The Relationship Between Knowledge Transfer, Team Learning, and Project Success in the Information Technology Field

Copyright © 2018 by Dixie D. O’Connell Overton, Ph.D., PMP

Library of Congress Control Number:       2018901628

ISBN:                   Hardcover                      978-1-5434-8354-3

                            Softcover                        978-1-5434-8353-6

                            eBook                              978-1-5434-8352-9

All rights reserved. No part of this book may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and retrieval system, without permission in writing from the copyright owner.

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Certain stock imagery © Getty Images.

Rev. date: 02/24/2018

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Table of Contents

List of Tables

List of Figures

Abstract

Acknowledgments

CHAPTER 1. INTRODUCTION

Introduction to the Problem

Background of the Problem

Statement of the Problem

Purpose of the Study

Significance of the Study

Research Questions and Hypotheses

Definition of Terms

Research Design

Assumptions and Limitations

Organization for Remainder of Study

CHAPTER 2. LITERATURE REVIEW

Introduction

Methods of Searching

Theoretical Orientation for the Study

Review of the Literature

Findings

Critique of Previous Research Methods

Summary

CHAPTER 3. METHODOLOGY

Introduction

Purpose of the Study

Research Questions and Hypotheses

Research Design

Target Population and Sample

Procedures

Instrument

Ethical Considerations

Summary

CHAPTER 4. RESULTS

Background

Description of the Sample

Data Analysis and Results

Hypothesis Testing

Summary

CHAPTER 5. DISCUSSION, IMPLICATIONS, RECOMMENDATIONS

Introduction

Summary of the Results

Discussion of the Results

Conclusions Based on the Results

Interpretation of the Findings

Limitations

Implications for Practice

Recommendations for Further Research

Conclusions

REFERENCES

STATEMENT OF ORIGINAL WORK

List of Tables

Table 1 Citations and Sources

Table 2 Demographic Characteristics

Table 3 Types of Data

Table 4 Age

Table 5 Project Roles

Table 6 Industry/Field in Which Project Was Conducted

Table 7 Size of Project Team

Table 8 Level of Project Complexity

Table 9 Descriptive Statistics

Table 10 Kolmogorov-Smirnov Test of Normality

Table 11 Reliability Coefficients

Table 12 Factor Score Weight Matrix

Table 13 Significance of Factor Structure

Table 14 Correlation Matrix

Table 15 Regression Weights for Research Question 1/Hypothesis 1

Table 16 Regression Weights for Research Question 2/Hypothesis 2

Table 17 Regression Weights for Research Question 3/Hypothesis 3

Table 18 Regression Weights for Research Question 4/Hypothesis 4

Table 19 Regression Weights for Research Question 5/Hypothesis 5

Table 20 Regression Weights for Research Question 6/Hypothesis 6

Table 21 Regression Weights for Research Question 7/Hypothesis 7

Table 22 Regression Weights for Research Question 8/Hypothesis 8

Table 23 Summary of Hypotheses Tested and Outcomes

Table 24 Comparison of Cronbach’s Alpha

List of Figures

Figure 1. Structural model

Figure 2. Confirmatory factor analysis

Figure 3. Path diagram for Research Question 1/Hypothesis 1

Figure 4. Path diagram for Research Question 2/Hypothesis 2

Figure 5. Path diagram for Research Question 3/Hypothesis 3

Figure 6. Path diagram for Research Question 4/Hypothesis 4

Figure 7. Path diagram for Research Question 5/Hypothesis 5

Figure 8. Path diagram for Research Question 6/Hypothesis 6

Figure 9. Path diagram for Research Question 7/Hypothesis 7

Figure 10. Path diagram for Research Question 8/Hypothesis 8

Dedication

There is nothing more appropriate than dedicating this academic milestone to the late Orville and Joyce O’Connell. No matter what you study, or how long you go to school, you never quit learning from your parents. Mom and Dad were with me when I started this journey but are only with me in spirit and memories at the completion. I feel a great sense of comfort knowing they are looking down on me from heaven with extreme pride. Thank you for molding me into the person I am today. I love you. I miss you.

Dixie O’Connell Overton

Abstract

A quantitative survey design was used to understand the relationship of the dynamic capabilities of knowledge sharing and team learning to project success controlling for information technology projects in the United States. The literature review considered the theories of organizational learning and knowledge management to understand the development of dynamic capabilities in project management. A high project failure rate has been reported for decades, and the relationship between dynamic capabilities and project success has not been well researched. The overall research question was: How does the model of knowledge transfer, team learning, and project success explain the relationship between project success and individual knowledge, knowledge articulation, and knowledge codification, controlling for the effects of information technology projects? Structural equation modeling was used to test the model and address the research questions and hypothesis. The population consisted of project professionals across industries that had worked on an information technology project in the United States in the past year. The final sample included 128 fully completed surveys. Forty-six percent of the variance in project success was explained by the final model. Knowledge articulation had the lowest correlation to cross-project learning, indicating that individual knowledge and knowledge codification are more important for cross-project learning. The data supported the model indicating that knowledge transfer activities impact both project learning and cross-project learning which contributes to project success in the IT field. Recommendations for further research include controlling for the level of project management maturity, the extent of IT infrastructure, and whether the project team members were co-located.

Acknowledgments

There are numerous people who helped me in varying ways during this journey. First, I would like to thank my mentor, Dr. Raj Singh, whose guidance, support, understanding, and encouragement was exceptional. I would also like to thank my committee members, Dr. Hung Kieu and Dr. José Nieves; their suggestions and feedback were instrumental. The Capella Dissertation Writers Retreat was extremely helpful, and I appreciate the coaching and recommendations from Dr. Rubye Braye, Dr. Edward Mason, Martha Ruddy, and Dr. Richard Shrek.

I would also like to thank Dr. Linda Edelman and Dr. Sue Newell for allowing me to use their survey instrument, without which I would not have been able to complete this research. Special thanks to Dr. Julie Conzelmann for her editing and formatting expertise. In addition, another special thank you to Dr. Harold Whitfield for his statistical prowess, which was of vital assistance in conducting SEM analysis.

I have a great deal of love and appreciation for my friend and sister, Deb Watson Lentsch, who always believed in me and was understanding when I said I didn’t have time to do fun things. Finally, I am grateful for my husband, Scott Whitney, for picking up the slack in the household chores and for sticking with me throughout this time. We were married only four months before I started this journey and are looking forward to a relationship that allows a little more time for each other.

CHAPTER 1. INTRODUCTION

Introduction to the Problem

There is a low success rate for information technology (IT) projects. The Standish Group has published the CHAOS Report every year since 1994. This report provides a snapshot of the status of IT projects in the software development realm. The 2015 report showed that the overall success rate had remained low, ranging from 27% to 31% for the last five years (Hastie & Wojewoda, 2015). In the same time period, the rate of challenged projects has varied from 49% to 56%. There has been very little change in project results, despite improvements in technology and in project management.

In addition, the failure rate of IT projects have seen limited improvements, varying only from 17% to 22% in the last 5 years (Hastie & Wojewoda, 2015; Levin, 2010). These ranges are also in agreement with the CHAOS Report published in 2001 (Lierni & Ribière, 2008), showing that no significant improvement was made in the past 15 years. Cerpa and Verner (2009) reported that projects fail for the same reasons they did 30 years ago. However, ways to increase project success have been suggested.

Effective knowledge management (KM) was shown to influence project success. Three of the top 10 factors identified by Hastie and Wojewoda (2015) that made IT projects more successful include individual knowledge, knowledge sharing, and knowledge transfer. Lack of individual and team knowledge is a risk to project performance (Lierni & Ribière, 2008); knowledge needs to be shared and successfully integrated (Levin, 2010). Gasik (2011) agreed that project knowledge management (KM) is one of the main success factors in project management. Knowledge management is particularly important for IT projects.

Greater challenges exist when the project involves a high degree of technology or is dissimilar to past projects, which is common in the IT field. Without knowledge transfer and sharing, organizations fail to continue practices that worked well, and fail to discontinue those that resulted in errors or rework, as evidenced in the findings of The Standish Group. In addition, KM is an important component of project team learning, which was identified by Jetu and Riedl (2012) as one of three main focuses that influence project success in IT projects. The goal of this study is an attempt to understand the importance of, and how, knowledge sharing can contribute to IT project success rates.

The project management field is increasing and is expanding to more industries. Between 2010 and 2020, the demand for project management professions is expected to grow 12% in the United States, according to the Project Management Gap Report (Project Management Institute [PMI], 2013). The report identified business services, manufacturing, finance and insurance, oil and gas, information services, construction, health care, and utilities as the leading project intensive industries. Projects are prioritized and initiated to meet an organization’s strategic goals (Levin, 2010). Therefore, learning how to improve success rates may benefit organizations and practitioners in many industry sectors.

All change in an organization happens through projects and programs (Project Management Institute [PMI], 2016, p. 14). The need for experienced and effective project managers can only increase as more and more changes are planned for organizations and industries. Of great importance to both scholars and practitioners is the need to identify skills and practices that increase IT project success rates.

The number of organizations that have a defined training plan and career path for project managers has remained the same since 2012 (PMI, 2016). Moreover, less than half of organizations surveyed by Project Management Institute (PMI) have a formal knowledge transfer process, which has actually decreased 5% from 2015 to 2016. Consequently, more projects are failing, resulting in monetary loss for the organization. Specifically, the report disclosed that $122 million is wasted for every $1 billion invested because of poor project performance; an increase of 12% from 2015 to 2016 (PMI, 2016).

In the early 21st century, research in project management expanded to include concepts such as organizational learning and knowledge management (Morris, 2013). One of the main success factors in project management, project knowledge management, was first mentioned in the literature in 1987 (Gasik, 2011). However, knowledge management has not received the attention as other areas in project management, such as risk management, quality management, or communications management (Gasik, 2011). The Project Management Book of Knowledge (PMBOK), the leading guidebook