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A Study on the Important Factors for Accounting Information Quality Impact on AIS Data Quality Outcomes

회계정보 품질에 영향을 미치는 요인이 회계정보시스템 데이터 품질에 미치는 영향

  • Kim, Kyung-Ihl (Division of Convergence Management, Korea National University of Transportation)
  • 김경일 (국립 한국교통대학교 융합경영학과)
  • Received : 2019.10.21
  • Accepted : 2019.12.20
  • Published : 2019.12.28

Abstract

AIS is one of the most critical systems in any organization. Data quality plays a critical role in a knowledge-based economy. The objective of this study is to identify the most important factors for accounting information quality and their impact on AIS data quality outcomes. This study includes an extensive literature review to identify a set of CSF for data quality. The study uses empirical data to test the research hypothesis and resluts show that the top three most important factors that affect AIS's data quality are toop management commitmentm the nature of the AIS and input controls. The study further uses regression analysis to test the effect of those factors on AIS data quality, finding that there is a significant positive relationship between the perceived performance of the three factors and AIS data quality putcomes. To be develop to AIS data quality further study for CSF's control methodology is necessary.

AIS는 여느 조직에서든 가장 중요한 시스템 중 하나인 바, 데이터품질은 지식기반 산업사회에 있어 정보시스템의 중요한 역할을 하게 된다. 본 연구의 목적은 회계정보 품질에 영향을 미치는 중요한 요인들을 식별하고 이 요인들이 AIS 데이터 품질을 산출함에 영향을 미치는 가를 확인하고자 함에 있다. 광범위한 문헌조사를 통하여 데이터 품질에 대한 일련의 CSF를 발견하고자 하였으며, 경험적 연구를 통하여 연구목적을 달성하고자 하였다. 연구결과 AIS 데이터 품질에 영향을 미치는 가장 중요한 요인은 최고경영자 결의, AIS 본연의 특성, 입력통제로 나타났으며, AIS 데이터 품질에 영향을 미치는 요인을 검증하기 위한 회귀분석을 통하여 상기 3개 요인을 인식하는 정도와 AIS 데이터 품질을 인식하는 수준 간에 매우 유의적인 관련이 있다는 것을 발견하였다. 본 연구를 통하여 AIS를 도입하고 운영함에 있어서는 회계정보의 품질을 영향을 미치는 요인들에 대한 조직 내의 통제활동에 기여할 수 있으며 통제방안에 대한 연구가 후속으로 연구되어야 할 것이다.

Keywords

References

  1. J. E. Olson. (2003). Data Quality : The Accuracy Dimentsion, Morgan.Kaufmann San Francisco, CA.
  2. C. X. Cai, R. W. Faff, D. Hillier & S. Mohamed. (2007). Exploring the link between information quality and systematic risk. Journal of Financial Research, 30(3), 335-353. DOI : 10.1111/j.1475-6803.2007.00217.x
  3. A. Niek (2018). S. Using structural technology acceptance models to segment intended users of a new technology: Propositions and an empirical illustration. Information Systems Journal, 28(5), 879-904. DOI :10.1111/isj.12172.
  4. S. W. Tee, P. L. Bowen, P. Doyle & F. H. Rohde. (2007). Factors influencing organizations to improve data quality in their information systems. Accounting and Finance, 47(2), 335-355. DOI : 10.1111/j/1467-629x.2006.00205.x
  5. R. R. Nelson, P. A. Todd, B. H. Wixom. (2005). Antecedents of Information and System Quality : AN Empirical Examination Within the Context of Data Warehousing. Journal of Management Information Systems, 21(4), 199-235. DOI : 10.1080/07421222.2005.11045823.
  6. D. I. Kim. (2019). The Impact of Internal Control System on Accounting Information's Usefulness. Journal of the Korea Convergence Society, 9(11), 307-313. DOI : 10.15207/JKCS.2018.9.11.307.
  7. G. E. Park & C. J. Kim. (2015). Quality Characteristics of Public Open Data. Journal of Digital Convergence, 13(10), 135-1468. DOI : 10.14400/JDC.2015.13.10.135.
  8. H. J. Jung. (2013). The Quality Analysis Model for Software Testing. Journal of Digital Convergence, 11(3), 293-298. https://doi.org/10.14400/JDPM.2013.11.3.293
  9. C. Firth. (1996). Data quality in practice: Experience from the frontline. In proceedings of Conference of Information Quality.
  10. A. Segev. (1996). On informatioin quality and the WWW impact, a position paper. In proceedings of Conference of Information Quality.
  11. A. Haug & J. S. Arlbjern. (2011). Barriers to master data quality. Journal of Enterprise Information Management, 24(3), 288-303. DOI : 10.1108/17410391111122862.
  12. K. I. Kim. (2018). Differences Between Client's and Supplier's receptions of IT Outsourcing Risks. Journal of Convergence for Information Technology, 8(5), 237-242. DOI : 10.22156/CS4SMB.2018.8.5.237
  13. J. C. Shin & K. I. Kim. 2016). A Study on the success factors in the Enterprise Information Systems introduced. Jourmal of Convergence Society for SMB, 6(4), 1-8. DOI : 10.22156/CS4SMB/2016/6/4/001
  14. J. E. Lee. (2018). The Effect of ERP System Quality on the System Use Satisfaction and on Individual and Organizational Performance. Journal of the Korea industrial information systems research, 23(4), 55-67. https://doi.org/10.9723/JKSIIS.2018.23.4.055
  15. M. C. Bliemer, J. M. Rose & C. G. Chorus. (2017). Detecting dominance in stated choice data and accounting for dominance-based scale differences in logit models. Transportation research, 102(8), 83-104. https://doi.org/10.1016/j.trb.2017.05.005