A design concept of big data analytics model for managers in hospitality industries

Seyedmohammad Mousavian; Shah J Miah; Yifan Zhong

doi:10.1007/s00779-023-01714-3

A design concept of big data analytics model for managers in hospitality industries

Pers Ubiquitous Comput. 2023 Feb 15:1-11. doi: 10.1007/s00779-023-01714-3. Online ahead of print.

Authors

Seyedmohammad Mousavian¹, Shah J Miah¹, Yifan Zhong²

Affiliations

¹ Newcastle Business School, University of Newcastle, Callaghan, NSW Australia.
² School of Management and Marketing, Curtin University, Perth, WA Australia.

Abstract

The hospitality and tourism sector has long played a significant role in Australia's economy, especially in regional areas. Due to the onslaught of COVID-19, numerous businesses have experienced lockdowns, restrictions, and closures due to the fact that people's activity in restaurants, shopping centers, and recreational destinations was restricted, and many other places went into hibernation. After about 2 years since the outbreak, businesses in this sector are gradually starting to reopen and revitalize themselves, but in order to have better decision support about the future of this sector, thus being able to plan, businesses are suffering from an effective analytics solution due to the lack of broken data trends. Starting from fresh day-to-day real-time big data, the study aims to develop a new data analytics model, adopting the design science research methodology, which can provide invaluable options and techniques to make prediction easier from immediate past datasets. This study introduces an innovative design artifact as a big data solution for hospitality managers to utilize analytics for predictive strategic decision-making in post-COVID situation. The artifact can also be generalized for other sectors with tailoring aspects which are subject to further studies. The proposed artifact is then compared with other design artifacts related to big data solutions where it outperforms them in terms of comprehensiveness. The proposed artifact also shows promises for primarily available UGC in managers' decision support aids.

Keywords: Big data; Data analytics; Decision support; Hospitality; Machine learning; Tourism.

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