مشخصات مقاله:
عنوان فارسی مقاله:
استفاده از تجزیه و تحلیل مقایسه ای کیفی مجموعه ای فازی برای درک دقیق کارآفرینی
عنوان انگلیسی مقاله:
Using fuzzy-set qualitative comparative analysis for a finer-grained understanding of entrepreneurship
کلمات کلیدی مقاله:
مجموعه فازی، کارآفرینی، داده های نامتقارن، بیرونی ها، پیکربندی
کلمات کلیدی انگلیسی:
Fuzzy set – Entrepreneurship – Asymmetric data – Outliers – Configurations
مناسب برای رشته های دانشگاهی زیر:
مدیریت
مناسب برای گرایش های دانشگاهی زیر:
کارآفرینی
وضعیت مقاله انگلیسی و ترجمه:
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فهرست مطالب:
Abstract
Keywords
Executive summary
1. Introduction
2. Fuzzy-set qualitative comparative analysis
3. Re-examining the data of a prior entrepreneurship study
3.1. Sufficiency Analyses for Independence-Oriented Intentions
3.2. Sufficiency analyses for growth-oriented intentions
4. Future research opportunities
4.1. Future research on entrepreneurial intentions
4.2. Using fsQCA for an interactive perspective of entrepreneurship
4.3. The fsQCA approach to entrepreneurship that is more activity based
4.4. The fsQCA approach to entrepreneurship that is cognitively hot
4.5. The fsQCA approach to entrepreneurship that is more compassionate and prosocial
5. Conclusion, limitations, and implications
5.1. Limitations
5.2. Implications
Acknowledgements
References
قسمتی از مقاله انگلیسی:
1. Introduction
Entrepreneurship theory has been built largely for testing by traditional symmetric analytical methods that seek to discover the general determinants of observed entrepreneurial phenomena. These methods, such as multiple regression analysis and structural equation modelling, require symmetric central tendency in the data employed, such that the mean and standard deviation statistics can be interpreted as representative of the data for each variable. Moreover, the data for each variable is assumed to be normally distributed around the sample (or by extension, the population) means. Symmetric methods effectively calculate the sample-average relationship between each independent variable and the dependent variable, to find the regression equation that purports to measure the influence of each independent variable on the specified dependent variable. The regression equation is the linear addition of the correlation coefficients of the independent variables with the dependent variable. Importantly, each independent variable is considered discretely, that is, holding constant the impact of all other independent variables on the dependent variable, except for a limited number of interactive terms that may be included in the analysis to reflect the hypothesized interdependence of two or more antecedent variables.