Exploring customer concerns on service quality under the COVID-19 crisis: a social media analytics study from the retail industry

Li, Xinwei, Xu, Mao, Zeng, Wenjuan, Tse, Mike ORCID: https://orcid.org/0000-0001-6174-0326 and Chan, Hing Kai 2023. Exploring customer concerns on service quality under the COVID-19 crisis: a social media analytics study from the retail industry. Journal of Retailing and Consumer Services 70 , 103157. 10.1016/j.jretconser.2022.103157

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Abstract

The COVID-19 pandemic has triggered a set of government policies and supermarket regulations, which affects customers' grocery shopping behaviours. However, the specific impact of COVID-19 on retailers at the customer end has not yet been addressed. Using text-mining techniques (i.e., sentiment analysis, topic modelling) and time series analysis, we analyse 161,921 tweets from leading UK supermarkets during the first COVID-19 lockdown. The results show the causes of sentiment change in each time series and how customer perception changes according to supermarkets’ response actions. Drawing on the social media crisis communication framework and Situational Crisis Communication theory, this study investigates whether responding to a crisis helps retail managers better understand their customers. The results uncover that customers experiencing certain social media interactions may evaluate attributes differently, resulting in varying levels of customer information collection, and grocery companies could benefit from engaging in social media crisis communication with customers. As new variants of COVID-19 keep appearing, emerging managerial problems put businesses at risk for the next crisis. Based on the results of text-mining analysis of consumer perceptions, this study identifies emerging topics in the UK grocery sector in the context of COVID-19 crisis communication and develop the sub-dimensions of service quality assessment into four categories: physical aspects, reliability, personal interaction, and policies. This study reveals how supermarkets could use social media data to better analyse customer behaviour during a pandemic and sustain competitiveness by upgrading their crisis strategies and service provision. It also sheds light on how future researchers can leverage the power of social media data with multiple text-mining methodologies.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Publisher:	Elsevier
ISSN:	0969-6989
Date of First Compliant Deposit:	3 October 2022
Date of Acceptance:	2 October 2022
Last Modified:	06 Jan 2024 03:34
URI:	https://orca.cardiff.ac.uk/id/eprint/153012

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