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Common difficulties across industries are discovered in data management, where handling the volume, variety, and quality of data is crucial for informed decisions in uncertain environments. In this context, rail management must navigate complex decision‐making to ensure safety, service continuity, and cost‐effectiveness. The 2020 Stonehaven derailment is an example of the increasing vulnerability of rail infrastructure to environmental factors and systemic failures. It emphasizes the need for resilient systems, proficient at preventative maintenance and adaptable to escalating challenges. These matters further accentuate the need for context‐dependent strategies that bridge theoretical insights and practical applications. This scoping review explores strategies for decision‐making under uncertainty across sectors such as civil infrastructure, agriculture, water management, and emergency response. It unfolds a selection of procedures addressing the impacts of extreme weather and other unexpected disruptions. It also sets a foundation for future research to support rail infrastructure adaptation to climate change by advocating the use of cybernetic principles and artificial intelligence (AI) to enhance decision‐making processes. Cybernetics enables collaborative human‐AI methods, improving adaptability and resilience. However, balancing and incorporating diverse stakeholder viewpoints into decision chains remains difficult. While promising, substantial research and system improvements are needed to fully harness the potential of AI. [ABSTRACT FROM AUTHOR]

This study offers fresh ontological insights by examining generative causality through the Qualitative Comparative Analysis (QCA) counterfactual lens, in conjunction with Critical Realism and the practice of retroduction. Specifically, it claims that Information Systems (IS) researchers could retroduce generative mechanisms by leveraging the QCA counterfactual approach to causation because retroduction is about conjecturing hypothetical mechanisms that would generate the outcome of interest in a counterfactual fashion. Drawing on an example of typological theorising, this study calls for a renewed effort in the use of retroduction in the study of IS phenomena. In addition, this study sheds new light on the overarching approach for conducting Critical Realist (case study) research. A number of theoretical, methodological, and practical implications are discussed. [ABSTRACT FROM AUTHOR]

This article introduces the 'House Model', an integrated framework consisting of four data governance modes, based on the urban and smart city vision, context, and big data technologies. The model stems from engaged scholarship, synthesizing and extending the academic debates and evidence from existing smart city initiatives. It provides a means for comparing cities in terms of their digitization efforts, helps the planning of more effective urban data infrastructures and guides future empirical research in this area. The article contributes to the literature examining the issue of big data and its governance in local government and smart cities. Points for practitioners: Data is a vital part of smart city initiatives. Where the data comes from, who owns it and how it is used are all important questions. Data governance is therefore important and has consequences for the overall governance of the city. The House Model presented in this article provides a means for organizing data governance. It relates questions of data governance to the history and vision of smart city initiatives, and provides a typology organizing these initiatives. [ABSTRACT FROM AUTHOR]

This study conceptualizes how fire management authorities can empower nonexpert public to participate in fire risk communication processes and increase their own responsibilities for managing fire preventive, protective and recovery processes effectively. Drawing narratives from 10 disaster management experts working at government institutions and nine micro‐entrepreneurs operating self‐sustaining businesses in different merchandized lines in Ghana, we analyzed the data thematically and explored new insights on mental models to generate a two‐way fire risk communication model. The findings suggest that fire management authorities planned fire disasters at the strategic level, collaborated with multiple stakeholders, disseminated information through many risk communication methods, and utilized their capabilities to manage fire at the various stages of fire risk communication, but the outcomes were poor. The micro‐entrepreneurs sought to improve fire management outcomes through attitude change, law enforcement actions, strengthened security and better public trust building. The study has implications for policymakers, governments, and risk communication authorities of developing countries to strengthen their fire disaster policies to minimize commercial fire incidents and address the damaging effects of fire on people's livelihoods, businesses, properties, and environments. Our proposed two‐way fire risk communication model is a new theoretical lens for experts and the nonexpert public to assess each other's beliefs about risk information and manage fire risk communication effectively at all stages. [ABSTRACT FROM AUTHOR]