Measuring performance of knowledge-intensive workgroups through social networks

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34 June 2009 ■ Project Management Journal ■ DOI: 10.1002/pmj

INTRODUCTION ■

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 & 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 & Thompson, 1995). Inproject environments, much emphasis is on models of the personal attrib-utes of project personnel that relate to performance—for example, teamleadership effectiveness (Thamhain, 2004), management and leadershipstyles (Dvir, Sadeh, & Malach-Pines, 2006; Turner & Müller, 2005), and softskills such as motivation (Muzio, Fisher, Thomas, & 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.

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 individual’s network properties, measured by social networkstructure, position, and tie variables, and its relationship with performance,measured by the individual’s 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.

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

ABSTRACT ■

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.

KEYWORDS: social network; structure; ties;position; performance; knowledge-intensivework

Project Management Journal, Vol. 40, No. 2, 34–58

© 2009 by the Project Management Institute

Published online in Wiley InterScience

(www.interscience.wiley.com)

DOI: 10.1002/pmj.20115

June 2009 ■ Project Management Journal ■ DOI: 10.1002/pmj 35

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 raisond’être 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 & 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.

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 & Dill, 1957; Leavitt,1951). Previous studies further demon-strate that actors with a dense socialnetwork perform better (Oh, Chung, &Labianca, 2004; Reagans & 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

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 & 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, & Chung,2006).

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.

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

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 & Caldwell, 1992; Cummings,2004; Podolny & Baron, 1997; Reagans &Zuckerman, 2001).

Research QuestionsGiven the arguments above, the follow-ing questions motivate this research:• How can individual performance be

understood through the emergentpatterns of social processes that con-stitute performance?

• How can it be evaluated?• What is the role of social influence

and social networks (that create suchinfluence) in understanding individ-ual performance?

• Why is understanding social networkstructure and position important forunderstanding individual perform-ance?

• How does one account for social fac-tors, apart from personal and demo-graphic factors, that are important forenhancing individual performance inproject environments?

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


Measuring Performance of Knowledge-Intensive WorkgroupsP

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framework, and the following sectionexplores some of the earliest works in thedomain of network structure and per-formance.

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.

The experiment consisted of fivepeople who had to communicate witheach other through enclosed cubicles

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.

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” and“Y” 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 “star”and 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

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Figure 1: Conceptual framework.

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Figure 2: The Y, star, circle, and line structures of communication.


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.

Inevitably, a thought-provokingfinding that emerged from this studyback then was that centralized struc-tures, such as the “star” (or “hub-spokes”or “wheel”) network, were far more con-ducive to performance (solving the puz-zle faster), in contrast to decentralized orflatter structures, such as the “circle”network. The crux of the argument isthat information flow is inefficient indecentralized networks and, therefore,less conducive to performance.

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 “star”structure. 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-channels”structure shown in Figure 3 (assumingthat there are no “ties-overloads”caused 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.

As Guetzkow and Simon (1955) andGuetzkow and Dill (1957) stipulate, theactors must solve two problems: (1) thatof developing an organization scheme

suitable for finding the common symbolwithin the constraints of their particularnetwork structures and (2) that of actu-ally identifying the common symbol.Ultimately, they would seek and find anorganizational structure that works andthen play with variations on it both tomaintain interest and to seek a “better”form.

The Bavelas and Leavitt experimentduring the early 1950s was a crucialmilestone, as it injected rejuvenatedvigor in the space of network structure-performance research. Their key findingwas that centralization leads toenhanced performance in simple tasksand that decentralization leads to effi-cient performance in complex tasks.Their conceptualization of the effectthat communication pattern in terms ofsocial structure and social position hason task performance opened up newresearch avenues. Consequently, itbegged new ideas and questions onunderstanding performance using asocial networks perspective.

Freeman’s Concept on Centrality

As described in the preceding section,the idea of centrality was applied to

human communication in the early1950s. Although the “structural central-ity and influence in group processes”hypothesis was proposed by Bavelas(1950) and reported in depth by Leavitt(1951), modifications and extension tothe original experiments provided con-tradictory and confusing results(Burgess, 1968). In the late 1970s,Freeman (1978) wrote a seminal articlethat clarified the conceptual founda-tions of centrality, which soon becamea core concept in social network stud-ies. His work laid the foundation forsocial network scholars to apply andextend the notion of structural central-ity at both the node and network level,conceptually and empirically.

Freeman (1978) reviewed literaturethat utilized the notion of centralityand showed that the concept wasapplied beyond “experimental groups’task-related” research such as in under-standing urban development, organi-zation of populated nations such asIndia, transportation and communica-tion networks in Russia, and patterns ofdiffusion of technological innovation inthe steel industry. Freeman (1978) thenreviewed the various measures and

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Figure 3: The “all channels” structure.



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overlapping concepts of centrality byunifying the measures, while clarifyingcentrality’s range and limits of potentialfor application.

In particular, he defined centralityin terms of point, “betweenness,” andcloseness centrality, with each havingimportant implications on social out-come and processes. According toFreeman (1978), “these kinds of central-ity imply three competing ‘theories’ ofhow centrality might affect groupprocesses.” In-degree centrality can bemeasured in terms of degree (the num-ber of ties to and from an actor).Structurally, centrality is measured interms of closeness (the extent to whichan actor is close to all others in the net-work) and “betweenness” (the extent towhich an actor lies in the shortest pathto all others in the network). Each cen-trality concept has been related toimportant social occurrences: in-degree centrality being viewed as animportant indicator of an actor’s com-munication activity; “betweenness”centrality being viewed as an indicatorof the potential of an actor’s control ofcommunication; and closeness as anindex of minimum cost of time and effi-ciency for communicating with otheractors in the network.

In a subsequent study, Freeman,Roeder, and Mulholland (1979) revertedto the classic experiment by Bavelas(1950) to study the effects of structuralcentrality on human communication.Using 100 volunteers (university stu-dents) as subjects for the experiment,Freeman et al. (1979) analyzed theresults and demonstrated that centrali-ty is an important structural factorinfluencing leadership, satisfaction,and efficiency. In particular, out of thethree concepts of structural centrality,only two demonstrated interestingresults and significance in its effect onperformance—namely, the control-based measure of betweenness and theactivity-based measure of degree. Theindependence index based on close-ness was “vaguely” related to experi-mental results. Interestingly, another

structural factor, the overall density ofcommunication paths in the structuralform, also turned out to be relevant inunderstanding performance. Back atthe time of this study, these resultsbreathed new life into the MIT labora-tory experiments at a time when theresearch avenues from the experimentsseemed to have stagnated, as it raisednew questions as to what kinds of struc-ture (influenced by individuals orgroups) influenced differing types ofperformance (task or contextual).

In another classical work on theeffects of network structure on innova-tion diffusion, Coleman, Katz, andMenzel (1957) attempted to understandthe underlying social processes thataffected 125 doctors’ rate of adoption ofa new drug. Results suggested that doc-tors who were generally more integratedwith their peers—that is, with densernetworks—were quicker to adopt thenew drug compared with those whowere more isolated. The results fromColeman et al.’s (1957) study suggest thatthe larger number of ties an individualhas results in a higher likelihood to dif-fuse innovation faster. These individualsare quicker to capitalize on the noveltyof the information and are thus in a posi-tion to enhance individual outcomessuch as performance. These results resonated strongly with the similarfindings about the density concept byFreeman et al. (1979) described above.Since then, the notion of centrality,density, and centralization were one ofthe key network measures used forstudying effects on individual andgroup outcomes such as task efficiency,productivity, improved performance,and project coordination, as well asfavorable attitudes toward task-relatedwork (Ahuja, Galletta, & Carley, 2003;Bonacich, 1991; Brass, 1981, 1985; Cross &Cummings, 2004; Cummings & Cross,2003; Faust, 1997; Hossain et al., 2006;Mullen, Johnson, & Salas, 1991; Pfeffer,1980; Salancik & Pfeffer, 1978; Sparrowe,Liden, Wayne, & Kraimer, 2001).

The contribution of Freeman’s(1978) work was so influential that the

notion of centrality is now almostalways attributed to him. By expoundingon the intuitive notions of centrality—closeness, degree, and “betweenness”—Freeman (1978) not only unified thesemeasures mathematically, but also provided their respective theoreticaland practical implications, which were key contributions to the networkstructure and task-performance re-search. That is, “betweenness” centrality(the extent of communication con-trolled) and degree centrality (theextent of communication activity) have been shown to influence perfor-mance from a network structure per-spective, while closeness centrality (theextent of communication efficiency)does not.

Burt’s Structural Holes TheoryA major caveat in previous studies ofnetwork structure such as that ofColeman et al. (1957) is that theyassume that individuals are able tomaintain ties within their personal net-work consistently over time. They alsoassume that each tie is a channelprovider of unique information or com-munication. These drawbacks are theparadoxical reasons why an extremelydense network may paralyze an indi-vidual’s ability to perform better.

In the early 1990s, Burt (1992)made an influential contribution to thenetwork paradigm and phenomena ofstructural effects on individual out-come by shifting the focus from net-work structure and network relations tonetwork position. Burt’s (1992) theoryon structural holes offers a novel andsubtle but interesting perspective inexplaining why some individuals per-form better and others do not. Forexample, it takes Coleman et al.’s(1957) study and its assumptions a stepfurther by offering an explanation as towhy social processes such as innova-tion diffusion may occur faster from astructural positional point of viewrather than from a relational perspec-tive. The theory is linked to personalitytheory, suggesting that the personal


attributes (such as locus of control,leadership skills, and ability to performwell) of an individual are associatedwith structural autonomy—an optimalsituation where an individual benefitsfrom nonredundant information bene-fits.

In contrast to network structureand relational ties, Burt (1992) arguesthat the structural configuration of anindividual’s social network that pro-vides optimized “brokerage” position iswhat dictates structural advantages,such as information novelty and con-trol. This argument is based on the factthat maximizing the number of ties(weak or strong) in an individual’s net-work does not necessarily provide suchbenefits. Instead, opportunity costscome into play, and the maintenance ofties becomes expensive in terms of timeand resources. Furthermore, as an indi-vidual’s personal network grows overtime, the extent of information comingfrom closely knit clusters tends tobecome redundant. Intuitively, an indi-vidual can maintain no more than 50 ormore close ties on a frequent basis. Thisfigure represents, at best, an individualstriving to maintain ties with his con-tacts. In particular, in a study byGurevitch (1961), where a diary ofacquaintances and friends was kept for100 days by 15 study participants, themaximum number of acquaintancesnoted was 658; the average was 500. Asone can imagine, to maintain ties withsuch numbers of contacts is sociallyexpensive and time-consuming. To thisend, Burt (1992) capitalizes on his theo-ry of structural holes by focusing on theimportance of structural position ratherthan structural properties such as den-sity or the size of the network.

The theory of structural holes issimple and intuitive yet empirically pro-found. “Holes” in the network refers tothe absence of ties that would otherwiseconnect unconnected clusters together.Individuals who bridge these holesattain an advantageous position thatyields information and control benefits.Therefore, structural holes theory is

based on the idea that actors are in a better position to benefit from inter-actions with others if the actors are con-nected to others who are not well connected themselves or are not wellorganized. In other words, the bridgingof connection to others provides oppor-tunities; the lack of connections amongthose others are the holes in the struc-ture (i.e., “structural holes”). Individualswho attain structural autonomy arethose who bridge all structural holeswhile the groups to whom the individ-ual is connected to are surrounded bystructural holes. Closer examination onthe crux of structural holes theoryreveals that it is based on the assump-tion of betweenness centrality: thatpower and influence accrue to thosewho broker connections betweenunconnected groups of people. It is thisconcept of betweenness centrality thatBurt (1992) capitalizes on and extendsto explain the role of “brokerage” as aform of obtaining structural autonomythat leads to improved performance,getting ahead, and obtaining goodideas. This theoretical contributionoffers a more insightful perspective onindividual performance, given thatGuetzkow and Simon (1955) note thatcentrality in itself is not always a keypredictor of individual performance.Instead, the theory offers insightfulexplanation beyond the concept of cen-trality and centralization in that an indi-vidual’s benefit accrues from the extentto which the individual’s network is effi-cient, effective, and constrained. Thefollowing section discusses networkefficiency and constraint in greaterdetail.

Network Efficiency and Effectiveness

In order to optimize a network by cap-italizing on structural holes, Burt(1992) claims that increasing networksize (number of direct contacts) with-out considering the diversity reachedby the contacts makes the networkinefficient in many ways. Therefore,the number of nonredundant contactsis important to the extent that redun-

dant contacts would lead to the samepeople and, hence, provide the sameinformation benefits. The term effec-tiveness is used to denote the averagenumber of people reached per primarycontact; while the term efficiency con-cerns the total number of people ofpeople reached with all primary con-tacts. Hence, effectiveness is about theyield per primary contact, while effi-ciency is about the yield of the entirenetwork. To illustrate, consider two net-work diagrams, first, an inefficient network (A) where “you” as the ego areconnected to the members of fourclusters of contacts who are all con-nected to each other within the clusterbut there are no intercluster connec-tions; and second, an efficient network(B) where “you” as the ego are con-nected to one member only from eachof the four clusters where all memberswithin the cluster are connected toeach other and there are no interclus-ter connections. In both networks,each cluster consists of four members.

Thus, in network A, the ego (“you”)maintains 16 ties with every contact inthe network. This represents a signifi-cant strain on the ego in terms of timeand opportunity cost that could beinvested in other contacts. Network B isfar more efficient, because the ego only needs to maintain ties with fourprimary contacts, thereby achievingefficiency at one-fourth of the costcompared with network A. Network B isfar more effective because the primarycontacts in this network are nonredun-dant in that they are connected to clus-ters that are not connected to eachother. An effective network thereforeregards the primary contacts as ports ofaccess to diverse clusters (because of nonredundancy), and thereforeachieves the yield of the entire network.

The term that Burt (1992) uses todenote effectiveness in networks iseffective size. In network A, the networksize is 16. The effective size, however, is4, because in effect the ego is able toobtain novel information and benefitsonly from the four clusters, which are



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not connected to each other exceptthrough “you.” The other three ties toeach of the clusters are redundantbecause they provide the same infor-mation that is available through thefourth. Efficiency in network A is there-fore 0.25 (measured as effective size[4]/network size [16]). In network B, thenetwork size is 4, and effective size is 4,resulting in perfect efficiency of 1 (4/4).Ideally, the number of nonredundantcontacts should increase with the num-ber of contacts to achieve optimal effi-ciency (i.e., 1). As one increases one’snumber of contacts and gradually startsto have a smaller number of nonredun-dant contacts, the individual’s networkefficiency decreases. Conversely, as thenumber of nonredundant contactsincreases relative to the lower numberof contacts, the individual’s networkefficiency increases.

Network Constraint

Constraint dictates the extent towhich an individual’s opportunitiesare limited by investing the bulk of hisor her network time and energy inrelationships that lead back to the sin-gle contact (Burt, 1992, p. 55). In otherwords, constraint measures thedegree to which an individual’s con-tacts are connected to each other andis therefore a proxy for redundancy ofcontacts. According to Hanneman(2001), constraint also measures theextent to which an ego is connected toothers who are connected to oneanother. So if the ego has many con-nections to others, who in turn havemany connections to still others, theego is quite constrained. At organiza-tional levels, individuals with highconstraint indices are unable to con-ceive novel ideas because of theredundant nature of information thatis sourced from a densely connectedgroup of individuals.

Previous research has consistentlydemonstrated that high-efficiency andlow-constraint indices are useful indi-cators of an individual’s ability to pro-duce good ideas (Burt, 2004), to “get

ahead” in terms of job performanceand promotion (Burt, 1992, 2005), andto enjoy greater career mobility(Podolny & Baron, 1997). In line withthese arguments, it is expected thatindividuals in knowledge-intensivework thrive on useful knowledge andinformation from peers. An individualin knowledge-intensive work with anefficient and low-constrained networkstructure is thus more likely to obtainuseful knowledge from diverse andnonredundant contacts, which hasbeen linked to improved performance.Therefore, the following hypotheses areformally derived:

H1a: Efficiency of an individual’s net-work position is positively associatedwith performance in knowledge-inten-sive work.

H1b: Constraint of an individual’s net-work position is negatively associatedwith performance in knowledge-inten-sive work.

Social Network Ties and PerformanceIt is obvious at this point, for social net-work scholars, that the raison d’être isthat the structure of relationshipsamong actors and the location of indi-vidual actors in the network haveimportant behavioral, perceptual, andattitudinal consequences both for theindividual units and for the system as awhole (Knoke & Kulinski, 1992). In thiscontext, individual or system-level out-comes are treated as a function of net-work structure and position (Gabbay &Leenders, 2001). However, at the indi-vidual level, the debate concentratesnot only on how structural positionimpacts individual performance, butalso concentrates on the relationalcomponents of an individual’s network.Evidence in the literature demonstratesthat just as structural position plays avital role in the effect of individual per-formance, tie strength has significanteffects as well (Borgatti, Jones, & Everett,1998; Hossain et al., 2006; Mehra, Kilduff,& Brass, 2001; Reagans & McEvily, 2003;Sparrowe et al., 2001).

Granovetter’s Theory on the Strength of Weak Ties

The most seminal work in social net-works with regards to the relationalcomponent of an individual’s socialnetwork almost always begins withGranovetter’s (1973) theory on thestrength of weak ties. Granovetter (1973)argues that individuals obtain new andnovel information from weak ties ratherthan from strong ties within the individ-ual’s group structure. The argument isbased on assumptions concerning thehomophilous nature of actors in a socialsystem, where strong ties tend to bondsimilar people to each other and wherethese similar people tend to clustertogether such that they all becomemutually connected. For this reason,information that originates and circu-lates at a high velocity among stronglytied cliques or clusters tends to becomeobsolete or redundant in a shortamount of time. Such network clustersor cliques of people bound together bystrong ties are therefore not conduciveto channels of innovation. That is, suchnetworks are closed networks and arenot highly receptive of new information.Granovetter (1973, p. 1373) suggests thatthe influx of new and novel informationmust therefore come from weak ties(hence, the theory of the strength ofweak ties), which serves as a bridge to adifferent cluster of people, from whichthe new information originates.

Although the strength of weak tiestheory has wide appeal, it has the draw-back of implying that maximizing thenumber of weak ties in one’s personalnetwork would yield novel informationbenefits that, in turn, allow one to per-form better. Furthermore, according toBurt (1992, p. 28), a weak tie is “thechasm spanned and the span itself. Bytitle and subsequent application, theweak tie argument is about the strengthof relationships that span the chasmbetween two social clusters” and there-fore not about information benefits. Bydefinition then, weak ties cannot pro-duce novel information benefits per se.Unlike structural holes, which provide


information benefits, weak ties obscurethe control benefits that come alongwith information benefits.

Strength of Strong Ties

Subsequent to the seminal study on thestrength of weak ties, several researchershave examined the dichotomy of thestrength of ties and associated it withindividuals and group outcomes.Krackhardt (1992), for instance, notesthat the “affect” level of strong ties isimportant and cannot be ignored. In hisstudy (Krackhardt, 1992) of a SiliconValley firm where advice and friendshipnetworks of 36 employees were com-pared, he concluded that strong tieswere particularly important, especiallyin the generation of trust within propa-gators of major organizational change.In another study of 127 knowledge-intensive workers from a pharmaceuti-cal company, a bank, and an oil and gascompany, Levin and Cross (2004) report-ed that the relationship between strongties and receipt of useful knowledge wasmediated by benevolence-based trustand competence-based trust. They alsofound evidence that strong ties, more sothan weaker ties, led to the receipt ofuseful knowledge for improving per-formance in knowledge-intensive work.However, when their research modelcontrolled for the two dimensions oftrust, the structural benefit of weak tiesemerged, suggesting that it was theweaker ties that provided access tononredundant information. In this con-text, the results are consistent with priorresearch by Hansen (1999), who investi-gated the association between tiestrength, transfer of complex knowl-edge, and performance in terms of proj-ect completion times by 41 differentsubunits within an organization. Theconceptual model postulated by Hansen(1999) is that strong ties facilitate• low search benefits with moderate

transfer problems where knowledge isnoncodified or dependent,

• low search benefits with few transferproblems where knowledge is codifiedor independent, and

weak ties facilitate• search benefits with severe transfer

problems where knowledge is noncod-ified or dependent,

• search benefits with few transfer prob-lems where knowledge is codified orindependent.

According to Hansen (1999), weakties facilitate faster project completiontimes when the task is simple andenables faster search for useful knowl-edge among other organizational sub-units. However, it is strong ties ratherthan weak ties that foster complexknowledge transfer, as the transferprocess is slowed down when knowl-edge is highly complex, where the com-plexity of knowledge is determined by the degree to which it is tacit and bywhether an individual is dependent onanother for transfer and acquisition ofknowledge. Similar findings were alsoechoed in the study of Reagans andMcEvily (2003) of a sociocentric net-work of 104 highly skilled employeeswithin a contract research and develop-ment (R&D) firm; this study found apositive association between tiestrength and the ease of knowledgetransfer in performing knowledge-intensive task activities. Furthermore,the authors also found that diversity ofties (in terms of network range or ties todifferent knowledge pools) was posi-tively associated with ease of knowl-edge transfer. Therefore, where knowl-edge-intensive work is involved andwhere knowledge transfer and receiptof useful information is crucial for per-formance, strong ties rather than weakties facilitate complex knowledge trans-fer, especially to heterogeneous audi-ences (Reagans & Zuckerman, 2001). Inother words, for an individual engagedin knowledge-intensive work to per-form well, the importance of strong tiesof an individual cannot be discounted.Stated formally:

H2a: Strong ties will be positively associ-ated with individual performance inknowledge-intensive work.

Degree and Diversity of Ties

Apart from observing tie strength, vari-ous studies also examined the numberof ties as a significant predictor of indi-vidual performance. In this context, thenumber of direct ties that the individualhas to other individuals in his or hernetwork is also termed network size(Burt, 1992, p. 16) or degree centrality(Freeman, 1978). In a study that exam-ined tie correlates of the individual per-formance of 101 engineers and 125consultants, Cross and Cummings(2004) found significant support for thepositive association between an indi-vidual’s number of ties (both from andto departments outside his or herdepartment) and individual perfor-mance. Furthermore, they also foundthat an individual’s number of ties tohigher hierarchy levels and those thatspanned physical (geographical) barri-ers were positively related to perform-ance. These findings suggest that tiesthat span departmental boundaries andgeographical barriers, including thosethat reach senior personnel, enhancethe diversity of information flow, espe-cially where complex work with highintegration of specialized knowledge isinvolved. According to Cummings(2004), individuals in workgroups aremore likely to perform better if theyexchange knowledge externally withmembers in their professional networkwho are structurally diverse. Structuraldiversity here refers to individual differ-ences in geographic locations, function-al assignments, reporting managers,and business units. For example, pastresearch has shown that geographicallocation influences what individualsexperience and who they interact with,and therefore generates novel task-related information and knowledge-sharing opportunities (Monge, Rothman,Eisenberg, Miller, & Kirste, 1985). In par-ticular, although internal and externalknowledge sharing has direct implica-tions on performance, the latter is morevaluable when individuals in the work-group are more structurally diverse.Therefore, individuals with higher



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reach and diversity of information aremore likely to be exposed to unique andrelevant knowledge that is helpful insolving complex problems. More for-mally:

H2b: Degree centrality is positively asso-ciated with individual performance inknowledge-intensive work.

H2c: Geographical tie diversity is posi-tively associated with individual per-formance in knowledge-intensive work.

H2d: Functional tie diversity is positive-ly associated with individual perform-ance in knowledge-intensive work.

To summarize, the previous sec-tions of the review up to this pointhave critically analyzed key literatureconcerning the relationship betweensocial network and task-related per-formance. In terms of social networkstructure, Bavelas (1950) and Leavitt(1951) demonstrated that hierarchicalor centralized structures perform bet-ter when tasks are simple, but thatdecentralized structures are more con-ducive toward lesser errors, satisfac-tion, and speed of task completion incomplex tasks. Freeman (1978) laterexpanded on the notion of centraliza-tion to show that performance wasrelated to the individual’s property ofcentrality attributed by the structure of the network. In particular, he identi-fied that degree centrality connotesintensity of communication flow, thatbetweenness centrality indicated com-munication power and influence, andthat closeness centrality indicated efficiency of information flow. Burt’s(1992) notion of structural holes builtfurther upon the assumption ofbetweenness centrality that advocatedthe idea of a brokerage position as pro-viding information and control bene-fits. In fact, this shift from the focus onnetwork structure to network positionwas instrumental, and paved the wayfor further research in delineating therelationship of communication pat-terns and performance at the indi-vidual level. At the relational level of

network structure, the main theoryreviewed was the strength of weak tiestheory (Granovetter, 1973), which stip-ulates that weak ties provide novel anduseful information, as opposed tostrong ties within a densely knit clusterof people. However, later research inthe dichotomy of the strength of tieshas also led to claims that strong anddiverse ties are equally and respective-ly important for performance. Thisresearch, therefore, amalgamates theseconcepts together to propose that net-work structure, position, and relationsindividually and jointly impact individ-ual performance. In the next section,the implications of personal attributesand its relevance to the network per-formance model are introduced andincorporated.

Personal Attributes, ProfessionalSocial Networks, and PerformanceAlthough it has been ascertained thatthe structure of relations among actorsand their network positions haveimportant behavioral, perceptual, andattitudinal consequences for individu-als, one cannot discount the impor-tance of personal attributes that affectan individual’s network structure, rela-tional aspects, and network position. Inthis section, the importance of person-al demographic attributes such as orga-nizational affiliation, qualification,experience, and gender and theireffects on social network attributes arehighlighted.

First, gender has long been recog-nized as an important factor linked toindividual outcomes in the social sci-ences. Men and women often differ inthe nature of their interpersonalexchanges and in their opportunitiesfor social interaction (Dykstra, 1990).The differences in the interpersonalstyle of women compared with menhave stimulated a good deal of interestas a possible source of variation in theinterpersonal aspects of a variety ofdomains, including areas of knowl-edge-intensive and geographically dis-tributed work (Aries, 1996; Eagly &

Johnson, 1990). For example, womentend to reveal more information aboutthemselves in conversations (Dindia &Allen, 1992), are better facilitators andmoderators in communication thantheir male counterparts, and are moreintimate, warmer, and more engaged innonverbal communication than men(Hall, 1984). In the medical work prac-tice domain, for example, it is no differ-ent. Female physicians facilitate a moreopen and equal interaction withpatients as well as with colleagues, anda therapeutic milieu that is differentthan that of male physicians(Verbrugge & Steiner, 1981; Weisman &Teitelbaum, 1989). More recently, ameta-analytic review of 26 articles from1967 to 2001 accounted for physiciangender effects in medical communica-tion. It was concluded that female phy-sicians engage in significantly moreactive partnership behaviors, positivetalk, psychosocial counseling, psy-chosocial question-asking, and emo-tionally focused talk than male physi-cians (Roter, Hall, & Aoki, 2002). In lightof these arguments, it is anticipatedthat the network structure, position,and ties will be influenced by suchattributes in the process of professionalsocialization. Therefore, the followinghypotheses can be postulated:

H3a: There is a significant difference indegree centrality between gender-basedego networks in knowledge-intensivework.

H3b: There is a significant difference inconstraint indices between gender-based ego networks in knowledge-inten-sive work.

H3c: There is a significant difference inprofessional tie diversity (geographicallyand functionally) between gender-based ego networks in knowledge-intensive work.

Other valid demographic attributesthat are speculated to influence networkstructure and performance, irrespec-tive of gender, are education and pro-fessional qualifications of individuals


in knowledge-intensive work. Inferringfrom previously discussed theoriesabout work structural diversity, ties,and performance (Cross & Cummings,2004; Cummings & Cross, 2003), andtechnological effects on performance(Aral, Brynjolfsson, & Alstyne, 2006), itis anticipated that individuals who areaccredited or affiliated with profession-al memberships related to their work orwho take on the responsibility of addi-tional functional tasks (such as hospitalappointments in primary care) willenjoy the advantage of greater diversityin professional network ties, a higherdegree of interaction, and a higher levelof performance as well. Therefore, thefollowing hypotheses are postulated:

H3d: There is a significant difference inego-network density between holdersand nonholders of professionalappointments in knowledge-intensivework.

H3e: There is a significant difference inego-network efficiency between holdersand nonholders of professionalappointments in knowledge-intensivework.

H3f: There is a significant difference indegree centrality between holders andnonholders of professional accredita-tions and memberships.

H3g: There is a significant difference infunctional tie diversity between holdersand nonholders of professional accredi-tations and memberships.

H3h: There is a significant difference inperformance attitude between holdersand nonholders of professional accredi-tations and memberships.

Toward a Social Networks–BasedModel for Performance Context of StudyIn order to validate the above hypothe-ses that form the conceptual and theoretical model for the study at anoperational level, the chosen context ofthe study is the rural general practi-tioners (GPs) of New South Wales,Australia. The Royal Australian College

of General Practitioners (RACGP) defines“general practice” as “the provision ofprimary continuing comprehensivewhole-patient medical care to individu-als, families, and their communities”(RACGP, 2004). Rural GPs are consid-ered knowledge-intensive workersbecause of the nature of their work,extensive medical expertise, highpatient-to-GP ratio, long work hours,usage of advanced medical technolo-gies, provision of diverse health careservices, and so on (Humphreys &Rolley, 1998). GPs working in ruralareas are geographically more occupa-tionally isolated from other practices.Furthermore, rural GPs often carry outprocedures in situations with limitedresources or personnel and are implic-itly required to adapt to the protocolsand codes of conduct of rural settings(Mellow, 2005). These problems andothers such as decreasing performanceas GPs age, lack of association withprofessional peers, obsolescence withmodern technology, and isolation fromcommunity not only hinder perfor-mance, but also make this study poten-tially interesting and practically useful(Choudhry, Fletcher, & Soumerai, 2005;Chung, Hossain, & Davis, 2005). It alsoprovides justification for the rural GPsas distributed knowledge-intensivework subjects for this study.

PerformancePerformance is essentially a multidi-mensional construct that varies in different contexts of work. Borman and Motowidlo (1993) conceptualizedperformance in terms of task-based performance and contextual-based per-formance. They defined task-based performance as “the proficiency withwhich job incumbents perform activi-ties that are formally recognized as partof their jobs, activities that contributeto the organization’s technical coreeither directly by implementing a partof its technological process or indirect-ly by providing it with needed materialsor services” (Borman & Motowidlo,1993, p. 75). Contextual performance,

by contrast, is defined as behavior thatsupports the broad organizational,social, and psychological environmentof the organization rather than theorganization’s technical core. In thisstudy, “performance” is about task-based activities that are core to a GP’spractice, such as the process of diagno-sis and prescription. In particular, itrelates to “the provision of primary con-tinuing comprehensive whole patientmedical care,” as per the definition ofgeneral practice.

Given that there is currently nogeneric performance index of GPs, aproxy to measure the effectiveness ofGPs in delivering medical care is toaccount for their behavior in actualpractice. Ideally, this would entailethnographic accounts of what wasactually observed in the consultationprocess and whether the practice canbe equated with components of “good-quality” patient care. However, thiswould most likely be an obtrusive andexpensive exercise time-wise andresource-wise. Therefore, an alternatemethod for studying behavior is to findout and empirically evaluate the self-perceived attitudes that the GP holdstoward the processes of medical care,as research in social psychology sug-gests a strong association between atti-tude and behavior.

Research in the area of social psy-chology suggests that the attitude that aperson holds toward any object is verylikely to be related to the overall patternof a person’s response to that object(Ajzen & Fishbein, 1980). Although therehave been previous studies showingonly weak correlations between meas-ures of attitudes and measures of behav-ior toward their objects (Breckler,Greenwald, & Pratkanis, 1989; Wicker,1969), Fishbein and Ajzen (1974) demon-strated that attitude and behavior arecorrelated given the following condi-tions: (1) when the observed behavior is judged to be relevant to the attitude,(2) when the attitude and behavior areobserved at comparable levels of speci-ficity, and (3) when mediation of the



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attitude-behavior relation by behavioralintentions is taken into account. Theirfindings were largely influential in thedevelopment of the theory of reasonedaction, or planned behavior, whichclaims that behavior is mediated bybehavioral intention, which in turn isinfluenced by attitude and social norms.Furthermore, Fazio (1986) demonstratedthat attitude and behavior are correlated(1) when the attitude is based on directexperience with the attitude object and(2) to the extent that the attitude is cog-nitively accessible.

Therefore, in work settings, atti-tudes about work practices are deter-mined by an individual’s past behaviorand how these behaviors come to beattributed to the environment or person.Using a social information-processingapproach to work attitudes and taskdesign, Salancik and Pfeffer (1978, p. 230) argue that “the process ofattributing attitudes from action is itselfaffected by (1) the individual’s commit-ment to the behavior, (2) the informa-tion about past behavior that is salientat the time the attitude is generated,and (3) social norms and expectationsthat affect what can be considered legit-imate or rational explanations for pastbehavior.” Plenty of evidence suggeststhat when individuals are committed toa situation, they tend to develop atti-tudes consistent with their commit-ment and their committing behavior(Igbaria & Tan, 1997; Kiesler, 1971).

In the case of general practice, GPs’practices are guided by physician char-ters, medical standards, and norms ofpractice. They are subject to norms ofdiagnosis and prescription, which arefairly standard in the consultationprocess. Moreover, the nature of theirtasks is also fairly specific to the extentthat their behavior affects the attitudeattribution process. The attitude for-mulated in turn is charged with emo-tion, which predisposes a class of medical actions toward consultationduring medical practice. Therefore,although behavior changes attitudes,attitudes are developed from justifying

past behavior. Given the above argu-ments, it is important to find out howGPs see their role in terms of theprocesses of care during the consulta-tion process; their perceptions are verylikely to influence their behavior in theconsultation, and they allow for com-parison with views held by other GPs(and patients, as well). Therefore, theargument acknowledges that althoughthe attitude of the GPs is not a sole orsufficient cause of behavior, it is a con-tributing cause. The theoretical modelfor the study is listed in Figure 4(Chung, 2008).

MethodologyA triangulation methodology was usedfor the study. First, qualitative interviewswere held with seven GPs, some ofwhom had dual job functions such asuniversity honorary associates and lec-turer. The qualitative interviews weresemistructured and were held for at least1 hour with each of the interviewees atappointed times over a 2-month period.The qualitative interviews were usefulfor exploring the validity and relevance of social network constructsused in the study. The interviews wereaudio-recorded, transcribed, and sum-marized as contact summary notes.Pattern-coding and memoing tech-niques were used to analyze the data.The analysis was used in conjunctionwith the existing literature to develop aconceptual model for the study.

An initial questionnaire survey wasdeveloped and pre-piloted among agroup of five students within theresearch laboratory. Ten copies of thesurvey were then sent out to rural GPs,with only three responding. With lowresponse rates, experts in the domain ofgeneral practice, including the formerpresident of a rural doctors’ associationin Australia, professor and head of dis-cipline of general practice in arenowned university, and rural GPs,were consulted about the survey instru-ment. Subsequently, the research designand theoretical constructs were furtherrefined. The experts also vetted the

instrument, which was then pretestedfor comprehension and ease of use. Thegeneral response from them was thatthe design of the network component inthe survey was visually complex andconfounding. As this not only detersresponse rates but also adds cognitiveload to survey completion, advice andsuggestions from the experts wereaccepted and the survey was modifiedaccordingly. The second version of thesurvey was designed for improved easeof comprehension and completion.Attribute items such as asking whetherthe GPs were trained overseas or locallywere included. Other items to deter-mine whether they were accredited withfellowships from the RACGP and theAustralian College of Rural and RemoteMedicine (ACRRM) were also includedin order to allow for cross-demographiccomparisons (although not shown inthis study). The survey was piloted to136 rural GPs practicing in two differentdivisions of rural general practice overthe period of December 2006 toFebruary 2007. Fifty-six GPs agreed to fillout the survey, thus achieving aresponse rate of about 41%. The surveywas mostly personally administered inorder to allow for capturing of surveyduration, respondent reaction, anderrors in the survey. Only one survey wasadministered over telephone, and fiveothers through postal mail.

After making cosmetic refinements,a final questionnaire was posted to1,488 GPs in the remaining 15 divisionsof rural general practice in New SouthWales. Telephone calls were made to thepractices 2 weeks in advance to gaugethe likelihood of survey response beforethe actual mail-out. Two weeks after themail-out, follow-up (reminder) callswere made to those who had not com-pleted the survey and who had previ-ously agreed to the survey. A total of 110rural GPs participated in the study,accounting for a response rate of 7.8%.

Egocentric Network MeasuresWe utilized the egocentric approach forcollecting network data because of


its practicality and feasibility. In thisapproach, the actor of interest isreferred to as the “ego,” while the actorswhom the “ego” considers as being hisor her affiliates, advisors, friends, or rel-atives are known as “alters” (Scott,2000). Name generators are used inorder to elicit alters’ names. In ourstudy, we used the following name

generator to elicit names from a GP’sprofessional network:

By “professional network,” we meanprofessional people with whom youassociate, interact or work for theprovision of care to patients (e.g.,nurses, admin staff, specialists,pathologists, doctors). Looking backover the last 6 months, please iden-

tify people (up to a maximum of 15)who are important in providing youwith information or advice for pro-viding care to patients.

Name interpreter questions are alsocommonly asked to elicit some attrib-ute data about the alters and ties. In ourcase, we requested GPs to indicate theoccupational code (e.g., nurse, practice

Performance AttitudesDegreeCentrality

Efficiency

Constraint

Tie Strength

FunctionalDiversity

GeographicalDiversity

Structure

Position

Relations

H2bH1a

H1b

H2a

H2d

H2c

Aspects ofInterpersonal

Care

Aspects ofTechnical

Care

H3b

H3a

H3c

H3c

H3d

H3e

H3f

H3g

Gender

ProfessionalAppointments

ProfessionalAccreditations

Performance Attitudes

Performance Attitudes

Aspects ofInterpersonal

Care

Aspects ofTechnical

Care

DegreeCentrality

Efficiency

Constraint

Tie Strength

FunctionalDiversity

GeographicalDiversity

Structure

Position

Relations

Density

H3h

Figure 4: Theoretical model for the study (above, with personal attributes; below, without) (Chung, 2008).



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manager, specialist) as well as the geo-graphical location (e.g., same practice,other practice) of each alter. Thestrength of each tie, measured by “timethe person has been known,” “frequen-cy of interaction,” “type of relationship,”and “degree of closeness” (Marsden &Campbell, 1984), was also solicited.Attribute data about the frequency ofinteraction via e-mail, telephone, andvideo conferencing were also includedin the instrument to segregate face-to-face and technology media interactions.

To determine the relationshipbetween elicited alters in order to com-plete the network structure, we askedGPs to determine how the members oftheir professional network relate to eachother based on a 5-point degree of close-ness scale ranging from “especially close”to “do not know each other.” That is, foreach alter nominated, the GP woulddetermine a closeness scale for everyother alter. Although this approach hasbeen criticized in the past for its recallreliability and accuracy (Bernard,Killworth, Kronenfeld, & Sailer, 1985),later studies confirmed that people also remembered long-term or typicalpatterns of interaction with other peoplerather well (Freeman, Romney, &Freeman, 1987). Furthermore, the freerecall method elicits a richer data on the social networks of people, whereasthe fixed-choice method influences people to elicit accurate information onthe most important relationships (i.e.,strong ties; Hammer, 1984).

Efficiency and Constraint

Effective size is a measure of the num-ber of alters minus the average degreeof alters within the ego network, notcounting ties to the ego (Burt, 1992, p. 55).The effective size of an actor’s (ego) network is thus:

where i is the ego, actor j is a primarycontact, and actor q is also a primarycontact who has strong ties with theego i (represented by strong tie piq) and

ajc1 � a

qpiq mjq d ,�q � i, j

actor j (represented by marginal tiemjq). Efficiency is measured by dividingthe effective size by the number ofalters in the ego’s network.

Ego constraint measures the oppor-tunities held back by the extent towhich the ego has invested time andenergy in relations with alters that leadback to a single contact (Burt, 1992, p. 55). In other words, it measures theextent to which the ego’s connectionsare to others who are connected to oneanother. Constraint on an actor’s net-work is defined as:

where i is the ego, actor j is a primarycontact, and actor q is also a primarycontact who has strong ties with the egoi (represented by piq) and actor j (repre-sented by pqj).

Degree Centrality

Degree centrality (CD) is measured asthe count of the ties (a) to the ego (pk)(Freeman, 1978). In graph theoreticalterms:

where a(pi, pk) � 1 if and only if they areconnected by a line, and 0 otherwise.

Tie Diversity

Diversity is measured using an entropy-based diversity index developed byTeachman (1980):

where if there are N possible states inwhich the system can be, Pi is the prob-ability that the system will be found instate i, the only exception being whenthe state is not represented, in whichcase the value is 0 (Ancona & Caldwell,1992). Tie diversity in this study ismeasured in terms of functional (occu-pational) and geographical diversity.Therefore, if a GP has ties to profession-

H � � as

i�1pi 1lnPi 2

CD1pk 2 � an

i�1a1pi, pk 2

apij � aq

piqpqj b2

,� q � i, j

als from diverse occupations, his or herfunctional diversity would be high.Similarly, if a GP has ties to profession-als who are from differing cities andtowns, his or her occupational diversitywould be considered high.

Performance MeasuresA validated and reliable questionnaire(Cockburn, Killer, Campbell, & Sanson-Fisher, 1987) for assessing GPs’ per-formance attitudes to medical care toascertain their perceived effectivenessof clinical and interpersonal care wasused in this study. In order to validatethe item sets to see if they could beclustered into few dimensions, a factoranalysis was conducted, first perform-ing the principal components analysiswithout rotation on the 17 items.Principal components analysis revealedthe presence of five factors with eigen-values greater than one. These factorsaccounted for 60.6% of the variance,which was considered adequate in pre-vious research (Hair, 1995). However,the first two factors captured muchmore of the variance (37.79%) than theother three factors. This was also clear-ly evidenced from the scree plot shownin Figure 5.

Using Catell’s (1966) scree test, itwas decided that two factors should beretained (note the clear break after thesecond component) for further inves-tigation. The second step involvedperforming the exact same steps butrotating the two factors to simpleorthogonal structure using directoblimin with Kaiser normalization. Thetwo-component solution explained atotal of 37.8% of the variance, with thefirst factor contributing 23.3% and thesecond factor contributing 14.5%.Finally, items with a high factor loadingof at least 0.40 on only one factor in thecomponent matrix were retained as list-ed in Table 1. One item (g15) that loadedrelatively low on both factors (lesserthan 0.4) was discarded. Factor 1 can bedescribed as aspects of interpersonalcare, and factor 2 as aspects of technicalcare; this is consistent with literature in


quality of care in general practice(Brook, McGlynn, & Shekelle, 2000;Campbell, Roland, & Buetow, 2000).

A reliability analysis demonstratedgood internal validity and consistencyfor both factors 1 (interpersonal care)and 2 (technical care), with Cronbach’sa � 0.771 and 0.720, respectively. Thescores were summed up, forming acomposite score for technical andinterpersonal care.

Demographic ItemsDemographic details were also solicit-ed from GPs. GPs were asked to statethe number of years they have beenpracticing as GPs and the number ofyears they have practiced in the cur-rent practice, in order to ascertain theiryears of experience. This is an impor-tant variable, as research has shownthat age and clinical experience arecovariates of performance. In particular,the systematic review of the literatureon GP experience and performance by Choudhry et al. (2005) shows thatquality of care delivered declines asGPs age. Other items asked in the sur-vey were: year of graduation, medicalcollege, and country from which the

GP graduated, gender, number of GPsin the practice, whether they had hos-pital appointments, and if they weremembers of the Fellowship of theAustralian College of Rural and RemoteMedicine and the Fellowship of theRoyal Australian College of GeneralPractitioners. Inclusion of these itemsets and others in the instrument wereconfirmed and validated with GPs fromNew South Wales, including those whoheld senior positions (e.g., president ofthe Rural Doctor’s Network and pre-sident of the RACGP) in the field ofgeneral practice.

ResultsResults from the respondents surveyedindicate that the typical rural GP hasbeen in rural practice for 20.24 years,with 13.63 years in the current practice.Although there are solo practices (a one-doctor-only practice), the typical ruralGP, as indicated by the results, workswith at least four other GPs in the same practice. Furthermore, 90 (or81.8%) are male and 20 (or 18.2%) arefemale, and 85 (or 77.3%) have hospitalappointments. Table 2 also lists the

descriptive statistics for the variables:network structure, position, ties, andperformance.

Given that there were no clear out-liers in the data distribution of all vari-ables and that the histograms of thedependent variables were fairly normal,we examined Pearson’s product-momentcorrelations indices. In summary, thecorrelations are suggestive of the fol-lowing:1. Demographics: Years in rural practice

and years in current practice (whichaccount for experience) are stronglycorrelated positively (r � 0.673, p �

0.000). In particular, the former vari-able is negatively associated withgeographical diversity (r � �0.231,p � 0.05), while the latter is negativelycorrelated with functional diversity (r � �0.230, p � 0.05).

2. Network Structure: As expected,there is a strong negative correlationbetween ego-network density andefficiency (r � �0.979, p � 0.000) anda strong positive correlation betweenego-network density and constraint(r � 0.455, p � 0.000). There is a smallnegative correlation between densityand average tie strength as well (r �

0.199, p � 0.05). Interestingly, degreecentrality is positively associated withgeographical diversity (r � 0.207, p �

0.05), functional diversity (r � 0.301,p � 0.01), and interpersonal care (r � 0.258, p � 0.01).

3. Network Position: Constraint is nega-tively associated with geographicaldiversity (r � �0.286, p � 0.01), func-tional diversity (r � �0.285, p � 0.01),and interpersonal care (r � �0.217,p � 0.05).

4. Network Relation: There exists amedium positive correlation betweengeographical diversity and functionaldiversity (r � 0.422, p � 0.000).

5. Performance: While there exists apositive correlation between inter-personal care and technical care (r �

0.230, p � 0.05), there are no othervariables that bear any significantcorrelations on technical care. Theother variables that bear significant

4

2

Eig

enva

lue

Component Number

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Figure 5: Scree plot of factor eigenvalues for performance attitudes items.



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correlations with interpersonal careare degree centrality (r � 0.258,p � 0.01) and constraint (r � �0.217,p � 0.05).

Hypothesis Testing

Efficiency and Performance

To test the first hypothesis (H1a), theindependent samples t-test was adopt-ed to test for the significant differencebetween two independent groups(high- and low-efficiency actors) on acontinuous measure of performanceattitude scores. A t-test allows us to findout if more efficient actors score higheron their attitudes to performance thanthose with lower efficiency. If the differ-ence between high- and low-efficiencyactors is statistically significant, then itis evidence that efficiency is associated

Table 1: Rotated pattern matrix for performance attitudes.

Item Component

No. Item Description 1 2

g8 Partnership with patient 0.813

g4 Counseling patients about personal problems helps them cope better 0.674

g9 Emotional support for patients 0.643

g6 Identifying modifiable risk factors is very important 0.633

g10 Be frank and open with patients 0.621

g3 Listening to patients’ worries is an important part of my role 0.561

g2 Ensuring that explanation of prescribed treatment is understood 0.504

g11 GPs are influential for lifestyle change 0.465

g16 Don’t help with psychological problems 0.723

g12 Treating physical disease is most important 0.676

g14 Patient involvement in treatment decisions 0.604

g13 Patients are likely to follow GP advice 0.598

g17 GP is responsible for medical problems 0.565

g5 My medical expertise is wasted because I see so many healthy patients 0.486

g7 Patients make convenience of me 0.444

g1 It is a waste of time trying to persuade patients to give up smoking 0.415

Note. Extraction Method: Principal Component Analysis. Rotation Method: Oblimin with Kaiser Normalization. Rotation converged in 11 iterations.

Table 2: Descriptive statistics.

Mean Median SD Min Max

DemographicsYears in rural practice 20.24 – 10.44 1 50Years in current practice 13.63 – 10.37 1 43No. of GPs in current practice 4.54 – 4.11 0 25

Network StructureDegree 8.77 8.0 3.94 1.00 15.0

Network PositionConstraint 0.402 0.35 0.197 0.078 1.00Efficiency 0.475 0.40 0.297 0.067 1.00

Relations (Ties)Geographic diversity 0.700 0.67 0.367 .000 1.71Functional diversity 0.799 0.86 0.447 .000 1.58

Performance (attitudes)Interpersonal care 47.45 48.00 6.25 13.00 56.00Technical care 39.07 40.00 7.05 19.00 53.00


with performance attitudes in knowl-edge-intensive work. Whether the asso-ciation is positively or negatively relatedcan be evidenced in the direction of thedifference (i.e., which group is higher).

The division or cut-point of thehigh- and low-efficiency groups wasmade first by sorting the data based onthe efficiency index in ascending order.This is equivalent to ranking the casesof data based on the efficiency index.The median of the index was then chosen as the cut-point that allowed fordivision of the groups based on effi-ciency. The median for the efficiencyscore was 0.406 in this study. Therefore,GPs who have an efficiency score ofgreater than or equal to the median aregrouped as the “high-efficiency group,”and those GPs with efficiency scoreslower than the median are termed the“low-efficiency group.”

For interpersonal care, the two effi-ciency groups (high-efficiency and low-efficiency) have no statistically signifi-cant difference in the interpersonalattitude care scores for the high (M �

47.85, SD � 5.67, n� 54) and low (M �

47.14, SD � 6.83, n � 55) efficiencygroups, t (107) � �0.587, p � 0.559 (two-tailed). The magnitude of the differ-ences in the means (mean difference ��0.70640, 95% CI: �3.093 to 1.680) isquite small (eta squared � 0.003).1

Therefore, the null hypothesis statingefficiency of an individual’s networkposition is not associated with attitudesto interpersonal care in knowledge-intensive work cannot be rejected. Inother words, there is no associationbetween efficiency and attitudes to per-formance with respect to interpersonalcare. With respect to attitudes to tech-nical care aspects, the two efficiencygroups also show no significant differ-ence in the technical care attitude scoresfor the high- (M � 38.68, SD � 7.23,n � 47) and low- (M � 39.52, SD � 6.86,n � 62) efficiency groups, t(107) � 0.933,

p � 0.508 (two-tailed). The magnitudeof the differences in the means (meandifference � 0.89630, 95% CI: �1.779to 3.572) is very small (eta squared �

0.004). Therefore, the null hypothesisthat the efficiency of an individual’snetwork position is not associated withtechnical care attitudes in knowledge-intensive work cannot be rejected.Consequently, there is no support forthe alternative hypothesis (H1a) thatefficiency is positively associated withattitudes to technical care.

Constraint and Performance

For H1b, the same principle using t-testwas applied as in testing for H1a. In thiscase, the median constraint score was0.355. GPs with constraint scoresgreater than or equal to this cut-pointwere termed a “high-constraint group”and those lesser than the median scorewere termed a “low-constraint group.”

In terms of attitudes to interperson-al care, the t-test reveals a significantdifference in the interpersonal care atti-tude scores of high-constraint groups(M � 45.69, SD � 7.18, n� 53) and low-constraint groups (M � 49.19, SD �

4.72, n � 56); t (107) � 3.021, p � 0.003(two-tailed). The magnitude of the differ-ence in the means (mean difference �3.49, 95% CI: 1.2024 to 5.7942) is mod-erate (eta squared � 0.079). The direc-tion of the difference shows that thelower-constraint group has a higherinterpersonal care mean of 49.1964 andthat the higher-constraint group has alower interpersonal care mean of45.6981 in attitudes to interpersonalcare. Further investigation from thecorrelation results in Table 3 shows asignificant negative correlation (r �

�0.217; p � 0.05) between constraintscores and attitudes to interpersonalcare. Consequently, there is no evi-dence to support the null hypothesisthat constraint in an individual’s net-work position is positively or not asso-ciated with attitudes to interpersonalcare. Therefore, there is sufficient evi-dence to support the hypothesis (H1b)stated in terms of attitudes to interper-

sonal care. For technical care attitudes,there is no significant difference in thescores of high-constraint groups (M �

38.02, SD � 6.97, n � 53) and low-constraint groups (M � 40.23, SD �

6.97, n� 56); t(107) � 1.657, p � 0.101(two-tailed). The magnitude (effectsize) of the differences in the means(mean difference � 2.2132, CI: �0.435to 4.86) is small (eta squared � 0.02).Therefore, the null hypothesis that con-straint in the individual’s network posi-tion is positively or not associated withtechnical care attitudes cannot be reject-ed. In other words, there is no supportfor the alternative H1b that constraint isnegatively associated with technical careattitudes and is thus rejected.

Strong Ties and Performance

Hypothesis 2a tests the positive associa-tion of strong ties with attitudes to inter-personal and technical care. As thedependent variables and the averagestrength of ties variable is reasonablynormally distributed, parametric tests tocompare groups (t-tests) may be con-ducted. In terms of hypothesis testing,the t-test tests the probability that thetwo sets of scores (strong tie and weaktie) came from the same population.This allows for the t-test to revealwhether there is a statistically significantdifference in the mean score for thestrong-tie group and the weak-tie group.

As stated earlier, the operationaliza-tion of tie strength is determined fromthe average of frequency of contact andcloseness measures of the “ego” GP to allother GPs within his or her network. Themaximum value for frequency of contacton a scale of “daily” (coded as 1 butreverse-coded for analysis as 5) to “lessoften” (coded as 5 but reverse-coded foranalysis as 1) is 5, which indicates fre-quent interaction and, intuitively, a veryclose tie. The maximum value for degreeof closeness on a scale of “especiallyclose” (coded as 1, but reverse-coded inanalysis as 4) to “distant” (coded as 4 butreverse-coded in analysis as 1) is 4,which indicates a stronger relationshipand a stronger tie. Therefore, in this

1Effect-size calculations are not shown. They are calculated

as .t2

t2 � (N1 � N2 � 2)



AP

ER

S

Tabl

e 3:

Pear

son'

s pr

oduc

t mom

ent c

orre

latio

n.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Year

s in

rura

l pra

ctic

e (1

)1

0.67

3 (*

*)�

0.04

4�

0.05

7�

0.16

40.

040

0.08

1�

0.09

4�

0.23

1 (*

)�

0.16

60.

003

�0.

140

Year

s in

cur

rent

0.

673(

**)

1�

0.03

80.

181

�0.

091

0.18

5�

0.14

6�

0.02

4�

0.13

7�

0.23

0 (*

)�

0.04

6�

0.05

7pr

actic

e (2

)

No o

f GPs

in c

urre

nt

�0.

044

�0.

038

10.

183

0.05

60.

130

�0.

145

0.00

1�

0.00

3�

0.04

00.

162

0.02

0pr

actic

e (3

)

Ego

dens

ity (4

)�

0.05

70.

181

0.18

31

0.03

60.

455

(**)

�0.

979

(**)

0.19

9 (*

)�

0.10

3�

0.01

6�

0.04

10.

064

Degr

ee (5

)�

0.16

4�

0.09

10.

056

0.03

61

�0.

720

(**)

�0.

180

�0.

196

(*)

0.20

7 (*

)0.

301

(**)

0.25

8 (*

*)0.

176

Cons

trai

nt (6

)0.

040

0.18

50.

130

0.45

5 (*

*)�

0.72

0 (*

*)1

�0.

296

(**)

0.26

5 (*

*)�

0.28

6 (*

*)�

0.28

5 (*

*)�

0.21

7 (*

)�

0.02

8

Effic

ienc

y (7

)0.

081

�0.

146

�0.

145

�0.

979

(**)

�0.

180

�0.

296

(**)

1�

0.16

00.

053

�0.

044

0.00

2�

0.08

3

Aver

age

tie s

tren

gth

(8)

�0.

094

�0.

024

0.00

10.

199

(*)

�0.

196

(*)

0.26

5 (*

*)�

0.16

01

�0.

184

0.13

9�

0.07

4�

0.11

8

Geog

raph

ical

div

ersi

ty (9

)�

0.23

1 (*

)�

0.13

7�

0.00

3�

0.10

30.

207

(*)

�0.

286

(**)

0.05

3�

0.18

41

0.42

2 (*

*)0.

026

�0.

080

Func

tiona

l div

ersi

ty (1

0)�

0.16

6�

0.23

0 (*

)�

0.04

0�

0.01

60.

301

(**)

�0.

285

(**)

�0.

044

0.13

90.

422

(**)

10.

126

0.06

3

Inte

rper

sona

l car

e (1

1)0.

003

�0.

046

0.16

2�

0.04

10.

258

(**)

�0.

217

(*)

0.00

2�

0.07

40.

026

0.12

61

0.23

0 (*

)

Tech

nica

l car

e (1

2)�

0.14

0�

0.05

70.

020

0.06

40.

176

�0.

028

�0.

083

�0.

118

�0.

080

0.06

30.

230

(*)

1

N11

011

011

010

910

910

910

911

010

910

911

011

0

*Cor

rela

tion

is s

igni

fican

t at t

he 0

.05

leve

l (tw

o-ta

iled)

.

**Co

rrel

atio

n is

sig

nific

ant a

t the

0.0

1 le

vel (

two-

taile

d).


study, the theoretical maximum valuethat the “average tie strength” can haveis 4.5 ((5 � 4)/2). In order to distinguisha strong tie from a weak tie, a cut-pointneeds to be determined from this theo-retical maximum value by dividing it by2. Therefore, the cut-point is 4.5/2 �

2.25. Consequently, GPs who have anaverage strength of tie score that isgreater than or equal to 2.25 are grouped“strong ties,” and those lesser than 2.25are termed “weak ties.”

With respect to interpersonal care,there is no significant difference in theattitudes scores to interpersonal care forGPs with strong ties (M � 47.26, SD �

7.19, n � 54) and GPs with weak ties (M � 47.64, SD � 5.25, n � 56); t (108) �

.320, p � 0.749 (two-tailed). The magni-tude of the differences in the means(mean difference � 0.38, 95% CI: �1.99to 2.76) is very small (eta squared �

0.0009). With regard to technical care,there is also no significant difference inthe attitude scores to technical care forGPs with strong ties (M � 19.45, SD �

5.81) and GPs with weak ties (M � 18.52,SD � 5.59); t(108) � �1.030, p � 0.305(two-tailed). The magnitude of the differ-ences in the means (mean difference ��1.3849, 95% CI: �4.049 to 1.279) is verysmall (eta squared � 0.009). Therefore,given the results, it is clear that there isnot sufficient evidence to reject the nullhypothesis that strong ties will not beassociated with individual perform-ance in knowledge-intensive work. Forthis reason, the alternative hypothesisH2a cannot be supported.

Degree Centrality and Performance

Again, the t-test was deployed to assessif there is a statistically significant dif-ference between the means of the per-formance attitude scores of those withhigh-degree centrality and those withlow-degree centrality. In order todichotomize groups into high- andlow-degree centrality, the mediandegree was chosen as the cut-point. Inthis study, the median degree centralityis 8. Therefore, those with a degree cen-trality lower than the median were

categorized as being in the “low-cen-trality group,” and those with degreecentrality equal to or greater than themedian were categorized as being inthe “high-centrality group.”

In terms of interpersonal care, thereis a significant difference in the atti-tudes scores to interpersonal care forGPs with high-degree centrality (M �

48.58, SD � 4.59, n � 64) and GPs withlow-degree centrality (M � 45.95, SD �

7.87, n � 45); t(107) � �2.189, p � 0.031(two-tailed). The magnitude of the differ-ences in the means (mean difference ��2.62, 95% CI: �4.99 to �0.247) is small(eta squared�0.04). Furthermore, inspe-cting the correlations, there exists avery significant positive correlation (r �

0.258; p � 0.01) between degree cen-trality scores and attitudes to interper-sonal care. Therefore, there is sufficientevidence to support the alternativehypothesis H2b in terms of attitudes to interpersonal care. With respect totechnical care, there is no significantdifference in the attitudes scores totechnical care for GPs with high-degreecentrality (M � 39.55, SD � 7.08, n � 64)and GPs with low-degree centrality (M �

38.6, SD � 6.99, n � 45); t(107) � �0.691,p � 0.491 (two-tailed). The magnitudeof the differences in the means (meandifference � �1.37, 95% CI: �3.66 to1.77) is very small (eta squared � 0.004).As such, the null hypothesis that thereis no association between degree cen-trality and interpersonal care cannot berejected. Therefore, there is sufficientevidence to reject the alternativehypothesis H2b in terms of attitudes totechnical care.

Geographical Tie Diversity andPerformance

Following the t-test method in the previ-ous tests, results revealed no significantdifference in the interpersonal care atti-tude scores of GPs with high geographi-cal diversity (M � 47.44, SD � 4.77, n �

54) and GPs with low geographicaldiversity (M � 47.46, SD � 7.44, n � 56);t(108) � 0.017, p � 0.987 (two-tailed).The magnitude of the differences in the

means (mean difference � 0.01984, 95%CI: �2.354 to 2.394) was very small (etasquared � 0.00). Therefore, the nullhypothesis that geographical diversity isnot associated with attitudes to inter-personal care cannot be rejected. Assuch, there is no evidence to supportthe alternative hypothesis H2c, and itshould thus be rejected. Similarly, thereis also no significant difference in thetechnical care attitude scores of GPswith high geographical diversity (M �

38.35, SD � 6.54, n � 54) and GPs withlow geographical diversity (M � 39.77,SD � 7.5, n � 56); t(108) � 1.054, p �

0.355. The magnitude of the differencesin the means (mean difference � 1.41,95% CI: �1.247 to 4.079) was very small(eta squared � 0.01). Therefore, the nullhypothesis cannot be rejected, and thealternative hypothesis that geographi-cal diversity is positively associated withattitudes to technical care should thusbe rejected. In conclusion, there is nosupport for H2c in terms of both inter-personal and technical care.

Functional Tie Diversity andPerformance

As discussed earlier, to test thishypothesis, the median (0.8676) waschosen as the cut-point. GPs, withfunctional diversity indices greaterthan equal to the median categorizedas a “high-functional diversity group”and those with indices lower than themedian value categorized as a “lowfunctional diversity group.” The t-testshows that for attitudes to interperson-al care, the two functional diversitygroups (high functional diversity andlow functional diversity) have no statis-tically significant difference in theinterpersonal attitude scores for thehigh functional diversity (M � 48.05,SD � 4.33, n � 54) and low functionaldiversity (M � 46.87, SD � 7.66, n � 56)groups. The probability value of the test(equal variances not assumed) is notless than or equal to 0.05, so the resultis not significant, t(108) � �0.999, p �

0.320 (two-tailed), with the magnitudeof the means (mean difference � �1.18,



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95% CI: �3.54 to 1.18) being very small(eta squared � 0.009). In other words,there is no association between func-tional diversity and attitudes to per-formance with respect to interpersonalcare. In terms of attitudes to technicalcare, the two functional diversitygroups also show no significant differ-ence in the technical care attitudescores for the high (M � 39.05, SD �

7.03, n � 54) and low (M � 39.08, SD �

7.13, n � 56) groups; t(108) � 0.025, p� 0.980 (two-tailed). The size of the dif-ference (mean difference � 0.03373,95% CI: �2.643 to 2.71) is extremelysmall (eta squared � 0.000). In conclu-sion, there is no support for the alter-native hypothesis (H2d) that function-al diversity is positively associated withattitudes to technical care.

Gender Effects

The independent samples t-test wasmost suited to test hypotheses H3a toH3c, as it tells us whether there is a sta-tistically significant difference in themean scores for the two groups (i.e., ofmale and female GPs). Statistically, it istesting the probability that the two setsof scores (for males and females) camefrom the same population.

For H3a, t-test results suggest thatthere is a statistical difference in thedegree centrality scores for males (M �

8.40, SD � 3.88) and females (M � 10.4,SD � 3.88); t(107) � 2.075, p � 0.04(two-tailed). The magnitude of the dif-ferences in the means (mean difference� 1.99, 95% CI: 0.088 to 3.9) was small(eta squared � 0.03). Furthermore, it isevident that the mean degree centralityscores of females are higher than thoseof males. Therefore, while there is sup-port for H3a, one can also infer thatfemales have higher-degree centralityin their ego networks than males inknowledge-intensive work.

For H3b, there is a statistical differ-ence in the constraint scores for males(M � 0.4192, SD � 0.206) and females (M � 0.3283, SD � 0.1317); t(43.005) �

�2.475, p � 0.017 (two-tailed). The mag-nitude of the differences in the means

(mean difference � �0.09086, 95% CI:�0.16489 to �0.01683) was near moder-ate (eta squared � 0.05). Furthermore,it is evident that the mean constraintscores of males are higher than those offemales. Therefore, although there issupport for H3b, one can also infer thatmales have higher constraint in theirego networks than females in knowl-edge-intensive work.

Regarding H3c, in terms of geo-graphical diversity, the results suggestthat there is a statistical difference in thegeographical diversity scores for males(M � 0.6613, SD � 0.376) and females (M� 0.8767, SD � 0.266); t(38.194) � 3.002,p � 0.005 (two-tailed). The magnitudeof the differences in the means (meandifference � 0.21538, 95% CI: 0.07019 to0.36058) was fairly moderate (etasquared � 0.07). Also, it is evident thatthe mean geographical diversity scoresof females are higher than those ofmales.

In terms of functional tie diversity, itappears that there also is a statisticaldifference in the functional diversityscores for males (M � 0.7347, SD � 0.446)and females (M � 1.0884, SD � 0.329);t(107) � 3.342, p � 0.001 (two-tailed).The magnitude of the differences in themeans (mean difference � 0.35375,95% CI: 0.14394 to 0.56357) was fairlymoderate (eta squared � 0.09). Further-more, it is evident that the mean func-tional diversity scores of females arehigher than those of males. Therefore,while there is strong support for H3c,one can also infer that females havehigher tie diversity in their ego net-works than their male counterparts inknowledge-intensive work.

Regarding H3d, it is clear that thereis a significant difference in the egonetwork density indices for holders ofhospital appointment (M �0.6557, SD �

0.328) and nonholders of hospital ap-pointment (M � 0.4674, SD � 0.377);t(107) � �2.426, p � 0.017 (two-tailed).The magnitude of the differences in themeans (mean difference � �0.188, 95%CI: �0.3420 to �0.0344) was small tonear moderate (eta squared � 0.05). In

particular, it also appears that thosewho hold professional appointmentsalso have a denser network than thosewho do not (by inspecting the means).Therefore, there is sufficient evidence toreject the null hypothesis in favor of H3d.

In terms of ego-network efficiency,it is clear that there is a significant dif-ference in the ego-network efficiencyindices for holders of hospital appoint-ment (M � 0.4327, SD � 0.283) andnonholders of hospital appointment (M � 0.6198, SD � 0.302); t(107) � 2.85,p � 0.005 (two-tailed). The magnitudeof the differences in the means (meandifference � �188, 95% CI: 0.0569 to0.3172) was moderate (eta squared �

0.07). In particular, it also appears thatthose who hold professional appoint-ments also have a less efficient networkthan those who do not (by inspectingthe means). Therefore, there is suffi-cient evidence to reject the nullhypothesis in favor of H3e.

With regard to H3f, fellowship accre-ditations are of two forms: Fellowship ofthe Royal Australian College of GeneralPractitioners (FRACGP) and Fellowshipof the Australian College of Rural andRemote Medicine (FACRRM). In terms ofthe FACRRM accreditation, there is a sig-nificant difference in the degree central-ity indices for holders of FACRRM (M �

10.55, SD � 4.13) and nonholders ofFACRRM (M � 8.125, SD � 3.69); t(107) �

�2.787, p � 0.004 (two-tailed). The mag-nitude of the differences in the means(mean difference � �2.42, 95% CI:�4.065 to �0.78818) was near moderate(eta squared � 0.05). In particular, italso appears that those who hold theFACRRM accreditation also have higher-degree centrality in their ego networksthan those who do not (by inspectingthe means). Therefore, there is suffi-cient evidence to reject the nullhypothesis in favor of H3f in terms ofFACRRM.

In terms of H3g, there is a signifi-cant difference in the functional diver-sity indices for holders of FACRRM (M �

0.944, SD � 0.31342) and nonholders ofFACRRM (M � 0.7472, SD � 0.47791);


t(107) � �2.49, p � 0.015 (two-tailed).The magnitude of the differences in themeans (mean difference � �0.19672,95% CI: �0.35408 to �0.03936) was nearmoderate (eta squared � 0.05). In par-ticular, it also appears that those whohold the FACRRM accreditation alsoexperience higher functional tie diver-sity in their professional networks thanthose who do not (by inspecting themeans). Therefore, there is sufficientevidence to reject the null hypothesis infavor of H3h in terms of FACRRM.

With respect to H3h: first, in termsof performance attitudes to interper-sonal care for FRACGP-accredited GPs,there is a significant difference in theinterpersonal care attitudes scores forholders of FRACGP (M � 49.068, SD �

4.353) and nonholders of FRACGP (M �

46.38, SD � 7.08); t(108) � �2.251,p � 0.026 (two-tailed). The magnitudeof the differences in the means (meandifference � �2.6893, 95% CI: �5.057to �321) was small (eta squared � 0.04).Second, in terms of performance atti-tudes to technical care for FRACGP-accredited GPs, there is a significantdifference in the technical care atti-tudes scores for holders of FRACGP (M � 41.34, SD � 6.80) and nonholdersof FRACGP (M � 37.56, SD � 6.85);t(106.331) � �3.469, p � 0.001 (two-tailed). The magnitude of the differ-ences in the means (mean difference ��6.6893, 95% CI: �10.512 to �2.866)was small (eta squared � 0.04).

Therefore, in terms of the associa-tion between FRACGP and perform-ance attitudes (for both technical andinterpersonal care), the evidence is suf-ficient to reject the null hypothesis andlend support to H4h. It is also clear thatthose who are professionally accreditedwith the FRACGP are more likely tohave a higher performance attitudescore than those who are not (byinspecting the means for both interper-sonal care and technical care attitudes).

ConclusionThe findings from the study providerich insights into the social network

model for explaining individual per-formance in geographically distributedknowledge-intensive work environ-ments. Although ego-network efficien-cy is claimed to be an influential predic-tor of performance (Aral et al., 2006),the results in this study indicate no sup-port for this particular variable in theresearch model. The correlation coeffi-cients, for instance, are indicative of thefact that there is no significant associa-tion between efficiency and perform-ance. This can be attributed to the factthat individuals (such as rural GPs)involved in knowledge-intensive workwithin dispersed communities andgroups are satisfied or are performingrelatively well with sources of informa-tion and advice based on their profes-sional network of peers and colleagues.It does not seem crucial for such knowl-edge workers to be efficient in terms ofobtaining information for the deliveryof quality care at the technical or interpersonal level. Unlike real estateprojects or corporate projects wherethe competition for information gainsis likely to provide competitive advan-tage so as to boost sales incentives,bonuses, and salary promotions (Burt,2007; Burt, Hogarth, & Michaud, 2000;Crowston, Sawyer, & Wigand, 2001),GPs in rural areas have no such motiva-tions, and the nature of their occupa-tion is noncompetitive.

Having said this, the study’s find-ings shift the focus toward the impor-tance of ego-network constraint. Anindividual’s professional network ishighly constrained to the extent thatthe individual seeks advice and infor-mation from peers and colleagues thatlead back to the same person. In thecontext of rural GPs, results show thatconstraint has a marginal detrimentaleffect on performance, not in terms oftechnical care but in terms of attitudesto interpersonal care. That is, the higherthe constraint for the individual GP, thelower the score for attitudes to interper-sonal care (r � �0.217, p � 0.05). Ahighly constrained professional net-work for a GP means that the GP seeks

advice and information from within hisor her professional network that leadsback to the same contact. This con-strains the GP from learning novelideas, interacting with a new anddiverse range of personnel in the relat-ed profession, who would contribute toa better level of interpersonal care.

Figure 6 illustrates a professionalnetwork of a rural GP. The rural GP isthe ego in the sociogram. The density ofthe overall network is 0.924 (whichindicates high cohesion and possiblyinformation redundancy). Despite thehigh density level, the ego’s constraintscore is 0.249 and is relatively uncon-strained compared with others in thenetwork. The size of the nodes alsoshows the relative constraint index (i.e.,larger nodes have a higher constraintindex). The ego GP has quite a largenumber of strong ties with others (col-ors of node depict occupation) in thenetwork, demonstrated by the thick-ness of lines (1 � distant, 5 � veryclose). Thicker lines denote strongerties. The low constraint score of the egoGP indicates his or her ability to seekadvice and information from nonre-dundant contacts.

With respect to performance interms of attitudes to interpersonal care,the finding of ego-network constraintbeing negatively associated with per-formance conforms with literature(Aral et al., 2006; Rosenthal, 1997).However, it was surprising and interest-ing to note that efficiency did not dis-play its hypothesized correlation withperformance in individual knowledge-intensive work. It can thus be speculat-ed that although the measures of ego-network position such as efficiencywere developed based on theories ofsocial structure and competition, itsdocumented effects toward perform-ance may not be clearly reflected orapplicable in occupational communi-ties of knowledge-intensive work whereindividuals are geographically dis-tributed in isolated settings. This natu-rally translates into an avenue for fur-ther research with respect to the effects



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of ego-network efficiency and perfor-mance in noncompetitive domains.

In terms of ego-network density,there was no association (negative orpositive) with performance. Degreecentrality, on the other hand, demon-strated significant positive associationbetween performance with respect toattitudes to interpersonal care, the cor-relation coefficient for the associationbeing r � 0.258, p � 0.01. Furthermore,the t-tests also demonstrated signifi-cant difference in the scores of attitudesto interpersonal care between twoindependent centrality groups (high-and low-centrality groups) of GPs. Inparticular, GPs with higher-degree cen-trality scored higher than those withlower-degree centrality in terms of atti-tude to interpersonal care. These find-

ings resonate with studies by Cross andCummings (2004), where significantsupport was found between degreecentrality (measured as an individual’sties to and from his or her department)and individual performance ratings inknowledge-intensive projects. Consensuscan, therefore, be reached regardingdegree centrality in that it representsthe extent of information activity,which in turn is conducive to perfor-mance (Freeman, 1978).

The ego-network ties hypotheseswere examined from the perspective ofstrength of relational ties and of theirdiversity. First, this research hypothe-sized that strong ties in an individual’sego network will be positively associat-ed with performance in knowledge-intensive work. Beginning with the

theory on the strength of ties (Granovetter,1973), arguments to formulate thehypothesis were put forward regardinghow strong ties connect individualswho work frequently with each other,how these individuals have greatermotivation to be of assistance, and howstrong ties are typically more availablethan weak ties (Granovetter, 1983).Furthermore, it was claimed that strongties generate trust that allows influx ofuseful knowledge (Levin & Cross, 2004;Reagans & McEvily, 2003) and that their“affect” level is conducive to perfor-mance (Krackhardt, 1992). Second, tiediversity in terms of geographical andfunctional (or occupational) diversitywas postulated to be positively associ-ated with performance. This hypothesiswas derived from previous research

Network Density � 0.924

GPPractice Manager

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SpecialistNurseReceptionistEgo

0.249 0.256

0.2490.249

0.249

0.249

0.255

0.250

0.2520.249

0.250

0.252

0.252

0.252

0.249

0.249

Ego

Figure 6: A GP's professional network.


primarily by Cummings (2004) andMonge et al. (1985), who found thatstructural diversity played an impor-tant role in obtaining new and novelinformation for performance. However,contrary to inferences from literature,findings from this study show no statis-tical support for the hypotheses thatstrong ties or tie diversity are positivelyassociated with individual perform-ance in knowledge-intensive work. Theresults are surprising, as they contra-dict past research, thus questioning thegeneralizability of the strong-tie and tiediversity hypotheses to the extent ofindividuals within geographically dis-tributed knowledge-intensive work. Inshort, this study argues that in ego networks of individuals in knowledge-intensive and geographically distributedwork, although demographic proper-ties (e.g., professional accreditation)are important, network structure (i.e.degree centrality) and network position(e.g., constraint) are equally potent pre-dictors of performance.

To summarize, empirical resultssuggest that network structure, posi-tion, and ties of knowledge workers playa crucial role in individual performance.In particular, degree centrality wasfound to be positively associated withperformance, while ego-network con-straint was found to be negatively corre-lated with performance. The resultsfrom this study resonate with findingsfrom past literature and extend tradi-tional theory of social networks andperformance within the micro-level to include geographically dispersedindividuals involved in knowledge-intensive work. For individuals in suchnoncompetitive settings, traditionalnetwork theories such as structural holestheory still apply. However, a key findingis that network structure is a muchmore potent predictor of performance,although network position is important.

Methodologically, the study con-tributes toward a triangulation approachthat utilizes both qualitative and quanti-tative methods for operationalizing thestudy. The quantitative method includes

a nontraditional “networks” method ofdata collection and analysis to serve as afine complement to traditional researchmethods in behavioral studies. The out-come is a valid and reliable survey instru-ment that allows the collection of bothindividual attributes and social networkdata. The instrument is theoreticallydriven, practically feasible to implement,time-efficient, and easily replicable forother similar studies.

At the domain level, key findingsfrom this study contradict previous lit-erature that suggests that professionalsin projects and occupational commu-nities such as GPs decline in perform-ance as they age. In fact, findings fromthis study suggest that age and experi-ence do not affect for performance.Furthermore, degree centrality is alsopositively associated with professionalaccreditations, such as the Fellowshipof the Royal Australian College ofGeneral Practitioners, which is con-ducive to performance in terms of atti-tudes toward interpersonal and techni-cal care. The contextual implicationfrom the quantitative and qualitativeevidence of this study is that whenstrategies for enhancing performance,such as improving attitudes to qualityof care at the technical and interper-sonal level, are contemplated, theimportance of social structure, posi-tion, and relationships in the profes-sional network needs to be consideredcarefully as part of the overall individ-ual-level, project-level, and organiza-tion-level goals. ■

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Kon Shing Kenneth Chung is a lecturer at theProject Management Graduate Programme atthe University of Sydney. He holds a PhD fromthe School of Information Technologies atSydney University and holds a BS with first-class honors in information systems and abachelor’s of commerce degree majoring inaccounting and information systems. His PhDresearch received the RailCorp Prize for“Addressing Society’s Needs in Research ProjectWork” at the 2005 Research Conversazionesheld at the University of Sydney. Prior to com-mencing his doctoral study, he was a systemsintegrator at Protocom Technologies and theTelstra Messaging (Text & SMS) Group. His workspanned several large-scale information tech-nology projects that involved development ofintegration and testing processes for mobileSMS text software from development to rollout in production environments. Prior toTelstra, he was online ad campaign coordinatorfor DoubleClick Australia (now acquired byGoogle). He currently enjoys teaching projectmanagement and research in the area of social networks and its influence on social out-comes such as coordination, performance, andinnovation. On a broader level, his currentresearch interest lies in the triangulation ofinterdisciplinary theories and methods fromsocial networks, sociology, information sys-tems, and management science. His recentwork has been published in top-tier conferences such as Computer-Supported Cooperative Work, European Conference onInformation Systems, Human-ComputerInteraction, and SIG-Management of Information Systems.

Liaquat Hossain is the director of the ProjectManagement Graduate Programme at theUniversity of Sydney. He was a postdoctoral candi-date at the Massachusetts Institute of TechnologyCenter for Technology, Policy, and IndustrialDevelopment during 1997. He completed his PhD ininformation and communications technology fromthe University of Wollongong during 1997. He com-pleted his MSc in computer and engineering man-agement in 1995 and bachelor’s of businessadministration in 1993 from Assumption Univer-sity. His work aims to explore the effects of differenttypes of social network structures and patterns ofinformation technology use on coordination in adynamic and complex environment. The primaryfocus of his research is in the area of networkanalysis of organizational and social systems. Heapproaches this using social networks theory andanalytical methods and applies theories and meth-ods from sociology and social anthropology tostudy coordination problem in a dynamic, complex,and distributed environment. He further appliesnetwork-based theories and methods to explorethe phenomenon of globally distributed work-groups (referred to as outsourcing in businessmanagement literature) and its management chal-lenges. Overall, he is interested in exploring theeffects of different types of social network struc-tures on coordination and organizational perform-ance from a theoretical and an applied perspective.In his research, he uses methods and analyticaltechniques from mathematical sociology (i.e.,social networks analysis), social anthropology(i.e., interview and field studies), and computer sci-ence (i.e., information visualization, graph theoret-ic approaches and data-mining techniques such asclustering) to explore coordination problems in adynamic, distributed, and complex setting.

Documents

Measuring performance of knowledge-intensive workgroups through social networks