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How do weather factors drive online reviews? The mediating role of online reviewers' affect.

Authors :
He, Dandan
Yao, Zhong
Zhao, Futao
Feng, Jiao
Source :
Industrial Management & Data Systems; 2020, Vol. 120 Issue 11, p2133-2149, 17p
Publication Year :
2020

Abstract

Purpose: The purpose of this paper is to investigate the mediating effect of online reviewers' affect (ORA) on the relationship between weather and online review ratings (ORR). Design/methodology/approach: The consumers' online review data were collected from the third-party restaurant website, and the weather data were obtained from the weather part of Chinese e-government website. SnowNLP was utilized to analyze sentiment and further extract ORA. Furthermore, the mediating effects of ORA on temperature and ORR, rain and ORR were explored separately using PROCESS 3 Macro Model 4, and the interaction effect of temperature and rain was tested through PROCESS 3 Macro Model 7. Findings: The findings of this work demonstrate that ORA mediates the relationship between temperature and ORR and the relationship between rain and ORR. Besides directly leading to higher ORR, a higher temperature can bring about higher ORR by elevating ORA. On the other hand, little rain and heavy rain have a direct negative influence on ORR, and they can also lead people into a bad mood state, thus leading to lower ORR. Furthermore, temperature moderates the effect of rain on ORA. When the temperature is higher, the differences of ORA are larger between different types of rain than that of lower temperature. Originality/value: This study appears to be the first to investigate the relationship among weather, ORA and ORR using online data. The results could help managers understand when consumers are more likely to provide negative eWOM under corresponding weather conditions and adopt appropriate strategies to improve ORR. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
02635577
Volume :
120
Issue :
11
Database :
Complementary Index
Journal :
Industrial Management & Data Systems
Publication Type :
Academic Journal
Accession number :
146653376
Full Text :
https://doi.org/10.1108/IMDS-02-2020-0121