Abstract The increased use of digital technologies in public administration has created new means for controlling the execution of administrative contracts. Of all these digital technologies, global positioning system (GPS) tracking has been recognized as a means to ascertain contractors’ observance of contract terms regarding dates and to identify possible delays in the execution of contractual obligations. The legal validity of GPS data has also posed a number of issues, including its authenticity, its accuracy, and the protection of contractors against false digital evidence. This research focuses on the legal implications of using GPS data as a basis for computing delay penalty calculations in administrative contracts. It also analyzes the possibility of using GPS data as reliable digital evidence to ascertain the contractor’s location and time of arrival and the legitimate use of the data to justify the authority of the administration to impose financial penalties on the contractor. This research is premised on the hypothesis that while GPS data can be used to improve administrative efficiency and accuracy in monitoring contractual obligations, its use must be accompanied by legal and technical verification to ensure the integrity and reliability of the data used. The importance of the stipulations made in contracts with regard to the use of GPS technology is also highlighted by the study, along with the need to ensure the availability of technology and procedural safeguards such as verification of the data, transparency of the data processing, and the availability of an opportunity for the contractors to dispute the data. The significance of providing an opportunity to dispute possible inaccuracies before imposing financial sanctions is also highlighted, thereby maintaining the concepts of fairness and due process through the judiciary in the field of administrative law. Ultimately, the research seeks to provide a balanced framework of law that addresses the interests of the administration in the effective supervision of the contract, as well as the right of the contractor to procedural protection. The establishment of effective digital verification mechanisms would enable the administration to make effective use of the GPS data while protecting the rights of the contractors.
Research shows that trust in AI is influenced by socio-ethical considerations, technical features of AI systems, and user characteristics. Yet, the mediating role of emotional response between perceived risk and trust remains underexplored, particularly across different AI contexts. This cross-sectional vignette experiment design aims to explore the relationship between users’ perceived potential risk, emotional response, and trust in AI, and examine how these relationships vary across different levels of automation and criticality. An online survey included a total of 639 participants including 316 from the UK and 323 from Arab Gulf Cooperation Council (GCC) countries. Participants rated their perceived risk, trust, and emotional response across four scenarios representing different combinations of automation (low/high) and criticality (low/high). Correlation results indicate a significant negative association between perceived risk and trust, as well as between emotional response and perceived risk. These associations were weakest in the low-automation, low-criticality scenario and strengthened with increasing criticality levels. Mediation analyses assessed the role of emotional response in the perceived risk–trust relationship. In the UK sample, emotional response fully mediated the risk–trust relationship in the low-automation, low-criticality scenario and partially mediated this relationship in the remaining scenarios. In the Arab sample, emotional response partially mediated the risk–trust relationship in all scenarios. Gender and age showed varying influences in both samples. These findings underscore the critical role of emotional response in shaping users’ trust across cultures and AI modalities, significantly influencing trust alongside cognition. Awareness of emotional sources enables users to build trust based on both rational and emotional aspects, highlighting the inadequacy of cognition alone in achieving effective trust calibration. Enhancing system performance, addressing perceived risks, and promoting positive affect can foster greater user trust.
PurposeDespite increasing interest in Inclusive Leadership (IL), its influence on frontline employees' proactive behaviours in diverse service environments like the United Arab Emirates (UAE) remains underexplored. Drawing on Social Exchange Theory (SET), this study investigates how IL promotes employees' Willingness to Report Customer Complaints (WRC) and engage in Extra-role Customer Service (ERS), with Psychological Safety (PS) mediating these relationships and Social Well-being (SW) moderating their effects.Design/methodology/approachData from 403 frontline employees in various service organisations was collected via convenience sampling and analysed using SmartPLS 3.3. Meanwhile, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to test the research hypotheses.FindingsIL enhances PS by fostering open communication, valuing diverse perspectives, and ensuring fair treatment. This encourages frontline employees to go beyond their duties in customer service and report issues to management for service improvement. Feeling psychologically safe makes employees more engaged in enhancing service quality. Moreover, SW strengthens these effects, making employees even more proactive.Practical implicationsManagers should adopt IL by being open, accessible, and fair, thereby strengthening PS and encouraging proactive behaviours. They must create a safe space where employees can voice concerns, share ideas, and learn from mistakes without fear of punishment. Enhancing SW through team activities, mentorship, and peer-support networks further motivates employees to improve service quality.Originality/valueThis study addresses two critical frontline behaviours, proactive customer service and WRC, within a single framework. It highlights how IL fosters PS, which in turn drives these behaviours, with SW strengthening these effects. By examining these dynamics in the UAE's diverse service sector, this study fills a key gap in Middle Eastern research and provides actionable insights for management.
The ever-changing nature of financial markets underscores the need for early warning mechanisms to prevent and mitigate systemic financial risks. This paper proposes a novel approach to measure and predict the higher-order moment risk spillovers, offering early warning risk detection signals in commodity markets by integrating machine learning and traditional quantitative modeling. We employ a combination of the Autoregressive Conditional Density (ARCD) model, the Time-Varying Parameter Vector Autoregression Extended Joint Connectedness (TVP-VAR-EJC) model, and the improved Graph Convolutional Network (IGCN) model with an edge-deletion method. Our results show significant heterogeneity between volatility and higher-order moment risk spillover. We document that energy and precious metals are the main net risk transmitter and receiver of the moment-based spillovers. The pairwise net spillover between energy and precious metals contributes the most to the total commodity risk spillover prediction. Our results show that the proposed IGCN model outperforms alternative deep learning models such as LSTM, GRU, and Transformer.
Early detection of brain tumors is crucial for improving patient survival rates and treatment options. Accurate classification and stratification of brain tumors are also critical for developing individualized treatment plans. Despite the increasing use of Magnetic Resonance Imaging (MRI) for brain evaluation and advances in AI-based detection techniques, building an accurate and efficient model to identify and classify brain tumors from MRI images remains a challenge. To tackle this issue, this study develops a deep Convolutional Neural Network (CNN)-based structure to automatically classify brain tumors into four prevalent groups: meningiomas, pituitary tumors, non-tumor tumors, and gliomas. To this end, the proposed deep CNN model utilizes a segmentation model and a preprocessing approach combined with the Capuchin Search Algorithm (CSA) to improve image contrast. These methods demand super-computing power and real-time performance, as well as parallel or distributed processing to further enhance their effectiveness. The proposed CNN model-based structure is used in the classifier to improve the diagnostic procedure for tumor classification. Using four broadly available reference datasets of varying complexity, along with tumor regions exhibiting varying degrees of variability, we trained the segmentation model and assessed the classification model. This enables us to perform a side-by-side comparison of the effects of the segmentation process on tumor classification. The efficiency level of the presented classification method was evaluated using many related metrics. On all four adopted datasets, the developed deep learning-based classification model performs better than many pre-trained models. The results showed that the proposed classification model achieved a maximum classification accuracy of 97.64
