Unified theory of acceptance and use of technology

The unified theory of acceptance and use of technology (UTAUT) is a technology acceptance model formulated by Venkatesh and others in "User acceptance of information technology: Toward a unified view".[1] The UTAUT aims to explain user intentions to use an information system and subsequent usage behavior. The theory holds that there are four key constructs: 1) performance expectancy, 2) effort expectancy, 3) social influence, and 4) facilitating conditions.

The first three are direct determinants of usage intention and behavior, and the fourth is a direct determinant of user behavior. Gender, age, experience, and voluntariness of use are posited to moderate the impact of the four key constructs on usage intention and behavior. The theory was developed through a review and consolidation of the constructs of eight models that earlier research had employed to explain information systems usage behaviour (theory of reasoned action, technology acceptance model, motivational model, theory of planned behavior, a combined theory of planned behavior/technology acceptance model, model of personal computer use, diffusion of innovations theory, and social cognitive theory). Subsequent validation by Venkatesh et al. (2003) of UTAUT in a longitudinal study found it to account for an impressive 70% of the variance in Behavioural Intention to Use (BI) and about 50% in actual use.[2]

Application

Extension of the theory

Criticism

See also

References

  1. Venkatesh et al. 2003
  2. Venkatesh, Viswanath; Morris, Michael G.; Davis, Gordon B.; Davis, Fred D. (2003-01-01). "User Acceptance of Information Technology: Toward a Unified View". MIS Quarterly. 27 (3): 425–478. JSTOR 30036540.
  3. T. Koivimäki, A. Ristola, and M. Kesti, “The perceptions towards mobile services: An empirical analysis of the role of use facilitators,” Personal & Ubiquitous Computing, vol. 12, no. 1, pp. 67–75, 2008
  4. A. Eckhardt, S. Laumer, and T. Weitzel, “Who influences whom? Analyzing workplace referents’ social influence on IT adoption and non-adoption,” Journal of Information Technology, vol. 24, no. 1, pp. 11–24, 2009
  5. L. Curtis, C. Edwards, K. L. Fraser, S. Gudelsky, J. Holmquist, K. Thornton, and K. D. Sweetser, “Adoption of social media for public relations by nonprofit organizations,” Public Relations Review, vol. 36, no. 1, pp. 90–92, 2010
  6. J. C. Verhoeven, D. Heerwegh, and K. De Wit, “Information and communication technologies in the life of university freshmen: An analysis of change,” Computers & Education, vol. 55, no. 1, pp. 53–66, 2010
  7. C.-P. Lin and B. Anol, “Learning online social support: An investigation of network information technology,” CyberPsychology & Behavior, vol. 11, no. 3, pp. 268–272, 2008
  8. T. A. Sykes, V. Venkatesh, and S. Gosain, “Model of acceptance with peer support: A social network perspective to understand employees’ system use,” MIS Quarterly, vol. 33, no. 2, pp. 371–393, 2009.
  9. Y.-S. Wang, M.-C. Wu, and H.-Y. Wang, “Investigating the determinants and age and gender differences in the acceptance of mobile learning,” British Journal of Educational Technology, vol. 40, no. 1., pp. 92–118, 2009.
  10. H.-W. Wang and S.-H. Wang, “User acceptance of mobile Internet based on the Unified Theory of Acceptance and Use of Technology: Investigating the determinants and gender differences,” Social Behavior & Personality: An International Journal, vol. 38, no. 3, pp. 415–426, 2010
  11. Bagozzi 2007
  12. E. M. van Raaij, and J. J. L. Schepers, “The acceptance and use of a virtual learning environment in China,” Computers & Education, vol. 50, no. 3, pp. 838–852, 2008
This article is issued from Wikipedia - version of the 11/14/2016. The text is available under the Creative Commons Attribution/Share Alike but additional terms may apply for the media files.