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      The impact of entrepreneurs’ decision-making style on SMEs’ financial performance

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      Journal of Entrepreneurship in Emerging Economies
      Emerald

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          Abstract

          Purpose

          This study aims to investigate the impact of entrepreneurs’ decision-making styles on enterprise performance and suggests several entrepreneurial ecosystems – factors are impacting this relationship. The authors extend this line of work by examining how regional entrepreneurial culture, educational institutional support and business and social networks mediating the relationship between entrepreneurs’ decision-making style and small medium enterprises (SME)s’ financial performance.

          Design/methodology/approach

          The data were collected through an e-survey of SME owners in New South Wales, Australia. This study developed a model combining a set of entrepreneurial ecosystem factors, entrepreneurs’ decision-making styles and SMEs’ financial performance. Data were analysed using partial least square structural equation modelling.

          Findings

          The results suggest regional entrepreneurial culture, educational institutional support and business and social networks mediate the relationship between entrepreneurs’ decision-making style and SMEs’ financial performance. Hence, this study developed a more complete methodical understanding of entrepreneurs’ decision-making styles and their impact on SMEs’ financial performance. This study provides deeper insights into the conditions and processes by which an entrepreneurs’ decision-making style impacts SMEs’ financial performance.

          Originality/value

          The focus of this study was to understand the relationship of entrepreneurs’ decision-making styles on SMEs’ financial performance. The authors identified that the entrepreneurs’ decision-making style positively impacts SMEs’ financial performance. This study augments the body of knowledge by proposing ways in how the entrepreneurs’ decision-making style can be more strengthened.

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          Most cited references125

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          Common method biases in behavioral research: A critical review of the literature and recommended remedies.

          Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.
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            Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

            G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.
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              Self-Reports in Organizational Research: Problems and Prospects

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                Author and article information

                Journal
                Journal of Entrepreneurship in Emerging Economies
                JEEE
                Emerald
                2053-4604
                2053-4604
                February 11 2022
                October 31 2023
                February 11 2022
                October 31 2023
                : 15
                : 5
                : 861-884
                Article
                10.1108/JEEE-03-2021-0099
                d620c8b5-9461-42fa-ab70-d3a989ba8357
                © 2023

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