Beyond Spectatorship: A Macro–Meso–Micro Framework for Understanding Social Well-being through Rugby Spectatorship

Authors

  • Koh Sasaki Nagoya University, Nagoya, Japan, Nippon Sport Policy Commission, Tokyo, Japan and Japanese Rugby Football Union, Tokyo, Japan https://orcid.org/0000-0001-6482-0073
  • Masato Tshuchida Japanese Rugby Football Union, Tokyo, Japan
  • Takashi Yamagami Japanese Rugby Football Union, Tokyo, Japan
  • Genichi Tamatsuka Japan Rugby League One, Tokyo, Japan
  • Hajime Shoji Japan Rugby League One, Tokyo, Japan
  • Takumi Kawahara Nippon Sport Policy Commission, Tokyo, Japan https://orcid.org/0009-0004-3745-3078
  • Noriyuki Sakamoto Nippon Sport Policy Commission, Tokyo, Japan https://orcid.org/0009-0002-5839-3057
  • Hiroshi Suzuki Nippon Sport Policy Commission, Tokyo, Japan & University of Tokyo, Tokyo, Japan
  • Ichiro Kono Nippon Sport Policy Commission, Tokyo, Japan and University of Tsukuba, Ibaraki, Japan https://orcid.org/0009-0002-1106-9137

DOI:

https://doi.org/10.63002/assm.405.1727

Keywords:

Rugby spectatorship, Social well-being, Explainable artificial intelligence, Text mining, Correspondence analysis, Co-occurrence network

Abstract

Sport spectatorship is increasingly recognized as a vital contributor to individual well-being and community development. However, the mechanisms through which sport experiences generate broader social well-being remain insufficiently understood, particularly when quantitative and qualitative perspectives are examined in isolation. The present study aimed to evaluate the multidimensional social value of rugby spectatorship by proposing an integrated macro–meso–micro analytical framework. Questionnaire data obtained from 14,851 Japanese rugby spectators were analyzed. Five psychosocial factors—Stress Relief, Work Efficiency, Social Bond, Physical Health, and Corporate Favorability—were specified as predictor variables, with Social Well-being as the primary outcome. At the macro level, correspondence analysis evaluated age-related variations in psychosocial values. At the meso level, a multilayer perceptron neural network (R² = 0.71) combined with explainable artificial intelligence (XAI)—including Random Forest Feature Importance, SHAP, and Standardized Partial Regression Coefficients—quantified predictor contributions. At the micro level, 12,238 open-ended responses regarding spectators' happiest moments were analyzed using morphological analysis and a Jaccard index-based co-occurrence network. The XAI results revealed that Corporate Favorability (41.7%) and Physical Health (22.0%) were the strongest predictors of Social Well-being, followed by Work Efficiency (13.3%), Stress Relief (13.0%), and Social Bond (10.1%). Correspondence analysis demonstrated distinct generational priorities: younger spectators prioritized relational and emotional outcomes (Social Bond, Stress Relief), whereas older spectators favored functional health and daily performance outcomes (Physical Health, Work Efficiency). Text mining further illustrates that spectator happiness stems from interconnected themes of match performance, social connection, and rugby-specific cultural values (e.g., "No Side" spirit, mutual respect). By integrating macro, meso, and micro perspectives, this study extends sport consumer behavior theories beyond individual consumption toward societal outcomes, providing empirical support for sport management, sponsorship evaluation, and Social Return on Investment (SROI) applications.

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Published

21-09-2026

How to Cite

Sasaki, K., Tshuchida, M., Yamagami, T., Tamatsuka, G., Shoji, H., Kawahara, T., Sakamoto, N., Suzuki, H., & Kono, I. (2026). Beyond Spectatorship: A Macro–Meso–Micro Framework for Understanding Social Well-being through Rugby Spectatorship. Advances in Social Sciences and Management, 4(05), 64–74. https://doi.org/10.63002/assm.405.1727