'Cultural Effect' in User-Generated Content: Evidence from Online Reviews

2013 ◽  
Author(s):  
Yili Hong ◽  
Chunxiao Li
Keyword(s):  
Online Reviews ◽  
User Generated Content ◽  
Cultural Effect

The importance of user-generated photos in restaurant selection

2019 ◽  
Vol 10 (1) ◽  
pp. 2-14 ◽  
Author(s):  
Bruno Oliveira ◽  
Beatriz Casais
Keyword(s):  
Design Methodology ◽  
Online Reviews ◽  
User Generated Content ◽  
Content Type ◽  
Online Platforms ◽  
Photo Sharing ◽  
Restaurant Managers ◽  
Marketing Activities ◽  
Hospitality Sector ◽  
Practical Implications

Purpose User-generated content and online reviews are highly relevant in purchase decision in the hospitality sector, including restaurants, but there is a lack of knowledge about the effect of sharing pictures in this context. This study aims to focus on the relevance of user-generated photos in online platforms for restaurants’ selection. Design/methodology/approach A research was conducted with a sample of 319 residents of Porto region, who had at least one meal in a restaurant over the 30 days before the answer of the survey and had searched online to select the restaurant. Findings The results show that while doing online research about restaurants, it is important for potential consumers to find pictures of food and physical evidences of restaurants generated by other users. Findings also show that consumers find user-generated photos especially at websites of reviews, although the importance of restaurant owned platforms, such as official social media pages and websites. Practical implications The research results appeal restaurant managers to understand the importance of user-generated photos in online platforms by promoting photo sharing in their restaurants with appropriate marketing activities for that purpose. Originality/value This paper expands the state-of-the-art about the importance of user-generated content, focusing on the importance of photos from restaurants shared by consumers in online platforms.

A hybrid recommender system for the mining of consumer preferences from their reviews

2019 ◽  
Vol 46 (5) ◽  
pp. 664-682
Author(s):  
Li Chen Cheng ◽  
Ming-Chan Lin
Keyword(s):  
Opinion Mining ◽  
Information Overload ◽  
Consumer Preferences ◽  
Online Reviews ◽  
User Generated Content ◽  
Mining Technique ◽  
Movie Recommendation ◽  
Hybrid Recommender System ◽  
Hybrid Recommender ◽  
Filtering Approach

Product review sites are widespread on the Internet and are rapidly gaining in popularity among consumers. This already large volume of user-generated content is dramatically growing every day, making it hard for consumers to filter out the worthwhile information which appears on the various review sites. There commendation system plays a significant role in solving the problem of information overload. This study proposes a framework which integrates a collaborative filtering approach and an opinion mining technique for movie recommendation. Within the proposed framework, sentiment analysis is first applied to the users’ reviews to detect consumer opinions about the movie they have watched and to explore the individual’s preference profile. Traditional recommendation models are overly dependent on preference ratings and often suffer from the problem of ‘data sparsity’. Experimental results obtained from real online reviews show that our proposed method is effective in dealing with insufficient data and is more accurate and efficient than existing traditional methods.

eWOW of Guests Regarding Their Hotel Experience

2020 ◽  
pp. 295-308
Author(s):  
Zelia Breda ◽  
Rui Costa ◽  
Gorete Dinis ◽  
Amandine Angie Martins
Keyword(s):  
Sentiment Analysis ◽  
Online Reviews ◽  
User Generated Content ◽  
Sources Of Information ◽  
Value For Money ◽  
Quality Of Sleep ◽  
Online Comments ◽  
Analytical Response ◽  
Source Of Information

Online comments are increasingly mentioned as an important source of information, simplifying consumers' buying decisions. Online user-generated content has become one of the main sources of information for tourists, who themselves become creators of their own online content. This chapter focuses on sentiment analysis of comments made on TripAdvisor regarding one resort located in the Algarve region, in Portugal. The resort has good reviews, which means that the eWOM is positive. The highest scores relate to the resort's cleanliness, location and quality of sleep, and those that were less relevant were the value for money, the rooms and the service. The most dominant emotion is joy, followed by an analytical response. Negative emotions, such as sadness and anger, were not found very often in the online reviews. These results could be explained by the quality of the service, the kindness of the staff, the facilities for children, the entertainment, and the location, attributes that were often highlighted in the comments.

Calibration of Voting-Based Helpfulness Measurement for Online Reviews: An Iterative Bayesian Probability Approach

2020 ◽  
Author(s):  
Xunhua Guo ◽  
Guoqing Chen ◽  
Cong Wang ◽  
Qiang Wei ◽  
Zunqiang Zhang
Keyword(s):  
User Study ◽  
Prediction Models ◽  
Online Reviews ◽  
User Generated Content ◽  
Bayesian Probability ◽  
Social Media Analytics ◽  
Science Literature ◽  
Data Set ◽  
Novel Approach ◽  
Wide Range

Voting mechanisms are widely adopted for evaluating the quality and credibility of user-generated content, such as online product reviews. For the reviews that do not receive sufficient votes, techniques and models are developed to automatically assess their helpfulness levels. Existing methods serving this purpose are mostly centered on feature analysis, ignoring the information conveyed in the frequencies and patterns of user votes. Consequently, the accuracy of helpfulness measurement is limited. Inspired by related findings from prediction theories and consumer behavior research, we propose a novel approach characterized by the technique of iterative Bayesian distribution estimation, aiming to more accurately measure the helpfulness levels of reviews used for training prediction models. Using synthetic data and a real-world data set involving 1.67 million reviews and 5.18 million votes from Amazon, a simulation experiment and a two-stage data experiment show that the proposed approach outperforms existing methods on accuracy measures. Moreover, an out-of-sample user study is conducted on Amazon Mechanical Turk. The results further illustrate the predictive power of the new approach. Practically, the research contributes to e-commerce by providing an enhanced method for exploiting the value of user-generated content. Academically, we contribute to the design science literature with a novel approach that may be adapted to a wide range of research topics, such as recommender systems and social media analytics.

How People Evaluate Online Reviews

2015 ◽  
Vol 45 (5) ◽  
pp. 719-736 ◽  
Author(s):  
David C. DeAndrea ◽  
Brandon Van Der Heide ◽  
Megan A. Vendemia ◽  
Mao H. Vang
Keyword(s):  
Impression Management ◽  
Online Reviews ◽  
Third Party ◽  
User Generated Content ◽  
Future Research ◽  
Web Building

The ability viewers have to contribute information to websites (i.e., user-generated content) is a defining feature of the participatory web. Building on warranting theory, this study examined how viewers’ evaluations of a target are more or less likely to be influenced by user-generated content. The results indicate that the more a target is perceived to be able to control the dissemination of user-generated reviews online, the less credence people place in those reviews when forming impressions of the target. In addition, the less people are confident that user-generated reviews are truly produced by third-party reviewers, the less people trust those reviews. The results provide novel support for warranting theory by illustrating how the warranting value of user-generated information can vary and thus differentially affect viewers’ evaluations of a target. The implications of the study’s results for warranting theory, online impression management, e-commerce, and future research are discussed.

scholarly journals In Mobile We Trust: The Effects of Mobile Versus Nonmobile Reviews on Consumer Purchase Intentions

2019 ◽  
Vol 56 (5) ◽  
pp. 791-808 ◽  
Author(s):  
Lauren Grewal ◽  
Andrew T. Stephen
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Online Reviews ◽  
Purchase Intentions ◽  
User Generated Content ◽  
Timely Manner ◽  
Perceived Effort

In the context of user-generated content (UGC), mobile devices have made it easier for consumers to review products and services in a timely manner. In practice, some UGC sites indicate if a review was posted from a mobile device. For example, TripAdvisor uses a “via mobile” label to denote reviews from mobile devices. However, the extent to which such information affects consumers is unknown. To address this gap, the authors use TripAdvisor data and five experiments to examine how mobile devices influence consumers’ perceptions of online reviews and their purchase intentions. They find that knowing a review was posted from a mobile device can lead consumers to have higher purchase intentions. Interestingly, this is due to a process in which consumers assume mobile reviews are more physically effortful to craft and subsequently equate this greater perceived effort with the credibility of the review.

Nature-based experiences in tree houses: guests’ online reviews

2019 ◽  
Vol 74 (3) ◽  
pp. 310-326 ◽  
Author(s):  
Ana Brochado
Keyword(s):  
Design Methodology ◽  
Qualitative Data ◽  
Opportunity To Learn ◽  
Online Reviews ◽  
User Generated Content ◽  
Computer Assisted ◽  
Content Type ◽  
Tourism Studies ◽  
Key Dimensions ◽  
Dominant Narratives

Purpose This study aims to examine nature-based tourists’ experiences in tree houses using user-generated content in Web reviews. The research objectives were to identify the main dimensions of tree house experiences and test whether these dimensions vary according to different traveller-type market segments. Design/methodology/approach A sample of 722 Web reviews was analysed using mixed content analysis methods. Leximancer software provided computer-assisted qualitative data analysis that identified the main themes, after which further qualitative analysis identified the key narratives associated with experiences. Findings The results reveal that tourists are extremely satisfied with their stays in tree houses. The main themes that encompass the dominant narratives are tree house, Costa Rica, staff, morning, walk, wildlife, rainforest, opportunity to learn, trip, experience and recommendation. The narratives vary according to type of traveller. Originality/value The valuable insights gained emphasise the advantages of using user-generated content in tourism studies. The results also offer a better understanding of the key dimensions of this type of nature-based tourism, including a graphic representation of the main themes and concepts in guests’ narratives. In addition, the findings emphasise that nature-based tourists are not a homogeneous group and that they can be segmented according to type of traveller.

scholarly journals EXPRESS: Mickey D’s Has More Street Cred Than McDonald’s: Consumer Brand Nickname Use Signals Information Authenticity

2021 ◽  
pp. 002224292199627
Author(s):  
Zhe Zhang ◽  
Vanessa M. Patrick
Keyword(s):  
Relationship Quality ◽  
Online Communication ◽  
Online Reviews ◽  
User Generated Content ◽  
Brand Strategy ◽  
Online Information ◽  
Brand Attachment ◽  
Brand Relationship ◽  
Twitter Data ◽  
Brand Relationship Quality

Consumers often observe how other consumers interact with brands to inform their own brand judgments. This research demonstrates that brand relationship quality-indicating cues, such as brand nicknames (e.g., Mickey D’s for McDonald’s and Wally World for Walmart), enhance perceived information authenticity in online communication. An analysis of historical Twitter data followed by six experiments (using both real and fictitious brands across different online platforms, e.g., online reviews and social media posts) show that brand nickname use in user-generated content signals a writer’s relationship quality with the target brand from the reader’s perspective, which the authors term inferred brand attachment (IBA). The authors demonstrate that IBA boosts perceived information authenticity and leads to positive downstream consequences, such as purchase willingness and information sharing. The authors also find that this effect is attenuated when brand nicknames are used in firm-generated content. How consumers’ relationships with brands are portrayed and perceived in a social context (e.g., via brand nickname use) serves as a novel context to examine user-generated content and provides valuable managerial insight regarding how to leverage consumers’ brand attachment cues in brand strategy and online information management.

scholarly journals Mining Express Service Innovation Opportunity from Online Reviews

2021 ◽  
Vol 33 (6) ◽  
pp. 0-0
Keyword(s):  
Text Mining ◽  
Customer Satisfaction ◽  
Sentiment Analysis ◽  
Service Innovation ◽  
Online Reviews ◽  
User Generated Content ◽  
Customer Demand ◽  
Market Competitiveness ◽  
Service Attribute ◽  
Express Delivery

In order to further meet the diversified needs of customers and enhance market competitiveness, it is necessary for express delivery enterprises to carry out service innovation. From the perspective of customer demand, we propose a framework for mining service innovation opportunities. This framework uses text mining to analyze user generated content and tries to provide a scientific service innovation scheme for express enterprises. Firstly, we crawl online reviews of express companies and use LDA model to identify service attributes. Secondly, customer satisfaction is calculated by sentiment analysis, and simultaneously, the importance of each service attribute is calculated. Finally, we carry out an opportunity algorithm with the results of text mining to quantify the innovation opportunities of service attributes. The results show that the framework can effectively identify service innovation opportunities from the perspective of customer demand. This study provides a new way to explore the direction of service innovation from the perspective of customer demand.

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