Nghia Nguyen, Thuy-Hien Nguyen, Yen-Nhi Nguyen, Dung Doan, Minh Nguyen and Van-Ho Nguyen
The purpose of this paper is to expand and analyze deeply customer emotions, concretize the levels of positive or negative emotions with the aim of using machine learning methods…
Abstract
Purpose
The purpose of this paper is to expand and analyze deeply customer emotions, concretize the levels of positive or negative emotions with the aim of using machine learning methods, and build a model to identify customer emotions.
Design/methodology/approach
The study proposed a customer emotion detection model and data mining method based on the collected dataset, including 80,593 online reviews on agoda.com and booking.com from 2009 to 2022.
Findings
By discerning specific emotions expressed in customers' comments, emotion detection, which refers to the process of identifying users' emotional states, assumes a crucial role in evaluating the brand value of a product. The research capitalizes on the vast and diverse data sources available on hotel booking websites, which, despite their richness, remain largely unexplored and unanalyzed. The outcomes of the model, pertaining to the detection and classification of customer emotions based on ratings and reviews into four distinct emotional states, offer a means to address the challenge of determining customer satisfaction regarding their actual service experiences. These findings hold substantial value for businesses operating in this domain, as the findings facilitate the evaluation and formulation of improvement strategies within their business models. The experimental study reveals that the proposed model attains an exact match ratio, precision, and recall rates of up to 81%, 90% and 90%, respectively.
Research limitations/implications
The study has yet to mine real-time data. Prediction results may be influenced because the amount of data collected from the web is insufficient and preprocessing is not completely suppressed. Furthermore, the model in the study was not tested using all algorithms and multi-label classifiers. Future research should build databases to mine data in real-time and collect more data and enhance the current model.
Practical implications
The study's results suggest that the emotion detection models can be applied to the real world to quickly analyze customer feedback. The proposed models enable the identification of customers' emotions, the discovery of customer demand, the enhancement of service, and the general customer experience. The established models can be used by many service sectors to learn more about customer satisfaction with the offered goods and services from customer reviews.
Social implications
The research paper helps businesses in the hospitality area analyze customer emotions in each specific aspect to ensure customer satisfaction. In addition, managers can come up with appropriate strategies to bring better products and services to society and people. Subsequently, fostering the growth of the hotel tourism sector within the nation, thereby facilitating sustainable economic development on a national scale.
Originality/value
This study developed a customer emotions detection model for detecting and classifying customer ratings and reviews as 4 specific emotions: happy, angry, depressed and hopeful based on online booking hotel websites agoda.com and booking.com that contains 80,593 reviews in Vietnamese. The research results help businesses check and evaluate the quality of their services, thereby offering appropriate improvement strategies to increase customers' satisfaction and demand more effectively.
Details
Keywords
Nhi Yen Nguyen, Hao Gia Tran, Dang Thanh Tra, Nhung Tuyet Le and Hien Thi Thuy Nguyen
This study aims to combine two theories, the Theory of Planned Behaviour (TPB) and the Norm Activation Model (NAM), to investigate the relationship between the awareness of…
Abstract
Purpose
This study aims to combine two theories, the Theory of Planned Behaviour (TPB) and the Norm Activation Model (NAM), to investigate the relationship between the awareness of reducing single-use plastic waste's environmental cost and the behaviour to limit the use of single-use plastic products (SUPPs) by FPT university students.
Design/methodology/approach
Quantitative research methodologies were employed on a sample of 506 university students. The survey data was then examined using SPSS, SPSS AMOS and SmartPLS software.
Findings
The overarching conclusion of the study is that awareness of reducing single-use plastic waste's environmental cost has a positive impact on FPT university students' behaviour to reduce their use of single-use plastic products. Another intriguing discovery is how socialisation of responsibility affects pro-environmental behaviour through the interplay between personal norms, subjective norms and behavioural intention.
Originality/value
This study on the relationship between SUPP low-consumption awareness and behaviour and mediating factors is a necessary foundation for future studies related to changing the behaviour of students using SUPPs. That will also be a solid foundation for practical plans to change behaviour using SUPPs through communication campaigns to increase awareness.
Details
Keywords
An H.K. Vo, Tuan-Duong Nguyen, Yen-Nhi Le, Huong Ngoc Quynh Cao, Van Ngoc Thanh Le and Khanh-Linh Huynh
Based on the model of Big-Five personality traits and theories of person–environment interaction, this study aims to investigate the moderating effects of personality traits on…
Abstract
Purpose
Based on the model of Big-Five personality traits and theories of person–environment interaction, this study aims to investigate the moderating effects of personality traits on innovativeness through knowledge sharing (KS).
Design/methodology/approach
A sample of 318 Vietnamese employees was collected. The hypothesized model was tested by using partial least squares structural equation modelling.
Findings
The results indicate that extraversion, agreeableness, conscientiousness and openness to experience have relationships with innovativeness through the mediating effect of KS. Furthermore, transformational leadership (TL) mitigates the positive relationship between agreeableness and openness to experience and innovativeness.
Practical implications
Based on the research results, the authors suggest several practical implications for enhancing employees' innovative organizational behaviours. Transformational leaders should be aware of and control the relationships with employees high in agreeableness and open to experience to ensure that employees' innovativeness can be freely developed.
Originality/value
This research systematically investigates the effect of each personality on employees' innovativeness. Furthermore, this study contributes to the leadership literature by suggesting the dark side of TL that can negatively influence the innovative ability of employees with certain personality traits.