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This study critically examines the transformative impact of the “North Sea TikTok” phenomenon on the marine tourism sector, emphasizing the role of employee training in fostering resilience and adaptability within marine economics and integrated marine systems. It delves into how viral social media trends influence marine tourism destinations, particularly the North Sea, affecting local economies, marine resource management and tourism strategies. By analyzing this trend, the paper seeks to uncover how marine tourism destinations can effectively respond to the challenges and opportunities presented by digital media-driven tourism.

Employing a multidisciplinary framework that merges insights from digital marketing, risk perception in tourism and human resource management, this paper provides a comprehensive qualitative analysis of the “North Sea TikTok” trend. Through a meticulous content analysis of viral videos and an examination of user engagement metrics, alongside a thorough review of contemporary literature in marine tourism and sustainability, the study unpacks the far-reaching implications of social media on marine tourism ecosystems.

The analysis reveals that the “North Sea TikTok” trend has markedly altered public perceptions of the North Sea, catalyzing a shift toward adventure and risk-taking tourism. This pivot promises economic rejuvenation for local tourism sectors and necessitates agile marine management strategies to accommodate the evolving demands. Implementing innovative employee training programs focusing on safety protocols, environmental conservation and digital engagement is central to managing these dynamics. The paper emphasizes integrating sustainable practices to ensure the equitable growth of marine tourism economies and environmental preservation.

This paper pioneers exploring the nexus between social media trends and their operational and strategic impacts on marine tourism management and economics. Synthesizing social media's viral dynamics with marine tourism development introduces groundbreaking insights into adapting marine tourism strategies in the digital age. It emphasizes the critical need for a skilled workforce capable of navigating the complexities of digital trend-driven tourism markets, proposing a novel model for employee training that aligns with the shifting paradigms of marine tourism engagement. This unique contribution advances academic discourse in marine economics and provides practical frameworks for stakeholders aiming to harness social media trends for sustainable tourism development.

In order to solve the problem of inaccurate calculation of index weights, subjectivity and uncertainty of index assessment in the risk assessment process, this study aims to propose a scientific and reasonable centralized traffic control (CTC) system risk assessment method.

First, system-theoretic process analysis (STPA) is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis. Then, to enhance the accuracy of weight calculation, the fuzzy analytical hierarchy process (FAHP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and entropy weight method are employed to calculate the subjective weight, relative weight and objective weight of each index. These three types of weights are combined using game theory to obtain the combined weight for each index. To reduce subjectivity and uncertainty in the assessment process, the backward cloud generator method is utilized to obtain the numerical character (NC) of the cloud model for each index. The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system. This cloud model is used to obtain the CTC system's comprehensive risk assessment. The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud. Finally, this process yields the risk assessment results for the CTC system.

The cloud model can handle the subjectivity and fuzziness in the risk assessment process well. The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.

This study provides a cloud model-based method for risk assessment of CTC systems, which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment, achieving effective risk assessment of CTC systems. It can provide a reference and theoretical basis for risk management of the CTC system.

With the increasing use of social media in operation and supply chain management (OSCM), it is of great importance for managers to consider risks in advance and take precautions against the risks that might arise from social media usage among supply chain members. The aim of this research is to identify and evaluate the risks related to the use of social media in OSCM.

An initial research of the literature revealed that there is no detailed risk categorization in this area. Current taxonomies on the business risks of social media usage were examined and integrated with classifications identified in a Delphi study. The authors empirically demonstrate how the determined risks are prioritized and how decision-makers may decide to manage risks effectively based on the analytical hierarchy process (AHP) method.

The findings of the research showed that reputation-associated risks such as criticism, language and loss of confidence should be prioritized over human, content and technical-associated risks.

To date, a comprehensive approach to determine risks arising from using social media in OSCM is missing. With the Delphi and AHP techniques, the authors provide a novel insight for managers to mitigate risks. The outcomes of this study may assist executives in achieving successful management of social media usage in OSCM. Besides, the proposed AHP model may provide guidelines and direction in this regard.

This article investigates the application of generative artificial intelligence (GenAI) in experiential learning for authentic assessment in higher education. Recognized for its human-like content generation, GenAI has garnered widespread interest, raising concerns regarding its reliability, ethical considerations and overall impact. The purpose of this study is to explore the transformative capabilities and limitations of GenAI for experiential learning.

The study uses “thing ethnography” and “incremental prompting” to delve into the perspectives of ChatGPT 3.5, a prominent GenAI model. Through semi-structured interviews, the research prompts ChatGPT 3.5 on critical aspects such as conceptual clarity, integration of GenAI in educational settings and practical applications within the context of authentic assessment. The design examines GenAI’s potential contributions to reflective thinking, hands-on learning and genuine assessments, emphasizing the importance of responsible use.

The findings underscore GenAI’s potential to enhance experiential learning in higher education. Specifically, the research highlights GenAI’s capacity to contribute to reflective thinking, hands-on learning experiences and the facilitation of genuine assessments. Notably, the study emphasizes the significance of responsible use in harnessing the capabilities of GenAI for educational purposes.

This research showcases the application of GenAI in operations management education, specifically within lean health care. The study offers insights into its capabilities by exploring the practical implications of GenAI in a specific educational domain through thing ethnography and incremental prompting. Additionally, the article proposes future research directions, contributing to the originality of the work and opening avenues for further exploration in the integration of GenAI in education.

There is a limited number of research work on critical success factors (CSFs) in public construction projects in Saudi Arabia. In response to this knowledge gap, the objective of this paper is to assess the impact of CSFs on the government construction projects in Saudi Arabia. The success factors are investigated from a broader consideration of failure criteria, from consideration of most effectiveness in successful project completion and also from consideration of the impact of implementing control processes for successful project completion.

This study has analysed the impact of success factors on construction projects in Saudi Arabia using a descriptive methodology. An exhaustive literature survey is undertaken to identify the success and failure factors related to government construction projects in Saudi Arabia. The survey data are sorted out and analysed by cost, schedule, technical, context and finance dimensions of the projects based on project types, engineering complexity, size, modality, jurisdictional control and funding approach. To evaluate the influence of success factors implementation, qualitative data were collected in a survey via a web-based questionnaire that was sent to officials working and occupying a responsible position in national project guidelines organizations and in government construction organizations in Saudi Arabia. In all, 28 CSFs were identified, ranked and evaluated for their impact on project success. The four identified factors belong to process categories of construction projects, nine factors belong to management of construction projects and 15 success factors are identified for impact assessment of implementation in construction projects.

The study's findings have identified and ranked the top five CSFs that significantly influence project outcomes, including meeting time targets, adhering to financial budgets, delivering desired outcomes for all stakeholders, effectively managing risks and assembling the appropriate team while optimizing resource allocation. Additionally, the research indicates that hindrances to projects primarily stem from execution, economic, human and political factors. The study advocates for strict controls over incomplete engineering designs and advises against contractors independently handling design work to ensure project success. Additionally, addressing contractors' qualifications and financial matters is crucial for project success. By highlighting these CSFs and challenges, the research provides actionable insights to enhance project management practices in the construction industry.

This study is limited to the infrastructure projects constructed by governmental bodies with the participation of officials from government organizations. Further study, including private projects and officials working on private projects, may be needed to generalized the research outcome.

Numerous studies have investigated CSFs in construction projects, but few have examined their relevance to Saudi Arabian government projects. This study aims to fill this gap by identifying key CSFs specific to Saudi Arabian public sector construction projects and assessing their impact on project success. It advocates for stringent controls in the Saudi Arabian construction sector, emphasizing the importance of preventing incomplete or altered engineering designs by contractors to increase the success rate of public sector projects. This research offers practical insights to stakeholders, advancing project management practices in Saudi Arabia's construction sector for improved outcomes and resource utilization.

Earthquakes pose a significant challenge to human safety and the durability of infrastructure, highlighting the urgent need for innovative disaster management strategies. This study addresses the gap in current earthquake disaster management approaches, which are often related to issues of transparency, centralization and sluggish response times. By exploring the integration of blockchain technology into seismic hazard management, the purpose of the research is to overcome these limitations by offering a novel framework for integrating blockchain technology into earthquake risk mitigation and disaster management strategies of smart cities.

This study develops an innovative approach to address these issues by introducing a blockchain-based seismic monitoring and automated decision support system for earthquake disaster management in smart cities. This research aims to capitalize on the benefits of blockchain technology, specifically its real-time data accessibility, decentralization and automation capabilities, to enhance earthquake disaster management. The methodology employed integrates seismic monitoring data into a blockchain framework, ensuring accurate, reliable and comprehensive information. Additionally, smart contracts are utilized to handle decision-making and enable rapid responses during earthquake disasters, offering an effective alternative to traditional approaches.

The study results highlight the system’s potential to foster reliability, decentralization and efficiency in earthquake disaster management, promoting enhanced collaboration among stakeholders and facilitating swift actions to minimize human and capital loss. This research lays the foundation for further exploration of blockchain technology’s practical applications in other disaster management contexts and its potential to transform traditional practices.

Current methodologies, while contributing to the reduction of earthquake-related impacts, are often hindered by limitations such as lack of transparency, centralization and slow response times. In contrast, the adoption of blockchain technology can address these challenges and offer benefits over various aspects, including decentralized control, improved security, real-time data accessibility and enhanced inter-organizational collaboration.

This research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.

Using the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).

The validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.

Land cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.

It helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.

The social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.

Landslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.

Addressing Environmental, Social and Governance (ESG) issues has become a critical aspect of business strategy. Since ESG has primarily focused on ratings and measures for reporting, there is a scarcity of methods to assist stakeholders in better comprehending corporate risk and addressing ESG-related issues and problems. The purpose of this paper is to propose a new model to narrow the critical gap.

This study is based on several well-known structural frameworks for managing risks and projects in various industries. Two case studies on topics related to environment (E) and social (S) responsibility are used to demonstrate the practical implementation of the CEPAR® model.

The CEPAR® model, a trademarked five-step methodology (the Challenge-Evaluation-Planning-Action-Review model) was developed by the International Chamber of Sustainable Development (ICSD). The method and guidelines are outlined for easier appreciation by stakeholders of corporations to analyze ESG-related challenges and dilemmas, then able to make principled decisions, take actions, and review the outcomes. Each phase of the new model adheres to the theoretical and practical frameworks for problem-solving and decision-making, emphasizing the iterate process of addressing challenges, evaluating materiality, planning actions, taking actions, and reviewing the outcomes.

The new model is applicable for business corporations and organizations seeking to gain insight and tackle crucial ESG issues, ultimately improving their short- and long-term decision-making and business opportunities.

Today’s world is struggling with the hardship of climate change that has drastically disturbed human life, wildlife and the earth’s biological system. This study aims to show how implementing climate change mitigation strategies and environmental protection measures can ensure sustainable development through collaborative efforts between governmental authorities and the nation’s populace.

An extensive literature review of studies is conducted from across the world concentrating on holistic, sustainable development, enabling a showcase of various conferences, action plans initiated and resolutions passed. VOSviewer software is used to quantify the results of bibliometric analysis and cluster analysis. A total of 260 research studies released between 1993 and 2022 on the Scopus platform are quantified in terms of topmost publications, collaborations among authors, citations index and year-wise publication. The search string has keywords including “climate change,” “sustainable development” and “environment protection.”

The study results revealed a steep increase in research publications in the last three years, from 2017 to 2021, which serves as the basis of the emergence of high-impact articles. The most cited document in this context throws light on assessing vulnerability to climatic risk and building adaptive capacity. It also draws attention to voluntary carbon markets’ rationale while condemning emission trading systems for climate change due to structural flaws, negative consequences and questionable emission-cutting effectiveness. Low energy demand, zero energy buildings and shared socioeconomic pathways should be implemented as strategies for sustainable development.

This study provides a significant opportunity to construct a valuable addition to mitigate climate change. Also, it shows a positive and significant correlation between mitigation and adaptation policies by analyzing publication efforts worldwide considering local climate risks and national adaptation mandates.

The originality of this study lies in its comprehensive approach, combining literature review, bibliometric analysis and cluster analysis to provide insights into current research trends, challenges and potential strategies for addressing climate change and promoting sustainable development. The study’s emphasis on the correlation between mitigation and adaptation policies adds practical significance to its findings.

The construction industry is one of the most hazardous working environments globally. Studies reveal that wearable sensing technologies (WSTs) have practical applications in construction occupational health and safety management. In the global south, the adoption of WSTs in construction has been slow with few studies investigating the critical drivers for its adoption. The purpose of this study is to investigate the factors driving WSTs adoption in Ghana where investment in such technologies can massively enhance health and safety through effective safety monitoring.

To meet the objectives of this study, research data was drawn from 210 construction professionals. Purposive sampling technique was used to select construction professionals in Ghana and data was collected with the use of well-structured questionnaires. The study adopted the fuzzy synthetic evaluation model (FSEM) to determine the significance of the critical drivers for the adoption of WSTs.

According to the findings, perceived value, technical know-how, security, top management support, competitive pressure and trading partner readiness obtained a high model index of 4.154, 4.079, 3.895, 3.953, 3.971 and 3.969, respectively, as critical drivers for WSTs adoption in Ghana. Among the three broad factors, technological factors recorded the highest index of 3.971, followed by environmental factors and organizational factors with a model index of 3.938 and 3.916, respectively.

Theoretically, findings are consistent with studies conducted in developed countries, particularly with regard to the perceived value of WSTs as a key driver in its adoption in the construction industry. This study also contributes to the subject of WSTs adoption and, in the case of emerging countries. Practically, findings from the study can be useful to technology developers in planning strategies to promote WSTs in the global south. To enhance construction health and safety in Ghana, policymakers can draw from the findings to create conducive conditions for worker acceptance of WSTs.

Studies investigating the driving factors for WSTs adoption have mainly centered on developed countries. This study addresses this subject in Ghana where studies on WSTs application in the construction process are uncommon. It also uniquely explores the critical drivers for WSTs adoption using the FSEM.