Unlocking sustainable supply chain performance through dynamic data analytics: a multiple mediation model of sustainable innovation and supply chain resilience

Piprani, Arsalan Zahid; Khan, Syed Abdul Rehman; Salim, R.; Rahman, Muhammad Khalilur

doi:10.1007/s11356-023-28507-8

Cited by 13 publications

(8 citation statements)

References 147 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Collaborative innovation means that enterprises in the supply chain can find partners, integrate their resources and technologies, and realize collaborative technology innovation and management innovation [ 52 ]. Product development refers to the time and cost required by a company to develop a new product after it has an insight into a business opportunity [ 53 ]. By strengthening the learning organization, collaborative innovation ability and product development capability of the supply chain, it can not only promote communication between all members of the supply chain, alleviate the "bullwhip effect", but also enhance the core competitiveness of the supply chain and improve the resilience of the supply chain.…”

Section: Supply Chain Resilience Evaluation Index Systemmentioning

confidence: 99%

Resilience evaluation of low-carbon supply chain based on improved matter-element extension model

Luo,

Kang,

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

How to evaluate the resilience level and change trend of supply chain is an important research direction in current supply chain management practice. This paper proposes a new method of supply chain resilience assessment based on hesitant fuzzy linguistic term set (HFLTS) and matter element extension theory. Firstly, based on the research status quo at home and abroad, a low-carbon enterprise supply chain resilience assessment index system is established, which includes six first-level indicators and corresponding 21 second-level indicators of product supply resilience, resource resilience, partner resilience, information response resilience, financial resilience and knowledge resilience. Secondly, HFLTS was used to collect expert opinions and Ordered Weighted Arithmetic (OWA) to calculate the expert composite language, by which the fuzzy evaluation matrix of supply chain resilience assessment indicators was obtained. Once again, the resilience indicator weights are determined based on a game-theoretic portfolio assignment method combining the best-worst method (BWM) and the CRITIC method. Finally, the nearness degree function is combined with the extension comprehensive evaluation method to improve the matter element extension model, and the supply chain resilience assessment model of low-carbon enterprises based on the game theory combination assignment-improved matter element extension is established. Taking X low-carbon enterprise as an example, the evaluation results show that the supply chain resilience level of this enterprise is II, and the eigenvalue of the grade variable is 2.69, and the supply chain resilience is shifting to III, and the supply chain resilience is shifting to III, which indicates that the supply chain resilience of this enterprise is being enhanced. Therefore, the improved matter element extension not only ensures the accuracy of the evaluation results, but also has higher prediction accuracy.

show abstract

Section: Supply Chain Resilience Evaluation Index Systemmentioning

confidence: 99%

Resilience evaluation of low-carbon supply chain based on improved matter-element extension model

Luo,

Kang,

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Therefore, the present study used DC theory because it emphasizes an organization's ability to recognize the potential benefits of new technologies like AICs and integrate them into their supply chain processes to bring real value [11]. By adopting AICs, organizations can enhance SCV [18], streamline operations, and respond more effectively to market demands [19]. DC theory also highlights the importance of building resilience to cope with disruptions and uncertainties.…”

Section: Theoretical Foundations and Hypothesis Developmentmentioning

confidence: 99%

“…DC theory also highlights the importance of building resilience to cope with disruptions and uncertainties. By adopting AI technologies, organizations can better predict and manage supply chain risks, leading to increased resilience and sustainability [19].…”

Section: Theoretical Foundations and Hypothesis Developmentmentioning

confidence: 99%

“…Additionally, SCV, achieved through real-time data monitoring and analytics, empowers organizations to identify potential risks and opportunities, optimize logistics, and make informed decisions that positively impact sustainability goals. Together, supply chain innovation capabilities and visibility contribute to a more resilient and environmentally responsible supply chain, ultimately enhancing overall SSCP [19]. AI-powered service innovation is projected to change service ecosystems, adoption, diffusion, organizational resources, and capabilities.…”

Section: Ic and Scv Influence On Sscpmentioning

confidence: 99%

See 1 more Smart Citation

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

Panigrahi,

Shrivastava,

Qureshi

et al. 2023

Sustainability

View full text Add to dashboard Cite

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.

show abstract

“…Prior research has delved into the impact of GSCM on diverse domains, encompassing organizational performance (Abdallah and Al-Ghwayeen, 2020), firm performance (Agyabeng-Mensah et al, 2020), environmental efficacy (Le, 2020), environmental consciousness (Sheng et al, 2023), as well as the challenges related to closed-loop and reverse supply chain processes (Piprani et al, 2023) and sustainable outcome (Alhamali, 2019). Recent scholarly literature has also examined the changing trends and forthcoming challenges in the domain of GSCM (Alhamali, 2019).…”

Section: Introductionmentioning

confidence: 99%

Exploration of the impact of green supply chain management practices on manufacturing firms’ performance through a mediated-moderated model

Zhaolei,

Nazir,

Hussain

et al. 2023

Front. Environ. Sci.

View full text Add to dashboard Cite

This study aimed to examine the impact of green supply chain management (GSCM) practices on the performance of manufacturing firms. It explored the mediating role of green innovation (GI) and the moderating role of information sharing (IS) and information technology innovation (ITI) on the relationship between GSCM practices and manufacturing firms’ performance. Data were collected through a structured questionnaire from employees working in manufacturing firms in Pakistan. Random sampling was used to select the participants. The hypotheses were tested using partial least squares (PLS) analysis. The study revealed that GSCM practices significantly positively impacted manufacturing firms’ performance. It highlighted the mediating role of green innovation, suggesting that GSCM practices fostered innovation and improved firm performance. The study also uncovered the moderating role of information sharing and IT innovation, indicating that the effectiveness of GSCM practices in driving performance varied based on the extent of information sharing and IT innovation within the industry. The findings of this study had practical implications for industry practitioners and policymakers. Implementing effective GSCM practices and promoting green innovation could enhance firm performance in the manufacturing sector. Recognizing the importance of information sharing and IT innovation was crucial, as they significantly enhanced the effectiveness and efficiency of GSCM practices. This study contributed to the existing literature by examining the relationships among GSCM practices, green innovation, information sharing, IT innovation, and manufacturing firm performance. It provided insights into the mechanisms through which GSCM practices impacted firm performance, offering a comprehensive understanding of the role of green innovation and the moderating effects of information sharing and IT innovation.

show abstract

Unlocking sustainable supply chain performance through dynamic data analytics: a multiple mediation model of sustainable innovation and supply chain resilience

Cited by 13 publications

References 147 publications

Resilience evaluation of low-carbon supply chain based on improved matter-element extension model

Resilience evaluation of low-carbon supply chain based on improved matter-element extension model

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

Exploration of the impact of green supply chain management practices on manufacturing firms’ performance through a mediated-moderated model

Contact Info

Product

Resources

About