Entertainment analysis in gaming model for business management with decision making and Machine learning model

• Published in: Entertainment computing 2025-01, Vol.52, p.100725, Article 100725
• Main Authors: Karthiga, M., Abirami, S.P., Arunkumar, B., Vanitha Sheba, M.
• Format: Article
• Language: English
• Subjects: Business Management; Decision Making; Entertainment Analysis; Gaming Model; Machine Learning
• ISSN: 1875-9521; 1875-953X
• DOI: 10.1016/j.entcom.2024.100725

•This study fills this gap by presenting a paradigm that uses the micro foundations of dynamic capacity to uncover the causes of algorithmic bias in marketing.•Purpose of this research is to create a fresh method to entertainment analysis for machine learning-based business management decision making.•In this study, a game model using fuzzy swarm Gaussian recursive neural networks is developed for entertainment analysis in social network-based business intelligence and decision making modelling.•The MSE, MAE, f-1 score, accuracy, precision, recall, and precision are all included in the experimental study.•We provide a methodology to provide a dynamic method management capacity to address algorithmic bias in ML-based marketing decision making by combining several points of view utilising theories as well as practices. Marketing strategies are being revolutionised by rise of consumer-generated data as well as expanding availability of Machine Learning (ML) tools. It is highly unlikely that academics and marketers fully comprehend the myriad of options that machine learning (ML) applications provide for establishing and preserving a competitive economic edge. Despite the catastrophic, unequal, oppressive effects of algorithmic bias on different client groups, research in this area is still lacking despite the enormous increase of algorithmic decision making in marketing. This study fills this gap by presenting a paradigm that uses the micro foundations of dynamic capacity to uncover the causes of algorithmic bias in marketing. Purpose of this research is to create a fresh method to entertainment analysis for machine learning-based business management decision making. In this study, a game model using fuzzy swarm Gaussian recursive neural networks is developed for entertainment analysis in social network-based business intelligence and decision making modelling. The MSE, MAE, f-1 score, accuracy, precision, recall, and precision are all included in the experimental study. We provide a methodology to provide a dynamic method management capacity to address algorithmic bias in ML-based marketing decision making by combining several points of view utilising theories as well as practices.