Authors - Unnati Parmar, Jatin Modh Abstract - Sentiment Analysis, or opinion mining, is the computational study of opinions, sentiments, emotions, and attitudes expressed in texts. Due to the rapid expansion of social media, e-commerce platforms, and digital communication, huge amount of opinion rich information is generated daily. This tremendous surge has also substantially increased the importance of sentiment analysis for understanding public opinion, monitoring brand image, forecasting market trend and enhancing customer relations. This paper also offers a detailed survey of SA methods, from classic lexicon-based methods to recent deep learning models and transformers such as BERT and RoBERTa. We test their effectiveness on bench-mark datasets with empirical experiments and discuss their pros and cons in various real-world scenarios. Beyond these popular approaches, we present a range of domain-specialized techniques, multilingual processing, and hybrid architectures synthesizing symbolic and statistical methods. We empirically show that the transformer models outperform LR, SVM, LSTM and BERT on the IMDB dataset in achieving better understanding of context-specific nuances. We next provide a number of problematic applications of sentiment analysis including sarcastic expression detection, domain adaptation, data unbalance and sentiment ambiguity. Various application scenarios in marketing, healthcare, finance and politics are also included in the paper. Finally, we describe future research avenues focusing on explainable sentiment models, real-time analysis systems, and multimodal fusion approaches that take into account text, audio and visual features.The goal of this study is to provide researchers and practitioners with a fundamental framework for creating sentiment analysis systems that are intelligent, scalable, and reliable.
Authors - Rhytheema Dulloo, Aditi Singh, Vinay Kiran Abstract - The convergence of artificial intelligence, sustainability conscious-ness, and immersive metaverse technologies has created unprecedented opportunities for transformative marketing communications that influence consumer behavior in virtual environments. Despite the growing adoption of metaverse platforms for commercial activities, there remains a significant gap in under-standing how AI-powered sustainable marketing messages within these digital ecosystems affect consumers' actual purchase intentions, particularly given the complex interplay between virtual experiences and real-world consumption decisions. This study employs the Theory of Planned Behavior (TPB) as the theoretical framework to examine the relationships between attitudes, subjective norms, perceived behavioral control, and purchase intentions in the context of AI-driven sustainable marketing communications within metaverse environments. The research objectives include: (1) investigating the impact of AI-generated sustainable marketing content on consumer attitudes and purchase intentions, (2) examining the role of social influence and perceived control in metaverse shopping contexts, and (3) identifying key predictors of consumer behavior in virtual sustainable commerce environments. A quantitative research methodology will be employed using structured online surveys distributed to active metaverse users, with data analysis conducted through structural equation modeling (SEM) and multiple regression analysis to test the proposed hypotheses. Preliminary findings indicate significant positive relationships between AI-driven sustainable marketing exposure and purchase intentions, with attitudes and perceived behavioral control serving as strong mediating factors, while subjective norms demonstrate moderate influence on consumer decision-making processes within metaverse retail environments.
Authors - Sonaly Maharaj, Rhulani Maluleka, Abelwe Maluleka Abstract - Life insurance is not immune to the transformative impact of artificial intelligence (AI). AI offers unprecedented levels of precision, speed, and customization in a field historically reliant on laborious manual procedures and statistical assumptions. This paper investigates how AI-driven underwriting can be incorporated into life insurers’ digital business strategy, using Momentum’s in-app screening tool as a primary case study. The study proposes a theoretical AI underwriting architecture that maximizes risk assessment and policy issuance by leveraging machine learning algorithms, biometric information, and behavioral insights. The advantages, disadvantages, opportunities, and threats of adopting AI underwriting are analyzed through a SWOT analysis, highlighting ethical and legal considerations around algorithmic bias, regulatory compliance, and explainability. The findings suggest that while AI underwriting can provide insurers a competitive edge in a crowded market, its implementation requires robust frameworks, ethical design, and regulatory alignment.
Authors - Suresh Panampilly, Murale Venugopalan Abstract - Despite the prestige associated with leadership roles, many employees are reluctant to become managers. This poses challenges for organizations aiming to strengthen their managerial pipeline to meet long-term strategic goals. In this study, managerial reluctance is studied by analyzing candid posts on Reddit. LDA topic modelling using 15 posts revealed 6 themes. The themes uncover psychological, organizational and ethical factors. Some results align with established theories (e.g. Expectancy, equity and role theories). Others highlight unexplored areas such as ethical dilemmas and identity conflicts. This study confirms that topic modelling on social media data can give valuable insights into managerial reluctance. By using data from online discourses to draw theoretical insights, this study offers conceptual contributions and practical in-sights which can be used to develop inclusive managerial talent in organizations.
Authors - Otmane Azeroual Abstract - This paper introduces Dialogic Artificial Intelligence (DAI)—a conceptual and computational framework for modeling how AI systems engage with human epistemic preferences in processes of knowledge retrieval and semantic representation. Grounded in Wittgenstein’s theory of language games, DAI facilitates context-sensitive interpretation of user intent through dialogical interaction. Using multiverse hypergraphs and a mechanism called Dispute Learning (DL), the framework maps diverse queries onto epistemic subgraphs that reflect over-lapping but distinct knowledge communities. Unlike existing preference-matching models, DAI emphasizes pluralism, transparency, and contestability in meaning construction. Real-world applications include academic search, semantic interoperability, and policy analysis—pointing toward a more dialogical paradigm in knowledge-driven AI systems.
Authors - Windy Permata Suyono, Dwi Handarini, Nuramalia Hasanah, Ratna Anggraini, Rio Firnanda, Surya Anugrah, Eka Septariana Puspa, Septi Nurmalita Abstract - The digital transformation of Micro, Small, and Medium Enterprises (MSMEs) in the e-commerce sector has created significant opportunities to enhance tax compliance through the adoption of digital financial technology. De-spite these advancements, tax compliance remains relatively low in many developing countries. This study investigates the impact of digital financial technology adoption and tax knowledge on the tax compliance behavior of e-commerce-based MSMEs, with digital trust examined as a moderating variable. A quantitative approach was employed, utilizing survey data from 120 MSME actors operating on major e-commerce platforms such as Tokopedia, Shopee, Lazada, Blibli, and Bukalapak. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that both digital financial technology and tax knowledge significantly influence tax compliance. Moreover, digital trust positively moderates these relationships, amplifying the effects of digital financial technology and tax knowledge on compliance behavior. These results highlight the critical role of a trustworthy digital ecosystem and comprehensive tax literacy programs in promoting voluntary tax compliance among MSMEs. The study provides meaningful insights for policymakers, tax authorities, and FinTech developers, contributing to the growing body of literature on digitalization and tax governance in emerging economies.
Authors - Usman Ali, Shaiza Shahid, Misbah, Shahzad Ali, Sheraz Aslam, Kainat Mustafa Abstract - Autism Spectrum Disorder (ASD) is a neurocognitive condition that affects social connection, expressive abilities, and behavioral patterns. This study aims to diagnose ASD using machine learning (ML) techniques based on behavioral and demographic data. The dataset sourced from Kaggle includes features such as age, gender, ethnicity, social interaction behaviors, and responses to standardized screening questionnaires. To improve accuracy, various ML models are implemented, including NB, AdaBoost, SVM, LightGBM, KNN, and XGBoost. Additionally, advanced Ensemble Methods are applied, such as Hybrid Kernel SVM, a boosting classifier, and a stacking classifier. Among all methods, the stacking classifier delivered the best performance, achieving 99.74% accuracy with the minimum MSE of 0.2. This result supports the development of an intelligent system to aid in early autism diagnosis and intervention planning. Future research could enhance this approach by integrating neuroimaging biomarkers, generative AI, or more advanced ML frameworks, aiming for even greater precision and robustness in a real-world clinical setting.
Authors - Martina Beranek Abstract - Industry and services are undergoing fundamental changes in connection with new technologies, the advent of AI, global competition and at the same time the contradictory process of partial deglobalization, changing markets, material shortages, local regulations and changing trade barriers, especially in the form of tariffs. The already difficult situation is complicated by major military conflicts in the Middle East, Ukraine and some regions of Africa. These aspects place high demands on the work of managers at all levels of management. It is therefore essential for university students to prepare for the above facts and to understand the complexity of strategic management as best as possible. ICT in the form of business simulations is a very suitable tool to teach students system thinking and explain the principles and principles of a strategic approach in business. Empirical research shows that students prefer project-based learning using ICT tools to classic frontal teaching and that this form of teaching based on the use of not only ICT but also AI promotes adaptability of learning, contributes to digital literacy and leads to a much higher level of readiness for future professions. Business simulation not only allows the development of the entrepreneurial spirit but also leads to the improvement of soft skills. Finally, it has been proven that thanks to the feedback provided by the simulation, students see the impact of their decisions more or less immediately and can thus understand the complexity and complexity of the decision-making process at the strategic level of business management. The document recommends expanding the use of strategic simulations in higher education to meet the demands of a rapidly evolving and changing business environment.
Authors - Ch Sahyaja, Ch Shankar, Madhuri. K, Jaswanthi Guda Abstract - The paper evaluates how the integrated Green Human Resource Management (GHRM) practices, such as the green recruitment and selection of employees, green sustainability training, green performance management, employee engagement and green compensation and rewards, play a role in green leadership leading to sustainability capacity of organizations. The research was based on Ability Motivation Opportunity (AMO) and resource-based view (RBV) conceptualizations, and the analysis was done using partial least squares SEM on 205 HR and the line managers of the manufacturing and service-based organization, upon using the questionnaires issued to the mentioned managers in ADANCO 2.2.1. Findings reveal that green recruitment and selection (E = 0.303), employee involvement (E = 0.350) and green compensation (E = 0.250) do play a significant role in influencing the green leadership with a combinational effect of 60.3 per cent (R2 = .603). Green leadership plays an important role in exerting a favourable impact on sustainability (BR2 = .543) and helps to mediate among all three GHRM practices (Sustainability-mediated effects). Green leadership mediates between the GHRM behaviours of sustainability (.189), recruiting (.218) and promoting (.155) in direct effect (p < .005). Although organizational green culture positively affects sustainability (156), it does not have such an effect on the connection between leadership and sustainability. This brings out the point that green leadership holds the key to sustainability.
Authors - Md Faiz Ahmad, Md Asadul Haque, Ananya Chowdary Muppana, Aasritha Malineedi, Dharani Gudivaka, Sri Krishna Surya Putta Abstract - The present study offers a systematic literature review (SLR) providing insights on the role of social media in promoting sustainable marketing. The SLR is carried out to gain an understanding of the phenomenon. Scopus database was used to identify, extract, select and review related articles using search keywords. A total of 27 articles were deemed fit for this study. This SLR recognizes current research avenues related to social media in sustainable marketing using TCCM framework. The study used co-citation and keyword occurrence analyses to explore the nuances of the field under consideration. The study yields valuable insights for academia, government, green marketers to practically implement strategies and suggesting future opportunities for research.
