Purpose: This paper aims to shed novel light to further the ongoing debate about the relationship between traditional sports and eSports by gathering empirical evidence on the role that eSports play on the consumption of traditional sports (i.e. live matches at the Stadium, TV matches spectating, merchandise or sponsor purchase), in the peculiar context of soccer. Design/methodology/approach: An extensive literature review on both sports and eSports consumption has informed the creation of a novel dataset through the design and administration of a structured questionnaire to Italian citizens 18+. Questions were about eSports and soccer consumption, information-seeking behaviour and psychometric factors. All constructs have been measured against validated scales. A total of 279 high-quality responses have been analysed through a prediction model based on regression trees in the Machine Learning domain. Findings: Results show that soccer consumption is predicted by the degree of vicarious achievement (positive effect), the degree of playing sport-related eSports (positive effect) and the degree of playing non-sport-related eSports (negative effect). Vertical analyses have been on sub-dimensions of soccer consumption (attending live matches at the Stadium, spectating TV matches, buying merchandise or sponsors’ products). Originality/value: To the best of our knowledge, this study is the first to offer empirical evidence to bridge two main limitations: the lack of studies about the eSports-soccer consumptions relationship and the reduction of soccer consumption as just Stadium attendance. Our results have both theoretical and practical implications.

Predicting soccer consumption: do eSports matter? Empirical insights from a machine learning approach

Lettieri E.;Orsenigo C.
2020-01-01

Abstract

Purpose: This paper aims to shed novel light to further the ongoing debate about the relationship between traditional sports and eSports by gathering empirical evidence on the role that eSports play on the consumption of traditional sports (i.e. live matches at the Stadium, TV matches spectating, merchandise or sponsor purchase), in the peculiar context of soccer. Design/methodology/approach: An extensive literature review on both sports and eSports consumption has informed the creation of a novel dataset through the design and administration of a structured questionnaire to Italian citizens 18+. Questions were about eSports and soccer consumption, information-seeking behaviour and psychometric factors. All constructs have been measured against validated scales. A total of 279 high-quality responses have been analysed through a prediction model based on regression trees in the Machine Learning domain. Findings: Results show that soccer consumption is predicted by the degree of vicarious achievement (positive effect), the degree of playing sport-related eSports (positive effect) and the degree of playing non-sport-related eSports (negative effect). Vertical analyses have been on sub-dimensions of soccer consumption (attending live matches at the Stadium, spectating TV matches, buying merchandise or sponsors’ products). Originality/value: To the best of our knowledge, this study is the first to offer empirical evidence to bridge two main limitations: the lack of studies about the eSports-soccer consumptions relationship and the reduction of soccer consumption as just Stadium attendance. Our results have both theoretical and practical implications.
2020
eSport
Machine learning
Marketing
Prediction
Soccer
Sport consumption
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1157160
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