The paper I’m going to be reviewing today is titled Generative Artificial Intelligence (AI) Technology Adoption Model for Entrepreneurs: Case of ChatGPT. It was published by Varun Gupta and Hongji Yang on January 5th, 2024 in a journal named Internet Reference Services Quarterly.
Let’s dive in!
Summary of the Paper
The paper deals with the process entrepreneurs go through when deciding to adopt generative AI technology (e.g., ChatGPT). The paper proposes a three-stage adoption model consisting of 1) Pre-Perception & Perception, 2) Assessment, and 3) Outcome; with each stage being influenced by one or more factors.
The model is an extension of the well-known technology acceptance model (TAM) developed by Fred D. Davis. However, Gupta and Yang’s model is unique in that it emphasises the importance of both utilitarian and hedonic values in the decision making process.
Alongside the importance of perceived ease of use and perceived usefulness (the main adoption factors in TAM), it also considers the importance of perceived enjoyment (a novel addition).
The model is relevant to both entrepreneurs and technology developers, as it presents a decision-making roadmap for adopting generative AI, like ChatGPT.
Quality of the Research
The research question and objective is clearly stated and well-articulated. The researchers state their aim to fill a gap in the literature by focusing on the generative AI adoption process for entrepreneurs.
Given that our understanding of user acceptance of AI technologies is still limited, and the rapid development and growing importance of AI in business operations, the research question is both timely and significant. In today’s technology-driven world, the focus on entrepreneurs and their decision-making processes regarding AI use is particularly relevant.
While there is existing research exploring the AI adoption process using an extended TAM model, Gupta and Yang’s research maintains originality by integrating emotional factors. This sets it apart from many existing frameworks that focus purely on utilitarian value.
Indeed, Gupta and Yang explore existing models through a thorough review of prior research. In their review, they cover a range of models and theories related to technology adoption and AI. The literature review is well-organised, providing a solid foundation for their proposed model.
While the paper does briefly touch on ethical considerations, such as the responsible use of AI, it could stand to delve deeper into these issues. Considering AI’s rapid advancement and greater integration into society, there is a need for a more comprehensive discussion on the ethical implications of its adoption, particularly among business leaders!
The Research Method
In this paper, the authors propose a conceptual model informed by existing adoption frameworks together with their extensive professional experience. The authors present their research as a series of hypotheses that require empirical testing and evaluation.
This particular research method aligns with the authors’ objectives in creating a new adoption model. However, its reliance on logical reasoning and theoretical assumptions is certainly a weak point, and the lack of any empirical data limits the ability to assess the model’s practical applicability.
While the authors’ conceptual approach may be adequate for the early stages of model development, empirical testing is necessary in order to evaluate the utility of the model, elicit the relationships between adoption factors, and, ultimately, assess its accuracy.
Nonetheless, the conclusions given in the paper are logically consistent with the theoretical framework presented, and the authors do call for future research to be carried out, openly acknowledging the need for empirical testing.
Quality of Presentation
The paper is well-written, with concise language that effectively communicates the authors’ objectives and ideas. The introduction discusses several aspects of generative AI, including existing adoption models. This serves to frame the research and illustrate its importance.
The structure of the paper is logical, ensuring its accessibility to a broad audience. There is a clear progression from introduction and literature review through to model development. The model’s three stages are well-defined, adoption factors are discussed in-depth, and a visual diagram aids reader comprehension.
Moreover, the authors employ consistent use of terminology, maintaining clarity throughout the paper. Key terms and concepts are well-defined and detailed explanations are given for more complex ideas. However, a table summarising the hypotheses and associated factors could enhance understanding.
Connection to Broader Themes and Implications for Future Research
The paper connects well with ongoing research into AI adoption and its developing role in business. The authors also provide a valuable contribution to the consideration of utilitarian and hedonic motivations in technology use. The use of TAM underscores the model’s connection to the study of technology adoption as a whole.
The focus of future research should be on empirically testing the proposed model across different entrepreneurial contexts. There is further potential to explore how the model can be adapted for other professional groups such as recruiters or executives, for example.
Lastly, there is an open question as to whether the model can be adapted to other AI systems, beyond ChatGPT.
Final Thoughts
All in All, Gupta and Yang’s AI adoption model for entrepreneurs is compelling in its logical derivation and incorporates some novel ideas regarding emotional factors. Despite needing empirical testing, Gupta and Yang’s research provides a valuable contribution to our limited understanding of AI adoption.
References
Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International Journal of Man-Machine Studies, 38(3), 475-487. https://doi.org/10.1006/imms.1993.1022
Duong, C. D., Vu, T. N., & Ngo, T. V. (2023). Applying a modified technology acceptance model to explain higher education students’ usage of ChatGPT: A serial multiple mediation model with knowledge sharing as a moderator. The International Journal of Management Education, 21(3). https://doi.org/10.1016/j.ijme.2023.100883
Gupta, V., & Yang, H. (2024). Generative Artificial Intelligence (AI) Technology Adoption Model for Entrepreneurs: Case of ChatGPT. Internet Reference Services Quarterly, 28(2), 223-242. https://doi.org/10.1080/10875301.2023.2300114
Kelly, S., Kaye, S.-A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77. https://doi.org/10.1016/j.tele.2022.101925
Premlatha, P. (2023). Ethics in artificial intelligence and its impact on leadership styles (Order No. 30566996). Available from ProQuest Dissertations & Theses Global. (2835726064). https://www.proquest.com/dissertations-theses/ethics-artificial-intelligence-impact-on/docview/2835726064/se-2
Stanford University. (2024). Artificial Intelligence Index Report 2024. https://aiindex.stanford.edu/wp-content/uploads/2024/05/HAI_AI-Index-Report-2024.pdf