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Purpose While a number of studies examined the eWOM (online word-of-mouth) factors affecting box office, the studies on the impact of review helpfulness on box office are lacking. The purpose of this paper is to fill the void in previous studies and further extend prior work regarding eWOM and box office. In order to explain the interaction effect of helpfulness with other variables on product sales, this study posits that review characteristics such as number of reviews, review rating, review length interact with review helpfulness to have an influence on box office. Further, as the studies that have examined whether eWOM factors are significant in box office performances for the international markets other than US are lacking, this study is targeting Korean markets to validate the effect of eWOM on box office. Design/methodology/approach This study used publicly available data from www.naver.com to build a sample of online review data concerning box office. The final sample of the study included 2090 movies. Findings The results indicated that in cases when the review is helpful, the number of reviews and review length are more greatly influencing box office. Review rating, review extremity, and helpfulness for reviewer are important determinants for review helpfulness. Practical implications Managers can concentrate on the review rating and review extremity of online customer reviews in the design of online sites for movies. The design of user review systems can follow the direction that promotes more helpfulness for online user reviews based on an enhanced understanding of what drives helpfulness voting. Originality/value Given that previous studies on the effect of review helpfulness on box office are lacking, it contributes to eWOM literature by investigating the impact of review helpfulness on box office revenue.

The television industry is changing, and with it, the small screen's potential to engage in debate and present valuable representations of American history. Founded in 1972, HBO has been at the forefront of these changes, leading the way for many network, cable, and streaming services into the \"post-network\" era. Despite this, most scholarship has been dedicated to analyzing historical feature films and documentary films, leaving TV and the long-form drama hungry for coverage. In History by HBO: Televising the American Past, Rebecca Weeks fills the gap in this area of media studies and defends the historiographic power of long-form dramas. By focusing on this change and its effects, History by HBO outlines how history is crafted on television and the diverse forms it can take. Weeks examines the capabilities of the long-form serial for engaging with historical stories, insisting that the shift away from the network model and toward narrowcasting has enabled challenging histories to thrive in home settings. As an examination of HBO's unique structure for producing quality historical dramas, Weeks provides four case studies of HBO series set during different periods of United States history: Band of Brothers (2001), Deadwood (2004–2007), Boardwalk Empire (2012–2014), and Treme (2010–2013). In each case, HBO's lack of advertiser influence, commitment to creative freedom, and generous budgets continue to draw and retain talent who want to tell historical stories. Balancing historical and film theories in her assessment of the roles of mise-en–scène, characterization, narrative complexity, and sound in the production of effective historical dramas, Weeks' evaluation acts as an ode to the most recent Golden Age of TV, as well as a critical look at the relationship between entertainment media and collective memory.

The motion picture industry is obviously highly visible in the literal sense. Further, it is such in the figurative sense in that data relative to the industry abound. However, as in most cases, one must take considerable care in the interpretation of the available data due to the manner in which they are often reported. That is, the data, especially the relevant financial data, tend to be reported in nominal or unadjusted terms, rather than in real terms, i.e. on an unadjusted basis relative to changes in prices. This article examines some of the data commonly reported relative to the motion picture industry over the 2000 - 2016 period (the span of the 21st century in which data are available, and assesses same trends therein, after making appropriate real adjustments to these data. This work is an update of a similar endeavor undertaken by the author for data covering the 1991 - 2001 period.

How to improve the predictive accuracy of box office revenue with social media data is a big challenge and is particularly important for movie distributors and cinema operators. In this research, we find that microblogging UGC (MUGC) is a significant predictor of box office revenue and has stronger predictive power than UGC on Douban! Movies (DUGC) based on our examination of 60 movies released in China in 2012. To increase the attendance rate of movies, cinema operators can consider previous valence and volume of MUGC before scheduling the current film screenings because these messages can quickly predict the future box office revenue of a movie. Besides, we find that the volume of enterprise microblogs (i.e., MGC) can predict both box office revenue and MUGC, indicating that movie distributors should optimize their online media strategy by shifting more resources to utilizing enterprise microblogging. Although rebroadcasting volume from microblogging platforms does not predict box office revenue directly, it can indirectly predict it via MGC. Accordingly, compared with third-party platforms, rebroadcasting as one of the key distinct functions of microblogging platforms also shows its usefulness in box office revenue prediction. Overall, metrics from microblogging platforms are more effective in predicting box office revenue than those from third-party platforms. In this research, we build a prediction model of movie box office revenue by empirically exploring its intricate relationships with user-generated content (UGC) as well as marketer-generated content (MGC) on a microblogging platform and UGC on a third-party platform. Our analyses are based on a panel vector autoregression (PVAR) model that is calibrated with a combination of data from Weibo (microblogging platform) and Douban! Movies (third party). Our empirical results show that microblogging UGC (MUGC) is a significant predictor of box office revenue and has stronger predictive power than UGC on Douban! Movies (DUGC). In addition, we find that the volume of enterprise microblogs (i.e., MGC) predicts box office revenue directly and also indirectly via MUGC, and MUGC thus exerts a partial mediating effect on the predictive relationship between the volume of enterprise microblogs and box office revenue. Finally, a prediction model of box office revenue using lagged box office revenue, MGC, MUGC, and DUGC is proposed, and its forecasting accuracy is found to outperform that of existing models. Managerial implications on utilizing social media for enterprises are provided. The e-companion is available at https://doi.org/10.1287/isre.2018.0797 .

PurposeThis study investigates the impact of cultural distance on foreign box office performance of East Asian cinematic production in European markets. Predicated on two dimensions of a film's cultural specificity, namely content- and aesthetics-based components, this research advances current knowledge on the moderating effects of cultural specificity.Design/methodology/approachThe authors compile a data set of 515 East Asian films released in European countries during the 2010–2018 period. Data are analyzed by hierarchical linear modeling.FindingsResults show that cultural distance plays a negative role in affecting foreign box office performance and that aesthetics specificity of films weakens such a relationship, while content specificity of films can further strengthen the relationship.Practical implicationsThe findings suggest that cultural specificity is a crucial element and a relevant marketing tool in the cross-country film trade. Film producers and distributors need to consider both distribution strategy and intercultural context in order to align effectively with differing cultural distance and specificity.Originality/valueThis study proposes a new categorization framework of cultural specificity and demonstrates the moderating roles of content and aesthetics specificity on the relationship between cultural distance and films' foreign box office performance. It offers implications for both theory and practice in global film marketing and trade.

Box office prediction is of great significance for understanding investment risks, class construction, promotion and distribution, and theater scheduling. However, due to the insufficient selection of influencing factors of movie box office, the currently existing prediction model restricts the prediction accuracy. A total of 34 influencing factors in 11 categories, such as heat index, movie types, release date, creators, first-day box office, were selected to study the prediction technology of movie box office. The Word2vec algorithm is used to construct a feature thesaurus for nouns in movie domain; adjectives and verbs with emotional coloring are used to construct an emotional dictionary based on the movie domain; and the TF-IDF algorithm is integrated to calculate the emotional scores of movie comments. A prediction method based on comments and Multivariate Linear Regression (MLR) is designed to analyze the relationship between the influencing factors and the movie box office, which provides an important basis for the prediction of the total box office, and also provides a decision-making reference for the movie industry and the related management departments. Incorporating comments as feature values to improve the accuracy, a prediction model based on comments and Convolutional Neural Network (CNN) is constructed. The results show that the average prediction accuracy of the MLR without comments, Back-Propagation Neural Network (BPNN), and CNN is 63.4%, 68.3%, and 71.9%, respectively, and after integrating the comments, the average prediction accuracy of the MLR and CNN is improved by 16.1% and 11.8%, respectively, and the prediction accuracy is significantly improved.

PurposeAnecdotal evidence suggests that casting actors with similar facial features in a movie can pose challenges in foreign markets, hindering the audience's ability to recognize and remember characters. Extending developments in the literature on the cross-race effect, we hypothesize that facial similarity – the extent to which the actors starring in a movie share similar facial features – will reduce the country-level box-office performance of US movies in East and South-East Asia (ESEA) countries.Design/methodology/approachWe assembled data from various secondary data sources on US non-animation movies (2012–2021) and their releases in ESEA countries. Combining the data resulted in a cross-section of 2,616 movie-country observations.FindingsActors' facial similarity in a US movie's cast reduces its box-office performance in ESEA countries. This effect is weakened as immigration in the country, internet penetration in the country and star power increase and strengthened as cast size increases.Originality/valueThis first study on the effects of cast's facial similarity on box-office performance represents a novel extension to the growing literature on the antecedents of movies' box-office performance by being at the intersection of the two literature streams on (1) the box-office effects of cast characteristics and (2) the antecedents, in general, of box-office performance in the ESEA region.

Artificial intelligence has been increasingly employed to improve operations for various firms and industries. In this study, we construct a box office revenue prediction system for a film at its early stage of production, which can help management overcome resource allocation challenges considering the significant investment and risk for the whole film production. In this research, we focus on China’s film market, the second-largest box office in the world. Our model is based on data regarding the nature of a film itself without word-of-mouth data from social platforms. Combining extreme gradient boosting, random forest, light gradient boosting machine, k-nearest neighbor algorithm, and stacking model fusion theory, we establish a stacking model for film box office prediction. Our empirical results show that the model exhibits good prediction accuracy, with its 1-Away accuracy being 86.46%. Moreover, our results show that star influence has the strongest predictive power in this model.

Chinese martial arts films are one of the Chinese film genres with wide dissemination and high popularity. However, the decline in box office in the North American market has weakened their ability to maintain an advantage, develop, and profit in long-term competition, that is, long-term competitiveness. In this study, data mining techniques were used to screen out Chinese martial arts films with long-term competitiveness on the Internet Movie Database (IMDb) from 2000 to 2020. The influencing factors and their influence on overseas audiences were analyzed to explore the long-term international influence of Chinese martial arts films in global dissemination. The long-term influence of films is different from the immediate influence reflected by box - office figures, and it places more emphasis on spiritual functions and cultural values. Chinese martial arts films are highly ‘Chinese - characteristic’ (incorporating traditional cultural elements, demonstrating national spirit and values, and highlighting the unique charm of China in many aspects). Their narrative and dissemination essentially represent Chinese culture, symbolize the country’s cultural soft power, and are also unique cultural and spiritual products.

Box office is a challenging and crucial task for the movie distributors in decision making. In recent years, movie reviews are widely posted and shared on intelligent multimedia systems and everywhere. In this work, we employ both the metadata of the movie and the sentiment information of the users’ reviews to establish an intelligent predicting model. In the sentiment polarity classification model, a co-attention network-based aspect-level sentiment analysis strategy is developed by using the specific word embedding representations from both the contexts and the aspect. Considering the movie success prediction, a Softmax Discriminant Classifier is used due to its capable of dealing with non-linear issues. The sentiments from review texts, together with the movie information are taken as input variables of the predictor. Experimental outcomes verify the working performance of the proposed method which indicates that our model can be further applied to the sentiment analysis and the predicting of movie success.