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

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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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2023. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.