QUALITY OF R&D INFORMATION IN THE DISCLOSURES OF PHARMACEUTICAL COMPANIES IN HUNGARY
Gréta Palánk - Eötvös Loránd University, Faculty of Economics, Egyetem tér 1-3, 1053 Budapest, Hungary
Éva Karai - Eötvös Loránd University, Faculty of Economics, Egyetem tér 1-3, 1053 Budapest, Hungary
DOI: https://doi.org/10.31410/Balkans.JETSS.2023.6.1.14-23
Gréta Palánk - Eötvös Loránd University, Faculty of Economics, Egyetem tér 1-3, 1053 Budapest, Hungary
Éva Karai - Eötvös Loránd University, Faculty of Economics, Egyetem tér 1-3, 1053 Budapest, Hungary
DOI: https://doi.org/10.31410/Balkans.JETSS.2023.6.1.14-23
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Balkans Journal of Emerging Trends in Social Sciences, (2023) , Vol 6, No 1
ISSN: 2620-164X |
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Abstract: The Hungarian Accounting Act and the International Financial Reporting Standards require different accounting treatments and disclosures for research and development activities. While examining ten years’ financial statements of five Hungarian pharmaceutical companies, we revealed the differences between the two accounting systems and evaluated the quality of the provided accounting information. Incorporating former researchers’ findings, the authors developed a criteria system for content analysis to examine the impact of accounting differences on the quality of accounting information. The financial statements presented on the IFRS basis provided more consistent high-quality information, while the disclosures prepared on the domestic accounting rules showed a variable picture.
Keywords: Accounting information, Research & Development, Hungarian Accounting Act, International Financial Reporting Standards.
JEL Classification M41 · O30 · D80
Keywords: Accounting information, Research & Development, Hungarian Accounting Act, International Financial Reporting Standards.
JEL Classification M41 · O30 · D80
REFERENCES
Azeroual, O., & Abuosba, M. (2017). Improving the data quality in the research information systems. International Journal of Computer Science and Information Security, 15(11), 82–86. arXiv:1901.07388.
Azeroual, O., Saake, G., & Wastl, J. (2018). Data measurement in research information systems: Metrics for the evaluation of data quality. Scientometrics, 115(3), 1271–1290. https://doi.org/10.1007/s11192-018-2735-5
Ballou, D. P., & Pazer, H. L. (1985). Modeling Data and Process Quality in Multi-Input, Multi-Output Information Systems. Management Science, 31(2), 150–162. https://doi.org/10.1287/mnsc.31.2.150
Cai, L., & Zhu, Y. (2015). The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Journal, 14(0), 2. https://doi.org/10.5334/dsj-2015-002
Eppler, M. J. (2001, November). A Generic Framework for Information Quality in Knowledge-intensive Processes. Proceedings of the Sixth International Conference on Information Quality, 329–346.
Floridi, L. (2013). Information Quality. Philosophy & Technology, 26(1), 1–6. https://doi.org/10.1007/s13347-013-0101-3
Gertz, M., Özsu, M. T., Saake, G., & Sattler, K. U. (2004, March). Report on the Dagstuhl Seminar “Data Quality on the Web.” ACM SIGMOD Record, 33(1), 127–132. https://doi.org/10.1145/974121.974144
Heinrich, B., Hristova, D., Klier, M., Schiller, A., & Szubartowicz, M. (2018). Requirements for Data Quality Metrics. Journal of Data and Information Quality, 9(2), 1–32. https://doi.org/10.1145/3148238
Huang, T., Li, J., Wu, F., & Zhu, N. (2020). R&D information quality and stock returns. Journal of Financial Markets, 57, pp. 1–19. https://doi.org/10.1016/j.finmar.2020.100599
Kahn, B. K., Strong, D. M., & Wang, R. Y. (2002). Information quality benchmarks: Product and service performance. Communications of the ACM, 45(4ve), 184–192. https://doi.org/10.1145/505999.506007
Katerattanakul, P., & Siau, K. (1999, December). Measuring Information Quality of Web Sites: Development of an Instrument. ICIS 1999 Proceedings, 279–285. https://doi.org/10.1145/352925.352951
Klein, B. (2002, December). When Do Users Detect Information Quality Problems on the World Wide Web? AMCIS 2002 Proceedings, 1101–1103.
Knight, S. A., & Burn, J. (2005). Developing a Framework for Assessing Information Quality on the World Wide Web. Informing Science: The International Journal of an Emerging Transdiscipline, 8, pp. 159–172. https://doi.org/10.28945/493
Madnick, S. E., Wang, R. Y., Lee, Y. W., & Zhu, H. (2009). Overview and Framework for Data and Information Quality Research. Journal of Data and Information Quality, 1(1), 1–22. https://doi.org/10.1145/1515693.1516680
Miller, H. (1996). The Multiple Dimensions of Information Quality. Information Systems Management, 13(2), 79–82. https://doi.org/10.1080/10580539608906992
Naumann, F., & Rolker, C. (2005). Assessment methods for information quality criteria. Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Informatik.
Shanks, G., & Corbitt, B. (1999, December). Understanding data quality: Social and cultural aspects. Proceedings of the 10th Australasian Conference on Information Systems, pp. 785–797.
Stvilia, B., Twidale, M. B., Smith, L. C., & Gasser, L. (2008). Information quality work organisation in Wikipedia. Journal of the American Society for Information Science and Technology, 59(6), 983–1001. https://doi.org/10.1002/asi.20813
Taleb, I., Serhani, M. A., & Dssouli, R. (2018, July). Big Data Quality: A Survey. 2018 IEEE International Congress on Big Data (BigData Congress). Big Data Congress 2018, San Francisco, USA. https://doi.org/10.1109/bigdatacongress.2018.00029
Tayi, G. K., & Ballou, D. P. (1998). Examining data quality. Communications of the ACM, 41(2), 54–57. https://doi.org/10.1145/269012.269021
Wang, R. Y., & Strong, D. M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12(4), 5–33. https://doi.org/10.1080/07421222.1996.11518099
REFERENCES
Azeroual, O., & Abuosba, M. (2017). Improving the data quality in the research information systems. International Journal of Computer Science and Information Security, 15(11), 82–86. arXiv:1901.07388.
Azeroual, O., Saake, G., & Wastl, J. (2018). Data measurement in research information systems: Metrics for the evaluation of data quality. Scientometrics, 115(3), 1271–1290. https://doi.org/10.1007/s11192-018-2735-5
Ballou, D. P., & Pazer, H. L. (1985). Modeling Data and Process Quality in Multi-Input, Multi-Output Information Systems. Management Science, 31(2), 150–162. https://doi.org/10.1287/mnsc.31.2.150
Cai, L., & Zhu, Y. (2015). The Challenges of Data Quality and Data Quality Assessment in the Big Data Era. Data Science Journal, 14(0), 2. https://doi.org/10.5334/dsj-2015-002
Eppler, M. J. (2001, November). A Generic Framework for Information Quality in Knowledge-intensive Processes. Proceedings of the Sixth International Conference on Information Quality, 329–346.
Floridi, L. (2013). Information Quality. Philosophy & Technology, 26(1), 1–6. https://doi.org/10.1007/s13347-013-0101-3
Gertz, M., Özsu, M. T., Saake, G., & Sattler, K. U. (2004, March). Report on the Dagstuhl Seminar “Data Quality on the Web.” ACM SIGMOD Record, 33(1), 127–132. https://doi.org/10.1145/974121.974144
Heinrich, B., Hristova, D., Klier, M., Schiller, A., & Szubartowicz, M. (2018). Requirements for Data Quality Metrics. Journal of Data and Information Quality, 9(2), 1–32. https://doi.org/10.1145/3148238
Huang, T., Li, J., Wu, F., & Zhu, N. (2020). R&D information quality and stock returns. Journal of Financial Markets, 57, pp. 1–19. https://doi.org/10.1016/j.finmar.2020.100599
Kahn, B. K., Strong, D. M., & Wang, R. Y. (2002). Information quality benchmarks: Product and service performance. Communications of the ACM, 45(4ve), 184–192. https://doi.org/10.1145/505999.506007
Katerattanakul, P., & Siau, K. (1999, December). Measuring Information Quality of Web Sites: Development of an Instrument. ICIS 1999 Proceedings, 279–285. https://doi.org/10.1145/352925.352951
Klein, B. (2002, December). When Do Users Detect Information Quality Problems on the World Wide Web? AMCIS 2002 Proceedings, 1101–1103.
Knight, S. A., & Burn, J. (2005). Developing a Framework for Assessing Information Quality on the World Wide Web. Informing Science: The International Journal of an Emerging Transdiscipline, 8, pp. 159–172. https://doi.org/10.28945/493
Madnick, S. E., Wang, R. Y., Lee, Y. W., & Zhu, H. (2009). Overview and Framework for Data and Information Quality Research. Journal of Data and Information Quality, 1(1), 1–22. https://doi.org/10.1145/1515693.1516680
Miller, H. (1996). The Multiple Dimensions of Information Quality. Information Systems Management, 13(2), 79–82. https://doi.org/10.1080/10580539608906992
Naumann, F., & Rolker, C. (2005). Assessment methods for information quality criteria. Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, Institut für Informatik.
Shanks, G., & Corbitt, B. (1999, December). Understanding data quality: Social and cultural aspects. Proceedings of the 10th Australasian Conference on Information Systems, pp. 785–797.
Stvilia, B., Twidale, M. B., Smith, L. C., & Gasser, L. (2008). Information quality work organisation in Wikipedia. Journal of the American Society for Information Science and Technology, 59(6), 983–1001. https://doi.org/10.1002/asi.20813
Taleb, I., Serhani, M. A., & Dssouli, R. (2018, July). Big Data Quality: A Survey. 2018 IEEE International Congress on Big Data (BigData Congress). Big Data Congress 2018, San Francisco, USA. https://doi.org/10.1109/bigdatacongress.2018.00029
Tayi, G. K., & Ballou, D. P. (1998). Examining data quality. Communications of the ACM, 41(2), 54–57. https://doi.org/10.1145/269012.269021
Wang, R. Y., & Strong, D. M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. Journal of Management Information Systems, 12(4), 5–33. https://doi.org/10.1080/07421222.1996.11518099
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