The unprecedented production and sharing of data, opinions, and comments among people on social media and the Internet in general has highlighted sentiment analysis (SA) as a key machine learning approach in scientific and market research. Sentiment analysis can extract sentiments and opinions from user-generated text, providing useful evidence for new product decision-making and effective customer relationship management. However, there are concerns about existing standard sentiment analysis tools regarding the generation of inaccurate sentiment classification results. The objective of this paper is to determine the efficiency of off-the-shelf sentiment analysis APIs in recognizing low-resource languages, such as Greek. Specifically, we examined whether sentiment analysis performed on 300 online ordering customer reviews using the Meaning Cloud web-based tool produced meaningful results with high accuracy. According to the results of this study, we found low agreement between the web-based and the actual raters in the food delivery services related data. However, the low accuracy of the results highlights the need for specialized sentiment analysis tools capable of recognizing only one low-resource language. Finally, the results highlight the necessity of developing specialized lexicons tailored not only to a specific language but also to a particular field, such as a specific type of restaurant or shop.

Employee satisfaction is a crucial factor affecting organizational performance, productivity, and overall workplace efficiency. This study investigates employment satisfaction within the Greek technology sector through sentiment analysis, focusing on employees’ responses through the Employee Experience-Satisfaction (EmEx-Sa) questionnaire. The study employs natural language processing (NLP) and, in particular, the lexicon-based sentiment analysis methodology to analyze data from 208 employees across the entirety of Greece, obtained from open and semi-open questions, multiple-choice alternatives, and demographic questions. The objective is to utilize data from sources such as the Michigan Organizational Assessment Questionnaire (MOAQ), Minnesota Satisfaction Questionnaire (MSQ) and Job Descriptive Index (JDI) to determine the primary elements that impact job satisfaction and, by applying principles of organizational ergonomics, gain insight into the attitudes and emotions of employees. Results reveal that the working environment (total sentiment score: 21.50) is the primary driver of positive sentiment, while salary (total sentiment score: −18.72) is the main source of dissatisfaction. Sentiment regarding superiors is more balanced, leaning slightly positive (total sentiment score: 0.02), but the analysis indicates opinions lack significant polarization. The findings delineate critical factors influencing job satisfaction, encompassing the work environment, leadership quality, salary, and opportunities for professional advancement. The research underscores the significance of internal marketing tactics in fostering engagement, trust, and transparency between management and employees and provides actionable suggestions for boosting working conditions, fostering employee well-being, and improving organizational performance, underscoring the strategic imperative of prioritizing job satisfaction.

Learning is fundamentally human, even as Artificial Intelligence (AI) challenges human exclusivity. AI, along with Virtual Reality (VR), emerges as a powerful tool that is set to transform higher education, the institutional embodiment of this pursuit at its highest level. These technologies offer the potential not to replace the human factor, but to enhance our ability to create more adaptive, immersive, and truly human-centric learning experiences, aligning powerfully with the emerging vision of Education 5.0, which emphasizes ethical, collaborative learning ecosystems. This research maps how AI and VR tools act as a disruptive force, examining additionally their capabilities and limitations. Moreover, it explores how AI and VR interact to overcome traditional pedagogy’s constraints, fostering environments where technology serves human learning goals. Employing a comprehensive two-month audit of over 60 AI, VR, and AI-VR hybrid tools, the study assesses their functionalities and properties such as technical complexity, cost structures, integration capabilities, and compliance with ethical standards. Findings reveal that AI and VR systems provide significant opportunities for the future of education by providing personalized and captivating environments that encourage experiential learning and improve student motivation across disciplines. Nonetheless, numerous challenges limit widespread adoption, such as advanced infrastructure requirements and strategic planning. By articulating a structured evaluative framework and highlighting emerging trends, this paper provides practical guidance for educational stakeholders seeking to select and implement AI and VR tools in higher education.

Employee satisfaction is a crucial factor affecting organizational performance, productivity, and overall workplace efficiency. This study investigates employment satisfaction through sentiment analysis, focusing on employees’ responses through the Employee Experience-Satisfaction (EmEx-Sa) questionnaire. The study employs natural language processing (NLP), and in particular the lexicon-based sentiment analysis methodology, to analyze data obtained from open and semi-open questions, multiple-choice alternatives, and demographic questions. The objective is to utilize data from sources such as the Michigan Organizational Assessment Questionnaire (MOAQ), Minneso-ta Satisfaction Questionnaire (MSQ) and Job Descriptive Index (JDI) to determine the primary el-ements that impact job satisfaction and gain insight into the attitudes and emotions of employees. The findings delineate critical factors influencing job satisfaction, encompassing the work envi-ronment, leadership quality, salary, and opportunities for professional advancement. Moreover, they demonstrate that sentiment analysis provides valuable insights into employee viewpoints, acting as a credible substitute for conventional research methods. This research enhances the un-derstanding of job satisfaction by identifying sentiment trends—positive, neutral, or negative—thereby enabling data-driven decision-making for organizational enhancement. The research un-derscores the significance of internal marketing tactics in fostering engagement, trust, and trans-parency between management and employees. The findings provide actionable suggestions for boosting working conditions, fostering employee well-being, and improving organizational per-formance, underscoring the strategic imperative of prioritizing job satisfaction.

This systematic literature review introduces managerial infophysics, a novel framework and metaparadigm that integrates Business Process Management (BPM) principles with entropy-based metrics to address organizational uncertainty and enhance decision-making in complex environments. Employing the PRISMA methodology, the review spans research from 2018 to 2024, drawing on a comprehensive search across 21 databases, each selected for its focus on peer-reviewed content in BPM, econophysics, and informatics. The identification stage yielded 16,101 records, which were rigorously evaluated to ensure the inclusion of the most current and valid research on these topics. This review highlights the potential for entropy-based metrics to quantify process variability, offering a dynamic alternative to traditional KPIs. Through an interdisciplinary synthesis, managerial infophysics is proposed as a metaparadigm, presenting a unified approach to managing complexity and uncertainty within structured organizational processes. Findings suggest that entropy-enhanced BPM frameworks not only improve operational predictability and resource allocation but also extend BPM's applicability to sectors with high variability, such as healthcare and finance. Despite challenges in aligning BPM’s efficiency orientation with entropy’s probabilistic insights, the curated evidence supports this interdisciplinary framework's role in fostering organizational resilience and adaptability. This review establishes managerial infophysics as a promising conceptual model, inviting further empirical validation for broader application.