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

Use of five-factor model of personality types in technology acceptance [Personality & TKMS series]

This is part 11 of a series of articles featuring edited portions of Dr. Maureen Sullivan’s PhD dissertation.

Many software and hardware users make purchase decisions based on their effective use of the respective products and their associated technical knowledge management systems (TKMSs). Users’ level of new technology acceptance determines whether they are willing to adopt TKMSs1.

For instance, Thompson, Higgins, and Howell2 observed users‘ behaviors while they were using PCs and added two more variables to the extended technology acceptance model (TAM2) that include the long-term effects of new technology and facilitating conditions. This research was conducted to help companies understand the potential reactions to the introduction of new technology by employees and consumers.

However, most past research focused on certain dimensions or constructs that prevented organizations and companies from completely understanding the reasons why customers or employees resisted accepting new technology. Consequently, Venkatesh, Morris, Davis, and Davis3 created an integrated model based on eight prominent models. This integrated model, the unified theory of acceptance and use of technology (UTAUT), addresses the intention of behavior and is comprised of four constructs: social influence, facilitation conditions, performance expectancy, and efforts expectancy4.

Moreover, prior research investigated the relationship between each of the four constructs and personality traits. For instance, Wang and Yang5 conducted a study that examined the relationship of personality traits with the UTAUT model based on online stock investment use. These researchers used the quantitative research method by distributing questionnaires to a contact person at eight major Taiwanese security companies who in turn distributed the questionnaires to their clients. Although the questionnaires were meant for clients with some investment experience, no specific filtering was applied upon distribution.

Similar to this researcher‘s study, the source of Wang and Yang’s questionnaires included the Venkatesh et al. instrument that measured UTAUT constructs, Costa and McCrae’s6 NEO-PI (form S) instrument, and an internet survey to measure internet experience. One result of this study suggested that the extraversion personality trait affected the investor‘s intention to use online investing systems. The other personality traits had varying results on the intention to use. “Data analyses suggest that personality traits play more important roles as moderators than as external variables”. Wang and Yang suggested that future research should include broader audiences (other countries). As a result, this research was distributed globally to reduce the limitations found in the Wang and Yang study.

Other past studies conducted by Connolly and Viswesvaran7, DeNeve and Cooper8, and Judge, Bono, and Locke9 found that the variables‘ personality traits and performances were positively correlated 10. Another study, performed by Gellatly11, studied the effect “conscientiousness had on job performance that resulted in the determination that performance expectancy was the conciliator between personality trait and job performance. As a result, Gellatly‘s study determined that persons exhibiting the conscientious personality trait set higher work goals and work harder to achieve their goals based on their belief that they can perform well at their jobs.

In contrast, Barrick and Mount‘s12 research was limited due to their inability to observe job performance characteristics because persons exhibiting the neuroticism personality trait were easily removed from their jobs.

Moreover, classifying personality traits through the five-factor model has allowed researchers to apply the five-factor model to medical research, allowing researchers and doctors to predict human behaviors13 14.

Consequently, this past research supports the exploration of the role of personality traits in the unified theory of acceptance and use of technology (UTAUT).

Next edition: Personality types and information systems adoption.


  1. Wang, H. I., & Yang, H. L. (2005). The role of personality traits in UTAUT model under online stocking. Contemporary Management Research, 1(1), 69-82.
  2. Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125-143. doi:10.2307/249443
  3. 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.
  4. Wang, H. I., & Yang, H. L. (2005). The role of personality traits in UTAUT model under online stocking. Contemporary Management Research, 1(1), 69-82.
  5. Wang, H. I., & Yang, H. L. (2005). The role of personality traits in UTAUT model under online stocking. Contemporary Management Research, 1(1), 69-82.
  6. Costa, P. T., & McCrae, R. R. (1995). Domains and facets: Hierarchical personality assessment using the revised NEO personality inventory. Journal of Personality Assessment, 64(1), 21-50. doi:10.1207/s15327752jpa6401_2
  7. Connolly, J. J., & Viswesvaran, C. (2000). The role of affectivity in job satisfaction: A meta-analysis. Personality and Individual Differences, 29(2), 265-281.
  8. DeNeve, K. M., & Cooper, H. (1998). The happy personality: A meta-analysis of 137 personality traits and subjective well-being. Psychological Bulletin, 124(2), 197-229. doi:10.1037/0033-2909.124.2.197
  9. Judge, T. A., Bono, J. E., & Locke, E. A. (2000). Personality and job satisfaction: The mediating role of job characteristics. Journal of Applied Psychology, 85(2), 237-49. doi:10.1037/0021-9010.85.2.237
  10. Wang, H. I., & Yang, H. L. (2005). The role of personality traits in UTAUT model under online stocking. Contemporary Management Research, 1(1), 69-82.
  11. Gellatly, I. R. (1996). Conscientiousness and task performance: Test of a cognitive process model. Journal of Applied Psychology, 81(5), 474-482. doi:10.1037/0021-9010.81.5.474
  12. Barrick, M. R., & Mount, M. K. (1991). The big five personality dimensions and job performance: A meta-analysis. Personnel Psychology, 44(1), 1-26. doi:10.1111/j.1744-6570.1991.tb00688.x
  13. Courneya, K. S., Friedenreich, C. M., Sela, R. A., Quinney, H. A., & Rhodes, R. E. (2002). Correlates of adherence and contamination in a randomized controlled trial of exercise in cancer survivors: An application of the theory of planned behavior and the five factor model of personality. Annals of Behavioral Medicine, 24(4), 257-268. doi:10.1207/S15324796ABM2404_02
  14. Hough, L. M. (1992). The ‘big five’ personality variables – construct confusion: Description versus prediction. Human Performance, 5(1/2), 139-155. doi:10.1207/s15327043hup0501&2_8
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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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