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Measuring how motivation affects information quality assessment: A gamification approach.
Marko Poženel1, Aljaž Zrnec1, Dejan Lavbič1
1University of Ljubljana, Faculty of Computer and Information Science, Ljubljana, Slovenia.
Plos One
|October 27, 2022
Summary
Assessor motivation significantly enhances information quality (IQ) measurability. Gamified surveys with diverse participants improved inter-rater agreement (ICC), moving from poor to moderate levels.
Area of Science:
- Information Science
- Human-Computer Interaction
- Data Quality Management
Background:
- Existing research highlights poor measurability of information quality (IQ) due to low inter-rater agreement (ICC).
- Low ICC values raise concerns about the reliability of IQ dimension evaluations.
- The impact of assessor motivation on IQ measurability remains underexplored.
Purpose of the Study:
- To investigate whether assessor motivation can improve the Intra-Class Correlation (ICC) in evaluating information quality (IQ) dimensions.
- To analyze the relationship between motivation levels and the reliability of IQ assessments.
- To determine if motivation enhances the overall measurability of IQ.
Main Methods:
- A gamified online survey was employed to gather participant views on IQ.
- A diverse participant pool was recruited to ensure research validity across different backgrounds.
- Intra-Class Correlation (ICC) was used to measure inter-rater agreement on IQ dimensions.
Main Results:
- Assessor motivation led to an average ICC increase of 0.27, improving measurability from poor (0.29) to moderate (0.56).
- A positive correlation was observed between motivation levels and ICC, with significant overall improvement compared to prior studies.
- Completeness and accuracy demonstrated the highest ICC gains among IQ dimensions.
Conclusions:
- Incorporating rater motivation substantially enhances the measurability of information quality.
- The findings suggest practical guidelines for future IQ research, emphasizing the role of motivation.
- Motivated assessors yield superior agreement levels, improving the interpretation and reliability of IQ evaluations.
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