Decision on the introduction of blockchain technology in green supply chains considering altruistic preferences

IF 1.8 Q3 MANAGEMENT

Journal of Modelling in Management Pub Date : 2024-08-02 DOI:10.1108/jm2-03-2024-0092

Wenxian Zhao

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引用次数: 0

Abstract

Purpose

This paper aims to examine the blockchain introduction and altruistic preference decisions of the supplier in agricultural food supply chains and discuss how the supplier decisions are influenced by blockchain technology and altruistic preference levels.

Design/methodology/approach

The author considers a single period two-level supply chain model to describe the supplier’s decisions. The supplier, as the leader of the game, decides whether to introduce blockchain technology and his own level of altruistic preferences. Consumers have environmental awareness and heterogeneity in green trust. Supply chain members determine their own product pricing and green effort level under Stackelberg game.

Findings

The results reveal the negative impact of unit verification cost in the technology introduction process on the supply chain. In addition, the supplier can adjust their profits by adjusting their altruistic preferences after introducing blockchain to offset the impact of blockchain through the influence of altruistic preferences as discussed by the author.

Originality/value

This paper investigates how the profits and green efforts of supply chain members are influenced by blockchain technology and altruistic preferences.

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考虑利他主义偏好，决定在绿色供应链中引入区块链技术

本文旨在研究农业食品供应链中供应商的区块链引入和利他偏好决策，并讨论供应商的决策如何受到区块链技术和利他偏好水平的影响。供应商作为博弈的领导者，决定是否引入区块链技术以及自身的利他偏好水平。消费者具有环保意识和绿色信任的异质性。研究结果表明，在技术引进过程中，单位验证成本会对供应链产生负面影响。此外，在引入区块链后，供应商可以通过调整利他主义偏好来调整利润，从而抵消区块链通过利他主义偏好的影响所带来的影响。

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.

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