| Description |
This dissertation consists of three essays studying the effect and causes of human distortions in the digital economy. In these essays, I use quasi- and actual experimental variation to explore how different types of human distortion affect the profitability of digital platform business models. The first essay studies the process of digitization of medicine delivery platforms in India. Digital platforms and firms in the sharing economy routinely invest in service-quality improvements and still grapple with service quality inconsistency. The impact of service inconsistency on platform member's rating behavior remains unexamined. I use unique data from an online medicine delivery platform in India that relies on delivery persons for their service delivery. Exploiting idiosyncratic variation in delivery person allocation, I find that when a customer encounters a well-performing delivery person, she will give a poor rating to the delivery person in the next encounter and vice-versa. This cyclical nature can be explained by the customer's prior expectations about the next delivery person based on her previous encounter with a delivery person. I further show that this effect is moderated by the nature of the ailment and the monetary value of the transaction. The second essay explores the role of relative economic status in the decision to contribute a review to a user community. I use field experiment data from Facebook and in collaboration with a digital wedding services platform. Potential review writers were encouraged to join the community - but I randomized whether the potential target community for these reviews appeared wealthy or poor. I find that the level of poverty in the surrounding area moderates how consumers respond to wealth or poverty depicted in advertising. The third essay is a collaboration with a ride-sharing platform in India. Online rating systems can lead, on occasion, to reviews that are unfair or unrepresentative of the true quality provided. On the one hand, receiving an unfairly low rating once might induce participants to exert more effort and receive a better rating the next time. On the other hand, it might dispirit participants and make them exert less effort. I use data from a ride-sharing platform in India where driver ratings were made particularly salient to the driver after each trip. Our data suggest that if a customer experiences a ride cancellation, they are more likely to unfairly blame the replacement driver. I use this as a exogenous source of unfair negative ratings for the driver. I show that drivers are more likely to respond negatively to a bad rating and receive subsequently bad ratings if they were blameless for the previous negative rating. This effect is larger in contexts where there is a higher potential for an emotional response and when there is a greater need for driver skill in the subsequent ride. These unfair ratings can lead drivers to leave the platform, suggesting a broader negative effect of unfair negative ratings on platform participation. |
