This study examines the impact of others' reviews (reviews, product ratings) on consumer responses (helpfulness & buying intention) in an online shopping platform. We propose that review features, such as review message construal and review inconsistency between review message valence and rating, determine review credibility as product-related information, which in turn influences helpfulness of review and buying intention toward the product. Specifically, low- level construal review messages will be perceived as more credible than high-level construal review messages, which affect helpfulness and buying intention. In addition, the effect of review message construal will be moderated by review inconsistency. The effect of the review message construal will be enhanced in the condition of review consistency (positive content-high rating & negative content-low rating), but it will be disappeared or attenuated in the condition of inconsistency (positive content-low rating, negative content-high rating).

One of the main challenges brands face nowadays is the ability to provide a real-life experience through online platforms. The aim of this study is to analyze an AR try-on app versus a website, considering consumers self-concept and testimonials. To this end, an online survey was conducted, in which respondents were exposed to two of four scenarios: AR APP or website experience, and positive versus negative reviews presence. Our findings indicate that ideal self-congruence impacts both, purchase intention and confidence. The present study positively contributes to the AR and self-concept literature, while opening new avenues of research for both academics and practitioners.

This study investigates how travellers adopt information from travel review websites, i.e., Tripadvisor and how online travel reviews influence their intention to visit a tourist attraction. Based on the Information Adoption Model (IAM), a conceptual model was developed and tested using the data obtained from 227 valid respondents.

Online communities are identified as people gathering online and communicating through the internet to share ideas, objectives, goals, without any geographical boundary. The growth of user-generated content created in online communities has transformed the way consumers search for and share information, particularly in the hospitality industry. Particularly, in the restaurant and food sectors due to the intangible nature of hospitality services, online reviews play an important role on consumer decisions. Furthermore, online reviews on restaurants are not only informational but also, they impact consumers’ choices regarding restaurants. Consequently, the nature of such user-generated content that is produced at a high speed and is diverse and rich should be treated and understood. This study proposes the first tailored BERTopic model together with sentiment analysis based on pre-trained BERT model that takes advantage of its novel sentence embedding for creating interpretable topics into the analysis of restaurant online reviews to determine how the customers elaborate their criteria in the context of certain experiences. An exploratory analysis is presented involving a large-scale review data set of 261,531 restaurant online reviews from 4 different countries retrieved from the eWOM community thefork.com. A broad list of the topics discussed by customers post-dining in restaurants is built. Insights into the behavior, experience, and satisfaction of the customers across the different restaurants are discovered. This approach and findings are encouraging hospitality managers in understanding customers’ perception, through which applicable marketing can be developed to attract and retain potential customers.

This study aims to address two important questions: will advertising on mobile short-form video apps jeopardize the value perception of luxury brands (RQ1), and if so, how will self-deprecating online reviews eliminate these negative effects (RQ2). An experimental design approach was employed to investigate the proposed research questions. Three experiments were conducted to test the hypotheses. SPSS was used for data analysis. The study 1 finds that compared with traditional media, advertising on mobile short-form video apps shortened the psychological distance between consumers and luxury, therefore has a more negative impact on consumers’ perception of luxury brands. The study 2 reveals that self-deprecating online reviews can eliminate the negative effects of advertising of luxury brands. On the basis of previous research, this paper proves the negative influence of social media on luxury brands in the scene of new social media-mobile short format video application. In addition, it also studies the moderating effect of online comments, especially self-deprecating comments, on consumers' perception of luxury brands. This study outlines theoretical contributions and practical implications for the luxury marketing management and made suggestions for future research in the field of luxury marketing in Social Media.

본 연구는 소비자가 경험재인 웹소설을 선택하는 상황에서 사실적 메시지와 평가적 메시지를 담은 온라인 리뷰를 보았을 때 더 유용하다고 판단하는 리뷰가 무엇인지 탐색하고, 이러한 유용성 평가의 차이에 대한 개인의 분석적 의사결정 성향 수준의 조절효과를 검증하기 위하여 실시되었다. 경험재를 구매하기 전 객관적 정보를 수집하는 소비 자의 성향에 근거하여 웹소설의 소비자는 사실적인 온라인 리뷰의 유용성을 더 높이 평가할 것으로 예상하였다. 또 한 인지적 성향에 따라 구분되는 의사결정 유형 중 분석적 의사결정자는 정확한 정보를 수집하여 논리적인 판단을 내린다. 따라서 분석적 의사결정 성향 수준이 높아짐에 따라 사실적인 온라인 리뷰의 유용성 평가가 높아질 것으로 예상하였다. 실험 1의 결과 사실적인 리뷰를 제시받은 집단이 평가적인 리뷰를 제시받은 집단보다 리뷰의 유용성을 높이 평가하였으며, 실제 웹소설 선택 상황과 유사하게 두 유형의 리뷰를 동시에 제시한 실험 2에서도 리뷰의 유용 성 평가에 대한 리뷰 메시지 유형의 주효과가 확인되었다. 또한 실험 2에서는 분석적 의사결정 성향 수준이 높아짐 에 따라 사실적인 리뷰의 유용성을 높이 평가하는 경향이 드러나 분석적 의사결정 성향 수준의 조절적 역할이 확인 되었다. 본 연구는 경험재인 웹소설을 선택하는 상황에서 리뷰 메시지 유형 및 소비자의 분석적 의사결정 성향 수준 이 소비자의 리뷰 유용성 평가에 영향을 끼친다는 사실을 확인함으로써 웹소설 소비자의 행동 양식을 밝혔다는 이론 적, 실무적 의의를 갖는다.

Online consumer activities have increased considerably since the COVID-19 outbreak. For the products and services which have an impact on everyday life, online reviews and recommendations can play a significant role in consumer decision-making processes. Thus, to better serve their customers, online firms are required to build online-centric marketing strategies. Especially, it is essential to define core value of customers based on the online customer reviews and to propose these values to their customers. This study discovers specific perceived values of customers in regard to a certain product and service, using online customer reviews and proposes a customer value proposition methodology which enables online firms to develop more effective marketing strategies. In order to discover customers value, the methodology employs a text-mining technology, which combines a sentiment analysis and topic modeling. By the methodology, customer emotions and value factors can be more clearly defined. It is expected that online firms can better identify value elements of their respective customers, provide appropriate value propositions, and thus gain sustainable competitive advantage.

This study analyzes the user-generated reviews of Paris-based Michelin three-star restaurants in terms of how they are discursively constructed. Using the reviews posted in Tripadvisor in 2019 as data, it examines how positive reviews (PR) and negative reviews (NR) are framed with distinct discursive practices. While PR and NR a re both characteriz ed by the discursive practice of highlighting professed culinary expertise of the reviewer, this feature is more foregrounded in NR, where the reviewer is generally more oriented to showing themselves as being entitled to write a review. In terms of communicative styles, PR is also characterized by a heavy use of symbolic and metaphoric language, while more ordinary style of language is used in NR, embedded in the context of critiquing specific items of dish or service. While PR and NR both tend to make references to Michelin star status as a basis of their evaluation, they were shown to differ in terms of the tones or keys used in describing chefs, and also in the way the target of evaluation is formulated. The findings shed light on how and why the members of foodie community construct the language the way they do, and have implications for genre analysis.

This paper presents a way of classifying qualitative online consumer reviews (OCRs) in terms of functional and emotional dimensions and measures the direct and indirect impact of both volume and valence of OCRs on product sales. Utilizing four million online postings across 342 mobile games for thirty months, the authors use text analysis and word classification and identify 74 representative words to describe the various levels of functional OCRs consisting of product quality, product innovativeness, price acceptability, and product simplicity, and emotional OCRs including anger, fear, shame, love, contentment, and happiness. They combine the resulting OCR volumes with weekly sales, resulting in 1,835 observations for analysis with hierarchical Bayesian methods. Results suggest that the volume and valence of aggregated functional OCRs and the valence of aggregated emotional OCRs have the positive effects on sales. The volume and valence of functional OCR subcategories have mixed effects on sales and the link is moderated by the share of emotional OCR subcategories. Further, a sales forecasting model which includes 13 variables of OCR subcategories shows the best predictive validity.

This study aims to analyse the overall sentiments of online reviews on restaurants in Malaysia using predictive text analytics. As we know in opinion mining, sentiment analysis is a prominent technique in predictive text mining. It is a technique that categorises opinions in unstructured text format into binary classification (ie. good or bad). The authors attempt to go beyond the binary classification by viewing texts as empirical entities derived using the Term Frequency - Inverse Document Frequency (TF-IDF) weighting algorithm. These empirical entities, based on online reviews of restaurants in Malaysia, are then manifested into hypothetically defined constructs closely reflecting their thematic and semantic nature. The were 4914 customer reviews from restaurants across 20 towns and cities in Malaysia scraped off TripAdvisor.com using web crawler tools. Then a series of analytical tests were carried out. First the online reviews were parsed, filtered and clustered using SAS Text Miner. Then the online reviews underwent the TF-IDF process to identify significant terms and weightages were assigned according to their importance. The TF-IDF process resulted in a series of important nouns and adjectives from the text corpus. Using these weightages of nouns and adjectives, the authors went on to thematise these terms based on their semantic nature to manifest hypothetical constructs. These constructs were based on the Mehrabian–Russell Stimulus Response Model. Subsequently the authors tested the associations among the constructs using variance-based and covariance-based Structural Equation Modelling (SEM). The authors were encouraged by this exploratory methodological approach in formulating predictive text analytics using SEM. Results indicated that sentiments were generally positive towards restaurants and the important terms derived were price, hospitality, location, waiting time, availability of parking and size of food portion.

Online review sites such as Booking.com or Tripadvisor are considered to be the most accessible and valuable feedback platform in the hospitality industry (Verma et al., 2012; Xiang & Gretzel, 2010; Yoo & Gretzel, 2008). To keep pace with customers’ use of social media, hotels have recently begun to use customer-generated content or online reviews to assist in decision-making (Chan & Guillet, 2011; Leung et al., 2013) since reviews can affect customer satisfaction and ultimately hotel sales and profitability (e.g. Ye et al., 2011; Zhang et al., 2011; Kim et al., 2016; Berezina et al., 2016;). However, limited research efforts have been made to understand customers’ satisfactory and unsatisfactory experiences by analysis of online reviews (Kim et al., 2016; Berezina et al., 2016; Rhee & Yang 2015 a;b; Levy et al., 2013; Li et al., 2013; Kim et al., 2015; 2016; Kwok & Xie, 2016). Furthermore, the effect of different service characteristics on hotel performance is expected to be assymetrical and non-linear (Mikulic & Prebežac, 2008; Füller et al. 2006; Kim et al., 2016; Zhang and Cole, 2016). The objective of this study is to analyse online reviews and determine whether different hotel service characteristics have assymetrical or symmetrical effects on hotel customer satisfaction. A total of 8.540 online customer reviews (from Booking.com) for 42 4 and 5 star hotels in Athens, Greece were analysed in terms of the overall score of the hotel and the individual service characteristics (cleanliness; location/access; personnel quality; installation quality; room quality; food quality; service process quality, and perceived value) for a 2-year period. Data was analyzed using penalty-reward analysis (Mikulic & Prebežac, 2008) and the three factor (satisfiers, dissatisfiers, hybrid) theory of customer satisfaction (Matzler & Sauerwein, 2002; Matzler et al., 2003). Results show that there are indeed asymmetric effects on customer satisfaction. The most powerful frustrators are cleanliness and perceived value and the highest impact dissatisfier is room quality, followed by installation quality and food quality. Only personnel quality and location/access are hybrid factors, meaning that they can have symmetric effects on customer satisfaction. Also, no characteristic was found to be a satisfier or delighter showing that delighting customers is very difficult. Results also differ according to reason for travel (leisure / business) and type of traveller (solo, groups, families, friends). The results of this study can serve as a guide for customizing hotel services for each type of customer. This can lead to higher customer satisfaction and higher perceived overall performance of hotels as expressed in online reviews. Also, higher review ratings can influence overall profits.

Considering that the effectiveness of ads varies according to the credibility of consumers, it is necessary to establish data regarding consumer credibility in relation to online reviews. To conduct a successful study on the marketing strategies of online reviews, it is also necessary to analyze the relationship between credibility and the various factors that influence the purchase intentions of consumers. Therefore, this study attempted to examine the relationship between consumer trust of on-line reviews, brand preference, ads credibility, and purchase intentions in relation to cosmetics. The study was conducted through a normative descriptive survey method using stimuli and a self-administered questionnaire. Analysis of the structural equation model was conducted for the data analysis. The results revealed that consumer reliance on online reviews of cosmetics influences brand preference, credibility of brand ads and purchase intentions. The results also revealed that consumers’ on-line reviews, brand preference, and trust of brand ads are important factors for increasing the purchase intentions. The mediation effect of brand preference and brands’ ads credibility were found in the process where on-line reviews exercise an influence on the purchase intentions. It was also found that brand preference has a stronger influence on purchase intention than credibility of brand ads. It was discovered that the credibility of on-line reviews directly influences purchase intentions more than indirectly influences. Considering the results of this study, programs that encourage customers to post on-line reviews, and strategies to promote brand preference by targeting groups that exhibit high trust in online reviews would be recommended.

The goal of this study is to get a better understanding of the relationship between online customer reviews (OCRs), product returns and sales after returns in online fashion. Furthermore, we generate deeper insights about the moderating role of mobile shopping usage, product involvement and brand equity in this context. We answer our research questions by empirically analyzing a unique data set from a European fashion e-commerce company. This study links a wide range of transaction data (2.5 billion page clicks, 46 thousand different products, 700 brands, 40 product categories, 72 million sold and 33 million returned items) with a large set of OCRs (0.9 million). Our results show that positive OCRs can lead to higher sales, lower returns, and better conversion rates. Considering higher search costs on mobile devices, we reveal a weaker impact of OCRs in the mobile than in the desktop sales channel. Furthermore, in line with involvement theory, we see a significant impact of product involvement in this context such as the influence of positive OCRs is stronger for high-involvement products than vice versa. Moreover, we find strong support for statements from brand signaling literature, that OCRs matter more for weak than for strong brands.

E-commerce is a global phenomenon that reshapes retailing and the appropriate multinational corporations. The goal of this study is to get a better understanding of the relationship between online customer reviews (OCRs), sales and sales after returns in the cross-national and cross-cultural context. We discuss our hypotheses by empirically analyzing a large and unique data set from a European fashion e-commerce company. This study links a wide range of transaction data (0.8 billion page clicks, 17 thousand different products, 499 brands, 50 product categories, 22 million sold and 11 million returned items) from six different countries (Austria, France, Germany, Italy, Netherlands, Poland) with a large set of OCRs (0.7 million). Our results show that positive OCRs can lead to higher sales and sales after returns with considerable cross-country differences. We argue that differences in culture provide a substantial explanation for these effects by using Hofstede's cultural framework.

We adopt a semi-grounded theory approach to investigate the impact of different review manipulation tactics. Shoppers take a negative view toward seller manipulations, but the degree of negativity varies across different tactics. Moreover, different manipulations tactics vary in the ease of detection, perceived unethicality, and the effect on consumer perceptions.

Given the increasing competition in the hospitality industry, a key question is to investigate how consumer-generated reviews affect the consumption decision of tourism services. Online reviews are regarded as one form of electronic word of mouth communication (Banerjee & Chua, 2016). While researchers have demonstrated the benefits of the presence of customer reviews on company sales, an issue scarcely investigated is how to assess the impact of informational cues on eWOM adoption for consumer decision-making and how individuals process and integrate conflicting opinions from other consumers. Drawing on dual process theories, this paper analyzes: (1) the impact of systematic information cues (informativeness, credibility and helpfulness of reviews) on eWOM adoption; (2) the moderating effect of conflicting reviews on the impact of eWOM adoption on behavioural intentions. The heuristic-systematic model HSM (Chaiken, 1980) is a widely recognized communication model that attempts to explain how people receive and process persuasive messages. As Zhang et al. (2014) advocated, the HSM provides broader explanations of individuals’ information processing behaviour in the context of online communities than do other models, such as ELM (elaboration likelihood model). We build up and test an expanded HSM model anchored in dual process literature, which includes the influence informativeness, credibility and helpfulness of mixed valence online reviews (systematic information cues) have on eWOM adoption which, in turn, influences behavioural intentions. In order to test the hypotheses of the model an experimental subjects-design was carried out using valence order: positive-negative vs. negative-positive as a condition. Data was collected in January 2016 using a sample of 908 Tripadvisor heavy-users. 461 interviewees answered in the POS-NEG condition and 447 in NEG-POS condition. Participants were instructed to imagine a situation where they were going out for dinner to an Italian restaurant with friends and they were told to read a total of 10 reviews about the restaurant in the same order they were displayed and answer the questions that followed. We used an experimental design. All variables were measured with seven point likert scales. Data analysis shows informativeness activates both review credibility and review helpfulness, which in turn influence eWOM adoption. When the sequence of Tripadvisor reviews begins with positive commentaries, eWOM is a significant driver of intention to visit the restaurant, but when the user reads negative commentaries followed by positive ones, the effect becomes non-significant. This study is novel because it examines the factors that drive consumers to adopt consumer generated content (eWOM) in tourism services and to make consumption decisions. This study demonstrates how systematic information cues and sequence of reviews influence on eWOM adoption and behavioural intentions. Firstly, consumer intentions to visit a restaurant are determined by the consumer's eWOM adoption, which, in turn, is determined by three information cues: informativeness, perceived credibility and helpfulness of the online reviews. Understanding the specific effects of different information cues on eWOM adoption seems to be particularly important given the tremendous competition in the tourism sector. Secondly, this study shows conflicting reviews affect the user in a complex way. When consumer reviews conflict, if the consumer reads positive reviews before the negative ones, eWOM adoption has a stronger influence on behavioural intentions. It seems that users attribute an opportunistic view to the negative comments mainly attributed to the lack of their informativeness, credibility and helpfulness. User behavioural intention to visit a restaurant is directed by systematic and heuristic information cues. Therefore, users examine content of online reviews carefully and they also are influenced by the sequence of comments.

Customers’ final purchase decisions for electronic products are understandably influenced by previous experiences, marketing messages such as price and promotion, and opinions from other consumers (Simonson and Rosen 2014). In particular, millions of product reviews are posted daily on online review boards or social media represent aggregate consumer preference data (Decker and Trusov 2010). Past studies analyzing online reviews or word-of-mouth (WOM) have focused more on the quantitative dimension of volume of WOM (or “how much people say”), but less on qualitative dimension of valence of WOM (or “what people say”) (Gopinath, Thomas, and Krishnamurthi 2014). However, recent studies have analyzed disaggregate-level UGC by performing text mining in addition to a general analysis of volume and valence of OUGC. Onishi and Manchanda (2012) investigate the relationship between movie sales and both TV advertising and blogs. Although the authors find that the volume and the valence of OUGC (i.e., blogs) are predictive of market outcomes, they retain only certain words (i.e., advertising, award, interesting, and viewed) that consumers would find useful, therefore having general predictive power for market outcomes. Gopinath, Thomas, and Krishnamurthi (2014) address the relationship between the content of online WOM, advertising, and brand performance of cell phones and find that the volume of OUGC does not have significant impact on sales, but only the valence of recommendation UGC has a direct impact. Liu, Singh, and Srinivasan (2015) find that both the volume and sentiments of Tweets do not outperform the information content of Tweets in predicting TV series ratings. Although these three papers have investigated the importance of qualitative UGC through text mining techniques, such studies have not accounted for the detailed dimensions of specific contents. For example, Onishi and Manchanda (2012) use only 4 words out of top 30 frequently cited words for their analysis, and Gopinath, Thomas, and Krishnamurthi (2014) classify the OUGC into three disaggregated dimensions (i.e., attribute, emotion, and recommendation) without further classifications of subcategories and valence of positivity and negativity. Liu, Singh, and Srinivasan (2015) mainly focus on positive and negative Tweet contents about TV shows, lacking further classification of functional and emotional dimensions. In contrast to these studies, this study aims to examine in-depth multidimensional aspects of the content of online reviews, i.e., qualitative UGC, and their impacts on product sales. In this process, we develop defensible measurements of UGC by executing a comprehensive empirical text analysis and evaluate the impact of measures of qualitative UGC relative to volume measure of quantitative UGC. Specifically, we analyze a large data set of UGC on the 350 most talked-about smartphone games from seven different genres (e.g., action, arcade, shooting, puzzle, role playing, simulation, and sports) over a 30 month period, August 2010 to February 2013. We utilize a theoretical framework that classifies qualitative UGC into two major perceptions of functional and emotional dimensions. Prior studies show that perceptions of both functional (cognitive) and emotional (affective) dimensions should be considered to investigate their effects on perceived user satisfaction (Coursaris and van Osch 2015) and online shopping behavior (Van der Heijden 2004). It is evident that both functional and emotional UGC influence consumers to purchase a focal product (Lovett, Peres, and Shachar 2013). The functional UGC relates to the positive and negative attributes and beliefs about a product, and the emotional UGC pertains to the feelings and emotions in response to product experience. As an example, consider one innovative car-racing mobile game which, although expensive, has 3D graphics and high level of complexity. After playing this game, consumers may express their feedback on this game online by describing it as well-made, unique, but sometimes fearful (because a high bill charge is expected from excessive playing time), and addictive (because they like the game too much to stop playing it). This type of online reviews contains different types of UGC: functional (e.g., quality, innovativeness) and emotional (e.g., fear). Another layer of our analysis involves the heterogeneity of impact on product sales across different qualitative UGCs. Specifically, we consider the effects of functional UGC on product sales across emotional contexts such as anger and happiness, in other words, a simultaneous association between functional UGC and emotional UGC. For example, although a consumer may be attracted by some reviews on the high quality graphics of a mobile game (functional UGC), she may hesitate to purchase this product because other reviews express their fear about high cost of purchasing virtual goods (emotional UGC). Accordingly, we expect the functional UGC’s effects on sales to be moderated (amplified or reduced) by emotional UGC. We accommodate such interaction effects in both aggregate and disaggregate models. To the best of our knowledge, this study is the first to empirically identify two dimensions of qualitative UGC (functional and emotional), and shed light on the effects of multidimensional UGC categories on sales. Our findings on the influence of qualitative UGC on product sales are quite different from the prevailing view that firms should pay attention more to the volume of UGC (Chevalier and Mayzlin 2006; Liu 2006) but little to the valence of UGC (Duan, Gu, and Whinston 2008; Godes and Mayzlin 2004; Liu 2006). Rather, our research is in line with recent three papers (Gopinath, Thomas, and Krishnamurthi 2014; Liu, Singh, and Srinivasan 2015; Onishi and Manchanda 2012) in terms of the importance of considering specific contents from a vast amount of text data. However, our paper provides two key contributions. First, we show that specific categories of qualitative online UGC such as functional and emotional variables can be used to predict product sales; this result will be of a high managerial relevance. Especially, traditional methods that use simple metrics such as volume and valence of UGC are less accurate than our method that employs a sophisticated, multidimensional content analysis. Second, the results offer guidance to firms in determining which specific UGC (quantitative or qualitative; functional or emotional; under what contexts) they should focus on for increasing the efficiency of their online marketing activities. Utilizing a large dataset of online reviews on 350 mobile games consisting of four million postings generated for thirty months, the authors identified 76 representative words to describe the functional and emotional UGC using text analysis and word classification. We combined the resulting UGC volumes with weekly sales, resulting in 1,835 observations for analysis with hierarchical Bayesian methods. We find that functional UGC includes 54 representative words to describe various levels of product quality, product innovativeness, price acceptability, and product simplicity, and emotional UGC includes 22 words to express anger, fear, shame, love, contentment, and happiness. The results show that the volume and valence of aggregated functional UGC and the share of aggregated emotional UGC have the positive effects on sales. The volume and valence of functional UGC subcategories have mixed effects on sales and the link is moderated by the share of emotional UGC subcategories. These results are in contrast to those in the literature. Further, a sales forecasting model which includes 13 variables of UGC subcategories shows the best predictive validity. The authors discuss the implications of these results for online marketers.