The Influence of Self-Regulation of Sharing Economy: Evidence from Unstructured Data
41 Pages Posted: 11 Sep 2017 Last revised: 29 Jul 2019
Date Written: July 28, 2019
The sharing economy has been experiencing rapid growth in recent years. Although researchers have explored the societal aspects of the sharing economy, regulatory issues have been overlooked. Notably, the sharing economy suffers from information asymmetry and a lack of government regulation, calling for the platform's self-regulation. We investigate the effectiveness of self-regulation disclosure when the disclosure conveys incomplete food-preparation hygiene quality information by releasing only inspection images. Using the data from a large peer-to-peer meal-sharing platform in China, we integrate an econometric analysis method with deep-learning and computer-vision techniques to conduct the analysis. We find that, although the inspection results disclosure, on average, leads to a sales increase to suppliers, disclosing image information can create misinterpretation, resulting in a non-monotonic sales impact as related to hygiene quality. We also find that competition and reputation are substitutes for the self-regulation mechanism. Our work extends the theoretical work of self-regulation by providing empirical evidence on its effectiveness and emphasizes the important role of unstructured information in regulation disclosure, which is usually neglected in a mandatory disclosure setting. Finally, because competition, reputation, and the self-regulation mechanism correspond to enforcement from the supplier, consumer, and platform sides, respectively, our analysis integrates enforcement by these three major parties of the sharing economy ecosystem.
Keywords: platform regulation, self-regulation, sharing economy, computer vision, deep learning, unstructured data, treatment effect, econometric analysis
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