26617 research outputs found
Sort by
To what extent does the role of product involvement shape consumer responses to AI-labeled advertisement?
No full textTO WHAT EXTENT DOES THE ROLE OF PRODUCT INVOLVEMENT SHAPE CONSUMER RESPONSES TO AI-LABELED ADVERTISEMENT?
To what extent does the role of product involvement shape consumer responses to AI-labeled advertisement? (1)
To What Extent Does the Role of Product Involvement Shape Consumer Responses to AI-Labeled Advertisement? (2)
1Abstract (7)
2Keywords (8)
3Introduction (9)
3.1Problem Statement (10)
3.2Approach (10)
Chapter (11)
4Research Context (12)
Chapter (14)
5Theoretical Background (15)
5.1Literature Review (15)
Figure 1. Elaboration Likelihood Model (Petty & Briñol 2012) (19)
Chapter (20)
Chapter (20)
Chapter (20)
Chapter (20)
Chapter (20)
Chapter (20)
Chapter (20)
Table 1. Overview of Existing Literature and Literature Gap (21)
5.2Hypotheses (22)
Figure 2. Conceptual Diagram (23)
Chapter (23)
6Methodology (24)
6.1Experimental Conditions & Group Assignment (24)
Chapter (24)
Table 2. Overview of planned methodology (25)
Table 3. Experimental Design (3x2 Mixed Design) (25)
6.2Stimuli Selection (25)
Figure 3. Group A (Non-AI-Generated/Unlabeled) Displayed Advertisement (27)
Figure 4. Group B (AI-Generated/Unlabeled) Displayed Advertisement (27)
Chapter (28)
Figure 5. Group C (AI-Generated/Labeled) Displayed Advertisement (28)
6.3Pretest (28)
6.4Final Survey (29)
7Empirical Analysis (30)
7.1Descriptive Statistics (30)
Table 4. Overview of data by descriptive category (30)
7.2Model-Free Evidence (31)
Figure 6. Model Free Evidence: Mean Perceived Advertisement Effectiveness Scores across 3 Testing Groups (31)
Figure 7. Model Free Evidence: Mean Perceived Trust Scores across 3 Testing Groups (32)
Figure 8. Model Free Evidence: Mean Purchase Intention Scores across 3 Testing Groups (32)
7.3Hypothesis Testing (33)
Table 5. Effect of AI Labeling on Consumer Perception (34)
Table 6. Effect of AI Labeling on Consumer Perception, including Controlled Variables (35)
Chapter (37)
Table 7. Effect of AI Labeling and Moderating Role of Product Involvement on Consumer Perception (37)
Table 8. Effect of AI Labeling and Moderating Role of Product Involvement on Consumer Perception, including Controlled Variables (39)
Chapter (39)
Table 9. Effect of AI- vs. Non-AI-Generated Content on Consumer Perception, including Controlled Variables (41)
Table 10. Effect of AI- vs. Non-AI-Generated Content on Consumer Perception including Controlled Variables (42)
Figure 9. Coefficient Plot displaying mean results per Model (43)
Chapter (43)
Chapter (43)
Table 11. Overview of Hypothesis Testing (44)
8Conclusion (45)
8.1Managerial Takeaways (45)
8.2Experimental Limitations (47)
8.3Further Areas of Research (48)
9References (50)
10Appendix (56)
10.1 Examples of AI-Generated Advertisement (56)
Chapter (56)
Figure 10. Mango AI-Generated Advertisement Campaign 2025 (New York Post 2024) (56)
Figure 11. Coca Cola Artificial Intelligence Campaign May 2023 (Pawar 2023) (56)
Figure 12. Serena Williams AI-generated Nike Campaign August 2022 (WPP 2022) (57)
10.2 Overview of current available literature and research gap (57)
Figure 13. Overview of current available literature and research gap (57)
10.3 Pre-Test Overview (58)
Figure 14. Pre-Test Mean Realism Ratings per Advertisement Condition (58)
Figure 15. Pretest Label Manipulation Check: Detection of Visual Differences (59)
Figure 16. Pretest Label Manipulation Check: AI Label Recognition per Product Type (n=60) (60)
Table 12. Pretest Results (60)
10.4 Final Survey (60)
10.5 Model-Free Evidence (61)
Figure 17. Model-Free Evidence: Mean Consumer Perception per Gender Category (61)
Figure 18. Model-Free Evidence: Mean Consumer Perception per Income Category (62)
Figure 19. Model-Free Evidence: Mean Consumer Perception per Education Category (63)
Figure 20. Model-Free Evidence: Mean Consumer Perception per Age Category (63)
Figure 21. Model-Free Evidence: Mean AI Familiarity, Attitude, and Usage Frequency across Age Groups (64)
Figure 22. Model-Free Evidence: Mean AI Familiarity, Attitude, and Usage Frequency across Income Groups (65)
Chapter (65)
Figure 23. Model-Free Evidence: Mean AI Familiarity, Attitude, and Usage Frequency across Education Groups (65)
10.6 Robustness Checks (66)
Chapter (66)
Table 13. Robustness Check Results for H1 Regression Model (66)
Table 14. Robustness Check Results for H2 Regression Model (67)
Chapter (68)
Table 15. Robustness Check Results for H3 Regression Model (68)
10.7 AI Use Overview (69)
Table 16. Overview of AI Tool Use (69
Implementation of the digital product passport in the electronic sector
No full textIMPLEMENTATION OF THE DIGITAL PRODUCT PASSPORT IN THE ELECTRONIC SECTOR
Implementation of the digital product passport in the electronic sector (1)
1. Introduction (8)
1.1 Background (8)
1.2 Electronic Sector (10)
2. Methodology (13)
3. Literature Review (15)
3.1 Definition and origin of DPP (15)
3.2 Regulations Supporting Digital Product Passport (DPP) (16)
3.3 Stakeholders Involved in DPP (18)
3.4 Functional Requirements for DPP Implementation (19)
3.4.1 Data Requirements (21)
3.4.1.1 Process Data (23)
3.4.1.2 Operational Data (24)
3.4.1.3 Product Disposal & Recycling Data (24)
3.4.1.4 Environmental Data (25)
3.4.2 Data Management (25)
3.4.3 Data Storage (27)
3.4.4 Operational Requirements (28)
3.5 Non-Functional Requirements for DPP Implementation (29)
3.5.1 Interoperability (29)
3.5.2 Accessibility (30)
3.5.3 Regulatory Compliance (31)
3.5.4 Confidentiality and Security (32)
3.5.5. Modularity and modifiability (33)
3.5.6 Portability (33)
3.6 Challenges the Electronic Sector is Currently Facing (34)
3.6.1 The Refurbishment Process (34)
3.6.1.1 Limited Visibility of Refurbishment Procedures (34)
3.6.1.2 The Gap in Smartphone Health and Repair Data (35)
3.6.1.3 Restricted Access to Repair Guidelines and Component Compatibility (35)
3.6.1.4 Lack of Universal Standards in Smartphone Refurbishment and Evaluation (36)
3.6.2 The Process of Recycling and Recovering WEEE (36)
3.6.2.1 Difficulty in Identifying Hazardous Components and Batteries (37)
3.6.2.3 Lack of Standardized WEEE Reporting Among EU Countries (37)
3.6.2.2 Limited Insight into Potential Material and Component Recovery (38)
3.7 Benefits of DPP Implementation in Electronic Sector (38)
3.8 Barriers in Implementation of DPP in Electronic Sector (40)
4. Data Collection (45)
An extensive literature review was carried out in the first phase by analyzing academic publications, market analyses, company websites, and EU regulatory documents. This review established a foundation for understanding the digital product passport a... (45)
4.1 Expert Interviews (45)
4.1.1 IOTA Technologies (45)
4.1.2 Protokol (47)
4.1.3 PicoNext (48)
4.2 Grounded Theory (50)
4.2.1 Theoretical Sampling (51)
4.2.2 Theoretical Sensitivity (51)
4.2.3 Steps of Grounded Theory Analysis (51)
5. Data Analysis (52)
5.1 Analysis on MAXDA (53)
5.1.1 Open Coding (53)
5.1.2 Axial Coding (55)
5.1.3 Selective Coding (56)
5.1.4 Theoretical Saturation and Reflexivity (57)
5.1.5 Emergent Theoretical Model (58)
5.1.5.1 Technical Infrastructure (58)
5.1.5.2 Data Governance (58)
5.1.5.3 System Readiness (59)
6. Conclusion (62)
6.1 Key findings (62)
6.2 Theory and Practice Contributions (63)
6.3 Limitations of the Study (64)
6.4 Concluding Remarks (64)
7. Appendix (66)
8. References (86
Circular practices within the beer industry
No full textCIRCULAR PRACTICES WITHIN THE BEER INDUSTRY
Circular practices within the beer industry (-
The intersection of tax law and artificial intelligence
No full textTHE INTERSECTION OF TAX LAW AND ARTIFICIAL INTELLIGENCE
The intersection of tax law and artificial intelligence (1
From tradition to transformation
No full textFROM TRADITION TO TRANSFORMATION
From tradition to transformation (1
Win, lose, game over?
No full textWIN, LOSE, GAME OVER?
Win, lose, game over? (2)
1. Abstract (6)
2. Keywords (6)
3. Introduction (7)
4. Theoretical Background (9)
4.1 Literature on CLV, Traditional Value Models (9)
4.1.1 The Quirks of Individual Customers (10)
4.1.2 Customer Satisfaction and CLV (11)
4.1.3 Firm Specific Metrics and CLV (11)
4.2 Sports Literature on Customer Engagement and Loyalty (12)
4.2.1 Fan Reactions to Team Performance (12)
4.2.2 Predicting Churn/Renewal Among Season Ticket Holders (13)
4.3 Expanding the CLV Framework in Sports (15)
5. Methodology (16)
5.1 Context of the study (16)
5.2 Research Design (17)
5.3 Hypothesis testing (17)
5.4 Procedures of the study (18)
5.5 Participants (18)
5.6 Data Analysis Procedure (19)
6.0 Results (20)
6.1 Statistical Summaries (21)
6.1.1 Customer Behaviour (21)
6.1.2 Team Performance (21)
6.1.3 Churn/Renewal/Survival Likelihood (23)
6.1.4 Spending Trends (25)
6.2 Cluster Analysis (27)
6.2.1 Cluster attributes (28)
6.2.3 Cluster 1 – New & Unsure (28)
6.2.4 Cluster 2 – Locked & Loyal (30)
6.2.4 Cluster 3 – Slow & Steady (32)
6.3 Regression Analysis (33)
6.3.1 All Customers (33)
6.3.2 Cluster by Cluster (34)
7.1 The Relationship Between CLV and TP (36)
7.2 Implications (39)
7.2.1 Practical Implications (39)
7.2.2 Theoretical Implications (41)
7.3 Limitations (42)
7.4 Conclusion (44)
8.0 References (46
Pricing of geopolitical risk in U.S. corporate bonds
No full textPRICING OF GEOPOLITICAL RISK IN U.S. CORPORATE BONDS
Pricing of geopolitical risk in U.S. corporate bonds (1
Flexible AI task scheduling for sustainable energy systems
No full textFLEXIBLE AI TASK SCHEDULING FOR SUSTAINABLE ENERGY SYSTEMS
Flexible AI task scheduling for sustainable energy systems (1
The implications of ESG greenwashing and ESG uncertainty on stock returns
No full textTHE IMPLICATIONS OF ESG GREENWASHING AND ESG UNCERTAINTY ON STOCK RETURNS
The implications of ESG greenwashing and ESG uncertainty on stock returns (1
Die Bedeutung von Nationalparks für eine nachhaltige Entwicklung in Österreich
No full textDIE BEDEUTUNG VON NATIONALPARKS FÜR EINE NACHHALTIGE ENTWICKLUNG IN ÖSTERREICH
Die Bedeutung von Nationalparks für eine nachhaltige Entwicklung in Österreich (1