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Leveraging artificial intelligence to facilitate green servitization: Resource orchestration and Re-institutionalization perspectives

Author

Listed:
  • Chowdhury, Soumyadeb
  • Ren, Shuang
  • Richey, Robert Glenn

Abstract

Research in operations and supply chain management (O&SCM) has highlighted drivers of digital servitization, along with the influence of Industry 4.0 technologies, such as artificial intelligence (AI). Amidst growing environmental concerns, green servitization (GS) has emerged as a strategic alternative to achieve sustainability. However, few studies have examined the interaction between AI capabilities and GS. To address this gap, this study integrates the resource orchestration theory and theory of institutional entrepreneurship for sustainable organizations, to develop a conceptual model examining the relationship between AI-driven decision support systems (ADSS) capabilities, supply-chain alertness (SCA), resource orchestration (REO), re-institutionalization (REI), circular economy practices (CEP), and GS. A survey was conducted with 248 UK supply chain managers and partial least squares-structural equation modelling was used for analysis. The results indicate that ADSS capabilities will significantly enhance SCA (β = 0.597), and the latter will significantly influence REO (β = 0.461), REI (β = 0.495), and CEP (β = 0.160). We also found that the CEP will significantly impact GS (β = 0.781). Both REO (β = 0.169) and REI (β = 0.142) significantly mediates the relationship between SCA and CEP, i.e., REO and REI will facilitate implementing CEP resulting from AI-driven SCA. These findings highlight the critical role of ADSS in enabling managers to orchestrate resources and implement new institutional frameworks, essential for adopting GS in the supply chain.

Suggested Citation

  • Chowdhury, Soumyadeb & Ren, Shuang & Richey, Robert Glenn, 2025. "Leveraging artificial intelligence to facilitate green servitization: Resource orchestration and Re-institutionalization perspectives," International Journal of Production Economics, Elsevier, vol. 281(C).
  • Handle: RePEc:eee:proeco:v:281:y:2025:i:c:s0925527325000040
    DOI: 10.1016/j.ijpe.2025.109519
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