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Job analytic information is central to a variety of human resource activities (e.g., selection system development, compensation system development, organizational design). It is also well understood that many jobs are changing due to forces such as technological advances, a desire for teamwork, and various organizational interventions. In addressing such changes when using job analytic information, human resource practitioners might be faced with questions about the currency, or "up-to-dateness," of their existing job analysis. Unfortunately, there is almost no guidance for researchers or professionals regarding what methods might be used to check job analyses for currency (i.e., if there are any changes to the job that might influence the job analysis, how does one systematically assess such changes). We review the scant literature in this area and offer a protocol that we developed and implemented within a litigious environment. The protocol is systematic and focused on job change. In order to be as efficient as possible (and sensitive to organizational resource expenditures), the protocol builds on prior job analytic information. Two examples of the protocol's implementation are provided. We also present a variety of lessons learned during the development and implementation of this relatively unique currency protocol, as well as a discussion of some possible variants of the method.

Duration estimation is shown to be affected by the scale, or size, of visual displays. In particular, verbal estimates of the duration of a fixed interval of time tend to increase as the size of a given visual display decreases. The results are considered from two general theoretical frameworks (psychobiological chronometer and information storage-size theories).

Ten video games currently used in commercial markets were subjected to pairwise similarity ratings. A multidimensional scaling of the responses revealed three underlying dimensions that accounted for 30% of the variance: destructiveness, dimensionality, and graphic quality. These three dimensions are readily interpretable, can be embedded within personality and motivation literatures, and have application to the design of computer-based training devices.

Objective: The objective was to review and integrate available research about the construct of state-level suspicion as it appears in social science literatures and apply the resulting findings to information technology (IT) contexts. Background: Although the human factors literature is replete with articles about trust (and distrust) in automation, there is little on the related, but distinct, construct of "suspicion" (in either automated or IT contexts). The construct of suspicion—its precise definition, theoretical correlates, and role in such applications—deserves further study. Method: Literatures that consider suspicion are reviewed and integrated. Literatures include communication, psychology, human factors, management, marketing, information technology, and brain/neurology. We first develop a generic model of state-level suspicion. Research propositions are then derived within IT contexts. Results: Fundamental components of suspicion include (a) uncertainty, (b) increased cognitive processing (e.g., generation of alternative explanations for perceived discrepancies), and (c) perceptions of (mal)intent. State suspicion is defined as the simultaneous occurrence of these three components. Our analysis also suggests that trust inhibits suspicion, whereas distrust can be a catalyst of state-level suspicion. Based on a three-stage model of state-level suspicion, associated research propositions and questions are developed. These propositions and questions are intended to help guide future work on the measurement of suspicion (self-report and neurological), as well as the role of the construct of suspicion in models of decision making and detection of deception. Conclusion: The study of suspicion, including its correlates, antecedents, and consequences, is important. We hope that the social sciences will benefit from our integrated definition and model of state suspicion. The research propositions regarding suspicion in IT contexts should motivate substantial research in human factors and related fields.

Emergent cyber-attack threats against cyber-physical systems can create potentially catastrophic impacts. The operators must intervene at the right moment when suspected attacks occur, without over-reliance on systems to detect the cyber-attacks. However, military operators are normally trained to trust, rather than suspect systems. We applied suspicion theory to explore how operators detect and respond to cyber-attacks against an unmanned ground vehicle (UGV) system in the operational context of a human-machine team (HMT). We investigated the relationships between the operator suspicion and HMT performance by conducting human-in-the-loop experiments on eight mission scenarios with 32 air-force officers. The experiment yielded a significant, negative relationship between operator suspicion and HMT performance (quantified both in terms of the desirability of decision response and the time to respond). Notably, operator suspicion increased with the combined effects of cyber-attacks and a sentinel alert but not with the alert alone. This finding was particularly meaningful for "false-negative" scenarios, in which no sentinel alert was sent despite cyber-attacks having occurred. Although the operators did not receive an alert, the operators grew more suspicious, seeking more information; it took longer for the operators to respond, and their decision responses were highly divergent (17.2% came with less-desirable responses, and 21.9% were considered instances of over-reliance). In contrast, in "false-positive" scenarios, 95.3% of the operator responses were highly desirable. This experiment has implications for the role of a sentinel alert in engineering trustworthy HMT systems so that the operators can quickly transition through state-suspicion to the most desirable decision.

Despite the importance that human error in the cyber domain has had in recent reports, cyber warfare research to date has largely focused on the effects of cyber attacks on the target computer system. In contrast, there is little empirical work on the role of human operators during cyber breaches. More specifically, there is a need to understand the human-level factors at play when attacks occur. This paper views cyber attacks through the lens of suspicion, a construct that has been used in other contexts, but inadequately defined, in prior research. After defining the construct of suspicion, the authors demonstrate the role that suspicion plays as the conduit between computer operators' normal working behaviors and their ability to alter that behavior to detect and react to cyber attacks. With a focus on the user, rather than the target computer, the authors empirically develop a latent structure for a variety of types of cyber attacks, link that structure to levels of operator suspicion, link suspicion to users' cognitive and emotional states, and develop initial implications for cyber training.