The impact of online reviews on hotel booking intentions and trust

A growing reliance on the Internet as an information source when making choices about tourism products raises the need for more research into electronic word of mouth. Within a hotel context, this study explores the role of four key factors that influence perceptions of trust and consumer choice. An experimental design is used to investigate four independent variables: the target of the review (core or interpersonal); overall valence of a set of reviews (positive or negative); framing of reviews (what comes first: negative or positive information); and whether or not a consumer generated numerical rating is provided together with the written text. Consumers seem to be more influenced by early negative information, especially when the overall set of reviews is negative. However, positively framed information together with numerical rating details increases both booking intentions and consumer trust. The results suggest that consumers tend to rely on easy-to-process information, when evaluating a hotel based upon reviews. Higher levels of trust are also evident when a positively framed set of reviews focused on interpersonal service.

The influence of online reviews on consumers’ attributions of service quality and control for service standards in hotels

Online travel reviews are emerging as a powerful source of information affecting tourists' pre-purchase evaluation of a hotel organization. This trend has highlighted the need for a greater understanding of the impact of online reviews on consumer attitudes and behaviors. In view of this need, we investigate the influence of online hotel reviews on consumers' attributions of service quality and firms' ability to control service delivery. An experimental design was used to examine the effects of four independent variables: framing; valence; ratings; and target. The results suggest that in reviews evaluating a hotel, remarks related to core services are more likely to induce positive service quality attributions. Recent reviews affect customers' attributions of controllability for service delivery, with negative reviews exerting an unfavorable influence on consumers' perceptions. The findings highlight the importance of managing the core service and the need for managers to act promptly in addressing customer service problems.

Leveraging product adopter information from online reviews for product recommendation

The availability of the sheer volume of online product reviews makes it possible to derive implicit demographic information of product adopters from review documents. This paper proposes a novel approach to the extraction of product adopter mentions from online reviews. The extracted product adopters are the ncategorise into a number of different demographic user groups. The aggregated demographic information of many product adopters can be used to characterize both products and users, which can be incorporated into a recommendation method using weighted regularised matrix factorisation. Our experimental results on over 15 million reviews crawled from JINGDONG, the largest B2C e-commerce website in China, show the feasibility and effectiveness of our proposed frame work for product recommendation.

Mining product adopter information from online reviews for improving product recommendation

We present in this article an automated framework that extracts product adopter information from online reviews and incorporates the extracted information into feature-based matrix factorization formore effective product recommendation. In specific, we propose a bootstrapping approach for the extraction of product adopters from review text and categorize them into a number of different demographic categories. The aggregated demographic information of many product adopters can be used to characterize both products and users in the form of distributions over different demographic categories. We further propose a graphbased method to iteratively update user- and product-related distributions more reliably in a heterogeneous user-product graph and incorporate them as features into the matrix factorization approach for product recommendation. Our experimental results on a large dataset crawled from JINGDONG, the largest B2C e-commerce website in China, show that our proposed framework outperforms a number of competitive baselines for product recommendation.

ASSESSING QUALITY IN HIGH-UNCERTAINTY MARKETS: ONLINE REVIEWS OF CREDENCE SERVICES

In economics of information theory, credence products are those whose quality is difficult or impossible for consumers to assess, even after they have consumed the product (Darby & Karni, 1973). This dissertation is focused on the content, consumer perception, and power of online reviews for credence services. Economics of information theory has long assumed, without empirical confirmation, that consumers will discount the credibility of claims about credence quality attributes. The same theories predict that because credence services are by definition obscure to the consumer, reviews of credence services are incapable of signaling quality. Our research aims to question these assumptions. In the first essay we examine how the content and structure of online reviews of credence services systematically differ from the content and structure of reviews of experience services and how consumers judge these differences. We have found that online reviews of credence services have either less important or less credible content than reviews of experience services and that consumers do discount the credibility of credence claims. However, while consumers rationally discount the credibility of simple credence claims in a review, more complex argument structure and the inclusion of evidence attenuate this effect. In the second essay we ask, “Can online reviews predict the worst doctors?” We examine the power of online reviews to detect low quality, as measured by state medical board sanctions. We find that online reviews are somewhat predictive of a doctor’s suitability to practice medicine; however, not all the data are useful. Numerical or star ratings provide the strongest quality signal; user-submitted text provides some signal but is subsumed almost completely by ratings. Of the ratings variables in our dataset, we find that punctuality, rather than knowledge, is the strongest predictor of medical board sanctions. These results challenge the definition of credence products, which is a long-standing construct in economics of information theory. Our results also have implications for online review users, review platforms, and for the use of predictive modeling in the context of information systems research.

Avoiding the dark side of positive online consumer reviews: enhancing reviews' usefulness for high risk-averse travelers

The influence of positive online consumer reviews on a traveler's decision making remains unclear. To better understand the perceived usefulness of online reviews, this study conducts two experiments using positive and negative online consumer reviews. Study results suggest that high risk-averse travelers find negative online reviews more useful than positive reviews. For positive online reviews, high-risk averse travelers feel expert reviewers' postings, travel product pictures, and well-known brand names enhance usefulness of the positive online reviews. These findings offer interesting implications for both marketing theory and practice.

Asymmetric effects of online consumer reviews

Consumers tend to seek heuristic information cues to simplify the amount of information involved in tourist decisions. Accordingly, star ratings in online reviews are a critical heuristic element of the perceived evaluation of online consumer information. The objective of this article is to assess the effect of review ratings on usefulness and enjoyment. The empirical application is carried out on a sample of 5,090 reviews of 45 restaurants in London and New York. The results show that people perceive extreme ratings (positive or negative) as more useful and enjoyable than moderate ratings, giving rise to a U-shaped line, with asymmetric effects: the size of the effect of online reviews depends on whether they are positive or negative.

High-Order Concept Associations Mining and Inferential Language Modeling for Online Review Spam Detection

Despite many incidents about fake online consumer reviews have been reported, very few studies have been conducted to date to examine the trustworthiness of online consumer reviews. One of the reasons is the lack of an effective computational method to separate the untruthful reviews (i.e., spam) from the legitimate ones (i.e., ham) given the fact that prominent spam features are often missing in online reviews. The main contribution of our research work is the development of a novel review spam detection method which is underpinned by an unsupervised inferential language modeling framework. Another contribution of this work is the development of a high-order concept association mining method which provides the essential term association knowledge to bootstrap the performance for untruthful review detection. Our experimental results confirm that the proposed inferential language model equipped with high-order concept association knowledge is effective in untruthful review detection when compared with other baseline methods.

“The impact of online ratings on video game sales”

Online third-party reviews have been grown over the last decade and they now play an important role as a tool for helping customers evaluate products and services that in many cases offer more than tangible features. This study intends to quantify the impact online ratings have over video game sales by conducting a linear regression analysis on 300 titles for the previous console generation (PlayStation® 3 and Xbox® 360) using a data from the video game industry to understand the existing influence on this particular market. The findings showed that these variables have a weak linear relationship thus suggesting that quality of a title explains little the commercial success of a video game and instead this should cover a wider range of factors. Afterwards, we compare results to previous ones and discuss the managerial implications for upcoming gaming generations.

Marketing plan for online aqctivity of Rota Vicentina boosting direct website sales

The nature tourism experienced a great expansion of its market with the appearance of different lifestyles. In this Work Project a study regarding the website direct sales of Rota Vicentina was developed. Its website shows the idea of being solely an information structure and not a purchase one, leading to a current absence of online sales. Hence, it is suggested the modification of its business model, using different instruments and channels. Some digital marketing recommendations were developed in order to boost website sales, such as a platform for online reviews, remarketing campaigns and social media activity.

Ergebnisse der Physiologie, biologischen Chemie und experimentellen Pharmakologie. Reviews of physiology, biochemistry, and experimental pharmacology.

Founded and for many years edited by L. Asher and K. Spiro.

A cross-sectional study assessing the association between online ratings and structural and quality of care measures : results from two German physician rating websites

BACKGROUND: Even though physician rating websites (PRWs) have been gaining in importance in both practice and research, little evidence is available on the association of patients' online ratings with the quality of care of physicians. It thus remains unclear whether patients should rely on these ratings when selecting a physician. The objective of this study was to measure the association between online ratings and structural and quality of care measures for 65 physician practices from the German Integrated Health Care Network "Quality and Efficiency" (QuE). METHODS: Online reviews from two German PRWs were included which covered a three-year period (2011 to 2013) and included 1179 and 991 ratings, respectively. Information for 65 QuE practices was obtained for the year 2012 and included 21 measures related to structural information (N = 6), process quality (N = 10), intermediate outcomes (N = 2), patient satisfaction (N = 1), and costs (N = 2). The Spearman rank coefficient of correlation was applied to measure the association between ratings and practice-related information. RESULTS: Patient satisfaction results from offline surveys and the patients per doctor ratio in a practice were shown to be significantly associated with online ratings on both PRWs. For one PRW, additional significant associations could be shown between online ratings and cost-related measures for medication, preventative examinations, and one diabetes type 2-related intermediate outcome measure. There again, results from the second PRW showed significant associations with the age of the physicians and the number of patients per practice, four process-related quality measures for diabetes type 2 and asthma, and one cost-related measure for medication. CONCLUSIONS: Several significant associations were found which varied between the PRWs. Patients interested in the satisfaction of other patients with a physician might select a physician on the basis of online ratings. Even though our results indicate associations with some diabetes and asthma measures, but not with coronary heart disease measures, there is still insufficient evidence to draw strong conclusions. The limited number of practices in our study may have weakened our findings.

Comentários online nos websites de comparação de hotéis

Com o rápido aumento da utilização da internet no mundo, os consumidores utilizam cada vez mais os websites para comparação, compra e venda de produtos. Atualmente os utilizadores da internet deixaram de procurar exclusivamente informação e tornaram-se eles próprios fornecedores de experiências através de comunidades online, que continuam em grande crescimento. Seguindo essa tendência, muitas indústrias escolheram a internet como canal de comunicação preferido e a indústria hoteleira não foge à regra. Assim a presente dissertação resulta numa pesquisa, em que o principal objetivo é entender de que forma os diversos fatores de informação presentes nos comentários online realizados nos websites de comparação de hotéis influenciam a utilização da informação pelo consumidor. Na presente metodologia é utilizado o modelo de Filieri e McLeay (2014), o tipo de inquérito utilizado é um questionário online para analisar quais os fatores que mais influenciam os consumidores a utilizarem a informação disponível nos comentários online. Os principais resultados obtidos neste estudo indicam que a precisão da informação, a consistência da informação e o ranking do alojamento influenciam a utilização da informação presente nos comentários online.

A review selection method using product feature taxonomy

As of today, online reviews have become more and more important in decision making process. In recent years, the problem of identifying useful reviews for users has attracted significant attentions. For instance, in order to select reviews that focus on a particular feature, researchers proposed a method which extracts all associated words of this feature as the relevant information to evaluate and find appropriate reviews. However, the extraction of associated words is not that accurate due to the noise in free review text, and this affects the overall performance negatively. In this paper, we propose a method to select reviews according to a given feature by using a review model generated based upon a domain ontology called product feature taxonomy. The proposed review model provides relevant information about the hierarchical relationships of the features in the review which captures the review characteristics accurately. Our experiment results based on real world review dataset show that our approach is able to improve the review selection performance according to the given criteria effectively.

Quality-aware review selection based on product feature taxonomy

As of today, user-generated information such as online reviews has become increasingly significant for customers in decision making process. Meanwhile, as the volume of online reviews proliferates, there is an insistent demand to help the users tackle the information overload problem. In order to extract useful information from overwhelming reviews, considerable work has been proposed such as review summarization and review selection. Particularly, to avoid the redundant information, researchers attempt to select a small set of reviews to represent the entire review corpus by preserving its statistical properties (e.g., opinion distribution). However, one significant drawback of the existing works is that they only measure the utility of the extracted reviews as a whole without considering the quality of each individual review. As a result, the set of chosen reviews may consist of low-quality ones even its statistical property is close to that of the original review corpus, which is not preferred by the users. In this paper, we proposed a review selection method which takes review quality into consideration during the selection process. Specifically, we examine the relationships between product features based upon a domain ontology to capture the review characteristics based on which to select reviews that have good quality and preserve the opinion distribution as well. Our experimental results based on real world review datasets demonstrate that our proposed approach is feasible and able to improve the performance of the review selection effectively.