Ali Akbar Sobhani - Ph.D. Student Department of Business Management, South Tehran Branch, Islamic Azad University, Tehran, Iran.
Asghar Moshabaki - Professor, Department of Business Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.
Abdullah Naami - Assistant Professor, Department of Business Management, South Tehran Branch, Islamic Azad University, Tehran, Iran.
Mohammad Ehsani - Professor, Sport Sciences, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran.

The aim of the current research is to identify the data-driven process of sports consumer motivation based on the relational marketing knowledge approach. This research is exploratory and qualitative in nature, utilizing the thematic analysis method. The research population consists of fans of Iran's Premier League football clubs, selected through purposeful sampling. Subsequently, data from ۱۸ interviews were analyzed using the thematic analysis method. Findings: The interviews yielded ۱۲۷۸ open codes, which were then condensed into ۱۹۱ unique codes and further refined into ۳۳ core codes. These core codes, identified during the selective coding stage, are categorized into ۶ groups. Three of these groups are associated with types of pleasure motivation, psychological and social factors, one with relational marketing, one with the use of social media, and one with behavioral goals and intentions. A hierarchical model was developed based on the relationships among these groups of motivations, relational marketing, social media usage, and behavioral intentions. This model can serve as a tool to understand the motivations of football club fans and leverage relational marketing and social media to enhance and solidify the enduring relationship between fans and clubs. It can also aid in addressing marketing challenges, revenue generation, financial independence of the club in a systematic manner, and resolving issues in this domain.

Customer Knowledge, Data-Driven Consumer Motivation, Relational Marketing Knowledge