Unified theory of acceptance and use of technology

Last updated September 27, 2024

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]^[2] 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 70% of the variance in Behavioural Intention to Use (BI) and about 50% in actual use.^[1]

Application

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.^[3]
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.^[4]
Curtis et al. applied UTAUT to the adoption of social media by 409 United States nonprofit organizations. UTAUT had not been previously applied to the use of social media in public relations. They found that organizations with defined public relations departments are more likely to adopt social media technologies and use them to achieve their organizational goals. Women considered social media to be beneficial, and men exhibited more confidence in actively utilizing social media.^[5]
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.^[6]
Welch et al. applied UTAUT to study factors contributing to Mobile learning adoption among 118 museum staff in England. UTAUT had not been previously applied to the use of just-in-time knowledge interventions to development technological knowledge within the museum sector. They found that UTAUT was useful in explaining the determinants of mobile learning adoption.^[7]

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.^[8]
Sykes et al. proposed a model of acceptance with peer support (MAPS), integrating prior research on individual adoption with research on social networks in organizations. They conducted a 3-month-long study of 87 employees in one organization and found that studying social network constructs can aid in understanding new information system use.^[9]
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.^[10]
Hewitt et al. extended the UTAUT to study the acceptance of autonomous vehicles. Two separate surveys of 57 and 187 individuals in the USA showed that users were less accepting of high autonomy levels and displayed significantly lower intention to use highly autonomous vehicles.^[11]
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. .l. 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.^[12]
Cheng-Min Chao developed and empirically tested a model to predict the factors affecting students' behavioral intentions toward using mobile learning (m-learning). The study applied the extended unified theory of acceptance and use of technology (UTAUT) model with the addition of perceived enjoyment, mobile self-efficacy, satisfaction, trust, and perceived risk moderators. The study collected data from 1562 respondents to conduct a cross-sectional study and employed a research model based on multiple technology acceptance theories.^[13]
Cimperman et al. developed an extended UTAUT model to analyze the acceptance rate of home telehealth services among older adults. The extended UTAUT model has six predictors and were empirically tested to be effective when predicting how a certain behaviour influences acceptance rate. ^[14]

Criticism

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.^[15]
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.^[16]
Li suggested that using moderators to artificially achieve high R² in UTAUT is unnecessary and also impractical for understanding organizational technology adoption, and demonstrated that good predictive power can be achieved even with simple models when proper initial screening procedures are applied. The results provide insights for organizational research design under practical business settings.^[17]

Related Research Articles

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The technology acceptance model (TAM) is an information systems theory that models how users come to accept and use a technology.

Uses and gratifications theory is a communication theory that describes the reasons and means by which people seek out media to meet specific needs. The theory postulates that media is a highly available product, that audiences are the consumers of the product, and that audiences choose media to satisfy given needs as well as social and psychological uses, such as knowledge, relaxation, social relationships, and diversion.

Diffusion of innovations is a theory that seeks to explain how, why, and at what rate new ideas and technology spread. The theory was popularized by Everett Rogers in his book Diffusion of Innovations, first published in 1962. Rogers argues that diffusion is the process by which an innovation is communicated through certain channels over time among the participants in a social system. The origins of the diffusion of innovations theory are varied and span multiple disciplines.

The theory of planned behaviour (TPB) is a psychological theory that links beliefs to behavior. The theory maintains that three core components, namely, attitude, subjective norms, and perceived behavioural control, together shape an individual's behavioral intentions. In turn, a tenet of TPB is that behavioral intention is the most proximal determinant of human social behavior.

The elaboration likelihood model (ELM) of persuasion is a dual process theory describing the change of attitudes. The ELM was developed by Richard E. Petty and John Cacioppo in 1980. The model aims to explain different ways of processing stimuli, why they are used, and their outcomes on attitude change. The ELM proposes two major routes to persuasion: the central route and the peripheral route.

Media richness theory (MRT), sometimes referred to as information richness theory, is a framework used to describe a communication medium's ability to reproduce the information sent over it. It was introduced by Richard L. Daft and Robert H. Lengel in 1986 as an extension of information processing theory. MRT is used to rank and evaluate the richness of certain communication media, such as phone calls, video conferencing, and email. For example, a phone call cannot reproduce visual social cues such as gestures which makes it a less rich communication media than video conferencing, which affords the transmission of gestures and body language. Based on contingency theory and information processing theory, MRT theorizes that richer, personal communication media are generally more effective for communicating equivocal issues in contrast with leaner, less rich media.

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Behavioural change theories are attempts to explain why human behaviours change. These theories cite environmental, personal, and behavioural characteristics as the major factors in behavioural determination. In recent years, there has been increased interest in the application of these theories in the areas of health, education, criminology, energy and international development with the hope that understanding behavioural change will improve the services offered in these areas. Some scholars have recently introduced a distinction between models of behavior and theories of change. Whereas models of behavior are more diagnostic and geared towards understanding the psychological factors that explain or predict a specific behavior, theories of change are more process-oriented and generally aimed at changing a given behavior. Thus, from this perspective, understanding and changing behavior are two separate but complementary lines of scientific investigation.

<span class="mw-page-title-main">Health belief model</span> Psychological model

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Media naturalness theory is also known as the psychobiological model. The theory was developed by Ned Kock and attempts to apply Darwinian evolutionary principles to suggest which types of computer-mediated communication will best fit innate human communication capabilities. Media naturalness theory argues that natural selection has resulted in face-to-face communication becoming the most effective way for two people to exchange information.

Social presence theory explores how the "sense of being with another" is influenced by digital interfaces in human-computer interactions. Developed from the foundations of interpersonal communication and symbolic interactionism, social presence theory was first formally introduced by John Short, Ederyn Williams, and Bruce Christie in The Social Psychology of Telecommunications. Research on social presence theory has recently developed to examine the efficacy of telecommunications media, including SNS communications. The theory notes that computer-based communication is lower in social presence than face-to-face communication, but different computer-based communications can affect the levels of social presence between communicators and receivers.

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The reasoned action approach (RAA) is an integrative framework for the prediction of human social behavior. The reasoned action approach states that attitudes towards the behavior, perceived norms, and perceived behavioral control determine people's intentions, while people's intentions predict their behaviors.

A Behavioral Change Support System (BCSS) is any information and communications technology (ICT) tool, web platform, or gamified environment which targets behavioral changes in its end-users. BCSS are built upon persuasive systems design techniques.

References

1 2 Venkatesh, Viswanath; Morris, Michael G.; Davis, Gordon B.; Davis, Fred D. (2003). "User Acceptance of Information Technology: Toward a Unified View". MIS Quarterly. 27 (3): 425–478. doi:10.2307/30036540. JSTOR 30036540. S2CID 14435677.
↑ Menon, Devadas; Shilpa, K (November 2023). ""Chatting with ChatGPT": Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model". Heliyon. 9 (11): e20962. doi: 10.1016/j.heliyon.2023.e20962 . ISSN 2405-8440. PMC 10623159 . PMID 37928033.
↑ Koivimäki, T.; Ristola, A.; Kesti, M. (2007). "The perceptions towards mobile services: An empirical analysis of the role of use facilitators". Personal and Ubiquitous Computing. 12 (1): 67–75. doi:10.1007/s00779-006-0128-x. S2CID 6089360.
↑ Eckhardt, A.; Laumer, S.; Weitzel, T. (2009). "Who influences whom? Analyzing workplace referents' social influence on IT adoption and non-adoption". Journal of Information Technology. 24 (1): 11–24. doi:10.1057/jit.2008.31. S2CID 42420244.
↑ Curtis, L.; Edwards, C.; Fraser, K. L.; Gudelsky, S.; Holmquist, J.; Thornton, K.; Sweetser, K. D. (2010). "Adoption of social media for public relations by nonprofit organizations". Public Relations Review. 36 (1): 90–92. doi:10.1016/j.pubrev.2009.10.003. S2CID 154466947.
↑ Verhoeven, J. C.; Heerwegh, D.; De Wit, K. (2010). "Information and communication technologies in the life of university freshmen: An analysis of change". Computers & Education. 55 (1): 53–66. doi:10.1016/j.compedu.2009.12.002.
↑ Welch, Ruel; Alade, Temitope; Nichol, Lynn (2020). "USING THE UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY (UTAUT) MODEL TO DETERMINE FACTORS AFFECTING MOBILE LEARNING ADOPTION IN THE WORKPLACE: A STUDY OF THE SCIENCE MUSEUM GROUP" (PDF). International Journal on Computer Science and Information Systems. 15 (1): 85–98. Retrieved 4 June 2021.
↑ Lin, C.-P.; Anol, B. (2008). "Learning online social support: An investigation of network information technology". CyberPsychology & Behavior. 11 (3): 268–272. doi:10.1089/cpb.2007.0057. PMID 18537495.
↑ Sykes, T. A.; Venkatesh, V.; Gosain, S. (2009). "Model of acceptance with peer support: A social network perspective to understand employees' system use". MIS Quarterly. 33 (2): 371–393. doi:10.2307/20650296. JSTOR 20650296.
↑ Wang, Y.-S.; Wu, M.-C.; Wang, H.-Y. (2009). "Investigating the determinants and age and gender differences in the acceptance of mobile learning". British Journal of Educational Technology. 40 (1): 92–118. doi:10.1111/j.1467-8535.2007.00809.x. S2CID 7092931.
↑ Hewitt, Charlie; Politis, Ioannis; Amanatidis, Theocharis; Sarkar, Advait (2019-03-17). "Assessing public perception of self-driving cars". Proceedings of the 24th International Conference on Intelligent User Interfaces. Marina del Ray California: ACM. pp. 518–527. doi:10.1145/3301275.3302268. ISBN 978-1-4503-6272-6. S2CID 67773581.
↑ Wang, H.-W.; Wang, S.-H. (2010). "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. 38 (3): 415–426. doi:10.2224/sbp.2010.38.3.415.
↑ Chao, Cheng-Min (2019). "Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model". Frontiers in Psychology. 10: 1652. doi: 10.3389/fpsyg.2019.01652 . ISSN 1664-1078. PMC 6646805 . PMID 31379679.
↑ Cimperman, Miha; Makovec Brenčič, Maja; Trkman, Peter (2016-06-01). "Analyzing older users' home telehealth services acceptance behavior—applying an Extended UTAUT model". International Journal of Medical Informatics. 90: 22–31. doi:10.1016/j.ijmedinf.2016.03.002. ISSN 1386-5056. PMID 27103194.
↑ Bagozzi, R.P. (2007). "The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift". Journal of the Association for Information Systems. 8 (4): 244–254. doi: 10.17705/1jais.00122 .
↑ van Raaij, E. M.; Schepers, J. J. L. (2008). "The acceptance and use of a virtual learning environment in China". Computers & Education. 50 (3): 838–852. doi:10.1016/j.compedu.2006.09.001. S2CID 5676829.
↑ Li, Jerry (2020), "Blockchain technology adoption: Examining the Fundamental Drivers", Proceedings of the 2nd International Conference on Management Science and Industrial Engineering, ACM Publication, April 2020, pp. 253–260. doi : 10.1145/3396743.3396750

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[Venkatesh_2003-1] 1 2 Venkatesh, Viswanath; Morris, Michael G.; Davis, Gordon B.; Davis, Fred D. (2003). "User Acceptance of Information Technology: Toward a Unified View". MIS Quarterly. 27 (3): 425–478. doi:10.2307/30036540. JSTOR 30036540. S2CID 14435677.

[2] Menon, Devadas; Shilpa, K (November 2023). ""Chatting with ChatGPT": Analyzing the factors influencing users' intention to Use the Open AI's ChatGPT using the UTAUT model". Heliyon. 9 (11): e20962. doi: 10.1016/j.heliyon.2023.e20962 . ISSN 2405-8440. PMC 10623159 . PMID 37928033.

[3] Koivimäki, T.; Ristola, A.; Kesti, M. (2007). "The perceptions towards mobile services: An empirical analysis of the role of use facilitators". Personal and Ubiquitous Computing. 12 (1): 67–75. doi:10.1007/s00779-006-0128-x. S2CID 6089360.

[4] Eckhardt, A.; Laumer, S.; Weitzel, T. (2009). "Who influences whom? Analyzing workplace referents' social influence on IT adoption and non-adoption". Journal of Information Technology. 24 (1): 11–24. doi:10.1057/jit.2008.31. S2CID 42420244.

[5] Curtis, L.; Edwards, C.; Fraser, K. L.; Gudelsky, S.; Holmquist, J.; Thornton, K.; Sweetser, K. D. (2010). "Adoption of social media for public relations by nonprofit organizations". Public Relations Review. 36 (1): 90–92. doi:10.1016/j.pubrev.2009.10.003. S2CID 154466947.

[6] Verhoeven, J. C.; Heerwegh, D.; De Wit, K. (2010). "Information and communication technologies in the life of university freshmen: An analysis of change". Computers & Education. 55 (1): 53–66. doi:10.1016/j.compedu.2009.12.002.

[7] Welch, Ruel; Alade, Temitope; Nichol, Lynn (2020). "USING THE UNIFIED THEORY OF ACCEPTANCE AND USE OF TECHNOLOGY (UTAUT) MODEL TO DETERMINE FACTORS AFFECTING MOBILE LEARNING ADOPTION IN THE WORKPLACE: A STUDY OF THE SCIENCE MUSEUM GROUP" (PDF). International Journal on Computer Science and Information Systems. 15 (1): 85–98. Retrieved 4 June 2021.

[8] Lin, C.-P.; Anol, B. (2008). "Learning online social support: An investigation of network information technology". CyberPsychology & Behavior. 11 (3): 268–272. doi:10.1089/cpb.2007.0057. PMID 18537495.

[9] Sykes, T. A.; Venkatesh, V.; Gosain, S. (2009). "Model of acceptance with peer support: A social network perspective to understand employees' system use". MIS Quarterly. 33 (2): 371–393. doi:10.2307/20650296. JSTOR 20650296.

[10] Wang, Y.-S.; Wu, M.-C.; Wang, H.-Y. (2009). "Investigating the determinants and age and gender differences in the acceptance of mobile learning". British Journal of Educational Technology. 40 (1): 92–118. doi:10.1111/j.1467-8535.2007.00809.x. S2CID 7092931.

[11] Hewitt, Charlie; Politis, Ioannis; Amanatidis, Theocharis; Sarkar, Advait (2019-03-17). "Assessing public perception of self-driving cars". Proceedings of the 24th International Conference on Intelligent User Interfaces. Marina del Ray California: ACM. pp. 518–527. doi:10.1145/3301275.3302268. ISBN 978-1-4503-6272-6. S2CID 67773581.

[12] Wang, H.-W.; Wang, S.-H. (2010). "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. 38 (3): 415–426. doi:10.2224/sbp.2010.38.3.415.

[13] Chao, Cheng-Min (2019). "Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model". Frontiers in Psychology. 10: 1652. doi: 10.3389/fpsyg.2019.01652 . ISSN 1664-1078. PMC 6646805 . PMID 31379679.

[14] Cimperman, Miha; Makovec Brenčič, Maja; Trkman, Peter (2016-06-01). "Analyzing older users' home telehealth services acceptance behavior—applying an Extended UTAUT model". International Journal of Medical Informatics. 90: 22–31. doi:10.1016/j.ijmedinf.2016.03.002. ISSN 1386-5056. PMID 27103194.

[Bagozzi_2007-15] Bagozzi, R.P. (2007). "The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift". Journal of the Association for Information Systems. 8 (4): 244–254. doi: 10.17705/1jais.00122 .

[16] van Raaij, E. M.; Schepers, J. J. L. (2008). "The acceptance and use of a virtual learning environment in China". Computers & Education. 50 (3): 838–852. doi:10.1016/j.compedu.2006.09.001. S2CID 5676829.

[Li2020-17] Li, Jerry (2020), "Blockchain technology adoption: Examining the Fundamental Drivers", Proceedings of the 2nd International Conference on Management Science and Industrial Engineering, ACM Publication, April 2020, pp. 253–260. doi : 10.1145/3396743.3396750

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