Examining the critical success factors of productive entrepreneurship: an ISM-MICMAC approach

IF 1.8 Q3 MANAGEMENT
Chinmaya Kumar Sahu, Rajeev Kumar Panda
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引用次数: 0

Abstract

Purpose

The concept of productive entrepreneurship has been recognised as a strategic approach to address the various challenges economies face, such as high unemployment, low economic growth and limited diversification. However, studies on the productive entrepreneurship’s critical success factors (CSFs) are rare and fragmented. Hence, this paper aims to identify the CSFs of productive entrepreneurship and determine their relationship among to offer a meaningful framework for enhancing the result of entrepreneurial activities in the emerging economy.

Design/methodology/approach

The authors conducted an extensive literature review and consulted experts to identify 14 CSFs for productive entrepreneurship. The technique of interpretive structural modelling (ISM) was used to determine the relationships and interconnectedness between these factors. In addition, this study used matrix of cross-impacts applied to a classification (MICMAC) analysis to determine the significance of CSFs in relation to the productive entrepreneurship.

Findings

The results indicate that the regulatory environment, incubators and accelerators and mentorship were the most influential factors for productive entrepreneurship in the Indian context. In contrast, social mobility and resilience were found to be the least influential factor.

Research limitations/implications

The study's findings can enable researchers, policymakers and entrepreneurs to make informed decisions and develop effective strategies to enhance the productive entrepreneurship.

Originality/value

The unique approach of research (ISM and MICMAC analysis) expands the frameworks of the entrepreneurship ecosystem with a comprehensive and dynamic emergent investigation into the foundation of productive entrepreneurship.

研究生产性创业的关键成功因素:ISM-MICMAC 方法
目的 生产性创业的概念已被公认为是应对各经济体面临的高失业率、低经济增长和有限多样化等各种挑战的战略方法。然而,有关生产性创业的关键成功因素(CSFs)的研究却很少且零散。因此,本文旨在识别生产性创业的 CSFs,并确定它们之间的关系,从而为提高新兴经济体创业活动的成果提供一个有意义的框架。研究采用了解释性结构建模(ISM)技术来确定这些因素之间的关系和相互联系。研究结果表明,在印度,监管环境、孵化器和加速器以及导师是对生产性创业影响最大的因素。原创性/价值独特的研究方法(ISM 和 MICMAC 分析)扩展了创业生态系统的框架,对生产性创业的基础进行了全面、动态的新兴调查。
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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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