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How do organizations respond to negative feedback regarding their innovation activities? In this chapter, the authors reconcile contradictory predictions stemming from behavioral learning and from the escalation of commitment (EoC) perspectives regarding persistence under negative performance feedback. The authors core argument suggests that the seemingly contradictory psychological processes indicated by these two perspectives occur simultaneously in decision makers but that the design of organizational roles and reward systems affects their prevalence in decision-making tasks. Specifically, the authors argue that for decision makers responsible for an individual project, responses given to negative performance feedback regarding a project are dominated by self-justification and loss-avoidance mechanisms predicted by the EoC literature, while for decision makers responsible for a portfolio of projects, responses to negative performance regarding a project are dominated by an under-sampling of poorly performing alternatives that behavioral learning theory predicts. In addition to assigning decision-making authority to different organizational roles, organizational designers shape the strength of these mechanisms through the design of reward systems and specifically by setting more or less ambiguous goals, aspiration levels, time horizons of incentives provided, and levels of failure tolerance.

In this study, the authors unpack the micro-level processes of knowledge accumulation (experiential learning) and knowledge application (problem solving) to examine how task allocation structures influence organizational learning. The authors draw on untapped potential of the classical garbage can model (GCM), and extend it to analyze how restrictions on project participation influence differentiation and integration of organizational members’ knowledge and consequently organizational efficiency in solving the diverse, changing problems from an uncertain task environment. To isolate the effects of problem or knowledge diversity and experiential learning, the authors designed three simulation experiments to identify the most efficient task allocation structure in conditions of (1) knowledge homogeneity, (2) knowledge heterogeneity, and (3) experiential learning. The authors find that free project participation is superior when the members’ knowledge and the problems they solve are homogenous. When problems and knowledge are heterogeneous, the design requirement is on matching specialists to problem types. Finally, the authors found that experiential learning creates a dynamic problem where the double duty of adapting the members’ specialization and matching the specialists to problem types is best solved by a hierarchic structure (if problems are challenging). Underlying the efficiency of the hierarchical structure is an adaptive role of specialized members in organizational learning and problem solving: their narrow but deep knowledge helps the organization to adapt the knowledge of its members while efficiently dealing with the problems at hand. This happens because highly specialized members reduce the necessary scope of knowledge and learning for other members during a certain period of time. And this makes it easier for the generalists and for the organization as a whole, to adapt to unforeseen shifts in knowledge demand because they need to learn less. From this nuanced perspective, differentiation and integration may have a complementary, rather than contradictory, relation under environmental uncertainty and problem diversity.

Entrepreneurs in a competitive economy face three fundamental problems. They need to search for and discover a business opportunity (Kirzner, 1973), evaluate it (Knight, 1921), and then seize the opportunity to reap entrepreneurial profits (Schumpeter, 1911) (Langlois, 2007). The problem that we address is how the ability to exploit business opportunities is influenced by entrepreneurial search and the economic organization of entrepreneurship (Arrow, 1962; Lippman & Rumelt, 2003b; Aghion et al., 2005; Foss, Foss, & Klein, 2007). In many cases, the discovery for a new business opportunity needs to be motivated by expected gains, since the search and evaluation of business opportunities is a costly, resource-consuming process (Denrell, Fang, & Winter, 2003; Nickerson & Zenger, 2004; Foss & Klein, 2005; Teece, 2007; Foss & Foss, 2008).¹ We show the critical role of expectations for understanding of the economic organization of entrepreneurship, and argue that transaction cost economics, with its insistence on bounded rationality, but farsighted contracting offers useful insights and presents rich opportunities for further theoretical and empirical research (cf. also Furubotn, 2002).

We extend the classical garbage can model to examine how individual differences in ability and motivation will influence organizational performance. We find that spontaneous coordination provided by an organized anarchy is superior when agents are equally competent. The Weberian bureaucracy of planned coordination is effective when problems require specialist knowledge. However, errors in matching problems to specialized agents are a central challenge for bureaucracies. Actual organizations, therefore, combine elements of organized anarchies and bureaucracies. Heterogeneous motivation compounds coordination problems, but is usually less important than competence. Our findings point to matching and interactive learning as fruitful areas for further study.

Corruption has traditionally been associated with an absence of pro-social norms such as trust and altruism. This paper challenges this view by examining market corruption – one-shot exchange transactions between strangers in the shadow of the law. The paper aims to propose that in the absence of repeat interactions and legal remedies to prevent contractual violations, acts of market corruption will require strong norms of generalized trust and altruism. As such, pro-social norms facilitate, rather than mitigate, market corruption.

The paper utilizes meta-analysis to examine the relationship between pro-social behavior in economic experiments and prevailing corruption levels.

The results from meta-analyses of both trust- and dictator game experiments show positive, significant relationships between pro-social norms and prevailing corruption levels.

The findings of the paper suggest the need for further research into the relationship between societal norms and different types of corruption.

Policymakers should be wary about attempting to combat corruption through bottom-up policies designed to strengthen pro-social norms. Such policies may be counter-productive in that they are likely to provide the breeding ground for more acts of market corruption.

Conventional wisdom suggests a negative association between pro-social norms and corruption levels. The paper proposes that the relationship is not that simple. Indeed, the meta-study findings suggest the reverse relationship in the case of petty (market) corruption.

To assess the impact of TCE on the field of strategy, we first quantified the distribution of TCE-related research articles across all disciplines and fields. Specifically, we identified every article that appeared in a journal included in the Institute for Scientific Information's (ISI's) Web of Knowledge between 1975 and 2008 and that included among its keywords some variation of “transaction costs.” We then removed those articles for which this term clearly did not refer to transaction costs of the Coasean kind (primarily articles in finance and computing, for which “transaction cost” has a different meaning). Finally, we categorized each journal according to its discipline or field. Granted, this requires some judgment, but we attempted to be objective in our categorizations.¹ As Table 1 shows, articles that are self-described as part of the TCE research stream have appeared more frequently in strategy journals than in the journals of any other discipline or field. We interpret this as evidence of TCE's impact on strategy, and of the importance of the strategy field to TCE.