modeling the knowledge perspective of it projects

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S4 2008 � Project Management Journal � DOI: 10.1002/pmj

INTRODUCTION �

Information technology (IT) projects often enable organizationalchange. Therefore, successfully implementing an IT-enabled change iscrucial for organizational success. IT projects consume a large share oforganizational capital expenditure and have been undertaken for over

40 years. While researchers and practitioners have reported various levels ofIT project performance (Jørgensen & Moløkken-O�stvold, 2006; Sauer,Gemino, & Reich, 2007; Standish Group, 1994), it can be argued that IT proj-ects are usually delivered over budget and past deadline. More troublingthough is the perception that IT-enabled change does not generate theorganizational value that is anticipated (Brynolfsson & Hitt, 1998; Roach,1989).

Much of the research over the past 20 years on IT projects has focused onidentifying and managing the risk factors that are inherent in implementingthese projects. Some of these identified risk factors include size, complexity,organizational support, use of methodologies, and change management.Prescriptions for achieving project success focus on the behaviors of projectmanagers and project sponsors and encourage them to manage change,engage more fully, use methodologies carefully, and govern wisely. Thisadvice seems to have been heeded, and project success has improved some-what over the past decade (Hartmann, 2006; Standish Group, 1994). Thisresearch perspective considers an IT project as an arena in which action isparamount and in which tasks, budgets, people, and schedules must bemanaged and controlled to achieve expected results. This is a useful view, asit encourages the project manager to scope work, manage time and budget,and monitor progress.

Another perspective that is gaining momentum views a project as a placein which learning and knowledge is paramount. In this view, projects func-tion as conduits for knowledge that enters through people, methodologies,and prior learning. During the project, knowledge must be transferred, inte-grated, created, and exploited to create new organizational value. Learningand teaching are occurring at every level of the project: the executive spon-sor, the project manager, and the business and technical teams. During theproject, knowledge is created and knowledge can be lost.

Within an IT project, this focus on knowledge yields new insightsbecause IT projects are primarily knowledge work. Furthermore, today’s ITprojects are often more about the integration of systems than the constructionof software. In this environment, the project manager’s primary task is tocombine multiple sources of knowledge about technologies and businessprocesses to create organizational value.

These and other views of the IT project are complementary. Each bringsinsights into the factors that impact project performance. In this article,however, we focus only on the knowledge perspective, leaving aside other

Modeling the Knowledge Perspectiveof IT ProjectsBlaize Horner Reich, Faculty of Business Administration, Simon Fraser University,Vancouver, CanadaAndrew Gemino, Faculty of Business Administration, Simon Fraser University, Vancouver, CanadaChris Sauer, Saïd Business School, Oxford University, Oxford, United Kingdom

ABSTRACT �

Information technology (IT) projects are often viewedas arenas in which action is paramount, and tasks,budgets, people, and schedules need to be managedand controlled to achieve expected results. This per-spective is useful because it encourages the projectmanager to scope work, manage time and budget, andmonitor progress. Another perspective views a projectas a place where learning and knowledge is para-mount. In this view, projects are seen as a conduit forknowledge, which enters through people, methodolo-gies, and prior learning. During the project, knowledgemust be transferred, integrated, created, and exploitedto create new organizational value. Knowledge iscreated, and knowledge can be lost. Within an IT project,this focus on knowledge yields new insights, becauseIT projects are primarily knowledge work. From thisperspective, the project manager’s primary task is tocombine multiple sources of knowledge about tech-nologies and business processes to create organiza-tional value. These and other views of the IT projectare complementary. However, this article focusesonly on the knowledge perspective, leaving asideother views. This article is designed to bring togetherthe empirical literature, which has investigated theimpact of knowledge perspectives on IT project per-formance, and to suggest a temporal model of thisperspective. In the first part of this article, we considerthe knowledge-based view of an IT project and suggestdefinitions and a typology of knowledge. Then theknowledge risks model (Reich, 2007) is used as aframework within which to collect and examine theempirical data that support the knowledge-based viewof an IT project. In the third part of this article, theproblem of modeling knowledge and learning within ITprojects is addressed. The study begins with theTemporal Model of IT Project Performance (Gemino,Reich, & Sauer, 2008) and discusses evidence that itsknowledge-based constructs and subconstructs are influential with respect to project performance.The article ends by proposing a temporal model of theknowledge perspective of an IT project. There are fiveconstructs in this model: knowledge resources,knowledge creation, knowledge loss, project perform-ance, and learning. The content of these constructsand their expected interaction is discussed. Althoughthis stream of work is at its early stages, hopefully itwill convince researchers that further investigationinto knowledge and learning within projects is war-ranted because it has the potential to impact both thetheory and performance of IT projects.

KEYWORDS: lessons learned; knowledgemanagement; risk management; learning inprojects

Project Management Journal, Vol. 39, Supplement,S4–S14©2008 by the Project Management InstitutePublished online in Wiley InterScience(www.interscience.wiley.com)10.1002/pmj.20056

2008 � Project Management Journal � DOI: 10.1002/pmj S5

views. We also bring together theempirical literature that has investigat-ed the impact of knowledge perspec-tives on IT project performance andsuggest a temporal model of this per-spective.

In the first section of this article, weexamine the knowledge-based view ofan IT project and suggest definitionsand a typology of knowledge. Then weuse the Knowledge Risks model (Reich,2007) as a framework within which tocollect and examine the empirical datathat supports the knowledge-basedview of an IT project. In the long term, with additional data, we shouldbe able to refine the model, so it can bemore parsimonious and focus only onthose areas of major importance toproject outcomes.

In this article’s third section, weconsider the problem of modelingknowledge and learning within IT proj-ects. We start with the Temporal Modelof IT Project Performance (Gemino,Reich, & Sauer, 2008) because it has ele-ments of knowledge embedded in it.We also discuss the evidence showinghow its knowledge-based constructsand subconstructs are influential.

We end by proposing a preliminarymodel of the knowledge perspective ofan IT project. This stream of work is atits early stages. We hope that this pres-entation will convince researchers thatfurther investigation into knowledgeand learning within projects is warrant-ed because it has the potential toimpact both the theory and the per-formance of IT projects.

A Knowledge-Based View ofIT ProjectsMany research projects have investigat-ed aspects of knowledge and learningin projects. (See Reich [2004] for areview.) This research emanates from avariety of sources, including softwareengineering, project management,management information systems, andorganizational learning disciplines.This research is fragmented at present,with little convergence on constructs,

measures, and models of learning.Most studies focus on a single aspect ofknowledge management (Reich, 2004),such as integration, transfer, lessonslearned, or knowledge systems.

Although research into knowledgeand learning in projects is at an earlystage, a model has been proposed thatattempts to provide a repository for allof the existing work (Reich, 2007). Thismodel of knowledge-based risks in ITprojects suggests that there are 10 areasof risk occurring within an IT project’sfour parts: inputs, processes, outputs,and governance structure. The modelwas generated from previous researchand has been supported by in-depthinterviews with project managers inNorth America and New Zealand. Itremains largely conceptual at thispoint. However, research studies overthe past decade have shown supportfor different elements of it.

In this section, we briefly introducethe conceptual framework of knowledgeand learning in IT projects. Then, we show the empirical support for ele-ments in this model.

The conceptual framework for theknowledge and learning perspectivewithin IT projects contains three parts:a. a typology of knowledge that is critical

to IT project success;b. a definition of knowledge manage-

ment in the context of IT projects; andc. a model that identifies the knowledge-

based risks in an IT project.

What Kinds of KnowledgeNeed to Be Managed?Reich (2007) proposes that four kinds ofknowledge are important within ITprojects. Much of the research onknowledge and learning in projects hasfocused on the first two types—processknowledge and domain knowledge.This emphasis will be reflected in theempirical evidence shown later in thisarticle.

The first type of knowledge—process knowledge—is the knowledgethat team members and sponsors haveabout the project structure, methodol-

ogy, tasks, and time frames (Chan &Rosemann, 2001; Meehan & Richardson,2002).

The second type of knowledge isdomain knowledge, the knowledge ofthe industry, firm, current situation,problem/opportunity, and potentialsolutions (including technology andbusiness process). This knowledgeencompasses three types of knowledgeidentified in Chan and Rosemann(2001): business, technical, and prod-uct knowledge. Domain knowledge isspread widely within and outside theproject team. The project sponsor maybe the most knowledgeable about theindustry and the problem or opportu-nity being addressed. Technical expertsinside and outside the company haveknowledge about the technologies thatcould be brought to bear. Project mem-bers will have deep knowledge aboutthe company and its business processes.Many researchers have discusseddomain knowledge as it relates to the issues of knowledge integration(Majchrzak & Beath, 2003; Nelson &Cooprider, 1996; Walz, Elam, & Curtis,1993) and coordination (Crowston &Kammerer, 1998; Faraj & Sproull, 2000),as well as learning (Stein & Vandenbosch,1996) and project governance (Henry,Kirsch, & Sambamurthy, 2003).

There are also two other kinds of knowledge. The third is institutionalknowledge. This knowledge is a mix ofan organization’s history, power struc-ture, and values; this knowledge istransferred via stories or anecdotes byorganizational insiders and observers.The fourth kind of knowledge necessaryin an IT project is cultural knowledge.This is the knowledge that project man-agers need to lead teams composed ofindividuals from differing disciplinesand cultures.

What Is Knowledge Managementin IT Projects?We developed the following definitionfrom our prior research into knowledgeand learning within IT projects. Itemphasizes the temporary nature of


Modeling the Knowledge Perspective of IT ProjectsP

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knowledge (i.e., knowledge loss) andthe need to create and integrate theknowledge from different sources.

Knowledge management in thecontext of a project is the application ofprinciples and processes designed tomake relevant knowledge available to the project team. Effective knowledgemanagement facilitates the creationand integration of knowledge, mini-mizes knowledge losses, and fillsknowledge gaps throughout the durationof the project.

What Knowledge-Based RisksExist in IT Projects?The 10 knowledge-based risks in ITprojects have been organized into afour-component model composed ofknowledge inputs, project governance,project operational phases, and proj-ect outputs. This model is outlined in

Figure 1. Each component is discussedbelow.

InputsThere are two main knowledge risks atthe beginning of a project: the failure tolearn from past projects and the failureto meet the project’s knowledge needsduring team selection.

Without access to lessons learnedfrom comparable projects, the teamwill lack important domain or institu-tional knowledge. If this knowledgewere available to the project, it couldimpact the emergent process knowl-edge and shape how the project man-ager plans and monitors the project(Thomas & Tjader, 2000).

Finding the right team members isimportant because the project managerwill want all relevant knowledge areasincluded in the team or available to the

team when they need it (Grant, 2006;Walz et al., 1993). When the team selec-tion process is flawed, the project man-ager will not know what the teamknows collectively or, more important,what it does not know.

Governance ProcessesProject governance is the responsibilityof several key positions: executivesponsor, project champion, projectmanager, and project steering commit-tee. From a knowledge perspective,effective project governance involvestwo issues—volatility and role under-standing.

Volatility risk in governance refersto the loss of any member of the gover-nance structure who controls or influ-ences project resources and direction.Whether moving a project manager orother executive away from a project is

Failure tolearn#10

Lessons notlearned

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Teamselectionflawed

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Plan

Design

Configure

Implement

Operational project processes

Lack ofknowledge map

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Inadequateknowledgeintegration

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Incompleteknowledge

Transfer#6

Exit of teammembers

#7

Loss betweenphases

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Projectinputs Project

outputs

Project processes

Project governance processes

Volatility ingovernance team

#3

Lack of roleknowledge

#4

Figure 1: The knowledge risks model of IT projects (Reich, 2007).


done for strategic reasons or whether itis totally unplanned, there are importantinstitutional, domain, and processknowledge losses that may cause targetsto be missed and benefits to go unreal-ized (Gemino et al., 2008).

Another risk is an executive spon-sor’s lack of role knowledge (Henry et al., 2003). First-time project sponsorsmay not know when to support theproject and when to tighten up the reins,or they may fail to differentiate betweenthose project issues that are seriousand those that are easily addressed.

Operational Processes—Plan, Design,Build, and ImplementWithin the main body of an IT projectare five knowledge risks: knowledgeintegration, knowledge transfer, loss ofteam members, lack of a knowledgemap, and loss between phases.

Knowledge integration is theprocess of bringing different specialistforms of knowledge (e.g., business andtechnical domain knowledge) togetherto address an identified issue and tocreate knowledge that is greater thanthe sum of its parts: a new idea, ashared understanding, or an integrativemodel (Garrety, Robertson, & Badham,2004; Huang & Newell, 2003;). Poordecisions can result when specialistknowledge is not integrated. For exam-ple, a technical specialist configuringan enterprise system may not under-stand clearly how the business processworks. Some research (Postrel, 2002;Tiwana, 2003) has tried to determinehow much knowledge needs to be inte-grated for optimum results.

Within a project that is implement-ing packaged software or new hard-ware, domain knowledge transfer fromvendor or consultant to the internalproject members is critical (Boh, 2003;Sense, 2003; Volkoff, Elmes, & Strong,2004; Walz et al., 1993). Apart from thedifficulty in explaining the inner work-ings of technology, there are problemsof agency inherent in this risk.

When key members of the teamleave the project, knowledge is lost

(Eskerod & Blichfeldt, 2005; Gable,Scott, & Davenport, 1998; Schindler &Eppler, 2003). Although project man-agers conceptually understand thatlosing key team members results inknowledge gaps, they often fail to createa plan to mitigate such losses.

Project managers and team mem-bers make a multitude of interrelateddecisions. The more difficult thedomain problems are, the more impor-tant it is that team members possess aknowledge map—showing the knowl-edge within the team (i.e., who knowswhat) and the knowledge available tothe team—that enables them to addresscomplex problems efficiently and effec-tively (Grant, 2006; von Stamm, 2005).

Because team composition oftenchanges from phase to phase in IT proj-ects, there is a significant risk that thedomain knowledge generated by onephase will be inadequately transmittedto the next phase. This problem is exac-erbated in long projects and withinvirtual project teams (Rus, Lindvall, &Sinha, 2001).

Project OutputsThe knowledge risk at the end of theproject is that lessons learned are rarelysatisfactorily captured (Middleton,1967; Williams, 2004, 2006).

An incomplete debriefing duringand at the end of a project leaves teammembers with a fragmented idea ofwhat was learned and why things wentwrong or right. When project managersfail to capture lessons learned, theyprevent team-level learning and hinderopportunities to improve organiza-tional competency in managing andcompleting projects.

Empirical Support for Knowledge-Based Risks in IT ProjectsOver the past decade, researchers havebeen trying to estimate the impact ofknowledge-based risks on project per-formance. Some risks (e.g., lack of aknowledge map) have been studied moreintensively than others (e.g., knowledgeloss between phases). In this section,we present a sample of the empirical

work done to date (Table 1), showingonly research that measured the impactof knowledge and learning on projectoutcomes. We discuss the major find-ings in the sections that follow.

Summary of the EmpiricalEvidence to DateFrom the empirical research publishedto date, we have identified severalobservations.

Although lessons learned is oftenconsidered to be the most importantelement of knowledge and learningwithin and across projects, the empiri-cal evidence suggests that the principleof gathering and disseminating lessonslearned is well understood in mostproject-oriented organizations butthat the practices for gathering anddisseminating lessons learned areimmature.

Expertise coordination is the mostwidely studied type of knowledge man-agement and shows a very strong influ-ence on IT project performance. Thisexpertise coordination can be thoughtof as complementary to administrativecoordination; both are necessary incomplex projects. What is missing fromthe literature is advice to the practitionerabout how to go about managingexpertise. Research connecting theconcept of expertise location manage-ment in organizations (Smith &McKeen, 2006) may be helpful in thisregard.

Volatility in the governance teamhas only recently emerged in researchbut shows promise with respect tohaving a significant impact on projectperformance. Research needs to bedone to determine the causes ofturnover and to investigate the varyingimpacts of these causes (i.e., voluntaryversus forced departure).

It has been accepted for some timethat the impact of the executive sponsoron IT project performance is signi-ficant. Gemino et al. (2008) have sepa-rated the action and knowledge perspectives to show that this impact ismade up of at least two components:



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K Risk Empirical Studies Linking This K Risk to Project Performance

Lessons learned Williams (2006), in a survey of 522 PMI members, found that only 36% of respondents felt that lessons learned werenot brought in transferred to other projects. However, 65% of respondents reported that lessons learned were sometimes or routinely

incorporated into processes.

Knowledge of Nidumolu (1995), in a survey of 64 projects, showed that project uncertainty has a direct effect on project performance.team members Wallace and Keil (2004) showed that a lack of knowledge within the project team (e.g., inadequately trained, and networks inexperienced, unfamiliar, lack specialized skills) and project manager (i.e., inexperienced) can impact both project

processes and project product outcomes.

Gemino et al. (2008) showed that knowledge resources significantly impacted project management practices and organizational support, which in turn impacted project performance.

Loss of a Parker and Skitmore (2005) surveyed 67 project managers in the aerospace industry. Respondents felt that themember of turnover of a project manager—during a project—negatively impacted both team and overall project performance.governance Sauer et al. (2007) surveyed 421 projects and found that the loss of a project manager (which happened, on average,team on one in every two projects) resulted in a total negative impact on project schedule, budget, and scope of 17%. They

also showed that changing the executive sponsor (which happened, on average, on one in every four projects)caused a 6% reduction in scope delivered.

Gemino et al. (2008), in a survey of 194 projects, showed that volatility has a very significant negative impact on process performance. The most important component within volatility was the loss of a project manager orexecutive sponsor.

Executive Karlsen and Gottschalk (2003), in a survey of 68 projects, showed that strategic knowledge transfer (the knowledge sponsor role that is transferred from the executive sponsor) has a direct impact on project outcomes relating to implementation knowledge and client benefits.

Gemino et al. (2008), in a survey of 194 projects, showed that knowledge resources significantly impacted project management practices and organizational support, which in turn impacted project performance. An influential component of the knowledge resources construct was executive sponsor knowledge.

Knowledge Tiwana, Bharadwaj, and Sambamurthy (2003) studied 133 projects and reported that the level of knowledge integrationintegration within the team was more important to project success than a pre-existing positive relationship between business

and IT.

Tiwana (2004), in a survey of 232 software projects, showed that knowledge integration (domain and technical knowledge) was associated with higher software development efficiency, greater development effectiveness,and fewer defects.

Knowledge Karlsen and Gottschalk (2003), in a survey of 68 projects, showed that serial transfer (within teams) and expert transfertransfer (from outside experts) had a direct impact on project outcomes relating to attainment of targets and system quality.

Loss of team members

Knowledge map Crowston and Kammerer (1998) studied a group of requirements engineers and were able to use theories of (i.e., expertise transactive memory (Wegner, 1987) and collective mind (Weick & Roberts, 1993) to explain software requirementscoordination) that were complete, accurate, and consistent.Theoretical bases Faraj and Sproull (2000) surveyed project team members and stakeholders working on 69 software developmentcome from projects. They found that by coordinating expertise, teams could improve their performance an additional 25%transactive above applying traditional project management practices.memory and

Yoo and Kanawattanachai (2001) tracked the progress of 38 virtual project teams and found that project success was collective mindstrongly influenced by each team member’s knowledge of other team members’ areas of expertise and by thetheories.team’s ability to harness this knowledge to achieve the project’s goals.

Akgün, Byrne, Keskin, Lynn, and Imamoglu (2005), in a survey of 69 projects, showed that transactive memory systems(i.e., people accessing knowledge through their network of contacts) affect project outcomes, especially when the task is complex.

Gemino et al. (2008), in a survey of 194 projects, showed that project management practices significantly influenced both project and product performance. Expertise coordination was the most influential component of project management practices.

Loss between phases

Lessons not Williams (2006), in a survey of 522 PMI members, found that only 47% of lessons learned were learned transferred from the individual to the project team.

Table 1: Empirical support for elements in the knowledge and learning model.


what the executive sponsor knows (e.g.,about project role, project manage-ment, technology, and the businessdomain) and what the executive spon-sor actually does during the project.Because the impact of role knowledgehas only recently been investigated, we cannot say that one influences theother. More research is needed toexplore these connections.

Several studies have shown that theknowledge possessed by team mem-bers impacts team performance. Thisneeds more study, since we do not yetknow what aspects of knowledge areinfluential or under what conditionsthese aspects affect performance.Gemino et al. (2008), however, haveshown that knowledge resources do notdirectly impact project performance;rather, these are mediated by projectmanagement practices and organiza-tional support.

Can We Model Project PerformanceUsing a Knowledge Lens?As far as we know, there is no modelthat fully incorporates both the actionand the knowledge perspectives of ITprojects and attempts to predict orexplain the variances in project per-formance. For this discussion, we drawupon a Temporal Model of IT ProjectPerformance (Gemino et al., 2008). Thismodel includes some knowledge ele-ments as well as the action perspective;it can be used as a starting point for thediscussion.

In the Temporal Model of IT ProjectPerformance (see Figure 2), the risksand resources in a project are modeledover time. These time elements are verysimple: T-1 (the time before the projectstarts), T-2 (the time during which theproject is conducted), and T-3 (the endof the project).

Two elements are modeled at T-1:the structural risks of the project (size,duration, budget, schedule, technicalcomplexity) and the knowledgeresources associated with the project(knowledge of the team, the executivesponsor, the client manager, and the

project manager as well as the uncer-tainty involving the requirements).Therefore, at T-1, the model separatesout knowledge resources from otherrisks.

At T-2, there are three constructs ofinterest: organizational supportresources, project managementpractices, and volatility risk. These con-structs contain elements from both theaction and knowledge perspectives.

The organizational supportresources construct is drawn from apure action perspective and measuresthe support and commitment behav-iors of the executive sponsor and clientmanager.

The project management practicesconstruct considers the administrativeactions of the project manager (use ofmethodologies, use of tools), processintegration (within the team and withthe users) and expertise coordination(knowledge mapping and sharing ofknowledge).

Volatility risk contains three sub-constructs: exogenous change (i.e.,changes in industry, company strategy),project target change (i.e., changes tobudget, schedule, or scope), andknowledge loss (i.e., loss of the projectmanager or executive sponsor).

At T-3, there are two aspects of proj-ect performance: process performance(budget and schedule attainment) andproduct performance (organizationalbenefits and quality). There are noknowledge outputs explicitly containedin this model.

This model was tested via a surveyof 194 projects from 194 different proj-ect managers. We used data from asurvey of PMI chapters in threedifferent U.S. cities, all located in Ohio.We used a formative approach toestimate the constructs, an approachthat involved using a number ofsubconstructs. To analyze this data, wethen used a partial least squares (PLS)approach. PLS is a structural equationmodeling technique utilizing aprincipal component-based approachto estimation. We selected PLS because

it is preferred over covariance-basedtechniques when developing theoriesand using formative constructs (Gefen,Straub, & Boudreau, 2000).

The significant relationships areshown in Figure 2 with arrows and apositive or negative sign. If there are nolines between two constructs, it meansthat there was not a significant relation-ship between these in the sample data.

The findings show partial supportfor the knowledge perspective of ITprojects. This support is shown both atthe construct level (i.e., knowledgeresources) and at the subconstruct level(i.e., expertise coordination withinproject management practices andknowledge loss within volatility risk).Each is discussed below.

Influence at the Construct Level:Knowledge ResourcesAt T-1, a project starts with an initialallotment of knowledge resources.Higher levels of this knowledge resourcepositively influence both organiza-tional support and project managementpractices. Project management prac-tices and organizational supportinfluence each other positively in a typeof virtuous cycle. Following the model,project management practice is seen as affecting product performancedirectly and process performance bothdirectly and indirectly, through organi-zational support.

Through this perspective, a projectmanager can be viewed as leveragingknowledge resources to manage theproject through project managementpractices so as to positively influenceorganizational support and eventually,both product and process perform-ance. From this perspective, knowledgeresources help the project managertake the necessary actions that will helpthe team achieve the project’s goals.

Another way of thinking about thevalue of initial knowledge resources isthat high levels of knowledge resourcesenable high levels of organizationalsupport and project management prac-tices, and that in this environment,



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organizational support can make itsfull impact on project success.

The Temporal Model of Project Per-formance therefore suggests a strongrole for initial knowledge resources.

Influence at the SubconstructLevel: Expertise Coordinationand Knowledge LossThe story of the impact of knowledgecontinues as the subconstructs under-lying the larger constructs are consid-ered.

Each construct in the model shownin Figure 2 above was formed by con-sidering a number of independent sub-constructs. The subconstructs shouldtherefore not be viewed, as is tradition-ally the case, as reflective measures ofthe major construct. Instead, weviewed the major construct as beingformed by separate and largely inde-pendent contributions from severalsources. For example, requirementsuncertainty was viewed as being animportant part of the knowledgeresources construct but was not expec-

ted to depend on or co-vary with othersubconstructs such as the knowledge ofthe team, the project manager, or thesponsor.

Table 2 shows the loadings of thesubconstructs from the constructs inFigure 2, which contain knowledgeperspectives.

We have already discussed theimpact of knowledge resources at the construct level. It is interesting tolook inside this construct and see thateach element—but most especiallyrequirements uncertainty and teamknowledge—is a significant contributor.

We have seen from Figure 2 thatproject management practices posi-tively influence both aspects of projectperformance—process and productperformance. When we looked insidethe components of project manage-ment practices, we saw that expertisecoordination, the knowledge compo-nent, has more influence than bothadministrative coordination (i.e., taskand time monitoring) and process inte-gration.

In the lower part of Figure 2, wefound that volatility risk negativelyinfluences process performance.Examining the components of volatilityrisk, we observed that the loss of aproject manager or executive sponsoris the most influential part of thisconstruct.

At this point in our investigation,we cannot be more specific about theimpact of knowledge components onproject performance, but there is astrong indication that they have animportant role to play.

A Preliminary Model of the KnowledgePerspective of IT ProjectsIt is interesting to note that two rela-tively independent efforts to model ITprojects (Gemino et al., 2008; Reich,2007) both resulted in temporal models.At present, we have only a verysimplistic understanding of the impactof time within a project on knowledgeand risk. Future research is needed toexplore the impact of time at a moregranular level.

We have shown the potential of theknowledge perspective to inform ourunderstanding of IT project perform-ance. We now move to consider how tomodel this perspective so that moreempirical work can be done to evaluatewhich aspects are the most influential.There are several changes that shouldbe made to the temporal model so thatit can fully specify the knowledge andlearning risks considered in Reich (2007):• At T-1, add elements to the knowledge

resources construct such as lessonslearned.

• At T-2, model knowledge creationthrough transfer and integration.

• At T-2, separate expertise coordina-tion from the other project manage-ment practices that manage tasks andtime. This might create overlapsbetween the action and knowledgeactivities of the project manager.Obtaining conceptual clarity couldprove to be a difficult task.

• At T-2, add the within-project learningthat is generated when teams debrief

Construct Loading

Subconstruct

Knowledge Resources

Requirements Uncertainty 0.578

PM Knowledge 0.320

Executive Sponsor Knowledge 0.464

Team Knowledge 0.845

Project Management Practices

Administrative Coordination 0.779

Expertise Coordination 0.901

Process Integration 0.370

Volatility Risks

Project Target Volatility 0.791

Knowledge Loss—of PM or Exec Sponsor 0.856

External Volatility 0.281

Table 2: Loading for selected subconstructs in the TMPP model.


between phases, examine theirassumptions at critical decisionpoints, and learn as they go.

• At T-2, model knowledge loss betweenphases and knowledge loss due to theloss of team members.

• At T-2, separate the aspects of volatili-ty that deal with knowledge loss dueto turnover of governance roles fromother aspects of volatility such asexogenous change and target change.

• At T-3, add the knowledge outputs ofan IT project. A first step could involvemodeling individual learning andorganizational learning as knowledgeoutputs.

Figure 3 shows a preliminary knowl-edge-based model for IT projects. Weacknowledge that this model considersonly the knowledge risks shown inReich (2007). This model admittedlydoes not consider the impact of culturaland institutional knowledge on projectsuccess and also may fail to capture theclient’s knowledge needs andcontributions. These and other short-comings are left for future research.

In this model, we suggest that fiveconstructs be used to model the knowl-edge perspective within IT projects:1. The first construct would contain the

initial level of knowledge resourcesavailable to the project. This wouldbe measured at T-1 through theknowledge inputs (e.g., knowledge ofteam members, project manager,executive sponsor, client manager,knowledge networks, lessonslearned) and the knowledge holes(e.g., requirements uncertainty).Knowledge resources would beexpected to positively influenceknowledge creation, since a higherlevel of knowledge within the projectwould enable more advanced meas-ures of knowledge integration, trans-fer, and coordination.

2. The second construct would repre-sent the knowledge lost during T-2.This might include the loss of peoplesuch as the project manager, projectsponsor, and team members as wellas other key sources of knowledgesuch as vendors and consultants.Knowledge loss would be expected to

vary indirectly with knowledge cre-ation, as higher levels of learningmight encourage project participantsto remain on the project, while signif-icant knowledge loss might slow orcripple the creation of new knowl-edge. Knowledge loss would beexpected to negatively influence bothproject and knowledge outcomes.

3. The third construct would representefforts taken by the project manager(and other project leaders) to createnew knowledge through integration,transfer, and coordination. It wouldalso represent the within-projectlearning accomplished by the projectparticipants. Knowledge creationwould be expected to positively influ-ence both project and knowledgeoutcomes.

4. The fourth construct would containthe traditional measures of projectsuccess: the attainment of targets(process performance) and theattainment of organizational value(product performance).

5. The fifth construct would model theknowledge outputs of the project.

Knowledgeresources

Organizationalsupport

resourcesProjectprocess

performance

Projectproduct

performance

Projectmanagement

practices

Volatilityrisk

Structuralrisk

(size and techcomplexity)

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Figure 2: Temporal model of IT project performance (Gemino, Reich, & Sauer, 2008).



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At the individual level, this wouldinvolve assessing increases in process,domain, institutional, and culturalknowledge stocks. At the organiza-tional level, this would involve meas-uring the overall capacity of theorganization to execute projects suc-cessfully (both by attaining targetsand by obtaining value).

There is much more work to bedone in order to understand the knowl-edge perspective of IT projects. In thisarticle, we have discussed the empiricalsupport for such a view and proposed apreliminary model. We hope that thiswork stimulates future discussion andfurther exploration. �

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Knowledgeresources

T-1:Knowledge resources

T-2:Knowledge creation and loss

T-3:Outcomes

Projectperformance:Process and

product

Learning:Individual andorganizational

Knowledgecreation:

Integration,transfer andcoordination

Within-projectlearning

Knowledgeloss

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Figure 3: A preliminary model of the knowledge perspective of IT projects.


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Blaize Horner Reich is a professor in the busi-ness school at Simon Fraser University,Vancouver, British Colombia, Canada. Beforeher graduate studies, she worked for manyyears in the IT industry as a practitioner, proj-ect manager, and consultant, focusing on the

financial services and utilities sectors. She iscurrently an editorial board member for sever-al academic journals and a corporate director.Her research has been published in a widerange of journals, including MIS Quarterly, theJournal of Management Information Systems,the International Journal of ProjectManagement, and the Project ManagementJournal. Her current project-related researchfocuses on (1) modeling risk and IT projectperformance, (2) the evolving role of IT project managers, and (3) the use of knowl-edge management concepts in IT project gov-ernance. She speaks frequently at academicand practitioner gatherings and, with colleagues, has launched a research-based,interactive Web site called PMPerspectives.orgfor project managers.

Andrew Gemino is an associate professor anddirector for the Faculty of BusinessAdministration at the Surrey Campus of SimonFraser University. His research interests includeinformation technology project managementand the evaluation of requirements develop-ment techniques. His work has been publishedin academic journals including the Journal ofMIS, Communications of the ACM, and theEuropean Journal of IS. He is funded throughgrants from the National Sciences and Research

Council (NSERC) and the Social Sciences andHumanities Research Council (SSHRC). Herecently coauthored a textbook entitledExperiencing MIS for Pearson Education Canada.He is president of the AIS Special Interest Groupon Systems Analysis and Design (SIGSAND). Healso volunteers his time in the SurgeonInformation System Working Group for theSurgical Oncology Network associated with theBC Cancer Agency.

Chris Sauer is a fellow in information manage-ment at Saïd Business School, University ofOxford, United Kingdom. He has 30 years’experience in the information technology (IT)industry as a computing professional, consultant,and academic. In the international community,he holds a number of positions including jointeditor-in-chief of the Journal of InformationTechnology. He is the author of many books,chapters, and journal articles. He contributed achapter to the AMA Handbook of ProjectManagement, which won the 2007 David I.Cleland Literature Award. He has conducted con-sulting and training assignments for a widerange of private- and public-sector organiza-tions. His research interests cover a wide scope,and his current primary research stream seeksto improve IT project outcomes through knowl-edge and risk management.

modeling the knowledge perspective of it projects

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