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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.

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).

This study aims to develop an architectural prototype of a Cyber-Physical System (CPS), as well as lay a technological foundation for future smart housing with improved health and well-being outcomes for its occupants.

This study deploys smart sensors to monitor the key environmental parameters of a house. Using Internet of Things technology, a prototype of a CPS has been developed for capturing the environmental conditions over time. A case study involving a property in New Zealand was undertaken to validate the prototype.

The study proposes a monitoring platform, enabled by the CPS and smart sensing devices, that collects, shares, stores, analyses and visualises indoor environment data. The reliability and accuracy of the monitoring system were enhanced by comparing the activity of house occupants with sensor data.

Due to limited time, the prototype was tested in one house for a period of one month. Air quality was not considered in this study. However, the work suggests that such an approach provides an effective solution for government organisations and housing agencies to collect information for the purpose of assessing building thermal performance.

This research proposes a new lens consisting of a home environment monitoring application with health and well-being implications. It could also be used to inform the future design of healthy homes and buildings, both in New Zealand and internationally.

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.

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.

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).

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.

Presents results of a nine‐year research and development project. At the beginning a scheduling simulation software was built and assessed in real contexts. Then it has grown into a technology for shop‐floor planning and control in the jobbing industry, developing now into a philosophy for strategic jobbing production management, named here as “Total punctuality approach”. Based on this approach, a conceptual model was developed to orientate control system design in the jobbing industry. The model was conceived on a manufacturing strategy base being clearly driven by punctuality which was identified as being the main order winning criteria in such markets, when quality and price issues have already been resolved. Flexibility, speed and costs were addressed as well according to their relative importance to competitiveness. Concepts extracted from the various technologies available are mixed up forming a special blend with regard to the total punctuality goal. Almost all aspects of the model were implemented in a software that has been successfully used in small Brazilian jobbing firms. Using the software, it is possible to assess a large range of shop‐floor management practices in terms of their contribution to the competitiveness of the company.

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.