This study investigates how interorganizational imitation influences management control decisions in a supply chain setting. Control design in interfirm exchanges is traditionally thought to be based on the principle of matching, where organizations install MCS that align with the transaction context. However, despite these theorized interrelationships, misaligned transactions commonly exist in practice. In this study, we propose a framework on the potential sources of such misalignment. We argue that control misalignment can be attributed to imitating behavior, by which organizations adopt MCS following the example of other organizations. Based on survey data collected from firms involved in a supply chain triad, we demonstrate that buyers control their upstream suppliers partially by imitating how their downstream customer controls them. Notably, buyers appear to imitate despite variations in transaction context, creating a basis for misalignment in line with our predictions.

This paper examines whether and how increased entry threat drives industry merger activity. We use the reduction in import tariffs as a natural experiment of exogenous increase in competitive intensity and study its effect on merger and acquisition (M&A) decisions. Our results indicate that competition drives M&As towards more efficient resource allocation. We first document that increased entry threat intensifies takeover activity, consistent with the argument that M&As are an efficient reaction to economic shocks. We also find that, after import tariff reductions, the selection of targets outside the industry becomes more efficient and industry rivals react more positively to those deals, suggesting that efficient non-horizontal deals signal the existence of investment opportunities outside the industry for the industry peers.

We propose two classes of semi-parametric estimators for the tail index of a regular varying elliptical random vector. The first one is based on the distance between a tail probability contour and the observations outside this contour. We denote it as the class of separating estimators. The second one is based on the norm of an arbitrary order. We denote it as the class of angular estimators. We show the asymptotic properties and the finite sample performances of both classes. We also illustrate the separating estimators with an empirical application to 21 worldwide financial market indexes.