Search results

1 – 10 of over 110000
To view the access options for this content please click here
Article
Publication date: 5 March 2021

Vinay Chittiprolu, Nagaraj Samala and Raja Shekhar Bellamkonda

In business, online reviews have an economic impact on firm performance. Customers’ data in the form of online reviews was used to understand the appreciation and service…

Abstract

Purpose

In business, online reviews have an economic impact on firm performance. Customers’ data in the form of online reviews was used to understand the appreciation and service complaints written by previous customers. The study is an analysis of the online reviews written by the customers about Indian heritage hotels. This study aims to understand the dimensions of service appreciation and service complaints by comparing positive- and negative-rated reviews and find the patterns in the determinants of the satisfaction and dissatisfaction of the customers.

Design/methodology/approach

A total of 23,643 online reviews about heritage hotels were collected from the TripAdvisor website by using a Web crawler developed in Python. A total of 1000 reviews were randomly selected for further analysis to eliminate the bandwagon effect. Unsupervised text mining techniques were used to analyze reviews and find out the interesting patterns in text data.

Findings

Based on Herzberg two-factor theory, this study found satisfied and dissatisfied determinants separately. The study revealed some common categories discussed by satisfied and dissatisfied customers. The factors which satisfy the customers may also dissatisfy the customers if not delivered properly. Satisfied customers mentioned about tangible features of the hotel stay, which includes physical signifiers, traditional services, staff behavior and professionalism and core products (rooms, food). However, most of the customers complained about intangible service problems, such as staff attitude, services failure, issues with reservation and food, value for money and room condition. The results are contradicting with commercial hotels-based studies owing to the unique services provided by heritage hotels.

Practical implications

The dimensions for satisfaction and dissatisfaction among customer of heritage hotels provide marketers to understand the real emotion and perception of the customers. As these dimensions were extracted through text mining of the reviews written by the customer of heritage hotels, the results would certainly give better insights to the hotel marketers.

Originality/value

The study is a rare attempt to study online reviews of customers on heritage hotels through a text mining approach and find the patterns in the behavior and the determinants of satisfaction and dissatisfaction of customers.

Details

International Journal of Culture, Tourism and Hospitality Research, vol. 15 no. 2
Type: Research Article
ISSN: 1750-6182

Keywords

To view the access options for this content please click here
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

Keywords

To view the access options for this content please click here
Article
Publication date: 21 May 2020

Jaylan Azer and Matthew Alexander

This study aims to show the impact of direct and indirect customers’ negatively valenced influencing behavior (NVIB) on other actors in online social networks.

Abstract

Purpose

This study aims to show the impact of direct and indirect customers’ negatively valenced influencing behavior (NVIB) on other actors in online social networks.

Design/methodology/approach

Four experiments were conducted in an online review setting that encompasses both restaurant and hotel reviews. The first study compares the impact of direct and indirect NVIB. The second, third and fourth studies measure this impact moderated by aggregate ratings, the volume of positive reviews and managerial responses.

Findings

Drawing on recent literature of customer engagement behavior, online reviews and social influence theory, this paper provides the first empirical results of the impact of direct and indirect NVIB, revealing the significant difference in their impact and the moderating role of the aggregate ratings, number of positive reviews and managerial responses on the cause-effect relationship between direct and indirect NVIB and other actors’ attitudes and behavioral intentions toward service providers.

Research limitations/implications

TripAdvisor reviews were selected for the reason of appropriateness rather than representativeness, using two service providers, hotels and restaurants.

Practical implications

This paper provides managers with new insights, which capture not only what customers say about service providers but also the impact of how they say it, suggesting that managers move beyond framing NVIB in generalized terms to considering the differences in the impact of its direct and indirect facets.

Originality/value

This paper is the first to provide empirical results about the significant difference in the impact of direct and indirect NVIB on other actors’ attitudes and behavioral intentions toward service providers, moderated by different heuristics, namely, ratings, volume of positive reviews and managerial responses.

To view the access options for this content please click here
Article
Publication date: 1 October 2019

Musarrat Shaheen, Farrah Zeba, Namrata Chatterjee and Raveesh Krishnankutty

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

To view the access options for this content please click here
Article
Publication date: 19 March 2018

Xun Xu

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

Abstract

Purpose

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

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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

Practical implications

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.

Originality/value

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.

Details

International Journal of Contemporary Hospitality Management, vol. 30 no. 3
Type: Research Article
ISSN: 0959-6119

Keywords

To view the access options for this content please click here
Article
Publication date: 6 September 2018

Pengfei Zhao, Ji Wu, Zhongsheng Hua and Shijian Fang

The purpose of this paper is to identify electronic word-of-mouth (eWOM) customers from customer reviews. Thus, firms can precisely leverage eWOM customers to increase…

Abstract

Purpose

The purpose of this paper is to identify electronic word-of-mouth (eWOM) customers from customer reviews. Thus, firms can precisely leverage eWOM customers to increase their product sales.

Design/methodology/approach

This research proposed a framework to analyze the content of consumer-generated product reviews. Specific algorithms were used to identify potential eWOM reviewers, and then an evaluation method was used to validate the relationship between product sales and the eWOM reviewers identified by the authors’ proposed method.

Findings

The results corroborate that online product reviews that are made by the eWOM customers identified by the authors’ proposed method are more related to product sales than customer reviews that are made by non-eWOM customers and that the predictive power of the reviews generated by eWOM customers are significantly higher than the reviews generated by non-eWOM customers.

Research limitations/implications

The proposed method is useful in the data set, which is based on one type of products. However, for other products, the validity must be tested. Previous eWOM customers may have no significant influence on product sales in the future. Therefore, the proposed method should be tested in the new market environment.

Practical implications

By combining the method with the previous customer segmentation method, a new framework of customer segmentation is proposed to help firms understand customers’ value specifically.

Originality/value

This study is the first to identify eWOM customers from online reviews and to evaluate the relationship between reviewers and product sales.

Details

Industrial Management & Data Systems, vol. 119 no. 1
Type: Research Article
ISSN: 0263-5577

Keywords

To view the access options for this content please click here
Article
Publication date: 24 October 2017

Run Hong Niu and Ying Fan

More and more customers refer to online reviews before making any purchasing decisions thanks to the increasing popularity of social media and online shopping. This…

Abstract

Purpose

More and more customers refer to online reviews before making any purchasing decisions thanks to the increasing popularity of social media and online shopping. This phenomenon has caught the attention of business managers who are increasingly aware that online reviews provide great opportunities to connect with current and potential customers. However, both practices and research on online review management from the businesses’ perspective are fragmented. The purpose of this paper is to develop an integrative framework that includes the key dimensions of an online review management system.

Design/methodology/approach

Based on the Grounded Theory approach, the authors conducted a multiple case study by analyzing the interviews with 11 hospitality services.

Findings

The authors found that an online review management system should go beyond the current norm of response management to incorporate key dimensions of formality, centralization, specialization, response customization, integration and review analytics.

Practical implications

The study provides a systematic guideline for online review management practices. The framework can be used as a tool for a business to evaluate existing online review management practices and develop/refine its online review management system.

Originality/value

The study contributes to online review management literature by developing a comprehensive framework to understand the structure and processes of online review management. The key dimensions of an online review management system identified in this study provide an initial measurement model for the online review management construct. Furthermore, the study provides a springboard for future empirical validation and refinement of the key factors for effective online review management.

Details

Journal of Service Theory and Practice, vol. 28 no. 1
Type: Research Article
ISSN: 2055-6225

Keywords

To view the access options for this content please click here
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. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0959-6119

Keywords

To view the access options for this content please click here
Article
Publication date: 2 April 2021

Huiliang Zhao, Qin Yang and Zhenghong Liu

The customer enables online reviews, discusses product features and enhances the user's experiences in online activities. Users generated product innovation and product…

Abstract

Purpose

The customer enables online reviews, discusses product features and enhances the user's experiences in online activities. Users generated product innovation and product reviews effect as market competition. This research study explains deep learning, online reviews and product innovation empirical evidence used by mobile apps.

Design/methodology/approach

Online reviews and product innovation are very important for every organization and firms to achieve a competitive advantage in a large business environment. When the authors see past traditional history, customers are not involved in product creating and innovating processes. Due to new technology changes, online systems and web 2.0 increase this ability.

Findings

For this research purpose, the authors use different analytical software to measure the impact among variables. This study is established on primary data; this study collected data from online customers and its users. For data collection, the authors use some questionnaires, and these questions are filled from 200 respondents.

Research limitations/implications

This research study used data from the Google app store – Google product selling application – and gathered customers' online reviews. Research found that customers' online reviews and deep learning positively and significantly influence product innovation through networking technology. This research-based online mobile application and its research reviews found that organizations convert their own business online and effectively and efficiently enhance creditability.

Originality/value

This research study used data from the Google app store Google product selling application and gathered customers' online reviews. Research founded that customers' online reviews and deep learning are positively and significantly influence product innovation through networking technology. This research-based online mobile application and its research reviews found that organizations convert their own business online and effectively and efficiently enhance creditability.

Details

Business Process Management Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-7154

Keywords

To view the access options for this content please click here
Article
Publication date: 10 March 2021

YooHee Hwang, Xingyu Wang and Aysin Pașamehmetoġlu

Online reviews are perceived as credible and trustworthy across various business sectors; thus, they influence customers’ purchase decisions. However, the potential role…

Abstract

Purpose

Online reviews are perceived as credible and trustworthy across various business sectors; thus, they influence customers’ purchase decisions. However, the potential role of customer online reviews as feedback for employee performance and employee reactions to customer reviews remain largely unclear. To address this knowledge gap, this study proposes that employee characteristics, namely, self-efficacy (Study 1) and moral identity (Study 2), moderate the effect of the valence of customer reviews on hospitality employees’ helping behavior.

Design/methodology/approach

The authors used a scenario-based, quasi-experimental design in two studies. They recruited a total of 215 frontline employees at independent casual dining restaurants in Istanbul, Turkey (Study 1) and 226 US residents who have worked in the restaurant industry for more than six months (Study 2). Multiple linear regressions via PROCESS and moderation analysis via Johnson–Neyman technique were used.

Findings

Study 1 demonstrates that when employees’ self-efficacy is low, positive (vs negative) customer reviews enhance employees’ helping behavior. By contrast, when employees’ self-efficacy is high, their helping behavior is invariantly high regardless of the valence of customer reviews. Study 2 reveals that when employees’ moral identity is low, their helping behavior decreases in the presence of negative (vs positive) customer reviews. Conversely, when employees’ moral identity is high, their helping behavior is similarly high regardless of the valence of customer reviews.

Practical implications

Hospitality managers may need to develop training programs to enhance their employees’ self-efficacy and moral identity. They may also provide necessary organizational support to induce their employees’ self-efficacy and moral identity, given that such psychological resources help buffer the dampening effect of negative reviews on helping behavior. Last, hospitality managers may consider incorporating customer reviews as part of employee performance feedback.

Originality/value

This study advances the understanding of employees’ responses to customer reviews, with the performance appraisal feedback framework as fresh theoretical lens. This study is among the first to demonstrate the relationship between the valence of customer reviews and subsequent helping behavior of employees toward customers. It also contributes to the emerging literature that identifies boundary conditions for employees’ responses to customer reviews.

Details

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

Keywords

1 – 10 of over 110000