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The paper aims to compare two general team identification processes of consumers’ in-group-favor and out-group-animosity responses to sports sponsorship.

The paper draws on two studies and four samples of professional baseball fans in Taiwan (N = 1,294). In Study 1, data from the fans of three teams were analyzed by using multi-group structural equation modeling to account for team effects and to consider parallel in-group-favor and out-group-animosity processes. In Study 2, the fans of one team were sampled and randomly assigned to assess the sponsors of one of three specific competitor teams to account for differences in team competition and rivalry. In both studies, these two processes were compared using patterns of significant relationships and differences in the indirect identification-attitude-outcome relationships.

Positive outcomes of in-group-favor processes were broader in scope and were more pronounced in absolute magnitude than the negative outcomes of out-group-animosity processes across all outcomes and studies.

The research was conducted in one country and considered the sponsorship of one sport. It is possible that the results could differ for leagues within different countries, more global leagues and different fan bases.

The results suggest that managers should carefully consider whether the negative out-group-animosity outcomes are actually present, broad enough or strong enough to warrant costly or compromising intervention, because they might not always be present or meaningful.

The paper demonstrates the comparatively greater breadth and strength of in-group-favor processes when compared directly to out-group-animosity processes.

The purpose of this paper is to use Twitter data to mine personality traits of basketball players to predict their performance in the National Basketball Association (NBA).

Automated personality mining and robotic process automation were used to gather data (player statistics and big five personality traits) of n = 185 professional basketball players. Correlation analysis and multiple linear regressions were computed to predict the performance of their NBA careers based on previous college performance and personality traits.

Automated personality mining of Tweets can be used to gather additional information about basketball players. Extraversion, agreeableness and conscientiousness correlate with basketball performance and can be used, in combination with previous game statistics, to predict future performance.

The study presents a novel approach to use automated personality mining of Twitter data as a predictor for future basketball performance. The contribution advances the understanding of the importance of personality for sports performance and the use of cognitive systems (automated personality mining) and the social media data for predictions. Scouts can use our findings to enhance their recruiting criteria in a multi-million dollar business, such as the NBA.

This paper studies the interactive valuation discourses of an online user community (transfermarkt.de) that seeks to determine market values for soccer players. Despite their seemingly casual nature, these values have featured in newspapers, transfer negotiations, academic research, and capital market communication – and have thus become reified.

The paper employs netnographic research methodology to collect and thematically analyze a wide range of user entries on the platform. These entries are studied using theoretical insights from the sociology of quantification and valuation.

The analysis reveals how values are constructed in constant interaction between value-proposing users and value-justifying “experts.” This dynamic form of relational valuation positions players relative to one another as well as to actual transactions on the transfer market. In the absence of authoritative guidelines, it is this possibility and affordance for interaction that enacts a coherent valuation regime. The paper further reveals the platform's response to a disruptive event, which risked bringing the user-expert dynamics to a halt, requiring intervention from the platform to repair its valuation frame.

The paper responds to increased scholarly interests in the valuation of professional athletes. It contributes to the extant literature on valuation, first, by analyzing the dynamic valuation work that feeds into the social construction of values and, second, by studying platform participation and user interaction in a socially engineered online space.

As the superiority of sports celebrities' endorsements has been questioned, the purpose of this study is to identify various types of endorsers' direct and indirect effects on consumers' purchase intentions.

Empirical data were collected from 240 useful responses to an online experiment, and research hypotheses were tested using (moderated) serial mediation analyses.

The study's findings indicate that an endorser has a positive influence on consumers' purchase intentions through their perceptions of the advertisement and the endorsed brand. A moderated serial mediation analysis finds differences in the four types of endorsers analyzed. A sports celebrity is the most effective type of endorser in increasing consumers' purchase intentions, whereas endorsements by company managers and peer consumers, while also positive, are less effective in influencing advertising outcomes. An expert's endorsement is comparable to that of a manager but not significant.

The generalizability of the study's findings is limited because of a restricted data sample, the use of fictitious endorsers and the limited number of product categories and brands analyzed.

The study systematically analyzes the behavioral influence of four types of endorsers on consumers' purchase intentions, mediated by their perceptions of the advertisements and the endorsed brand. The results of this analysis extend the current state of endorsement research, indicating that endorsements should be integrated into companies' marketing strategies and provide marketing professionals practical guidance on which type of endorser is most effective in influencing advertising outcomes.