مطالب

Digitalization is rapidly changing employment dynamics, demanding an understanding of how digital technologies impact employability. This study provides a comprehensive analysis of the relationship between digitalization and employability through a hybrid approach combining bibliometric analysis with a systematic theoretical review, based on the 4Ws framework (What, When, Where, and Why). Through the examination of thematic trends spanning the years 2010 to 2023, this study reveals significant domains in which digital transformation is influencing employability. The results underscore three primary thematic categories: the evolution of employment models catalyzed by digital technologies, the shift from Industry 4.0 to Industry 5.0, and theoretical advancements that concentrate on the informal economy alongside comparative analyses. This research contributes to addressing theoretical gaps regarding the lasting impact of digitalization on labor markets, with a particular focus on skill acquisition and job security. It presents targeted approaches for scholars, educators, and industry stakeholders to improve employability amid technological change. These include creating adaptive skill development programs, using AI in workforce management, and encouraging policies that enhance workers’ adaptability to new digital innovations. By presenting clear insights on how digitalization may affect employability, this research aims to enable more informed decisions for designing educational strategies and labor policies.

As user needs change frequently over time, software systems must evolve; therefore, increased software complexity inevitably violates software engineering principles. The violations of these principles are called anti-patterns, which differ from bugs and faults, and can occur at various levels ofion; finally, they reduce software quality. Anti-patterns can occur in various software, including web applications, and their prediction can effectively help prevent their occurrence. The anti-patterns prediction process at different levels ofion utilizes software features, whose threshold values impact the accuracy of this process. This study presents an improved component-level feature, called weighted content similarity, to more accurately detect component dependencies by minimizing the influence of common words that are often used in comments but are worthless in identifying the relationship between components. Therefore, the comment words are weighted using TF-IDF values. F-Measure values are calculated to show the greater impact of our proposed weighted feature compared to structural, topological, and content similarity features on detecting dependencies between components of an open-source system. The prediction of component anti-patterns, such as cyclic and hub-like dependencies, will be possible with the help of dependency detection. The average F-Measure of topological features in OpenJPA 2.0.0 software is 0.73, content similarity features is 0.76, and weighted content similarity features is 0.88. Therefore, the F-Measure of our weighted content similarity feature is 0.12 higher than the unweighted content similarity feature and is 0.15 higher than the topological feature.  So, it is more effective than these two features in predicting dependencies between components using machine learning algorithms.

Background : As digital transformation accelerates globally, effective IT governance has become critical for organizational success. With global spending on IT governance and risk management projected to reach $16 billion by 2025, emerging technologies such as artificial intelligence (AI), blockchain, and cloud computing are introducing new governance complexities that demand adaptive strategies. Objective : The article explores the key factors and anticipated trends in IT governance that are expected to shape organizational management by 2025. The aim is to understand how evolving technological landscapes influence governance models and risk management practices. Method : A qualitative methodology was adopted, involving a systematic review of 100 scholarly and industry articles focused on recent trends and future directions in IT governance. The analysis highlights issues related to risk management, regulatory compliance, cybersecurity, and technology integration. Results : The review revealed that 83% of organizations reported significant governance challenges due to technological disruption, while 68% indicated a transition toward decentralized governance models, particularly within blockchain-based systems. Additionally, AI-powered decision-making tools are projected to be adopted by over 70% of large enterprises for IT governance functions by 2025. Conclusion : The findings underscore the growing need for flexible and adaptive IT governance frameworks that align with both agile and traditional business objectives. By anticipating and addressing future risks and compliance demands, organizations can enhance their current governance strategies to remain resilient and competitive in the digital era.