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This chapter introduces dividend smoothing, presents theories to explain dividend smoothing behavior, and analyzes how different levels of business environment affect dividend smoothing. First, dividend smoothing describes a mechanism in which a firm is reluctant to reduce dividends and only increases dividends when its earnings increase permanently. In practice, dividend smoothing behavior is found in both developed and developing countries. Firms in developed countries are more likely to smooth dividends than those in developing countries. Second, although Miller and Modigliani (1961) posit that investors are indifferent between stable and unstable dividend payments in a perfect environment, market frictions in the real world make stable and unstable dividends have different effects on firm value. Three common frictions are information asymmetry, agency problem, and investors' demand for income smoothing. Due to information asymmetry between insiders and outsiders, firms tend to smooth their dividends to signal outside investors about their quality. In addition, dividend smoothing may be the substitute for weak corporate governance and/or the outcome of free cash absorption behavior. Besides, dividends are more convenient for investors' consumption; therefore, firms are more likely to smooth dividends in order to satisfy investors' demand for smooth income. Finally, as a special dividend decision, dividend smoothing is also affected by an internal micro (industry) and macro-environment. Dividend smoothing theories are the behind mechanisms to explain these effects.

This paper empirically investigates whether trade openness (TO) in Brazil, Russia, India, China and South Africa (BRICS) countries affects how banks might employ loan loss provisions (LLPs) to smooth out their earnings and how adopting the International Financial Reporting Standards (IFRS) can mitigate it.

The analysis includes 78 commercial banks from five BRICS nations and spans 2014 through 2020. To test these hypotheses, the authors utilized a fixed-effect and two-step system panel generalized methods of moments (GMM) estimator.

TO positively affects income smoothing (earnings management) across BRICS commercial banks. The effect is clearer in banks that make financial reports under the IFRS. Path analysis reveals that the effect of TO is driven by nonperforming loans (NPLs). Additionally, the IFRS restricts earnings management in the BRICS banking sector when a better institutional environment is present. The authors found that accounting rules (IFRS) and enforcement (better institutional settings) interact to enhance earnings’ quality.

The relationship between TO and bank earnings management practices is important for understanding the complex interplay between trade and finance and ensuring financial stability, investor confidence and regulatory compliance. This study recommends better regulations and governance mechanisms for financial reports in emerging nations like BRICS. Additionally, macro-prudential regulators and banking supervisors should work closely to ensure transparent TO decisions with improved discipline, institutional quality and regulatory support to enhance bank stability.

The study finds evidence of bank income smoothing in the BRICS and introduces TO as a determinant. It also identifies the evolving role of IFRS in the presence of higher institutional quality and TO, thereby expanding the financial reporting literature.

This study examines the impact of competition and concentration on bank income smoothing in Central and Eastern European (CEE) countries.

The two-step system GMM method was used to analyse the impact of competition and concentration on bank income smoothing in 17 CEEs from 2004 to 2015.

Loan loss provisions (LLPs) are negatively related to bank competition and concentration. The authors find no evidence for income smoothing using LLPs in a high-competition or high-concentration environment.

A limitation of the study is that the analysis was restricted to commercial banks. The authors did not examine investment banks or microfinance banks in this study. Also, not having access to databases does not allow them to include recent years in the study.

CEE commercial banks will likely keep fewer provisions or engage in under-provisioning when they face intense competition, and this can expose them to credit risk, which may threaten their stability.

This study is the first to investigate the effect of concentration and competition on income smoothing among CEE banks.

Prior studies generally focus on income smoothing through discretionary accruals and document that managers have incentives to smooth earnings due to various reasons. This paper aims to focus on income smoothing through research and development (R&D) management and examine whether and how income smoothing through R&D management affects credit rating agencies’ perception of firm risk.

The authors use financial statement data from the CRSP/Compustat Merged data set universe for the period from 1992 to 2019 after excluding financial and utility industries. The authors follow the model for credit ratings used in previous literature to test the hypothesis. Specifically, the authors use an ordered probit model to express credit ratings as a function of income smoothing attributes.

The authors find that R&D-based income smoothing improves a firm’s credit rating. However, the positive effect of R&D-based income smoothing on credit ratings is less than that of accruals-based income smoothing. This study also shows that the positive effect of R&D-based income smoothing is more pronounced for firms less subject to opportunistic incentives, further strengthening the notion that managers smooth earnings through R&D management to provide more informative earnings.

This study contributes to the income smoothing literature in several ways. First, the authors contribute to the research by showing that managers’ income smoothing activity through R&D management positively affects firms’ credit rating. Second, the authors also document the relative benefits of the two different income smoothing techniques in terms of improving credit agencies’ perception of firms’ creditworthiness.

This paper aims to examine the association between income smoothing and the cost of debt in two different countries, namely, the UK and Nigeria.

The authors used a sample from listed firms in the UK and Nigeria during 2000–2019. The study hypotheses are examined by implementing quantitative methods, including panel regression analysis, cross-sectional regression analysis and parametric independent samples t-test.

The results reveal that Nigerian companies have a substantially higher cost of debt and are more active in using income-smoothing practices. However, the relationship between income smoothing and the cost of debt is not found to be statistically significant in both countries. Besides, the results of this study show that financial leverage, profitability, company size and asset turnover are significantly associated with the cost of debt.

The study contributes to the existing literature by providing new insights concerning the contrast between developed and developing countries in financial and reporting issues.

This paper aims to investigate bank earnings management using loan loss provision. The paper examines income smoothing, which is a type of earnings management. It compares the income smoothing behaviour of banks in the UK, France, South Africa and Egypt.

The study uses the panel fixed effect regression methodology to analyse bank income smoothing.

The findings show that bank income smoothing is present in the UK and Egypt and absent in France and South Africa. Banks in Egypt used LLPs to smooth income before the global financial crisis. Meanwhile, bank income smoothing is pronounced in France during and after the financial crisis but was absent in the pre-crisis period. Also, bank income smoothing is reduced in countries that (1) have strict banking supervision, (2) adopt common law particularly the United Kingdom, and by countries that adopt civil law, particularly France and Egypt. Bank earnings management is greater in countries that (3) adopt a mixed legal system, particularly South Africa, and in countries that adopt International Financial Reporting Standards accounting standards.

The implication of the findings is that country differences may affect banks' incentive to smooth income using loan loss provision.

The novelty of this paper is that it explicitly analyses specific countries that have different supervisory regimes, different structure and accounting rules.

This paper aims to investigate the performance of two novel numerical methods, the face-based smoothed finite element method (FS-FEM) and the edge-based smoothed finite element method (ES-FEM), which employ linear tetrahedral elements, for the purpose of strength assessment of a high-speed train hollow axle.

The calculation of stress for the wheelset, comprising an axle and two wheels, is facilitated through the application of the European axle strength design standard. This standard assists in the implementation of loading and boundary conditions and is exemplified by the typical CRH2 high-speed train wheelset. To evaluate the performance of these two methods, a hollow cylinder cantilever beam is first used as a benchmark to compare the present methods with other existing methods. Then, the strength analysis of a real wheelset model with a hollow axle is performed using different numerical methods.

The results of deflection and stress show that FS-FEM and ES-FEM offer higher accuracy and better convergence than FEM using linear tetrahedral elements. ES-FEM exhibits a superior performance to that of FS-FEM using linear tetrahedral elements, showing accuracy and convergence close to FEM using hexahedral elements.

This study channels the novel methods (FS-FEM and ES-FEM) in the static stress analysis of a railway wheelset. Based on the careful testing of FS-FEM and ES-FEM, both methods hold promise as more efficient tools for the strength analysis of complex railway structures.

This study aims to present the optimization of parameters and effects of annealing and vapor smoothing post-processing treatments on the surface roughness and tensile mechanical properties of fused deposition modeling (FDM) printed acrylonitrile butadiene styrene (ABS).

Full-factorial test matrices were designed to determine the most effective treatment parameters for post-processing. The parameters for annealing were temperature and time, whereas the parameters for the vapor smoothing were volume of acetone and time. Analysis of surface roughness and tensile test results determined influences of the levels of parameters to find an ideal balance between mechanical properties and roughness.

Optimal parameters for vapor smoothing and annealing were determined. Vapor smoothing resulted in significantly higher improvements to surface roughness than annealing. Both treatments generally resulted in decreased mechanical properties. Of all treatments tested, annealing at 100 °C for 60 min provided the greatest benefit to tensile properties and vapor smoothing with 20 mL of acetone for 15 min provided the greatest benefit to surface roughness while balancing effects on properties.

Vapor smoothing and annealing of FDM ABS have typically been studied independently for their effects on surface roughness and material properties, respectively, with varying materials and manufacturing methods. This study objectively compares the effects of each treatment on both characteristics simultaneously to recommend ideal treatments for maximizing the balance between the final quality and performance of FDM components. The significance of the input variables for each treatment have also been analyzed. These findings should provide value to end-users of 3D printed components seeking to balance these critical aspects of manufacturing.

Non-linear power–voltage characteristics of solar cell and frequently changing output due to variation in solar irradiance caused by movement of clouds are the major issues need to be considered in photovoltaic (PV) penetration to maintain the power quality of the grid. It is important for a PV module to always function at its maximum available power point to increase the efficiency and to maintain the grid stability. A possible solution to mitigate these generation fluctuations is the use of an electric double-layer capacitor or supercapacitor energy storage device, which is an efficient storage device for power smoothing applications. This study aims to propose a power smoothing control approach to smoothen out the output power variations of a solar PV system using a supercapacitor energy storage device.

To extract the maximum possible power from a PV panel, there are several maximum power points tracking (MPPT) algorithms developed in literature. Fuzzy logic controller-MPPT method is used in this work as it is a very efficient and popular technique which responds quickly under varying ecological conditions, reduced computational complexity and does not depend on any system constraints. Fuzzy logic-based MPPT controller by Boost DC–DC converter is developed for operating the PV panels at available maximum power point. Fuzzy logic-proportional integral (PI) charge controller is implemented by Buck–Boost converter to provide the constant current and suitable voltage for supercapacitor and to achieve better power smoothing. PI charge controller is preferred in this work as it offers better outcomes and is very easy to implement.

Simulation results conclude that the proposed power smoothing control approach can efficiently smooth out the power variations under variable irradiance and temperature situations. To confirm the accurateness of the proposed system, it is validated for poly-crystalline PV module and comparison of results is done by using different case study with and without the use of an energy storage system under change in irradiance condition. The proposed system is developed and examined on MATLAB/Simulink environment.

The performance comparison between PV power output with and without the use of a supercapacitor energy storage device under different Case Studies shows that the improved performance in smoothing of power output was achieved with the use of a supercapacitor energy storage device.

Despite an enormous body of literature on conflict management, intra-group conflicts vis-à-vis team performance, there is currently no study investigating the conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams.

This study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology.

Results from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by power-conflict prevention, prevention of task disputes and prevention of Process conflicts respectively. Results also show that relationship and power conflicts interact on the one hand, while task and relationship conflict prevention also interact on the other hand, thus, suggesting the prevention of one of the conflicts could minimise the outbreak of the other.

The study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations.

The study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. This study urges organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team.

The study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams.

The study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power conflict, process, relationship and task conflicts; to encourage Big Data implementation.