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The purpose of this paper is to (n=472) examine how negative, user-generated content on Facebook impacts stakeholders’ perceptions of the organization.

At Phase 1, stakeholders’ perceptions about the organization – public relationship, corporate social responsibility, attitude toward the organization, and reputation of the organization were assessed. A week later, at Phase 2, participants were exposed to negative Facebook comments. This study employed the theory of inoculation as a way to bolster stakeholders’ attitudes to protect against attitude shift following exposure to negative, user-generated comments.

Paired sample t-tests indicate stakeholders’ perceptions of the organization – public relationship and corporate social responsibility significantly decrease after stakeholders read negative, user-generated content. The pattern of means supports the idea inoculation can prevent against attitude shift.

Strategic communication professionals should be aware of the impact negative posts can have and develop a strategy to respond to negative comments on Facebook.

There is limited experimental research examining the impact of negative Facebook posts on stakeholders. It extends current literature and provides practitioners with some guidance on the impact of negative, user-generated content.

The broad goal of the study is to determine how travelers communicate in the cyberspace in relation to their positive and negative experiences they had when staying in a particular hotel. Further goals of this study include identifying the main themes that motivate consumers to evaluate hotel experiences in online environments and categorize the most frequently mentioned areas in the online hotel reviews.

Content analysis techniques were applied by using the software tool NVivo 8 in order to analyze comments extracted using an automated web spider. The spider extracted qualitative data in the form of reviews and comments and quantitative data in the form of demographic information and ratings. The reviews were considered as a primary data for analysis, these reviews portrayed both positive and negative experiences. During this process, the spider collected data on 3,124 hotels and 17,357 traveler reviews from the TripAdvisor site.

By reviewing and understanding traveler comments of their hotel experiences, managers could gain knowledge concerning which element influence to form a positive brand image. Cleanliness of the hotel generally is a common concern in traveler's expectations. Words about deficiency of cleanliness (dirty) appeared more regularly when travelers write negative reviews about the hotel. Travelers showed to be more likely to write positive reviews of hotels with convenient location to good areas such as attractions, shopping, airports, and restaurants. The data in this research shows that travelers can be positively influenced by quality of service received a friendly and well trained staff. When travelers are pleased with the quality of human contact offerings of a well‐trained employee, they tend to feel more satisfied and to form a positive brand image that it is translated into a positive review.

Limitations could be listed using a relatively small sample size, and a relatively limited geographical capacity. Future studies are advised to include bigger sample sizes and also advised to explore a diverse pool of geographical.

The study identifies the possible areas that hoteliers need to pay close attention to improve service. Further, it is one of the first studies in hospitality that highlights strategies to create and reinforce brand image by using online reviews.

The purpose of this paper is to construct a multi-relational network for an online sharing platform in the age of the sharing economy, to identify the factors impacting users’ product adoption behavior and to predict consumers’ purchases of user-generated products on the platform.

The study conducted multi-relational network analyses of five different sub-networks in identifying influential factors for e-book adoption. Meanwhile, the study adopted machine learning methods with different classification algorithms and feature sets to predict users’ purchasing behaviors.

The authors found that an individual’s adoption of a product was correlated with his or her purchasing habits and collaboration with others on the online sharing platform. Through the inclusion of network features, the authors were able to build a predictive model that forecasted consumers’ purchases of user-generated e-books with reasonable accuracy.

The interdisciplinary approach used in the study can serve as a good reference for identifying factors impacting the product adoption behavior of users in the online sharing platform, through employing different sociological and computational methods.

The outcome of the study has provided important managerial implications, especially for the design of social commerce platform in the age of the sharing economy.

The authors verified the social influence impacting consumers’ product adoption behavior and shed light on the value of collaboration in the age of the sharing economy.

The study was the first to identify user-generated e-book adoption on an online sharing platform from a multi-relational network perspective. The idea and the approach supplied a new method of behavioral analysis in the context of a sharing economy.

The purpose of this paper is to analyze user-generated comments posted on social media while live matches were being played during the Cricket World Cup 2015.

Data were collected from Yahoo! Cricket (YC), a website that allows people to submit comments during live matches. The comments were qualitatively analyzed using the grounded theory approach.

The key finding of this paper is that people like to consume live sporting events in an online social setting rather than as isolated individuals. In addition, the use of the grounded theory approach helped uncover several new findings related to people’s use of social media during live matches.

Since this paper studied the case of the Cricket World Cup 2015 and collected data from YC, caution is advocated in generalizing its findings.

Scholarly interest on the use of social media during live sporting events is growing. Building on such works, this paper highlights how user-generated comments posted during the Cricket World Cup 2015 – mostly by individuals within the Indian subcontinent – intersected with broader issues such as culture, identity, politics and religion.

This study aims to examine how different hospitals utilize social media to communicate risk information about COVID-19 with the communities they serve, and how hospitals' social media messaging (firm-generated content and their local community's responses (user-generated content) evolved with the COVID-19 outbreak progression.

This research proposes a healthcare-specific social media analytics framework and studied 68,136 tweets posted from November 2019 to November 2020 from a geographically diverse set of ten leading hospitals' social media messaging on COVID-19 and the public responses by using social media analytics techniques and the health belief model (HBM).

The study found correlations between some of the HBM variables and COVID-19 outbreak progression. The findings provide actionable insight for hospitals regarding risk communication, decision making, pandemic awareness and education campaigns and social media messaging strategy during a pandemic and help the public to be more prepared for information seeking in the case of future pandemics.

For hospitals, the results provide valuable insights for risk communication practitioners and inform the way hospitals or health agencies manage crisis communication during the pandemic For patients and local community members, they are recommended to check out local hospital's social media sites for updates and advice.

The study demonstrates the role of social media analytics and health behavior models, such as the HBM, in identifying important and useful data and knowledge for public health risk communication, emergency responses and planning during a pandemic.

The body of scholarship on YouTube is an expanding area of scholarly inquiry. Existent research indicates that music videos are one of the most salient features of YouTube. Interactionist research about popular music has provided important insights through interviews with fans and audience members; however, this work has yet to examine audience engagement with music videos on YouTube. Using Qualitative Media Analysis, I illustrate how the researcher of popular music can work with user comments collected from YouTube. Thematic understandings largely consistent with nostalgia that emerged from an analysis of user-generated comments in response to selected music videos on YouTube are explored. I conclude by suggesting some directions for future research.

Campaigns to promote the COVID-19 vaccination messages to vaccine-hesitant consumers in the late stages of the pandemic are often met with resistance. This study aims to explore a way to leverage positive emotions induced from entertainment media consumption to promote vaccination messages to this audience group.

An online experiment was conducted with vaccine-hesitant consumers (N = 409). Participants viewed personally relevant entertainment music videos or mundane videos and vaccinated messages embedded in user-generated comments.

Data revealed that feelings of inspiration and nostalgia induced from entertainment media consumption increased vaccination intentions via increased risk perceptions and reduced anti-vaccination attitudes.

Social marketers should consider leveraging the combined effect of entertainment media-induced positive emotions and user-generated comments to motivate behavioral change among vaccine-hesitant individuals in the late stages of the COVID-19 pandemic.

The present study adds to social marketing literature by showing mechanisms that positive emotions induced from entertainment social media consumption might lead to health behavioral change.

Social media use has increased in recent years, and businesses are looking to capitalize on the plethora of marketing opportunities afforded by this digital shift by paying attention to user-generated content (UGC) posted on review websites. Leveraging UGC can help small businesses gain a competitive advantage over late-adopters. At the same time, there is evidence to suggest that small businesses do not have the time, resources or skill level to properly use social media to create a competitive advantage. This paper aims to explore how wine tourism businesses can analyze consumer feedback on online review websites to evaluate customer perceptions and expectations and generate more effective ways to improve customer satisfaction.

The qualitative method of thematic analysis was used to map out consumer reviews online to assess service satisfaction and dissatisfaction. A total of 848 reviews were gathered and qualitatively analyzed from two online review websites (TripAdvisor.com and Yelp.com) using open and axial coding and thematic analysis.

The results show that wine consumers are interested in the hedonic aspects of their experience, are most often attracted to wineries as a special outing and focus on factors such as scenery and atmosphere, service quality and products in their reviews. Hence, service and sales personnel have a key opportunity to capitalize on generating better service experiences through social media analysis.

The present study fills a gap by providing a more in-depth, qualitative exploration of the wine consumers’ psychology and experience, including factors such as atmosphere and special occasions. Furthermore, this study uses interpretive, manual coding to pick up on nuanced themes that are often missed by using automated qualitative analysis software or by looking at frequency counts in isolation.

The chapter investigates the evaluation and rating practice of individual travelers, through the examination of user-generated comments on the Internet. The study focuses on determining the most mentioned attributes of the accommodation experience, with consideration given to nationality differences. The individual evaluations of 40 Istanbul hotels are examined through an analysis of guest comments and hotel ratings posted in the Booking.com web site. The results obtained through content analysis provide knowledge to the accommodation industry in Istanbul regarding the areas in need of improvement, with consideration to variations among guests from different nationalities.

This study aims to use big data analysis and sheds light on key hotel features that play a role in the revisit intention of customers. In addition, this study endeavors to highlight hotel features for different customer segments.

This study uses a machine learning method and analyzes around 100,000 reviews of customers of 100 selected hotels around the world where they had indicated on Trip Advisor their intention to return to a particular hotel. The important features of the hotels are then extracted in terms of the 7Ps of the marketing mix. This study has then segmented customers intending to revisit hotels, based on the similarities in their reviews.

In total, 71 important hotel features are extracted using text analysis of comments. The most important features are the room, staff, food and accessibility. Also, customers are segmented into 15 groups, and key hotel features important for each segment are highlighted.

In this research, the number of repetitions of words was used to identify key hotel features, whereas sentence-based analysis or group analysis of adjacent words can be used.

This study highlights key hotel features that are crucial for customers’ revisit intention and identifies related market segments that can support managers in better designing their strategies and allocating their resources.

By using text mining analysis, this study identifies and classifies important hotel features that are crucial for the revisit intention of customers based on the 7Ps. Methodologically, the authors suggest a comprehensive method to describe the revisit intention of hotel customers based on customer reviews.