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Unified theory of acceptance and use of technology

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Title: Unified theory of acceptance and use of technology  
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Subject: Technology acceptance model, Lazy user model, Technological change, Product management, Marketing
Collection: Marketing, Product Management, Technological Change
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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 four key constructs: 1) performance expectancy, 2) effort expectancy, 3) social influence, and 4) facilitating conditions; being the first three direct determinants of usage intention and behavior, and the fourth a direct determinant of use 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. 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 (citation?).


  • Application 1
  • Extension of the theory 2
  • Criticism 3
  • See also 4
  • References 5
  • References 6


  • Koivumäki et al. applied UTAUT to study the perceptions of 243 individuals in northern Finland toward mobile services and technology and found that time spent using the devices did not affect consumer perceptions, but familiarity with the devices and user skills did have an impact.[2]
  • Eckhardt et al. applied UTAUT to study social influence of workplace referent groups (superiors, colleagues) on intention to adopt technology in 152 German companies and found significant impact of social influence from workplace referents on information technology adoption.[3]
  • Curtis et al. applied UTAUT to the adoption of [4]
  • Verhoeven et al. applied UTAUT to study computer use frequency in 714 university freshmen in Belgium and found that UTAUT was also useful in explaining varying frequencies of computer use and differences in information and communication technology skills in secondary school and in the university.[5]

Extension of the theory

  • Lin and Anol postulated an extended model of UTAUT, including the influence of online social support on network information technology usage. They surveyed 317 undergraduate students in Taiwan regarding their online social support in using instant messaging and found that social influence plays an important role in affecting online social support.[6]
  • Sykes et al. proposed a model of acceptance with peer support (MAPS), integrating prior research on individual adoption with research on [7]
  • Wang, Wu, and Wang added two constructs (perceived playfulness and self-management of learning) to the UTAUT in their study of determinants of acceptance of mobile learning in 370 individuals in Taiwan and found that they were significant determinants of behavioral intention to use mobile learning in all respondents.[8]
  • Wang and Wang extended the UTAUT in their study of 343 individuals in Taiwan to determine gender differences in mobile Internet acceptance. They added three constructs – perceived playfulness, perceived value, and palm-sized computer self-efficacy to UTAUT and chose behavioral intention as a dependent variable. They omitted use behavior, facilitating conditions, and experience. Also, since the devices were used in a voluntary context, and they found that most adopters were ages 20–35, they omitted voluntariness and age. Perceived value had a significant influence on adoption intention, and palm-sized computer self-efficacy played a critical role in predicting mobile Internet acceptance. Perceived playfulness, however, did not have a strong influence on behavioral intention, but this may have been due to service or network communication quality issues during the study.[9]


  • Bagozzi critiqued the model and its subsequent extensions, stating “UTAUT is a well-meaning and thoughtful presentation,” but that it presents a model with 41 independent variables for predicting intentions and at least 8 independent variables for predicting behavior,” and that it contributed to the study of technology adoption “reaching a stage of chaos.” He proposed instead a unified theory that coheres the “many splinters of knowledge” to explain decision making.[10]
  • van Raaij and Schepers criticized the UTAUT as being less parsimonious than the previous Technology Acceptance Model and TAM2 because its high R2 is only achieved when moderating key relationships with up to four variables. They also called the grouping and labeling of items and constructs problematic because a variety of disparate items were combined to reflect a single psychometric construct.[11]

See also


  1. ^ Venkatesh et al. 2003
  2. ^ 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
  3. ^ 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
  4. ^ 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
  5. ^ 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
  6. ^ 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
  7. ^ 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.
  8. ^ 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.
  9. ^ 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
  10. ^ Bagozzi 2007
  11. ^ 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


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