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Improving the quality of manually acquired data: Applying the theory of planned behaviour to data quality

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  • Murphy, Glen D.

Abstract

The continued reliance of manual data capture in engineering asset intensive organisations highlights the critical role played by those responsible for recording raw data. The potential for data quality variance across individual operators also exposes the need to better manage this particular group. This paper evaluates the relative importance of the human factors associated with data quality. Using the theory of planned behaviour this paper considers the impact of attitudes, perceptions and behavioural intentions on the data collection process in an engineering asset context. Two additional variables are included, those of time pressure and operator feedback. Time pressure is argued to act as a moderator between intention and data collection behaviour, while perceived behavioural control will moderate the relationship between feedback and data collection behaviour. Overall the paper argues that the presence of best practice procedures or threats of disciplinary sanction are insufficient controls to determine data quality. Instead those concerned with improving the data collection performance of operators should consider the operator's perceptions of group attitude towards data quality, the level of feedback provided to data collectors and the impact of time pressures on procedure compliance. A range of practical recommendations are provided to those wishing to improve the quality of their manually acquired data.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:94:y:2009:i:12:p:1881-1886
    DOI: 10.1016/j.ress.2009.05.008
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    References listed on IDEAS

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    1. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    2. Betsch, Tilmann & Haberstroh, Susanne & Molter, Beate & Glockner, Andreas, 2004. "Oops, I did it again--relapse errors in routinized decision making," Organizational Behavior and Human Decision Processes, Elsevier, vol. 93(1), pages 62-74, January.
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    Cited by:

    1. Barabadi, Abbas & Tobias Gudmestad, Ove & Barabady, Javad, 2015. "RAMS data collection under Arctic conditions," Reliability Engineering and System Safety, Elsevier, vol. 135(C), pages 92-99.
    2. Nguyen, Lynda & Murphy, Glen & Chang, Artemis, 2014. "The construction of social identity in newly recruited nuclear engineering staff: A longitudinal study," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 14-28.
    3. Mathieu Payette & Georges Abdul-Nour, 2023. "Machine Learning Applications for Reliability Engineering: A Review," Sustainability, MDPI, vol. 15(7), pages 1-22, April.
    4. 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.
    5. 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.
    6. 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.

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