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

The Happiness Premium: The Impact of Emotion on Individuals’ Willingness to Pay in Online Auctions

Much research across various disciplines has studied individuals’ bidding behavior in online auctions. Emotion is an important factor affecting individual behavior, but we know little about its effects in online auctions. We conducted a lab experiment to investigate the impact of positive emotion on individuals’ willingness to pay in online auctions. We found that individuals with positive emotions bid more than those with neutral emotions; that is, they paid a “happiness premium” of about 10 percent. The effect size was medium (Cohen’s d = 0.51). This study contributes to electronic commerce literature by identifying emotion as an important factor affecting online auction behavior. The findings also provide guidance to auction website design: websites can increase bid amounts by inducing positive emotions in potential customers

The Devil is in The Details: Measuring Sensory Processing Sensitivity Using Natural Language Processing

Personality traits play a strong role in our perceptions, attitudes, and decision-making behaviors in our daily lives, including our choices of words and writing patterns. While prior Information Systems (IS) research on personality typically used the Big Five personality traits as a theoretical framework, we look into measuring a comparatively new inherent personality trait, sensory processing sensitivity, using natural language processing. We collect data on twenty general essay questions from along with self-reported sensory processing sensitivity survey questions from 241 participants. We categorize participants based on survey questions with multiple methods and derive different features from the textual data. Our results show almost perfect agreement among the different methods categorizing a highly sensitive person versus a non-highly sensitive person. The initial analysis demonstrates that certain features can be of great potential in measuring sensory processing sensitivity in written text

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

Crossing the Uncanny Valley? Understanding Affinity, Trustworthiness, and Preference for More Realistic Virtual Humans in Immersive Environments

Developers have long strived to create virtual avatars that are more realistic because they are believed to be preferred over less realistic avatars; however, an “Uncanny Valley” exists in which avatars that are almost but not quite realistic trigger aversion. We used a field study to investigate whether users had different affinity, trustworthiness, and preferences for avatars with two levels of realism, one photo-realistic and one a cartoon caricature. We collected survey data and conducted one-on-one interviews with SIGGRAPH conference attendees who watched a live interview carried out utilizing two avatars, either on a large screen 2D video display or via 3D VR headsets. 18 sessions were conducted over four days, with the same person animating the photo realistic avatar but with different individuals animating the caricature avatars. Participants rated the photo-realistic avatar more trustworthy, had more affinity for it, and preferred it as a virtual agent. Participants who observed the interview through VR headsets had even stronger affinity for the photo-realistic avatar and stronger preferences for it as a virtual agent. Interviews further surprisingly suggested that our ability to cross the Uncanny Valley may depend on who controls the avatar, a human or a virtual agent

Beyond deep fakes: Conceptual framework, applications, and research agenda for neural rendering of realistic digital faces

Neural rendering (NR) has emerged as a novel technology for the generation and animation of realistic digital human faces. NR is based on machine learning techniques such as generative adversarial networks and is used to infer human face features and their animation from large amounts of (video) training data. NR shot to prominence with the deep fake phenomenon, the malicious and unwanted use of someone’s face for deception or satire. In this paper we demonstrate that the potential uses of NR far outstrip its use for deep fakes. We contrast NR approaches with traditional computer graphics approaches, discuss typical types of NR applications in digital face generation, and derive a conceptual framework for both guiding the design of digital characters, and for classifying existing NR use cases. We conclude with research ideas for studying the potential applications and implications of NR-based digital characters

Celebrity at Your Service: The Effects of Digital-Human Customer Service Agents

It is now possible to create digital humans that look and sound like real human celebrities. However, it is unclear whether celebrity effects from product endorsements observed in marketing research transfer to digital-human celebrities providing customer service. We conducted an experiment to investigate the effects of a digital-human celebrity as a customer service agent. We used a state-of-the-art neural rendering method to generate a digital human of Hugh Jackman. Our results show that users’ perceived celebrity of digital-human customer service agents leads to higher perceived ability, benevolence, and integrity of the agents, increasing the perception of trustworthiness and the intention to use the service. Also, when digital-human agents make a mistake, customers forgive celebrity agents more than non-celebrity agents. Contrary to what the prior literature suggests, whether the digital-human agents are controlled by a human or by AI has no influence on the impact of errors on perceived trustworthiness. However, the AI-controlled agents increase the willingness to use the service, though they are perceived to be less benevolent

Facing the Artificial: Understanding Affinity, Trustworthiness, and Preference for More Realistic Digital Humans

In recent years, companies have been developing more realistic looking human faces for digital, virtual agents controlled by artificial intelligence (AI). But how do users feel about interacting with such virtual agents? We used a controlled lab experiment to examine users’ perceived trustworthiness, affinity, and preference towards a real human travel agent appearing via video (i.e., Skype) as well as in the form of a very human-realistic avatar; half of the participants were (deceptively) told the avatar was a virtual agent controlled by AI while the other half were told the avatar was controlled by the same human travel agent. Results show that participants rated the video human agent more trustworthy, had more affinity for him, and preferred him to both avatar versions. Users who believed the avatar was a virtual agent controlled by AI reported the same level of affinity, trustworthiness, and preferences towards the agent as those who believed it was controlled by a human. Thus, use of a realistic digital avatar lowered affinity, trustworthiness, and preferences, but how the avatar was controlled (by human or machine) had no effect. The conclusion is that improved visual fidelity alone makes a significant positive difference and that users are not averse to advanced AI simulating human presence, some may even be anticipating such an advanced technology

Intelligence Augmentation: Towards Building Human-Machine Symbiotic Relationship

Artificial intelligence, which people originally modeled after human intelligence, has made significant advances in recent years. These advances have caused many to fear that machines will surpass human intelligence and dominate humans. Intelligence augmentation (IA) has the potential to turn the tension between the two intelligence types into a symbiotic one. Although IA has not gained momentum until recent years, the idea that machines can amplify human abilities has existed for many decades. Expanded from a panel discussion on Intelligence Augmentation at the 2020 International Conference of Information Systems (ICIS), we define IA in light of its history and evolution and classify IA based on its capabilities, roles, and responsibilities. Based on reviewing the IA literature in terms of research themes, enabling technology, and applications, we identify key research issues, challenges, and future opportunities