As the studies and empirical data indicate that innovations are a significant factor for improving competitiveness, we believe that scientific research in this area may provide support for those who seek to make their business more innovative. There are many works dedicated to general effect of innovations on growth, but less are focused on the role of innovations in certain sectors. Such sector is might be the wine industry. The wine industry in Bulgaria has a long history, experience and has the potential for development. That is why we examine the scope of innovations that can be implemented in the wine industry. Alongside we study the potential of Bulgarian wine industry. The results show that the wine producers in Bulgaria make slow steps towards more contemporary development of the sector, but the more significant results are yet to come.

Generative artificial intelligence (GAI) is increasingly used in higher education for explanation, content generation, feedback, and personalized learning. However, conventional prompt-response chatbots may provide insufficient support for complex academic tasks requiring planning, evidence organization, comparison, justification, verification, and reflection. This study proposes the Agentic GAI-Supported Learning Framework for Higher Education (AGAI-HE), which positions GAI agents as bounded, human-supervised learning partners rather than substitutes for students or instructors. The framework compares traditional e-learning, GAI-chatbot-supported learning, and GAI-agent-supported learning. Agentic support covers task contracting and interpretation, decomposition and planning, evidence organization, alternative generation, comparison and decision, feedback and refinement, and learner verification and reflection. It also incorporates human supervision, academic integrity, privacy safeguards, and instructor validation. An exploratory perception-based validation involving 130 students in an e-commerce learning context found that both AI-supported conditions were rated more positively than traditional e-learning for learning enhancement, personalization, decision-making support, and workflow organization. However, the difference between GAI agents and GAI chatbots was not statistically significant for the matched composites. The findings provide initial perception-based support for agentic GAI but do not demonstrate superior learning outcomes. Its educational value depends on making multi-step reasoning more structured, visible, verifiable, and accountable.

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.

Social service professionals operate in legally sensitive, administratively intensive, and context-dependent environments in which decision-making requires the simultaneous interpretation of regulatory norms, institutional procedures, and individual case circumstances. This paper proposes a conceptual model of a Personal Legal and Social Artificial Intelligence (AI) Assistant intended to support professional decision-making in social services, and demonstrates its functionality through a working prototype. The model is formulated as a domain-specific retrieval-augmented generation (RAG) framework in which a controlled legal and social document corpus is processed through text extraction, chunking, semantic indexing via SentenceTransformer embeddings, top-k retrieval through cosine similarity, and bounded large-language-model reasoning to produce grounded and explainable responses. The proposed framework is informed by three successive prototype versions and by observed sensitivity to corpus scope, document prioritization, and prompt constraints. The current prototype version operates on a prioritized corpus of sixteen Bulgarian normative acts complemented by three supplementary resources, comprising 883 indexed fragments, and uses DeepSeek as the reasoning model accessed through the OpenRouter API. The functionality of the model is validated through a representative use case concerning child protection, in which the prototype identifies the applicable legal provisions, exposes the retrieved documentary evidence, and generates a four-part structured analysis comprising legal qualification, applicable provisions, legal consequences, and recommendations for action. The main contribution lies in the formalization and prototype-level demonstration of a domain-specific AI assistant that combines legal grounding, social-context awareness, and bounded language-model reasoning for trustworthy decision support in regulated social-service practice.

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.