{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T01:33:14Z","timestamp":1784079194669,"version":"3.55.0"},"reference-count":107,"publisher":"Emerald","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,9]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Generative artificial intelligence (GEN-AI) is becoming increasingly vital across various business sectors, particularly in the context of supply chain sustainability. This study focused on the manufacturing sector, examining how technology readiness (TR) affects the adoption of GEN-AI and its impact on supply chain efficiency (SCE), circular supply chain performance (CSCP) and sustainable supply chain performance (SSCP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design\/methodology\/approach<\/jats:title>\n                    <jats:p>The study utilised stimulus-organism-response (S-O-R) theory and structural equation modelling through SPSS and AMOS to analyse survey data collected from 226 manufacturing firms in India.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>The research found that TR has a positive influence on GEN-AI adoption, significantly mediating the relationship between TR and circular and SSCP. However, GEN-AI adoption was found to have a negative but statistically insignificant impact on SCE, suggesting that the TR of a firm does not enhance efficiency with GEN-AI adoption. This implies that higher TR may not improve SCE with GEN-AI adoption in the manufacturing sector, particularly at the early stage of GEN-AI adoption, which is still evolving.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Practical implications<\/jats:title>\n                    <jats:p>The empirical findings reveal that the adoption of GEN-AI to solve efficiency issues is still in its infancy. Nevertheless, it has significant potential due to a high degree of awareness. Managers must provide comprehensive training and adoption strategies to drive transformation. By integrating GEN-AI, organisations can meet environmental standards while gaining competitive advantages. A focus on TR and the integration of GEN-AI will strengthen supply chains and reshape supply chain management priorities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Originality\/value<\/jats:title>\n                    <jats:p>This study connects GEN-AI adoption to a firm\u2019s TR and illustrates how the S-O-R theory can explain supply chain outcomes. It achieves this by adapting the S-O-R framework to consider technological readiness as an internal organisational stimulus, offering a practical application at the managerial level. By doing so, it contributes a novel perspective on how internal capability \u201ctechnological readiness\u201d can drive GEN-AI adoption and improve sustainable CSCP.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1108\/jeim-02-2025-0072","type":"journal-article","created":{"date-parts":[[2025,9,12]],"date-time":"2025-09-12T01:35:33Z","timestamp":1757640933000},"page":"736-758","source":"Crossref","is-referenced-by-count":18,"title":["Generative artificial intelligence adoption for achieving supply chain\u00a0efficiency, circularity and sustainability"],"prefix":"10.1108","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6508-8002","authenticated-orcid":true,"given":"Gunjan","family":"Malhotra","sequence":"first","affiliation":[{"name":"Institute of Management Technology Ghaziabad Department of Operations Management, , ,","place":["Ghaziabad, 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