A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews

Meek, Stephanie; Wilk, Violetta; Lambert, Claire

doi:10.1016/j.jbusres.2020.12.001

Cited by 66 publications

(51 citation statements)

References 56 publications

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“…In the Research Theme section, we categorized the main themes into three levels: Individual, Organization, and Societal levels, combining all papers in each theme and manually extracting the future research directions and implication at each level. Then, we performed content analysis using Leximancer Software ( Leximancer, 2022 ) a reliable tool for content analysis used by multiple bibliometric studies (e.g., Meek et al, 2021 , Tiwary et al, 2021 ). The software automatically analyzes textual data using Bayesian learning algorithms and provides concepts and high-level themes with interactive visualization and text output.…”

Section: Future Research Directionmentioning

confidence: 99%

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COVID-19 research in management: An updated bibliometric analysis

Hashemi

Rajabi

Alejandro

2022

Journal of Business Research

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Section: Future Research Directionmentioning

confidence: 99%

“…Then the software generates concept seeds using the proximity value of the words and forms a thesaurus through supervised or unsupervised machine learning methods. Finally, the thesaurus is used to build the network of concepts and themes ( Meek et al, 2021 ).…”

Section: Future Research Directionmentioning

confidence: 99%

COVID-19 research in management: An updated bibliometric analysis

Hashemi

Rajabi

Alejandro

2022

Journal of Business Research

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“…Schwarz's Feeling-As-Information Theory can explain this argument: that emotion, as a source of information and subjective experience, follows the same principles as when other information is used [19]. Other studies show negative emotions such as fear and guilt toward sustainable issues (e.g., climate change), impact positive environmentally sustainable behaviors (e.g., traveler's perceptions and intentions toward sustainable accommodation, restaurant, and eco-friendly apparel) [20][21][22][23][24]. Therefore, types of messages (positive or negative) influence communication effectiveness related to both emotional and rational appeal [25].…”

Section: Feeling As Informationmentioning

confidence: 99%

Emotion or Information: What Makes Consumers Communicate about Sustainable Apparel Products on Social Media?

2022

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This study investigated how sustainability-related messages of cotton textile and apparel products influence consumers’ attitudes toward the message and interaction with other consumers in social media. Three hundred and eighty-eight online survey data of US consumers investigated the effects of perceived information and emotion toward sustainability practice messages on attitude toward and social interaction with other consumers and the effects of attitude on social interaction in social media. The results confirm that emotion is an influential variable, and, specific, positive emotion is an influential variable for attitude, and attitude influences social interaction with other consumers of sustainability practice messages. Negative emotions influence both attitude and social interaction. The information does not influence attitude but directly impacts social interaction, which may bring the purchase intention of sustainable fashion products. Thus, apparel marketers should consider emotionally sustainable promotion messages when tailoring their brand communications on social media. This study helps clarify the relationships between emotion and social interaction for sustainable fashion products. It also contributes to the theoretical foundation and has implications for sustainable fashion marketing and management in social media.

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“…Consumers tend to make consumption decisions based on restaurant ratings and reviews (Mathayomchan and Taecharungroj, 2020; Meek et al , 2021; Zhu et al , 2020; Kwon et al , 2021). Researchers have found that in contrast to online ratings, potential consumers often refer to reviews from other customers who have already spent money making purchase decisions (Kwon et al , 2021).…”

Section: Discussionmentioning

confidence: 99%

What affects the online ratings of restaurant consumers: a research perspective on text-mining big data analysis

Mehraliyev

et al. 2022

IJCHM

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Purpose Despite a significant focus on customer evaluation and sentiment analysis, limited attention has been paid to discrete emotional perspective in terms of the emotionality used in text. This paper aims to extend the general-sentiment dictionary in Chinese to a restaurant-domain-specific dictionary, visualize spatiotemporal sentiment trends, identify the main discrete emotions that affect customers’ ratings in a restaurant setting and identify constituents of influential emotions. Design/methodology/approach A total of 683,610 online restaurant reviews downloaded from Dianping.com were analyzed by a sentiment dictionary optimized by the authors; the main emotions (joy, love, trust, anger, sadness and surprise) that affect online ratings were explored by using multiple linear regression methods. After tracking these sentiment review texts, Latent Dirichlet Allocation (LDA) and LDA models with term frequency-inverse document frequency as weights were used to find the factors that constitute influential emotions. Findings The results show that it is viable to optimize or expand sentiment dictionary by word similarity. The findings highlight that love and anger have the highest effect on online ratings. The main factors that constitute consumers’ anger (local characteristics, incorrect food portions and unobtrusive location) and love (comfortable dining atmosphere, obvious local characteristics and complete supporting services) are identified. Different from previous studies, negativity bias is not observed, which poses a question of whether it has to do with Chinese culture. Practical implications These findings can help managers monitor the true quality of restaurant service in an area on time. Based on the results, restaurant operators can better decide which aspects they should pay more attention to; platforms can operate better and can have more manageable webpage settings; and consumers can easily capture the quality of restaurants to make better purchase decisions. Originality/value This study builds upon the existing general sentiment dictionary in Chinese and, to the best of the authors’ knowledge, is the first to provide a restaurant-domain-specific sentiment dictionary and use it for analysis. It also reveals the constituents of two prominent emotions (love and anger) in the case of restaurant reviews.

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A big data exploration of the informational and normative influences on the helpfulness of online restaurant reviews

Cited by 66 publications

References 56 publications

COVID-19 research in management: An updated bibliometric analysis

COVID-19 research in management: An updated bibliometric analysis

Emotion or Information: What Makes Consumers Communicate about Sustainable Apparel Products on Social Media?

What affects the online ratings of restaurant consumers: a research perspective on text-mining big data analysis

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