Gabriella Vindigni

Motivations and Performance Conditions for Ethnic Entrepreneurship

Ethnic entrepreneurship has become a popular concept in a modern multi–cultural society. This paper seeks to offer an overview of the potential of ethnic entrepreneurship for solving inter alia the structural unemployment problems of ethnic groups in cities. There is a general lack of empirical evidence on this issue in the current literature. The present paper aims to fill this gap by addressing in particular the critical success conditions for ethnic entrepreneurs. The focus of the research is on variations in success across three ethnic groups in the Amsterdam area. By means of structured personal interviews with many ethnic entrepreneurs, a systematic qualitative data base was created. The paper sets out to identify empirically the driving forces for business success, such as education or the role of informal networks. The explanatory framework deployed for the identification of these qualitative success factors for distinct ethnic groups is based on a particular, recently developed artificial intelligence method, viz. rough set analysis. This multidimensional classification approach appears to be able to identify various important factors for the motivation and performance of ethnic enterprises. Two major findings emerge from this investigation: (i) performance conditions vary across ethnic groups, and (ii) informal networks are crucial for business success.

Breeding places for ethnic entrepreneurs: a comparative marketing approach

This paper aims to examine the performance conditions of ethnic (migrant) entrepreneurs in a modern economy. After a broad overview of key issues, an analytical tool from marketing theory is proposed, based on the five Ps (Product, Price, Place, Personnel and Promotion). Next, an empirical application is presented, in which results from an in-depth interview study on Moroccan entrepreneurs in Amsterdam are discussed. Given the linguistic and qualitative information in our data base, two recently developed pattern recognition methods for categorized information, namely Apriori and rough set methods, are deployed in order to derive meaningful association and classification rules that are helpful to identify conditional success or performance rules.

Profitability of wine grape growing in the EU member states

The aim of this work is to examine the profitability of European Union wine grape growing by comparing some economic indicators extrapolated from information in the Farm Accountancy Data Network, data banks already widely used to analyse the income of European agricultural holdings. The results show that in recent years the profitability of European wine grape growing has grown overall, but to overcome the current economic crisis and face the challenge of “New World” wine producers, it is necessary to make an effort towards public intervention to increase the profitability of wine farms. In this context, the new reform of wine Common Market Organisation (2008) and new Common Agricultural Policy (2007–2013) aimed at improving the profitability of wine grape growing by reducing production costs, increasing added value of wine, promoting the participation of farmers in food quality schemes, etc.

Food security at agricultural sustainability: an overview of critical success factors

This paper offers an overview of factors that are decisive for productivity increase in the agricultural sector (both farming and agro‐food). An attempt is made to explain differences in total factor productivity in agriculture in different countries by means of meta‐analysis, in particular, by using rough set theory as a framework for comparative study. The main aim is to derive the drivers of changes in agricultural food production with a view to conditional future predictions of an “if … then” nature. The empirical application to OECD countries is used to illustrate the potential of this new approach for identifying critical success factors in agriculture with a view to future food security objectives.

Spatial dynamics of government policy: an artificial intelligence approach to comparing complex systems

Complexity is concerned with the unpredictable nature of non-linear and dynamic systems. Complexity can relate to a dynamic causal sequence of events at an object-specific micro-level [such as in the case of the weather, business performance, market impact of innovation, individual well-being, etc.], but it may also refer to the outcomes of repeated experiments in a semi-controlled setting. A comparison of the results of case studies is a good illustration of the latter interpretation of complexity. In this case, it is useful to see to what extent the outcome is shaped by the systemic background and the specific research methodologies used.