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Journal articles on the topic 'Movie Box Office'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Hu, Ya-Han, Wen-Ming Shiau, Sheng-Pao Shih, and Cho-Ju Chen. "Considering online consumer reviews to predict movie box-office performance between the years 2009 and 2014 in the US." Electronic Library 36, no. 6 (December 10, 2018): 1010–26. http://dx.doi.org/10.1108/el-02-2018-0040.

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Purpose The purpose of this paper is to combine basic movie information factors, external factors and review factors, to predict box-office performance and identify the most crucial factor of influence for box-office performance. Design/methodology/approach Five movie genres and first-week movie reviews found on IMDb were collected. The movie reviews were quantified using sentiment analysis tools SentiStrength and Stanford CoreNLP, in which quantified data were combined with basic movie information and external environment factors to predict movie box-office performance. A movie box-office performance prediction model was then developed using data mining (DM) technologies with M5 model trees (M5P), linear regression (LR) and support vector regression (SVR), after which movie box-office performance predictions were made. Findings The results of this paper showed that the inclusion of movie reviews generated more accurate prediction results. Concerning movie review-related factors, the one that exhibited the greatest effect on box-office performance was the number of movie reviews made, whereas movie review content only displayed an effect on box-office performance for specific movie genres. Research limitations/implications Because this paper collected movie data from the IMDb, the data were limited and primarily consisted of movies released in the USA; data pertaining to less popular movies or those released outside of the USA were, thus, insufficient. Practical implications This paper helps to verify whether the consideration of the features extracted from movie reviews can improve the performance of movie box-office. Originality/value Through various DM technologies, this paper shows that movie reviews enhanced the accuracy of box-office performance predictions and the content of movie reviews has an effect on box-office performance.

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Feng, Kaicheng, and Xiaobing Liu. "Adaptive Attention with Consumer Sentinel for Movie Box Office Prediction." Complexity 2020 (December 7, 2020): 1–9. http://dx.doi.org/10.1155/2020/6689304.

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To improve the movie box office prediction accuracy, this paper proposes an adaptive attention with consumer sentinel (LSTM-AACS) for movie box office prediction. First, the influencing factors of the movie box office are analyzed. Tackling the problem of ignoring consumer groups in existing prediction models, we add consumer features and then quantitatively analyze and normalize the box office influence factors. Second, we establish an LSTM (Long Short-Term Memory) box office prediction model and inject the attention mechanism to construct an adaptive attention with consumer sentinel for movie box office prediction. Finally, 10,398 pieces of movie box office dataset are used in the Kaggle competition to compare the prediction results with the LSTM-AACS model, LSTM-Attention model, and LSTM model. The results show that the relative error of LSTM-AACS prediction is 6.58%, which is lower than other models used in the experiment.

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Lee, Sangjae, and Joon Yeon Choeh. "Movie Production Efficiency Moderating between Online Word-of-Mouth and Subsequent Box Office Revenue." Sustainability 12, no. 16 (August 14, 2020): 6602. http://dx.doi.org/10.3390/su12166602.

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The studies are almost nonexistent regarding production efficiency of movies which is determined based on the relationship between movie resources powers (powers of actors, directors, distributors, and production companies) and box office. Our study attempts to examine how efficiency moderates the relationship between eWOM (online word-of-mouth) and revenue, and to show the difference in prediction performance between efficient and inefficient movies. Using data envelopment analysis to suggest efficiency of movies, movie efficiency negatively moderates the effects of review depth and volume on subsequent box office revenue compensating negative effects of smaller box office in previous period while efficiency exert a positive moderating effect on the influences of review rating and the number of positive reviews on revenue. This shows that review depth and volume are affected by the slack of movie resources powers for inefficient movies, and high rating and positive response for efficient movies to affect revenue. The results of decision trees, k-nearest-neighbors, and linear regression analysis based on ensemble methods using eWOM or movie variables indicate that the movies with the inefficient movie resources powers are providing greater prediction performance than movies with efficient movie resources powers. This show that diverse variation in the efficiency of movie resources powers contributes to prediction performance.

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Barranco, Raymond E., Nicole E. Rader, and Anna Smith. "Violence at the Box Office." Communication Research 44, no. 1 (July 8, 2016): 77–95. http://dx.doi.org/10.1177/0093650215614363.

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The negative effects of violent content in movies have recently been a hot topic among both researchers and the general public. Despite growing concern, violence in movies has persisted over time. Few studies have examined why this pattern continues. To fill this gap in the literature, we examine how Motion Picture Association of America (MPAA) movie rating descriptors predict ticket sales of 2,094 movies from 1992 to 2012. We test the validity of three theoretical models: (1) the reflective model, (2) the reactance model, and (3) the market model. We find that violent content is linked neither to violence in the broader U.S. culture (i.e., the reflective model) nor to a psychological reactance by adolescents (i.e., the reactance model). Rather, we find, especially among PG-13 (parents strongly cautioned) movies, that violent content leads to increased ticket sales, suggesting that market demand (i.e., audience preferences) is responsible for continued violent content. We discuss the implications of our findings.

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Hung, Yu-Chen, and Chong Guan. "Winning box office with the right movie synopsis." European Journal of Marketing 54, no. 3 (February 24, 2020): 594–614. http://dx.doi.org/10.1108/ejm-01-2019-0096.

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Purpose Consumers often search for movie information and purchase tickets on the go. A synopsis is often provided by producers and theatres in mobile apps and websites. However, to the best of the authors’ knowledge, little research has investigated whether the synopsis has an impact on a movie’s box office. This research uses computerized text analysis in examining the influence of linguistic cues of a synopsis on the movie’s financial performance. This paper aims to show that language choice in a synopsis is a significant factor in predicting box office performance. Design/methodology/approach A total usable sample of 5973 movies was collected using a web crawler. Computerised text analysis using linguistic inquiry and word count was adopted to analyse the movie synopses data. The empirical study comprises two phases. Phase 1 used exploratory factor analysis on 50 per cent of the sample (Sample 1) to establish the dimensionality of psychological processes as reflected in the linguistic expressions. The analysis identified 11 linguistic variables that loaded on four dimensions. The factor structure was replicated on an independent sample (Sample 2) using confirmatory factor analysis. Phase 2 tested the hypotheses using structure equation modelling. Findings Results show that consistency between movie genres and linguistic cues in a film synopsis promotes movie box office revenue when linguistic cues shown in the synopsis confirm a consumer’s expectancies about a focal movie genre. Conversely, a synopsis reduces the movie box office revenue when the linguistic cues shown disconfirm the genre-based expectancies. These linguistic cues exert similar effects on action and crime films but different effects on comedies and drama films. Research limitations/implications It is likely that consumer tastes and linguistic styles of film synopses have evolved over time. As a cross-sectional study, such changes were not taken into consideration in the current research. A longitudinal study in the future can reveal the dynamic relationship between film synopses and audience. Practical implications Managerially, the findings show that a synopsis is an effective communication touch point to position a movie. This research provides concrete guidelines in crafting synopses with the “rights words’ aligned with movie-goers’ expectations within each specific genre. Beyond movie consumption, the research findings can be applied to other entertainment products, such as TV series and books. Originality/value To our knowledge, this research is the first in studying the linguistic cues in synopses and its relation to box office performance. It addresses this knowledge gap by answering the basic question of whether movie synopses matter. Methodically, the paper marks the first attempt to use the two-step structural equation modelling method on computerised content analysis data.

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Garcia-del-Barrio, Pedro, and Hugo Zarco. "Do movie contents influence box-office revenues?" Applied Economics 49, no. 17 (August 27, 2016): 1679–88. http://dx.doi.org/10.1080/00036846.2016.1223828.

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Ahmad, Ibrahim Said, Azuraliza Abu Bakar, Mohd Ridzwan Yaakub, and Mohammad Darwich. "Sequel movie revenue prediction model based on sentiment analysis." Data Technologies and Applications 54, no. 5 (October 8, 2020): 665–83. http://dx.doi.org/10.1108/dta-10-2019-0180.

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PurposeSequel movies are very popular; however, there are limited studies on sequel movie revenue prediction. The purpose of this paper is to propose a sentiment analysis based model for sequel movie revenue prediction and to propose a missing value imputation method for the sequel revenue prediction dataset.Design/methodology/approachA sequel of a successful movie will most likely also be successful. Therefore, we propose a supervised learning approach in which data are created from sequel movies to predict the box-office revenue of an upcoming sequel. The algorithms used in the prediction are multiple linear regression, support vector machine and multilayer perceptron neural network.FindingsThe results show that using four sequel movies in a franchise to predict the box-office revenue of a fifth sequel achieved better prediction than using three sequels, which was also better than using two sequel movies.Research limitations/implicationsThe model produced will be beneficial to movie producers and other stakeholders in the movie industry in deciding the viability of producing a movie sequel.Originality/valuePrevious studies do not give priority to sequel movies in movie revenue prediction. Additionally, a new missing value imputation method was introduced. Finally, sequel movie revenue prediction dataset was prepared.

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Mohanty, Saurav, Nicolle Clements, and Vipul Gupta. "Investigating the Effect of eWOM in Movie Box Office Success Through an Aspect-Based Approach." International Journal of Business Analytics 5, no. 1 (January 2018): 1–15. http://dx.doi.org/10.4018/ijban.2018010101.

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This study examines the influence of Electronic Word of Mouth (eWOM) on the box office revenue generation of movies in the U.S domestic market using the technique of Aspect-Based Sentiment Analysis (ABSA) and aspect identification. The analysis was conducted on the sentiment score and frequency of five movie aspects from the user reviews collected from high grossing 2014 movies. This study revealed a significant dependence on the aspect-based sentiment frequency of the movie's Story aspect. Surprisingly, the data also showed a strong dependence of movie success on the negative sentiment frequency on the Casting aspect. The findings of the study suggest that the eWOM present in online movie reviews can be used to predict the performance of a movie at the box office by monitoring the aspect's frequency of sentiment, which can be referred to as a metric of the online “buzz” of the movie.

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Lee, O.-Joun, Seung-Bo Park, Daul Chung, and Eun-Soon You. "Movie Box-office Analysis using Social Big Data." Journal of the Korea Contents Association 14, no. 10 (October 28, 2014): 527–38. http://dx.doi.org/10.5392/jkca.2014.14.10.527.

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Powers, Robert A. "Activities for Students: Big Box—Office Bucks." Mathematics Teacher 94, no. 2 (February 2001): 112–18. http://dx.doi.org/10.5951/mt.94.2.0112.

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One of the challenges that teachers face when they teach data analysis to their students is getting them to see the “big picture.” Using multiple representations is one way to help students make sense out of a large amount of data. For example, the data can be recorded in a table, graphed in a rectangular-coordinate system, and analyzed for an equation of best fit. These techniques are standard practice in data analysis. However, furnishing an interesting context for teaching these methods is sometimes difficult. One way to motivate students to see the big picture of data analysis is to explore an example from the motion-picture business. Since the release of James Cameron's movie Titanic in December 1997, the film has become a part of the popular culture. It earned more than $1 billion worldwide at the box office, which makes it the highest-grossing film of all time. Inevitable articles and programs documented and commented on the success of this epic film. One article, in particular, in Newsweek was titled “Our Titanic Love Affair” (Ansen 1998). A simple statement of earnings trends appeared in a caption next to a graph showing the longevity of the weekend revenues generated by the movie. It read as follows: “Most hit movies enjoy big opening weekend sales, then revenues decline quickly.” Both the graph and the caption raise two interesting questions: What do the graphs and trends of “most hit movies” look like? And how are the weekend earnings of Titanic different?

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Zhang, Xin-Jie, Yong Tang, Jason Xiong, Wei-Jia Wang, and Yi-Cheng Zhang. "How Network Topologies Impact Project Alliance Performance: Evidence from the Movie Industry." Entropy 21, no. 9 (September 3, 2019): 859. http://dx.doi.org/10.3390/e21090859.

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In many industries, partners are interconnected in project alliances that have limited lifespans and clearly-defined boundaries. The transparency of the movie industry provides a unique opportunity to study how alliance network topologies impact the performance of project alliances from the perspectives of social networks and organization theories. In this work, we compiled a massive movie dataset and constructed alliance networks for both movie production and distribution companies. Using the box office as the proxy for the financial performance of a movie project alliance, this research investigates how the two alliance networks impact the box office. We introduce the social network properties of degrees, centralities, and structural holes as alliance network variables into empirical regression models. The results show that alliance networks have a significant influence on the box office. The degrees of production companies and the structural holes of distribution companies are especially important to achieve success in the box office. The results add new evidence for the study of the movie economy and alliance networks. Meanwhile, this work also provides implications for the movie industry by revealing that it is essential to wisely choose partners that are appropriately embedded in alliance networks for the success of a movie project.

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Yu, Kuo-Ting, Hsi-Peng Lu, Chih-Yu Chin, and Yu-Shiuan Jhou. "Box office performance: Influence of online word-of-mouth on consumers’ motivations to watch movies." Social Behavior and Personality: an international journal 47, no. 10 (October 22, 2019): 1–17. http://dx.doi.org/10.2224/sbp.8162.

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Content created by the movie industry is high-risk, as production costs and marketing budgets are massive but box office results can be volatile. We examined the effect of online word-of-mouth on consumers’ motivation and intention to watch movies. The proposed model was tested in a survey with 337 consumers using a structural equation modeling approach. The results showed that movie reviews by professional movie media writers had a substantial positive impact on consumers’ intrinsic motivation for presenting themselves, via transmitting their values and expressing personal favor by watching movies. Popular media also had a positive influence on the intrinsic motivation of perceived enjoyment, and social media had the broadest influence on consumers’ intrinsic motivation. Thus, movie makers and marketers should focus on the critical platform of online word-ofmouth to enhance consumers’ motivation to watch movies.

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Larceneux, Fabrice. "Buzz and Recommendations on the Internet. What Impacts on Box-Office Success?" Recherche et Applications en Marketing (English Edition) 22, no. 3 (September 2007): 43–62. http://dx.doi.org/10.1177/205157070702200304.

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Potential moviegoers may find information on the Internet on movie critics' recommendations and web users' comments on movies they have seen. The more a recommendation is shared by a large number of individuals, the greater is the buzz. This research explores to what extent this online buzz can be a predictor and/or modifier of box-office results, particularly the week following the launch. A field experiment on 534 movies shows that the buzz, and more specifically critics' recommendations appearing on the allocine.com website, is significantly correlated with box-office results after the first week. Various developments show that these online recommendations may not only predict but also influence box-office results.

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Gaenssle, Sophia, Oliver Budzinski, and Daria Astakhova. "Conquering the Box Office: Factors Influencing Success of International Movies in Russia." Review of Network Economics 17, no. 4 (December 19, 2018): 245–66. http://dx.doi.org/10.1515/rne-2019-0017.

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Abstract This paper empirically examines factors influencing box office success of international movies in Russia between 2012 and 2016. It adds to existing research on national movie markets, by highlighting the relevance of differences in culture, institutions, language, and consumption habits for movie success. Three groups of success factors are distinguished: distribution related (e.g. budget, franchise), brand and star effects (e.g. top actors or directors), and evaluation sources (e.g. critics and audience rating). We add novel region-specific variables like seasonality, time span between the world and local release, attendance of international stars at Russian movie premieres, and title adaptation to Russian culture. The results indicate that budget, franchise, employment of popular actors and directors, electronic word of mouth and audience ratings exert a significantly positive influence on Russian box office success. However, we find significantly negative effects for international critics and, interestingly, the adaption of movie titles. The main contributions of our study are (i) success factors vary between countries with different cultures, (ii) region-specific factors matter, and consequently (iii) results from one market (e.g. the US) cannot easily be generalised.

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Chon, Bum Soo, Sung Bok Park, and Ah Reum Jo. "The Effects of Movie Stars on Box-Office Performances." Journal of Image and Cultural Contents 18 (October 31, 2019): 363–89. http://dx.doi.org/10.24174/jicc.2019.10.18.363.

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Fetscherin, Marc. "The Main Determinants of Bollywood Movie Box Office Sales." Journal of Global Marketing 23, no. 5 (November 29, 2010): 461–76. http://dx.doi.org/10.1080/08911762.2010.521117.

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Gopinath, Shyam, Pradeep K. Chintagunta, and Sriram Venkataraman. "Blogs, Advertising, and Local-Market Movie Box Office Performance." Management Science 59, no. 12 (December 2013): 2635–54. http://dx.doi.org/10.1287/mnsc.2013.1732.

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Antipov, Evgeny, and Elena Pokryshevskaya. "Accounting for latent classes in movie box office modeling." Journal of Targeting, Measurement and Analysis for Marketing 19, no. 1 (March 2011): 3–10. http://dx.doi.org/10.1057/jt.2011.3.

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Zhou, Yao, Lei Zhang, and Zhang Yi. "Predicting movie box-office revenues using deep neural networks." Neural Computing and Applications 31, no. 6 (August 1, 2017): 1855–65. http://dx.doi.org/10.1007/s00521-017-3162-x.

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Bae, Giwoong, and Hye-jin Kim. "The impact of movie titles on box office success." Journal of Business Research 103 (October 2019): 100–109. http://dx.doi.org/10.1016/j.jbusres.2019.06.023.

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Peukert, Christian, Jörg Claussen, and Tobias Kretschmer. "Piracy and box office movie revenues: Evidence from Megaupload." International Journal of Industrial Organization 52 (May 2017): 188–215. http://dx.doi.org/10.1016/j.ijindorg.2016.12.006.

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Nelson, Randy A., and Robert Glotfelty. "Movie stars and box office revenues: an empirical analysis." Journal of Cultural Economics 36, no. 2 (February 11, 2012): 141–66. http://dx.doi.org/10.1007/s10824-012-9159-5.

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Kim, Jin Woong, and Young Doc Seo. "Consumer Consideration Factors and Determinants of Movie Box-Office Performance by Movie Genre." Journal of Arts and Cultural Management 12, no. 2 (December 30, 2019): 9–51. http://dx.doi.org/10.15333/acm.2019.12.30.9.

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Chiu, Ya-Ling, Ku-Hsieh Chen, Jying-Nan Wang, and Yuan-Teng Hsu. "The impact of online movie word-of-mouth on consumer choice." International Marketing Review 36, no. 6 (November 11, 2019): 996–1025. http://dx.doi.org/10.1108/imr-06-2018-0190.

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Purpose 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? Design/methodology/approach 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. Findings 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. Practical implications 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. Originality/value 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.

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Lu, Wei, and Ruben Xing. "Research on Movie Box Office Prediction Model With Conjoint Analysis." International Journal of Information Systems and Supply Chain Management 12, no. 3 (July 2019): 72–84. http://dx.doi.org/10.4018/ijisscm.2019070104.

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Based on the Chinese film market, considering the influence factors of the movie box office (MBO) from multiple dimensions, and using the conjoint-analysis method with a questionnaire survey and an expert interview to determine the main index system affecting MBO, this article then establishes a MBO forecast model through the neural network BRP method. In combination with the actual data of the film market along with the empirical analysis and verification this article provides valuable investment reference for film risk control and movie investment decisions.

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Ryu, Ji Youn, and Hee Sun Park. "Do I Like This Movie? Movie Ratings, Box-Office Profit, and Factors Affecting Ratings." Asian Communication Research 15, no. 3 (December 31, 2018): 20–46. http://dx.doi.org/10.20879/acr.2018.15.3.20.

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Hossein, Najafi, and Darryl W. Miller. "Predicting motion picture box office performance using temporal tweet patterns." International Journal of Intelligent Computing and Cybernetics 11, no. 1 (March 12, 2018): 64–80. http://dx.doi.org/10.1108/ijicc-04-2017-0033.

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Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

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Kim, Yon Hyong, and Jeong Han Hong. "A Study for the Drivers of Movie Box-office Performance." Korean Journal of Applied Statistics 26, no. 3 (June 30, 2013): 441–52. http://dx.doi.org/10.5351/kjas.2013.26.3.441.

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De Vany, Arthur S., and W. David Walls. "Estimating the Effects of Movie Piracy on Box-office Revenue." Review of Industrial Organization 30, no. 4 (June 2007): 291–301. http://dx.doi.org/10.1007/s11151-007-9141-0.

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Gaikar, Dipak Damodar, Bijith Marakarkandy, and Chandan Dasgupta. "Using Twitter data to predict the performance of Bollywood movies." Industrial Management & Data Systems 115, no. 9 (October 19, 2015): 1604–21. http://dx.doi.org/10.1108/imds-04-2015-0145.

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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.

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Chen, Xinlei, Yuxin Chen, and Charles B. Weinberg. "Learning about movies: the impact of movie release types on the nationwide box office." Journal of Cultural Economics 37, no. 3 (October 29, 2012): 359–86. http://dx.doi.org/10.1007/s10824-012-9189-z.

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Nanda, Madhumita, Chinmay Pattnaik, and Qiang (Steven) Lu. "Innovation in social media strategy for movie success." Management Decision 56, no. 1 (January 8, 2018): 233–51. http://dx.doi.org/10.1108/md-04-2017-0429.

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Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

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Belvaux, Bertrand, and Séverine Marteaux. "Web User Opinions as an Information Source. What Impact on Cinema Attendances?" Recherche et Applications en Marketing (English Edition) 22, no. 3 (September 2007): 65–81. http://dx.doi.org/10.1177/205157070702200305.

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Digital communication technologies such as the Internet intensify and accelerate the phenomenon of information diffusion by social exchange. The aim of this paper is to assess whether product introduction strategies should be modified in the light of this phenomenon. We conducted an empirical analysis on the movie market to compare the ability of web user opinions with classic indicators such as the mass media and critics to predict box-office takings. The results show that all three variables influence movie attendances. Promotion of a movie has no influence on web user opinions, but critics are influenced negatively for low-budget movies and positively for large-budget movies. Critic and web user advice merges only for large-budget movies.

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Bi, Guang, and David E. Giles. "Modelling the financial risk associated with U.S. movie box office earnings." Mathematics and Computers in Simulation 79, no. 9 (May 2009): 2759–66. http://dx.doi.org/10.1016/j.matcom.2008.04.014.

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Eliashberg, Jehoshua, Sam K. Hui, and Z. John Zhang. "Assessing Box Office Performance Using Movie Scripts: A Kernel-Based Approach." IEEE Transactions on Knowledge and Data Engineering 26, no. 11 (November 2014): 2639–48. http://dx.doi.org/10.1109/tkde.2014.2306681.

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朱, 得. "The Social Responsibility Performance of Movie Stars and the Box Office." Statistics and Application 09, no. 02 (2020): 218–23. http://dx.doi.org/10.12677/sa.2020.92024.

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Lee, Sangjae, Bikash KC, and Joon Yeon Choeh. "Comparing performance of ensemble methods in predicting movie box office revenue." Heliyon 6, no. 6 (June 2020): e04260. http://dx.doi.org/10.1016/j.heliyon.2020.e04260.

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姚, 雅琪. "Prediction Research on Movie Box Office Based on Stacking Ensemble Learning." Statistics and Application 10, no. 02 (2021): 193–208. http://dx.doi.org/10.12677/sa.2021.102019.

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Gao, Weihe, Li Ji, Yong Liu, and Qi Sun. "Branding Cultural Products in International Markets: A Study of Hollywood Movies in China." Journal of Marketing 84, no. 3 (March 27, 2020): 86–105. http://dx.doi.org/10.1177/0022242920912704.

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Cultural products are a major component of the world economy and are responsible for a growing share of U.S. exports. The authors examine brand name strategies when cultural products are marketed in foreign countries. Incorporating the unique characteristics of these products, the authors develop a theoretical framework that integrates similarity, which focuses on how the translated brand name relates to the original brand name, and informativeness, which focuses on how the translated brand name reveals product content, to study the impact of brand name translations. The authors analyze Hollywood movies shown in China from 2011 to 2018. The results show that higher similarity leads to higher Chinese box office revenue, and this effect is stronger for movies that perform better in the home market (i.e., the United States). When the translated title is more informative about the movie, the Chinese box office revenue increases. The informativeness effect is stronger for Hollywood movies with greater cultural gap in the Chinese market. Moreover, both similarity and informativeness effects are strongest when the movie is released and reduce over time. This research provides valuable guidance to companies, managers, and policy makers in cultural product industries as well as those in international marketing.

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Zhuang, Weiling, Barry Babin, Qian Xiao, and Mihaela Paun. "The influence of movie's quality on its performance: evidence based on Oscar Awards." Managing Service Quality 24, no. 2 (March 4, 2014): 122–38. http://dx.doi.org/10.1108/msq-11-2012-0162.

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Purpose – 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. Design/methodology/approach – The data set consists of a sample of 332 movies released between 2000 and 2008. Regression was used to test the study hypotheses. Findings – 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. Research limitations/implications – 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. Practical implications – 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. Originality/value – 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.

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Chen, Runyu, Wei Xu, and Xinghan Zhang. "Dynamic box office forecasting based on microblog data." Filomat 30, no. 15 (2016): 4111–24. http://dx.doi.org/10.2298/fil1615111c.

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Movies, as one of the most rapidly developing industries? outcomes, have gained much attention these years. Especially in China, the world? s second largest film market with a rapid growing speed, many film companies intend to foresee the future box office in advance to better arrange their income and expenditure. Unlike some traditional forecasting model based on several movie-related features, this paper comprehensively utilizes the real-time social media, microblog, to realize a more accurate weekly box office forecasting model. The features weekly extracted from microblogs can be divided into count based features and context based features, along with the existing box office and the screen arrangements, to predict the box office in next week. For count based features, not only the total volume of related microblogs and the diffusion effect considers the number of followers, several unnoticed features like authentication users, gender ratio and mobile-users ratio are also introduced into the original predicting model. For content based features, a duplicate semantic analysis method is proposed. The number of tweets which can indeed influence others? purchase decision, along with the number of tweets with positive and negative influence is the results of the analysis system. On this basis, guided effect for each influential tweets are identified by the praise, comment and retweet times. Some machine learning models are then adopted after using genetic algorithm (GA) for feature selection. The empirical study shows that our research can dynamic forecast box office with a sustainable good performance.

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D.A., Olubukola, Stephen O.M., Funmilayo A.K., Ayokunle O., Oyebola A., Oduroye A., Wumi A., and Yaw M. "Movie Success Prediction Using Data Mining." British Journal of Computer, Networking and Information Technology 4, no. 2 (September 22, 2021): 22–30. http://dx.doi.org/10.52589/bjcnit-cqocirec.

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The movie industry is arguably one of the biggest entertainment sectors. Nollywood, the Nigerian movie industry produces tons of movies for public consumption, but only a few make it to box-office or end up becoming blockbusters. The introduction of movie success prediction can play an important role in the industry not only to predict movie success but to help directors and producers make better decisions for the purpose of profit. This study proposes a movie prediction model that applies data mining techniques and machine learning algorithms to predict the success or failure of an upcoming movie (based on predefined parameters). The parameters needed for predicting the success or failure of a movie include dataset needed for the process of data mining such as the historical data of actors, actresses, writers, directors, marketing and production budget, audience, location, release date, and competing movies on same release date. This model also helps movie consumers to determine a blockbuster, hit, success rating and quality of upcoming movies before deciding on a movie ticket. The data mining techniques was applied to Internet Movie Database MetaData which was initially passed through cleaning and integration process.

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Hwangbo, Hyunwoo, and Jonghyuk Kim. "A Text Mining Approach for Sustainable Performance in the Film Industry." Sustainability 11, no. 11 (June 9, 2019): 3207. http://dx.doi.org/10.3390/su11113207.

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Many previous studies have shown that the volume or valence of electronic word of mouth (eWOM) has a sustainable and significant impact on box office performance. Traditional studies used quantitative data, such as ratings, to measure eWOM. However, recent studies analyzed unstructured data, such as comments, through web-based text analysis. Based on recent research trends, we analyzed not only quantitative data, like ratings, but also text data, like reviews, and we performed a sentiment analysis using a text mining technique. Studies have also examined the effect of cultural differences on the decision-making processes of individuals and organizations. We applied Hofstede’s cultural theory to eWOM and analyzed the moderating effect of cultural differences on eWOM influence. We selected 338 films released between 2006 and 2015 from the BoxOfficeMojo database. We collected ratings and reviews, box office revenues, and other basic information from the Internet Movie Database (IMDb). We also analyzed the effects of cultural differences, such as power distance, individualism, uncertainty avoidance, and masculinity, on box office performance. We found that user comments have a greater impact on film sales than user ratings, and movie stars and co-production contribute to box office success. We also conclude that cultural and geographical differences moderate the sentiment elasticity of eWOM.

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Ding, Ding, and Chong Guan. "Movie Release Dates and Box Office Success under Word-of-mouth Advertising." Academy of Management Proceedings 2015, no. 1 (January 2015): 14292. http://dx.doi.org/10.5465/ambpp.2015.14292abstract.

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Derrick, Frederick W., Nancy A. Williams, and Charles E. Scott. "A two-stage proxy variable approach to estimating movie box office receipts." Journal of Cultural Economics 38, no. 2 (January 13, 2013): 173–89. http://dx.doi.org/10.1007/s10824-012-9198-y.

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Liu, Ting, Xiao Ding, Yiheng Chen, Haochen Chen, and Maosheng Guo. "Predicting movie Box-office revenues by exploiting large-scale social media content." Multimedia Tools and Applications 75, no. 3 (October 2, 2014): 1509–28. http://dx.doi.org/10.1007/s11042-014-2270-1.

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Ma, Haoxiang, Jong Min Kim, and Eunkyung Lee. "Analyzing dynamic review manipulation and its impact on movie box office revenue." Electronic Commerce Research and Applications 35 (May 2019): 100840. http://dx.doi.org/10.1016/j.elerap.2019.100840.

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Yue, Yang. "The effects of movie piracy on box-office revenue: an empirical analysis of the Chinese movie market." Journal of Applied Economics 23, no. 1 (January 1, 2020): 618–55. http://dx.doi.org/10.1080/15140326.2020.1812477.

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Baranchuk, Nina, Seethu Seetharaman, and Andrei Strijnev. "Revenue Sharing Vertical Contracts in the Movie Industry: A Theoretical Analysis." Review of Marketing Science 17, no. 1 (November 18, 2019): 81–116. http://dx.doi.org/10.1515/roms-2019-0059.

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Abstract For many years, the movie industry has been characterized by a unique (compared to other industries) type of vertical contracting practice, called sliding-scale contracting whereby the distributor (studio) takes a much larger (usually around 70%) share of box-office revenues than the exhibitor (theater) in the week of a movie’s release, with the exhibitor’s share increasing, in gradual steps, over subsequent weeks. In this paper, we propose a game-theoretic model that provides a new rationale for these contracting choices. Specifically, we show that these contracts effectively resolve conflicts of interest between studios and theaters over movie release timing and display length, in a way that is beneficial for both parties. Our model also stipulates conditions under which sliding scale become dominated by aggregate deals, i.e. deals based on total rather than weekly box office revenue. The testable predictions based on these conditions can be used by future empirical research once the available evidence on the use of aggregate deals in practice goes beyond anecdotal.

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Çağlıyor, Sandy, Başar Öztayşi, and Selime Sezgin. "Forecasting US movies box office performances in Turkey using machine learning algorithms." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 6579–90. http://dx.doi.org/10.3233/jifs-189120.

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The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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