Understanding the challenges of entrepreneurship in emerging economies: a grey systems-based study with entrepreneurs in Brazil

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2024-09-16 DOI:10.1108/jm2-03-2024-0078

Wilhelm K.K. Abreu, Tiago F.A.C. Sigahi, Izabela Simon Rampasso, Gustavo Hermínio Salati Marcondes de Moraes, Lucas Veiga Ávila, Milena Pavan Serafim, Rosley Anholon

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Abstract

Purpose

This research aims to understand the primary challenges encountered by entrepreneurs operating in emerging economies, where entrepreneurship plays a vital role. The study places a particular emphasis on entrepreneurs in Brazil.

Design/methodology/approach

The research methodology involved the analysis of data obtained from interviews, using both content analysis and Grey Relational Analysis techniques.

Findings

The analysis revealed several prominent difficulties that entrepreneurs face in these domains. These challenges encompassed issues such as grappling with intricate taxation systems and the associated tax burden, navigating government bureaucracy, securing access to essential financing and initial investments, contending with the absence of supportive government programs and addressing the dynamic nature of market conditions. The findings on the most critical barriers reveal potential pathways for entrepreneurs, policymakers and universities to act in developing the entrepreneurial ecosystem in emerging economies.

Originality/value

The insights garnered from this research have the potential to inform the formulation of robust public policies aimed at fostering entrepreneurship and innovation in emerging countries. Furthermore, these findings can serve as a valuable resource for planning initiatives designed to train engineers to become successful entrepreneurs.

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了解新兴经济体的创业挑战：对巴西创业者进行的基于灰色系统的研究

目的本研究旨在了解在新兴经济体经营的企业家所遇到的主要挑战，在这些经济体中，创业精神发挥着至关重要的作用。研究方法包括使用内容分析和灰色关联分析技术分析从访谈中获得的数据。研究结果分析揭示了创业者在这些领域面临的几个突出困难。这些困难包括应对错综复杂的税收制度和相关税收负担、与政府官僚机构周旋、确保获得必要的融资和初始投资、与缺乏支持性政府计划作斗争以及应对市场条件的动态性质等问题。关于最关键障碍的研究结果揭示了创业者、政策制定者和大学在新兴经济体发展创业生态系统的潜在途径。原创性/价值本研究获得的见解有可能为制定旨在促进新兴国家创业和创新的有力公共政策提供参考。此外，这些发现还可作为宝贵的资源，用于规划旨在培训工程师成为成功企业家的计划。

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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.