The Unified Theory of Acceptance and Use of technological (UTAUT) analyzes technological acceptance, influenced by performance expectancy, effort expectancy, social influence, and facilitating factors.
The expansion of the e-commerce industry and the advent of digital technologies, including big data, artificial intelligence, cloud computing, and robots, propel the adoption of new technology within organizations. Venkatesh, Morris, Davis & Davis (2003) [1] aimed to provide a comprehensive theory of technology acceptance by synthesizing essential characteristics that forecast behavioral intention and usage.
The UTAUT theoretical model posits that technology usage is influenced by behavioral intention. The perceived probability of technology adoption relies on the direct impact of four essential constructs: performance expectancy, effort expectancy, social influence, and facilitating factors. The influence of predictors is mitigated by age, gender, experience, and voluntary usage.
Venkatesh et. al. (2003) compared eight leading adoption models (TRA, TAM, TPB, DOI, MPCU, SCT, MM, C-TAM-TPB) and fused their most predictive constructs into one framework. Their longitudinal field study across four organisations showed UTAUT explained ≈ 70 % of the variance in behavioural intention and ≈ 50 % in actual use—far higher than any single predecessor.[1]
The various factors which build our intentions and are related to behaviour.
Figure: UTAUT 1 (Venkatesh et al., 2003)
1.1 UTAUT 1
| Construct | Definitions[2] | Simple example | Typical survey item* |
| Performance Expectancy (PE) | Performance expectancy is defined as “the degree to which an individual believes that using the system will help him or her to attain gains in job performance.” | “Will this tech help me achieve results?” | Using the system improves my job performance. |
| Effort Expectancy (EE) | Effort expectancy is defined as “the degree of ease associated with the use of the system.” | “How easy will it be?” | Learning to operate the system is easy for me. |
| Social Influence (SI) | Social Influence is defined as “the degree to which an individual perceives that important others believe he or she should use the new system.” | “Do people who matter think I should use it?” | My manager expects me to use the system. |
| Facilitating Conditions (FC) | Facilitating conditions is defined as “the degree to which an individual believes that an organisation’s and technical infrastructure exists to support the use of the system.” | “Do I have the resources & support?” | I have the necessary resources to use the system. |
PE, EE and SI → Behavioural Intention (BI) → Use Behaviour (UB).
FC + BI also predict UB directly. Four moderators—age, gender, experience, voluntariness—change the strength of each link (e.g., EE matters more to older novices).[3]
*All items were validated with structural-equation modelling in the original article.
Key takeaways:
- Strive to develop a unified model that encapsulates the core of eight prior innovation acceptance models.
- Aims to provide a comprehensive understanding of all aspects influencing behavioral intention about the adoption of new technologies.
- Developed to assess the implementation of a novel technology within an enterprise (not intended for the consumer market).
1.2 UTAUT 2
UTAUT 2 — the consumer-centric upgrade to UTAUT
| Original UTAUT (2003) | UTAUT 2 (2012) | |
| Context | Organisational / workplace | Everyday consumer technologies (m-commerce, wearables, e-wallets) |
| Core drivers of intention | Performance Expectancy, Effort Expectancy, Social Influence | + Hedonic Motivation (fun), Price Value (cost–benefit), Habit (automaticity) |
| Moderators | Age, gender, experience, voluntariness | Keeps age–gender–experience; drops voluntariness (consumer use is typically voluntary) |
| Explained variance (BI) | ≈ 70 % | ≈ 74 % in the original validation across mobile-payment & m-commerce scenarios.[4] |
Why the extension was needed
Venkatesh, Thong & Xu found that factors like enjoyment and price sensitivity—weak in office roll-outs—drive consumer tech uptake. By weaving these into UTAUT, the model gained predictive power without ballooning the survey length.
The three new constructs
| Construct | Definition | Typical item |
| Hedonic Motivation (HM) | The sheer fun or pleasure from using the tech | “Using the app is enjoyable.” |
| Price Value (PV) | Net value after weighing monetary cost against benefits | “The subscription fee is acceptable given the convenience.” |
| Habit (HT) | The extent to which past use makes future use automatic | “Using contactless pay has become a routine for me.” |
HM and PV shape Behavioural Intention; Habit influences both intention and actual use. Age and gender moderate all three relationships—e.g., HM’s pull is stronger for young users, PV for older price-conscious segments.
1.3 UTAUT: A synthesis of extensions
- Endeavor to augment the elucidated variance of UTAUT and UTAUT 2
- Developed for the contexts of both employees and consumers.
| Level | What’s added | Why researchers add it | Typical domains |
| New belief constructs | Trust / Perceived Security & Privacy Perceived Risk Computer / Mobile Self-Efficacy Satisfaction / Service Quality | Core UTAUT assumes organisational infrastructures and clear accountability; consumers and patients do not. Extra beliefs capture vulnerability and confidence issues that dominate adoption decisions outside the workplace. | m-banking & FinTech, tele-health, social commerce, government portals |
| Context moderators | Culture (individualism–collectivism, uncertainty avoidance) Income / Education Regulatory climate | Cross-country meta-analyses show the weight of Social Influence and Facilitating Conditions swings with cultural tightness, resources and legal protections. | Cross-national studies, low- and middle-income country (LMIC) settings |
| Outcome extensions | Use Continuance / Loyalty Word-of-Mouth Intentions | For apps & platforms, first-time use is only half the battle; scholars track habit formation, churn and advocacy. | Subscription apps, e-learning MOOCs, social platforms |
Adaptations
| Sector | High-impact add-ons | Illustrative findings |
| Mobile Health (m-health) | Trust, Perceived Risk, Data Privacy | Adding these lifted explained variance in BI from 64 % → 78 % among Indian urban users.[5] |
| E-Government | Institutional Trust, Perceived Service Quality | “Trust-integrated UTAUT” predicted portal uptake 2× better than classic UTAUT in China’s digital-tax service.[6] |
| Social Commerce | Social Support, Anxiety, Innovativeness | Meta-UTAUT model explained 71 % of adoption intention in a Bangladesh S-commerce study.[7] |
| Higher Education LMS | Computer Self-Efficacy, Facilitating Conditions, Hedonic Motivation | Systematic review finds self-efficacy consistently amplifies Effort Expectancy → BI path.[8] |
References
[1] Venkatesh, V., Morris, M. G., & Davis, G. B. D. a. F. D. (2003). User acceptance of information Technology: toward a unified view. www.jstor.org. https://doi.org/10.2307/30036540https://www.jstor.org/stable/30036540
[2] Marikyan, D., & Papagiannidis, S. (2023). Unified Theory of Acceptance and Use of Technology. https://open.ncl.ac.uk/theory-library/unified-theory-of-acceptance-and-use-of-technology.pdf
[3] Same as 1.
[4] Venkatesh, V., & Xu, J. Y. L. T. a. X. (2012). Consumer Acceptance and use of Information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
[5] Mensah, I. K., Zeng, G., & Mwakapesa, D. S. (2022). The behavioral intention to adopt mobile health services: The moderating impact of mobile self-efficacy. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.1020474
[6] Dwivedi, Y. K., Rana, N. P., Kuttimani Tamilmani, & Raman, R. (2020). A meta-analysis based modified unified theory of acceptance and use of technology (meta-UTAUT): a review of emerging literature. Current Opinion in Psychology, 36, 13–18. https://doi.org/10.1016/j.copsyc.2020.03.008
[7] Sarker, P., Hughes, L., Malik, T., & Dwivedi, Y. K. (2025). Examining consumer adoption of social commerce: An extended META-UTAUT model. Technological Forecasting and Social Change, 212, 123956. https://doi.org/10.1016/j.techfore.2024.123956
[8] Xue, L., Rashid, A. M., & Ouyang, S. (2024). The Unified Theory of Acceptance and Use of Technology (UTAUT) in Higher Education: A Systematic review. SAGE Open, 14(1). https://doi.org/10.1177/21582440241229570
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