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This study aims to use the voice of the customer (VoC) strategy to collect user-generated content (UGC) compare customer expectations with reality, make the necessary improvements for the business and create personalized strategies for each customer to maximize revenue, focus on hospitality industry in Vietnam market.

This study proposes a synthesis of techniques for a deep understanding of the VoC based on online reviews in the hospitality industry. First, 409,054 comments were collected from websites in the hospitality sector. Second, the data will be organized, stored, cleaned, analyzed and evaluated. Next, research using business intelligence (BI) solutions integrating three models, including net promoter score (NPS), graph model and latent Dirichlet allocation (LDA), based on natural language processing (NLP) technique, experiment on Vietnamese and English data to explore the multidimensional voice of customer’s row. Finally, a dashboard system will be implemented to visualize analysis results and recommendations on marketing strategies to improve product and service quality.

Experimental results allow analysts and managers to “listen to the customer’s voice” accurately and effectively, identify relationships between entities, topics of discussion in favor of positive and negative trends.

The novelty in this study is the integration of three models, including NPS, graph model and LDA. These models are combined based on the BI solution and NLP technique. The study also conducted experiments on both Vietnamese and English languages, which ensures more effective practical application.

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.

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.

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.

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.

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.

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.

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.

This paper aims to examine the impacts of financial and nonfinancial incentives on the development of employee work motivation and knowledge sharing in the postpandemic environment. The paper further investigates the role of transformational leadership as a moderator in enhancing the relationship between work motivation and knowledge sharing.

Adopting a quantitative approach, the study uses data collected from multiple informants, specifically senior managers, in Nepalese manufacturing and service business-to-business (B2B) firms.

Contrary to prior research, the results reveal that nonfinancial incentives have a stronger impact on work motivation in the postpandemic era. This enhanced work motivation, in turn, contributes to knowledge sharing, with transformational leadership further strengthening the relationship.

The findings suggest that B2B firms should consider moving toward leveraging nonfinancial incentives to motivate employees to develop knowledge sharing initiatives, especially in challenging circumstances such as those experienced in the postpandemic era. In addition, it is recommended that chief executive officers adopt a transformational leadership style to facilitate effective knowledge sharing within their firms.

In a developing economy and amid the challenges of the global pandemic, there has been limited research exploring the possible effects that financial and nonfinancial incentives could have on work motivation and knowledge sharing. This research bridges this gap by providing a fresh perspective on work motivation and knowledge management in B2B firms, contributing novel insights to the literature.

The main purpose of this study is to reveal the degree of association between lean, building information modeling (BIM) and construction project success. The study further intends to provide strategies for high and low associations of the factors.

Lean construction and BIM are two important applications that have recently gained popularity in terms of enhancing project success. Considering this impact, this study investigates the synergy between lean construction and BIM and determines to what extent these two contribute to the success of the projects. As a first step, lean, BIM and project success were examined based on an in-depth literature review. In the second stage, a structural equation model (SEM) was established to reflect the relationship among these three through hypotheses. Then, a questionnaire was designed and administered to the construction professionals experienced in both lean and BIM implementation. The SEM was tested using Analysis of Moment Structures (AMOS), an SPSS tool.

The results indicated that lean implementation has a significant and positive impact on BIM implementation and project success. On the other hand, BIM implementation had a lower significant impact on project success than lean implementation construct.

The results of this study can be used by both policymakers and industry practitioners in terms of developing strategies for effectively using both lean and BIM. The researchers can further develop other implementation models to investigate whether these concepts are more effective in increasing project success when used integratively.

This study considers both the impact of lean and BIM on project success through input from construction practitioners working on large projects. This way, the study fosters the use of lean, BIM or lean–BIM together in construction projects to enhance project success.

This study aims to determine the effect of psychological capital (PsyCap) on innovative work behavior (IWB) by incorporating the mediating role of work engagement and examine the moderating role of transformational leadership in the relationship between PsyCap and IWB.

Using a correlational design, this research involved 270 nurses from seven public hospitals across Iraq, selected through purposive sampling. Data analysis was conducted using partial least squares structural equation modeling (SmartPLS 3).

The distinctness of the variables used in this study was confirmed by confirmatory factor analysis. The findings show that a PsyCap had a positive influence on IWB directly and indirectly through the mediating of work engagement, and transformational leadership positively moderates the relationship between a PsyCap and IWB in such a way that with high transformational leadership behavior, the relationship will be strengthened.

By focusing on Iraqi nurses, this study not only contributes to the existing literature on PsyCap and IWB but also underscores the unique contextual challenges faced by health-care professionals in conflict-affected areas. The findings emphasize the importance of nurturing leadership qualities to foster a resilient and innovative nursing workforce in such settings.