IDEAS home Printed from https://ideas.repec.org/r/inm/ormnsc/v31y1985i2p150-162.html
   My bibliography  Save this item

Modeling Data and Process Quality in Multi-Input, Multi-Output Information Systems

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Prat, Nicolas & Madnick, Stuart E., 2008. "Evaluating and Aggregating Data Believability across Quality Sub-Dimensions and Data Lineage," Working papers 40085, Massachusetts Institute of Technology (MIT), Sloan School of Management.
  2. Choo Yeon Kim & Seong Soo Cha, 2023. "Effect of SNS Characteristics for Dining Out on Customer Satisfaction and Online Word of Mouth," SAGE Open, , vol. 13(3), pages 21582440231, September.
  3. Hazen, Benjamin T. & Weigel, Fred K. & Ezell, Jeremy D. & Boehmke, Bradley C. & Bradley, Randy V., 2017. "Toward understanding outcomes associated with data quality improvement," International Journal of Production Economics, Elsevier, vol. 193(C), pages 737-747.
  4. Donald Ballou & Richard Wang & Harold Pazer & Giri Kumar Tayi, 1998. "Modeling Information Manufacturing Systems to Determine Information Product Quality," Management Science, INFORMS, vol. 44(4), pages 462-484, April.
  5. Kon, Henry B. & Siegel, Michael D., 2003. "Error browsing and mediation : interoperability regarding data error," Working papers #94-15, Massachusetts Institute of Technology (MIT), Sloan School of Management.
  6. Xiao, Yu & Lu, Louis Y.Y. & Liu, John S. & Zhou, Zhili, 2014. "Knowledge diffusion path analysis of data quality literature: A main path analysis," Journal of Informetrics, Elsevier, vol. 8(3), pages 594-605.
  7. Sabrina Sicari & Cinzia Cappiello & Francesco Pellegrini & Daniele Miorandi & Alberto Coen-Porisini, 2016. "A security-and quality-aware system architecture for Internet of Things," Information Systems Frontiers, Springer, vol. 18(4), pages 665-677, August.
  8. Bernd Heinrich & Marcus Hopf & Daniel Lohninger & Alexander Schiller & Michael Szubartowicz, 2021. "Data quality in recommender systems: the impact of completeness of item content data on prediction accuracy of recommender systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 389-409, June.
  9. M N Jalil & R A Zuidwijk & M Fleischmann & Jo A E E van Nunen, 2011. "Spare parts logistics and installed base information," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 442-457, March.
  10. Hazen, Benjamin T. & Boone, Christopher A. & Ezell, Jeremy D. & Jones-Farmer, L. Allison, 2014. "Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications," International Journal of Production Economics, Elsevier, vol. 154(C), pages 72-80.
  11. Ramayya Krishnan & James Peters & Rema Padman & David Kaplan, 2005. "On Data Reliability Assessment in Accounting Information Systems," Information Systems Research, INFORMS, vol. 16(3), pages 307-326, September.
  12. Bettina Distel & Holger Koelmann & Ralf Plattfaut & Jörg Becker, 2022. "Watch who you trust! A structured literature review to build a typology of e-government risks," Information Systems and e-Business Management, Springer, vol. 20(4), pages 789-818, December.
  13. Risto Silvola & Janne Harkonen & Olli Vilppola & Hanna Kropsu-Vehkapera & Harri Haapasalo, 2016. "Data quality assessment and improvement," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 22(1), pages 62-81.
  14. Roman Lukyanenko & Jeffrey Parsons & Yolanda F. Wiersma, 2014. "The IQ of the Crowd: Understanding and Improving Information Quality in Structured User-Generated Content," Information Systems Research, INFORMS, vol. 25(4), pages 669-689, December.
  15. So Sohn & Yoon Kim, 2013. "Behavioral credit scoring model for technology-based firms that considers uncertain financial ratios obtained from relationship banking," Small Business Economics, Springer, vol. 41(4), pages 931-943, December.
  16. Jalil, M.N. & Zuidwijk, R.A. & Fleischmann, M. & van Nunen, J.A.E.E., 2009. "Spare Parts Logistics and Installed Base Information," ERIM Report Series Research in Management ERS-2009-002-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
  17. Klein, Barbara D., 2001. "Detecting errors in data: clarification of the impact of base rate expectations and incentives," Omega, Elsevier, vol. 29(5), pages 391-404, October.
  18. Xiangyu Chang & Yinghui Huang & Mei Li & Xin Bo & Subodha Kumar, 2021. "Efficient Detection of Environmental Violators: A Big Data Approach," Production and Operations Management, Production and Operations Management Society, vol. 30(5), pages 1246-1270, May.
  19. Xue Bai & Manuel Nunez & Jayant R. Kalagnanam, 2012. "Managing Data Quality Risk in Accounting Information Systems," Information Systems Research, INFORMS, vol. 23(2), pages 453-473, June.
  20. Jingran Wang & Yi Liu & Peigong Li & Zhenxing Lin & Stavros Sindakis & Sakshi Aggarwal, 2024. "Overview of Data Quality: Examining the Dimensions, Antecedents, and Impacts of Data Quality," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 1159-1178, March.
  21. J. Vaníček, 2006. "Software and data quality," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 52(3), pages 138-146.
  22. Svetlana JESIĻEVSKA & Daina ŠĶILTERE, 2019. "Infrastructure, Employment And Income Convergence," Romanian Journal of Economics, Institute of National Economy, vol. 49(2(58)), pages 62-72, December.
  23. repec:jtr:journl:v:4:y:2012:i:1:p:12-37 is not listed on IDEAS
  24. Klein, B. D. & Rossin, D. F., 1999. "Data quality in neural network models: effect of error rate and magnitude of error on predictive accuracy," Omega, Elsevier, vol. 27(5), pages 569-582, October.
  25. Zhang, Xiaodong & Patino-Echeverri, Dalia & Li, Mingquan & Wu, Libo, 2022. "A review of publicly available data sources for models to study renewables integration in China's power system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
  26. Rajiv D. Banker & Robert J. Kauffman, 2004. "50th Anniversary Article: The Evolution of Research on Information Systems: A Fiftieth-Year Survey of the Literature in Management Science," Management Science, INFORMS, vol. 50(3), pages 281-298, March.
  27. Benita M. Gullkvist, 2013. "Drivers of change in management accounting practices in an ERP environment," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 6(2), pages 149-174, September.
  28. Debabrata Dey & Subodha Kumar, 2013. "Data Quality of Query Results with Generalized Selection Conditions," Operations Research, INFORMS, vol. 61(1), pages 17-31, February.
  29. Nathalie Demoulin & Kristof Coussement, 2018. "Acceptance of text-mining systems: The signaling role of information quality," Post-Print hal-02111772, HAL.
  30. Tavakkoli, Sakineh & Macknick, Jordan & Heath, Garvin A. & Jordaan, Sarah M., 2021. "Spatiotemporal energy infrastructure datasets for the United States: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
  31. Dongpu Fu & Yili Hong & Kanliang Wang & Weiguo Fan, 2018. "Effects of membership tier on user content generation behaviors: evidence from online reviews," Electronic Commerce Research, Springer, vol. 18(3), pages 457-483, September.
  32. Michnik, Jerzy & Lo, Mei-Chen, 2009. "The assessment of the information quality with the aid of multiple criteria analysis," European Journal of Operational Research, Elsevier, vol. 195(3), pages 850-856, June.
  33. Koziel, Sylvie & Hilber, Patrik & Westerlund, Per & Shayesteh, Ebrahim, 2021. "Investments in data quality: Evaluating impacts of faulty data on asset management in power systems," Applied Energy, Elsevier, vol. 281(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.