Market Engineering : : Insights from Two Decades of Research on Markets and Information.

• Author: Gimpel, Henner.
• TeilnehmendeR: Krämer, Jan.; Neumann, Dirk.; Pfeiffer, Jella.; Seifert, Stefan.; Teubner, Timm.; Veit, Daniel J.; Weidlich, Anke.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2021.; ©2021.
• Year of Publication: 2021
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (244 pages)

Table of Contents

• Intro
• Preface
• Contents
• Information and Market Engineering at KIT: Quo Vadis?
• 1 Introduction
• 2 A Brief Overview of Past IM Research
• 3 Present and Future of IM Research
• 3.1 Smart Grids and Energy Markets
• 3.1.1 Support Systems for Energy Consumers
• 3.1.2 Innovative Energy Market Designs
• 3.2 Business Data Analytics
• 3.2.1 Recommendation Systems Innovation
• 3.2.2 Data Markets and Platforms
• 3.2.3 Modelling Asset Development
• 3.3 Electronic Markets and User Behaviour
• 3.3.1 Affective Experience in Knowledge Work
• 3.3.2 Methods and Models for Adaptive Systems
• 3.4 Digital Experience and Participation
• 3.4.1 User Experience in Immersive Systems
• 3.4.2 Participatory and Collaborative Information Systems
• 4 Concluding Thoughts
• References
• Karlsruhe Decision &amp
• Design Lab (KD2Lab)
• Market Success: The Quest for the Objectives and Success Factors of Markets
• 1 Introduction
• 2 Theoretical Background
• 2.1 Market Design and Market Engineering
• 2.2 Market Structure and Success
• 3 Market Success Framework
• 3.1 Market Objectives
• 3.1.1 Short-Run Efficiency
• 3.1.2 Long-Run Efficiency
• 3.1.3 Support of the Broader Economy and Society
• 3.1.4 Security of Supply
• 3.1.5 Distributive Justice
• 3.1.6 Environmental Protection
• 3.1.7 Human Health and Flourishing Protection
• 3.2 Market Success Factors
• 3.2.1 Objective Orientation
• 3.2.2 Participation
• 3.2.3 Incentives for Market-Conducive Behavior
• 3.2.4 Integration and Interconnection
• 3.2.5 Coordinated Timing
• 3.2.6 Realizability in IT Systems
• 3.2.7 Legitimacy
• 3.2.8 Legality
• 3.2.9 Technological Factors
• 3.2.10 Transparency
• 3.2.11 Simplicity
• 4 Intended Usage and Limitations of the Market Success Framework
• 5 Conclusion
• References.
• Decision Analytics for Initial Public Offerings: How Filing Sentiment Influences Stock Market Returns
• 1 Introduction
• 2 Related Work
• 2.1 IPO Filings
• 2.2 Sentiment Analysis in the Finance Discipline
• 2.3 Information Processing Theory of IPO Filings
• 3 Research Methodology: Sentiment Analysis of IPOs
• 3.1 Preprocessing IPO Filings
• 3.2 Method for Sentiment Analysis
• 4 Empirical Evaluation: Analyzing Sentiment of IPO Filings
• 4.1 IPO Filing Corpus, News Corpus, and Stock Market Data
• 4.2 Descriptive Statistics of Stock Market Returns
• 4.3 Control Variables
• 4.4 Regression Design
• 4.5 Results: Linking Sentiment and Stock Market Performance
• 4.6 Results: Analyzing the Persistence of Sentiment Impact Over Time
• 4.7 Results: Prospectus Sentiment Versus News Sentiment
• 5 Conclusion
• References
• Taxonomy Development for Business Research: A Hands-On Guideline
• 1 Introduction
• 2 The Process of Taxonomy Building
• 2.1 Selection of Research Material and Sampling
• 2.2 Inductive Content Analysis Procedures
• 2.3 Clustering of Raw Characteristics
• 2.4 Formative Pretests and Deductive Content Analysis Procedures
• 2.5 Quantitative Clustering of Manifestations
• 2.6 Summative Checks of Taxonomic Quality
• 2.7 Limitations of the Method
• 3 Conclusion
• References
• Understanding Emotions in Electronic Auctions: Insights from Neurophysiology
• 1 Introduction
• 2 Theoretical Background
• 2.1 Affective Processing and Emotions
• 2.2 Framework for Emotional Bidding
• 2.3 Neurophysiological Measurements
• 3 Empirical Results
• 3.1 Impact of the Auction System and Environment on a Bidder's Current Emotional State (P1)
• 3.2 Impact of a Bidder's Current Emotional State on Their Bidding Behavior (P2)
• 3.3 Immediate Emotions in Response to Auction Events (P3).
• 3.4 Immediate Emotions in Response to Auction Outcome (P4)
• 4 Discussion
• References
• Studying Conceptual Modeling Processes: A Modeling Tool, Research Observatory, and Multimodal Observation Setup
• 1 Conceptual Models and Conceptual Modeling
• 2 Multimodal Observation Setup
• 3 Modeling Tool and Research Observatory
• 4 Data Integration, Data Analysis, and Lessons Learned
• References
• Engineering Energy Markets: The Past, the Present, and the Future
• 1 Introduction
• 2 Energy Sector Developments
• 2.1 Emergence of Power Markets
• 2.2 Introduction of Emissions Trading
• 2.3 Empowering the Demand Side
• 2.3.1 Modeling Flexibility
• 2.3.2 Marketing Flexibility
• 2.3.3 Group Formation for Flexibility Aggregation
• 2.4 Renewable Energy Integration and Congestion Management
• 2.4.1 Congestion Management in the Transmission Grid
• 2.4.2 Market-Based Congestion Management at the Distribution Level
• 2.5 Peer-to-Peer Energy Markets
• 3 The Next Steps for Decentralized Energy Markets
• 4 Conclusion and Outlook
• References
• Can Immersive Systems Help Address Sustainability Goals? Insights from Research in Information Systems
• 1 Introduction
• 2 Immersive Systems as a Tool to Increase Awareness
• 3 Immersive Systems as a Tool to Increase Motivation to Participate
• 4 Immersive Systems as a Tool for Smart Information Transfer
• 5 Immersive Systems as a Tool for Learning to Act
• 6 Conclusion and Outlook
• References
• How at the Institute of Information Systems and Marketing One Thing Leads to Another and Eventually Resultsin a Low-Trade Theorem
• 1 Requirements
• 2 Setting
• 3 Model
• 4 Excursus
• 5 Digression
• 6 Assessment
• References
• On the Potency of Online User Representation: Insights from the Sharing Economy
• 1 Introduction
• 2 Theoretical Background
• 2.1 Signaling and Social Presence Theory.
• 2.2 Framework of User Representation Artifacts
• 3 User Representation Artifacts
• 4 Case Study: User Representation on Airbnb
• 5 Discussion
• 6 Concluding Note
• References
• Legal Tech and Lawtech: Towards a Framework for Technological Trends in the Legal Services Industry
• 1 Introduction
• 2 Background/Foundations
• 2.1 The Role of Technology in Different Areas of Law
• 2.2 Recent Trends in Legal Technology
• 2.3 Lawtech Research
• 3 A Framework for Lawtech Applications
• 3.1 B2C Applications
• 3.2 B2B Applications
• 3.3 Internal Applications
• 4 Discussion and Conclusion
• References
• The Socialoid: A Computational Model of a City
• 1 Introduction
• 2 Background: Smart Cities and Urban Data Modeling
• 3 Modeling for Scientific Applications and Policy Purposes
• 4 Modeling Example
• 4.1 Generalized Optimization
• 4.2 Heuristic Optimization
• 4.3 Designing MAD Models
• 4.4 Using the MAD Models
• 5 Discussion and Future Work
• References
• Data Analytics for Smart Decision-Making and Resilient Systems
• 1 Introduction
• 2 Data Analysis for Decision-Making
• 3 Resilient Systems and the Supporting Role of Data Analytics
• 4 Use Case 1: Improving Employee Experience Through the Listen-Learn-Act Approach and Reasoning on Semi-structured Data
• 5 Use Case 2: Manufacturing
• 6 Use Case 3: Purchasing and Logistics
• 7 Use Case 4: Software Transition
• 7.1 Key Challenges in Transition Projects
• 7.2 AI-Automated Platform with Consistent Digitalization
• 7.3 Resilience with the Platform
• 8 Conclusion
• References
• Academic Poem for Christof.