Ann Terlaak

A Framework for Analyzing Environmental Voluntary Agreements

In the 1990s, Environmental Voluntary Agreements (VAs) involving regulatory agencies and industry have emerged as the promise of the future in environmental policy circles. The collaborative mechanisms of VAs can be conducive to the development of innovative solutions, which regulators or firms would have been unlikely to develop alone. From a business perspective, participation in VAs can reduce the burden of regulation, facilitate the communication of environmental improvements, and allow firms to be ahead of competition for environmental products. However, the benefits of participating in VAs can be outweighed by high transaction and administration costs if VAs are not properly designed. This article discusses when participation in a VA offers strategic opportunities and when joining a VA might turn into a costly enterprise.

Decomposing Uncertainty and Its Effects on Imitation in Firm Exit Decisions

This study examines the effects of different uncertainty types on interorganizational imitation in firm exit decisions. We draw on herding models to conceptualize exit decisions as being based on a firms private information, which the firm updates with information inferred from observing the actions of others. We posit that different types of uncertainty differentially affect this observational learning process; in particular, we propose that certain uncertainty types attenuate rather than foster observational learning and subsequent imitation. We test this theory using a 29-year panel data set on the exit of private venture capital firms. Our results indicate that observational learning does influence imitation in firm exit decisions, and they also suggest that a common belief-that uncertainty enhances imitation-does not apply to all types of uncertainty. Specifically, we find that uncertainty fosters imitation only when it is idiosyncratic to the firm; uncertainties that are common to all firms, in contrast, actually reduce reliance on observational learning. By decomposing uncertainty into different types and explicating their effects on imitation, we demonstrate that this relationship is more nuanced than previously assumed and, in addition, highlight the role of deliberate information processing in imitation.