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The purpose of this paper is to examine how movie studios develop an integrated social media strategy to achieve box office success. Departing from prior studies which focus on single social media platforms, this study examines the role of integrated social media promotion strategy using multiple social media platforms on movie success in the Bollywood movie industry.

This study adopts an in-depth and comprehensive case study approach to examine the promotional strategies adopted through YouTube, Facebook and Twitter throughout the life cycle of the movie and its impact on the box office success of the movie.

The study provides three major findings. First, the social media promotional strategy was centred on developing appropriate content to match the unique characteristics of the social media platforms. While Facebook was utilised primarily to connect audiences through organising fun events, Twitter was used to retweet the positive word-of-mouth generated from the audiences. Second, emphasis on promotional strategy through social media platforms in the post-release stage of the movie was found to be equally important as the pre-release stage. Finally, the social media platforms were utilised to develop emotional connection with the audience by promoting the content through which the audience identified themselves with the main protagonist of the movie.

This study is among the very few studies which examines the role of integrative social media strategy on the box office success in the movie industry. This study emphasises the way firms can utilise the synergies across different social media platforms to achieve success in the movie industry.

Given the high uncertainty in the quality perception of experiential products, manufacturers use signals to influence consumers’ decisions. In the movie industry, literature shows that performance of the main channel (e.g. cinema) strongly influences the performance of auxiliary channels (e.g. DVDs). The success of a movie in the home country is also to be resonated by its good performance in host countries. However, the cultural contingency of these success-breeds-success (SBS) effects has not been examined. This paper aims to test the influence of cultural values on the SBS effects across channels and countries.

Borrowing concepts from the signaling literature and analyzing DVD sales data from six international markets using a multilevel mixed-effects model, the study finds that culture plays a significant role to influence both SBS effects.

In countries with low power distance, short-term orientation and high indulgence, consumers who purchase from auxiliary channels are more likely to be influenced by the box office performance of movies. Meanwhile, cultural distance between the home and host nations significantly decreases the cross-national SBS effect.

The findings are likely to be generalized to online auxiliary channels of movies, but empirical testing is required to ensure that no major adaptation is required in the process. Future research can also extend the framework of this paper to include more countries into the analysis and investigate cultural variables beyond Hofstede’s dimensions.

This paper suggests that the SBS effects may vary across nations. When managers plan for the sequential distributions of experiential products, the cultural values of target markets should be considered to decrease the uncertainty in sales prediction.

This paper contributes to the existing literature by investigating the international auxiliary channels of movies and incorporating cultural values into the framework of sequential distributions. To the best of the authors’ knowledge, this study is the first to test the links between the main and auxiliary channels from an international marketing perspective.

The purpose of this paper is to investigate temporal tweet patterns and their effectiveness in predicting the financial performance of a movie. Specifically, how tweet patterns are formed prior to and after a movie’s release and their usefulness in predicting a movie’s success is explored.

Volume was measured and sentiment analysis was performed on a sample of Tweets posted four days before and after the release of 86 movies. The temporal pattern of tweeting for financially successful movies was compared with those that were financial disappointments. Using temporal tweet patterns, a number of machine learning models were developed and their predictive performance was compared.

Results show that the temporal patterns of tweet volume, length and sentiment differ between “hits” and “busts” in the days surrounding their releases. Compared with “busts” the tweet pattern for “hits” reveal higher volume, shorter length, and more favourable sentiment. Discriminant patterns in social media features occur days in advance of a movie’s release and can be used to develop models for predicting a movie’s success.

Analysis of temporal tweet patterns and their usefulness in predicting box office returns is the main contribution of this research. Results of this research could lead to development of analytical tools allowing motion picture studios to accurately predict and possibly influence the opening night box-office receipts prior to the release of the movie. Also, the specific temporal tweet patterns presented by this work may be applied to problems in other areas of research.

Wall Street hedge fund manager Kim Meyer is considering investing in an SFA (slate financing arrangement) in Hollywood. Dave Griffith, a Hollywood producer, is pitching for the investment and has conducted a broad analysis of recent movie data to determine the important drivers of a movie’s success. In order to convince Meyer to invest in an SFA, Griffith must anticipate possible questions to maximize his persuasiveness.

Students will analyze the factors driving a movie’s revenue using various statistical methods, including calculating point estimates, computing confidence intervals, conducting hypothesis tests, and developing regression models (in which they must both choose the relevant set of independent variables as well as determine an appropriate functional form for the regression equation). The case also requires the interpretation of the quantitative findings in the context of the application.

Electronic word-of-mouth (eWOM) is very important for consumer decision making; previous international product diffusion studies have investigated eWOM and cultural factors that influence consumers’ acceptance of new products, but they have not adequately compared the differences in these factors between the USA and China. Therefore, the purpose of this paper is to compare the impact of eWOM on consumer choices in China and the USA. The authors addressed the following questions: What are the cross-cultural differences in consumers’ eWOM behavior between the USA and China: Which genres of Hollywood movies have better cross-culture predictability in terms of box office performance; and What factors affect the success of Hollywood movies in entering the Chinese market?

Real eWOM data were collected from two online movie review websites, IMDb.com (the USA) and Douban.com (China), from January 2010 to December 2015. In addition, box office revenue information was collected from BoxOfficeMojo.com. The authors used an independent sample t-test to check whether the differences in consumers’ eWOM behavior between China and the USA and different types of movie lead to cultural discount differences. Furthermore, a log-linear regression model is used to examine which factors influence the commercial success of new movies.

There are specific similarities and differences between the American and Chinese movie markets. First, the results show that American consumers are more engaged in online review systems and tend to submit extreme reviews, but Chinese consumers tend to submit moderate reviews on movies, and the eWOM variance there is smaller than in the USA. Second, genres are useful variables as indicators of movie content; the genres of comedy and drama are not popular in the Chinese market. Finally, eWOM variance has a positive impact on box office in China, but eWOM variance has no impact on the US box office. In addition, the interactive effect of the average rating and eWOM variance on sales is positively significant in China. Importantly, the one-star reviews have a negative impact on the Chinese box office, but it has no impact on US box office.

Understanding how cultural factors influence consumer eWOM communication will help managers to better apply this new marketing communication tool to create more aggressive and targeted promotional plans. Marketers may use eWOM behavior to better respond to and target consumers to overcome barriers to the selection of their products by consumers. Therefore, more effective management of eWOM can improve the acceptance of and preference for products in different cultural consumer groups.

This study expands the existing body of knowledge on eWOM and international marketing literature. Clearly, culture is an important determinant of eWOM’s impact on sales. In addition, it provides strategic direction and practical implications for eWOM communication management in cross-cultural settings.

The purpose of this paper is to develop and empirically test a new framework that shows how different signals of movie quality along with key control variables affect consumers’ post-consumption evaluations, critics’ reviews (CR), and movie box office revenues.

The data set consists of a sample of 332 movies released between 2000 and 2008. Regression was used to test the study hypotheses.

The results suggest that the three signals of movie quality exhibit different effects on three movie performance measures. Of the three cues, the peripheral quality signal is positive related to movie box, moviegoers’ evaluations (ME), and CR. Furthermore, star performance quality is positive related to both ME and CR. Surprisingly, overall quality signal does not display any influence on movie performances.

The primary limitation is the use of cross-sectional study design and future research should apply for time-series technique to test the relationships between movie quality signals and movie performances.

The findings suggest that consumers and critics evaluate movie qualities based on various movie quality signals. Furthermore, the characteristics of movies also have mixed impacts on movie performances. Movie studios may take these findings into account to produce better movies.

This study proposes and empirically tests the impacts of three groups of movie signals – peripheral quality signal, star performance quality signal, and overall quality signal on motion picture performance. This study contributes to service quality literature and signal theory by categorizing different Academy Awards into three groups of quality signals and by empirically testing the proposed relationships.

The purpose of this paper is to provide insightful advice that can improve the practice of using consumers’ pre-launch awareness and preference (AP) changes to predict the sales of new movies.

This paper applies a new movie box-office revenue forecasting model based on consumers’ weekly AP measures, to take advantage of the industry’s practice of using weekly survey data containing the AP measures of upcoming new movies. Specifically, a sales forecasting model is developed on the basis of the theory that the combination of the nature of new product preference (positive vs negative) and the timing of new product awareness (early vs recent) influences entertainment product sales.

This paper shows that early awareness consumers are as important as late awareness consumers in determining new product sales, suggesting that more marketing resources need to be allocated earlier than currently practiced. This paper also shows that when negative preferences dominate positive preferences well ahead of a product’s release, marketing efforts cannot overcome the negative sentiment of the market. Finally, the empirical application illustrates that three consumer segments varying in product expertise and consumption frequency reveal different AP patterns among high-, medium- and low-performance products.

This paper is intended to provide insightful advice that can improve the AP-based approach in entertainment industries. Toward that end, the authors emphasize two major aspects in association with new entertainment product sales: rethinking survey-based AP measures and examining heterogeneous consumer segments’ differential AP patterns.

The purpose of this paper is to address the shortcomings of limited research in forecasting the power of social media in India.

This paper uses sentiment analysis and prediction algorithms to analyze the performance of Indian movies based on data obtained from social media sites. The authors used Twitter4j Java API for extracting the tweets through authenticating connection with Twitter web sites and stored the extracted data in MySQL database and used the data for sentiment analysis. To perform sentiment analysis of Twitter data, the Probabilistic Latent Semantic Analysis classification model is used to find the sentiment score in the form of positive, negative and neutral. The data mining algorithm Fuzzy Inference System is used to implement sentiment analysis and predict movie performance that is classified into three categories: hit, flop and average.

In this study the authors found results of movie performance at the box office, which had been based on fuzzy interface system algorithm for prediction. The fuzzy interface system contains two factors, namely, sentiment score and actor rating to get the accurate result. By calculation of opening weekend collection, the authors found that that the predicted values were approximately same as the actual values. For the movie Singham Returns over method of prediction gave a box office collection as 84 crores and the actual collection turned out to be 88 crores.

The current study suffers from the limitation of not having enough computing resources to crawl the data. For predicting box office collection, there is no correct availability of ticket price information, total number of seats per screen and total number of shows per day on all screens. In the future work the authors can add several other inputs like budget of movie, Central Board of Film Certification rating, movie genre, target audience that will improve the accuracy and quality of the prediction.

The authors used different factors for predicting box office movie performance which had not been used in previous literature. This work is valuable for promoting of product and services of the firms.

The purpose of this paper is to examine whether the sharing of movie trailers on video-sharing social media has an impact on box-office revenue.

From December 2013 to November 2014, view statistics were collected for movie trailers from YouTube and matched with the box-office revenue of the relevant movies from Box Office Mojo. Based on the 72 sample movies, a panel simultaneous equation model was applied.

The results show that sharing of a movie trailer has a positive impact on the box-office revenue of the movie, and this effect is greater in the early stage of a movie release than in the later stage.

This study analyzes, from electronic word-of-mouth (eWOM) and social influence perspectives, the effects of video-sharing activities on social media and their impact on sales with an example that is representative of entertainment goods or movie trailers.

This study reveals that promotion through video-sharing social media websites can serve as an essential marketing tool for entertainment goods such as movies, while the sharing of a movie trailer has a positive influence on the box-office revenue of the movie.

Focusing on the sharing features of video-sharing social media websites, this study contributes to enhancing our understanding of the predictive power of consumers’ video-sharing and expanding current research on eWOM and social influence.