the influence of collaborative technology knowledge on advice network structures

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The influence of collaborative technology knowledge on advice network structures

Mark Keith a,⁎, Haluk Demirkan b, Michael Goul b

a Department of Information & Decision Management, College of Business, West Texas A&M University, United Statesb Department of Information Systems, W. P. Carey School of Business, Arizona State University, United States

a b s t r a c ta r t i c l e i n f o

Article history:Received 23 February 2010Received in revised form 3 June 2010Accepted 25 July 2010Available online 30 July 2010

Keywords:Decision makingSocial networksInformation and knowledge sharingCollaborative technologyWeb 2.0Group structureTask environment uncertaintyEntrainment theoryAssymetric adaptability

This research describes an experiment designed to understand how an individual's knowledge concerningtask-critical technologies influences the structure of their advice network relationships. The results indicatethat an individual's technology knowledge leads them to become more central depending on the type oftechnology, their formal group structure, and task uncertainty. These results contribute to the theory onadvice networks by demonstrating how individual knowledge, task uncertainty, and group departmentationinfluence the evolution of an advice network structure. It suggests that managers should make informeddecisions about the formal group structuring and technology training which can improve their employee'sadvice networks.

© 2010 Elsevier B.V. All rights reserved.

1. Introduction

Because of the increasing need for knowledge sharing, coordina-tion, and creativity among employees, organizations are becomingmore interested in technologies designed to support collaborationamong users such as the so-called “Web 3.0” technologies, groupsupport systems, etc. [7,13,24,43]. However, there is little evidenceconcerning the actual effects these technologies may have to bringpeople together who need to collaborate and share knowledge [50].Taking a social network perspective, this paper reports the results of aquasi experiment which tests how technologies designed to supportcollaboration influence the structure of a group's knowledge sharingrelationships (i.e. advice network structure).

Advice network research has demonstrated that the structure of anindividual's knowledge sharing relationshipswill significantly influencetheir work performance [2,15,21,42,44,45]. Therefore, managers wouldbe wise to help their employees foster positive advice networks forknowledge exchange in order to improve overall unit performance.However, there is little research evidence concerning how anindividual's advice network structure forms— orwhat role collaborativetechnologies play in that formation. Social exchange theory posits thatindividuals form relationships based on the resources that others haveto offer, and that emergent exchange relationships will becomemutually beneficial to both parties [19,31]. In certain environments,

the most valuable resource that employees can obtain through socialexchange relationships with other employees is knowledge [27]. Inenvironments where collaboration is technology-enabled, one knowl-edge area of importancemay be of a technology-oriented nature. On theother hand, such knowledge may have little effect on how advicenetwork structures emerge over time. Given this interesting disparity,the specific research question addressed by this study is how does anindividual's knowledge concerning collaborative technologies influence thestructure of their advice network?Or, in otherwords, do individual'swithexpertise in task-relevant collaborative technologies become more “indemand” in their organization's knowledge sharing networks?

Answering this research question depends on a variety of otherfactors which also influence advice network formation. For example,those who share similar demographics and personality traits tend toform knowledge sharing relationships [3,35]. Also, people tend toform relationships with those in close physical proximity to them [5]or who hold central ranks and positions [36]. Perhaps moreimportantly, the need for knowledge sharing (and collaborativetechnology) is likely to depend on nature of the task environment[28]. For example, Nidumolu [40] found that when there is greateruncertainty in the task environment, horizontal coordination (i.e. thecoordination represented by advice networks) is more effective thanvertical coordination (i.e. coordination which follows the formal linesof authority). Therefore, the impact of task uncertainty should also beinvestigated as a moderator of the relationships between anindividual's collaborative technology knowledge and their positionin the advice network.

Decision Support Systems 50 (2010) 140–151

⁎ Corresponding author. Tel.: +1 806 367 2560; fax: +1 806 651 2969.E-mail address: [email protected] (M. Keith).

0167-9236/$ – see front matter © 2010 Elsevier B.V. All rights reserved.doi:10.1016/j.dss.2010.07.010

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In addition, this relationship is also likely to depend on thetype of group structure which individuals work within. Forexample, in project-based environments such as software devel-opment and R&D, organizations often use cross-functional (or“matrix”) groups [20,30]. In this case, employees belong to afunctionally structured “home” group as well as one or moredivisionally structured, temporary project groups. In the functionalgroups, employees work on similar tasks with similar roleswhereas in divisional groups, they work on different tasks indifferent roles. Moon et al., [39] discovered that the type of groupdepartmentation (functional versus divisional) has a significantimpact on the knowledge sharing that takes place within thegroup. In addition, as group structures change, employees have anopportunity to develop new knowledge sharing relationships andtrim others. As a result, the influence of an individual'scollaborative technology knowledge on their advice networkposition may also depend on the type of group departmentationindividuals work within and the nature of structural changes overtime.

To investigate these relationships, a controlled experiment isperformed which tests the influence of an individual's collaborativetechnology knowledge on their advice network position in conditionsof high and low task uncertainty and in functional and divisionalgroup structures over time. The experiment is based on the advicenetworks of 99 graduate students working on a variety of groupprojects. By measuring the influence of collaborative technologyknowledge on the quality of an individual's advice network position,this study offers a unique perspective on the effectiveness ofcollaborative technologies (versus traditional technology) in accom-plishing task-critical objectives. The overall findings implicate that anindividual's technology skills and knowledge play a significant role intheir position within an advice network. In addition, it contributes tothe literature and theory on advice networks by demonstrating howindividual knowledge, task environment uncertainty, and groupdepartmentation influence the emergence of the advice networkstructure.

In Section 2, we review the relevant literature and theories andoutline hypotheses. Section 3 describes the methodology and settingused to test the hypotheses. Section 4 summarizes the results of thestudy. Section 5 discusses the results, implications, and limitations ofthe study and Section 6 concludes.

2. Literature review and hypotheses

The value of an organization's advice network can be consideredin terms of the social capital which it offers. Social capital refers tothe value that comes from the quality of an individual's socialrelationships and their position in the social structure [6,14,42]. Or,as Burt [10] explains, while human capital refers to an individual'sability, social capital refers to their opportunity. It is the value thatcomes from knowing what knowledge individuals possess, wherethey are located, and how to get it from them. Groups with greatersocial capital can make better use of the knowledge and informationthey have because it's easier to transfer to those who don't have it[24]. The advice network literature has many examples of howgreater social capital leads to greater performance for individualsand groups [2,21,41,44,45]. If organizations want to improve theirperformance by improving their social capital, they need tounderstand: (1) how their advice networks form, and (2) whatfactors influence their development.

In order to understand how advice networks form, they need tobe measured. Social network analyses (SNA) are the primarymechanism used to measure the structure of social networks (orin this case, advice networks) [51] which consist of a series ofnodes (individuals) and ties (relationships) between nodes [26].An individual's network centrality is one of the most common

variables measured in advice networks. There are multipleversions of centrality, but basically, it indicates how well-connected an individual is within a network [22]. In general,highly central individuals tend to be higher performers [2,45] andbetter at coordinating the actions of others [32]. An individual'scentrality is one way in which their social capital can beoperationalized [11].

Several advice network studies demonstrate the factorsinfluencing an individual's centrality such as personality anddemographics [3,35], physical proximity [5], and rank and position[36]. However, little research examines the effects of knowledgeon centrality. Burkhardt and Brass [8] performed a related studywhen they discovered that individuals who adopted new task-critical technologies earlier than others tend to become central intheir advice networks (if they weren't central already). Althoughthey did not explicitly measure the employee's technologyknowledge, it can be inferred that it was the knowledge employ-ees had concerning the new technology which caused them tobecome central, because the advice network which was measuredwas based on the knowledge employees shared. In agreement(and as stated above), social exchange theory posits thatindividuals form relationships based on the resources othershave to offer such as knowledge [19,31]. This leads to the firsthypothesis:

H1. (Knowledge) Individual technology knowledge is positivelyrelated to advice network centrality.

Technologies come in many degrees of complexity, function,and capability to reduce uncertainty. This being the case, it shouldbe expected that different technologies will have differing effectson an individual's advice network centrality — as long as thetechnology is relevant to the task being performed. The nextsection discusses the effects of collaborative versus traditionaltechnologies.

2.1. Technology and uncertainty

Different technologies offer different capabilities for informa-tion processing and collaboration. Some technologies are rathersimple and may only offer limited information processing. Moreadvanced technologies may offer greater processing for data,workflow, and collaboration. Basically, the type of technologyknowledge individuals seek from others depends on the amount ofuncertainty in the task.

Media richness theory posits that optimal performance isachieved for a group task when the demands for communicationrichness are met by capabilities of the communication media usedto perform the task (e.g. highly uncertain environments requiredhighly rich media) [16,17]. Face-to-face communication is consid-ered the “richest” form because it occurs in the same time andsame place. At the other end of the spectrum, email communica-tion occurs across space and time making it much less rich. Theappropriate degree of media richness depends on the level of taskuncertainty. For example, a task with low uncertainty may becompleted using simple office software for word processing,spreadsheets, and presentations which are shared and transferredby email. On the other hand, highly uncertain projects may requiregreater communication capabilities making Google Docs moreappropriate, because it facilitates real-time, distributed onlinecollaboration. As a result, individuals with expertise concerningrich technologies will be in demand when project uncertaintyincreases. However, richer communication is not always better.Advanced technologies are also likely to be complex and requireadvanced knowledge. If the task is simple, time and cost may besaved by using only those technologies which are needed.

141M. Keith et al. / Decision Support Systems 50 (2010) 140–151


In summary, when task uncertainty is high, the individuals withthe most knowledge concerning communication-rich technologieswhich offer greater collaborative capabilities will be more central.When task uncertainty is low, individuals with the most knowledgeconcerning communication-poor technologies will be relatively morecentral. Therefore, the following hypotheses are important:

H2a. (High Uncertainty)When task uncertainty is high, knowledge ofcommunication-rich technology will be more related to centralitythan knowledge of communication-poor technology.

H2b. (Low Uncertainty) When task uncertainty is low, knowledge ofcommunication-poor technology will be more related to centralitythan knowledge of communication-rich technology.

These hypotheses are based on the contingency perspective takenby information processing theory [23]. When uncertainty is high,technology which offers greater information processing capability ismore useful. Therefore, individuals who are knowledgeable aboutthose technologies will be in greater demand. On the other hand,when uncertainty is low, organizations will not invest in technologieswith advanced information processing capabilities because it wouldbe wasteful and unnecessary. Therefore, individuals who areknowledgeable about the technologies with more basic informationprocessing capabilities will be the most valuable. From a managerialperspective, these hypotheses have implications for the types oftechnologies they should invest in and the types of employees theyshould hire.

Although the influence of various types of technology knowledgeon an individual's advice network centrality can be discovered at agiven point in time, many environments are constantly changing.Project teams dissolve and then re-form causing major shifts in thegroup of co-workers with which a particular individual will work. As aresult, it would also be useful to know how this knowledge/centralityrelationship is affected over time as group structures change. The nextsection discusses this issue.

2.2. Group structure and advice networks

Besides the level of uncertainty, the relationship between technol-ogy knowledge and advice network centrality may also depend onhow the organization is formally structured. For example, people whowork or reside near each other tend to form knowledge sharingrelationships [5] which are stronger and more stable than those whoare further apart [38]. However, task environments can be quitevolatile. While a particular individual may be highly central in theadvice network during one task, he or she might become less centralafter group structures change, and they're working with an entirelydifferent set of people on a different task. However, few studies havetested how group structures influence advice network structure.

According to Thompson [48], managers group people andpositions in the way that minimizes the cost of coordinatingworkflow activities. Those individuals who need to communicatethe most because they are heavily interdependent on each otherwill be grouped in a work team. Obviously, the particularemployee make-up of formal groups is likely to impact theknowledge sharing relationships which form. However, the extentand formation of these relationships may depend on the groupdepartmentation. Group departmentation refers to how positionsor roles are grouped to handle the coordination of tasks [48]. Mostcommonly, group departmentation is considered as being func-tional or divisional. Functional groups are based on performingsimilar functions or tasks. Their activities must be coordinatedwith other groups to accomplish an overall objective. Divisionalgroups perform all of the necessary tasks, but only for a particularproduct line or geographic region.

These two forms of group structure have important implica-tions for how advice networks may form. For example, becauseteam members in functional groups perform the same task, theywill benefit more from knowledge and advice sharing [39]. On theother hand, members of divisional groups will not have as muchneed or benefit from internal knowledge sharing. They may needto coordinate with other group members to complete the overallobjective, but they will not be able to seek advice concerning theirown task from others who perform different tasks from them. So,the relationship between technology knowledge and advicenetwork centrality is also moderated by group departmentation(See Fig. 1):

H3. (Functional Groups) The relationship between technologyknowledge and advice network centrality is stronger in functionallystructured groups than in divisionally structured groups.

In other words, an employee's knowledge will be more valuable toother co-workers when they are all performing similar tasks. Forexample, a software developer who could really use some help onhow to debug a particular software program is more likely to seekknowledge from others in his or her group if everyone is debuggingthe same program.

According to structural contingency theory, organizations changetheir structure to fit their task environment [9,18]. Because marketsand task environments are constantly changing, the most optimalstructure for organizations changes as well. Therefore, managers maychoose to change their group structures between functional anddivisional forms. However, Moon et al. [39] discovered that thesechanges can have unintended side effects. They found that theconsequences of changing group structure depend on the nature anddirection of the change. Specifically, a change from functional todivisional (F2D) might be more advantageous in terms of knowledgesharing than a change from divisional to functional (D2F). Theirreasoning is based on entrainment theory which posits that the normsand habits in a social system persist (or become “entrained”) overtime even after their operational value is gone [1]. The implication isthat the good knowledge sharing habits of functional groups maycarry over after a switch to a divisional structure for a certain amountof time. Alternatively, divisional groups which change to functionalstructures may experience a dip in performance until they developgood knowledge sharing cultures. This phenomenon was termedasymmetric adaptability [39].

Based on this, teams that make the D2F switch should developimproved knowledge sharing patterns and the relationship betweentechnology knowledge and centrality will strengthen because of thenew opportunity to work with others performing similar tasks. On

Fig. 1. Theoretical model predicting network centrality.

142 M. Keith et al. / Decision Support Systems 50 (2010) 140–151


the other hand, teams which make the F2D switch will experiencelittle or no decrease in the need for technology knowledge sharingbecause they have developed a culture of knowledge sharing whichwill persist for a while even though they are performing less similartasks. Therefore, the strength of the relationship between knowl-edge and advice network centrality should remain the same in theshort run.

H4a. (D2F) When group structures change from divisional tofunctional, the strength of the relationship between technologyknowledge and centrality increases.

H4b. (F2D) When group structures change from functional todivisional, the strength of the relationship between technologyknowledge and centrality remains the same in the short run.

Fig. 2 summarizes the hypotheses along the dimensions ofgroup departmentation and uncertainty. Basically, if an employeedevelops good knowledge sharing relationships with those aroundhim while working on similar tasks in a functionally structuredgroup, he is likely to continue in open communication with histeammates even after a switch to a divisional structure. However,if an employee starts out in a divisionally structured group and isunused to sharing knowledge, it will take some time for him todevelop knowledge sharing relationships after a switch to afunctional group, even though there are greater benefits from itthen.

In summary, an individual's technology knowledge should leadthem to become more central in their advice networks. Whenuncertainty is high, knowledge concerning advanced technologieswhich offer greater capabilities for processing information will have astronger relationship to an individual's network centrality whilesimpler technologies will be more useful in conditions of lowuncertainty. Group departmentation will also influence this relation-ship with functional groups supporting a stronger knowledge/centrality relationship. Groups making the F2D switch will beginwith a strong knowledge/centrality relationship which will remainstrong in the short run. Groups making the D2F switch will begin witha lower knowledge/centrality relationship which will strengthen afterswitching to functional groups. The following section describes themethodology used to test these hypotheses.

3. Methodology

3.1. Sample and demographics

To test these hypotheses, an SNA was performed with graduatestudents in the business school of a large public university in thewestern United States. Specifically, five sections of two differentgraduate level information systems courses were selected. Eachcourse had 28 to 35 participants for a total of 99 participants. 60of those 99 were enrolled in both courses (see Fig. 3). Course 1had 70 participants between two sections and Course 2 had 89

Fig. 2. Effect of asymmetric adaptability.

Fig. 3. Description of project groups.



participants among three sections. Each course had two projectsduring the semester. Among the four total projects, there were 69distinct project teams of three to five individuals each (most weregroups of four).

Of those who chose to indicate their area of study, 56participants were in the Master of Science in Information Systems(MSIM) program, 24 were in the Master of Accounting Informa-tion Systems (MAIS) program, and 10 were in the Master ofBusiness Administration (MBA) program. Over two thirds ofparticipants had between 3 and 20 years of professional workexperience with an overall average of seven years. One third of theparticipants were female, and the average age was 33. While themajority of participants were U.S. citizens, many came from othercountries including Vietnam, China, Japan, Mexico, Trinidad,Tanzania, Zambia, Serbia, India, Taiwan, Pakistan, Australia,England, Israel, and Korea. Because the test environment wasbased in a business school setting, it was designed to be as similaras possible to a professional organization.

3.2. Measures

The measurements include snapshots of the advice networks attwo time periods including the advice network centrality for eachindividual, task uncertainty, group departmentation and controlvariables. The remainder of the section reviews and describes eachmeasurement.

3.2.1. Individual advice network centralityAdvice networks were measured in the typical fashion [2,45] by

asking, “Who do you go to for knowledge or advice for…” andproviding a list of names of every individual in the class. Consequent-ly, a separate network was measured for each section of each course.Response rates range from 80 to 100% for each networkmeasurement.In-degree centrality was measured by using Stephenson and Zelen's[46] closeness index as used in Baldwin's [2] study of MBA teams. In-degree refers to counting only those relations with an individualspecified by other individuals, thereby removing any self-reportingbias. The closeness index denotes the degree to which an individual isclose to all other actors in the network whether directly or indirectly(i.e., friend of a friend of a friend). For descriptive statistics only, in-degree centrality is measured separately for relationships within andbetween the formal group structures. In-degree centrality is a simplecount of the number of incoming ties to each node. Because groupsizes vary to some extent, these centrality scores are normalized bygroup size for the within-group scores and by class size for between-group scores.

3.2.2. Task uncertaintyTask uncertainty varied between the two courses by manipulat-

ing the group projects assigned by the instructors. The group projectfor Course 2 was designed to be the “low uncertainty” condition. Itwas a typical business-oriented written report and presentation tobe completed by teams of four to five members. The only technologyrequired by the project was basic office productivity software forword processing, presentations, web research, and email commu-nication. Dividing work and roles among team members was arelatively simple task. The group project for Course 1 was designedto be the “high uncertainty” condition. The participants wererequired to develop a reusable learning object (RLO). RLOs are web-based interactive “chunks” of independent e-learning modules [37].RLOs consist of multiple sub-objects which are grouped together tocompose a lesson. The sub-objects might consist of Flash demon-strations, “wizards,” and tutorials. Participants indicated that theyhad no prior experience with this kind of project from any othercourse they had taken making it relatively more uncertain than theproject of Course 2.

3.2.3. Functional vs. divisionalTo simulate the complexities of a typical work environment with

multiple group projects and changing group structures, the partici-pants were assigned two separate group projects in both courses.Group composition changed completely from project to project (i.e.no two participants were in the same group twice). The first projectwas due at the midpoint of the semester and the second was due atthe semester's end. Group departmentation was manipulated by theinstructors by changing the group make-up and nature of theassignments from the first project to the second.

In Course 1, the first group project was to create an entire RLO onone particular topic. Although the participants could divide up thework any way they pleased, they were all working on the same topicand could benefit from helping each other representing a functionalgroup structure. However, each group was assigned a different topic.After this project was submitted, the participants were rearrangedinto new groups so that no participant was working with any priorteammates. Therefore, each participant within the second projectgroups had a different skill set which they had developed from thefirst project. The second project was to combine each of the differenttopic areas into a new RLO. This allowed each participant tocontribute by developing an RLO sub-object about their own topicarea of expertise. This represents a divisional structure relative tothe Project 1 because each member worked on separate topics.

In Course 2, the first group project was a report and present on avariety of critical issues concerning the topic of project management.During the first portion of the semester, the participants had learnedmuch about project management and were exposed to a wide varietyof topics. Participants were asked to divide up the project assignmentso that each group member worked on a separate topic — thisrepresents a divisional structure. As in Course 1, the groups wererearranged after the first project so that no two members were in thesame group for the second project. The second group project requiredparticipants to select one particular project management issue andwrite a research paper in greater depth on the topic. Because allparticipants were working on the same topic, this project grouprepresented a functional structure relative to the first group project.

In summary, the two sections of Course 1 made an F2D switch,whereas the three sections of Course 2 made a D2F switch. It isimportant to note that this design has a potential confoundinginfluence between the direction of group change and level ofuncertainty. However, Moon et al.'s [39] theory on asymmetricadaptability did not depend on the level of uncertainty. It only positsthat habits will persist when changing structures. Therefore, while aconfounding affect is still possible, there is no theoretical reason toexpect this. Although it was not possible to strictly enforce functionaland divisional roles in every group, the requirements were developedto encourage and reward similar roles for groups that alignedfunctionally and segregated roles for groups that are aligneddivisionally.

Network surveys were administered at the end of each projectwithin each section of each course. In other words, the surveyquestion measuring advice network structure was, “Who did you goto for knowledge and advice concerning [Project 1/Project 2]?”Therefore, each section had its advice network measured separately.In total, 10 networks were measured — two for each of the five totalsections.

3.2.4. Technology knowledgeMeasuring technology knowledge was performed using a

survey with a list of potentially useful technologies. Eachparticipant was asked what their level of knowledge was witheach technology. The responses were given on a Likert-type scaleranging from one to five with one being “Not skilled” and fivebeing “Very skilled.” The list of technologies itself was developedby a pre-survey of the participants that asked which types of



technologies they had used during that semester. The pre-surveywas administered the week before the first group projects weredue to discover which technologies the participants were using forthose particular projects. Therefore, the technology knowledge inquestion only concerned technologies which were actually beingused to complete the tasks. The resulting list had technologiesranging from high to low degrees of collaboration including email,web searching, instant messaging, online discussion boards,electronic portfolios (e.g. Blackboard), video conferencing, audioconferencing, word processing software, spreadsheet software,presentation software, Google Docs & Spreadsheets, blogs, Wikis,and ThinkTank™. ThinkTank™ is an advanced collaborative groupsupport tool created in juncture with GroupSystems™. It allowedparticipants to write, outline, brainstorm, vote, etc. electronically,both synchronously and asynchronously, and in a distributedInternet environment. Because the participants were likely to havehad no experience with ThinkTank™, they were given a classroomdemonstration and training session at the beginning of thesemester.

3.3. Controls

Several control variables were measured that had antecedents tocentrality already existing in the advice network literature. Demo-graphics such as age, gender, and ethnicity have all been identified assignificant precursors to advice network relationships and areincluded in this study (Ibarra, 1992; 1993). Level of education isalso a significant indicator but is not included because the participantsare at roughly similar levels (i.e., they are all at the same point in theirgraduate education). The participant's degree program (Master ofBusiness Administration, Master of Science in Information Manage-ment, and Master of Accounting Information Systems) was alsoincluded. The Euclidean distance measure of these items was createdto capture individual's demographic similarity to their classmates[35]. This score is multiplied by −1 so that larger numbers indicategreater demographic similarity.

Similarities in personality characteristics have also been found tobe significant indicators of relationship forming in advice networks(i.e. those with similar personality traits tend to form relationshipswith each other). As used by Klein et al. [35], the InternationalPersonality Item Pool (IPIP) was used to measure the participant'spersonality [25]. It is a 50-item standardized instrument thatmeasures the “big five” personality factors (extraversion, agreeable-ness, conscientiousness, neuroticism, and openness to experience).Students rated howmuch they agreewith each item on a 5-point scale(1=strongly disagree, 5=strongly agree). Another Euclidean dis-tance measure was created based on personality scores to capture thesimilarity of participants to their classmates. This score is alsomultiplied by−1 so that higher numbers indicate greater personalitysimilarity.

4. Experimental results

Table 1 reports descriptives and correlations for each includedvariable. Fig. 4 plots the average individual closeness centrality foreach course from Project 1 to Project 2. The participants in bothcourses begin with high average centrality in Project 1 (Course1=13.1, Course 2=13.94) and then seem to trim nonessentialrelationships in Project 2 for a lower average rate (Course 1=8.76,Course 2=9.57).

Based on the closeness centrality scores used in Fig. 4, the twocourses appear to follow a near identical pattern of relationshiptrimming (i.e. participant's centrality scores reduced as a result offewer advice network relationships). Fig. 5a and b demonstrate theaverage individual in-degree centrality both within and betweengroups. The within-group centrality scores are standardized by groupsize and the between-group centrality is standardized by class size.From these figures, it is evident that groups largely maintained theirwithin-group relationships from Project 1 to Project 2 while severingmost of their between-group ties. This makes sense because eventhough project groups changed and new assignments were given,these second group projects were still based on the content of the firstgroup projects. Therefore, there would be less uncertainty aftercompleting Project 1 and less need for knowledge sharing relation-ships as a result. This is corroborated by Nidumolu's [40] finding thatthe rate of horizontal coordination is positively related to the level oftask uncertainty.

Table 1Descriptive statistics and correlations.

Mean Std. Dev. 1 2 3 4 5 6 7

1 Course 1 Project 1 13.10 2.792 Course 1 Project 2 8.76 2.42 0.1433 Course 2 Project 1 13.94 3.24 0.593*** 0.222*4 Course 2 Project 2 9.57 3.68 −0.533*** 0.242* −0.435**5 Demographics distance −0.79 0.25 0.219* 0.207* 0.005 0.0326 Personality distance −1.84 0.61 0.083 0.057 0.072 −0.070 −0.1207 CP tech. knowledge 4.25 1.01 0.127 0.007 −0.114 0.216* 0.113 0.1398 CR tech. knowledge 1.95 1.07 0.335** 0.202 0.051 −0.057 0.189* 0.234* 0.546***

Note: *** Correlation is significant at the pb0.001 level; ** pb0.05 level; * pb0.01 level.

Fig. 4. Average individual closeness centrality over time.



4.1. Technology knowledge

Each project technology was categorized as either communica-tion-rich or CP. This dichotomy was based on an exploratory factoranalysis of the participant's responses (95% response rate). Usingvarimax rotation and the principle component analysis extractionmethod, the factor analysis demonstrated convergent and discrimi-nant validity (See Table 2). The technologies of instant messaging,discussion boards, electronic portfolios, and audio conferencing didnot load well on either factor and were removed from the analysis(Fig. 6). Of the ten remaining technologies, five loaded on the

communication-rich factor and five on the communication-poorfactor. Cronbach's alpha was also computed for the technologies ineach factor. Both factors demonstrated reliability above 0.80 (α=0.86for poor, α=0.83 for rich). The technologies loading on thecommunication-poor factor included email, web searching, wordprocessing, spreadsheets, and presentation software. The technolo-gies which loaded on the communication-rich factor were moreadvanced and are used to support greater amounts of groupcoordination. These included video conferencing, Google Docs,blogging, Wikis, and ThinkTank™.

4.2. Advice network centrality

To measure the relationship between technology knowledgeand advice network centrality in varying conditions of uncertaintyand group departmentation, several multiple regression modelswere developed. Tables 3 and 4 report the coefficients andstandard errors of the models for Projects 1 and 2 in the highuncertainty and low uncertainty courses respectively. The reducedmodel of each project regression includes the effects of technologyknowledge only. The full models include covariates for theEuclidian distance measure of an individual's demographics (age,gender, and race) and personality (extraversion, agreeableness,conscientiousness, neuroticism, and openness to experience). Inother words the variable for demographics similarity is a measureof how close an individual is across all demographics measured toevery other participant in their class. Similarly, the personalitysimilarity variable is a measure of how similar an individual'spersonality is to every other participant in the class across all fivetraits. Variance inflation factors and tolerance scores revealed nomajor collinearity among the variables.

According to the regressionmodels, the support for the knowledgehypothesis was mixed, but positive overall. Technology knowledgewas significantly related to centrality in advice networks. However,the relationship depended on the level of task uncertainty and groupdepartmentation. Table 3 displays the regression results for bothprojects of Course 1 (high uncertainty). In support of the highuncertainty hypothesis, knowledge concerning communication-richtechnology was significantly related to advice network centrality inProject 1 (b=0.949, p=0.013) whereas communication-poor tech-nology knowledge was not (b= −0.258, p=0.519).

The models for Project 2 demonstrate the relationships after achange in group structure from F2D. The F2D hypothesis predictedthat the knowledge/centrality relationship would persist despite thechange in group departmentation. The regression results for Project 2

Table 2Descriptive statistics and correlations.

CP CR

Email 0.72 0.15Web research 0.70 0.25Word processing 0.88 0.15Spreadsheets 0.84 0.15Presentations 0.84 0.07Video conferencing 0.23 0.75Google Docs and Spreadsheets 0.32 0.64Blogging 0.27 0.72Wikis 0.07 0.74ThinkTank™ 0.04 0.84

Notes: Principle Component Analysis with Varimax rotation.

Fig. 6. Participant's average technology knowledge by type.

Fig. 5. Average in-degree centrality both within and between groups.



demonstrate that by the time the network had been measured, therelationship between group knowledge and centrality was stillmoderately significant (b=0.553, p=0.095). To test the differencein the knowledge/centrality relationship between Project 1 andProject 2, a repeated measures multivariate analysis of variance(MANOVA) was performed because the same subjects were beingcompared from the first project to the second (See Appendix A). Thewithin-subject effects tests revealed that the knowledge/centralityrelationship during the second project was no different for thecommunication-poor technology (F=0.102, p=0.751) and onlymarginally “less significant” for the communication-rich technology(F=3.609, p=0.062). Evidently, the seven weeks between Project 1and Project 2 was long enough that the need for sharing communi-cation-rich technology knowledge began to decrease. However, at thep=0.05 level, the F2D hypothesis is supported since the change inneither technology knowledge relationship was significant.

Table 4 displays the regression results for both projects of Course 2(low uncertainty). Contrary to the low uncertainty hypothesis,communication-poor technology knowledge was not related toadvice network centrality (b=−0.614, p=0.141) — at least whilethe groups were divisional. However, the model for Project 2 inTable 4 shows that when the participants made the D2F switch forProject 2, communication-poor technology knowledge did become asignificant predictor of advice network centrality (b=1.234,p=0.007), thus confirming the D2F hypothesis. Surprisingly, com-munication-rich technology knowledge became a significant negativepredictor of centrality (b=−0.901, p=0.042).

Another repeated measures MANOVA was performed to examinethe change in the knowledge/centrality relationship betweenProjects 1 and 2 of Course 2 (D2F condition). According to the D2Fhypothesis, the relationship should significantly strengthen after thegroups change to a functional departmentation where they couldbenefit more from knowledge sharing. As expected, the test forwithin-subjects effects revealed that the relationship did signifi-cantly improve for communication-poor technology (F=7.171,p=0.009), thus confirming the D2F hypothesis1 (See Appendix Aor details).

5. Discussion

From these results, it is clear that the technology knowledgeindividuals possess has an effect on their positions within advice

networks. However, this effect also depends on the level of taskuncertainty and the type of group departmentation. Table 5 sum-marizes the hypotheses and their results.

Although technology knowledgewas not a significant indicator ofadvice network centrality in the divisional project of the lowuncertainty condition, the knowledge hypothesis is supportedoverall. Communication-poor technology knowledge was signifi-cantly related to centrality in Project 2 of Course 2 (p=0.007) whilecommunication-rich technology knowledge was related to centralityin Project 1 (p=0.013) and marginally in Project 2 (p=0.095) ofCourse 1. However, it is clear that the relationship depends on boththe level of uncertainty in the task environment and group structure.The high uncertainty hypothesis was supported in that communica-tion-rich technology knowledgewasmore important in conditions ofhigh task uncertainty (high p=0.013b low p=0.487 in Project 1;high p=0.095b low p=0.220 in Project 2). The low uncertaintyhypothesis received partial support. Although communication-poortechnology knowledge was a better indicator of centrality thancommunication-rich knowledge in environments of low task uncer-tainty, this relationship only held in a functional group structure(p=0.007bp=0.042). It may be that the divisional structures of thegroups in Course 2 did not require significant amount of knowledgesharing. Also, the participants may have been competent enoughwith the communication-poor technologies that little, if any,knowledge sharing was required.

The functional groupshypothesiswas supported in that the functionalgroups of both courses demonstrated a stronger knowledge/centralityrelationship than their divisional counterparts (e.g. communication-poortechnology knowledge of Project 2 p=0.007bp=0.141; communica-tion-rich technology knowledge of Project 1 p=0.013bp=0.095).However, this finding does not imply that functional group structuresare inherently better. It only supports the notion that when groupmembers perform similar functions, they can benefit more fromknowledge sharing [39]. Finally, D2F and F2D hypotheses were alsosupported. When groups made the D2F switch, the importance ofcommunication-poor technology knowledge significantly increased(F=7.171, p=0.009). In addition, when groups made the F2D shift,the levels of communication-rich technology knowledge sharingremained the same or experienced only a “marginal” decrease(F=3.609, p=0.062).

One surprising finding was that, in Course 2 (low uncertaintycondition), communication-rich technology knowledge had a signif-icant negative relationship with centrality (p=0.042). Why wouldthose with advanced technology knowledge be avoided in the advicenetwork? There are several possible explanations for this finding, oneof which is offered by Casciaro and Lobo [12]. They found that whenpeople needed help and could seek it from either “competent” otherswho were difficult to approach or good friends who were “lesscompetent,” they often still sought help from their friends. It could be

1 The communication-rich technology relationship also improved significantly(F=6.021, p=0.016), but in the opposite direction — meaning that those withhigher levels of communication-rich technology were more likely to be de-central inthe social network.

Table 3Coefficients and standard errors of Course 1 models (F2D switch).

Project 1 Project 2

Variables Reduced Full Reduced Full

b s. e. b s. e. b s. e. b s. e.

Demographics similarity −0.265 0.709 −0.631 0.620Personality similarity 0.318 0.660 0.879 0.577CP technology knowledge −0.219 0.388 −0.258 0.398 −0.345 0.347 −0.454 0.348CR technology knowledge 0.981** 0.364 0.949* 0.373 0.637† 0.326 0.553† 0.326

R2 0.116 0.122 0.056 0.106F 4.211* 2.148† 1.907 1.837d. f. 66 66 66 66

Note: *** Significant at level pb0.001; ** pb0.01; * pb0.05; † pb0.10.



that in Course 2, those with communication-rich technology knowl-edge also happened to be those who would be less central in afriendship network.

This research has several practical implications for organizations.First, technology knowledge plays a significant role in determiningan individual's advice network centrality. A possible implication ofthis is that those who lack relevant domain knowledge may still findthemselves central, useful, and needed in their organizations if theyhave expertise in task-relevant technology knowledge. As a result,employees should keep their technology skills current and not shyaway from opportunities to improve.

Second, collaborative technologies are considered valuable tothose working in conditions of high task uncertainty. While this studydoesn't directly measure communication-rich technology value, theirinfluence on the advice networks in this study demonstrate that usersperceive communication-rich technologies to hold value for helpingthem to accomplish tasks over that of communication-poor technol-ogies in highly uncertain environments.

In addition, organizations can make more informed decisionsabout the types of technology they invest in by examining their advicenetworks and the expertise of their employees. Similarly, managerswho want to improve the connectivity of their employees' advicenetworks can provide training that will influence the sources ofknowledge in the advice network. This study also demonstrates thatmanagers can have a degree of indirect control over how their advicenetworks are formed by the decisions they make for employeetraining and improvement. For example, if certain individuals who areconsidered to be subject matter experts of a particular technology (orother knowledge domain) are heavily drawn upon for knowledge andinformation by everyone else, managers may decide to provide

training on that technology to increase the number of subject matterexperts. This would be very helpful since subject matter experts canoften cause severe bottlenecks in project performance [33].

Also, this research should be of particular interest to organizationsemploying project-based cross-functional groups desiring to improveproject coordination. For example, in Keith et al.'s [33] case study,software engineers belonged to functional “home” groupswhichwerebased on performing certain types of development functions. Asprojects were funneled into the software development unit, theywerethen assigned to a project manager who selected individuals fromvarious functional groups to join a temporary project teamwhich wasdivisionally structured. As a result, group members were constantlyswitching from functional to divisional groups and back again. If aproject manager selects certain individuals based on their advicenetwork centrality, they may be disappointed to find that thoseindividuals might not be as useful (or central) once they are part of adifferently structured project group.

This study also has other theoretical implications for advicenetwork research in the information systems literature. For example,the results provide evidence concerning the impact that an indivi-dual's technology knowledge has on their advice network position —

an impact currently under-investigated. For the literature onknowledge sharing, the results demonstrate the effects of taskenvironment uncertainty and group departmentation on the actualstructure of knowledge sharing relationships by relying on an SNArather than survey items concerning perceived levels of knowledgesharing.

As with most experimental research, this work comes withlimitations and boundaries which suggest fruitful areas for futureresearch. For example, the controlled experiment context of this studyallows certain variables to be studied in detail. However, it requiredthe use student participants because few organizations would bewilling to allow researchers to manipulate variables such as groupdepartmentation and task uncertainty because of the potentialnegative impacts to performance. While the use of students inexperimental studies concerning teamwork, technology usage, andoutcomes is not uncommon [2,4,47], they may not accurately depicthow phenomena occur in organizational practice. To improve thegeneralizability of this study, future research should triangulate thesefindings by measuring (if not manipulating) the variables in anorganizational setting.

Also, there are possibly some confounding influences frommeasuring separate networks which are likely to be highly related.For example, since many participants were registered in bothcourses, the networks of one course are likely to be influenced bythe networks of the other course. And the networks based on Project1 are likely to influence the networks of Project 2. This is a typicalproblem in field research. We are limited by the context of theenvironment studied. However, this is unlikely to affect the overallresults. Overlapping networks may inflate the total number of

Table 4Coefficients and standard errors of Course 2 models (D2F switch).

Variables Project 1 Project 2

Reduced Full Reduced Full

b s. e. b s. e. b s. e. b s. e.

Demographics Similarity 1.534* 0.736 −1.289 0.830Personality Similarity 1.075 0.685 −0.587 0.772CP Technology Knowledge −0.650 0.419 −0.614 0.413 1.284** 0.462 1.234** 0.466CR Technology Knowledge 0.487 0.394 0.522 0.387 −0.855† 0.434 −0.901* 0.437R2 0.031 0.106 0.090 0.122F 1.308 2.346 4.004* 2.756*d. f. 83 83 83 83

Note: *** Significant at level pb0.001; ** pb0.01; * pb0.05; † pb0.10.

Table 5Hypotheses and results.

H1 (Knowledge): Technology knowledge is positively related tocentrality.

Supported

H2a (High Uncertainty): In high task uncertainty, CR technologyknowledge is more related to centrality than CP technologyknowledge.

Supported

H2b (Low Uncertainty): In low task uncertainty, CP technologyknowledge is more related to centrality than CR technologyknowledge.

Partiallysupported

H3 (Functional Groups): The relationship between technologyknowledge and advice network centrality is stronger infunctional groups than divisional groups.

Supported

H4a (D2F): When groups make a D2F switch, the strength of therelationship between technology knowledge and advicenetwork centrality increases.

Supported

H4b (F2D): When groups make a F2D switch, the strength of therelationship between technology knowledge and advicenetwork centrality remains the same in the short run.

Supported



knowledge sharing relationships in each network because indivi-duals have multiple opportunities to seek knowledge. But, therelationships are still formed based on the knowledge individualspossess and the knowledge needed for that particular task. In thecase of these 99 graduate students, they were already extremelyfamiliar with each other because they had taken each course for theprior year with the same group of students (as designed by thegraduate programs). Therefore, being in class with the same studentswas unlikely to cloud the results any further. In addition, thisscenario replicates the complexities of a project-based workenvironment. It is not uncommon for employees to be assigned tomultiple projects with many of the same people.

Finally, while this experiment was designed to measure andmanipulate those variables which were critical to determiningadvice network structure, there are certainly many other variableswhich can andwill influence advice network formation. For example,this study examined the effects of technology knowledge only.Whiledeveloping a taxonomy of all the different types of knowledge whichmay influence an individual's advice network position would beideal, it was beyond the scope of this study. However, because othertypes of knowledge were not measured, it leaves the resultssomewhat open to alternative interpretations. For example, knowl-edge of communication-rich technologies may be highly related to aparticular type of domain-relevant knowledge which is the actualpredictor of centrality scores. Although measuring knowledge isnotoriously difficult [34], testing a more comprehensive or generalscale of knowledge would be a good idea for future research. Alongthose lines, it would also be useful to examine longitudinally whateffect an individual's centrality has on their level of knowledgeversus the effect of their knowledge on their centrality to betterunderstand the endogeneity issue.

Also, while your knowledge may determine why others formrelationships with you, your individual transactive memory, orknowledge of “who knows what,” likely determines who you go tofor knowledge [29]. In other words, measuring transactivememory in addition to knowledge would help predict bothincoming and outgoing knowledge sharing relationships. An

individual's transactive memory may also be a better predictorof their ability to fill structural holes [10] than knowledge alone.Future research should include both facets of knowledge — “what Iknow” and “what I know about what others know.” Similarly,future research should examine how additional characteristics ofthe task environment can moderate the relationship betweenknowledge and advice network structure. For example, this studymanipulated high and low levels of uncertainty. However, Tush-man and Nadler [49] also consider information sharing to be afunction of the complexity and interdependence of the taskenvironment. For example, interdependence may cause an in-crease in the connectivity of the information-sharing networkwhile complexity causes an increase in the knowledge sharingnetwork. The difference is that one network is used to coordinateworkflow while the other network coordinates knowledge. Insummary, future research can build upon the findings of this studyby examining knowledge, task environment, and structuralvariables in greater breadth and detail.

6. Conclusion

In conclusion, this study found that an individual's knowledge ofusing work-related technologies is an important indicator of theiradvice network centrality. The relationship depends, however, onthe type of knowledge and uncertainty of the task. Specifically,highly uncertain tasks place individuals with knowledge concerningcollaborative technologies in central network positions whereastasks with lower uncertainty place individuals with less-collabora-tive, communication-poor technology knowledge in central posi-tions. In addition, the relationship between technology knowledgeand advice network centrality is affected by the type of groupdepartmentation. Functional groups tend to foster the knowledge/centrality relationship to a greater degree than divisional groups. Inaddition, as groups change structure and departmentation, thestrength of the knowledge/technology relationship depends on thedirection of the change. It is hoped that this work will foster interest

Appendix A. Repeated measures MANOVA results for F2D and D2F hypotheses.

Table 6Tests of within-subjects effects for the difference in the relationship between knowledge and centrality for the participants in class 1 (F2D condition).

Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared Power

Centrality Sphericity Assumed 1583.08 1 1583.08 0.010 0.921 0.000 0.051Greenhouse–Geisser 1583.08 1 1583.08 0.010 0.921 0.000 0.051Huynh–Feldt 1583.08 1 1583.08 0.010 0.921 0.000 0.051Lower-bound 1583.08 1 1583.08 0.010 0.921 0.000 0.051

Centrality * Personality Sphericity Assumed 134289.19 1 134289.19 0.843 0.362 0.014 0.148Greenhouse–Geisser 134289.19 1 134289.19 0.843 0.362 0.014 0.148Huynh–Feldt 134289.19 1 134289.19 0.843 0.362 0.014 0.148Lower-bound 134289.19 1 134289.19 0.843 0.362 0.014 0.148

Centrality * Demographics Sphericity Assumed 131302.58 1 131302.58 0.824 0.368 0.013 0.145Greenhouse–Geisser 131302.58 1 131302.58 0.824 0.368 0.013 0.145Huynh–Feldt 131302.58 1 131302.58 0.824 0.368 0.013 0.145Lower-bound 131302.58 1 131302.58 0.824 0.368 0.013 0.145

Centrality * CP Technology Sphericity Assumed 16227.97 1 16227.97 0.102 0.751 0.002 0.061Greenhouse–Geisser 16227.97 1 16227.97 0.102 0.751 0.002 0.061Huynh–Feldt 16227.97 1 16227.97 0.102 0.751 0.002 0.061Lower-bound 16227.97 1 16227.97 0.102 0.751 0.002 0.061

Centrality * CR Technology Sphericity Assumed 574806.51 1 574806.51 3.609 0.062 0.056 0.464Greenhouse–Geisser 574806.51 1 574806.51 3.609 0.062 0.056 0.464Huynh–Feldt 574806.51 1 574806.51 3.609 0.062 0.056 0.464Lower-bound 574806.51 1 574806.51 3.609 0.062 0.056 0.464

Error (Centrality) Sphericity Assumed 9716784.51 61 159291.54Greenhouse–Geisser 9716784.51 61 159291.54



in the use of SNAs to study the impacts of technology on the wayindividuals share knowledge and coordination within organizations.

References

[1] D. Ancona, C. Chong, Entrainment: pace, cycle, and rhythm in organizationalbehavior, in: L. Cummings, B.M. Staw (Eds.), Research in Organizational Behavior,JAI Press, Greenwich, CT, 1996.

[2] T.T. Baldwin, M.D. Bedell, J.L. Johnson, The social fabric of a team-based MBAprogram: network effects on student satisfaction and performance, Academy ofManagement Journal 40 (6) (1997).

[3] P. Balkundi, M. Kilduff, Z.I. Barsness, J.H. Michael, Demographic antecedents andperformance consequences of structural holes in work teams, Journal ofOrganizational Behavior 28 (2) (2007).

[4] A. Bhattacherjee, G. Premkumar, Understanding changes in belief and attitudetoward information technology usage: a theoretical model and longitudinal test,MIS Quarterly 28 (2) (2004).

[5] S.P. Borgatti, R. Cross, A relational view of information seeking and learning insocial networks, Management Science 49 (4) (2003).

[6] S.P. Borgatti, P.C. Foster, The network paradigm in organizational research: areview and typology, Journal of Management 29 (6) (2003).

[7] E. Brynjolfsson, A.P. McAfee, M.E. Mangelsdorf, T.M. Laseter, E. Rabinovich, K.K.Boyer, M.J. Rungtusanatham, The future of the web, MIT Sloan ManagementReview 48 (3) (2007).

[8] M.E. Burkhardt, D.J. Brass, Changing patterns or patterns of change: the effects of achange in technology on social network structure and power, AdministrativeScience Quarterly 35 (1) (1990).

[9] T. Burns, G.M. Stalker, The Management of Innovation, Tavistock, London, 1961.[10] R.S. Burt, The contingent value of social capital, Administrative Science Quarterly

42 (2) (1997).[11] R.S. Burt, Structural holes versus network closure as social capital, in: N. Lin, K.S.

Cook, R.S. Burt (Eds.), Social Capital: Theory and Research, Aldine Transaction,New Brunswick, 2001.

[12] T. Casciaro, M.S. Lobo, Competent jerks lovable fools, and the formation of socialnetworks, Harvard Business Review 83 (6) (2005).

[13] C.M.K. Cheung, M.K.O. Lee, A theoretical model of intentional social action inonline social networks, Decision Support Systems 49 (1) (2010).

[14] C.M. Chiu, M.H. Hsu, E.T.G. Wang, Understanding knowledge sharing in virtualcommunities: an integration of social capital and social cognitive theories,Decision Support Systems 42 (3) (2006).

[15] J.N. Cummings, R. Cross, Structural properties of work groups and theirconsequences for performance, Social Networks 25 (3) (2003).

[16] R.L. Daft, R.H. Lengel, Organizational information requirements: media richnessand structural design, Management Science 32 (5) (1986).

[17] R.L. Daft, R.H. Lengel, L.K. Trevino, Message equivocality, media selection, andmanager performance: implications for information systems, MIS Quarterly 11(3) (1987).

[18] L. Donaldson, The Contingency Theory of Organizations, Sage Publications,Thousand Oaks, 2001.

[19] R.M. Emerson, Social-exchange theory, Annual Review of Sociology (1976) 2.[20] J.A. Espinosa, J.N. Cummings, J.M. Wilson, B.M. Pearce, Team boundary issues

across multiple global firms, Journal of Management Information Systems 19 (4)(2003).

[21] S. Faraj, L. Sproull, Coordinating expertise in software development teams,Management Science 46 (12) (2000).

[22] L. Freeman, Centrality in social networks: conceptual clarification, SocialNetworks 1 (3) (1979).

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

[24] D. Ganley, C. Lampe, The ties that bind: social network principles in onlinecommunities, Decision Support Systems 47 (3) (2009).

[25] L.R. Goldberg, J.A. Johnson, H.W. Eber, R. Hogan, M.C. Ashton, C.R. Cloninger, H.G.Gough, The international personality item pool and the future of public-domainpersonality measures, Journal of Research in Personality 40 (1) (2006).

[26] M. Granovetter, Economic action and social structure: the problem of embedded-ness, American Journal of Sociology 91 (3) (1985).

[27] R.M. Grant, Toward a knowledge-based theory of the firm, Strategic ManagementJournal (1996) 17.

[28] W. He, K.K. Wei, What drives continued knowledge sharing? An investigation ofknowledge-contribution and -seeking beliefs, Decision Support Systems 46 (4)(2009).

[29] J. He, B.S. Butler, W.R. King, Team cognition: development and evolution insoftware project teams, Journal of Management Information Systems 24 (2)(2007).

[30] M. Hoegl, K. Weinkauf, H.G. Gemuenden, Interteam coordination, projectcommitment, and teamwork in multiteam R&D projects: a longitudinal study,Organization Science 15 (1) (2004).

[31] C.G. Homans, Social Behavior: Its Elementary Forms, Harcourt, Brace & World,New York, 1961.

[32] L. Hossain, A. Wu, K.K.S. Chung, Actor centrality correlates to project basedcordination, Proceedings of the Conference on Computer Supported CooperativeWork, Alberta, Canada, 2006.

[33] M. Keith, H. Demirkan, M. Goul, Coordination network analysis: a researchframework for studying the organizational impacts of service-orientation inbusiness intelligence, Proceedings of the Hawaii International Conferenc onSystem Sciences, Waikaloa, Hawaii, 2007.

[34] A.W. King, C.P. Zeithaml, Measuring organizational knowledge: a conceptual andmethodological framework, Strategic Management Journal 24 (8) (2003).

[35] K.J. Klein, B.C. Lim, J.L. Saltz, D.M. Mayer, How do they get there? An examinationof the antecedents of centrality in team networks, Academy of ManagementJournal 47 (6) (2004).

[36] J.R. Lincoln, J. Miller, Work and friendship ties in organizations: comparativeanalysis of relational networks, Administrative Science Quarterly 24 (2) (1979).

[37] Q.T. Liu, Z.K. Yang, K. Yan, D. Wu, Reusable learning object and its strategies for e-learning, in: W.Y. Liu, Y. Shi, Q. Li (Eds.), Advances in Web-Based Learning,Springer-Verlag, Berlin, Berlin, 2004.

[38] P.R. Monge, E.M. Eisenbert, Emergent communication networks, in: M. Jablin, L.L.Putnam, K.H. Roberts, L.W. Porter (Eds.), Handbook of Organizational Commu-nication: An Interdisciplinary Perspective, FSage, Newbury Park, 1987.

Table 7Tests of within-subjects effects for the difference in the relationship between knowledge and centrality for the participants in class 2 (D2F condition).

Source Type III Sum of Squares df Mean Square F Sig. Partial Eta Squared Power

Centrality Sphericity Assumed 1220046.41 1 1220046.41 16.76 0.000 0.181 0.981Greenhouse–Geisser 1220046.41 1 1220046.41 16.76 0.000 0.181 0.981Huynh–Feldt 1220046.41 1 1220046.41 16.76 0.000 0.181 0.981Lower-bound 1220046.41 1 1220046.41 16.76 0.000 0.181 0.981

Centrality * Personality Sphericity Assumed 142600.80 1 142600.80 1.96 0.166 0.025 0.282Greenhouse–Geisser 142600.80 1 142600.80 1.96 0.166 0.025 0.282Huynh–Feldt 142600.80 1 142600.80 1.96 0.166 0.025 0.282Lower-bound 142600.80 1 142600.80 1.96 0.166 0.025 0.282

Centrality * Demographics Sphericity Assumed 849667.86 1 849667.86 11.6 0.001 0.133 0.921Greenhouse–Geisser 849667.86 1 849667.86 11.6 0.001 0.133 0.921Huynh–Feldt 849667.86 1 849667.86 11.6 0.001 0.133 0.921Lower-bound 849667.86 1 849667.86 11.6 0.001 0.133 0.921

Centrality * CP Technology Sphericity Assumed 521828.97 1 521828.97 7.17 0.009 0.086 0.753Greenhouse–Geisser 521828.97 1 521828.97 7.17 0.009 0.086 0.753Huynh–Feldt 521828.97 1 521828.97 7.17 0.009 0.086 0.753Lower-bound 521828.97 1 521828.97 7.17 0.009 0.086 0.753

Centrality * CR Technology Sphericity Assumed 438169.24 1 438169.24 6.02 0.016 0.073 0.678Greenhouse–Geisser 438169.24 1 438169.24 6.02 0.016 0.073 0.678Huynh–Feldt 438169.24 1 438169.24 6.02 0.016 0.073 0.678Lower-bound 438169.248 1 438169.24 6.02 0.016 0.073 0.678

Error (Centrality) Sphericity Assumed 5530438.45 76 72768.92Greenhouse–Geisser 5530438.45 76 72768.92Huynh–Feldt 5530438.45 76 72768.92Lower-bound 5530438.45 76 72768.92



[39] H. Moon, J.R. Hollenbeck, S.E. Humphrey, D.R. Ilgen, B. West, A.P.J. Ellis, C. Porter,Asymmetric adaptability: dynamic team structures as one-way streets, Academyof Management Journal 47 (5) (2004).

[40] S. Nidumolu, The effect of coordination and uncertainty on software projectperformance: residual performance risk as an intervening variable, InformationSystems Research 6 (3) (1995).

[41] W. Oh, J. Choi, K. Kim, Coauthorship dynamics and knowledge capital: thepatterns of cross-disciplinary collaboration in IS research, Journal of ManagementInformation Systems 22 (3) (2005).

[42] H. Oh, G. Labianca, M.H. Chung, A multilevel model of group social capital,Academy of Management Review 31 (3) (2006).

[43] M. Parameswaran, A.B. Whinston, Research issues in social computing, Journal ofthe Association for Information Systems 8 (6) (2007).

[44] R. Reagans, E.W. Zuckerman, Networks, diversity, and productivity: the socialcapital of corporate R&D teams, Organ Science 12 (4) (2001).

[45] R.T. Sparrowe, R.C. Liden, S.J. Wayne, M.L. Kraimer, Social networks and theperformance of individuals and groups, Academy of Management Journal 44 (2)(2001).

[46] K. Stephenson, M. Zelen, Rethinking centrality, Social Networks 11 (1) (1989).[47] K.S. Suh, Y.E. Lee, The effects of virtual reality on consumer learning: an empirical

investigation, MIS Quarterly 29 (4) (2005).[48] J.D. Thompson, Organizations in Action, McGraw-Hill, New York, 1967.[49] M.L. Tushman, D.A. Nadler, Information processing as an integrating concept in

organizational design, Academy of Management Review 3 (3) (1978).[50] C.Y. Wang, H.Y. Yang, S.C.T. Chou, Using peer-to-peer technology for knowledge

sharing in communities of practices, Decision Support Systems 45 (3) (2008).[51] S. Wasserman, J. Galaskiewicz, Advances in Social Network Analysis, Sage

Publications, Thousand Oaks, CA, 1994.

Mark Keith is an Assistant Professor in the Information & Decision Managementdepartment at the College of Business at West Texas A&M University. His recentresearch interests concern the effectiveness of IT projects including how advicenetworks can impact project success as well as the development of new service-oriented IT project methodologies. His research has appeared in the Journal of theAssociation for Information Systems, INFORMS Decision Analysis, and the InternationalJournal of Human-Computer Studies, as well as numerous conference proceedings.

Haluk Demirkan is a Clinical Associate Professor of Information Systems and a ResearchFaculty of Center for Services Leadership at theW. P. Carey School of Business at ArizonaState University. His doctorate is in Information Systems and Operations Managementfrom University of Florida, and his research in service science, and sustainable service-oriented data warehousing and business intelligence solutions have included recentjoint industry-academic research projects with industry experts from American Express,Intel, IBM, Teradata and MicroStrategy. His research appears in a number of journals,including Journal of Service Research, JMIS, JAIS, EJOR, IEEE Transactions, ECRA, ISFrontiers, CACM, ISEBM, International Journal of Services Science, Decision SciencesJournal of Innovative Education, and other leading journals. He has authored or co-authored over forty articles in refereed journals or conference proceedings. He hasfifteen years of consulting experience in the areas of service-oriented informationmanagement, systems and technology solutions, information supply chain manage-ment, and decision support data warehousing solutions with Fortune 100 companies.He is the recent recipient of the IBM Faculty Award for a research project titled “DesignScience for Self-Service Systems”. He also serves as an advisory board member for theTeradata University Network and the Global Text Project, and serves as an Officer for theAIS SIG on Decision Support, Knowledge and Data Management Systems.

Michael Goul is a Professor and Chair of the Department of Information Systems and aResearch Faculty member of Center for Services Leadership at the W. P. Carey School ofBusiness at Arizona State University. His recent research interests are in the area ofservice computing, smart and self-service, and advanced analytics. He has served asjournal editor, special issue editor, Association for Information Systems (AIS) VicePresident, AIS Conference and Program Chair, and Chair of the AIS special interest groupin decision support, knowledge and data management systems (SIGDSS). Dr. Goulrecently held a sabbatical appointment as a William J. Clinton Distinguished UniversityFellow at the newest presidential school — the Clinton School of Public Service, and hethe Executive Director of the Teradata University Network.


the influence of collaborative technology knowledge on advice network structures

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