Search

Purpose: The author explores the challenges to implementation of Industry 5.0 in the manufacturing sector, considering the developing economy context and studying the causal relationships among factors using an advanced causal modelling technique, the Grey Influence Analysis (GINA). The challenges were further classified based on importance and their influencing power. Design/methodology/approach: The author uses the novel causal modelling technique of GINA to study and understand the influence relations among the challenges to implementation of Industry 5.0. Findings: Based on the results from the expert response-based study in the Indian manufacturing industry, it is seen that the Regulatory challenges (RGC) appear to be the most important challenge that needs to be tackled first, while implementing Industry 5.0. Integration with existing systems and Ethical challenges (ETC) emerge as the second and third most important in the category of challenges, as per the results from the GINA analysis. Research limitations/implications: The RGC and the ETC need to be addressed prior to implementation and it is necessary to check whether the technologies comply with regulations and whether it creates serious job displacements. While implementation, the challenges with integration to existing systems can be appropriately tackled. Practical implications: As a practical implication of the study, the author suggests that a proactive and reactive approach can be adopted to implement the Industry 5.0 concepts to reality. The RGC can be viewed for the adoption of technology, integration challenges can be viewed by understanding the existing systems, and ETC can be addressed by understanding the workforce in combination with technologies. Originality/value: The shift in focus on sustainability and resilience has transformed the conventional industries to think beyond efficiency and productivity, toward being more responsible to society. The study analyzes the challenges to implementation of Industry 5.0 and the causal relations among them considering an expert response-based study. [ABSTRACT FROM AUTHOR]

Evolutionary theory has made large impacts on our understanding and management of the world, in part because it has been able to incorporate new data and new insights successfully. Nonetheless, there is currently a tension between certain biological phenomena and mainstream evolutionary theory. For example, how does the inheritance of molecular epigenetic changes fit into mainstream evolutionary theory? Is niche construction an evolutionary process? Is local adaptation via habitat choice also adaptive evolution? These examples suggest there is scope (and perhaps even a need) to broaden our views on evolution. We identify three aspects whose incorporation into a single framework would enable a more generalised approach to the understanding and study of adaptive evolution: (i) a broadened view of extended phenotypes; (ii) that traits can respond to each other; and (iii) that inheritance can be non‐genetic. We use causal modelling to integrate these three aspects with established views on the variables and mechanisms that drive and allow for adaptive evolution. Our causal model identifies natural selection and non‐genetic inheritance of adaptive parental responses as two complementary yet distinct and independent drivers of adaptive evolution. Both drivers are compatible with the Price equation; specifically, non‐genetic inheritance of parental responses is captured by an often‐neglected component of the Price equation. Our causal model is general and simplified, but can be adjusted flexibly in terms of variables and causal connections, depending on the research question and/or biological system. By revisiting the three examples given above, we show how to use it as a heuristic tool to clarify conceptual issues and to help design empirical research. In contrast to a gene‐centric view defining evolution only in terms of genetic change, our generalised approach allows us to see evolution as a change in the whole causal structure, consisting not just of genetic but also of phenotypic and environmental variables. [ABSTRACT FROM AUTHOR]

Experience shows that Agile project management tools such as Atlassian Jira capture the state of EAS projects by relying solely on expert judgement that is not supported by any knowledge model. Therefore, the assessment of project content against strategic objectives and business domain features are not supported by any tool. This is one of the reasons why Agile project management still does not provide sufficient EAS project delivery results. In order to address this problem, the Enterprise Application Software (EAS) development using Agile project management is summarized in a conceptual model. The model highlights the knowledge used and indicates its nature (empirical or causal digitized). The modified Agile management process we have developed and described in previous works is based on causal knowledge models that supports EAS development and Agile management processes. The purpose of this article is to specify knowledge repository to ensure the Agile management solutions of an EAS project are aligned with strategic goals and business domain causality. It is worth noticing that strategic goals have been identified and specified as capabilities using some enterprise architecture framework (NAF, MODAF, ArchiMate, etc.). The novelty of the proposed method is incorporating the business domain causal knowledge modelling approach into the Agile project management process. The causal knowledge unit is considered as a Management Transaction (MT), which includes closed loop dependence of its components. The modified Agile activity hierarchy (theme, initiative, epic, user story) defines the required content of their mutual interactions. An important new results obtained are the conceptual model of causal knowledge base (KB) and specification of enhanced Agile management tool components: project management database and project state assesment knowledge base. Causal KB includes specification of causal knowledge unit (MT metamodel) and specifications of traditional and causal Agile hierarchy meta-models. These conceptual models define the causal knowledge components necessary to evaluate the state of Agile activities in the EAS development project using intelligent Agile project management tool. [ABSTRACT FROM AUTHOR]

Planning for the future plays a pivotal role in a competitive business world. Scenario analysis is a popular tool for exploring plausible futures and planning. However, the practice of scenario planning is often qualitative, unstructured, and time-consuming. We propose a structured technology development framework by categorising the qualitative variables impacting technology development and identifying their causal relationships. We then use causal loops and expert opinions and the Decision Making Trial and Evaluation Laboratory (DEMATEL) method for scenario planning and futures studies. We present a case study in the communications industry to demonstrate the applicability of the proposed framework. [ABSTRACT FROM AUTHOR]

The Agile approach and tools are popular for the management of Enterprise Application Software (EAS) development. This article focuses on the issue of inconsistency between strategic business objectives and the functionality of the software developed. Agile management tools lack the functionality of EAS project activities coordination. This article aims to rethink Agile project management using the causal modelling approach. A causal model of Agile project management using a management transaction (MT) concept was developed. The notion of the space of processes was used to identify the MTs location along the axes of aggregation, generalization, and time and to formalize their interaction specifications. Taxonomy of the coordination meta-types and types was developed using the identifiers of the MTs. The modified Agile activities hierarchy was developed, and vertical and horizontal causal interactions between Agile activities were identified. This modified Agile management model helps to consistently track the integrity of EAS project content. Complexity indicators were introduced to evaluate the EAS project complexity and their average and normalized values are presented. Additional attributes in the Agile management tool Jira are proposed. Monitoring mismatch between strategic business objectives and development activities content helps to improve the success of EAS projects delivery. [ABSTRACT FROM AUTHOR]

Aim: To advance our understanding of the mechanisms that mediate the relationships between global climatic and anthropogenic processes and pathogen occurrence, it is crucial to evaluate the exact pathways connecting the ecological mediators and the pathogen responses across spatial and temporal heterogeneities at various scales. We investigated the pathways connecting these two types of heterogeneities in sand stabilization that were created by contrasting forces of various human activities and long‐term droughts, and pathogen occurrence in host populations. The considered candidate ecological mediators were various components of host community structure, arthropod vector traits, and the pathogen occurrence in these vectors. Location: North‐western Negev Desert's sands in Israel. Time period: 1982–2018. Major taxa studied: Gerbillus andersoni, Gerbillus floweri, Gerbillus gerbillus, Mycoplasma, Bartonella, Synosternus cleopatrae. Methods: We combined information from satellite images, 36 years of rodent censuses, a natural experiment, and causal modelling. Results: We found evidence that the spatial heterogeneity in sand biocrusts is largely correlated with structural differences between host communities, especially at medium spatial scales. Pathogen sampling, followed by causal modelling, suggested that the cascading effect of sand stabilization on pathogen occurrence is mainly mediated through changes in host community structure and vector burdens. Importantly, we found that structural changes in the same host community can simultaneously amplify and dilute different pathogens. Main conclusions: These findings suggest that global processes can translate into local processes, where the importance of the mediation effects depend on the magnitude of environmental heterogeneity. These mediation effects can benefit some organisms while adversely affecting others. [ABSTRACT FROM AUTHOR]

I appraise some areas of recent achievement in economic methodology by identifying four topics on which there will likely be heavy exogenously generated demand for methodological innovation over coming years, and asking what foundations have been set for this work. The topics in question are economists' role in policy formation, macroeconomic management, causal and structural modeling of economic processes, and welfare with non-standard and dynamic utility. [ABSTRACT FROM AUTHOR]

Research Questions: Does being in the social welfare slow-onset disaster quadrant help in garnering resources during the sudden disaster? Theory: Humanitarian supply chain has been traditionally explained as a system involved in mobilizing people, resources, skills and knowledge for disaster relief operations. The established classification of disasters includes natural and man-made disasters. These are further classified into sudden- and slow-onset disasters. Social welfare supply chains happen to be a distinctive type of humanitarian supply chain working in slow-onset disasters such as poverty and drought. To understand the complex systems like humanitarian supply chain, system dynamics modelling is used. Type of the Case: Study of a phenomenon. Basis of the Case: In this study, we proposed a humanitarian supply chain case and system dynamics model that works as a social welfare supply chain. In the face of a calamity, operations are ramped up for the sudden-onset conditions. After the initial phase is over, operations are ramped down and again become stable. Protagonist: Absent. Findings: The study tests the results of four policy measures (a) increasing goodwill, (b) decreasing stringent directive, (c) increasing donor attitude and (d) a combination of all of these measures. These measures are additive in nature, and a humanitarian supply chain can use these policy measures to respond to a sudden disaster. Discussions: The case discusses various policy measures taken by the humanitarian supply chain during a sudden disaster. It also explores whether being in the slow-onset quadrant helps tack sudden disasters like earthquakes or terrorist attacks. An organization can start with decreasing the stringent measures in the case of a sudden disaster. Increasing donor attitude and goodwill requires long-term outreach efforts. [ABSTRACT FROM AUTHOR]

Summary: A common problem in educational evaluation is estimating causal effects of interventions from non‐experimental data on students. Scores from standardized achievement tests often are used to adjust for differences in background characteristics of students in different non‐experimental groups. An open question is whether, and how, these adjustments should account for the errors in test scores as measures of latent achievement. The answer depends on what information was used to assign students to non‐experimental groups. Using a case‐study of estimating teacher effects on student achievement, we develop two novel empirical tests about what information is used to assign students to teachers. We demonstrate that assignments are influenced by both information that is unobserved by the researcher, and error prone test scores. We develop a model that is appropriate for this complex selection mechanism and compare its results with common simpler estimators. We discuss implications for the broader problem of causal modelling with error prone confounders. [ABSTRACT FROM AUTHOR]

I focus on token, deterministic causal claims as they feature in causal explanations. Adequately handling absences is difficult for most causal theories, including theories of causal explanation. Yet so is adequately handling cases of late preemption. The best account of absence-causal claims as they appear in causal explanations is Jonathan Schaffer's quaternary, contrastive account. Yet Schaffer's account cannot handle preemption. The account that best handles late preemption is James Woodward's interventionist account. Yet Woodward's account is inadequate when it comes to absences. I propose an account that handles both absences and preemption by transposing Schaffer's account into an interventionist framework. [ABSTRACT FROM AUTHOR]

Landscape resistance reflects how difficult it is for genes to move across an area with particular attributes (e.g. land cover, slope). An increasingly popular approach to estimate resistance uses Mantel and partial Mantel tests or causal modelling to relate observed genetic distances to effective distances under alternative sets of resistance parameters. Relatively few alternative sets of resistance parameters are tested, leading to relatively poor coverage of the parameter space. Although this approach does not explicitly model key stochastic processes of gene flow, including mating, dispersal, drift and inheritance, bias and precision of the resulting resistance parameters have not been assessed. We formally describe the most commonly used model as a set of equations and provide a formal approach for estimating resistance parameters. Our optimization finds the maximum Mantel r when an optimum exists and identifies the same resistance values as current approaches when the alternatives evaluated are near the optimum. Unfortunately, even where an optimum existed, estimates from the most commonly used model were imprecise and were typically much smaller than the simulated true resistance to dispersal. Causal modelling using Mantel significance tests also typically failed to support the true resistance to dispersal values. For a large range of scenarios, current approaches using a simple correlational model between genetic and effective distances do not yield accurate estimates of resistance to dispersal. We suggest that analysts consider the processes important to gene flow for their study species, model those processes explicitly and evaluate the quality of estimates resulting from their model. [ABSTRACT FROM AUTHOR]

Purpose - The purpose of this paper is to adapt a causal modelling approach to investigate the organisational collaboration and information technology (IT) project management issues concerning the planning and adoption of inter-organisational IT systems across NHS hospital pathology departments. Design/methodology/approach - The researchers adopted an interpretive research approach utilising qualitative methods and in particular template analysis. Themes and categories were initially derived from a review of the literature based on critical success factors from enterprise resource planning (ERP) projects. Findings - A causal loop modelling approach was adapted to define key linkages between success factors. This highlighted virtuous and vicious cycles associated with IT project management and team collaboration - influencing the adoption process of large-scale integrated IT systems. Research limitations/implications - Single case studies can be problematic in terms of generalising from the research. The study had a limited number of interview participants due to the focus on strategic management. Future studies could extend the number of stakeholders involved and also focus on end-users of services, such as clinicians in primary care. Practical implications - The discussion and conclusions assess the appropriateness and utility of using a causal modelling approach, and an adapted causal loop model, to inform more effective approaches to IT planning, project management, team collaboration and adoption of integrated systems. Social implications - The findings indicate that a more enhanced understanding of project team collaboration involving technical, administrative and clinical stakeholders has the potential to inform more effective strategies for modernisation of hospital clinical services such as pathology. Originality/value - This study investigates the anatomy of a high-profile IT project under the umbrella of strategic modernisation of health services. Privileged access to key stakeholders has enabled the development of a causal model for IT project collaboration and management. This will form the basis for further development of more refined models to enhance project outcomes in the future. [ABSTRACT FROM AUTHOR]

Across many academic disciplines visualisation and notation systems are used for modelling data and developing theory, but in child development visual models are not widely used; yet researchers and students of developmental difficulties may benefit from a visualisation and notation system which can clearly map developmental outcomes and trajectories, and convey hypothesised dynamic causal pathways. Such a system may help understanding of existing accounts and be a tool for developing new theories. We first present criteria that need to be met in order to provide fully nuanced visualisations of development, and discuss strengths and weaknesses of the visualisation system proposed by Morton. Secondly, we present a tool we have designed to give more precise accounts of development while also being accessible, intuitive and visually appealing. We have called this an Accessible Cause-Outcome Representation and Notation System (ACORNS). This system provides a framework for clear mapping and modelling of developmental sequences, illustrating more precisely how functions change over time, how factors interact with the environment, and the absolute and relative nature of causal outcomes. We provide a new template, a set of rules for the appropriate use of boxes and arrows, and a set of visually accessible indicators that can be used to show more precisely relative rates, degrees and variance of functioning over different capacities at different time points. We have designed ACORNS to give a precise and clear visualisation of how development unfolds; allowing the representation of less 'static' and more transactional models of developmental difficulties. We hope ACORNS will help students, clinicians and theoreticians across disciplines to better represent nuances of debates, and be a seed for the development of new theory. [ABSTRACT FROM AUTHOR]

Background. Declining residual renal function, as indicated by the glomerular filtration rate (GFR), is associated with an increased mortality risk in patients with end-stage renal disease (ESRD) on dialysis.Methods. We monitored GFR and mortality in 1800 haemodialysis (HD) and peritoneal dialysis (PD) patients in 1996–2006. We used a marginal structural model to estimate the causal effects both of GFR when it was not completely lost and of the subsequent full loss of GFR on mortality, avoiding the drawbacks of standard regression models that include covariates to adjust for confounding. Instead, effect estimates were adjusted for possible baseline and time-varying confounders using inverse probability weighting.Results. We estimated a hazard ratio (HR) corresponding to the effect of the full loss of GFR on mortality, as compared to not having fully lost GFR, of 1.50 [95% confidence interval (CI) 1.09–2.07]. The HR corresponding to the effect of GFR when GFR is not (yet) fully lost on mortality was 0.97 (95% CI 0.92–1.02) (per mL/min/1.73m2). We found no significant difference in the effect of GFR on mortality between patients starting on PD and HD.Conclusions. Preventing or delaying the full loss of GFR can improve survival in dialysis patients. This supports the importance that is given to the effect of treatment options for patients with ESRD on the rate of decline of the residual renal function. [ABSTRACT FROM PUBLISHER]

In their recent comment in this journal, T. M. Blackburn and colleagues called into question the use of standardized partial regression modelling (also called path analysis and structural equation modelling) when null expectations for regression coefficients are not zero. Here, we answer their critique by showing how randomization can be used to illuminate and interpret causal modelling in analyses that have non-zero expectations. Causal modelling is a powerful tool that can yield novel insights in biogeography when properly interpreted. [ABSTRACT FROM AUTHOR]

If a clinician has to make decisions on diagnosis and treatment, he or she is confronted with a variety of causal theories. In order to compare these theories a neutral terminology and notational system is needed. The Causal Modelling framework involving three levels of description – biological, cognitive and behavioural – has previously been used to compare causal accounts for dyslexia and autism. In this article we present this framework and explore its application to four causal theories of conduct disorder. We discuss the problems we encountered in this application and evaluate both the framework and the theories of conduct disorder. It was possible to capture parts of the theories of conduct disorder in the Causal Modelling framework but a multi-model approach may be necessary for the alternative theories of conduct disorder we evaluate. The application of the framework helps to see the relationships among the various theories of aspects of conduct disorder and demonstrates the need for more explicitness in the causal theories. [ABSTRACT FROM AUTHOR]

As global conservation actions become more urgent, informed decision-making requires robust analyses of the costs and benefits of policy options, based on available evidence. Recovery planning for threatened or endangered species must assume a cause-and-effect relationship between proposed management interventions and population responses. However, a significant portion of current knowledge about threatened or endangered species is derived from observational studies because experiments that fully meet random and controlled design criteria are largely infeasible or unethical. Large-scale field experiments are becoming more common, yet the greater uncertainty generated by what remain fundamentally observational studies can lead researchers to weak inferences about causal mechanisms, creating debate and confusion among decision-makers, planners and stakeholders. This has been an acute problem facing conservationists and governments as they struggle with the successful recovery of species in decline. In other domains where experimental evidence is difficult to collect, causal modelling has been adopted to identify causal relationships from observational data, based on a set of strong assumptions and identification rules. In Canada, significant and ongoing efforts have had limited success in reversing the population decline of woodland caribou (Rangifer tarandus caribou). We examine the scientific framework for woodland caribou recovery efforts through the lens of causal modelling, highlighting feasible steps that could be taken to improve the rigour of causal inferences. • Policy decisions require strong cause-effect inferences to predict success of interventions. • Causal inferences about woodland caribou declines are constrained by observational data. • Causal modelling improves inferences for policy decisions in fields similarly constrained. • Policy interventions to arrest caribou declines can benefit from insights from causal modelling. [ABSTRACT FROM AUTHOR]

Inferring causality has long been a challenging task in environmental impact studies and monitoring programs, mostly because of the problem of confounding bias, i.e. the difficulty of separating impact from natural variation. Traditional approaches for dealing with confounding, despite improvements in study design and statistical analysis, are inadequate. Using aquatic biota as a case study, this review explains the limitations of traditional methods used to separate the impact of human-made pollution from natural variation in the environment. Advantages and disadvantages of the traditional and novel techniques are enumerated. Bayesian networks (BNs) and structural equation modelling (SEM) as causal modelling techniques are introduced as approaches to improve environmental impact monitoring. [ABSTRACT FROM AUTHOR]