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Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics
... If the review is professional , make your English professional . 11 Please ensure your generated review English level is similar to the original review English level . First , identify the English level of review and generate according to it . ...
... this is a must Listing 2: AI Agent Prompts C Appendix C This appendix outlines the review feedback tips to improve the quality and usefulness of user reviews, as generated by the language model. The prompts provided are informed by recent research [9,11,27,34,37,39,40,42,44,45,54] on the characteristics of highly valuable reviews. 1 Don 't tell users to rate the review on a scale when you give tips . We already have a star rating system . ...
... 9 The researchers found that restaurants and online review platforms should encourage review writers to provide reaction words that communicate feelings , emotions , and subjective evaluations , as well as experiences in their reviews . 10 The researchers found that the readability of a review text is correlated with the perceived helpfulness of the reviews 11 Researchers found that reviews with precise or easy -to -understand writing styles will receive more helpfulness votes . 12 The researchers found that reviews expressing extreme sentiment would be considered valuable . ...
Online reviews help people make better decisions. Review platforms usually depend on typed input, where leaving a good review requires significant effort because users must carefully organize and articulate their thoughts. This may discourage users from leaving comprehensive and high-quality reviews, especially when they are on the go. To address this challenge, we developed Vocalizer, a mobile application that enables users to provide reviews through voice input, with enhancements from a large language model (LLM). In a longitudinal study, we analysed user interactions with the app, focusing on AI-driven features that help refine and improve reviews. Our findings show that users frequently utilized the AI agent to add more detailed information to their reviews. We also show how interactive AI features can improve users’ self-efficacy and willingness to share reviews online. Finally, we discuss the opportunities and challenges of integrating AI assistance into review-writing systems.
... Therefore, identifying the factors that influence the perceived helpfulness of online reviews is crucial in online hospitality marketing. Many existing studies have considered factors that influence review helpfulness from three main perspectives: (a) reviewer impact, such as reviewer experience, expertise level [6,[9][10][11] , and reviewer historical performance [12,13] ; (b) review informativeness, such as review length [6,12,14,15] , the number of user-generated photos (UGPs) [16][17][18] , valence, and rating [6,14,19,20] ; and (c) hotel managerial response (MR) [19,21] . ...
... Reviewer historical performance serves as an indicator of the unobservable quality signal that can be measured by the total votes on all reviews written by a reviewer [12] . There are inconsistent conclusions on whether the relationship between reviewer historical performance and review helpfulness is positive [13] or nonsignificant [12] . ...
... Review timeliness has been measured using different methods; for example, measurements based on the number of elapsed days since the review was posted revealed that reviews posted more recently tended to obtain more helpfulness notes than older reviews [13,39] . In other words, review timeliness has a positive effect on review helpfulness [9,40] . ...
Many existing studies have considered the factors influencing review helpfulness, mainly focusing on reviewer impact, review informativeness, and managerial response, based on signaling theory. However, previous studies have simply regarded these factors as independent signals, thus ignoring their in-depth transmission and reception processes. The conclusions about the impact of reviewers on review helpfulness are also inconsistent due to the inaccurate measurement of variables. To fill the above gaps, we followed the signaling timeline theoretical framework used in signaling theory and employed a bootstrapping analysis to examine how reviewer impact, review informativeness, and hotel managerial responses interact to influence review helpfulness. In this study, we used a unique dataset that included official labels from one leading online travel agency. The results show that reviewer impact may affect review helpfulness sequentially through review informativeness and hotel managerial response. Furthermore, by using official labels, both reviewer expertise and reviewer experience significantly affect review helpfulness. Finally, we discuss the theoretical and practical implications of these findings.
... For example, Hu and Yang (2021) contend that the more information is available about a reviewer, the more confident and trustworthy the reviewer is. Other indicators of reviewer credibility are the number of helpfulness votes (Zhang and Watts, 2008), reviewer levels on the platform (Fang et al., 2016), expertise (Banerjee et al., 2017;Cheung and Ho, 2015), number of reviews (O'Connor, 2008), number of followers (Cheung and Ho, 2015), reviewer experience (Banerjee et al., 2017;Kwok and Xie, 2016), and rating behavior (Fang et al., 2016;Wu et al., 2021). ...
... For example, Hu and Yang (2021) contend that the more information is available about a reviewer, the more confident and trustworthy the reviewer is. Other indicators of reviewer credibility are the number of helpfulness votes (Zhang and Watts, 2008), reviewer levels on the platform (Fang et al., 2016), expertise (Banerjee et al., 2017;Cheung and Ho, 2015), number of reviews (O'Connor, 2008), number of followers (Cheung and Ho, 2015), reviewer experience (Banerjee et al., 2017;Kwok and Xie, 2016), and rating behavior (Fang et al., 2016;Wu et al., 2021). ...
... First, by incorporating reviewers' historical rating behavior, it builds upon previous research on the helpfulness of reviews and extends the investigation to a critical yet understudied aspect: IMDS 124,8 reviewers' historical ratings. Research on the factors influencing review helpfulness has traditionally explored various dimensions, including review characteristics, such as message length, readability, linguistics, quality, and ratings (Fang et al., 2016;Huang et al., 2015;Korfiatis et al., 2012;Yang et al., 2019), as well as reviewer attributes, including information disclosure, expertise, and attractiveness (Li et al., 2019;Liu and Park, 2015;Zhu et al., 2014). Despite a wealth of studies assessing the diverse variables affecting review helpfulness, a notable research gap remains in understanding how reviewers' historical ratings contribute to their perceived helpfulness of their reviews, particularly within the context of defining reviewer credibility. ...
Purpose
As online reviews have become a potent marketing tool, the underlying motivation has come into question. Focus has shifted towards assessing reviewer credibility before appraising online review credibility. Guided by source credibility theory, this study investigates the effect of reviewers’ historical ratings on review helpfulness to gain insight into the role of reviewer credibility.
Design/methodology/approach
We explore readers’ underlying psychological processes using web data analysis (Study 1) and experiments (Study 2). Study 1 empirically examines the effect of reviewers’ historical ratings on review helpfulness using 100,621 reviews authored by 890 TripAdvisor reviewers. Study 2 involves two experiments with 328 participants to scrutinize the readers’ underlying mechanisms in establishing reviewer credibility, with a specific focus on the effect of reviewers’ historical ratings.
Findings
When a reviewer’s historical ratings are predominantly extreme, readers perceive the reviewer as less credible, leading to decreased helpfulness in reviews authored by that reviewer. Interestingly, high negativity in historical ratings does not have a significant effect on either reviewer credibility or review helpfulness.
Originality/value
This study offers two significant contributions to the existing literature. First, it extends previous research on review helpfulness by incorporating reviewers’ historical rating behavior. This provides a more comprehensive understanding of the factors that influence the perceived usefulness of reviews. Second, it integrates two distinct research methods: TripAdvisor web data analysis and experiments. This methodological synthesis enhances the robustness of the study by offering a more nuanced and well-rounded perspective on the dynamics between reviewers’ historical ratings and perceived helpfulness of reviews.
... On the other hand, massive amounts of information make consumers feel overwhelmed when making hotel selection decisions. To address the problem of information overload, many studies have explored various perspectives, such as the management of online reviews [3], the usefulness of online reviews [4,5], and hotel categorization [6]. Additionally, hotel recommendation systems have proven to be effective tools in coping with information overload [7]. ...
... Table 6 illustrates the optimal and worst reference points for the attribute "Rooms". Subsequently, the delight value, regret value and weight of each online review are obtained according to Eqs. (2)(3)(4). Content courtesy of Springer Nature, terms of use apply. Rights reserved. ...
Optimizing hotel recommendation systems based on consumer preferences is crucial for online hotel booking platforms. The purpose of this study is to reveal differences in hotel recommendation results for different types of consumers by considering consumer expectations. Specifically, this study introduces an online hotel recommendation method that considers three preferences for five types of consumers (business, couples, families, friends, and solo): attribute importance, consumer expectations, and actual hotel attribute performance. Here, consumer expectations are expressed in the form of the 2-tuple. 2-tuple expectations mean that customers can not only express specific demands but also express the probability of meeting the demands. Further, using three different consumer preferences, a similarity measurement model is constructed to recommend hotels for different types of consumers. This study puts this innovative method to the test using a dataset covering 40 hotels in the Beijing area and analyzes the impact of three preferences for different types of consumers on their hotel recommendation results. The method introduced in this study has two management implications. On the one hand, the recommendation method based on consumer preferences can optimize hotel recommendation systems and help online hotel booking platforms improve the accuracy of recommendation results. On the other hand, the proposed method can offer valuable insights to hotel managers, helping them measure their competitiveness and providing guidance for developing service improvement strategies.
... Similarly, another interviewee believes that a review that is errorfree 'gives the impression that the reviewer is someone whose words can be trusted'. In addition, the readability of the text has an impact on how reviews are perceived as valuable [50] and consequently, the purchase intention of consumers. With respect to the ELM, simple text requires less cognitive effort and is therefore more useful than complicated text [51]. ...
... This is because a large number of reviews can enhance consumer confidence, which, in turn, can reduce consumer uncertainty and increase purchase intentions [35]. However, it is important to note that the abundance of OCRs available to consumers may cause information overload [50], [63]. Therefore, these results need to be interpreted with caution, and further research is required to determine the optimal number of reviews required for Arab consumers to make informed purchase decisions. ...
... Em virtude do seu potencial, Filieri (2015) e Romeu et al. (2022) apontam que sites com este formato podem representar para os seus usuários fontes de informação credíveis, influenciando o processo de escolha e compra desse tipo de consumidor, atentos às indicações hospedadas em plataformas avaliativas. Assim, como estas avaliações demarcamse pelo quesito experiencial, as revisões online servem de influência sobre as percepções e expectativas positivas ou negativas de desempenho dos estabelecimentos (Fang et al., 2016). ...
... Por exemplo, de modo geral os consumidores são influenciados por comentários positivos que avaliam atributos como sabor, serviço, preço, cardápio e qualidade (Ramadanoğlu & Ince Karaçeper, 2023), por fatores que interferem na experiência, como autenticidade, memorabilidade, novidade, hospitalidade e interações sociais (Kodaş, 2023), e por características demográficas do autor e de inteligibilidade do texto da avaliação (Fang et al., 2016). ...
Objetivo: Analisar o comportamento dos usuários da seção de restaurantes do Tripadvisor à luz da Teoria do Comportamento Planejado, considerando os antecedentes da intenção de fazer uso da plataforma e o seu efeito no comportamento de boca a boca. Método/abordagem: Pesquisa exploratório-dedutiva que verificou as relações entre construtos da TCP e de escalas de boca a boca. Os dados amostrais foram coletados através de um questionário, totalizando 111 respondentes, com procedimentos de análise exploratória preliminar, análise descritiva da amostra, testes de normalidade, análise fatorial confirmatória e modelagem de equações estruturais. Principais Resultados: Observou-se a influência das opiniões nas normas subjetivas e na familiaridade com a plataforma. Ainda, as atitudes em relação à utilização da plataforma, as normas subjetivas e do controle percebido dos indivíduos como preditores da intenção de uso. E, por fim, a intenção de uso foi considerada como preditora do comportamento de emissão do boca a boca positivo. Contribuições teóricas/práticas/sociais: Melhor compreensão teórica sobre as relações entre o papel do boca a boca recebido e da familiaridade nos antecedentes da intenção de buscar informações na plataforma Tripadvisor, assim como o efeito dessas intenções no comportamento de emitir boca a boca. Quanto à aplicação prática, têm implicações diretas para os proprietários e clientes de restaurantes, além da própria gestão da plataforma. No que concerne as contribuições sociais, a discussão sobre explorar de melhor maneira características “family friendly”, uma vez que famílias mais numerosas parecem não sofrer influência das avaliações no site. Originalidade/relevância: Apesar da seção de Restaurantes da plataforma Tripadvisor ter sido objeto de estudos na literatura de turismo e hospitalidade, curiosamente, vem sendo preterida em comparação a outras seções na plataforma, mesmo existindo relevância deste tipo de tecnologia para micro e pequenos empreendimentos alimentares. Neste contexto, apresenta uma lacuna referente ao engajamento de consumidores em comportamentos de boca a boca, sobretudo no Brasil.
... The information of word-of-mouth and consumer preferences is mainly acquired via sentiment analysis on various attributes of a product, containing numerous consumers' subjective emotions. Note that such emotions may not completely relate to the product itself, but other influential factors, e.g., characteristics of review providers (Fang et al., 2016) and even societal factors such as social networks of reviewers and followers (Cheng & Ho, 2015). In this regard, the forecasting results may be biased only integrating the information of word-of-mouth or consumer preferences, especially for automobile products. ...
As one source of user-generated content, online reviews embed vast quantities of important business information, significantly affecting consumer demand. In this study, we aim to propose a new forecasting approach to predict the demand for new energy vehicles (NEVs) by incorporating perceived quality measures extracted from online reviews into the traditional Bass model. To this end, we consider three crucial dimensions (i.e., emotional experience, defect perception, and brand/product image) and adopt text analysis techniques to mine perceived quality information from online reviews for NEVs comprehensively. Coping with the limited datasets, we further dynamically incorporate the mined perceived quality into the Bass model to improve the accuracy of new energy vehicle (NEV) demand forecasting. Finally, we meticulously conduct a series of experiments with crawled online reviews and historical sales of distinct NEV models. The experimental results demonstrate that the perceived quality measures identified from online reviews jointly affect the consumers’ purchasing decisions, and effectively enhance the performance of the NEV demand forecasting. Furthermore, some interesting and important findings are achieved based on the proposed methodology, including the time-lag effect of perceived quality on consumers’ purchasing decisions and the formulation of specific product strategies based on demand trends.
... In Cluster #2 ("Hotel") the authors provide valuable insights into the importance of online reputation and trust in the hotel industry, highlighting the need for hotels to manage online reviews and responses effectively to improve their performance and reputation. The papers cover a range of topics related to the business value of consumer reviews and management responses to hotel performance [68,101,132], the influence of eWOM [107], the perceptions and evaluations of prospective customers toward an online negative review and any accompanying hotel response [57,117], the effects of consumer-autogenerated content [39,41] especially the content posted on social media, such as Facebook and others [62]. For academics, marketing, and e-commerce executives, these insights underscore the critical role of online reputation management in shaping consumer perceptions and influencing their decisions. ...
Recognizing the challenges identified in the vast literature exploring the intellectual landscape of Online Reputation Management (ORM) in the realm of e-commerce, this study performs a quantitative bibliometric analysis, specifically a co-citation analysis using CiteSpace software, to find thematic clusters in a sample of 1136 papers containing 48,385 cited references. This is the first co-citation analysis of ORM literature that cluster the intellectual structure and identifies both the intellectual turning points and burst papers. The results reveal 14 distinct co-citation clusters, each representing a unique thematic structure. An in-depth analysis further characterizes the clusters, ranging from the impact of online reputation on the hospitality industry to frameworks explaining trust formation in e-commerce. Additionally, the study identifies intellectual turning points by assessing betweenness centrality, highlighting four seminal papers that have strongly influenced the field. Furthermore, burst detection analysis uncovers the temporal dynamics of research trends, showcasing the enduring influence of certain clusters and the transient nature of burst patterns. The novelty and importance of the results from the detailed burst detection analysis lie in identifying a significant evolution in research focus over time. Initially, research was concentrated on foundational studies and understanding customer behavior. It then shifted towards practical applications in specific industries, particularly in hospitality and online reviews. In recent years, the emphasis has been on integrating ORM into broader business strategies, especially within e-commerce and the collaborative economy. This research not only contributes to a deeper understanding of ORM, but also serves as a valuable guide for researchers, practitioners, and policymakers in the evolving landscape of online reputation in e-commerce.
... Additionally, social media influencers who consistently share informative content can build emotional connections with their followers, positively impacting their inclination to purchase the recommended products or brands (Ki et al., 2020). Brands that are associated with endorsers perceived as trustworthy tend to enjoy higher levels of brand credibility and brand attitude, resulting in increased perceived brand value and purchase intention (Fang et al., 2016;Wang and Scheinbaum, 2018;Koay, 2021). Based on these insights, Hypothesis 2 is proposed as: ...
This chapter aims to explore the impact of perceived trustworthiness toward Gen Z social media influencers on Gen Z restaurant customers’ perceived brand value and purchase intentions. This study collected online survey data from 312 Gen Z restaurant customers in the United States in the spring of 2023. The partial least squares structural equation modeling (PLS-SEM) was used to analyze the data and concluded that Gen Z customers’ perceived high trustworthiness toward Gen Z social media influencers had a positive impact on increasing brand value perception and purchase intentions. This study did not identify the significant effect differences between the social influencer’s follower group and the non-follower group. The results of this chapter provide a better understanding of Gen Z consumer behavior and how their consumption behavior is influenced by social media influencers in the same generation.
... Objective word-of-mouth contains more product information, which can help the receiver reduce the cognitive and search costs when making purchase decisions (Park et. al.,2015;Fang et al., 2016). The customer buying decision preference framework of brands and businesses in social media proposed by researchers is based on the relationship between impression management (construction, control and determine) and electronic word-of-mouth (message direction, search behavior, quality and involvement), and contextual outlook (integration, acceptance and evaluate) and electronic word-of-mouth. ...
... Undoubtedly, then, the quantity of helpfulness votes is a useful indicator for potential customers (Lui et al., 2018), but if negative comments receive a large number of helpfulness votes, the management may face challenges. Most research quantifies review helpfulness by the number of helpful votes (Fang, Ye, Kucukusta, & Law, 2016;Lui et al., 2018), and in line with that previous research, we used the number of "helpful" votes to quantify review helpfulness in this study. ...
... This mechanism could be deliberate, such that consumers actively try to mirror reviews that are rated as helpful (Eberhard et al., 2018), effectively constructing hypotheses about potential features of a review that could lead to a positive evaluation by others. If reviewers' perceptions were accurate (or they had in-depth knowledge of the academic literature), they could determine that reviews rated as helpful are those that include emotional language (Ahmad & Laroche, 2015), are more fluently written (Fang et al., 2016;Kronrod & Danziger, 2013;Moore, 2015;van Laer et al., 2018), are written by people who share broader consensus about a product (Naylor et al., 2011), or appear to have been written with more effort (Grewal & Stephen, 2019). Equally, consumers could avoid features of reviews that are too polarizing (Schoenmueller et al., 2020) or give the impression that they have been written by someone who did not purchase a product (Anderson & Simester, 2014). ...
Product reviews on e-commerce platforms can have a pronounced effect on consumers’ decisions. Less is known, however, whether the reviews written by others can shape a person’s own written opinion of a product. We hypothesized that people who compose reviews on digital storefronts will try to imitate successful reviews, such that their content will show similarity with other reviews displayed at the time of writing. More specifically, we predicted that reviews would be more semantically similar to the most successful, salient, and readily accessible reviews written by others. To investigate this hypothesis, we extracted over 3 million reviews from a major online distribution platform and traced the reviews that were displayed at the time when each review was being composed. Using word embeddings from a pretrained language model, we quantified the semantic similarity between a given review and other reviews that were visible (or not) to a user. We found that reviewers imitate the most helpful reviews written by others, especially those that are visually salient. Their reviews, in turn, gather more helpfulness ratings in the future, leading to a cascade of similar reviews. Our findings suggest that the default sorting and display format of reviews on online platforms will have a pronounced effect on the style and content of new reviews.
... Due to information overload and the presence of false comments, consumers frequently fail to read and screen helpful content (Cao et al., 2021;Fang et al., 2016;Liu et al., 2020). Direct conversation and contact with others can boost trust in the livestreamed shopping scenario. ...
The aim of this study is to identify the impact of live stream marketing on consumer trust. Data was collected using a questionnaire developed based on prior works and administered electronically to consumers who use live stream marketing before making their purchasing decisions in Saudi Arabia. A total of 350 responses were used for data analysis purposes, which was carried out via SmartPLS 4.0 software. The findings of the study demonstrate a significant and positive impact of live stream marketing as conceptualized in terms of five dimensions (information, attractiveness, entertainment, expert, and social interaction) on consumer trust. In accordance with the research's findings, researchers advise live stream marketers to learn everything they can about the things they are selling because doing so will enable customers to make well-informed choices.
... There are several public user-generated review datasets in the travel domain, mainly crawled from leading online travel platforms such as TripAdvisor and Booking.com. For example, Fang et al. [8] crawled 41k reviews for attractions in New Orleans to analyze the perceived helpfulness of reviews (judged by the number of votes). Tsamis et al. [31] crawled 65k English hotel reviews from TripAdvisor and trained a DNN on helpfulness prediction. ...
User-generated reviews significantly influence consumer decisions, particularly in the travel domain when selecting accommodations. This paper contribution comprising two main elements. Firstly, we present a novel dataset of authentic guest reviews sourced from a prominent online travel platform, totaling over two million reviews from 50,000 distinct accommodations. Secondly, we propose an innovative approach for personalized review ranking. Our method employs contrastive learning to intricately capture the relationship between a review and the contextual information of its respective reviewer. Through a comprehensive experimental study, we demonstrate that our approach surpasses several baselines across all reported metrics. Augmented by a comparative analysis, we showcase the efficacy of our method in elevating personalized review ranking. The implications of our research extend beyond the travel domain, with potential applications in other sectors where personalized review ranking is paramount, such as online e-commerce platforms.
... Many tourism and hospitality researchers have examined electronic word-of-mouth (eWOM) by examining data from major review sites, such as tripadvisor (Fang et al., 2016) and Booking.com (Mellinas et al., 2015(Mellinas et al., , 2016Mellinas & Martin-Fuentes, 2021), while others have compared review sites (Xiang et al., 2017). ...
The Bayesian approach to data analysis is useful when the variables considered are already subjective or abstract, as is the case with online consumer reviews and ratings in tourism research. The Bayesian framework provides a method for combining observed data from prominent e-commerce platforms with other prior information, such as expert knowledge. Also, Bayesian statistical modelling has several advantages when the sample size of observed data is small. However, a source of uncertainty is introduced into the analysis by eliciting a unique prior distribution that adequately represents the expert’s judgement. We focus on the problem in a formal Bayesian robustness context by assuming that the hospitality manager is unable to choose a functional form for the prior distribution but that he or she may be able to restrict the possible priors to a class that is suitable for quantifying the practitioner’s uncertainty. Our interest is: We propose a new distribution that is suitable for fitting the rating data.
We have shown how the practitioner can introduce his judgements about the feeling parameter using an appropriate prior distribution and
We develop a Bayesian robust methodology to manage hospitality managers’ uncertainty using a class of prior distributions suitable for quantifying the practitioner’s uncertainty.
These ideas were illustrated using real data. We demonstrate that the Bayesian robustness methodology proposed allows us to manage this uncertainty in our model by using classes of prior distributions and how the measures of interest are transformed into intervals of interest that will allow the manager to make decisions.
... it describes how consumers respond to price changes or product or service modifications (Yue et al., 2020). customers are willing to pay more when perceived value exceeds cost (Fang et al., 2016). this pricing response is extremely valuable for marketing strategies (rundh, 2013). ...
This study examines how price sensitivity parameters affect customer lifetime value in the luxury business. Quality, position, information, time, and customer lifetime were examined as price sensitivity factors followed by a conceptual model and research hypotheses were produced based on previous studies. Secondary analysis and in-depth interviews with industry specialists and customers. A representative sample of 232 A class consumers was included in a survey that was given to A-class customers in Egypt. A new level of price sensitivity known as ‘quality positioning value’ was found through the use of factor analysis. Multiple discriminant analysis was performed to validate the hypotheses and Cronbach’s alpha was utilised to assess the reliability of the data. This analysis sheds light on the effect of price sensitivity on customer lifetime value in the luxury industry.
... The impact of online reviews on consumer behavior can differ based on various factors, including when they are presented, the popularity of the product, and the characteristics of the consumer (Camilleri, 2020;Tamimi & Sebastianelli, 2016;Zhu & Zhang, 2010). Furthermore, the structure and qualities of online reviews, such as their ease of understanding, the profile of the reviewer, and the perceived trustworthiness, have a substantial impact on how consumers perceive and decide to make purchases (Cheung et al., 2012;Fang et al., 2016;Jin et al., 2021). In addition, online reviews have a significant impact on sales. ...
This study offers a thorough examination of online reviews to gain insights into visitor opinions and experiences at Kuta Beach, a renowned tourist attraction in Bali, Indonesia. Using advanced techniques in sentiment analysis and data analytics, the research investigates 3,726 online reviews from January 2016 until October 2023, carefully analysing the content and emotional tone of each review. The research indicates that Kuta Beach is generally well-regarded, with a median review rating of 5 and a mean sentiment polarity of 0.41. However, there are significant issues raised about cleanliness and overcrowding. Aspect-Based Sentiment Analysis (ABSA) has identified cleanliness and crowdedness as important factors that impact visitor satisfaction. The findings of the study indicate that it is vital to address these concerns in order to improve the visitor experience and maintain the beach's popularity. In addition, the research provides valuable suggestions for enhancing visitor satisfaction, including addressing overcrowding, improving cleanliness, and fostering connections with local communities to promote sustainable tourism development. This analysis not only offers a glimpse into current visitor experiences but also enhances our understanding of consumer sentiments in the age of the internet, which is crucial for effective tourism management and marketing strategies.
... People have started to utilize online reviews to express their thoughts on things as a result of the growing usage of the internet and online social media (Chakraborty, 2019). Even many people consider Internet reviews to be more trustworthy than other, more traditional sources of information (Fang et al., 2016). The digital form of CSR, known as DSR, is used by businesses today to fulfil customer requests and promote consumer purchase intent. ...
Purpose
The study specifically seeks to comprehend the impact of online corporate social responsibility (CSR) initiatives on consumer behaviour, with a focus on Generation Y consumers. It also aims to examine how, particularly within Ghanaian manufacturing firms, the views of Generation Y consumers regarding digital social responsibility (DSR), and how it moderates the relationship between brand loyalty and purchase intention.
Design/methodology/approach
This study takes a quantitative approach, using information gathered via a survey questionnaire from 611 Generation Y consumers in Ghana. Examining the connections between DSR, customer engagement, brand loyalty and purchase intention is the main goal of the investigation. Structural equation modelling (SEM) methods are used in the study to examine the data gathered and verify the proposed linkages.
Findings
The study reveals a strong positive relationship between corporate social responsibility (DSR) and purchase intention, mediated by consumer engagement and brand loyalty. However, it does not suggest Generation Y's attitudes towards DSR moderating this relationship. The study underscores the importance of DSR for Ghanaian manufacturing businesses.
Originality/value
By studying the relatively unexplored idea of DSR and its effects on consumer behaviour in developing nations – especially in the context of Ghanaian manufacturing enterprises – this study adds to the body of current work. This study sheds light on the ways in which DSR affects Generation Y customers' intentions to buy by examining the mediating roles of brand loyalty and consumer engagement.
... Online customer reviews (OCRs) in the tourism context and online hotel reviews (OHRs) generally refer to the feedback left by customers about their experiences. However, they differ in focus and scope: OCR in the tourism context encompasses customer reviews on a wide range of products, businesses and services, such as transportation and ancillary services used at or while travelling to the destination, restaurants, events, places, and attractions visited and their related experiences [44][45][46]. OHRs have a much narrower scope and are reviews related explicitly to hotels and other types of accommodations that tend to focus on aspects directly related to the hotel stay, such as room quality, service, value, cleanliness, location, check-in and check-out, and facilities [47]. Although many studies analyse tourism-related reviews as a whole in tourism and hospitality reviews [48,49], this study separates online hotel reviews from all other tourism-related online reviews and is the first to investigate this. ...
This study conducts a bibliometric analysis of online customer and hotel review research, aiming to provide insights into where each field comes from, stands now and ought to go in the future. In particular, this study examines how the existing research on online customer reviews can benefit future hotel review research. Data collected from Web-of-Science and Scopus created an expanded network of 797 core articles and 19,374 citations to identify intellectual structures, developing trends, and future research gaps. This study offers a visual overview of journals, institutions, countries, research themes and authors to assess the overall directions hotels can take. It underscores the necessity for rigorous and relevant research amid the proliferation of online reviews and emphasises the imperative for academia to bridge the gap between theoretical insights and practical applications within the dynamic tourism industry. This study provides researchers and industry professionals with useful tools to understand and deal with the complexities of online reviews. It also highlights the important role these reviews play in shaping the future of tourism strategies.
... However, none of the above approaches have applied topic modeling, and sentiment analysis approaches to examine the concept of user experience and niche tourism, as well as the tourists' perception of those concepts. More user-oriented research has focused on predicting reviews' usefulness [15,16], identifying suitable attractions' recommendations for users [17,18], and extracting certain user profiles [19]. While these works study individual user behavior, they do not examine the correlation between user profiles and tourist experiences to better understand different market segments. ...
This study explores the Experience Economy dimensions within fishing tourism, adopting a data science perspective by employing web crawling for extensive data gathering on reviewer profiles from TripAdvisor. Utilizing Natural Language Processing (NLP) techniques, it scrutinizes the relationship between Experience Economy dimensions and user profiling aspects. Findings reveal "Entertainment" as the predominant dimension in tourist reviews, followed by "Aesthetic," "Educational," and "Escapist." Particularly notable are the frequent co-occurrences of "Entertainment"-"Aesthetic" and "Educational"-"Entertainment" pairs. The practical implications underscore the potential socioeconomic benefits of fishing tourism for local communities and fishermen, emphasizing the necessity for governmental support in terms of infrastructure, leadership, legislation, and financial backing to foster sustainable development in this sector. Notably, this research stands as a pioneering effort in its focus on Experience Economy dimensions and user profiling within the context of fishing tourism, drawing data from business pages and user profiles.
... However, none of the above approaches have applied topic modeling and sentiment analysis approaches to examine the concept of user experience and niche tourism, as well as the tourists' perception of those concepts. More user-oriented research has focused on predicting reviews' usefulness [21,22], identifying suitable attractions' recommendations for users [23,24], and extracting certain user profiles [25]. While these works study individual user behavior, they do not examine the correlation between user profiles and tourist experiences to better understand different market segments. ...
Fishing tourism, a niche yet burgeoning sector within the broader tourism industry, presents a unique opportunity to explore the dynamics of the Experience Economy. This study delves into the intricacies of fishing tourism through the lens of data science, leveraging web crawling techniques to gather extensive data from reviewer profiles on TripAdvisor. By employing natural language processing (NLP) techniques, the study investigates the correlation between Experience Economy dimensions and user profiling aspects within this domain. The findings of this study shed light on the predominant dimensions of the Experience Economy within fishing tourism. “Entertainment” emerges as the primary dimension, closely followed by “Aesthetic”, “Educational”, and “Escapist” elements. Notably, the study reveals frequent co-occurrences of certain dimension pairs, such as “Entertainment”–“Aesthetic” and “Educational”–“Entertainment”, underscoring the multifaceted nature of the fishing tourism experience. The implications of this research extend beyond academic discourse to practical considerations for stakeholders in the fishing tourism sector. By highlighting the socioeconomic benefits inherent in fishing tourism for local communities and fishers, the study emphasizes the importance of governmental support. This support, in the form of infrastructure development, effective leadership, legislative measures, and financial backing, is deemed essential for fostering sustainable growth and development in this sector. This study stands as a pioneering endeavor within the realm of fishing tourism research, particularly in its focus on Experience Economy dimensions and user profiling. By drawing data from both business pages and user profiles on TripAdvisor, it provides a comprehensive understanding of the intricate interplay between tourist experiences and the broader economic and social landscape of fishing tourism destinations.
... Nowadays, the emergence of online reviews has become a crucial source of information for potential tourists when making their final decisions [5][6][7]. According to Fang [8], 65% of tourists search the internet before choosing a destination, and 69% of their decisions are influenced by online reviews. Thus, the impact of online reviews on potential tourists' attitudes is evident in reality. ...
In the era of social media, online reviews have become a crucial factor influencing the exposure of tourist destinations and the decision-making of potential tourists, exerting a profound impact on the sustainable development of these destinations. However, the influence of review valence on visit intention, especially the role of affective commitment and reputation (ability vs. responsibility), remains unclear. Drawing on emotion as a social information theory, this paper aims to elucidate the direct impact of different review valences on tourists’ visit intentions, as well as mediating mechanisms and boundary conditions. Three experiments indicate that positive (vs. negative) reviews can activate stronger affective commitment and visit intention, with affective commitment also playing a mediating role. Additionally, destination reputation significantly moderates the after-effects of review valences. More specifically, a responsibility reputation (compared with an ability reputation) weakens the effect of negative valence on affective commitment and visit intention. This study provides valuable theoretical insights into how emotional elements in online reviews influence the emotions and attitudes of potential tourists. Particularly for tourism managers, review valence and responsibility reputation hold practical significance in destination marketing.
... Hotels' overall quality is often inferred via the use of designations, such as star rating (Fang et al., 2016;Dioko et al., 2013), with one star suggesting basic amenities and comfort requirements and five stars denoting luxury of the same and more (UNWTO, 2015). As such, hotel rating systems aim ultimately to enlighten potential visitors on what to anticipate ahead of time in order to close the gap between expected and experienced facilities and service delivery (Sufi & Singh, 2018;Rhee & Yang, 2015). ...
This chapter discusses the impact of Cozy Days, a short-term rental management company in the Algarve, on the tourism and economic dynamics of the destination. Cozy Days' operations optimise property profitability during the peak season and provide additional revenue during off-peak periods, particularly by attracting digital nomads and senior tourists. Many accommodations would remain closed during the off-season; however, the company's strategy has been crucial in mitigating this seasonality, favouring more balanced and sustainable financial management throughout the year. The chapter emphasises the importance of separately analysing the performance of high and off-season periods. This approach allows for an understanding of fluctuations in consumption patterns and visitor preferences, enabling Cozy Days to tailor its offerings to the specific needs of each season. Thus, an analysis of the tourist profile is presented. This strategic segmentation is fundamental to maximising operational efficiency and increasing profitability by enhancing amenities and services.
The increasing significance of virtual word of mouth (v-WOM) in the digital era has the potential to revolutionize consumer decision-making across a wide range of markets such as tourism. The objective is to review the literature on v-WOM published between 2013 and 2023 and the research employs bibliometric analysis to examine 718 documents from the Scopus database. For performance and science mapping analysis, Biblioshiny and VOSviewer were utilized. The growing trend in publication and citation numbers shows that v-WOM is a growing field and the prominent journals involving tourism research are Tourism Management and International Journal of Hospitality Management. Robert Law is a well-known author and China has the greatest number of publications. The conceptual themes of v-WOM literature were formed by the co-occurrence analysis of keywords and bibliographic coupling networks. In order to provide insights for the future, a word cloud analysis of 549 documents published in the previous four years was also carried out.
The concept of consumer reviews dates back to traditional word-of-mouth recommendations, which have always played a crucial role in consumer decision-making. However, with the advent of the internet and the rapid growth of e-commerce platforms, these reviews have moved online, becoming more accessible and influential than ever. Online Consumer Reviews (OCRs) have become a pivotal resource for consumers seeking information and advice on products and services. They offer valuable insights from experienced individuals, aiding potential buyers in making informed decisions. This transformation in consumer behavior didn’t occur overnight but is the result of a gradual evolution in digital communication, e-commerce, and consumer trust.
The purpose of the present study is three-fold: to provide a comprehensive analysis of the rise and popularity of online consumer reviews, to assess the impact of OCRs on consumer buying behavior and to identify gaps and provide recommendations for future research to develop a more coherent understanding of OCRs and their impact. To conduct a comprehensive assessment of previous research work on Online Consumer Reviews (OCRs), this study adopts an extensive literature review approach. Based on the extensive literature review, a conceptual framework is developed. This framework aims to categorize and explain three broad factors of OCRs- Online review credibility, source credibility and platform credibility that influence consumer purchase decisions. The framework serves as a tool for researchers to understand the triggers that result in OCRs impacting consumer behavior. Thus, the novel contribution of this study lies in its comprehensive aggregation and presentation of the most pertinent information on OCRs, offering a clear depiction of the current state of literature. This foundation facilitates the identification of future research opportunities, aiming to propel the field forward. Thus, it can be concluded that online consumer reviews have been revolutionized and play a major role in the decision to purchase products.
Purpose
This study investigates how the complexity of sentiment in online reviews affects perceived helpfulness. Analyzed over 730,000 reviews from Tripadvisor.com, the research explores how information overload and increased cognitive load impact consumer decision-making.
Design/methodology/approach
This study applied the BERT deep learning model to analyze sentiment complexity in online reviews. Based on cognitive load theory, we examined two key factors: the number of attributes mentioned in a review and the variation in sentiment valence of across attributes to evaluate their impact on cognitive load and review helpfulness.
Findings
The results show that a higher number of attributes and greater variation in sentiment valence increase cognitive load, reducing review helpfulness. Reviewers’ expertise and review readability further moderate these effects, with complex or expert-written reviews worsening the negative impact.
Originality/value
This research introduces a method for measuring attribute-level sentiment complexity and its impact on review helpfulness, emphasizing the importance of balancing detail with readability. These findings provide a foundation for future studies on review characteristics and consumer behavior.
Consumers increasingly read and post online reviews through mobile devices. Drawing upon the motivation, opportunity, and ability framework, we hypothesize the interesting interplay of mobile reviewing, firm review solicitation and reviewer popularity on review helpfulness. Using a large‐scale data set of 1,024,062 reviews from TripAdvisor, we reveal the double‐edged sword effect of mobile reviewing on review helpfulness. Compared to nonmobile reviews, mobile reviews are more emotional, less cognitive and more perceptual, which enhances review helpfulness; however, they are also shorter and less readable, which hurts review helpfulness. Furthermore, hotel review solicitation and reviewer popularity moderate on the aforementioned effects. Compared to organic reviewing, solicitated reviewing weakens both the negative and positive effects of mobile reviewing and elicits more cognitive, more readable, longer but less experiential (i.e., emotional and perceptual) mobile reviews than nonmobile reviews. Meanwhile, compared to unpopular reviewers, popular reviewers mainly strengthen the positive effect of mobile reviewing and write more experiential, more readable, but less cognitive and shorter mobile reviews than nonmobile reviews. Our findings offer practical guidelines for marketers to leverage the power of mobile reviews and downplay their disadvantages, and meanwhile produce meaningful implications for eWOM marketing.
This study utilizes source credibility theory to examine how two manners of expression act as cues for source trustworthiness and expertise, impacting potential tourists’ adoption intentions. Importantly, we explore the interplay between source identity and the manner of expression. Throughout this investigation, four empirical studies were undertaken, integrating social media analytics and three experiments. Findings show that high effort and certainty expressions increase potential tourists’ willingness to adopt information, mediated by benevolence-based and competence-based trust, respectively. Particularly, high effort expression is more effective when the source is a tourist, while high certainty expression is more effective with local residents. The research findings suggest that platforms and destination managers should encourage local residents to focus on enhancing certainty expression when generating user-generated content, while encouraging tourists to focus on enhancing effort expression. Additionally, these findings provide insights for crafting guides tailored to different identity information sources.
This study aimed to empirically verify the impact of tourism destination image on perceived usefulness from the perspective of construal‐level theory. It also attempted to compare the results of the survey and data mining techniques regarding methodology. An online survey was initially conducted, and statistical analysis was performed using the R program. Concurrently, 25,952 reviews from TripAdvisor were gathered using a web crawler, Octoparse, and analyzed using the Tidyverse approach. The results indicated that, in the probabilistically distant group, affective destination image had a significant effect on perceived usefulness, whereas cognitive image did not affect perceived usefulness. Contrastingly, in the probabilistically close group, cognitive images had a stronger effect on perceived usefulness than affective images. Both the influential relationship between affective and cognitive images and the comparison of the two research methods were partially supported. These findings have theoretical and practical implications.
This study investigates the consumer cost efficiency of travel agency bookings in Cabanatuan City, addressing questions regarding price competitiveness, service quality, and consumer preferences. The research employs a cross-sectional design, utilizing a survey questionnaire to gather data from 100 travelers who booked through various travel agencies in the city. Purposive sampling ensures representation across demographics and travel preferences. Statistical analysis reveals insights into consumer behavior and cost efficiency. Findings indicate that most travelers in Cabanatuan City are young, educated, and primarily female travelers, with a preference for leisure trips and family packages. In-person bookings were favored, emphasizing value- added services like travel insurance. Regarding price competitiveness, respondents perceive travel agency prices as competitive and aligned with service quality, indicating a willingness to pay for value. The transparency of fees and charges is highly valued, and the highest satisfaction is clarity regarding booking fees. Customization options for travel packages are essential, particularly in accommodation, transportation, and activities. Customer service quality, especially responsiveness and friendliness, significantly impacts overall satisfaction. In conclusion, the study concludes that travel agencies must tailor their offerings and promotions to meet diverse consumer demands, focusing on transparency, price competitiveness, and service quality to enhance customer satisfaction and loyalty. The findings underscore the importance of special offers, customizable packages, and excellent customer service in attracting and retaining customers in a competitive market. Recommendations include implementing a consumer cost efficiency plan that addresses these key factors to promote customer happiness and loyalty.
The rise of health 2.0 technologies has led to an increasing number of patients seeking medical advice in online health communities. This has ignited scholarly interest in exploring patients’ online doctor choice behaviors. While many researchers have examined the various determinants that impact this decision-making process, relatively few have delved into doctors’ functional quality embedded in doctor-patient interactions, and fewer have considered the role of patient health literacy. Thus, this study focuses on doctors’ functional quality, online reputation, and patient health literacy. Grounded in the Elaboration Likelihood Model and service quality theory, this study analyzes 143,895 doctor-patient text interactions involving 764 doctors. Results indicate that both functional quality and online reputation significantly influence patients’ choices of doctors. Additionally, patient health literacy moderates the impact of doctors’ online reputation on patient decisions. The findings contribute theoretical insights for researchers and practical implications for doctors and platform administrators.
Urban Green Spaces have become essential tools for promoting public health, especially in over-urbanized cities. Following visitors’ outdoor recreation experiences, this paper aims to identify visitors’ complaints about nation gardens in Istanbul. The study site consisted of 12 nation gardens in Istanbul. Initially, 38,965 Google Maps reviews about the nation gardens through December 2022 were screened. Then, 4143 comments containing the visitors’ complaints were identified. After gathering these comments in an Excel spreadsheet, the data was imported to MAXQDA software. Braun and Clarke’s six-phased thematic analysis approach was applied to explore codes and themes. Considering the visitors’ complaints, 91 codes were generated. Relevant codes are categorized under five themes: outdoor areas/physical characteristics, facilities, visitors’ interactions, accessibility/entrance management, and restrictions and security implications. Outdoor areas/physical characteristics, facilities, and visitor interactions are the most common complaints, followed by accessibility and security. Park planners can use visitor feedback to improve existing parks and design new ones, maximizing individual and social benefits, such as health outcomes, and ensuring the effective use of green spaces.
Purpose
Most previous studies predicting review helpfulness ignored the significance of deep features embedded in review text and instead relied on hand-crafted features. Hand-crafted and deep features have the advantages of high interpretability and predictive accuracy. This study aims to propose a novel review helpfulness prediction model that uses deep learning (DL) techniques to consider the complementarity between hand-crafted and deep features.
Design/methodology/approach
First, an advanced convolutional neural network was applied to extract deep features from unstructured review text. Second, this study used previous studies to extract hand-crafted features that impact the helpfulness of reviews and enhance their interpretability. Third, this study incorporated deep and hand-crafted features into a review helpfulness prediction model and evaluated its performance using the Yelp.com data set. To measure the performance of the proposed model, this study used 2,417,796 restaurant reviews.
Findings
Extensive experiments confirmed that the proposed methodology performs better than traditional machine learning methods. Moreover, this study confirms through an empirical analysis that combining hand-crafted and deep features demonstrates better prediction performance.
Originality/value
To the best of the authors’ knowledge, this is one of the first studies to apply DL techniques and use structured and unstructured data to predict review helpfulness in the restaurant context. In addition, an advanced feature-fusion method was adopted to better use the extracted feature information and identify the complementarity between features.
Despite calls for journalists and media agencies to address a disconnect between news audiences and news prose, content continues to increase in its difficulty to read and comprehend for the masses. While readability is often associated with audience comprehension and engagement, studies have neglected to assess whether readability is a factor in audience assessments of the credibility of content. Using an online experimental design, this study examines whether readability acts as a heuristic that helps news consumers make credibility judgments of news. Results show that readability tends not to be a predictor of credibility perception, regardless of partisanship strength or media use preferences. Theoretical and practical implications are discussed.
Consumer-generated content (CGC) is growing in importance. Especially online travel reviews written by consumers are ever more available and used to inform travel-related decisions. A Web-based survey of users of the most prominent travel review site, TripAdvisor, was conducted to investigate how other travellers' reviews inform the trip planning process. Since current CGC statistics show generational and gender differences, the study also aimed at examining whether those carry over into the realm of travel review use. The results show that reviews are used mostly to inform accommodation decisions and are currently not used much for en route travel planning. Gender differences were found for perceived impacts of reviews, with females reaping greater benefits from using reviews, especially in terms of enjoyment and idea generation. Age differences occurred across a variety of perceptions and use behaviours. Implications for travel marketing and travel information systems design are provided.
Keywords: online travel reviews; consumer generated content; trip planning; age differences; gender differences.
This study presents the results of an initial phase of a project seeking to convert texts into a more accessible form for people with
autism spectrum disorders by means of text simplification technologies.
Random samples of Simple Wikipedia articles are compared with texts from News, Health, and Fiction genres using four standard readability indices (Kincaid, Flesch, Fog and SMOG) and sixteen linguistically motivated features. The comparison of readability indices across the four genres indicated that the Fiction genre was relatively easy whereas the News genre was relatively difficult to read.
The correlation of four readability indices was measured, revealing that they are almost perfectly linearly correlated and that this correlation is not genre dependent. The correlation of the sixteen
linguistic features to the readability indices was also measured. The results of these experiments indicate that some of the linguistic
features are well correlated with the readability measures and that these correlations are genre dependent. The maximum correlation
was observed for fiction.
Virtual communities rely on active and committed members who make regular contributions. These contributors typically get psychological and social rewards for their virtual community activities. Given their importance for community sustainability, it is essential for community platforms to understand who their most active members are and what status seeking strategies they employ. This research looks at TripAdvisor and its dedicated Destination Experts to examine how localized the expertise is in this community, how the community acknowledges expertise, whether status seeking strategies can be detected, and if the Destination Experts differ from general reviewers. The findings reveal that Destination Experts are active throughout the TripAdvisor platform and are indeed important and influential pillars of the community.
While the proliferation of online review websites facilitate travellers' ability to obtain information (decrease in search costs), it makes it difficult for them to process and judge useful information (increase in cognitive costs). Accordingly, this study attempts to identify the factors affecting the perceived usefulness of online consumer reviews by investigating two aspects of online information: (1) the characteristics of review providers, such as the disclosure of personal identity, the reviewer's expertise and reputation, and (2) reviews themselves including quantitative (i.e., star ratings and length of reviews) and qualitative measurements (i.e., perceived enjoyment and review readability). The results reveal that a combination of both messenger and message characteristics positively affect the perceived usefulness of reviews. In particular, qualitative aspects of reviews were identified as the most influential factors that make travel reviews useful. The implications of these findings contribute to tourism and hospitality marketers to develop more effective social media marketing.
The quality of service received from an e-auction website owes much to the use of self-service technology. Researchers have proposed that user personality traits and user perceptions of the service provider have an impact on individual use of web-based technology. This paper describes the development and validation of a conceptual model illustrating the impact of both the personality trait of technology readiness and the perception of online service provider trustworthiness on intentions to continue with the service. Using survey data collected from e-auction users in China and employing SEM analysis procedures, we found that both technology readiness and perceived service provider trustworthiness had an impact on user satisfaction of e-auction services which, in turn, had an impact on intentions to continue with the e-auction services. Implications and future research directions are also included in the study.
Online word-of-mouth (WOM) such as consumer opinions, user experiences, and product reviews has become a major information source in consumer purchase decisions. Prior research on online WOM effect has focused mostly on low-involvement products such as books or CDs. For these products, retailer-hosted (internal) WOM is shown to influence sales overwhelmingly. Numerous surveys, however, suggest consumers often conduct pre-purchase searches for high-involvement products (e.g., digital cameras) and visit external WOM websites during the search process. In this study, we analyze the relative impact of external and internal WOMs on retailer sales for high-involvement products using a panel of sales and WOM data for 148 digital cameras from Amazon.com and three external WOM websites (Cnet, DpReview, and Epinions) over a four-month period. The results suggest that a retailer's internal WOM has a limited influence on its sales of high-involvement products, while external WOM sources have a significant impact on the retailer's sales. The findings imply that external WOM sources play an important role in the information search process.
Detecting low observable targets within sea clutter at low grazing angle is one of the research hotspots in radar signal processing community. In this paper, we have proposed a novel method based on polarimetric decomposition theorem. The polar characteristics of sea clutter has been analyzed, with the parameters after the decomposition of target scattering matrix. The scattering entropy and the scattering angle are the key parameters to discriminate the target from the sea clutter. The technique is designed to suppress unwanted sea clutter at polarimetric domain. Datasets from real marine radar are used to illustrate the performance of the new approach.
This article investigates the main and interactional effects of review valence and the presence of source identity on consumer perception of credibility of an online review and initial trust of travel services being reviewed. An experimental design is developed involving 639 travel consumers. Results indicate that a negative online review is deemed more credible than a positive online review, while a positive online review leads to a greater initial trust than a negative review. These findings apply when the identity of the reviewer is disclosed. However, when the reviewer’s identity is not disclosed, there is no significant difference between positive and negative reviews either in terms of perceived credibility or impact on consumer trust. Theoretical and managerial implications, limitations and future directions are also discussed.
Online reviews have received much attention recently in the literature, as their visibility has been proven to play an important role during the purchase process. Furthermore, recent theoretical insight argue that the votes casted on how helpful an online review is (review helpfulness) are of particular importance, since they constitute a focal point for examining consumer decision making during the purchase process. In this paper, we explore the interplay between online review helpfulness, rating score and the qualitative characteristics of the review text as measured by readability tests. We construct a theoretical model based on three elements: conformity, understandability and expressiveness and we investigate the directional relationship between the qualitative characteristics of the review text, review helpfulness and the impact of review helpfulness on the review score. Furthermore, we examine whether this relation holds for extreme and moderate review scores. To validate this model we applied four basic readability measures to a dataset containing 37,221 reviews collected from Amazon UK, in order to determine the relationship between the percentage of helpful votes awarded to a review and the review text’s stylistic elements. We also investigated the interrelationships between extremely helpful and unhelpful reviews, as well as absolutely positive and negative reviews using intergroup comparisons. We found that review readability had a greater effect on the helpfulness ratio of a review than its length; in addition, extremely helpful reviews received a higher score than those considered less helpful. The present study contributes to the ever growing literature on on-line reviews by showing that readability tests demonstrate a directional relationship with average length reviews and their helpfulness and that this relationship holds both for moderate and extreme review scores.
Despite hospitality and tourism researchers’ recent attempts on examining different aspects of online word-of-mouth [WOM], its impact on hotel sales remains largely unknown in the existing literature. To fill this void, we conduct a study to empirically investigate the impact of online consumer-generated reviews on hotel room sales. Utilizing data collected from the largest travel website in China, we develop a fixed effect log-linear regression model to assess the influence of online reviews on the number of hotel room bookings. Our results indicate a significant relationship between online consumer reviews and business performance of hotels.
The authors investigate how critics affect the box office performance of films and how the effects may be moder- ated by stars and budgets. The authors examine the process through which critics affect box office revenue, that is, whether they influence the decision of the film going public (their role as influencers), merely predict the deci- sion (their role as predictors), or do both. They find that both positive and negative reviews are correlated with weekly box office revenue over an eight-week period, suggesting that critics play a dual role: They can influence and predict box office revenue. However, the authors find the impact of negative reviews (but not positive reviews) to diminish over time, a pattern that is more consistent with critics' role as influencers. The authors then compare the positive impact of good reviews with the negative impact of bad reviews to find that film reviews evidence a negativity bias; that is, negative reviews hurt performance more than positive reviews help performance, but only during the first week of a film's run. Finally, the authors examine two key moderators of critical reviews, stars and budgets, and find that popular stars and big budgets enhance box office revenue for films that receive more neg- ative critical reviews than positive critical reviews but do little for films that receive more positive reviews than neg- ative reviews. Taken together, the findings not only replicate and extend prior research on critical reviews and box office performance but also offer insight into how film studios can strategically manage the review process to enhance box office revenue.
We are in the midst of a technological revolution whereby, for the first time, researchers can link daily word use to a broad array of real-world behaviors. This article reviews several computerized text analysis methods and describes how Linguistic Inquiry and Word Count (LIWC) was created and validated. LIWC is a transparent text analysis program that counts words in psychologically meaningful categories. Empirical results using LIWC demonstrate its ability to detect meaning in a wide variety of experimental settings, including to show attentional focus, emotionality, social relationships, thinking styles, and individual differences.
Many consumers consult online reviews before making (online) travel arrangements. Yet, little is known about the impact of these reviews on consumer decision making. This research applies consideration set theory to model the impact of online hotel reviews on consumer choice. An experimental study (N = 168) that includes review valence (positive vs. negative reviews), hotel familiarity (well-known vs. lesser-known hotels), and reviewer expertise (expert vs. non-expert reviewers) as independent factors shows that on average, exposure to online reviews enhances hotel consideration in consumers. This is because positive as well as negative reviews increase consumer awareness of hotels, whereas positive reviews, in addition, improve attitudes toward hotels. These effects are stronger for lesser-known hotels. Reviewer expertise has only a minor – positive – influence on review impact.
Consumer-generated content (CGC) is growing in importance. Especially online travel reviews written by consumers are ever more available and used to inform travel-related decisions. A Web-based survey of users of the most prominent travel review site, TripAdvisor, was conducted to investigate how other travellers’ reviews inform the trip planning process. Since current CGC statistics show generational and gender differences, the study also aimed at examining whether those carry over into the realm of travel review use. The results show that reviews are used mostly to inform accommodation decisions and are currently not used much for en route travel planning. Gender differences were found for perceived impacts of reviews, with females reaping greater benefits from using reviews, especially in terms of enjoyment and idea generation. Age differences occurred across a variety of perceptions and use behaviours. Implications for travel marketing and travel information systems design are provided.
As online reviews increasingly become part of the purchasing process, it is important to understand which components of these reviews consumers consider most helpful in facilitating the purchase decision process. Online retailer and rating websites with more helpful reviews offer greater potential value to their consumers. Through two studies, we seek to identify and better understand what makes a helpful consumer review. After an open-ended analysis of the qualities of a review identified by subjects' as helpful, we conducted a controlled experiment that manipulated both the review content and the description of the reviewer. One key finding is that reviews written by a self-described expert are more helpful than those that are not. This information can provide guidance to online retailers and rating websites in their efforts to provide value to their customers.
Customer reviews are increasingly available online for a wide range of products and services. They supplement other information provided by electronic storefronts such as product descriptions, reviews from experts, and personalized advice generated by automated recommendation systems. While researchers have demonstrated the benefits of the presence of customer reviews to an online retailer, a largely uninvestigated issue is what makes customer reviews helpful to a consumer in the process of making a purchase decision. Drawing on the paradigm of search and experience goods from information economics, we develop and test a model of customer review helpfulness. An analysis of 1,587 reviews from Amazon.com across six products indicated that review extremity, review depth, and product type affect the perceived helpfulness of the review. Product type moderates the effect of review extremity on the helpfulness of the review. For experience goods, reviews with extreme ratings are less helpful than reviews with moderate ratings. For both product types, review depth has a positive effect on the helpfulness of the review, but the product type moderates the effect of review depth on the helpfulness of the review. Review depth has a greater positive effect on the helpfulness of the review for search goods than for experience goods. We discuss the implications of our findings for both theory and practice.
This study examines the persuasive effect and awareness effect of online user reviews on movies' daily box office performance. In contrast to earlier studies that take online user reviews as an exogenous factor, we consider reviews both influencing and influenced by movie sales. The consideration of the endogenous nature of online user reviews significantly changes the analysis. Our result shows that the rating of online user reviews has no significant impact on movies' box office revenues after accounting for the endogeneity, indicating that online user reviews have little persuasive effect on consumer purchase decisions. Nevertheless, we find that box office sales are significantly influenced by the volume of online posting, suggesting the importance of awareness effect. The finding of awareness effect for online user reviews is surprising as online reviews under the analysis are posted to the same website and are not expected to increase product awareness. We attribute the effect to online user reviews as an indicator of the intensity of underlying word-of-mouth that plays a dominant role in driving box office revenues.
Online product reviews may be subject to self-selection biases that impact consumer purchase behavior, online ratings’ time series, and consumer surplus. This occurs if early buyers hold different preferences than do later consumers about the quality of a given product. Readers of early product reviews may not successfully correct for these preference differences when interpreting ratings and making purchases. In this study, we develop a model that examines how idiosyncratic preferences of early buyers can affect long-term consumer purchase behavior as well as the social welfare created by review systems. Our model provides an explanation for the structure of product ratings over time, which we empirically test using online book reviews posted on Amazon.com. Our analysis suggests that firms could benefit from altering their marketing strategies such as pricing, advertising, or product design to encourage consumers likely to yield positive reports to self-select into the market early and generate positive word-of-mouth for new products. On the other hand, self-selection bias, if not corrected, decreases consumer surplus.
This article explores the implications that dispositional optimism holds for physical well-being. Research is reviewed that links optimism to a number of different positive health-relevant outcomes, ranging from the development of physical symptoms to recovery from coronary artery bypass surgery. Additional findings are described which suggest that these beneficial effects are partly due to differences between optimists and pessimists in the strategies that they use to cope with stress. A number of other potential mediators are also discussed, including some that are physiologic in nature. The article closes with a discussion of the relationships between our own theoretical account of the effects of optimism and several other conceptual approaches.
User-generated content on social media platforms and product search engines is changing the way consumers shop for goods online. However, current product search engines fail to effectively leverage information created across diverse social media platforms. Moreover, current ranking algorithms in these product search engines tend to induce consumers to focus on one single product characteristic dimension (e.g., price, star rating). This approach largely ignores consumers' multidimensional preferences for products. In this paper, we propose to generate a ranking system that recommends products that provide, on average, the best value for the consumer's money. The key idea is that products that provide a higher surplus should be ranked higher on the screen in response to consumer queries. We use a unique data set of U.S. hotel reservations made over a three-month period through Travelocity, which we supplement with data from various social media sources using techniques from text mining, image classification, social geotagging, human annotations, and geomapping. We propose a random coefficient hybrid structural model, taking into consideration the two sources of consumer heterogeneity the different travel occasions and different hotel characteristics introduce. Based on the estimates from the model, we infer the economic impact of various location and service characteristics of hotels. We then propose a new hotel ranking system based on the average utility gain a consumer receives from staying in a particular hotel. By doing so, we can provide customers with the "best-value" hotels early on. Our user studies, using ranking comparisons from several thousand users, validate the superiority of our ranking system relative to existing systems on several travel search engines. On a broader note, this paper illustrates how social media can be mined and incorporated into a demand estimation model in order to generate a new ranking system in product search engines. We thus highlight the tight linkages between user behavior on social media and search engines. Our interdisciplinary approach provides several insights for using machine learning techniques in economics and marketing research.
Online reviews provide a valuable resource for potential customers to make purchase decisions. However, the sheer volume of available reviews as well as the large variations in the review quality present a big impediment to the effective use of the reviews, as the most helpful reviews may be buried in the large amount of low quality reviews. The goal of this paper is to develop models and algorithms for predicting the helpfulness of reviews, which provides the basis for discovering the most helpful reviews for given products. We first show that the helpfulness of a review depends on three important factors: the reviewerpsilas expertise, the writing style of the review, and the timeliness of the review. Based on the analysis of those factors, we present a nonlinear regression model for helpfulness prediction. Our empirical study on the IMDB movie reviews dataset demonstrates that the proposed approach is highly effective.
With the rapid growth of the Internet, the ability of users to create and publish content has created active electronic communities that provide a wealth of product information. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers to locate the best reviews and understand the true underlying quality of a product. In this paper, we reexamine the impact of reviews on economic outcomes like product sales and see how different factors affect social outcomes such as their perceived usefulness. Our approach explores multiple aspects of review text, such as subjectivity levels, various measures of readability and extent of spelling errors to identify important text-based features. In addition, we also examine multiple reviewer-level features such as average usefulness of past reviews and the self-disclosed identity measures of reviewers that are displayed next to a review. Our econometric analysis reveals that the extent of subjectivity, informativeness, readability, and linguistic correctness in reviews matters in influencing sales and perceived usefulness. Reviews that have a mixture of objective, and highly subjective sentences are negatively associated with product sales, compared to reviews that tend to include only subjective or only objective information. However, such reviews are rated more informative (or helpful) by other users. By using Random Forest-based classifiers, we show that we can accurately predict the impact of reviews on sales and their perceived usefulness. We examine the relative importance of the three broad feature categories: “reviewer-related” features, “review subjectivity” features, and “review readability” features, and find that using any of the three feature sets results in a statistically equivalent performance as in the case of using all available features. This paper is the first study that integrates eco- - nometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their helpfulness and economic impact.
This paper explores effects of the emotions embedded in a seller review on its perceived helpfulness to readers. Drawing on frameworks in the emotion and cognitive processing literatures, we propose that over and above a well-known negativity bias, the impact of discrete emotions in a review will vary, and that one source of this variance is reader perceptions of reviewers’ cognitive effort. We focus on the roles of two distinct, negative emotions common to seller reviews: anxiety and anger. In Studies 1 and 2, experimental methods were utilized to identify and explain the differential impact of anxiety and anger in terms of perceived reviewer effort. In Study 3, seller reviews from Yahoo! Shopping websites were collected to examine the relationship between emotional review content and helpfulness ratings. Our findings demonstrate the importance of examining discrete emotions in online word-of-mouth, and they carry important practical implications for consumers and online retailers.
Assessing the quality of user generated con- tent is an important problem of Web 2.0. Currently, most web sites need their users to rate content manually, which is labour intensive and thus happens rarely. The au- tomatic systems in the literature are limited to one kind or domain of discourse. We propose a system to assess the quality of user generated discourse automatically. Our system learns from human ratings by ap- plying SVM classification based on features such as Surface, Lexical, Syntactic, Forum specific and Similarity features. Our system has also shown to be adapt- able to dierent domains of discourse in our experiments on three dierent web fo- rum data sets. The system outperformed the majority class baseline for all three data sets. Our best performing system configu- ration achieves an accuracy of 89.1%, which is significantly higher than the baseline of 61.82%.
This article is Robert Gunning's own assessment of the achieve ments of the Fog Index after twenty years of use. Those in terested in readability formulas will be happy to learn a little more not only about the history and development of the Fog Index but about the applica tions that have been made of it in business and industry, and also in newspaper and govern ment work. Mr. Gunning is well aware, of course, of some of the weaknesses of the index, but obviously he also has rea son to be proud of its achieve ments.
An important area of information systems (IS) research has been the identification of the individual-level beliefs that enable technology acceptance such as the usefulness, reliability, and flexibility of a system. This study posits the existence of additional beliefs that inhibit usage intentions and thus foster technology rejection rather than acceptance. We theorize that these inhibitors are more than just the antipoles of enablers (e.g., the opposite of usefulness or reliability) and so are distinct constructs worthy of their own investigation. Inhibitors are proposed to have effects on usage intentions beyond that of enablers as well as effects on enablers themselves. We report on a series of empirical studies designed to test the existence and effects of inhibitors. A candidate set of six inhibitors is shown to be distinct from enablers. These inhibitors are subsequently tested in a field study of 387 individuals nested within 32 different websites. Effects at both individual and website unit levels of analysis are tested using multilevel modeling. We find that inhibitors have negative effects on usage intentions, as well as on enablers, and these effects vary contingent upon individual or website unit levels of analysis. The overall results support the existence and importance of inhibitors in explaining individual intent to use—or not use—technology.
Drawing from cognitive response models of persuasion, functional emotion theories, and theoretical and empirical work on the influence of message-relevant and message-irrelevant affect on attitudes, this paper presents a model of persuasion that suggests that discrete, message-induced negative emotions influence attitudes through a complex process that centers around the notions of motivated attention and motivated processing. Emotion type, expectation of the message containing reassuring information, argument strength, presence of peripheral cues, emotional intensity, and emotion placement within a message are expected to mediate information processing depth, message acceptance or rejection, and information recall. This model attempts to bridge the gap between the “emotional” and “rational” approaches to persuasion, and it extends current theorizing in the area of emotion and attitude change by (a) linking the concepts of motivated attention and motivated processing to that of expectation of message reassurance, and (b) considering the persuasive effects of negative emotions other than fear, like anger, disgust, sadness, and guilt.
Crowd-funded markets have recently emerged as a novel source of capital for entrepreneurs. As the economic potential of these markets is now being realized, they are beginning to go mainstream, a trend reflected by the explicit attention crowdfunding has received in the American Jobs Act as a potential avenue for economic growth, as well as the recent focus that regulators such as the U.S. Securities and Exchange Commission have placed upon it. Although the formulation of regulation and policy surrounding crowd-funded markets is becoming increasingly important, the behavior of crowdfunders, an important aspect that must be considered in this formulation effort, is not yet well understood. A key factor that can influence the behavior of crowd funders is information on prior contribution behavior, including the amount and timing of others’ contributions, which is published for general consumption. With that in mind, in this study, we empirically examine social influence in a crowd-funded marketplace for online journalism projects, employing a unique data set that incorporates contribution events and Web traffic statistics for approximately 100 story pitches. This data set allows us to examine both the antecedents and consequences of the contribution process. First, noting that digital journalism is a form of public good, we evaluate the applicability of two competing classes of economic models that explain private contribution toward public goods in the presence of social information: substitution models and reinforcement models. We also propose a new measure that captures both the amount and the timing of others’ contribution behavior: contribution frequency (dollars per unit time). We find evidence in support of a substitution model, which suggests a partial crowding-out effect, where contributors may experience a decrease in their marginal utility from making a contribution as it becomes less important to the recipient. Further, we find that the duration of funding and, more importantly, the degree of exposure that a pitch receives over the course of the funding process, are positively associated with readership upon the story’s publication. This appears to validate the widely held belief that a key benefit of the crowdfunding model is the potential it offers for awareness and attention-building around causes and ventures. This last aspect is a major contribution of the study, as it demonstrates a clear linkage between marketing effort and the success of crowd-funded projects.
User-Generated Content (UGC) on social media platforms and product search engines is changing the way consumers shop for goods online. However, current product search engines fail to effectively leverage information created across diverse social media platforms. Moreover, current ranking algorithms in these product search engines tend to induce consumers to focus on one single product characteristic dimension (e.g., price, star rating). This approach largely ignores consumers ’ multi-dimensional preferences for products. In this paper, we propose to generate a ranking system that recommends products that provide on average the best value for the consumer’s money. The key idea is that products that provide a higher surplus should be ranked higher on the screen in response to consumer queries. We use a unique dataset of U.S. hotel reservations made over a three-month period through Travelocity, which we supplement with data from various social media sources using techniques from text mining, image classification, social geotagging, human annotations, and geo-mapping. We propose a random coefficient hybrid structural model, taking into consideration the two sources of consumer heterogeneity the different travel occasions and different hotel characteristics introduce. Based on the estimates from the model, we infer the economic impact of various location and service characteristics of hotels. We then propose a new hotel ranking
Building on the interpersonal evaluation theory in social psychology, this study explores the existence of a negativity bias in evaluating the helpfulness of online reviews, i.e., whether users perceive a negative review to be more helpful than a positive review. An analysis of 7659 book reviews from Amazon.co.uk shows that a negativity bias disappears after controlling for moderating factors related to evaluation quality such as readability and length. The finding demonstrates that the negativity bias suggested by the social psychology literature is not readily applicable to consumer-generated online reviews. The study contributes to the theorization of word-of-mouth by exploring the qualitative characteristics of consumer-generated reviews in addition to their valence. The study also makes a theoretical contribution to information systems research by introducing and extending the interpersonal evaluation theory to online review research.
Combining econometric analysis with text mining techniques, this study attempts to explore why some online product reviews have no usefulness rating through examining review posting time and text features. Later posting time may reduce the probability of some online reviews being seen and thus lead to their being not rated for usefulness. Besides, the neutral diagnosticity of reviews reflected from the text features may cause difficulty for readers to judge and evaluate the usefulness of these reviews. Our study finds that, though not being seen due to later posting time obviously explains no usefulness rating for some online reviews, the neutral diagnosticity of these reviews is also an important and non-neglectable cause for their lack of usefulness rating. Further, we identify the text features which may lead to the neutral diagnosticity of the review. Our study has implications for online product reviews website managers in identifying and dismissing the reviews with no usefulness rating to improve readers' information retrieving efficiency and also for reviewers in improving the quality of their reviews.
User-supplied reviews are widely and increasingly used to enhance e- commerce and other websites. Because reviews can be numerous and varying in quality, it is important to assess how helpful each review is. While review helpfulness is currently assessed manu- ally, in this paper we consider the task of automatically assessing it. Experi- ments using SVM regression on a vari- ety of features over Amazon.com product reviews show promising results, with rank correlations of up to 0.66. We found that the most useful features in- clude the length of the review, its uni- grams, and its product rating.
Consumer-generated product reviews have proliferated online, driven by the notion that consumers' decision to purchase or not purchase a product is based on the positive or negative information about that product they obtain from fellow consumers. Using research on information processing (Chaiken 1980) as a foundation, we suggest that in the context of an online community, reviewer disclosure of identity-descriptive information is used by consumers to supplement or replace product information when making purchase decisions and evaluating the helpfulness of online reviews. Using a unique dataset based on both chronologically compiled ratings as well as reviewer characteristics for a given set of products and geographical location-based purchasing behavior from Amazon, we provide evidence that community norms are an antecedent to reviewer disclosure of identity-descriptive information. Amazon members rate reviews containing identity-descriptive information more positively, and the prevalence of reviewer disclosure of identity information is associated with increases in subsequent online product sales. In addition, we show that when reviewers are from a particular geographic location, subsequent product sales are higher in that region, thus highlighting the important role of geography in electronic commerce. Taken together, our results suggest that identity-relevant information about reviewers shapes community members' judgment of products and reviews. Implications for research on the relationship between online reviews and sales, peer recognition systems, and conformity to online community norms are discussed.
With the rapid growth of the Internet, the ability of users to create and publish content has created active electronic communities that provide a wealth of product information. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers to locate the best reviews and understand the true underlying quality of a product. In this paper, we re-examine the impact of reviews on economic outcomes like product sales and see how different factors affect social outcomes such as their perceived usefulness. Our approach explores multiple aspects of review text, such as subjectivity levels, various measures of readability and extent of spelling errors to identify important text-based features. In addition, we also examine multiple reviewer-level features such as average usefulness of past reviews and the self-disclosed identity measures of reviewers that are displayed next to a review. Our econometric analysis reveals that the extent of subjectivity, informativeness, readability, and linguistic correctness in reviews matters in influencing sales and perceived usefulness. Reviews that have a mixture of objective, and highly subjective sentences are negatively associated with product sales, compared to reviews that tend to include only subjective or only objective information. However, such reviews are rated more informative (or helpful) by other users. Further, reviews that rate products negatively can be associated with increased product sales when the review text is informative and detailed.By using Random Forest based classiers, we show that we can accurately predict the impact of reviews on sales and their perceived usefulness. We examine the relative importance of the three broad feature categories: 'reviewer-related' features, 'review subjectivity' features, and 'review readability' features, and find that using any of the three feature sets results in a statistically equivalent performance as in the case of using all available features. This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their helpfulness and economic impact.
One of the problems in public education and mass communication is how to tell whether a particular piece of writing is likely to be readable to a particular group of readers. Two major solutions are possible: measuring and predicting readability. Measuring, by judgments or tests, involves using readers. Predicting by readability formulas, does not involve readers but instead uses counts of language elements in the piece of writing. This article reviews formulas and related predictive devices since 1960. Four categories are presented: 1) recalculations and revisions of existing formulas; 2) new formulas, for general purpose or special purpose use; 3) application aids, for both manual and machine use; and, 4) predictions of readability for foreign languages. It concludes with suggestions for choosing a formula, based upon the following considerations: 1) special versus general needs, 2) manual versus machine application, 3) simple versus complex formulas, 4) word length versus word list formulas; and, 5) sentence length versus sentence complexity. Finally, the article stresses that formulas provide good indices of difficulty, but do not indicate causes of difficulty or say how to write readably.
New types of document collections are being developed by various web services. The service providers keep track of non-textual features such as click counts. In this paper, we present a framework to use non-textual features to pre- dict the quality of documents. We also show our quality measure can be successfully incorporated into the language modeling-based retrieval model. We test our approach on a collection of question and answer pairs gathered from a community based question answering service where people ask and answer questions. Experimental results using our quality measure show a signiflcant improvement over our baseline.
Online communities displaying textual postings require measures to combat information overload. One popular approach is to ask participants whether or not messages are helpful in order to then guide others to interesting content. Adopting a well-established framework for assessing data quality, we examine the nature of "helpfulness."We study consumer reviews at Amazon.com, deriving 22 measures quantifying their textual properties, authors' reputations and product characteristics. Confirmatory factor analysis reveals five underlying quality dimensions representing reviewers' reputations in the community, the topical relevancy of the reviews, the ease of understanding them, their believability and objectivity. A correlation and regression analysis confirms that these dimensions are related to the helpfulness scores assigned by community participants. However, it also uncovers a strong relationship between the chronological ordering of reviews and helpfulness, which both community participants and designers should keep in mind when using this method of social navigation.
The creation of online consumer communities to provide product reviews and advice has been touted as an important, albeit somewhat expensive component of Internet retail strategies. In this paper, we characterize reviewer behavior at two popular Internet sites and examine the effect of consumer reviews on firms' sales. We use publicly available data from the two leading online booksellers, Amazon.com and BarnesandNoble.com, to construct measures of each firm's sales of individual books. We also gather extensive consumer review data at the two sites. First, we characterize the reviewer behavior on the two sites such as the distribution of the number of ratings and the valence and length of ratings, as well as ratings across different subject categories. Second, we measure the effect of individual reviews on the relative shares of books across the two sites. We argue that our methodology of comparing the sales and reviews of a given book across Internet retailers allows us to improve on the existing literature by better capturing a causal relationship between word of mouth (reviews) and sales since we are able to difference out factors that affect the sales and word of mouth of both retailers, such as the book's quality. We examine the incremental sales effects of having reviews for a particular book versus not having reviews and also the differential sales effects of positive and negative reviews. Our large database of books also allows us to control for other important confounding factors such as differences across the sites in prices and shipping times.
Analysis of decision making under risk has been dominated by expected utility theory, which generally accounts for people's actions. Presents a critique of expected utility theory as a descriptive model of decision making under risk, and argues that common forms of utility theory are not adequate, and proposes an alternative theory of choice under risk called prospect theory. In expected utility theory, utilities of outcomes are weighted by their probabilities. Considers results of responses to various hypothetical decision situations under risk and shows results that violate the tenets of expected utility theory. People overweight outcomes considered certain, relative to outcomes that are merely probable, a situation called the "certainty effect." This effect contributes to risk aversion in choices involving sure gains, and to risk seeking in choices involving sure losses. In choices where gains are replaced by losses, the pattern is called the "reflection effect." People discard components shared by all prospects under consideration, a tendency called the "isolation effect." Also shows that in choice situations, preferences may be altered by different representations of probabilities. Develops an alternative theory of individual decision making under risk, called prospect theory, developed for simple prospects with monetary outcomes and stated probabilities, in which value is given to gains and losses (i.e., changes in wealth or welfare) rather than to final assets, and probabilities are replaced by decision weights. The theory has two phases. The editing phase organizes and reformulates the options to simplify later evaluation and choice. The edited prospects are evaluated and the highest value prospect chosen. Discusses and models this theory, and offers directions for extending prospect theory are offered. (TNM)
D. is a Professor of Information Systems in the School of Management at the Harbin Institute of Technology
- Qiang Ye
- Ph
Qiang Ye, Ph.D. is a Professor of Information Systems in
the School of Management at the Harbin Institute of
Technology (yeqiang@hit.edu.cn)
Econometrics analysis
- W H Greene
Greene, W. H. (2011). Econometrics analysis (7 ed.). Upper Saddle River, NJ: Prentice
Hall.
- B Fang
B. Fang et al. / Tourism Management 52 (2016) 498e506 1007e1016.
is a Professor of Technology Management in the School of Hotel & Tourism Management at The Hong Kong Polytechnic University
- Rob Law
- D Ph
Rob Law, Ph.D., is a Professor of Technology Management
in the School of Hotel & Tourism Management at The Hong
Kong Polytechnic University (rob.law@polyu.edu.hk).
Personal profile information as cues of credibility in online travel reviews. Information and communication techonologies in tourism 2013
- H L Park
- Z Xiang
- B Josiam
- H M Kim
Park, H. L., Xiang, Z., Josiam, B., & Kim, H. M. (2013). Personal profile information as
cues of credibility in online travel reviews. Information and communication
techonologies in tourism 2013. In C. Lorenzo, & X. Zheng (Eds.), ENTER 2013
Proceedings (pp. 230e241). Austria. Berlin Heidelberg: Springer-Verlag.
Internet leads travel
- M Collie
Collie, M. (2014). Internet leads travel. Geton.com. Retrieved from http://www.geton.
com/blog/geton/internet-leads-travel.
is an Assistant Professor in the School of Hotel & Tourism Management at The Hong Kong Polytechnic University
- Ph D Deniz Kucukusta
Deniz Kucukusta, Ph.D., is an Assistant Professor in the
School of Hotel & Tourism Management at The Hong Kong
Polytechnic University (deniz.kucukusta@polyu.edu.hk).
Technology readiness, trustworthiness, and use of e-auctions
- J Lu
- L Hayes
- C.-S Yu
- L.-Z Wang
Lu, J., Hayes, L., Yu, C.-S., & Wang, L.-Z. (2010). Technology readiness, trustworthiness, and use of e-auctions. In Proceedings of Southwest decision sciences Institute
conference Dallas, TX (pp. 1e16).