26617 research outputs found
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Deconstructing gender stereotypes in AI
No full textDECONSTRUCTING GENDER STEREOTYPES IN AI
Deconstructing gender stereotypes in AI (1)
List of Figures (6)
List of Tables (6)
Abstract (8)
1 Introduction (9)
1.1 Research Problem (9)
1.2 Research Direction (11)
1.3 Research Methodology (11)
2 Conceptual Background (13)
2.1 What Do We Mean With “AI”? (13)
2.2 Gender As A Social Construct (13)
2.3 Why Perceived Gender Matters (15)
3 Structured Literature Review (17)
3.1 Literature Search (17)
3.2 PRISMA Diagram (18)
3.3 A Concept-Centric Literature Review (20)
3.4 Literature Review Findings (30)
3.4.1 Agent Form and Modality (43)
3.4.2 Gender Representation: Explicit, Implicit, and “Neutral” (43)
3.4.3 Task Context and Role Fit (44)
3.4.4 User Factors: Gender, Culture, and Age (44)
3.4.5 Outcomes: Warmth, Competence, Trust, Satisfaction, Humanness (45)
3.4.6 Methods and Theory (45)
3.4.7 Mechanism and Boundary Conditions (45)
3.5 Research Gap & Research Questions (46)
4 Research Model & Hypotheses (49)
5 Experiment Implementation (55)
5.1 Experiment Design (55)
5.1.1 Phase 1 (Within-Subjects) (55)
5.1.2 Phase 2 (Between-Subjects) (56)
5.1.3 Demographics (56)
5.2 Pre-Test (57)
5.3 Procedure (58)
5.4 Participants and Sampling (59)
5.5 Bias Analysis (60)
5.6 Measures And Constructs (62)
6 Analysis (65)
6.1 Demographics (65)
6.2 Construction (65)
6.3 Descriptive Statistics (67)
6.4 Hypotheses Testing (68)
6.4.1 Assumption Checks (69)
6.4.2 H1 Default-Male Attribution (69)
6.4.3 H2 Participant-Gender Moderation (69)
6.4.4 H3 Match / Mismatch Stereotype Effects (71)
6.4.4.1 Competence (71)
6.4.4.2 Warmth (71)
6.4.4.3 MANCOVA (72)
6.4.4.4 Pairwise Contrasts (Estimated Marginal Means) for Competence (73)
6.4.5 Response Time Analysis (73)
7 Results (76)
7.1 H1 Default-Male Attribution (76)
7.2 H2 Participant- Gender Moderation (78)
7.3 H3 Match / Mismatch stereotype effects (79)
8 Discussion (81)
8.1 Theoretical Implications (83)
8.1.1 Conditions Suppressing the Male Default (83)
8.1.2 Why Anglophone Studies Often Find Male Defaults (84)
8.1.3 Measurement Considerations (85)
8.1.4 Domain-Sensitive Stereotype Activation and Trait Diagnosticity (85)
8.1.5 Asymmetric Mismatch and Status Beliefs (86)
8.1.6 Cue Competition and Micro-Linguistic Gendering (88)
8.1.7 Familiarity as a Global Booster (90)
8.1.8 Measurement Implications (92)
8.2 Practical Implications (92)
8.2.1 Persona Strategy and Language (93)
8.2.2 Familiarity and User Experience (93)
8.2.3 Personalization and Control (94)
8.2.4 Cultural and Linguistic Localization (94)
8.2.5 Governance and Documentation (94)
8.2.6 Measurement and Monitoring (95)
8.2.7 Domain Fit without Stereotype Reinforcement (96)
8.2.8 Team Practices (96)
8.3 Limitations & Future Research (96)
9 Conclusion (98)
References (99)
Appendix A (120)
Appendix B (145
SEC 10-K report risk factor text embedding clustering
No full textSEC 10-K REPORT RISK FACTOR TEXT EMBEDDING CLUSTERING
SEC 10-K report risk factor text embedding clustering (1)
Introduction (3)
Motivation (3)
Research questions (4)
Structure of the thesis (4)
Theoretical background (5)
Clustering (5)
Parametric mixture models (6)
Setup (6)
Parameter estimation: EM algorithm (6)
Metrics of clustering quality (7)
Directional statistics and distributions on the hypersphere (8)
Poisson Kernel-based and Spherical Cauchy distributions (8)
Literature Review (10)
Clustering of directional data via the Spherical Cauchy and Poisson Kernel-Based distributions (10)
Text analysis of financial reports (11)
Statistical Inference (13)
Previous result (13)
Reparametrization (14)
Observed Fisher information matrix (15)
Statistical significance tests (18)
Tests for single coefficients (19)
Joint tests for covariate coefficient vectors (19)
Wald-type tests (19)
Max-type tests (20)
Score-based wild bootstrap tests (21)
Application methodology (23)
Data (23)
SEC 10-K files (23)
Company market data (24)
Text embedding model (24)
High-dimensional data visualization (25)
Clustering algorithms and software (26)
Statistical inference model implementation (26)
Simulation study (27)
Observed Fisher information calculation comparison (27)
Embedding dimensionality effect on test power and size (28)
Spherical Cauchy Distribution (28)
Poisson Kernel-Based Distribution (29)
SEC 10K report clustering (31)
Clustering of text embeddings (31)
Statistical inference (32)
Financial interpretation (33)
Conclusions and discussion (38)
Bibliography (40)
Appendix (44)
SEC 10-K report download and embedding implementation (Python) (44)
Function definition (44)
Raw text download usage (50)
Text embedding model and metadata download (51)
Statistical inference for PKBD and SC clustering covariates implementation (C++/Rcpp) (52)
Simulation study implementation (R) (63)
SEC 10-K report clustering and statistical inference (R) (73)
Embedding clustering (73)
Statistical inference (77
Die Berichterstattung linksliberaler Medien über die Letzte Generation und Fridays for Future
No full textDIE BERICHTERSTATTUNG LINKSLIBERALER MEDIEN ÜBER DIE LETZTE GENERATION UND FRIDAYS FOR FUTURE
Die Berichterstattung linksliberaler Medien über die Letzte Generation und Fridays for Future (1
Exploring coopetition as an implementation strategy for the Austrian carbon capture and storage (CCS) ecosystem
No full textEXPLORING COOPETITION AS AN IMPLEMENTATION STRATEGY FOR THE AUSTRIAN CARBON CAPTURE AND STORAGE (CCS) ECOSYSTEM
Exploring coopetition as an implementation strategy for the Austrian carbon capture and storage (CCS) ecosystem (1)
Abstract (2)
List of Figures (4)
List of Tables (4)
List of Abbreviations (5)
1. Introduction (6)
1.1 Research Problem & Gap (6)
1.2 Research Objectives & Questions (7)
1.3 Methodology, Scope, and Structure (8)
2. Literature Review (9)
2.1 Overview of Carbon Management (9)
2.2 Application of CCS as a Climate Mitigation Strategy (10)
2.3 Implementation Challenges along the CCS Value Chain (13)
2.4 Theoretical Framework: Coopetition (16)
2.5 Coopetition in CCS Ecosystems (19)
3. Methodology (20)
3.1 Research Design and Data Collection (20)
3.2 Data Analysis (22)
4. Results (25)
4.1 Feasibility of Coopetition in the Scope of Capture Technologies (25)
4.2 Feasibility of Coopetition in the Scope of Energy Supply (28)
4.3 Feasibility of Coopetition in the Scope of Transport Infrastructure (29)
4.4 Feasibility of Coopetition in the Scope of CO₂ Storage (33)
4.5 Feasibility of Coopetition in the Scope of Lobby Work (36)
4.6 Feasibility of Coopetition in the Scope of Public Perception (37)
4.7 Feasibility of Coopetition across the CCS Value Chain (39)
5. Discussion (41)
5.1 Feasibility of Coopetition in the Austrian CCS Ecosystem (41)
5.2 Theoretical Implications (44)
5.3 Practical Implications (49)
A Practical Application of Coopetition: the CCS Alliance Austria (CCS-AA) (51)
5.4 Limitations (53)
5.5 Future Research Directions (54)
6. Conclusion (55)
References (58
Verständnis für Risiko(management) und Versicherung
No full textVERSTÄNDNIS FÜR RISIKO(MANAGEMENT) UND VERSICHERUNG
Verständnis für Risiko(management) und Versicherung (1
The influence of artificial intelligence on augmented strategic decision-making
No full textTHE INFLUENCE OF ARTIFICIAL INTELLIGENCE ON AUGMENTED STRATEGIC DECISION-MAKING
The influence of artificial intelligence on augmented strategic decision-making (2
Competing discourse about female gender performance among social media influencers on Instagram
No full textCOMPETING DISCOURSE ABOUT FEMALE GENDER PERFORMANCE AMONG SOCIAL MEDIA INFLUENCERS ON INSTAGRAM
Competing discourse about female gender performance among social media influencers on Instagram (1
Die Mitgliedschaft als Erfolgsfaktor
No full textDIE MITGLIEDSCHAFT ALS ERFOLGSFAKTOR
Die Mitgliedschaft als Erfolgsfaktor (1
Premiumsegment KMU Einzelhandel und die Auswirkungen von Omnichannel-Strategien auf deren Unternehmenserfolg
No full textPREMIUMSEGMENT KMU EINZELHANDEL UND DIE AUSWIRKUNGEN VON OMNICHANNEL-STRATEGIEN AUF DEREN UNTERNEHMENSERFOLG
Premiumsegment KMU Einzelhandel und die Auswirkungen von Omnichannel-Strategien auf deren Unternehmenserfolg (1