Leader Ethical Decision-Making in Organizations: Strategies for Sensemaking

Organizational leaders face environmental challenges and pressures that put them under ethical risk. Navigating this ethical risk is demanding given the dynamics of contemporary organizations. Traditional models of ethical decision-making (EDM) are an inadequate framework for understanding how leaders respond to ethical dilemmas under conditions of uncertainty and equivocality. Sensemaking models more accurately illustrate leader EDM and account for individual, social, and environmental constraints. Using the sensemaking approach as a foundation, previous EDM models are revised and extended to comprise a conceptual model of leader EDM. Moreover, the underlying factors in the model are highlighted—constraints and strategies. Four trainable, compensatory strategies (emotion regulation, self-reflection, forecasting, and information integration) are proposed and described that aid leaders in navigating ethical dilemmas in organizations. Empirical examinations demonstrate that tactical application of the strategies may aid leaders in making sense of complex and ambiguous ethical dilemmas and promote ethical behavior. Compensatory tactics such as these should be central to organizational ethics initiatives at the leader level.     

ROBUST ASSET ALLOCATION WITH BENCHMARKED OBJECTIVES

In this paper, we introduce a new approach for finding robust portfolios when there is model uncertainty. It differs from the usual worst‐case approach in that a (dynamic) portfolio is evaluated not only by its performance when there is an adversarial opponent (“nature”), but also by its performance relative to a stochastic benchmark. The benchmark corresponds to the wealth of a fictitious benchmark investor who invests optimally given knowledge of the model chosen by nature, so in this regard, our objective has the flavor of min–max regret. This relative performance approach has several important properties: (i) optimal portfolios seek to perform well over the entire range of models and not just the worst case, and hence are less pessimistic than those obtained from the usual worst‐case approach; (ii) the dynamic problem reduces to a convex static optimization problem under reasonable choices of the benchmark portfolio for important classes of models including ambiguous jump‐diffusions; and (iii) this static problem is dual to a Bayesian version of a single period asset allocation problem where the prior on the unknown parameters (for the dual problem) correspond to the Lagrange multipliers in this duality relationship. This dual static problem can be interpreted as a less pessimistic alternative to the single period worst‐case Markowitz problem. More generally, this duality suggests that learning and robustness are closely related when benchmarked objectives are used.     

The Changing Rewards to Science: The Case of Biotechnology

Little is known about the form and or magnitude of compensation provided university-based scientists working on firm R&D. This is unfortunate, given the important role that university-based scientists play in R&D, and the growing literature concerning compensation and innovation. This paper sheds some light on these issues by examining the compensation of university-based scientists involved with 52 biotech firms that made an initial public offering between March of 1990 and November of 1992. Although the stock holdings of the university scientists are of particular interest, additional forms of compensation received by the scientists are also examined.We find that approximately 10 percent of the university-based scientists affiliated with these companies hold sufficient options or stock to require disclosure at the time of the public offering. A far larger proportion has an equity position in the firm. In many instances the scientists also receive consulting fees or salary from the firm and enter into licensing agreements with the firm. In addition to providing information concerning the compensation of university-based scientists, the empirical work suggests that the rewards to science can be significantly greater than previous work would suggest.