The Impact of Technology People and Organizational Compatibility Variables on the Use of Knowledge Management Systems

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1 The Impact of Technology People and Organizational Compatibility Variables on the Use of Knowledge Management Systems Di Ye 1 Zhenyu Liu 1 Tung-Ching Lin 2 Yung-Chih Liou 3 1Department of Management Science, Xiamen Universiy 2 Department of Information Management, National Sun Yat-Sen University 3 Department of Information Management, National Kaohsiung Marine University Abstract Based on contingency theory, this study attempts to explore KMS usage and user s performance from a different yet complementary perspective by investigating compatibility/fit between diverse variables related to KMS usage. Adopting the four sub-systems (technology, organization, people, and task) categorized in Leavitt s Diamond Model, this research paper discusses the compatibility between technology and organization (TOC), between technology and people (TPC), and that between technology and task (TTC). The three compatibility variables impact on KMS usage and user s performance is examined using our research model and statistically attested hypotheses proposed in this paper. Keywords: Compatibility, Knowledge Management, KMS Usage, Contingency Theory 1. Introduction According to Markus and Robey, past research investigating the MIS implementation can basically be grouped into three main streams: that based on people-determined theory, on system-determined theory and that on interaction theory. The research basis of this paper is fundamentally in line with interaction theory. Technology-Task-Fit theory (TTF) has traditionally been used in MIS study to investigate the compatibility and fitness of technology and task. In this paper we try to extend the discussion of compatibility/fit to dimensions beyond merely that between technology and task. Based on Leavitt s reputed Diamond Model, our research explores and integrates the divergent strains of compatibility, technology-people compatibility (TPC), technology-organization compatibility (TOC), and technology-task compatibility (TTC/F), in an effort to give a comprehensive view into how these contingency variables affect and determine the usage behavior and performance.besides the currently widely used determined research models (people-determined theory and system-determined theory), we find it may prove important and fruitful to examine the compatibility between technology and other contingency variables for the use of KMS. The concept of compatibility, as it sporadically appeared in past studies and often was 1

2 deemed as a separate and independent factor, actually is a multi-level and multi-oriented one which requires further clarification, and from the understanding of different sorts of compatibility there will emerge a clearer picture of how and to what degree individual factors influence the use of KMS and its performance. 2. The Research Model and Hypotheses 2.1 Research model In Accordance with earlier literatures and practice, we devise a research model as shown in Figure 1. Technology Task compatibility H 5 H 1 H 6 Technology people compatibility H 2 KMS Usage H 4 Performanc e H 3 Technology Organization H 7 Figure 1-Research Model 2.2 Research Hypotheses In Goodhue and Thompson s user-specific construct, TTF(as show in Figure 2) is based on two important assumptions: 1) TTF will strongly influence user beliefs about consequences of utilization; and, 2) these user beliefs will have an effect on utilization. They argued that an IT system will be used if, and only if, the functions available to the user support the activities of the user. Higher degrees of fit lead to expectations of positive consequences of use by the individuals choosing to use the technology. Rational, experienced users will choose those tools and methods that enable them to complete the task with the greatest net benefit. Information Technology that does not offer sufficient advantage will not be used. Task Characteristics Technology Characteristics Task and Technology Fit Utilization Performance Impacts Figure 2-Task and Technology Fit Model Related studies have largely confirmed the relevance of the TTF construct to assess and predict system usage.based on TTF model, Dishaw and Strong explained 2

3 those factors which lead to the use of the software maintenance support tools. They found that the fit between software tool functionality and the maintenance task activities is a primary driving force to use tools. TTF model has since been employed in various fields, including WEB sites usage, mobile IS, and the utilization of M-commerce. Therefore, we posit that technology-task compatibility has positive impact on the use of KMS and hypothesize: H 1 : Technology-Task Compatibility positively influences the use of KMS. A key element in Innovation Diffusion Theory (IDT) is the compatibility between an innovation and a potential adopter s existing values, needs, and past experiences. Technology-people compatibility has also been found to be relevant to adoption decisions in general, IT usage, and to the perceptions of adopting an IT innovation. When a user believes that a technology can help promote the deeply held values and achieve the self-concept of the way one would like to work, he or she is more likely to develop positive use behaviors. Likewise, it is unlikely that a use would perceive the various advantages of using IS/IT if its use is in fact not compatible with his or her past experience or preferred work style. Thus we form our second hypothesis: H 2 :Technology-People Compatibility positively influences the use of KMS. Schultz and Slevin highlighted the need for technological innovations to have both organizational and technical validity. Organizational validity evaluates if the technological innovations are compatible with existing organizational attitudes, beliefs, and value systems. Technical validity, on the other hand, refers to an innovation s compatibility with existing information systems such as hardware and software used. Markus and Robey assumed that organizational validity refers to the "fit" between an information system and its organizational context of use, including the organization s structure, politics, and environment. Ramiller drawn on literature addressing innovation-organization fit and the implementation of new technologies, then modified and extended the conceptualization of perceived compatibility. Besides, researchers also pointed out that the organization s culture, especially top management support, training, and consensus with organizational objectives is crucial to implementation success of IS. Therefore, we suggest that the organizational compatibility may have positive impact on use of KMS, the following hypothesis is posited: H 3 :The Technology-Organization compatibility positively influences the use of KMS. Several studies tested the association between system use and individual performances and the association was found to be significant in each of the researches. Goodhue and Thompson proposed a comprehensive technology-to-performance (TPC) model in which characteristics of information technology, tasks, and those of the individual user are taken as explanatory variables to gauge technology use and individual performance. The purpose of KMS is to increase individual knowledge by promoting the capabilities of knowledge searching, tacit knowledge sharing, rapid knowledge transfer, and so forth. Once individuals bear excellent knowledge, their performance present. Dalkir further suggested that KMS provides advantages for individuals such as speeding individuals learning process, increasing productivity and abilities of solving problem, and improving decision quality. Hence, the more use of KMS, the more knowledge, and the better performance.gray argued that increase in team knowledge variety and sharing lead to improvements in the effectiveness of the 3

4 solutions generated by a team. In addition he proposed that ongoing KMS use increases employee specialization, which in turn reinforces the use of KMS. Hence, the following hypothesis is posited: H 4 :The use of KMS will increase the individual s performance. Despres and Chauvel proposed the implementation of KMS should be considered as a concept of contingency organizations with different contexts should develop their distinct KM strategies. They stressed the relationship between KMS strategy and KMS performance has to be moderated by factors such as organizational context and culture of knowledge, etc. In other words, the compatibility/fit between KMS and related contextual factors plays a crucial role on the performance. Similarly, Oh and Pinsonneault suggested that alignment between the patterns of relevant contextual, structural, and strategic factors lead to better IS performance, whereas misalignment results in performance erosion. Their study results clearly confirms the positive relationship between compatibility and performance. Goodhue and Thompson argued that not only does high TTF increase the likelihood of utilization, but it also improves an individual user s performance impact. At any given level of utilization, a system with higher TTF will lead to better performance, since it more closely meets the task needs of the individual. Through a survey research, Weill and Olson suggested that the better the fit between IS and organizational contingency variables (e.g., individual task, organizational structure, etc.), the better the IS use and success (as described in Figure 3). Therefore, we propose the following hypotheses: H 5 : The Technology-Task compatibility positively influences the individual s performance. H 6 : The Technology-People compatibility positively influences the individual s performance. H 7 : The Technology-Organization compatibility positively influences the individual s performance. Fit Contingency MIS MIS Firm Variables Performance Performance Strategy Management Satisfaction Structure Implementation Success Size Structure Effectiveness Environment Development Innovativeness Individual Task Figure 3-Representatives of Contingency Theory in MIS Research Financial Volume 3. Research Method The model of this study was tested by survey method, and the measurements were developed by identifying and integrating items from a comprehensive literature 4

5 review. Some modification was made on the existing scale to make them more suitable in the context of the use of KMS. 3.1 Data Collection Since our study focus is on the use of KMS, the target population should comprise the users of KMS in companies. In the process of sampling, we selected 600 persons randomly from a list of more than 2000 alumni from a Business Administration School of a university located in southern Taiwan. or local geographical location, lower cost involved, and likely faster responses, etc. 4. Data Analysis and Results In this paper Partial Least Square (PLS) is used for data analysis, and the software tool used is SmartPLS 2.0. PLS is a latent structural equation modeling technique that utilizes a component-based approach to estimation. Such an approach requires minimal demands on sample size and residual distributions. According to Chin et al, PLS is perfectly suited for testing complicated relationships on account of its avoiding inadmissible solutions and factor indeterminacy. The capacity of PLS in exploring complex relationships has been affirmed in many other studies, and has been gaining interest and more application in the fields of MIS, IS and KM. Data analysis is carried out in accordance with a two-stage methodology accessing the measurement model and analyzing the structure mode. 4.1 Measurement Model To validate our measurement model, reliability, convergent validity and discriminant validity are assessed. The evaluation result that all CRs are above 0.7. Besides, Cronbach s alpha, the most commonly-used test methodology in Likert Scale, is simultaneously evaluated. All the Cronbach s alpha values fall between and , which are above the threshold (0.7) proposed by Nunnally. Therefore, results are indicative of adequate reliability in main research constructs. Those indicators which are trimmed away from our data set for unqualified indicators loading values less than 0.5. After the trimming, the new factor loading values are recalculated.the satisfactory result showed that all AVE values exceed the recommended threshold value of 0.5 and exhibit acceptable convergent validity.all diagonal values are greater than their corresponding off-diagonal values, which suggests the respective constructs exhibit acceptable discriminant validity. 4.2 Analysis of the Structural Model Structural model is then analyzed with bootstrapping technique. The explanatory power of the structural model is evaluated by R 2 value. Besides, in order to examine if each hypothesis is supported, we evaluate t-statistic for the standardized path coefficient. All the path coefficients and explained variances for the model using a bootstrapping procedure are shown in Figure 4. 5

6 Technology-Task Compatibility *** ** Technology-People Compatibility *** * Use of KMS R 2 = *** Individual Performance R 2 =.19 Technology-Organization Compatibility * Significant at 0.05 ** Significant at 0.01 *** Significant at Figure 4-Structure Model and the Paths Coefficients From the statistics, we draw our conclusion, which is to be discussed in terms of relationships as follows: 1. Relationship between three compatibility variables and the use of KMS Figure 4 shows the effect of technology-task compatibility (TTC) on Use of KMS is positive and significant (β=0.5505, p<0.001). Similarly, the link between technology-people compatibility (TPC) and Use of KMS is supported positively and significantly ( β =0.444, p<0.001); the path from technology-organization compatibility (TOC) to Use of KMS(β=0.3453, p<0.05) is also positive and significant. Based on these figures, the three main hypotheses (H1 ~ H3) of our model strongly supported. 2. Relationship between three compatibility variables and individual performance Although the path from technology-people compatibility to individual performance is positive and significant (β=0.2959, p<0.01), the other two links between compatibility and individual performance are not supported, which indicates this group of relationships are only weakly supported. 3. Relationship between the use of KMS and individual performance The link between Use of KMS and performance in the direction predicted by the model is significant (β=0.149, p<0.001). As to how to evaluate the reliability of a PLS model, Hulland proposed that R 2 values are the best indicators. In this study the three compatibility beliefs totally explain 41% of the variance in self-reported usage (Hypotheses are strongly supported.), while 19% of the variance in individual performance is accounted for by the self-reported usage and the three compatibility beliefs (Hypotheses are weekly supported comparatively.). The hypotheses proposed and the results of structural model assessment are summarized in Table 1. 6

7 Table 1. Summary of Hypotheses Test Results Hypothesis Path coefficient T-Statistic Result H1: The Technology-Task compatibility positively influences use of the KMS *** Support H2: The Technology-People compatibility positively influences use of the KMS *** Support H3: The Technology-organization compatibility positively influences use of the KMS * Support H4: KMS use will increase the individual performance *** Support H5: The Technology-Task compatibility positively Not influences the individual s performance. Support H6: The Technology-People compatibility positively influences the individual s performance ** Support H7: The Technology-organization compatibility Not positively influences the individual s Support performance. p*<0.05,p**<0.01,p***< Discussion and Implication 5.1 Discussion The empirically tested results suggest that each of the three types of compatibility variables has positively significant influence on the use of KMS. Among the three, TPC (Technology-People Compatibility) has significant effect on individual performance. The research find of this paper is elaborated as follows. 5.2 The impact of TTC (Technology-Task Compatibility) on the use of KMS From the analysis above, we draw the conclusion that technology-task compatibility has the most influence on the use of KMS, which indicates that the compatibility between KMS and tasks plays an important role in the use of KMS. This result is in alignment with previous KMS research studies as proposed by Hansen et al.. Hansen et al. stresses the point that there is no uniform but only a fit approach for managing knowledge, and organizations should choose their own KMS according to their needs of tasks. Once an organization adopts a wrong approach, not only will the users resist the use of the KMS, but also it will diminish the performance of the KMS. 5.3 The impact of TPC (Technology-people Compatibility) on the use of KMS and individual performance. Our result in this aspect is consistent with the previous research studies. Agarwal and Karahanna posited causal linkages between TPC and IS usage and performance, and identified compatibility as a factor which has a significant positive effect on perceived ease of use and perceived usefulness. The concept of technology-people compatibility has been used by some researchers to make complement to TAM. Algahtani and King, by drawing the variable of compatibility into the discussion of TAM, confirmed that technology-people compatibility had a significant effect on user s beliefs and contributed most to usage. 5.4 The impact of TOC (Technology-Organization Compatibility) on the use of 7

8 KMS Our result shows that the more compatibility KMS has with organization s culture and work practices, the more usage the users will have. This result is consistent with Schultz and Slevin s findings which confirm the importance of organizational and technical validity in ensuring implementation success. Our conclusion on TOC is also in line with Rogers s argument that innovations are more likely to be adopted when they are compatible with an organization s values, previously introduced ideas, and needs. In this context, KMS should provide functions of well knowledge sharing, brainstorming, and expert communications, etc. 6. Conclusion By adopting Contingency Theory and Diamond Model, we extend and integrate the concept of technology-related compatibility, which previously was often dealt with incompletely and separately in earlier literatures, to build a comprehensive research model. The impact of diverse strains of compatibility (TTC, TPC and TOC) on the use and performance of KMS is investigated and discussed in detail. Our result shows most of the hypotheses are significantly supported, especially the relationship between compatibility variables and the use of KMS. Since the new contingency research stream is evidently dissimilar to the two commonly-used determined research approaches(people-determined models such as TPB and TAM, and system-determined models such as IS Success Model), the study result of this paper may thus provide a fresh perspective to and shed some light on the future study of KMS or even other kinds of information system. Implication for Practitioners Besides the attention paid to system determined-factors, such as system quality and information quality of KMS, company s chief knowledge officers (CKO) should take into account the following compatibility-related factors when implementing KMS: 1) On technology-task compatibility. Different KMS has different functions. The functions of a KMS which supports tasks requiring explicit knowledge reuse is definitely different from the functions of a KMS which supports tasks that require tacit knowledge sharing and exploration. Similarly, the KMS focusing on tasks in need of knowledge creation is different from the one focusing on tasks in want of best practice transfer. Likewise, a KMS which supports R&D department will have different requirements than a KMS which supports manufacturing section. To motivate the frequent use of certain KMS, an organization has to take into account the various distinct functions served by different KMS, making sure the KMS in use can meet and in compatible with the need of tasks. 2) On technology-people compatibility. Individuals differ in their background, mental model, work style and philosophy, so IS managers are well advised to figure out ways to make the functions, procedures and interfaces of a KMS highly compatible with users values, beliefs and expectations. The compatibility between KMS and its users is positively associated to users intention to use, perceived usefulness, perceived ease of use, and causes less cognitive dissonance and metal burden. To achieve this end, personnel in management level may try to encourage and involve the KMS users in two-way communication. The more communication there is with both side, the more understanding of users values and needs the KMS managers will have. Increasing user involvement and participation in the process of designing a KMS will help the design team understand 8

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