Online hotel booking decisions based on price complexity, alternative attractiveness, and confusion

Purpose The importance of online reviews on online hotel booking has been widely acknowledged. However, not all online reviews affect consumers equally. Compared with common online reviews, key online reviews (KORs) have a greater influence on consumers' decisions and online hotel booking. This study takes the first step to investigate the factors affecting the identification of KORs and the role of KORs in online hotel booking.Design/methodology/approach To test the research hypotheses, this study develops a crawler to obtain 551,600 online reviews of 650 hotels in ten representative large cities in China. This study first uses a binary logistic regression to identify KORs by combining review content quality and reviewer characteristics and then uses a log-regression model to investigate the role of KORs in online hotel booking.Findings This study mined the factors affecting the identification of KORs by analyzing review contents and reviewer characteristics. Our results revealed that KORs play a mediating role in the effects of review content and reviewer characteristics on online hotel booking.Originality/value This study focuses on KORs, which have received limited attention in research but are important to practitioners. Specifically, this study investigates the antecedents and consequences of KORs. Our results enable hotel managers to manage online reviews effectively, particularly KORs.

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Examining User Switch between Mobile Stores

Information Resources Management Journal ◽

10.4018/irmj.2016040101 ◽

2016 ◽

Vol 29 (2) ◽

pp. 1-13 ◽

Cited By ~ 6

Author(s):

Tao Zhou

Keyword(s):

Service Quality ◽

Social Influence ◽

Information Quality ◽

Switching Behavior ◽

Quality Information ◽

System Quality ◽

Push Factors ◽

Factors Affecting ◽

Critical Issues ◽

Alternative Attractiveness

Retaining users and curbing their switching behavior are critical issues for mobile stores. Drawing on the push-pull-mooring (PPM) model, this research identified the factors affecting user switches between mobile stores. The push factors include dissatisfaction with system quality, information quality and service quality. The pull factor is alternative attractiveness. The mooring factors include switching costs and social influence. The results indicated that user switches receive influences from all three kinds of factors. Hence mobile stores need to be concerned with the effects of push, pull and mooring factors in order to curb users' switching behavior.

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