The Link Between the Data Producers’ Knowing-Why and their Intention to Enter Data Correctly

Haegemans, Tom; Snoeck, Monique; Lismont, Jasmien; Lemahieu, Wilfried

doi:10.1109/cbi.2017.20

Cited by 2 publications

(1 citation statement)

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“…Collecting high-quality data is related to knowing why the data is needed and what it will be used for (Lee and Strong 2003). Furthermore, it is necessary to know why the data is important (Haegemans et al 2017). To study social influence, this study also measures the importance of knowing who is going to use the data.…”

Section: Ux Context: Social Influencementioning

confidence: 99%

Analysis of manual data collection in maintenance context

Mahlamäki¹,

Nieminen²

2019

JQME

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Purpose The purpose of this paper is to identify details of technological, organizational and people (TOP) factors affecting maintenance technicians’ use of computerized maintenance management systems (CMMS) in manual collection of asset data. Design/methodology/approach In addition to TOP factor details, results from six case studies in Finland, India and the Caribbean are presented. Interviews and observations clarify the role of TOP factors in CMMS use in industrial maintenance. Findings In total, 17 detailed TOP factors are identified and criteria for analyzing CMMS contexts with them are defined. Analyzing the cases with these factors reveals that technicians who collect good quality data have received good training and instructions for the CMMS, are competent, and understand how manually collected data benefits them in their own work. However, even these sites struggle with the usability of the CMMS. Research limitations/implications The 17 TOP factors and the criteria for CMMS evaluation extend understanding on context and usability in manual data collection. Case study method does not imply the relative importance of the TOP factors, which calls for future research using quantitative methods. Practical implications Management can use the criteria to analyze the context of manual data collection for improvements, e.g., in CMMS usability. Originality/value Insights from industrial environments and a new way of studying contextual factors of CMMS use are presented. The results extend a data quality research framework with details to manual data collection and define the TOP factors in CMMS context.

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Section: Ux Context: Social Influencementioning

confidence: 99%