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The current study aims to investigate the impact of perceived blockchain-related information transparency on consumers’ intention to purchase organic food. This study examines how perceived blockchain- related information transparency, directly and indirectly, affects purchase intentions through attitudes, perceived behavioural control and subjective norms. Additionally, the study explores how blockchain-based trust moderates the influence of perceived blockchain-related information transparency on these factors and the intention to purchase organic food.

Based on the theory of planned behaviour framework and a sample of 5,326 consumers, this study uses partial least squares structural equation modelling to test the research model.

This study finds that perceived blockchain-related information transparency directly enhances consumers’ attitudes towards organic food purchase, perceived behavioural control, subjective norms and intention to purchase organic food. Additionally, perceived blockchain-related information transparency indirectly affects consumers’ intention to buy organic food through three antecedents of the theory of planned behaviour model. Notably, these indirect effects were moderated by consumers’ blockchain-based trust.

This study provides recommendations for leveraging blockchain to enhance transparency and build trust, which could boost consumer engagement and organic food purchases.

This research contributes to blockchain literature by empirically examining the role of perceived blockchain-related transparency and blockchain-based trust in consumers’ purchasing decisions regarding organic food. It provides valuable insights into the consumer-centric benefits of blockchain technology. Furthermore, this study also contributes to the literature on organic food, particularly its promotion through blockchain technology.

This study aims to deeply understand customer experiences toward Internet of Things (IoT) applications in retail by developing machine learning models for aspect-based sentiment analysis (SA). It includes creating a related terms dictionary and proposing implications for retail businesses in Vietnam based on these analyses. The ultimate goal is to gain insights into customer opinions and assist administrators in formulating effective digital transformation and business strategies within the Vietnamese market.

Initially, this research uses qualitative methods to identify different aspects of customer experience at stores equipped with IoT applications. Then, quantitative methods were applied through classification machine learning models which were trained on the annotated data set to classify comments into aspects and sentiments. Finally, the classification results were analyzed and visualized to draw implications about customer opinions of these stores.

This study collected 77,042 customers’ comment from potential and actual customers who have ever shopped at retail stores with IoT applications deployed worldwide, identified ten new aspects of customer experience in this field and built a dictionary of related terms. Furthermore, this study contributed two efficient ensemble models with an accuracy of 81% and 89% for analyzing aspects and customer sentiments, respectively. This study also proposes implications for managers regarding the use of IoT technology in retail stores to improve shopping experiences for customers.

This study’s findings help managers develop appropriate digital transformation and business strategies for integrating IoT technology into retail stores, especially for retail businesses in the Vietnamese market based on the analysis results and proposed model.