Julie Hermans

Ambitious Entrepreneurship: A Review of Growth Aspirations, Intentions, and Expectations

In the study of entrepreneurial behavior types, “ambitious entrepreneurship” recently emerged as a new research concept. Unfortunately, a systematic overview of what is known (and not known) about this topic is missing. In particular, insights into the various definitions, measures, and antecedents of ambitious entrepreneurship are lacking. In this chapter, we offer a state-of-the-art review and analysis of extant research on ambitious entrepreneurship. We structure the literature review by providing insights into antecedents of ambitious entrepreneurship, and extensively discuss the conceptualization and operationalization of this research concept. We clarify the differences between related concepts such as growth intention, expectation, and aspiration, and argue how all these concepts fit into a unifying framework of ambitious entrepreneurship. We summarize promising future research avenues for the study of ambitious entrepreneurship, both from a methodological and a conceptual point of view.

Contingent knowledge transfers in university–industry R&D projects

This paper explores knowledge transfers inside University–Industry collaborative research as the objective and scope of R&D activities vary. First, we contribute to a dynamic perspective on inter-organizational knowledge transfers by providing a new typology of University–Industry collaborative research. Second, we provide propositions related to contingent knowledge transfers, i.e. alignment between the nature of a project and the expected transfers. If such an alignment should ideally be built starting in the design phase, we demonstrate that a lack of alignment can be corrected as the collaborative research is iterated. Those iterations are experienced by partners as highly emotional events, influencing the rest of the collaborative work through what we call the pivot complex.

LSP based comparison of 3D ear models

Ear biometric authentication is considered to be an important aspect of human identification and is, among other techniques, used in victim identification for practical reasons. State-of-the-art techniques transform 2D ear photos to 3D ear models to adequately cope with geometrical and photometric normalisation issues. From each 3D ear model a feature list is extracted and used in the comparison process. In this paper we study how automated comparison of 3D ear models can be improved by soft computing techniques. More specifically we investigate and illustrate how multiple-criteria decision support techniques, which are based on fuzzy set theory, can be used for fine-tuning the ear comparison process. Point-to-point matching schemes are enriched with Logic Scoring of Preference (LSP) multiple-criteria decision support facilities. In this way valuable knowledge of forensic experts on ear identification aspects can be incorporated in the comparison process. The benefits and added value of the approach are discussed and demonstrated by an illustrative example.

The added value of implicit motives for management research Development and first validation of a Brief Implicit Association Test (BIAT) for the measurement of implicit motives

Many Management (sub-)disciplines, from Organizational Behavior and Marketing to Accounting and Strategy, are interested in antecedents and consequences of individual attitudes and traits. A key aspect of personality profiles are explicit and implicit motives. Yet, Management scholars mainly focus on explicit motives, with limited attention to implicit motives. We argue that this state of affairs probably came into being because current Management researchers mainly rely on implicit motive measures that are either difficult to apply or to develop, hampering researchers from applying implicit motive measures. To overcome the downsides of available instruments, we develop a Brief Implicit Association Test (BIAT) as an efficient, reliable and valid measure of implicit motives, particularly the needs for achievement, affiliation and power. To explore our BIAT’s predictive validity, we apply this measure to a specific research domain within Management: Entrepreneurship. We examine implicit motives’ association with entrepreneurial self-efficacy, business founding, and financial profitability. Our results show that the introduction of implicit motives can unlock stranded discussions in this research domain. Overall, we argue that implicit motives can help to push the boundaries of the study of deep-level attributes in a wide range of organizational and managerial settings.

Knowledge Transfer in or Through Clusters: Outline of a Situated Approach

Clusters usually assume a dynamic of innovation at the crossroad between proximity and distance. On the one hand, proximity triggers trust and a sense of common understanding between members that allow for the transfer of knowledge, especially its tacit components. But, at the same time, the innovativeness of the cluster also depends on distance: participants from different organizations with different skills, objectives, and interests interact in a joint network. It creates a complex context for knowledge sharing, full of creative tensions and power issues.