Sanja Pfeifer

A Crossnational Prediction Model for Business Success

In this study, the Lussier (1995) success prediction model, originally developed using U.S. data, is tested using a sample of firms from Central Eastern Europe. The same factors found to be predictors of success in the U.S. (staffing, education level, use of professional advice, and planning) were also predictors of success and failure in Central Eastern Europe. All these factors have to do with the firms human resources. These findings should lead to reconsideration of preconceptions existing in Central Eastern Europe regarding small business, as in many of its countries it is commonly believed that human resources have little to do with business success and failure.

A Comparison of Business Success versus Failure Variables between U.S. and Central Eastern Europe Croatian Entrepreneurs

In this study, 15 success versus failure variables were tested for differences between U.S. and Central Eastern Europe Croatian (CEEC) entrepreneurs. Nine of the values were significantly different at the .05 level and two at the .10 level (73%). U.S. Entrepreneurs started with greater capital, had more years of management experience, developed more detailed planning, made greater use of professional advice, had more college graduates, sold products with better product life-cycle timing, started their business during better economic times, were older, included more partnerships, and had more parents who owned a business than CEEC entrepreneurs, while CEEC entrepreneurs had an easier time staffing than U.S. entrepreneurs. The Lussier (1995) U.S. success versus failure prediction model was tested using logistic regression (S/F = f staffing, education, use of professional advice, planning) and it was also a significant predictor in CEEC. The findings should help lead to redefining entrepreneurship in CEEC, as many of its countries commonly believe that human resources have little to do with business success and failure. As the view of human resources changes, more resources should be allocated to develop employees.

Shaping the Entrepreneurial Mindset: Entrepreneurial Intentions of Business Students in Croatia

Business students from a public university in roatia participated in an international study on entrepreneurial self‐efficacy, identity, and education. The results of this preliminary empirical research indicate that the main predictors of the entrepreneurial intentions in roatia are strength of entrepreneurial identity aspiration and entrepreneurial self‐efficacy. These two main constructs mediate the number of personal, situational, or contextual factors, including education. Empirical analysis supports the majority of ocial ognitive areer heory hypothesized interaction between control variables and main constructs such as self‐efficacy, positive outcome expectations, and entrepreneurial identity. These findings thus fill the gap in the empirical evidence of the theoretical framework validity derived from different contexts.

The Relationship between Entrepreneurial Activities, National and Regional Development and Firm Efficiency—Global Entrepreneurship Monitor (GEM)-based Evidence from Croatia

This study analyses the dynamics, structure and connections between entrepreneurial activity, economic development and firm efficiency. While the usual presumption on the relationship between these variables implies straightforward, linear and positive impacts, empirical evidence shows that those impacts are significant, more complex and less straightforward. The evidence of entrepreneurial activity in Croatia shows that the early stages of entrepreneurship development are very dynamic and volatile. Furthermore, significant inter-regional differences exist in entrepreneurial activity, firm performance and economic development across six Croatian regions. Correlations between entrepreneurial activity, firm performance and economic development are significant but depend on whether the entrepreneurial activity is opportunity or necessity based. This study confirms the theoretical presumption about complex and multilayered connections between different types of entrepreneurship activity and economic development.

Exploring Entrepreneurs’ Motivation: Comparison of Croatia, European Post-socialist and Developed Countries

This article explores the entrepreneurial profiles of Croatian entrepreneurs and compares them with the profiles of entrepreneurs of post-socialist and developed European countries. Specifically, logistic regression analysis was used to find the best descriptors of the opportunity and necessity-driven entrepreneurs in Croatia, post-socialist and developed European countries. Results indicate that chances for engaging in opportunity-driven entrepreneurship in Croatia are increasing for men and those who perceive their income level and competencies for running a business as high. The likelihood of engagement in opportunity-driven entrepreneurial activity in the post-socialist context has been related to the age rather than gender and innovations in the product/market matrix or higher export expectations. The developed countries’ datasets reveal that opportunity-driven entrepreneurs have been related to higher expectations of growth through new job openings.

A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

Abstract Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

Gender Patterns of Businesses with Growth Potential in Croatia

The literature review presented in the chapter confirms that researchers’ interest is more focused on why and how businesses are created, and much less why and how they grow. There is a shortage of empirical evidence of growth influencers, as well as evidence about the intensity and patterns of the interaction between internal factors and entrepreneurship ecosystem in the process of growing a venture. The authors identify this gap and combine different angles (theory of firm growth, entrepreneurship, inclusion, macroeconomic aspects of using resources) with a gender aspect (inclusion), in exploring why and how businesses grow. Using this angle and aggregated Global Entrepreneurship Monitor (GEM) data from the 2003–2013 period, the chapter presents several gender patterns of businesses with growth potential (innovative products, innovative technology, and competitiveness) in Croatia.