Understanding and Supporting Decision-Making in Electronic Auctions: A NeuroIS Approach

Making use of the potential of NeuroIS, I apply a NeuroIS approach in this thesis to further the understanding of decision-making and to analyze the opportunities for NeuroIS in decision-support, both in electronic auctions

Information Management and Market Engineering. Vol. II

The research program Information Management and Market Engineering focuses on the analysis and the design of electronic markets. Taking a holistic view of the conceptualization and realization of solutions, the research integrates the disciplines business administration, economics, computer science, and law. Topics of interest range from the implementation, quality assurance, and advancement of electronic markets to their integration into business processes and legal frameworks

Live Biofeedback in Electronic Markets

Decisions in electronic markets are frequently made under time pressure and in competition to others. Both factors can cause the decision maker to experience high levels of arousal. Without sound emotional processing, arousal can have detrimental effects on decision making. In this thesis the use of live biofeedback to support emotion perception and thus, to facilitate emotion regulation during emotionally charged decision making is evaluated. Based on a systematic literature review existing live biofeedback research is analyzed in Chapter 2. A transmission model for live biofeedback is developed that classifies the main components of live biofeedback applications and the flow of information in form of transmission signals. To address the identified research gaps, three experimental studies (study I-III) are designed that investigate the effects of arousal and the use of live biofeedback in electronic markets. Study I in Chapter 3 examines how arousal affects purchasing decisions with and without social interaction to analyze the context dependence of the effects of arousal on decision making. The results reveal that in auctions, where social interaction is a key characteristic, arousal increases final prices. Purchasing decisions without social interaction, however, are not affected by arousal. As social interaction has been identified as an essential factor for arousal to affect decision making, the subsequent studies II and III investigate the effects of live biofeedback in markets experiments that involve social interaction. Study II in Chapter 4 evaluates the effects of live biofeedback on emotional processing in the context of auction bidding. Without prior biofeedback training this novel user interface element alters decision making processes at a cognitive and affective level. Study participants, who suppress emotional expressions, experience higher levels of physiological arousal. When provided with live biofeedback, this effect is mitigated. Furthermore, participants who receive live biofeedback show increased coherence of physiological and perceived arousal. Study III in Chapter 5 examines the use of biofeedback in a game that has frequently been used to model financial markets, that is, the beauty contest game. In this study, participants complete a training in order to familiarize with the live biofeedback prior to the experiment. The analysis reveals that live biofeedback increases arousal perception and reduces suppression of emotional expressions. Importantly, participants who receive live biofeedback yield higher decision making quality. In summary, this thesis provides further insights into the effects of arousal on behavior and how live biofeedback affects emotional processing and decision making in electronic markets. The results of this thesis suggest that live biofeedback is a promising tool to support emotion perception, regulation, and decision making of market participants

Using Live Biofeedback for Decision Support: Investigating Influences of Emotion Regulation in Financial Decision Making

The influence of emotions on decision making is widely accepted, however, research investigat-ing emotions within decision support systems is scarce. Previous research showed that via emo-tion regulation decision makers can significantly improve their decisions. However, a sound perception of emotions is a foundation for beneficial emotion regulation. In this paper, we pro-pose the use of live biofeedback–the provision of real-time information about a person’s current physiological state–as a potential feature for decision support systems in online environments. We developed a research model and hypothesize that live biofeedback can moderate (i) the in-fluence of the decision environment on decision makers’ physiological states and (ii) the deci-sion makers’ perceptions of their emotional states. Within the current research a live biofeed-back prototype for decision support in the context of financial trading was implemented. We aim at testing the hypothesized moderating effects of the developed decision support system in the controlled environment of a laboratory market experiment

Measuring Emotions in Electronic Auctions

This book develops a structured methodology that allows to systematically analyze emotions in auctions. It provides a unified framework for emotional bidding in auctions, which comprises the bidders\u27 processes of cognitive reasoning and emotional processing, and a methodology for measuring physiological correlates of human emotional processing in economic experiments is proposed: physioeconomics. Finally, an experiment is presented which investigates the impact of clock speeds in Dutch auctions

Emotions and Emotion Regulation in Economic Decision Making

By employing the methodology of experimental economics, the thesis examines the influence of emotions on decision making in electronic auction markets. Subjects\u27 emotional processes are measured by psychophysiological indicators, helping to decipher the coherence of information, emotion (regulation) and decision making. Four chapters build the main body of the thesis and all are constructed similarly: introduction, design, method, results, limitations, theoretical and managerial implications

Incorporating Emotional Information in Decision Systems

Abstract – The media equation [22] states that users react to systems as they would to another person, while continuously emitting social signals. Today’s users expect systems to be empathetic and understand these social signals. Decision systems are a specific sub-branch, facing the need to incorporate affective information, to facilitate users to maximize their cognitive resources. To this end, we attempt to incorporate affective information in the form of physiology to learn users ’ decision behavior. In a controlled experiment, we record participants ’ decisions and measure physiological signals elicited from subjects. To predict the binary decision to buy or sell, three algorithms, multi-layer perceptron, radial basis function, and decision trees, are compared, and they yield recognition rates of 76%, 73 % and 77.2 % respectively. Taking these results, we propose that a decision tree with feature-level fusion, factors in affective information in this controlled context best. These results however have to be extrapolated to decision contexts that elicit emotions more strongly. Keywords—Multimodal Systems, Emotion, User Behavior

Social Preferences under Risk - Peer Types and Relationships in Economic Decision Making

This work considers economic decision making, particularly scenarios in which the decision maker faces the presence of other (private) persons. It draws inferences for the design, operation, and use of e-commerce platforms from an economic and information systems perspective and is oriented along two vital concepts therein: social preferences and risk. The role of the type as well as the relation towards the reference person is explored from an empirical and game theoretical perspective

Using Wearable Devices for Non-invasive, Inexpensive Physiological Data Collection

Using sensors to gather physiological data about users can provide valuable insights for Information Systems (IS) research that are not availed through traditional measures. While useful in many laboratory settings, many of these physiological sensors (e.g., fMRI, EEG, EKG, etc.) are impractical and severely limited in other scenarios due to (1) prohibitive cost, (2) small sample size, (3) invasiveness, and (4) the difficulty to match psychological traits to physiological measures. In this study, we demonstrate how inexpensive consumer-grade wearable technologies overcome these first three limitations while we extend existing research on exploring the fourth limitation