Interacting Like Humans? Understanding the Effect of Anthropomorphism on Consumer’s Willingness to Pay in Online Auctions

Most research examining individuals’ bidding behavior in online auctions has used the lens of a rational decision making process. However, bidding behavior is also influenced by non-rational factors. Anthropomorphism, attributing human characteristics to a non-human object, has been studied in many disciplines, but has not been investigated in online auctions. This study aims to identify whether auditory and visual design factors for a non-human product would induce anthropomorphism and impact individuals' bidding decision. Results show that visual design induces individuals’ anthropomorphism and also impacts bidding decisions

Face It, Users Don’t Care: Affinity and Trustworthiness of Imperfect Digital Humans

Digital humans are growing in application and popularity, both as avatars for people and as standalone artificial intelligence-controlled agents. While the technology to make a digital human look more realistic is improving, we know little about how realistic they need to be. Humans are exceptionally good at identifying imperfect digital reproductions of human faces, so it has been reasoned that the slightest imperfections in the visual design of digital humans may translate into reduced acceptance and effectiveness. The broadly held wisdom is that digital humans should be photorealistic and indistinguishable from real people. To examine this common belief we collected data on individuals’ affinity and trustworthiness in photorealistic digital humans when engaged in a product bidding situation, along with a human presenter with varying degrees of video imperfections. The results reveal that participants noticed some of the video imperfections, but this did not adversely affect their willingness to pay, affinity, or trust. We found that once digital humans become close to realistic, users simply do not care about visual imperfection

Dynamic lot-sizing in sequential online retail auctions

Retailers often conduct non-overlapping sequential online auctions as a revenue generation and inventory clearing tool. We build a stochastic dynamic programming model for the seller's lot-size decision problem in these auctions. The model incorporates a random number of participating bidders in each auction, allows for any bid distribution, and is not restricted to any specific price-determination mechanism. Using stochastic monotonicity/stochastic concavity and supermodularity arguments, we present a complete structural characterization of optimal lot-sizing policies under a second order condition on the single-auction expected revenue function. We show that a monotone staircase with unit jumps policy is optimal and provide a simple inequality to determine the locations of these staircase jumps. Our analytical examples demonstrate that the second order condition is met in common online auction mechanisms. We also present numerical experiments and sensitivity analyses using real online auction data.Auctions/bidding Dynamic programming e-Commerce