تجزیه و تحلیل شبکه های بیزی برای پیش بینی دینامیکی از شاخص فعالیت های کارآفرینی مرحله اولیه

Entrepreneurship plays a critical role for the development and well-being of society. Illustration of its dynamic relationship with entrepreneurial attitudes and aspirations can provide a guideline for the cause of such activities. However, a time-lagged causal relationship among these concepts has not yet been established. In this study, we examine a dynamic relationship among early stage entrepreneurial attitudes, activities, and aspirations using Bayesian network (BN) analysis. In addition, we propose an early stage entrepreneurial activity index that can predict the percentage of both nascent entrepreneur and new business owner using the variables related to entrepreneurial attitudes of the previous year. This index, in turn, can be used to predict various aspects of entrepreneurial aspiration of the following year. The proposed index turns out to have very high prediction accuracy and is expected to provide effective policies to boost future entrepreneurial activity and aspiration.

The Global Entrepreneurship Monitor (GEM) project is “an annual assessment of the entrepreneurial activity, aspirations and attitudes of individuals across a wide range of countries (Bosma, Wennekers, & Amoros, 2011)”. The GEM focuses on three main objectives: “(1) to measure differences in entrepreneurial attitudes, activity and aspirations among economies, (2) to uncover factors determining the nature and level of national entrepreneurial activity, and (3) to identify policy implications for enhancing entrepreneurship in an economy (Bosma et al., 2011).” To identify the flow of entrepreneurial activities related to national economic growth from a society, culture, and politics, the GEM study constructed a conceptual framework of the original GEM model (Bygrave, Hay, Ng, & Reynolds, 2003). GEM considers that national economic growth is the result of such set of entrepreneurship activities. The initial GEM model was to develop to integrate advances in understanding the entrepreneurial process and to allow for further exploration of patterns detection (Herrington, Kew, & Kew, 2010). On the other hand, recently a GEM model was reworked based on the combination of three main components: entrepreneurial attitudes, entrepreneurial activity, and entrepreneurial aspirations (Bosma et al., 2011). According to Hessels, van Gelderen, and Thurik (2008), entrepreneurial motivations of perceived opportunity for entrepreneurship have relevance to entrepreneurial aspirations. In addition, if an individual exhibits positive attitudes toward entrepreneurship, the tendency to be a potential entrepreneur or get involved in entrepreneurial activity will increase (Krueger, 2007 and Krueger and Brazeal, 1994). Entrepreneurial attitudes, entrepreneurial activity, and entrepreneurial aspirations are latent variables and are comprised of many observable variables. However, none of the existing studies consider the dynamic causal relationship among the observable variables of entrepreneurial attitudes, activity, and aspirations, although Sohn and Ju (submitted for publication) attempted to model the relationship among these three factors. In addition, no research was conducted to reflect potential time-lagged causal relationship among them. If such a relationship is identified, it can be utilized to predict the degree of future entrepreneurial activity levels of various nations and corresponding entrepreneurial policies can be set to take necessary actions to vitalize entrepreneurial activities. The main purpose of this study is to identify a dynamic causal relationship among measurement variables of entrepreneurial attitudes, early stage entrepreneurial activity, and aspirations at a national level. We utilize a Bayesian network (BN) to identify such a relationship. The BN is a powerful formalism for representing the joint probability distribution of a set of related variables. It can represent an area of knowledge and uncertainties of related variables, enabling reasoning with uncertainties. Moreover, we propose an index for early stage entrepreneurial activity that can be used to predict the level of future early stage entrepreneurial activity reflecting causal relationships found through BN. We compare this index to the Total early-stage Entrepreneurial Activity (TEA) index (Bosma, Acs, Eutio, Coduras, & Levie, 2009). The TEA index is a composite measure of the current year’s degree of early stage entrepreneurial activity representing the percentage of both nascent entrepreneur and new business owner and therefore cannot be used for prediction. We expect that our findings will contribute to the understanding of the dynamic relationship among national entrepreneurial attitude, activity, and aspirations. This can be used as feedback information to boost entrepreneurship. This paper is organized as follows. In Section 2, we review the existing literature of GEM. In Section 3, we introduce a proposed BN as well as the data used in this study. In Section 4, we discuss the results of our analysis. Finally, we conclude our study and suggest areas for future research in Section 5.

This study examined the time-lagged dynamic relationship between entrepreneurial attitudes, activity, and aspirations, which are factors newly included in the GEM conceptual model. Bayesian network (BN) analysis was used to investigate the dynamic causal relationship among measurement variables of these three factors. With respect to perceived capabilities and entrepreneurial intention the previous year, entrepreneurial attitudes positively affected both nascent entrepreneurship and new business ownership activities of the current year. Those attributes also positively affected active new business ownership. In addition, nascent entrepreneurship activity was influenced by new business ownership activity in the same year. Our proposed BN model has the ability to predict TEA index and the individual activity rates of nascent entrepreneurship and new business ownership. Our empirical analysis showed the superior prediction ability of this proposed model. Predicting TEA or individual entrepreneurship activity rates is crucial for boosting these activities. Policies can be prepared to increase entrepreneurship attitudes for different age groups in different countries. This study is a pioneering work on examining the time-lagged relationships among the observational variables of entrepreneurial attitudes, activity, and aspirations. Although some causal relationships were detected in our empirical results, room for further research remains. In terms of methodology, a factor based structural relationship can be examined. This kind of theory-based approach will provide more robust results and is left for further research.