Learning technologies often do not meet the university requirements for learner engagement via interactivity and real-time feedback. In addition to the challenge of providing personalized learning experiences for students, these technologies can increase the workload of instructors due to the maintenance and updates required to keep the courses up-to-date. Intelligent chatbots based on generative artificial intelligence (AI) technology can help overcome these disadvantages by transforming pedagogical activities and guiding both students and instructors interactively. In this study, we explore and compare the main characteristics of existing educational chatbots. Then, we propose a new theoretical framework for blended learning with intelligent chatbots integration enabling students to interact online and instructors to create and manage their courses using generative AI tools. The advantages of the proposed framework are as follows: (1) it provides a comprehensive understanding of the transformative potential of AI chatbots in education and facilitates their effective implementation; (2) it offers a holistic methodology to enhance the overall educational experience; and (3) it unifies the applications of intelligent chatbots in teaching–learning activities within universities.

The goal of this study is to examine and identify the factors influencing customer attitude toward and intention to use digital wallets (electronic wallets, e-wallets) during and after the COVID-19 pandemic. A total of 257 correctly fulfilled questionnaires from an online survey were summarized. The main features of e-wallet payment systems were classified with a focus on consumer satisfaction via the integration of classic and modern data analysis methods. Structural Equation Modeling (SEM) was preferred to reveal the dependencies between the variables from e-wallets users’ perspective. The designed model can discover and explain the underlying relationships that determine the e-wallets’ adoption mechanism. The obtained results lead to specific recommendations to stakeholders in the value chain of payment processing. Financial regulatory authorities could employ the presented results in planning the development of payment systems. E-commerce marketers could utilize the proposed methodology to assess, compare and select an alternative way for order payment. E-wallet service providers could establish a reliable multi-criteria system for the evaluation of digital wallet adoption. Being aware of the most important components of e-wallets value, managers can more effectively run and control payment platforms, enhance customer experience, and thus improve the company’s competitiveness. As the perceived value of customer satisfaction is subjective and dynamic, measurements and data analysis should be conducted periodically.

TikTok supports fashion discovery and creator-mediated brand communication. This study examines associations among TikTok fashion-brand content involvement, brand awareness, brand engagement, and purchase-related intentions among Bulgarian users and illustrates a human-governed analytical architecture for structured and open survey data. An online survey yielded 295 responses; 268 eligible TikTok users comprised the sample. The architecture organizes primary measurement and structural analysis, secondary prediction and moderation, exploratory response-structure and text analysis, and descriptive multi-criteria synthesis within processing stages. Content involvement was positively associated with awareness (β = 0.683), engagement (β = 0.512), and purchase-related intentions (β = 0.283). Awareness was associated with engagement (β = 0.300) but had no incremental direct association with purchase-related intentions; engagement showed the strongest direct association (β = 0.496). The model explained 52.5% of purchase-related-intention variance, and PLS-PM prediction produced lower errors than the training-mean benchmark for all four outcome indicators. No demographic interaction received Holm-adjusted support. Illustrative multi-criteria synthesis ranked engagement first, involvement second, and awareness third under sample-derived criteria. Dictionary-assisted open-response categories concerning presentation, creators, reviews, demonstrations, music, and humor were manually checked against the responses. All reported relationships are cross-sectional associations and do not establish causal effects, intervention effectiveness, or marketing priorities. Concentrated sampling, broad working constructs, and internal validation require cautious interpretation and independent external replication.

TikTok supports fashion discovery and creator-mediated brand communication. This study examines associations among TikTok fashion-brand content involvement, brand awareness, brand engagement, and purchase-related intentions among Bulgarian users and illustrates a human-governed analytical architecture for structured and open survey data. An online survey yielded 295 responses; 268 eligible TikTok users comprised the sample. The architecture organizes primary measurement and structural analysis, secondary prediction and moderation, exploratory segmentation and text analysis, and descriptive multi-criteria synthesis within processing stages. Content involvement was positively associated with awareness (β = 0.683), engagement (β = 0.512), and purchase-related intentions (β = 0.283). Awareness was associated with engagement (β = 0.300) but had no incremental direct association with purchase-related intentions; engagement showed the strongest direct association (β = 0.496). The model explained 52.5% of purchase-related-intention variance, and PLS-PM prediction produced lower errors than the training-mean benchmark for all four outcome indicators. No demographic interaction received Holm-adjusted support. Illustrative multi-criteria synthesis ranked engagement first, involvement second, and awareness third under sample-derived criteria. Dictionary-assisted open-response categories concerning presentation, creators, reviews, demonstrations, music, and humor were manually checked against the responses. Cross-sectional self-reports, concentrated sampling, broad working constructs, and internal validation require cautious, noncausal interpretation and independent external replication.

Short-form video (SFV) marketing has become a prominent format in digital advertising by combining compressed audiovisual storytelling, feed-based discovery, algorithmic visibility, and rapid audience interaction. This study examines how content- and creator-related factors shape consumers’ value-based responses to short-video marketing and how these responses influence purchase intention. Data were collected through an online survey of 409 respondents in Bulgaria. The analytical design integrates descriptive statistics, clustering, sentiment analysis, partial least squares structural equation modeling (PLS-SEM), and machine learning (ML) prediction to examine both explanatory relationships and predictive performance. From a business intelligence perspective, the proposed workflow transforms structured survey responses and open-ended consumer feedback into actionable insights for audience segmentation, consumer-response prediction, campaign prioritization, and e-commerce decision support. The final structural model treats creating shared values (CSV) as a value-based attitudinal response to SFV marketing. The results show that clarity, willingness to use, similarity, empathy, and likability positively affect CSV, which in turn positively affects purchase intention. The findings indicate that short videos are most persuasive when they communicate clearly, feel relatable, create emotional resonance, and reduce friction in audience engagement. ML models further suggest that the selected perception variables have strong predictive value for consumer responses in this sample. The study contributes to digital persuasion, e-commerce analytics, and business intelligence research by offering an integrated empirical framework for assessing short-video marketing effectiveness.

Short-form video (SFV) marketing generates structured consumer ratings and unstructured feedback that can be analyzed for e-commerce decision support. This study examines associations between five content- and creator-related perceptions, Creating Shared Value (CSV), and purchase intention among 409 respondents in Bulgaria. From a business intelligence (BI) perspective, this study combines descriptive profiling, clustering, sentiment analysis, formative partial least squares (PLS) path modeling, group-difference testing, and machine-learning prediction to produce descriptive profiles, explanatory associations, consumer segments, text-analytic findings, and predictive results. CSV is specified as a formative composite of eight noninterchangeable product/economic, community/social, and relational indicators associated with short-form video creators. Clarity (β = 0.260), willingness to use (β = 0.226), similarity (β = 0.205), likability (β = 0.177), and empathy (β = 0.130) are positively associated with CSV; CSV is positively associated with purchase intention (β = 0.567). The model explains 69.1% of CSV variance and 32.2% of purchase-intention variance. Six formative weights are significant, all absolute loadings exceed 0.72, and indicator VIFs remain below 5. The group-difference hypothesis is partially supported by 18 omnibus ANOVAs with Holm correction for multiple testing. Significant differences in both CSV and purchase-intention means are found across age groups and categories based on the number of influencers followed. Significant differences in purchase-intention means are also found across social-media use-frequency and daily SFV-viewing-time categories. Clustering, sentiment-analysis, and machine-learning results are interpreted as complementary BI outputs rather than evidence of causal effects or validated campaign effectiveness.