Abstract/Summary

In an era of rapid technological advancements, social robots have become increasingly prevalent across various sectors (Vishwakarma et al., 2024). As of June 2023, the UK government committed £2.4 billion to implementing AI and robotic technologies, including the use of social robots within the NHS (Hooson and Pratt, 2024). Despite their growing global use in industries such as healthcare, airports and entertainment (Moriuchi and Murdy, 2024), the UK has limited exposure to social robots (IFR, 2022). Prior research within human to robot interaction literature had identified several challenges that warrant further investigation. These challenges include the adaptation of existing technological adoption frameworks and to further understand the roles of risks, motivations and users’ intentions to accept new technologies (Chatterjee et al., 2023). Societal concerns of emerging technologies remain a prominent issue, with individuals still reluctant to accept social robots primarily due to issues related to risks and privacy (Liu and Hancock, 2024; Lutz and Tamo-Larrieux, 2020; Lenca and Fosch-Villaronga, 2019; Pavlou, 2001). This research offers new insights into how various factors influence users' intentions to adopt social robots, by modifying the most up-to-date version of the latest technology adoption framework through employing the UTAUT2. Another contribution of this thesis resided in the restructuring of the UTAUT2 model to explore alternative pathways through which users' intentions to accept social robots are formed. By testing these varying pathways, this research has offered an alternative understanding of how varying factors interact and influence users' behavioural intentions. This approach moves beyond the traditional inclusion of antecedent variables towards behavioural intentions, which has been a limitation within adoption frameworks over the past two decades.