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This study aimed to examine the impact of learning organization (LO) on job satisfaction and individual performance in Vietnamese enterprises. The study further explores the mediating effect of job satisfaction on the relationship between learning organization and employee performance.

Data were collected from 653 employees from various types of organizations in Vietnam. Structural equation modeling was implemented to test the hypotheses.

The results revealed that the proposed research model was supported. Results indicated that LOs positively influenced employees’ job satisfaction and the broader range of their individual performance. In addition, employees’ job satisfaction motivated them to achieve higher performance levels. The study also found a mediating effect of job satisfaction on the relationship between LO and employee performance. The results underscore the importance of implementing an LO culture for individual outcomes such as job satisfaction and employee performance in the Vietnamese cultural context, which is based on socialism and Confucianism.

To the best of the authors’ knowledge, this is the first study to examine the relationships among LO, job satisfaction and individual employee performance in the Vietnamese context. The results offer a deeper understanding of the LO concept in the Vietnamese cultural context and highlight the cultural impact on the LO concept and its effects. The results suggest how the LO concept is applied in the Vietnamese context.

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.