Artificial intelligence adoption in supply chain risk management: Scale development and validation

Paul Souma Kanti (), Riaz Sadia and Das Suchismita
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Paul Souma Kanti: S P Jain School of Global Management, Mumbai
Riaz Sadia: S P Jain School of Global Management, Mumbai, India and Dubai, UAE
Das Suchismita: S P Jain School of Global Management, Mumbai

HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ECONOMICS AND BUSINESS ADMINISTRATION, 2022, vol. 12, issue 2, 15-32

Abstract: Artificial Intelligence (AI) can play an important role in the post-Covid-19 world to proactively enable the identification, assessment, and mitigation of supply chain risks as well as provide managerial insights for responding to those risks. There has been a growing interest among supply chain executives to adopt AI for Supply Chain Risk Management (SCRM). The purpose of this paper is to develop an instrument to assess and measure the factors influencing the adoption of AI in SCRM. The development of the instrument has been done in stages covering factor identification, item generation, pre-testing, pilot testing, and scale validation. Data has been collected through a survey of supply chain executives, risk professionals, and AI consultants across manufacturing, wholesale trade, retail trade, and services industries in India. The questionnaire has been pre-tested based on interviews with nine industry experts and two academicians. The scale has been assessed for reliability and validity using Confirmatory Factor Analysis. The scale generated consists of eight factors that are modeled as latent variables covering a total of twenty-eight items. The systematic approach followed resulted in a scale fulfilling a need for the creation of an empirically validated instrument for AI adoption studies in the field of SCRM. This instrument can be used by supply chain executives and researchers to examine and measure factors that influence the adoption of AI in SCRM for the selected industries.

Keywords: artificial intelligence; scale development; scale validation; SCRM; supply chain risk management (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:bjw:econen:v:12:y:2022:i:2:p:15-32

DOI: 10.46223/HCMCOUJS.econ.en.12.2.2142.2022

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