Since its emergence, the field of sport consumer behavior research (SCBR) has witnessed exponential growth in both popularity and complexity over the past two decades. With the proliferation of digital information, globalization of sports organizations, fans, and players’ real-time connections and interactions, the field is reaching an inflection point at which access to extensive quantities of data, novel contexts, and advanced statistical methods are readily available to retest and falsify the conventional assumptions and established frameworks. For instance, the previous research in sports marketing has failed to examine how cultural and geographical heterogeneities might affect managerial strategies that have been predominantly contingent upon empirical evidence collected from North America. Considering the scarcity of scholarly engagement focusing on the Asian context, Yoshida and Heere (2015) developed five research propositions through identifying various cultural and ideological considerations to build a foundation for guiding future cross-context research in sports marketing.

From a philosophy of science perspective, two complementary approaches dominate the practice of contemporary knowledge construction and diffusion in SCBR: hypothetico-deductive reasoning and observational-inductive reasoning. Assuming the singularity of truth to be explored, researchers who follow the post-positivist paradigms subscribe to the deductive "scientific" approach of applying and falsifying existing theories through hypothesis development and testing. The alternative inductive model, on the contrary, calls for scholars to examine context-specific factors that enable the development of new insights into inquiries of phenomena. To this end, this Research Topic echoes James' Zeigler's lecture in 2018 to call for a need to embrace both the breadth and depth of philosophical paradigms in guiding the conduct of scientific inquiries through employing different ontological and epistemological principles by featuring perspectives from the Asia Pacific region to which relatively less attention has been paid in the extant sports consumer behavior research. Without a systematic endeavor to expand the boundary applicable to different sociocultural economic settings, the field will be challenged in further cultivating and advancing pertinent theoretical development.

Methodologically, sport consumer behavior research has mainly relied on cross-sectional, single-source design, self-reported survey data to test theories and the derivative correlational nomological net, failing to provide compelling evidence for causal relationships among the variables and facing possible common method variance or mono-method bias. Relatively, less attention has been given to replications of programmatic empirical studies, inference of causation, and longitudinal research design. To bridge this gap, this Research Topic will also survey various advanced quantitative methods to demonstrate how researchers can benefit from these analytical tools to advance the practice of theory development, justification, and refinement and enhance validity, reliability, and scientific rigor of sport consumer behavior research. Collectively, our goal is to offer a bridge to demonstrate how new research contexts combined with methodological robustness can add heuristic value in promoting reciprocity in theory development and advancing knowledge discovery and diffusion in sport consumer behavior research.

We are especially interested in empirical, theoretical, systematic review, mini-review papers focusing on a wide spectrum of issues as the following:

• Bayesian network and the evolution of nomological net in sport consumer behavior research (SCBR).
• Plausible causal inference with mediation and sweet spot analysis in SCBR.
• Developing counterfactual models.
• Endogeneity and the selection of instrument variables using machine learning for causal inference from observational (or non-experimental) data.
• Enhancing the robustness, precision, and heterogeneity of causal inference in SCBR using meta-analysis.
• Establishing causality with regression-discontinuity and difference-in-difference designs in SCBR.
• Identifying unobserved consumer heterogeneity using unsupervised machine learning.
• Theory testing with lab, field, and natural experiments in both controlled and naturalistic settings.
• Addressing social and selection biases, confounding effects, and other common method biases in SCBR.
• Applications of digital dexterity in SCBR.

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