The effect of consumer group breadth and depth on movie sales: the mediating effect of eWOM-to-viewing ratio

PurposeThe purpose of this study is to analyze the effect on movie performance of the breadth and depth of consumer groups targeted by movies and to analyze the ways in which electronic word-of-mouth (eWOM) mediates these relationships.Design/methodology/approachFor empirical analysis, 45 days of sales and eWOM data for 63 movies released in Korea in 2017 were collected, and a panel regression analysis was conducted on a total of 2,835 data items. In addition, the analysis was rigorously verified through three robustness tests.FindingsThe breadth and depth of consumer groups targeted by movies have a non-linear relationship with sales, and this relationship is mediated by the eWOM performance of social media websites. In addition, eWOM performance has a non-linear relationship with sales, and these effects differ depending on the type of eWOM platform involved.Originality/valueThe effects of the breadth and depth of the consumer groups targeted by movies on eWOM performance and movie performance have not been sufficiently investigated. This paper expands on previous studies that reported a linear relationship between eWOM and sales by finding that the effects of consumer group breadth and depth on sales are not linear in terms of the mediation of eWOM performance. In addition, a new research direction is suggested by conceptualizing consumer group breadth and depth using eWOM data, on which basis the new concept of eWOM-to-viewing ratio (eWOM ratio) is proposed.

A Parsimonious Predictive Model of Movie Performance

In this paper, the authors develop a parsimonious model that offers early prediction of potential success of a movie. In order to achieve this, a broad look at the drivers of movie success is required. Supply chain members will be making decisions regarding movie popularity with regard to licensing contracts, forecasting toy purchases, cross-promotions, etc. at varying times before a movie is released. A simple forecasting approach using publicly available data could improve supply chain decision making. Prior literature suggested that the virtual movie stock market, HSX, was a good predictor. Using a small set of variables including view counts, likes, and dislikes did offer some predictive value. However, HSX produces a forecast that dominates prior models while using a single readily available public data. Further, the HSX-based prediction showed consistency and convergence across a significant breadth of time.

Using Twitter data to predict the performance of Bollywood movies

Purpose – The purpose of this paper is to address the shortcomings of limited research in forecasting the power of social media in India. Design/methodology/approach – 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. Findings – 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. Research limitations/implications – 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. Originality/value – 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.