The infusion of service robots in conjunction with artificial intelligence and machine learning in service deliveries across different service industries and settings has attracted significant attention from business practitioners (Lelieveld and Wolswinkel, 2017; Beck and Libert, 2017; McKinsey and Co, 2017; Microsoft, 2018) and service scholars (Wirtz et al. 2018; Huang and Rust, 2017; Marinova et al., 2017; van Doorn et al., 2017). While there are significant cost and productivity advantages to organizations in introducing service robots to replace people on the frontline (Grewal et al., 2017, marketers, policy makers and society at large have understandably expressed concern at the impact of such technological advances on employees. However little consideration has been given to how the consumers will be impacted, and indeed accept the technology (Wirtz et al. 2018).
Calling the new paradigm ‘Service encounter 2.0”, Larivière and colleagues (2017) suggest customers must develop new skills and competences so that they can be ready, together with service employees, to undertake the role of enabler (i.e., supporting employees and/or technology in the service encounter), innovator (i.e., partaking in new service development and delivery), coordinator (i.e., being a resource integrator), and differentiator (i.e., innovating services and technology for individual needs). However, a critical issue remains unanswered – that is, how can service organizations best develop, capture and deliver an engaging customer experience in the new world where customers interact with robots on a regular basis across multiple service contexts?
Therefore, this research analyzes consumer perceptions of service robot infusion at the front line in various service contexts. Moreover, we examine in how far these perceptions contribute to the overall service robot experience. To explore this topic, we use qualitative interviews to collect in-depth insights into consumers’ perceptions, attitude and evaluation of service robots and their application in different service settings (Patton 2015). Based on insights from literature and the results of the 70 qualitative interviews, we propose the service robot acceptance model (sRAM) that reflects customer experience (encompassing customers’ cognitive, emotional, behavioural, sensorial, and social responses to their interactions with employees, technology, and the organization; Lemon and Verhoef, 2016), and ultimately better understand customer service-robot adoption, which is driven by not only reliable delivery of the core service (functional or outcome quality), but also the manner in which the service is delivered (technical or process quality). The sRAM combines functional elements (ease of use, perceived usefulness and social norms), social-emotional elements (humanness, social interactivity, social presence), relational elements (trust, rapport) and individual characteristics (technology anxiety, need for personal contact, privacy concerns) to explain consumer adoption of service robot. Theoretical and managerial implications will be discussed.