These last years have seen the arise of immersive technologies (Mixed Reality continuum (Flavián et al., 2019)), which saw application and adoption in several industries. Historically, technology acceptance and adoption have been highly investigated, and numerous theoretical models have been developed in this regard, such as the Technology Acceptance Model (TAM) (Davis, 1989), the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), and the Augmented Reality Acceptance Model (Marto et al., 2023), highlighting the most influential drivers to the adoption. But drivers are not the only factor contributing to technological diffusion, in fact, barriers to the adoption are relevant as well. In this domain, studies have been performed to assess factors of resistance to this technology in specific sectors such as construction (Maqsoom et al., 2023) and manufacturing (Schein and Rauschnabel, 2023). It is then important to note that literature on one side suggests that “better theories or models of user resistance would lead to better implementation strategies and desired implementation outcomes” (Laumer and Eckhardt, 2012, p.67), while on the other side critiques classical models such as TAM for the lack of barriers and situational factors (Benbasat and Barki, 2007).

It becomes clear the importance of developing a new model, able to incorporate both drivers and barriers to the adoption of this technology, while also considering situational factors that may influence users’ intentions to adopt Mixed Reality technologies. This thesis wants to investigate these three aspects, with the goal of building a robust theoretical model, able to fill the current research gap in the domain of Mixed Reality adoption.  Such model would benefit the research world, by providing a solid reference point in the field, and would also benefit practitioners, by providing relevant insights about the most influential factors to take into account.

The proposed methodology is:

• Perform a literature review with the scope of identifying the most relevant and influential models, barriers and drivers to the adoption of immersive technologies, and situational factors.
• Identification of a specific current research gap to be filled with the research.
• Development of a theoretical model and identification of measurement metrics
• Design of a data collection phase to empirically validate the theoretical model (proposed methodology: online survey to be distributed).
• Data collection.
• Analysis of the gathered data, with the aim of testing the research hypotheses.
• Discussion of the results and identification of managerial and policymakers’ implications.

Literature:

Flavián et al., (2019). The impact of virtual, augmented and mixed reality technologies on the customer experience. Journal of Business Research 100 (2019) 547-560.

Davis, (1989). Perceived usefulness perceived ease of use and user acceptance of information technology. MIS Quarterly vol. 13, no. 3, pp. 319–340.

Venkatesh et al., (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly vol. 27, No.3, pp.425-478.

Marto, A.; Gonçalves, A.; Melo, M.; Bessa, M.; Silva, R. ARAM: A Technology Acceptance Model to Ascertain the Behavioural Intention to Use Augmented Reality. J. Imaging 2023, 9, 73.

Maqsoom et, al. (2023). Drivers of, and Barriers to, the Adoption of Mixed Reality in the Construction Industry of Developing Countries. Buildings 13(4).

Schein and Rauschnabel, (2023). Augmented Reality in Manufacturing: Exploring Workers’ Perceptions of Barriers. IEEE transactions on engineering management, vol. 70, no. 10.

Laumer and Eckhardt, (2012). Why Do People Reject Technologies: A Review of User Resistance Theories. Information Systems Theory, Y. Dwivedi, M. Wade, and S. Schneberger, Eds. New York, NY, USA: Springer, 2012, pp. 63–86.

Benbasat and Barki, (2007). “Quo vadis, TAM?,” J. Assoc. Inf. Syst., vol. 8, no. 4, pp. 211–218.