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Article
Publication date: 27 June 2022

Toan Pham-Khanh Tran, Ngoc Phu Tran, Phuc Van Nguyen and Duc Hong Vo

The effects of government expenditure on the shadow economy have been investigated. However, the effect from a moderating factor that affects this relationship has been largely…

Abstract

Purpose

The effects of government expenditure on the shadow economy have been investigated. However, the effect from a moderating factor that affects this relationship has been largely ignored in the existing literature. This paper investigates how fiscal deficit moderates the effects of government expenditure on the shadow economy for 32 Asian countries for the past two decades since 2000.

Design/methodology/approach

The authors use various techniques, which allow cross-sectional dependence and slope homogeneity in panel data analysis, to examine this relationship in both the long run and short run. The analysis also considers the marginal effects of government expenditure on the shadow economy at different degrees of fiscal deficits.

Findings

Empirical findings from this paper indicate that an increase in government expenditure and fiscal deficit will increase the shadow economy size. Interestingly, the effects of government expenditure on the shadow economy will intensify with a greater degree of the budget deficit. The authors also find that enhancing economic growth to improve income per capita and extending international trade appears to reduce the shadow economy in the Asian countries.

Practical implications

The authors consider that policies targeting reducing shadow economy should follow conventional economic policies on economic growth, unemployment and inflation.

Originality/value

To the best of the authors’ knowledge, this is the first empirical study conducted to examine the moderating role of fiscal deficit in the government expenditure–shadow economy nexus in Asian countries.

Details

International Journal of Emerging Markets, vol. 19 no. 2
Type: Research Article
ISSN: 1746-8809

Keywords

Article
Publication date: 31 May 2023

Vani Aggarwal and Nidhi Karwasra

The purpose of this study is to provide a comprehensive analysis on the economic relationship between trade openness and economic growth and to identify current developments…

Abstract

Purpose

The purpose of this study is to provide a comprehensive analysis on the economic relationship between trade openness and economic growth and to identify current developments, potential research area and future directions. The emphasis is on the identification of annual growth of publications, country-wise distribution, publication pattern, intellectual structure and cluster analysis of scientific production in this field.

Design/methodology/approach

This study used evaluative techniques, text mining approach and performance analysis to identify possible patterns and correlation and to measure the impact of authors/citations/scientific production. Further, this study used the bibliometric mapping to represent the structural features of scientific production. This study emphasized on identification of the research hotspots based on occurrence of indexed keywords, productive researchers and journals during 2000–2022. Further, cluster analysis is performed using VOS viewer to analyze the current dynamics and future direction of the association between trade openness and economic growth (Eck and Waltman, 2011). Also, co-citation analysis is used in this study to identify the relations among authors or journals or documents using citation data, whereas the bibliographic coupling/mapping is intended to analyze the citing documents. Similarly, co-word analysis is used to study the article keywords that are mainly used to assess the conceptual structure of a concerning subject.

Findings

Economic growth is a function of trade openness, and it is important to analyze the relationship between trade openness and economic growth. Trade openness tends to become more liberalized over time, to contribute more to economic growth. Empirical evidence suggested that there exists a strong association between trade openness and economic growth. Further, keyword timeline analysis illustrated that the linkage between trade openness and economic growth is current area of interest among researchers. As per bibliometric analysis, China, Pakistan and Malaysia are the three most prolific countries in the terms of published articles on this theme. However, the most influential publications based on h-index and citation on trade openness–economic growth relationship is produced by Turkey. Based on cluster analysis, this study suggests that researchers are currently working on trade openness–economic growth relationship with other variables such as FDI, financial development, labor force, environment degradation and carbon emission, while in future, researchers could work on variables such as technology and sustainable development.

Research limitations/implications

There are some limitations of this study. The first limitation is the authors have used Scopus database, leaving the possibility for future research to use Web of Science, Google Scholar or other similar sources. The second limitation is that the authors have used search terms “trade openness “and “economic growth,” although research could be performed using synonyms or even relevant terms in other languages.

Practical implications

Cluster analysis suggested that researchers are currently working on trade openness–economic growth relationship with other variables such as FDI, financial development, labor force, environment degradation and carbon emission, while in future, researchers could work on variables such as technology and sustainable development. Therefore, this study identified the potential research area in this research domain.

Originality/value

To confirm the originality of this study, to the best of the authors’ knowledge, this is the first study to combine bibliometric analysis and cluster analysis on trade openness–economic growth relationship. This study makes a comparison with phenomena/processes/events in contemporary economic and social reality in the field of trade openness and economic growth relationship.

Details

Competitiveness Review: An International Business Journal , vol. 34 no. 2
Type: Research Article
ISSN: 1059-5422

Keywords

Article
Publication date: 17 July 2023

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

Journal of Hospitality and Tourism Insights, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9792

Keywords

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