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Proceedings of the 40th Hawaii International Conference on System Sciences - 2007

APPLYING THE REPGRID TECHNIQUE TO RESOLVE COGNITIVE CONFLICTS AMONG EXPERTS DURING KNOWLEDGE CAPTURE Ananth Chiravuri American University of Sharjah [email protected]

Derek Nazareth University of WisconsinMilwaukee [email protected]

Abstract There is an urgent need to understand and resolve conflict that may occur among multiple experts during the knowledge acquisition process so that it remains less of a “bottleneck”. Our study addresses this need by examining the effectiveness of the Repertory Grid technique (Repgrid) in resolving cognitive conflicts among multiple experts in virtual teams during the process of knowledge capture. While the main study is ongoing, we present preliminary results from the pilot study conducted using real world experts. The results indicate that Repgrid technique is an effective technique in achieving consensus.

1. Introduction and Motivation As we move to a knowledge-based economy, the challenge of how knowledge workers find relevant information assumes greater importance. The management of knowledge has become critical for firms. However, very often, firms find that the processes of knowledge management are dependent on experts who are seldom available in the same geographic location. Consequently, this may have accelerated the movement of firms towards the use of virtual teams because such teams are particularly useful to firms when needed skills are not available locally. Despite the growing importance of virtual teams, research on virtual teams in Information Systems (IS) has largely focused on cross-functional virtual teams within firms [33]. Although studies have started to examine the limited research on “cross-organizational virtual teams”, there exists a paucity of research on knowledge capture and sharing using virtual teams [33]. Clearly, there is a need to examine issues related to knowledge capture using virtual teams.

K. Ramamurthy University of WisconsinMilwaukee [email protected]

One of the findings from the limited research on knowledge management in virtual teams is that knowledge sharing is advanced by capturing and evenly distributing knowledge, of both content and context, to all team members [7] and ensuring that informal knowledge sharing opportunities are not suppressed. In effect, studies [44] seem to suggest that a shared understanding among members of a virtual team is necessary for effective knowledge management. However, there are difficulties in achieving shared understanding because it requires that members start with a common set of norms, context, and problem definitions [33]. This may not be always possible because virtual teams may consist of experts with different organizational backgrounds, task domains and mental models leading to disagreement on a given set of knowledge [36]. It is quite possible that members or experts in a virtual team will filter information through their mental models, consequently giving rise to a potentially broad range of misunderstandings [28]. This in turn may lead to communication obstacles, and perhaps conflicts, among experts during the process of knowledge capture using virtual teams [59]. Indeed, recent studies in conflict management acknowledge that conflicts increase with an increase in physical distance and may be an issue in the case of geographically dispersed teams such as virtual teams [6, 40]. It is, therefore, necessary that firms examine the issue of conflicts and their resolution among experts in virtual teams so that a shared understanding can be facilitated in a shorter period. While there are many dimensions of conflict, this study deals with conflicts that arise from the cognitive states and cognitive limitations of members, that is, cognitive or task based conflicts. We examine cognitive conflicts that arise among multiple experts in virtual teams because experts, when unable to interact with other experts face to face, may tend to concentrate more on the task at hand leading to more task based conflicts. We

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specifically chose virtual teams for this study because they mimic the real-world issues of distributed pockets of knowledge i.e., experts are not always available at one location [4, p.3]. The techniques we propose are applicable to face-to-face teams as well, though we have not investigated their effectiveness in this study context. In short, we argue that understanding cognition and the mental models of various members of a virtual team may prove to be important in order to successfully resolve conflicts that arise during the knowledge elicitation process. It should be noted that in the knowledge acquisition context conflict arises because different experts employ different patterns and heuristics to address the problem at hand. This conflict could render the acquired knowledge less useful, as it may produce contradictory recommendations. This can be eliminated or minimized by removing some or all of the conflicting knowledge. An alternative approach involves examining whether it is possible to have the experts resolve the conflict through some form of consensus formation. This is a major objective of our study where we attempt to resolve conflicts among experts and achieve consensus (defined as the general agreement among group members or movement from more to less disagreement with respect to the acquired set of knowledge [4, p.6]) by using the Repertory Grid Technique, a cognition based technique. This technique permits a knowledge engineer to collect, in a short period of time, the opinions of various experts on a given issue. It also provides the experts and the knowledge engineer with an opportunity to seek explanations, leading to a better understanding of the assumptions and biases of other experts. This process, therefore, helps a knowledge engineer in a faster reconciliation of conflicts among experts than traditional techniques such as the interview process where a knowledge engineer collects knowledge from one expert at a time, making the whole process very tedious. It may also become difficult for a knowledge engineer to reconcile conflicts using one on one interview because experts do not get a chance to interact directly with others and seek further clarifications. To summarize, the objective of our ongoing study is to study the effectiveness of the Repgrid technique in resolving cognitive or task based “conflicts” that may arise among multiple experts in virtual teams during the process of knowledge acquisition. Research is yet to examine the effectiveness of different conflict resolution techniques on group outcomes of virtual teams during the process of knowledge acquisition and therefore the findings from our study are expected to contribute to the theory and practice in this area. Since our study

is in progress, we present preliminary results of the pilot study in this paper. The remainder of this paper is structured as follows: The next section provides the theoretical background to this study. Following this, we present our proposition, methodology and findings from the pilot study. Finally, we conclude with a discussion on the potential implications of this study.

2. Conflict in Knowledge Management As indicated earlier, the objective of this section is to provide a brief theoretical background to the relevant topics of this pilot study. These are discussed next.

2.1 Knowledge Management and Knowledge Acquisition For the purposes of this study, we define “knowledge management” as the tools, technologies, practices and incentives deployed by an organization to “know what it knows” and to make this knowledge available to people who need to know it when they need to know it [32]. In general, knowledge management may be largely regarded as a process consisting of several steps such as knowledge creation, knowledge storage and retrieval, knowledge transfer and knowledge application [1]. While managing each one of these sub processes is important for the overall success of a knowledge management program, knowledge storage stands out because it involves knowledge acquisition or knowledge engineering [19]. Knowledge acquisition refers to “the process of extracting, structuring and organizing knowledge from several knowledge sources, usually human experts, so that the problem solving expertise can be captured and transformed into a computer readable form” [31, Ch.2-p.1). It is an extremely important process to manage because it still continues to remain a bottleneck in the development of knowledge management systems or expert systems [19]. Many previous studies have examined this issue and generally agree that “the success of the knowledge acquisition processes often depends on factors that serve to make up what has been called the knowledge acquisition context or environment” [18, 23]. Although there are many contextual factors influencing the knowledge acquisition processes such as problem domain factors, organizational factors, physical factors and human factors, we restrict our examination to human experts in order to limit the scope of this study.

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The fact that human factors such as the performance of an expert or a knowledge engineer can play a major role in the knowledge acquisition process has been noted by several research studies [12, 18]. However, most previous studies on the role of human factors in knowledge acquisition have focused primarily on certain traits of human experts such as level of expertise and problem solving style. As a result, other expert attributes like mental models have received little attention [23]. While this study attempts to address this shortcoming partly by attempting to resolve conflicts that may be caused from the different mental models of experts, it is important to first understand “how” knowledge is acquired from experts. 2.1.1 Knowledge Acquisition from Experts. Firms may capture the required knowledge from either single experts or multiple experts. For simple problems, a knowledge engineer may use the opinion of a single expert. While this strategy may be useful for narrow domains, it may begin to falter as the domains get complex. This may force firms to adopt the second strategy of using multiple experts in small groups, as explained next. Experts can be used in small groups or teams either in a traditional sense or in a virtual sense. Traditional teams or face-to-face teams are those teams that do all their work face to face with or without any technological support [21]. Because of the major limitations of the face to face strategy such as “huge cost in having all experts at one location”, “groupthink” and “group size”, firms are increasingly using virtual teams, which are temporary “groups of geographically and/or organizationally dispersed coworkers that are assembled using a combination of telecommunications and information technologies attempting to accomplish an organizational task” [58, p.18]. Like face to face teams, virtual teams have limitations too, many of which arise from the fact that experts are dispersed and may communicate asynchronously. Since the focus of this study is on virtual teams, we discuss virtual teams in greater detail next.

2.2 Virtual Teams Recent studies in IS have recognized the importance of virtual teams because of “their potential to enable work across distances, time zones and geographical and organizational boundaries with links strengthened by webs of communication technologies” [47, p.27]. Virtual teams have been defined as being “composed of coworkers geographically and organizationally linked through

telecommunications and information technologies attempting to achieve an organizational task” [58, p.17]. Firms use virtual teams to bring experts from different firms, disciplines and industries in order to stimulate innovation [49] and consequently, virtual teams have become increasingly common. Prior research on virtual teams has looked at issues such as knowledge sharing [21], motivating team member involvement [41], decision making effectiveness [52], and trust [45]. Findings from these studies indicate a lack of consensus as explained next. While a few studies have indicated that virtual teams were able to generate a higher number ideas or perspectives and conduct more in depth analyses leading to higher quality [42], these findings are in contrast to what other studies report [9]. The authors find that decision quality is lower for virtual teams even though they generated the same number of ideas as face-to-face teams using GSS. Similarly, other studies suggest that virtual teams took more time to reach a decision and had difficulty coming to a consensus [22]. This lack of consensus emerging due to conflict makes it more essential that researchers address the issue of conflict management during knowledge capture in virtual teams [47]. Conflict management in virtual teams is especially important to study because studies have shown that conflict management behavior is an important determinant of group processes and performance [60] and when managed effectively, it leads to the development of an appropriately effective level of synergy among members of a virtual team [13]. However, conflict is a very broad term and therefore it is essential that we examine the various types of conflict in order to focus on the type of conflict that may occur between experts in virtual teams. This is done next.

2.3 Conflict Conflict has been long examined in IS from the perspective of inter-organizational systems [30], GDSS [46] and systems development [50], to name a few. There are many types of conflicts that can result in both positive and negative effects. In general, some types of conflict are desirable because they may lead to higher quality solutions that in turn may lead to improved organizational decision-making [53]. For example, conflict has been considered important in requirements analysis because it may lead to a much better understanding of the domain [15]. It is, therefore, important to understand the different types of conflict before discussing techniques to resolve

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conflicts arising from different mental models of individuals. 2.3.1 Types of Conflict. Conflict has been said to primarily consist of two dimensions: issue based and interpersonal [10]. Between these two, issue based conflict is desirable because it helps the groups achieve a greater understanding of the issue by bringing out the different views and issues of the task. In contrast, interpersonal conflict is targeted at persons within the group instead of the task, leading to dysfunctional group behavior. We explain this in some detail next. a. Affective Conflict. Affective conflict or relationship conflict [26, 27] or interpersonal conflict [10, 17] occurs when “two interacting social entities, while trying to solve a problem together, become aware that their feelings and emotions regarding some or all the issues are incompatible” [48, p. 21). Interpersonal conflict is undesirable because it tends to draw attention away from the task and affects group functioning [10]. It also results in the use of sub-optimal strategies for conflict resolution, for example: Participants in a negotiation may decide not to cooperate with each other for emotional reasons (distributive strategy) instead of engaging in a “winwin” situation (integrative strategy) leading to a smaller share of the overall “pie” [38]. b. Substantive Conflict. This type of conflict is also known as task conflict [17, 26, 27] or cognitive conflict [3, 51] or issue based conflict [10]. This type of conflict refers to the “disagreements among group member’s ideas and opinions of the task being performed” [27, p.288] or “differences in interpretations and judgments brought about by high equivocality surrounding the decision issues facing the group” [51, p.226]. Although this type of conflict frequently arises in group decision making [54], it is desirable because it brings out the differing views and issues of the task and helps the group to come to better solutions [11] and a higher post-meeting consensus [51]. It also preempts meeting hazards such as groupthink [39]. In spite of these advantages, studies have recommended that cognitive conflict be minimized. The rationale being a lower level of cognitive conflict leads to better understanding and make it easier to attain consensus where it is possible [54]. The authors also note that the level of cognitive conflict is initially high and gradually declines and that the structure of conflict changes over time. As indicated earlier, although there exist several types of conflict, it is not feasible to examine all of these given the limited scope of this study and

therefore, we attempt to focus only on task related conflict or substantive conflict or cognitive conflict among experts in virtual groups. We base our examination using prior research on cognitive conflict, which points out that cognitive conflict arises “as members draw upon their past experience, training, or departmental affiliation to produce initial interpretations; individual differences in these areas may result in a range of competing interpretations in the group” [51, p.227). This strongly suggests that cognitive conflict may be caused by differences in cognition, including mental models. Since conflicts among experts with different mental models are primarily based on cognition, it is possible that techniques based on cognition may be more successful in resolving them. Repertory Grid Technique (Repgrid) is one such technique which uncovers the different mental models that individuals use to structure and interpret knowledge [57]. Although studies have used Repgrid for varied reasons such as to elicit knowledge [24, 43] and to conduct cross cultural research [25], there is hardly any research that has examined its use in resolving conflicts among experts in virtual teams. Hence, we explain more about Repgrid as a technique next.

2.4 Repertory Grid Technique Repgrid is a “cognitive mapping technique that attempts to describe how people think about the phenomena in their world” [57, p.40]. Repgrid is based on Kelly’s Personal construct psychology (PCP) theory [29], which argues that individuals use their own “personal constructs” or “mental models” to understand and interpret events that occur around them. In other words, such constructs are used to a construct a “view of the world” [20]. One of the basic assumptions of PCP is that people make sense of the events around them by organizing them into categories according to their similarities and differences [34]. It is from this process of contrast and discrimination, known as “construing” that bipolar personal constructs emerge [29]. Repgrid consists of three major components: Elements, Constructs and Links [14]. While elements are the objects of attention within the domain of investigation, constructs represent the research participant’s interpretation of the elements [57]. Finally links refer to indicators such as ratings and rankings, which are used to relate elements with constructs. Repgrid is a useful technique because it provides data that can be analyzed both qualitatively and quantitatively using statistical methods [57]. More importantly, Repgrid may be able to help resolve conflicts among experts by producing

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cognitive maps that can display the understandings held in common by these groups. The maps reveal the differences in constructs between the experts and can provide the platform upon which the overall group can collectively diagnose disagreements. In addition, individual experts can gain greater awareness of what the issue looks like from the others’ standpoint [16]. Advantages such as the ones explained above make the Repgrid an ideal technique to create consensus in our study, whose propositions and methodology are explained in the next section

3. Methodology 3.1 Proposition A study done by Mortensen and Hinds [40] examined the role of conflict and shared identity in both collocated and geographically dispersed teams. The authors report that task conflict decreased with increased shared team identity in the case of geographically dispersed teams. This may be explained using the social identity theory [55, 56]. Social identity theory states that an individual categorizes himself/herself and others around him/her into two distinct groups: the “in-group” and the “outgroup”. An individual’s “in-group” consists of other individuals who are perceived to be similar to him/her. All those individuals perceived to be dissimilar to the individual in question are part of his/her “out-group”. Research has indicated that the perception of “otherness” has lead to, among other things, higher levels of conflict [62]. These findings allow us to posit that task conflict in virtual teams should decrease when members of that team experience an increase in shared team identity because it would lead to a perception of others being in their “in-group”. Specifically, we propose that there would be a faster understanding of the other group members’ views leading to a quicker formation of shared team identity when using a Repgrid. Our contention is that Repgrid allows for a faster collection of the underlying constructs of members, thereby permitting a given member to examine the mental models of other members of the team. Once a member learns the positions of his/her fellow members, it may lead him/her to examine the biases in his/her thinking or in the member’s thinking and correct them, leading to a faster shared understanding. A shared team identity may in turn reduce task based conflict among the members of such virtual teams because of the formation of “ingroups” or in corollary, increase the post meeting consensus.

In addition, studies examining group conflict management have noted that the way conflict was managed in groups influenced the relationship between task conflict and group performance [8]. Results indicate that the relationship between task conflict and group performance was positive when conflict was actively managed and turned negative, when conflict was passively managed. We may infer from such findings that the way conflict is managed can influence consensus. Conflict, when actively managed, may lead to members of a group to openly discuss their differences and exchange information to solve problems together [8]. This in turn may lead to a higher degree of consensus among group members than techniques that passively manage conflict among group members. Since we are only concerned with task based conflict, we believe that these findings may also apply to members of virtual teams. Repgrid is one of the few techniques that lend structure to the resolution of conflicts by collecting, and rating (or ranking or voting) constructs in a structured manner. This technique actively manages conflict because it allow differences among members of teams to surface, facilitate discussions to resolve such conflicts and come to a consensus. Our proposition from these arguments is given as follows: P1: Repgrid technique will reduce conflict and create consensus among experts.

3.2 Research Model For the purposes of this study, our model looked at conflict not only in terms of outcomes and also in terms of a process. We attempted to influence the level of consensus by the conflictive nature of the task, the rationale being that fragmented thinking results in closed mental models, which in turn leads to conflict [3]. Conflict that results from closed mental models may be a “response to the threat posed by competing values that confront one's cherished beliefs” [5]. Therefore, it followed that conflictive tasks that would lead to divergence in opinion and low convergence in decision strategies would lead to greater conflict. Conflict, in such situations, can be resolved by capturing and sharing the assumptions/mental models of the individuals with respect to their convictions with other participants in the group so that a dialogue can be initiated to resolve the biases and come to a consensus. We used the Repgrid technique to achieve this.

3.3 Research Method

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Experiments are an important source of information and are of two kinds, field and laboratory. Field experiments allow the examination of a phenomenon in settings that are closer to the real world. This makes a field experiment more realistic and increases the external validity of the research findings. Therefore, we considered this approach for our study and used real world experts in our study. Subjects were asked to resolve conflicts, which occurred amongst them and come to a consensus by using the Repgrid technique and respond to questionnaires on the experimental outcomes.

3.4 Variables The independent variable in our study was a set of three cases/scenarios that made use of the Repgrid technique to resolve such inherent conflicts. The dependent variable was the group consensus. As noted, consensus refers to the general agreement among group members and we define level of consensus as the degree of agreement generated among team members [4, 36] in the rating of constructs. We calculated consensus and/or disagreement using three methods that were based on the Euclidean distance, the approach by Whitworth and Felton [61] and the normalized method.

3.5 Controlled Variables 3.5.1 Group Size. In line with other studies, we used a group size of five because it is considered an “optimal” size in the literature on “groups”. Using an odd size of groups would also prevent a stalemate when arriving at a consensus. 3.5.2 Task. The conflictive tasks, which deal with different issues in network design, were chosen after extensive validation by a panel of experienced network professionals. The three cases were modeled on the cognitive conflict tasks of McGrath’s [35] task circumplex. A conflictive task was chosen so that there was ambiguity leading to many solutions, generating conflict among the experts. The cases involved identification of factors that were relevant to network design, assessment of their relative importance, and specialization to specific cases involving network design or redesign. More specifically, the cases covered different configurations of organization structure and location, different needs for networking performance, and different networking technologies. Case 1 described the issues that arose from a campus LAN. Case 2

centered on issues from using WAN and ATM technology. Finally, Case 3 presented issues relating to the migration of LAN. 3.5.3 Procedures. The subjects interacted with the experimenter who played the role of a knowledge engineer. Interaction occurred over five rounds, with the following major tasks assigned in each round. To begin with, experts were asked to read the three networking cases/scenarios and elicit solutions for the problems in the form of constructs. (To clarify, we wanted to capture knowledge or expertise in general to design telecom networks of situations collectively represented by the three cases.) Specifically, each team member was asked to elicit constructs using a dyadic approach, i.e., each subject was asked to consider any two given cases and elicit the constructs on the basis of similarities and differences. Experts could provide as few or as many factors or constructs as they wished. The data from this round were consolidated into a set of relevant factors, after eliminating overlap and addressing the use of synonyms. A total of 30 factors were identified in this round. For the second round, the experts were presented with the set of 30 factors they generated and asked to rate their importance to the network design problem in general. Ratings were collected on a 1-7 point scale (1=Not Important and 7=Very Important). To reduce ambiguity and facilitate data collection, each factor was characterized by bipolar extremes. Data collected in this round served as the basis for assessing conflict among the experts. Each expert was required to rate the importance of all factors – no omissions were permitted, as this would skew the conflict measure. The assessments were the analyzed through the Repgrid technique, thereby permitting clustering on two separate dimensions, factor and subject, as illustrated in Figure 1. This allowed visualization of the relationship between factors as well as among experts. The feedback on individual ratings and group consensus was presented in round three. Experts were able to view the Repgrid analysis, thus permitting them to get a better interpretation of potential conflict between experts, while also explicating the relationship between factors. Data on the revised importance of the factors was collected in this round. Rounds four and five narrowed the analysis to the three cases outlined earlier. In round four, experts were asked to rate the relative importance of the 30 factors in the specific context of each of the three cases. Data from this round was processed using the Repgrid technique, and the results

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communicated to the experts, including dissemination of both clustering views. Experts then re-rated the importance of each of the factors in round 5, and were debriefed on the findings.

3.6 Sample

responses to the measurement scales to reflect a shared meaning. Our subjects for the pilot study (and the main study) consisted of real world networking experts with a minimum experience of 18 months. Examples of job titles of subjects were Networking Managers, Networking Executives and Networking Professors.

The unit of analysis in our model was a group. As indicated earlier, although groups consist of individuals, studies on group phenomenon are not allowed to consider the individuals as a unit of analysis [50]. Hence, we aggregated the individual

Figure 1. Repertory Grid Analysis

4. Preliminary Findings We tested our proposition given above by conducting a pilot study using five real world experts. As indicated earlier, the experts made use of the Repertory Grid technique to try to resolve their disagreements and come to a consensus. We calculated the degree of agreement/disagreement using three measures. The first measure was that proposed by Whitworth and Felton [61], which assesses consensus based on the level of agreement when selecting from nominal values. This measure, though insightful, proved less satisfactory than desired. Distance measures were rather high, and improvements from one round to the next appeared

modest. The assumption of a nominal scale led to high disagreement measures, and the lack of directionality and distance made this less effective. Thus for example, a scenario where two experts rated a factor’s importance as 6 and 7 would generate a disagreement contribution that was assessed the same as whether the two experts rated the same factor as 1 and 7. An alternative measure that normalized the ratings was devised to compensate for the lack of direction and difference. Using this measure, the levels of disagreement were more in line with expectations. In addition, the difference between ratings on successive rounds was more pronounced.

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Finally, Euclidean distance between the two rounds was also considered as a measure of disagreement. While this is computationally simple, it does suffer from problems regarding missing data values. Detailed results of the pilot group study are presented in Table 1. Table 1. Disagreement measures for the pilot group Change by round 1 2 3 D(W,F) D(N) D(Eucl)

Round

Case

2

-

-

-

-

3 4

1 2 3 1 2 3

-9.46% -5.88% 2.59% 0.45%

-14.84% -2.88% 3.59% -14.64%

-10.17% -4.37% 1.59% -13.86%

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Notes: 1= Disagreement calculated using Whitworth and Felton method 2= Disagreement calculated using the Normalized Method 3= Disagreement calculated using the Euclidean Distance Method

At an overall level, disagreement decreased by around 9.5% using the Whitworth and Felton method and by 10.2% using the Euclidean distance method. Results were also similar when considering each task individually, although results were more pronounced using the Euclidean distance method. In the case of task 1, disagreement decreased by 5.9% using the Whitworth and Felton method, by 2.9% using the normalized method and by 4.4% using the Euclidean distance method. Similarly, results for task 3 show that disagreement decreased by 0.45 % using the Whitworth and Felton method, by 14.6% using the Normalized method and by 13.9% using the Euclidean distance method. However for task 2, all methods indicated a marginal increase in disagreement. Disagreement increased by 2.6% using the Whitworth and Felton method, by 3.6% using the normalized method and by 1.6% using the Euclidean distance method. Next, we discuss the results and limitations of our study.

5. Discussion and Limitations Findings from the pilot study broadly supported our proposition indicating that the Repgrid technique was able to reduce disagreement and increase consensus among the experts. This was observed for

the overall problem-solving approach (rounds 2 to 3), as well as for its application to individual scenarios (rounds 4 and 5). At the individual case level, consensus improved for cases 1 and 3 but not for case 2. It is possible that disagreement among experts increased leading to a lack of consensus because some experts felt very strongly about the role of technology (ATM) and its benefits and stuck with their position. This was the only case which involved the application of a specific networking technology ATM. Also, as in real life, experts could not see eye to eye on the importance of corporate mandate in the context of case 2. This makes sense because every expert experiences a unique organizational culture in their firms. Finally, we believe that the lack of adequate use of the technique could partly explain the increase in disagreement for case 2. Consensus for each case was calculated after a round and disagreement in case 2 could actually decrease with increased usage. Future research could examine this issue. While it is desirable to eliminate conflict altogether, in the context of this research, that would require all experts to rate each design criteria identically on a 7 point scale – a most unlikely scenario. That would strongly suggest deindividualization of the experts, which is clearly undesirable. As is, the technique is subject to some degree of known group losses and concerns like groupthink. Presenting experts with their own ratings in comparison to the rest of the group’s ratings is likely to encourage movement towards the rest of the group, particularly when the expert is far from the rest of the group, and there are limited opportunities to discuss these differences. In addition, the results reported are from a single pilot study which has been conducted to investigate the feasibility of capturing expertise from multiple experts dealing with task-based conflictive contexts in connected multiple sessions over an extended period of time in a virtual setting. Though the results are suggestive, claims of definitive superiority of the Repgrid technique to eliminate conflict and improve consensus would most certainly be premature at this stage.

5. Conclusion This study attempted to address the paucity of research on knowledge acquisition from experts in virtual teams. Specifically, we attempted to examine the effectiveness of the Repertory Grid technique in resolving group conflicts during the process of knowledge capture, and consequently increase

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consensus. Preliminary findings from the pilot study lend support to our proposition by indicating that Repgrid reduced the overall (across all IT network design case scenarios) disagreement and increased agreement or consensus. Repgrid was equally successful in increasing consensus in two out of the three cases. It is therefore worthwhile for knowledge engineers to examine using the Repgrid technique to resolve cognitive conflicts that may arise during knowledge acquisition among multiple experts in virtual teams. We believe that the findings from our study will contribute to the limited research on knowledge management and mental models in the context of virtual teams.

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