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This study aims to explore whether seeking recommendations for doctors from offline word-of-mouth or online reviews influences patient satisfaction after treatment, and how the source of recommendation affects this effect.

Using a unique dataset of more than three million reviews from a popular Chinese online health community, this study used the coarsened exact matching method and built fixed-effect models to conduct empirical analysis.

The results suggest that selecting doctors according to recommendations can improve patient satisfaction and mitigate their dissatisfaction when encountering service failures. However, online recommendations were found to be less effective than offline sources in improving patient satisfaction.

This study provides important insights into patient satisfaction and doctor-patient relationships by revealing the antecedents of satisfaction and the potential for improving this relationship. It also contributes to the understanding of how recommendations in the healthcare context can improve patient satisfaction and alleviate the negative impact of service failures.

Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users’ satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles.

Association rule mining is applied to users’ news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website.

The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users’ online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance.

Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user’s online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.

Consumers increasingly depend on the internet as the information source to make their hospitality decisions, which highlights the need for more research in online recommendation. Due to the globalization, culture and its effects on marketing become an increasingly important subject to investigate. Therefore, this paper aims to offer a cross-cultural investigation of consumers’ different trustworthiness and credibility perceptions when facing online recommendations from different information resources.

This research uses the source-credibility theory to examine consumers’ responses to online recommendations from two sources. Participants were recruited from two equivalent marketing panels in each culture. A 2 (online recommendation source: in-group vs out-group) by 2 (culture: American vs Chinese) between-subjects quasi-experiment was conducted to test the hypotheses.

The results demonstrate that culture moderates consumer responses to the two types of online sources. Chinese consumers, due to their more collectivist nature, exhibit higher levels of purchase intent when the recommendation originates from an in-group rather than from an out-group. Such differences are not observed among the more individualist American consumers. Furthermore, trustworthiness plays an important role in influencing Chinese consumers’ perception of recommendation credibility and the consequent purchase intent.

This research provides guidelines to hospitality practitioners when developing their social networking sites and online marketing strategies across different cultures.

The current study conducts an in-depth investigation of cultural differences in consumers’ perceptions of and reactions to online recommendations from other customers with various social distances.

Blogging has become part of a consumer's decision making process when shopping online; however, the understanding of blog recommendation's effect on consumer purchase decision is still vague. The purpose of this study is to examine whether the blog reader's trusting belief in the blogger is significant in relation to the perceived usefulness of the blogger's recommendations; and how the blog reader's perceptions influence his/her attitude and purchasing behavior online. The moderating effect of blogger's reputation on readers’ purchasing intentions is also tested.

Based on various theories, a model was proposed in this study. A survey involving 327 blog readers as participants was analyzed in the empirical study to investigate whether the usefulness of bloggers’ recommendations and trusting beliefs toward blogger had influence on consumers’ attitudes and behavioral intentions toward online shopping.

The results indicated that perceived usefulness of bloggers’ recommendations and trust had significant influential effect on blog users’ attitude towards and intention to shop online. Moreover, the findings showed that different determinants affected the users of perceived‐high‐reputation and perceived‐low‐reputation blogs.

The findings suggest bloggers’ electronic word‐of‐mouth (eWOM) to be a promising marketing strategy for increasing sales. The marketers should provide free trial products and services to the perceived‐high‐reputation bloggers who, as valued opinion leaders, will influence and prompt others to shop online through a trusting effect. As for perceived‐low‐reputation bloggers, the marketing strategists should strive to emphasize the usefulness of products and services being marketed, so these perceived‐low‐reputation bloggers can focus more on describing the advantages and benefits of products or services discussed in their blogs.

Providing online news recommendations to users has become an important trend for online media platforms, enabling them to attract more users. The purpose of this paper is to propose an online news recommendation system for recommending news articles to users when browsing news on online media platforms.

A Collaborative Semantic Topic Modeling (CSTM) method and an ensemble model (EM) are proposed to predict user preferences based on the combination of matrix factorization with articles’ semantic latent topics derived from word embedding and latent topic modeling. The proposed EM further integrates an online interest adjustment (OIA) mechanism to adjust users’ online recommendation lists based on their current news browsing.

This study evaluated the proposed approach using offline experiments, as well as an online evaluation on an existing online media platform. The evaluation shows that the proposed method can improve the recommendation quality and achieve better performance than other recommendation methods can. The online evaluation also shows that integrating the proposed method with OIA can improve the click-through rate for online news recommendation.

The novel CSTM and EM combined with OIA are proposed for news recommendation. The proposed novel recommendation system can improve the click-through rate of online news recommendations, thus increasing online media platforms’ commercial value.

This purpose of this paper is to analyze how consumers’ online recommendations affect the omnichannel webrooming experience based on the internet, physical and mobile channels.

Two experimental studies are implemented. Study 1 analyzes the impact of an online review on the physical interaction with the product. Study 2 modifies the moment of receiving the online recommendation and its social tie.

Webrooming improves the shopping experience. Online recommendations from anonymous customers increase confidence in the product’s adequacy, although this effect depends on the moment of receiving the recommendation and the level of confidence before interacting physically with the product. Friend recommendations reinforce preferences regardless of previous online experiences.

This research examines the effects of different types of online recommendations on offline shopping experiences, choice and confidence. Confidence is stressed as a key variable in omnichannel behavior.

The findings offer practical value for electronic word-of-mouth marketing, omnichannel marketing, as well as online and physical channel management.

This is one of the first studies that examine the impact of online consumer recommendations on shopping experiences combining online, mobile and physical channels. The results reveal the importance of recommendations’ source and moment of reception for determining consumers’ preferences, choice and confidence.

La presente investigación analiza cómo las recomendaciones online afectan a la experiencia webrooming omnicanal, basada en el canal físico, online, y móvil.

Se llevaron a cabo dos experimentos. El Estudio 1 analiza el impacto de una revisión online positiva en la interacción posterior con el producto. El Estudio 2 modifica el momento de recibir la recomendación y el vínculo social entre emisor y receptor.

El proceso webrooming mejora la experiencia de compra. Las recomendaciones online de clientes anónimos incrementan la auto-confianza sobre la adecuación del producto, aunque este efecto depende del momento de recibir la recomendación y del nivel de auto-confianza previo a la interacción física con el producto. Las recomendaciones de amigos refuerzan las preferencias, independientemente de la experiencia online previa.

Esta investigación examina los efectos de diferentes tipos de recomendaciones online en experiencias offline, le elección y la auto-confianza. La auto-confianza se revela como una variable clave del comportamiento omnicanal.

Los resultados ofrecen implicaciones para la gestión del marketing boca-oído y omnicanal, así como la gestión de la experiencia en el canal físico y el online.

Este es uno de los primeros estudios que analizan el impacto de recomendaciones online en experiencias de compra que combinan canales online, offline y móvil. Los resultados revelan que la importancia de la fuente y del momento de recibir la recomendación determinan las preferencias, elección, y auto-confianza de los consumidores.

Comercio minorista, Omnicanal, Webrooming, Auto-confianza, Boca-oído electrónico, Vínculo social

Trabajo de investigación

The purpose of this paper is to develop a novel recommendation method using online reviews with emotional preferences for facilitating online purchase decisions. This leads to better use of information-rich online reviews for providing users with personalized recommendations.

A novel method is developed for producing personalized recommendations in online purchase decision-making. Such a method fuses the belief structure and the Shapley function together to effectively deal with the emotional preferences in online reviews and adequately tackle the interaction existent between product criteria with the use of a modified combination rule for making better online recommendations for making online purchase decisions.

An example is presented for demonstrating the applicability of the method for facilitating online purchase. The results show that the recommendation using the proposed method can effectively improve customer satisfaction with better purchase decisions.

The proposed method can better utilize online reviews for satisfying personalized needs of consumers. The use of such a method can optimize interface design, refine customer needs, reduce recommendation errors and provide personalized recommendations.

The proposed method adequately considers the characteristics of online reviews and the personalized needs of customers for providing customers with appropriate recommendations. It can help businesses better manage online reviews for improving customer satisfaction and create greater value for both businesses and customers.

The purpose of this paper is to study the use of online recommendation systems on e‐commerce sites is which becoming more common as marketers recognize their potential to improve their own operations as well as consumers' shopping experiences. Since some consumers question the credibility of these systems, this study compares responses to such systems (classified based on their source into seller and third party systems) with responses to recommendations coming directly from other consumers. The latter may also be better suited for consumers today since many of them utilize direct information from social media on a daily basis. Past research indicates that reactions to such recommendations may depend on the types of goods they describe and therefore this study also tests whether consumer responses vary with types of goods. The study examines consumer reactions to recommendations designed for search, experience, and credence goods. Finally, this study also explores the most desired features of recommendations to help marketers come up with the most effective recommendations that help facilitate purchasing decisions.

The study surveys a convenience sample of 202 undergraduate students to test these objectives. It was a 3 (product types) by 3 (recommendation types) factorial design with multiple dependent variables and three covariates.

The study reveals that, irrespective of the product type, consumers react differently to the three types of recommendations that are tested. This study shows that consumers have the most positive attitudes and most frequently utilize recommendations coming directly from other consumer. This suggests that more attention should be directed to these recommendations in marketing theory and practice. Consumers also hold more positive attitudes towards third‐party recommendation systems than recommendation systems coming from the seller. They also have more positive reactions toward recommendations designed for search and experience goods rather than credence products. Finally, the study also examines the usefulness of different characteristics of these recommendations to help online managers develop most effective recommendations online and finds that it varies with different types of recommendations and products for which recommendations are used.

In addition to the recommendation systems that have been explored in the past (seller and third party systems), the study examines reactions to recommendations coming directly from other consumers, as these recommendations may be better suited for today's audiences. The study shows which recommendation type is best received and most frequently used online. It also tests reactions to recommendations designed for different types of goods. This study includes credence goods, in addition to search and experience products, since consumer reactions to recommendations designed for credence goods have not been yet explored in the past research. It also found that recommendations are better received for goods with a higher number of search features. Finally, the study explores the specific features of different recommendation types and based on the findings proposes how these online recommendations should be structured to be most effective.

Online advertisement brings huge revenue to many websites. There are many types of online advertisement; this paper aims to focus on the online banner ads which are usually placed in a particular news website. The investigated news website adopts a pay-per-ad payment model, where the advertisers are charged when they rent a banner from the website during a particular period. In this payment model, the website needs to ensure that the ad pushed frequency of each ad on the banner is similar. Under such advertisement push rules, an ad-recommendation mechanism considering ad push fairness is required.

The authors proposed a novel ad recommendation method that considers both ad-push fairness and personal interests. The authors take every ad’s exposure time into consideration and investigate users’ three different usage experiences in the website to identify the main factors affecting the interests of users. Online ad recommendation is conducted on the investigated news website.

The results of the experiments show that the proposed approach performs better than the traditional approach. This method can not only enhance the average click rate of all ads in the website but also ensure reasonable fairness of exposure frequency of each ad. The online experiment results demonstrate the effectiveness of this approach.

Existing researches had not considered both the advertisement recommendation and ad-push fairness together. With the proposed novel ad recommendation model, the authors can improve the ad click-through rate of ads with reasonable push fairness. The website provider can thereby increase the commercial value of advertising and user satisfaction.

The aim of this paper is to examine the impact of three main technologies on converting browsers into customers: impact of review rating (location rating and service rating), recommendation and search listings.

This paper estimates conversion rate model parameters using a quasi-likelihood method with the Bernoulli log-likelihood function and parametric regression model based on the beta distribution.

The results show that a high rank in search listings, a high number of recommendations and location rating have a significant and positive impact on conversion rates. However, service rating and star rating do not have a significant effect on conversion rate. Furthermore, room price and hotel size are negatively associated with conversion rate. It was also found that a high rank in search listings, a high number of recommendations and location rating increase online hotel bookings. Furthermore, it was found that a high number of recommendations increase the conversion rate of hotels with low ranks.

The findings show that hotels’ location ratings are more important than both star and service ratings for the conversion of visitors into customers. Thus, hotels that are located in convenient locations can charge higher prices. The results may also help entrepreneurs who are planning to open new hotels to forecast the conversion rates and demand for specific locations. It was found that a high number of recommendations help to increase the conversion rate of hotels with low ranks. This result suggests that a high numbers of recommendations mitigate the adverse effect of a low rank in search listings on the conversion rate.

This paper contributes to the understanding of the drivers of conversion rates in online channels for the successful implementation of hotel marketing.