Walden University

Ph.D Dissertation OralIntegrated Knowledge Management

Framework for Addressing Information Technology Project Failures

(IKMFAITPF)

Walden University

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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

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The StudyResearch 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?

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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

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Conceptual model of the research

Research Model

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People

Projects

ProcessPatterns of IT projects failures that

relate to KM Proposed KM Framework

Expanded Conceptual Framework of the Proposed Research

Data from

- IT projects Cases

Measuring the Performance of the

Framework

Project Selection-using purposeful

sampling

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.

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Validity - InternalConformability

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.

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Validity - ExternalGeneralizability

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.

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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.9

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)10

Results - 2

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S. No

KM Pattern Case 1

Case 2

Case 3

Case 5

Case 6

1 Knowledge Area (KA)

Lack of systems analysis skills


Unclear deliverables


Detailed scope challenges

Mutually dependent projects’ deliverables

Detailed scope challenges

Lack of vendor product knowledge


Lack of business systems knowledge

Lack of new application knowledge based on vendor product


Lack of quality guidelines

Incomplete and informal quality guidelines

No formal quality guidelines

Needed technical, procedural, soft skill and process knowledge

Needed technical, procedural, soft skill and process knowledge

Needed to follow informal processes

Needed to follow informal processes

Results - 3

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S. No

KM Pattern Case 1

Case 2

Case 3

Case 5

Case 6

2 Knowledge Producer (KP)

Vendor team had expertise on vendor product

Business team had domain knowledge expertise

Team relied for expertise on one person

Team members worked independently rather than collaboratively

Project teams had technology knowledge of the vendor product

Project team had domain knowledge expertise

Technology team had expertise in technology areas

Challenges in technical areas such as deployments to end users

Client teams had domain knowledge

3 Knowledge Consumer (KC)

-same as above - -same as above-

-same as above-

-same as above-

-same as above-

4 Knowledge Base (KB)

No formal or structured documentation

Process and design-related formal and structured documentation

Some formal and structured documentation

Some formal and informal documentation

Some informal documentation

Informal documentation did not cater to the needs of the project

Insufficient documentation for the project

Insufficient documentation for the project

Insufficient documentation

Insufficient documentation

Results - 4

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S. No

KM Pattern Case 1

Case 2

Case 3

Case 5

Case 6

5 Knowledge Flow (KF)

Insignificant or minimal knowledge transfer

Knowledge transfer was insignificant or minimal

Poor or insignificant knowledge transfer

Insufficient knowledge transfer

Insignificant or minimal knowledge transfer

6 Knowledge Inhibitors (KI)

Virtual team environment

Virtual environment

Virtual environment

Virtual environment

Virtual environment

Multiple countries Multiple countries

Multiple countries

Multiple countries

Multiple countries

Multiple project assignments




Initial members left and new members joined


Members left the project


New members joined the project

People left and new people joined





Recurring problems

Time and budget constraints

No rewards or recognition




70% interactions (average)





Results - 5

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S. No

KM Pattern Case 1

Case 2

Case 3

Case 5

Case 6

7 Knowledge Accelerators (KA)

None

None

None

Five instances of rewards and recognition

None

8 Knowledge Distribution (KD)

None

None

None

None

None

9 Un-codified and Un-abstracted Knowledge (UUK)

Needed procedural knowledge

None

None

None

None

10 Training and Learning (TA)

No formal training No formal training

No formal training

No formal training

Some training on technology

No e-learning sessions





No coaching No coaching No coaching No coaching Some level of mentoring

Data Analysis - 1

Research Question 3: How can the development of a KM framework help to improve the success of IT projects?

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The Framework

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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?

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Measuring Mechanism for Integrated Knowledge Management Framework for Addressing Information-

Technology Project Failures

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Measuring the Framework–Project Benefit and Overall Benefits

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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.

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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.

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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.

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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.

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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.

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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

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