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Sistemas de recomendação: análise da intenção de compra dos consumidores
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Resumo(s)
O crescimento acelerado do comércio eletrónico e a disseminação de tecnologias
baseadas em inteligência artificial trouxeram os sistemas de recomendação para o centro
da experiência digital contemporânea. Estes mecanismos, capazes de analisar grandes
volumes de dados e identificar padrões de comportamento, têm como objetivo reduzir a
sobrecarga de informação e apoiar decisões de compra mais rápidas e direcionadas aos
consumidores. Apesar do seu uso generalizado em plataformas internacionais, permanece
a necessidade de compreender a forma como os consumidores reagem a tais sistemas em
contextos específicos.
Assim, esta dissertação toma como objeto de estudo a plataforma online da
Worten, analisando o impacto de diferentes dimensões dos sistemas de recomendação —
personalização, utilidade percebida, confiança e transparência — na intenção de compra
dos consumidores portugueses. Recorre-se a uma metodologia quantitativa, com
aplicação de questionários e posterior tratamento estatístico, de modo a testar hipóteses
fundamentadas na literatura sobre comportamento do consumidor e aceitação
tecnológica.
Espera-se que os resultados contribuam para uma compreensão mais abrangente
do papel desempenhado pelos sistemas de recomendação atuais, não só enquanto
facilitadores da experiência de compra, mas também como instrumentos capazes de gerar
confiança e fidelização. Do ponto de vista prático, o estudo procura fornecer orientações
a empresas do setor, ajudando-as a equilibrar inovação tecnológica com práticas éticas de
transparência e proteção da privacidade. Em termos académicos, o trabalho acrescenta
evidência empírica ao debate sobre a relação entre tecnologia, confiança e
comportamento de consumo em ambientes digitais.
The rapid growth of e-commerce and the spread of technologies based on artificial intelligence have placed recommender systems at the center of the contemporary digital experience. These mechanisms, capable of analyzing large volumes of data and identifying behavioral patterns, aim to reduce information overload and support faster and more targeted purchase decisions. Despite their widespread use on international platforms, there remains a need to understand how consumers react to such systems in specific national contexts. Thus, this dissertation takes the online platform Worten as its case study, analyzing the impact of different dimensions of recommender systems — personalization, perceived usefulness, trust, and transparency — on the purchase intentions of Portuguese consumers. A quantitative methodology was adopted, involving the application of questionnaires and subsequent statistical analysis, in order to test hypotheses grounded in the literature on consumer behavior and technology acceptance. The results are expected to contribute to a broader understanding of the role played by recommender systems, not only as facilitators of the shopping experience but also as instruments capable of generating trust and loyalty. From a practical perspective, the study seeks to provide guidance to companies in the sector, helping them balance technological innovation with ethical practices of transparency and privacy protection. From an academic standpoint, the research adds empirical evidence to the debate on the relationship between technology, trust, and consumer behavior in digital environments.
The rapid growth of e-commerce and the spread of technologies based on artificial intelligence have placed recommender systems at the center of the contemporary digital experience. These mechanisms, capable of analyzing large volumes of data and identifying behavioral patterns, aim to reduce information overload and support faster and more targeted purchase decisions. Despite their widespread use on international platforms, there remains a need to understand how consumers react to such systems in specific national contexts. Thus, this dissertation takes the online platform Worten as its case study, analyzing the impact of different dimensions of recommender systems — personalization, perceived usefulness, trust, and transparency — on the purchase intentions of Portuguese consumers. A quantitative methodology was adopted, involving the application of questionnaires and subsequent statistical analysis, in order to test hypotheses grounded in the literature on consumer behavior and technology acceptance. The results are expected to contribute to a broader understanding of the role played by recommender systems, not only as facilitators of the shopping experience but also as instruments capable of generating trust and loyalty. From a practical perspective, the study seeks to provide guidance to companies in the sector, helping them balance technological innovation with ethical practices of transparency and privacy protection. From an academic standpoint, the research adds empirical evidence to the debate on the relationship between technology, trust, and consumer behavior in digital environments.
Descrição
Palavras-chave
Inteligência artificial Sistemas de recomendação Comportamento do Consumidor Intenção de compra Artificial Intelligence Recommendor Systems Consumer Behavior Purchase Intension