ILLUSTRATING THE PERCEPTION OF STUDENTS TOWARDS AUTONOMOUS SERVICE ROBOTS IN THE TOURISM INDUSTRY: AN EXPLORATORY STUDY

Abstract

Research purpose – The purpose of the study was to explore the students’ perceptions towards autonomous service robots, using interviews and, in a novel manner, also their drawings, to generate data. The paper contributes to current knowledge claims around the Techno-Economic Paradigm by providing a greater understanding of, and student response to, autonomous service robots. Additional insights were gained on transformational skills set for tourism practitioners. Design/Methodology/Approach – A qualitative approach followed a participatory research design which was set in a public higher education institution. Undergraduate tourism and hospitality students who had previous exposure and engagement with robots were the purposive sample. The paper draws on the Techno-Economic Paradigm to illuminate how, according to students’ experiences and perceptions, autonomous robots interact in, and disrupt, the tourism industry. Findings – Four main drawings essentially expressed tourism and hospitality students’ perceptions of autonomous service robots in the tourism industry. Centrally there are opportunities for the absorption of robots in certain sectors of the tourism industry. Despite greater use of robots in service-driven industries like tourism, it remains challenging to establish the right balance between humans and robots, and up- and re-skilling transformation would be required of those working in the tourism industry and those studying towards a tourism qualification. This study advances that additional research is still required, including longitudinal studies on the effects of autonomous services in the tourism industry, as well as students’ perception on the use of robots, re-skilling as well as ethical risks to customers, the greater value to the economy and those working in the tourism industry. Originality of the research – The article contributes to the use of visual methodology as part of data generation, specifically how students’ perceptions regarding autonomous robots in the tourism industry were graphically distilled using this methodology.