Mapping the factors influencing artificial intelligence adoption in auditing: a bibliometric analysis

Artificial intelligence has emerged as a decisive force in the auditing profession because it enhances automation, improves fraud detection, and strengthens professional judgment. However, the academic literature still lacks an integrated view of the factors that shape its adoption in auditing. This study addresses this gap by examining the intellectual structure and research trends on artificial intelligence adoption in auditing from 2016 to 2025 through a bibliometric approach. Data were obtained from the Dimensions database, and 210 English-language journal articles were retained after screening. The analysis employed text-based co-occurrence techniques to identify the main research themes and conceptual linkages. The results reveal five dominant lines of work: the transformation of internal and financial audits, the use of data analytics and digital tools, the adoption of new technologies and their effects on efficiency within audit firms, auditors’ perceptions and behavioral responses, and the broader opportunities and challenges facing the auditing profession. These findings show a progression from conceptual discussions toward empirical examinations that consider organizational, ethical, and strategic implications. The study offers a consolidated overview of how artificial intelligence adoption has evolved in auditing and provides a reference point for future investigations seeking to promote responsible and sustainable technological integration in assurance practices

Digital Approaches to Planning Post-School Transition Program for School-Aged Individuals with Disabilities

Introduction: individuals with disabilities often face significant barriers in accessing employment after completing school, particularly in regions where structured transition services are limited. In Indonesia, including the city of Magelang, post-school transition programs remain underdeveloped. The aim of this research is to provide post school transition program for work age individuals with disabilities in the city of Magelang, Central Java Method: this study employed a descriptive research design to map the population of work aged individuals with disabilities (19–50 years old) in Magelang District, Central Java. The dataset containing names and addresses was obtained from the District Office of Manpower. There were 256 work-aged individuals with disabilities. Based on the list, the researchers did home visits and interviewed them to get data on their types of disabilities, educational background, working experiences, training experiences, needs for training, and types of training needed. Data were analyzed descriptively Results: There are 256 work aged individuals with disabilities in the city of Magelang, consisting of 110 mildly disabled, 97 moderately disabled and 49 severely disabled. Seventy eight of them had no education at all. Special attention should be paid to 78 individuals who do not work and want to work (there are 122 individuals who do not work, but do not want to work, might be due to the severely of their disabilities or there are others who support their lives) Conclusions: Special efforts are needed to provide education to this group, especially those with mild or moderate disabilities, since basic education becomes compulsory in Indonesia. Recommendations are offered for individuals who did not work and wanted to work

The impact of online search intensity and the moderating role of marketing intensity on the market value of listed travel and airline companies in Vietnam

This study elucidates the complex relationship between online search intensity and the market value of listed travel and airline companies in Vietnam, examining the moderating role of marketing intensity within this dynamic. By employing a Fixed Effects Model (FEM) with Driscoll-Kraay standard errors on an unbalanced panel dataset of 24 firms from 2015 to 2024 (208 observations), the research reveals that online search intensity (ln_SVI), a proxy for public attention, exerts a negative and statistically significant impact on firm market value (Tobin\u27s Q) (β = -0,189, p < 0,01), suggesting that surges in search volume are more indicative of crises than of positive interest. However, the study\u27s core finding is that marketing intensity (MKT) plays a crucial positive moderating role, capable of mitigating and even reversing this negative impact, as evidenced by a highly significant interaction coefficient (β = 0,875, p < 0,01). The research identifies a critical marketing expenditure threshold of 21,6 %, at which the adverse effect of online attention is completely neutralized, with further analysis showing this mitigating role is particularly pronounced for travel companies but not statistically significant for the airline sector. Consequently, the study concludes that online attention is a double-edged sword, and that marketing is not merely an expense but a strategic investment in "reputational assets," acting as an effective risk management tool that safeguards firm value against information shocks

The Influence of Workload, Work Shifts, Dual Role Conflict on Performance Through Work Stress In Female Nurses of RSUD Makassar City 2025

Introduction: The nursing profession has a high risk of experiencing work stress due to large workloads and responsibilities, which have a negative impact on the quality of their services and performance. Method: This study used a quantitative approach with a cross-sectional design. The sample of this study was 104 respondents selected by probability sampling technique. Physical workload was measured by %CVL (Cardio Vascular Load), NASA-TLX mental workload, work shift, dual role conflict, work stress and performance using measured using a questionnaire. Data analysis using AMOS-based path analysis. Results: Based on the analysis at 95% confidence level (Cl = 0,05), nurses\u27 performance was significantly affected by physical workload (p = 0,000), mental workload (p = 0,007), work shift (p = 0,031), as well as work stress (p = 0.000), while work stress was mainly mediated by mental workload (p = 0,015); indirect effect = -0,022) and dual role conflict (p = 0,000; indirect effect = -0,042), while physical workload (p = 0,112; indirect effect = -0,111) and work shifts (p = 0,075; indirect effect = -0,064) had no significant effect on stress nor indirectly on performance. Conclusion: Nurses\u27 performance is influenced directly by workload, work shift and work stress and indirectly by mental workload and dual role conflict.

Jobsheet-Based Digital learning Media: A Pathway to Intelligent Education

Introduction: this study aims to design and develop digital job sheet-based learning media to improve the quality of learning in Software Engineering courses at universities.Methods: the study uses a Research and Development (R&D) design with the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). The media developed integrates Flip PDF, YouTube, Google Sheets, and Quizizz. Validity testing was conducted by experts in subject matter, media, and language; practicality testing involved lecturers and students; while effectiveness testing used a single-group pretest-posttest design with paired t-test analysis and N-Gain.Results: the validation results showed an average of 85,68 % with a category of highly valid. Practicality obtained 86,33 % (lecturers) and 84,05 % (students), indicating that the media is very practical. Effectiveness analysis showed a significant increase in student learning outcomes (p < 0,05) with a moderate N-Gain score (0,46).Conclusions: this study provides empirical evidence that the integration of digital job sheets with multimedia elements supports the principles of constructivist learning and increases interactivity and learning outcomes. The developed media can be a reference for smart learning inovation in the future

A Thematic Analysis of Professional Gaps, Experiences, and Systemic Challenges in Integrating Artificial Intelligence in Healthcare

Introduction: artificial intelligence (AI) is increasingly transforming clinical decision-making and diagnostic accuracy across healthcare systems worldwide. However, despite growing interest in Saudi Arabia, the perceptions and readiness of healthcare professionals toward AI integration remain underexplored. This study aimed to evaluate healthcare professionals’ perceptions, experiences, and challenges regarding the use of AI-powered diagnostic and predictive analytics tools in hospital settings in Jeddah, Saudi Arabia, and to examine the factors influencing their adoption and confidence levels.Methods: a descriptive cross-sectional study was conducted among 240 healthcare professionals, including physicians, nurses, specialists, and allied health staff from selected hospitals in Jeddah. Data were collected using a validated bilingual questionnaire assessing familiarity, training, usage patterns, perceived benefits, and barriers to AI implementation. Quantitative data were analyzed using descriptive statistics and Chi-square tests, while qualitative responses underwent thematic SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis.Results: overall, 59.2% of participants reported using AI tools, primarily in diagnostic imaging. Although most participants demonstrated moderate familiarity with AI, only 30% expressed confidence in AI-based diagnostics. Significant associations were observed between professional roles, years of experience, and AI utilization (p < 0.05). Major challenges included limited training, cost, and lack of institutional support. SWOT analysis revealed a strong willingness to adopt AI but highlighted patient resistance and ethical concerns as persisting threats.Conclusion: AI integration in Saudi hospitals is advancing yet constrained by training and trust gaps. Strengthening institutional frameworks, implementing national AI competency programs, and aligning initiatives with Vision 2030 are essential to ensure effective, ethical, and sustainable AI adoption

Digital Synergy in Educational Supervision: A Technology-Driven Model for Collaborative School Excellence

The rapid digital transformation in education has reshaped supervisory practices, demanding systems that move beyond administrative routines toward collaborative, transparent, and data-informed approaches. While digital tools have become increasingly accessible, many schools still employ them only for basic reporting, leaving unrealized the potential for technology to strengthen professional interaction and institutional improvement. This study aimed to evaluate a technology-mediated collaborative supervision model grounded in the concept of digital synergy, examining how the integration of digital platforms, data literacy, and collaborative supervisory routines contributes to managerial quality, teacher performance, and school effectiveness. Using a qualitative case study design, data were collected from five public senior high schools in Padang City through document analysis, semi-structured interviews with principals and vice principals, and a Likert-scale questionnaire that assessed supervisory needs. Thematic analysis revealed that school leaders exhibit high awareness of the importance of digital tools, yet operational use remains limited, with technology functioning primarily as an administrative mechanism rather than an instrument for reflective supervision or joint decision-making. Collaborative supervision is widely valued but lacks structured digital routines to support shared analysis and sustained collegial engagement. Data-driven planning remains fragmented, with supervision results seldom informing strategic documents such as the RKJM. Despite these limitations, evidence of emerging digital synergy is visible in improved communication patterns and increased accessibility of supervisory records. The study concludes that technology-mediated supervision can enhance school performance when digital infrastructure, data competencies, and collaborative leadership are developed cohesively within an ethical and participatory supervisory culture.

Moving Out Of The Class And Developing Environmental Awareness: Experiential Learning Based Ecopedagogy In Action

Introduction: Integrating experiential learning into ecopedagogy provides a practical framework for fostering students’ ecological intelligence and environmental awareness through authentic, context-based experiences. However, conventional science education in Indonesia remains dominated by classroom-based instruction, limiting students’ engagement with real environmental issues. This study aimed to develop and implement the Experiential Learning–Based Ecopedagogy (ELCoCare) model to bridge theoretical understanding with environmental action.Method: This study employed a descriptive qualitative approach to design, implement, and evaluate an experiential learning-based ecopedagogy model (ELCoCare) to enhance students’ environmental awareness. The model comprised five experiential stages—experiencing, reflecting, constructing, performing, and continuing—integrated with ecopedagogical principles. Data were collected through observations, questionnaires, reflective journals, and interviews, and analyzed using a mixed-methods approach that combined descriptive statistics and thematic analysis.Results: The findings indicated a marked improvement in students’ environmental awareness, reflective thinking, and sense of ecological responsibility. Participants exhibited higher sensitivity to local environmental problems and developed innovative solutions through vertical and straightforward farming projects. The structured ELCoCare cycle effectively linked theoretical knowledge with action-oriented sustainability practices.Conclusions: The ELCoCare model successfully enhanced students’ environmental awareness and ecological intelligence by merging experiential learning with ecopedagogy. The approach promoted critical reflection, problem-solving, and sustainable behavior, preparing students to act as environmentally responsible citizens. The model demonstrates strong potential for integration into higher education curricula to strengthen sustainability-oriented teaching and learning

Performance-Based Adaptive Revenue Sharing in the Indonesian Natural Rubber Supply Chain: Model Design, AHP Calibration, and Simulation Evidence

Introduction: The study addressed income inequities in Indonesia’s natural rubber supply chain by redesigning revenue sharing among farmers, collectors, processors and exporters in Padang Lawas, North Sumatra, aligning income allocation with observable contributions and risk.Methods: The study employed a developmental research approach to specify a performance-based, adaptive revenue-sharing model, calibrate it with Analytic Hierarchy Process (AHP) weights, and test it through multi-scenario simulations using field parameters on dry rubber content, market price, on-time delivery and actor-specific risk; the stability and consistency of the AHP weighting vector were assessed.Results: The AHP weights were stable, prioritising product quality, followed by price and timeliness, while risk received a smaller but meaningful weight; the consistency ratio remained below 0,10. Relative to a fixed baseline split, the adaptive mechanism reallocated income toward verified performance improvements, increasing farmers’ share from 11 % to 22 % under a moderate-price scenario with high on-time delivery, while preserving incentive compatibility and channel coordination.Conclusions: The study transformed revenue sharing from a static proportional rule into a transparent, auditable, learning-oriented mechanism that operationalised distributive justice through normalised performance indices. The model could be encoded in contract clauses with measurable quality metrics and public price benchmarks and implemented with low-cost traceability; broader field pilots and multi-region validation were required to generalise the results

Predictive model of complex networks for geopolitical conflicts in global trade: the case of Ecuador

Introduction: the development of Latin American countries requires clear economic integration strategies that reduce the risks associated with geopolitical conflicts. In an international scenario marked by trade tensions and rivalries between powers, the ability to anticipate risk scenarios becomes a strategic tool for guiding national policies and decisions. The objective is to develop a predictive model based on complex networks to assess geopolitical conflicts arising from global trade, applied in Ecuador.Method: the study relied on the structural-functional systemic method to comprehensively analyze the interactions between economic and political factors. A bibliographic review was conducted in high-impact databases, Scopus and Web of Science, complemented by official reports and statements from heads of state. The model was built using Gephi 1.10.1 software, which allowed for a visual representation of trade and geopolitical networks. To assess its robustness, the Historical Validation (Backtesting) method was applied, comparing the model\u27s predictions with events that occurred during the reference period.Results: the results showed high accuracy in identifying geopolitical conflicts, successfully predicting trade tensions with Ecuador\u27s main trading partners, although discrepancies were observed in the actual intensity of some events.Conclusions: while the model is an effective tool for anticipating risks and guiding trade strategies, it requires adjustments that incorporate qualitative and contextual variables. These findings reinforce the need to complement quantitative approaches with interdisciplinary analysis to increase predictive capacity in complex scenarios.