供应链金融生态系统关键成功因素建模的综合方法

IF 1.8 Q3 MANAGEMENT
Prasad Vasant Joshi, Bishal Dey Sarkar, Vardhan Mahesh Choubey
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引用次数: 0

摘要

目的供应链金融(SCF)已成为促进增长和为全球供应链提供灵活性的重要因素。因此,了解有助于供应链金融生态系统(SCFE)取得成功的因素变得至关重要。本研究旨在确定发展高效、有效的供应链金融生态系统的关键成功因素(CSFs)。根据这些因素的特点,本研究打算将这些因素归类为构造,并进一步建立 CSF 之间的层次关系。根据专家的反馈意见,对 21 个拟议因素中的 16 个选定因素进行了探索性因素分析(EFA),并将这些因素归类为四个构造。通过识别和最终确定 SCFE 的 CSF,建立了总体解释结构模型(TISM)。该模型在各因素之间建立了层次关系。研究结果该研究为高效和有效的 SCF 生态系统确定了重要的 CSF。通过使用 EFA 分析 CSF,建立了四个构造。通过 TISM 对最终确定的 16 个 CSFs 进行建模,并进一步建立 CSFs 之间的层次关系,得出结论认为政府政策和行业增长是最强的驱动力,而金融吸引力是最弱的驱动力。根据所确定的 CSF 和构建要素,研究发现,要使 SCF 生态系统取得成功,经济生态系统的存在可为 SCFE 的整体发展提供一个促进框架。此外,合作伙伴之间的相互信任可促进更好的关系,并带来财务可行性,为所有利益相关者提供商业机会。 这项研究将有助于全球的 SCF 合作伙伴了解确保发展互利 SCF 生态系统的 CSF,并为供应链合作伙伴提供灵活性。CSF 将为政策制定者和金融中介机构提供见解,为发展更好的 SCF 生态系统提供有利环境。此外,买家和卖家也将了解 CSF,从而在他们之间建立更好的关系,最终有助于全球业务的发展。原创性/价值本研究确定了 SCF 生态系统的 CSF。研究确定了重要的因素,并使用 EFA 将其分类。与现有文献不同的是,本文发展了 CSF 之间的层次关系,并建立了一个高效和有效的 SCF 生态系统模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An integrated approach for modeling critical success factors for supply chain finance ecosystem

Purpose

Supply chain finance (SCF) has become a vital ingredient that fosters growth and provides flexibility to the global supply chain. Thus, it becomes essential to understand the factors that contribute to the success of the supply chain finance ecosystem (SCFE). This study aims to identify the critical success factors (CSFs) for the development of an efficient and effective SCFE. Based on their characteristics, the study intends to classify the factors into constructs and further establish a hierarchical relationship among the CSFs.

Design/methodology/approach

The study is based on empirical data collected from 221 respondents based on administered questionnaires. Exploratory factor analysis (EFA) is carried out on 16 selected factors (out of 21 proposed factors) based on the feedback of the experts and the factors were classified into four constructs. The total interpretive structural modeling (TISM) model was developed by identifying and finalizing CSFs of the SCFE. The model developed a hierarchical relationship between the various factors.

Findings

The study identified significant CSFs for the efficient and effective SCF ecosystem. Four constructs were developed by analyzing CSFs using the EFA. The finalized 16 CSFs modeled through the TISM and further hierarchical relationship established between the CSFs concludes that governmental policies and sectoral growth are the strongest driving forces and financial attractiveness is the weakest driving force. Based on the CSFs and the constructs identified, it was found that for the success of the SCF ecosystem, the existence of an economic ecosystem provides a facilitating framework for the overall development of the SCFE. Also, the trustworthiness among the partners fosters better relationships and results in financial feasibility and offers business opportunities for all the stakeholders.

Practical implications

This study will help the SCF partners across the globe understand the CSFs that ensure development of mutually beneficial SCF ecosystems and provide flexibility to the supply chain partners. The CSFs would provide insights to the policymakers and the financial intermediaries for providing a conducive environment for the development of a better SCF ecosystem. Also, the buyers and sellers would understand the CSFs that would develop better relationships among them and ultimately help in development of business across the globe.

Originality/value

The study identifies the CSFs for the SCF ecosystem. The study ascertains the significant factors and classifies them into clusters using EFA. Unlike the literature available, the paper develops the hierarchical relationship between the CSFs and develops a model for an efficient and effective SCF ecosystem.

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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
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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