Is a picture worth a thousand words? Understanding the role of review photo sentiment and text-photo sentiment disparity using deep learning algorithms

Li, Hengyun; Ji, Haipeng; Liu, Hongbo; Cai, Danting; Gao, Huicai

doi:10.1016/j.tourman.2022.104559

Cited by 48 publications

(10 citation statements)

References 69 publications

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“…Second, this study is consistent with others that demonstrate that consumer interpretations of the valence of reviews, as measured by the overall rating of the business, follow a non-linear rather than a linear pattern (Lai et al , 2021; Li et al , 2022; Shin and Nicolau, 2022). Others (Bridges and Vásquez, 2018) show that extremely positive reviews could be interpreted by consumers as less meaningful than those giving lower ratings, although they represent important informational cues.…”

Section: Resultssupporting

confidence: 90%

Daily online review sentiment and hotel performance

Nicolau

Xiang

Wang

2023

IJCHM

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Purpose This paper aims to investigate the links between daily review sentiment and the hotel performance measures of occupancy rate (OR), average daily rate (ADR) and revenue per available room (RevPAR). Design/methodology/approach The authors conducted review sentiment analyses in three moments (−1, −7 and −14 days) before arrival time using a data set of budget hotel performance and online reviews. The aim was to identify the effect of review sentiment in the budget hotel market on the three performance metrics. Findings Daily sentiment positively affects ADR and negatively affects OR and RevPAR, but only up to a certain threshold, after which the trend reverses. Prices increase with the level of sentiment, and high prices lead to low OR and RevPAR only when the sentiment scores are low. When they are high, they are associated with low rates, which lead to high OR and RevPAR. Research limitations/implications Daily review sentiment can be viewed as a valuable “barometer” indicating a hotel’s daily operational effectiveness. Daily sentiment can thus allow hotel managers to adjust their dynamic pricing strategies more accurately. Originality/value This study identifies daily sentiment as an alternative predictor of hotel performance. In addition to the roles of valence and volume in the decision-making process, the authors found that daily review sentiment can be an “in-the-moment” factor with a high impact, encouraging consumers to complete their transactions. This study suggests that aggregated measures such as the total number of reviews and overall ratings of the hotel should not be the sole consideration in reputation management.

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Section: Resultssupporting

confidence: 90%

Daily online review sentiment and hotel performance

Nicolau

Xiang

Wang

2023

IJCHM

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“…As a robustness check, to determine if scholars working with ML simply omitted to define and use the term “analytics”, we developed a further query using the keywords “machine learning” OR “deep learning” matched with the hospitality and tourism words “hospitality”, “hotel”, “touris*”, “travel*” and “leisure” in the title, abstract and keywords and identified 159 articles (some of them overlapping with the articles generated by our main query represented in Figure 1). Those articles deploy some form of ML to obtain their results, but do not use the term “analytics” explicitly (Li et al , 2022) and in most of the cases (85.5%) they do not even use the term artificial intelligence (Huang et al , 2022). This suggests that scholars generating ML- and AI-based analytics in hospitality and tourism either do not mention the word “analytics” at all or do not label them as “cognitive analytics” and, in most of the cases, they do not even use the term “artificial intelligence”.…”

Section: Resultsmentioning

confidence: 99%

“…Third, cognitive analytics is missing in the terminology deployed by hospitality and tourism management scholars. However, there is a growing body of research that leverages ML to analyze data and therefore it seems that several scholars (e.g., Li et al, 2022) are working de facto in the field of cognitive analytics but do not use the label 'cognitive', or simply are not aware of this terminology. Fourth, neither AI nor ML are explicitly linked to analytics.…”

Section: Discussionmentioning

confidence: 99%

A critical reflection on analytics and artificial intelligence based analytics in hospitality and tourism management research

Mariani

Wirtz

2023

IJCHM

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Purpose This work consists of a critical reflection on the extent to which hospitality and tourism management scholars have accurately used the term “analytics” and its five types (i.e. descriptive, exploratory, predictive, prescriptive and cognitive analytics) in their research. Only cognitive analytics, the latest and most advanced type, is based on artificial intelligence (AI) and requires machine learning (ML). As cognitive analytics constitutes the cutting edge in industry application, this study aims to examine in depth the extent cognitive analytics has been covered in the literature. Design/methodology/approach This study is based on a systematic literature review (SLR) of the hospitality and tourism literature on the topic of “analytics”. The SLR findings were complemented by the results of an additional search query based on “machine learning” and “deep learning” that was used as a robustness check. Moreover, the SLR findings were triangulated with recent literature reviews on related topics (e.g. big data and AI) to generate additional insights. Findings The findings of this study show that: there is a growing and accelerating body of research on analytics; the literature lacks a consistent use of terminology and definitions related to analytics. Specifically, publications rarely use scientific definitions of analytics and their different types; although AI and ML are key enabling technologies for cognitive analytics, hospitality and tourism management research did not explicitly link these terms to analytics and did not distinguish cognitive analytics from other forms of analytics that do not rely on ML. In fact, the term “cognitive analytics” is apparently missing in the hospitality and tourism management literature. Research limitations/implications This study generates a set of eight theoretical and three practical implications and advance theoretical and methodological recommendations for further research. Originality/value To the best of the authors’ knowledge, this is the first study that explicitly and critically examines the use of analytics in general, and cognitive analytics in particular, in the hospitality and tourism management literature.

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“…Furthermore, customer reviewers have a significant impact on consumers' purchase intentions (Verhagen and Van Dolen, 2011;Li et al, 2022). Consumers usually pay attention to other consumers' usage experiences, quality evaluations, and performance feedback, which make them more likely to buy the products.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, customer reviewers have a significant impact on consumers' purchase intentions (Verhagen and Van Dolen, 2011; Li et al. , 2022).…”

Section: Introductionmentioning

confidence: 99%

How do purchase preferences moderate the impact of time and price sensitivity on the purchase intention of customers on online-to-offline (O2O) delivery platforms?

Cao,

Wang

2024

BFJ

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PurposeThe purpose of this study is to test the impact of time and price sensitivity on consumer satisfaction and purchase intention on online-to-offline (O2O) takeout platforms and explore the moderating effect of purchase preference on time sensitivity and satisfaction, as well as price sensitivity and satisfaction, in order to guide market pricing.Design/methodology/approachA structural equation model (SEM) of customer purchase intention was constructed, and the relationships between the variables (time sensitivity, price sensitivity, satisfaction and purchase intention) were examined. The completed questionnaires of 349 respondents were collected from the Questionnaire Star platform in China. The research model and hypotheses were then tested. Analytic hierarchy procedure was used to determine the moderating effect of purchase preference. Finally, the study proposes a pricing strategy for customer-active selective services.FindingsSatisfaction positively influences purchase intention, and price sensitivity significantly increases satisfaction and further increases purchase intention; however, time sensitivity negatively affects satisfaction. Specifically, purchase preference has strongly moderated the relationship between time, price sensitivity and satisfaction. In addition, the findings show that when purchase preference is high, the effect of price sensitivity on satisfaction is stronger, suggesting the importance of purchase preference in strengthening purchase intentions. The research work recommends a pricing strategy involving value-added pricing primarily for time-sensitive customers, which can help build a high-end brand image and reduce price competition. Reduced pricing is mainly for price-sensitive customers, which is conducive to stimulating consumption within a specific time. This pricing strategy is important for adjusting market sensitivity and flexibility.Originality/valueThis research provides new ideas for related disciplines and guidance for the differentiated pricing and promotion of takeout platforms, as well as a theoretical basis for the diversified development of takeout platforms, improvement of personalized service quality and enhancement of customer stickiness. This study fills gaps in the existing literature on the moderating effect of purchase preference on time sensitivity and satisfaction and price sensitivity and satisfaction.

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Is a picture worth a thousand words? Understanding the role of review photo sentiment and text-photo sentiment disparity using deep learning algorithms

Cited by 48 publications

References 69 publications

Daily online review sentiment and hotel performance

Daily online review sentiment and hotel performance

A critical reflection on analytics and artificial intelligence based analytics in hospitality and tourism management research

How do purchase preferences moderate the impact of time and price sensitivity on the purchase intention of customers on online-to-offline (O2O) delivery platforms?

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