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This study aimed to identify and analyse the key factors influencing the adoption of e-government services and to discern their implications for various stakeholders, from policymakers to platform developers.

Through a comprehensive review of existing literature and detailed analysis of multiple studies, this research organised the influential factors based on their effect: highest, direct and indirect. The study also integrated findings to present a consolidated view of e-government adoption drivers.

The research found that users' behaviour, attitude, optimism bias and subjective norms significantly shape their approach to e-government platforms. Trust in e-Government (TEG) emerged as a critical determinant, with security perceptions being of paramount importance. Additionally, non-technical factors, such as cultural, religious and social influences, play a substantial role in e-government adoption decisions. The study also highlighted the importance of performance expectancy, effect expectancy and other determinants influencing e-government adoption.

While numerous studies have explored e-government adoption, this research offers a novel classification based on the relative effects of each determinant. Integrating findings from diverse studies and emphasising non-technical factors introduce an interdisciplinary approach, bridging the gap between information technology and fields like sociology, anthropology and behavioural sciences. This integrative lens provides a fresh perspective on the topic, encouraging more holistic strategies for enhancing e-government adoption globally.

In the realm of construction education, the investigation of immersive learning and extended reality (XR) technologies has experienced a surge in recent times. Nevertheless, there remains a notable lack of comprehension surrounding the most efficient ways to integrate these technologies into tailored teaching approaches for vocational construction training. This research study aims to pinpoint the key factors that construction vocational education and training (VET) providers must consider when introducing XR technologies into their training schemes.

This study conducted an in-depth literature review to develop an initial framework to summarise training, technology and institutional factors influencing the educational-technology integration of XR technologies in VET. In addition, this study utilised a Delphi technique, including semi-structured group discussions and two rounds of online follow-up questionnaires, to capture VET experts’ judgements on the importance of decision-making criteria.

This study has identified the critical factors to be considered in the VET sector when adopting XR technologies. Findings revealed institutional factors were the most important criteria for participants, followed by training and technology factors.

The current decision-making process focuses on selecting XR technologies rather than evaluating their performance after implementation. Therefore, more research is needed to monitor the implementation of this technology in curricula from a senior management perspective. This will help to understand the cost and value factors related to the value proposition of XR technologies in courses.

To ensure the success and long-term viability of the technology-curriculum interface, it is important to consider factors such as the availability of technical and educational support, data security and cost-effectiveness. It is also crucial to focus on ease of use and content development that emphasises instruction to create engaging content for learners.

The potential impact of this study is underpinned by two facts: (1) it constitutes the first effort made in the field to comprehensively elicit VET expert judgements in relation to XR technologies, and (2) it offers decision-making criteria that are at play in seeking to take advantage of high-cost technologies that are rapidly evolving. While there is no simple checklist for XR implementation, this study takes a step further to identify significant factors influencing XR integration in vocational construction training.

This study aims to develop an interface management risk interaction modeling and analysis methodology applicable to complex systems in high-speed rail construction projects, reveal the interaction mechanism of interface management risk and provide theoretical support for project managers to develop appropriate interface management risk response strategies.

This paper introduces the association rule mining technique to improve the complex network modeling method. Taking China as an example, based on the stakeholder perspective, the risk factors and significant accident types of interface management of high-speed rail construction projects are systematically identified, and a database is established. Then, the Apriori algorithm is used to mine and analyze the strong association rules among the factors in the database, construct the complex network, and analyze its topological characteristics to reveal the interaction mechanism of the interface management risk of high-speed rail construction projects.

The results show that the network is both scale-free and small-world, implying that construction accidents are not random events but rather the result of strong interactions between numerous interface management risks. Contractors, technical interfaces, mechanical equipment, and environmental factors are the primary direct causal factors of accidents, while owners and designers are essential indirect causal factors. The global importance of stakeholders such as owners, designers, and supervisors rises significantly after considering the indirect correlations between factors. This theoretically explains the need to consider the interactions between interface management risks.

The interaction mechanism between interface management risks is unclear, which is an essential factor influencing the decision of risk response measures. This study proposes a new methodology for analyzing interface management risk response strategies that incorporate quantitative analysis methods and considers the interaction of interface management risks.

The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

Despite significant progress in Iran's immunization programs, vaccine policymaking in the country still faces various challenges and shortcomings. To address these issues and ensure sustained progress toward achieving comprehensive vaccination policies, it is essential to identify the critical factors influencing vaccine policies in Iran. Our study aims to provide evidence-based insights that can inform the development of effective and equitable vaccine strategies, leading to a more sustainable and efficient approach to vaccination in the country.

This mixed-method study aimed to analyze the factors influencing the future of human vaccine policy using Cross Impact Analysis. Firstly, a scoping review was conducted to identify the factors affecting the future of human vaccine development. Secondly, a semi-structured interview was conducted with experts in this field to add more factors and confirm the identified factors within the Iranian context. Finally, a Cross-Impact Analysis (CIA) approach was applied to comprehend the complex relationships between the identified factors. Thematic analysis was used for the qualitative data, and MICMAC analysis was applied to characterize the relationships between the factors.

Seventeen key driving force factors were identified through comprehensive review and interviews. These factors were assigned weighted values ranging from zero to three and subsequently analyzed using MICMAC software. Employing the Cross-Impact Analysis (CIA) technique, the study characterized the impact of each factor on vaccine policy and elucidated the intricate interactions between them. The findings underscored that robust leadership and governance, an innovative ecosystem, and well-established immunization information systems emerged as pivotal driving forces shaping vaccine policy in Iran.

While this study contributes valuable insights into the driving factors influencing vaccine policy in Iran, it is important to acknowledge several limitations. The results rely on the subjective perceptions of a diverse group of specialists, and future research could delve into additional factors in other countries to identify common themes and differences.

This study provides evidence to assist policymakers in making informed decisions regarding vaccines in Iran. The findings suggest that enhancing access to vaccines, fostering trust in the healthcare system, and prioritizing equity in distribution can contribute to increased vaccination rates and a reduction in vaccine-preventable diseases.

This study provides a unique contribution to the field of vaccine policy by utilizing the cross-impact analysis to examine the complex interactions among various factors. The results of this analysis demonstrate that these interactions can significantly impact the overall system, highlighting the need for policymakers to consider multiple factors when formulating effective strategies. By revealing the significance of these interactions, this research offers valuable insights into the development of successful policies that can shape a desirable future for vaccine policy in Iran. Future studies could ratify the findings from this research by applying other methodological approaches.

Organisational strategy becomes reality by connecting organisation’s resources and capabilities in daily operations, and physical workspace is one of the environments in which this takes place. This study aims to explore to what extent factors required for successful strategy implementation are considered when designing, using and managing workspaces of knowledge-intensive organisations.

For the study, managers in 25 large and medium-sized knowledge-intensive organisations were interviewed. The semi-structured interviews focused on organisation’s strategy, strategy implementation practices and workspace design and management. To form a comprehensive framework of strategy implementation success factors for the study, the factors of 11 frameworks were analysed, grouped and renamed.

Current workspace design, usage and management mainly support human-related strategy implementation factors. However, both organisation- and human-related factors are needed for the strategy implementation to be successful. Therefore, the organisations studied may have unused potential in their workspaces to ensure strategy-aligned operations and behaviour.

Due to the potential imbalance between organisation- and human-related strategy implementation factors, a more holistic, organisational-level approach to workspace design, usage and management is recommended to ensure the success of strategy implementation.

Workspaces have extensively been studied from individual strategy implementation factors’ as well as employees’ perspectives. Prior to this work, there are only few studies exploring workspace in the holistic, strategy implementation context.

A new triage method, MBCE (Medical Bio Social Ethics), is presented with social justice, bio, and medical ethics for critical resource distribution during a pandemic. Ethical triage is a complex and challenging process that requires careful consideration of medical, social, cultural, and ethical factors to guide the decision-making process and ensure fair and transparent allocation of resources. When assigning priorities to patients, a clinician would evaluate each patient’s medical condition, age, comorbidities, and prognosis, as well as their cultural and social background and ethical factors.

A statistical analysis shows no interactions among the ethical triage factors. It implies the ethical components have no moderation effect; hence, each is independent. The result also points out that medical and bioethics may have an affinity for interactions. In such cases, there seem to be some ethical factors related to bio and medical ethics that are correlated. Therefore, the triage team should be careful in evaluating patient cases. The algorithm is explained with case histories of the selected patient. A group of triage nurses and general medical practitioners assists with the triage.

The MBCE triage algorithm aims to allocate scarce resources fairly and equitably. Another ethical principle in this triage algorithm is the principle of utility. In a pandemic, the principle of utility may require prioritizing patients with a higher likelihood of survival or requiring less medical care. The research presents a sensitivity analysis of a patient’s triage score to show the algorithm’s robustness. A weighted score of ethical factors combined with an assessment of triage factors combines multiple objectives to assign a fair triage score. These distinctive features of the algorithm are reasonably easy to implement and a new direction for the unbiased triage principle.

The idea is to make decisions about distributing and using scarce medical resources. Triage algorithms raise ethical issues, such as discrimination and justice, guiding medical ethics in treating patients with terminal diseases or comorbidity. One of the main ethical principles in triage algorithms is the principle of distributive justice.

This article presents a comprehensive analysis of innovation in micro, small and medium enterprises (MSMEs) in India, focusing on the barriers and facilitators within their internal and external ecosystems.

A self-administered questionnaire was used to collect data from 1430 MSMEs across India, employing Structural Equation Modeling (SEM) to analyze the relationships between internal and external factors and innovation adaptation.

The findings indicate that factors such as top management and organization structure, communication, technological capability and adaptation and organizational culture have a positive impact on innovation adaptation within the internal environment. Conversely, employee and market orientation, as well as financial factors, have a negative influence. Regarding the external environment, industry and competitive analysis, internationalization and partner alliances were found to positively affect innovation adaptation, whereas the country's infrastructure and policies had a negative impact.

The study emphasizes that MSMEs have the potential to leverage their internal and external environments to foster innovation within their organizations.

We propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.

We estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.

We find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

We propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

This study examines the impact of outdoor environments in public rental housing complexes on residents’ psychological restoration, taking into account the interconnectedness of physical and psychological factors in human health. Drawing on Kaplan and Kaplan’s Attention Restoration Theory and Ulrich’s Supportive Design Theory, the research investigates the factors influencing residents’ psychological restoration within these outdoor spaces.

The Perceived Restorativeness Scale (PRS), which is based on the Attention Restoration Theory and the Zuckerman Inventory of Personal Reactions (ZIPERS) are used to assess residents’ restorative experiences. Field research was conducted to collect data on the outdoor environments, and surveys were administered to the residents. The study analyzes the data using SPSS, including both factor and correlation analyses, to explore the relationship between the restorative effect and emotional factors.

The study verified a significant influence of positive emotions in ZIPERS on PRS’ overall restorative effect, thus supporting the utilization of both PRS and ZIPERS factors together to assess comprehensively the impact of outdoor environments on residents’ psychological restoration.

By employing a multidimensional approach involving residents’ experiences and emotions, this study quantified emotional and psychological data, which were hard to quantify. These results provide a basis for developing more objective restoration environment design guidelines and programs in the future.