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THE IMPACT OF SEARCH QUALITY ON E-COMMERCE SALES
(Manuscript, 2026-05) Myropolskyi, Mark
This capstone project examines the impact of internal search performance on customer behavior and e-commerce outcomes using real search data from EVA.UA. Internal search is treated not only as a navigation tool, but also as a mechanism that shapes product discovery, customer experience, and the allocation of economically valuable traffic. The study aims to understand whether query popularity, query characteristics, platform differences, or traffic concentration across demand segments drive search-related business performance.
The empirical analysis is based on a large real-world dataset that includes more than 42 million search events and more than 6 million unique queries, as extracted from the original raw data. After data cleaning, the study applies query-level and event-level analysis to evaluate search demand distribution, conversion patterns, add-to-cart behavior, platform differences, weighted and unweighted performance metrics, frequency deciles, and revenue concentration proxies. Numeric SKU-like queries and low-quality noise were filtered out to reflect genuine customer intent better.
The results show that search traffic is highly concentrated in a small subset of high frequency queries. The top frequency decile accounts for a disproportionate share of total search traffic and revenue proxy. However, conversion differences across frequency segments are relatively small, which suggests that revenue concentration is driven more by traffic allocation than by substantially better query performance. The analysis also shows that query length has a non-linear relationship with conversion: medium-length queries tend to perform best. In contrast, very long queries yield unstable results due to a low sample size. Platform analysis reveals meaningful behavioral differences: web demonstrates the highest purchase conversion, iOS shows the highest add-to-cart rates, and Android underperforms on both conversion and downstream funnel efficiency.
The findings suggest that the major business opportunity is not limited to further optimizing already dominant queries, but also to improving the exposure and handling of underutilized demand, especially on mobile platforms. These results contribute to understanding how internal search influences e-commerce performance and offer practical implications for search optimization, product discovery strategy, and customer
experience management.
ISO 13485:2016 IMPLEMENTATION AND INTERNAL AUDIT READINESS IN A UKRAINIAN MEDICAL DEVICE MANUFACTURER
(Manuscript, 2026-05) Zabroda, Daryna
This capstone examines the implementation of ISO 13485:2016 quality management systems and internal audit readiness within a Ukrainian medical device manufacturer. The research addresses the problem of the gap between formal certification and actual operational effectiveness of quality
management systems in emerging regulatory environments.
This research is guided by the following research questions: how the level of ISO 13485 implementation affects regulatory readiness and compliance performance; how internal audit practices influence the recurrence of nonconformities; and how management involvement impacts CAPA effectiveness.
The aim of the research is to evaluate the relationship between implementation depth, audit performance, and the effectiveness of corrective action.
A mixed-methods single-case study design was applied. Quantitative data were collected through a clause-based gap analysis and internal audit metrics, while qualitative data were obtained through structured document analysis of QMS documentation. The sample consists of one Ukrainian medical device manufacturing organization in the pre-certification stage.
The results indicate a moderate level of ISO 13485 implementation (70.5%), with stronger performance in documentation and weaker integration in monitoring and improvement processes. Internal audits effectively identify nonconformities but have a limited impact on preventing recurrence (32% repeat rate). CAPA processes demonstrate moderate effectiveness, with delays and incomplete closure. The findings suggest that implementation depth, rather than certification status, determines regulatory readiness and quality performance. Management involvement is critical for improving CAPA effectiveness and reducing recurring issues.
The research contributes to theory by emphasizing implementation maturity and provides practical recommendations for improving QMS effectiveness in Ukrainian medical device organizations.
FOUNDER VISIBILITY, REPUTATION, AND TRUST IN STAKEHOLDER SUPPORT DURING BUSINESS CRISIS: A COMPARATIVE STUDY OF WARTIME B2B CASES
(Manuscript, 2026-05) Shcherbyna, Alina
This research aims to examine how founder visibility and reputation influence partner support during crisis situations in B2B contexts. The study focuses on understanding the mechanisms through which reputation acts as a key driver of partner behavior, particularly in high-uncertainty environments such as operational disruptions caused by external shocks (e.g., war-related damage to business infrastructure).
The research explores how visibility contributes to reputation formation and how, in turn, reputation affects partners’ willingness to provide support, including financial flexibility, continued collaboration, and accelerated decision-making. Through qualitative and quantitative analysis, the study seeks to develop a conceptual model linking founder visibility, reputation, and partner support in crisis conditions.
The object of the research is B2B partnership relationships in crisis situations, particularly in cases of sudden operational disruption affecting companies’ ability to deliver products or services.
The subject of the research is the role of founder reputation, shaped in part by public visibility, in influencing partner support behaviors during crisis situations. This includes trust formation, perceived credibility, risk tolerance of partners, and decision-making regarding continued cooperation or withdrawal.
Research Results: Research Results: the study revealed significant differences in the scale and mobilization of partner support during business crises depending on the founder’s visibility, reputation, communication style, and embeddedness in business networks. Analysis of the interview responses from both cases produced more than 100 initial codes, which were consolidated into seven major themes: depth of relationship, reputational trust, personal communication, founder visibility and personal brand, founder leadership and influence, trust formation mechanisms, and support decision drivers.
TACTICAL COMBAT CASUALTY CARE (TCCC) AI DECISION SUPPORT SYSTEM
(Manuscript, 2026) Piltiai, Roman
Tactical Combat Casualty Care (TCCC) serves as the definitive standard for battlefield trauma management, yet the application of these protocols under high-stress combat conditions remains a significant challenge. Cognitive overload and lack of immediate expert guidance can lead to critical errors in pre-hospital care. This capstone project addresses this gap by engineering an Intelligent TCCC Tutor and Decision Support System tailored for dynamic environments. The proposed solution leverages a cloud-native, serverless architecture on Amazon Web Services (AWS) to ensure high availability, scalability, and low-latency performance. By integrating Generative AI and Large Language Models (LLMs), the system interprets complex user inputs and provides real-time, context-aware guidance aligned with the MARCH algorithm. The application was designed using modern software engineering principles, prioritizing modularity and enterprise-scale security standards. The resulting system demonstrates how advanced cloud computing and artificial intelligence can be effectively operationalized to enhance medical training and decision-making. This project not only delivers a functional prototype for tactical education but also establishes a reference architecture for future AI-driven systems in high-stakes domains.
DEVELOPMENT OF IMAGE CLASSIFICATION APPROACHES BASED ON DIMENSIONALITY REDUCTION METHODS
(Manuscript, 2026) Avhust, Viktor
Principal Component Analysis (PCA) is a powerful tool for reducing the dimensionality of image data while retaining critical information. This capstone project explores the application of PCA in image recognition, focusing on its use in simplifying high-dimensional image datasets to improve recognition efficiency and performance. The study focuses on two primary applications: recognizing handwritten digits (HWD) and classifying galaxies using three different types of classification. Custom software was developed to implement these image classification tasks, integrating dimensionality reduction techniques with classification algorithms to achieve high accuracy rates. The results demonstrate the effectiveness of these approaches in handling high-dimensional datasets, paving the way for using the method and implementing it in human detection software using HWD and tools to support astronomers in their research.