Dirk Martignoni

Evaluating the New: The Contingent Value of a Pro-Innovation Bias

It is a central tenet in the literature on organizational change that firms need to explore novel courses of action in order to adapt and survive. Should firms thus exhibit a “pro-innovation bias” when evaluating novel decision alternatives? Or should firms rather assess new opportunities as objectively as possible? Our analysis of a simulation model suggests that a pro-innovation bias can have exploration-enhancing effects that increase long-run performance in complex and stable environments, but can also decrease performance substantially if the bias becomes too pronounced. However, under most other conditions, an unbiased, objective evaluation of novel opportunities is most effective. We also identify a set of contingency factors that strongly affect the value of a pro-innovation bias, which may explain why it is that we see so few firms with such a bias.

Organizational learning with forgetting: Reconsidering the exploration–exploitation tradeoff

Prior exploration–exploitation models of organizational learning generally neglect forgetting. This study models organizational learning with forgetting and derives some novel implications. Most noteworthy, our findings point out limits to the contention that promoting rapid learning undermines long-run knowledge. Slower learning is not always better. When agents are subject to forgetting, raising the rate of interpersonal learning often enhances the diversity of beliefs within an organization, as well as the number and range of aspects of the environment that organizational members come to know. The rate of learning that maximizes organizational knowledge or diversity varies with the rate of forgetting. Organizations need not sacrifice diversity as they gain knowledge. Analyses of our model indicate that knowledge and diversity are positively correlated across organizations. Implications of forgetting redirect theorists, empirical researchers, and managers toward alternatives to some conclusions from prior exploration–exploitation modeling studies.

Fortune Favors Fools: How Confidence Can Compensate for Competence in Learning

An important strategic challenge facing entrepreneurs and managers is the need to make choices across a set of policy alternatives (e.g., strategies, technologies, product designs), the merits of which are not initially well known. The efficacy of these choices is an increasing function of knowledge — competence that takes the form of accurate beliefs about the relative merits of the alternatives. Yet knowledge has a second, equally important but underexplored, dimension related to confidence in those beliefs. We examine confidence from a behavioral theory perspective that points to its role in learning processes, rather than a psychological perspective. We employ a formal computational model of learning where uncertainty may make any particular experience with an alternative misleading or unrepresentative. While our model is general in its applicability, we apply it to the case of an entrepreneur pursuing a particular market opportunity. Confidence acts to moderate an entrepreneur’s willingness to disregard feedback that conflicts with her beliefs. We find that there are conditions (e.g., high uncertainty) under which confidence in beliefs, rather than the accuracy of those beliefs, is the primary driver of the efficacy of entrepreneurial choice. One implication of this observation is that a less competent (less accurate) but more confident entrepreneur may outperform a highly competent entrepreneur lacking confidence in her accurate beliefs.

E Pluribus Unum: Organizational Size and the Efficacy of Learning

Learning from experience is a central theme in the management literature. While in general experiential learning is viewed as efficacious, the literature increasingly points to the difficulties inherent in the learning process — many of which stem from a deficit of information about the merits of alternative solutions. It seems plausible that larger organizations, with their capacity to simultaneously pursue a variety of potential solutions to a given challenge, may overcome this deficit. Such a perspective suggests that the efficacy of an organizations learning process should be an increasing function of organizational size. While this logic is intuitively appealing, we find that it does not fully capture the nuances of the organizational learning process. We employ a computational model and find that larger organizations, as characterized by their scale in pursuing parallel initiatives: (a) explore less than smaller organizations, (b) are less likely to discover the very best alternative, and yet (c) on average identify better alternatives. Increasing the number of parallel initiatives guides the search process towards viable alternatives, but it does so at the cost of inhibiting search breadth. Thus, in our model, the characteristics of learning by larger organizations do not result from differences in inertia or incentives that may impede learning and innovation, but rather from the properties of the organizational learning process itself.

How Can Imitation Increase Inter-Firm Heterogeneity?

Imitation is thought to lead to a decrease in inter-firm heterogeneity. This outcome rests on the assumption that imitation engenders only one type of implication for the imitating firm: an endowment of knowledge (technologies, product designs, strategies, etc.) about successful practices that makes the imitator more similar to its target. Yet research in the Carnegie tradition points to a second mechanism — imitation moderates a firms post-imitation adaptation, which we term the generative effect of imitation. In this paper, we use a computational model to examine the implications of this dual role of imitation for inter-firm heterogeneity. As intuition suggests, we find the endowment effect of imitation reduces inter-firm heterogeneity. However the generative effect of imitation is double-edged: it tends to make firms more similar in practices but more different in performance. Our results suggest that the generative effect of imitation produces a bimodal performance distribution; some firms achieve significant benefits from imitation, while many others find themselves significantly worse-off than they would have been had they foregone imitation. Because of its generative effect, imitation can lead to an increase in performance heterogeneity.

Learning Through Inaccurate Replication

Replicating a successful template or best practice across time (temporal replication) or across a number of different economic settings (spatial replication) is an important strategy for organizational growth and performance improvement. In this paper, we use an NK landscape model to examine how organizations may innovate and adapt to their environment through inaccurate replication. We identify conditions under which inaccurate replication can result in higher long-run performance than accurate replication. We also uncover the specific mechanisms through which replication errors may affect organizational performance. In that, our study also sheds a new light on how organizations may learn from (replication) errors.