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Close interaction between universities, industries, and governments has given rise to hybrid organizations incorporating economic development alongside scientific research and higher education. We will approach this phenomenon and the related organization-theoretical problems by looking at two cases of discipline making to discuss the potential of the concept of organizational field introduced by the neoinstitutionalist school of organization theory. As this concept presumes the Bourdieusian theory of social fields, we will consider possibilities of reflective contesting of the states of doxa in discipline making in regard to organizational aspects of disciplinary boundaries in the university-centered system of higher education, its demarcation to business and schooling, as well as to the related ideology of professionalism and science policy. We will also comment on the Bourdieusian conceptuality inscribed in the neoinstitutionalist metaphor of organizational field from the perspective of systems theory inspired by Luhmann. This is because we believe that further development of the semantic focus in the problem of disciplinary boundaries would benefit from Luhmannian tools designed to grasp organizations as social systems that facilitate interrelations of differentiated function systems relevant for discipline making in current technoscience.

Many recent studies have voiced the growing concern that the body of knowledge that springs from organization science is hardly taken notice of in management practice. This has given rise to urgent calls for making organization research more relevant to practitioners and an intensive debate on how to realize this aim has set in (e.g., Hodgkinson, Herriot, & Anderson, 2001; Rynes, Bartunek, & Daft, 2001; MacLean & MacIntosh, 2002; Baldridge, Floyd, & Markoczy, 2004; Van de Ven & Johnson, 2006). In most of the existing literature one can identify three main reasons for the observable lack of connection between organization research and practice: research is not sufficiently focused on the “real” problems of practitioners (e.g., Rynes, McNatt, & Breetz, 1999), research results are not properly disseminated to practitioners (e.g., Spencer, 2001), and the language of science is not properly translated into the language practitioners' use (e.g., Starkey & Madan, 2001; Van de Ven & Johnson, 2006). The underlying assumption is that if scientists redressed these shortcomings, their findings would be utilized by practitioners and thus the gap between theory and practice would be bridged.

Taiwan serves as a case study to investigate the association between the expansion and reform of higher education and the growth of science production. More specifically, what driving forces facilitated the growth of science production in different types of Taiwanese universities and other sectors, from 1980 to 2011.

The contribution charts differential contributions to overall production. Taiwanese data from Thomson Reuters’ Science Citation Index Expanded (SCIE) is analyzed to show the expansion of the higher education system and its relationship to the production of science. The author uses sociological organization theories to facilitate our understanding of how and why the landscape of science production changed.

Results show that the growth of science production is associated with processes of isomorphism and competition within the higher education system. Findings also suggest that universities quickly seized upon external opportunities and turned themselves into what is known as the “knowledge conglomerate.” Unique organizational features bolster universities’ position as the driving force behind advancing national innovation.

This study extends previous research by examining multiple sectors of higher education, using longitudinal and recent data, and highlighting themes that have been ignored or overlooked, such as competition and collaboration among universities and industry partners.

This chapter introduces readers to a complex adaptive systems approach for integrating research on genes, behavior, and social structures/institutions. Until recently, scientists have resorted to reductionism as a decoding and epistemological strategy for understanding human health. The complex bonds among health’s biological, behavioral, and social dimensions, however, cannot be fully grasped with reductionist schemas. Moreover, because reducing and simplifying can lead to incomplete understanding of phenomena, the resulting deficient knowledge has the potential to be harmful.

To achieve its purpose, this primer will: (1) introduce fundamental notions from complexity science, useful for inquiry and practice integrating research on genes, behavior, and social structures; (2) outline selected methodological strategies employed in studying complex adaptive/dynamic systems; (3) address the question, “Specifically, how can a dynamic systems approach be helpful for integrating research on genes, behavior, and social structures/institutions, to improve the public’s health?”; and (4) provide examples of studies currently deploying a complexity perspective.

The originality/value of this primer rests in its critique of the research status quo and the proposition of an alternative lens for integrating genomic, biomedical, and sociological research to improve the public’s health. The topic of complex adaptive/dynamic systems has begun to flourish within sociology, medicine, and public health, but many researchers lack exposure to the topic’s basic notions and applications.

Certain elements of Hayek’s work are prominent precursors to the modern field of complex adaptive systems, including his ideas on spontaneous order, his focus on market processes, his contrast between designing and gardening, and his own framing of complex systems. Conceptually, he was well ahead of his time, prescient in his formulation of novel ways to think about economies and societies. Technically, the fact that he did not mathematically formalize most of the notions he developed makes his insights hard to incorporate unambiguously into models. However, because so much of his work is divorced from the simplistic models proffered by early mathematical economics, it stands as fertile ground for complex systems researchers today. I suggest that Austrian economists can create a progressive research program by building models of these Hayekian ideas, and thereby gain traction within the economics profession. Instead of mathematical models the suite of techniques and tools known as agent-based computing seems particularly well-suited to addressing traditional Austrian topics like money, business cycles, coordination, market processes, and so on, while staying faithful to the methodological individualism and bottom-up perspective that underpin the entire school of thought.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

We discuss the impact of complexity science on the design and management of health care organizations over the past decade. We provide an overview of complexity science issues and their impact on thinking about health care systems, particularly with the rising importance of information systems. We also present a complexity science perspective on current issues in today’s health care organizations and suggest ways that this perspective might help in approaching these issues.

We review selected research, focusing on work in which we participated, to identify specific examples of applications of complexity science. We then take a look at information systems in health care organizations from a complexity viewpoint.

Complexity science is a fundamentally different way of understanding nature and has influenced the thinking of scholars and practitioners as they have attempted to understand health care organizations. Many scholars study health care organizations as complex adaptive systems and through this perspective develop new management strategies. Most important, perhaps, is the understanding that attention to relationships and interdependencies is critical for developing effective management strategies.

Increased understanding of complexity science can enhance the ability of researchers and practitioners to develop new ways of understanding and improving health care organizations.

This analysis opens new vistas for scholars and practitioners attempting to understand health care organizations as complex adaptive systems. The analysis holds value for those already familiar with this approach as well as those who may not be as familiar.

This chapter discusses the emerging network science approach to the study of complex adaptive systems and applies tools derived from statistical physics to the analysis of tourism destinations. The authors provide a brief history of network science and the characteristics of a network as well as different models such as small world and scale free networks, and dynamic properties such as resilience and information diffusion. The Italian resort island of Elba is used as a case study allowing comparison of the communication network of tourist organizations and the virtual network formed by the websites of these organizations. The study compares the parameters of these networks to networks from the literature and to randomly created networks. The analyses include computer simulations to assess the dynamic properties of these networks. The results indicate that the Elba tourism network has a low degree of collaboration between members. These findings provide a quantitative measure of network performance. In general, the application of network science to the study of social systems offers opportunities for better management of tourism destinations and complex social systems.

In his 1931 unpublished “Surplus Product” manuscript Sraffa used an open–closed distinction to explain the relationship between the “economic field” and distribution. This chapter examines Sraffa’s thinking in this regard, and shows how it allowed him to resolve a problem he encountered in his early objectivist representation of commodity production in economies with a surplus. The chapter argues that Sraffa adopted a view different from Bertalanffy’s general systems theory understanding of open and closed systems developed around the same time in such a way as to address the specific nature of economics. The chapter compares two related interpretations of Sraffa’s thinking in regard to the open–closed distinction developed by Arena and Ginzburg, and also addresses how Sraffa’s thinking regarding open and closed systems compares with similar thinking of Wittgenstein and Gramsci. The concluding discussion contrasts Sraffa’s causal reasoning with mainstream economics’ ceteris paribus method of causal reasoning.