Use of machine learning technology for tourist and organizational services: high-tech innovation in the hospitality industry

Abstract

Purpose Technological innovation has been changing the tourism industry precipitously and making the holiday experience more enjoyable and easier than before. The purpose of this study is to identify the current and future changes by the machine learning (ML) system as artificial intelligence in the hospitality industry. Design/methodology/approach This study has a descriptive research approach because building knowledge on technology and applying this knowledge to a tourism research are still new extensions in social studies, especially in tourism. Findings This research shows the value of using ML in the quality of data, features and algorithms besides stating the difficulties of data analysis in hospitality. This research also provides a comparison of automated ML techniques and the use of a robot for customer services in the hotel. Originality/Value This research contributes to the tourism and technology literature by shedding light on the use of ML in tourism advancement to predict future business conditions, revenue, challenges and also to identify the current trend of tourist demand.

Antecedents of human-robot collaboration: theoretical extension of the technology acceptance model

“…In social science research, robotics collaboration is a fairly new idea related to the human experience of human–robot teamwork in the workplace (Liboni et al , 2019). Robotics can help industrial companies provide effective services that increase the company’s productivity and reduce the workload of human employees (Ivanov et al , 2018; Parvez, 2020). This study presents a research model based on TAM to determine human employees’ intentions in HRC by examining the effects of antecedents, including perceived usefulness, perceived ease of use, robot advantages, robot disadvantages and robot user motivation and robot awareness.…”

“…In the tourism and hospitality industry, robots fulfill various customer requests (Tuncer et al, 2021) or carefully complain as support for human employees or in the absence of employees (Christou et al, 2020). Therefore, the use of AI or machine learningbased service robots in tourism is popular among customers and employees, as robots reduce employee workload and complete important tasks quickly and accurately (Parvez, 2020). Bowen and Morosan (2018) found that AI robots will provide a competitive advantage in solving all challenges by 2030.…”

Analysing the impact of artificial intelligence on the competitiveness of tourism firms: a modified total interpretive structural modeling (m-TISM) approach

“…The workforce training to improve their skills is necessary to avoid dysfunctionality between the technology and the workforce that can gravely hamper the firm competitiveness (Bonometti, 2009). The skilled workforce can become an asset for the firm as they can indulge in predictive analytics in terms of the future of the firm's business with the help of big data analytics and the use of AI (Parvez, 2020).…”

Some Key Technology Trends in Tourism Industry

“…In addition, the subsets of AI, that is machine learning or neural networks, have been used for analyzing data related to tourism, in forms of association and classification learning, in cases such are forecasting of tourists' expenses, the number of tourist arrivals prediction, as well as in analyzing tourists' profiles, customer experience, etc. (Gajdošík, Marciš, 2019;Parvez, 2020) When it comes to new term named cultural tourism, "Improving Sustainable Development Policies and Practices to Assess, diversify, and foster Cultural Tourism in European regions and areas" (IMPACTOUR) is the project which supposed to create an innovative and user-friendly methodology and tool based on combination of data analytics algorithms and AI and machine learning strategies in order to measure and assess the impact of cultural tourism on European economic and social development. It has been funded by the H2020 Framework Program of the European Commission, from January 2020 until December 2022, with participation of 12 partners from 10 European countries.…”