Purpose

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.

Design/methodology/approach

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).

Findings

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.

Originality/value

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.

中文翻译：

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一种机器学习方法来预测球迷对赞助商态度的分类

 目的

本研究旨在开发并验证准确的机器学习模型，根据 NBA 球迷对体育赞助的看法将他们分类为有意义的类别。此外，通过预测 NBA 球迷对赞助商的态度强度，作者打算确定影响预测的具体特征，讨论这些发现，并为体育赞助领域的学者和从业者提供启示。

设计/方法论/途径

本研究使用了通过 Amazon Mechanical Turk (MTurk) 招募的 1,142 名 NBA 球迷作为样本。粉丝认同、赞助契合度、行为意图、赞助商利他动机、赞助商规范动机、赞助商利己动机被调查作为预测变量，而粉丝对赞助商的态度被收集作为因变量。通过探索性因子分析（EFA）和验证性因子分析（CFA）验证测量模型后，使用LASSO回归、SVM、KNN、RF和XGboost来开发和验证预测模型。

 发现

RF模型在预测球迷对赞助商的态度强度方面最准确，AUC为0.919，灵敏度为0.872，特异性为0.828，PPV为0.873，NPV为0.828，准确度为0.848。模型中最有影响力的特征是“0.301 的拟合度”。 “球迷对赞助商规范动机的认知”、“支持赞助商的行为意图”、“球迷对自己喜欢的球队的认同”、“球迷对赞助商利他动机的认知”和“球迷对赞助商利己动机的认知”分别以下降的顺序表现出来。命令。

 原创性/价值

这项研究是体育赞助领域首次利用机器学习模型准确地将球迷对赞助商的态度强度分类为高或低，并根据球迷对赞助过程的看法来阐述球迷对赞助商的态度是如何形成的。