Errors in data sources of information product (IP) manufacturing systems can degrade overall IP quality as perceived by consumers. Data defects from inputs propagate throughout the IP manufacturing process. Information Quality (IQ) research has focused on improving the quality of inputs to mitigate error propagation and ensure an IP will be fit for use by consumers. However, the feedback loop from IP consumers to IP producers is often incomplete since the overall quality of the IP is not based solely on quality of inputs but rather by the IP’s fitness for use as a whole. It remains uncertain that high quality inputs directly correlate to a high quality IP. The methods proposed in this paper investigate the effects of intentionally decreasing, or disrupting, quality of inputs, measuring the consumers' evaluations as compared to an undisrupted IP, and proposes scenarios illustrating the advantage of these methods over traditional survey methods. Fitness for use may then be increased using those attributes deemed “important” by consumers in future IP revisions.