Understanding Adaptability: A Prerequisite for Effective Performance within Complex Environments: Volume 6

Preface

Adaptability is becoming a hallmark of effective performance at all levels and types of organizations. Individuals within both public and private organizations are having to adapt to technological changes and the tempo of operations in dynamic, competitive environment. Corresponding teams within these organizations have to adapt to changes in structure, membership, and environmental conditions. At an organizational level public and private corporations are competing in an environment that is increasingly global, technological, and high-risk. The increased need for adaptability is not only seen within public and private organizations. The US military is facing an increasingly complex geopolitical environment that also demands adaptability in order to be effective. For example, within the military, individuals are having to adapt to asymmetric threats, increased joint operations, and network capabilities. Teams are having to adapt to a wide variety of environmental and team composition factors (e.g., warfighting-peacekeeping, teams comprised of coalition partners of multiple nationalities), and at an organizational level the military is having to adapt to changes in the geopolitical environment. The above conditions are but a few which highlight that as complexity rises it is no longer acceptable to only be able to perform well when things go as expected; instead individuals, teams, and organizations must be able to continuously adapt their knowledge and skills in order to remain competitive in environments which are fluid, often ambiguous, and where multiple pathways to goal attainment exist.

Work organizations and the employees within these organizations face considerable environmental pressures requiring adaptive change. Several forces have contributed to this need for great adaptation. These are described in many excellent sources (e.g., Cascio, 2003; Ilgen & Pulakos, 1999); here we briefly review their implications for individual adaptability.

Although models have been published in the literature covering various aspects of the job performance domain (e.g., technical performance, contextual performance), researchers have recently recognized a void in these models and have called for their expansion to include adaptive performance components (Campbell, 1999; Hesketh & Neal, 1999; London & Mone, 1999; Murphy & Jackson, 1999). Toward this end, Pulakos, Arad, Donovan, and Plamondon (2000) developed a taxonomy of adaptive job performance similar to the model of job performance developed by Campbell, McCloy, Oppler, and Sager (1993). This model contained eight dimensions of adaptive job performance. Pulakos et al. began their research with a review of various literatures on adaptability and identified six different aspects of adaptive performance. These are shown in Table 1, along with the research references from which they were derived. The diversity of substantive areas that are represented in the research articles cited in Table 1 is a testament to the perceived importance of adaptability across a number of behavioral disciplines. Although the idea that adaptive performance is multi-dimensional was reasonable based on the wide range of behaviors “adaptability” has encompassed in the literature (for example, adapting to organizational change, different cultures, different people, new technology), no published research prior to Pulakos et al. had systematically defined or empirically examined specific dimensions of adaptive job performance. Pulakos et al. conducted two studies to refine the six-dimension model of individual adaptive job performance derived from the literature. In Study 1, over 1,000 critical incidents from 21 different jobs were content analyzed, yielding an eight-dimension taxonomy of adaptive performance. That is, the critical incident analysis produced two additional adaptive performance dimensions that are shown at the bottom of Table 1.

This chapter addresses adaptation to dynamic, novel and uncertain military environments. These environments require a shift from a maneuver warfare paradigm to an asymmetric world where shifting alliances, questionable civilian loyalties, opaque cultures, and the requirement to maintain peace one day and combat the next makes for a particularly confusing situation. This new warfare paradigm requires adaptation to an uncertain, complex environment.

The initial section discusses a general cognitive model of visualization called RAVENS and its importance for adaptation developed specifically to address complex military environments. RAVENS posits that humans are inherently flexible decision makers and situation awareness depends on the ability of humans to create narrative visualizations that capture the overall context of complex military environments. Using the framework as a guideline, we will examine two important visualization research programs whose purpose is to allow military operators to rapidly adapt to volatile situations. The first program investigates cognitive effects such as the framing bias and their possible interactions with a variety of display concepts during a series of missile defense simulations. The experimenters presented risk as a spatial representation of uncertainty and target value that emphasized either expected population lost or expected population saved. The second program investigated the feasibility of using visualizations generated from Scheherazade (a coevolutionary algorithm) to aid MI analysts in predicting emergent tactics of terrorist groups during urban operations. Finally, we discuss the value of these approaches for providing coherent narrative understanding as called for in the RAVENS model.

As operational environments become increasingly fluid, organizations are turning to teams as a proven performance arrangement to structure complex work. Teams are ubiquitous in modern organizations because they can be used to create synergies, streamline workflow, deliver innovative services, satisfy incumbent needs, maximize the benefits of technology connecting distributed employees, and seize market opportunities in a global village. Teams are also increasingly used because coordinating the “…activities of individuals in large organizations is like building a sand castle using single grains of sand” (West, Borrill, & Unsworth, 1998, p. 6).

Barriers to cultural adaptability include perceptual, interpretive, and evaluative biases. Differences in culturally based perceptual patterns can be problematic given that interpretation and evaluation of behavior is a critical element of teamwork. Altogether, perceptual patterns are “selective, learned, culturally determined, consistent, and inaccurate” (Adler, 1986, p. 54). Selective exposure, selective attention, and selective retention are all hallmarks of the process of perception. Bagby (1970) demonstrated how perceptual patterns become selective even in childhood. He had American and Mexican children watch a bullfight and a baseball game simultaneously using a tachistoscope. When asked what they had seen, the American children claimed to have watched a baseball game, and the Mexican children claimed to have watched a bullfight. Neither group was aware that they had been presented two stimuli simultaneously. Both groups of children selected stimuli that had meaning for their culture and ignored or forgot the stimuli that had no meaning for them. The children's culture predisposed them to notice some things and not others. Perceptual selectivity is a key barrier to cultural adaptability and influences both interpretation and evaluation.

Adaptive capacity has commonly been defined as the “general ability of institutions, systems, and individuals to adjust to potential damage, to take advantage of opportunities, or to cope with the consequences” (http://www.greenfacts.org). Adaptive capacity is herein described as the ability to facilitate the process of adaptive team performance and the resulting outcome of team adaptation (see Stagl, Burke, Salas, & Pierce, this volume). More specifically, although often spoken of with regard to environmental and global changes, it is spoken of here with regard to the ability of individuals (and correspondingly teams) to recognize and understand contextual changes, dynamically revise and implement plans accordingly, and learn from each implementation so as to be better prepared in the future.

In complex environments, the use of technology to enhance the capability of people is commonplace. In rapidly changing and often unpredictable environments, it is not enough that these human-automated “teams” perform well when events go as expected. Instead, the human operators and automated aids must be flexible, capable of responding to rare or unanticipated events. The purpose of this chapter is to discuss the Framework of Automation Use (Dzindolet, Beck, Pierce, & Dawe, 2001) as it relates to adaptive automation. Specifically, our objectives are to: (1) examine a number of factors that determine how people can effectively integrate their activities with their machine partners in fluid environments and (2) consider the implications of these findings for future research.

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).