Artificial Intelligence as a Driver of Competitiveness in Micro and Small Enterprises: A Systematic Review on the Democratization of Data-Driven Marketing

Maria Antonia Benati

Abstract


Purpose: This study analyzes the strategic implications of integrating Artificial Intelligence (AI) into the marketing of Micro and Small Enterprises (MSEs). It investigates how automation and predictive analytics mitigate competitive asymmetries against large corporations, identifying trends and barriers in contemporary literature.

Methodology: A qualitative, exploratory approach using a systematic review via the PRISMA protocol. The corpus was selected from Scopus and Web of Science (2014–2024), prioritizing high-impact journals and seminal works on digital transformation.

Results: Evidence shows that AI democratizes high-performance marketing through accessible personalization and operational efficiency tools. However, effectiveness depends on managerial digital literacy and overcoming capital scarcity and cultural resistance to change.

Research limitations: The study relies on secondary data without primary empirical validation. The rapid obsolescence of generative AI may also limit the long-term permanence of certain instrumental analyses.

Practical implications: AI adoption requires prior data sanitization and digital training. Using low-cost tools, MSEs can scale personalized service. Findings guide institutional bodies, like SEBRAE, in developing targeted capacitation programs for regional small-scale enterprises.

Originality: This work shifts the AI debate from global corporations to MSEs in emerging economies. It proposes a conceptual framework integrating classical marketing theory with algorithmic disruption, providing theoretical foundations for small business survival in the data intelligence era.

Keywords: Artificial intelligence; digital marketing; micro and small enterprises; competitiveness; digital transformation.

DOI: https://doi.org/10.58869/SPM/05


Full Text:

PDF

References


Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Harvard Business Review Press.

Baker, T., & Nelson, R. E. (2005). Creating something from nothing: Resource construction through entrepreneurial bricolage. Administrative Science Quarterly, 50(3), 329–366. https://doi.org/10.2189/asqu.2005.50.3.329

Bardin, L. (2011). Análise de conteúdo. Edições 70.

Belk, R. (2021). Ethical issues in service robotics. Journal of Service Management, 32(2), 151–167.

Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital business strategy: Toward a next generation of insights. MIS Quarterly, 37(2), 471–482.

Chui, M., Manyika, J., & Miremadi, M. (2018). What AI can and can’t do (yet) for your business. McKinsey Quarterly, 3(1), 1–10.

Davenport, T. H. (2006). Competing on analytics. Harvard Business Review, 84(1), 98–107.

Davenport, T. H., 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.

https://doi.org/10.1007/s11747-019-00696-0

Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.

Grönroos, C. (1994). From marketing mix to relationship marketing: Towards a paradigm shift in marketing. Management Decision, 32(2), 4–20.

Gummesson, E. (2002). Total relationship marketing. Butterworth-Heinemann.

Gupta, S., & Khan, M. (2024). The role of AI chatbots in enhancing customer experience: A study of digital engagement. Journal of Digital Marketing Trends, 12(1), 45–58.

Hoffman, D. L., & Novak, T. P. (2018). Consumer and object experience in the internet of things: An assemblage theory approach. Journal of Consumer Research, 44(6), 1178–1204.

Huang, M. H., & Rust, R. T. (2021). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 30–41.

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15–25.

Kotler, P. (1967). Marketing management: Analysis, planning, and control. Prentice-Hall.

Kotler, P., Kartajaya, H., & Setiawan, I. (2017). Marketing 4.0: Moving from traditional to digital. Wiley.

Kotler, P., Kartajaya, H., & Setiawan, I. (2021). Marketing 5.0: Technology for humanity. Wiley.

Kumar, V., Ashraf, A. S., & Nadeem, W. (2024). Revolutionizing marketing through AI-driven customer insights. International Journal of Applied Management, 6(2), 112–128.

Levitt, T. (1960). Marketing myopia. Harvard Business Review, 38(4), 45–56.

Lusch, R. F., & Vargo, S. L. (2014). Service-dominant logic: Premises, perspectives, possibilities. Cambridge University Press.

Martin, L. M., & Matlay, H. (2001). "Blanket" approaches to promoting ICT in small firms: Some lessons from the DTI ladder of adoption model. Internet Research, 11(5), 399–410.

McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60–68.

Mitchell, T. M. (1997). Machine learning. McGraw Hill.

Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097.

Ngai, E. W., & Wu, Y. (2022). Machine learning in marketing: A review and agenda for future research. International Journal of Information Management, 63, 102451.

Porter, M. E., & Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard Business Review, 92(11), 64–88.

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.

Rust, R. T. (2020). The future of marketing is digital, personalized, and adaptive. Journal of Marketing, 84(1), 1–15.

Sebrae. (2023). O impacto da inteligência artificial nas micro e pequenas empresas brasileiras. Relatório de Pesquisa.

Senyapar, H., & Nurgul, E. (2024). Ethical practices in AI-driven marketing: Transparency and consumer rights. European Journal of Applied Business and Management, 10(1), 88–104.

Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, 135–146.

Teixeira, S., & Lopes, J. (2024). AI trends in SME: The case of Portugal and Brazil. International Journal of Applied Management, 7(1), 22–39.

Toffler, A. (1980). The third wave. William Morrow.

Vargo, S. L., & Lusch, R. F. (2004). Evolving to a dominant logic for marketing. Journal of Marketing, 68(1), 1–17.

Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1(1), 100002.

Wamba, S. F., Gunasekaran, A., Akter, S., Bhattacharya, M., Dubey, R., & Childe, S. J. (2017). Big data analytics and business process innovation. Business Process Management Journal, 23(3), 477–499.

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

Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96(4), 114–123.

Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs.




Copyright (c) 2026 European Journal of Applied Business and Management

 

European Journal of Applied Business and Management

ISSN: 2183-5594

DOI: https://doi.org/10.58869/EJABM

Indexing:

EBSCO | CROSSREF | GOOGLE SCHOLAR | LATINDEX | DRJI | ICI JOURNALS MASTER | REDIB | MIAR