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The purpose of this paper is to argue for an improved conceptualisation of health service research, using Stengers' (2018) metaphor of “slow science” as a critical yardstick.

The paper is structured in three parts. It first reviews the field of health services research and the approaches that dominate it. It then considers the healthcare research approaches whose principles and methodologies are more aligned with “slow science” before presenting a description of a “slow science” project in which the authors are currently engaged.

Current approaches to health service research struggle to offer adequate resources for resolving frontline complexity, principally because they set more store by knowledge generalisation, disciplinary continuity and integrity and the consolidation of expertise, than by engaging with frontline complexity on its terms, negotiating issues with frontline staff and patients on their terms and framing findings and solutions in ways that key in to the in situ dynamics and complexities that define health service delivery.

There is a need to engage in a paradigm shift that engages health services as co-researchers, prioritising practical change and local involvement over knowledge production. Economics is a research field where the products are of natural appeal to powerful health service managers. A “slow science” approach adopted by the embedded Economist Program with its emphasis on pre-implementation, knowledge mobilisation and parallel site capacity development sets out how research can be flexibly produced to improve health services.

The purpose of this research is to systematically review the properties of supply chains demonstrating that they are complex systems, and that the management of supply chains is best achieved by steering rather than controlling these systems toward desired outcomes.

The research study was designed as both exploratory and explanatory. Data were collected from secondary sources using a comprehensive literature review process. In parallel with data collection, data were analyzed and synthesized.

The main finding is the introduction of an inductive framework for steering supply chains from a complex systems perspective by explaining why supply chains have properties of complex systems and how to deal with their complexity while steering them toward desired outcomes. Complexity properties are summarized in four inter-dependent categories: Structural, Dynamic, Behavioral and Decision making, which together enable the assessment of supply chains as complex systems. Furthermore, five mechanisms emerged for dealing with the complexity of supply chains: classification, modeling, measurement, relational analysis and handling.

Recognizing that supply chains are complex systems allows for a better grasp of the effect of positive feedback on change and transformation, and also interactions leading to dynamic equilibria, nonlinearity and the role of inter-organizational learning, as well as emerging capabilities, and existing trade-offs and paradoxical tensions in decision-making. It recognizes changing dynamics and the co-evolution of supply chain phenomena in different scales and contexts.

How a micro-founded discipline such as economics could deal with the increasing global economic reality? This question has been asked frequently since the last economic crisis that appeared in 2008. In this challenging context, some commentators have turned their attention to a new area of knowledge coming from physics: econophysics which mainly focuses on a macro-analysis of economic systems. By showing that concepts used by econophysicists are consistent with an existing economic knowledge (developed by J.S. Mill), the purpose of this paper is to claim that an interdisciplinary perspective is possible between these two communities.

The authors propose a historical and conceptual analysis of the key concept of emergence to emphasize the potential bridge between econophysics and economics.

Six methodological arguments will be developed in order to show the existence of conceptual bridges as a necessary condition for the elaboration of a common language between economists and econophysics which would not be superfluous, in this challenging context, to clarify the growing complexity of economic phenomena.

Although the economics and econophysics study same the complex economic phenomena, very few collaborations exist between them. This paper paves a conceptual/methodological path for more collaboration between the two fields.

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?

The term “agent-based modelling” (ABM) is a buzzword which is widely used in the scientific literature even though it refers to a variety of methodologies implemented in different disciplinary contexts. The numerous works dealing with ABM require a clarification to better understand the lines of thinking paved by this approach in economics. All modelling tasks are a means and a source of knowledge, and this epistemic function can vary depending on the methodology. this paper is to present four major ways (deductive, abductive, metaphorical and phenomenological) of implementing an agent-based framework to describe economic systems. ABM generates numerous debates in economics and opens the room for epistemological questions about the micro-foundations of macroeconomics; before dealing with this issue, the purpose of this paper is to identify the kind of ABM the author can find in economics.

The profusion of works dealing with ABM requires a clarification to understand better the lines of thinking paved by this approach in economics. This paper offers a conceptual classification outlining the major trends of ABM in economics.

There are four categories of ABM in economics.

This paper suggests a methodological categorization of ABM works in economics.

Managers’ work is surrounded by complex environments, from which they need to learn, in order to understand them. However, complexity poses several challenges to managerial learning, for which usually management educational programs have not prepared managers. Thus, the purpose of this paper is to explore such challenges and possible ways to overcome them.

This is a conceptual paper that explores in depth the issue of managerial learning challenges in a complex world. Managers face these challenges during their practice, yet sometimes management education has not prepared them for this.

Three managerial learning challenges due to complexity are identified. First, through cognition and cognitive structures, managers simplify the world around them. Nevertheless, biases, inertia and inaccuracy emerge, as managers’ mental models are not truly capable of capturing complexity. Second, managers look for information to aid them in their learning processes, but the information they gather is sometimes bogus, invalid or unfounded. Third, managers could seek for support from management research to improve their learning. However, given management research intricacies, limitations and particularities, a learning challenge emerges as well, as management research has been rarely capable to capture complexity.

Having explored these managerial learning challenges due to complexity, this paper discusses a carefulness-based management learning ideal, which by being underpinned by the quality of carefulness and the related concepts of critical thinking, negative capability and a deep learning style, suggests a potential new way to approach management learning in light of complexity.

The purpose of this paper is to study the relationship between organizational adaptability, institutional leadership and business process reengineering performance using the tested complexity theory in a developing economy setting.

This study is correlation and cross-sectional and adopts institutional-level data collected via questionnaires from reengineered microfinance institutions in Uganda. Cluster analysis as data mining technique was used to classify cases based on respondents’ opinions into homogeneous clusters. Nvivo was used to understand the perceptions of business process reengineering performance based on qualitative data. The authors used structural equation modeling to derive the predictive model of business process reengineering performance in a developing world setting.

The authors find that organizational adaptability and institutional leadership are key predictors of business process reengineering performance. Results reveal a predictive model of 61 per cent based on structural equation modeling for the study variables. Cluster analysis as data mining approach explored complex patterns of reengineered business processes.

The use of cluster analysis is susceptible to problems associated with sampling error and absence of fit indices. However, the likelihood of these problems is reduced by the interaction with the data, practical implications and use of smart partial least square to generate structural equations based on derived measurement models of each study variable.

Policymakers of Bank of Uganda, Ministry of Finance and Economic Planning, should develop sound policies in relation to knowledge management, institutional leadership and adaptive mechanisms to enhance business process reengineering performance to take advantage of new knowledge opportunities for the improvement of their businesses.

Given the results from structural equations generated, managers need to consider institutional leadership and organizational adaptability as key drivers of business process reengineering performance in microfinance institutions. The results confirm the significant role of institutional leadership, organizational adaptability in determining business process reengineering performance outcomes.

Unlike most of the business process reengineering literature, this study contributes to literature by domesticating and testing complexity theory to explain business process reengineering performance in developing economies.

This paper aims to analyse the most significant disruptive events affecting tourism during the twenty-first century, particularly the 2020 COVID-19 pandemic.

Based on a thorough literature review, this study takes a complexity science approach to the field of tourism to shed light on the challenges of disruptive events in tourism systems.

Focusing on the COVID-19 pandemic, in particular, this study acknowledges that disruptive events are complex and have tremendous impacts on several areas of society: people’s psychological well-being and the health-care system, as well as social, economic, cultural, technological, environmental and political dimensions. Whether they occur alone or interact, these dimensions add varying levels of complexity to the tourism system. In response, the tourism industry can adopt a resilience model as a crisis management tool to address disruptive events affecting this field.

As this paper is mainly theoretical, future empirical research will contribute to refining the findings and testing the usefulness of the proposed model.

The paper looks at examples of successful and unsuccessful of COVID-19 outbreak management in various countries to analyse issues such as crisis management, resilience and tools for coping with the impacts of disruptive events.

This theoretical paper proposes a first taxonomy of the multidimensional impacts of twenty-first-century disruptive events on tourism and dissects the phases of crisis management, with a corresponding conceptual model.

本文分析了21世纪影响旅游业的最为重要的破坏性事件, 尤其关注2020年新型冠状病毒肺炎大流行

本文将复杂性科学方法应用于旅游领域, 通过全面的文献综述, 揭示破坏性事件给旅游系统带来的挑战。

本文承认破坏性事件, 特别是新型冠状病毒肺炎大流行, 复杂且影响巨大, 涉及心理、医疗、社会、经济、文化、技术、环境和政治等诸多方面。这些影响不管是单一的还是相互作用的, 都在不同程度上增加了旅游系统的复杂性。旅游业可以将弹性模型作为危机管理工具, 以应对其领域内的破坏性事件。

本文首次提出了21世纪破坏性事件对旅游业的多维影响的分类法, 并对危机管理的各个阶段进行了剖析。本文还提出了一个综合模型。

由于本文以理论为主, 未来的实证研究将有助于完善研究结果和验证所提出模型的实用性。

本文着眼于不同国家新冠疫情管理的成功和失败案例, 分析危机管理、复原力以及应对破坏性事件影响的方法等问题。

Identificar los eventos disruptivos mundiales más importantes que han afectado el turismo en el siglo XXI poniendo especial atención en la pandemia causada por el COVID-19 en el 2020.

Mediante un enfoque a las ciencias de la complejidad aplicado al turismo y una exhaustiva revisión bibliográfica, este artículo esclarece el reto que significa un evento disruptivo en el turismo.

Los eventos disruptivos, en particular el COVID-19, son complejos y generan un alto impacto tanto en el aspecto psicológico, sanitario, social, económico, cultural, tecnológico, medioambiental y político. En su interacción, todos ellos añaden un diferente grado de complejidad al sistema del turismo. Este artículo propone adoptar un modelo de resiliencia como herramienta de gestión de crisis para afrontar eventos disruptivos en el campo del turismo.

Proponer una primera clasificación de los impactos multidimensionales de los eventos disruptivos del siglo XXI en el turismo y un desglose de las fases de gestión de crisis, como así también proponer un modelo integrador de ambos aspectos propios de un evento disruptivo.

Debido al carácter teórico de este artículo, el modelo integrador sugerido representa un marco prometedor para futuras investigaciones en el plano empírico.

Este artículo presta atención a aquellos países que han gestionado la pandemia de forma exitosa o no, para así tener una mejor noción de gestión de crisis y herramientas para hacer frente a futuros eventos disruptivos.