In our introductory article, we established the landscape of technology adoption through the lens of a trifecta: Organizational, User, and Consumer Adoption. We positioned Organizational Adoption—the C-Suite’s strategic focus—as the apex. We now turn our attention to the other two domains, which together form the deeply human side of the adoption equation: the internal employee’s decision to use a new system and the external customer’s choice to integrate a technology into their lives.
This branch of our series, The User’s Journey, delves into the rich intellectual history of individual technology acceptance and use. We will explore the evolution of the models designed to explain and predict the most fundamental of behaviors: the decision by a person to either embrace or reject a new technology.
The Core Question: Deconstructing the “Acceptance” Decision
At the heart of this entire field of study lies a foundational puzzle: What are the key cognitive levers that determine whether an individual will accept and use a new technology? Is it a purely rational calculation of costs and benefits? Is it the influence of peers and managers? Or is it a deeper, more personal assessment of one’s own capabilities and the intrinsic enjoyment derived from the experience?
The answer, as decades of research have shown, is a complex interplay of all these factors. Understanding this decision-making process is not merely an academic exercise; it is critical for realizing the value of any technological investment. A perfectly designed enterprise system is worthless if employees refuse to use it, and a groundbreaking consumer app will fail if customers do not see its value or find it too difficult to learn. This branch traces the scholarly quest to identify, measure, and understand these critical human factors.
From Psychology to Practice: A Narrative Arc
The intellectual history of individual adoption models follows a clear and fascinating narrative arc, moving from the general to the specific and from fragmentation to synthesis.
- The Foundational “Grandparents”: The earliest attempts to model this behavior did not originate in information systems research but drew from a rich tapestry of established theories. Social psychology gave us the powerful intention-behavior link through the Theory of Reasoned Action (TRA) [1] and the Theory of Planned Behavior (TPB) [2]. Sociology provided the Diffusion of Innovations (DOI) Theory [3], which described how ideas spread through social systems. Other key building blocks included Social Cognitive Theory (SCT) [4], which highlighted the role of self-efficacy, and the Motivational Model (MM) [5], which explored intrinsic drivers for technology use. These culminated in early hybrid models like the Model of PC Utilization (MPCU) [6], which attempted to synthesize these diverse perspectives.
- The Watershed Moment: The field was revolutionized by the development of the Technology Acceptance Model (TAM) [7]. It offered a powerful and, crucially, parsimonious explanation for technology use, focusing on two core beliefs: Perceived Usefulness and Perceived Ease of Use. For decades, TAM became the dominant theoretical lens for researchers.
- Expansion and Unification: Following TAM’s success, the next period was characterized by expansion and refinement. Researchers extended the original model to increase its explanatory power, resulting in TAM 2 [8] and TAM 3 [9]. This era of energetic model-building eventually culminated in a landmark effort to synthesize the core elements of eight competing models into a single, Unified Theory of Acceptance and Use of Technology (UTAUT) [10].
- Broadening the Lens: Context and Personality: With a unified model established for organizational users, research began to branch out. Scholars adapted the unified theory for the consumer world, resulting in UTAUT2 [11], which added crucial constructs like hedonic motivation and price value. Concurrently, other researchers argued that general models were insufficient, developing specialized frameworks like the Task-Technology Fit (TTF) Model [12], which emphasizes the alignment of a tool with a user’s specific job functions. Finally, another stream of research looked inward, proposing that adoption is heavily influenced by an individual’s innate personality traits toward technology, leading to the Technology Readiness Index (TRI) [13].
Roadmap for this Branch
This narrative provides the structure for the articles to come. Our exploration of the user’s journey is organized as follows:
First, we’ll look at the foundational theories. Then, we’ll do a deep dive into TAM, the model that changed everything. From there, we’ll explore its direct successors before examining the ambitious UTAUT model that sought to unify the field. After establishing this core lineage, our focus will broaden to see how these theories were adapted for the consumer context, explore specialized models where context is king, and finally, consider the crucial role of an individual’s innate readiness for technology.
This journey will provide a comprehensive understanding of how the field has evolved, from its psychological roots to the sophisticated, unified models used today. In our next article, we will begin at the beginning, with a deep dive into the bedrock theories that made everything else possible.
The Complete Branch: A Glance Ahead
- Article 1.1: The Bedrock – Foundational Theories That Shaped Tech Acceptance
- Article 1.2: The Game Changer – A Deep Dive into the Technology Acceptance Model (TAM)
- Article 1.3: Expanding the Classic – The Evolution to TAM 2, TAM 3, and C-TAM-TPB
- Article 1.4: The Grand Unification – The Unified Theory of Acceptance and Use of Technology (UTAUT)
- Article 1.5: Beyond the Office – UTAUT2, Consumer Context, and Modern Syntheses
- Article 1.6: Context is King – Specialized Individual Adoption Models
- Article 1.7: Are You Ready? The Role of Technology Readiness (TRI & TRAM)
References
[1] Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley.
[2] Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
[3] Rogers, E. M. (1962). Diffusion of Innovations. Free Press of Glencoe.
[4] Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall.
[5] Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111-1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x
[6] Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: Toward a conceptual model of utilization. MIS Quarterly, 15(1), 125-143. https://doi.org/10.2307/249443
[7] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
[8] Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
[9] Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
[10] 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. https://doi.org/10.2307/30036540
[11] Venkatesh, V., Thong, J. Y. L., & Xu, 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
[12] Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213-236. https://doi.org/10.2307/249244
[13] Parasuraman, A. (2000). Technology Readiness Index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307-320. https://doi.org/10.1177/109467050024001