Professors on the Board: Do They Contribute to Society Outside the Classroom?

According to our data, 38.5 % of S&P 1500 firms have at least one professor on their boards. Given the lack of research examining the roles and effects of academic faculty as members of boards of directors (professor–directors) on corporate outcomes, this study investigates whether firms with professor–directors are more likely to exhibit higher corporate social responsibility (CSR) performance ratings. Results indicate that firms with professor–directors do exhibit higher CSR performance ratings than those without. However, the influence of professor–directors on firm CSR performance ratings depends on their academic background—the positive association between the presence of professor–directors and firm CSR performance ratings is significant only when their academic background is specialized (e.g., science, engineering, and medicine). Finally, this positive association weakens when professor–directors hold an administrative position at their universities

Supporting dynamic change detection: using the right tool for the task

Detecting task-relevant changes in a visual scene is necessary for successfully monitoring and managing dynamic command and control situations. Change blindness—the failure to notice visual changes—is an important source of human error. Change History EXplicit (CHEX) is a tool developed to aid change detection and maintain situation awareness; and in the current study we test the generality of its ability to facilitate the detection of changes when this subtask is embedded within a broader dynamic decision-making task. A multitasking air-warfare simulation required participants to perform radar-based subtasks, for which change detection was a necessary aspect of the higher-order goal of protecting one’s own ship. In this task, however, CHEX rendered the operator even more vulnerable to attentional failures in change detection and increased perceived workload. Such support was only effective when participants performed a change detection task without concurrent subtasks. Results are interpreted in terms of the NSEEV model of attention behavior (Steelman, McCarley, & Wickens, Hum. Factors 53:142–153, 2011; J. Exp. Psychol. Appl. 19:403–419, 2013), and suggest that decision aids for use in multitasking contexts must be designed to fit within the available workload capacity of the user so that they may truly augment cognition