IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v193y2017icp737-747.html
   My bibliography  Save this article

Toward understanding outcomes associated with data quality improvement

Author

Listed:
  • Hazen, Benjamin T.
  • Weigel, Fred K.
  • Ezell, Jeremy D.
  • Boehmke, Bradley C.
  • Bradley, Randy V.

Abstract

Business analytics is driving the way organizations compete. However, the decisions made as a result of any analytics process are greatly dependent on the quality of the data from which they are based. Scholars suggest several data quality improvement methods, and commercial software is now available that can help improve data quality. Although organizations are motivated to find ways to improve analytic capabilities and using these methods has been shown to improve some measures of data quality, there is little understanding of the tangible and intangible outcomes of employing these methods. In this study, we explore outcomes that arise from a data quality improvement process implementation in an operations management environment. Over a three-year period, we conducted a longitudinal single case study at an organization that maintains a large fleet of aircraft, collecting and analyzing qualitative interviews and observations. The findings suggest outcomes (both positive and cautionary) associated with the implementation of data quality improvement processes. These include increased stakeholder commitment to data quality and business analytics, as well as an over-emphasis on program metrics to the peril of operational outcomes, among others. From the basis of our findings, we construct a research agenda, which includes testable Propositions regarding outcomes of data quality improvement.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:proeco:v:193:y:2017:i:c:p:737-747
    DOI: 10.1016/j.ijpe.2017.08.027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527317302803
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2017.08.027?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Robert M. Grant, 1996. "Prospering in Dynamically-Competitive Environments: Organizational Capability as Knowledge Integration," Organization Science, INFORMS, vol. 7(4), pages 375-387, August.
    2. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    3. Donald P. Ballou & Harold L. Pazer, 1985. "Modeling Data and Process Quality in Multi-Input, Multi-Output Information Systems," Management Science, INFORMS, vol. 31(2), pages 150-162, February.
    4. Wu, Zhang & Jiao, Jianxin & He, Zhen, 2009. "A single control chart for monitoring the frequency and magnitude of an event," International Journal of Production Economics, Elsevier, vol. 119(1), pages 24-33, May.
    5. 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.
    6. Robert G. Fichman, 2004. "Real Options and IT Platform Adoption: Implications for Theory and Practice," Information Systems Research, INFORMS, vol. 15(2), pages 132-154, June.
    7. Tan, Kim Hua & Zhan, YuanZhu & Ji, Guojun & Ye, Fei & Chang, Chingter, 2015. "Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph," International Journal of Production Economics, Elsevier, vol. 165(C), pages 223-233.
    8. Even, Adir & Shankaranarayanan, G. & Berger, Paul D., 2010. "Managing the Quality of Marketing Data: Cost/benefit Tradeoffs and Optimal Configuration," Journal of Interactive Marketing, Elsevier, vol. 24(3), pages 209-221.
    9. Ou, Yanjing & Wu, Zhang & Tsung, Fugee, 2012. "A comparison study of effectiveness and robustness of control charts for monitoring process mean," International Journal of Production Economics, Elsevier, vol. 135(1), pages 479-490.
    10. Paul A. Pavlou & Omar A. El Sawy, 2006. "From IT Leveraging Competence to Competitive Advantage in Turbulent Environments: The Case of New Product Development," Information Systems Research, INFORMS, vol. 17(3), pages 198-227, September.
    11. Akter, Shahriar & Wamba, Samuel Fosso & Gunasekaran, Angappa & Dubey, Rameshwar & Childe, Stephen J., 2016. "How to improve firm performance using big data analytics capability and business strategy alignment?," International Journal of Production Economics, Elsevier, vol. 182(C), pages 113-131.
    12. Andrew Frank, 2008. "Analysis of dependence of decision quality on data quality," Journal of Geographical Systems, Springer, vol. 10(1), pages 71-88, March.
    13. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    14. Jay R. Galbraith, 1974. "Organization Design: An Information Processing View," Interfaces, INFORMS, vol. 4(3), pages 28-36, May.
    15. Porter, Leslie J. & Rayner, Paul, 1992. "Quality costing for total quality management," International Journal of Production Economics, Elsevier, vol. 27(1), pages 69-81, April.
    16. 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.
    17. Craig W. Fisher & InduShobha Chengalur-Smith & Donald P. Ballou, 2003. "The Impact of Experience and Time on the Use of Data Quality Information in Decision Making," Information Systems Research, INFORMS, vol. 14(2), pages 170-188, June.
    18. Wickham, Hadley, 2014. "Tidy Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 59(i10).
    19. Fred D. Davis & Richard P. Bagozzi & Paul R. Warshaw, 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science, INFORMS, vol. 35(8), pages 982-1003, August.
    20. William H. DeLone & Ephraim R. McLean, 1992. "Information Systems Success: The Quest for the Dependent Variable," Information Systems Research, INFORMS, vol. 3(1), pages 60-95, March.
    21. R. G. Dyson & M. J. Foster, 1982. "The relationship of participation and effectiveness in strategic planning," Strategic Management Journal, Wiley Blackwell, vol. 3(1), pages 77-88, January.
    22. Dutta, Debprotim & Bose, Indranil, 2015. "Managing a Big Data project: The case of Ramco Cements Limited," International Journal of Production Economics, Elsevier, vol. 165(C), pages 293-306.
    23. Fosso Wamba, Samuel & Akter, Shahriar & Edwards, Andrew & Chopin, Geoffrey & Gnanzou, Denis, 2015. "How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study," International Journal of Production Economics, Elsevier, vol. 165(C), pages 234-246.
    24. Lee Ho, Linda & Quinino, Roberto Costa, 2013. "An attribute control chart for monitoring the variability of a process," International Journal of Production Economics, Elsevier, vol. 145(1), pages 263-267.
    25. Campbell, Donald T., 1979. "Assessing the impact of planned social change," Evaluation and Program Planning, Elsevier, vol. 2(1), pages 67-90, January.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Sofia Pinheiro Melo & Alexander Barke & Felipe Cerdas & Christian Thies & Mark Mennenga & Thomas S. Spengler & Christoph Herrmann, 2020. "Sustainability Assessment and Engineering of Emerging Aircraft Technologies—Challenges, Methods and Tools," Sustainability, MDPI, vol. 12(14), pages 1-27, July.
    2. Vigan Raca & Goran Velinov & Stefan Dzalev & Margita Kon-Popovska, 2022. "A Framework for Evaluation and Improvement of Open Government Data Quality: Application to the Western Balkans National Open Data Portals," SAGE Open, , vol. 12(2), pages 21582440221, June.
    3. Kohtamäki, Marko & Einola, Suvi & Rabetino, Rodrigo, 2020. "Exploring servitization through the paradox lens: Coping practices in servitization," International Journal of Production Economics, Elsevier, vol. 226(C).
    4. Ayan Chatterjee & Debmallya Chatterjee, 2024. "A Journey of Business Analytics in Improving Supply Chain Performance: A Systematic Review of Literature," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 49(2), pages 337-361, May.
    5. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Bryde, David J. & Giannakis, Mihalis & Foropon, Cyril & Roubaud, David & Hazen, Benjamin T., 2020. "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations," International Journal of Production Economics, Elsevier, vol. 226(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Roßmann, Bernhard & Canzaniello, Angelo & von der Gracht, Heiko & Hartmann, Evi, 2018. "The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 135-149.
    3. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    4. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    5. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Roubaud, David & Fosso Wamba, Samuel & Giannakis, Mihalis & Foropon, Cyril, 2019. "Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain," International Journal of Production Economics, Elsevier, vol. 210(C), pages 120-136.
    6. Wang, Hui & Gong, Qiguo & Wang, Shouyang, 2017. "Information processing structures and decision making delays in MRP and JIT," International Journal of Production Economics, Elsevier, vol. 188(C), pages 41-49.
    7. Brinch, Morten & Gunasekaran, Angappa & Fosso Wamba, Samuel, 2021. "Firm-level capabilities towards big data value creation," Journal of Business Research, Elsevier, vol. 131(C), pages 539-548.
    8. Venkatesh Mani & Catarina Delgado & Benjamin T. Hazen & Purvishkumar Patel, 2017. "Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain," Sustainability, MDPI, vol. 9(4), pages 1-21, April.
    9. Pan Liu & Shu-ping Yi, 2018. "A study on supply chain investment decision-making and coordination in the Big Data environment," Annals of Operations Research, Springer, vol. 270(1), pages 235-253, November.
    10. Arunachalam, Deepak & Kumar, Niraj & Kawalek, John Paul, 2018. "Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 416-436.
    11. Pan Liu & Shu-ping Yi, 2018. "Investment decision-making and coordination of a three-stage supply chain considering Data Company in the Big Data era," Annals of Operations Research, Springer, vol. 270(1), pages 255-271, November.
    12. Claudio Vitari & Elisabetta Raguseo, 2019. "Big data analytics business value and firm performance: Linking with environmental context," Post-Print hal-02293765, HAL.
    13. S. Vijayakumar Bharathi, 2017. "Prioritizing and Ranking the Big Data Information Security Risk Spectrum," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 18(3), pages 183-201, September.
    14. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Papadopoulos, Thanos & Luo, Zongwei & Wamba, Samuel Fosso & Roubaud, David, 2019. "Can big data and predictive analytics improve social and environmental sustainability?," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 534-545.
    15. Patrick Mikalef & Ilias O. Pappas & John Krogstie & Michail Giannakos, 2018. "Big data analytics capabilities: a systematic literature review and research agenda," Information Systems and e-Business Management, Springer, vol. 16(3), pages 547-578, August.
    16. Elisabetta Raguseo & Claudio Vitari, 2017. "Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects," Grenoble Ecole de Management (Post-Print) halshs-01923259, HAL.
    17. Lodemann, Sebastian & Kersten, Wolfgang, 2021. "Supply chain analytics implementation: A TOE perspective," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 411-434, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    18. Raut, Rakesh D. & Mangla, Sachin Kumar & Narwane, Vaibhav S. & Dora, Manoj & Liu, Mengqi, 2021. "Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    19. Samuel Fosso Wamba & Angappa Gunasekaran & Rameshwar Dubey & Eric W. T. Ngai, 2018. "Big data analytics in operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 1-4, November.
    20. Akhtar, Pervaiz & Tse, Ying Kei & Khan, Zaheer & Rao-Nicholson, Rekha, 2016. "Data-driven and adaptive leadership contributing to sustainability: global agri-food supply chains connected with emerging markets," International Journal of Production Economics, Elsevier, vol. 181(PB), pages 392-401.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:193:y:2017:i:c:p:737-747. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.