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Are managerial pressure, technological control and intrinsic motivation effective in improving data quality?

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  • Molina, Roger
  • Unsworth, Kerrie
  • Hodkiewicz, Melinda
  • Adriasola, Elisa

Abstract

Can data collectors be “pushed†into collecting high quality data or would being “pulled†be more effective? This paper finds that managers should be careful of the degree to which “push†factors, such as managerial pressure and technological input control, are relied upon. While they may be helpful for motivating those data collectors who are not intrinsically motivated, they are either not helpful or may discourage those data collectors who are intrinsically motivated. Instead, self-concordance may act as a longer-term, more stable approach to increasing the motivation of data collectors and thus increasing the quality of data that enter reliability systems. This study uses a sequential mixed-method approach involving interviews with 20 data collectors and a quantitative survey of 109 data collectors in a water utility. It examines the interactive effect of managerial pressure, technological input control and self-concordance on data collection performance.

Suggested Citation

  • Molina, Roger & Unsworth, Kerrie & Hodkiewicz, Melinda & Adriasola, Elisa, 2013. "Are managerial pressure, technological control and intrinsic motivation effective in improving data quality?," Reliability Engineering and System Safety, Elsevier, vol. 119(C), pages 26-34.
    • Handle: RePEc:eee:reensy:v:119:y:2013:i:c:p:26-34
    DOI: 10.1016/j.ress.2013.04.009
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    References listed on IDEAS

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    1. Unsworth, Kerrie & Adriasola, Elisa & Johnston-Billings, Amber & Dmitrieva, Alina & Hodkiewicz, Melinda, 2011. "Goal hierarchy: Improving asset data quality by improving motivation," Reliability Engineering and System Safety, Elsevier, vol. 96(11), pages 1474-1481.
    2. Murphy, Glen D., 2010. "Testing a tri-partite contingent model of engineering cultures: A pilot study," Reliability Engineering and System Safety, Elsevier, vol. 95(10), pages 1040-1049.
    3. Podsakoff, Nathan P. & Podsakoff, Philip M. & Kuskova, Valentina V., 2010. "Dispelling misconceptions and providing guidelines for leader reward and punishment behavior," Business Horizons, Elsevier, vol. 53(3), pages 291-303, May.
    4. Murphy, Glen D., 2009. "Improving the quality of manually acquired data: Applying the theory of planned behaviour to data quality," Reliability Engineering and System Safety, Elsevier, vol. 94(12), pages 1881-1886.
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    Cited by:

    1. Robin Le Conte Des Floris & Cédric Dalmasso & Pierre Jouvelot, 2022. "The impact of motivation on the quality of project management data: an emailbased communication case study," Post-Print hal-03662794, HAL.
    2. Haegemans, Tom & Snoeck, Monique & Lemahieu, Wilfried, 2018. "Entering data correctly: An empirical evaluation of the theory of planned behaviour in the context of manual data acquisition," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 12-30.

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