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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。

Two valuable pieces of information – reviews and their corresponding numerical ratings – are accessible to potential customers before they make a purchasing decision. An extensive body of marketing literature has scrutinized the influence of customers’ reviews by linking such aspects as the volume and valance of reviews with product sales and customers’ purchase intention. The aim of this study, for which dual coding theory was used, was to understand the relationship between reviews and their corresponding numerical ratings.

The authors used the latent Dirichlet allocation technique to categorize customers’ reviews. The present findings contribute to the literature by showing the underlying mechanisms that customers use to interpret reviews and associate them with numerical ratings.

The gradient boosted decision tree model demonstrates that non-abstract-dominant reviews (reviews mainly consist of tangible objects, actions, events or affective words) are significant predictors of their corresponding numerical ratings. However, abstract-dominant reviews (i.e. those consisting primarily of intangible objects, events or actions) cannot predict their associated numerical ratings.

The present findings contribute to the literature by showing the underlying mechanisms that customers use to interpret reviews and associate them with numerical ratings.

The existing collaborative filtering algorithm may select an insufficiently representative customer as the neighbor of a target customer, which means that the performance in providing recommendations is not sufficiently accurate. This study aims to investigate the impact on recommendation performance of selecting influential and representative customers.

Some studies have shown that review helpfulness and consistency significantly affect purchase decision-making. Thus, this study focuses on customers who have written helpful and consistent reviews to select influential and representative neighbors. To achieve the purpose of this study, the authors apply a text-mining approach to analyze review helpfulness and consistency. In addition, they evaluate the performance of the proposed methodology using several real-world Amazon review data sets for experimental utility and reliability.

This study is the first to propose a methodology to investigate the effect of review consistency and helpfulness on recommendation performance. The experimental results confirmed that the recommendation performance was excellent when a neighbor was selected who wrote consistent or helpful reviews more than when neighbors were selected for all customers.

This study investigates the effect of review consistency and helpfulness on recommendation performance. Online review can enhance recommendation performance because it reflects the purchasing behavior of customers who consider reviews when purchasing items. The experimental results indicate that review helpfulness and consistency can enhance the performance of personalized recommendation services, increase customer satisfaction and increase confidence in a company.

This paper aims to investigate an integrated, holistic assessment of the characteristics by which consumers judge non-sponsored product review video (PRV) source, message and medium components as credible, and how these are linked to personal values for a deeper understanding of multidimensional credibility assessments of PRVs.

Employing a means-end approach, the authors draw on credibility theory and the persuasion knowledge model to analyse data from 21 in-depth semi-structured laddering interviews.

First, the authors demonstrate distinctive contributions of the video modality towards PRV credibility assessments and the interplay between specific PRV characteristics, cognitive and socio-emotional consequences, and personal values in an ongoing process of credibility assessment. Second, high persuasion knowledge creates awareness of the potential phoniness of the market, revealing a dark side to PRV use even in non-sponsored PRV seemingly created and shared as an act of benevolent concern between consumers.

This paper focused on the credibility of non-sponsored PRVs, future studies might investigate motivations and attributes by which users judge sponsored reviews. Also, the roles of specific product categories and existing brand trust on PRVs credibility provide avenues for further research.

This research offers practical implications for reviewers and brand managers to leverage the unique informational values of video by focusing on the interplay between credibility attributes and customer values.

This work advances credibility theory in the PRV context by examining how non-sponsored PRVs are evaluated as credible, by highlighting consumer persuasion knowledge and scepticism and including the holistic effects of the interplay between source, message and video format characteristics and by linking these to consumers’ goals and values.

The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews can help predict product sales.

The authors designed a big data architecture and deployed Node.js agents for scraping the Amazon.com pages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales.

This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with sentiments and discounts are more important than the individual predictors of discounts, sentiments or online volume.

This study designed big data architecture, in combination with sentimental and neural network analysis that can facilitate future business research for predicting product sales in an online environment. This study also employed a predictive analytic approach (e.g. neural network) to examine the variables, and this approach is useful for future data analysis in a big data environment where prediction can have more practical implications than significance testing. This study also examined the interplay between online reviews, sentiments and promotional strategies, which up to now have mostly been examined individually in previous studies.

One of the major challenges within the airline industry is to keep pace with the changing customer perception toward their service quality. This paper aims to demonstrate how sentiment analysis of user-generated big data can be used to research airline service quality as a more comprehensive alternative to other survey-based models by investigating real-time passenger insights.

The present research uses the case of Indigo airlines by studying passenger's trip advisor reviews regarding the low-cost commercial airline service. The authors analyzed 1,777 passenger reviews, which were classified, to uncover sentiments for five dimensions of airline service quality (AIRQUAL).

The findings of the study demonstrate the need for harnessing the brand-related user-generated content shared on online platforms to identify the critical attributes for airline service quality. Further, through the application of sentiment analysis, the paper provides much-needed clarity in the processing of user-generated content. It illustrates the investigation of passenger interactions as a reflection of their satisfaction, expectation, intention and overall opinion toward the airline service quality.

The analytical framework adopted in the study for examining user-generated content (UGC) can be functional for the marketing managers and equip them for handling large-scale data readily available in action-oriented interactive marketing research.

This paper demonstrates how sentiment analysis of user-generated data can be used to research airline service quality as a more comprehensive alternative to other survey-based models. The study supplements the methodological advances in the field of UGC analysis and adds to the existing knowledge domain.

The purpose of this paper is to understand the differences between organic (i.e. naturally occurring) and sponsored (i.e. marketer influenced) consumer-generated blog reviews, by examining how blog source and review valence can impact upon perceived credibility, brand attitude, and behavioural intentions.

This study used an online experiment, where participants were randomly assigned to one of six conditions in a 2 (blog source) × 3 (review valence) between-subjects factorial design.

The findings indicate that balanced reviews are perceived to be the most credible. Negative reviews led to the lowest ratings on brand attitude and purchase intention, while positive reviews led to the highest ratings on these two constructs. However, the effects of review valence were not found to differ based upon whether a blog was organic or sponsored.

The findings of this study extend and contribute to the knowledge of the impact of source and valence in electronic word of mouth settings. Moreover, the findings of this study provide practical insight into how consumers react to the sponsorship of blog web sites on the internet.

With the increasing importance of technology in hospitality and tourism, technology-driven service innovation has been a salient topic discussed from both customers’ and suppliers’ perspectives. However, there has been a lack of research that provides an overview of research on technology-driven service innovation. The purpose of this study is to review current discussions on technology-driven service innovation and provide directions for future studies in the hospitality and tourism literature.

A total of 82 articles on technology-driven service innovation were collected from top-tier hospitality and tourism journals. The papers were analyzed using content analysis to derive key topics discussed in the literature. Such discussions were made by different service innovation categories, antecedents, outcomes and theories. Future research agendas were suggested based on the research gap found in the literature.

The results indicate that prior discussions on technology-driven service innovation viewed technology as a service or service delivery method, with limited focus on management, marketing and institutional service innovation. In addition, the study reveals five key topics that need further discussion, such as cocreative technology, human resources management, strategy management, emerging technology and digital transformation.

While there have been increasing studies that reveal determining roles of technology in service innovation, scarce research introduced the new concept of technology-driven service innovation, suggesting a comprehensive approach. By adopting the unique approach of technology-driven service innovation, the research reveals the multifaceted roles of technology in service innovation and areas that need further discussion to implement highly sustainable strategies.

The research adds to the knowledge of technology-driven service innovation by providing a holistic view of current discussions, finding research gaps and proposing future research agendas for extended discussion.

Falsified online reviews (FORs) are the published/viewable consumer-generated online content regarding a firm (or its representatives) or its services and goods that is, to some degree, untruthful or falsified. The purpose of this study is first to explore the nature of FORs, focusing on reviewers' interpretations and refections on falsity, intent, anonymity and the target of their FOR. Secondly, the authors examine the valence and veracity dimensions of FORs and introduce a typology to differentiate their variations.

using an exploratory research design, 48 interviews were conducted with participants who post online reviews on social media about their experiences with food and beverage serving outlets.

The results show four common forms of FORs on social media. These are reviews focused on equity equalizing, friendly flattery, opinionated opportunism and malicious profiteering.

The authors provide exploratory and in-depth information via interviews, but do not analyse the content of FORs.

Firms should be aware of varieties of FORs and that these may not be limited to malicious content. This is important in terms of showing that in dealing with FORs, a one-size-fits-all approach will not work. FORs are not always entirely fabricated, and instead various levels of falseness are observed, ranging from slight alterations to complete fabrications.

Previous research explored how to identify and differentiate FORs from truthful ones, focusing on the reviews or how they are perceived by readers. However, comparatively little is known of the reviewers of FORs. Hence, this study focuses on reviewers and offers new insights into the nature of FORs by identifying and examining the main forms of FORs on social media.

Repeat customers play an important role in the restaurant sector. Previous studies have confirmed the positive effect of managerial responses on customer relationship management. However, the practice of managerial response strategies toward repeat customers in the restaurant sector remains unclear. This study aims to explore how social influence and the revisit intention of customers affect the responding behavior of restaurant managers.

This study collects information of 251,944 customer reviews and managerial responses from 1,272 restaurants on Yelp (a leading restaurant review website around the world) and builds four econometric models (with restaurant and month fixed effects) to test the hypotheses empirically.

The empirical results show that restaurant managers are less likely to respond to reviews posted by repeat customers (10% lower than that of new customers). This effect is moderated by customer social influence, which entails that repeat customers with great social influence are more likely to receive managerial responses. Moreover, reviews from repeat customers who have had a longer time since their last consumption are also more likely to receive managerial responses.

The results present implications for restaurant managers in business practice regarding managerial response. Managers should take advantage of platform designs and tools (i.e. customer relationship management programs to keep track of repeat customers) to locate repeat customers and avoid the potential negative effects caused by their selected response strategies.

To the best of the authors’ knowledge, this study is among the first attempts to examine empirically how restaurant managers respond to reviews generated by repeat customers in real business practice and reveals what drives such activities from the perspectives of social influence and revisit intention.