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Freelancing platforms have gained tremendous popularity, connecting millions of employers and freelancers worldwide. We examine whether profile pictures on such platforms may facilitate hiring biases based on appearance-based perceptions of a freelancer's fit for the job (e.g., whether the applicant looks like a programmer). We collect data from Freelancer.com for all jobs posted between January-June 2018 that ended in a contract, resulting in 79,038 jobs with 2,462,043 applications from 220,385 freelancers. Leveraging computer vision techniques, we find that freelancers with pictures perceived as high fit (or who “look the part”) are more likely to be hired. More importantly, we show that such a bias goes above and beyond known prejudice variables such as demographics and attractiveness. Interestingly, we discover that “looking the part” is a complement rather than a substitute for online reputation. We further conduct two experiments to explore the underlying mechanisms behind these findings. We find that when the reputation system is extremely positive, as in most freelancing platforms, employers use profile pictures as tiebreakers to choose among similar applicants. We also show that freelancers, especially those who “do not look the part,” may mitigate such biases by strategically selecting backgrounds and accessories in their profile pictures to enhance their chances of being hired.

Jisu Kim, Doctoral Student, University of Washington, Foster School of Business

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Loyalty Program Enhancement Strategies: The Dynamic Effects of Relational States

Synopsis

Loyalty programs have been referred to as the ubiquitous customer relationship management tool. However, despite the growing practice and research in loyalty programs, the field remains unclear on the effectiveness of its key enhancement strategies such as points multiplier and expiring reward promotions. This article proposes a conceptual model that synthesizes extant insights on loyalty program enhancements, empirically analyzes longitudinal data from a global financial services firm to test the model, and conducts two experiments to reveal the mechanisms. The proposed conceptual model is grounded in gratitude-based social exchange theory to parsimoniously explain the differential impact of the key loyalty program enhancements (i.e., points multiplier and expiring promotions) on customer performance that depends on the types of customer relational states (i.e., customer temporal state and loyalty program state). Through a multi-method approach, this article suggests a set of managerial takeaways and future research avenues in loyalty programs literature. With an integrative theory of loyalty program enhancements, this paper delineates the trade-offs and mechanisms associated with the key loyalty program enhancement strategies and customer relational states.