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A influência da reputação online na competitividade das empresas rent-a-car: análise comparativa na Península Ibérica entre regiões continentais e ilhas
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A transformação digital do setor do turismo alterou drasticamente a forma como os consumidores pesquisam, avaliam e selecionam produtos e serviços, atribuindo às plataformas digitais um papel central na construção da reputação das organizações. Desta maneira, as avaliações publicadas pelos utilizadores constituem uma importante fonte de informação para a compreensão da experiência do cliente, influenciando a perceção de qualidade, a confiança dos consumidores e a competitividade das empresas. No setor do rent-a-car, caracterizado por uma grande concorrência e por uma grande dependência da satisfação dos clientes, a gestão da reputação online assume uma importância crescente, tornando-se um elemento estratégico para a diferenciação e sustentabilidade das organizações.
Esta investigação teve como principal objetivo analisar a influência da reputação online na competitividade das empresas de rent-a-car, através de uma análise comparativa entre diferentes localizações da Península Ibérica e das ilhas de Portugal e Espanha. Para o efeito, foi adotada uma abordagem quantitativa baseada na análise de avaliações publicadas em plataformas digitais, recorrendo à aplicação de técnicas de mineração de texto e de modelação por equações estruturais. Concretamente, foram utilizadas a Análise de Tópicos (Latent Dirichlet Allocation – LDA), a Análise de Sentimentos, as Regras de Associação e a modelação Partial Least Squares (PLS), com recurso aos softwares Orange Data Mining, BigML e SmartPLS. Estas metodologias permitiram identificar os principais temas abordados pelos utilizadores, analisar a polaridade emocional das avaliações, explorar as relações entre diferentes atributos do serviço e compreender os fatores que influenciam a satisfação dos clientes.
Os resultados evidenciam que a reputação online das empresas de rent-a-car é influenciada por várias dimensões da experiência do cliente, destacando-se a qualidade do atendimento, a transparência das condições contratuais, o estado das viaturas, a eficiência dos processos de levantamento e devolução e a atuação dos colaboradores. Verificou-se também que as avaliações positivas estão quase sempre associadas à qualidade do atendimento, ao profissionalismo dos trabalhadores e à eficiência do serviço, enquanto as avaliações negativas se relacionam sobretudo com cobranças adicionais, questões contratuais e situações em que as expectativas dos clientes não foram correspondidas. Adicionalmente, a análise de sentimentos revelou um grande volume de emoções positivas, especialmente de confiança e satisfação, reforçando a importância da experiência global do cliente na construção da reputação digital das empresas.
Os resultados da modelação PLS confirmaram ainda que fatores como a comunicação, a localização do serviço e a qualidade da experiência no momento da entrega da viatura exercem uma influência significativa na satisfação dos clientes, contribuindo para uma perceção mais favorável do serviço prestado.
Em conclusão, os resultados demonstram que a reputação online constitui um fator importante para a competitividade das empresas de rent-a-car, influenciando a perceção dos consumidores e a sua intenção de escolha. Demonstram, ainda, que a combinação de técnicas de mineração de texto e de modelação PLS permite transformar grandes volumes de dados não estruturados em conhecimento útil para a gestão, apoiando a identificação de oportunidades de melhoria e a definição de estratégias orientadas para o reforço da qualidade do serviço e da experiência do cliente. Esta investigação reforça o conhecimento sobre a reputação online no setor do rent-a-car e evidencia o potencial da inteligência artificial e da análise de dados como instrumentos de apoio à tomada de decisão em contexto turístico.
The digital transformation of the tourism sector has significantly changed the way consumers search for, evaluate and select tourism products and services, making digital platforms a key element in shaping organizations’ online reputation. In this context, online customer reviews have become an important source of information for understanding customer experiences, influencing consumers' perceptions of service quality, trust and companies' competitiveness. Within the car rental sector, characterized by intense competition and a strong dependence on customer satisfaction, effective online reputation management has become a strategic factor for business differentiation and long-term sustainability. The main objective of this study was to analyze the influence of online reputation on the competitiveness of car rental companies through a comparative analysis of customer reviews from different locations across the Iberian Peninsula and the islands of Portugal and Spain. A quantitative research approach was adopted, combining text mining techniques with Partial Least Squares Structural Equation Modelling (PLS-SEM). Specifically, Latent Dirichlet Allocation (LDA), Sentiment Analysis, Association Rules and Partial Least Squares (PLS) were applied using Orange Data Mining, BigML and SmartPLS software. These techniques made it possible to identify the main discussion topics, analyse the emotional polarity of customer reviews, explore the relationships between different service attributes and examine the factors influencing customer satisfaction. The findings revealed that the online reputation of car rental companies is influenced by several dimensions of the customer experience, particularly service quality, transparency of contractual conditions, vehicle condition, the efficiency of vehicle pick-up and return procedures, and staff performance. The results also showed that positive reviews are predominantly associated with service quality, staff professionalism and operational efficiency, whereas negative reviews are mainly related to additional charges, contractual issues and situations in which customer expectations were not fully met. Furthermore, sentiment analysis revealed a predominance of positive emotions, particularly trust and satisfaction, reinforcing the importance of the overall customer experience in shaping companies' online reputation. The PLS analysis further demonstrated that communication, service location and the vehicle pick-up experience significantly influence customer satisfaction, which in turn positively affects customers' perceptions of the vehicle return process and the overall service experience. Overall, the results confirm that online reputation is a key determinant of competitiveness in the car rental industry, influencing consumers' perceptions and decision-making processes. Furthermore, the combination of text mining techniques and PLS modelling proved to be an effective approach for transforming large volumes of unstructured data into valuable managerial insights, supporting service improvement initiatives and strategic decision-making. Consequently, this research contributes to a better understanding of online reputation in the car rental sector and highlights the potential of artificial intelligence and data analytics as valuable tools for tourism management.
The digital transformation of the tourism sector has significantly changed the way consumers search for, evaluate and select tourism products and services, making digital platforms a key element in shaping organizations’ online reputation. In this context, online customer reviews have become an important source of information for understanding customer experiences, influencing consumers' perceptions of service quality, trust and companies' competitiveness. Within the car rental sector, characterized by intense competition and a strong dependence on customer satisfaction, effective online reputation management has become a strategic factor for business differentiation and long-term sustainability. The main objective of this study was to analyze the influence of online reputation on the competitiveness of car rental companies through a comparative analysis of customer reviews from different locations across the Iberian Peninsula and the islands of Portugal and Spain. A quantitative research approach was adopted, combining text mining techniques with Partial Least Squares Structural Equation Modelling (PLS-SEM). Specifically, Latent Dirichlet Allocation (LDA), Sentiment Analysis, Association Rules and Partial Least Squares (PLS) were applied using Orange Data Mining, BigML and SmartPLS software. These techniques made it possible to identify the main discussion topics, analyse the emotional polarity of customer reviews, explore the relationships between different service attributes and examine the factors influencing customer satisfaction. The findings revealed that the online reputation of car rental companies is influenced by several dimensions of the customer experience, particularly service quality, transparency of contractual conditions, vehicle condition, the efficiency of vehicle pick-up and return procedures, and staff performance. The results also showed that positive reviews are predominantly associated with service quality, staff professionalism and operational efficiency, whereas negative reviews are mainly related to additional charges, contractual issues and situations in which customer expectations were not fully met. Furthermore, sentiment analysis revealed a predominance of positive emotions, particularly trust and satisfaction, reinforcing the importance of the overall customer experience in shaping companies' online reputation. The PLS analysis further demonstrated that communication, service location and the vehicle pick-up experience significantly influence customer satisfaction, which in turn positively affects customers' perceptions of the vehicle return process and the overall service experience. Overall, the results confirm that online reputation is a key determinant of competitiveness in the car rental industry, influencing consumers' perceptions and decision-making processes. Furthermore, the combination of text mining techniques and PLS modelling proved to be an effective approach for transforming large volumes of unstructured data into valuable managerial insights, supporting service improvement initiatives and strategic decision-making. Consequently, this research contributes to a better understanding of online reputation in the car rental sector and highlights the potential of artificial intelligence and data analytics as valuable tools for tourism management.
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Dissertação de mestrado
Palavras-chave
Reputação online Rent-a-car Competitividade Mineração de texto Análise de sentimentos Latent Dirichlet allocation Partial least squares Online reputation Car rental Competitiveness Text mining Sentiment analysis