Artificial Intelligence-Enabled Digital Marketing Strategies: An Empirical Investigation of Competitive Advantage and Marketing Performance

Author

Mr. D Dev Srikar, Dr. K. Krishna Rao, Mr. B. Hiteesh Sri Ram

Abstract

 Purpose. This study examines how AI-enabled digital marketing strategies (AI personalization, predictive analytics, AI digital advertising, AI content automation and AI marketing automation) relate to competitive advantage and marketing performance, and whether competitive advantage transmits part of the effect of AI-enabled marketing on marketing performance. 

Design. A cross-sectional survey design was specified using a 38-item questionnaire (8 profile items and 30 five-point Likert items). The analysis used a simulated sample of 250 marketing professionals and was run in Microsoft Excel: descriptive statistics, Cronbach's alpha, Pearson correlations, chi-square tests of independence, one-way ANOVA with Bonferroni post hoc comparisons, multiple and simple linear regression, and a Sobel test of mediation.

 Findings. All constructs were reliable (α = .76–.83). The five AI strategies jointly explained 42.4% of the variance in marketing performance; AI personalization (β = .30), AI digital advertising (β = .22) and predictive analytics (β = .16) were significant, whereas content automation (β = .11, p = .060) and marketing automation (β = .05, p = .438) were not. AI-enabled marketing predicted competitive advantage (β = .56), competitive advantage predicted marketing performance (β = .50), and the indirect effect was significant (Sobel z = 3.47, p < .001), indicating partial mediation. AI adoption differed by organization size, χ²(6, N = 250) = 29.66, p < .001, Cramér's V = .24. 

Implications. Competitive value from AI appears to come from customer-facing, insight-driven applications rather than from automation alone. 

Originality. The study tests several AI marketing applications in one integrated model rather than in isolation.

Keywords

artificial intelligence; digital marketing; competitive advantage; marketing performance; personalization; predictive analytics; mediation analysis

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References

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.

Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.

Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.

Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49(1), 30–50.

Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135–155.

Manis, K. T., & Madhavaram, S. (2023). AI-enabled marketing capabilities and the pursuit of competitive advantage. Journal of Business Research.

Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

Sobel, M. E. (1982). Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology, 13, 290–312.

Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.

Wedel, M., & Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121.

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