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This paper studies the association between earnings opacity and corporate social responsibility disclosures of firms listed on the Vietnamese Stock Exchange.

We utilize a dataset comprising a sample of all listed Vietnamese firms for the period of 2014–2022. Data regarding corporate social responsibility information are gathered manually. Following Dechow et al. (1995), Kothari et al. (2005) and Bhattacharya et al. (2003), earnings opacity is measured by using three proxies, including abnormal accruals, earnings smoothing and loss avoidance. Our hypothesis was tested via ordinary least squares (OLS) regressions. To address endogeneity problems, we use the two-stage instrumental variable method (IV-2SLS) as well as the generalized method of moments (GMM) to ensure the robustness of our results.

We find that earnings opacity is positively related to corporate social responsibility disclosures. Cross-sectional analyses indicate that managers of firms disguise their opportunistic behaviour by disclosing more information about corporate social responsibility. The evidence also shows that firms experience long-run underperformance when having higher earnings opacity and greater sustainability disclosures. Our results remain robust even after correcting for endogeneity using the IV approach and the GMM method.

Evidence from this study can serve as a warning signal to the investment community, highlighting that some methods aimed at enhancing a firm’s corporate social responsibility disclosures might be used to obstruct other unethical activities. Moreover, the results of this study can help regulators gain a better comprehension of firms' reporting patterns concerning corporate social responsibility initiatives. It should not only reform the corporate social responsibility regulation but also impose stronger litigation for firms to enhance the quality of corporate social responsibility disclosures.

We are the first to present evidence regarding the relationship between earnings opacity and corporate social responsibility disclosure in Vietnam.

The study aims to determine the the optimal value of output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The output parameters of a variable compression ratio (CR) diesel engine are investigated at different loads, CR and fuel modes of operation experimentally. The performance parameters like brake thermal efficiency (BTE) and brake specific energy consumption (BSEC), whereas CO emission, HC emission, CO₂ emission, NO_x emission, exhaust gas temperature (EGT) and opacity are the emission parameters measured during the test. Tests are conducted for 2, 6 and 10 kg of load, 16.5 and 17.5 of CR.

In this investigation, the first engine was fueled with 100% diesel and 100% Calophyllum inophyllum oil in single-fuel mode. Then Calophyllum inophyllum oil with producer gas was fed to the engine. Calophyllum inophyllum oil offers lower BTE, CO and HC emissions, opacity and higher EGT, BSEC, CO₂ emission and NO_x emissions compared to diesel fuel in both fuel modes of operation observed. The performance optimization using the Taguchi approach is carried out to determine the optimal input parameters for maximum performance and minimum emissions for the test engine. The optimized value of the input parameters is then fed into the prediction techniques, such as the artificial neural network (ANN).

From multiple response optimization, the minimum emissions of 0.58% of CO, 42% of HC, 191 ppm NO_x and maximum BTE of 21.56% for 16.5 CR, 10 kg load and dual fuel mode of operation are determined. Based on generated errors, the ANN is also ranked for precision. The proposed ANN model provides better prediction with minimum experimental data sets. The values of the R² correlation coefficient are 1, 0.95552, 0.94367 and 0.97789 for training, validation, testing and all, respectively. The said biodiesel may be used as a substitute for conventional diesel fuel.

The blend of Calophyllum inophyllum oil-producer gas is used to run the diesel engine. Performance and emission analysis has been carried out, compared, optimized and validated.

This study aims to make a distinction between actualized and claimed affordances of blockchain by examining how a specified user group interprets and translates the actualized affordances from a known use context into their existing practices. This allows us to develop and advance the concept of affordances-in-practice as an enactment of action possibilities through practices in a specified use context.

We focus on the field of sustainable investment (SI) and its relation to emerging blockchain technologies in the pursuit of sustainable development goals (SDGs). We used a field study involving 29 interviews with SI practitioners and blockchain entrepreneurs in South Africa, supplemented with an analysis of 91 practitioner and industry documents.

Our findings show that when there is a lack of actual use cases in the field of SI, the claimed affordances of blockchain are subject to a sensemaking process, which considers how action possibilities can be enacted and transformed through practices and how institutional constraints and socio-cognitive barriers can determine the available action possibilities.

A notable limitation relates to the relative novelty and emerging status of blockchain. As affordances are based on available information and experience, this leaves room for claimed affordances. We discuss the implications of the interplay of the actualized and claimed affordances in blockchain applications in the field of SI.

We discuss the practical implications of addressing claimed affordances and field opacity in the SI field.

To the best of the authors’ knowledge, this is the first study to examine blockchain affordances for good in the context of achieving SDGs through SI. Our affordances-in-practice framework holds theoretical promise to pinpoint and explain how practices can shape action possibilities despite having difficulties in evaluating the underlying technological potentialities.

The purpose of this paper is to examine failures in internal and external controls associated with sovereign wealth funds (SWFs), using three caselets to illustrate the fraud triangle theory factors.

This study uses a qualitative research approach. Caselets are used to illustrate the fraud triangle factors associated with SWFs.

Ideally, SWFs would be characterized by opacity and the strategic flexibility to advance political goals, but this operational agility facilitates an environment ripe for fraud, in large part because there is little transparency with regard to their regulatory structure. Elements of the fraud triangle inherent in the structure of SWFs contribute to the fraud found in the three case examples.

The authors use three SWF fraud cases rather than statistical sampling of all SWFs, which limits the generalizability of the findings. Future research should explore additional recommendations for the evaluation of SWF governance.

The overlap between public sector governance and SWF governance creates an environment amenable to fraud, and as a result, fraud has occurred in several SWFs. Governance recommendations should take into account the lessons learned from previous SWF fraud cases.

Ideally, SWFs would be characterized by opacity and the strategic flexibility to advance political goals, but this operational agility may also facilitate an environment ripe for fraud, in large part because there is little transparency with regard to their regulatory structure.

To the best of the authors’ knowledge, this paper is the first to identify the fraud triangle risk factors associated with sovereign wealth funds using SWF fraud caselets.

This study aims to examine stock price synchronicity during the COVID-19 crisis using 32,452 firms from 61 countries. This paper explores the impact of government effectiveness on synchronicity while distinguishing between developed and emerging markets.

The research analysis employs ordinary OLS pooled regression analysis.

This paper presents worldwide evidence that stock price synchronicity was significantly higher during February and March 2020. This paper shows that synchronicity increased with the intensity of the crisis. In addition, the government's role reduced the COVID-19 impact on synchronicity, which was stronger in developed markets than in emerging markets.

The novelty of the study lies in documenting the impact of the COVID-19 pandemic on stock price synchronicity. The findings add to a deeper understanding of market behavior amid significant disruptive shocks.

This paper aims to examine the impact of traditional banks’ financial technology (FinTech) adoption on corporate loan spreads and lending practices.

This study examines the impact of FinTech adoption by banks on corporate loan spreads and lending practices. By analyzing data from bank 10-K filings, we develop a novel metric to assess FinTech adoption at the individual bank level. Our analysis reveals a significant positive correlation between increased FinTech adoption and higher corporate loan spreads, particularly for loans that are relatively informationally opaque. This causality is further validated through a quasi-natural experiment. Additionally, we identify trends toward loans with smaller sizes and longer maturities in banks with advanced FinTech integration.

Using a sample of corporate loans issued from 1993 to 2020, this paper documents a significant positive relationship between a bank’s increased FinTech adoption and higher loan spreads. This correlation is especially noticeable for loans that are informationally opaque. Moreover, the paper reveals trends toward smaller loan sizes and longer maturities with advanced FinTech integration in banks. Overall, these findings indicate FinTech enhances efficiency in processing hard information and holds the potential to enhance financial inclusion.

This paper contributes to two significant strands of finance literature. First, it highlights how banks with advanced FinTech integration gain advantages through enhanced processing of hard information. Furthermore, it underscores the role of FinTech in promoting financial inclusion, particularly for those borrowers facing informational opacity.

Financial technologies form the heart of considerable disruptive innovation. Fintech is the emerging financial infrastructure for modern business. Big data are the feedstock for artificial intelligence (AI) that drives many fintech sectors – start-up finance, commodities and investment instrumentation, payment systems, currencies, exchange markets/trading platforms, market-failure response forensics, underwriting, syndication, risk assessment, advisory services, banking, financial intermediaries, transaction settlement, corporate disclosure, and decentralized finance. This chapter demonstrates how analyzing big data, largely processed through cloud computing, drives fintech innovations, scholarship, forensics, and public policy. Despite their apparent virtues, some fintech mechanisms can externalize various social costs: flawed designs, opacity/obscurity, social media (SM) influences, cyber(in)security, and other malfunctions. Fintech suffers regulatory lag, the delay following the introduction of novel fintechs and later assessment, development, and deployment of reliable regulatory mechanisms. Big data can improve fintech practices by balancing three key influences: (1) fintech incentives, (2) market failure forensics, and (3) developing balanced public policy resolutions to fintech challenges.

Information transparency is an important factor in enhancing trust and promoting interfirm cooperation. By combining transaction cost theory and institutional theory, this study aims to examines whether host-country firms’ information transparency prompt multinational enterprises’ (MNEs) to choose the joint venture entry mode for outward foreign direct investment (OFDI).

Using Heckman two-stage estimation method, this study examines Chinese listed manufacturing firms for the period 2014–2019.

The findings indicate that the higher the information transparency of host-country firms, the higher the possibility of MNEs choosing the joint venture entry mode for OFDI. This study further finds that the positive relationship between host country firms’ information transparency and the possibility of choosing the joint venture entry mode is enhanced by institutional distance, but weakened by MNEs’ host-country experience.

How to choose the appropriate entry mode of OFDI in the internationalization strategy is an important issue for MNEs to consider. As the postpandemic world is characterized by increased global risks, decoupling of economies, disruption of global value chains and the retreat of globalization (Contractor and Cantwell, 2022), how to further strengthen cooperation, reduce the cost and risk of MNEs and truly realize common construction and sharing is one of the hot issues in both practice and research.

This chapter explores the role of artificial intelligence (AI), particularly its subfield of machine learning (ML) methods, as a core technology of the fintech revolution in the financial services industry. It simplifies some of the complex concepts related to AI by introducing the main ML paradigms and related techno-methodic aspects. This chapter uses real-world examples to illustrate how next-generation AI powered by ML is transforming the financial services industry. Next, in illustrating the risks associated with AI adoption, this chapter discusses the need for regulation to address the essential facets of AI governance, including transparency, accountability, ethics, and responsible use. Lastly, it looks at emerging regulatory approaches across leading global jurisdictions. The primary goal is to give readers an initial understanding of AI's profound impact on the financial sector.

Awarding contracts based solely on the lowest price is unsuitable for every project. Consequently, most procurement systems in developed countries have progressed to the multicriteria selection practices (MSPs) for tender evaluation. MSPs consider a range of quality measures, such as completion time, life cycle cost, functional characteristics, environmental impact and innovation, alongside bid price. This study examines the prevailing MSPs in Sri Lankan public tender evaluations to enhance the effectiveness of the local tender evaluation process.

A desk study approach was employed to collect bidding documents, resulting in the identification of 66 documents. A systematic screening process was then applied to identify those bidding documents that incorporated MSPs. Subsequently, content analysis was conducted to determine the common features of the functions used in MSPs.

The study identified six primary functions related to MSPs incorporated in the bidding documents to procure building and substation projects. Three functions follow the price-to-quality method, while the remaining three follow the quality-to-price method. Among these identified functions, four functions employ objective evaluation criteria, such as thickness, capacity and operational loss. The other two functions utilize subjective evaluation criteria, such as the project’s design and technical specifications. Contract awarding will be based on either the highest score or the lowest bid, depending on the function type.

This study’s originality lies in exploring MSPs in the Sri Lankan public tender evaluation process and in disclosing their characteristics to promote the MSPs in Sri Lanka and developing countries.