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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

Food delivery robot service (FDRS) is a novel service using autonomous delivery robots that alters human delivery in mobile food ordering. Despite FDRS companies’ skyrocketing service expansions throughout the USA, there has been limited understanding of customer perceived value, customer satisfaction and loyalty which is imperative to promote repeat sales. Therefore, this study aims to examine customer perceived value of FDRS, identify antecedents of customer satisfaction with FDRS and investigate the additional impact of customer satisfaction and loyalty toward FDRS and the restaurant using FDRS.

As a sample population, this study targeted the US customers over 18 years old and have used FDRS within the past 12 months. With a total of 323 responses, descriptive statistics, confirmatory factor analysis and structural equation modeling were conducted for data analysis.

The findings of this study demonstrate the positive impacts of functional, price and emotional value on customer satisfaction. The current study also shows that customer satisfaction positively influences customer loyalty toward FDRS and the restaurant.

To the best of the authors’ knowledge, the current study is arguably the first to investigate customers’ post-purchase experience with FDRS, which enhances understanding of customer behavior toward the service. Moreover, with a multidimensional consumption value approach from the theory of consumption values, this study provides a single framework to explore the relations between customer perceived value, satisfaction and loyalty in the FDRS context.

食品配送机器人服务（FDRS）是一种使用自主配送机器人改变移动食品订购的新型服务。尽管FDRS公司在美国各地的服务扩张迅猛, 但对于顾客感知价值、顾客满意度和忠诚度的理解仍有限, 而这对于促进重复销售至关重要。因此, 本研究旨在考察顾客对FDRS的感知价值, 确定影响顾客对FDRS满意度的先决条件, 并调查顾客对FDRS及使用FDRS的餐厅的满意度和忠诚度的影响。

本研究以美国18岁以上并且在过去12个月内使用过FDRS的顾客为样本, 共收集到323份回答。采用描述性统计、确认性因素分析和结构方程模型进行数据分析。

本研究的结果显示功能性、价格和情感价值对顾客满意度有积极影响。当前研究还表明, 顾客满意度对FDRS及餐厅的忠诚度有积极影响。

本研究可以说是首次调查顾客对FDRS购买后的体验, 从而增进对顾客对该服务行为的理解。此外, 本研究采用了来自消费价值理论的多维消费价值方法, 为探索FDRS背景下顾客感知价值、满意度和忠诚度之间的关系提供了一个单一的框架。

This research aims to analyze the customer satisfaction associated with experiences from 14 ecological hotels in Ecuador by exploring online guest reviews and classifying the most influential factors.

This study applied big data exploration, semantic network analysis, EFA and linear regression. It processed 22,629 online reviews from Google/travel, extracting 100 words with the highest frequency. In addition, CONCOR analysis built a comprehensive structural model gathering essential keywords. Furthermore, exploratory factor analysis and regression were conducted to explore the elements that best express customer satisfaction in ecological hotels.

The words such as green, sustainable, recycle, environment and ecological were not found among the main attributes extracted. Nonetheless, the keywords obtained reflect customer satisfaction, revealing that green practices do not affect comfort and the guests' experience. CONCOR analysis displayed four categories associated with satisfaction: tangibles, experience, location and empathy. Then, EFA restructured and revealed the factors: facilities feature, assurance, reliability, location and experience. Lastly, the regression disclosed location, assurance and facilities features as the most significant factors for customer satisfaction in the 14 ecological hotels. The terms related to the hotel area, staff care and hotel amenities were decisive for guests.

This study demonstrated that employee courtesy and location are the keys to enhancing customer experience and satisfaction. Hotel managers must promote green attributes and practices to increase customer awareness through constant staff training and information disclosure in common areas.

These findings provide insight and empirical evidence for hoteliers to understand how and what guest perceive to be green practices. By identifying the main features or concepts associated with satisfaction in Ecuador's green hotels, hoteliers could address new strategies to respond to expectations, effectively satisfy customers and provide a superior experience.

Tourists’ destination image is crucial for visiting intentions. An ancient capital with diverse characteristics is an important component of China’s urban tourism. The purpose of this paper is to address the following questions: what are the differences and commonalities of the perceived destination image of ancient capitals? What makes the difference of the perceived destination image in these cities? Aside from the exterior factors, are there internal factors of cities that influence tourists’ cognition and perception of destination image?

The comment text data of Baidu tourism website were used to determine the differences in the destination images of China’s four great ancient capitals: Beijing, Xi’an, Nanjing and Luoyang. ROST content mining and semantic network analysis were for differences and commonalities of the perceived destination image, and correlation analysis was used to explore the internal factors of cities that influence tourists’ cognition and perception of destination image.

Though the same as ancient capital, the four ancient capitals’ images are far apart; historical interests are the core of tourism experience in ancient capital city; image perception is from physical carrier, history and culture, and human cognition; tourist’ destination affect of ancient capital is most from its history and culture; protecting identity and maintaining daily life are crucial for ancient city tourism.

Previous studies on ancient capitals have focused on the invariable identity of ancient capitals’ destination images, and left a gap on determining from where the invariable identity comes in general and how much it influences destination image. This gap was addressed in this study, by analyzing the destination images of four ancient capitals in China as cases. In this way, this study provided reference to the other ancient cities worldwide.