| Issue: | 2026 №2 (94) |
| Section: | Management |
| UDK: | 424.11.785 |
| DOI: | https://doi.org/10.33271/ebdut/94.151 |
| Article language: | Ukrainian |
| Pages: | 151-157 |
| Title: | Information quality in the paradigm of information support for economic research: a theoretical perspective |
| Author: | Dzhandzhhava N. V., Simon Kuznets Kharkiv National University of Economics |
| Annotation: | Methods. The article applies a set of general scientific and specific research methods: terminological analysis to clarify the content of the concept of «information quality», the comparative method to compare scientific approaches to its interpretation, systematization and grouping to identify information quality criteria in economic research, and logical generalization to formulate proposals for ensuring an appropriate level of the research information base. Results. It is substantiated that under the conditions of digitalization of economic science, not only the accessibility of information increases, but also the risk of using unverified, outdated, fragmented or methodologically weak data. The expediency of understanding information quality as a set of properties that determine the possibility of its correct, evidence-based and effective use in economic research is proved. The criteria of information quality are systematized by two levels of importance: critical criteria, without which information cannot be used, and additional criteria that require preliminary processing. It is shown that the formal presence of a reference to a source does not guarantee its scientific suitability, since the origin of data, the reputation of the publication, the methodology of data collection and the possibility of verification are decisive. Novelty. The content of the concept of «information quality» is clarified specifically for the field of economic research; an approach to ranking information sources is proposed; and the need for express testing of information sources according to the criteria of relevance, accuracy, reliability, meaningfulness, timeliness, sufficiency and adaptability is substantiated. Practical value. The results can be used by researchers, higher education students, editorial boards of professional journals and experts when selecting a source base, checking the correctness of empirical data and increasing the evidentiary value of economic research. The proposed approach helps minimize information distortions, improve the quality of argumentation and strengthen the reproducibility of scientific results in practice. |
| Keywords: | Information quality, Economic research, Information support, Data reliability, Information relevance, Information accuracy, Digitalization, Scientific sources, Quality criteria, Source ranking, Express testing, Research evidence |
| File of the article: | EV20262_151-157.pdf |
| Literature: | - 1. Andryeyeva, H.I., & Yaroshenko, O.S. (2013). Do pytannia yakosti informatsiynoho zabezpechennia analizu hospodarskoyi diyalnosti. Efektyvna ekonomika, (2). Retrieved from http://www.economy.nayka.com.ua/?op=1&z=1835
- 2. Heryak, Yu.M., & Berko, O.Yu. (2024). Problemy kontrolyu za yakistyu danykh u rozpodilenykh informatsiynykh systemakh. Stan, dosiahnennia ta perspektyvy informatsiynykh system ta tekhnolohiy, 98-100. https://doi.org/10.23939/sisn2024.16.191
- 3. Neustroyev, Yu.H. (2021). Rol informatsiynykh tekhnolohiy u zabezpechenni ekonomichnoyi bezpeky krayiny. Investytsiyi: praktyka ta dosvid, (8). DOI:10.32702/23066814.2021.8.40
- 4. Okhrimenko, H. (2022). Analitychna kultura ta yakist informatsiyi u diyalnosti menedzhera informatsiyno-komunikatyvnoyi sfery v Ukrayini: proektnyy pidkhid. Ahora. Zhurnal sotsialnykh nauk, (1), 61-71. https://doi.org/10.25264/26.01.2023-1/1-61-71
- 5. Sariohlo, V.H. (2021). Mikrodani u sotsialno-ekonomichnykh doslidzhennyakh. Natsionalna akademiia nauk Ukrayiny, Instytut demohrafiyi ta sotsialnykh. doslidzhen imeni. M.V. Ptukhy. Uman: Vydavets «Sochinskyy M.M.».
- 6. Serkutan, T.V., & Vlasenko, D.S. (2020). Informatsiyne zabezpechennia kompleksnoho doslidzhennya rynku. Aktualni problemy ekonomiky, 11(233) DOI:10.32752/1993-6788-2020-1-233-88-94
- 7. Cai, L., & Zhu, Y. (2015). Challenges of Data Quality and Data Quality Assessment v Big Data Era. Data Science Journal, (14). https://doi.org/10.5334/dsj-2015-002
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