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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).

This chapter addresses quality management (QM) content on the process quality management (PQM) level in the high-technology industry of semiconductor manufacturing. Identifying critical components of a manufacturing or service process and improving them to ensure superior quality at economic costs is the overall goal of PQM. Deming was a prominent proponent of PQM as a means to optimize the performance of a product or process. In optimizing the performance of a product or process, good design practices require the evaluation of designs from a process perspective. Advanced design techniques, namely design of experiments (DOEs), are cornerstone to the optimization process, to design management, and in turn to PQM. This chapter investigates the use of DOEs in the manufacture of semiconductors. Specifically, two underlying assumptions impact operations managers using DOEs: solution differences/similarities in underlying DOE optimization methods and marginal rates of substitution. Perhaps unknown to the user, DOE optimization techniques carry strong assumptions regarding these characteristics. This chapter investigates two commonly used DOE optimization approaches applied to the operational control of semiconductor wafer production, and demonstrates that each method contains assumptions about these characteristics, which are not intuitively evident to a user.

This chapter uses the concept of real-world experiments to describe ecological activity that occurs in the wider human community. The case discussed is an ecological restoration project at Montrose Point, a peninsula in Lake Michigan on the North Side of Chicago. In the restoration of Montrose Point, the scientific importance of ecological science is not juxtaposed with the seemingly irrational ideas of the human community, but the human community becomes a part of scientific work. Like experiments in the laboratory, real-world experiments often bring surprises. However, if ecological restoration is recognized to be inherently uncertain, surprises become opportunities to learn rather than failures. The chapter concludes with a discussion how a real-world experimental strategy can help to handle surprises via alternate phases of corroboration by recursive practice.

Independent travelers are those vacationers who have booked only a minimum of their transportation and accommodation arrangements prior to departure on the vacation. Independent travel is an important and growing sector of worldwide tourism. Choice of vacation itinerary for the independent vacation represents a complex series of decisions regarding purchase of multiple leisure and tourism services. This chapter builds and tests a model of independent traveler decision-making for choice of vacation itinerary. The research undertaken employs a two-phase, inductive–deductive case study design. In the deductive phase, the researcher interviewed 20 travel parties vacationing in New Zealand for the first time. The researcher interviewed respondents at both the beginning and the end of their New Zealand vacations. The study compares pre-vacation research and plans, and actual vacation behaviors, on a case-by-case basis. The study examines case study narratives and quantitative measures of crucial variables. The study tests two competing models of independent traveler decision-making, using a pattern-matching procedure. This embedded research design results in high multi-source, multi-method validity for the supported model. The model of the Independent Vacation as Evolving Itinerary suggests that much of the vacation itinerary experienced in independent travel is indeed unplanned, and that a desire to experience the unplanned is a key hedonic motive for independent travel. Rather than following a fixed itinerary, the itinerary of an independent vacation evolves as the vacation proceeds. The independent traveler takes advantage of serendipitous opportunities to experience a number of locations, attractions and activities that they had neither actively researched nor planned.

The Cyclic Innovation Model is applied to a new process for the production of fine chemicals and pharmaceuticals using a combination of ionic liquids and supercritical carbon dioxide. This multi-value innovation combines economic growth with environmental concerns and social value. The most important obstacles in the implementation of this new technology are the successful life cycle management of current production plants, the linearity of current innovation thinking, and a perceived high risk of adoption.

Many universities are currently investing significant sums of money into refurbishing existing learning spaces and/or building further infrastructure (including Next Generation Learning Spaces (NGLS)) to support learning and teaching in the face-to-face context. While this is usually welcome by staff and students, there is often a concern that designs are not informed by input from appropriate stakeholders.

This chapter brings together information from a range of sources to provide practical ideas and advice on designing robust, whole-of-lifecycle evaluations for learning space projects. By incorporating pre- and post-occupancy stages, involving a wide array of stakeholders and looking beyond surveys and focus groups as evaluation techniques, universities can ensure that future designs take into consideration the experiences and context of staff and students at the institution as well as lessons learned from previous projects.

Purpose – This chapter argues that the concept of agility is an effective robust framework for designing sustainable health care systems.

Design/methodology/approach – This case study of Alegent Health was based on 7 years of data collection. It includes observations of meetings, large-group interventions, and other activities; site visits to different hospitals in the system to observe changes in practice; interviews with Alegent Health executives, primary care physicians, hospital presidents, specialist physicians and physician groups, and health systems staff and nurses; and a variety of archival data including meeting minutes, video tapes, conference proceedings, and web site material.

Findings – The Alegent Health system has evolved over time according to the principles of agility. It built a series of new capabilities that contribute to improved clinical outcomes, sustained financial results, and more socially and ecologically responsible results. Designing health care systems based on agility is a more effective and sustainable approach than relying on legislative or other criteria.

Originality/value – The discussion of sustainability in health care has focused primarily on specific projects or how to respond to specific technological, regulatory, or clinical changes. Alegent Health's experience provides important lessons, opportunities, and challenges that can help advance our understanding of effective health care and use organizational agility to create more sustainable health care systems. This chapter provides health care system administrators an alternative design option.

Considering sustainability and resilience together is crucial in food supply chain (FSC) management, as it ensures a balanced approach that meets environmental, economic and social needs while maintaining the system's capacity to withstand disruptions. Towards this, a multi-objective optimisation model is proposed in this study to create an integrated sustainable and resilient FSC. The proposed model employs four objective functions – each representing a dimension of sustainability and one for resilience and utilises an augmented ϵ-constraint method for solving. The findings highlight the interplay between sustainability aspects and resilience, illustrating that overemphasis on any single dimension can adversely affect others. Further, the proposed model is applied to the case of Indian mango pulp supply chain and several inferences are derived. The proposed model would assist decision-makers in making a well-balanced choice based on sustainability and resilience considerations.

Health-care systems currently face great challenges, including an increasing elderly population. To respond to this problem, a hospital emergency department, three municipalities, and self-employed general practitioners in Denmark decided to collaborate with the aim of reorganizing treatment of elderly acute ill patients. By establishing a small-scale collaborative community and through an action research process, we show, how to jointly explore and develop a new organization design for in-home hospital treatment that enables the health professionals to collaborate in new ways, and at the same time to investigate and improve this cocreation process and codesign of knowledge among multiple different stakeholders.