Critical research directions for AI-driven KM in sustainability
| Research direction | Critical rationale and proposed focus |
|---|---|
| 1. Integrating robust theoretical perspectives | Theoretical fragmentation limits knowledge building. Future studies should center on the KBV, using DC and STS to explain how AI-generated assets drive sustainable competitive advantage |
| 2. Advancing methodological diversity | The dominance of cross-sectional designs obscures process dynamics. Research should combine longitudinal tracking, participatory action research and computational qualitative methods (e.g. NLP) to uncover how KM practices evolve alongside AI adoption |
| 3. Embedding human-centric AI and Industry 5.0 | Techno-optimism often ignores human agency. Research should investigate human–AI collaboration to mitigate sociocultural inhibitors, reduce technostress and foster human creativity within sustainable KM ecosystems |
| 4. Mapping multi-actor ecosystems and value co-creation | Superficial treatment of interorganizational dynamics ignores trust and coordination. Network-level studies are needed to examine governance structures and overcome knowledge-sharing inhibitors in the sustainable supply chain |
| 5. Tailoring solutions for startups and emerging economies | Geographic bias and the neglect of resource-limited organizations perpetuate sustainability inequities. Scholars should develop and validate lean AI–KM approaches suitable for SMEs, startups and underrepresented regions lacking advanced digital infrastructure |
| 6. Operationalizing ethical governance and social sustainability | Inadequate attention to ethics creates systemic risks. Rigorous empirical testing of accountability mechanisms, algorithmic bias detection and participatory design is urgently needed to ensure AI–KMS support social equity and the UN SDGs |
| Research direction | Critical rationale and proposed focus |
|---|---|
| 1. Integrating robust theoretical perspectives | Theoretical fragmentation limits knowledge building. Future studies should center on the KBV, using |
| 2. Advancing methodological diversity | The dominance of cross-sectional designs obscures process dynamics. Research should combine longitudinal tracking, participatory action research and computational qualitative methods (e.g. |
| 3. Embedding human-centric | Techno-optimism often ignores human agency. Research should investigate human–AI collaboration to mitigate sociocultural inhibitors, reduce technostress and foster human creativity within sustainable |
| 4. Mapping multi-actor ecosystems and value co-creation | Superficial treatment of interorganizational dynamics ignores trust and coordination. Network-level studies are needed to examine governance structures and overcome knowledge-sharing inhibitors in the sustainable supply chain |
| 5. Tailoring solutions for startups and emerging economies | Geographic bias and the neglect of resource-limited organizations perpetuate sustainability inequities. Scholars should develop and validate lean AI–KM approaches suitable for SMEs, startups and underrepresented regions lacking advanced digital infrastructure |
| 6. Operationalizing ethical governance and social sustainability | Inadequate attention to ethics creates systemic risks. Rigorous empirical testing of accountability mechanisms, algorithmic bias detection and participatory design is urgently needed to ensure AI–KMS support social equity and the |