A Data-Science Decision-Support Framework for Responsible AI-Enabled Marketing, Visualization, And Consumer Engagement in The United States

Abstract

Artificial intelligence (AI) and data science are increasingly transforming marketing decision-making by enabling organizations to integrate consumer data, generate predictive intelligence, visualize complex information, personalize engagement, and automate selected decision processes. However, the organizational value of AI-enabled marketing depends not only on analytical capability but also on the interpretability, governance, and responsible application of AI-generated insights. This study develops and evaluates an integrated data-science decision-support framework for responsible AI-enabled marketing in the United States by examining the effects of Data-Science Marketing Intelligence (DSMI), AI-Enabled Marketing Visualization (AIMV), Responsible AI Governance (RAIG), and Consumer Engagement Analytics and Intelligence (CEAI) on Responsible AI-Enabled Marketing Decision-Support Performance (RAMDSP). Grounded in Organizational Information Processing Theory, the study employs a quantitative, positivist, deductive, explanatory, and cross-sectional survey design. Data were obtained from 292 usable responses from professionals working in marketing, analytics, artificial intelligence, customer relationship management, consumer insights, customer experience, marketing technology, and AI governance across selected U.S.-based consumer-oriented industries. Reliability, descriptive statistics, Pearson correlation, and multiple linear regression were applied to evaluate the proposed relationships. The measurement instrument demonstrated strong reliability, with construct-level Cronbach’s alpha values ranging from .85 to .91 and an overall alpha of .93. The regression model was statistically significant, F(4,287) = 182.68, p < .001, explaining 71.8% of the variance in RAMDSP (R² = .718; adjusted R² = .714). RAIG demonstrated the strongest standardized contribution (β = .31), followed by CEAI (β = .29), DSMI (β = .24), and AIMV (β = .19), with all relationships significant at p < .001. The findings support all four hypotheses and indicate that responsible AI-enabled marketing performance depends on complementary analytical, interpretive, governance, and consumer-intelligence capabilities.