Journal of Information Technology Management (مدیریت فناوری اطلاعات)

This study explores the psychological impact of Internet addiction, comparing the functioning of Internet-addicted individuals to their non-addicted counterparts. A survey of 175 undergraduates from a Malaysian public university assessed their perceptions of 18 statements reflecting key facets of psychological well-being, including autonomy, environmental mastery, positive relationships, personal growth, purpose in life, and self-acceptance. Significant differences were observed in 14 out of 18 statements, with small to medium effect sizes, indicating meaningful disparities between the two groups. The findings suggest that Internet-addicted individuals are more likely to experience challenges in psychological well-being, often turning to the Internet as a coping mechanism for dissatisfaction or unhappiness. This study underscores the need for targeted interventions in the realm of information technology to promote healthier Internet use and address its psychological effects.

Problems caused by the growth of traffic in cities require modern management approaches to improve the situation with the routing of traffic flow. This article aims to develop a conceptual system that uses computer vision technology to collect and process data from vehicles. It describes the technology of computer vision as an opportunity to improve routing algorithms by processing large data streams that reflect real situations and causes that affect route optimization. The result of using the color vision system is to provide more accurate and timely information to drivers, allowing them to make informed decisions about their routes. This will reduce traffic congestion, improve transport efficiency, and minimize the negative impact on the environment.

This paper investigates the success of the EHR system and its impacts on the knowledge-sharing process by healthcare professionals in a public medical complex in Saudi Arabia. It aims to reexamine and evaluate the usefulness of the updated DeLone & McLean IS success model considering IS in the healthcare context. The study is based on a quantitative methodology conducted at a public medical complex in Saudi Arabia. The data analysis was performed by combining descriptive and exploratory analysis. SPSS was used to test the constructs' validity and scale reliability. Additionally, using AMOS to test the model fit and examine the direct and indirect relationships among dependent and independent variables, structural equation modeling was performed. The results support the role of EHR use in enhancing knowledge-sharing practices within the medical care complex. The findings show that EHR users appreciate knowledge transfer and collaboration between medical staff. The findings suggest that the EHR’s characteristics of information quality, system quality, and service quality promote medical care knowledge-sharing through system satisfaction and use. This study helps medical staff and health decision-makers to understand the EHR benefits and the medical digital innovations that may contribute to improving the work conditions at care organizations. It shows that healthcare organizations can identify various benefits from the use of the EHR**, ** especially in terms of knowledge management and sharing to propose better medical services. The paper contributes to the existing empirical literature by demonstrating the confirmation of the D&M Model as a relevant instrument for IS success within the healthcare sector.

Consumers rely on social media opinions to make product choices and purchases. With the popularity of web-based platforms like Tripadvisor, consumers express their opinions and feelings about food quality, service, and other aspects affecting restaurants through comments. Hence, analyzing these comments can be valuable for others to choose a restaurant or to improve and develop their products and brands. Sentiment analysis utilizes text mining methods to extract, identify, and study emotions and subjective perceptions. Since consumers can use comments to choose a restaurant, this study seeks to provide sentiment analysis of their reviews on the Tripadvisor website about Iranian restaurants. This study is applied in nature, aiming to analyze and manually label 4000 comments from the Tripadvisor website regarding restaurants in ten tourist cities across Iran. It uses a standard extended long short-term memory algorithm for sentiment analysis, a deep learning neural network, and Python text mining packages for modeling. The results indicate that the F-Measure for all aspects exceeds 80%, indicating sufficient efficiency and accuracy of the aspect-based sentiment analysis model for restaurant reviews. The most significant features for customers of Iranian restaurants are the food and the atmosphere. This study represents one of the initial efforts to analyze comments posted on the Tripadvisor website concerning Iranian restaurants. Business owners in the tourism industry, especially restaurant owners, can use the proposed model to automatically and quickly analyze customer feedback, improve performance, and gain a competitive edge. The proposed model can also assist users of online platforms in analyzing the opinions of others, enabling them to make informed decisions more efficiently.

Knowledge Management (KM) is essential in various sectors, including vocational higher education. A literature review revealed that adopting KM can improve innovation and organizational performance in Higher Education Institutions (HEIs). However, some HEIs in developing countries have yet to realize the benefits of KM, and its implementation still needs improvement. The KM process is affected by several issues, including organizational barriers, knowledge hoarding, a lack of a knowledge-sharing culture, ineffective KM mechanisms, and resistance to technology. Nevertheless, only a few researchers have investigated the antecedents of KM, particularly in vocational higher education institutions. This study aims to evaluate the influence of KM foundation factors on KM processes, innovation, and organizational performance in a vocational HEI, specifically the School of Meteorology, Climatology, and Geophysics (STMKG) in Indonesia. It also examines the mediating effects of KM processes and innovation and provides recommendations for improving STMKG's KM foundation. An explanatory research approach was applied in this study, incorporating both qualitative and quantitative techniques. The results show that Organizational Structure (OS) and Technological Factors (IT) significantly influence KM processes. Innovation (IN) is also a significant mediator between KM processes and Organizational Performance (OP). The practical implication of this study is that it provides recommendations for crucial KM factors based on Importance-Performance Analysis (IPA) for policymakers to improve the foundation of KM at STMKG. Additionally, the study contributes to the academic field by providing insights for further research on KM development in vocational higher education institutions.

This study aims to apply automatic machine-learning approaches using genetic algorithms to enhance heart disease prediction. Heart disease has remained the major cause of mortality in the world, necessitating an effective and timely diagnosis. Most current diagnostic and assessment processes are lengthy and expensive, relying heavily on clinical expert knowledge. To help address these issues, machine learning approaches, which derive their utility from examining substantial datasets for the recognition of patterns, have emerged as a potential solution, providing solutions beyond those achievable by human recognition alone. Genetic algorithms are also suited to addressing these issues as they mimic natural evolution to perfect high-caliber machine-learning models, feature selection, and parameter selection in machine-learning applications. This study examines the utilization of genetic algorithms working alongside AutoML frameworks to improve accuracy in heart disease predictions. Reducing to the best combination of attributes and the optimum parameters for each attribute is a time-consuming task, so automating this aspect of the process allows for more accurate and prompt predictions, consequently reducing the manual work. The AutoML approach followed in this research is TPOT, which uses genetic algorithms to ascertain optimally designed machine-learning pipelines. The application of AutoML, together with genetic algorithms, is the most prominent finding that yields a significant improvement in the quality of the predictions for heart disease compared to the traditional assessment approaches, with an accuracy of 93.8%. This approach will enhance diagnostic accuracy and enable early diagnosis, thereby reducing the likelihood of misdiagnoses or ineffective treatments and ultimately lowering associated costs.

This research delves into the complex relationship between dynamic managerial capabilities (DMCs) and innovation performance, examining the moderating effects of activity type, export level, and firm size. Employing rigorous ANOVA methodologies, the study elucidates the nuanced and conditional impacts of managerial actions on innovation outcomes. A significant aspect of this investigation is the classification of managers based on their specific DMC components, a novel contribution to the field that enhances our understanding of how different managerial competencies shape innovation. The results indicate substantial interactions between DMCs and the outlined contextual factors. Notably, Type 1 managers, distinguished by their superior managerial human capital, expansive social networks, and propensity for intuitive decision-making, demonstrate a robust positive effect on innovation across varied activities. In contrast, Type 9 managers, who possess limited managerial human capital yet maintain extensive social networks, display performance variability contingent on operational contexts. In export-centric firms, both Type 1 and Type 5 managers emerge as key drivers of innovation, adeptly maneuvering the complexities of international markets through their strategic acumen and flexibility. Moreover, the effectiveness of DMCs is significantly modulated by firm size, with micro and small enterprises deriving optimal benefits from a multifaceted managerial skill set, whereas larger corporations exhibit a greater reliance on established systemic processes. This research lays the groundwork for subsequent inquiries into the strategic deployment of DMCs in diverse organizational scenarios and offers critical insights for enhancing innovation-led development.