{"title":"An integrated approach for modeling critical success factors for supply chain finance ecosystem","authors":"Prasad Vasant Joshi, Bishal Dey Sarkar, Vardhan Mahesh Choubey","doi":"10.1108/jm2-01-2023-0007","DOIUrl":null,"url":null,"abstract":"<h3>Purpose</h3>\n<p>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.</p><!--/ Abstract__block -->\n<h3>Design/methodology/approach</h3>\n<p>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.</p><!--/ Abstract__block -->\n<h3>Findings</h3>\n<p>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.</p><!--/ Abstract__block -->\n<h3>Practical implications</h3>\n<p>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.</p><!--/ Abstract__block -->\n<h3>Originality/value</h3>\n<p>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.</p><!--/ Abstract__block -->","PeriodicalId":16349,"journal":{"name":"Journal of Modelling in Management","volume":"849 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Modelling in Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/jm2-01-2023-0007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.