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Article
Publication date: 31 May 2021

Xiaofan Lai, Fan Wang and Xinrui Wang

Online hotel ratings, a form of electronic word of mouth (eWOM), are becoming increasingly important to tourism and hospitality management. Using sentiment analysis based…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

Details

International Journal of Contemporary Hospitality Management, vol. 33 no. 6
Type: Research Article
ISSN: 0959-6119

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Article
Publication date: 11 February 2021

Xiaolin (Crystal) Shi and Zixi Chen

This study aims to examine the factors influencing hotel employee satisfaction and explores the different sentiments expressed in these factors in online reviews by hotel…

Abstract

Purpose

This study aims to examine the factors influencing hotel employee satisfaction and explores the different sentiments expressed in these factors in online reviews by hotel type (premium versus economy) and employment status (current versus former).

Design/methodology/approach

A total of 78,535 online reviews by employees of 29 hotel companies for the period of 2011-2019 were scraped from Indeed.com. Structural topic modeling (STM) and sentiment analysis were used to extract topics influencing employee satisfaction and examine differences in sentiments in each topic.

Findings

Results showed that employees of premium hotels expressed more positive sentiments in their reviews than employees of economy hotels. The STM results demonstrated that 20 topics influenced employee satisfaction, the top three of which were workplace bullying and dirty work (18.01%), organizational support (16.29%) and career advancement (8.88%). The results indicated that the sentiments in each topic differed by employment status and hotel type.

Practical implications

Rather than relying on survey data to explore employee satisfaction, hotel industry practitioners can analyze employees’ online reviews to design action plans.

Originality/value

This study is one of only a few to use online reviews from an employment search engine to explore hotel employee satisfaction. This study found that workplace bullying and dirty work heavily influenced employee satisfaction. Moreover, analysis of the comments from previous employees identified antecedents of employees’ actual turnover behavior but not their turnover intention.

Details

International Journal of Contemporary Hospitality Management, vol. 33 no. 6
Type: Research Article
ISSN: 0959-6119

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Article
Publication date: 7 January 2021

Alekh Gour, Shikha Aggarwal and Mehmet Erdem

The dynamic yet volatile nature of tourism and travel industry in a competitive environment calls for enhanced marketing intelligence and analytics, especially for those…

Abstract

Purpose

The dynamic yet volatile nature of tourism and travel industry in a competitive environment calls for enhanced marketing intelligence and analytics, especially for those entities with limited marketing budgets. The past decade has witnessed an increased use of user-generated content (UGC) analysis as a marketing tool to make better informed decisions. Likewise, textual data analysis of UGC has gained much attention among tourism and hospitality scholars. Nonetheless, most of the scholarly works have focused on the singular application of an existing method or technique rather than using a multi-method approach. The purpose of this study is to propose a novel Web analytics methodology to examine online reviews posted by tourists in real time and assist decision-makers tasked with marketing strategy and intelligence.

Design/methodology/approach

For illustration, the case of tourism campaign in India was undertaken. A total of 305,298 reviews were collected, and after filtering, 276,154 reviews were qualified for analysis using a string of models. Descriptive charts, sentiment analysis, clustering, topic modeling and machine learning algorithms for real-time classification were applied.

Findings

Using big data from TripAdvisor, a total of 145 tourist destinations were clustered based on tourists’ perceptions. Further exploration of each cluster through topic modeling was conducted, which revealed interesting insights into satisfiers and dissatisfiers of different clusters of destinations. The results supported the use of the proposed multi-method Web-analytics approach.

Practical implications

The proposed machine learning model demonstrated that it could provide real-time information on the sentiments in each incoming review about a destination. This information might be useful for taking timely action for improvisation or controlling a service situation.

Originality/value

In terms of Web-analytics and UGC, a comprehensive analytical model to perform an end-to-end understanding of tourist behavior patterns and offer the potential for real-time interpretation is rarely proposed. The current study not only proposes such a model but also offers empirical evidence for a successful application. It contributes to the literature by providing scholars interested in textual analytics a step-by-step guide to implement a multi-method approach.

Details

International Journal of Contemporary Hospitality Management, vol. 33 no. 2
Type: Research Article
ISSN: 0959-6119

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Article
Publication date: 16 March 2012

M. Geetha and Jensolin Abitha Kumari

The purpose of this paper is to provide a detailed analysis of the usage pattern of non‐revenue earning customers (NREC) who cause revenue churn in the company and are…

Abstract

Purpose

The purpose of this paper is to provide a detailed analysis of the usage pattern of non‐revenue earning customers (NREC) who cause revenue churn in the company and are susceptible to churn in the near future. These NREC customers were analyzed to discern a pattern in their usage and to serve as proactive measure to prevent customer churn.

Design/methodology/approach

Data from a leading telecom service provider were analyzed. The company has around seven lakh consumer mobile users. Within the seven lakhs consumer mobile users around two lakh customers are active users, i.e. revenue earning customers. This group of active customers also consists of around 37,388 customers who move to dormant state (from revenue earning to non‐revenue earning) every month. These customers were analyzed to understand their susceptibility to churn.

Findings

Analysis of revenue dump data indicates consumers with overall usage revenue per minute greater than 75 paise (USD 0.01) and those with greater usage of value added services are susceptible to churn. Also based on the nature of calls, churn occurs with the subscribers making more calls to other networks rather than to the same network.

Research limitations/implications

In a fiercely competitive market, service providers constantly focus on customer retention. The study has high importance as it helps to find out the customers who are likely to churn. This would help telecom companies create proactive rather than reactive strategies toward customer churn.

Originality/value

Earlier studies identified the reasons for customer churn and attributed the same to it. The authors propose that prior to customer churn there is a distinct shift in his/her usage pattern with the current service provider and this behavior is termed revenue churn. This revenue churn ultimately leads to customer churn from the network. This revenue churn is not explored much in detail in the literature.

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Article
Publication date: 29 July 2014

Geetha M. and Gitanjali Naidu

The purpose of this paper is to analyze the attribute preferences of buyers of branded pulses and to study the differences in preferences between consumers who purchase…

Abstract

Purpose

The purpose of this paper is to analyze the attribute preferences of buyers of branded pulses and to study the differences in preferences between consumers who purchase from traditional retail stores and those who purchase from modern retail stores.

Design/methodology/approach

A total of 300 respondents (150 respondents from traditional and 150 respondents from modern retail outlet) participated in the study. Conjoint analysis was used to assess the consumers’ attribute preferences for branded pulses.

Findings

For both traditional and modern retail outlets, profile with highest utility was the profile with established brand, low price, high quality and normal packaging.

Research limitations/implications

Shoppers of traditional and modern retail outlets have similar attribute preferences for branded pulses. Hence, it can be concluded that the purchase point makes no difference in consumer attribute preferences.

Practical implications

Results indicate that in both traditional and modern retail outlet customers prefer the same profile of attributes. Two important attributes determining their purchase are also the same. Hence a company entering into the sale of branded pulses will have to focus on these two important attributes irrespective of the purchase point.

Originality/value

The topic is relatively less researched in emerging markets especially where both branded pulses and organized retail are in their nascent stages.

Details

South Asian Journal of Global Business Research, vol. 3 no. 2
Type: Research Article
ISSN: 2045-4457

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Article
Publication date: 20 October 2020

Ree Chan Ho, Madusha Sandamali Withanage and Kok Wei Khong

With the growth of social media and online communications, consumers are becoming more informed about hotels' services than ever before. They are writing online review to…

Abstract

Purpose

With the growth of social media and online communications, consumers are becoming more informed about hotels' services than ever before. They are writing online review to share their experiences, as well as reading online review before making a hotel reservation. Hotel customers considered it as reliable source and it influences customers' hotel selection. Most of these reviews reside in unstructured format, scattered across in the Internet and inherently unorganized. The purpose of this study was to use predictive text analytics to identify sentiment drivers from unstructured online reviews.

Design/methodology/approach

The research used sentiment classifications to analyze customers' reviews on hotels from TripAdvisor. In total, 9,286 written reviews by hotel customers were scrapped from 442 hotels in Malaysia. A detailed text analytic was conducted and was followed by a development of a theoretical framework based on the hybrid approach. AMOS was used to analyze the relationship between customer sentiments and overall review rating.

Findings

With the use of Structural Equation Modeling (SEM) and clustering technique, a list of sentiment drivers was detected, i.e. location, room, service, sleep, value for money and cleanliness. Among these variables, service quality and room facilities emerged as the most influential factors. Sentiment drivers obtained in this study provided the insights to hotel operators to improve the hotel conditions.

Research limitations/implications

Although this study extended the existing literature on sentiment analysis by providing valuable insights to hoteliers, it is not without its limitations. For instance, online hotel reviews collected for this study were limited to one specific online review platform. Despite the large sample size to support and justify the findings, the generalizability power was restricted. Thus, future research should also consider and expand to other type of online review channels. Therefore, a need to examine these data reside various social media applications, i.e. Facebook, Instagram and YouTube.

Practical implications

This study highlights the significance of hybrid predictive model in analyzing the unstructured hotel reviews. Based on the hybrid predictive model we developed, six sentiment drivers emerged from the data analysis, i.e. location, service quality, value for money, sleep quality, room design and cleanliness. This consideration is critical due to the ever-increasing unstructured data resides in the online space. This explores the possibility of applying data analytic technique in a more efficient manner to obtain customer insights for hotel managerial consideration.

Originality/value

This study analyzed customer sentiments toward the hotel in Malaysia with the use of predictive text analytics technique. The main contribution was the list of sentiment drivers and the insights needed to improve the hotel conditions in Malaysia. In addition, the findings demonstrated motivating findings from different methodological perspective and provided hoteliers with the recommendation for improved review ratings.

Details

Asia-Pacific Journal of Business Administration, vol. 12 no. 3/4
Type: Research Article
ISSN: 1757-4323

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Article
Publication date: 3 December 2020

Rishi Dwesar and Debajani Sahoo

Increased global air travel and competition in the airline industry entail better service delivery and failure management. This study examines how airline type, failure…

Abstract

Purpose

Increased global air travel and competition in the airline industry entail better service delivery and failure management. This study examines how airline type, failure criticality and the traveller's culture influence travellers' airline evaluations of service failure.

Design/methodology/approach

The study uses a large data set of customers' online reviews and incorporates quantitative and qualitative feedback from 20 major airlines across the world. Semantic tagging, sentiment and multivariate analyses have been used to analyse the data.

Findings

Failure criticality and travellers' cultural backgrounds significantly affect airline evaluations after service failures. Moreover, failure criticality influences evaluations of travellers from individualistic cultures more severely. Contrary to expectations, full-service airlines were evaluated positively after less critical service failures.

Practical implications

The findings support that customers undergo different emotional states when they experience service failure. Understanding these internal emotional sensitivities and how services would be judged by travellers across cultures can help airlines to better manage their service recovery efforts and to strategise prioritisation of scarce resources.

Originality/value

Though airline service failure has been well researched, this study examines the role of culture in service failure evaluations. The study uses a novel method to analyse a large data set of both quantitative and qualitative traveller feedback useful in service recovery management.

Details

Management Decision, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0025-1747

Keywords

Content available
Article
Publication date: 26 January 2018

M. Lilibeth Fuentes-Medina, Estefanía Hernández-Estárico and Sandra Morini-Marrero

The purpose of this paper is to identify the critical success factors of emblematic hotels from the perspective of the guest, by analysing the direct activities that make…

Abstract

Purpose

The purpose of this paper is to identify the critical success factors of emblematic hotels from the perspective of the guest, by analysing the direct activities that make up the value chain of these types of establishments.

Design/methodology/approach

The authors use the case study methodology to derive conclusions that contribute to the development of a theory about the success factors of emblematic hotels. The case selected is the Spanish Tourist Parador chain. The authors carried out over a period of two years a data mining analysis of the online comments posted by its guests.

Findings

The results indicate that the attributes of location and facilities are critical success factors expected a priori given the nature of the business of such establishments, based on the singular nature of the buildings. Another critical success factor is personnel, which seems to indicate that the Paradors support their business model by employing highly qualified staff, but give less attention to restaurant services or the room, according to guest perceptions.

Originality/value

The paper provides required evidence on the critical success factors of emblematic hotels adapting Porter’s value chain, for the tourism accommodation sector, through the analysis of direct value chain activities. In addition, the existing literature is broadened by taking a perspective scarcely studied, the guest perception of hotel establishments, online content posted by the user on the establishment’s website, rather than simply considering the traditional views of the experts/managers, through structures questionnaires. Besides, the results provide practical and useful implications for the managements of the emblematic hotels under study.

Details

European Journal of Management and Business Economics, vol. 27 no. 1
Type: Research Article
ISSN: 2444-8451

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Article
Publication date: 17 May 2021

Sayeh Bagherzadeh, Sajjad Shokouhyar, Hamed Jahani and Marianna Sigala

Research analyzing online travelers’ reviews has boomed over the past years, but it lacks efficient methodologies that can provide useful end-user value within time and…

Abstract

Purpose

Research analyzing online travelers’ reviews has boomed over the past years, but it lacks efficient methodologies that can provide useful end-user value within time and budget. This study aims to contribute to the field by developing and testing a new methodology for sentiment analysis that surpasses the standard dictionary-based method by creating two hotel-specific word lexicons.

Design/methodology/approach

Big data of hotel customer reviews posted on the TripAdvisor platform were collected and appropriately prepared for conducting a binary sentiment analysis by developing a novel bag-of-words weighted approach. The latter provides a transparent and replicable procedure to prepare, create and assess lexicons for sentiment analysis. This approach resulted in two lexicons (a weighted lexicon, L1 and a manually selected lexicon, L2), which were tested and validated by applying classification accuracy metrics to the TripAdvisor big data. Two popular methodologies (a public dictionary-based method and a complex machine-learning algorithm) were used for comparing the accuracy metrics of the study’s approach for creating the two lexicons.

Findings

The results of the accuracy metrics confirmed that the study’s methodology significantly outperforms the dictionary-based method in comparison to the machine-learning algorithm method. The findings also provide evidence that the study’s methodology is generalizable for predicting users’ sentiment.

Practical implications

The study developed and validated a methodology for generating reliable lexicons that can be used for big data analysis aiming to understand and predict customers’ sentiment. The L2 hotel dictionary generated by the study provides a reliable method and a useful tool for analyzing guests’ feedback and enabling managers to understand, anticipate and re-actively respond to customers’ attitudes and changes. The study also proposed a simplified methodology for understanding the sentiment of each user, which, in turn, can be used for conducting comparisons aiming to detect and understand guests’ sentiment changes across time, as well as across users based on their profiles and experiences.

Originality/value

This study contributes to the field by proposing and testing a new methodology for conducting sentiment analysis that addresses previous methodological limitations, as well as the contextual specificities of the tourism industry. Based on the paper’s literature review, this is the first research study using a bag-of-words approach for conducting a sentiment analysis and creating a field-specific lexicon.

论可推广性的情感分析法以创建酒店字典:以TripAdvisor酒店评论为样本的大数据分析

摘要

研究目的

对于在线游客评论的研究在过去的几年中与日俱增, 但是仍缺乏有效方法能在有限的时间喝预算内提供终端用户价值。本论文开发并测试了一套情感分析的新方法, 创建两套酒店相关的词库, 此方法超越了标准词典式分析法。

研究设计/方法/途径

研究样本为TripAdvisor酒店客户评论的大数据, 通过开发崭新的有配重的词库法, 来开展两极式情感分析。这个崭新的具有配重的词库法能够呈现透明化和可复制的程序, 准备、创建、并检验情感分析的词条。这个方法用到了两种词典(有配重的词典L1和手动选择的词典L2), 本论文通过对TripAdvisor大数据进行使用词类划分精准度, 来检测和验证这两种词典。本论文采用两种热门方法(公共词典法和复杂机器学习算法)来对比词典的准确度。

研究结果

精确度对比结果证实了本论文的方法, 相较于机器学习算法, 显著地超越了以字典为基础的方法。研究结果还表明, 本论文的方法可以就预测用户情感趋势进行推广。

研究实际启示

本论文开发并验证了一项方法, 这种方法通过创建可信的词典进行大数据分析, 以判定用户情感。本论文创建的L2酒店词库对分析客人反馈是可靠有用的工具, 这个词库还能帮助酒店经理了解、预测、以及积极相应客人的态度和改变。本论文还提出了一项可以了解每个用户情感的简易方法, 这项方法可以通过对比的方式来检测和了解客人不同时间的情感变化, 以及根据其不同背景和经历的不同用户之间的变化。

研究原创性/价值

本论文提出并检测了一项新方法, 这项情感分析方法可以解决之前方法的局限并立脚于旅游行业。基于文献综述, 本论文是首篇研究, 使用词库法来进行情感分析和创建特别领域词典的方式。

Details

Journal of Hospitality and Tourism Technology, vol. 12 no. 2
Type: Research Article
ISSN: 1757-9880

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Article
Publication date: 17 September 2020

Elise Wong, S. Mostafa Rasoolimanesh and Saeed Pahlevan Sharif

This study aims to investigate the relationships between service quality, perceived value and hotel guest satisfaction, drawing upon data from TripAdvisor – an online…

Abstract

Purpose

This study aims to investigate the relationships between service quality, perceived value and hotel guest satisfaction, drawing upon data from TripAdvisor – an online travel agent (OTA) platform. The study also investigates the mediating role of perceived value on the relationship between service quality and satisfaction, as well as the moderating role of hotel star ratings on all direct and indirect relationships.

Design/methodology/approach

Data for this study were collected via Web scraping from August–October 2018. Data were collected from 192 three- to five star-rated hotels in Kuala Lumpur, Malaysia. Partial least squares – structural equation modeling was used for data analysis. Furthermore, importance-performance map analysis (IPMA) was performed to identify the most important items of service quality and perceived value in improving customer satisfaction.

Findings

The findings of this study provide support for all direct and indirect relationships for three-star and four- and five-star hotels. Moreover, the results indicate that perceived value mediates the relationship between service quality and customer satisfaction. These results support the moderating role of hotel star ratings for the relationship between service quality and perceived value. The results also show that after perceived value, three-star hotels looking to improve customer satisfaction should prioritize improving the quality of their services, sleep quality, cleanliness and rooms. Four- and five-star hotels, on the other hand, should prioritize service, cleanliness, room and sleep quality.

Originality/value

OTA platforms collect a wealth of data pertaining to large number of hotels; nevertheless, few studies to date have drawn on this data to examine a pre-determined conceptual framework developed based on the literature. As such, this study makes a valuable methodological contribution to the tourism and hospitality literature. In terms of theoretical contributions, this study examines the mediating role of perceived value between service quality and satisfaction using OTA data. In addition, this study assesses the moderating role of hotel star ratings for the direct and indirect effects of service quality on satisfaction. Using IPMA, this study compares the importance and performance of service quality indicators to generate satisfaction between three-star and four- and five-star hotels.

研究目的

本论文检测了服务质量、价值感知、和酒店顾客满意度之间的关系, 使用TripAdvisor的数据—OTA。本论文还检测了价值感知对服务质量和满意度之间的中介作用, 以及酒店星级评价对其中直接和间接关系的调节作用。.

研究设计/方法/途径

本论文采样通过网络爬虫技术, 截取了2018年八月至十月之间的数据。研究样本为192家马来西亚Kuala Lumpur地区的三星-五星酒店。样本分析方法为PLS-SEM。此外, 本论文采样IPMA分析法来找出提高顾客满意度中的服务质量和价值感知中最重要的因子。.

研究结果

研究结果指出了三星、四星、五星酒店的直接和间接关系。此外, 研究还显示了服务质量和顾客满意度关系的价值感知中介作用。研究结果还指出了酒店星级评价对服务质量和价值感知关系的调节作用。此外, 研究还指出, 除了价值感知, 如果三星酒店想提高顾客满意度, 那么他们应该优先提高其服务质量、睡眠质量、清洁度、和房间。另一方面, 四星和五星酒店应该优先提高其服务质量、清洁度、房间、和睡眠质量。.

研究原创性/价值

OTA平台搜集大量酒店数据, 但是很少作品研究这些数据, 以检测根据文献提出的理论模型。因此, 本论文在方法论上对旅游酒店文献做出宝贵贡献。理论贡献而言, 本论文使用OTA数据检测了价值感知对服务质量和满意度关系之间的中介作用。此外, 本论文检测了酒店星级评价对服务质量和满意度之间直接和间接关系的调节作用。本论文使用IPMA方法, 比较各种服务质量指标的重要性对在三星、四星、五星酒店的提高满意度的不同作用。.

Details

Journal of Hospitality and Tourism Technology, vol. 11 no. 3
Type: Research Article
ISSN: 1757-9880

Keywords

1 – 10 of 169