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Management frameworks (MFs) and management system standards (MSSs) are essential tools for improving organisational management practises. They inherently include a range of fundamental building blocks that facilitate the creation of structured management systems. However, these building blocks have not yet been holistically identified or unified into a consilient taxonomy. Addressing this research gap, this study conducts a comprehensive review of 415 academic papers and theses, 47 ISO MSSs, and 79 MFs sourced from scholarly databases and official publications. Utilising a novel heuristic methodology, this study integrates a literature review, clustering, text mining analytics, and an expert review to develop a Consilient Building Block Taxonomy (CBBT). This taxonomy categorises the foundational components of MFs and MSSs, presenting them as a structured framework that unifies these elements into a cohesive system. By providing a systematic classification, the CBBT serves as a foundation for the development of a Unified Singular Management System (USMS). The proposed taxonomy enhances operational coherence, strategic alignment, and efficiency by consolidating the core aspects of diverse management systems. This study concludes with insights into how the CBBT can be leveraged to achieve integration and unification in management practises, offering significant potential for both research and practical applications.

The Kingdom of Saudi Arabia (KSA) has the largest construction market in the gulf region. Nevertheless, the sector faces issues related to inefficiency and ineffectiveness in project delivery. This research aims to explore the impact of current practices across projects lifecycles, and to utilize findings to develop an integrated strategic construction project management framework (ISCPMF) that may pave the way to efficient and effective project implementation. To achieve this objective, the authors have traced the implementation processes of nine projects for data collection. This was based on a deductive approach with preconceived themes. Within-case and cross-case analysis was conducted. The data was complemented by holding three separate focus-group discussions with a total of nineteen participants, and the initial findings were cross-checked with six experts. The deficiencies that surround the pre-construction phase and disconnected activities that are carried out in different timespans represent the first barrier to implement projects successfully. This is coupled with low capacities contractors and non-proactive construction teams that lack a management toolbox to alleviate accumulated issues and control project progress. The unavailability of infrastructure and utilities did not ease construction nor made inspection possible, which led to late occupancy of facilities, waste of resources and failure to deliver the desired benefits effectively. The adoption of ISCPMF will institutionalize and bridge project phases. This may play a vital role in implementing projects efficiently and effectively and building data to benefit future projects. Though the research is limited to higher education facilities, the findings may be generalized to public construction projects.

BACKGROUND AND OBJECTIVES: Climate change mitigation and food security are critical global priorities, with greenhouse gas emissions, particularly carbon dioxide, significantly contributing to climate change. With an annual per capita greenhouse gas emission of 7.5 tons of carbon dioxide equivalent, Indonesia is confronted with a considerable obstacle in its efforts to decrease these emissions. The objective of this study is to analyze the carbon footprints of employees within a research institution in Indonesia, thereby elucidating individual contributions to greenhouse gas emissions and highlighting significant areas for the development of targeted mitigation strategies. METHODS: Data were collected through an online survey from 77 government employees at the National Research and Innovation Agency, Indonesia, focusing on activities with high emission potential: transportation, food consumption, electricity use, waste creation, and other daily activities. The process of calculating the individual carbon footprint entailed converting activity data into carbon dioxide emissions, employing the appropriate emission factors for accuracy. The analysis included a breakdown of emissions by scope (direct and indirect) and demographic factors such as gender and income level. FINDINGS: revealed that the mean carbon footprint for employees was 7.42 tons of carbon dioxide equivalent annually, which is considerably greater than the national average of 2.03 tons of carbon dioxide equivalent per year. Scope 1 emissions contributed 13 percent, primarily from employee bus usage, while Scope 2 emissions (indirect emissions from electricity use) accounted for 18 percent. A significant portion of emissions, amounting to 69 percent, originated from Scope 3 activities, with food consumption and transportation identified as primary factors contributing to this total. Men exhibited a higher personal carbon footprint than women, driven by greater travel and food consumption, while women had higher emissions from entertainment activities. A correlation exists between higher income levels and a greater personal carbon footprint, emphasizing how socioeconomic status affects carbon emissions. CONCLUSION: The elevated individual carbon footprint among research institution employees underscores the need for targeted strategies to reduce emissions in high-income and high-consumption groups. Future research should focus on behavioral interventions, the establishment of institutional policies that encourage sustainable practices, and comparative analyses across various sectors. Targeting particular sources of emissions and the factors that influence them allows for the formulation of effective strategies aimed at minimizing individual carbon footprints, thereby contributing to global efforts in climate change mitigation.

This study presents a novel fully distributed and cooperative demand side management framework based on adaptive diffusion strategy. In this approach, each customer autonomously and without any need for the global information, minimises his incommodity function. The proposed framework has ability to track drifts resulting from the changes in the customer preferences and conditions or any rapidly changing price parameter coming from the wholesale market. In this scenario, the customers aim at maximising their individual utility functions; while the utility company aims at minimising the smart grid total payment (i.e. maximisation of the social welfare). The authors show that there is no need for the utility company to participate in the scheduling program for maximising social welfare. This measurement is maximised adaptively when the customers minimise their incommodity. Moreover, the authors provide a detailed analysis of the robustness of the proposed strategy in the presence of imperfect communication/computation conditions. Numerical results show that the proposed framework performs well, is scale free, and can achieve lower peak‐to‐average ratio of the total energy demand compared with that achieved by the game theoretical methods.

This paper is based on the premise that public officials in developing countries lack the necessary skills to implement Knowledge Management (KM) successfully, so a framework is required to facilitate this process. South Africa is the case study. It is therefore necessary to develop a Knowledge Management Implementation Framework (KMIF). Consequently, one of the objectives of this paper is to validate this need and then outline a KMIF that can help government departments in developing countries implement KM and foster a KM culture. A mixed methodology approach was used, combining qualitative and quantitative data collection. Based on the Taro Yamane formula, 139 people were selected from a target population of 221 officials involved in KM in the South African government. DATAtab, a web-based statistics application, was used to analyze the responses. A comprehensive review of several secondary literature sources was carried out. For the literature review, relevant peer-reviewed articles were downloaded from Google Scholar, ResearchGate, Scopus, and Phil Papers. The study posits that officials charged with KM implementation in the South African government lack the necessary implementation skillset, that a need for a KMIF exists, and subsequently outlines a three-stage KMIF to facilitate their efforts. This study recommends that the proposed three-stage KMIF be adopted since it will provide the government (i) a simplified and structured way of realizing KM; (ii) it will be an effective tool that officials can use to guide them on how to implement KM, and (iii) it will cultivate a KM culture within the government. Even though the study is original to the South African government, the findings, however, may be applied to other emerging governments in Africa and beyond. Despite its theoretical nature, the paper lacks empirical validation, leaving it open to further investigation.

Assessment tools are needed to evaluate agronomic management effects on critical soil functions such as carbon sequestration, nutrient cycling and water partitioning. These tools need to be flexible in terms of selection of soil functions to be assessed and indicators to be measured to ensure that assessments are appropriate for the management goals. The soil management assessment framework (SMAF) is being developed to meet this need. The SMAF uses soil physical, chemical and biological indicator data to assess management effects on soil function using a three-step process for (1) indicator selection, (2) indicator interpretation and (3) integration into an index. While SMAF is functional in its present format, it is intended to be malleable so that user needs can be met. Development of additional indicator interpretation scoring curves is one way that this framework can be expanded. Scoring curve development is a multi-step process of identifying an indicator, determining the nature of the relationship of the indicator to a soil function, programming an algorithm and/or logic statements describing that relationship and validating the resulting scoring curve. This paper describes the steps involved in developing an SMAF scoring curve. Scoring curves for interpreting water-filled pore space (WFPS) and Mehlich extractable potassium (K) were developed using the described protocol. This protocol will assist users of the SMAF in understanding how the existing scoring curves were developed and others interested in developing scoring curves for indicators that are not in the current version.

Data management is a crucial requirement due to the autonomous and constrained nature of Unmanned Aerial Vehicles (UAVs), Internet of Things (IoTs), and the aviation domain. The autonomous and restricted nature of these sectors increases the need for a shared, distributed database, strong access control management, consensus in autonomous decision‐making, and effective communication across diverse protocols and devices. This research presents a comprehensive approach and offers a new viewpoint to the field of blockchain while establishing a fundamental baseline for future improvements in data management systems and addressing the shortcomings of previously proposed existing frameworks in order to fulfill the complex needs of secure data management. This study contributes to the advancement of secure and efficient data management systems by implementing robust data monitoring for error detection, ensuring data integrity, and enabling encrypted or anonymous data sharing based on sensitivity levels. Additionally, the integration of diverse devices, enforcement of immutable regulations compliance, and development of permissioned blockchain systems for identity management further enhance the system's capabilities, offering comprehensive solutions for modern data management challenges. In the tests, the proposed framework showed increased successful transactions in all rate controllers. Besides, effect of the validator number on throughput and latency is tested and analyzed thoroughly. Data management is a crucial requirement due to the autonomous and constrained nature of Unmanned Aerial Vehicles (UAVs), Internet of Things (IoTs) and the aviation domain. This research underscores the shortcomings of previously suggested and existing frameworks emphasizing the absence of a comprehensive approach that successfully incorporates blockchain technology to fulfill the complex needs of secure data management. It offers a solution that carefully controls communication across different channels and protocols, ensures data confidentiality, promotes transparency and self‐management following secrecy requirements, and maintains unchangeable records within a distributed domain. The framework's scalability and viability were confirmed by simulations on the Ethereum private blockchain, representing a substantial advancement in addressing the highlighted deficiencies in secure data management. This research presents a comprehensive approach that was not included in previous studies.

The global ubiquity and demonstrated toxicity of microplastics has led governments around the world to express the need for a risk assessment on microplastics. To conduct a risk assessment, scientists often draw upon frameworks from other contaminants, however we argue that microplastics are a unique pollutant and thus require a unique framework. Microplastics are a multidimensional contaminant, differing in size, shape, polymer type, and chemical cocktail. Each of these dimensions may influence the toxicity of the particle. Furthermore, microplastic pollution exists as a complex and dynamic mixture of particles, that varies over temporal and spatial scales. Thus, we propose a multidimensional risk framework for microplastics that incorporates, rather than simplifies, the multidimensionality of the contaminant as well as the contaminant mixture. With this framework, we can calculate a particle-specific hazard value that describes the potential for a single particle to cause harm based on its chemical and physical properties. The particle-specific hazard values can then be combined based on the number and type of particles in an environmental sample to inform the overall hazard value of the sample. The risk of the sample can then be calculated, which is dependent on the overall hazard value and the concentration of particles in the sample. Risk values among samples in the environment can be compared to illustrate differences among locations or seasons, or can be placed in a management framework with thresholds to guide regulatory decisions. To demonstrate the utility of our proposed framework, we perform a case study using data from San Francisco Bay. Our proposed framework is just that, and requires new research for application. To strengthen the ability of this framework to accurately predict risk, we propose a testing scheme that prioritizes strategic experimental designs that will increase our understanding of how each dimension of microplastics affect the toxicity (or hazard value) of a particle.

Floods are one of the persistent major risk drivers impacting the Cuvelai-Etosha basin of northern Namibia. Locally known as Efundja, this disruptive event negatively impacts particularly the rural population, who have limited resources to combat its effects. Being mostly subsistence farmers in isolated communities, the floods wreak havoc with their homesteads, harvests, animals, and general way of life by cutting them off from their fields, neighbours, and essential services for prolonged periods. This study investigates the impacts and coping mechanisms of rural communities regularly affected by Efundja. Data was collected from four groups of respondents through interviews and focus groups. These were heads of households in the affected rural communities, the community leaders, local councillors and national government officials involved in disaster mitigation. This ensured a comprehensive picture of the impacts.ContributionDespite the presence of a national disaster risk management strategy, the national disaster response mechanism rather reactively responds to the hazard as opposed to being proactive. Results indicates that the strategy is not fully implemented and the parts that are implemented functions as a top-down approach. Respondents reported a wide range of impacts and a general inability to effectively cope with Efundja, coupled with an absence of their voices in deliberations about risk reduction matters. Additions to the current disaster risk management strategy is proposed and several recommendations derived from the research results concludes the article. Should these recommendations be implemented into the Namibian disaster risk management strategy, Efundja as risk driver will also become an agent of change.

PurposeThe global health crisis represents an unprecedented opportunity for the development of artificial intelligence (AI) solutions. This paper aims to integrate explainable AI into the decision-making process in emergency scenarios to help mitigate the high levels of complexity and uncertainty associated with these situations. An AI solution is designed to extract insights into opioid overdose (OD) that can help government agencies to improve their medical emergency response and reduce opioid-related deaths.Design/methodology/approachThis paper employs the design science research paradigm as an overarching framework. Open-access digital data and AI, two essential components within the digital transformation domain, are used to accurately predict OD survival rates.FindingsThe proposed AI solution has two primary implications for the advancement of informed emergency management. Results show that it can help not only local agencies plan their resources for timely response to OD incidents, thus improving survival rates, but also governments to identify geographical areas with lower survival rates and their primary contributing factor; hence, they can plan and allocate long-term resources to increase survival rates and help in developing effective emergency-related policies.Originality/valueThis paper illustrates that digital transformation, particularly open-access digital data and AI, can improve the emergency management framework (EMF). It also demonstrates that the AI models developed in this study can identify opioid OD trends and determine the significant factors improving survival rates.