https://www.um.edu.mt/library/oar/handle/123456789/4610 2026-06-11T14:26:23Z 2026-06-11T14:26:23Z Gdakowicz, Anna Łatuszyńska, Małgorzata https://www.um.edu.mt/library/oar/handle/123456789/147324 2026-06-11T10:18:25Z 2025-01-01T00:00:00Z

Title: Bridging the gap : performance evaluation of classical and modern discretization techniques in real estate data Authors: Gdakowicz, Anna; Łatuszyńska, Małgorzata Abstract: PURPOSE: The aim of this study is to evaluate the effectiveness of selected discretization methods for continuous variables using the example of real estate area—one of the key attributes in property analysis. The study addresses the challenge of transforming continuous variables into discrete forms with minimal information loss, a process crucial for data mining, statistical modeling, and classification tasks.; DESIGN/METHODOLOGY/APPROACH: Thirteen discretization methods were applied to a dataset of 3,732 residential real estate listings from the Szczecin housing market between 2017 and 2021. The methods include classical approaches with predefined class parameters (equal width and equal frequency), expert-driven methods, quantile-based techniques, clustering (kmeans), and supervised learning approaches such as entropy minimization and 1R. The evaluation criteria included the deviation of grouped results from ungrouped data (arithmetic mean difference and loss function), and the number of classes, treated as a nominant. A linear ordering technique (Hellwig’s method) was used to rank the methods.; FINDINGS: The method based on expert-defined class width (Method 4) showed the highest consistency with the original data, followed by Scott’s rule (Method 2) and the entropy-based supervised method (Method 11). Contrary to expectations, quantile-based methods and commonly used rules such as Freedman–Diaconis or square-root yielded unsatisfactory results, either due to oversimplification (too few intervals) or excessive granularity (too many classes).; PRACTICAL IMPLICATIONS: The results underline the importance of selecting discretization methods tailored to the characteristics of the variable and research context. In particular, they demonstrate the value of domain expertise in guiding discretization decisions in real estate analytics, improving data quality for downstream analysis such as classification, segmentation, or regression.; ORIGINALITY/VALUE: This study is one of the first to systematically compare a broad spectrum of discretization methods in the context of real estate data. It introduces a comprehensive evaluation framework combining statistical accuracy and interpretability. The findings contribute to both methodological development in data pre-processing and practical decision-making in real estate market research.

2025-01-01T00:00:00Z Zwiech, Patrycja https://www.um.edu.mt/library/oar/handle/123456789/147315 2026-06-11T08:47:32Z 2025-01-01T00:00:00Z

Title: Workplace well-being as a priority for HR strategy in the context of young employees' expectations Authors: Zwiech, Patrycja Abstract: PURPOSE: The research gap lies in the need for comprehensive insight into specific HR strategies that organizations can implement to create a mentally healthy work environment. This article examines the impact of employee perceptions of workplace well-being, in the context of generational differences, on HR strategies and presents new HR strategies to improve employee well-being. The paper addresses two questions: How do generational differences in perceptions of well-being influence HR strategies? and What changes should HR strategies in specific areas of human resources management implement to better incorporate workplace well-being?; DESIGN/METHODOLOGY/APPROACH: To achieve the research objectives and identify effective strategies to improve employee well-being in the workplace, a literature review was conducted, enabling a comprehensive analysis of the topic.; FINDINGS: Adding a generational perspective to HR strategy is crucial, as differences in the perception of workplace well-being between Generations Z and X influence the design of HR policies. Research shows that existing generational differences in the perception of workplace well-being among Generation X and Z employees are reflected in expectations regarding employer branding, organizational culture, internal communication, development and training, and the segmentation of well-being activities. Furthermore, the trend of workplace well-being in human resources management will become increasingly integrated with specific HRM functions in the coming years. This article presents proposed changes in the following areas of human resources management: recruitment and selection, employee evaluation, employee development, motivation and remuneration, interpersonal relationship management, communication, and work organization.; PRACTICAL IMPLICATIONS: Managers will better understand the crucial role workplace wellbeing can play in improving productivity. These findings will help organizations and human resources (HR) departments understand the importance of employee well-being in the workplace.; SOCIAL IMPLICATIONS: This research will help improve the personal and social lives of employees and managers by better understanding workplace well-being. This will have further economic implications, such as higher organizational performance.; ORIGINALITY/VALUE: This research makes important scientific and practical contributions to workplace well-being. It is crucial to reassess areas of human resource management, such as recruitment and selection, employee assessment, employee development, motivation and remuneration, interpersonal relationship management, communication, and work organization, in the context of employee well-being perceptions across generations, to adapt to the new demands of the new work environment. An integrated approach that encompasses strategies within employer branding, organizational culture, internal communication, development and training, and the segmentation of well-being activities ensures improved workplace well-being.

2025-01-01T00:00:00Z Ziemba, Paweł Gago, Izabela Różański, Rafał https://www.um.edu.mt/library/oar/handle/123456789/147306 2026-06-11T08:14:28Z 2025-01-01T00:00:00Z

Title: Classification of water samples using a limited set of characteristics for the economics of water resource management Authors: Ziemba, Paweł; Gago, Izabela; Różański, Rafał Abstract: PURPOSE: The purpose of this article is to develop a framework for reducing water quality assessment parameters while maintaining the precision and accuracy of evaluation.; DESIGN/METHODOLOGY/APPROACH: The study was based on machine learning methods, including classification and feature selection techniques. Specifically, data discretization, correlation-based feature selection, and the following classification algorithms were applied: artificial neural network, decision tree, random forest, and support vector machine. The study also utilized two datasets describing water quality based on physical, chemical, and biological parameters.; FINDINGS: The conducted research indicates that the correlation-based feature selection filter, combined with other machine learning methods, is an effective tool that enables a substantial reduction in the number of parameters used for water quality assessment without any loss of accuracy or precision.; PRACTICAL IMPLICATIONS: The study’s findings make it possible to optimize the water quality assessment process by reducing the number of required chemical, physical, and biological tests and analyses. This reduction can lower the costs of data collection, analysis, and interpretation, minimize data gaps, and increase monitoring frequency – thereby economically rationalizing water quality testing procedures.; ORIGINALITY/VALUE: The development of a framework for reducing water quality assessment parameters while maintaining precision and accuracy constitutes a contribution to the field of water quality assessment methods and the economic rationalization of water quality testing.

2025-01-01T00:00:00Z Zdybicki, Tomasz https://www.um.edu.mt/library/oar/handle/123456789/147305 2026-06-11T08:06:15Z 2025-01-01T00:00:00Z

Title: Project-lifecycle data as the conceptual basis for data-driven governance in global IT programmes Authors: Zdybicki, Tomasz Abstract: PURPOSE: This paper presents a case study of a global IT programme in the pharmaceutical industry, demonstrating that a structured data model can serve as a basis of data-driven programme governance. Coherent, accountable, and predictable data flows play an important role in enhancing predictability, transparency, and operational efficiency of largescale initiatives, the study contends.; DESIGN/METHODOLOGY/APPROACH: Qualitative case study design was employed. As an active participant in the programme, the author did participatory observation, information-flow mapping, and data-model design as part of the research. Empirical material consisted of operational data from Jira, Monday.com, ServiceNow, and Google Sheets. Project documentation and meeting records also formed part of the evidence.; FINDINGS: The analysis identified severe fragmentation of data sources as well as ambiguity of ownership, with implications for decision time delays, duplication of tasks, and less audit trail. The development yielded a consolidated model that was Single Source of Truth based. It helps to organize roles, processes, and responsibilities along common datasets in which responsibility for decisions is traceable, thereby providing a means of bringing back uniformity and responsibility for decisions made.; PRACTICAL IMPLICATIONS: The proposed methodology can be utilised in another, equally complex programme to reduce coordination cost, build better data stewardship, and allow for evidence-based policy choice. It establishes an approach of logical integration of operational execution with strategic governance through orderly flows of information.; ORIGINALITY/VALUE: By demonstrating that a data model constitutes a management approach, the research connects the project governance and data governance literature. It provides a repeatable model for converting decentralized project ecosystems into coherent, data-driven governance systems.

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