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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.

The study of group dynamics was central to the field of organization development at its inception. More recently, there has been a move away from considering irrational and unconscious dynamics in organizational life and more attention focused on rational and observable behavior that can be measured and quantified. We introduce the tool, Beneath the Surface of the Burke-Litwin Model, that invites consideration of how the overt behavior of individuals, groups, and entire systems is linked to covert dynamics. This more comprehensive view of organizational life provides scholar-practitioners with a systemic perspective, a view of covert dynamics by organizational level, and support for the ongoing development of one’s capacity for using self-as-instrument when engaged in organization development and organization change efforts.

This chapter introduces the negotiagram as a tool that contributes to the study of the negotiation process. The negotiagram is a construct that facilitates an understanding of interactions between negotiators and their temporal context. Given that the negotiation process can be seen as a system, system dynamics (SD) are used to explore the interactions and complexities in the proposed construct, especially when it comes to circumstances with a high degree of uncertainty, such as an economic crisis or disruptive innovation in the industry. The results suggest that the causal configurations that SD establish lead to non-linear relationships and feedback loops that direct behavior within negotiation, an understanding that further leads stakeholders to a holistic vision and the opportunity to change, manage, and control a negotiation as a system.

Social enterprises (SEs), part of the third sector, are hybrid organizations combining the pursuit of social scopes with commercial business solutions. In seeking for social value, they pair for-profit and non-profit features, thereby compensating for shortcomings of both the public sector and the commercial market. Therefore, the performance management of such organizations assumes a crucial relevance. Among the available tools, the balanced scorecard (BSC) aims to capture performance multidimensionality, at the same time fostering legitimacy towards stakeholders.

In general terms, the BSC has the limit to follow a linear and static logic of construction and functioning. For this reason, scholars combine it with system dynamics (SD) to create dynamic balanced scorecards (DBSCs). However, literature seems to devote scarce attention to the adoption of such analytic tools in the third sector, particularly in SEs. This chapter wants to contribute to bridging this gap by proposing a tailored application in the context of a social cooperative, active in the clothing recycle and in the re-integration of disadvantaged social categories. By referring to previous literature about DBSC, two modelling strategies are identified: the BSC-driven and the SD-driven. The latter, based on inductive reasoning, is the one privileged for the study because of its wider flexibility. The modelling outputs consider different perspectives than the ones within traditional BSCs, contain elements of circular causality and show how financial and non-financial performances interplay and co-determine each other. Insights from the proposed model can be useful to support both decision-making and stakeholder engagement.

Several different simulation techniques, such as discrete event simulation (DES), system dynamics (SD) and agent-based modelling (ABM), have been used to model complex construction systems such as construction processes and project management practices; however, these techniques do not take into account the subjective uncertainties that exist in many construction systems. Integrating fuzzy logic with simulation techniques enhances the capabilities of those simulation techniques, and the resultant fuzzy simulation models are then capable of handling subjective uncertainties in complex construction systems. The objectives of this chapter are to show how to integrate fuzzy logic and simulation techniques in construction modelling and to provide methodologies for the development of fuzzy simulation models in construction. In this chapter, an overview of simulation techniques that are used in construction is presented. Next, the advancements that have been made by integrating fuzzy logic and simulation techniques are introduced. Methodologies for developing fuzzy simulation models are then proposed. Finally, the process of selecting a suitable simulation technique for each particular aspect of construction modelling is discussed.

This chapter uses a system dynamics approach to do a constructive replication (Lykken, 1968; Kelly, Chase, & Tucker, 1979; Hendrick, 1990) and extension of Reeves-Ellington's (this volume) timescape theory illustrated in his case study carried out at different hierarchical levels in Procter & Gamble. The timescape theory of temporal fit consists of two time perspectives – business time and social time – that compete for application. The senior-management level plays a key role in determining which timescape dominates. Reeves-Ellington argues that his findings show that organizational performance diminishes when there is a lack of fit between the timescapes of senior management and those of other levels of management. Our system dynamics model tests this notion and finds that the timescape case does not allow sufficient time to clearly demonstrate the hypothesized fit effects. In addition to timescape fit, environmental consumer demand aspects, which were not considered in the original case, are argued to affect Reeves-Ellington's performance measures. The system dynamics model's general emphasis on temporality and feedback provide especially for the constructive replication and extension of the timescape theory.

Multiteam systems (MTSs) have been employed across numerous organizations and occupations (e.g., healthcare, emergency disaster response, business, and military) to achieve complex goals over time. As MTSs are inherently different than team level and organizational level theories, this chapter highlights the defining features of these dynamic systems through a temporal lens. Thus, the main purpose of our chapter is to address the challenges and issues concerning MTSs over time in order to provide a future agenda to guide researchers and practitioners.

To explore temporality throughout this chapter, we leverage two key MTSs frameworks along with contributions from the literature to produce a review, which demonstrates the extent of MTS theoretical and practical findings. After reviewing the definitional components of MTSs, we highlight various compositional, linkage, and developmental attributes that operate within a system. We then expand upon these attributes to consider the structural features of the system that enhance boundaries between component teams (i.e., differentiation) and may disrupt the system over time (i.e., dynamism).

After reviewing and integrating current MTS literature, we provide a new conceptual framework for MTSs and their temporal complexities. We offer several methodologies that managers and researchers can employ to assess these complex systems and suggest practical recommendations and areas for future research as we continue to study MTSs.

Our original conceptual framework considers MTSs through a dynamic lens developing over time and suggests the need for future research to build upon this perspective.

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.

Kalecki's theory of the business cycle is rightly renowned for various reasons: in particular, besides itself providing an original contribution, it set the framework for Kalecki's ideas on effective demand, for his anticipation of a number of Keynesian elements, and for the development of Kalecki's related themes such as income determination and distribution. Although the secondary literature (both technical and descriptive) on this subject is immense, a specific aspect seems to deserve further reflection.