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AI Chatbot Adoption in SMEs for Sustainable Manufacturing Supply Chain Performance: A Mediational Research in an Emerging Country

Author

Listed:
  • Rashmi Ranjan Panigrahi

    (GITAM School of Business, GITAM (Deemed to Be University), Visakhapatnam 530045, India)

  • Avinash K. Shrivastava

    (International Management Institute Kolkata, Kolkata 700027, India)

  • Karishma M. Qureshi

    (Department of Mechanical Engineering, Parul Institute of Technology, Parul University, Waghodia 391760, India)

  • Bhavesh G. Mewada

    (Department of Mechanical Engineering, Parul Institute of Technology, Parul University, Waghodia 391760, India)

  • Saleh Yahya Alghamdi

    (Department of Industrial Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia)

  • Naif Almakayeel

    (Department of Industrial Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia)

  • Ali Saeed Almuflih

    (Department of Industrial Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia)

  • Mohamed Rafik N. Qureshi

    (Department of Industrial Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi Arabia)

Abstract

AI chatbots (AICs) have the potential to increase the sustainability of a manufacturing supply chain (SC) through sales engagement and customer engagement to accomplish various activities related to logistics and SC in real time. Industry 4.0 (I4.0) has opened up several opportunities with internet-based technologies, along with challenges for small and medium enterprises (SMEs). SMEs are beginning to adopt such technologies for their competitive advantages and the required sustainability in the manufacturing supply chain. AICs may help in accomplishing supply chain visibility (SCV) to enhance sustainable supply chain performance (SSCP). Innovation capability (IC) is also due to disruptive technologies being adopted by SMEs. The present research investigates the role of AICs in SCV and IC, which lead to SSCP, by employing structural equation modeling (SEM). An empirical study based on dynamic capability (DC) theory was carried out using 246 responses, and later Smart PLS-4.0 was used for SEM. The analysis revealed that AICs positively influence SCV and IC to support SSCP. SCV and IC also partially mediate the relationship between the adoption of AICs and SSCP.

Suggested Citation

  • Rashmi Ranjan Panigrahi & Avinash K. Shrivastava & Karishma M. Qureshi & Bhavesh G. Mewada & Saleh Yahya Alghamdi & Naif Almakayeel & Ali Saeed Almuflih & Mohamed Rafik N. Qureshi, 2023. "AI Chatbot Adoption in SMEs for Sustainable Manufacturing Supply Chain Performance: A Mediational Research in an Emerging Country," Sustainability, MDPI, vol. 15(18), pages 1-18, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13743-:d:1240083
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    References listed on IDEAS

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    1. Hair, Joe F. & Howard, Matt C. & Nitzl, Christian, 2020. "Assessing measurement model quality in PLS-SEM using confirmatory composite analysis," Journal of Business Research, Elsevier, vol. 109(C), pages 101-110.
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    Cited by:

    1. Mirela Cătălina Türkeș & Cristian-Silviu Bănacu & Laurențiu Stoenică, 2024. "The Effect of Supply Chain Sustainability Practices on Romanian SME Performance," Sustainability, MDPI, vol. 16(7), pages 1-28, March.
    2. Karishma M. Qureshi & Bhavesh G. Mewada & Sumeet Kaur & Saleh Yahya Alghamdi & Naif Almakayeel & Ali Saeed Almuflih & Mohamed Rafik Noor Mohamed Qureshi, 2023. "Sustainable Manufacturing Supply Chain Performance Enhancement through Technology Utilization and Process Innovation in Industry 4.0: A SEM-PLS Approach," Sustainability, MDPI, vol. 15(21), pages 1-20, October.

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