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The purpose of this study is to propose a methodological approach for modeling catastrophic consequences caused by black swan events, based on complexity science, and framed on Feyerabend’s anarchistic theory of knowledge. An empirical application is presented to illustrate the proposed approach.

Thom’s nonlinear differential equations of morphogenesis are used to develop a theoretical model of the impact of catastrophes on international business (IB). The model is then estimated using real-world data on the performance of multinational airlines during the SARS-CoV-2 (COVID-19) pandemic.

The catastrophe model exhibits a remarkable capability to simultaneously capture complex linear and nonlinear relationships. Through empirical estimations and simulations, this approach enables the analysis of IB phenomena under normal conditions, as well as during black swan events.

To the best of the author’s knowledge, this study is the first attempt to estimate the impact of black swan events in IB using a catastrophe model grounded in complexity theory. The proposed model successfully integrates the abrupt and profound effects of catastrophes on multinational corporations, offering a critical perspective on the theoretical and practical use of complexity science in IB.

The purpose of this paper is twofold: (1) to map and analyze existing complexity typologies and (2) to develop a framework for characterizing complexity-based approaches.

This study was conducted in three stages: (1) initial identification of typologies related to complexity following a structured procedure based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol; (2) backward and forward review to identify additional relevant typologies and (3) content analysis of the selected typologies, categorization and framework development.

Based on 17 selected typologies, a comprehensive overview of complexity studies is provided. Each typology is described considering key concepts, contributions and convergences and differences between them. The epistemological, theoretical and methodological diversity of complexity studies was explored, allowing the identification of the main schools of thought and authors. A framework for characterizing complexity-based approaches was proposed including the following perspectives: ontology of complexity, epistemology of complexity, purpose and object of interest, methodology and methods and theoretical pillars.

This study examines the main typologies of complexity from an integrated and multidisciplinary perspective and, based on that, proposes a novel framework to understanding and characterizing complexity-based approaches.

Project complexity is a critical issue that has increasingly attracted attention in both academic and practical circles. However, there are still many gaps in the research on project complexity, such as the differentiated conceptualization of complexity and disjointed operationalization in the measurements. Therefore, this paper aims to conduct a systematic and detailed literature review on the concept, dimensions, assessment, and underlying mechanisms of project complexity.

A systematic literature review methodology was applied to search and synthesize the research on project complexity, and a final sample of 74 journal articles was identified.

This study first summarizes the concepts of project complexity from three different theoretical perspectives, and then identifies different approaches of measurement, evaluation, or simulation to assess project complexity. This paper finally establishes an integrative framework to synthesize the antecedents, mediators and moderators, and outcomes of project complexity, generating four suggestions for future research.

This study summarizes the definition and operationalization of project complexity to reduce the discrepancies in the existing research and offers an integrative framework to offer a broad overview of the current understanding of project complexity, providing a potential way forward for addressing project complexity.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

Traditional approaches to organizational performance management that emphasize objectivity, control and predictability are rapidly losing relevance in an environment characterized by increasing levels of complexity and dynamism. This paper draws on complexity theory to suggest a new paradigm for managing performance in organizations.

The paper draws on the common features of complex systems and the corresponding concept of emergence to revisit key themes in organizational performance management and propose a set of implications for research and practice.

Understanding organizations as complex systems and performance as an emergent property of such systems leads to a set of new research questions, the adoption of alternative methods and the formulation of novel propositions. It also has various implications for both academic research and managerial practice, from moving away from the traditional notion of organizational alignment to adopting a more explicit stakeholder-based view in the design and use of measurement systems.

The paper highlights the great potential of complexity theory for addressing contemporary issues in the field of organizational performance management and charting the landscape for its future development.

Online question-and-answer (Q&A) communities serve as important channels for knowledge diffusion. The purpose of this study is to investigate the dynamic development process of online knowledge systems and explore the final or progressive state of system development. By measuring the nonlinear characteristics of knowledge systems from the perspective of complexity science, the authors aim to enrich the perspective and method of the research on the dynamics of knowledge systems, and to deeply understand the behavior rules of knowledge systems.

The authors collected data from the programming-related Q&A site Stack Overflow for a ten-year period (2008–2017) and included 48,373 tags in the analyses. The number of tags is taken as the time series, the correlation dimension and the maximum Lyapunov index are used to examine the chaos of the system and the Volterra series multistep forecast method is used to predict the system state.

There are strange attractors in the system, the whole system is complex but bounded and its evolution is bound to approach a relatively stable range. Empirical analyses indicate that chaos exists in the process of knowledge sharing in this social labeling system, and the period of change over time is about one week.

This study contributes to revealing the evolutionary cycle of knowledge stock in online knowledge systems and further indicates how this dynamic evolution can help in the setting of platform mechanics and resource inputs.

A slew of conventional change models and theories appear in the extant change literature. Despite being theoretically sound, these a priori structured approaches to organizational change management have questionable application given the rapidly changing business environments. Novel approaches, offering greater flexibility to fast changing external conditions, may offer superior models to organizational change and organizational transformation (OT) in particular. In this paper, the application of a complex adaptive system (CAS) framework, from complexity theory (CT), for managing OT is assessed theoretically.

A conceptual paper.

A review of the extant change literature suggests that current approaches and models for organizational change are limited in their ability to reflect OT responses to today's highly dynamic external environments. New models are required to inform and guide organizations. A new model, i.e. CAS framework, is deemed suitable to guide the OT implementation.

This paper critically analyses different approaches to change management, consolidates CAS framework, reviews its applications in the field of management and presents a case for CAS's application for the management of OT.

This paper introduces a conceptual lens for leading social change in slums and informal settlements. In line with this aim, the purpose of this case study is to describe the public problem-solving approach of a social change organization situated in an informal settlement through the lens of adaptive leadership, complexity theory and social change leadership (SCL).

This paper follows an engaged reflection tradition. First, the author-practitioners describe an informal settlement case hereafter called ISC in southeast Iran where many people have historically remained undocumented and uneducated. Using complex adaptive systems theory, adaptive leadership and SCL as the conceptual lens, the paper analyzes ISC as a complex adaptive context in which the community and the government are in tension in solving problems, particularly illiteracy. The instrumental case study draws from participant observation and document analysis to describe and examine the endeavors of a community office operating within ISC. Through this reflective analysis, the authors illustrate how a social change organization can effectively tackle public issues like illiteracy within informal settlements.

This paper applies complexity leadership theory to a social context. The study illustrates how social change organizations can support the transformation of informal spaces into adaptive spaces to enact social change.

This paper reflects on engagement activity near the insecure borders of Iran, Afghanistan and Pakistan. By extending an organizational-level theory to the public sphere, this paper contributes theoretically to the complexity theory literature. Moreover, it provides a practical insight for community development and slum upgrading projects.

By adopting network analytic techniques, this paper aims to examine interlocking directorates among firms operating in the hospitality services sector in seven major Italian tourism destinations.

The authors collected information for all the hotel corporations whose headquarters are located in the seven top Italian destinations: Florence, Milan, Naples, Rimini, Rome, Turin and Venice. Data come from the Analisi Informatizzata delle Aziende Italiane database by Bureau Van Dijk and were used to build a network where the nodes are board members (people) and corporations (hotels) and the links represent the membership of individuals in the boards. From this, with a one-mode projection, the authors obtain two networks: people and corporations. The overall networks’ structures are analysed by assessing their connectivity characteristics.

The findings indicate a relatively low number of interlocks that signals a high degree of fragmentation, showing that the interconnections (both within and between destinations) are scarce. This suggests that in absence of formalized cooperation arrangements, corporations might collaborate informally.

This work extends previous research on complexity in business settings, focusing specifically on service companies whose output depends on multiple interactions and helps clarifying coopetition practices of hospitality service firms. Policymaking perspectives are discussed as well as managerial viewpoints.

Not many studies of the interlocking directorates in the hospitality domain exist. This paper uses network analysis for a better understanding of the cooperative practices and the formal social structures of the Italian hospitality industry and derives a series of implications important for both researchers and practitioners while also looking at potential future studies.

Artificial intelligence (AI) is the most progressive commodity among current information system applications. In-house development and sales of beneficial products are difficult for many software development and service companies (SDSCs). SDSCs have some implicit concerns about implementing AI software development due to the complexity of AI technology; they require an evaluation framework to avoid development failure. To fill the void, this study identified the factors influencing SDSCs when developing AI software development.

Based on complex adaptive systems theory, three aspects were developed as the main factors of hierarchy, namely, employees' capabilities, environmental resources and team capabilities. Fuzzy analytic hierarchy process (FAHP) was used to assess the SDSCs' attitude. Based on SDSCs, attitudes toward implementing AI software projects were collected to calculate the hierarchy of factors.

The outcome of FAHP is used as understanding the key factors of SDSCs for selecting an AI software project, toward the improvement of overall project planning. Employees' stress resistance was considered as a priority for the project, although professional AI skills and resources were also important.

This study suggested three variables developed using complex adaptive systems. This study contributes to a better understanding of the critical aspects of developing AI software projects in SDSCs. The study's findings have practical and academic implications for SDSCs and subsequent academic development, broadening the scope of AI software development research.