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EMAC 2023 Annual

Incentivizing User Input for Data Enrichment

Published: May 24, 2023


Sven Beisecker, WHU - Otto Beisheim School of Management; Christian Schlereth, WHU - Otto Beisheim School of Management


Companies increasingly rely on individual-level data to make decisions. To collect such data, they often ask users to manually enrich existing data sources. This paper studies how such manual data enrichment can best be incentivized. Across two field experiments, we differentiate incentives that benefit participants themselves and incentives that benefit others and measure their effect on (i) participation in manual data enrichment and (ii) the quality of information shared. The studies are conducted in a restaurant where guests have the chance to scan an NFC-enabled drinking glass (Smartglass) using their smartphones and can subsequently provide information through a mobile-optimized survey in return for an incentive. Our results suggest that the effect of incentive amount on the quality of information follows a U-shaped pattern, while participation is rather inelastic to different incentives. We explain our findings based on the theory of self-concept maintenance.