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This study aims to examine entertainment TV shows' social media accounts to theoretically and practically explore the relationship between social media engagement and the performance (represented by ratings) of such shows.

By using the data of a popular TV show in the USA, The Voice, the present study examined the messages on the Facebook fan page of the show and how these messages correlated with the ratings of the show. Social media usage data in the course of three seasons (Seasons 10–12, 82 episodes in total) were collected from Facebook (N = 1,192,722 messages). Both regression and sentiment analysis were performed.

Overall, the findings revealed positive relationships of TV show ratings with both passive social media engagement (Facebook likes) and the number of official posts. However, active social media engagement was not positively related to show ratings.

By enhancing understanding of audience engagement with social media, our research extends knowledge related to the nature and development of viewer involvement with entertainment across different media platforms. Our results also help clarify how interpersonal communication (social media comments) and mass communication (TV programs) intersect. Practically, the findings could be applied to improve the interaction of TV audiences with show content, provide insights into the future of social TV development and inform decision-making amongst TV industry professionals.

This paper aims to examine the effects of traditional customer satisfaction (CS) relative magnitude and social media review ratings on hotel performance and to explore which online travel intermediaries’ review ratings serve as the most reliable and valid predictor for hotel performance.

In 2014, CS and hotel performance data were collected from the internal database of full-service hotels operated and managed by a large hotel chain in the USA. Each property’s social media review ratings data were hand-collected from major online travel intermediaries and social media websites.

The results of this study indicate that social media review rating is a more significant predictor than traditional CS for explaining hotel performance metrics. Additionally, the social media review rating of TripAdvisor is the best predictor for hotel performance out of the other intermediaries.

This research contributes to the hospitality literature because it examines the incremental explanatory power of social media review rating and traditional CS on hotel performance. Among the leading online travel intermediaries, the findings show that TripAdvisor’s social media review rating has the most salient effect on hotel performance.

The result of this study provides useful practical implications for hotel marketers and revenue managers. This study assists hotel marketers and revenue managers in better allocating their budget for marketing and suggests ways for channel optimization.

The finding of this study will help revenue managers, marketing managers, and hotel owners make decisions regarding their marketing budget allocation to their social media marketing campaign and select the optimal online travel intermediaries as part of their channel management strategies.

Social media (e.g., e-WOM) and traditional media (e.g., media coverage) serve different roles in a firm's marketing activities and also interact with each other, which in turn affect the market outcome. In addition, how market outcome affects the two types of media in turn has not been examined, which brings the need for a holistic framework. The rare study that examines this relation mostly relies on the volume of media rather than the valence. This study examines the interdependent relation between the volume and valence of social media, the volume of traditional media and TV ratings.

Forty-one South Korean TV drama shows from October 2014 to March 2016 were analyzed using the 3SLS estimation to examine the interdependent relation between the variables.

First, the volume of traditional media has a negative effect on the volume of social media. Second, ratings negatively affect the valence of social media. Third, the volume of traditional media is found to have a negative effect on ratings. This is explained by the displacement effect.

This study is one of the very few studies that examine the interdependent relation between various earned media and market outcomes in one framework. In addition, it has originality in that it considers the valence of social media, which is an important dimension in analyzing earned media. Our results show negative effects of news media on TV ratings and e-WOM, which diverge from common intuition.

Social media channels provide an avenue for expressing views about different services/products. However, unlike merchandise/company websites (where users can post both reviews and ratings), it is not possible to understand user's ratings for a particular service-related comment on social media unless explicitly mentioned. Predicting ratings can be beneficial for service providers and prospective customers. Additionally, predicting ratings from a user-generated content can help in developing vast data sets for recommender systems utilizing recent data. The aim of this study is to predict ratings more accurately and enhance the performance of sentiment-based predictors by combining it with the emotional content of textual data.

This study had utilized a combination of sentiment and emotion scores to predict the ratings of Twitter posts (3,509 tweets) in three different contexts, namely, online food delivery (OFD) services, online travel agencies (OTAs) and online learning (e-learning). A total of 29,551 reviews were utilized for training and testing purposes.

Results of this study indicate accuracies of 58.34%, 57.84% and 100% in cases of e-learning, OTA and OFD services, respectively. The combination of sentiment and emotion scores showed an increase in accuracies of 19.41%, 27.83% and 40.20% in cases of e-learning, OFD and OTA services, respectively.

Understanding the ratings of social media comments can help both service providers as well as prospective customers who do not spend much time reading posts but want to understand the perspectives of others about a particular service/product. Additionally, predicting ratings of social media comments will help to build databases for recommender systems in different contexts.

The uniqueness of this study is in utilizing a combination of sentiment and emotion scores to predict the ratings of tweets related to different online services, namely, e-learning OFD and OTAs.

Online B2B markets offer buyers a new source of information provided by social media signals about suppliers. These signals have not yet received much attention in the supplier selection literature. This study advances our understanding of how buyers respond to social media signals in the supplier selection process.

We develop a choice-based conjoint experimental design to isolate and manipulate two signals from social media: volume (the number of ratings) and valence (average evaluation of the ratings). We test how these signals are interpreted in the context of varying deal sizes and price points.

Both volume and valence are positively correlated with supplier selection. However, (1) the signals exhibit diminishing returns and (2) the efficacy of valence is interpreted in the context of volume. We also find that (3) there is no influence of the deal size and that (4) the relationships between signals and supplier selection are negatively moderated by deviations from the reference price.

Social media signals should be considered in supplier selection decisions as they convey valuable information to the buyer. However, signals go through a process of interpretation which has implications for buyers, suppliers, and owners of online B2B markets.

Our research opens new lines of inquiry in behavioural operations management research regarding the mechanisms by which buyers interpret social media signals and how these ultimately influence their choice.

The purpose of this study is to explore the role of the physical environment in patient satisfaction ratings as communicated in narratives on the social media platform such as Facebook.

Publicly available Facebook reviews (n = 4,502) of a reputable healthcare system in the USA were analyzed. A thematic analysis was conducted to explore architectural elements of the physical environment that play a role in patient satisfaction.

Facebook reviews were examined for the presence of design-related factors within the physical environment. Of the 627 posts (14 per cent) with relevant content, 56 involved factors related to the physical environment. The factors include: location, parking, cleanliness, privacy, waiting rooms, music and temperature. The results showed that environmental and design-related factors are part of patient satisfaction in hospitals.

Not all Facebook reviews contain narrative information. Nevertheless, the impact of the built environment can manifest in online reviews of healthcare systems. Future patient satisfaction research should examine variables related to the built environment on social media ratings.

Social media feedback about the physical environment can help in understanding factors influencing patient satisfaction, which can have an implication for architectural design.

The patient satisfaction is related to the physical environment of healthcare facilities. Some social media narratives reflect it and can be used to improve patient satisfaction.

Although some studies examine social media narratives on patient satisfaction, fewer studies examine these narratives in relation to the built environment. Created by a team of interdisciplinary researchers, this study provides a novel approach to examine social media ratings.

The purpose of this paper is to explore the interrelationship between television (TV) consumption (viewing ratings), engagement behaviors of different actors on Twitter (TV programs, media, celebrities and viewers) and the content of engagement behaviors (affective, program-related and social content).

TV ratings and Twitter data were obtained. The content of tweets was analyzed by means of a sentiment analysis. A vector auto regression model was used to understand the interrelationship between tweets of different actors and TV consumption.

First, the results showed a negative interrelationship between TV viewing and viewers’ tweeting behavior. Second, tweets by celebrities and media exhibited similar patterns and were both affected mostly by the number of tweets by viewers. Finally, the content of tweets matters. Affective tweets positively relate to TV viewing, and program-related and social content positively relates to the number of tweets by viewers.

The findings help us understand the online engagement ecosystem and provide insights into drivers of TV consumption and online engagement of different actors.

The results indicate that content producers may want to focus on stimulating affective conversations on Twitter to trigger more online and offline engagement. The results also call for rethinking the meaning of TV metrics.

While some studies have explored viewer interactions on Twitter, only a few studies have looked at the effects of such interactions on variables outside of social media, such as TV consumption. Moreover, the authors study the interrelations between Twitter interactions with TV consumption, which allows us to examine the effect of online engagement on offline behaviors and vice versa. Finally, the authors take different actors into account when studying real-life online engagement.

To test if the factors “reviewer location” and “time frame” have any impact on the prediction results when predicting online product ratings from user reviews.

Reviews and ratings are scraped for the product “The Secret” book through Web pages of e-commerce websites like Amazon and Flipkart. Such data is used for training the model to predict ratings of similar products based on reviews data in various other social media platforms like Facebook, Quora and YouTube. After data preprocessing, sentiment analysis is used for opinion classification. A multi-class supervised support vector machine is used for feature classification and predictions. The four models produced in the study have a prediction accuracy of 79%. The data collection is done based on a specific geographical location and specific time frame. Post evaluating the predictions, inferential statistics are used to check for significance.

There will be an impact on the ratings predicted from the reviews that belong to a particular geographic location or time frame. The ratings predicted from such reviews help in taking accurate decisions as they are robust and informative.

This study is confined to a single product and for cross domain social media pages, only Facebook, YouTube and Quora data are considered.

Provides credible ratings of a product/service on all cross domain social media pages making the initial screening process of purchase decisions better.

Many studies explored the usefulness of reviews for rating prediction based on review nature. This study aims to identify the usefulness of reviews based on factors that would reduce uncertainty in the purchase process.

To empirically verify whether patient hospital satisfaction ratings on social media such as Yelp provide similar information as the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) surveys.

OLS and ordinal regressions performed on secondary data obtained from Yelp.com and 2016 Hospital Compare database disclosed by CMS.

Results show that the patient hospital satisfaction ratings from Yelp can predict the patient experience of care domain scores obtained through the annual HCAHPS surveys and are also positively and significantly correlated to the overall hospital quality performance scores given by CMS.

Study suggests that social media patient review information could be used to supplement the information obtained from HCAHPS surveys, thereby providing hospitals more accurate information about their patient experiences.

Hospital leaders need not wait an entire year to receive their HCHAPS scores to know about the issues related to their patient experience that need improvement and can periodically refer to free Yelp patient review scores on Yelp.com to obtain similar information.

To the best of knowledge, this research is the first to empirically demonstrate that patient reviews freely obtained from social media sites like Yelp can provide similar information as obtained from HCAHPS surveys and can thus be used to supplement HCAHPS.

The purpose of this study is to examine how corporate communication of their social programs on their websites affects the ratings of those programs by independent rating agencies. Firms expend resources on corporate social programs (CSPs) to promote their corporate social responsibility and sustainability credentials. Stakeholders, however, often respond to such “self-promotion” with skepticism because they believe that there are inconsistencies between corporate claims and actions. This research draws on attribution theory as a framework to examine how the perceived CSP performance of firms by uncontrollable sources are affected when firms disseminate CSP information on firm websites, i.e. a controllable source, where their claims may not be verifiable.

This study uses a two-step, mixed method study for the analysis using data from Fortune 500 companies. A qualitative content analysis process identifies the interfaces of CSP and their communications on firms’ website. The process allows the authors to collect CSP data systematically from firm websites and to identify relevant variables through the patterns that emerge from the analysis. The findings are used in a quantitative analysis to study how the patterns underlying CSP communication on their websites affect the ratings of firms’ CSP by independent rating agencies.

Results show that the location, the manner, the content and the scope of CSP information dissemination on firm websites, as well as perceived commitment to CSP identified on the website are important drivers of perceived CSP performance. A robustness check using an alternative independent rating of CSP also provides results that are supportive of the findings. In addition, the effects are found to differ by sector of operation, firm age and profitability.

This research suggests that communication of CSPs at controllable sources of firm information dissemination can have a significant effect on the evaluation of CSP at uncontrollable sources when such communication facilitates the assessment of other information from a firm to determine the motive underlying a firm’s CSP.

The findings show that firms and managers can influence the perceived ratings, rankings or scores of their CSP by stakeholders when they put the right information at the right place on their corporate websites. One of the findings shows that even moderate levels of CSP commitment demonstrated on firm websites result in positive perceptions of CSP, which has marked practical implications.

The findings show that integrating even a medium level of commitment to CSP increases the positive perceptions of a firm’s CSP. Thus, society benefits from the firm’s action without a substantial impact on the firm’s profits.

This research shows that firm-controlled sources of CSP information dissemination to stakeholders can affect uncontrollable sources of CSP information evaluation.