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Data quality and firm performance: empirical evidence from the Korean financial industry

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Abstract

Despite popular belief that timely and precise data are important and indispensable to good decisions and that good decisions are related to better firm performance, empirical research that examines the effect of data quality on firm performance is still scarce. How great an impact does data quality have on firm performance? This study empirically investigates the effect of firm-level data quality on firm performance in the Korean financial industry during 2008–2010. The results show that commercial banks have high-quality data, while credit unions have comparatively low-quality data. They also show that better data quality has a positive influence on sales, operating profit, and value added. Improving the level of data quality management maturity by one can increase firm performance by 33.7 % in sales, 64.4 % in operating profit, and 26.2 % in value added.

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References

  1. Ballou DP, Pazer HL (1995) Designing information systems to optimize the accuracy-timeliness tradeoff. Inform Syst Res 6(1):51–72

    Article  Google Scholar 

  2. Bank of Korea (2011) Economic statistics systems. http://ecos.bok.or.kr

  3. Batini C, Cappiello C, Francalanci C, Maurino A (2009) Methodologies for data quality assessment and improvement. ACM Comput Surv 41(3) Article 16:1–52

    Google Scholar 

  4. Caballero I, Caro A, Calero C, Piattini M (2008) IQM3: information quality management maturity model. J Univers Comput Sci 41(22):3658–3685

    Google Scholar 

  5. Cappiello C, Francalanci C, Pernici B (2004) Time-related factors of data quality in multichannel information systems. J Manag Inform Syst 20(3):71–92

    Google Scholar 

  6. DeLone WH, McLean ER (1992) Information systems success: the quest for the dependent variable. Inform Syst Res 3(1):60–95

    Article  Google Scholar 

  7. DeLone WH, McLean ER (2003) The DeLone and McLean model of information systems success: a ten-year update. J Manag Inform Syst 19(4):9–30

    Google Scholar 

  8. DeLone WH, McLean ER (2004) Measuring e-commerce success: applying the DeLone & McLean information systems success model. Int J Electron Commer 9(1):31–47

    Google Scholar 

  9. Eckerson WW (2002) Data quality and the bottom line: achieving business success through a commitment to high quality data. TDWI report series, The Data Warehousing Institute. http://download.101com.com/pub/tdwi/Files/DQReport.pdf

  10. Financial Supervisory Service of Korea (2011) Financial statistics information system. http://fisis.fss.or.kr

  11. Haug A, Zachariassen F, van Liempd D (2011) The costs of poor data quality. J Ind Eng Manag 4(2):168–193

    Google Scholar 

  12. Kim JK, Xiang JY, Lee S (2009) The impact of IT investment on firm performance in China: an empirical investigation of the Chinese electronics industry. Technol Forecas Soc Change 76(5):678–687

    Article  Google Scholar 

  13. Korea Database Agency (KDB) (2011) Database quality certification. http://www.dqc.or.kr

  14. Lee S, Kim SH (2006) A lag effect of IT investment on firm performance. Inform Res Manag J 19(1):43–69

    Article  Google Scholar 

  15. Lee S, Xiang JY, Kim JK (2011) Information technology and productivity: empirical evidence from the Chinese electronics industry. Inform Manag 48(2–3):79–87

    Article  Google Scholar 

  16. Madnick SE, Lee YW, Wang RY, Zhu H (2009) Overview and framework for data and information quality research. ACM J Data Inform Qual 1(1) Article 2:1–22

    Google Scholar 

  17. Miles MB (1979) Qualitative data as an attractive nuisance: the problem of analysis. Adm Sci Q 24(4):590–601

    Article  Google Scholar 

  18. Petter S, McLean ER (2009) A meta-analytic assessment of the DeLone and McLean IS success model: an examination of IS success at the individual level. Inform Manage 46(3):159–166

    Article  Google Scholar 

  19. Pipino LL, Lee YW, Wang RY (2002) Data quality assessment. Commun ACM 45(4):211–218

    Article  Google Scholar 

  20. Redman TC (1995) Improve data quality for competitive advantage. Sloan Manage Rev 36(2):99–107

    Google Scholar 

  21. Redman TC (1998) The impact of poor data quality on the typical enterprise. Commun ACM 41(2):79–82

    Article  Google Scholar 

  22. Statistics Korea (KOSTAT) (2011) http://www.kostat.go.kr/portal/korea/index.action

  23. Tee SW, Bowen PL, Doyle P, Rohde FH (2007) Factors influencing organizations to improve data quality in their information systems. Account Finance 47(2):335–355

    Article  Google Scholar 

  24. Wang RY, Storey VC, Firth CP (1995) A framework for analysis of data quality research. IEEE Trans Knowl Data Eng 7(4):623–639

    Article  Google Scholar 

  25. Wang RY, Strong DM (1996) Beyond accuracy: what data quality means to data consumers. J Manag Inform Syst 12(4):5–34

    Google Scholar 

  26. Wixom BH, Watson HJ (2001) An empirical investigation of the factors affecting data warehousing success. MIS Quarterly 25(1):17–41

    Article  Google Scholar 

  27. Zhu K, Kraemer KL, Xu S, Dedrick J (2004) Information technology payoff in e-business environments: an international perspective on value creation of e-business in the financial services industry. J Manag Inform Syst 21(1):17–54

    Google Scholar 

Download references

Acknowledgments

This work was supported by a Grant from the Kyung Hee University in 2010 (KHU-20100847).

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Correspondence to Jae Kyeong Kim.

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Xiang, J.Y., Lee, S. & Kim, J.K. Data quality and firm performance: empirical evidence from the Korean financial industry. Inf Technol Manag 14, 59–65 (2013). https://doi.org/10.1007/s10799-012-0145-6

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