We present an interdisciplinary exploration of technostress in knowledge-intensive organizations, including both business and healthcare settings, and its impact on a healthy working life. Technostress, a contemporary form of stress induced by information and communication technology, is associated with reduced job satisfaction, diminished organizational commitment, and adverse patient care outcomes. This article aims to construct an innovative framework, called The Integrated Technostress Resilience Framework, designed to mitigate technostress and promote continuous learning within dynamic organizational contexts. In this perspective article we incorporate a socio-technical systems approach to emphasize the complex interplay between technological and social factors in organizational settings. The proposed framework is expected to provide valuable insights into the role of transparency in digital technology utilization, with the aim of mitigating technostress. Furthermore, it seeks to extend information systems theory, particularly the Technology Acceptance Model, by offering a more nuanced understanding of technology adoption and use. Our conclusion includes considerations for the design and implementation of information systems aimed at fostering resilience and adaptability in organizations undergoing rapid technological change.

This study explores the intricate relationship between emotional cues present in food delivery app reviews, normative ratings, and reader engagement. Utilizing lexicon-based unsupervised machine learning, our aim is to identify eight distinct emotional states within user reviews sourced from the Google Play Store. Our primary goal is to understand how reviewer star ratings impact reader engagement, particularly through thumbs-up reactions. By analyzing the influence of emotional expressions in user-generated content on review scores and subsequent reader engagement, we seek to provide insights into their complex interplay. Our methodology employs advanced machine learning techniques to uncover subtle emotional nuances within user-generated content, offering novel insights into their relationship. The findings reveal an inverse correlation between review length and positive sentiment, emphasizing the importance of concise feedback. Additionally, the study highlights the differential impact of emotional tones on review scores and reader engagement metrics. Surprisingly, user-assigned ratings negatively affect reader engagement, suggesting potential disparities between perceived quality and reader preferences. In summary, this study pioneers the use of advanced machine learning techniques to unravel the complex relationship between emotional cues in customer evaluations, normative ratings, and subsequent reader engagement within the food delivery app context.

Recovery and resilience plan (RRP) approved by the European Commission fosters the development of lifelong learning programs to upgrade employees’ skills and knowledge for digital and green transitions. Within higher education, the field of information and communication technology (ICT) is also a priority area, so we compared the demographic variables of students enrolled in formal first-cycle higher education programs in ICT with those enrolled in lifelong ICT programs within the framework of the Advanced Computer Skills project funded by the RRP in Slovenia. The results show that formal first-cycle higher education in the field of ICT remains strongly male-dominated, whereas, among participants in lifelong learning, the percentage of females stands out. Bachelor programs in ICT are primarily enrolled by young people aged up to 24 years, while shorter university-based lifelong learning programs attract mostly older participants with higher completed formal education and from a broader range of prior educational backgrounds. Finally, when all three variables (gender, age and level of prior formal education) are considered, participants in lifelong learning are much more similar to part-time students than full-time bachelor ICT students, although the percentage of men in formal education is still predominant even in part-time studies. The research findings highlight the need for further efforts to offer lifelong learning in ICT to enable individuals to improve their employment prospects, progress in the workplace or even change their field of work.

The COVID-19 pandemic has fundamentally transformed the global education landscape, compelling institutions to adopt e-learning as an essential tool to sustain academic activities. This research examines the critical impact of e-learning on arts and science college students in Coimbatore, with an emphasis on its influence on their readiness for campus recruitment. Using a survey of 300 students, this study investigates their perceptions of online education, highlighting both its advantages, such as flexibility and accessibility, and its challenges, including engagement barriers and technical limitations. Data was collected through structured questionnaires and analyzed using statistical methods to draw meaningful insights. The research also explores the efficacy of online assessments in recruitment processes and assesses students’ awareness of available e-learning platforms and courses. The urgency of this study lies in addressing the pressing need to optimize digital education models as institutions globally transition toward blended learning post-pandemic. The findings underline the dual potential and limitations of e-learning, concluding with actionable recommendations to enhance its effectiveness, particularly in preparing students for competitive employment opportunities.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.

The purpose of this study is to investigate different factors associated with remote online home-based learning (thereafter named OHL), including technical system quality, perceived quality of contents, perceived ease of use, and perceived usefulness in relation to the satisfaction of undergraduate students following the post-COVID-19 pandemic in Malaysia. Additionally, the mediating roles of attitude are also investigated. Two hundred questionnaires were distributed using judgmental sampling method and 156 completed responses were collected. The data were subsequently analyzed using PLS-SEM. The findings imply that the OHL system is an effective method although it is challenging to operate. In terms of perceived technical system quality, OHL is currently more gratifying for students; however, some have reported that the quality of the content delivered via the remote system is still unsatisfactory. Moreover, the study found that attitude is a significant determinant of undergraduates’ satisfaction with OHL. This study contributes to the advancement of current knowledge by inspecting the factors of the Undergraduate Level OHL System using the mediating roles of attitude. In terms of underpinning theories, Technology Acceptance Model and Information System Model were employed as the guiding principles of the current study.