Search results

The purpose of this paper is to study how CEO power impact corporate tax avoidance. In particular, this paper aims to empirically examine the moderating impact of institutional ownership on the relationship between CEO power and corporate tax avoidance.

The multivariate regression model is used for hypothesis testing using a sample of 308 firm-year observations of Tunisian listed companies during the 2013-2019 period.

The results show that CEO power is negatively associated with corporate tax avoidance and that institutional ownership significantly accentuates the CEO power’s effect on corporate tax avoidance. This implies that CEOs, when monitored by institutional investors, behave less opportunistically resulting in less tax avoidance.

Our findings have significant implications for managers, legislators, tax authorities and shareholders. They showed that CEO duality, tenure and ownership can mitigate the corporate tax avoidance in Tunisian companies. These findings can, hence, guide the development of future regulations and policies. Moreover, our results provide evidence that owning of shares by institutional investors is beneficial for reducing corporate tax avoidance. Thus, policymakers and regulatory bodies should consider adding regulations to the structure of corporate ownership to promote institutional ownership and consequently control corporate tax avoidance in Tunisian companies.

This study differs from prior studies in several ways. First, it addressed the emerging market, namely the Tunisian one. Knowing the notable differences in institutional setting and corporate governance structure between developed and emerging markets, this study will shed additional light in this area. Second, it proposes the establishment of a moderated relationship between CEO power and corporate tax avoidance around institutional ownership. Unlike prior studies that only examined the simple relationship between CEO power and corporate tax avoidance, this study went further to investigate how institutional ownership potentially moderates this relationship.

This study aims to investigate whether various types of risks faced by the publicly listed commercial banks of India and Bangladesh are driven by market power and provides comparative insights from both economies.

By using the adjusted Lerner index to gauge bank market power and applying the generalised methods of moments (GMM) regression approach, the research delved into the relationship between bank market power and three distinct facets of risk across a sample of 26 publicly listed commercial banks in India and 22 listed banks in Bangladesh spanning from 2011 to 2022.

The results indicate that for Bangladesh, both “competition fragility” and “competition stability” viewpoints coexist simultaneously across all risk types, supporting a nonlinear relationship between market power and risk. However, in the Indian context, a nonlinear association exists only in the case of credit risk, while the relationship with insolvency risk is linear, substantiating the “competition fragility view”. Apart from market power and bank-specific variables, GDP growth rate has emerged as a prominent driver across all risk categories in both countries.

The filtration of banks is a limitation that might have influenced the outcomes. This study recommends that the Reserve Bank of India encourages further bank consolidation. Along the same line, Bangladesh Bank should closely oversee the growing competitive landscape. Furthermore, the regulators must monitor the elevated levels of non-performing loans to reduce credit risk so as to bolster the stability of their respective banking sectors.

This comparative study is the first attempt to analyse the market power and risk relationship and includes a novel bank-specific variable, i.e. technology, apart from other established variables.

The ongoing paradigm shift in the energy sector holds paramount implications for the realization of the sustainable development goals, encompassing critical domains such as resource optimization, environmental stewardship and workforce opportunities. Concurrently, this transformative trajectory within the power sector possesses a dual-edged nature; it may ameliorate certain challenges while accentuating others. In light of the burgeoning research stream on open innovation, this study aims to examine the intricate dynamics of knowledge-based industry-university-research networking, with an overarching objective to elucidate and calibrate the equilibrium of ambidextrous innovation within power systems.

The authors scrutinize the role of different innovation organizations in three innovation models: ambidextrous, exploitative and exploratory, and use a multiobjective decision analysis method-entropy weight TOPSIS. The research was conducted within the sphere of the power industry, and the authors mined data from the widely used PatSnap database.

Results show that the breadth of knowledge search and the strength of an organization’s direct relationships are crucial for ambidextrous innovation, with research institutions having the highest impact. In contrast, for exploitative innovation, depth of knowledge search, the number of R&D patents and the number of innovative products are paramount, with universities playing the most significant role. For exploratory innovation, the depth of knowledge search and the quality of two-mode network relations are vital, with research institutions yielding the best effect. Regional analysis reveals Beijing as the primary hub for ambidextrous and exploratory innovation organizations, while Jiangsu leads for exploitative innovation.

The study offers valuable implications to cope with the dynamic state of ambidextrous innovation performance of the entire power system. In light of the findings, the dynamic state of ambidextrous innovation performance within the power system can be adeptly managed. By emphasizing a balance between exploratory and exploitative strategies, stakeholders are better positioned to respond to evolving challenges and opportunities. Thus, the study offers pivotal guidance to ensure sustained adaptability and growth in the power sector’s innovation landscape.

The primary originality is to extend and refine the theoretical understanding of ambidextrous innovation within power systems. By integrating several theoretical frameworks, including social network theory, knowledge-based theory and resource-based theory, the authors enrich the theoretical landscape of power system ambidextrous innovation. Also, this inclusive examination of two-mode network structures, including the interplay between knowledge and cooperation networks, unveils the intricate interdependencies between these networks and the ambidextrous innovation of power systems. This approach significantly widens the theoretical parameters of innovation network research.

The energy generation process through photovoltaic (PV) panels is contingent upon uncontrollable variables such as wind patterns, cloud cover, temperatures, solar irradiance intensity and duration of exposure. Fluctuations in these variables can lead to interruptions in power generation and losses in output. This study aims to establish a measurement setup that enables monitoring, tracking and prediction of the generated energy in a PV energy system to ensure overall system security and stability. Toward this goal, data pertaining to the PV energy system is measured and recorded in real-time independently of location. Subsequently, the recorded data is used for power prediction.

Data obtained from the experimental setup include voltage and current values of the PV panel, battery and load; temperature readings of the solar panel surface, environment and the battery; and measurements of humidity, pressure and radiation values in the panel’s environment. These data were monitored and recorded in real-time through a computer interface and mobile interface enabling remote access. For prediction purposes, machine learning methods, including the gradient boosting regressor (GBR), support vector machine (SVM) and k-nearest neighbors (k-NN) algorithms, have been selected. The resulting outputs have been interpreted through graphical representations. For the numerical interpretation of the obtained predictive data, performance measurement criteria such as mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE) and R-squared (R²) have been used.

It has been determined that the most successful prediction model is k-NN, whereas the prediction model with the lowest performance is SVM. According to the accuracy performance comparison conducted on the test data, k-NN exhibits the highest accuracy rate of 82%, whereas the accuracy rate for the GBR algorithm is 80%, and the accuracy rate for the SVM algorithm is 72%.

The experimental setup used in this study, including the measurement and monitoring apparatus, has been specifically designed for this research. The system is capable of remote monitoring both through a computer interface and a custom-developed mobile application. Measurements were conducted on the Karabük University campus, thereby revealing the energy potential of the Karabük province. This system serves as an exemplary study and can be deployed to any desired location for remote monitoring. Numerous methods and techniques exist for power prediction. In this study, contemporary machine learning techniques, which are pertinent to power prediction, have been used, and their performances are presented comparatively.

This paper aims to investigate the influence of composite solder joint preparation on the thermal properties of metal-oxide-semiconductor field-effect transistors (MOSFETs) and the mechanical strength of the soldered joint.

Reinforced composite solder joints with the addition of titanium oxide nanopowder (TiO₂) were prepared. The reference alloy was Sn99Ag0.3Cu0.7. Reinforced joints differed in the weight percentage of TiO₂, ranging from 0.125 to 1.0 Wt.%. Two types of components were used for the tests. The resistor in the 0805 package was used for mechanical strength tests, where the component was soldered to the FR4 substrate. For thermal parameters measurements, a power element MOSFET in a TO-263 package was used, which was soldered to a metal core printed circuit board (PCB) substrate. Components were soldered in batch IR oven.

Shear tests showed that the addition of titanium oxide does not significantly increase the resistance of the solder joint to mechanical damage. Titanium oxide addition was shown to not considerably influence the soldered joint’s mechanical strength compared to reference samples when soldered in batch ovens. Thermal resistance R_thj-a of MOSFETs depends on TiO₂ concentration in the composite solder joint reaching the minimum R_thj at 0.25 Wt.% of TiO₂.

Mechanical strength: TiO₂ reinforcement shows minimal impact on mechanical strength, suggesting altered liquidus temperature and microstructure, requiring further investigation. Thermal performance: thermal parameters vary with TiO₂ concentration, with optimal performance at 0.25 Wt.%. Experimental validation is crucial for practical application. Experimental confirmation: validation of optimal concentrations is essential for accurate assessment and real-world application. Soldering method influence: batch oven soldering may affect mechanical strength, necessitating exploration of alternative methods. Thermal vs mechanical enhancement: while TiO₂ does not notably enhance mechanical strength, it improves thermal properties, highlighting the need for balanced design in power semiconductor assembly.

Incorporating TiO₂ enhances thermal properties in power semiconductor assembly. Optimal concentration balancing thermal performance and mechanical strength must be determined experimentally. Batch oven soldering may influence mechanical strength, requiring evaluation of alternative techniques. TiO₂ composite solder joints offer promise in power electronics for efficient heat dissipation. Microstructural analysis can optimize solder joint design and performance. Rigorous quality control during soldering ensures consistent thermal performance and mitigates negative effects on mechanical strength.

The integration of TiO₂ reinforcement in solder joints impacts thermal properties crucial for power semiconductor assembly. However, its influence on mechanical strength is limited, potentially affecting product reliability. Understanding these effects necessitates collaborative efforts between researchers and industry stakeholders to develop robust soldering techniques. Ensuring optimal TiO₂ concentration through experimental validation is essential to maintain product integrity and safety standards. Additionally, dissemination of research findings and best practices can empower manufacturers to make informed decisions, fostering innovation and sustainability in electronic manufacturing processes. Ultimately, addressing these social implications promotes technological advancement while prioritizing consumer trust and product quality in the electronics industry.

The research shows the importance of the soldering technology used to assemble MOSFET devices.

The current and modern electrical distribution networks, named smart grids (SGs), use advanced technologies to accomplish all the technical and nontechnical challenges naturally demanded by energy applications. Energy metering collecting is one of these challenges ranging from the most basic (i.e., visual assessment) to the expensive advanced metering infrastructure (AMI) using intelligent meters networks. The AMIs’ data acquisition and system monitoring environment require enhancing some routine tasks. This paper aims to propose a methodology that uses a distributed and sustainable approach to manage wide-range metering networks, focused on using current public or private telecommunication infrastructure, optimizing the implementation and operation, increasing reliability and decreasing costs.

Inspired by blockchain technology, a collaborative metering system architecture is conceived, managing massive data sets collected from the grid. The use of cryptography handles data integrity and security issues.

A robust proof-of-concept simulation results are presented concerning the resilience and performance of the proposed distributed remote metering system.

The methodology proposed in this work is an innovative AMI solution related to SGs. Regardless of the implementation, operation and maintenance of AMIs, the proposed solution is unique, using legacy and new technologies together in a reliable way.

The purpose of this paper is to develop a load detection method for domestic induction cooktops. The solution aims to minimize its impact in the converter power transmission while enabling the estimation of the equivalent electrical parameters of the load. This method is suitable for a multi-output resonant inverter topology with shared power devices.

The considered multi-output converter presents power devices that are shared between several loads. Thus, applying load detection methods in the literature requires a halt in the power transfer to ensuring safe operation. The proposed method uses a complementary short-voltage pulse to excite the induction heating (IH) coil without stopping the power transfer to the remaining IH loads. With the current through the coil and the analytical equations, the equivalent inductance and resistance of the load is estimated. The precision of the method has been evaluated by simulation, and experimental results are provided.

The measurement of the current through the induction coil as a response to a short-time single-pulse voltage variation provides enough information to estimate the load equivalent parameters, allowing to differentiate between no-load, non-suitable IH load and suitable IH load situations.

The proposed method provides a solution for load detection without requiring additional circuitry. It aims for low power transmission to the load and ensures zero-voltage switching and reduced peak current even in no-load cases. Moreover, the proposed solution is extensible to less complex converters, as the half bridge.

The study evaluates the influence of human capital efficiency (HCE) and market power on bank performance.

The study employs two measures of bank performance: profitability and stability. Unbalanced panel data of 35 banks operating in Kenya for 2005–2020 collected from published financial statements is utilized. The study employs the feasible generalized least squares (FGLS) method in the analysis and the two-step system generalized method of moments (GMM) for robustness check.

The study affirms an inverted U-shaped relationship between market power and bank performance. The effect of market power on bank profitability is enhanced when a bank has highly efficient human capital. Further, HCE significantly impacts bank stability for banks with low HCE. Interestingly, a further increase in HCE narrows the net interest margins for banks with high HCE, conferring welfare benefits to customers as interest rate spreads shrink.

This study provides important insights into the role of human capital in bank performance. First, banks ought to invest in promoting HCE through training and development. As regulators root for bank consolidation, attention to HCE is imperative for fostering profitability and stability.

The study fills an essential gap in the literature by evaluating the effect of firm-level market power on bank performance in an emerging market. We adopt a novel stochastic frontier estimator to generate the Lerner index. Further, this is the first study known to the authors to evaluate the effect of market power on bank performance in the context of human capital efficiency variations.

The purpose of this study is to establish the relationship between chief executive officer (CEO) power, audit committee effectiveness and earnings quality in regulated firms in Uganda.

The authors employed cross-sectional and correlational research designs, based on a sample of 136 regulated firms in Uganda. Data were collected using a questionnaire survey from Chief Finance Officers and Chief Audit Executives. Data were analyzed using a Statistical Package for Social Sciences and Partial Least Squares Structural Equation Modeling.

Results indicate that CEO power causes negative variances in earnings quality. The results also reveal that audit committee effectiveness positively relates relatively similarly with earnings quality. In addition, CEO power and audit committee effectiveness are negative and significantly related. The results further indicate that CEO power and earnings quality are mediated by audit committee effectiveness.

CEO power creates an opaque accounting environment which may leave the stakeholders unable to evaluate the true economic reality of the firm. Audit committee effectiveness is an important enabler for reporting high-quality earnings even in the presence of a powerful CEO.

This study contributes toward a methodological stance of using perceptions to understand earnings quality in regulated firms in Uganda. This is probably the first study that has specifically explored earnings quality using only the fundamental qualitative characteristics of accounting information (as proxies) as enshrined in the Conceptual Framework for Financial Reporting 2018 particularly in Uganda since Her adoption of International Financial Reporting Standards in 1998. Second, the indirect effect of audit committee effectiveness and CEO power is tested.

Endurance time is an important factor limiting the progress of flapping-wing aircraft. In this study, this paper developed a prototype of a double-wing flapping-wing micro air vehicle (FMAV) that mimics insect-scale flapping wing for flight. Besides, novel methods for optimal selection of motor, wing length and battery to achieve prolonged endurance are proposed. The purpose of this study is increasing the flight time of double-wing FMAV by optimizing the flapping mechanism, wings, power sources, and energy sources.

The 20.4 g FMAV prototype with wingspan of 21.5 cm used an incomplete gear flapping wing mechanism. The motor parameters related to the lift-to-power ratio of the prototype were first identified and analyzed, then theoretical analysis was conducted to analyze the impact of wing length and flapping frequency on the lift-to-power ratio, followed by practical testing to validate the theoretical findings. After that, analysis and testing examined the impact of battery energy density and efficiency on endurance. Finally, the prototype’s endurance duration was calculated and tested.

The incomplete gear facilitated 180° symmetric flapping. The motor torque constant showed a positive correlation with the prototype’s lift-to-power ratio. It was also found that the prototype achieved the best lift-to-power ratio when using 100 mm wings.

A gear-driven flapping mechanism was designed, capable of smoothly achieving 180° symmetric flapping. Besides, factors affecting long-duration flight – motor, wings and battery – were identified and a theoretical flight duration analysis method was developed. The experimental result proves that the FMAV could achieve the longest hovering time of 705 s, outperforming other existing research on double-wing FMAV for improving endurance.