Organizational Adoption of Artificial Intelligence in Supply Chain Risk Management

Paul, Souma Kanti; Riaz, Sadia; Das, Suchismita

doi:10.1007/978-3-030-64849-7_2

Cited by 10 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As per the TOE framework, the research model considers the technological, organizational, and environmental contexts. Paul et al (2020) proposed a conceptual model for the adoption of AI in SCRM based on the TOE framework and introduced new factors based on a qualitative study in India. This study adopts and further builds on this conceptual model.…”

Section: Research Model and Hypothesesmentioning

confidence: 99%

Adoption of Artificial Intelligence in Supply Chain Risk Management

Paul

Riaz²,

Das

2022

Journal of Global Information Management

Self Cite

View full text Add to dashboard Cite

The study aims to examine factors that influence the adoption-diffusion process of Artificial Intelligence (AI) in Supply Chain Risk Management (SCRM) across manufacturing, wholesale trade, retail trade, and transportation industries in India. As part of this study, eleven constructs that influence the adoption-diffusion stages of AI in SCRM were identified and examined. A survey was conducted to collect data from supply chain executives, risk professionals, and AI consultants across the manufacturing, wholesale trade, retail trade, and transportation industries in India. Partial least squares structural equation modeling (PLS-SEM) was used to study the data. Results show that these factors have varying degrees of influence and direction on the three stages of adoption of AI in SCRM. The study will enable the leadership team in the organizations to build a roadmap for the adoption, implementation, and routinization of AI in SCRM.

show abstract

Section: Research Model and Hypothesesmentioning

confidence: 99%

Adoption of Artificial Intelligence in Supply Chain Risk Management

Paul

Riaz²,

Das

2022

Journal of Global Information Management

Self Cite

View full text Add to dashboard Cite

show abstract

“…While the contexts are based on the TOE framework, many of the factors are different when compared to past literature on technology adoption, and they are unique to the needs of AI adoption in SCRM for industries in India. Considering past research on AI adoption in SCRM (Paul et al, 2020) and taking into consideration feedback from SMEs, the following factors have been determined for each of the three contexts.…”

Section: Identification Of Constructsmentioning

confidence: 99%

“…While applications of AI in SCRM have been studied in detail, there is a gap related to the study of factors that influence the adoption of AI in SCRM at an organizational level. Paul, Riaz, and Das (2020) have proposed a conceptual model for the adoption of AI in SCRM based on the TOE framework and introduced new factors based on a qualitative study in India, the study does not provide a measurement instrument. Moreover, while past research has covered adoption studies on technologies like predictive analytics, big data analytics, and other similar technologies (Banerjee & Banerjee, 2017;Chen, Preston, & Swink, 2015;Malladi, 2013), that provide a measurement instrument, there is no relevant study on the adoption of AI in the context of SCRM that has provided an empirically tested measurement instrument.…”

Section: Introductionmentioning

confidence: 99%

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

Kanti¹,

Sadia²,

Das³

2022

HCMCOUJS - ECONOMICS AND BUSINESS ADMINISTRATION

View full text Add to dashboard Cite

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.

show abstract

“…Nychas et al [69] describe the potential of internet technologies for dealing with perishable products, while the Smith publication [70] discusses the potential added value that AI can bring to agriculture in the next decade; both also fail to provide concrete examples of possible applications. Finally, Paul et al [71] discuss the application of AI adoptions in the field of SCRM. Based on a qualitative study in India they propose a research model on the implementation of AI in SCRM at an organizational level.…”

Section: Number Of Published Papersmentioning

confidence: 99%

A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management

Schroeder

Lodemann

2021

Logistics

View full text Add to dashboard Cite

The main objective of the paper is to analyze and synthesize existing scientific literature related to supply chain areas where machine learning (ML) has already been implemented within the supply chain risk management (SCRM) field, both in theory and in practice. Furthermore, we analyzed which risks were addressed in the use cases as well as how ML might shape SCRM. For this purpose, we conducted a systematic literature review. The results showed that the applied examples relate primarily to the early identification of production, transport, and supply risks in order to counteract potential supply chain problems quickly. Through the analyzed case studies, we were able to identify the added value that ML integration can bring to the SCRM (e.g., the integration of new data sources such as social media or weather data). From the systematic literature analysis results, we developed four propositions, which can be used as motivation for further research.

show abstract

Organizational Adoption of Artificial Intelligence in Supply Chain Risk Management

Cited by 10 publications

References 13 publications

Adoption of Artificial Intelligence in Supply Chain Risk Management

Adoption of Artificial Intelligence in Supply Chain Risk Management

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

A Systematic Investigation of the Integration of Machine Learning into Supply Chain Risk Management

Contact Info

Product

Resources

About