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Comparative Analysis of X-Y-Z Generation Entrepreneurs in a Semi-Peripheral EU Member Country: Insights from Regularized Regression Techniques

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  • Marton Gosztonyi

Abstract

Purpose: The aim of our research is to deeply analyze entrepreneurial dynamics across generations X, Y, and Z, enhancing understanding of generational shifts and offering insights for future tailored entrepreneurship policies and development programs. This study serves as a foundation for stakeholders to address the unique challenges and opportunities presented by each generational cohort. Design/Methodology/Approach: In our paper, we conduct a nuanced comparative analysis of entrepreneurs from Generation X, Y, and Z within a semi-peripheral European Union member state, employing Ridge, Lasso, and Elastic Net regression techniques. Utilizing a sophisticated system-level approach, we devised a quint-segment model capable of encapsulating the generational disparities in a comprehensive manner. Findings: Our findings delineate a pronounced polarization within the sector, highlighting a notable intergenerational coexistence particularly between Generations Y and Z. Despite the distinct socio-economic backgrounds and entrepreneurial approaches prevalent amongst these generational cohorts, there emerges a remarkable alignment in self-perception and economic trust between Generation Y and Z entrepreneurs. Conversely, this shared perspective markedly diverges from that held by Generation X individuals, spotlighting a significant generational schism in the appraisal of the business environment and the evolving role of education and training across these generations. Practical Implications: In light of emergent entrepreneurial paradigms, it is imperative for policymakers and educational institutions to recalibrate, cognizant of Generations Y and Z's proclivity for informal pedagogical modalities and networking. Business support mechanisms, notably incubators, are enjoined to refine their approaches, accentuating Gen Z's predilection for trust-anchored mentorship. Concurrently, investors and governmental entities must reconfigure strategies, attentive to dynamic sectoral and capital sourcing shifts. As workplace ethos undergoes transformation, enterprises should champion inclusivity, with advisory services emphasizing bespoke, trust-centric advisement. Originality/Value: The paper presents a novel systemic analysis of entrepreneurial dynamics across generations offering fresh insights particularly on the economic and self-perception dimensions of Generations Y and Z in juxtaposition with Generation X. Through a quint-segment model and five predictive models, the study not only corroborates existing literature but also unveils unique intergenerational discrepancies and convergences, thereby enriching the understanding of generational shifts in entrepreneurial realms. This research holds significant implications for shaping future entrepreneurship policies and tailoring business development programs, emphasizing the importance of recognizing generational nuances in the entrepreneurial ecosystem.

Suggested Citation

  • Marton Gosztonyi, 2023. "Comparative Analysis of X-Y-Z Generation Entrepreneurs in a Semi-Peripheral EU Member Country: Insights from Regularized Regression Techniques," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 191-217.
  • Handle: RePEc:ers:journl:v:xxvi:y:2023:i:4:p:191-217
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    References listed on IDEAS

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    1. Maribel Guerrero & Francisco Liñán & F. Rafael Cáceres-Carrasco, 2021. "The influence of ecosystems on the entrepreneurship process: a comparison across developed and developing economies," Small Business Economics, Springer, vol. 57(4), pages 1733-1759, December.
    2. Zhe Cao & Xianwei Shi, 2021. "A systematic literature review of entrepreneurial ecosystems in advanced and emerging economies," Small Business Economics, Springer, vol. 57(1), pages 75-110, June.
    3. Alberto Chong & Luisa Zanforlin, 2001. "Technological adaptation, trade, and growth," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 137(4), pages 565-592, December.
    4. Paul Cilliers, 2001. "Boundaries, Hierarchies And Networks In Complex Systems," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 5(02), pages 135-147.
    5. Bai, Peng, 2009. "Sphericity test in a GMANOVA-MANOVA model with normal error," Journal of Multivariate Analysis, Elsevier, vol. 100(10), pages 2305-2312, November.
    6. Silke Eisenbeiss & Daan Knippenberg & Clemens Fahrbach, 2015. "Doing Well by Doing Good? Analyzing the Relationship Between CEO Ethical Leadership and Firm Performance," Journal of Business Ethics, Springer, vol. 128(3), pages 635-651, May.
    7. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    8. Simon, Noah & Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2011. "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i05).
    9. Alina Daniela MIHALCEA & Andreea MITAN & Alexandra VI?ELAR, 2012. "Generation Y: Views on Entrepreneurship," Economia. Seria Management, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 15(2), pages 277-287, December.
    10. Sanjeev Bhojraj & Charles M. C. Lee & Derek K. Oler, 2003. "What's My Line? A Comparison of Industry Classification Schemes for Capital Market Research," Journal of Accounting Research, Wiley Blackwell, vol. 41(5), pages 745-774, December.
    11. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    12. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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    More about this item

    Keywords

    XYZ generation; entrepreneurs; complex system; regularized regression.;
    All these keywords.

    JEL classification:

    • L26 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Entrepreneurship
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • O52 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Europe

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