THE EFFECT OF COLLABORATIVE TECHNOLOGY CHARACTERISTICS ON KNOWLEDGE MANAGEMENT IN AN INTERDEPARTMENTAL ENVIRONMENT

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1 THE EFFECT OF COLLABORATIVE TECHNOLOGY CHARACTERISTICS ON KNOWLEDGE MANAGEMENT IN AN INTERDEPARTMENTAL ENVIRONMENT Wesley Shu, National Central University, , Chia-Sheng Lin, National Central University, George Wang, National Tsing-Hua University, ABSTRACT The present study compared the effectiveness of wikis and an alternative (non-wiki) collaboration technology for knowledge management in an interdepartmental setting. Most off-location or virtual team researchers continue to view wikis exclusively as virtual communication or collaboration writing tools, even though they can also be seen as tools for collaborative tasking, including knowledge management. In doing so we attempted to answer the following question: Can differences in collaborative technology characteristics between a wiki and a non-wiki medium, along with differences in the nature of the task, affect knowledge management? We concluded that wikis are a better knowledge management tool in both preference and intellective tasks. Collaboration, Knowledge Management, Interdepartmental, Group Support System INTRODUCTION Several studies have been conducted aimed at understanding how best to use wikis for knowledge management [1-5]. Willem and Buelens [6] posited that effective interdepartmental knowledge sharing is required by both public sector organizations and corporations. However, no one has studied this implementation in an interdepartmental setting. Specifically, the following critical question has been left untouched: Are wikis more effective than other systems for knowledge management? The present study compared the effectiveness of wikis and an alternative (non-wiki) collaboration technology for knowledge management in an interdepartmental setting. Internet technology has enabled corporations of all sizes to expand beyond their geographical boundaries. However, along with such global expansion comes the increasing complexity of managing interdepartmental relationships and knowledge management capabilities. Past studies have shown that most corporate decisions are the result of collaboration among departments, and the soundness of the decisions depends largely on how well these interdepartmental relationships are maintained [7]. Interdepartmental collaboration studies that predate the standardization of electronic mail protocols have largely been based on such communication modes as face-to-face meetings, memoranda, and phone calls. When personal workstations began to gain popularity with enterprises in the mid-1980s, technologies such as teleconferencing and emerged as the primary choices for interdepartmental communication. More recent studies have branched out to cover Group Support Systems (GSS), Computer Mediated Communication

2 (CMC), and Virtual Teams (VT), all of which incorporate these newer communication technologies. Yet, researchers have overlooked those technological characteristics that are responsible for collaboration success. For our study we chose wikis, the proven mass collaboration platforms made famous by Wikipedia. Past research on wikis has focused mainly on their application as online collaborative writing tools. As the technology has matured, wikis have been adopted as a media for synchronous or asynchronous communication, and as vehicles for knowledge sharing, report assessment, and online learning [8]. The versatility of wikis can be attributed to their core collaboration features, namely, synchronous and asynchronous editing capability, topic association and linkage, change history recording, and a centralized relational database-management system. It is because of these characteristics that wikis have become many multinational corporations media of choice for collaboration projects across national borders. Yet most off-location or virtual team researchers continue to view wikis exclusively as virtual communication or collaboration writing tools, even though they can also be seen as tools for collaborative tasking, including knowledge management. Three recent exceptions are a study by Hester [9], which uncovered a positive relationship between wiki technology and task structure, a study by Silvia and Iryna [10], which led the authors to conclude that wikis are suitable platforms for collaborative problem solving, and a study by Pei Lyn Grace Tay [11], which provides insight into the virtues of wikis for knowledge sharing. Unfortunately, many studies of virtual teams have been rooted in overly vague definitions of team, which resulted in failure to adequately distinguish between interdepartmental and intradepartmental collaboration. The purpose of the present study was to explore whether the collaborative technology characteristics typified by wikis facilitate the kind of cohesive tasking that leads to improvement in knowledge management. In doing, so we attempted to answer the following question: Can differences in collaborative technology characteristics between a wiki and a non-wiki medium, along with differences in the nature of the task, affect knowledge management? LITERATURE REVIEW Interdepartmental collaboration Researchers have tried to understand what it takes to make interdepartmental collaboration more efficient. Nauta et al. [12] found that communication plays an important role in interdepartmental cooperation, problem solving, and goal setting. Goodhue et al. [13] analyzed the effect of team member familiarity on collaborative performance, with an emphasis on issues that might arise between members of departments that have decidedly different goals. Cummings and Kiesler [14] found that relationships in distributed projects were not as strong at the outset as those in collocated projects. In the distributed projects, team members were more likely to be strangers or mere acquaintances when the project started, which means they began their collaboration with weaker relationships. Such a circumstance tends to create interdepartmental communication conflicts [15]. With respect to goal disparity, Nauta [12] has noted that interdepartmental goal incompatibility can hamper overall organizational effectiveness. When interdepartmental goals are at least partly incompatible with the overall goals of the organization, a dilemma is created.

3 With respect to cognitive discrepancies, Jehn & Mannix [15] asserted that the more that individuals or group of individuals share similar backgrounds and cognitive values, the lower the potential for conflict. With respect to social identity, Ashforth and Mael [16] argued that individuals identify better with their departments than with the organization as a whole. People in the same department are likely to identify with one another through shared goals or functional similarities. However, identification across departments requires a completely different perspective; for instance, research and development (R & D) departments are likely to have different goals than marketing departments, because they differ in their objectives and desire for autonomy. Marketers are concerned primarily with identifying product-market windows and how quickly products can be launched, whereas the major concern of R & D is how quickly the product can be made technically feasible and functionally effective. Although both kinds of department play a key role in developing a marketable product, differences in the interpretation of launch timing can easily create conflict. Interdepartmental teamwork can suffer from another kind of dilemma: although groups consisting of people familiar with one another may be better equipped psychologically to resolve conflicts effectively, they may be less likely than groups of strangers to experience the knowledge asymmetries from which cognitive conflicts arise (Gruenfeld et al., [17]. On the other hand, groups of strangers, although likely to know different facts and have different perspectives, may lack the social ties and interpersonal knowledge to tap into the essence and prospective advantages of their diversity. Finally, traditional interdepartmental teamwork is bound by physical and synchrony constraints. Meetings are often too short for an effective consensus to emerge. Geographical separation can often cause valuable time to be wasted in commuting, make it difficult to set mutually agreeable meeting times, and create hardship in providing effective project supervision. As a result, group members often find it hard to maintain cooperative enthusiasm. The above studies clearly show that although interdepartmental collaboration has proven to be necessary and beneficial, it is often marked by frustration. Web 2.0 technologies, such as wiki, can potentially provide relief, because they enable a decidedly different mode of collaboration. Wiki technology Web 2.0 technology enables remarkable interactivity and creates many new collaboration models, such as Wikipedia. We are encouraged not only by its popularity but also by its unique features: simple and parallel editing, version control, and real-time updates. Bean and Hott [18] described the bottleneck effect, whereby updates are delayed because data entry is centrally managed. Instead of control centers, wikis serve as platforms and central repositories. This makes asynchronous cooperation and international operations possible. Wikis work to level the playing field and allow all opinions to be heard. In a typical corporate hierarchy, opinions from more highly ranked individuals are likely to be valued more than those of the people below them. Wikis avoid this problem. They also permit efficiency. As Bean and Hott [18] commented, instead of back-and-forth exchanges of e- mail attachments or discussion boards, wikis allow direct exchanges with a central repository. Unlike the blogs or micro-blogs available today, wikis allow bi-directional communication, which makes wiki a dynamic process that closely resemble real-life communication

4 exchange. Mattison [19] pointed out that, in contrast to blogs where articles are written mainly by individuals, wikis are an example of group-work where authors can see what others write and offer their own thoughts. Most wikis provide forums where authors can discuss and resolve conflicting opinions before they are posted. Lastly, the entire wiki methodology is built on trust, which means that all entries are assumed to be genuine and correct, and filters are activated only when necessary. This trust is enhanced because wikis continually encourage the elaboration of facts, a process that in turn harnesses the power of diverse individuals to create collaborative works globally [RW.ERROR - Unable to find reference:2499]. Knowledge management Knowledge management (KM) has been studied widely, but our focus has been on the specific knowledge capabilities outlined by Gold, Malhotra, and Segars [20], whose constructs have been applied by many other researchers [21-26]. Gold et al. proposed that a knowledge infrastructure (internal KM) consisting of technology, structure, and culture along with a knowledge process architecture (external KM) of acquisition, conversion, application, and protection are essential organizational capabilities or preconditions for effective knowledge management. In our experiment setting, since none of our participants had any corporate influence in the past and that there was no predetermined corporate structure or cultural setting, the knowledge infrastructure mentioned above was effectively controlled for in our comparison of wiki and non-wiki. As far as knowledge process architecture is concerned, the three key knowledge processes acquisition, conversion, and application were embedded in the tasks (see Section 0 Wikis as knowledge management platforms In the past 20 years, many organizations have begun to understand the importance of company-wide KM as a key to competitiveness and productivity [27]. Wikis are readily available and convenient tools for both KM and collaboration. Many enterprises such as Motorola use wikis as internal KM systems [RW.ERROR - Unable to find reference:2499] ; for example, IBM uses them to obtain product knowledge and insights from their component brokers [28]. Organizations must focus on their own learning to sustain growth if they are to compete successfully in the global market [29]. Wikis have been successful in helping companies integrate information from diverse sources into a streamlined and easily accessible knowledge base [28]; thus, they have become good tools for organization learning. King [30] posited that wiki innovation focuses on a codification strategy as distinct from a personalization strategy. Such KM codification strategies are exemplified by the creation of knowledge repositories. Wikis enable people to create and store knowledge collaboratively [31]. This accumulation of knowledge allows innovators to absorb and use this knowledge to generate innovation [32] and then to organize the collected body of thought. These tasks are important for studying organizational learning and knowledge creation [33]. Despite these benefits, organizational resistance to electronic teamwork infrastructure remains high. Greiner and Metes [34] explained that many people are reluctant to use the electronic infrastructure to do their work, which may be highly communicative, knowledge intensive and ego-involved. They communicate best face-to-face, are resistant to wide-

5 spread electronic information sharing and don t like the planning and formality that are required to make virtual processes work. Nonetheless, these authors believe that virtual teaming is an optimal way to work in the current environment of time compression, distributed resources, increasing dependence on knowledge-based input, a premium on flexibility and adaptability, and the availability of networks for electronic information gathering and communication. Task classification Our study focused on tasks that are commonly encountered in the decision-making process by task teams and within organizations. According to McGrath [35], there are 2 types of collaborative tasks: intellective tasks and preference tasks. The purpose of intellective tasks is to solve problems that have anticipated outcomes. The outcomes of preference tasks, on the other hand, are uncertain; the final agreement depends on team members values and beliefs. According to [36], team members will collaborate differently given different task classifications and collaborative technologies, resulting in differences in task fit. The nature of preference tasks suggests to us that they are more likely than intellective tasks to generate group discussion, exchanges of opinion, and alternative interpretations of the task, thereby making the need for a sound collaborative platform more prominent. Task-technology fit In Goodhue s [13] Task/Technology Fit (TTF) model, the usefulness of a tool for performing a task is highly correlated with how well the task collocates with the tool s functionality. The main thrust of TTF theory is that measurable performance is possible only if the science and technology jointly collocate with the needs of the mission. For example, collaboration between team members in a collaborative support system is sufficient for effective problem solving and is not subject to geographical and time constraints [37]. Moreover, collaborative methods are more efficient than traditional methods [38, 39]. We believe that the TTF model is appropriate for our study for 3 reasons. First, wikis and traditional collaboration methods differ with respect to synchronization; our goal is to find out which technology mode (wiki or non-wiki) is better suited for tasks requiring extensive asynchronous collaboration. Second, task/technology fit predicts task performance. As noted in Section 0, although interdepartmental collaboration is necessary, it usually does not yield the expected level of performance. Thus, analyzing the performance is vital. Third, the TTF model differs from utilization models such as UTAUT [40] in that it provides for a direct measurement of performance. As Goodhue stated, Utilization of a poor system (i.e., one with low TTF) will not improve performance, and poor systems may be utilized extensively due to social factors, habit, ignorance, availability, etc., even when utilization is voluntary. When technology use is not voluntary, in which case it is considered to be a contaminant in utilization models, performance is not measured at all [41]. Our model, informed by the literature review above, is depicted in Figure 1.

6 Collaborative Task Figure 1 Research Model Intellective Type Preference Type Collaborative Technologies Task/Technology Fit Knowledge Capability Knowledge Acquisition Knowledge Conversion Knowledge Application Wikis Non-Wikis Our research extends previous research on virtual teams to address interdepartmental collaboration, with the following 2 differences. First, we took account of the fact that most interdepartmental collaboration is ad-hoc; previous studies used technologies that required extensive preparation, such as system configuration and testing, as well as session planning and facilitation [42]. Second, interdepartmental collaboration involves heterogeneous participants, which has not been a point of emphasis in team studies. Studies applying Goodhue s TTF model have been limited to the measurement of performance. Our study went differently by measuring knowledge capability. We used scales developed by Gold, Malhotra, and Segars [20] and other researchers [43-47] to evaluate 3 components of knowledge capability pertinent to wikis and expressed through teamwork: knowledge acquisition, knowledge conversion, and knowledge application. HYPOTHESIS DEVELOPMENT As mentioned above, according to the TTF model, the usefulness of a tool for performing a given task is highly correlated with how well the task collocates with the tool s functionality. Potential users must be thoroughly convinced that a technology can help them complete their mission before they are prepared to adopt it [48]. For example, individuals who draw upon a collaborative support system that enables them to solve problems unencumbered by geographical and time constraints are more efficient and successful than those lacking such a system [49]; [50]. Groups that adopt a group support system are also more motivated than other groups to express their ideas [51]. This is probably because their members can avoid the possibility of face-to-face confrontation, which can lead to coercion and embarrassment [52]. Additionally, arrangements that support parallel editing and allow multiple participants to instantly share and express their opinions, ideas, information, and presentations are far more efficient than face-to-face meetings, where participants must wait for permission to speak (Berndt, 1992). Based on these arguments, we proposed the following hypotheses: H1a: In the context of interdepartmental collaboration, wiki groups manifest better task/technology fit after the task than before the task. H1b: In the context of interdepartmental intellective tasking, wiki groups manifest better task/technology fit after the task than before the task.

7 H1c: In the context of interdepartmental preference tasking, wiki groups manifest better task/technology fit after the task then before the task. H2a: In the context of interdepartmental collaboration, wikis provide a better task/technology fit than non-wiki technologies. H2b: In the context of interdepartmental intellective tasking, wikis provides a better task/technology fit than non-wiki technologies. H2c: In the context of interdepartmental preference tasking, wikis provides a better task/technology fit than non-wiki technologies. According to Choo and Neto [53], the management of organizational knowledge is really about managing the context and conditions by which knowledge can be created, shared, and put to use towards the attainment of organizational goals. Nonaka, Toyama, and Konno [54] labeled a similar enabling context by the term ba, which refers, for example, to time, place, relationship with others and situations for the appearance of knowledge (Choo & Neto, 2010). This concept led us to posit 3 hypotheses related to our 3 components of knowledge capability. First, wikis provide an enabling context for knowledge acquisition because they are a place where people can share opinions and store knowledge. Second, wikis provide an enabling context for knowledge conversion because participants can cross-edit, which allows them to filter and consolidate their ideas. They can also get ideas from other participants and internalize them as their own. Third, wikis provide an enabling context for knowledge application because they allow participants to learn from one another. Applications of knowledge thus can be easily assimilated. Although wikis cannot create a mental ba that generates emotion, cognition, and meaning [55], people can use them to construct a personal working environment containing key elements of ba. Thus, wikis serve as a kind of physical ba. Once again, the usefulness of a tool for performing a task is highly correlated with how well the task collocates with the tool s functionality. Hence the following hypotheses: H3a: The better the task/technology fit, the better a team s knowledge acquisition. H3b: The better the task/technology fit, the better a team s knowledge acquisition when the task is of the intellective type. H3c: The better the task/technology fit, the better a team s knowledge acquisition when the task is of the preference type. H4a: The better the task/technology fit, the better a team s knowledge conversion. H4b: The better the task/technology fit, the better a team s knowledge conversion when the task is of the intellective type. H4c: The better the task/technology fit, the better a team s knowledge conversion when the task is of the preference type. H5a: The better the task/technology fit, the better a team s knowledge application. H5b: The better the task/technology fit, the better a team s knowledge application when the task is of the intellective type. H5c: The better the task/technology fit, the better a team s knowledge application when the task is of the preference type. EXPERIMENTAL DESIGN AND PARTICIPANTS We adopted a 2 2 factorial experiment design for the experiment. The between-subject factors were mode of collaboration (wiki and non-wiki) and task type (intellective and

8 Post-Task Pre-Task preference). The wiki group used the wiki platform offered by Google with the rolling back, locking in, and history review functions. The non-wiki group used s and MS Word to communicate. The within-subjects variable was time of measurement (pre-task and posttask). The 4 dependent variables were task/technology fit, knowledge acquisition, knowledge conversion, and knowledge application. A total of 80 MBA students from a prominent Taiwan university served as participants. They were divided into 20 teams of 4 members each. To ensure inter-group validity, we required that each team contain at least 2 members who had never worked with each other in the past. With this proviso, it is highly unlikely that members of different departments worked together in the past for an extended period of time. Ten of the 20 teams were randomly assigned to the wiki group and the other 10 to the nonwiki group. Five teams were randomly assigned an intellective task and the other 5 a preference task. The design is illustrated in Figure 2. Figure 2 Experimental Design Intellective Task Wiki Preference Task Intellective Task Non-Wiki Preference Task To ensure our sample size was large enough to minimize Type I error (falsely rejecting the null hypothesis) and Type II error (falsely accepting the null hypothesis), we performed a power analysis to determine the optimal sample size for each cell of the design, using the formula described by List, Sadoff, and Wagner [56]: n = 2(t α/2 + t β ) 2 ( σ δ )2, where n = sample size, t = t-value (type I error), t = t-value (type II error), = population standard deviation, and = the minimum detectable mean outcome difference between the wiki and non-wiki conditions.

9 Setting power at 0.80 and 0.90 and the criterion alpha level at 0.05, we determined that the ideal minimum treatment cell sizes were 16 and 21. As the number of participants in each cell of our study was 20, we concluded that our sample was large enough to detect the desired effects if real. Tasks Under the leadership of Steve Jobs and his successor, Apple has been able to maintain an iconic status. The Apple brand projects an image of innovation and success. The company in our study was modeled on Apple, and through in-depth analysis we were able to identify the factors that made it successful. Discussions surrounding the tasks involved 2 management-related issues. For the intellective task, we first asked the teams to collect quantifiable data about Apple s net worth, total number of employees, and future equity holdings. We then asked the teams to briefly describe Apple s industry reach at the time of the experiment. The preference task was far more complicated because there were no definitive answers to the questions posed. The teams were asked, first, to use a SWOT analysis to evaluate Apple s current competitiveness. They were then asked to evaluate Apple s current strategy for brand promotion. EXPERIMENTAL PROCEDURE, MEASURES, AND PSYCHOMETRIC TESTS Questionnaire The questions used for task/technology fit measurements were taken from Jarupathirun and Zahedi [57]. The measures used for knowledge acquisition, knowledge conversion, and knowledge application were taken from Gold, Malhotra, and Segars (2001). All responses were recorded on 5-point Likert scales. The questionnaire was divided into 3 parts presented as Google docs at different stages of the experiment. Part I asked for personal information, such as gender, academic department, weekly computer usage, prior teamwork experience, and prior experience in team collaboration using wiki. Based on this information, the team members were reassigned such that at least 2 members of a given team had never worked with each other before. Then Part II was administered. It asked the participants for their opinions about the task/technology fit, productivity, decision quality, and satisfaction. Part III asked for the same opinions after the wiki procedure was implemented. Experimental procedure Before the formal experiment, the wiki group was given a 20-minute wiki tutorial including the use of the wiki website editor, page and platform management, and version non-wiki functions. Exercises were then given to ensure that all participants were familiar with the wiki environment. The experiment consisted of the following sequence of events: (1) 5 minutes to complete Part II of the questionnaire, (2) 10 minutes for oral-only discussion, (3) 25 minutes for discussion aided by wiki or non-wiki tools, (4) 10 minutes for a selfcheck on the work performed, and (5) 5 minutes to complete Part III of the questionnaire. The team members were physically separated from one another by partitions or distance [58]The floor plan was designed to encourage real intra- and inter-group communication,

10 as shown in Figure 3. The numbers represent the participants. Participants 1 and 2 were from the same department and Participants 3 and 4 were from a different department. Participants were free to talking to each other verbally or communicated through wiki (for wiki group) or /word (for non-wiki groups). Figure 3 Floor Plan Pilot test One week prior to the wiki procedure, a pilot-test was performed on Part II to test the questionnaire s reliability and validity. A total of 35 questionnaires were sent out, 31 of which provided valid data. Reliability Tests After converting the data for SPSS analysis, we found that all the scales representing the constructs were reliable, except for knowledge acquisition, the Cronbach alpha for which was below 0.7 [59]. We deleted one item from this scale and ran the analysis again, finding that the alpha exceeded the 0.7 threshold. The alpha for the combined scales (i.e., Part II of the questionnaire) was The alphas are reported in in Table 1. Table 1 Alpha Results for Pilot Test Construct Cronbach's Alpha Task/technology fit Knowledge acquisition Knowledge conversion Knowledge application Validity Tests We invited 10 scholars with domain knowledge and extensive experience with wiki and technology research to examine the above revised questionnaire. Two MIS professors checked the internal validity of the questions, and 3 Ph.D. candidates did a further

11 evaluation. Five MIS professionals were invited to check for content validity; i.e., whether the topics asked about are really important for task/technology fit. All 10 judges agreed that the questionnaire can measure what it is supposed to measure and that all dimensions are essential to the evaluation of wiki and task/technology fit. To test the validity of the constructs, we began by applying the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy to determine if the scales were suitable for factor analysis [60]. The obtained value was 0.59, which is higher than the threshold of 0.5. Thus, the scales were factorable. To extract the factors, we used principal component analysis. A varimax rotation was then applied using Kaiser normalization. The rotation converged after 8 iterations. The factor analysis yielded 4 factors corresponding to the 4 psychological dimensions (tasktechnology fit, knowledge acquisition, knowledge conversion, and knowledge application). However, one of the questions from task-technology fit did not load on another, similar factor and was removed. Demographic, reliability, and validity analyses for the main test Sample Characterization The distribution of sample characteristics of the 80 participants is given in Table 2. Table 2 Sample Characteristics Characteristic Category Sample Size Percentage Gender Male Female Past experience in group collaboration Average hours per day spent on the computer 1 to 2 times to 4 times to 6 times to 8 times to 10 times Beyond 11 times to 1 hours to 2 hours to 3 hours to 4 hours to 5 hours to 6 hours Over 6 hours Age (years) Under Over Next, we conducted a one-way analysis of variance to determine whether our demographic variables, such as gender, age, online collaborative experience, had any effect on our constructs. The analysis showed that they did not.

12 Reliability Tests Cronbach s alpha was used as the main criterion of reliability. The values are satisfactory. Validity Tests We conducted the same factorability test with KMO that we used for the pilot test. The KMO value was 0.859, which means the data were adequately factorable. Construct validity has 2 components: convergent validity and discriminant validity. Convergent validity is achieved when the factor loadings for the items representing a given dimension are high and similar to one another. Convergent validity is achieved when the following 3 conditions are met: (1) all the standardized factor loadings exceed 0.5, (2) the composite reliability is greater than 0.7, and (3) the average variance extracted (AVE) exceeds 0.5 [61]Discriminant validity means that the factor loadings for the items representing a given dimension are consistently higher than the loadings of these same items on the other dimensions. Discriminant validity is achieved when the square root of the AVE of a construct is greater than the correlation between that construct and another construct [61]. Table 3 shows that convergent validity was achieved. Table 3 Measures of Convergent and Discriminant Validity Construct Factor Composite AVE Loading Reliability AVE Task/technology fit Knowledge acquisition Knowledge conversion Knowledge application Model Fit We next sought to determine whether our model was the best of the available choices the question of model fit. Table 4 lists the statistics of our model for absolute, incremental, and parsimonious fit. The results show that our model fit was good according to all 3 criteria. We used structural equation modeling to verify the model assumptions and model fit. The results show that most of the goodness-of-fit indices exceeded their standard values, indicating that the data fit our proposed model well. Table 4 Measures of Model Fit Statistic Value Threshold Result Absolute fit χ2/df < 3 Good GFI > 0.8 Good RMR < 0.08 Good RMSEA < 0.1 Good Incremental fit TLI > 0.9 Good IFI > 0.9 Good CFI > 0.9 Good Parsimonious fit

13 Statistic Value Threshold Result PGFI > 0.5 Good PNFI > 0.5 Good 5.5 HYPOTHESIS TESTS Before we conducted the hypothesis tests, we determined that the wiki and non-wiki groups had similar mean scores pre-task, thereby providing a common baseline for evaluating the changes at post-task (see Table 5). Table 5 Comparisons of Pre-task Results on Task/technology Fit for the Wiki and Non-wiki Groups on the Intellective and Preference Tasks Task Group/Time Sample Size Mean SD t p Intellective Wiki Pre-task Non-wiki Pre-task Preference Wiki Pre-task Non-wiki Pre-task We then conducted t-tests to further evaluate the hypotheses. H1a-c predict that the task/technology fit demonstrated marked improvement for wiki group after the assignment of the tasks than prior to the assignment. The hypothesis was supported for either overall (H1a), the intellective task (H1b) or the preference task (H1c). Next, to test H2a-c, we compared the post-task results for the wiki and non-wiki groups on task/technology fit. H2a, H2b, and H2c were all supported, showing that after the task the wiki group demonstrated better task/technology fit than non-wiki group. Next, to test H3a-c through H5a-c, we compared the 3 KM measures (knowledge acquisition, knowledge conversion, and knowledge application). Levene s test showed significant heterogeneity of variance on all these variables, so the unequal variances version of the t-test was employed to test these hypotheses. We found that H3a, H3b, H4a, H4b, H5a, and H5b were supported, but H3c, H4c, and H5c were not. This means that the hypotheses were supported for the intellective task but not the preference task. All the hypothesis test results are listed in Table 6. Table 6 Hypothesis Test Results Measure Task Hypothesis Group / Time Mean SD t p Task-technology fit Overall H1-a Wiki Pre-task Wiki Post-task

14 Measure Task Hypothesis Group / Time Mean SD t p Intellective H1-b Wiki Pre-task Wiki Post-task ** Preference H1-c Wiki Pre-task Wiki Post-task ** Overall H2-a Wiki Post-task Non-wiki Post-task ** Intellective H2-b Wiki Post-task Non-wiki Post-task ** Preference H2-c Wiki Post-task Non-wiki Post-task ** Overall H3-a Wiki Post-task Non-wiki Post-task * Knowledge acquisition Intellective H3-b Wiki Post-task Non-wiki Post-task * Preference H3-c Wiki Post-task Non-wiki Post-task Overall H4-a Wiki Post-task Non-wiki Post-task * Knowledge conversion Intellective H4-b Wiki Post-task Non-wiki Post-task * Preference H4-c Wiki Post-task Non-wiki Post-task Overall H5-a Wiki Post-task Non-wiki Post-task ** Knowledge application Intellective H5-b Wiki Post-task Non-wiki Post-task ** Preference H5-c Wiki Post-task Non-wiki Post-task * p < 0.05 ** p < 0.01

15 DISCUSSION AND CONCLUSION This study explored the impact of different technologies on knowledge management capabilities in preference and intellective tasks in an interdepartmental context. Our wiki design simulated the conditions in which interdepartmental collaboration occurs. Groups used wiki or non-wiki technologies as their main mode of collaboration. The wiki groups were rated higher than the non-wiki groups on task/technology fit. They were also rated higher overall and on the intellective task on the constructs that measured knowledge management capabilities. This is likely because wikis response celerity, editing simplicity, and version control changed the nature of the collaboration for the interdepartmental teams. Wikis differ from other asynchronous computer-aided platforms in that they offer a virtual environment where the implementation of collaboration is faithfully and continuously recorded at a centralized location. They also offer participants equal opportunity to enter their own input and overwrite each other s input iteratively until a mutually agreeable output can be registered. In other words, in contrast to many traditional collaboration methods, wikis prevent any single individual from controlling the course of the collaboration. However, we did not find that wikis worked better than non-wikis on the preference task. This finding supports the conclusion of Hollingshead, McGrath and O'Connor [62] that task characteristics indeed have an effect on group performance. A possible explanation of our finding is that, from the information technology system point of view, wikis can be regarded as generic applications that were not developed to accommodate the specific inhouse needs of an organization [63]. Such generic technologies are better suited to wellstructured and well-articulated tasks. Intellective tasks thus may be a better fit with wikis because they have expected outcomes. Post-task interviews revealed that the wiki platform helped the groups performing intellective tasks to converge their heterogeneous thoughts and save consensus thoughts or ideas in the repository. Because such ideas are well articulated, people can utilize them more easily. Because they occur in intellective tasks, they are also more easily absorbed, filtered, assimilated, dissimilated and integrated than those that come forth in preference tasks. Thus, the knowledge management capabilities are exercise significantly better in intellective tasks than in preference tasks. Because wikis are drawing more attention for group activities, the next step in this research line should be to improve wiki functionality, so that the knowledge capabilities of a group can also be manifested in preference tasks. Another goal of future studies should be to understand how wikis can foster ba. The results of the present study show only that the characteristics exemplified by wikis are good for knowledge management. They do not show us how to manage these characteristics so they can form a ba. To fully understand this mechanism, we may need to create a process or procedure based on a knowledge-management paradigm such as Nonaka s Socialization- Externalization-Combination-Internalization model [54]. The specific goals of such research would be to evaluate the wiki ba and to analyze the contributions of each KM process to organizational performance.

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