Evaluation of Statistical Machine Learning Methods and Service Design Techniques Applications in Hotel Industry

Allam, Batta Rabie; Bahy El-Din, Mohamed Hany; Sayed, Mohammed Saber

doi:10.21608/jaauth.2023.225321.1489

Evaluation of Statistical Machine Learning Methods and Service Design Techniques Applications in Hotel Industry

Document Type : Original Article

Authors

¹ Minia University/ Faculty of Tourism and Hotels/ Hotel Management Department

² Hotel Management Department, / Faculty of Tourism and Hotels/ Helwan University

³ Hotel Management, Faculty of Tourism, Helwan University

10.21608/jaauth.2023.225321.1489

Abstract

Business process management (BPM) is an essential discipline for any organization hoping to find creative answers to challenging business issues and provide fresh chances for progress and competitive advantage. Using service design techniques and statistical methods (SMs) as supporting tools for BPM initiatives in hotel industry is not always expressed in management literature and their mutual relations with quality management objectives are not provided, which implies difficulties when to put the notion of service process into practice. The study aims to integrate statistical methods (SMs) in BPM initiatives deployed in hotels and providing insights on the relevant statistical techniques of improving decisions-making process and how these reasons mutually relate to quality management practices in hotels. Through a comprehensive qualitative and quantitative study that included in-depth semi-structured interviews with five-star hotel managers in Egypt, the authors have attempted to investigate this understudied topic. The study concluded that the level of coverage of BPM core areas is low in hotel industry and there is a need to support BPM approach with appropriate tools and techniques, especially statistical methods that are necessary to better manage service quality and performance of operation processes.

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