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The prevailing conceptualization of information system (IS) capabilities, rooted in the resource-based view (RBV) framework, tends to focus on unique firm resources. In the digital age, as emphasized by dynamic capabilities (DC), resource reconfiguration is critical in maintaining strategic advantage. This paper focuses on big data analytics capabilities (BDAC) from a DC perspective to present a novel conceptualization of BDAC–DC. We examine its effects on product, business model and business process innovation, including the effects of enterprise architecture (EA) on the BDAC business model innovation relationship.

This research presents a novel DC-based BDAC conceptualization, operationalized as a hierarchical construct. A survey-based approach is used for data collection and data analysis is done using partial least squares structural equation modeling (PLS-SEM).

The novel conceptualization and the effects of BDAC DC on BDA sensing-seizing and reconfiguration capacities support BDAC’s functional and evolutionary roleplay. Empirical results confirm the positive effects of BDAC–DC on first-order value targets (innovation) and the moderating effects of EA.

The novel BDAC–DC conceptualization has several implications for BDAC, DC, EA and business value research. Practicing managers must adopt a multifaceted approach to BDAC development by considering non-technical and organizational factors, collaborate with their business counterparts to explore unique big data ideas, initiate proof-of-concept projects to secure support and allocate resources synchronously, considering a multidimensional view of the process, product and business model innovation.

Practicing managers must adopt a multifaceted approach to BDAC development by considering non-technical and organizational factors, collaborate with their business counterparts to explore unique big data ideas, initiate proof-of-concept projects to secure support and allocate resources synchronously, considering a multidimensional view of the process, product and business model innovation for synergistic outcomes.

To the best of our knowledge, this research is the first attempt toward DC-based BDAC conceptualization, empirical validation of first-order effects on various forms of innovation and the often-overlooked role of critical EA capability.

This study aims to understand the kind of content and context that effectively create higher fan social media engagement (SME) through pre-match content posted by sports teams.

This research examines the effect of inspirational, informational, entertaining and warmth content appeal on affective and cognitive responses from fans in the form of likes and shares. Messages on X (previously Twitter), chosen as a representative social media platform, from the teams participating in the Indian Premier League, were analysed using regression models to validate the proposed model empirically.

For sports clubs, entertaining, warmth and inspirational content is more effective than information content in generating likes on social media. Content with high vividness is effective only for sports teams with high performance. Fans of low-performance teams exhibit higher responsiveness to content with inspirational appeal.

This research contributes to the sports marketing literature by examining the influential role of warmth and inspirational content in generating higher SME in the pre-match context.

This study provides prescriptions to sports clubs for leveraging social media platforms to engage their fans through appropriate content. Given the growth of sports leagues in developing and developed countries, this study provides guidelines to sports clubs for effective social media marketing.

To the best of the authors’ knowledge, this study is among the first to integrate social identity theory and elaboration likelihood model theoretical frameworks to study fan engagement with social media content posted by sports clubs.