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This study aims to develop and validate an accurate machine learning model to categorize NBA fans into meaningful clusters based on their perceptions of sport sponsorship. Additionally, by predicting the intensity of NBA fans’ attitudes toward sponsors, the authors intend to identify the specific features that influence prediction, discuss these findings and offer implications for academics and practitioners in sport sponsorship.

This study used a sample of 1,142 NBA fans who were recruited through Amazon Mechanical Turk (MTurk). Fans identification, sponsorship fit, behavioral intentions, sponsor altruistic motive, sponsor normative motive, sponsor egoistic motive were surveyed as predictors, whereas fans’ attitudes toward sponsors was collected as the dependent variable. The LASSO regression, SVM, KNN, RF and XGboost were used to develop and validate the prediction model after verifying the measurement model by the Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

The RF model had the best accurate in predicting the intensity of fans’ attitudes toward sponsors, achieving an AUC of 0.919 with a sensitivity of 0.872, a specificity of 0.828, a PPV of 0.873, a NPV of 0.828 and an accuracy of 0.848. The most influential feature in the model was “the fit of 0.301”. “Fans’ perceptions of sponsor’s normative motive”, “behavioral intentions supporting sponsors”, “fans’ identification with their favorite team”, “fans’ perceptions of sponsor’s altruistic motive” and “fans’ perceptions of sponsor’s egoistic motive” were exhibited in descending order.

This study is the first in sport sponsorship to accurately classify the intensity of fans’ attitudes toward sponsors as either high or low using machine learning models, and to formulate how fans’ attitudes formed toward sponsors from their perceptions of sponsorship process.

This study aims to examine the role of different forms of corporate social advocacy (CSA) in shaping individuals’ attitudinal and behavioral intentions towards companies taking their public stand on controversial socio-political issues. With an online experiment as the research method, this study tests whether depicting nonpolitical or political behaviors in CSA messages increases individuals’ positive behavioral intentions.

This study uses a single factor between subject online experiment. A total of 135 US young adults were recruited through a Qualtrics online panel. Three social media mockups were created to manipulate three levels of actions in CSA messages (no action, nonpolitical action and political action). Participants viewed one of those social media posts depicting presented actions to counter anti-LGBTQ + legislation in the USA and answered questions about values-driven motives behind CSA, brand preference and positive word-of-mouth (WOM) intention.

Participants displayed higher levels of brand preference when they viewed CSA messages depicting the company’s political action intended to repel anti-LGBTQ + legislation. Participants showed more positive WOM intentions towards the company when they perceived its political actions as more values-driven.

The findings of this study offer practical insights to companies when designing CSA messages and strategies. The results of this study indicate that the presence of political actions in CSA communication increases individuals’ positive behaviors towards companies. The results also suggest that depicting altruistic motives behind CSA leads individuals to talk about companies more in positive ways.

This study is one of the early studies investigating the impact of various forms of CSA on individuals’ attitudinal and behavioral intentions to companies practicing CSA. This study provides practical implications on how to effectively appeal individuals’ favorable attitudes and behaviors towards CSA. In particular, this research presents the importance of action aspects in individuals’ attitudes toward corporations’ CSA messages.