Walden University

1. Ph.D Dissertation OralIntegrated Knowledge Management Framework for Addressing Information Technology Project Failures(IKMFAITPF) Walden University

2. The Problem • Synthesis of a Knowledge Management (KM) framework for minimizing the Information Technology (IT) project risks to increase project success rate, which impacts organizations positively, while inculcating knowledge sharing culture among team members. Drivers of the problem • IT projects, close to 71% (Harman, 2006; Sauer & Gemino & Reich, 2007), fail to deliver due to scope, time, quality, staff and money related problems. • Lack of applicability of existing knowledge management (KM) frameworks that can be applied directly to information technology (IT) projects (Burgess, 2005; Ermine & Boughzala & Toukara, 2006; Fichman & Kell & Tiwana, 2005; Iyer & Shankarnarayan & Wyner, 2006; Jewels & Ford, 2006; Haas, 006; Kalpic & Bernus, 2006; Kane & Pretorius & Steyn, 2005; Landaeta, 2008; Lee & Anderson, 2006; Ragsdell & Oppenhiem, 2006; Whelton & Ballard & Tommelein, 2002). • Mobile knowledge workforce and assets. • Increasing globalization and distributed teams. • Involvement of multiple cultures and interactions. • Complexity in IT projects along with fast changing industrial and organizational changes. • Lack of or poor software standards • Task complexity, project complexity, and software complexity • Flexibility of IT projects • Fluid building blocks • Knowledge assets – people, projects and process

3. The Study Research Goals • G1 - Identify and assess KM related project failures due to five major variables – scope, time, quality, money and staff. • G2 - Build integrated KM framework that addresses these failures and enhances the project success rates. • G3 - Devise a measuring system that quantifies the value add of such a system. Research Questions Question for G1 • Q1: How scope, time, quality, money and staff factors, that relate to knowledge management, impact IT projects to fail? Questions for G2 • Q2: How can these factors be addressed to improve IT project success rate by using a knowledge management framework? • Q3: How can the development of a KM framework help in increasing the rate of success of IT projects? Question for G3 • Q4: How can the development of performance management system measure the success of the framework?

4. Theoretical Framework • Basic theories by Nonaka and Takeuchi (1994) and Boisot (1998) on KM are the foundational theories for this research study – theoretical lens through which this research looks at the problem. • SECI model – Socialization, Externalization, Combination, and Internalization • I-Space – knowledge flow • Un-coded to codified • Concrete to abstract • Undiffused to diffused

5. Conceptual model of the research Research Model

6. Research Method • Qualitative Research Methodology • Multiple case study method. • Discovering the facts about an object, entity or unit of analysis – in this study – IT project about why and how of the story. • Purposeful sampling (sampling of IT projects and not people) • IT Projects implemented during 2007-2010 within financial service industry in the USA. • Projects that had worked through similar environment • Project/Software methodologies adopted • Lack of formal knowledge management infrastructure • Organizational level of maturity, Capability Maturity Model Integrated (CMMI) • Projects that were challenged (a subcategory of failed projects). • These projects were implemented successfully with additional project resources – scope, time, quality, staff and money. • Semi-structured Face-to-Face Interviews • Interviewed the project manager and the project lead for each of the five projects selected through purposeful sample method. • Questionnaire A - for purposeful sampling of the projects. • Questionnaire B – for capturing qualitative data from project managers. • Questionnaire C – for capturing qualitative data from project leads. • Transcription and coding • Interviews conducted, were audio recorded and then transcribed. • Coding of the transcripts were done based on the themes identified through qualitative data that related to KM activities and tasks. • Used NVivo version8 for coding into themes and patterns of themes.

7. Validity - Internal Conformability • Taken field notes and used to correct and minimize error in analysis. • Arranged multiple interview sessions to capture data based on the previous sessions. • Semi-structured questionnaires guided the researcher through asking questions where necessary and appropriate for data consistency. Member checking • At the end of the transcription, the researcher interacted with project managers and project leads to make sure that what was said was interpreted correctly. In few cases the researcher corrected the information, although these were found to be minor. For example project schedules, budget and how they view project success were elaborated, corrected, and interpreted. Transferability • The research was aided by semi-structured interview instrument which defined the scope of questions to be asked for all the project managers and/or project leads. • Purposeful sampling with a defined criteria for the project selection provides transparency on the research method used.

8. Validity - External Generalizability • There was no selection of people in this research, although project managers and project leads were the participants. So there were limited idiosyncratic settings within the sample. • Balanced breadth and depth of the data collected • Multiple cases were selected • Five projects were selected • Each of the interview sessions lasted for 60 to 90 minutes for project manager or project lead and spanned into multiple sessions. • Research data covered multiple aspects of project failures to cover depth required. Dependability • Used negative cases • Utilized multiple interview sessions and rephrased the same questions where appropriate. • Examined themes at the end of initial analysis and made sure that the there was no disconfirming evidence.

9. Reliability • Multiple case - Five projects and each project constituted a single case in this qualitative case study research. • Multiple organizations were involved from where the projects were sampled. • Multiple listening's of the tapes with help of digital recordings, and with flexibility of variations in the speed of play of the audio recordings. • Multiple transcriptions (four times) of audio recorded files in phases with reexamination the transcriptions.

10. Results - 1 • Research Question 1: How do scope, time, quality, and staff factors relate to KM influence IT project failure? • Research Question 2: How can these factors be addressed through a knowledge management framework to improve the success rate of IT projects? • Project 1 – quality, scope, time, money and staff • Project 2 – staff, quality, time, scope, and money • Project 3 – scope, time, quality, and money • Project 5 – time, quality, staff, and time • Project 6 – money, staff, time, scope, and quality • Patterns identified from the data • Knowledge Area (KA) • Knowledge Producer (KP) • Knowledge Consumer (KC) • Knowledge Base (KB) • Knowledge Flow (KF) • Knowledge Inhibitors (KI) • Knowledge Accelerators (KA) • Knowledge Distribution (KD) • Un-codified and Un-abstracted Knowledge (UUK) • Training and Learning (TA)

11. Results - 2

12. Results - 3

13. Results - 4

14. Results - 5

15. Data Analysis - 1 • Research Question 3: How can the development of a KM framework help to improve the success of IT projects?

16. The Framework

17. Data Analysis - 3 • Research Question 4: How can the development of a performance-management system be used to measure the success of the KM framework?

18. Measuring Mechanism for Integrated Knowledge Management Framework for Addressing Information-Technology Project Failures

19. Measuring the Framework–Project Benefit and Overall Benefits

20. Summary of Findings and Conclusions - 1 • Teams worked in predominantly virtual environments. • Inferior knowledge flow. • Inhibiting factors of knowledge flow identified • No significant knowledge accelerating factors recognized. • Documentation was not sufficient and/or did not meet the project teams’ needs. • The integrated KM framework addresses these through increasing acceleration factors for knowledge flow and decreasing inhibitors. • Formalizing knowledge and structuring knowledge allows teams to interact efficiently and share knowledge, via knowledge base, effectively in virtual environments. • The return on investment (ROI) on the investment for the framework allows the project leaders to assess value in implementing it.

21. Summary of Findings and Conclusions - 2 • Identify and divide knowledge specific areas into learning objectives that are measurable. • Rate the knowledge articles based the value addition to the project teams and reward the members of the teams who produced the knowledge. • Evaluate the overall project benefit through the measuring systems prescribed. • The value addition in incremental as more projects follow through the framework the more knowledge artifacts get collected and the more knowledge equity for the organization. • The entire framework should be considered as a process and must be treated just like any other process within the project development environment.

22. Summary of Findings and Conclusions - 3 • The framework requires a onetime investment on the KM infrastructure. • Project managers should assume responsibility for implementing the framework for their projects. • While the project leaders have an option on how much they want to invest in the KM framework in setting time for KM activities, the recommendation of at least 5% buffer time should be allowed to trigger formal knowledge flow. • The framework indirectly creates knowledge sharing culture and allows this culture to grow into a standard and is a continuous process. • The knowledge culture gets embedded in people minds and accelerates the habit in others while working with them.

23. Future Research Potential • Repeat the research in other industries such as healthcare, hospitals, pharmaceuticals, automobile, and education and compare the results. • Identify the primary KM component(s) for each industry. • Identify inhibiting and accelerating factors or lack there of. • The measuring method prescribed deserves its own research and ample research opportunities in that area. • Contribute to benchmarking KM measurements.

24. References - 1 • Nonaka, I. (1990). Redundant, overlapping organizations: A Japanese approach to managing the innovation process,. California Management Review, 32(3), 27-38. • Nonaka, I. (1994). A dynamic theory of organizational knowledge creation. Organization Science, 5(1). • Nonaka, I., & Takeuchi, H. (1995). The knowledge creating company: how Japanese companies create the dynamics of innovation. New York, NY: Oxford University Press. • Boisot, M. H. (1998). Knowledge assets: Securing competitive advantage in information economy: Oxford, England: Oxford University Press. • Burgess, D. (2005). What motivates employees to transfer knowledge outside their work unit. Journal of Business Communication, 42(4), 324-348. • Ermine, J.-L., Boughzala, I., & Tounkara, T. (2006). Critical knowledge map as a decision tool for knowledge transfer actions. The Electronic Journal of Knowledge Management, 4(2), 129-140. • Iyer, B., Shankarnarayanan, G., & Wyner, G. (2006). Process coordination requirements implications for the design of knowledge management systems. Journal of Computer Information Systems. • Jewels, T., & Ford, M. (2006). Factors influencing knowledge sharing in information technology projects. e-Service Journal.

25. References - 2 • Haas, M. R. (2006). Knowledge gathering, team capabilities, and project performance in challenging work environments. Management Science, 52(8), 1170-1184. • Kalpic, B., & Bernus, P. (2006). Business process modeling through the knowledge management perspective. Journal of Knowledge Management, 10(3), 40-56. • Kane, Hilary., & Ragsdell, Gillian., & Oppenhiem, Charles. (2006). Knowledge management methodologies. Electronic Journal of Knowledge Management, 4(2). • Landaeta, R. E. (2008). Evaluating benefits and challenges of knowledge transfer across projects. Engineering Management Journal, 20(1). • Lee, Gwanhoo (2003).  The flexibility and complexity of information systems development projects: Conceptual frameworks, measures, and empirical tests. Ph.D. dissertation, University of Minnesota, United States -- Minnesota. Retrieved July 27, 2009, from Dissertations & Theses: Full Text.(Publication No. AAT 3092759). Lee, L. S., & Anderson, R. M. (2006). An exploratory investigation of the antecedents of the IT project management capability. e-Service Journal. • Whelton, M., Ballard, G., & Tommelein, I. D. (2002). A knowledge management framework for project definition. ITcon 7. • Wyner, E., McDermott, R., & Snyder, W. M. (2000). Cultivating communities of practice. Boston, MA: Massachusetts Institute of Technology. • Sauer, C., Gemino, A., & Reich, B. H. (2007). The impact of size and volatility on IT project performance. Communications of the ACM, 50(11), 79-84. Retrieved from http://portal.acm.org/citation.cfm?doid=1297797.1297801