Infusing stakeholder agency theory with insights from behavioural agency theory, we describe a frame‐dependent relationship between CEO stock option incentives and tax avoidance. Our theoretical framework highlights the role of competing shareholder demands in providing a salient reference point for a CEO contemplating the implications of tax avoidance for their stock option wealth. In a study of 2,573 publicly listed U.S. firms between 1993 and 2014, we show that the implications of CEO stock option incentives are contingent on whether the firm’s effective tax rate is anticipated to be below or above the tax rate of peer firms – an outcome that the CEO can cast as balancing stakeholder demands. Consistent with our theoretical reasoning, we also show that, both above and below this reference point, the implications of option incentives for corporate tax avoidance are amplified by the level of activist institutional ownership and attenuated by the CEO’s ability to unwind their bond with shareholders through hedging. In doing so, our study offers an impetus for a broader stakeholder approach to governance research examining CEO incentive alignment.

We study a single‐period inventory planning problem for a category of substitutable products. This is an important practical problem facing category managers who have to maintain high service levels for constantly expanding product catalogs. We formulate the problem as a stochastic optimization model that minimizes the total stocking cost subject to service level requirements, which consist of product‐specific and category‐wide targets for inventory availability (ready rates) through the selling season. Our model accounts for stochastic customer arrivals, captures stockout‐based substitutions, and determines initial stocking quantities jointly for all products. Recognizing the challenges that these aspects pose in solving the problem, we propose an optimization‐based method that estimates the ready rates using a deterministic approximation and discretizes the selling season into a finite number of time intervals. This novel modeling approach permits us to recast the stochastic optimization model as a deterministic mixed integer linear program that can accommodate several common stockout‐based substitution schemes. We characterize the worst‐case behavior of this approach to develop performance guarantees. We also implemented and applied this model to randomly generated numerical instances featuring different types of product differentiation and varying in parameter values. We observe that the approach is robust to changes in problem parameter values and yields solutions very quickly, outperforming an enumeration‐based alternative, a practical heuristic, and an approach based on extant literature. Finally, we applied our approach to data from a re‐seller of Information Technology products. Results illustrate that our approach scales well and has the potential to generate savings in inventory costs.

Recognizing that supervisor–subordinate dyads exist within a broader organizational hierarchy, we examine how the individual's role within the organizational hierarchy influences perceptions of abusive supervision. Specifically, we examine how supervisors' abusive behaviors are perceived by abusive supervisors' managers as well as abusive supervisors' subordinates. Drawing on role theory, we propose that these perceptions will differ. Further, we suggest that these differences will be reflected in different relationships between manager-rated abusive supervision and subordinate-rated abusive supervision and managers' evaluations of supervisor performance. Results from manager–supervisor–subordinate triads indicate differences between managers' and subordinates' view of abusive supervision. Further, managers' perceptions of abuse were related to supervisors' in-role performance, whereas subordinates' perceptions of abuse were related to workgroup performance. In Study 2, we replicate these findings and expand our investigation to an examination of supervisors' contextual performance. Additionally, we examine another contextual characteristic—aggressive climate—and demonstrate it influences how abusive supervision relates to managerial evaluations of supervisor performance. Future research and managerial implications are discussed.

In this study, we examined how perceived overqualification influences employees' career distress and career planning. Specifically, we drew on role identity theory to hypothesize that perceived overqualification is positively related to individuals' career identity. Based on internal self-processing dynamics of role identity, we further hypothesized that career identity predicts reduced career distress and increased career planning. We expected career identity to mediate the effects of overqualification on career distress and career planning. Based on the symbolic interactionism perspective of identity, we hypothesized that this mediation is moderated by leader humility so that overqualified employees exhibit stronger career identities in the presence of a humble leader. We found support for our hypotheses in a multi-wave time-lagged study of 220 supervisor–subordinate dyads from 50 groups. Overall, our studies highlight that perceived overqualification can have positive effects on employees and organizations under appropriate management conditions. We discuss theoretical and practical implications of these results.

We propose a new highly flexible and tractable Bayesian approach to undertake variable selection in non-Gaussian regression models. It uses a copula decomposition for the joint distribution of observations on the dependent variable. This allows the marginal distribution of the dependent variable to be calibrated accurately using a nonparametric or other estimator. The family of copulas employed are "implicit copulas" that are constructed from existing hierarchical Bayesian models widely used for variable selection, and we establish some of their properties. Even though the copulas are high dimensional, they can be estimated efficiently and quickly using Markov chain Monte Carlo. A simulation study shows that when the responses are non-Gaussian, the approach selects variables more accurately than contemporary benchmarks. A real data example in the Web Appendix illustrates that accounting for even mild deviations from normality can lead to a substantial increase in accuracy. To illustrate the full potential of our approach, we extend it to spatial variable selection for fMRI. Using real data, we show our method allows for voxel-specific marginal calibration of the magnetic resonance signal at over 6000 voxels, leading to an increase in the quality of the activation maps.

On p. 130 of his 1947's seminal paper, Barnard1 wrote: “we propose that in our ordering, the two points which, respectively, have the same abscissa or the same ordinate as (a, b), and which lie further from the diagonal PR, shall be considered as indicating wider differences than (a, b) itself. Thus, […] the points immediately above and immediately to the left of the point ‘x’ are reckoned to indicate wider differences than the point ‘x’ itself. This condition implies that the set of points indicating differences as wide or wider than (a, b) will have a shape property vaguely related to convexity, and we call it the ‘C condition’.” It appears clear from this quotation how Barnard himself did not refer to a mathematical definition of a convex set strictu sensu but, instead, was providing the reader with the intuition of convexity linked to his definition. In this spirit, since our paper2 was a review paper for non‐experts, our intent was to point out that there is no obvious connection with the usual notion of a convex set, which is usually a condition that ensures convergence of algorithms to a unique optimum. We were not aware of the extended notions of convexity geometry or polyomino convexity that have appeared in the literature, and we thank the authors for pointing out that Barnard convex sets do satisfy these definitions. We would suggest that in future, when quoting Barnard convexity, it will be noted that it is not related to closure under linear combination. In any case, we prefer to emphasise the monotonicity properties of the generating statistic and how this affects computation of the exact or quasi‐exact P value.

In the last decades, statistical methodology has developed rapidly, in particular in the field of regression modeling. Multivariable regression models are applied in almost all medical research projects. Therefore, the potential impact of statistical misconceptions within this field can be enormous Indeed, the current theoretical statistical knowledge is not always adequately transferred to the current practice in medical statistics. Some medical journals have identified this problem and published isolated statistical articles and even whole series thereof. In this systematic review, we aim to assess the current level of education on regression modeling that is provided to medical researchers via series of statistical articles published in medical journals. The present manuscript is a protocol for a systematic review that aims to assess which aspects of regression modeling are covered by statistical series published in medical journals that intend to train and guide applied medical researchers with limited statistical knowledge. Statistical paper series cannot easily be summarized and identified by common keywords in an electronic search engine like Scopus. We therefore identified series by a systematic request to statistical experts who are part or related to the STRATOS Initiative (STRengthening Analytical Thinking for Observational Studies). Within each identified article, two raters will independently check the content of the articles with respect to a predefined list of key aspects related to regression modeling. The content analysis of the topic-relevant articles will be performed using a predefined report form to assess the content as objectively as possible. Any disputes will be resolved by a third reviewer. Summary analyses will identify potential methodological gaps and misconceptions that may have an important impact on the quality of analyses in medical research. This review will thus provide a basis for future guidance papers and tutorials in the field of regression modeling which will enable medical researchers 1) to interpret publications in a correct way, 2) to perform basic statistical analyses in a correct way and 3) to identify situations when the help of a statistical expert is required.

We specify a Bayesian, geoadditive Stochastic Frontier Analysis (SFA) model to assess hospital performance along the dimensions of resources and quality of stroke care in German hospitals. With 1,100 annual observations and data from 2006 to 2013 and risk-adjusted patient volume as output, we introduce a production function that captures quality, resource inputs, hospital inefficiency determinants and spatial patterns of inefficiencies. With high relevance for hospital management and health system regulators, we identify performance improvement mechanisms by considering marginal effects for the average hospital. Specialization and certification can substantially reduce mortality. Regional and hospital-level concentration can improve quality and resource efficiency. Finally, our results demonstrate a trade-off between quality improvement and resource reduction and substantial regional variation in efficiency.

PURPOSE: In this study, we investigated the commitment of cultural minorities and majorities in organizations. We examined how contextual factors, such as pressure to conform and leadership styles, affect the commitment of minority and majority members. DESIGN/METHODOLOGY/APPROACH: A field study was conducted on 107 employees in a large multinational corporation. FINDINGS: We hypothesize and found that cultural minorities felt more committed to the organization than majority members, thereby challenging the existing theoretical view that cultural minorities will feel less committed. We also found that organizational pressure to conform and effective leadership increased the commitment of minorities. IMPLICATIONS: Our findings indicate that organizational leaders and researchers should not only focus on increasing and maintaining the commitment of minority members, but should also consider how majority members react to cultural socialization and integration processes. The commitment of minority members can be further enhanced by effective leadership. ORIGINALITY/VALUE: In this study, we challenge the existing theoretical view based on similarity attraction theory and relational demography theory, that cultural minorities would feel less committed to the organization. Past research has mainly focused on minority groups, thereby ignoring the reaction of the majority to socialization processes. In this study, we show that cultural minorities can be more committed than majority members in organizations. Therefore, the perceptions of cultural majority members of socialization processes should also be considered in research on cultural diversity and acculturation.

This article considers panel data models in the presence of a large number of potential predictors and unobservable common factors. The model is estimated by the regularization method together with the principal components procedure. We propose a panel information criterion for selecting the regularization parameter and the number of common factors under a diverging number of predictors. Under the correct model specification, we show that the proposed criterion consistently identifies the true model. If the model is instead misspecified, the proposed criterion achieves asymptotically efficient model selection. Simulation results confirm these theoretical arguments.

This paper studies panel data models with unobserved group factor structures. The group membership of each unit and the number of groups are left unspecified. We estimate the model by minimizing the sum of least squared errors with a shrinkage penalty. The number of explanatory variables can be large. The regressions coefficients can be homogeneous or group specific. The consistency and asymptotic normality of the estimator are established. We also introduce new C -type criteria for selecting the number of groups, the numbers of group-specific common factors and relevant regressors. Monte Carlo results show that the proposed method works well. We apply the method to the study of US mutual fund returns and to the study of individual stock returns of the China mainland stock markets. p