Occupational safety in construction and mining requires coordination between preventive, supervisory, organisational, and enforcement mechanisms. This study examined the legal and institutional framework governing occupational safety in Kazakhstan’s construction and mining sectors and compared it with selected international approaches through a risk-governance perspective. A comparative legal analysis was conducted using seven common dimensions: employer duties, risk assessment and prevention, worker participation and training, internal monitoring and audit, state inspection and specialised supervision, accident and incident reporting, and enforcement and sanctions. The legal analysis was supplemented by descriptive official statistics on occupational injuries and labour inspection. Kazakhstan combines employer duties, internal occupational safety control, worker participation, state labour inspection, industrial-safety supervision, administrative and criminal liability, and civil compensation. National statistics showed that the number of persons affected by work-related accidents increased from 2,449 in 2022 to 2,670 in 2023 and then decreased to 2,471 in 2024 and 2,328 in 2025. In 2023, mining and quarrying accounted for 20.5% of affected persons and construction for 14.1%. In 2024, state labour inspectors conducted 5,577 inspections and identified 8,122 labour-law violations, including 1,835 occupational safety and health violations. The findings support selective adaptation of international practices, particularly stronger supervisory coordination, internal risk assessment and audit, worker participation and training, and improved digital reporting. Aggregated data do not permit causal attribution of injury trends to individual enforcement measures.
In higher education, learning value increasingly drives students’ continuous engagement with Generative AI tools such as ChatGPT. By re-conceptualizing the UTAUT framework, this study examines university students’ post-adoption and continuance usage intention towards ChatGPT. The study positioned learning value as a mediating variable linking performance expectancy, effort expectancy, perceived trust and hedonic motivation with continuance usage intention. With data collected from 351 university students, this study combines PLS-SEM and ANN analysis to understand both linear and nonlinear relationships among the variables. The findings establish that learning value is the strongest driver of continuance usage intention and significantly mediates the effect of performance expectancy, effort expectancy and hedonic motivation while perceived trust has a significant direct influence on continuance intention, with no mediating effect with learning value. Effort expectancy, on the other hand, does not have a significant influence on continuance intention, suggesting that ease of use has become a hygiene factor in the post-adoption learning context. The results highlight the importance of re-conceptualizing the UTAUT model towards a learning-centered approach where educational value, an interactive user interface and trustworthiness drive continuous usage intention of Generative AI tools. The findings highlight the importance of integrating Gen-AI tools into pedagogical designs that promote critical thinking rather than using them for mere task completion. This paper suggests longitudinal and cross-country comparisons examining the ethical and responsible use of ChatGPT in the learning process of university students as the scope for future research.
Household adaptation is crucial for building the resilience of coastal communities in Bangladesh, which face a high risk of impacts on their livelihoods and well-being due to climate change. This research aims to examine the behavioural determinants of household adaptation, and combines Protection Motivation Theory and the Theory of Planned Behaviour. It specifically focuses on the impact of climate risk perception, environmental knowledge, self-efficacy, and perceived response effectiveness on household adaptation behaviour by taking into account the mediating role of adaptation intention and the moderating role of community resilience. The data were taken from 230 households from six climate-vulnerable coastal districts (Khulna, Satkhira, Bagerhat, Barguna, Patuakhali, and Bhola) using a structured questionnaire with 5-point Likert scale items. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to analyse the proposed model. The results indicate that climate risk perception, environmental knowledge, self-efficacy, and perceived response effectiveness are significant factors that positively contribute to the formation of adaptation intention, which then determines household adaptation behaviour. The four antecedent variables' relationships with adaptation behaviour are also found to be significantly mediated by the variable adaptation intention. In addition, community resilience positively moderates between the relationship between adaptation intention and household adaptation behaviour, suggesting that community resilience can help to translate the intention to adapt into actual adaptation behaviour. The study is a contribution to the climate adaptation literature, as it proposes an integrated behavioural framework, and provides interesting policy implications for strengthening climate resilience through improved risk communication, environmental education, capacity building and community-based adaptation projects in climate-vulnerable coastal areas.
The accommodation sector in South Africa was among the economy's most severely impacted sectors during the COVID-19 pandemic, yet individual firms’ responses to the financial crisis, and the factors that differentiated business survival from bankruptcy, remain empirically under-researched. This paper addresses this gap. Data were obtained from a cross-sectional, self-administered online survey of 477 accommodation establishments in all nine provinces, conducted in 2020 while strict lockdown rules were in force. The paper examines how establishments responded to the crisis and how their characteristics and financial position related to perceived bankruptcy risk. Frequencies described the extent of disruption; exploratory factor analysis (EFA) identified the main coping types; Kruskal-Wallis and Mann-Whitney tests compared coping across establishment groups; binary logistic regression modelled perceived bankruptcy risk; and K-means cluster analysis grouped establishments by coping pattern. The EFA produced four coping dimensions: operational downsizing, workforce management, financial strategy and support, and business suspension and contraction (overall α = 0.834). Ability to meet fixed costs was the only significant correlate of perceived bankruptcy risk (OR = 5.874, p < 0.001); revenue decline and establishment size were not significant. Size and category, but not province, separated three coping groups (Passive Withdrawers, Comprehensive Responders and Selective Adapters). When asked about hypothetical lockdown lengths, operators expected them to last at most six months. From a Resource-Based View perspective, financial slack held before the crisis, the ability to pay fixed costs without income- best separated vulnerable from less vulnerable firms. Larger establishments with more resources took a wider range of actions. Because the authors did not measure dynamic capabilities directly, they treat these actions as operational adjustments rather than evidence of dynamic capabilities. The authors propose a Financial Vulnerability Index (FVI) for future crisis research. Managers should build cost flexibility and reserves in normal times, and banks and government should direct crisis support at liquidity and fixed costs, for example, payment holidays on loans, rates and utilities, and deliver it quickly and widely instead of relying mainly on access to credit.
Cloud computing technology can provide several benefits to organizations. However, several threats and risks can significantly impact the information technology infrastructure in public clouds, potentially affecting organizational processes. A structured approach is needed to effectively identify, assess, and mitigate emerging threats and risks. However, widely adopted frameworks do not yet fit the conditions and characteristics of public cloud. Therefore, the purpose of this study is to develop a model that helps in identifying, assessing, and mitigating risks in public cloud adoption in organizations. In developing this model, the design science research methodology framework was adopted, as it is commonly used in research related to computer science and information systems. The results show that the model has three main phases in the model components: initiation (three sub-components), assessment (four sub-components), and mitigation (three sub-components). Risk factors and mitigation strategies have been adapted to the public cloud environment, which can assist in implementing the risk mitigation model. The proposed risk mitigation model is appropriate and suitable for use in public cloud environment adoption, following rigorous in-depth interviews. The academic findings can contribute to the development of risk management models, which include the identification of threat types, controls, risk factors, and mitigation strategies that have been adapted and mapped within the public cloud context. Practical benefits can help mitigate risks and potential impacts in public cloud environments.
