Abstract

We investigate the participation and effectiveness of paid endorsers in sponsored tweet campaigns. We manipulate the financial pay rate offered to endorsers on a Chinese paid endorsement platform, where payouts are contingent on participation rather than engagement outcomes. Hence, our design can distinguish between variation in participation and variation in outcomes, even if people select to endorse only specific tweets. Also, the lack of compensation for effort allows one to attribute differences in outcomes to precontractual selection rather than postcontractual behavior. The main finding is that endorsers exhibited adverse selection. Several observed and unobserved endorser characteristics associated with a higher propensity to participate had a negative association with being an effective endorser given participation. This adverse selection results in a conundrum when trying to recruit a sizable number of high-quality endorsers. Only 9.5%–11.8% of the endorsers were above the median in both the propensity to participate and the propensity to be effective compared to a benchmark of 25% in the absence of any association. A simulation analysis of various targeting approaches that leverages our data of actual endorsements and outcomes shows that targeting candidate endorsers by scoring and ranking them using models taking into account adverse selection on observables improves campaign outcomes by 13%–55% compared to models ignoring adverse selection. This paper was accepted by Eric Anderson, marketing. Funding: This project was made possible by financial support extended to J. Peng through a Penn Lauder Center for International Business Education & Research PhD Grant and a Wharton Baker Retailing Center PhD Research Grant. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2019.01897 .

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