| _version_ | 1866901481566765056 |
|---|---|
| author | Meritta M Johnson |
| author_facet | Meritta M Johnson |
| contents | <p><span>The proliferation of artificial intelligence in marketing practices has intensified consumer concerns regarding ethical implications, yet no validated instrument exists to systematically measure consumer perceptions of AI ethics in branding contexts. This study develops and empirically validates an Ethical Branding Heuristics Index (EBHI) through a mixed-methods approach combining exploratory factor analysis, confirmatory factor analysis, and multiple regression modeling across three industry sectors (technology, retail, and financial services). Data from 1,247 consumers revealed a five-factor structure encompassing Transparency Perception (α = .89), Algorithmic Fairness Concern (α = .86), Data Privacy Assurance (α = .91), Human Agency Preservation (α = .84), and Outcome Accountability (α = .87). Cross-industry validation demonstrated strong predictive validity for brand trust (R² = .67), purchase intention (R² = .54), and brand advocacy (R² = .61). The EBHI provides marketing practitioners and researchers with a psychometrically robust tool for assessing and predicting consumer ethical perceptions in AI-enabled branding contexts, contributing to the emerging field of algorithmic marketing ethics.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18104006 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Empirical Validation of an Ethical Branding Heuristics Index (EBHI) for AI Marketing Meritta M Johnson <p><span>The proliferation of artificial intelligence in marketing practices has intensified consumer concerns regarding ethical implications, yet no validated instrument exists to systematically measure consumer perceptions of AI ethics in branding contexts. This study develops and empirically validates an Ethical Branding Heuristics Index (EBHI) through a mixed-methods approach combining exploratory factor analysis, confirmatory factor analysis, and multiple regression modeling across three industry sectors (technology, retail, and financial services). Data from 1,247 consumers revealed a five-factor structure encompassing Transparency Perception (α = .89), Algorithmic Fairness Concern (α = .86), Data Privacy Assurance (α = .91), Human Agency Preservation (α = .84), and Outcome Accountability (α = .87). Cross-industry validation demonstrated strong predictive validity for brand trust (R² = .67), purchase intention (R² = .54), and brand advocacy (R² = .61). The EBHI provides marketing practitioners and researchers with a psychometrically robust tool for assessing and predicting consumer ethical perceptions in AI-enabled branding contexts, contributing to the emerging field of algorithmic marketing ethics.</span></p> |
| title | Empirical Validation of an Ethical Branding Heuristics Index (EBHI) for AI Marketing |
| url | https://doi.org/10.5281/zenodo.18104006 |