Due to uncertainties regarding quality in terms of both product and seller, information asymmetry is a significant problem for consumers in the online marketplace. Online sellers are keen to improve their sales by highlighting quality signals such as price, reputation, and e-WOM. In traditional...

This paper examines the influence of information disclosure on lenders' behaviors in a crowdfunded supply chain finance (SCF) market, a novel “FinTech” initiative that transforms the existing value chains via alternative leading. Unlike prior research that takes a static view and focuses on...

Inspired by the evaluation mode theory, we show online vendors mispredict consumers' responses to different types of sales displays. While vendors predict that consumers evaluate a featured product more positively in a cooperative sales promotion (CSP; i.e., multiple stores promoting...

Reputation system has been an important component of improving online markets’ efficiency by reducing uncertainty about the quality of the sellers. Most, if not all, reputation systems examined in the extant literature reflect the sellers’ long-term accumulative reputation, which has several...

In this paper, we investigate the optimal reward scheme design problem for online crowdfunding projects where project backers' reward item selection is assumed to follow the multinomial logit choice model. Our model shows that project creator's effective marginal profit from each reward item is...

Extending recent work on individual-level decision in the emerged reward-based crowdfunding market, we formulated a Panel Vector Auto Regression Model with exogenous variables to examine whether investors' Inertia Behavior (IB) exists in their repeated investment decisions (i.e., which reward...

We use controlled lab experiments to test whether the market efficiency being affected by unfair negative ratings, and whether announcing the percentage of unfair ratings in the market makes any difference. We find that the market efficiency is higher than no rating information case even when...

Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data? In this work, we answer these key questions by conducting (to our best...