15.3 8.9 61.1 6.1 3.4368 1.08072 190 * Source: SPSS output of the survey, 2021 Among the questions posed to the respondents on the subsets of project risk was whether technical complexity of IT projects had a marked effect on their implementation and 54.2% representing 103 out of 190 respondents agreed. This question returned a mean score of 3.47. 57.4% representing 109 out of 190 agreed on relative project size has a significant impact on project implementation. This question returned a mean score of 3.52. 60.5% representing 115 out of 190 agreed on the urgency with which a project must be completed has a significant impact on project implementation. This question returned a mean score of 3.48. 54.2% representing 103 out of 190 agreed on the degree to which a project is vital has a significant impact on its execution. This question returned a mean score of 3.38. 61.6 % representing 117 out of 19 agreed on the project implementation is frequently hampered by specification revisions. This question returned a mean score of 3.45. 61.1% representing 117 out of 190 agreed on the project implementation is frequently hampered by technological uncertainty. This question returned a mean score of 3.43. 4.4.6 Summary of survey results for the dependent variable Nine statements were presented to respondents to look at different issues that can measure the dependent variable (implementation of IT projects). The following table summarizes the views of respondents with regard to implementation of IT projects. N St. Deviation Mean Strongly Agree Agree Statements Neutral Strongly disagree Disagree Table 4. 8 Responses Related to Implementation of IT projects N 6.1 The bank’s IT initiatives are completed on 37 66 25 53 19 2.6368 1.21268 190 schedule and within the budget. % 19.5 34.7 13.2 27.9 4.9 6.2 The projects were finished on time and on N 67 77 budget. % 35.3 40.5 4.7 27.9 4.2 6.3 The projects were finished on schedule and under N budget. % 56 29.5 70 7 41 16 36.8 3.7 21.6 8.4 2.4263 1.33426 190 6.4 The bank's project deployment yielded the anticipated commercial benefits. 43 22.6 72 4 58 13 37.9 2.1 30.5 6.8 2.6105 1.31169 190 29 67 10 68 16 35.3 5.3 35.8 8.4 2.8684 1.28424 190 8 4.2 3 1.6 2.5842 1.16871 6 3.2 9 4.7 2.5053 1.13970 190 2.1421 1.16647 190 N % 6.5 During the project life cycle, the scope and goals N of the project are not changed needlessly. % 15.3 9 13.5 5.8 6.6 Project deliverables usually satisfy key stakeholders in the bank. 6.7 The bank anticipated the project to meet all of its quality criteria, which it did N % N % 33 39 20.5 78 22 49 41.1 11.6 25.8 86 14 48 45.3 7.4 25.3 6.8 The initiatives were done in a way that will help them achieve their intended aim 6.9 The initiatives' planned objectives were met with great success. * Source: SPSS output of the survey, 2021 N % N % 34 17.9 64 33.7 85 18 44.7 9.5 79 12 41.6 6.3 17.4 47 24.7 26 13.7 2.1421 1.20660 2.4211 1.12291 190 190 190 As Table 4.8 shows the majority of the respondents were unsatisfied with questions related to implementation of IT projects. When the respondent asked that the bank’s IT initiatives are completed on schedule and within the budget 34.7% with an average mean of 2.63 were in disagreed level, when asked the projects were finished on time and on budget,35.3% of the respondent with an average mean of 2.14 were in agreement level of strongly disagree, when asked the projects were finished on schedule and under budget 36.8% the respondent with an average mean of 2.42 were in agreement level of disagree, when asked the bank's project deployment yielded the anticipated commercial benefits 37.9% and 22.6% of the respondent with an average mean of 2.61 were in agreement level of disagree and strongly disagree respectively , when asked during the project life cycle, the scope and goals of the project are not changed needlessly 35.8% of the respondent with an average mean of 2.86 were in agreement level of agree, when asked if project deliverables usually satisfy key stakeholders in the bank 41.1% of the respondents with an average mean of 2.58 were in agreement level of disagree , when asked the bank anticipated the project to meet all of its quality criteria, which it did 45.3% of the respondents with an average mean of 2.42 were in agreement level of disagreement, when asked if the initiatives were done in a way that will help them achieve their intended aim 44.7 of the respondents with an average mean of 2.50 were in agreement level of disagreement, when finally asked if the initiatives' planned objectives were met with great success 41.6% of the respondents with an average mean of 2.14 were in agreement level of disagree. From the responses, it can be seen that the respondent disagreed that the overall implementation of IT projects was successful and effectively implemented. From all response we can conclude that majority of the respondent believe the IT projects are facing problems during implementation. Thus, the selected commercial banks should pay more attention to the project in order to realize the benefits after the implementation of the project. 4.2.9. Summary of frequency results of each variable and Mean Score for dependent and independent variables As it is known, the mean value or score of a certain set of data is equal to the sum of all the values in the data set divided by the total number of values. In this context, the mean of the response and score of all statements for each independent variables and dependent variable is calculated and the meaning of the score is interpreted accordingly. In this research project procurement management, project scope management, project management methodology and executive commitment, were the independent variables and project risk was moderator variable and implementation of IT projects as dependent variable. Statements under this variable are scaled 1 to 5 with a meaning of 1= strongly disagree, 2=disagree, 3=neutral, 4=agree, and 5=strongly agree. To match the result of the mean score of each variable with the respondent level agreement in Likert scale and to summarize the narrative outcomes; the researcher used criterion-referenced definitions for rating scales to describe the collected data. Unlike norm-referenced scales, criterionreferenced scale measure response scores against a fixed set of criteria. 4.4.6 Aggregation of Independent Variables With independent variables having met the reliability test, items under each variable were aggregated and the average shown (mean and standard deviation). From the descriptive statistics, project procurement management had the highest rating but the second lowest variation in responses (M = 2.8825, S.D = 0.74155). Project scope management had the second highest rating with the second highest variation in responses (M = 2.8240, S.D = 0.77243). Project management methodology had the third highest rating but lowest variation in responses (M = 2.8088, S.D = 0.71381). The variable which recorded the lowest rating was executive commitment but had the highest variation in responses (M = 2.6322, S.D = 0.93357). From the given scores, executive commitment became the worst predictor followed by project management methodology. The second-best predictor was project scope management with the best predictor being project procurement management. The aggregation is shown in Table 4.10; Table 4.10: Summary of Means and Standard Deviations Variable Mean Std. Dev. Min Max Project procurement management – X1 3.2433 0.65196 1.67 5.00 Project scope management – X2 3.2649 0.73004 1.56 5.00 Project management methodology – X3 3.0667 0.64361 1.33 5.00 Executive commitment – X4 3.0461 0.76637 1 5.00 Ranked on scale: (Strongly Disagree = 0 – 1.8; Disagree = 1.8 – 2.6; Neutral = 2.6 – 3.4; Agree = 3.4 – 4.2; Strongly Agree = 4.2 – 5.0). Source; (MacEachern, 1982) Based on the Likert scale “3" means “neither agree nor disagree, while value “4” means “agree”, etc. Here in criterion-referenced scale, if value of 3 is recorded as any of the subsequent measurement, it means that level is neither high nor low, or in other words, it is in “average or medium level”. If a value of (4) is obtained, it means s “high” level. Similarly, value one (1) and five (5) mean “very low” level and “very high” level respectively while value two (2) means “low” level. Table 4. 11: Mean score result summary according to Criterion–referenced scale Variables PPM PSM Mean Score 3.24 3.26 Degree/level of agreement N N Description M PMM EC 3.06 M 3.04 PR IITP 3.54 2.47 N N A N M M H M *Source: SPSS output of the survey, 2021 As it is shown in the Table 4.11, the average mean score of the respondents for PPM, PSM, PMM, EC, PR, is 2.88, 2.82, 2.80, 2.63 and 3.46respectively towards “implementation of IT projects.” Based on criterion- referred definitions of Table 4.10, the mean of PPM, PSM PMM and EC, shown as medium, this implies that the response for those individual question to those independent variables were neutral level of agreement. Respondents’ opinion on project risk shown as high; this implies that the response for individual questions to this independent variable was respond to an agreed level and those respondents have high scored relative to this moderator variable. The grand or cumulative mean score of the dependent variable implementation of IT projects is 2.47. This implies that all and nearly all have got a neutral level of agreement which indicates that the status of implementation of IT projects in mind of employees has moderate successful implementation. 4.4.7 Qualitative Analysis This study employed both qualitative and quantitative means in obtaining data. Arising from the concept of triangulation, data was obtained from respondents through open and closed ended questions. Methodological triangulation entails combining both quantitative and qualitative data collection methods (Creswell & Creswell, 2017), based on the rationale that a single data collection method is insufficient to provide adequate and accurate research results. A wide array of open-ended questions related to the study objectives were included in the questionnaire and content analysis done by use of SPSS version 25. Jjjjjjjjjjjjjjjjjjj(((((yikeral) Ffffffff 4.5 Tests of Assumptions Having explored the independent variables through descriptive statistical analysis, the study sought to establish the relationship between these independent variables with the dependent variable. This meant a bivariate nature of the relationship between the variables had to be established. Correlation analysis was used to evaluate the strength and direction of the relationship among the variables and linear regression used to determine the nature of the relationships. The researcher applied inferential statistics to test the study hypotheses and reject or fail to reject the null hypotheses. At 5% level of significance, the null hypothesis was rejected if the p value was < 0.05. 4.5.1 Normality Tests for Variables Assumption 1 - Normality Distribution Test Multiple regressions require the independent variables to be normally distributed. Skewness and kurtosis are statistical tools that enable the researcher to check if the data is normally distributed or not. According to Smith and Wells (2006), kurtosis is defined as “property of a distribution that describes the thickness of the tails. The thickness of the tail comes from the number of scores falling at the extremes relative to the Gaussian/normal distribution” Skewness is a measure of symmetry. A distribution or data set is symmetric if it looks the same to the left and right of the center point. If the skewness and kurtosis test results of the data are within the acceptable range (-1.0 to +1.0), it can be concluded that the data is normally distributed. For this purpose and taste of normal distribution, the kurtosis and skewness results are shown in table 4.15 Table 4. 15: Normality of data distribution Descriptive Statistics N Mean Std. Deviation Skewness Kurtosis Statistic Statistic Std. Error Statistic Std. Error .466 -.060 .176 -.515 .351 PPM Statistic Statistic 190 3.26466 PSM 190 3.3023 .71523 -.306 .176 -.315 .351 PMM EC PR CEB 190 190 190 190 3.0774 3.0658 3.5884 3.1947 .65235 .65235 .71245 .64355 -.026 .090 -.805 -.266 .176 .176 .176 .176 -.431 -.644 1.015 -.610 .351 .351 .351 .351 PMPCC IITP Valid N (Listwis e) 190 3.0314 190 2.7180 190 .69275 .92283 .057 .126 .176 .176 -.511 -1.050 .351 .351 Source: SPSS output of survey questionnaire, 2021 The acceptable range for normality for both statistics is between -1.0 and + 1.0. as shown in table 4.15, all variables for both skewness and kurtosis statistics are fall in the acceptable standard of normality (-1.0 -, +01.0). Graphically this normality assumption distribution is shown below. Also, it is recommended that normality be assessed both visually using instruments like Quantile – Quantile (Q-Q) plots and through other normality tests such as Shapiro-Wilk. To test the significance of departure from normality, Q-Q plots were done and the results shown in figures 4.2, 4.3, 4.4, 4.5 and 4.6. 4.5.1 (a) Normal Q-Q plot of project procurement management Departure from normality for project procurement management was not so pronounced and the same was confirmed from the approximation of line of fit in Figure 4.2. Data was therefore near to normal distribution and could be used in regression analysis. Figure 4.2: Normal Q-Q plot of project procurement management 4.5.1 (b) Normal Q-Q plot of project scope management For the independent variable project scope management, departure from normality was also not so much as can be seen from the approximation line of fit. This was a confirmation that data was near normal in its distribution and could therefore be used in regression analysis. This is shown in Figure 4.3; Figure 4.3: Normal Q-Q plot of project scope management 4.5.1 (c) Normal Q-Q plot of Project management methodology The departure from normality for independent variable project management methodology was also not very far off the line of approximation of fit. That was a confirmation that the data was almost normally distributed and hence could be used for regression analysis. This is illustrated in Figure 4.4; Figure 4.4: Normal Q-Q plot of Project management methodology 4.5.1 (d) Normal Q-Q plot of Executive commitment For the independent variable executive commitment, departure from normality was also not so much as shown by the approximated line of fit in Figure 4.5. This confirmed that the data was near normal distribution and could therefore be used in regression analysis. Figure 4.5: Normal Q-Q plot of Executive commitment 4.5.1 (e) Normal Q-Q plot of Implementation of IT projects (Dependent variable) Data for the dependent variable, implementation of IT projects, was not far off the approximation line of fit and could therefore be used in regression analysis. This is shown in Figure 4.6; Figure 4.6: Normal Q-Q plot of Implementation of IT projects 4.5. Multicollinearity Multicollinearity refers to the situation in which the independent/predictor variables are highly correlated. When independent variables are multicollinear, there is “overlap” or sharing of predictive power. This may lead to the paradoxical effect, whereby the regression model fits the data well, but none of the predictor variables has a significant impact in predicting the dependent variable. This is because when the predictor variables are highly correlated, they share essentially the same information. Thus, together, they may explain a great deal of the dependent variables, but may not individually contribute significantly to the model. Meaning, they can be considered as one variable than two separate variables. The existence of multicollinearity can be checked using “Tolerance” and “VIF” values for each predictor variable. Tolerance values less than 0.10 and VIF (variance inflation factor) greater than 10 indicates the existence of multicollinearity (Robert, 2006). As can be seen from the table below, multicollinearity is not an issue for this current data. Table 4. 16: Multicollinearity Test Table Coefficientsa Model 1 (Constant) Collinearity Statistics Tolerance VIF PPM .524 1.910 PSM .485 2.076 PMM EC PR CEB PMPCC .487 .434 .867 .405 .316 2.055 2.305 1.154 2.469 3.163 Source: SPSS output of survey questionnaire, 2021 As it is stated above for the assumption to be met values of Variance Inflation Factor (VIF) scores must be below 10, and tolerance scores to be above 0.1; which is the case in as shown in table 4.16, the tolerance and VIF of project procurement management, project scope management, project management methodology, executive commitment, cost estimating and budget, project management and controlling capability 0.524,0.485,0.487,0.434,0.867,0.405, and 0.316 respectively. For this reason, this research model fits the requirement and collinearity is not a problem. 4.5.1 Correlation Analysis for the Linear Relationship between the Study Variables The researcher ran a correlation matrix to establish if there existed a relationship between the variables. Pearson Product Moment Correlation was used for the correlation analysis, with (r) being used to determine the linear relationship between the study variables. According to Mugenda et al., (2012), the correlation coefficient yields a statistic that ranges between -1.0 (perfect negative correlation) to 1.0 (perfect positive correlation) and it shows the magnitude of the relationship between two variables. How big the correlation coefficient value is points to a stronger association between two variables. A zero value of (r) shows that there is no association between two variables. The correlation coefficients were computed for each pair of the variables and the results shown in the correlation matrix (Table 4.12). Table 4.12: Correlation Analysis Results for Study Variables Project Pearson 1 procurement Correlation management Sig. (2-tailed) N 190 Project scope Pearson .567** 1 management Correlation Sig. (2-tailed) .000 N 190 190 Project Pearson .480** .554** 1 management Correlation methodology Sig. (2-tailed) .000 .000 N 190 190 190 ** ** Executive Pearson .516 .519 .550** 1 commitment Correlation Sig. (2-tailed) .000 .000 .000 N 190 190 190 190 ** * Project risk Pearson .205 .179 .181* .194 1 ** Correlation Sig. (2-tailed) .004 .014 .012 .007 N 190 190 190 ** ** Cost Pearson .633 .634 .607** estimating Correlation and budget Sig. (2-tailed) .000 .000 .000 N 190 190 190 ** ** Project Pearson .491 .613 .659** management Correlation and Project Sig. (2-tailed) .000 .000 .000 control N 190 190 190 capability Implementati Pearson .512** .429** .563** on of IT Correlation projects Sig. (2-tailed) .000 .000 .000 N 190 190 190 **. Correlation is significant at the 0.01 level (2-tailed). *. Correlation is significant at the 0.05 level (2-tailed). 190 190 .528 .289** 1 ** .000 .000 190 190 190 ** .726 .323 .626** 1 ** .000 190 .000 190 .000 190 190 .665 .197** .643** .677** ** .000 190 .007 190 .000 190 .000 190 The findings showed that implementation of IT projects had a high correlation with cost estimating and budgeting (r = .633, p-value < 0.001). That meant that a positive change in cost estimating and budgeting resulted in effective implementation of IT projects. Also, commercial banks that focused on executive commitment in projects recorded improved effectiveness in implementation of IT projects as indicated by a significant correlation value (r = .516, p-value < 0.001). The study findings also showed that implementation of IT projects and adoption of project management and control had a relatively significant relationship (r = .491, p-value < 0.001). It showed that banks that embraced project management and control achieved effective implementation of IT projects. Project risk being the moderating variable also showed a weak positive correlation with implementation of IT projects (r = .205, p-value <0.001). The p-values for project risk were above the criteria of α < 0.05 and therefore they were not statistically significant. Since all variables returned a positive correlation as shown in Table 4.12, they were therefore subjected to further regression analysis to determine their individual contributions. 1 4.6 Regression Analysis Results The researcher used multiple regression analysis to determine the linear statistical relationship between the independent, moderating and dependent variables of the study. The null hypotheses of the study were tested using linear regression models. F- test was used to test the validity of the model while (r2) was used to measure the model’s goodness of fit. The regression coefficient was used to describe the results of regression analysis and outline the nature and intensity of the relationships between the study variables. 4.6.1 Influence of project procurement management on Implementation of IT projects To find out the influence of project procurement management (X1) on implementation of IT projects (Y), a regression model was fitted to the data and it was found to be statistically significant (F (1, 188) = 66.797, p = .000). The co-efficient of determination (R2) of .262 was an indicator that project procurement management explained a paltry 26.2% variation in improvement of implementation of IT projects. The adjusted R2 explained 25.8% variation while the remainder could be explained by other factors not included in the model. R value of .512 indicated a strong positive correlation between project procurement management and implementation of IT projects. The hypothesis to be tested was H01: Project procurement has no significant influence on implementation of IT projects by commercial banks in Ethiopia. The survey results showed that there was a strong positive relationship between project procurement management and implementation of IT projects by commercial banks in Ethiopia (β1= .708, t = 8.173, p-value < .001). The regression model fitted to test the relationship was; Y = β0 + β1X1 + ε; where; Y = Implementation of IT projects, β0 = Constant, X1 = project procurement management, ε = Error term The null hypothesis stating that project procurement management has no significant influence on implementation of IT projects by commercial banks in Ethiopia (H01: β1= 0) was therefore rejected (β1= .708, t = 8.173, p-value <.001) and a conclusion drawn that project procurement management (X1) has a marginal influence on implementation of information technology projects by commercial banks in Ethiopia (Y). The resulting regression model was; Y = 0.407 + 0.708X1 The model equation shows that standardized implementation index of information technology projects will increase by .708 units with one unit increase in the index of project procurement management. Table 4.13: Regression analysis results on executive commitment and implementation of IT projects a) Model Summary Change Statistics Std. Error of R R Adjusted the Square Model R Square R Square Estimate Change F Change df1 df2 Sig. F Change a 1 .512 .262 .258 .79480 .262 66.797 1 188 .000 a. Predictors: (Constant), Project procurement management b)ANOVAa Sum of Model Squares df Mean Square 1 Regression 42.196 1 42.196 Residual 118.760 188 .632 Total 160.956 189 a. Dependent Variable: implementation of IT projects b. Predictors: (Constant), Project procurement management F 66.797 Sig. .000b c)Coefficientsa Standardize Unstandardized d 95.0% Confidence Interval Coefficients Coefficients for B Correlations Partia Par Model 1 B Std. Error (Constant) .407 .289 Project .708 .087 Beta .512 t Sig. Lower Bound Upper Bound Zero-order 1.410 .160 -.162 .976 8.173 .000 .537 .879 .512 procurement management a. Dependent Variable: implementation of IT projects Pearson product-moment correlation coefficient for project procurement management and implementation of IT projects (r =.512, p-value = .000) was significant at .05 level of significance. Regression analysis carried out showed that project procurement management had a marginal influence on implementation of information technology projects by commercial banks in Ethiopia. These results on the influence of project procurement management on implementation of information technology projects were consistent with earlier studies conducted by Cooper, Schindler, and Sun (2006) asserts that procurement suppliers, subsidiaries, logistics and manufacturing companies that affect the exchange of goods and services also face procurement challenges and this also affects effective implementation of projects. 4.6.2 Influence of Project scope management on Implementation of IT Projects In establishing the influence of project scope management (X2) on implementation of IT projects (Y), the regression model was found to be significant (F (1, 188) = 42.448, p – value 0.000), indicating that user project scope management was a valid predictor in the model. The coefficient of determination (R2) value of .184 implied that project scope management independently explained 18.4% variation in effective implementation of information technology projects. The adjusted R2 explained 18% and therefore the remainder could be explained by other factors not included in the model. The R value of .429 indicated a moderate positive correlation between project scope management and implementation of IT projects. The hypothesis to be tested was H02: project scope management has no significant influence on implementation of information technology projects by commercial banks in Ethiopia. l .512 t .51 2 The survey results showed that there was a positive relationship between project scope management and implementation of information technology projects by commercial banks in Ethiopia (β2 = .554, t = 6.515, p-value < 0.001). The regression model fitted to test the relationship was; Y = β0+ β2X2 + ε. The null hypothesis H02: project scope management has no significant influence on implementation of information technology projects by commercial banks in Ethiopia was therefore rejected (β2 = .554, t = 6.515, p-value < 0.001) and a conclusion drawn that: project scope management (X2) moderately influenced implementation of information technology projects (Y). The model equation was; Y = .889 + .554X2 where; Y = Implementation of IT projects and X2 = project scope management. The beta coefficient for project scope management was significant (β2 = .554, t = 6.515, p-value < 0.001), implying that for every single unit increase in the index of project scope management, there is an improvement index of .554 in effectiveness of information technology project implementation as shown in Table 4.14. Table 4.14: Regression results for the relationship between user involvement and implementation of IT projects. a) Model Summary Change Statistics Std. Error Mod R Adjusted of the R Square F el R Square R Square Estimate Change Change df1 df2 a 1 .429 .184 .180 .83573 .184 42.448 1 188 a. Predictors: (Constant), Project scope management b) ANOVAa Sum of Squares Model 1 Regression df Mean Square 29.648 1 29.648 Residual 131.309 188 .698 Total 160.956 189 a. Dependent Variable: Implementation of IT projects b. Predictors: (Constant), Project scope management F 42.448 Sig. .000b Sig. F Change .000 C)Coefficientsa Unstandardized Standardized Coefficients Coefficients Model B Std. Error 1 (Constant) .889 .287 Project scope .554 .085 t Sig. Beta 95.0% Confidence Interval for B Lower Bound .429 Upper Bound 3.097 .002 .323 1.456 6.515 .000 .386 .721 Correlations Zero- Part order ial Part .42 .429 .429 management a. Dependent Variable: Implementation of IT projects Similarly, findings of this study are consistent with Dekkers & Forselius (2007), Increase IT project success with concrete scope management, presents that through concrete scope management processes, IT project managers can learn and embrace proven approaches that measure the size of software projects, streamline the requirements articulation and management, and impose solid change management controls, to keep projects on time and on budget. 4.6.3 Influence of Project Management methodology on Implementation of IT projects As shown in Table 4.16, the regression model of project management methodology (X3) and implementation of information technology projects was significant (F (1, 188) = 87.034, p-value < 0.001), confirming that project management methodology was a valid predictor in the model. The coefficient of determination (R2) was .316 implying that 31.6% improvement in effective implementation of information technology projects could be explained by project management methodology adopted. Adjusted R2 was .313 meaning 31.3% was explained by project management methodology and the rest could be attributed to other factors not captured in the model. R score of .563 indicated a moderate positive correlation between project management methodology and implementation of information technology projects. The standard error of .765 indicated the deviation from the line of best fit. The hypothesis to be tested was H03: Project management methodology has no significant influence on implementation of information technology projects by Ethiopian commercial banks. The survey results showed that there was a positive relationship between project management approach and implementation of information technology projects (β3 = 9 .796, t = 9.329, p-value < 0.001). In order to test the relationship, the regression model fitted was Y = β0 + β4X4+ ε; The null hypothesis H03 stating that project management methodology has no significant influence on implementation of information technology projects by commercial banks in Ethiopia (H03: β3= 0) was therefore rejected (β3 =.796, t = 9.329, p-value < 0.001) and a conclusion drawn that indeed project management methodology (X3) influences implementation of IT projects (Y). The resulting regression model was; Y = 2.69 + .796X3; where; Y = Implementation of IT projects and X3 = Project management approach The beta coefficient for project management approach was significant (β4 = .284, t = 4.410, p-value < 0.001), implying that for every one unit varying in the index of project management methodology, there was a .796 index improvement in effectiveness of implementation of IT projects. Table 4.16: Regression results for the relationship between project management methodology and implementation of IT projects Model Summary Mod el R Adjusted Square R Square R a 1 .563 .316 Std. Error of the Estimate .313 Change Statistics R Square Change .76500 .316 F Change df1 87.034 df2 1 188 a. Predictors: (Constant), Project management methodology ANOVAa Sum of Squares Model 1 Regression df Mean Square 50.934 1 50.934 Residual 110.022 188 .585 Total 160.956 189 a. Dependent Variable: Implementation of IT projects b. Predictors: (Constant), Project management methodology F 87.034 Sig. .000b Sig. F Change .000 Coefficientsa Unstandardize Standardized 95.0% Confidence d Coefficients Coefficients Interval for B Std. Model 1 B Error Correlations Lower Beta t Sig. Bound Par Upper Bound (Constant) .269 .268 1.003 .317 -.260 .798 Project .796 .085 .563 9.329 .000 .628 .964 Zero-order .563 Partial .563 .56 management 3 methodology a. Dependent Variable: Implementation of IT projects Similarly, this study’s findings concur with Mohabuth’s (2017) study of 720 information technology projects that found that the use of an inappropriate methodology is actually the most critical risk driver for sub-optimal project implementation. Therefore, matching the project type and information technology project methodology would be expected to enhance chances of effective implementation of information technology projects by Ethiopian commercial banks. Špundak (2014) researched on the issue of introducing agile methods to traditional environments and noted that larger organizations contained some unique management challenges. 4.6.4 Influence of Executive Commitment on Implementation of IT projects To find out the influence of executive commitment (X4) on implementation of IT projects (Y), a regression model was fitted to the data and it was found not to be statistically significant (F (1, 188) = 149.102, p-value< .001). The co-efficient of determination (R2) of .0442was an indicator that executive commitment explained a paltry 44.2% variation in improvement of implementation of IT projects. The adjusted R2 explained 43.9% variation while the remainder could be explained by other factors not included in the model. R value of .665 indicated a weak positive correlation between executive commitment and implementation of IT projects. The hypothesis to be tested was H04: Executive commitment has no significant influence on implementation of IT projects by commercial banks in Ethiopia. The survey results showed that there was a moderate positive relationship between executive commitment and implementation of IT projects by commercial banks in Ethiopia (β4=.771, t = 12.211, p-value <0.001). The regression model fitted to test the relationship was; Y = β0 + β4X4+ ε; t where; Y = Implementation of IT projects, β0 = Constant, X4 = Executive commitment, ε = Error term The null hypothesis stating that executive commitment has no significant influence on implementation of IT projects by commercial banks in Ethiopia (H04: β4= 0) was therefore rejected (β4= .771, t = 12.211, p-value < .001) and a conclusion drawn that executive commitment (X4) has a moderate influence on implementation of information technology projects by commercial banks in Ethiopia (Y). The resulting regression model was; Y = 0.355 + 0.771X4 The model equation shows that standardized implementation index of information technology projects will increase by .771 units with one unit increase in the index of executive commitment. Table 4.13: Regression analysis results on executive commitment and implementation of IT projects a) Model Summary Change Statistics Adjuste Std. Error Mo R dR of the R Square del R Square Square Estimate Change F Change df1 df2 a 1 .665 .442 .439 .69099 .442 149.102 1 a. Predictors: (Constant), Executive commitment Sig. F Change 188 .000 b) ANOVAa Sum of Model Squares df Mean Square 1 Regression 71.192 1 71.192 Residual 89.765 188 .477 Total 160.956 189 a. Dependent Variable: Implementation of IT projects b. Predictors: (Constant), Executive commitment F 149.102 Sig. .000b Coefficientsa Standardize Unstandardized d 95.0% Confidence Coefficients Coefficients Interval for B Model B 1 (Constant) .355 Std. Error .200 Beta t Sig. Lower Bound 1.775 .078 -.040 Correlations Upper Zero- Partia Bound order .749 l Part Executive .771 .063 .665 12.211 .000 .646 .895 .665 .665 commitment a. Dependent Variable: Implementation of IT projects These results on the influence of executive commitment on implementation of information technology projects were consistent with earlier studies by Yunis et al., (2013) who indicated that among all the subsets of executive commitment, executive management support has been suggested to be the primary determinant for effective implementation of IT projects. This is probably because executive management support drives and influences other executive commitment subsets. Imreh et al., (2011) and Mansor et al., (2011) also emphasize that no project can be completed successfully unless the project manager secures commitment from the senior management. This implies that for any effective project implementation, there is necessity for sustained executive management commitment to provide resources, authority and influence. Consistently, Dyck and Majchrzak (2012) found that executive management commitment has a positive impact on effective implementation of IT projects. 4.5.6. Project management and project control capability on implementation of IT projects To find out the influence of PMCC (X4) on implementation of IT projects (Y), a regression model was fitted to the data and it was found not to be statistically significant (F (1, 188) = 159.187, p-value< .001). The co-efficient of determination (R2) of .459was an indicator that PMCC explained a paltry 45.9% variation in improvement of implementation of IT projects. The adjusted R2 explained 45.6% variation while the remainder could be explained by other factors not included in the model. R value of .677 indicated a weak positive correlation between PMCC and implementation of IT projects. The hypothesis to be tested was H07: PMCC has no significant influence on implementation of IT projects by commercial banks in Ethiopia. The survey results showed that there was a moderate positive relationship between PMCC and implementation of IT projects by commercial banks in Ethiopia (β7=.902, t = 12.617, p-value <0.001). The regression model fitted to test the relationship was; Y = β0 + β7X7+ ε; where; Y = Implementation of IT projects, β0 = Constant, X7 = PMCC, ε = Error term .665 The null hypothesis stating that executive commitment has no significant influence on implementation of IT projects by commercial banks in Ethiopia (H07: β7= 0) was therefore rejected (β7=.902, t = 12.617, p-value <0.001) and a conclusion drawn that PMCC (X7) has a moderate influence on implementation of information technology projects by commercial banks in Ethiopia (Y). The resulting regression model was; Y = -0.16 + 0.902X7 The model equation shows that standardized implementation index of information technology projects will increase by .902 units with one unit increase in the index of executive commitment. Model Summary Std. Mo del R .677a 1 a. Change Statistics Adjuste Error of R dR the R Square Square Square Estimate Change .459 .456 .68088 Sig. F F Change .459 df1 df2 159.187 Change 1 188 .000 Predictors: (Constant), Project management and Project control capability ANOVAa Sum of Squares Model 1 df Mean Square F Regression 73.799 1 73.799 Residual 87.157 188 .464 160.956 189 Total Sig. .000b 159.187 a. Dependent Variable: Implementation of IT projects b. Predictors: (Constant), Project management and Project control capability Coefficientsa Unstandardize Standardized 95.0% Confidence d Coefficients Coefficients Interval for B Std. Model 1 (Constant) Project management B Error Beta t Lower Upper Zero- Parti Sig. Bound Bound order al Part .677 .677 -.016 .222 -.074 .941 -.455 .422 .902 .071 .677 12.617 .000 .761 1.043 and Project control capability a. Dependent Variable: Implementation of IT projects Correlations .677 This means there is a very high relationship between PMCC and implementation of IT projects where the increase (good condition) in PMCC process could also result in an increase in the success of implementation of IT projects. This is also supported by literature and empirical evidence that confirms project management and control capability is influential in the implementation of IT projects (Ngai et al., 2008; Tsai et al, 2011; Zhang et al., 2003). This finding implies that PMCC is one of the critical factors for the successful implementation of IT projects in Ethiopian commercial banks. Having effective project management and controlling capability enables organizations to plan, coordinate, and monitor various implementation activities. In addition, it also monitors activities to ensure that the stated objectives during implementation of IT projects. 4.6.3 Influence of Project cost estimation and budget on Implementation of IT projects As shown in Table 4.16, the regression model of project cost estimation and budget(X6) and implementation of information technology projects was significant (F (1, 188) = 132.709, p-value < 0.001), confirming that CEB was a valid predictor in the model. The coefficient of determination (R2) was .414 implying that 41.4% improvement in effective implementation of information technology projects could be explained by CEB adopted. Adjusted R2 was .414 meaning 41.4% was explained by CEB and the rest could be attributed to other factors not captured in the model. R score of .643 indicated a moderate positive correlation between project CEB and implementation of information technology projects. The standard error of .708 indicated the deviation from the line of best fit. The hypothesis to be tested was H06: Project CEB has no significant influence on implementation of information technology projects by Ethiopian commercial banks. The survey results showed that there was a positive relationship between project CEB and implementation of information technology projects (β6 = .922, t = 11.520, p-value < 0.001). In order to test the relationship, the regression model fitted was Y = β0 + β6X6+ ε; The null hypothesis H06 stating that project CEB has no significant influence on implementation of information technology projects by commercial banks in Ethiopia (H06: β6= 0) was therefore rejected (β6 = .922, t = 11.520, p-value < 0.001) and a conclusion drawn that indeed project CEB (X6) influences implementation of IT projects (Y). The resulting regression model was; Y = -2.29 + .922X6; where; Y = Implementation of IT projects and X6 = project cost estimation and budget the beta coefficient for project CEB was significant (β6 = .922, t = 11.520, p-value < 0.001), implying that for every one unit varying in the index of project CEB, there was a .922 index improvement in effectiveness of implementation of IT projects. Table 4.16: Regression results for the relationship between project management methodology and implementation of IT projects Model Summary Mo del R .643a 1 Change Statistics Adjuste Std. Error R dR of the R Square Square Square Estimate Change F Change df1 df2 .414 .411 .70843 .414 132.709 1 Sig. F Change 188 .000 a. Predictors: (Constant), Cost estimation and budget ANOVAa Sum of Squares Model 1 df Mean Square Regression 66.604 1 66.604 Residual 94.353 188 .502 160.956 189 Total F Sig. .000b 132.709 a. Dependent Variable: Implementation of IT projects b. Predictors: (Constant), Cost estimation and budget Coefficientsa Unstandardi 95.0% zed Standardized Confidence Coefficients Coefficients Interval for B Correlations Std. Erro Model 1 (Constant) Cost estimation B r Lower Beta t Sig. Upper Zero- Parti Bound Bound order al -.229 .261 -.877 .381 -.744 .286 .922 .080 .643 11.520 .000 .764 1.080 and budget a. Dependent Variable: Implementation of IT projects .643 .643 Part .643 These results on the influence of project cost and budget on implementation of information technology projects were consistent with earlier studies by Mir et al., (2014), for organizations running several short-term customer projects, managing project cost and budget is designed to control their resources in a given activity within the constraints of time, cost, acceptable level of performance and good customer relations. These results confirm that adequate project cost and budget have an inner feeling of loyalty and responsibility to the project. 4.6.5 Joint Influence of Independent Variables on Implementation of IT projects Multiple regression was carried out where all the variables were aggregated to assess their collinearity with implementation of IT projects. A multiple regression model was fitted to the data and it was found to be statistically significant (F (7,182) = 40.566, p-value < 0.001). The seven variables’ R2 was .609 meaning that they jointly explained 60.9% variation in effective implementation of information technology projects. The hypothesis to be tested was that the joint factors of implementation of projects have no significant influence on implementation of information technology projects by commercial banks in Ethiopia. To test the hypothesis, the following model was fitted; Y = β0 + β1X1+ β2X2 + β3X3 + β4X4 + β5X5 + β6X6 ε; where; Y = Implementation of IT projects X1 = Project procurement management X2 = Project scope management X3 = Project management methodology X4 = Executive commitment X5 =Adequate project cost estimation and budget X6 = Project management and project control capability ε= Error term (referring to other factors not captured in the study but had influence). The regression model fitted was given by; Y= – 0.718 + 0.117X1 – 0.270X2 + 0.110X3 + 0.3506X4 + 0.499X5 + 0.389X6 Under joint influence of factors, the null hypothesis stated that factors of implementation of projects have no significant influence on implementation of information technology projects by commercial banks in Ethiopia. Regression analysis results revealed that Project procurement management had a positive and significant relationship with implementation of information technology projects. We therefore reject the null hypothesis and conclude that Project procurement management (X1) has significant influence on implementation of information technology projects (Y). User involvement had a positive and significant effect on implementation of information technology projects (H02: β2 = 0), since t = 2.210, p-value =.029). We therefore reject the null hypothesis and conclude that user involvement (X2) has a significant influence on implementation of information technology projects (Y). It means a unit increase in user involvement causes 0.151 improvement in implementation of information technology projects when all the other variables are held constant. Project team capability had a positive and significant effect on implementation of information technology projects (H03: β3 = 0), since t = 2.190, p-value =.030). We reject the null hypothesis and conclude that project team capability (X3) has a significant influence on implementation of IT projects (Y). It means a unit increase in project team capability causes 0.187 improvement in implementation of information technology projects when all the other variables are held constant. Project management approach had a positive but insignificant effect on implementation of information technology projects (H04: β4 = 0), since t = 1.926, p-value =.056). We fail to reject the null hypothesis and conclude that project management approach (X4) has no significant influence on implementation of information technology projects (Y). It means a unit varying in project management approach causes 0.146 improvement in implementation of information technology projects. In the model equation, the significant terms were the constant (t = 5.913, p<0.001), user involvement (t = 2.210, p = 0.029) and project team capability (t = 2.190, p = 0.030). Therefore, the model equation implies that for one unit increase in the index of user involvement, effective implementation of IT projects improves by an index of .151 when all the other variables are held constant. Effective implementation of IT projects will improve by an index of 0.187 with a unit increase in the index of project team capability, holding other variables constant and lastly, for one unit increase in the index of project management approach, there is a corresponding improvement in implementation of IT projects by an index of 0.146, holding other variables constant. Model Summary Mo del R Adjusted Square R Square R 1 .781 a .609 Change Statistics Std. Error of the Estimate .594 R Square Change .58773 F Change .609 df1 40.566 df2 7 Sig. F Change 182 .000 a. Predictors: (Constant), Project management and Project control capability, Project risk, Project procurement management, Project management methodology, Project scope management, Executive commitment, Adequate cost estimation and budget ANOVAa Sum of Squares Model 1 Mean Square df F Regression 98.089 7 14.013 Residual 62.868 182 .345 160.956 189 Total Sig. 40.566 .000b a. Dependent Variable: Implementation of IT projects b. Predictors: (Constant), Project management and Project control capability, Project risk, Project procurement management, Project management methodology, Project scope management, Executive commitment, Adequate cost estimation and budget Coefficientsa Unstandardized Standardized Coefficients 95.0% Confidence Coefficients Interval for B Std. Model 1 (Constant) Project procurement B Error -.718 .299 .117 .089 -.270 Beta t Sig. Correlations Lower Upper Zero- Bound Bound order Partial Part -2.402 .017 -1.308 -.128 .085 1.321 .188 -.058 .292 .512 .097 .061 .086 -.209 -3.133 .002 -.440 -.100 .429 -.226 -.145 .110 .094 .077 1.167 .245 -.076 .295 .563 .086 .054 .350 .082 .302 4.288 .000 .189 .510 .665 .303 .199 -.066 .064 -.051 -1.029 .305 -.194 .061 .197 -.076 -.048 .499 .104 .348 4.781 .000 .293 .705 .643 .334 .221 .389 .110 .292 3.548 .000 .173 .606 .677 .254 .164 management Project scope management Project management methodology Executive commitment Project risk Adequate cost estimation and budget Project management and Project control capability a. Dependent Variable: Implementation of IT projects