knowledge management and project-based knowledge in it projects: a model and preliminary empirical...

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International Journal of Project Management 30 (2012) 663–674www.elsevier.com/locate/ijproman

Knowledge management and project-based knowledge in it projects: A modeland preliminary empirical results☆

Blaize Horner Reich a,⁎, Andrew Gemino a, Chris Sauer b

a Simon Fraser University, Canadab University of Oxford, UK

Received 8 September 2011; received in revised form 21 December 2011; accepted 22 December 2011

Abstract

In this research, we investigate how IT-enabled business projects can be managed to contribute value to the client organization. We take aknowledge view of this issue, and conceptualize knowledge management as a three dimensional concept comprising knowledge stock, enablingenvironment and knowledge practices. We suggest that knowledge management enables the creation and alignment of three types of project-based knowledge that are critical to achieving desired business outcomes: technical design knowledge, organizational change knowledge and busi-ness value knowledges

We test this model with survey data from 212 IT project managers from around the world. The results statistically support the model's concep-tualisation of the key constructs and show that knowledge management within IT projects contributes to the creation and alignment of the impor-tant project-based knowledges.

This study contributes to research into IT projects by 1) integrating the wide variety of knowledge management literature into a singlemanagerially-useful construct, 2) developing a model which connects knowledge management, through knowledge practices to the creation andalignment of project-based knowledges, and 3) demonstrating the validity of the model, its constructs and measures. The model has the potentialto influence research into IT projects and to guide project executives towards the achievement of business value.© 2012 Elsevier Ltd. APM and IPMA. All rights reserved.

Keywords: IT projects; Project management; Knowledge management; Business value; Knowledge alignment; Project-based knowledges

1. Introduction

As pressures in the new economy have made organizationsfocus on innovation as a means of survival, they have increasing-ly turned to information technology (IT) to support and imple-ment their plans for change. As a result, IT spending now totalsover $3.5 Trillion USD worldwide (Computerworld, 2011).However, in spite of increasing investment in information tech-nology and in the professionalization of project managers, IT's

☆ The research team is very grateful for their sustained financial support fromthe social sciences and Humanities Research Council of Canada. Without thissupport, the research would not have been possible.⁎ Corresponding author.E-mail addresses: [email protected] (B.H. Reich), [email protected]

(A. Gemino), [email protected] (C. Sauer).

0263-7863/$36.00 © 2012 Elsevier Ltd. APM and IPMA. All rights reserved.doi:10.1016/j.ijproman.2011.12.003

contribution to productivity gains have declined since 2000(PWC, 2008) and most organizations struggle to achieve strongbusiness value from their IT investments (Bowen et al., 2007).Although there has been significant emphasis in the researchand practitioner literature about how project managers shouldact to deliver projects on time and on budget (e.g. Rubinstein,2007), research has shown that senior project managers and ex-ecutive sponsors increasingly measure performance using themetric of business value (Sauer and Reich, 2009; Winter etal., 2006).

Our overall research program seeks to understand factorsthat predict the attainment of business value from IT-enabledbusiness projects. Although business value is often realizedafter a project is completed, we take a within-project view, try-ing to determine what actions a project manager might take tohelp the client firm receive the benefits it desired from the

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http://dx.doi.org/10.1016/j.ijproman.2011.12.003




664 B.H. Reich et al. / International Journal of Project Management 30 (2012) 663–674

project. Our assumption is that a well-executed IT project canposition the client firm to realize value and that there arewithin-project actions that are influential in this regard.

Projects can be examined from a variety of perspectives(Bredillet, 2007, 2008; Sauer and Reich, 2007; Söderlund,2005). In this research project, we take a knowledge-based view.We see IT-enabled business projects as knowledge-intensiveprojects and seek to model and test assumptions about how knowl-edge management within an IT-enabled business project influ-ences the attainment of business value. We model knowledgemanagement as a three dimensional construct, and suggest thatthe goal of knowledge management within a project is to constructthree kinds of project-based knowledge which, if aligned, willcontribute to the achievement of business value.

Our empirical model has been tested with a sample of 212project managers from North America and Europe.

The first half of this paper introduces the theoretical model,traces its origins in the literature on knowledge managementand project management, and ends with hypotheses. The sec-ond half reports on the testing of this model, including measuredevelopment, survey deployment and data analysis. A discus-sion section concludes the paper.

This research contributes to research into IT projects by (1)integrating the wide variety of knowledge management litera-ture into a single three-dimensional construct, by (2) develop-ing a model which connects knowledge management to thecreation and alignment of three project based knowledgetypes and (3) demonstrating the validity of the model, its con-structs and measures. The model, when further developed, hasthe potential to influence research in IT projects and to guideproject managers, client managers, and project sponsors to-wards the achievement of Business Value.

2. The theoretical model

The linkage between knowledge management and projectsuccess is particularly relevant to IT projects because the taskof building or implementing IT-enabled business systems is aknowledge-intensive activity (Peppard, 2007). Whereas con-struction projects involve large quantities of physical materials,IT projects work with knowledge as their core input material.Further, because the project team is a temporary organization,team members may have few shared experiences, knowledgebases, or routines. The project manager must develop a projectenvironment in which knowledge is created, shared, and uti-lized to produce the results desired by the client organization.For these reasons, effective knowledge management within aproject context should contribute to the attainment of valuefrom projects.

2.1. Research into knowledge in related disciplines

Knowledge management is a conceptualization that has ap-plication in many different domains. Researchers in a range ofengineering and management disciplines have examined as-pects of knowledge and learning and their impact on various

outcomes, including core capabilities (Kotnour, 1999), teamlearning (Akgün et al., 2005), team satisfaction (Janz andPrasarnphanich, 2003), and project success (Karlsen andGottschalk, 2003, 2004). To develop our model, we examinedliterature on knowledge management from many disciplinesto understand the core concepts and perspectives of each. Inthe sections below, we very briefly note some key knowledgemanagement concepts from the Management Information Sys-tems (MIS), software engineering, project management, organi-zational theory, and organizational behaviour literatures.Following these synopses, we identify six key points to takeforward into a theoretical model.

The MIS and software engineering literatures recognise theimportance of knowledge management (Aurum et al., 2008;Corbin et al., 2007; DeSouza et al., 2006) and point to its lim-ited application in practice (Aurum et al., 2008). Publishedstudies make five contributions to the development of ourtheoretical model: (1) they provide empirical evidence thatknowledge and knowledge management significantly affectperformance in IT projects (Faraj and Sproull, 2000; Geminoet al., 2008; Tiwana, 2004); (2) they highlight the importanceof modelling at the level of specific knowledges (Tiwana,2004); (3) they provide relevant constructs such as projectknowledge resources (Gemino et al., 2008) and expertise coor-dination (Faraj and Sproull, 2000; He et al., 2007); (4) they in-troduce the idea of team-based knowledge (He et al., 2007); and(5) they introduce the concept of project alignment as a knowl-edge process (Jenkin and Chan, 2006).

Researchers in the project management domain suggest that thetraditional assumption about the importance of control needs to besupplemented by ideas about experimentation, innovation, knowl-edge management and learning (e.g. Akgün et al., 2005; Grant,2006; Reich, 2007; Reich and Wee, 2006; Sauer and Reich,2009; Sense, 2003). Some researchers have connected knowledgeand learning with project performance (Reich et al., 2008).

The organizational and management literature includes tworelevant theories that are based in knowledge concepts andhave been applied to projects: organizational control theory(Choudhury and Sabherwal, 2003; Kirsch, 1996, 1997; Liu etal., 2003; Ouchi, 1977, 1979, 1980) and information processingtheory (Galbraith, 1973, 1977; Winch, 2002). Empirical studiesin this literature show a strong correlation between project man-agement and knowledge management practices (McElroy,2000) and between good knowledge management practicesand project performance (Leseure and Brookes, 2004).

Research in organizational behaviour offers relevantinsights into the knowledge practices of teams through conceptssuch as the shared or team mental models (Cannon-Bowers etal., 1993; Lee, 2007; Rico et al., 2008), and transactive memorysystems and the collective mind (Austin, 2003; Yoo andKanawattanachai, 2001; Zhang et al., 2007).

This short review demonstrates that there is support in exist-ing literature for the core thesis — that project success is influ-enced by knowledge management. However, the diversity ofknowledge management literature makes it challenging to de-velop managerially-oriented models. We use the followingkey ideas to frame the model:

665B.H. Reich et al. / International Journal of Project Management 30 (2012) 663–674

• There is a stock of knowledge that includes knowing at theindividual, group and organization levels (Crossan et al.,1999; Prieto and Revilla, 2006).

• Project managers can actively manage knowledge stocksthrough knowledge practices such as expertise coordination(Faraj and Sproull, 2000; Habib, 2008; He et al., 2007).

• The enabling environment in an organization is influential insharing and transferring knowledge stocks through practices(Bohn, 1994; Chang, 2008; Vera and Crossan, 2003);

• The actual practices that teams apply to access, create andprocess knowledge will produce knowledge instrumental tothe achievement of business value (Ashurst et al., 2008).

• Multiple knowledges are required in delivering an IT project(Leseure and Brookes, 2004);

• These knowledges need to be aligned (Pee et al., 2008;Tiwana et al., 2003)

The following sections describe the model and conceptualizeits main elements.

2.2. Knowledge Management and Project-Based Knowledges

Fig. 1 represents the two principal elements in the model —Knowledge Management, and Project-based Knowledges. Itrepresents knowledge management as consisting of three di-mensions — knowledge stock, enabling environment andknowledge practices. It proposes that knowledge is generatedthrough a range of knowledge practices, using knowledgestock as input and operating within an enabling environment.Taken together, knowledge management influences the crea-tion of three key types of project-based knowledge and also in-fluences the alignment between these types of knowledge.

2.3. Knowledge management

We define Knowledge Management in a project context asthe management activities required to source the KnowledgeStock, create the Enabling Environment, and manage the

EnablingEnvironment

KnowledgePractices

KnowledgeStock

Knowledge Management

H1b

H2a

H1c

H2c

H2b

H1a

Fig. 1. Knowledge management dimens

Knowledge Practices to result in an aligned set of project-based knowledges.

Each of these elements is described below with relevantsupporting literature.

2.3.1. Knowledge stockThe concept of Knowledge Stock represents the total cogni-

tive capacity available to the project at the individual, groupand project organization levels (Crossan et al., 1999; Prietoand Revilla, 2006). The importance of knowledge stock hasbeen recognised within the literature on knowledge loss andfailure to learn (Eskerod and Blichfeldt, 2005; Gable et al.,1998; Parker and Skitmore, 2005; Schindler and Eppler, 2003).

The concept of knowledge stock is comprised of two parts.The first is the store of knowledge that is possessed by or em-bodied in members of the project (Henry et al., 2003; Walz etal., 1993), including vendors and consultants (Mitchell, 2006;Owen et al., 2004) as well as that inherent in project processesand design methods including explicit knowledge representedin documents, models, designs, and other repositories (Arthuret al., 2001) and meta-knowledge such as knowledge maps(Faraj and Sproull, 2000). The second part is the potential to in-crease knowledge including the project's absorptive capacity—the ability to absorb a diverse range of knowledges and makeuse of them (Cohen and Levinthal, 1990; Szulanski, 1996),and its access to sources of knowledge external to the projectsuch as knowledge networks (Ancona and Caldwell, 1992;Henderson, 1994; Henderson and Cockburn, 1994; Leonard-Barton, 1992; Nagarajan and Mitchell, 1998). Combiningthese two aspects into a definition — Knowledge Stock withinan in IT-enabled business projects is the relevant domain knowl-edge of the IT team, the Business Team and the Governanceteam”.

2.3.2. Enabling environmentPrior research has identified two broad conditions that pro-

mote effective knowledge practices. One is technological, theother social. Together these comprise the Enabling Environ-ment. Our definition is: “The Enabling Environment within

Aligned Project-BasedKnowledges

Organizational Solution

Technical Solution

Desired BusinessValue

Project-Based Knowledges

ions and project based knowledges.


an IT-enabled business project is a combination of the techno-logical and social aspects of a project that facilitate KnowledgePractices”.

The technological conditions that support Knowledge Practicesconsist of physical resources, typically the IT infrastructure includ-ing the communications infrastructure, project websites, shared re-positories and other elements of a technology-based knowledgemanagement system (Earl, 2001).

The social conditions relate to organizational resources, typ-ically the project's organizational structures and processes andthe project climate. The organizational structures and processescan be seen as defining the formal knowledge channels that sup-port knowledge transfer and creation (Galbraith, 1977). Theseinclude arrangements such as committees, working groups,and liaison groups that help define who will be involved ingroup-based Knowledge Practices and in what ways. There isalso a cultural or project climate element to the social conditionsthat influences how readily learning and knowledge sharingoccur within the project.

The Enabling Environment facilitates or inhibits the intensityand effectiveness with which Knowledge Practices employ theproject's Knowledge Stock. For example, the availability ofchannels that permit access to external sources of expertise islinked to higher performance (Ancona and Caldwell, 1992;Henderson, 1994; Henderson and Cockburn, 1994; Leonard-Barton, 1992; Nagarajan and Mitchell, 1998).

2.3.3. Knowledge practicesKnowledge Practices are the activities that generate usable

knowledge, either in explicit or tacit forms. The knowledge man-agement literature offers high level models of knowledge prac-tices using concepts such as socialization, internalization,combination and externalization (Nonaka and Takeuchi, 1995).However, these concepts are difficult to operationalize and havenot been used in project management research. The best knownoperationalization of knowledge practices is Faraj and Sproull's(2000) expertise coordination. It has several dimensions, namelyknowledge appreciation, knowledge “mapping”, and knowledgesharing. Based on the most influential aspects of expertise coordi-nation in the context of multiple teams in an IT project, we defineknowledge practices as ``actions taken to map and share knowl-edge within and between the IT, business and governance teamsin an IT-enabled business project``.

2.4. Project-based knowledges

As the Project Management Institute explains via the 42 pro-cesses of the Project Management Body of Knowledge (ProjectManagement Institute, 2008), there are many explicit knowledgeartefacts created, stored, accessed, used and updated throughouta project (Reich andWee, 2006). For the purposes of this research,Markus's (2004) emphasis on the technical artefact, the organiza-tional transformations which are needed to implement it and therole of sponsors in achieving business value leads us to focus onthree areas of domain knowledge: knowledge of desired businessvalue (developed by the sponsors or, in our terminology, thegovernance team), knowledge of the organizational solution

(developed by the business team), and knowledge of the technicalsolution (developed by the IT team).

Our belief is that knowledge management creates specificbodies of knowledge within a project; knowledge that is essen-tial to the successful completion of project goals. Some of thisknowledge will remain tacit, but much of it needs to be madeexplicit, so that it can be examined, verified, shared and madecorrect and complete.

We recognize that other types of knowledge such as processknowledge (Ahn and Chang, 2004) have been identified in theproject management literature. Although we accept that suchknowledge is critical to attainment of schedule and budget tar-gets, our model focuses on the domain knowledge that is criti-cal for the realization of business value.

Each knowledge type is defined below, located within ap-propriate literature. We then identify a set of shared characteris-tics that each type of knowledge should exhibit.

2.4.1. Knowledge of desired business valueThe importance of clear objectives (Pinto and Slevin, 1987)

and a clear understanding of the desired value (Ward andDaniel, 2005) are important success factors for IT projects.

Some writers on the dynamics of strategic IT have producedevidence of the emergent nature of business value (Ciborra,1991; Yetton et al., 1994). They show that opportunities mayemerge during a project through learning about the business ap-plication of the technology. Therefore, Knowledge of DesiredBusiness Value needs to be a dynamic concept such that itcan develop and be modified throughout the project lifecycle.

We define Knowledge of the Desired Business Value as a“dynamic shared understanding of the business objectivesthat the project is expected to deliver”. The project must contin-ually review whether what it is producing will lead to businessvalue. This knowledge must be shared across a sufficientlywide constituency and it must be explicit and appropriatelyconcrete.

2.4.2. Knowledge of the organizational solutionThe IT project literature has increasingly recognized that

benefits are only secured if a new system is accompanied bybusiness process and organizational change (Ashurst et al.,2008; Kohli and Grover, 2008; Markus, 2004; Peppard andDaniel, 2007). Alignment models embody the recognition thatstrategy, structure, process and people need to be aligned tocore technology systems to achieve high performance (ScottMorton, 1991).

We use the term “knowledge of the organizational solution”to reflect the need to have a clear understanding about whatorganizational changes (e.g. changes to structure, processes, in-centives, skills, and culture) are needed in order to fully utilizethe IT artefact and realize value.

We use the words organizational solution rather than thebusiness solution both to explicitly include organizations suchas non-profit and government organizations, and also to includesolutions that run beyond the boundaries of a single businessentity, for example by integrating a complex supply chain.


The literature on the dynamics of strategic IT (Ciborra,1991; Yetton et al., 1994) also applies to the organizational so-lution. That is, knowledge of the organizational solution willemerge dynamically. We define Knowledge of the Organiza-tional Solution as “the dynamic shared understanding of thechanges that need to be made in the organization in order toutilize the technical solution to enable the attainment of the de-sired business value”.

2.4.3. Knowledge of the technical solutionOver the last fifteen years, industry has increasingly developed

architect roles at the corporate and project levels. The task of theproject technical architect is to develop a satisfactory technical so-lution in a manner consistent with corporate architectural stan-dards (Pearlson and Saunders, 2006; Zachman, 1999). Not onlymust the project technical architect know what technology cando, how it works and what new technology is emerging, he/shemust also understand the corporate architecture.

Technical knowledge is also subject to dynamic change.New technologies can emerge and supersede old ones withinthe time frame of medium to large projects.

We define Knowledge of the Technical Solution as a “dy-namic, shared understanding of the architecture and infra-structure of the technical solution within the context of anywider architectural standards or infrastructure standards andconstraints”.

2.5. Alignment of project-based knowledges

In the strategy literature, alignment commonly refers to theextent to which internal firm resources match the needs of theexternally-focused competitive strategy (Leavitt and Whisler,1958; Scott Morton, 1991). In the IT literature it refers to theextent to which the IT function supports the business (Chanand Reich, 2007; Henderson and Venkatraman, 1992).

Although a discussion of alignment among knowledges isseemingly to introduce a new meaning for the term, there areexisting concepts to guide us. It is implicit in our model that wesee alignment of knowledges as involving knowledge sharingacross individuals. Thus concepts relating to team cognition arerelevant (He et al., 2007) to signify overlap or coherence amongindividuals with potentially different expertises or knowledgebases. Empirical research has reported that shared mental modelsinfluence team performance positively (Levesque et al., 2001).Related concepts include transactive memory (Akgün et al.,2005; Yoo and Kanawattanachai, 2001) and integrative capability(Mitchell, 2006).

In our context, alignment is defined as “the level of congruencebetween the three Project-Based Knowledges”. An image ofknowledge alignment might be a set of three cogs, representingknowledge of business value, knowledge of the organizational so-lution, and knowledge of the technical solution. If the knowledgesare aligned, when one shifts, the others will also move. They areout of alignment when change in one set of knowledge fails to trig-ger an appropriate change in the other two.

For example, suppose that during a project, there is a changein organizational structure which separates two previously

integrated business functions. This change may affect plansthat were in place to transform the culture and skills in the clientorganization. In terms of our model, if the implications of thestructural change on the project are recognized and adjustmentsmade, we have a change in knowledge of the organizational so-lution. Further, if this change is translated into recognition thattwo distinct sets of financial and management reports are need-ed, then the knowledge of the technical solution has beenaligned with the knowledge of the organizational solution.Alignment involves continuing feedback or mutual adaptationamong the three project-based knowledges.

2.6. Hypotheses

The model in Fig. 1 posits several hypotheses about the re-lationship between the dimensions of knowledge managementand creation and alignment of project based knowledges.

H1a. Knowledge Stock available within the project team willhave a significant positive effect on the quality of documentedProject Based Knowledges. This hypothesis reflects the beliefthat people who know more will use their knowledge to pro-duce better quality documents than people who know less.

H1b. The Enabling Environment created in the project will havea significant positive effect on the quality of documented ProjectBasedKnowledges. This hypothesis reflects the belief that technol-ogy and a positive learning culture will encourage project teammembers to work together and develop high quality documents.

H1c. The Knowledge Practices developed in the project will havea significant positive effect on the quality of documented ProjectBased Knowledges. This hypothesis reflects the belief that whenproject team members share their knowledge with each other,the integrated knowledge that results improves the quality of theartefacts.

H2a. Knowledge Stock available within the project team willhave a significant positive effect on the alignment of documentedProject Based Knowledges. This hypothesis reflects the belief thatpeople's level of expertise and knowledge impacts their ability tosee the connection between different artefacts and that the moreconnection they see, the more they will work to align the artefacts.

H2b. The Enabling Environment evidenced in the project willhave a significant positive effect on the alignment of documentedProject Based Knowledges. The hypothesis reflects the belief thattechnology and a positive learning culture will enable peoplefrom different parts of the project to work together. As theywork together, they will understand the connection between thedifferent artefacts of the project and will work to align them.

H2c. The Knowledge practices used in the project will have a sig-nificant positive effect on the alignment of documented ProjectBased Knowledges. This hypothesis reflects the belief that themore project team members share their knowledge with eachother, the more they understand the connection between differentparts of the project and work towards creating artefacts that arealigned.


3. Empirical investigation

3.1. Sample development

An online survey instrument was created using www.SurveyMonkey.com to collect data from practicing projectmanagers. The survey underwent three phases of testing. Inthe first phases, the research team scrutinized each question en-suring that the concepts were named identically wherever theyoccurred and that there was only one concept in each question.This was necessary because the items had been taken from anumber of sources and there was little convergence in the liter-ature on items to measure knowledge and knowledge manage-ment. In the second phase, 5 senior project managers wereasked to complete the survey and provide comments on thesuitability and composition of the questions. Based on these re-sponses, several questions were rewritten to make them moreunderstandable.

The third phase consisted of a pilot study of 25 project man-agers, recruited from two local project management organiza-tions. This third phase allowed us to do some preliminarytesting of the constructs in the model and several changeswere made as a result.

The distribution of the survey was done in 2 stages. In thefirst stage, we talked with many chapters of project manage-ment associations, asking them to contact their members witha request to fill out the survey. Responses were positive, butchapters were only willing to post invitations on their websiteor social media site. We pursued this strategy for 3 monthsand finally abandoned it because there were not enough re-sponses to test the model.

In our second stage, we revised the survey substantially,bringing the number of questions down from 104 to 62, whileattempting to measure our concepts validly. We hoped thatthis more streamlined survey would appeal to our next targets.The Computer Weekly publishing group and the InformationSystems Special Interest Group were willing to send out a no-tice and reminder to their members. We sent an email to 4200of the general management members in the Computer Weeklydatabase and received a response from 108 participants (2.5%participation) of which 54 completed the survey (1% valid re-sponses) to the final question. Although this response rate islow, it is a reasonable response since this was a managementaudience, not a project management audience.

We sent a request for participation to the PMI IS Communi-ty of Practice (ISCOP) group that numbered approximately10,000 PMI member addresses. We received 365 participants(3.6%), of which 198 completed the survey to the final question(2% valid responses).

We tested, using 2 tailed t-tests, data from these two groupsto ensure that they were similar in key areas including budget,duration, person months, and elapsed time. We found no signif-icant differences. We therefore combined the two datasets foran initial sample of 252.

Since we asked project managers about their most recentlycompleted projects, the sample included projects that had beencompleted or abandoned. Our interest was in completed projects,

so we eliminated 17 abandoned projects from the sample of 252leaving 235 completed projects. There were 4 abandoned projectsin the Computer Weekly sample (8% abandoned rate) and 13abandoned projects from the ISCOP sample (7% abandoned rate).

We also filtered for outlier projects. Outlier projects weredefined on several dimensions. We looked for projects thatwere too small (b $10,000 USD in budget or less than1 month in duration) or too large (N $1 Billion USD). In addi-tion were looked for anomalies in the data such as a projectend date earlier than the start date and other simple qualitychecks. Overall we found a total of 23 outliers. Removing theabandoned and outlier projects from the initial sample left afinal sample of 212. It should be noted that some of the data re-cords in the final sample of 212 had questions that were not an-swered. Since regression requires complete data sets, thesample size for the regressions is lower than the total samplesize of 212.

Our unit of analysis is the individual project. Respondentswere asked to provide information about the most recent projectthey had completed (either implemented or cancelled). Themost recently completed project was chosen to ensure that re-spondents were considering projects for which there was a de-fined outcome while maintaining reasonable recall of projectdetails. It also denied respondents a choice as to what projectto report, thereby increasing the validity of the sample.

The average reported project budget was $3.38 million USDwith an average effort of 228 person months and an average dura-tion of 14.6 months. On average, each project had an average ofover 20 full time equivalent (FTE) positions working on the pro-ject with 11.38 FTE working in Information technology rolesand 8.72 working in Business roles. Respondents indicated that92% of the projects were completed and 8% cancelled. The ratioof cancelled projects is similar to industry reports (Rubinstein,2007; Sauer et al., 2007) suggesting the responses have some ex-ternal validity. The respondents were project managers, with 61%working as employees and 39% as external contractors. Approxi-mately 21% of the respondents were females.

3.2. Measures

Measures for the knowledge management dimensions weretaken from a variety of sources and, where necessary, were for-mulated by the research team.

In order to test the concepts in the model, we first had to con-ceptualize an IT project from a knowledge perspective. Theoreti-cally, there are three different and important knowledge sourceswithin an IT project. First, there are people who comprise the“IT Team”. This concept was defined in the survey as follows:

“IT Team includes people on the project with a technicallyoriented role and focus including people trained to config-ure a software package. These people can be from the clientor a consulting organization”

Second, there is the “business team”, the people who designthe business processes, and determine what changes need to bemade to process, structure, and skills to successfully deliver

http://www.SurveyMonkey.com

http://www.SurveyMonkey.com


business value. This group of people were defined in the surveyas follows:

“Business Team includes people on the project with a businessoriented role and focus. These people can be from the client ora consulting organization”Third, there is the “Governance team”, the person or people

who have overall responsibility for the progress and outcomes
of the project. This concept was defined as follows:
“Governance Team includes the Executive Sponsor, ProjectManager, Client Manager and other major stakeholders(e.g. Steering Committee Members, Project Champion) withauthority over the direction of the project”

A fourth concept, called “the Project Team” was also used inthe survey. This name was used to denote the core team, madeup of members of the IT team and the Business Team.

Table 1Independent variables — survey items and descriptive statistics.

Construct Item name and survey question

Knowledge stock KStock1 At the start of the project, the Busineshad all the knowledge and expertise needed to cOrganizational Change PlanKStock2 At the start of the project, the Governahad all the knowledge and expertise needed to ddesired Organizational BenefitsKStock3 At the start of the project, the IT Teamknowledge and expertise needed to create the TDesign

Enabling environment EnabEnv1 The Project Team members viewedas having a knowledge and learning - orientatiowithin the projectEnabEnv2 Members of the IT Team and the Buwere easily able to meet face-to-face throughouEnabEnv3 The Project Team members had acceknowledge management system (e.g., project podocument repository)EnabEnv4 Members of the IT team and the Bustrusted each other to act professionally and comEnabEnv5 Members of the Project Team recognpotential value of their peers’ expertise

Knowledge practices KPract1 Business Team members shared their kand expertise with IT Team membersKPract2 Members of the Governance Team shaknowledge and expertise with members of the PKPract3 During the project, the IT Team and thTeam formally shared information (e.g., meetinstatus reports)KPract4 During the project, the IT Team and thTeam informally shared information (e.g., sharistories, social interaction)KPract5 Business Team members knew whichhad knowledge and expertise that was relevantKPract6 IT Team members knew which Businemembers had knowledge and expertise that wastheir workKPract7 IT Team members shared their knowleexpertise with Business Team members

3.2.1. Knowledge stockKnowledge Stock was measured in a direct manner, in that

we asked one question about the domain knowledge of each ofthe IT, business, and governance team (taken from concepts inHe et al. (2007) and Faraj and Sproull (2000)) and anchoredthis measure to the relevant knowledge artefact. The questionsare shown in Table 1 with the variable names and descriptivestatistics.

3.2.2. Enabling environmentThe enabling environment for learning and knowledge sharing

was conceptualized as having two dimensions — capability andwillingness. To measure capability, we asked two questions, oneabout physical proximity (from Akgün et al., 2005) and theother about access to a project knowledge management system.

To measure the team's willingness to learn from each other,we took two questions from the absorptive capacity variableused by Tiwana and McLean (2005). We added another

Mean Std, Dev. N

s Teamreate the

4.12 1.81 202

nce Teamefine the

4.48 1.79 199

had all theechnical

4.32 1.87 209

themselvesn

5.27 1.50 197

siness Teamt the project

4.79 2.06 206

ss to artal,

5.18 1.93 207

iness Teampetently

5.39 1.75 208

ized the 5.35 1.56 206

nowledge 5.36 1.54 203

red theirroject Team

4.90 1.69 194

e Businessgs,

5.66 1.61 204

e Businessng personal

5.52 1.56 203

IT Team membersto their work

5.40 1.47 200

ss Teamrelevant to

5.47 1.42 204

dge and 5.57 1.39 202


“trust” question taken from concepts in Quigley et al. (2007)and Akgün et al. (2005). Survey questions with the variablenames and descriptive statistics are shown in Table 1.

3.2.3. Knowledge practicesThis was the most challenging section of the knowledge

management concept to model and measure, due to the lackof consensus in the literature about knowledge practices. How-ever, we began with a set of questions to measure both theknowledge map and the knowledge sharing within the expertisecoordination construct (Faraj and Sproull, 2000; Tiwana andMcLean, 2005). We focused on cross-team expertise coordina-tion for the first five questions. We then added two questions,taken from Akgün et al. (2005) about knowledge practices ata more granular level. Survey questions with the variablenames and descriptive statistics are shown in Table 1.

3.2.4. Project-Based KnowledgesAs the concept of project-based knowledges originated with-

in our research team, we had no archetypes to draw on whenconstructing questions. Our conclusion was that the quality ofeach project-based knowledge should be evaluated directlywith the hypothesis being that higher levels of knowledge man-agement would lead to higher quality project-based knowledge.Survey questions with the variable names and descriptive statis-tics are shown in Table 2.

3.2.5. Alignment of Project-Based KnowledgesWe focused on artefacts when measuring alignment. As impor-

tant as it is to create a comprehensive technology design, organiza-tional change plan and benefits statement, it is critical that theseartefacts are aligned if they are to support the desired results. Weasked two questions about alignment of project-based knowledgeartefacts, believing that the technical design plan needed to bealignedwith the organizational change plan and the organizationalchange plan needed to be aligned with the statement of desired

Table 2Dependent variables – survey items and descriptive statistics.

Construct Item name and survey question Mean Std,Dev.

N

Project-basedknowledge

PBKOrgBen A comprehensivestatement of desired OrganizationalBenefits was created

4.88 1.80 198

PBKChgPln A comprehensiveOrganizational Change Plan wascreated

4.47 1.89 184

PBKTechDe A comprehensiveTechnical Design was created

5.52 1.49 199

Aligned project-based knowledge

ATechDeOrgChgPln TheTechnical Design was appropriateto support the delivery of theOrganizational Change Plan

5.34 1.55 182

AOrgChgPlnOrgBen TheOrganizational Change Plan wasappropriate to deliver the desiredOrganizational Benefits

4.89 1.78 175

organizational benefits. The survey questions with the variablenames and descriptive statistics are shown in Table 2.

3.3. Results

3.3.1. Preliminary tests: independent scale measuresAll statistics provided in this section were developed using

SPSS version 14.0. Three independent measures were createdfor this study: 1) Knowledge Stock (3 items), 2) Enabling Envi-ronment (5 items) and 3) Knowledge Practices (7 items). De-scriptive statistics for each of the items used in this analysisare provided in Table 1.

A factor analysis including the 15 independent measuresyielded three factors with eigenvalues greater than 1.0. Cronbachalpha test scores were used to test the validity of individual scales.A threshold of 0.70 was used in assessing the scales as suggestedby Nunnally (1978) and Cortina (1993). All three scalesprovided alphas above the threshold level as shown in Table 3(Knowledge Stock=.778, Enabling Environment= .806, Knowl-edge Practices=.859). Three scale variables were then createdby averaging the raw scores across items in the scale.

3.3.2. Preliminary tests: dependent scale measuresTwo dependent measures were created for this study: 1) Pro-

ject Based Knowledges (3 items), 2) Aligned Project BasedKnowledges (2 items). Descriptive statistics for each of theitems used in the dependent scales are provided in Table 2.

Preliminary tests on the internal validity of the dependentmeasures focused on factor analysis and Cronbach's alpha.Both principal component analyses produced a strong singlefactor with Eigenvalues above the threshold of 1.0. The Cron-bach alpha test scores provided alphas above the thresholdlevel as shown in Table 3 (Project Based Knowledge= .744;Aligned Project Based Knowledge= .773). Two dependent var-iables were then created by averaging the raw scores across theitems in each scale.

Descriptive statistics and correlations for the resulting threeindependent and two dependent scales are provided below inTable 3. Note that regression analysis requires full data acrosseach of the scale items so the sample size used in regressionmay differ from the sample size available in the descriptive sec-tion. The scale measures in Table 3 were used to develop thetwo regression analyses described in the following section.

3.3.3. Regression analysisBefore performing the analysis, the assumptions underlying re-

gression were considered. Histograms of each variable showed nosignificant signs of non-normality, Visual inspection of scatterplots showed on obvious non-linear relationships between the vari-ables used in the models. A plot of the standardized residuals bythe regression predicted value showed no indication of heterosce-dasticty in either regression. Multicollinearity was assessed usingthe Variance Inflation factor (VIF) reported in the final columnof Tables 4 and 5. A tolerance of less than 0.20 or a VIF levelsover 5 suggests the presence of significant multicollinearity (Hairet al., 2006). Results show no significant effect of multicollinearityin either regression.

Table 3Descriptive statistics and correlations for scale measures.

Scale variables N Mean Std.Dev

Alpha Averageknowledgestock

Averageenablingenvironment

Averageknowledgepractices

Averageproject-basedknowledge

Average alignmentof project-basedknowledge

Average knowledge stock 211 4.32 1.51 .78 1.0Average enabling environment 211 5.19 1.29 .81 .32 1.0Average knowledge practices 207 5.42 1.12 .86 .25 .47 1.0Average project-based knowledges 206 4.98 1.44 .74 .34 .44 .38 1.0Average alignment of project-based knowledges 187 5.12 1.55 .77 .30 .35 .29 .72 1.0


Two regression analyses were created. Both regressions used aGeneral Least Squares linear procedure using unstandardized var-iable. The first (Regression 1) regressed the dependent variable ofProject Based Knowledges against the three independent mea-sures. Results for this regression are shown in Table 4. Regres-sion 1 was used to test hypotheses H1a, H1b and H1c. Thesecond regression (Regression 2) regressed the dependent vari-able of Aligned Project Based Knowledges against the three inde-pendent measures and was undertaken to test hypotheses H2a,H2b and H2c. Results for regression 2 are shown in Table 5.

Regression 1 produced a model that provided a significanteffect of independent variables on Project Based Knowledges(Adjusted R2= .24, F=21.94, pN .000). Results in Table 4 indi-cate that the three independent variables taken as a whole had asignificant relationship with the dependent variable. In addi-tion, the impact of each variable was positive and significant(Knowledge Stock t=3.30, p= .001; Enabling environmentt=2.19, p= .03; Knowledge Practices t=2.21; t= .028). The re-sults support Hypotheses H1a, H1b, and H1c at or above the95% confidence level.

Regression 2 produced a model that provided a significanteffect of independent variables on Project Based Knowledges(Adjusted R2= .15, F=12.16, pN .000). Results provided inTable 5 indicated that the three independent variables taken asa whole had a significant relationship with the dependent vari-able. The effects of the Knowledge Stock and Enabling Envi-ronment variables were insignificant (t=1.64, p=0.10;t=1.77, p= .08 respectively) whereas the effects of KnowledgePractices were significant (t=2.25, p= .03). The results supportHypothesis H2c and do not support H2a or H2b at the 95% con-fidence level.

Table 4Results for regression 1.

Dependent variable: average project-based knowledges

R2=.25, adjusted R2=.24, F=21.94, pN .000

Model Unstandardized coefficients S

B Std. error B

(Constant) 1.54 0.46Average knowledge stock 0.22 0.07 0Average enabling environment 0.22 0.10 0Average knowledge practices 0.25 0.12 0

4. Discussion

This discussion provides an overall summary of the aims andresults of this research, provides a practice-based implication ofthese preliminary findings, and discusses future research.

4.1. Summary

This paper introduced a theoretical model proposing thatknowledge management could usefully be conceptualized ashaving three dimensions: Knowledge Stock, Enabling Environ-ment, and Knowledge Practices. We proposed that artefactscontaining each of three types of domain knowledge producedin an IT project – knowledge of the desired business value,the organizational change plan and the technical design –need to be created and then kept in alignment. We then hypoth-esized that higher levels of knowledge management would pos-itively impact the creation and alignment of these knowledgeartefacts.

Items for each construct were developed from the literature,and, based on data from a sample of 212 IT-enabled businessprojects, demonstrated internal validity. The hypothesized rela-tionships were tested using hierarchical regression techniques.

The results of the first regression (Table 4) demonstrated asignificant positive relationship between each of the three ele-ments of knowledge management (Knowledge Stock, EnablingEnvironment and Knowledge Practices) and the developmentof project-based knowledge artefacts. This suggests that devel-oping a comprehensive set of documents which describe thetechnical design, organizational change plan and business ben-efits was strongly supported by having a high level of expertise,

tandard T Sig. Collinearity statistics

eta Tolerance VIF

3.37 0.00.23 3.30 0.0 0.82 1.22.20 2.19 0.03 0.48 2.10.19 2.21 0.03 0.49 2.05

Table 5Results for regression 2.

Dependent variable: average alignment of project-based knowledges

R2=.17, adjusted R2=.15, F=12.16, pN .000

Model Unstandardized coefficients Standard t Sig. Collinearity statistics

B Std. error Beta Tolerance VIF

(Constant) 1.85 0.56 3.30 0.00Average knowledge stock 0.13 0.08 0.12 1.64 0.10 0.84 1.20Average enabling environment 0.21 0.12 0.17 1.77 0.08 0.51 1.95Average knowledge practices 0.30 0.13 0.21 2.25 0.03 0.53 1.89


by creating a trusting and technologically supportive environ-ment, and by mapping and sharing knowledge between theIT, business and governance teams. The fact that the three di-mensions of knowledge management produce similar levels ofinfluence on the production and comprehensiveness of individ-ual artefacts suggests that each element plays a unique andcomplementary role within a project team.

The results of the second regression (Table 5) showed thatonly Knowledge Practices exhibited a significant positive rela-tionship with the alignment of the project-based knowledge ar-tefacts. Knowledge Practices (i.e. mapping and sharingknowledge) had significant effects, but adding more knowledge(i.e. higher levels of Knowledge Stock) or more support (i.e.higher levels of trust or technology) were not as directly relatedto the development of aligned knowledge.

These two findings suggest that smart people, enabled bytechnology and social support, can create high quality individ-ual design documents. However, it takes a deliberate, managedset of knowledge practices to ensure that these documents arealigned with each other. In other words, without mindfulknowledge sharing practices, the design documents may notsupport each other and therefore may not enable businessvalue to be created for the organizational client.

4.2. Implication for practice

One practice-based implication of these preliminary findingsis that the project manager, who creates the conditions forknowledge sharing and oversees the knowledge practices, hasa significant impact on the ability of the project team to createhigh quality knowledge artefacts and to keep them aligned.This impact is not simply dependent on the initial competencelevels of team members, but also follows through to the generalenvironment for knowledge sharing and the practices that aresupported within the project. Therefore, a project managerdoes not need to staff the project with the most accomplishedteam members in order to develop high quality results; itmight be more effective to staff the project with competentteam members who are willing to engage in effective knowl-edge practices and share their knowledge. Tiwana et al.(2003) came to an analogous conclusion, showing that knowl-edge integration fully mediated a pre-existing relationship be-tween IS and Business. In other words, project teams inorganizations with a poor IS-Business relationship can performwell, if the project itself achieves knowledge integration. The

second regression suggests that the key to this alignment is inthe practices that the project managers develops and supports.It is not good enough to have smart people on your project orthe latest in technical support for distributed meetings. Whatis most important is to establish and maintain effective knowl-edge practices to develop alignment. These findings underscorethe importance of the actions of the project manager in complexIT-enabled business projects.

4.3. Future research

Findings from this research support and extend prior work.For example, the concept of enabling environment includes el-ements trust and willingness to share as noted in Quigley et al.(2007) and Akgün et al. (2005). He et al. (2007) recognized theimportance of knowledge stock and many researchers, includ-ing Faraj and Sproull (2000), have demonstrated the impor-tance of mapping and sharing practices on projectperformance variables. By combining these elements, we havestarted the process of building a comprehensive knowledgemanagement construct. Our conceptualization is not theoreti-cally complete; more research is needed.

This research relied on data about a project supplied by theproject manager. A stronger design would use a matched sam-ple of project manager and project sponsor so that the indepen-dent and dependent data are collected from differentindividuals. We have not yet been able to accomplish this.

It is important to note that while the effects of KnowledgePractices are significant, their overall explanatory power onboth the development of project based knowledge artefactsand the alignment of these artefacts remains limited as indicatedby the relatively low adjusted R2. The full story has thereforenot been told. Other factors, such as organizational supportfrom the governance team, complexity and size of the project,the level of volatility, or the competence of the project managerare likely to influence the ability of the team to keep their plansaligned as the project unfolds. Understanding these contextualfactors will likely enable researchers to provide more sophisti-cated support for project managers.

Additionally, the differential effect of both KnowledgeStock and Enabling Environment is interesting, suggestingthat these variables can help in creating comprehensive state-ments of project based knowledge, but are less effective as en-ablers of the alignment of those knowledges. Although it seemsreasonable to assume that a certain level of knowledge stock


and enabling environment are critical for the success of anyknowledge practice, it is not clear yet what the relationship is.Testing for non-linear relationships or boundary conditionswould help to explain this unexpected finding.

The overarching aim of this team's research is to better un-derstand what knowledge-based factors impact the overall at-tainment of business value in IT projects. This paper providesa first step in this process. The findings demonstrate that the ac-tive management of knowledge practices can have significanteffects on the alignment of knowledges across what are oftenconsidered separate teams in an IT-enabled business project.What is not clear is whether the aligned knowledge will leadto increased performance and to what degree this relationshipcan be influenced. Further research is needed to investigatewhether and how the creation and alignment of project-basedknowledges can have an impact on business outcomes.

References

Ahn, J., Chang, S., 2004. Assessing the contribution of knowledge to business per-formance: the KP3 methodology. Decision Support Systems 36 (4), 403–416.

Akgün, A.E., Byrne, J., Keskin, H., Lynn, G.S., Imamoglu, S.Z., 2005. Knowl-edge networks in new product development projects: a transactive memoryperspective. Information Management 42 (8), 1105–1120.

Ancona, D.G., Caldwell, D.F., 1992. Bridging the boundary: external activityand performance in organizational teams. Administrative Science Quarterly37 (4), 634–665.

Arthur, M., De Fillippi, R., Jones, C., 2001. Project-based learning as the inter-play of career and company non-financial capital. Management Learning 32(1), 97–117.

Ashurst, C., Doherty, N., Peppard, J., 2008. Improving the impact of IT devel-opment projects: the benefits realization capability model. European Journalof Information Systems 17 (4), 352–370.

Aurum, A., Daneshgar, F., Ward, J., 2008. Investigating knowledge manage-ment practices in software development organisations — an Australian ex-perience. Information and Software Technology 50 (6), 511–533.

Austin, J.R., 2003. Transactive memory in organizational groups: the effects ofcontent, consensus, specialization, and accuracy on group performance.Journal of Applied Psychology 88 (5), 866.

Bohn, R.E., 1994. Measuring and managing technological knowledge. SloanManagement Review 36 (1), 61–73.

Bowen, P., Cheung, M., Rohde, F., 2007. Enhancing IT governance practices: amodel and case study of an organization's efforts. International Journal ofAccounting Information Systems 3 (1), 191–221.

Bredillet, C.N., 2007. From the editor: exploring research in project manage-ment part 3. Project Management Journal 38 (4), 2–4.

Bredillet, C.N., 2008. From the editor: exploring research in project manage-ment part 4. Project Management Journal 39 (1), 2–6.

Cannon-Bowers, J.A., Salas, E., Converse, S., 1993. Shared mental models inexpert team decision making. In: Castellan, N.J.J. (Ed.), Individual andGroup Decision Making. Lawrence Erlbaum and Associates, Hillsdale,NJ, pp. 221–266.

Chan, Y., Reich, B.H., 2007. IT alignment: what have we learned? Journal ofInformation Technology 22 (4), 297–315.

Chang, K.T., 2008. Psychological contracts and knowledge exchange in virtualteams. Paper presented at the ICIS 2008 Proceedings., Paris. Paper 146.

Choudhury, V., Sabherwal, R., 2003. Portfolios of control in outsourced softwaredevelopment projects. Information Systems Research 14 (3), 291–314.

Ciborra, C.U., 1991. From thinking to tinkering: the grassroots of strategic in-formation systems. Proceedings of the Twelfth International Conferenceon Information Systems, 283.

Cohen, W.M., Levinthal, D.A., 1990. Absorptive capacity: a new perspective onlearning and innovation. Administrative Science Quarterly 35 (1), 128–152.

Computerworld, 2011. Global IT Spending to total $US3.6 trillion in 2011(at)http://www.computerworld.co.ke/articles/2011/01/11/global-it-spending-total-us36-trillion-20112011(Retrieved October 31, 2011).

Corbin, R.D., Dunbar, C.B., Zhu, Q., 2007. A three-tier knowledge manage-ment scheme for software engineering support and innovation. Journal ofSystems and Software 80 (9), 1494–1505.

Cortina, J.M., 1993. What is coefficient alpha? An examination of theory andapplications. Journal of Applied Psychology 78 (1), s 98–s 104.

Crossan, M.M., Lane, H.W., White, R.E., 1999. An organizational learningframework: from intuition to institution. Academy of Management Review24 (3), 522–537.

Desouza, K.C., Awazu, Y., Baloh, P., 2006. Managing knowledge in global soft-ware development efforts: issues and practices. IEEE Software 23 (5), 30–37.

Earl, M., 2001. Knowledge management strategies: toward a taxonomy. Journalof Management Information Systems 18 (1), 215–233.

Eskerod, P., Blichfeldt, B.S., 2005. Managing team entrees and withdrawalsduring the project life cycle. International Journal of Project Management23 (7), 495–503.

Faraj, S., Sproull, L., 2000. Coordinating expertise in software developmentteams. Management Science 46 (12), 1554–1568.

Gable, G.G., Scott, J.E., Davenport, T.D., 1998. Cooperative ERP life-cycleknowledge management. The 9th. Australasian Conference on InformationSystems, 227.

Galbraith, J.R., 1973. Designing Complex Organizations. Addison-Wesley,Reading, MA.

Galbraith, J.R., 1977. Organization Design. Addison-Wesley Publishing Com-pany, Reading, MA.

Gemino, A., Reich, B.H., Sauer, C., 2008. A temporal model of informationtechnology project performance. Journal of Management Information Sys-tems 24 (3), 9–44 (Winter 2007).

Grant, K.P., 2006. Leveraging project team expertise for better project solu-tions. PMI Research Conference 2006,PMI, Montreal, Canada.

Habib, J., 2008. The dynamics of knowledge creation within innovation processfrom case studies to agent based modeling. Paper presented at the ICIS 2008Proceedings., Paris. Paper 33.

Hair, J.F., Anderson, R., Tatham, R.L., Black, W.C., 2006. Multivariate DataAnalysis. Prentice Hall, Upper Saddle River, N.J.

He, J., Butler, B.S., King, W.R., 2007. Team cognition: development and evo-lution in software project teams. Journal of Management Information Sys-tems 24 (2), 261–292.

Henderson, R., 1994. The evolution of integrative capability: innovation in cardio-vascular drug discovery. Industrial and Corporate Change 3 (3), 607–630.

Henderson, R., Cockburn, I., 1994. Measuring competence? Exploring firm ef-fects in pharmaceutical research. Strategic Management Journal 15, 63–84.

Henderson, J.C., Venkatraman, N., 1992. Strategic alignment: a model for orga-nizational transformation through information technology. In: Kochan,T.A., Unseem, M. (Eds.), Transforming Organizations. Oxford UniversityPress, Oxford and New York.

Henry, R.M., Kirsch, L.J., Sambamurthy, V., 2003. The role of knowledge ininformation technology project governance. The 24th International Confer-ence on Information Systems. , Seattle, USA.

Janz, B.D., Prasarnphanich, P., 2003. Understanding the antecedents of effec-tive knowledge management: the importance of a knowledge-centered cul-ture. Decision Sciences 34 (2), 351–384.

Jenkin, T.A., Chan, Y.E., 2006. Exploring the IS Project Alignment Construct,Queen's School of Business working paper; Kingston, Ontario, Canada.

Karlsen, J.T., Gottschalk, P., 2003. An empirical evaluation of knowledgetransfer mechanisms for it projects. The Journal of Computer InformationSystems 44 (1), 112–119.

Karlsen, J.T., Gottschalk, P., 2004. Factors affecting knowledge transfer in ITprojects. Engineering Management Journal 16 (1), 3–10.

Kirsch, L.J., 1996. The management of complex tasks in organizations: control-ling the systems development process. Organizational Science 7 (1), 1–21.

Kirsch, L.J., 1997. Portfolios of control modes and IS project management. In-formation Systems Research 8 (3), 215–239.

Kohli, R., Grover, V., 2008. Business value of IT: an essay on expanding re-search directions to keep up with the times. Journal of the Association forInformation Systems 9 (1), 23–39.

http://www.computerworld.co.ke/articles/2011/01/11/global-it-spending-total-us36-trillion-2011

http://www.computerworld.co.ke/articles/2011/01/11/global-it-spending-total-us36-trillion-2011


Kotnour, T., 1999. A learning framework for project management. Project Man-agement Journal 30 (2), 32–38.

Leavitt, H.J., Whisler, T.L., 1958. Management in the1980s. Harvard BusinessReview 36 (6), 41–48.

Lee, M.Y., 2007. Understanding Changes in Team-Related and Task-RelatedMental Models and Their Effects on Team and Individual Performance,Doctor of Philosophy edn, The Florida State University.

Leonard-Barton, D., 1992. Core capabilities and core rigidities: a paradox in manag-ing new product development. Strategic Management Journal 13 (5), 111–125.

Leseure, M.J., Brookes, N.J., 2004. Knowledge management benchmarks forproject management. Journal of Knowledge Management 8 (1), 103–116.

Levesque, L.L., Wilson, J.M., Wholey, D.R., 2001. Cognitive divergence andshared mental models in software development project teams. Journal of Or-ganizational Behavior 22 (2), 135–144.

Liu, L., Yetton, P., Sauer, C., 2003. The effects of organizational control onproject performance in construction and IT services companies. 14th Aus-tralasian Conference on Information Systems.

Markus, M.L., 2004. Technochange management: using IT to drive organiza-tional change. Journal of Information Technology 19 (1), 4–20.

McElroy,M.W., 2000. Integrating complexity theory, knowledgemanagement andorganisational learning. Journal of Knowledge Management 4 (3), 195–203.

Mitchell, V.L., 2006. Knowledge integration and information technology pro-ject performance. MIS Quarterly 30 (4), 919–939.

Nagarajan, A., Mitchell, W., 1998. Evolutionary diffusion: internal and externalmethods used to acquire encompassing, complementary. Strategic Manage-ment Journal 19 (11), 1063–1078.

Nonaka, I., Takeuchi, H., 1995. The Knowledge Creating Company: How Jap-anese Companies Create the Dynamics of Innovation. Harvard BusinessSchool Press, Boston, Mass.

Nunnally, J.C., 1978. Psychometric Theory, 2nd ed. McGraw Hill, New York,New York.

Ouchi, W.G., 1977. The relationship between organizational structure and orga-nizational control. Administrative Science Quarterly 22 (1), 95–113.

Ouchi, W.G., 1979. A conceptual framework for the design of organizationalcontrol mechanisms. Management Science 25 (9), 833–848.

Ouchi, W.G., 1980. Markets, bureaucracies, and clans. Administrative ScienceQuarterly 25 (1), 129–141.

Owen, J., Burstein, F., Mitchell, S., 2004. Knowledge reuse and transfer in aproject management environment. Journal of Information TechnologyCases and Applications 6 (4), 21–35.

Parker, S.K., Skitmore, M., 2005. Project management turnover: causes and ef-fects on project performance. International Journal of Project Management23 (3), 205–214.

Pearlson, K.E., Saunders, C.S., 2006. Managing and Using Information Sys-tems: A Strategic Approach, 3rd ed. John Wiley and Sons, New Jersey.

Pee, L.G., Kankanhalli, A., Kim, H., 2008. Examining knowledge sharing in ISdevelopment projects: a social interdependence perspective. Paper presentedat the International Research Workshop on IT Project Management 2008,Paris, pp. 61–73.

Peppard, J., 2007. The conundrum of IT management. European Journal of In-formation Systems 4 (16), 336–345.

Peppard, J., Daniel, E., 2007. Managing the realization of business benefitsfrom IT investments. MIS Quarterly Executive 6 (1), 1–11.

Pinto, J.K., Slevin, D.P., 1987. Critical factors in successful project implemen-tation. IEEE Transactions on Engineering Management 34 (1), 22–27.

Prieto, I.M., Revilla, E., 2006. Assessing the impact of learning capability onbusiness performance: empirical evidence from Spain. Management Learn-ing 37 (4), 499–522.

Project Management Institute, 2008. A Guide to the Project Management Bodyof Knowledge, 4th ed. Project Management Institute, Pennsylvania, USA.

PWC (Price Waterhouse Coopers), 2008. Why Isn't IT Spending Creating moreValue(at) http://www.pwc.com/en_US/us/increasing-it-effectiveness/assets/it_spending_creating_value.pdf2008(retrieved Oct 31 2011).

Quigley, N.R., Tesluk, P.E., Locke, E.A., Bartol, K.M., 2007. A multilevel in-vestigation of the motivational mechanisms underlying knowledge sharingand performance. Organization Science 18 (1), 71–88.

Reich, B.H., 2007. Managing knowledge and learning in it projects — a con-ceptual framework and guidelines for practice. Project Management Journal38 (2), 5–17.

Reich, B.H., Wee, S.Y., 2006. Searching for knowledge in the PMBOK® guide.Project Management Journal 37 (2), 11–27.

Reich, B.H., Gemino, A., Sauer, C., 2008. Modeling the knowledge perspectivein IT projects. Project Management Journal 39, S4–S14 (Supplement).

Rico, R., Sánchez-Manzanares, M., Gil, F., Gibson, C., 2008. Team implicit co-ordination processes: a team knowledge-based approach. Academy of Man-agement Review 33 (1), 163–184.

Rubinstein, D., 2007. Standish Group Report: There's Less Development ChaosToday. Software Development Times http://www.sdtimes.com/article/story-20070301-01.html. (accessed July 21, 2007).

Sauer, C., Reich, B.H., 2007. What do we want from a theory of project man-agement? A response to Rodney Turner. International Journal of ProjectManagement 25 (1), 1–2.

Sauer, C., Reich, B.H., 2009. Rethinking IT project management: evidence of anewmindset and its implications. International Journal of Project Management27 (2), 182–193.

Sauer, C., Gemino, A., Reich, B.H., 2007. The impact of size and volatility onIT project performance. Communications of the ACM 50 (11), 79–84.

Schindler, M., Eppler, M.J., 2003. Harvesting project knowledge: a review ofproject learning methods and success factors. International Journal of Pro-ject Management 21 (3), 219–228.

Scott Morton, M., 1991. The Corporation of the 1990s: Information Technologyand Organizational Transformation. Oxford University Press, Oxford.

Sense, A., 2003. Learning generators, project teams re-conceptualized. ProjectManagement Journal 34 (3), 4–12.

Söderlund, J., 2005. What project management really is about: alternative per-spectives on the role and practice of project management. International Jour-nal of Technology Management 32 (3), 371–387.

Szulanski, G., 1996. Exploring internal stickiness: impediments to the transfer of bestpractice within the firm. Strategic Management Journal 17, 27–43 (winter).

Tiwana, A., 2004. An empirical study of the effect of knowledge integration onsoftware development performance. Information and Software Technology46 (13), 899–906.

Tiwana, A., McLean, E.R., 2005. Expertise integration and creativity in infor-mation systems development. Journal of Management Information Systems22 (1), 13–43.

Tiwana, A., Bharadwaj, A., Sambamurthy, V., 2003. Antecedents of IS devel-opment capability: a knowledge integration perspective. The 24th Interna-tional Conference on Information Systems, Seattle, USA, pp. 246–258.

Vera, D., Crossan, M., 2003. Organisational learning and knowledge manage-ment: toward an interactive framework. In: Easterby-Smith, M., Lyles, M.(Eds.), Handbook of Organisational Learning and Knowledge Management.Blackwell, Oxford, pp. 123–141.

Walz, D.B., Elam, J.J., Curtis, B., 1993. Inside a software design team: knowl-edge acquisition, sharing and integration. Communications of the ACM 36(10), 63–77.

Ward, J., Daniel, E., 2005. Benefits Management: Delivering Value from IS andIT Investments. Wiley, Chichester.

Winch, G., 2002. Managing Construction Projects: An Information ProcessingApproach. Blackwell, Oxford.

Winter, M., Smith, C., Morris, P., Cicmil, S., 2006. Directions for future researchin project management: the main findings of UK government-funded researchnetwork. International Journal of Project Management 24 (8), 638–649.

Yetton, P.W., Johnston, K.D., Craig, J.F., 1994. Computer-aided architects: a casestudy of IT and strategic change. Sloan Management Review 35 (4), 57–67.

Yoo, Y., Kanawattanachai, P., 2001. Developments of transactive memory sys-tems and collective mind in virtual teams. International Journal of Organiza-tional Analysis 9 (2), 187–208.

Zachman, J.A., 1999. A framework for information systems architecture. IBMSystems Journal 38 (2), 454.

Zhang, Z., Hempel, P.S., Han, Y., Tjosvold, D., 2007. Transactive memory sys-tem links work team characteristics and performance. Journal of AppliedPsychology 92 (6), 1722–1730.

http://www.pwc.com/en_US/us/increasing-it-effectiveness/assets/it_spending_creating_value.pdf

http://www.pwc.com/en_US/us/increasing-it-effectiveness/assets/it_spending_creating_value.pdf

http://www.sdtimes.com/article/story-20070301-01.html

http://www.sdtimes.com/article/story-20070301-01.html

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