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

Bibliographic Details
Main Author: Gimpel, Henner.
Other Authors: Krämer, Jan., Neumann, Dirk., Pfeiffer, Jella., Seifert, Stefan., Teubner, Timm., Veit, Daniel J., Weidlich, Anke.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2021.
Edition:1st ed.
Subjects:
Electronic books.
Online Access:Click to View
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.

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