Purpose
This study aims to examine whether and how managerial ability is associated with the relation between product market competition and earnings management. The authors argue that high-ability managers may moderate the underlying relations in both directions, and they are likely to trade off relative costs between accrual-based earnings management (AEM) and real earnings management (REM).
Design/methodology/approach
This study uses ordinary least square regressions to examine the association of managerial ability on the relations between product market competition and earnings management. The paper follows prior literature to measure managerial ability, product market competition and earnings management.
Findings
This study shows empirical evidence that high-ability managers in high-competition industries are likely to engage in AEM but less likely to engage in REM. These findings overall indicate that high-ability managers in high-competition industries trade-off between different forms of earnings management based on their relative costliness and choose the one that is relatively less costly.
Practical implications
This study has important practical implications as the findings identify situations when important stakeholders, such as the board of directors and investors, may take precautions to prevent managers’ opportunistic behaviors. The findings of this study also might be helpful for firms when it comes to selecting managers. The findings may provide some input to the firms in considering the risks and benefits trade-offs of recruiting a high versus low-ability manager in a more or less competitive environment.
Originality/value
The findings of this study show new insight into how managerial ability moderates the relation between product market competition and different types (i.e. accrual-based and real activity-based) of earnings management.
Protective coatings based on two dimensional materials such as graphene have gained traction for diverse applications. Their impermeability, inertness, excellent bonding with metals, and amenability to functionalization renders them as promising coatings for both abiotic and microbiologically influenced corrosion (MIC). Owing to the success of graphene coatings, the whole family of 2D materials, including hexagonal boron nitride and molybdenum disulphide are being screened to obtain other promising coatings. AI-based data-driven models can accelerate virtual screening of 2D coatings with desirable physical and chemical properties. However, lack of large experimental datasets renders training of classifiers difficult and often results in over-fitting. Generate large datasets for MIC resistance of 2D coatings is both complex and laborious. Deep learning data augmentation methods can alleviate this issue by generating synthetic electrochemical data that resembles the training data classes. Here, we investigated two different deep generative models, namely variation autoencoder (VAE) and generative adversarial network (GAN) for generating synthetic data for expanding small experimental datasets. Our model experimental system included few layered graphene over copper surfaces. The synthetic data generated using GAN displayed a greater neural network system performance (83-85% accuracy) than VAE generated synthetic data (78-80% accuracy). However, VAE data performed better (90% accuracy) than GAN data (84%-85% accuracy) when using XGBoost. Finally, we show that synthetic data based on VAE and GAN models can drive machine learning models for developing MIC resistant 2D coatings.
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