Abstract
Humanoid robots have proven to be a critical technology for humans as AI technology advances exponentially. While today’s social robots do not look and behave like the ones in Hollywood movies like The Terminator, they proved to be integral in managing the COVID-19 pandemic and beyond (Ladeira et al., 2023). Their increased usage and popularity have led to an expected increase of US$6.20 billion in the social robotics market between 2022-2027. In hospitality, they have been used as concierges, servers, porters, tour guides, helpers, and even recently for providing recommendations such as movie recommendations, etc. (Cervone et al., 2015; Herse et al., 2018; Woiceshyn et al., 2017). Businesses are deploying social robots because they can help cut down labour costs, enhance productivity, and improve service delivery (Khoa et al., 2023). Additionally, past studies have shown that customers are typically more engaged when interacting with social robots than virtual assistants, kiosks, chatbots, or tablets (Brengman et al., 2021; De Carolis et al., 2024). Although researchers have investigated the use of social robots to provide recommendations, an extensive literature review has shown that efforts to understand Willingness to Accept Social Robot Recommendations (WASRR) have been limited. Most of the studies either focused on system design (e.g.,De Carolis et al., 2020; Woiceshyn et al., 2017) or robot modalities to understand the acceptance of recommendations (e.g.,Abdi et al., 2022; Okafuji et al., 2024; Shiomi et al., 2013). Furthermore, most of the studies use a behavioural measure consisting of yes/no measurements. Models such as sRAM and scales such as SRIW, have also been developed to measure the acceptance of social robots. While some scales measure technology acceptance and can be adapted to measure social robot acceptance, it is essential to remember that accepting social robots in services does not mean customers will also accept recommendations from these social robots. The real success of using social robots to provide recommendations can be measured when customers delightfully accept these recommendations. Once accepted, it can lead to higher customer satisfaction and sales (Yoon et al., 2013). Previous researchers have called for more empirical work that assesses human responses and participation in human-robot interactions (Reeves & Hancock, 2020). Thus, this exploratory study aims to develop a scale for measuring WARR in the hospitality industry.
Original language | English |
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Title of host publication | ANZMAC 2024: AI for Sustainable Marketing: Bridging Innovation and Responsibility, Conference Proceedings, 2-4 December 2024, Tasmania, Australia |
Editors | Denni Arli, Linda Robinson |
Publisher | Australian and New Zealand Marketing Academy |
Pages | 125-128 |
Number of pages | 4 |
Publication status | Published - Dec 2024 |
Event | Australian and New Zealand Marketing Academy. Conference - Hobart, Australia Duration: 2 Dec 2024 → 4 Dec 2024 |
Conference
Conference | Australian and New Zealand Marketing Academy. Conference |
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Abbreviated title | ANZMAC |
Country/Territory | Australia |
City | Hobart |
Period | 2/12/24 → 4/12/24 |