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This paper aims to identify the interaction of different intervention strategies implemented in Malaysia towards flattening the curve of COVID-19 cases. Since the outbreak of COVID-19, many approaches were adopted and implemented by the Malaysian government. Some strategies gained quick wins but with negative unintended consequences after execution, whereas other strategies were slow to take effect. Learning from the previous strategies is pivotal to avoid repeating mistakes.

This paper presents the cause, effect of and connection among the implemented COVID-19 intervention strategies using systems thinking through the development of a causal loop diagram. It enables the visualisation of how each implemented strategy interacted with each other and collectively decreased or increased the spread of COVID-19.

The results of this study suggested that it is not only essential to control the spread of COVID-19, but also to prevent the transmission of the virus. The Malaysian experience has demonstrated that both control and preventive strategies need to be in a state of equilibrium. Focusing only on one spectrum will throw off the balance, leaving COVID-19 infection to escalate rapidly.

The developed feedback loops provided policy makers with the understanding of the merits, pitfalls and dynamics of prior implemented intervention strategies before devising other effective intervention strategies to defuse the spread of COVID-19 and prepare the nation for recovery.

Crowdfunding (CF) is a digital-financial innovation that, bypassing credit crisis, bank system rigidities and constraints of the capital market, is allowing new ventures and established companies to get the needed funds to support innovations. After one decade of research, mainly focused on relations between variables and outcomes of the CF campaign, the literature shows methodological lacks about the study of its overall behavior. These reflect into a weak theoretical understanding and inconsistent managerial guidance, leading to a 27% success ratio of campaigns. To bridge this gap, this paper embraces a “complex system” perspective of the CF campaign, able to explore the system's behavior of a campaign over time, in light of its causal loop structure.

By adopting and following the document model building (DMB) methodology, a set of 26 variables and mutual causal relations modeled the system “Crowdfunding campaign” and a data set based on them and crafted to model the “Crowdfunding campaign” with a causal loop diagram. Finally, system archetypes have been used to link the causal loop structure with qualitative trends of CF's behavior (i.e. the raised capital over time).

The research brought to 26 variables making the system a “Crowdfunding campaign.” The variables influence each other, thus showing a set of feedback loops, whose structure determines the behavior of the CF campaign. The causal loop structure is traced back to three system archetypes, presiding the behavior in three stages of the campaign.

The value of this paper is both methodological and theoretical. First, the DMB methodology has been expanded and reinforced concerning previous applications; second, we carried out a causation analysis, unlike the common correlation analysis; further, we created a theoretical model of a “Crowdfunding Campaign” unlike the common empirical models built on CF platform's data.

This study aims to describe the compounding factors in a complex emergency, which exacerbate a cholera epidemic among vulnerable populations due to supply chain disruptions. Basic needs such as food, medicine, water, sanitation and hygiene commodities are critical to reduce the incidence rate of cholera and control the spread of infection. Conflicts cause damage to infrastructure, displace vulnerable populations and restrict the flow of goods from both commercial and humanitarian organizations. This study assesses the underlying internal and external factors that either aggravate or mitigate the risk of a cholera outbreak in such settings, using Yemen as a case study.

This study adopts a system dynamics methodology to analyze factors that influence cholera outbreaks in the context of the Yemeni Civil War. A causal loop diagram with multiple components was constructed to represent the complexities of humanitarian situations that require critical decision-making. The model was built using data from humanitarian organizations, non-governmental organizations and practitioners, along with literature from academic sources. Variables in the model were confirmed through semi-structured interviews with a field expert.

Compounding factors that influenced the cholera outbreak in Yemen are visualized in a causal loop diagram, which can improve the understanding of relationships where numerous uncertainties exist. A strong link exists between humanitarian response and the level of infrastructure development in a country. Supply chains are affected by constraints deriving from the Yemeni conflict, further inhibiting the use of infrastructure, which limits access to basic goods and services. Aligning long-term development objectives with short-term humanitarian response efforts can create more flexible modes of assistance to prevent and control future outbreaks.

The model focuses on the qualitative aspects of system dynamics to visualize the logistics and supply chain-related constraints that impact cholera prevention, treatment and control through humanitarian interventions. The resulting causal loop diagram is bounded by the Yemen context; thus, an extension of the model adapted for other contexts is recommended for further study.

This study presents a systematic view of dynamic factors existing in complex emergencies that have cause-and-effect relationships. Several models of cholera outbreaks have been used in previous studies, primarily focusing on the modes and mechanisms of transmission throughout a population. However, such models typically do not include other internal and external factors that influence the population and context at the site of an outbreak. This model incorporates those factors from a logistics perspective to address the distribution of in-kind goods and cash and voucher assistance.

This study has been aligned with six of the United Nations Sustainable Development Goals (SDGs), using their associated targets in the model as variables that influence the cholera incidence rate. Recognizing that the SDGs are interlinked, as are the dynamic factors in complex humanitarian emergencies, the authors have chosen to take an interdisciplinary approach to consider social, economic and environmental factors that may be impacted by this research.

This paper provides an insight into the underlying inter-relations of internal and external factors present in the context of a cholera outbreak in a complex crisis. Supply chains for food; water, sanitation and hygiene; and health products are crucial to help prevent, control and treat an outbreak. The model exposes vulnerabilities in the supply chain, which may offer guidance for decision makers to improve resilience, reduce disruptions and decrease the severity of cholera outbreaks.

This study aims to focus on building a conceptual closed-loop vaccine supply chain (CLVSC) to decrease vaccine wastage and counterfeit/fake vaccines.

Through a focused literature review, the framework for the CLVSC is described, and the system dynamics (SD) research methodology is used to build a causal loop diagram (CLD) of the proposed model.

In the battle against COVID-19, waste management systems have become overwhelmed, which has created negative environmental and extremely hazardous societal impacts. A key contributing factor is unused vaccine doses, shown as a source for counterfeit/fake vaccines. The findings identify a CLVSC design and transshipment operations to decrease vaccine wastage and the potential for vaccine theft.

This study contributes to establishing a pandemic-specific VSC structure. The proposed model informs the current COVID-19 pandemic as well as potential future pandemics.

A large part of the negative impact of counterfeit/fake vaccines is on human well-being, and this can be avoided with proper CLVSC.

This study develops a novel overarching SD CLD by integrating the epidemic model of disease transmission, VSC and closed-loop structure. This study enhances the policymakers’ understanding of the importance of vaccine waste collection, proper handling and threats to the public, which are born through illicit activities that rely on stolen vaccine doses.

The study aims to summarise the literature on cancer care pathways at the diagnostic and treatment phases. The objectives are to find factors influencing the delivery of cancer care pathways; to highlight any interrelating factors; to find gaps in the literature concerning areas of research; to summarise the strategies and recommendations implemented in the studies.

The study used a qualitative approach and developed a causal loop diagram to summarise the current literature on cancer care pathways, from screening and diagnosis to treatment. A total of 46 papers was finally included in the analysis, which highlights the recurring themes in the literature.

The study highlights the myriad areas of research applied to cancer care pathways. Factors influencing the delivery of cancer care pathways were classified into different albeit interrelated themes. These include access barriers to care, hospital emergency admissions, fast track diagnostics, delay in diagnosis, waiting time to treatment and strategies to increase system efficiency.

As far as the authors know, this is the first study to present a visual representation of the complex relationship between factors influencing the delivery of cancer care pathways.

This paper applies systems thinking modelling to enhance the dynamic understanding of how to nurture an innovative culture in healthcare organisations to develop the innovation system in practice and speed up the innovative work. The model aims to provide a holistic view of a studied healthcare organisation's innovation processes, ranging from managerial values to its manifestation in improved results.

The study is based on empirical material from a healthcare unit that, within a few years, changed from having no innovations to repeatedly generating innovations. The study uses the modelling language of causal loop diagrams (CLDs) in the system dynamics methodology to identify the key important aspects found in the empirical material.

The proposed model, based on the stories of the interviewees, explores the dynamics of inertia when nurturing an innovative culture, identifying delays attributed to the internal change processes and system relationships. These findings underscored the need for perseverance when developing an innovative culture in the entrepreneurial phases.

The approach of using systems thinking to make empirical healthcare research results more tangible through the visual notations of CLDs and mental simulations is believed to support exploring complex phenomena to induce and nurture both individual and organisational learning.

The results from this approach provide deepened analysis and provoke the systems view to explain how the nurturing of the culture can accelerate the innovation processes, which helps practitioners and researchers to further expand their understanding of their healthcare contexts.

Due to the unknown location, size and timing of disasters, the rapid response required by humanitarian operations (HO) faces high uncertainty and limited time to raise funds. These harsh realities make HO challenging. This study aims to systematically capture the complex dynamic relationships between operations in humanitarian settings.

To achieve this goal, the authors undertook a systematic review of the extant academic literature linking HO to system dynamics (SD) simulation.

The research reviews 88 papers to propose a taxonomy of different topics covered in the literature; a framework represented through a causal loop diagram (CLD) to summarise the taxonomy, offering a view of operational activities and their linkages before and after disasters; and a research agenda for future research avenues.

As the authors provide an adequate representation of reality, the findings can help decision makers understand the problems faced in HO and make more effective decisions.

While other reviews on the application of SD in HO have focused on specific subjects, the current research presents a broad view, summarising the main results of a comprehensive CLD.

This study aims to find a dynamic model in an effort to optimize tourism performance in ecotourism destinations. The model structure is built based on competitive performance in geographic areas and the application of ecotourism elements that are integrated with big data innovation through artificial intelligence technology.

Data analysis is performed through dynamic system modeling. Simulations are carried out in three models: First, existing simulation models. Second, Scenario 1 is carried out by utilizing a causal loop through innovation of big data-based artificial intelligence technology to ecotourism elements. Third, Scenario 2 is carried out by utilizing a causal loop through big data-based artificial intelligence technology on aspects of ecotourism elements and destination competitiveness.

This study provides empirical insight into the competitiveness performance of destinations and the performance of implementing ecotourism elements if integrated with big data innovations that will be able to massively demonstrate the growth of sustainable tourism performance.

This study does not use a primary database, but uses secondary data from official sources that can be accessed by the public.

The paper includes implications for the development of intelligent technology based on big data and also requires policy innovation.

Sustainable tourism development.

This study finds the expansion of new theory competitiveness of ecotourism destinations.

This study aims to determine the factors and dynamic systems behaviour of essential medicine stockout in public health-care supply chains. The authors examine the constraints and effects of mental models on medicine stockout to develop a dynamic theory of medicine availability towards saving patients’ lives.

This study uses a mixed-method approach. Starting with a survey method, followed by in-depth interviews with stakeholders within five health-care supply chains to determine the dynamic feedback leading to stockout and conclude by developing a network mental model for medicines availability.

The authors identified five constraints and developed five case mental models. The authors develop a dynamic theory of medicine availability across cases and identify feedback loops and variables leading to medicine availability.

The need to include mental models of stakeholders like manufacturers and distributors of medicines to understand the system completely. Group surveys are prone to power dynamics and bias from group thinking. This survey’s quantitative output could minimize the bias.

This study uniquely uses a mixed-method of survey method and in-depth interviews of experts to assess the essential medicine stockout in Nigeria. To improve medicine availability, the authors develop a dynamic network mental model to understand the system structure, feedback and behaviour driving stockouts. This research will benefit public policymakers and hospital managers in designing policies that reduce medicine stockout.

The coronavirus disease (COVID-19) pandemic has emerged as an unprecedented health crisis worldwide and heavily disrupted the healthcare supply chain. This study focuses on analysing the different types of disruptions occurring in personal protective equipment (PPE) supply chains during the COVID-19 pandemic and on proposing mitigation strategies that are fit to the global scale and many interdependencies that are characteristic for this pandemic. The authors construct a conceptual system dynamics model (SD) based on the literature and adjusted with the use of empirical data (interviews) to capture the complexity of a global supply chain and identify leverage points (mitigation strategies).

This research follows a mix-methods approach. First, the authors developed a conceptual framework based on four types of disruptions that usually occur during health emergencies (direct effect, policy, supply chain strategy, and behaviourally induced disruptions). Second, the authors collected and analysed data from interviews with experts in the PPE supply chain. Based on the interviews data, the authors developed a conceptual system dynamics (SD) model that allows to capture the complex and dynamic interplay between the elements of the global supply chain system, by highlighting key feedback loops, delays, and the way the mitigation strategies can impact on them. From this analysis, the authors developed four propositions for supply chain risk management (SCRM) in global health emergencies and four recommendations for the policy and decision makers.

The SD model highlights that without a combination of mitigation measures, it is impossible to overcome all disruptions. As such, a co-ordinated effort across the different countries and sectors that experience the disruptions is needed. The SD model also shows that there are important feedback loops, by which initial disruptions create delays and shortages that propagate through the supply chain network. If the co-ordinated mitigation measures are not implemented early at the onset of the pandemic, these disruptions will be persistent, creating potential shortages of PPE and other critical equipment at the onset of a pandemic – when they are most urgently needed.

This research enriches the understanding of the disruptions of PPE supply chains on the systems level and proposes mitigation strategies based on empirical data and the existing literature.