The business model of Chinese movies

Purpose The motion picture industry is a cultural and creative industry. Unlike its US counterpart, the Chinese motion picture industry is still developing. Therefore, learning from the US market, the purpose of this paper is to analyze the business model of Chinese movies from the perspective of new product diffusion. Design/methodology/approach Based on 66 movies released in the US and 21 movies released in China, this paper first compares the diffusion curves of Chinese and US movies through the movie life cycle and box office trends. Next, it analyzes the moviegoing behaviors of Chinese and US audiences based on the innovation and imitation coefficients in the Bass model. Finally, it compares the attention to information of Chinese and US audiences from the perspective of interpersonal word-of-mouth (WOM). Findings In the USA, a movie’s highest weekly box office is usually in its opening week, followed by a weekly decline in revenue; in China, there is no difference in box office performance between the first two weeks, but a weekly decline in revenue similarly follows. US audiences pay more attention to advertisements for movies than WOM recommendations, while Chinese people pay more attention to WOM recommendations. Neither the Chinese nor the US market differs in the volume of WOM between the first week before release and the opening week, and these two weeks are the most active period of WOM in both markets. Practical implications During the production phase for Chinese movies, we should satisfy opinion leaders’ needs. During the distribution phase, we should not only focus on market spending before the movie’s release, but also increase market spending in the opening week. During the theater release phase, we should stimulate WOM communication between moviegoers and thereby attract many more opinion seekers. Originality/value Few studies have investigated the Chinese motion picture industry from the perspective of new products. This paper compares and analyzes the diffusion of Chinese and US movies using the Bass model of new product diffusion, providing systematic theoretical guidelines for the commercial operation of the Chinese motion picture industry.

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PSO Based Grey Bass Model for Simulating New Product Diffusion

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f8212.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 2006-2011

Keyword(s):

Subjective Experience ◽

New Product ◽

Study Data ◽

Least Square ◽

Percentage Error ◽

Bass Model ◽

Product Diffusion ◽

Raw Data ◽

New Product Diffusion ◽

Potential Capacity

The Bass model is one of the basic models for new product diffusion analysis. But the Bass model requires a large quantity of raw data to determine parameters of Bass model and this model also uses the potential capacity of market based on the subjective experience. To solve the problem of necessity of large raw data of Bass model, Wang put forward the Grey Bass model. Wang used non-linear least square (NLS) method to find the parameters of Grey Bass model and to assess the potential capacity of market. In the present paper a more appropriate method for Grey Bass equation is offered which estimates potential capacity of market even if the sample size is small. The proposed model is based on the minimization of sum of square of error between actual and predicted data using Particle Swarm Optimization (PSO) technique. Using the case study data, as used by Wang, the accuracy of the improved method is investigated. The results show that the mean absolute percentage error (MAPE) in the present case is 6.52 % compared to 7.93% reported by Wang.

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A Stochastic Formulation of the Bass Model of New-Product Diffusion

SSRN Electronic Journal ◽

10.2139/ssrn.319460 ◽

2002 ◽

Author(s):

Shun Chen Niu

Keyword(s):

New Product ◽

Bass Model ◽

Product Diffusion ◽

Stochastic Formulation ◽

New Product Diffusion

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Consumer susceptibility to social influence in new product diffusion networks: how does network location matter?

European Journal of Marketing ◽

10.1108/ejm-06-2019-0491 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Honghong Zhang ◽

Xiushuang Gong

Keyword(s):

Social Network ◽

Social Influence ◽

Betweenness Centrality ◽

New Product ◽

Ordinary Least Squares ◽

Least Squares Regression ◽

Product Diffusion ◽

Content Type ◽

New Product Diffusion ◽

High Degree

Purpose This study aims to empirically investigate how susceptibility to social influence in new product adoption varies with one’s structural location in a social network. Design/methodology/approach The social network data were collected based on a sociometric network survey with 589 undergraduate students. Social network analysis and ordinary least squares regression analyses were used to test the hypotheses. Findings This study finds that consumers with high degree centrality (i.e. hubs) who have a large number of connections to others and consumers with high betweenness centrality (i.e. bridges) who connect otherwise distant groups in social networks are both less sensitive to informational influence from others. More importantly, the authors find evidence that consumers with moderate levels of degree/betweenness centrality are more susceptible to normative influence and status competition than those with low or high degree/betweenness centrality. The inverse-U patterns in the above relations are consistent with middle-status conformity and anxiety. Research limitations/implications This research complements social influence and new product diffusion research by documenting important contingencies (i.e. network locations) in consumer susceptibility to different types of social influence from a social network perspective. Practical implications The findings will assist marketers to leverage social influence by activating relevant social ties with effective messages in their network marketing strategies. Originality/value This research provides a better understanding of the mechanisms driving susceptibility to social influence in new product diffusion.

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The NLS-based Grey Bass Model for Simulating New Product Diffusion

International Journal of Market Research ◽

10.2501/ijmr-2017-045 ◽

2017 ◽

Vol 59 (5) ◽

pp. 655-669 ◽

Cited By ~ 7

Author(s):

Yanyu Wang ◽

Lingling Pei ◽

Zhengxin Wang

Keyword(s):

Subjective Experience ◽

Least Squares Method ◽

New Product ◽

Small Sample ◽

Grey Theory ◽

Bass Model ◽

Product Diffusion ◽

New Product Diffusion ◽

Potential Market ◽

Market Capacity

To solve the problems inherent in the existing Bass model, this paper develops a grey Bass model using a non-linear least squares method (NLS) and provides the whitenisation solution of differential equations. A Bass model exploits the specific advantage in simulating and predicting new product diffusion. Unfortunately, the existing Bass model has two problems: one lies in the conflict between the small sample support in new product diffusion and the large sample requirement in Bass model estimations; the other is over-reliance on the subjective experience in estimating potential market capacity. Although Wang et al. (2011) proposed the grey Bass model to solve the first problem, the second problem remains untouched. Based on this work by Wang and colleagues, the improved method described in this paper is not only suitable for the small sample situation, but also directly estimates potential market capacity. Using the WeChat case, the authors test the improved method's estimation and prediction effects. The results show that the estimations for internal coefficient, external coefficient and potential market capacity are all significant at the 1% level, and the prediction effect in grey theory critical level reaches level 1. Additionally, internal and external sample prediction are both consistent with the raw data and company report.

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Managing initial expectations when word-of-mouth matters

European Journal of Marketing ◽

10.1108/ejm-10-2015-0752 ◽

2017 ◽

Vol 51 (1) ◽

pp. 123-156 ◽

Cited By ~ 6

Author(s):

Sangyoon Yi ◽

Jae-Hyeon Ahn

Keyword(s):

Word Of Mouth ◽

New Products ◽

New Product ◽

Product Diffusion ◽

Content Type ◽

Consumer Expectation ◽

New Product Diffusion ◽

Initial Expectation ◽

Expectation Management ◽

Product Valuation

Purpose Consumer expectation not only influences purchase decision but also post-purchase satisfaction and word-of-mouth (WOM). This study aims to develop theories of initial expectation management by suggesting when it is desirable for new products to raise or lower consumer expectations. It systematically examines the interplay of product value and consumer heterogeneity in the dynamic process of new product diffusion under competition. Design/methodology/approach Drawing on traditional diffusion and choice models, this study develops an agent-based model to formalize and analyze how consumers’ initial expectations of a new product influence the interdependent processes of product sales, consumer satisfaction and WOM. The simulation analyses in controlled settings help understand the underlying mechanisms in a stepwise manner. Findings The results show that, although the optimal strategy for low-value products is to induce consumer expectations higher than product value, high-value products are better introduced with expectations formed close to it. The results also highlight an important drawback of “under-promising” strategies in reducing the base and volume of WOM. Further, the analysis illustrates how consumer heterogeneities in product valuation and initial expectation affect the effectiveness of expectation management. For high-value products, both heterogeneities reduce the effectiveness of the optimal strategy. For low-value products, however, value heterogeneity enhances the effectiveness, whereas expectation heterogeneity reduces it. Practical implications Firms introducing new products should be sensitive to how consumers value the product and form expectations about it. Different from firms that must rely on aggressive advertising to sell inferior products by building up high expectations, those with superior products can rely more on the power of consumer WOM, which is much less costly and thus gives them a competitive advantage. Firms should also pay attention to how diversified the consumers are in product valuation and expectation. The expectation management strategy is more effective when consumers form more similar expectations. Inferior firms may leverage this mechanism to neutralize their disadvantages. Originality/value The articulated mechanisms help push forward the research on new product diffusion and consumer expectation management. To the best of the authors’ knowledge, this is one of the first studies to systematically analyze the impact of consumer heterogeneity on the effectiveness of expectation management.

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Can search engine data improve accuracy of demand forecasting for new products? Evidence from automotive market

Industrial Management & Data Systems ◽

10.1108/imds-08-2018-0347 ◽

2019 ◽

Vol 119 (5) ◽

pp. 1089-1103 ◽

Cited By ~ 1

Author(s):

Dongha Kim ◽

JongRoul Woo ◽

Jungwoo Shin ◽

Jongsu Lee ◽

Yongdai Kim

Keyword(s):

Search Engine ◽

Diffusion Model ◽

New Product ◽

Product Diffusion ◽

Internet Search ◽

Content Type ◽

Search Volume ◽

New Product Diffusion ◽

Proposed Model ◽

The Relationship

Purpose The purpose of this paper is to analyze the relationship between new product diffusion and consumer internet search patterns using big data and to investigate whether such data can be used in forecasting new product diffusion. Design/methodology/approach This research proposes a new product diffusion model based on the Bass diffusion model by incorporating consumer internet search behavior. Actual data from search engine queries and new vehicle sales for each vehicle class and region are used to estimate the proposed model. Statistical analyses are used to interpret the estimated results, and the prediction performance of the proposed method is compared with other methods to validate the usefulness of data for internet search engine queries in forecasting new product diffusion. Findings The estimated coefficients of the proposed model provide a clear interpretation of the relationship between new product diffusion and internet search volume. In 83.62 percent of 218 cases, analyzing the internet search pattern data are significant to explain new product diffusion and that internet search volume helps to predict new product diffusion. Therefore, marketing that seeks to increase internet search volume could positively affect vehicle sales. In addition, the demand forecasting performance of the proposed diffusion model is superior to those of other models for both long-term and short-term predictions. Research limitations/implications As search queries have only been available since 2004, comparisons with data from earlier years are not possible. The proposed model can be extended using other big data from additional sources. Originality/value This research directly demonstrates the relationship between new product diffusion and consumer internet search pattern and investigates whether internet search queries can be used to forecast new product diffusion by product type and region. Based on the estimated results, increasing internet search volume could positively affect vehicle sales across product types and regions. Because the proposed model had the best prediction power compared with the other considered models for all cases with large margins, it can be successfully utilized in forecasting demand for new products.

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A New Grey Bass Equation for Modelling New Product Diffusion

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.291-294.3033 ◽

2013 ◽

Vol 291-294 ◽

pp. 3033-3036 ◽

Cited By ~ 1

Author(s):

Zheng Xin Wang

Keyword(s):

Nonlinear Programming ◽

New Product ◽

Power Exponent ◽

Power Model ◽

Bass Model ◽

Product Diffusion ◽

New Model ◽

New Product Diffusion ◽

Grey Verhulst Model ◽

Nonlinear Programming Method

A novel grey Bass model with a power exponent (Grey Bass Power Model) was proposed, and the related parameter-packets of the model were deduced. A nonlinear programming method was employed to optimize the grey parameters and improve the accuracy of the grey Bass model. The results show that GM(1,1) model, GPM (1,1) model, grey Verhulst model and grey Bass model are the special examples of grey Bass power model. The actual example of product diffusion shows the modelling accuracy has been remarkably improved by using the new model.

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