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Clawback provisions entitle shareholders to recover previously awarded incentive compensation after the discovery of accounting manipulation or misconduct. The author evaluates the impact of clawback enforcement heterogeneity on the horizon of executive compensation.

The author provides empirical tests to evaluate the impact of clawback adoption decisions. The author deals with the endogeneity of clawback adoption decisions through an instrumental variables strategy that exploits the transmission of governance choices within firms’ networks.

While the author finds that clawback adoption reduces the frequency of accounting manipulation, this reduction is accompanied by heterogeneous effects on the horizon of executive pay across firms. Clawback adopters with high director independence, high leverage, high managerial termination payments and low executive ownership tilt their compensation toward the short-term.

The results, robust to alternative specifications, suggest that clawbacks allow strong-enforcement firms to tilt compensation toward the short-term, offsetting some of the direct manipulation disincentives generated by the clawback. The stock market reacts positively to the adoption in firms with weak enforcement, suggesting that clawbacks significantly reduce the managers’ rent-extraction capacity.

Using a novel empirical and identification approach, the results suggest that clawbacks allow strong-enforcement firms to tilt compensation toward the short-term, offsetting some of the direct manipulation disincentives generated by the clawback.

This study examines the value implications of oil price uncertainty for investors in diversified firms using a sample of 922 USA firms from 2001 to 2019.

Our study employs a panel dataset to examine the value implications of oil price uncertainty for diversified firm investors. We consider several alternative specifications to account for unobserved factors and measurement errors that could potentially bias our results. In particular, we use alternative measures of the excess value of diversified firms and oil price uncertainty, additional control variables, fixed-effects models, the Oster test, impact threshold for confounding variable (ITCV) analysis, two-stage least square instrumental variable (2SLS-IV) analysis and the system-GMM model.

We find that the excess value of diversified firms, relative to a benchmark portfolio of single-segment firms, increases with high oil price uncertainty. The impact of oil price uncertainty is asymmetric, as corporate diversification is value-increasing for diversified firm investors only when the volatility is due to positive oil price changes and amidst supply-driven oil price shocks. The excess value increases irrespective of diversified firms’ financial constraints and oil usage. Diversified firms become conservative in their internal capital allocations with high oil price uncertainty. Such conservatism is value-increasing for diversified firm investors, as it supports higher performance in response to oil price uncertainty.

Our study has three important implications: first, they are relevant to investors in understanding the portfolio value implications of oil price uncertainty. Second, they are helpful for firm managers while comprehending the value-relevant implications of internal capital allocations. Finally, our findings are policy relevant in the context of the future of diversified firms in developed markets.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.

This paper aims to explore the relationship between strategic internationalization decisions and dynamic capabilities deployment for the internationally growing firm (IGF). Dynamic capabilities refer to a firm’s ability to adapt proactively to a changing business environment, emphasizing the importance of “doing the right things” rather than just “doing things right.

Literature-based, this paper proposes a model that links internationalization decisions and dynamic capabilities deployment, offering valuable insights for both research and practical application.

The study highlights that the IGF – focused on expansion and growth abroad – faces unique complexities that demand “doing the right things” in terms of strategic internationalization decisions. Three critical organizational capabilities – knowledge transfer, knowledge recombination and learning capabilities – are mechanisms linking strategic internationalization decisions to dynamic capability deployment in the IGF. These organizational capabilities enable the IGF to act entrepreneurially and deploy dynamic capabilities across borders.

The model provides a practical framework illustrating the interconnectedness of strategic internationalization decisions and their combined effects on the ability of IGF to deploy dynamic capabilities to adapt to a changing global environment.

This research addresses a gap in the literature, challenging the conventional assumption that dynamic capabilities precede firms’ decisions to internationalize and that these dynamic capabilities can only be enhanced abroad.