FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384199462> ?p ?o ?g. }

• W4384199462 abstract "Purpose Decision-making, reinforced by artificial intelligence (AI), is predicted to become potent tool within the domain of supply chain management. Considering the importance of this subject, the purpose of this study is to explore the triggers and technological inhibitors affecting the adoption of AI. This study also aims to identify three-dimensional triggers, notably those linked to environmental, social, and governance (ESG), as well as technological inhibitors. Design/methodology/approach Drawing upon a six-step systematic review following the preferred reporting items for systematic reviews and meta analysis (PRISMA) guidelines, a broad range of journal publications was recognized, with a thematic analysis under the lens of the ESG framework, offering a unique perspective on factors triggering and inhibiting AI adoption in the supply chain. Findings In the environmental dimension, triggers include product waste reduction and greenhouse gas emissions reduction, highlighting the potential of AI in promoting sustainability and environmental responsibility. In the social dimension, triggers encompass product security and quality, as well as social well-being, indicating how AI can contribute to ensuring safe and high-quality products and enhancing societal welfare. In the governance dimension, triggers involve agile and lean practices, cost reduction, sustainable supplier selection, circular economy initiatives, supply chain risk management, knowledge sharing and the synergy between supply and demand. The inhibitors in the technological category present challenges, encompassing the lack of regulations and rules, data security and privacy concerns, responsible and ethical AI considerations, performance and ethical assessment difficulties, poor data quality, group bias and the need to achieve synergy between AI and human decision-makers. Research limitations/implications Despite the use of PRISMA guidelines to ensure a comprehensive search and screening process, it is possible that some relevant studies in other databases and industry reports may have been missed. In light of this, the selected studies may not have fully captured the diversity of triggers and technological inhibitors. The extraction of themes from the selected papers is subjective in nature and relies on the interpretation of researchers, which may introduce bias. Originality/value The research contributes to the field by conducting a comprehensive analysis of the diverse factors that trigger or inhibit AI adoption, providing valuable insights into their impact. By incorporating the ESG protocol, the study offers a holistic evaluation of the dimensions associated with AI adoption in the supply chain, presenting valuable implications for both industry professionals and researchers. The originality lies in its in-depth examination of the multifaceted aspects of AI adoption, making it a valuable resource for advancing knowledge in this area." @default.
• W4384199462 created "2023-07-14" @default.
• W4384199462 creator A5002840809 @default.
• W4384199462 creator A5059391208 @default.
• W4384199462 date "2023-07-17" @default.
• W4384199462 modified "2023-09-25" @default.
• W4384199462 title "Artificial intelligence in supply chain decision-making: an environmental, social, and governance triggering and technological inhibiting protocol" @default.
• W4384199462 cites W1979290264 @default.
• W4384199462 cites W1998485133 @default.
• W4384199462 cites W2001977159 @default.
• W4384199462 cites W2004605055 @default.
• W4384199462 cites W2008170547 @default.
• W4384199462 cites W2013494753 @default.
• W4384199462 cites W2015453663 @default.
• W4384199462 cites W2017073647 @default.
• W4384199462 cites W2028994818 @default.
• W4384199462 cites W2033693670 @default.
• W4384199462 cites W2057242481 @default.
• W4384199462 cites W2068154806 @default.
• W4384199462 cites W2085723930 @default.
• W4384199462 cites W2132697108 @default.
• W4384199462 cites W2150416324 @default.
• W4384199462 cites W2208053283 @default.
• W4384199462 cites W2302535939 @default.
• W4384199462 cites W2489461319 @default.
• W4384199462 cites W2560860553 @default.
• W4384199462 cites W2726904498 @default.
• W4384199462 cites W2730265215 @default.
• W4384199462 cites W2755051898 @default.
• W4384199462 cites W2791592925 @default.
• W4384199462 cites W2889731880 @default.
• W4384199462 cites W2893740026 @default.
• W4384199462 cites W2903175657 @default.
• W4384199462 cites W2905671087 @default.
• W4384199462 cites W2947788863 @default.
• W4384199462 cites W2955294344 @default.
• W4384199462 cites W2995946162 @default.
• W4384199462 cites W2997788383 @default.
• W4384199462 cites W2998574317 @default.
• W4384199462 cites W3007397514 @default.
• W4384199462 cites W3034251721 @default.
• W4384199462 cites W3034465384 @default.
• W4384199462 cites W3038156993 @default.
• W4384199462 cites W3095984860 @default.
• W4384199462 cites W3097839885 @default.
• W4384199462 cites W3112603225 @default.
• W4384199462 cites W3118651707 @default.
• W4384199462 cites W3131345956 @default.
• W4384199462 cites W3136944422 @default.
• W4384199462 cites W3142712062 @default.
• W4384199462 cites W3148196797 @default.
• W4384199462 cites W3163163834 @default.
• W4384199462 cites W3164963453 @default.
• W4384199462 cites W3166950124 @default.
• W4384199462 cites W3173781911 @default.
• W4384199462 cites W3178853696 @default.
• W4384199462 cites W3180686387 @default.
• W4384199462 cites W3181784162 @default.
• W4384199462 cites W3194368124 @default.
• W4384199462 cites W3197052056 @default.
• W4384199462 cites W3207433782 @default.
• W4384199462 cites W3208324058 @default.
• W4384199462 cites W3212467746 @default.
• W4384199462 cites W4210758929 @default.
• W4384199462 cites W4214809906 @default.
• W4384199462 cites W4224210268 @default.
• W4384199462 cites W4225012614 @default.
• W4384199462 cites W4225015806 @default.
• W4384199462 cites W4225129558 @default.
• W4384199462 cites W4226165328 @default.
• W4384199462 cites W4226529654 @default.
• W4384199462 cites W4232488826 @default.
• W4384199462 cites W4283695239 @default.
• W4384199462 cites W4292063383 @default.
• W4384199462 cites W4292263282 @default.
• W4384199462 cites W4292986062 @default.
• W4384199462 cites W4295947586 @default.
• W4384199462 cites W4296512863 @default.
• W4384199462 cites W4296905257 @default.
• W4384199462 cites W4302774341 @default.
• W4384199462 cites W4304959477 @default.
• W4384199462 cites W4306972426 @default.
• W4384199462 cites W4307486628 @default.
• W4384199462 cites W4307956789 @default.
• W4384199462 cites W4309194907 @default.
• W4384199462 cites W4313368041 @default.
• W4384199462 cites W4318426690 @default.
• W4384199462 cites W4318777803 @default.
• W4384199462 cites W4323044237 @default.
• W4384199462 cites W89786255 @default.
• W4384199462 doi "https://doi.org/10.1108/jm2-01-2023-0009" @default.
• W4384199462 hasPublicationYear "2023" @default.
• W4384199462 type Work @default.
• W4384199462 citedByCount "0" @default.
• W4384199462 crossrefType "journal-article" @default.
• W4384199462 hasAuthorship W4384199462A5002840809 @default.
• W4384199462 hasAuthorship W4384199462A5059391208 @default.
• W4384199462 hasConcept C10138342 @default.
• W4384199462 hasConcept C108713360 @default.
• W4384199462 hasConcept C112930515 @default.

• next