School of Management (capstones)

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https://er.auk.edu.ua/handle/234907866/3

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Browsing School of Management (capstones) by Subject "artificial intelligence"

Now showing 1 - 7 of 7
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    AI IN THE BOARDROOM: DIRECTOR SENSEMAKING IN UKRAINIAN CORPORATE GOVERNANCE
    (Manuscript, 2026) Skorupych, Artem
    While artificial intelligence increasingly influences organizational decision-making, how corporate board directors make sense of AI for governance purposes remains underexplored, particularly in non-Western and high-stress contexts. This study investigates three questions: how boards currently engage with AI, how board composition and organizational context shape directors' perspectives, and what governance practices directors identify as necessary for responsible adoption. Using a qualitative design, the research collected data through semi-structured interviews (n=5) and written qualitative surveys (n=17) with Ukrainian board directors across banking, technology, energy, healthcare, and other sectors. Analysis followed Gioia-inspired methodology, progressing from first-order concepts through interpretive themes to aggregate theoretical dimensions. Findings reveal directors hold a dialectical understanding of AI—simultaneously recognizing its potential to address cognitive constraints (information overload, backward focus, data fragmentation) while creating governance risks (explanation difficulties, accountability ambiguity, judgment erosion). Board composition, particularly the mix of technical and traditional expertise, systematically shapes these perspectives, while Ukrainian wartime conditions create paradoxical pressures making AI both more urgent and more risky. Directors converge on governance practices emphasizing human-in-the-loop principles, formal frameworks, transparency, and director capability-building. The study contributes to bounded rationality, upper echelons, and socio-technical systems theories while demonstrating how extreme contexts function as theoretical microscopes, revealing dynamics relevant to boards globally.
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    AI INTEGRATION FOR TEST CASES GENERATION AND MAINTENANCE: OPTIMIZING TEST TEAM WORKFLOW
    (Manuscript, 2026-05) Stepaniuk, Taras
    Manual quality assurance workflows in fast-paced software delivery increasingly struggle to keep pace with rapid code evolution, with QA teams spending disproportionate effort on interpreting requirements and maintaining test documentation. This research investigates whether an Artificial Intelligence-driven middleware can optimize this workflow by automating the synthesis of requirements, design, source code, and existing test documentation into actionable testing artifacts. A middleware solution was designed and implemented on the Fastlane framework, integrating data from Asana, Figma, GitLab, and TestRail, and leveraging the OpenAI GPT-4.1 model through a structured Chain-of-Thought prompt. The evaluation combined quantitative KPI tracking across 24 production tasks with qualitative feedback from three QA engineers. The results demonstrate a 68% reduction in the test documentation effort ratio, a decrease in the median number of dev/test iterations from three to one, and a doubling of the single-iteration resolution rate. Qualitative analysis confirmed accelerated feature comprehension and reduced cognitive load. The study validates a hybrid human–AI model of quality assurance and defines a roadmap toward autonomous test maintenance.
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    AN AI-DRIVEN PREDICTIVE MODEL FOR SCREENING LEGAL PROFESSIONALS IN INTERNATIONALLY ORIENTED UKRAINIAN IT COMPANIES
    (Manuscript, 2026) Dzoban, Volodymyr
    This applied research remedies a pressing talents issue for legal departments in Ukrainian IT companies because typical credentials are poor predictors for success in a fast-moving technology sector. The author created and tested a screening tool based on large language model and statistical modelling techniques to better evaluate candidates in an initial assessment stage. Using a dataset of 269 legal professionals who were 178 candidates for a major Ukrainian IT company and 91 professionals in the LinkedIn networking group, this study applied logistic regression analysis and narrative coding to explore predictors for success measured as acceptance of a job offer, retention for 24 months, and delivery of satisfactory performance. Results demonstrated that a background in the information technology industry, English language skills, foreign transaction experience, interest in technology, and a business focus were strong predictors for success, while traditional credentials such as university name, prior employment with a law firm, and membership in a bar association lacked predictive power. The model has 78.4% classification accuracy with 81.2% sensitivity and 75.6% specificity in cross-validation with AUC=0.78. It was used for creating a screening tool based on GPT for candidate screening where output classifies candidate data into organized assessments with scoring points for strong traits, areas of concern, and interview recommendations. Pilot testing showed a 60% reduction in screening time while maintaining quality. This study addresses a repeatable approach for statistical model development in the context of a particular legal staff environment and a usable screening tool for the technology sector legal recruitment. The results of this research challenge traditional forms of credentialism associated with legal recruitment and establish that culture fit factors potentially outperform legal credentials.
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    ASSESSING THE IMPACT OF KNOWLEDGE-SEEKING PRACTICES, COLLABORATION, AND AI TOOLS ON TICKET ESCALATION IN HEALTHCARE IT SUPPORT: A LOGISTIC REGRESSION ANALYSIS
    (Manuscript, 2026-05) Lahus, Valeriia
    This study investigates the factors influencing the escalation of support tickets to programmers in a healthcare IT environment. It focuses on three factors: knowledge-seeking practices (use of requirements and documentation), cross-team consultation, and the use of an artificial intelligence (AI) chatbot. Besides escalation frequency, the study examines valid escalation, defined as cases where escalation is justified and confirmed as a system defect, deficiency, or enhancement. A quantitative research approach was applied using logistic regression on a dataset of 150 support tickets to evaluate the main and interaction effects of these factors on escalation outcomes. The results show that knowledge-seeking practices have a statistically significant impact on both escalation and valid escalation. The use of requirements increases the likelihood of escalation while improving the accuracy of escalation decisions. Cross-team consultation and AI chatbot use do not have statistically significant independent effects. However, the interaction between requirements and AI chatbot is significant, indicating that AI can reduce escalation when combined with structured documentation. The findings suggest that effective technical support depends not only on reducing escalations but also on improving their accuracy and justification. The study recommends strengthening knowledge management, improving documentation quality and accessibility, and integrating AI tools with system requirements to optimize support operations in healthcare IT systems.
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    EVALUATING THE OPERATIONAL IMPACT OF AI CHATBOTS AND OWNERSHIP STRUCTURE IN HEALTHCARE IT SERVICE MANAGEMENT
    (Manuscript, 2026-05) Klimocych, Anastasiia
    The increasing adoption of artificial intelligence (AI) chatbots in healthcare IT Service Management (ITSM) is often framed as a broadly applicable approach for improving efficiency and reducing operational workload. However, limited empirical evidence exists regarding how organizational ownership structures and global time‑zone separation influence the realized value of such technologies. This study examines the operational impact of AI chatbot deployment within a globally distributed healthcare IT support environment, focusing on investigation‑phase support for specialized genetics systems. Using a quantitative, quasi‑experimental design, the study analyzes task‑level metadata from an internal ticket management system across two six‑month periods before and after AI implementation. The analysis combines longitudinal comparison of ticket volume, temporal latency modeling using Total Resolution Latency (TRL) and Lost Day Share of TRL, and interaction analysis using factorial ANOVA to assess the moderating role of ownership structure. The results show that AI chatbot deployment is associated with reduced manual workload and lower asynchronous coordination delay. However, these improvements are not uniform. While average coordination costs are similar across ownership models, hierarchical structures exhibit greater variability and higher exposure to extreme delays. Direct ownership models derive greater operational benefit from AI by converting structured diagnostic input into faster resolution. The study concludes that AI is not a universally effective intervention; its operational value is contingent upon alignment with ownership architecture and coordination pathways, with implications for healthcare ITSM design and global support strategy.
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    MANAGEMENT TRANSFORMATION IN THE UKRAINIAN VACATION SHORT-TERM RENTAL MARKET: LEVERAGING AI TOOLS FOR MARKETING AS A COMPETITIVE ADVANTAGE AND RESOURCE OPTIMIZATION
    (Manuscript, 2026) Danchak, Nestor
    The rapidly growing artificial intelligence industry is transforming hospitality operations, with applications spanning automated customer service, content generation, and workflow optimization. While large hotel chains have more resources to adopt enterprise AI solutions, small vacation rental operators managing cabins, cottages, and nature-based properties face critical challenges: dependence on online travel agencies (OTAs) extracting 15-25% commission fees while controlling customer relationships, combined with resource constraints limiting competitive response capabilities. This capstone develops a management framework enabling small Ukrainian vacation rental operators to leverage accessible AI tools for marketing automation, platform independence, and resource optimization. The research employs qualitative methodology: empathy mapping and customer journey analysis. Applying them across nine in-depth interviews with vacation rental guests. Analysis identifies five distinct guest segments (Aesthetic Sensualists, Comfort Planners, Nature Explorers, Festive Socializers, Retreat Seekers) and three universal friction points: insufficient online brand presence, slow communication response times, and content-audience mismatch. The resulting framework integrates four automated components implemented through a phased 12-week roadmap, reducing weekly operator time to 2-3 hours while targeting 30-40% direct booking conversion within six months. While specific AI tools evolve rapidly (ChatGPT, Make.com, and Google Vision AI may be superseded), the framework emphasizes goal-oriented methodology: defining clear key performance indicators (KPIs), validating tool effectiveness against strategic objectives, and continuously adapting technology choices to serve business outcomes rather than pursuing technology for its own sake. This research proposes an actionable framework enabling resource-constrained operators to potentially achieve competitive advantages through strategic AI adoption. The framework design suggests significant time savings and platform independence opportunities.
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    USE OF ARTIFICIAL INTELLIGENCE IN THE MANAGEMENT OF SECONDARY EDUCATION INSTITUTIONS IN UKRAINE
    (Manuscript, 2026) Vyslynskyi, Bohdan
    The management of secondary education institutions increasingly relies on digital tools to support administrative decision-making and organizational processes. While Artificial Intelligence (AI) has been widely discussed in the context of teaching and learning, yet its use in secondary school management remains underexplored. This study examines institutional readiness, current patterns of AI use, and perceived risks of AI adoption in the management of secondary education institutions in Ukraine. Using a mixed-methods approach, the study combines data from an online survey of 43 urban school administrators with insights from ten semi-structured interviews. The findings show a state of moderate readiness, as most schools indicated some level of infrastructure and strategic planning for AI integration. However, only a few cases of systematic use of AI have been reported in administrative practices. In most cases, respondents apply AI in isolated instances or pilot projects. AI applications are therefore most common in relatively simple administrative areas, such as scheduling and resource allocation, while their use remains rare in more complex areas, including decision-making and performance monitoring. The main barriers to wider AI adoption include concerns related to data protection, regulatory uncertainty, insufficient funding, and limited staff capacity. These findings suggest that effective AI integration in school management requires clear governance frameworks, targeted professional development, and upgrades to digital infrastructure.