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Photos are powerful tools to attract individuals’ attention and convey service experiences. Yet exactly how visual cues in a photo contribute to the perceptions of the staged servicescape, and how these perceptions inspire online booking/reservation behaviors, remains underexplored. Addressing the gap, this study aims to uncover (1) how perceptual information mediated by an online photo contributes to the formation of consumers' holistic perceptions of the service environment and (2) how such consumers' holistic perceptions further influence customers' online purchasing behaviors.

This research adopts an innovative crowdsourcing approach and refers to field data on consumers' online hotel booking behaviors to examine relationships among inferred servicescape dimensions, consumers' holistic perceptions of the mediated servicescape and their actual online booking/reservation behaviors (e.g. page-view and meta-click behaviors).

Confirmatory factor analysis and path analysis indicated that five mediated servicescape dimensions (i.e. color, lighting, furnishings, layout and style) contribute significantly to consumers' perceptions of the mediated servicescape (CPMS) and exert different impacts on CPMS. Connecting the crowdsourced rating and consumer behavioral data, CPMS is found to influence consumers' aggregated page-view and meta-click behavior, especially in the US market.

Building upon servicescape theory, the medium theory and the online booking literature, this research proposes a novel conceptual framework of CPMS to theorize the process by which visual cues in online photos contribute to CPMS and subsequent online purchase behaviors. Findings from this research extend Bitner's servicescape framework to mediated service contexts and provide practical implications for promoting service businesses.

This research aims to investigate the impacts of misleading hotel website photos on consumers’ brand trust, emotional responses and negative word-of-mouth (WOM) intention. The magnitude of these impacts in different contexts was examined.

This study employed a 2 (hotel segment: economy vs upscale) × 2 (expected product experience: hedonic vs utilitarian) scenario-based experimental design. A total of 240 consumers participated in this study.

The study results show that, in the case of misleading hotel website photos, brand trust was lower for the upscale hotel. Consumers experienced greater anger and regret in the upscale hotel situation and were most angry if they stayed at an upscale hotel for a hedonic purpose. The eWOM intention was higher in the upscale hotel situation. In addition, it was found that hotel physical environment was more important for female and married consumers.

A hypothetical brand name was used in this study. Therefore, brand attitude changes and the influences of brand loyalty on consumers’ responses to misleading website photos were not examined.

Upscale hotels and hotels that target leisure consumers need to make an effort to use truthful website photos and ensure that the physical environment is well maintained.

No prior research investigated the impact of misleading hotel website photos. This research fills this gap in the hospitality literature.

This study aims to examine the effects of user-generated photos (UGPs) and review valence (RV) on hotel guests’ perceived service quality, perceived price, perceived overall image and booking intention.

An online experiment where respondents were randomly assigned to one of the six conditions in a 2 (UGPs: provided vs not provided) × 3 (RV: positive vs neutral vs negative) between-subjects factorial design was used. The data of the study was collected from the travelers who used an online hotel review site to book a hotel at least once in the past 12 months. An independent sample t-test and analysis of variance were used to analyze the data of this study.

The results indicated that UGPs and RV significantly influenced hotel guests’ service quality, price, overall image perceptions and booking intention. The interaction effects of UGPs and RV indicated that positive online hotel reviews with UGPs had higher impact on hotel guests’ service quality, price, overall image perceptions and booking intention than neutral and negative online hotel reviews with UGPs.

The understanding of the effects of UGPs and RV on guests’ price, service quality, overall image perception and booking intention can help hotel managers and social media website designers to better promote the hotel and provide efficient online hotel booking environment.

This study builds the relationships between UGPs and RV and hotel guests’ perceived price, perceived service quality, perceived overall image and booking intention, which are crucial factors regarding online hotel marketing.

评估用户生成的照片对酒店客人价格、服务质量、整体形象感知和预订意愿的影响

本研究旨在探索用户生成的照片 (UGP) 和评论效价 (RV) 对酒店客人感知服务质量、感知价格、感知整体形象和预订意图的影响。

本研究采用受试者间因子设计进行了一项在线实验, 其中受访者被随机分配到 2（UGP：提供与未提供）× 3（RV：阳性 vs. 中性 vs. 阴性）中的六个条件之一。该研究的数据是从过去 12 个月内使用在线酒店评论网站至少预订一次酒店的旅行者那里收集的。使用独立样本 t 检验和方差分析 (ANOVA) 来分析研究数据。

结果表明, UGP 和 RV 显着影响酒店客人的服务质量、价格、整体形象认知和预订意愿。 UGP 和 RV 的交互作用表明, 与 UGP 的中性和负面在线酒店评论相比, 带有 UGP 的正面在线酒店评论对酒店客人的服务质量、价格、整体形象感知和预订意愿的影响更大。

了解UGPs和RV对客人价格、服 务质量、整体形象感知和预订意愿的影响, 可以帮助酒店管理者和社交媒体网站设计师更好地宣传酒店, 提供高效的在线酒店预订环境。

本研究建立了UGP和RV与酒店客人感知价格、感知服务质量、感知整体形象和预订意愿之间的关系, 这些是在线酒店营销的关键因素。

This study aims to provide a critical reflection of the application of image recognition techniques in visual information mining in hospitality and tourism.

This study begins by reviewing the progress of image recognition and advantages of convolutional neural network-based image recognition models. Next, this study explains and exemplifies the mechanisms and functions of two relevant image recognition applications: object recognition and facial recognition. This study concludes by providing theoretical and practical implications and potential directions for future research.

After this study presents different potential applications and compares the use of image recognition with traditional manual methods, the main findings of this critical reflection revolve around the feasibility of the described techniques.

Knowledge on how to extract valuable visual information from large-scale user-generated photos to infer the online behavior of consumers and service providers and its influence on purchase decisions and firm performance is crucial to business practices in hospitality and tourism.

Visual information plays a crucial role in online travel agencies and peer-to-peer accommodation platforms from the side of sellers and buyers. However, extant studies relied heavily on traditional manual identification with small samples and subjective judgment. With the development of deep learning and computer vision techniques, current studies were able to extract various types of visual information from large-scale datasets with high accuracy and efficiency. To the best of the authors’ knowledge, this study is the first to offer an outlook of image recognition techniques for mining visual information in hospitality and tourism.

This study aims to investigate the influence of the reviewed establishment’s price level and the user’s social network size and reputation status on consumers’ tendency to post more visual imagery content. Furthermore, it explores the moderating effects of user experiences and geographic distance on these dynamics.

This study adopts a multi-method approach to explore both the determinants behind the sharing of user-generated photos in online reviews and their internal mechanisms. Using a comprehensive secondary data set from Yelp.com, the authors focused on restaurant reviews from a prominent tourist destination to construct econometric models incorporating time-fixed effects. To enhance the robustness of the authors’ findings, the authors complemented the big data analysis with a series of controlled experiments.

The reviewed establishments price level and the users reputation status and social network size incite corresponding motivations conspicuous display “reputation seeking” and social approval motivating users to incorporate more images in reviews. “User experiences can amplify the influence of these factors on image sharing.” An increase in the users geographical distance lessens the impact of the price level on image sharing, but it heightens the influence of the users reputation and social network size on the number of shared images.

As a result of this study, high-end establishments can increase their online visibility by leveraging user-generated visual content. A structured rewards program could significantly boost engagement by incentivizing photo sharing, particularly among users with elite status and extensive social networks. Additionally, online review platforms can enhance users’ experiences and foster more dynamic interactions by developing personalized features that encourage visual content production.

This research, anchored in trait activation theory, offers an innovative examination of the determinants of photo-posting behavior in online reviews by enriching the understanding of how the intricate interplay between users’ characteristics and situational cues can shape online review practices.

This study aims to investigate the promoting effects of the quantity and quality of online review user-generated photos (UGPs) on perceived review usefulness. The research further tests the hindering effect of human facial presence in review photos on review usefulness.

Based on review samples of restaurants in a tourist destination Las Vegas, this study used an integrated method combining a machine learning algorithm and econometric modeling.

Results indicate that the number of UGPs depicting a restaurant’s food, drink, menu and physical environment has positive impacts on perceived review usefulness. The quality of online review UGPs can also enhance perceived review usefulness, whereas facial presence in these UGPs hinders perceived review usefulness.

Findings suggest that practitioners can implement certain tactics to potentially improve consumers’ willingness to share more UGPs and UGPs with higher quality. Review websites could develop image-processing algorithms for identifying and presenting UGPs containing core attributes in prominent positions on the site.

To the best of the authors’ knowledge, this study is the first to present a comprehensive analytical framework investigating the enhancing or hindering roles of review photo quantity, photo quality and facial presence in online review UGPs on review usefulness. Using the heuristic-systematic model as a theoretical foundation, this study verifies the additivity effect and attenuation effect of UGPs’ visual elements on judgements of online review usefulness. Furthermore, it extends scalable image data analysis by adopting a deep transfer learning algorithm in hospitality and tourism.

Hotels provide high-quality guest experiences to generate perceived value for money (PVM), positively influencing word-of-mouth (WOM) and electronic word-of-mouth (eWOM) communication. This study aims to (1) verify the impacts of the perceived quality by the guests about their experiences in hotels on their PVM; (2) inspect the influence of guests' perception of hotel prices on PVM; (3) examine the impacts of guest PVM on their hotel experiences on WOM and eWOM and (4) investigate the consequences of the hotel guests' behavioural engagement on social networking sites (HGBE-SNS) on eWOM.

This quantitative and descriptive study consists of a survey with 371 guests who evaluated their experiences at three hotels in Brazil. PLS-SEM tested the hypothetical model that resorted to the stimulus-organism-response theory (S-O-R), proposed by Mehrabian and Russell (1974). Cluster Analysis compared the PVM, WOM and eWOM of groups of hotel guests with different levels of social media engagement.

Perceived quality by hotel guests positively impacts PVM. Perceived price negatively influences PVM. PVM had a positive and robust impact on WOM. PVM impacts and explains weakly eWOM. In contrast, HGBE-SNS affects and better explains eWOM than PVM.

This unprecedented investigation concomitantly exhibits the relationships between perceived quality, price, PVM, WOM, eWOM and HGBE-SNS. Hotels must offer high perceived quality experiences to influence PVM and WOM positively. PVM is unable to stimulate eWOM strongly. HGBE-SNS is pivotal for guests to share their hotel experiences through eWOM. This study suggests marketing strategies for hospitality companies to amplify customer engagement on SNS.

In the field of hospitality, most studies use English reviews and neglect non-English sources. The purpose of this paper is to exploit a predictive framework for review helpfulness that can process both Chinese and English textual comments.

This study develops some methods for feature extraction from Chinese online reviews, extracts more comprehensive predictors and proposes a novel prediction framework of classification before regression. Hofstede’s cultural theory is used to explain differences in the determinants of the helpfulness of reviews in Chinese and English.

The findings reveal that travelers from various countries do have discrepant perspectives on reviews helpfulness. Chinese tourists pay more attention to the reviewer profiles, whereas American tourists pay more attention to the review-related features.

This research offers hoteliers with actionable implications for meeting the needs of travelers from dissimilar cultural societies. The authors’ prediction framework can be used by website developers to create a review helpfulness rating system that allows visitors to acquire beneficial information.

On the one hand, the methods developed for extracting features of Chinese review, the hybrid set of features with several novel predictors and the prediction framework proposed in this study contribute to the methodology. On the other hand, this study is one of the few articles based on Hofstede’s cultural theory to guide a cross-cultural study on reviews helpfulness in hotel sector, which in turn contributes to the theory.

The purpose of this paper is to describe and position the state-of-the-art of text and image mining methods in business research. By providing a detailed conceptual and technical review of both methods, it aims to increase their utilization in service research.

On a first stage, the authors review business literature in marketing, operations and management concerning the use of text and image mining methods. On a second stage, the authors identify and analyze empirical papers that used text and image mining methods in services journals and premier business. Finally, avenues for further research in services are provided.

The manuscript identifies seven text mining methods and describes their approaches, processes, techniques and algorithms, involved in their implementation. Four of these methods are positioned similarly for image mining. There are 39 papers using text mining in service research, with a focus on measuring consumer sentiment, experiences, and service quality. Due to the nonexistent use of image mining service journals, the authors review their application in marketing and management, and suggest ideas for further research in services.

This manuscript focuses on the different methods and their implementation in service research, but it does not offer a complete review of business literature using text and image mining methods.

The results have a number of implications for the discipline that are presented and discussed. The authors provide research directions using text and image mining methods in service priority areas such as artificial intelligence, frontline employees, transformative consumer research and customer experience.

The manuscript provides an introduction to text and image mining methods to service researchers and practitioners interested in the analysis of unstructured data. This paper provides several suggestions concerning the use of new sources of data (e.g. customer reviews, social media images, employee reviews and emails), measurement of new constructs (beyond sentiment and valence) and the use of more recent methods (e.g. deep learning).