This article analyzes the impact of not controlling for “demographic sample” differences on research results in the area of comparative family/nonfamily business research. Using different statistical methods with and without control for “demographic sample” differences, the results show that controlling for these firm demographics in a bivariate as well as a multivariate framework is very important to discover “real” differences between family and nonfamily firms. We found “real” differences for export, budgeting, variable reward systems, profitability and gender, educational degree, and tenure of the CEO. Strategy, networking, long‐term planning and control systems, perceived environmental uncertainty, growth, and management training, classified by prior empirical research as different between family and nonfamily firms, do not differ.

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In this article, we build a conceptual framework that models the influence of social capital as a multidimensional concept on strategic resource acquisition through interorganizational networks. Interorganizational networks are considered as effective when they allow for the acquisition of strategicresources. Our conceptual framework reflects that network effectiveness is dependent on the structural and the relational dimension of social capital. The main focus is on how the relational dimension of social capital – in this article conceptualized as trust – in interorganizational networks can directly and indirectly influence the acquisition of strategic resources through those networks. Based on the network literature, social capital literature and the literature on trust, we seek to develop propositions that detail the relationships among trust, interorganizational network characteristics, strategic resource acquisition/network effectiveness and performance. Basically, we argue 1) that different types of trust will have a different impact on network effectiveness, 2) that the level of trust will influence network effectiveness, and 3) that the interaction between trust and other variables, such as structural dimension variables, are fundamental for analyzing network effectiveness.

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