ABCs of KMPersonality types and acceptance of technical knowledge management systems (TKMS)

Personality types and acceptance of technical knowledge management systems (TKMS) series: Introduction

This is part 1 of a series of articles featuring edited portions of Dr. Maureen Sullivan’s PhD dissertation.

Organizations are facing challenges of global competitiveness due to the current changing business environment. Neto and Loureiro stated1, “the quest for competitiveness and sustainability has led to recognition of the efficient use of information and communication technologies as a vital ingredient for survival and profitability in this knowledge-based economy”. In a competitive economy, organizations face challenges like high market volatility, shortened product lifecycle, rapid technological changes, and downsizing. To meet these challenges, organizations must learn to manage knowledge. This is crucial to creating and maintaining a competitive advantage and innovation. Edvinsson and Malone2 indicated that to meet these challenges and to improve business processes, organizations must identify pertinent knowledge in an organization. Therefore, company organizational knowledge is a key asset in organizations and it must be protected to ensure success.

Efficient use of internal organizational knowledge can prove to be an asset to organizations and help organizations achieve a competitive advantage. Smuts, Van der Merwe, Loock, and Kotze3 assert that a knowledge management system (KMS) can enable increased knowledge sharing, both externally and internally towards an organizational goal such as gaining a competitive advantage. However, KMSs must be successfully implemented to achieve organizational goals, such as gaining a competitive advantage. A potential factor in this success is related to the personality types of KMS users.

This research addresses a subset of knowledge management systems that supports the technical aspects and users of a KMS. Technical KMSs (TKMSs), technical subsets of KMS, are knowledge management systems that store technical information about various software and hardware systems. Software and hardware manufacturers have developed and distributed TKMSs to its customers without examining the personality types that contribute to the acceptance of these systems4. System user satisfaction and effectiveness may be affected by individual differences (personality types) of its users and may affect the organization‘s competitive advantage5.

Despite the extensive past research (e.g., Alavi & Leidner6) in knowledge management (KM) related initiatives and the large financial investment in developing and implementing TKMSs-both externally and internally, not much research has been conducted to determine the actual acceptance of TKMSs and their relationship to personality types of its users7. Information systems (IS) literature has not outlined the individual differences as they relate to personality types8. This issue was studied by determining what the relationship was between acceptance of technical knowledge management systems and user personality types.

Background of the study

Organizations today are focusing on obtaining and maintaining a competitive advantage via the use knowledge management. Knowledge management has been defined as “a practice that finds valuable information and transforms it into necessary knowledge critical to decision making and action” 9. Organizations have used technology to implement their knowledge management goals resulting in various forms of knowledge management systems (KMSs), including technical knowledge management systems (TKMS). Additionally, a competitive advantage can be realized when customers and employees effectively and continually use the KMSs.

Researchers in the last two decades have concentrated on theory-based research of information systems usage that included investigating the variables around technology acceptance and how systems are used 10 11 12. One variable, user initial acceptance of any information systems is a key first step to achieving IS success. However, Bhattacherjee13 notes information system (IS) continued usage, another variable, directly affects the potential success of the information system. Accordingly, continued usage of an IS, directly relates to the success of an information system. In fact, some corporate failures can often be attributed to occasional and improper long-term use of these business critical information systems14. Accordingly, continued usage of an IS, directly relates to the success of an information system. The theory of continuance is not new in the information systems (IS) research arena. The IS implementation literature has noted the theory of continuance in various forms.

Past research studies have focused on system performance, usefulness, or on how the system aligns with the organizational business strategy15. Some of these studies report the failures and major setbacks organizations have experienced with such systems16. These failures are often a result of resistance from employees during implementation of new systems or from the lack of continued system usage17.

Since users have become consumers and make decisions on whether to accept or to continue using the system, increased attention should be paid to each user and their differences. These differences can be distinguished by a user‘s personality. Due to its ability to determine human conduct in many types of situations, personality is more applicable to individual differences18, and should have implications in IS-related activities 19. While this is true, little research exists that address a user‘s inner personality during the post-adoption continued usage context that has become more important to the success of IS in organizations.

The issue of the relationships between personality types and technical knowledge management systems (TKMS) acceptance is addressed in this research. The key factors in this dissertation research are personality types (independent variables) of the users, as measured by the Five Factor Model (FFM) and their acceptance of technical knowledge management systems as measured by technology acceptance model (TAM; dependent variable). The current body of literature includes information about knowledge management systems (a type of information system) acceptance and the correlation of behavior factors and acceptance of IS systems. However, the current body of research does not detail the relationship of the acceptance of knowledge management systems and the personality types of users. Alavi and Leidner20 indicated that in order for KMS research and development (R&D) to be reliable, it should continue to build on existing literature in similar fields.

Statement of the problem

TKMSs are not achieving the usage (acceptance) and the benefits that have been forecasted and are therefore, not enhancing competitive advantage and profits in organizations21. Akhavan, Jafari, and Fathian22 mention that many knowledge management system efforts (like TKMSs) are costly, doomed, and mention that the failure rate of KM has been listed at 50% by some researchers. However, they state that “Daniel Morehead, director of organizational research at British Telecommunications PLC in Reston, says the failure rate is closer to 70%”, and that Liam Fahey, Babson College adjunct professor, indicates that the reliance on technology has caused the high failure rates in KM initiatives. The desired benefits of technical knowledge management systems, as measured by acceptance, are not being consistently attained. Realizing their potential requires additional management knowledge concerning user personality factors that affect and contribute to its acceptance.

Next edition: KM: definition, history, & current trends.

References:

  1. Neto, R. C. D., & Loureiro, R. S. (2009). Knowledge management in the Brazilian agribusiness industry: A case study at Centro de Tecnologia Canavieira Sugarcane Technology Center). Electronic Journal of Knowledge Management, 7(2), 199-296.
  2. Edvinsson, L., & Malone, M. (1997). Intellectual capital: Realizing your company’s true value by finding its hidden brainpower. NY: Harper Business.
  3. Smuts, H., Van der Merwe, A., Loock, M., & Kotze, P. (2009, October). A framework and methodology for knowledge management systems implementation. Proceedings of the 2009 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists, South Africa. 70-79.
  4. Telvent. Author. (2010). Technical support. Retrieved from http://www.telvent-gis.com/support/
  5. Devaraj, S., Easley, R. F., & Crant, M. J. (2008). How does personality matter? relating the five-factor model to technology acceptance and use. Information Systems Research, 19(1), 93-105. doi:10.1287/isre.1070.0153
  6. Alavi, M., & Leidner, D. E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107-136. doi:10.2307/3250961
  7. Ong, C. S., & Lai, J. Y. (2007). Measuring user satisfaction with knowledge management systems: Scale development, purification and initial test. CyberPsychology & Behavior, 23(3), 1329-1346. doi:10.1016/j.chb.2004.12.012
  8. Devaraj, S., Easley, R. F., & Crant, M. J. (2008). How does personality matter? relating the five-factor model to technology acceptance and use. Information Systems Research, 19(1), 93-105. doi:10.1287/isre.1070.0153
  9. Van Beveren, J. (2002). A model of knowledge acquisition that refocuses knowledge. Journal of Knowledge Management, 6(1), 18-23. doi:10.1108/13673270210417655
  10. Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481. doi:10.1111/j.1540-5915.1996.tb00860.x
  11. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. doi:10.1287/mnsc.46.2.186.11926
  12. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
  13. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370. doi:10.2307/3250921
  14. Lyytinen, K., & Hirschheim, R. (1987). Information systems failures: A survey and classification of the empirical literature. Oxford Surveys in Information Technology, 4, 257-309.
  15. Chua, A., & Lam, W. (2005b). Why km projects fail: A multi-case analysis. Journal of Knowledge Management, 9(3), 6-17. doi:10.1108/13673270510602737
  16. Lucier, C., & Torsiliera, J. (1997). Why knowledge programs fail. Strategy and Business, 4, 14-28.
  17. Lin, M. Y., & Ong, C. S. (2010). Understanding information systems continuance intention: A five-factor model of personality perspective. PACIS 2010 Proceedings, Taipei, Taiwan.
  18. Eysenck, H. J., & Eysenck, M. W. (1985). Personality and individual differences: A natural science approach. NY: Plenum Press.
  19. Devaraj, S., Easley, R. F., & Crant, M. J. (2008). How does personality matter? relating the five-factor model to technology acceptance and use. Information Systems Research, 19(1), 93-105. doi:10.1287/isre.1070.0153
  20. Alavi, M., & Leidner, D. E. (2001). Review: Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly, 25(1), 107-136. doi:10.2307/3250961
  21. Comb, C. (2004). Assessing customer relationship management strategies for creating competitive advantage in electronic business. Journal of Knowledge Management Practice, 5. Retrieved from http://www.tlainc.com/articl72.htm
  22. Akhavan, P., Jafari, M., & Fathian, M. (2005). Exploring failure-factors of implementing knowledge management systems in organizations. Journal of Knowledge Management Practice, 6. Retrieved from http://www.tlainc.com/articl85.htm
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Maureen Sullivan

Dr. Maureen Sullivan is an information technology official in the US federal government workspace. She also teaches technology courses at a Maryland community college. Dr. Sullivan is continuing her research in technical knowledge management systems.

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