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Previous research into online reviews in the hospitality industry has focused mainly on big companies; thus, it is not yet known whether its findings apply also to small and medium enterprises (SMEs), the most abundant in the sector. Focusing on online reviews in the hospitality sector, this study aims to analyse whether firm size moderates the relationship between online review valence and customer responses.

This study uses a 2 (positive vs negative online review) × 2 (SME vs big company) experimental research design conducted in two hospitality settings, hotels and restaurants.

The impact of online reviews on customer responses is less intense for smaller hospitality companies.

This study incorporates firm size as a moderator of the relationship between online review valence and customer responses in two hospitality settings, restaurants and hotels.

以往针对酒店业的在线评论研究主要集中在大型企业上, 因此这些研究结果是否也适用于中小企业尚不清楚, 而中小企业在该行业中最为普遍。本研究重点研究了酒店业中的在线评论, 分析了企业规模是否在在线评论极性与客户反应之间的关系中起到调节作用。

本研究采用2（正面与负面在线评论）×2（中小企业与大型企业）的实验研究设计, 并在两个实验环境下（酒店和餐饮）进行研究。

对规模较小的酒店企业来说, 在线评论对客户反应的影响并不强烈。

本研究将企业规模作为餐厅和酒店行业中在线评论极性和客户反应关系的调节变量。

This methodological paper demonstrates how service firms can use digital technologies to quantify and predict customer evaluations of their interactions with the firm using unstructured, qualitative data. To harness the power of unstructured data and enhance the customer-firm relationship, the use of computerized text analysis is proposed.

Three empirical studies were conducted to exemplify the use of the computerized text analysis tool. A secondary data analysis of online customer reviews (n = 2,878) in a service industry was used. LIWC was used to conduct the text analysis, and thereafter SPSS was used to examine the predictive capability of the model for the evaluation of customer-firm interactions.

A lexical analysis of online customer reviews was able to predict evaluations of customer-firm interactions across the three empirical studies. The authenticity and emotional tone present in the reviews served as the best predictors of customer evaluations of their service interactions with the firm.

Computerized text analysis is an inexpensive digital tool which, to date, has been sparsely used to analyze customer-firm interactions based on customers' online reviews. From a methodological perspective, the use of this tool to gain insights from unstructured data provides the ability to gain an understanding of customers' real-time evaluations of their service interactions with a firm without collecting primary data.

This research contributes to the growing body of knowledge regarding the use of computerized lexical analysis to assess unstructured, online customer reviews to predict customers' evaluations of a service interaction. The results offer service firms an inexpensive and user-friendly methodology to assess real-time, readily available reviews, complementing traditional customer research. A tool has been used to transform unstructured data into a numerical format, quantifying customer evaluations of service interactions.

Prior hospitality studies have reviewed review trustworthiness and perceived price as predictors of restaurant selection. However, the impacts of these two factors may vary by sales promotion and customer types. This study aims to determine whether sales promotions and customer type are the key elements that facilitate behavioral intentions by moderating the linkage between perceived price and behavioral intentions as well as the linkage between online review trustworthiness and behavioral intentions.

Analysis of the responses of 533 individuals familiar with the Michelin Guide for restaurants in Seoul provided evidence supporting a sales promotion theory wherein promotions signal benefits in consumers’ minds.

The findings show that when perceived price is positive and the trustworthiness of online reviews is high, repeat customers prefer mixed coupons to price discounts. Notably, the results indicate that when the trustworthiness of online reviews is high, first-time customers also prefer mixed coupons to price discounts. Furthermore, the findings suggest that negative evaluations of perceived price increase the impact of mixed coupons by signaling to first-time customers that given restaurants’ offerings provide monetary benefits regardless of their intentions to revisit said restaurants.

The study findings provide insights that should help managers better understand various levels of promotion. Managers can design their pricing strategies to strengthen customers’ motivations to visit their restaurants – the very thing customers often seek in sales promotions.

This study provides indisputable evidence for a sales promotion theory, wherein promotions signal benefits in consumers’ minds; however, it also shows that first-time and repeat customers do not respond equally to sales promotions.

Online hotel ratings, a form of electronic word of mouth (eWOM), are becoming increasingly important to tourism and hospitality management. Using sentiment analysis based on the big data technique, this paper aims to investigate the relationship between customer sentiment and online hotel ratings from the perspective of customers’ motives in the context of eWOM, and to further identify the moderating effects of review characteristics.

The authors first retrieve 273,457 customer-generated reviews from a well-known online travel agency in China using automated data crawlers. Next, they exploit two different sentiment analysis methods to obtain sentiment scores. Finally, empirical studies based on threshold regressions are conducted to establish the asymmetric relationship between customer sentiment and online hotel ratings.

The results suggest that the relationship between customer sentiment and online hotel ratings is asymmetric, and a negative sentiment score will exert a larger decline in online hotel ratings, compared to a positive sentiment score. Meanwhile, the reviewer level and reviews with pictures have moderating effects on the relationship between customer sentiment and online hotel ratings. Moreover, two different types of sentiment scores output by different sentiment analysis methods verify the results of this study.

The moderating effects of reviewer level and reviews with pictures offer new insights for hotel managers to make different customer service policies and for customers to select a hotel based on reviews from the online travel agency.

This paper contributes to the literature by applying big data analysis to the issues in hotel management. Based on the eWOM communication theories, this study extends previous study by providing an analysis framework for the relationship between customer sentiment and online hotel ratings from the perspective of customers’ motives in the context of eWOM.

Leisure and tourism activities have proliferated and become important parts of modern life, and the hotel industry plays a necessary role in the supply for and demand from consumers. The purpose of this paper is to develop guidelines for hotel service development by applying a service development approach integrating Kansei engineering and text mining.

The online reviews represent the voice of customers regarding the products and services. Consumers’ online comments might become a key factor for consumers choosing hotels when planning their tourism itinerary. With the framework of Kansei engineering, this paper adopts text mining to extract the sets of Kansei words and hotel service characteristics from the online contents as well as the relationships among Kansei words, service characteristics and these two sets. The relationships are generated by using link analysis, and then the guidelines for hotel service development are proposed based on the obtained relationships.

The results of the present research can provide the hotel industry a comprehensive understanding of hotels’ customers opinions, and can offer specific advice on how to differentiate one’s products and services from competitors’ in order to improve customer satisfaction and increase hotels’ performance in the end. Finally, this study finds out the service development guidelines to meet customers’ requirements which can provide suggestions for hotel managers. The implications both for academic and industry are also drawn based on the obtained results.

Now, in the internet era, consumers can comment on their hotel living experience directly through the internet. The large amount of user-generated content (UGC) provided by consumers also provides chances for the hospitality industry to understand consumers’ opinions through online review mining. The UGC with consumers’ opinions to hotel services can be continuously collected and analyzed by hoteliers. Therefore, this paper demonstrates how to apply the hybrid approach integrating Kansei engineering and online review mining to hotel service development.

This study aims to investigate the online customer review behavior and determinants of overall satisfaction with hotels of travelers in various travel group compositions.

The author collected data from online reviews of travelers in various travel group compositions from 600 hotels in 100 of the largest cities in the USA from Booking.com and used latent semantic analysis (LSA) to identify the positive and negative factors from online reviews of travelers in various travel group compositions. Then, text regression was used to determine the influential factors of overall satisfaction of travelers in various travel group compositions.

It was found in this study that not all the positive and negative textual factors mined from travelers’ online reviews significantly influenced their overall satisfaction. In addition, the determinants of traveler satisfaction were different when travelers were in different travel group compositions.

The author found similar online review behavior, but different basic, excitement and performance factors of travelers in different travel group compositions.

This study helps hoteliers understand customers’ perception of the specific attributes of their products and services, which provides a guideline for businesses to design the priority rule to improve these corresponding attributes and use market segmentation strategy when dealing with customers in different travel group compositions.

The author examined and compared the online review behavior and determinants of satisfaction using the factors mined from online reviews between travelers in various travel group compositions. This study combined customer ratings with textual reviews and predicted customer ratings from the factors extracted from textual reviews using LSA and text regression.

The purpose of the study is to identify critical value-creating elements of luxury services expressed in ratings and reviews posted on third-party sites and examine cross-cultural differences. To this end, the research analyzed online ratings and reviews of luxury hotels posted on TripAdvisor from customers of four European regions (East, North, South and West).

Eight hundred thirty-eight online user-generated ratings and reviews of luxury hotels were analyzed quantitatively using MANOVA and qualitatively using text analysis.

The study findings support (a) that product and physical evidence are the most critical experiential elements of luxury hotels' offerings and (b) cultural differences among tourists from various regions of Europe in their hotel ratings and reviews. Specifically, Eastern and Northern Europeans are more generous in their review ratings than western and southern Europeans. Moreover, eastern Europeans value the hotel's physical evidence/environment whereas western Europeans prioritize the core product (room and food) followed by the physical environment/servicescape. Southern Europeans and Northern Europeans value most the personnel, followed by the physical environment and the core product, respectively.

Cultural differences provide several implications with regard to luxury services segmentation, social media management, service marketing mix development and hotel promotion.

The value of this study originates from studying post–purchase customer behavior in luxury services from a cross-cultural perspective. Moreover, identifying critical aspects of value-creating customer experience in a luxury context adds to the available literature.

This study aims to explore the hidden connectivity among words by semantic network analysis, further identify salient factors accounting for customer satisfaction of coffee shops through analysis of online reviews and, finally, examine the moderating effect of business types of coffee shops on customer satisfaction.

Two typical major procedures of big data analytics in the hospitality industry were adopted in this research: one is data collection and the other is data analysis. In terms of data analysis, frequency analysis with text mining, semantic network analysis, CONCOR analysis for clustering and quantitative analysis with dummy variables were performed to dig new insights from online customer reviews both qualitatively and quantitatively.

Different factors were extracted from online customer reviews contributing to customer satisfaction or dissatisfaction, and among these factors, the brand-new factor “Sales event” was examined to be significantly associated with customer satisfaction. In addition, the moderating effect of business types on the relationship between “Value for money” and customer satisfaction was verified, indicating differences between customers from different types of coffee shops.

The present study broadened the research directions of coffee shops by adopting online customer reviews through relative analytics. New dimensions such as “Sales event” and detailed categorization of “Coffee quality”, “Interior” and “Physical environment” were revealed, indicating that even new cognition could be generated with new data source and analytical methods. The industry professionals could develop their decision-making based on information from online reviews.

The present study used online reviews to understand coffee shop costumer experience and satisfaction through a set of analytical methods. The textual reviews and numeric reviews were concerned simultaneously to unearth qualitative perception and quantitative data information for customers of coffee shops.

本研究的目的在于通过网络评论分析了解网络评论中关键词之间的隐含联系, 然后探究影响咖啡店顾客满意度的因素, 最后验证不同咖啡店经营类型的调节作用。

本研究使用文本挖掘的频率分析、语义网络分析、聚类分析和通过虚拟变量进行的定量分析, 从网络评论中挖掘对咖啡店行业的新见解。此外, 还检验了咖啡店经营类型的调节作用。

从网络评论中提取影响顾客满意度的不同因素, 探索出“销售活动”对顾客满意度的显著影响。同时, 相较于连锁咖啡店, 独立经营咖啡店对“物有所值”到顾客满足度的关系具有调节作用。  

本研究通过使用相关的分析方法对顾客网络评论进行分析, 拓宽了咖啡店研究的方向。研究结果发现“销售活动”和“咖啡品质”的细分等新方面, 揭示了利用新的数据源和分析方法可以为相关产业提供全新的认知。本研究的研究结果表明行业从业者可以根据顾客网络评论来制定相应的营销策略。

本研究利用网络顾客评论及相关分析方法, 了解顾客咖啡店体验及满意度。本研究同时利用文本评论和数字评论来挖掘和分析咖啡店顾客的定性感知和定量数据信息。

关键词咖啡店 在线顾客评论 文本挖掘 语义网络分析 经营类型

文章类型： 研究型论文

Este estudio intenta explorar las relaciones encubiertas de palabras, mediante el análisis de redes semánticas, pero aún más, identificar los factores destacados que explican la satisfacción de los clientes de las cafeterías a través del análisis de reseñas online y, por último, examinar el efecto moderador de los tipos de negocios de cafeterías en la satisfacción al cliente.

En esta investigación se adoptaron dos procedimientos principales de análisis de “Big Data” en la industria hotelera, uno es la recopilación de datos y el otro es el análisis de datos. En términos de análisis de datos, se efectuó un análisis de frecuencia con minado de texto (text mining), análisis de red semántica, análisis CONCOR para agrupamiento y análisis cuantitativo con variables ficticias para extraer nuevas perspectivas de las reseñas de los clientes online, tanto cualitativa como cuantitativamente.

Diferentes factores fueron extraídos de las reseñas de clientes online que contribuyen a la satisfacción o insatisfacción de estos y, entre estos factores, se examinó que el nuevo factor “Evento de Ventas” está significativamente asociado con la satisfacción al cliente. Además, se verificó el efecto moderador de los tipos de negocios entre la relación de “Valor por Dinero” y la satisfacción del cliente, indicando las diferencias entre los clientes de distintos tipos de cafeterías.

El presente estudio amplia la perspectiva de investigación de las cafeterías, al adoptar las reseñas de clientes online a través de un análisis relativo. Se revelaron nuevas dimensiones como “Evento de Ventas” y la categorización detallada de la “Calidad del Café”, “Interior” y “Entorno Físico”, lo que indica que se podría generar una nueva cognición con una nueva fuente de datos y métodos analíticos. Los profesionales de la industria podrían llevar a cabo la toma de decisiones en función de la información obtenida a través de las reseñas en línea.

El presente estudio utilizó reseñas de clientes online para comprender la experiencia y satisfacción de los clientes de las cafeterías a través de un conjunto de métodos analíticos. Las revisiones numéricas y de texto se tomaron en cuenta simultáneamente para revelar la percepción tanto cualitativa como cuantitativa de la información de los clientes de las cafeterías.

Cafetería, Reseñas de clientes online, Minado de texto, Análisis de redes semánticas, Tipos de negocios

Trabajo de investigación

The purpose of this paper is to investigate social shopping deals and their impacts on review metrics at an online review site, Yelp and to compare the review metrics of the restaurant businesses and the health and wellness businesses to understand how social shopping deals affects them.

This study adopts a multiple regression model to analyse the effect of seven independent variables on the dependent variable, the growth rate of reviews which is a proxy of sales growth. This sample consisted of the review data of 134 merchants which offered social shopping deals at Groupon in 2011. The online review data of these merchants were collected in 2015 to analyse the relationship between the deals and the grow rate of the reviews.

For the restaurant businesses, there is a positive persuasive effect of Groupon customers’ review score on the growth rate of the reviews and consequently on the sale growth. For the health and wellness businesses, there are a positive persuasive effect of the regular customers’ review score on the growth rate of the reviews and a negative awareness effect of the number of Groupon reviews on the growth rate of the reviews. The review data also show that the Groupon customers of the health and wellness businesses are three times more likely to post their reviews than those of the restaurant businesses.

First, while the author limited the study to the seven independent variables, additional variables may exist. These additional variables may also influence the number of reviews, too. Future research needs to identify such variables to build a comprehensive model. Second, future research needs to address other types of businesses, such as education and entertainment, and compare differences between them. Third, while the study focussed on the review score and the number of reviews, a more in-depth analysis of the comments using sentiment analysis and social network analysis may shed additional insights on their review activities.

Despite the potential significance of customers’ reviews about social shopping deals, the critical mass of empirical studies still lacks in this area. The study contributes to the literature of this field by investigating the effect of social shopping deals on the customers’ online reviews. This study provides practical guidance for the improvement of online reviews about social shopping deals.

Electronic commerce (e-commerce) is growing rapidly and the e-retailers are finding it pertinent to enhance customers’ online shopping experiences and engage them with e-commerce portals. Against this backdrop, the purpose of this study is to develop a conceptual model of customer engagement, where credibility and usefulness of online reviews are found to trigger the adoption of reviews and customer trust that augments customer engagement.

A survey method design has been used to capture responses from 219 young customers (university students) of a reputed university in India. The hypothesized relationships have been examined through multiple regression analysis.

The findings of this study corroborate that the credibility and information usefulness of online reviews induce the adoption of reviews and propensity to trust e-commerce websites. The propensity to trust the reviews has been found to lead the adoption of reviews. The adoption of reviews is found to have a significant impact on the customer’ engagement with these portals.

The present study contributes to the theories of online marketing in the space of e-shopping, online reviews, customer trust, customer engagement and online shopping behavior. Further, this study provides a framework for managers to engage customers by triggering customers’ online trust through the facilitation of credible and useful reviews.

The study aims at understanding the role of different attributes associated with the online reviews’ credibility and information usefulness in driving customer engagement with specific focus on online shopping through the utility of online devices. The study is one of the pioneering empirical studies that explore the role of online reviews in driving customer engagement.