How Can Digital Technology Help the Hospitality Supply Chain Cope with the Impact of the COVID-19?

Zhang, Yajing (2022) How Can Digital Technology Help the Hospitality Supply Chain Cope with the Impact of the COVID-19? [Dissertation (University of Nottingham only)]

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Abstract

The use of digital technologies has been one of the most notable trends of the past few years. It has a crucial role in improving efficiency, especially in the supply chain sector. In the wake of COVID-19, a series of quarantines and lockdowns have made the hospitality industry one of the worst affected sectors. With the use of digital technologies in the economics, operations and services of the hospitality supply chain, it is worth exploring whether these technologies can help the hospitality supply chain. A large body of previous literature has analysed the impact of digital technologies and COVID-19 on the hospitality industry. Still, few scholars have examined the effect and help of digital technologies on the supply chain of the hospitality industry. Using the Jin Jiang Hotel Group as a case study, this paper aims to analyse how digital technology has helped the hospitality supply chain cope with the impact of COVID-19 and what challenges remain. Based on the analysis and discussion, the paper concludes that VR technology can help the hospitality supply chain cope with the economic losses caused by COVID-19 by increasing the desire of consumers to buy. Big data and cloud computing technologies can help the hospitality supply chain to optimise its operational processes by sharing data and resources. AI and IoT technologies can help the hospitality supply chain to enhance the consumer experience under COVID-19 through intelligent facilities and contactless services. The paper also provides a reference for how hospitality supply chains can cope with COVID-19 and similar impacts in the future.

Item Type: Dissertation (University of Nottingham only)
Keywords: Hospitality supply chain, digital technologies, COVID-19, Virtual Reality; Artificial Intelligence; Internet of Things; Big Data
Depositing User: ZHANG, YAJING
Date Deposited: 30 Jun 2023 13:41
Last Modified: 30 Jun 2023 13:41
URI: https://eprints.nottingham.ac.uk/id/eprint/70261

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