Effect of B&amp;B host responses to online reviews on subsequent reviews: the moderating effects of class level

Lan, Tao; Feng, Xiaodong; Zeng, Zhimin

doi:10.1108/tr-10-2020-0474

Cited by 6 publications

(4 citation statements)

References 55 publications

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“…Compared to other data sets, Ctrip offers a variety of products, attracting a wide range of consumers. Recently, Ctrip has been the data source of many studies on big data in China’s tourism research (Lan et al , 2021; Yang et al , 2022). Therefore, using Ctrip as the data source made the sample widely representative.…”

Section: Methodsmentioning

confidence: 99%

Destination image: a consumer-based, big data-enabled approach

Zhong

Morrison

Zheng

et al. 2023

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Purpose This study aims to use a bottom-up, inductive approach to derive destination image attributes from large quantities of online consumer narratives and establish a destination classification system based on relationships among attributes and places. Design/methodology/approach Content and social network analyses were used to explore the consumer image structure for destinations based on online narratives. Cluster analysis was then used to group destinations by attributes, and ANOVA provided comparisons. Findings Twenty-two attributes were identified and combined into three groups (core, expected, latent). Destinations were classified into three clusters (comprehensive urban, scenic and lifestyle) based on their network centralities. Using data on Chinese tourism, the most mentioned (core) attributes were determined to be landscape, traffic within the destination, food and beverages and resource-based attractions. Social life was meaningful in consumer narratives but often overlooked by researchers. Practical implications Destinations should determine into which category they belong and then appeal to the real needs of tourists. Destination management organizations should provide the essential attributes while paying greater attention to highlighting the destinations’ social life atmosphere. Originality/value This research produced empirical work on Chinese tourism by combining a bottom-up, inductive research design with big data. It divided the 49 destinations into three categories and established a new system based on rich data to classify travel destinations.

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Section: Methodsmentioning

confidence: 99%

Destination image: a consumer-based, big data-enabled approach

Zhong

Morrison

Zheng

et al. 2023

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“…Ctrip was chosen as the research object. Ctrip is commonly employed in tourist behavior research (Lan et al, 2021; X. Li et al, 2022; W.…”

Section: Methodsmentioning

confidence: 99%

Personalized Tourism Recommendations and the E-Tourism User Experience

Yang

Zhang

Zhou

2023

Journal of Travel Research

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Previous research indicates that personalized tourism recommendation (PTR) is becoming increasingly important in tourism marketing. However, many areas of PTR remain unexplored. This study is based on Stimulus-Organism-Response theory; integrated constructs from PTR, big data, and artificial intelligence; and the technology acceptance model. The quantitative approach was conducted through an online survey from 496 users of Ctrip. PLS-SEM was used to test the collected data. Three factors were found to stimulate consumers’ perceptions of PTR: perceived personalization, visual appearance, and information quality. Consumers’ reactions to PTR can be divided into an internal processing organism, which includes the perception of the technology as “technology trust” and the perception of the recommended content as “PTR attitude.” This study contributes to the literature on smart tourism and marketing by developing and empirically testing an integrated model and providing a guide to determine users’ trust and attitudes toward PTR or other personalized e-services.

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“…Existing research generally analyzes the managerial responses of hosts based on volume, emotional valence and text length and then discusses their impact on consumers (Lan et al , 2022; Ye et al , 2023). The research on hosts’ managerial responses to positive reviews based on warmth and competence is limited.…”

Section: Literature Review and Research Backgroundmentioning

confidence: 99%

Seize the favorable impression: how hosts should manage positive online reviews

Wang,

Jia

2023

IJCHM

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Purpose Positive reviews can enrich the favorable impression of peer-to-peer accommodation products, and seizing this impression is vital for hosts. This study aims to focus on hosts’ response strategies to positive reviews and their effects. Design/methodology/approach This study categorizes hosts’ response strategies to positive reviews into cordial and tailoring responses. This study empirically analyzes the influence of these response strategies on subsequent review volumes using 1,283 valid listings and zero-inflation negative binomial regression models. Findings While hosts use cordial responses more, tailoring responses are more likely to drive subsequent reviews. In addition, when the host chooses entirely shared accommodation or sets a high price, the facilitating effect of the two response strategies on subsequent reviews weakens. Research limitations/implications This study enriches the knowledge system on managerial responses by proposing two specific response strategies to positive reviews that can be adopted by peer-to-peer accommodation hosts and by finding the promoting impact of these strategies on subsequent review volumes. Practical implications This study recommends that peer-to-peer accommodation hosts adopt cordial and tailoring responses to encourage subsequent consumer reviewing behavior. Originality/value As an early attempt to explore hosts’ responses to positive reviews and their impacts on subsequent review volumes, this study provides valuable insights into further research on positive review response strategies in the digital space.

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Effect of B&B host responses to online reviews on subsequent reviews: the moderating effects of class level

Cited by 6 publications

References 55 publications

Destination image: a consumer-based, big data-enabled approach

Destination image: a consumer-based, big data-enabled approach

Personalized Tourism Recommendations and the E-Tourism User Experience

Seize the favorable impression: how hosts should manage positive online reviews

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