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Article
Publication date: 25 June 2024

Haiqing Shi, Taiwen Feng, Lucheng Chen and Xiaoping Lu

Despite the growing interest in enhancing mass customization capability (MCC), firms still have little knowledge of dealing with the superimposed challenges of increased market…

Abstract

Purpose

Despite the growing interest in enhancing mass customization capability (MCC), firms still have little knowledge of dealing with the superimposed challenges of increased market uncertainty and supply chain disruptions. Based on the dynamic capability view, this study focuses on the impacts of frequent sensing and reconfiguring processes on MCC and the mediating roles of proactive and reactive supply chain resilience (SCR).

Design/methodology/approach

We collected survey data from 277 manufacturing firms and conducted a structural equation model to test hypotheses.

Findings

The results reveal that although its direct effect on MCC is insignificant, sensing process improves MCC indirectly via reactive SCR. Our findings also show that reconfiguring process enhances MCC both directly and indirectly via reactive SCR.

Originality/value

This study provides theoretical and practical insights into how to combine dynamic capability and SCR to strengthen MCC.

Details

Journal of Manufacturing Technology Management, vol. 35 no. 7
Type: Research Article
ISSN: 1741-038X

Keywords

Article
Publication date: 31 October 2024

Md. Ramjan Ali, Sharfuddin Ahmed Khan, Yasanur Kayikci and Muhammad Shujaat Mubarik

Blockchain technology is one of the major contributors to supply chain sustainability because of its inherent features. However, its adoption rate is relatively low due to reasons…

Abstract

Purpose

Blockchain technology is one of the major contributors to supply chain sustainability because of its inherent features. However, its adoption rate is relatively low due to reasons such as the diverse barriers impeding blockchain adoption. The purpose of this study is to identify blockchain adoption barriers in sustainable supply chain and uncovers their interrelationships.

Design/methodology/approach

A three-phase framework that combines machine learning (ML) classifiers, BORUTA feature selection algorithm, and Grey-DEMATEL method. From the literature review, 26 potential barriers were identified and evaluated through the performance of ML models with accuracy and f-score.

Findings

The findings reveal that feature selection algorithm detected 15 prominent barriers, and random forest (RF) classifier performed with the highest accuracy and f-score. Moreover, the performance of the RF increased by 2.38% accuracy and 2.19% f-score after removing irrelevant barriers, confirming the validity of feature selection algorithm. An RF classifier ranked the prominent barriers and according to ranking, financial constraints, immaturity, security, knowledge and expertise, and cultural differences resided at the top of the list. Furthermore, a Grey-DEMATEL method is employed to expose interrelationships between prominent barriers and to provide an overview of the cause-and-effect group.

Practical implications

The outcome of this study can help industry practitioners develop new strategies and plans for blockchain adoption in sustainable supply chains.

Originality/value

The research on the adoption of blockchain technology in sustainable supply chains is still evolving. This study contributes to the ongoing debate by exploring how practitioners and decision-makers adopt blockchain technology, developing strategies and plans in the process.

Details

Industrial Management & Data Systems, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0263-5577

Keywords

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