Measuring performance of knowledge-intensive workgroups through social networks

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<ul><li><p>PA</p><p>PE</p><p>RS</p><p>34 June 2009 Project Management Journal DOI: 10.1002/pmj </p><p>INTRODUCTION </p><p>The performance of individuals in knowledge-intensive work in anyform of organization remains critical to the success of both individual-level and organization-level goals. Understanding the factors thatenhance and diminish the performance levels of individuals is there-fore a necessity for monitoring and managing performance. Accordingly, agrowing body of research in management and organizational psychologyproposes to understand performance by decomposing its constructs basedon task level and contextual levels (Borman &amp; Motowidlo, 1993). Theoriesfrom information systems (IS) research, for example, suggest that individualperformance can be understood by examining the task-technology fitwithin organizational human resources (Goodhue &amp; Thompson, 1995). Inproject environments, much emphasis is on models of the personal attrib-utes of project personnel that relate to performancefor example, teamleadership effectiveness (Thamhain, 2004), management and leadershipstyles (Dvir, Sadeh, &amp; Malach-Pines, 2006; Turner &amp; Mller, 2005), and softskills such as motivation (Muzio, Fisher, Thomas, &amp; Peters, 2007; Peterson,2007). However, these models do not account for the importance of socialprocesses that weave together a rich fabric of human or technology-enabledsocial and professional relationships that contribute largely toward per-formance. To this end, an emergent discipline of social networks theory andresearch takes as its central premise the embeddedness (Granovetter, 1985,p. 1065) of individuals in social networks. The novelty of this stream ofresearch lies in how it draws on the structural properties of individuals in asocial network to explain outcomes such as individual performance.</p><p>In this article, we examine the inherent relationship between profession-al network structure and individual performance by developing a theoreticalframework based on existing literature in the sociology, information science,and management science disciplines. By obtaining a pattern of network ofadvice-seeking interactions, we examine the fine-grained associationsbetween an individuals network properties, measured by social networkstructure, position, and tie variables, and its relationship with performance,measured by the individuals performance attitude about the various dimen-sions of task-level activities. The following section lays the conceptual foun-dation for the study, followed by the epistemological stance taken and theoretical justification of the hypotheses developed for the study. The latterparts of the article discuss the methodology for the study, including the sur-vey development procedure, followed by results and discussion.</p><p>Measuring Performance ofKnowledge-Intensive WorkgroupsThrough Social NetworksKon Shing Kenneth Chung, Project Management Graduate Programme, University ofSydney, AustraliaLiaquat Hossain, Project Management Graduate Programme, University of Sydney,Australia</p><p>ABSTRACT </p><p>In this article, we examine the effect of socialnetwork position, structure, and ties on the per-formance of knowledge-intensive workers indispersed occupational communities. Usingstructural holes and strength-of-tie theory, wedevelop a theoretical framework and a valid andreliable survey instrument. Second, we applynetwork and structural holes measures forunderstanding its association with perform-ance. Empirical results suggest that degreecentrality in a knowledge workers professionalnetwork positively influences performance use,whereas a highly constrained professional net-work is detrimental to performance. The find-ings show that social network structure andposition are important factors to consider forindividual performance.</p><p>KEYWORDS: social network; structure; ties;position; performance; knowledge-intensivework</p><p>Project Management Journal, Vol. 40, No. 2, 3458</p><p> 2009 by the Project Management Institute</p><p>Published online in Wiley InterScience </p><p>( </p><p>DOI: 10.1002/pmj.20115</p></li><li><p>June 2009 Project Management Journal DOI: 10.1002/pmj 35</p><p>BackgroundSocial NetworksBy social network, we mean a con-stituent of two or more actors (individ-uals) who are connected through oneor more relationships such as providingadvice, information, and so on. Socialnetwork studies have long been con-cerned with exploring structural and tieeffects, with a view toward illuminatingand explaining patterns of relation-ships in order to infer some outcome.For social network scholars, the raisondtre is that the structure of relation-ships among actors and the location ofindividual actors in the network haveimportant behavioral, perceptual, andattitudinal consequences both for theindividual units and for the system as awhole (Knoke &amp; Kulinski, 1992). At theindividual level, the debate concen-trates on how the structural position ofan individual in the network impacts anoutcome, such as performance, of thatperson.</p><p>Social Networks and PerformanceNetwork effects on individuals abilityto perform better have been document-ed in studies on communications, soci-ology, and social psychology (Coleman,1988; Guetzkow &amp; Dill, 1957; Leavitt,1951). Previous studies further demon-strate that actors with a dense socialnetwork perform better (Oh, Chung, &amp;Labianca, 2004; Reagans &amp; McEvily,2003). Furthermore, actors who are richin structural holes (i.e., having connec-tions to social clusters or groups whoare themselves not well connected) arebetter situated in their social networkto obtain, control, and broker informa-tion (Burt, 1992). Beginning with earlyliterature about communication pat-terns and the performance of groupsand individuals in project and organi-zational contexts, researchers demon-strated that, rather than being remote,impersonal, and rigid, knowledge-intensive work was actually communal,reflecting a strong interpersonal net-work of interconnected workers. Thestudies also suggested that informal</p><p>networks were equally or more impor-tant than formal networks in knowl-edge-intensive work, with the premisebeing that individual performance wasa function of network structure(Gabbay &amp; Leenders, 2001). In fact,studies relating network structures toperformance have shown that in-degree centrality, betweenness central-ity, and density in network structuressuch as advice networks were related to coordination and project-relatedperformance (Hossain, Wu, &amp; Chung,2006).</p><p>Problems Related to GeographicallyDistributed Knowledge-Intensive WorkThe quality of job performance inknowledge-intensive work is affectedby a variety of factors, such as experi-ence, education, keeping abreast ofwork-related and technologicalchanges, and so on. Holding such indi-vidual properties constant, perform-ance to a large extent is the product ofobtaining the right information toaccomplish the task at hand or to solvecomplex problems. For example, find-ing information and finding expertisefor handling the right information iscrucial for job performance. However,although knowledge and expertise arecritical resources, their mere presenceis insufficient to produce high-qualitywork. As Faraj and Sproull (2000) argue,expertise must be managed and coordi-nated in order to leverage its potential.This entails knowing where expertise islocated and where it is needed, andbringing needed expertise to bear. Thisproblem is accentuated when geo-graphical barriers are imposed. Grinter,Herbsleb, and Perry (1999) argue thatirrespective of the area of expertise,product structure, processes, and cus-tomized steps in organizational work,one of the most pertinent problems isthe location of expertise. </p><p>In distributed project environ-ments especially, Cross and Cummings(2004) claim that individuals who arenot aware of the location of expertiseelsewhere and who have fewer ties</p><p>spanning organizational and geo-graphical boundaries will have difficul-ty obtaining useful information forwork purposes. Furthermore, there isplenty of literature that emphasizes theimportance of social and professionalnetwork structure, position, and tiediversity. For instance, individuals whotend to be in closed networks tend tohave nondiverse ties, and the interac-tions are usually with the same individu-als. Such individuals are less successfulin adapting to a changing environ-ment and in receiving useful and novelinformation, and their work is thusmarked with low-quality performance(Ancona &amp; Caldwell, 1992; Cummings,2004; Podolny &amp; Baron, 1997; Reagans &amp;Zuckerman, 2001).</p><p>Research QuestionsGiven the arguments above, the follow-ing questions motivate this research: How can individual performance be</p><p>understood through the emergentpatterns of social processes that con-stitute performance?</p><p> How can it be evaluated? What is the role of social influence</p><p>and social networks (that create suchinfluence) in understanding individ-ual performance?</p><p> Why is understanding social networkstructure and position important forunderstanding individual perform-ance?</p><p> How does one account for social fac-tors, apart from personal and demo-graphic factors, that are important forenhancing individual performance inproject environments?</p><p>In order to shed light on the abovephilosophical questions, one needs toexplore possible answers by reviewingthe literature in the area of social net-works and performance. While there iscurrently a lack of literature that tiesthese constructs together coherently inproject contexts, it is important thatthese constructs are explored individual-ly, jointly, and holistically in a sequentialmanner. Figure 1 depicts a conceptual</p></li><li><p>36 June 2009 Project Management Journal DOI: 10.1002/pmj </p><p>Measuring Performance of Knowledge-Intensive WorkgroupsP</p><p>AP</p><p>ER</p><p>S</p><p>framework, and the following sectionexplores some of the earliest works in thedomain of network structure and per-formance.</p><p>Classical Works of NetworkStructure and Performance Bavelas-Leavitt ExperimentOne of the earliest studies that relatedsociometric aspects of human commu-nication patterns to performance wasthat of the Bavelas-Leavitt Experiment(Bavelas, 1950; Leavitt, 1949), alsoknown as the MIT experiment. Drawingfrom the assumption that (1) success ofany classes of tasks depends upon aneffective flow of communication (hold-ing the nature and content of the com-munication constant) and (2) a fixedcommunication pattern affects taskperformance and individual outcome,the motivating question in the studyasked, Under what principles may apattern of communication be deter-mined that will in fact be a fit one foreffective and efficient human effort?The question sought to answer, througha laboratory-controlled experiment,how social network structure measuredin terms of patterns of communicationaffects the work and life of individualswithin groups.</p><p>The experiment consisted of fivepeople who had to communicate witheach other through enclosed cubicles</p><p>in order to solve a puzzle. Each subjectwas given five symbols from a set of six.All had unique symbols, but there wasone symbol from each group of five thatwas common to all of the groups. Thepuzzle was solved when a consensuswas reached as to what the commonsymbol was. The experiment was trialed15 times. None of the subjects kneweach other, nor did they know the num-ber of subjects in the study, or the configuration of the communicationstructure. The channels and flow ofcommunication was controlled by theexperimenter. The subjects could passas many messages as they wantedthrough the cubicle lines, depending onthe structure of the communicationchannels as shown in Figure 2.</p><p>The performance of network struc-tures was evaluated on the basis of pat-tern comparison and node-level analysis.Performance of the task-orientedgroups was measured in terms of thetime required to complete the puzzleand the number of errors made in theprocess of guessing the right answer.When the patterns of various structureswere compared, the completion time(i.e., time that it took to complete thepuzzle) for groups using the star andY structures was on average relativelyshorter than for groups using the otherstructures (the circle and line struc-tures). The explanation offered byLeavitt (1951) was that centralizationwas key to influencing performance.Using centralization (measured as thesum of the internal distances of nodes xto y) as an operational construct, it wasfound that patterns that demonstratedhigher centralization performed better.When the subjects channeled all infor-mation through a central actor, theinformation was better coordinated andshared. Thus, groups using the starand the Y structures also used theleast number of messages comparedwith groups using the other structuresand also made the fewest errors. Whennode-level analysis was conducted, itwas discovered that structures that hadhigher centralization also tended tohave a leader emerge during the taskprocesses. The leaders emerged at posi-tions of highest centrality (measured by</p><p>Social Network</p><p>PersonalAttributes</p><p>Performance</p><p>Figure 1: Conceptual framework.</p><p>B C</p><p>A</p><p>D</p><p>E</p><p>B</p><p>C</p><p>A</p><p>D</p><p>E</p><p>B</p><p>C</p><p>A</p><p>D</p><p>E</p><p>B</p><p>C</p><p>A</p><p>D</p><p>E</p><p>Figure 2: The Y, star, circle, and line structures of communication.</p></li><li><p>June 2009 Project Management Journal DOI: 10.1002/pmj 37</p><p>the degrees of communication activity).Thus, the Y and star structures hadnodes with extremely high-degree cen-trality compared with other nodes with-in the structure, which led to better performance. </p><p>Inevitably, a thought-provokingfinding that emerged from this studyback then was that centralized struc-tures, such as the star (or hub-spokesor wheel) network, were far more con-ducive to performance (solving the puz-zle faster), in contrast to decentralized orflatter structures, such as the circlenetwork. The crux of the argument isthat information flow is inefficient indecentralized networks and, therefore,less conducive to performance. </p><p>However, later research byGuetzkow and Simon (1955) revealedthat decentralized structures actuallyworked better than centralized struc-tures when tasks become more com-plex. Complexity of tasks results inproblems and subtasks that cannot behandled by an individual alone. Thesame is true when central nodes areoverwhelmed with communications. Inthis context, the circle structureworked much better than the starstructure. The performance of the struc-tures depended more on how well thechannels of communication were usedthan on the structure per se. In contrastto the star structure, the all-channelsstructure shown in Figure 3 (assumingthat there are no ties-overloadscaused by having excessive ties pernode) provides a reconfigurable capaci-ty for task-relevant communication. Forthe actors, this allows an opportunity tonegotiate about the directions of com-munication, details regarding what thetask type is, and whether specific nodesare to be brokers of the communication.The resulting communication patternstend to be potentially more efficientthan if the network structure and flow ofinformation were rigid in form. </p><p>As Guetzkow and Simon (1955) andGuetzkow and Dill (1957) stipu...</p></li></ul>


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