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Auswirkungen von Kinderarmut auf den Schulerfolg
No full textAUSWIRKUNGEN VON KINDERARMUT AUF DEN SCHULERFOLG
Auswirkungen von Kinderarmut auf den Schulerfolg (1
How can cities attract young high potentials to settle
No full textHOW CAN CITIES ATTRACT YOUNG HIGH POTENTIALS TO SETTLE
How can cities attract young high potentials to settle (1)
Abstract (3)
List of abbreviations (4)
Table of Contents (5)
1 Introduction (7)
2 Literature Review (8)
2.1 Concept of "High Potentials" (8)
2.2 Factors Influencing Relocation Decisions of Young Professionals (9)
2.2.1 Economic factors (11)
2.2.2 Non-economic factors (12)
2.3 Case Studies of Cities Competing for High Potentials (12)
2.3.1 New York City (14)
2.3.2 Singapore (14)
2.3.3 Amsterdam (15)
2.3.4 Berlin (16)
2.3.5 London (16)
2.3.6 Vienna (17)
2.4 Lessons learned (18)
2.4.1 Economic Opportunities and Career Development (18)
2.4.2 Livability and Infrastructure (19)
2.4.3 Cultural Openness and Inclusivity (19)
2.4.4 Innovation Ecosystem and Entrepreneurship (19)
2.4.5 Administrative Efficiency and Flexibility (20)
2.5 Gaps in the Existing Literature (20)
2.6 Conclusion (21)
3 Methodology (21)
3.1 Quantitative Phase (22)
3.2 Qualitative Phase (24)
4 Results (25)
4.1 Quantitative Results (25)
4.1.1 General Perception of Vienna (25)
4.1.2 Perception by Age Group (<30) (26)
4.1.3 Perception by Gender (27)
4.1.4 Perception by Educational Background (27)
4.2 Qualitative Results (28)
4.2.1 High Potentials’ Perspective (28)
4.2.2 Employers’ Perspective (31)
4.2.3 Experts’ Perspective (31)
5 Discussion (32)
5.1 Interpretation of Findings (32)
5.1.1 Interpretation of Findings: Quantitative Study (32)
5.1.2 Interpretation of Findings: Qualitative Study (33)
5.2 Comparison with Literature (35)
5.3 Implications (37)
5.3.1 Implications: City Decision-Makers (37)
5.3.2 Implications: Employers (37)
5.3.3 Implications: Innovation Ecosystems (38)
5.3.4 Implications: Research (38)
5.4 Theoretical and Practical Contributions (39)
5.5 Limitations (39)
5.5.1 Limitations of Quantitative Phase (39)
5.5.2 Limitations of Qualitative Phase (40)
6 Conclusion (41)
6.1 Summary of Findings (41)
6.2 Answer to the Research Question (41)
6.3 Case study for Vienna (42)
6.3.1 Status Quo (42)
6.3.2 Recommendations for relevant stakeholders (42)
6.4 Concluding remarks (43)
7 References (44)
8 List of Tools (53)
9 Acknowledgements (54)
10 Appendix (55)
10.1 Appendix A: HousingAnywhere International Rent Index: Amsterdam, Lisbon, Berlin, Madrid, Brussels, Vienna (HousingAnywhere.com, 2025) (55)
10.2 Appendix B: Table of exemplary Excel formulas used to analyze the quantitative survey (56)
10.3 Appendix C: Education Analysis from the perspectives of New Zealand, Denmark, and the Netherlands (57)
10.4 Appendix D: 15 interview summaries from interviews with high potentials (58)
10.5 Appendix E: 2 interview summaries from interviews with HR Managers (88)
10.6 Appendix F: 2 interview summaries from interviews with experts (90)
10.7 Appendix G: Case Study Vienna - Strengths, Weaknesses, and Strategic implications (93
Herausforderungen und Strategien im Talentmanagement
No full textHERAUSFORDERUNGEN UND STRATEGIEN IM TALENTMANAGEMENT
Herausforderungen und Strategien im Talentmanagement (1
The halo effect of slow motion
No full textTHE HALO EFFECT OF SLOW MOTION
The halo effect of slow motion (1)
1 Abstract (10)
2 Keywords (11)
3 Introduction (12)
4 Theoretical Background (15)
4.1 Sensory and Visual Processing in Advertising (15)
4.1.1 Sensory Marketing Concepts and Relevance (15)
4.1.2 Visual Attention and Perception Mechanisms (16)
4.1.3 Effects of Visual Speed Manipulation (17)
4.1.4 Slow Motion Effects on Cognitive and Emotional (19)
4.2 The Halo Effect in Brand Perception (20)
4.2.1 The Halo Effect and Attribution Theory (20)
4.2.2 The Halo Effect of Slow Motion (21)
4.3 Brand Personalities and Consumer Perception (23)
4.3.1 Definition and Dimensions of Brand Personalities (23)
4.3.2 Brand Personality and Advertising Strategy (24)
4.4 FMCG Advertising and Brand Perception (27)
4.4.1 FMCG Characteristics and Challenges (27)
4.4.2 Advertising Impact in FMCG Markets (28)
4.4.3 Importance of Brand Perception in Consumer Decision-Making (29)
4.5 Moderating Factors (30)
4.5.1 General Moderators in Advertising Effectiveness (30)
4.5.2 Moderators of Visual and Temporal Stimuli (31)
5 Research Gap and Hypothesis (33)
6 Methodology (39)
6.1 Experimental Design (39)
6.2 Experimental Setup (39)
6.3 Stimuli Development and Product Selection (41)
6.3.1 Brand Personality Priming (41)
6.3.2 Product and Brand Selection (41)
6.3.3 Video Stimuli and Motion Type Manipulation (42)
6.4 Procedure and Data Collection (44)
6.4.1 Participants and Recruitment (44)
6.4.2 Study Procedure and Technical Implementation (44)
6.5 Measures (44)
6.5.1 Attention and Manipulation Check (44)
6.5.2 Dependent Variables (45)
6.6 Data Analysis (46)
6.6.1 Preliminary Analysis Procedures (46)
6.6.2 Hypothesis Testing (47)
6.6.3 Exploratory Analysis (47)
6.7 Validity and Limitations (48)
7 Results (49)
7.1 Sample Overview and Demographics (49)
7.2 Manipulation Check and Dependent Variables Overview (50)
7.3 Hypothesis Testing (52)
7.3.1 Brand Personality Perception by Motion Type (52)
7.3.2 Brand-Ad Fit by Brand Personality and Motion Type (53)
7.3.3 Evaluation by Brand Personality and Motion Type (54)
7.4 Overall Effects of Motion Type (55)
7.5 Brand-Ad Fit as a Predictor of Evaluative Response (57)
7.6 Exploratory Analyses (58)
7.6.1 Evaluative Subdimensions (58)
7.6.2 Gender Effects (62)
7.6.3 Familiarity Effects on Priming and Brand-Ad Fit (63)
8 Discussion (64)
8.1 Summary of Key Findings (64)
8.2 Theoretical Implications (65)
8.3 Practical Implications (67)
8.4 Methodological Considerations (70)
8.5 Future Research Directions (71)
9 Conclusion (74)
10 References (76)
11 Appendix (87)
Appendix A: Literature Table (87)
Appendix B: Survey Flow and Questionnaire (94)
Appendix C: Supplementary Figures, Tables and Statistical Outputs (103
Impact of managerial sentiment during earnings calls of tech firms: predicting market reactions
No full textIMPACT OF MANAGERIAL SENTIMENT DURING EARNINGS CALLS OF TECH FIRMS: PREDICTING MARKET REACTIONS
Impact of managerial sentiment during earnings calls of tech firms: predicting market reactions (1)
List of Figures (ii)
List of Tables (ii)
Glossary (iii)
Introduction (1)
Literature (2)
Behavioral Finance and Sentiment Theory (2)
Measuring Sentiment in Financial Disclosures (3)
Managerial Sentiment and Stock Market Reactions (5)
Executive Sentiment in Earnings Calls (6)
Sentiment and Technology Sector Characteristics (7)
Research Hypotheses (8)
Data (9)
Data sources (9)
Earnings Calls Transcripts (9)
Stock Market Data (9)
Company-Specific Financial and Macroeconomic Data (10)
Definition of Milestone Variables (10)
Descriptive Statistics (10)
Sample Construction (11)
Additional Criteria and Data Winsorization (12)
Methodology (12)
Event Study Framework (12)
Manager Sentiment Analysis (14)
Econometric Models (15)
Base Regression (15)
Additional Analyses (16)
Sentiment Score Dynamics and Milestone Regression (18)
Results (19)
Does managerial sentiment in earnings calls of U.S. technology firms shape investor behavior? (19)
Base Regressions (19)
Additional Analyses (23)
Have executives of technology companies adjusted their sentiment in earnings calls over time? (26)
Conclusion (28)
Figures and Tables (33)
Appendix (45
Stock market and real economy interactions in financial extreme events
No full textSTOCK MARKET AND REAL ECONOMY INTERACTIONS IN FINANCIAL EXTREME EVENTS
Stock market and real economy interactions in financial extreme events (1)
Abstract (2)
Table of Figures (4)
1. Introduction (5)
2. Literature Review (8)
2.1. The relationship between stock markets and the real economy (8)
2.2. Sectoral Differences (11)
2.3. Stock returns and real economy in financial extreme events (13)
3. Methodology (16)
3.1. Empirical Model and Technical Setup of the Regression (16)
3.2. Sectoral Analysis Approach (18)
3.3. Rolling Window Approach & Robustness Checks (19)
4. Data (21)
4.1. Dependent Variables: Monthly Stock Market Returns (21)
4.2. Independent Variables: Macroeconomic Variables (23)
5. Results (25)
5.1. Results of the FTSE 100 (Baseline & Interaction Model) (25)
5.2. Sectoral Analysis: Macroeconomic Effects on Major FTSE 100 Sectors (27)
5.3. Cross Validation with S&P 500 (30)
5.4. Rolling Window Approach & Robustness Checks (33)
5.5. Main Results compared to Literature (34)
6. Discussion (38)
7. Conclusion (40)
8. References (42)
9. Appendix (48
Comparing firm-created vs. organic engagement effects on firm outcomes
No full textCOMPARING FIRM-CREATED VS. ORGANIC ENGAGEMENT EFFECTS ON FIRM OUTCOMES
Comparing firm-created vs. organic engagement effects on firm outcomes (1)
Acknowledgements (3)
1 Abstract (6)
2 Keywords (6)
3 Introduction (7)
4 Literature review (9)
4.1 Theoretical foundations (9)
4.3 Organic Consumer Engagement (14)
4.4 Cross-channel & synergy/competition effects (16)
4.5 Conceptualization: model & hypotheses (18)
5 Data and Methodology (20)
5.1 Data preparation (20)
5.2 Data analysis (21)
6 Results (24)
7 Discussion (34)
7.1 Theoretical contribution (34)
7.2 Managerial implications (35)
7.3 Limitations and future research (37)
8 References (39)
9 Appendix (42)
9.1 Appendix A - Regression Assumption Testing (42)
9.2 Appendix B - Regression Results for Shop Visits (43)
9.3 Appendix C - Regression Results for Shop Sales (44)
9.4 Appendix D - Regression Results for Shop Visits and GT_data * trnd_PI Interaction (45)
9.5 Appendix E - Regression Results for Shop Sales and GT_data * trnd_PI Interaction (46