Blogs, Advertising, and Local-Market Movie Box Office Performance

Gopinath, Shyam; Chintagunta, Pradeep K.; Venkataraman, Sriram

doi:10.1287/mnsc.2013.1732

Cited by 139 publications

(101 citation statements)

References 32 publications

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“…The findings reported impact of online buzz valence on dependent variable, i.e., weekly box office revenue. In comparison, more recent studies reported in a blog analyses that box office ticket sales of movies can be affected by the valence of online buzz information collected from Yahoo Movie and blog posts (Gopinath, Chintagunta, and Venkataraman, 2013). Chen, Stephen (2001) in the research work of assessing the impact of internet on brands claims that easy access to online customer reviews has led some audiences (consumers) to suggest that alternative assurances of product quality and performance.…”

Section: Review Of Literaturementioning

confidence: 99%

A study on parameters of online reviews content that influence consumers buying behaviour- an Indian perspective

devedi¹,

Sujatha²,

Pathak³

2017

JBRMR

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The 21 The present study tries to fill the gap in the literature relating online product or service reviews in influencing the consumer buying behaviour. The objective of the study is to understand the parameters of review content on which consumer check online reviews before buying a product or service. To achieve the research objectives of the present study, a descriptive research design is used with primary data collection methodology through a structured questionnaire. The sample size for the study is 104. The analysis was conducted through mean, one sample t-test and correlations. The study identified that consumers view online reviews such as understanding quality of the product, product usage information, cost benefit of the product, information relevant to deals or discounts, and product information on warranty or guarantee or replacement details, Brand reputation among reviewers, Sales service availability for deciding to buy. It was observed that parameters of online review content influence the consumer's decision to buy.

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Section: Review Of Literaturementioning

confidence: 99%

A study on parameters of online reviews content that influence consumers buying behaviour- an Indian perspective

devedi¹,

Sujatha²,

Pathak³

2017

JBRMR

View full text Add to dashboard Cite

show abstract

“…Failing to properly control for quality heterogeneity can lead to omitted variable bias or potential endogeneity between the movie's box office and the network embeddedness of its team members. To address this potential endogeneity, prior work suggests exploiting the panel data structure and incorporating movie-level fixed effects (Elberse 2007;Gopinath, Chintagunta, and Venkataraman 2013). However, only one observation of the cumulative revenue exists for each movie.…”

Section: Measures and Modelingmentioning

confidence: 99%

The role of network embeddedness in film success

Packard

Aribarg

Eliashberg

et al. 2016

International Journal of Research in Marketing

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In the early stage of film development when producers assemble a development team, it is important to understand the means by which different team members may contribute to the film's box office. Building upon theories from marketing and sociology, we propose that these contributions arise from team members' positions, or embeddedness, in a social network weaved through past film collaborations. These collaborations provide team members with opportunities to draw knowledge and skills from the network for new film projects. Our conceptual framework accentuates two aspects of network embeddedness: positional embeddedness (PE)-how well a person is tied to well-connected others, and junctional embeddedness ( JE)-the extent to which a person bridges sub-communities in the industry. We examine how the importance of PE and JE varies by functional role (cast versus crew), and is moderated by the film's studio affiliation.Analyzing more than 15,000 industry professionals over nearly two decades of film collaborations, this research reveals crucial and divergent relationships: while high PE is more valuable for the cast, high JE is critical for the crew. This role distinction also depends on a film's studio affiliation. Managerially, these findings provide guidance to film executives and producers in revenue maximization through strategic team assembly, and to talents in career management. The Role of Network Embeddedness in Film Success ABSTRACTIn the early stage of film development when producers assemble a development team, it is important to understand the means by which different team members may contribute to the film's box office. Building upon theories from marketing and sociology, we propose that these contributions arise from team members' positions, or embeddedness, in a social network weaved through past film collaborations. These collaborations provide the members with the opportunities to draw knowledge and skills from the network for new film projects. Our conceptual framework accentuates two aspects of network embeddedness: positional embeddedness (PE) -how well a person is tied to well-connected others, and junctional embeddedness (JE) -the extent to which a person bridges sub-communities in the industry. We examine how the importance of PE and JE varies by functional role (cast versus crew), and is moderated by the film's studio affiliation.Analyzing more than 15,000 industry professionals over nearly two decades of film collaborations, this research reveals crucial and divergent relationships: while high PE is more valuable for the cast, high JE is critical for the crew. This role distinction also depends on a film's studio affiliation. Managerially, these findings provide guidance to film executives and producers in revenue maximization through strategic team assembly, and to talents in career management.

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“…Quality perceptions consist of three components: (1) abstract dimensions of intrinsic attributes (e.g., safety, durability), (2) perceived monetary price, and 3) reputation formed through advertising and brand name [61]. 2 A few notable exceptions include [19,34,43,44,56]. But they collect online buzz data from a single source, such as a movie review site (e.g., Yahoo!Movie) [19,43], an online forum [34], or a single firm [56].…”

Section: Introductionmentioning

confidence: 99%

“…2 A few notable exceptions include [19,34,43,44,56]. But they collect online buzz data from a single source, such as a movie review site (e.g., Yahoo!Movie) [19,43], an online forum [34], or a single firm [56]. adjust their prices up or down by small increments [10].…”

Section: Introductionmentioning

confidence: 99%