Eric Weisz, David M. Herold, Nadine Kathrin Ostern, Ryan Payne, Sebastian Kummer
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A five-stage AI collaboration framework for supply chains is presented, supporting managers to classify the supply chain collaboration stage in a company’s AI journey.Design/methodology/approachUsing existing literature on AI technology and collaboration and its effects of information sharing and trust, we present two frameworks to clarify (a) the interrelationships between information sharing, trust and AI capabilities and (b) develop a model illustrating five AI application stages how AI can be used for supply chain collaborations.FindingsWe identify various levels of interdependency between trust and AI capabilities and subsequently divide AI collaboration into five stages, namely complementary AI applications, augmentative AI applications, collaborative AI applications, autonomous AI applications and AI applications replacing existing systems.Originality/valueSimilar to the five stages of autonomous driving, the categorisation of AI collaboration along the supply chain into five consecutive stages provides insight into collaborations practices and represents a practical management tool to better understand the utilisation of AI capabilities in a supply chain environment.","PeriodicalId":54683,"journal":{"name":"Online Information Review","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence (AI) for supply chain collaboration: implications on information sharing and trust\",\"authors\":\"Eric Weisz, David M. 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Artificial intelligence (AI) for supply chain collaboration: implications on information sharing and trust
PurposeManagers and scholars alike claim that artificial intelligence (AI) represents a tool to enhance supply chain collaborations; however, existing research is limited in providing frameworks that categorise to what extent companies can apply AI capabilities and support existing collaborations. In response, this paper clarifies the various implications of AI applications on supply chain collaborations, focusing on the core elements of information sharing and trust. A five-stage AI collaboration framework for supply chains is presented, supporting managers to classify the supply chain collaboration stage in a company’s AI journey.Design/methodology/approachUsing existing literature on AI technology and collaboration and its effects of information sharing and trust, we present two frameworks to clarify (a) the interrelationships between information sharing, trust and AI capabilities and (b) develop a model illustrating five AI application stages how AI can be used for supply chain collaborations.FindingsWe identify various levels of interdependency between trust and AI capabilities and subsequently divide AI collaboration into five stages, namely complementary AI applications, augmentative AI applications, collaborative AI applications, autonomous AI applications and AI applications replacing existing systems.Originality/valueSimilar to the five stages of autonomous driving, the categorisation of AI collaboration along the supply chain into five consecutive stages provides insight into collaborations practices and represents a practical management tool to better understand the utilisation of AI capabilities in a supply chain environment.
期刊介绍:
The journal provides a multi-disciplinary forum for scholars from a range of fields, including information studies/iSchools, data studies, internet studies, media and communication studies and information systems.
Publishes research on the social, political and ethical aspects of emergent digital information practices and platforms, and welcomes submissions that draw upon critical and socio-technical perspectives in order to address these developments.
Welcomes empirical, conceptual and methodological contributions on any topics relevant to the broad field of digital information and communication, however we are particularly interested in receiving submissions that address emerging issues around the below topics.
Coverage includes (but is not limited to):
•Online communities, social networking and social media, including online political communication; crowdsourcing; positive computing and wellbeing.
•The social drivers and implications of emerging data practices, including open data; big data; data journeys and flows; and research data management.
•Digital transformations including organisations’ use of information technologies (e.g. Internet of Things and digitisation of user experience) to improve economic and social welfare, health and wellbeing, and protect the environment.
•Developments in digital scholarship and the production and use of scholarly content.
•Online and digital research methods, including their ethical aspects.