In response to growing sustainability imperatives and environmental uncertainties, startups are increasingly expected to embed green values into their strategic and operational processes. However, their limited resources, organisational learning base, and process maturity often hinder their capacity to achieve sustainable competitive advantage. Drawing on an integrative review of the literature, the study conceptually examines how AI-driven knowledge management (KM) contributes to the sustainability of startups. Based on the Resource-Based View and Dynamic Capability Theory, this paper identifies and integrates six core constructs: Green Entrepreneurial Orientation (GEO), Green Knowledge Management (GKM), Dynamic Capabilities (DCs), Big Data Analytics and Artificial Intelligence (BDA-AI), and Sustainable Competitive Advantage (SCA). These constructs are synthesised into a conceptual model that explains the mechanisms through which green strategic intent and knowledge practices interact with digital enablers to foster sustainable advantage. The proposed model highlights three key pathways. First, GEO is positioned as a strategic antecedent that directly enhances GKM and SCA, indicating that a green entrepreneurial mindset can drive sustainability-oriented knowledge processes and competitive outcomes. Second, GKM enhances DCs, which in turn contributes to SCA, with DCs mediating the GKM-SCA relationship, reinforcing the role of dynamic adaptability in transforming knowledge into sustainable advantage. Third, BDA-AI is proposed as a moderator that strengthens the effects of GKM on DCs and DCs on SCA by accelerating sensing, learning, and reconfiguration processes, particularly in uncertain and resource-constrained environments. Building upon these interrelationships, eight hypotheses are developed to construct the conceptual framework and provide a basis for future empirical validation. This study advances the theoretical discourse on sustainable entrepreneurship by integrating sustainability, KM, and AI perspectives. It offers practical insights for startups and policymakers seeking to leverage digital technologies for long-term viability. While conceptual, the proposed model provides a foundation for empirical validation and future refinements to account for contextual dynamics such as industry-specific challenges and policy influences.
Cai et al. (Fri,) studied this question.