Small- and medium-sized enterprises (SMEs) increasingly need to reconfigure how they create, deliver, and capture value in response to digitalisation, sustainability demands, and environmental uncertainty. This study systematically reviews 162 English-language articles published in SSCI-indexed journals to integrate fragmented research on business model innovation (BMI) in SMEs. Descriptive analysis, keyword co-occurrence analysis, and article-level qualitative content analysis are combined. The findings identify four broad knowledge domains and show that 41 studies treat BMI primarily as an outcome, 44 as an organisational process, 17 as an antecedent of subsequent outcomes, and 36 as part of a combined causal relationship; a further 24 studies are descriptive or typological. Across these roles, the literature explains BMI through resource mobilisation, entrepreneurial action logics, absorptive and dynamic capabilities, experimentation, and network mobilisation. Its effects on performance, growth, resilience, internationalisation, and sustainability depend on complementary resources, implementation capabilities, stakeholder alignment, and environmental conditions. The evidence supports an adaptive and iterative interpretation of SME BMI, but direct evidence of recursive feedback remains limited. Resource constraints also have conditional effects, stimulating bricolage and focused experimentation in some circumstances while inhibiting substantive change when shortages become severe. The review provides an integrative synthesis specific to SMEs and advances digital business model innovation research by explaining how digital pressures are translated into changes in value proposition, value creation and delivery, and value capture through organisational and relational mechanisms.

Digital platform firms accumulate business knowledge that can support AI-related business model innovation (BMI), yet how dynamic capabilities connect this legacy with new value arrangements remains insufficiently specified. Drawing on interviews, archival materials, and observations, this study reconstructs the 2016–March 2026 evolution of Alpha, a Chinese enterprise spend management platform. The findings trace three developmental configurations: single-scenario platformization, full-scenario integration, and movement toward AI agents. Sensing identifies changing service opportunities, seizing commits resources to successive service configurations, and reconfiguring redeploys supplier relationships and codified rules. Their differentiated but interdependent roles shape value proposition, value creation and delivery, and value capture. Early transaction linkages and subsequent workflow integration establish a cumulative knowledge foundation for AI-related renewal. We conceptualize knowledge-to-AI conversion as an emerging process within reconfiguring, connecting accumulated knowledge with rule-informed task assistance, reusable functions, and retained human approval. The study contributes a process model linking changing capability objects, functional knowledge redeployment, and differentiated BMI element changes during platform renewal toward AI agents.

HRMARS is well established journal publisher based in Pakistan. It is publishing prominent journals in social sciences since 2011.

HRMARS is well established journal publisher based in Pakistan. It is publishing prominent journals in social sciences since 2011.