International entrepreneurship : exploring the logic and utility of individual experience through comparative reasoning approaches

In this paper, we suggest that individual experience and reasoning, as applied to new endeavors in internationalization, are concepts with high potential to advance conceptual and empirical research in international entrepreneurship (IE). Experience is known to be important in internationalization, but the logic or reasoning with which it is applied is insufficiently understood. Cognitive, comparison-based reasoning theories explain how individuals draw on experience to make sense of uncertain, novel, and complex situations. Drawing on two such theories, heuristics and analogical reasoning, we delineate the logic of experience and advance speculative propositions on its utility in the context of internationalization research

An Open Invitation to Open Innovation: Guidelines for the Leadership of Open Innovation Processes

Leadership is crucial for the successful internal implementation of open innovation (OI) initiatives. Based on empirical evidence from the leadership, organizational change, and OI literatures, this chapter provides practical guidelines that can help leaders overcome some of the most common internal impediments to OI adoption. Specifically, it stresses the critical role that leaders can play in (1) getting people on board by creating and promoting a shared vision of OI, (2) earning employee commitment to OI by showing in words and deeds that it is more than just a new flavor of the month, (3) reshaping the definition of success by challenging assumptions about appropriate behaviors and aligning the reward/incentive systems, and (4) contributing to internal capability building by developing individuals and facilitating the development of internal networks. Moreover, it provides some practical strategies for leaders interested in embedding OI into their organizations

A Cognitive Model of Multi-Objective Multi-Concept Formation

Abstract. The majority of previous computational models of high-order human cognition incorporate gradient descent algorithms for their learning mechanisms and strict error minimization as the sole objective of learning. Recently, however, the validity of gradient descent as a descriptive model of real human cognitive processes has been criticized. In the present paper, we introduce a new framework for descriptive models of human learning that offers qualitatively plausible interpretations of cognitive behaviors. Specifically, we apply a simple multi-objective evolutionary algorithm as a learning method for modeling human category learning, where the definition of the learning objective is not based solely on the accuracy of knowledge, but also on the subjectively and contextually determined utility of knowledge being acquired. In addition, unlike gradient descent, our model assumes that humans entertain multiple hypotheses and learn not only by modifying a single existing hypothesis but also by combining a set of hypotheses. This learning-by-combination has been empirically supported, but largely overlooked in computational modeling research. Simulation studies show that our new modeling framework successfully replicated observed phenomena.