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This study aims to investigate the relationship between corporate sustainability performance and earnings management in emerging East Asian economies.

The authors base on the triple bottom line approach to measure corporate sustainability performance. In terms of earnings management, two models are applied to detect real activities manipulation and discretionary accruals. The authors use panel data analysis of 410 listed non-financial firms in emerging East Asian economies from 2016 to 2020 that are collected from the Thomson Reuters Eikon database.

The authors find a negative influence of corporate sustainability performance on real activities manipulation and discretionary accruals. The findings highlight the long-term perspective of sustainable development strategies in relation to earnings management. The authors conclude that sustainable firms in emerging East Asia are less likely to engage in earnings management.

The study would be of interest to investors who need more detailed assessments of financial reporting quality to facilitate their investment decision-making and to policymakers who need more understanding of business practices and reporting behaviors of East Asian firms.

The study has shed light on the role of corporate sustainability performance in constraining earnings management and the role of corporate ethics in providing transparent and reliable financial reporting in emerging East Asian economies.

Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.

Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.

Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.

This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.

Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.

By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.

This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.

Most new media research focuses on behavior as a measure of engagement, while the psychological state of being occupied with its content has received little attention. This study examined news engagement beyond pure action observation by exploring young people’s psychological experiences with the news.

The study carried out a digital native’s survey on 212 people (18–28 years). The focus of the survey was on understanding individuals’ engagement with online news using affective and cognitive components. The authors compared the influence of each type of engagement on youth consumption of and attitudes toward online news.

The results of the hierarchical regression analysis showed that affective engagement can be a stronger predictor of online news consumption than cognitive engagement. While affective engagement significantly predicts positive attitudes toward online news, cognitive engagement had no significant effect.

These findings suggest that “engaging the heart” is more influential than “engaging the mind” in drawing young people to the news in today’s information environment. The study thus contributes to the understanding of the cognitive and emotional focus on news content and their importance in shaping young people’s expectations of online news. The findings from this study could have broader implications for future trends in online news consumption.

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.

The study aims to evaluate the influence of capital structure decisions and asset structure on firms' performance for East African listed nonfinancial firms.

The research is descriptive and employs secondary data from the East African capital markets' websites. The generalized method of moments approach is used to estimate the relationship due to its ability to account for endogeneity problems.

The result shows that capital structure decisions and asset structure strongly influence the firms' performance. When long-term debts, short-term debts and tangible fixed assets increase, the return on total assets increases. An increase in the total debt ratio raises the return on equity (ROE). However, the increase in long-term debt lowers the ROE.

The results will help investors and potential investors decide on a financing policy that maximizes performance. Likewise, governments and other policymakers review the capital markets' frameworks to attract institutional and individual investors to the markets for financial availability and to increase profitability.

The research provides evidence on the influence of capital structure decisions and asset structure on firms' performance. Furthermore, its results contribute to firms' financing policy formulation and the corporate finance literature.

This study aims to investigate the effect of the presence of women in top executive positions on financial reporting quality (FRQ) and the role of external audit in enhancing the role of women in top executive positions.

This study uses a sample of 644 Vietnamese-listed firms from 2010 to 2020 and applies fixed-effect and dynamic system generalized method of moments techniques for empirical models to test the related hypotheses.

First, this study found a U-shaped relationship between women on the board and FRQ as well as women on the audit committee and FRQ. Second, female CEOs are positively associated with FRQ in small firms but there is no evidence of this in large firms. Third, a female chief accountant can enhance FRQ. Finally, external audit quality can reduce the negative effect of women on the board and the audit committee on FRQ and increase the positive impact of female chief accountants on FRQ.

The results support all risk-averse, ethical sensitivity and glass ceiling hypotheses in different contexts. This study provides important implications for firms to enhance FRQ by nominating women in a majority of top executive positions and simultaneously using high-quality external audit services.

The impact of women in top executive positions on controlling FRQ in different contexts is an original contribution to gender in management literature.

This paper analyzes the effects of the monetary stance on the media's favorable (or otherwise) attitude to the State Bank of Vietnam's (SBV) monetary policy using monthly data from 2011 to 2021. Monetary stance is a multivariate index based on the growth rates of money supply and domestic credit. A large set of articles published in five Vietnam daily newspapers are utilized to construct a view of the media's favorableness to the monetary policy.

This paper uses hand-collected data from 211 articles published in five newspapers from December 2011 to September 2021 in order to examine the relationship between the monetary stance and the media's favorableness to monetary policy. Following the studies of He and Pauwels (2008) and Xiong (2012), the authors constructed a multivariate stance index to capture most of the important changes in the SBV's monetary policy stance.

The study's main findings are that a change in monetary stance from easing to neutral/tightening, or from neutral to tightening, is greatly appreciated by the media. The study's findings are robust, especially in terms of alternative measures of the media's favorableness and monetary policy variables.

These findings have important policy implications for implementing SBV's monetary policy.

The main contribution of this paper is that the authors are the first to study the nexus of multivariate monetary stance and the media's favorableness to a central bank's non-inflation-targeting mandate. In particular, the study’s findings confirm that the SBV's multivariate monetary stance affects the media's favorableness, whereas the effect of inflation is statistically insignificant.

Little is known about how religious-related beliefs (i.e. karmic beliefs and beliefs in a just world) might affect consumers to engage in more environmentally friendly behavior. Drawing on the justice motive theory, this study aims to explore the individual, complementary, congruent and incongruent impacts of karmic beliefs and belief in a just world on consumers’ proenvironmental consumption.

A sample of 736 consumers was recruited by means of a mall-intercept survey in the most populus cities in Vietnam. An advanced technique – polynomial regression with response surface analysis – was used to test the hypothesized model, whereas the PROCESS macro approach was used to estimate indirect effects.

The findings indicate that while karmic beliefs do not directly affect proenvironmental consumption, they can inspire environmentally friendly behaviors through belief in a just world. High levels of both beliefs enhance proenvironmental consumption, but imbalanced levels erode such behaviors. Importantly, the findings revealed gender differences concerning the role of religious beliefs in influencing proenvironmental consumption among male and female consumers.

The findings of this study offered several theoretical and managerial implications for proenvironmental consumption.

The research provides new insights into how religious beliefs (karmic beliefs and belief in a just world) affect, interact, balance and imbalance with each other to trigger proenvironmental consumption. The research also contributes to the sustainable consumption literature by indicating the mediation roles of belief in a just world and proenvironmental consumption intention in the relationship between beliefs in karma and actual proenvironmental consumption behavior, as well as offering empirical evidence about the gender differences regarding these relationships.

Public–private partnership (PPP) projects play a pivotal role in fostering infrastructure development in developing countries. This study aims to identify major barriers and critical success factors (CSFs) for PPP projects in Vietnam.

The study initially conducted an extensive literature review and interviewed industry experts to compile a comprehensive list of barriers and success factors. Subsequently, a survey involving 250 construction professionals was conducted to assess these identified factors. The research used both ANOVA test and hierarchical regression analysis to explore the relationship between participants’ characteristics and assessments.

The results could provide a valuable reference for practitioners by assessing barriers and CSFs in PPP projects. Specifically, the ANOVA analysis indicated a positive correlation between barrier assessment and participants’ experience, while the influence of participants’ sector on the evaluation was minor. Furthermore, the hierarchical regression analysis demonstrated that participants’ sector did not moderate the relationship between barrier assessment and industry experience.

This research contributes to the existing literature by offering empirical insights into barriers and CSFs specific to PPP projects in a developing country context. The findings highlight the crucial role of experienced professionals in ensuring PPP project success. Moreover, the study provides construction practitioners with a better understanding of the essential factors and supports the development of effective management strategies for future PPP projects.

Construction sustainability (CS) is a strategic reaction to the sustainability expectations of the construction industry's external stakeholders. The extant literature has viewed the environmental, social and economic dimensions of CS as having independent effects on financial performance. Due to the influence of common stakeholders, however, interactions in these dimensions will be present in their effect on financial performance. Accordingly, this study identifies the mechanisms of the interactions between the three CS dimensions and how they jointly affect financial performance.

Content analysis of GRI reports of 60 large construction organisations, followed by a hierarchical regression analysis was used to identify the interactions between environmental, social and economic CS in their effect on financial performance.

Economic CS was found to indirectly, and not directly, affect financial performance, the effect being mediated by both environmental and social CS. Environmental CS was found to have a strong negative effect on financial performance, whilst social CS was found to have a strongly significant positive effect on financial performance.

The motivation for engaging in CS is that investment in economic CS will have a positive effect on both environmental and social CS outcomes, which, in turn can have a combined effect on financial performance.

This is one of the first studies investigating the effect of interactions between the environmental, social and economic CS dimensions on the financial performance of construction organisations. It is also one of the first studies that applies a sociotechnical framework to this relationship.