Online Reputation Mechanisms and the Decreasing Value of Chain Affiliation

2018 ◽  
Vol 55 (5) ◽  
pp. 636-654 ◽  
Author(s):  
Brett Hollenbeck
Keyword(s):  
Online Reviews ◽  
Consumer Information ◽  
Brand Reputation ◽  
Quality Signals ◽  
Small Market ◽  
Large Panel ◽  
Reputation Mechanisms ◽  
Independent Hotels ◽  
To Come ◽  
Online Reputation

This article investigates the value of business format franchising and how it is changing in response to a large increase in consumer information provided by online reputation mechanisms. Theory has suggested that much of the value of chain affiliation to firms comes from the ability of chain partners to use the same name, imagery, logo, and marketing to create a common brand reputation and signal specific qualities in settings with asymmetric information between buyers and sellers. As more information becomes available, consumers should rely less on branding for quality signals, and firms’ ability to extend reputations across heterogeneous outlets should decrease. To examine this empirically, the author combines a large panel of hotel revenues with millions of online reviews from multiple platforms. Chain-affiliated hotels earn substantially higher revenues than equivalent independent hotels, but this premium has declined by over 50% from 2000 to 2015. This can be largely attributed to an increase in online reputation mechanisms, and this effect is largest for low-quality and small-market firms. Measures of the information content of online reviews show that as information has increased, independent hotel revenue has grown substantially more than chain hotel revenue. This result should be viewed as descriptive, with attempts to come to near causality including the use of machine learning to derive latent dimensions of firm quality from the text of online reviews. Finally, the correlation between firm revenue and chain-wide reputation is decreasing, whereas the correlation with individual hotel reputation is increasing.

scholarly journals Electronic word-of-mouth marketing on Amazon: Exploring how and to what extent Amazon reviews affect sales

2020 ◽  
Vol 12 (1) ◽  
pp. 50-79 ◽  
Author(s):  
Kristine D'Arbelles ◽  
Pauline Berry ◽  
Ashika Theyyil
Keyword(s):  
Word Of Mouth ◽  
Brand Management ◽  
High Volume ◽  
Online Reviews ◽  
Reputation Management ◽  
Electronic Word Of Mouth ◽  
Organizational Leaders ◽  
Brand Reputation ◽  
Product Sales ◽  
Online Reputation

Consumers today base many of their decisions on peer referrals and online reviews. With the omnipresence of social media and online reviews, electronic word-of-mouth marketing (eWOM) has become a priority for many companies for both business growth and reputational management. The objective of this study is to examine the effectiveness of eWOM and its impact on sales. This study also seeks to help organizational leaders understand the significance of eWOM and its role in effective consumer and stakeholder relations, and in overall brand management. The researchers of this project explored eWOM by examining Amazon reviews from two different Kickstarter companies to determine which elements of online reviews impact product sales. By overlaying Amazon review data and sales figures from each Kickstarter company, researchers were able to determine the review factors that companies should focus on to increase their sales and grow their brands. The results of this study show that products with a high volume of positive reviews made by verified purchasers positively correlate to product sales.   Keywords: electronic word-of-mouth marketing, Amazon, online reviews, Kickstarter, sales, reputational management, brand reputation, online reputation management

Examining the Effect of Positive Online Reviews on Consumers' Decision Making

2019 ◽  
Vol 27 (3) ◽  
pp. 159-181 ◽  
Author(s):  
Lin Xiao ◽  
Yuan Li
Keyword(s):  
Decision Making ◽  
Perceived Risk ◽  
Online Survey ◽  
Source Credibility ◽  
Online Reviews ◽  
Consumer Information ◽  
Elaboration Likelihood ◽  
Argument Quality ◽  
Likelihood Model ◽  
Potential Risks

Online reviews play an important role in consumers' decision making. However, limited studies have been conducted to understand the effects of online reviews on consumers' behavior. Drawing upon the Elaboration Likelihood Model and the valence framework, a research model was developed to investigate the perceived benefits and potential risks brought by positive online reviews. The moderating effect of review skepticism was also examined. Data were collected through on online survey based on consumers' perceptions of the positive reviews from restaurants and food businesses and analyzed with partial least squares. The results indicated that argument quality and source credibility influence information usefulness, which further influences consumers' behavioral intentions. The influence of positive online reviews on perceived risk differs between high and low skepticism consumers. This research offers a more in-depth understanding of consumer information processing in an online context and benefits practitioners by allowing them to better understand consumers.

Reputation management in cyberspace: evidence from Jordan’s luxury hotel market

2019 ◽  
Vol 10 (1) ◽  
pp. 107-120 ◽  
Author(s):  
Zaid Alrawadieh ◽  
Mithat Zeki Dincer
Keyword(s):  
Response Rate ◽  
Online Reviews ◽  
Reputation Management ◽  
Future Research ◽  
Content Type ◽  
Luxury Hotels ◽  
Hotel Managers ◽  
Online Reputation ◽  
Luxury Hotel ◽  
Managerial Response

PurposeDrawing on a sample of 520 negative reviews posted on TripAdvisor against all five-star hotels operating in Petra, Jordan, the purpose of this paper is to evaluate the response of luxury hotels to negative online reviews by considering the Response Rate (RR), the Response Time (RT) and the Response Content (RC).Design/methodology/approachA deductive content analysis was used on hotels’ managerial responses. Based on the literature review, a four-construct scheme was identified to guide the analysis including Appreciation; Apology; Explanation; and Incentive. The managerial responses were carefully read and manually coded based on the four-construct scheme. The time between the review posting date and the date of the managerial response was also recorded. Luxury hotel managers were also surveyed to obtain insights into their perceptions and practices with respect to online reputation management.FindingsThe findings call into question luxury hotels’ awareness of the harmful impact of negative online reviews. Specifically, the findings suggest that less than half of the negative reviews received a managerial response, and that more than half of these were standardized and did not refer to the issues raised in the reviews. The low response rate coupled with the hotel managers’ consensus on the importance of answering all online reviews indicates inconsistency between hotel managers’ perceptions and practices with regard to online reputation management.Originality/valueThe paper adds to the ongoing debate on reputation management in the hospitality industry by considering the managerial response to negative online reviews. The paper discusses several managerial implications for hotel managers as well as avenues for future research.

scholarly journals Extremely Negative Ratings and Online Consumer Review Helpfulness: The Moderating Role of Product Quality Signals

2020 ◽  
pp. 004728752091678 ◽  
Author(s):  
Raffaele Filieri ◽  
Claudio Vitari ◽  
Elisabetta Raguseo
Keyword(s):  
Product Quality ◽  
Online Reviews ◽  
Average Score ◽  
Consumer Review ◽  
Quality Signals ◽  
Review Helpfulness ◽  
Online Consumer Review ◽  
The Relationship ◽  
Moderating Role

Contrasting findings about the role of extremely negative ratings (ENRR) are found in the literature, thus suggesting that not all ENRR are perceived as helpful by consumers. In order to shed light on the most helpful ENRR, we have drawn on negativity bias and signaling theory, and we have analyzed the moderating role of product quality signals in the relationship between ENRR and review helpfulness. The study is based on the analysis of 9,479 online reviews, posted on TripAdvisor.com, pertaining to 220 French hotels. The findings highlight that ENRR is judged as being more helpful when the hotel has been awarded a certificate of excellence, and when the average rating score and the hotel classification are higher. On the basis of these results, we recommend that managers of higher category hotels, with a certificate of excellence and with higher average score ratings, pay more attention to extremely negative judgments.

How hotel responses to online reviews differ by hotel rating: an exploratory study

2015 ◽  
Vol 7 (3) ◽  
pp. 242-250 ◽  
Author(s):  
HyeRyeon Lee ◽  
Shane C. Blum
Keyword(s):  
Web Sites ◽  
Exploratory Study ◽  
Online Reviews ◽  
Third Party ◽  
Rating System ◽  
Exploratory Research ◽  
Content Type ◽  
Star Rating ◽  
Hotel Managers ◽  
Online Reputation

Purpose – The purpose of this paper is to investigate how hotels respond to online reviews on a third-party Web site (such as TripAdvisor) based on the hotel’s star rating. Design/methodology/approach – Content analysis was used to compare responses to online hotel reviews at five different levels of hotel based on a star-rating system ranging from one star to five stars. Findings – Most hotel managers’ response rates were low, and they paid the most attention to positive comments. Managers at four- and five-star hotels more often responded to negative online reviews. Guest service manager was the most common job title of managers who responded to guests’ reviews. Research limitations/implications – This paper is limited to an analysis of ten hotels, two for each of the five-star ratings. More hotel cases with long-term data collection involving the use of the star-rating system may provide more insights on this discussion. Practical implications – The exploratory study sought to identify strategies for managing online reviews in the lodging industry. Hotel managers should respond to negative online reviews with appreciation, apology and an explanation of what went wrong. Moreover, hotels may need a designated person to observe and respond to guest comments on their Web sites and third-party Web sites. A designated person is also needed to monitor online comments and communicate with guests to better manage the hotel’s online reputation. Originality/value – As an exploratory research project, this paper expands the understanding of hotel managers’ responses to their guests’ online reviews in an attempt to identify best practices for the industry.

“Please write a (great) online review for my hotel!” Guests’ reactions to solicited reviews

2017 ◽  
Vol 24 (2) ◽  
pp. 148-158 ◽  
Author(s):  
Francesca Magno ◽  
Fabio Cassia ◽  
Attilio Bruni
Keyword(s):  
Management Strategy ◽  
Online Reviews ◽  
Psychological Reactance ◽  
Reputation Management ◽  
Online Review ◽  
Knowledge Gap ◽  
Behavioral Reactions ◽  
Online Reputation

With the aim of enhancing their online reputation, several hospitality businesses have started soliciting their guests to write online reviews. Available studies have not yet evaluated the effects of this strategy. To fill this knowledge gap, this study draws on the theory of psychological reactance and investigates guests’ attitudinal and behavioral reactions to received solicitations. Evidence collected from a sample of Italian travelers indicates that soliciting reviews has both benefits and drawbacks: It increases the number of reviews for the business, but it also irritates a significant share of guests. Particularly high levels of irritation arise when a business explicitly asks its guests to write positive reviews. The implications of these findings for the reputation management strategy of hospitality businesses are discussed.

scholarly journals Machine learning Techniques for Hotel Online Reputation

2019 ◽  
Vol 8 (9) ◽  
pp. 3004-3007
Keyword(s):  
Machine Learning ◽  
Online Reviews ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Machine Learning Technique ◽  
Learning Techniques ◽  
Learning Technique ◽  
Learning Reinforcement ◽  
Online Reputation ◽  
Fake Reviews

Now days when someone decide to book a hotel, previous online reviews of the hotels play a major role in determining the best hotel within the budget of the customer. Previous Online reviews are the most important motivation for the information that are used to analyse public opinion. Because of the high impact of the reviews on business, hotel owners are always highly concerned and focused about the customer feedback and past online reviews. But all reviews are not true and trustworthy, sometime few people may intentionally generate the fake reviews to make some hotel famous of to defame. Therefore it is essential to develop and propose the techniques for analysis of reviews. With the help of various machine learning techniques viz. Supervised machine learning technique, Text mining, Unsupervised machine learning technique, Semi-supervised learning, Reinforcement learning etc we may detect the fake reviews. This paper gives some notions of using machine learning techniques in analysis of past online reviews of hotels, Based on the observation it also suggest the optimal machine learning technique for a particular situation

Consumer Information Sharing

2010 ◽  
pp. 676-682 ◽  
Author(s):  
Jonathan Foster ◽  
Angela Lin
Keyword(s):  
Social Media ◽  
Information Sharing ◽  
Online Reviews ◽  
Consumer Information ◽  
Product Reviews ◽  
Purchasing Decisions ◽  
Life Project ◽  
Software Applications ◽  
The Social ◽  
Other Consumers

One area of e-business that has visibly changed in the last few years is the capacity of the Internet for supporting consumer-to-consumer information sharing. By using a variety of social media software applications such as online reviews, blogs, social tagging, and wikis, consumers are increasingly able to generate and share content about the products and services that are available in the marketplace. Collectively the labor expended by consumers in generating such content is considerable, influencing other consumers’ perceptions of these products and services and informing their purchasing decisions. It has been estimated for example that more than 5 million customers have reviewed products on the Amazon.com site, with many more making purchasing decisions informed by reading such reviews (Amazon, 2008). According to the findings of a recent Pew Internet & American Life Project survey, consumer generated information sources such as product reviews and blogs are also considered equally as important as commercial information, e.g. manufacturers’ specifications, when making a purchasing decision (Horrigan, 2008). This article aims to provide an up-to-date review of the practice of consumer information sharing. First the different kinds of information sought by consumers are identified; second the social media software applications that consumers use to create, organize and share information with other consumers are discussed; and finally consideration is given to the marketing implications of consumer information sharing and how e-businesses can utilize social media for developing and managing relations with their customers.

How do consumers process online hotel reviews?

2015 ◽  
Vol 6 (2) ◽  
pp. 113-126 ◽  
Author(s):  
Ellen EunKyoo Kyoo Kim ◽  
Chung Hun Lee
Keyword(s):  
Web Sites ◽  
Online Reviews ◽  
The Body ◽  
Consumer Information ◽  
Content Type ◽  
Consumer Review ◽  
Online Consumer ◽  
Online Consumer Review ◽  
The Impact ◽  
Practical Implications

Purpose – The purpose of this paper is to examine how consensus and sequence of electronic word-of-mouth (eWOM) presented on online hotel review Web sites affect consumers’ attitudes toward the company and intention to stay at a hotel. Design/methodology/approach – This experiment used a 2 (consensus: high/low) × 3 (sequence: positive-negative, neutral, negative-positive) between-subjects design. A total of 165 usable data samples were gathered. Both consensus and sequence were manipulated. Findings – The study revealed that the review consensus overrides the impact of the review sequence such that when review ratings are substantially consistent, consumers’ attitudes and intentions to stay at a hotel are not influenced by the sequence of reviews. Research limitations/implications – Other variables such as prior experience with the hotel or biases toward the hotel can affect consumer reactions to such online reviews. Future studies need to reflect on such variables that can moderate or mediate the impact of eWOM consensus and sequence. Practical implications – Our findings suggest that the online consumer review summary information should be used to control the customer message process and when consumer reviews conflict, managers should take note of the sequence in which consumers read the reviews. Originality/value – This paper adds to the body of scholarly research related to consumer information processing and further demonstrates how individuals integrate opinions from several reviews, especially in the online context.

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