Tacit meaning in disguise: Hidden metaphors in new product development and market making

This article explores the role of metaphor in product development processes and market making. Based on a sociocognitive perspective of innovation dynamics and required learning by market actors, the potential of metaphors for mental model development during new product development (NPD) processes is investigated. Three roles for metaphors as cognitive focusing devices for the co-evolution of producers' and consumers' mental models are inferred: mental model communication, mental model matching, and mental model creation. These roles are illustrated by examples that reinforce the need for creativity in applying metaphors as cognitive focusing devices in NPD and market making.     

Old flames never die – the role of binding social ties for corporate entrepreneurship in family firms

This paper investigates the role of social ties and family embeddedness for corporate entrepreneurship in family firms. Family firms are mostly characterized by close and often inseparable ties between the dominant family coalition and the firm and offer specific resources within a context of both rational as well as non-rational factors that influence entrepreneurial strategies. We empirically test (n =?181) the relationship between binding social ties and innovation, strategic renewal, and corporate venturing. Our findings indicate a strong significance for networks and close and stable relationships both to inside the firm and the outside in decision making for corporate venturing and innovation. In contrast, the results for strategic renewal show no relevance of strong social ties. We link up with the debate on the role of owners as an important stakeholder group.

     

ML– and semiparametric estimation in logistic models with incomplete covariate data

ML–estimation of regression parameters with incomplete covariate information usually requires a distributional assumption regarding the concerned covariates that implies a source of misspecification. Semiparametric procedures avoid such assumptions at the expense of efficiency. In this paper a simulation study with small sample size is carried out to get an idea of the performance of the ML–estimator under misspecification and to compare it with the semiparametric procedures when the former is based on a correct assumption. The results show that there is only a little gain by correct parametric assumptions, which does not justify the possibly large bias when the assumptions are not met. Additionally, a simple modification of the complete case estimator appears to be nearly semiparametric efficient.