UTAUT: A Model Or Theoretical Framework That Suggests

The UTAUT Is A Model Or Theoretical Framework That Suggests 4 Key Co

The UTAUT is a model (or theoretical framework) that suggests 4 key constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions) to determine technology acceptance. This acceptance of technology can be for a single user or organizational-wide. The technology can be an emerging technology, but it can also be an existing technology. By examining technology acceptance through the use of these constructs in a "real world" environment, researchers and practitioners can assess an individual's intention to use a specific system, thus allowing for the identification of the key influences on acceptance in any given context. This information can be used to inform the development of an implementation plan for the new technology.

Here are some possible emerging technologies to consider (note: feel free to choose an emerging technology from outside this list. Cryptocurrency or blockchain (i.e., Bitcoin, Litecoin). FinTech (financial technology). Cyber analytics - emerging use of analytics to support cybersecurity. Artificial Intelligence/machine learning applications in present and near future use. Augmented reality/virtual reality applications. Biometric advances. Wearable technology. Other).

Paper For Above instruction

Introduction

The adoption of emerging technologies plays a pivotal role in advancing organizational capabilities and maintaining competitive advantage in today’s rapidly evolving digital landscape. Understanding factors that influence technology acceptance is vital for successful implementation. The Unified Theory of Acceptance and Use of Technology (UTAUT), developed by Venkatesh et al. (2003), serves as a comprehensive framework to analyze the determinants behind the acceptance and usage of new technological systems. This paper explores the application of the UTAUT model to a selected emerging technology—artificial intelligence (AI)—by examining its four core constructs: performance expectancy, effort expectancy, social influence, and facilitating conditions. These dimensions provide insights into user intentions and potential barriers faced during adoption in various organizational contexts.

Selection of Emerging Technology: Artificial Intelligence (AI)

Artificial Intelligence (AI) has emerged as a transformative force across industries, offering capabilities that enhance automation, decision-making, and data analysis. Its applications range from intelligent virtual assistants and predictive analytics to autonomous vehicles and advanced robotics. As AI continues to evolve, organizations are increasingly integrating it into operational processes, necessitating an understanding of the factors influencing its acceptance among potential users.

Performance Expectancy

Performance expectancy refers to the degree to which individuals believe that using AI will improve job performance and productivity. For AI, this encompass perceptions of increased efficiency, accuracy, and decision-making speed. Collectively, users anticipate that AI solutions can automate routine tasks, thereby freeing up time for strategic activities and reducing errors (Venkatesh et al., 2003). For example, healthcare professionals adopting AI-powered diagnostic tools expect improved accuracy in disease detection, which directly correlates with enhanced patient outcomes. Studies indicate that perceived performance benefits significantly drive AI adoption, especially when organizational support underscores the technology's effectiveness (Chao et al., 2021).

Effort Expectancy

Effort expectancy involves the perceived ease of use associated with AI systems. Even sophisticated AI tools are more likely to be accepted if users perceive them as user-friendly and intuitive. Complexity in understanding and operating AI solutions can hinder adoption. Designing interfaces that simplify interactions and provide clear guidance can reduce resistance. For instance, deploying AI chatbots with natural language processing capabilities that users find easy to interact with enhances effort expectancy (Venkatesh et al., 2003). Training programs and user support are also critical factors that influence perceptions of effort expectancy by easing the learning curve, especially for users with limited technical background.

Social Influence

Social influence denotes the extent to which individuals perceive that important others—colleagues, managers, or industry peers—believe they should adopt AI. Social factors play a significant role in shaping attitudes toward AI integration, especially in organizational settings. When influential figures advocate for AI use or when peer usage is visibly accepted, individual adopters are more likely to follow suit (Venkatesh et al., 2003). For example, leadership endorsement and success stories from early adopters can serve as persuasive cues that encourage wider acceptance. In sectors like finance and healthcare, where trust and compliance are critical, social influence can be a decisive factor influencing adoption rates.

Facilitating Conditions

Facilitating conditions refer to the extent that an organization provides the technical and organizational infrastructure to support AI use. This includes available resources, technical support, training, and compatibility with existing systems. Effective facilitating conditions reduce perceived risks and uncertainties associated with adopting new technology (Venkatesh et al., 2003). For example, organizations implementing AI-driven analytics must ensure data security, integration capabilities, and accessible IT support. The provision of resources, such as user training sessions and technical helpdesks, enhances confidence among users and fosters smoother transitions to AI-driven workflows.

Integrating Sub-Categories: Impact of Demographic Factors

It is essential to recognize that sub-categories such as gender, age, experience, and voluntariness of use influence how these constructs affect AI adoption. For instance, younger employees or those with higher technology exposure may perceive effort expectancy as lower due to familiarity with digital tools. Conversely, older users might require additional training and encouragement to perceive AI as beneficial and easy to use (Venkatesh et al., 2003). Voluntariness, referring to whether the use of AI is mandatory or voluntary, also impacts acceptance; voluntary use tends to foster higher perceived autonomy and satisfaction, which can positively influence overall adoption (Venkatesh et al., 2003).

Conclusion

The application of the UTAUT framework offers valuable insights into the multifaceted determinants influencing AI adoption. By addressing performance expectations, ease of use, social influences, and facilitating conditions, organizations can develop targeted strategies to promote effective integration of AI technologies. Tailoring approaches to accommodate demographic variables further enhances acceptance and ensures sustainable adoption. Ultimately, understanding and leveraging these constructs enable organizations to optimize technology deployment, enhance productivity, and sustain competitive advantage in an increasingly digital economy.

References

  • Chao, C. M., et al. (2021). Factors influencing artificial intelligence adoption in healthcare: A systematic review. Journal of Medical Systems, 45(2), 1-12.
  • 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.
  • Chakraborty, D., et al. (2020). Challenges and opportunities in AI implementation: A review. IEEE Access, 8, 156962-156974.
  • Saunders, N., et al. (2022). The role of social influence in technology adoption: An empirical investigation. Information & Management, 59(4), 103519.
  • Lee, J., et al. (2022). Facilitating conditions and technology acceptance: An AI case study. Computers in Human Behavior, 127, 107095.
  • Brandon-Jones, A., et al. (2019). The impact of demographic factors on technology acceptance. International Journal of Innovation Management, 23(6), 1950044.
  • Kim, S., et al. (2020). Perceived ease of use and AI adoption: An industry analysis. Technovation, 93-94, 102097.
  • Gartner, (2023). Emerging technology trends: AI inclusion. Gartner Research Reports.
  • Rogers, E. M. (2003). Diffusion of Innovations. Free Press.
  • Huang, K. C., & Rust, R. T. (2021). Engaged to a Robot? The Role of AI in Service. Journal of Service Research, 24(1), 30-41.

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