Business enterprises

Businesses are the driving force behind economic systems and are the lifeline of the community as they help in the prosperity and growth of the nation. Hence it is important for the business to succeed in the market. The business’s success provides economic stability and sustainability that helps preserve resources for future generations. The success of a business is not only important to the owners but is also critical to the regional/domestic economic system, or even the global economy. Recent years have witnessed many new emerging businesses with tremendous success, such as Google, Apple, Facebook etc.. Yet, millions of businesses also fail or fade out within a rather short period of time. Finding patterns/factors connected to the business rise and fall remains a long-lasting question that puzzles many economists, entrepreneurs, and government officials. Recent advancements in artificial intelligence, especially machine learning, has lent researchers the powers to use data to model and predict business success. However, due to the data-driven nature of all machine learning methods, existing approaches are rather domain-driven and ad-hoc in their design and validations, particularly in the field of business prediction. The main challenge of business success prediction is twofold: (1) Identifying variables for defining business success; (2) Feature selection and feature engineering based on three main categories Investment, Business, and Market, each of which is focused on modeling a business from a particular perspective, such as sales, management, innovation etc.

Researchers and policy makers consider entrepreneurship to be a major source of economic development and competitiveness. Determinants of entrepreneurship have been studied at individual, regional and national levels. Even though research indicates that variation in the levels of entrepreneurship across regions within nations is greater than the national differences and that these differences persist over time (Bosma & Schutjen 2009, 2011; Fritsch & Mueller 2006; Sternberg 2004; Tamásy 2006), we still do not know the full range of regional level determinants of entrepreneurship. I drew from Wennekers’ (2006) framework and link two lines of research (international entrepreneurship and international management) to examine the effects of institutional, economic, technological and cultural contexts on entrepreneurship across within-country regions developed ten hypotheses regarding the relationship of institutional, economic, technological and cultural context to entrepreneurship. I tested these hypotheses within Europe using the regional classification scheme developed by the European Union. Data for the variables came from the European Values Survey, European Social Survey, Eurostat, World Bank, International Social Security Association, Eurobarometer and the Global Competitiveness Report. To test the hypothesized relationships, I use Hierarchical Linear Modeling (HLM 6.0.) The results indicate that there is a positive relationship between institutional trust, Long Term Orientation and entrepreneurship levels across regions. In conclusion, examination of region-level predictors of entrepreneurship must include different measures of entrepreneurship to provide more accurate understanding and to inform policy makers.