scholarly journals Effect of online word-of-mouth variables as predictors of box office

2016 ◽  
Vol 29 (4) ◽  
pp. 657-678 ◽  
Author(s):  
Seonghyeon Jeon ◽  
Young Sook Son
Keyword(s):  
Word Of Mouth ◽  
Box Office ◽  
Online Word Of Mouth

Box office performance: Influence of online word-of-mouth on consumers’ motivations to watch movies

2019 ◽  
Vol 47 (10) ◽  
pp. 1-17
Author(s):  
Kuo-Ting Yu ◽  
Hsi-Peng Lu ◽  
Chih-Yu Chin ◽  
Yu-Shiuan Jhou
Keyword(s):  
Intrinsic Motivation ◽  
Structural Equation ◽  
Word Of Mouth ◽  
Positive Impact ◽  
Positive Influence ◽  
Production Costs ◽  
Equation Modeling ◽  
Box Office ◽  
Online Word Of Mouth ◽  
Marketing Budgets

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.

scholarly journals Movie Production Efficiency Moderating between Online Word-of-Mouth and Subsequent Box Office Revenue

2020 ◽  
Vol 12 (16) ◽  
pp. 6602
Author(s):  
Sangjae Lee ◽  
Joon Yeon Choeh
Keyword(s):  
Production Efficiency ◽  
Word Of Mouth ◽  
Linear Regression Analysis ◽  
Ensemble Methods ◽  
Prediction Performance ◽  
Negative Effects ◽  
K Nearest Neighbors ◽  
Box Office ◽  
Online Word Of Mouth ◽  
The Relationship

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.

Impact of Blogs on Sales Revenue

2013 ◽  
pp. 106-120
Author(s):  
Guoying Zhang ◽  
Alan J. Dubinsky ◽  
Yong Tan
Keyword(s):  
Content Analysis ◽  
Network Analysis ◽  
Word Of Mouth ◽  
Estimating Equation ◽  
Opinion Leaders ◽  
Future Research ◽  
Analysis Methodology ◽  
Box Office ◽  
Online Word Of Mouth ◽  
High Influence

In this study, blog data were collected and network parameters were captured to represent three common measurements of online Word-Of-Mouth: intensity, influence level, and dispersion. These parameters were then analyzed using a General Estimating Equation (GEE) model to test their effects on average weekly movie box office receipts. Findings indicated that all three parameters were significant in the model. The aggregated degree, representing WOM intensity, was positively significant, which was consistent with results from extant research. Further, diameter of a network, representing WOM dispersion, was observed to be positively significant, which validated the importance of spreading WOM as far as possible. Counter-intuitively, the aggregated size node, representing WOM influence level, was ascertained to be negatively significant, which might be explained by the possible negative stance from opinion leaders with high influence level. Applying network analysis methodology to blog entries, the present work differentiated itself from extant WOM literature that has focused chiefly on content analysis. The findings also provided managerial insights to companies interested in utilizing blogs as online WOM for marketing initiatives and implications for future research.

The interactive impact of online word-of-mouth and review helpfulness on box office revenue

2018 ◽  
Vol 56 (4) ◽  
pp. 849-866 ◽  
Author(s):  
Sangjae Lee ◽  
Joon Yeon Choeh
Keyword(s):  
Word Of Mouth ◽  
Content Type ◽  
Factors Affecting ◽  
Box Office ◽  
Online Word Of Mouth ◽  
Final Sample ◽  
Online User Reviews ◽  
Review Helpfulness ◽  
Product Sales ◽  
The Impact

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.

Impact of Blogs on Sales Revenue

2011 ◽  
Vol 3 (2) ◽  
pp. 60-74 ◽  
Author(s):  
Guoying Zhang ◽  
Alan J. Dubinsky ◽  
Yong Tan
Keyword(s):  
Content Analysis ◽  
Word Of Mouth ◽  
Estimating Equation ◽  
Opinion Leaders ◽  
Future Research ◽  
Sales Revenue ◽  
Analysis Methodology ◽  
Box Office ◽  
Online Word Of Mouth ◽  
High Influence

In this study, blog data were collected and network parameters were captured to represent three common measurements of online Word-Of-Mouth: intensity, influence level, and dispersion. These parameters were then analyzed using a General Estimating Equation (GEE) model to test their effects on average weekly movie box office receipts. Findings indicated that all three parameters were significant in the model. The aggregated degree, representing WOM intensity, was positively significant, which was consistent with results from extant research. Further, diameter of a network, representing WOM dispersion, was observed to be positively significant, which validated the importance of spreading WOM as far as possible. Counter-intuitively, the aggregated size node, representing WOM influence level, was ascertained to be negatively significant, which might be explained by the possible negative stance from opinion leaders with high influence level. Applying network analysis methodology to blog entries, the present work differentiated itself from extant WOM literature that has focused chiefly on content analysis. The findings also provided managerial insights to companies interested in utilizing blogs as online WOM for marketing initiatives and implications for future research.

scholarly journals Sales Prediction by Integrating Heat and Sentiments of Product Dimensions

2018 ◽  
Author(s):  
Xiaozhong Lyu ◽  
Cuiqing Jiang ◽  
Yong Ding ◽  
Zhao Wang ◽  
Yao Liu
Keyword(s):  
Big Data ◽  
Empirical Study ◽  
Prediction Model ◽  
Word Of Mouth ◽  
Prediction Models ◽  
Movie Industry ◽  
Topic Analysis ◽  
Box Office ◽  
Online Word Of Mouth ◽  
Sales Prediction

The accuracy of sales prediction models based on the big data of online word-of-mouth (eWOM) is still not satisfied. We argue that eWOM contains heat and sentiments of different product dimensions, which can improve the accuracy of these models. In this paper, we propose a dynamic topic analysis (DTA) framework in order to extract heat and sentiments of product dimensions from the big data of eWOM. Finally, we propose an autoregressive-heat-sentiment (ARHS) model, which integrates heat and sentiments of dimensions into the baseline predictive model. The empirical study in movie industry confirms that heat and sentiments of dimensions can improve the accuracy of sales prediction model. ARHS model is better for movie box-office revenue prediction than other models.

scholarly journals Sales Prediction by Integrating Heat and Sentiments of Product Dimensions

2018 ◽  
Author(s):  
Xiaozhong Lyu ◽  
Cuiqing Jiang ◽  
Yong Ding ◽  
Zhao Wang ◽  
Yao Liu
Keyword(s):  
Big Data ◽  
Empirical Study ◽  
Prediction Model ◽  
Word Of Mouth ◽  
Prediction Models ◽  
Movie Industry ◽  
Topic Analysis ◽  
Box Office ◽  
Online Word Of Mouth ◽  
Sales Prediction

The accuracy of sales prediction models based on the big data of online word-of-mouth (eWOM) is still not satisfied. We argue that eWOM contains heat and sentiments of different product dimensions, which can improve the accuracy of these models. In this paper, we propose a dynamic topic analysis (DTA) framework in order to extract heat and sentiments of product dimensions from the big data of eWOM. Finally, we propose an autoregressive-heat-sentiment (ARHS) model, which integrates heat and sentiments of dimensions into the baseline predictive model. The empirical study in movie industry confirms that heat and sentiments of dimensions can improve the accuracy of sales prediction model. ARHS model is better for movie box-office revenue prediction than other models.

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