We develop and test a model that extends the understanding of how people react to news of organizational unethical behavior and how such reactions impact stock performance. We do so by taking into account the interplay between the features of specific unethical acts and the features of the organizational context within which unethical acts occur. We propose a two-stage model in which the first stage predicts that unethical acts that benefit the organization are judged less harshly than are unethical acts that benefit the actor, when the organization is seen as pursuing a moral goal (e.g., producing inexpensive medicine rather than tobacco products). In such cases, the motives behind the unethical act are construed as an individuals' intentions to pursue a moral end. The second stage of our model connects moral judgment to action against the organization as a whole. We propose that moral judgments of an unethical act are more likely to translate into negative economic consequences for the organization when the unethical act is seen as benefiting the organization, because in such cases the organization is construed as an accomplice. Study 1 is an event study of stock market reactions to organizational unethical behavior in which the features of organizational unethical behavior were operationalized by coding media coverage of unethical acts. Study 2 is an experiment that used news stories to manipulate features of unethical behavior and measured participants' estimates of stock performance, while incentivizing participants for accuracy. Both studies found support for our model.

The lives of U.S. soldiers in combat depend on complex weapon systems and advanced technologies. In combat conditions, the resources available to support the operation and maintenance of these systems are minimal. Following the failure of a critical system, technical support personnel may take days to arrive via helicopter or ground convoy—leaving soldiers and civilian experts exposed to battlefield risks. To address this problem, the U.S. Army Communications Electronics Command (CECOM) developed a suite of systems, Virtual Logistics Assistance Representative (VLAR), with a single purpose: to enable a combat soldier to maintain critical equipment. The CECOM VLAR team uses an operations research (OR) approach to codifying expert knowledge about Army equipment and applying that knowledge to troubleshooting equipment diagnostics in combat situations. VLAR infuses a classic knowledge-management spiral with OR techniques: from socializing advanced technical concepts and eliciting tacit knowledge, to integrating expert knowledge, to creating an intuitive and instructive interface, and finally, to making VLAR a part of a soldier’s daily life. VLAR is changing the Army’s sustainment paradigm by creating an artificial intelligence capability and applying it to equipment diagnostics. In the process, it has generated a sustainable cost-savings model and a means to mitigate combat risk. Through 2015, VLAR saved the Army $27 million in direct labor costs from an investment of $8 million by reducing the requirement for technical support personnel. We project additional direct costs savings of $222 million from an investment of $60 million by the end of 2020. Most importantly, VLAR has prevented an estimated 4,500 casualties by reducing requirements for helicopter and ground-convoy movements. This translates to short- and long-term medical cost savings of over $9 billion. In this paper, we discuss the OR methods that underpin VLAR, at the heart of which lie causal Bayesian networks, and we detail the process we use to translate scientific theory and experiential knowledge into accessible applications for equipment diagnostics.