In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate quality constructs relevant to contemporary technologies, including cyber-physical systems, cloud services, and crowdsensing, holds promise for further enriching our understanding of the evolving digital landscape.

Stroke prediction and prevention is an important focus in healthcare due to the significant morbidity and mortality associated with strokes. In this study, we investigate using Generative Adversarial Networks (GANs) to augment a stroke dataset and evaluate the effects on prediction performance. The original dataset contained patient medical records and demographics used to predict stroke occurrence. We trained a GAN on these data and generated synthetic samples to augment the training set. Five machine learning models were developed on the original and augmented datasets, including decision tree, k-nearest neighbors, random forest, Support Vector Machine (SVM), and logistic regression classifiers. Experiments indicate statistically significant improvements in prediction accuracy, F1-score, specificity, and sensitivity with GAN augmentation across all models. The random forest classifier achieved the highest average accuracy of 0.981 on augmented data, versus 0.967 on original data. GANs prove effective for tackling class imbalance and enabling more robust stroke prediction from limited real-world data. This demonstrates the potential of data augmentation and generative models to enhance healthcare Artificial Intelligence (AI) applications.