Adoption of artificial intelligence tools in digital marketing: empirical evidence and challenges among small women’s fashion retail firms in Sobral, Brazil
Adoption of artificial intelligence tools in digital marketing: empirical evidence and challenges among small women’s fashion retail firms in Sobral, Brazil
DOI:
https://doi.org/10.51473/rcmos.v1i2.2025.1885Keywords:
Artificial intelligence; Digital marketing; Small businesses; Innovation; Brazil.Abstract
The incorporation of artificial intelligence (AI) into digital marketing has expanded opportunities for automation, personalization, and data-informed decision-making. This article examines awareness, usage patterns, and perceived impacts of AI tool adoption among small women’s fashion retail firms in Sobral, Ceará, Brazil. A mixed-methods design was employed, combining quantitative and qualitative evidence collected through a structured questionnaire administered in person to 71 firms. Closed-ended items were analyzed using descriptive statistics (frequency distributions), and open-ended responses were examined through thematic analysis. Findings indicate limited familiarity with AI in the digital marketing context: 46% of respondents reported not knowing or being unsure about the concept, and 27% reported using AI-related tools, most commonly chatbots, social media caption generators, and ChatGPT. Reported outcomes included perceived sales increases and improved customer engagement, although many respondents described difficulties in objectively assessing effects due to limited use of metrics and partial understanding of the technologies involved. Key barriers to adoption included lack of technical training, perceived implementation costs, time constraints, and uncertainty regarding practical applications in routine business activities. Nonetheless, firms expressed strong interest in learning how to apply AI to marketing practices. The results suggest that broader diffusion of AI among small businesses depends on tailored training initiatives and institutional partnerships that support strategic use and performance measurement in local contexts.
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Copyright (c) 2025 Ellen Victor da Silva, Rogeane Morais Ribeiro, Raimundo Pedro Justino de Orlanda (Autor)
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