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

Personality & technical knowledge management systems research: Implications for researchers [Personality & TKMS series]

This is the final part (part 21) of a series of articles featuring edited portions of Dr. Maureen Sullivan’s PhD dissertation.

Many researchers have investigated the acceptance of various information technology systems, including knowledge management systems1. Researchers have also investigated the relationship of personality traits with certain jobs in the information systems arena2. Additionally, 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 used3 4 5. Moreover, past research studies have focused on system performance, usefulness, or how the system aligns with organizational business strategy6.

The results of this study expand the research in how the Big Five personality traits influence behavior by examining their relationship with technology acceptance as it relates to knowledge management systems and technical knowledge management systems (TKMSs). This research study also broadens the research on extraversion and extends the understanding of openness to experience. Although neuroticism, conscientiousness, and agreeableness are the Big Five traits with the fewest significant relationships with the technical acceptance model (TAM), it was discovered that these traits seem to be important for the relationship to TAM with TKMSs.

More importantly, the results support theoretical perspectives such as interactional psychology and person-situation interactionism7 8, which emphasize that we can gain a greater understanding of behaviors such as knowledge sharing by developing and testing theories of personality-situation interactions rather than focusing exclusively on trait-based theories that tend to highlight the inherent positive or negative aspects of personality traits9 10. 11

In the past, the five-factor model (FFM) of personality has been widely used and applied to research in the field of management and psychology, but rarely has it been discussed in the information systems field. In fact, Devaraj, Easley, & Crant12 noted:

Personality has been largely ignored in the [management information systems] literature over the past two decades. However, the field of personality psychology has significantly advanced since that time, and the FFM has sparked renewed theory and empirical investigation in other disciplines.

This research integrates the constructs of the FFM into the technology acceptance of TKMSs by examining how personality constructs influence perceived usefulness and ease of use and potential acceptance of TKMSs.

Therefore, conducting a study that connects the area of personality traits with the design of TKMSs adds to the body of knowledge and confirms its acceptability and possible future uses in not only software and hardware manufacture companies but also commercial and governmental organizations. Information technology and psychology researchers should evaluate the potential of this correlation and continue to research the potential uses of these relationships.

Recommendations for future research

Various recommendations for future research and practice can be cited based on the results of this study. The lack of research in the area of correlating personality traits with technology acceptance sparked this study and research should be continued to ascertain how personality traits can contribute to technology acceptance. Moreover, organizations, technical and nontechnical, should continue to use the results of these types of studies in implementing business strategies for competitive advantage. Specific recommendations for future research are discussed in the following paragraphs.

The area of correlating personality types or traits with technology acceptance has been limited; however, the future of this research is vast. For instance, this study could be repeated by determining the correlation of technology acceptance, if any, with the combination of all five personality types together. The research model for this study evaluated the personality types separately. Realistically, most people have a combination of personality types and an evaluation of a combination of personality types may achieve different results for the acceptance of TKMSs.

The results of the study were evaluated using linear regression methods, limiting the ability to obtain additional data on the relationships between the variables. Researchers could evaluate the results of this study using multiple regression methods. Using the multiple regression method may allow researchers to find out what multiple indicators best predict whether a relationship exists between personality types and the acceptance of TKMSs.

This research study was limited in that it did not investigate the relationship of demographics with personality traits and the acceptance of TKMSs. Demographic data collected in this study could be used to determine if demographics affect the outcome of the research. For instance, researchers could determine if the age of the study participant affected the results of this study. Additionally, educational level could be used to see if the data for the TAM would change based on increasing levels of education. Moreover, demographic data could be used to add a different dimension to the relationship of personality types to acceptance of TKMSs.

This research study was generalized to the usage of any TKMS. Researchers could modify this study to evaluate the technology acceptance of a specific TKMS. This study was limited in that it did not specify a particular TKMS. Therefore, the study participants answered questions based on a variety of experiences with different TKMSs. Future researchers could submit their results to industry to assist organizations in determining which persons (of a specific personality type) to hire for acceptance and usage of specific TKMSs.

This research study was also limited in that it did not ask questions regarding study participants‘ level of usage with TKMSs. Researchers could conduct this study and request that study participants enter their level of usage with TKMSs. The level of usage and experience with TKMSs may produce different results in the level of technology acceptance despite the personality type of the study participant.

The preceding are just a few recommendations for future research in the area of relating personality types to technology acceptance of TKMSs.


  1. 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
  2. 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
  3. 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
  4. 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.
  5. 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.
  6. Chua, A., & Lam, W. (2005a). Knowledge management abandonment: An exploratory examination of root causes. Communications of the Association of Information Systems, 2005(16), 723-743.
  7. Schneider, B. (1982). Interactional psychology and organizational behavior. Michigan State University East Lansing Department of Psychology.
  8. Tett, R. P., & Guterman, H. A. (2000). Situation trait relevance, trait expression, and cross-situational consistency: Testing a principle of trait activation. Journal of Research in Personality, 34(4), 397-423.
  9. George, J. M., & Zhou, J. (2001). When openness to experience and conscientiousness are related to creative behavior: An Interactional Approach. Journal of Applied Psychology, 86(3), 513-524.
  10. Tett, R. P., & Burnett, D. D. (2003). A personality trait-based interactionist model of job performance. Journal of Applied Psychology, 88(3), 500–517.
  11. Wang, S., Noe, R. A., & Wang, Z. M. (2011). Motivating knowledge sharing in knowledge management systems: A quasi–field experiment. Journal of Management, 1–32. doi:10.1177/0149206311412192
  12. 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
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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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