Critical factors that affect the functioning of a research and evaluation capacity building partnership: A causal loop diagram

Introduction Public health policy and practice is strengthened by the application of quality evidence to decision making. However, there is limited understanding of how initiatives that support the generation and use of evidence in public health are operationalised. This study examines factors that support the internal functioning of a partnership, the Western Australian Sexual Health and Blood-borne Virus Applied Research and Evaluation Network (SiREN). SiREN aims to build research and evaluation capacity and increase evidence-informed decision making in a public health context. Methods This study was informed by systems concepts. It developed a causal loop diagram, a type of qualitative system model that illustrated the factors that influence the internal operation of SiREN. The causal loop diagram was developed through an iterative and participatory process with SiREN staff and management (n = 9) via in-depth semi-structured interviews (n = 4), workshops (n = 2), and meetings (n = 6). Results Findings identified critical factors that affected the functioning of SiREN. Central to SiREN’s ability to meet its aims was its capacity to adapt within a dynamic system. Adaptation was facilitated by the flow of knowledge between SiREN and system stakeholders and the expertise of the team. SiREN demonstrated credibility and capability, supporting development of new, and strengthening existing, partnerships. This improved SiREN’s ability to be awarded new funding and enhanced its sustainability and growth. SiREN actively balanced divergent stakeholder interests to increase sustainability. Conclusion The collaborative development of the diagram facilitated a shared understanding of SiREN. Adaptability was central to SiREN achieving its aims. Monitoring the ability of public health programs to adapt to the needs of the systems in which they work is important to evaluate effectiveness. The detailed analysis of the structure of SiREN and how this affects its operation provide practical insights for those interested in establishing a similar project.

The Dynamics of Product Development in Software Startups

Software startups are increasingly under high pressure to deliver successful products to survive and thrive in the modern highly competitive technology market. Larger organizations with deep pockets can replicate the same business ideas used by startups with relative ease. So how does the average startup stand a chance at succeeding at this seemingly David vs. Goliath contest? This article looks at the available literature and identifies such factors that can affect the success of software development startups. Using causal loop constructs from the field of system dynamics, the interactions among the various identified factors are visualised to reveal the dynamics of the system. The result is as a three-dimensional view of success factors in form of time, capital and (product) differentiation. The modelled system is then simulated, and the resultant trend is reviewed and interpreted. This research acts as ground work for analysing the workings of software development startups and sets the stage for a more holistic study of the area, upon which further research can be carried out.

Diverse Approaches to Creating and Using Causal Loop Diagrams in Public Health Research: Recommendations From a Scoping Review

Objectives: Complex systems thinking methods are increasingly called for and used as analytical lenses in public health research. The use of qualitative system mapping and in particular, causal loop diagrams (CLDs) is described as one promising method or tool. To our knowledge there are no published literature reviews that synthesize public health research regarding how CLDs are created and used.Methods: We conducted a scoping review to address this gap in the public health literature. Inclusion criteria included: 1) focused on public health research, 2) peer reviewed journal article, 3) described and/or created a CLD, and 4) published in English from January 2018 to March 2021. Twenty-three articles were selected from the search strategy.Results: CLDs were described as a new tool and were based upon primary and secondary data, researcher driven and group processes, and numerous data analysis methods and frameworks. Intended uses of CLDs ranged from illustrating complexity to informing policy and practice.Conclusion: From our learnings we propose nine recommendations for building knowledge and skill in creating and using CLDs for future public health research.

Optimization model: the innovation and future of e-ecotourism for sustainability

PurposeThis 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.Design/methodology/approachData 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.FindingsThis 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.Research limitations/implicationsThis study does not use a primary database, but uses secondary data from official sources that can be accessed by the public.Practical implicationsThe paper includes implications for the development of intelligent technology based on big data and also requires policy innovation.Social implicationsSustainable tourism development.Originality/valueThis study finds the expansion of new theory competitiveness of ecotourism destinations.