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Knowledge Management Systems and Task-Technology Fit Research Paper


Introduction

In the modern post-capitalist society, economic resources are not only limited to land, labor, and financial capital (Kuo & Lee, 2011). On the contrary, intellectual capability and knowledge are some of the elements that are increasingly becoming some of the most important capital assets. These assets are unique for they hinge on the knowledge possessed by organizations’ workforces. The job-specific tasks undertaken by the workforces play an important role in firms’ quest to develop a strong competitive advantage.

Considering the growth in significance of knowledge as one of the core organizations assets, firms’ management teams assume the responsibility of ensuring that they exploit their workforces’ knowledge optimally. This aspect has led to increased incorporation of Knowledge Management (KM) in firms operation. According to Alavi and Leidner (2001), KM presumably enhances innovativeness and responsiveness in organizations.

To be effective in exploiting and utilizing their workforces’ knowledge, firms have increasingly incorporating Knowledge Management Systems (KMS), which include technologies that assist organizations in their knowledge management efforts (Nevo, 2003). Knowledge Management Systems promote firms a competitive advantage by enhancing, creation, storage and retrieval, transfer and utilization of knowledge (Alavi & Leidner, 2001).

Despite firms’ effort to investment in KMS, some organizations do not attain the desired outcome due to the existence of mismatch between the implemented KMS design and the workforces’ knowledge. This scenario calls for organizations to configure the KMS in such a way that it aligns with tasks various tasks undertaken. Consequently, firms’ management teams have to ensure that there is a sufficient task-technology fit.

The task-technology fit theory pioneered under the initiative of Dale Goodhue and Ronald Thompson and postulates that the extent of fit between a particular information systems technology and task influences task performance and utilization of the implemented technology (Larsen, Sorebo, & Sorebo, 2009).

One of the ways through which firms can achieve this goal is by integrating task-technology fit model. This paper seeks to illustrate the impact of task-technology fit model on firms’ effectiveness and efficiency in utilizing knowledge management systems.

Task-technology fit (TFF) model overview

The TTF model was postulated with the objective of explaining Information Systems utilization by organizations (Teo & Men, 2008). The TTF model is based on the assumption that the value derived from a particular Information System (IS) depends on the effective with which the system assists the users in executing the specific tasks for which it was designed (Turner, Biros, & Moseley, 2008).

According to McCarthy (2002), the TTF theory postulates that effective utilization of a particular technology depends on its ability to meet the user’s needs. Therefore, the technology must have features that sustain the fit with regard to the prevailing task.

A task-technology under-fit means that an organization is not effectively utilizing its capability thus limiting its ability to attain the intended benefits (Lize & Bonnie, 1998). Alternatively, an over-fit means that the organization has excessive capability that the firm is not in a capacity to exploit. Additionally, a task-technology over-fit means that the organization has invested too much in IS technologies. Goodhue and Thompson (1995) are of the opinion that the best IS technology is the one that has the best task-technology fit.

A high degree of fit between the task and technology contributes to improved performance. This aspect emanates from the fact that an organization’s workforce is able to execute tasks more effectively and efficiently. The cost of executing tasks also reduces adequately (Kuo & Lee, 2011).

Attainment of a high technology-task fit also increases the probability of continued utilization of technology. The proponents of this model are of the opinion that Information Systems is only useful if there is a strong relationship between the system’s functionality and the user’s task requirement.

Additionally, figure 1 makes it evident that task-technology fit is a product of incorporating three main elements, which include task characteristics, technology characteristics and individual characteristics. The task characteristics entail concepts such as knowledge tackiness and task interdependence while technology characterizes entail compatibility and quality (Teo & Men, 2008).

Task-technology fit model

Figure: Task-technology fit model

Characteristics of TTF

Various models have been formulated to explain the technology-to-performance model. One of these models is the technology-to-performance chain [TPC]. The model appreciates the fact that the implemented Information Systems technology must be utilized effectively and a fit between technology and task must prevail.

This strategy is the only way through which the implemented IS technologies can contribute towards improvement in organizational performance. TFF model constitutes a number of characteristics that contributes towards the attainment of the intended outcome. The main characteristics relate to tasks, technology, individuals, utilization, performance and fit perspectives.

Tasks: Goodhue and Thompson (1995) define tasks to include the various actions undertaken by individuals with the objective of transforming inputs into outputs. The tasks undertaken vary and can fall into various bases such as their degree of complexity, difficulty, behavior requirement, and unitary tasks amongst others. Some tasks may require the user rely on certain ISs. For example, some tasks may require the user to deal with unpredictable situations that the firm may experience in the future, which may force the user to rely on the operational information that has been stored on the organization’s database.

Technology: With regard to Information Systems, technology entails the computer systems [such as the software, data and hardware] and various user support services that are critical in the process of executing diverse tasks. The support services may relate to the training and help lines offered to assist the user in the course of executing the intended task.

Individuals: These include the organizations’ workforces that intend to utilize the technology in performing various tasks. In a bid to utilize the technology effectively, individuals must have the necessary training, motivation and computer experience. Lai and Hung (2009) opine that individual characteristics may affect the effectiveness and efficiency with which the technology is utilized.

Task-technology fit [TFF]: This aspect refers to the extent to which technology enables individuals accomplish various tasks. According to Goodhue and Thompson (1995), TFF also refers to the degree of correspondence between individual abilities, technology functionality and task requirement. Additionally, to attain a high degree of fit, it is paramount for there to be a relatively high degree of match between the task and the individual characteristics.

Utilization: This element entails the act of make use of various computer technologies to accomplish certain tasks. Technology utilization can be determined through various constructs such as the frequency of within which a particular technology is used and the number of tasks that a particular technology is employed to complete. For there to be a high degree of correspondence, it is critical for organizations to ensure that there is a relatively high degree of connection between the technology, the individual and the task.

Performance: This element refers to the ability of the implemented technology to assist individuals accomplish the portfolio of task for which it was designed. A high level of performance is an indicator of improved efficiency, effectiveness and quality. Ensuring TTF does not only enhance its utilization but also improves system performance. Consequently, attaining a high level of TTF contributes towards improved performance hence accomplishing the individual’s task needs (Goodhue & Thompson, 1995).

Feedback: Upon implementing the IS technology, an effective form of feedback must be ensured. The feedback should detail the experience of using the implemented IS technology. The feedback is necessary in that it aids in identifying the necessary improvements hence enhancing task-technology fit.

Impact of the task technology fit theory on the knowledge management system

Currently, the survival of firms in different economic sectors is dependent on the effectiveness with which they implement and utilize various IS technologies. One such technology is knowledge management system. According to Lin and Huang (2008), utilization of KMS is one of the main considerations of the management information systems community.

For there to be sufficient utilization of the implemented KMS, it is paramount for firms’ management teams to ensure a high level of congruency between the perceived capabilities of the KMS, the needs of the portfolio of tasks to be undertaken and the users’ competence (Gebauer & Ginsburg, 2006).

By ensuring a high level of task-technology fit, the users develop a positive perception regarding the implemented KMS technology, which emanates from the fact that they appreciate the knowledge management system’s responsiveness to tasks that it is designed to undertake. This objective is attainable by ensuring that the technology is congruent with the task for which it is designed. Consequently, task-technology fit theory contributes towards effective implementation of the KMS.

One of the ways through which this element is beneficial is that it ensures that the new technology does not result in the users experiencing injuries. Therefore, user friendliness of the implemented KMS is attained which culminates in creation of a high level of user satisfaction. In summary, TTF theory leads to increased KMS functionality. Kuo and Lee (2011) and (Lin & Huang 2008) further posit that incorporating task-technology fit theory culminates in attainment of a high level of task compatibility

Nevo (2003) is of the opinion that ensuring a high level of task-technology fit culminates in development of a strong organizational culture regarding utilization of the KMS technologies.

For example, employees appreciate and recognize the importance of utilizing the technology in executing various decisional and operational tasks. This means that the usefulness of the KMS technology is increased. For example, in the course of making various decisions, the top management team may increasingly rely on information systems such as Decision Support Systems.

The effectiveness and efficiency of the decision making process develops a positive impact on the firm’s management team regarding its usage (Clay 2006). The ultimate effect is development of an organizational culture that appreciates the KMS technology. The organizational culture is impacted by developing a high level of self-efficacy amongst individuals. As a result, the probability of continued usage of the KMS is increased.

According to Nevo (2003), the TTF enhances knowledge generation, codification and transfer. For example, task-technology fit ensures collection and storage of data necessary for the firm’s intelligence needs such as decision-making. The information provided is useful in various knowledge management activities such as executing tasks that depend on information availability.

Ultimately, the effectiveness of the KMS improves whereas the users of the KMS develop a positive perception regarding the favorability and compatibility of the implemented KMS technology in helping them execute their tasks (Pai, 2012).

Ensuring a high level of task-technology fit does not only increase usage of the KMS, but also enhances innovation and development of the KMS (Tatnall, 2007). For example, the increased usage of the KMS culminates in the creation of new knowledge, amongst the users. This assertion emanates from the fact that the task-technology fit and KMS culminates towards the provision of sufficient and up-to-date information through the various knowledge tools implemented such as the Decision Support Systems, and Expert Support Systems.

Lin and Huang (2008) are of the opinion that KMS covers various business intelligence areas such as improved collaboration, knowledge mapping and distributed learning. The theory asserts that implementation of TTF presents organizations with an opportunity to ameliorate their tasks. In summary, one can assert that the TTF theory contributes towards the creation of a sense of fit between the various organizational tasks and the implemented KMSs (Lin & Huang, 2008).

Conclusion

Knowledge is one of the most important assets in the success of firms in the modern business environment. This aspect emanates from the fact that it contributes towards development of a high competitive advantage, which has led to incorporation of knowledge management in firms’ operation. Therefore, to enhance utilization of knowledge in firms’ operation, firms are increasingly integrating emerging computer technologies such as management information systems.

One such system is the knowledge management system. The KMS assists organizational workforces in the process of executing various tasks such as decision-making. The core objective of implementing the KMS is to assist employees to improve their performance. Despite their implementation, the utilization of the KMS is dependent on the prevailing task-technology fit. The task-technology fit refers to the degree of congruency between the various organizational tasks and the implemented technology.

In a bid to achieve a high degree of task-technology fit with regard to KMS, it is critical for firms’ management teams to implement the task-technology fit model effectively. Therefore, to achieve this goal, firms’ management teams must take into account a number of components.

Some of the most important components include task characteristics, technology characteristics and individual characteristics. Ensuring a high degree of task-technology fit contributes towards development of improved usefulness of technology amongst the users coupled with continued utilization of the technology. Additionally, ensuring task-technology fit with regard to KMS culminates in improved performance of the employees, which leads to improvement of a firm’s competitive advantage.

Reference List

Alavi, M., & Leidner, D. (2001). Knowledge management and knowledge management systems: conceptual foundations and research issues. MIS Quarterly, 25(1), 107-136.

Clay, P. (2006). Factors contributing to user choice between codification and personalization based on knowledge management systems: A task-technology fit perspective. Indiana, IN: Indiana University.

Gebauer, J., & Ginsburg, M. (2006). . Urbana: University of Illinois. Web.

Goodhue, D. & Thompson, R. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236.

Kuo, R., & Lee, G. (2011). Knowledge management systems adoption: exploring the effects of empowering leadership, task-technology fit and compatibility. Behavior & Information Technology, 30(1), 113-129.

Lai, H., & Hung, S. (2009). Influence of user expertise, task complexity and knowledge management support on knowledge management support on knowledge seeking strategy and task performance. Changhua, China: Chienkuo Technology University.

Larsen, T., Sorebo, A., & Sorebo, O. (2009). The role of task-technology fit as users’ motivation to continue information system use. Oslo, Norway: Elsevier.

Lin, T., & Huang, C. (2008). Understanding knowledge management systems usage antecedents: An integration o social cognitive theory and task-technology fit. Oslo: Elsevier.

Lize, Z., & Bonnie, B. (1998). A theory of task/technology fit and group support systems effectiveness. MIS Quarterly, 22(3), 313-334.

McCarthy, R. (2002). management systems. Web.

Nevo, D. (2003). . Web.

Pai, J. (2012). Knowledge integration, a task-technology fit and e-business implementation; an empirical study. African Journal of Business Management, 6(47), 11609-11615.

Tatnall, A. (2007). Handbook on advancements in smart antenna technologies for wireless networks. Hershey, PA: Idea Group Incorporation.

Teo, T., & Men, B. (2008). Knowledge management portals in Chinese consulting firms: a task-technology fit perspective. European Journal of Information Systems, 17(2), 557-574.

Turner, J., Biros, D., & Moseley, M. (2008). “KMS-Fit”: A case based exploration of task-technology fit in a knowledge management context. Knowledge Management & E-Learning: An International Journal, 1(2), 120-138.

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IvyPanda. (2019, July 5). Knowledge Management Systems and Task-Technology Fit. Retrieved from https://ivypanda.com/essays/knowledge-management-systems-and-task-technology-fit/

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IvyPanda. "Knowledge Management Systems and Task-Technology Fit." July 5, 2019. https://ivypanda.com/essays/knowledge-management-systems-and-task-technology-fit/.

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IvyPanda. 2019. "Knowledge Management Systems and Task-Technology Fit." July 5, 2019. https://ivypanda.com/essays/knowledge-management-systems-and-task-technology-fit/.

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IvyPanda. (2019) 'Knowledge Management Systems and Task-Technology Fit'. 5 July.

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