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Analyzing superstars’ power using support vector machines

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Abstract

The main objective of this paper is to explain the influence that superstars have over spectators. The most significant contributions in the field of persuasion are discussed. This theoretical framework suggests some hypotheses that are tested using the data of an empirical study based on a survey of moviegoers. Support vector machine (SVM) is used for data analysis and pattern discovery. The SVM prediction capacity is benchmarked against that from a linear regression and multinomial logit. Results show that the SVM has considerable promise for analyzing spectators’ behavior. The results of this analysis allow us to extract some significant conclusions and implications for the process of creating and maintaining the power of a superstar.

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Notes

  1. We thank a referee for suggesting this implication.

  2. Source: AIMC (2011).

  3. Source: Adapted from Hoyer and Brown (1990).

  4. Source: Adapted from Christensen (2004).

  5. Source: Adapted from Laros and Steenkamp (2005).

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Acknowledgments

This research has been partially supported by the mobility programme of the Campus of International Excellence of the University of Oviedo. The authors would like to thank professor Jie Zhang (University of Maryland) and Manuel Chica Serrano (European Centre of Soft Computing) for their comments of revised versions of this paper.

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Authors and Affiliations

  1. School of Business and Economics, University of Oviedo, Avda. Del Cristo s/n, 33071, Oviedo, Asturias, Spain

    Ana Suárez-Vázquez

  2. Artificial Intelligence Center, University of Oviedo, Campus de Viesques, 33204, Gijón, Asturias, Spain

    José R. Quevedo

Authors
  1. Ana Suárez-Vázquez
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  2. José R. Quevedo
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Correspondence to Ana Suárez-Vázquez.

Appendices

Appendix 1

figure a
  1. 3.

    Could you tell us what your cinema attendance is? Footnote 2

figure b
  1. 4.

    Please tell us your degree of agreement with the following sentences referring to a number of superstars Footnote 3. Use a scale from 1 to 5 where 1 means total disagreement and 5 means total agreement.

figure c
  1. 5.

    Now we are going to give you a set of attributes. Please tell us which of the previous superstars has each of these attributes Footnote 4. Answer on a scale from 1 to 5, where 1 means He/she does not have this attribute at all and 5 means that He/she definitely has this attribute.

figure d
  1. 6.

    Regarding the next set of emotions, indicate the degree of relationship between each emotion and each of the superstars. Footnote 5 Answer on a scale from 1 to 5 where 1 means that you do not identify this emotion with that superstar at all and 5 means that you perceive a clear relationship between the superstar and the emotion.

figure e
  1. 7.

    Imagine you know each of the following actors/actresses is the lead star of a new film and you do not know any other feature about that film. Would the presence of that star encourage you to see the film? Answer on a scale from 1 to 5, where 1 means the presence of the star will definitely not encourage me to see the film and 5 means the presence of the star will definitely encourage me to see that film.

figure f
figure g

Appendix 2

See Tables 6 and 7.

Table 6 Mean absolute errors-Multiclass loss for spectators with low interest in the cinema market (Gaussian kernel)
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Table 7 Mean absolute errors-multiclass loss for spectators with high interest in the cinema market (Gaussian kernel)
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Suárez-Vázquez, A., Quevedo, J.R. Analyzing superstars’ power using support vector machines. Empir Econ 49, 1521–1542 (2015). https://doi.org/10.1007/s00181-015-0923-1

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