This study examines the adoption and usability of lifestyle (LS) apps, considering demographic factors like age and education that influence adoption decisions. The study employed a mixed-methods design, combining an experiment (spanning 14 weeks of app use) with semi-structured interviews and periodic measurements. The researchers employed the Mobile Application Usability Questionnaire (MAUQ) to identify pivotal aspects of standalone app usability, interface satisfaction, and usefulness at various stages of use, with a particular emphasis on the experiences of Hungarian students (n = 36). The results demonstrate that health-related factors have a significant impact on students’ behavior and evaluation of lifestyle apps over the 14-week period. Overall, the analyzed LS apps demonstrated positive outcomes in terms of supporting subject health and significantly improving the perceived health state. The findings highlight both practical and theoretical contributions to the field of mobile health applications, suggesting avenues for further research to either confirm or challenge existing theories.

Manuscript type: Research paper. Research aims: This study aims to explore the determinants of voluntary IFRS application in listed firms in Vietnam. Design/methodology/approach: Analyzing data from 552 public companies listed on the Ho Chi Minh Stock Exchange and Ha Noi Stock Exchange during 2019–2022, this study employs a logistic model with robust analysis. Research findings: The findings indicate that voluntary IFRS application is positively associated with firm size, leverage, internationalization, corporate efficiency, state ownership, and foreign ownership. Particularly, internationalization has the most significant impact on voluntary IFRS application. Theoretical contribution/originality: These findings of this study align with positive accounting theory, which proposes how factors affect voluntary IFRS application. Practitioner/policy implications: Policymakers should consider these findings when developing or revising policies concerning voluntary IFRS application, particularly for state-owned and foreign-owned companies. Research limitation: This study spans from 2019 to 2022, during which economic and regulatory conditions may have fluctuated, potentially impacting the results. Moreover, the data on voluntary IFRS adoption were collected through surveys, which may be subject to respondent bias and dependent on participants’ understanding and willingness to provide accurate information.

This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.

Blockchain technology is poised to significantly transform the corporate world, heralding a new era of innovation and efficiency. Over the past few years, its impact has been noted by leaders, academics, and government representatives around the globe this growing interest underscores businesses’ need to evolve and reconsider traditional operational models. To remain competitive, organizations must embrace this change. Before introducing such ground-breaking technology, it is crucial to assess the motivations of primary stakeholders concerning its implementation. This study looks into what influences the use of Blockchain technology in the oil and gas sector, primarily using a quantitative survey of Iraqi oil and gas companies. A questionnaire was distributed among 250 top-level managers, senior executives, project managers, and IT managers for analyzing the data, the study employs the Structural Equation Modelling-Partial Least Squares (SEM-PLS) technique, with Smart PLS for data processing. The findings suggest that the intention to utilise blockchain technology is influenced by one’s attitude towards it. Competitive pressure (environmental factors), functional benefit, and privacy/security (technological factors) significantly affect blockchain adoption intention. Nevertheless, there was no discernible correlation between regulatory backing and the desire to use Blockchain. Additionally, cost concern and perceived risk (organizational factors) two factors contribute negatively to the perception of blockchain technology. Besides the direct relationship, the findings revealed that attitude toward blockchain technology mediate the relationship between cost concern, perceived risk, and intention to adopt Blockchain. Built upon the Technology-Organization-Environment (TOE) model and the Theory of Reasoned Action, this research offers a comprehensive framework for investigating the intention to adopt blockchain technology. The results enhance both theoretical understanding and practical implementation by providing valuable insights into the emerging area of blockchain adoption intentions.

The main purpose of this research is to investigate the cash holdings behaviour on sectoral level for South African firms listed on the Johannesburg Stock Exchange (JSE). The accounting cash ratio is used to identify abnormal (excess) cash holdings for the firms listed on the JSE. This informed the panel regression analysis to identify cash holdings determinants on a sectoral level. The sample data included 255 firms of which 102 represent Financial Firms and 153 represent Non-Financial Firms for 2005 to 2019. The findings show the significant internal and external determinants of cash holdings. Comparing coefficient sizes, this research finds that financial and non-financial sectors with abnormal (excess) cash holdings exhibit higher coefficient sizes as opposed to sectors without. As a result, the higher coefficient size shows that the internal and external determinants of cash holdings have a greater effect on the cash holding levels of these sectors. The implications of the findings of this study are that each sector operates differently and that each firm within each sector has differing cash management policies and procedures. Therefore, analyzing cash holdings behaviour on an aggregated level and assuming that all sectors and firms within the collective operate the same is an erroneous assumption, as shown by this study. This research firstly contributed by introducing the use of the accounting cash ratio to indicate the presence of abnormal (excess) cash holdings. Most research focus on cash holdings of Non-Financial Firms. Therefore, the second contribution of this research is that both Non-Financial and Financial Firms with and without abnormal (excess) cash holdings were included to identify determinants of cash holdings, this was also done on a sectoral level.