Great Valley Center

This paper analyzes the impact of COVID-19 on the supply chain and production, investigating countermeasures for industrial recovery. In particular, the study examines how COVID-19 has affected the raw material supply chain, production, and outages on a real case study, that is, Turkey’s Glass-Reinforced Plastic (GRP) pipe industry. Using two- and three-way analysis of variance (ANOVA), significant negative impacts on the raw material supply chain are identified with 95% confidence. To enhance decision-making, the fuzzy q-rung orthopair set (FQROPS) and entropy-based multi-criteria decision-making (MCDM) methods are integrated in the baseline method. Specifically, ANOVA-identified factors, such as cost, supply continuity, production capacity, and risk level, are used as criteria in the MCDM analysis. Entropy determined criteria weights and FQROPS evaluate alternatives based on their proximity to the ideal solution. Findings show that significant disruptions occurred due to the pandemic. In addition, the MCDM analysis reveals that pre-pandemic conditions for key materials, such as fiberglass and resin, were significantly more favorable in terms of cost, supply continuity, production capacity, and risk levels. This integrated approach provides strategic insights for managing supply chains and production in the GRP pipe industry during and after pandemic events.

This paper investigates the general problem of comparing multidimensional simulation output with a given data set (e.g., real-world historical data). This problem frequently arises in verification, validation, and calibration of simulation models with spatial output statistics as in weather/climate, epidemic, swarm/crowd, social systems, communication networks, and many other applications where the simulation output is distributed across various locations or geographical regions. In the case of univariate simulation output, two-sample statistical hypothesis tests such as the t -test are commonly used. For simulation models with multidimensional and spatial output statistics, the Hotelling’s two-sample test is widely used as the benchmark method in the simulation literature. However, the Hotelling’s test assumes that the two samples come from multivariate Gaussian distributions with equal covariance matrices, which may not be the case in many applications. To address this gap, this paper proposes a double-bootstrap method based on the Wasserstein distance for comparing two multidimensional samples. Unlike the Hotelling’s test and other parametric approaches, the proposed method does not require restrictive distributional assumptions, enabling a wider range of applications and contributing to verification, validation, and calibration of simulation models with multidimensional output. Computational experiments are performed to assess the test power, and the results indicate that the proposed method outperforms the Hotelling’s test and various other approaches. The proposed method’s applicability is illustrated through two examples related to random walk of swarm particles on a two-dimensional space and a realistic engineering application involving simulation of unmanned aerial vehicle (UAV) communication systems.

Abstract We examine the impact of takeover vulnerability on firm‐specific climate change exposure using sophisticated text‐based metrics generated from machine learning algorithms and a measure of takeover vulnerability primarily derived from state takeover laws. Based on a large sample of more than 28,000 observations, our findings show that firms more vulnerable to hostile takeovers have higher exposure to physical climate risks, as managers focus on short‐term financial performance over long‐term risk mitigation. These firms also reduce regulatory risk exposure by adopting stringent compliance measures and improving environmental practices to avoid penalties and attract investors. However, they are less likely to invest in new business opportunities related to climate change, prioritizing stability over potential gains in new business opportunities. Propensity score matching and entropy balancing confirm the robustness of these results. Our research provides insights into how hostile takeover threats influence corporate environmental strategies.

Abstract We show the influence the size of a corporate board has on firms' ESG controversies. Our analysis suggests that businesses with larger boards are more effective in mitigating ESG controversies. Specifically, a rise in board size by one standard deviation results in a decline in ESG controversies by 4.30%. Our findings corroborate the anticipation that businesses need the board's advice to prevent ESG controversies. Thus, larger boards, with more human capital and more interactions with stakeholders, promote sustainability more effectively. Moreover, we find that the effect of board size is less pronounced during a stressful time but is more evident in companies with more agency problems. Further analysis validates the findings, that is, propensity score matching, entropy balancing, an instrumental‐variable analysis, and GMM dynamic panel data analysis.

Exploiting a novel measure of takeover vulnerability mainly based on state legislations, we explore the effect of hostile takeover threats on credit ratings. Our results reveal that companies with more takeover exposure are assigned significantly better credit ratings. In particular, a rise in takeover vulnerability by one standard deviation results in an improvement in credit ratings by 7.89%. Our findings are consistent with the view that the disciplinary mechanism associated with the takeover market mitigates agency problems and ultimately raises firm value. Further analysis corroborates our conclusion, including propensity score matching, entropy balancing, and an instrumental-variable analysis. As our proxy for takeover susceptibility is plausibly exogenous, our results are more likely to show a causal effect.

PurposeThe purpose of this study is to contribute to the debate in the literature about generalist CEOs by exploring the effect of board governance on CEO general managerial ability, focusing on one of the most crucial aspects of the board of directors, board size. Prior research shows that smaller boards constitute a more effective governance mechanism and therefore are expected to reduce agency costs.Design/methodology/approachThe authors estimate the effect of board size on CEO general managerial ability, using a fixed-effects regression analysis, propensity score matching, as well as an instrumental-variable analysis. These techniques mitigate endogeneity greatly and make the results much more likely to show causality.FindingsThe results show that firms with smaller board size are more likely to hire generalist CEOs. Specifically, a decline in board size by one standard deviation raises CEO general managerial ability by 15.62%. A lack of diverse experiences in a small board with fewer directors makes it more necessary to hire a CEO with a broad range of professional experiences. Furthermore, the agency costs associated with generalist CEOs are greatly diminished in firms with a smaller board. Hence, firms with a smaller board are more inclined to hire generalist CEOs.Originality/valueAlthough prior research has explored the effects of board size on various corporate outcomes, strategies and policies, this study is the first to investigate the effect of board size on CEO general managerial ability. This study contributes to the literature both in corporate governance and on CEO general managerial ability.