A hybrid framework to prioritize the performance metrics for Blockchain technology adoption in manufacturing industries

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-09-17 DOI:10.1108/jm2-02-2024-0058

Shweta V. Matey, Dadarao N. Raut, Rajesh B. Pansare, Ravi Kant

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Abstract

Purpose

Blockchain technology (BCT) can play a vital role in manufacturing industries by providing visibility and real-time transparency. With BCT adoption, manufacturers can achieve higher productivity, better quality, flexibility and cost-effectiveness. The current study aims to prioritize the performance metrics and ranking of enablers that may influence the adoption of BCT in manufacturing industries through a hybrid framework.

Design/methodology/approach

Through an extensive literature review, 4 major criteria with 26 enablers were identified. Pythagorean fuzzy analytical hierarchy process (AHP) method was used to compute the weights of the enablers and the Pythagorean fuzzy combined compromise solution (Co-Co-So) method was used to prioritize the 17-performance metrics. Sensitivity analysis was then carried out to check the robustness of the developed framework.

Findings

According to the results, data security enablers were the most significant among the major criteria, followed by technology-oriented enablers, sustainability and human resources and quality-related enablers. Further, the ranking of performance metrics shows that data hacking complaints per year, data storage capacity and number of advanced technologies available for BCT are the top three important performance metrics. Framework robustness was confirmed by sensitivity analysis.

Practical implications

The developed framework will contribute to understanding and simplifying the BCT implementation process in manufacturing industries to a significant level. Practitioners and managers may use the developed framework to facilitate BCT adoption and evaluate the performance of the manufacturing system.

Originality/value

This study can be considered as the first attempt to the best of the author’s knowledge as no such hybrid framework combining enablers and performance indicators was developed earlier.

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为制造业采用区块链技术确定绩效指标优先次序的混合框架

目的通过提供可视性和实时透明度，区块链技术（BCT）可在制造业中发挥重要作用。采用 BCT 技术后，制造商可以实现更高的生产率、更好的质量、灵活性和成本效益。本研究旨在通过一个混合框架，对可能影响制造业采用 BCT 的性能指标和推动因素进行优先排序。设计/方法/途径通过广泛的文献综述，确定了 4 个主要标准和 26 个推动因素。采用毕达哥拉斯模糊分析层次过程（AHP）方法计算促进因素的权重，并采用毕达哥拉斯模糊综合折中方案（Co-Co-So）方法对 17 个绩效指标进行优先排序。然后进行了敏感性分析，以检查所开发框架的稳健性。研究结果根据研究结果，数据安全促进因素是主要标准中最重要的，其次是以技术为导向的促进因素、可持续性和人力资源以及与质量相关的促进因素。此外，绩效指标排名显示，每年的数据黑客攻击投诉、数据存储容量和可用于业务连续性测试的先进技术数量是最重要的三个绩效指标。敏感性分析证实了该框架的稳健性。从业人员和管理人员可以使用所开发的框架来促进 BCT 的采用并评估制造系统的绩效。原创性/价值据作者所知，这项研究可以说是首次尝试，因为此前还没有开发过这种将使能因素和绩效指标相结合的混合框架。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Modelling in Management MANAGEMENT-

CiteScore

5.50

自引率

12.50%

发文量

期刊介绍： Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.