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In this chapter, we review empirical research evidence on the relationship between stressors and catecholamines (i.e., adrenaline and noradrenaline) and cortisol. With respect to acute stressors, both laboratory and field research have shown that the exposure to stressors leads to an increase in catecholamine and cortisol levels. With respect to more chronic stressors, research evidence is less consistent. Chronic mental workload was found to be related to elevated adrenaline levels. With respect to cortisol responses the interaction between workload and other variables seems to play a role. Empirical studies suggest that chronic stressors affect the responsivity to acute stressors. Research showed that after the exposure to stressors catecholamine and cortisol recovery is delayed.

This chapter provides an updated review of research examining the relationship between occupational stressors and job performance. We begin by presenting an eight-category taxonomy of workplace stressors and we then review theories that explain the relationships between workplace stressors and job performance. The subsequent literature review is divided into two sections. In the first section, we present a summary of Jex's (1998) review of research on the job stress–job performance relationship. In the second section, we provide an updated review of the literature, which includes studies that have been published since 1998. In this review, we evaluate how well the contemporary research has dealt with weaknesses and limitations previously identified in the literature, we identify and evaluate current trends, and we offer recommendations and directions for future research.

This chapter evaluates a model of the organizational context of burnout with direct reference to a new measure, the Areas of Worklife Scale (AWS). The model proposes a structured framework for considering six areas of worklife – workload, control, reward, community, fairness, and values – that have resonated through the literature on burnout over the previous two decades. The chapter presents extensive data on the AWS, testing a model of the six areas’ interrelationships as well as their overall relationship to the three aspects of burnout. The results of these analyses are discussed in reference to the psychometric qualities of the measure and the implications of a structured approach to work environments for future development of research on burnout. Implications for developing workplace interventions are also considered.

Karasek's (1979) job demands-control model is one of the most widely studied models of occupational stress (de Lange, Taris, Kompier, Houtman, & Bongers, 2003). The key idea behind the job demands-control model is that control buffers the impact of job demands on strain and can help enhance employees’ job satisfaction with the opportunity to engage in challenging tasks and learn new skills (Karasek, 1979). Most research on the job demands-control has been inconsistent (de Lange et al., 2003; Van Der Deof & Maes, 1999), and the main reasons cited for this inconsistency are that different variables have been used to measure demands, control, and strain, not enough longitudinal research has been done, and the model does not take workers’ individual characteristics into account (Van Der Deof & Maes, 1999). To address these concerns, expansions have been made on the model such as integrating resources, self-efficacy, active coping, and social support into the model (Demerouti, Bakker, Nachreiner, & Schaufeli, 2001b; Johnson & Hall, 1988; Demerouti, Bakker, de Jonge, Janssen, & Schaufeli, 2001a; Landsbergis, Schnall, Deitz, Friedman, & Pickering, 1992). However, researchers have only been partially successful, and therefore, to continue reducing inconstencies, we recommend using longitudinal designs, both objective and subjective measures, a higher sample size, and a careful consideration of the types of demands and control that best match each other theoretically.

Over the past few years, mathematical and computational models of organizations have attracted a great deal of interest in various fields of scientific research (see Lin & Carley, 1993 for review). The mathematical models have focused on the problem of quantifying the structural (mis)match between organizations and their tasks. The notion of structural congruence has been generalized from the problem of optimizing distributed decision-making in structured decision networks (Pete, Pattipati, Levchuk, & Kleinman, 1998) to the multi-objective optimization problem of designing optimal organizational structures to complete a mission, while minimizing a set of criteria (Levchuk, Pattipati, Curry, & Shakeri, 1996, 1997, 1998). As computational models of decision-making in organizations began to emerge (see Carley & Svoboda, 1996; Carley, 1998; Vincke, 1992), the study of social networks (SSN) continued to focus on examining a network structure and its impact on individual, group, and organizational behavior (Wellman & Berkowitz, 1988). Most models, developed under the SSN, combined formal and informal structures when representing organizations as architectures (e.g., see Levitt et al., 1994; Carley & Svoboda, 1996). In addition, a large number of measures of structure and of the individual positions within the structure have been developed (Roberts, 1979; Scott, 1981; Wasserman & Faust, 1994; Wellman, 1991).

Fig. 1 presents the interface used by our pilots to fly the UAV simulation.

UAVs have been used by military forces since at least the War of Attrition – fought between Egypt and Israel between 1967 and 1970 – when the Israeli Army modified radio-controlled model aircraft to fly over the Suez Canal and take aerial photographs behind Egyptian lines (Bolia, 2004). Although the Israelis ill advisedly abandoned the concept before the Yom Kippur War, it was taken up by several nations in the ensuing decades, and today UAVs are regarded as a routine component of surveillance operations, having played a significant role in both Afghanistan and Iraq.

A fundamental issue driving much of the current research is the design of the interface between humans and ROVs. Autonomous robots are sufficiently different from most computer systems as to require new research and design principles (Adams & Skubic, 2005; Kiesler & Hinds, 2004). Previous work on coordination between humans and automated agents has revealed both benefits and costs of automation for system performance (Parasuraman & Riley, 1997). Automation is clearly essential for the operation of many complex human–machine systems. But in some circumstances automation can also lead to novel problems for operators. Automation can increase workload and training requirements, impair situation awareness and, when particular events co-occur in combination with poorly designed interfaces, lead to accidents (e.g., Degani, 2004; Parasuraman & Riley, 1997).

Wide area search munitions (WASMs) are a cross between an unmanned aerial vehicle and a munition. With an impressive array of onboard sensors and autonomous flight capabilities WASMs might play a variety of roles on the modern battle field including reconnaissance, search, battle damage assessment, or communications relay.

Nursing comprises interactions with patients which may require emotional labor. This study clarifies the relation of emotional labor with the three burnout dimensions within the context of the Demand Control Support model in nurses. We used the Dutch Questionnaire on Emotional Labor (D-QEL) to measure surface acting, deep acting, suppression, and emotional consonance. In line with other studies, job characteristics were significantly related to emotional exhaustion and surface acting was significantly related to emotional exhaustion and depersonalization. Emotional consonance, the situation where somebody effortlessly feels the emotion that is required, is related to personal accomplishment.