BACKGROUND: Statistical model building requires selection of variables for a model depending on the model's aim. In descriptive and explanatory models, a common recommendation often met in the literature is to include all variables in the model which are assumed or known to be associated with the outcome independent of their identification with data driven selection procedures. An open question is, how reliable this assumed "background knowledge" truly is. In fact, "known" predictors might be findings from preceding studies which may also have employed inappropriate model building strategies. METHODS: We conducted a simulation study assessing the influence of treating variables as "known predictors" in model building when in fact this knowledge resulting from preceding studies might be insufficient. Within randomly generated preceding study data sets, model building with variable selection was conducted. A variable was subsequently considered as a "known" predictor if a predefined number of preceding studies identified it as relevant. RESULTS: Even if several preceding studies identified a variable as a "true" predictor, this classification is often false positive. Moreover, variables not identified might still be truly predictive. This especially holds true if the preceding studies employed inappropriate selection methods such as univariable selection. CONCLUSIONS: The source of "background knowledge" should be evaluated with care. Knowledge generated on preceding studies can cause misspecification.

Research Summary: In this article, we explore the interaction between internationalization and language strategy. We identify a range of language coping mechanisms that internationalizing firms use in response to the multilingualism they encounter. Learning outcomes and strategy implications of each of these mechanisms are identified. We then build a conceptual model to depict how, over time, interaction and influence between internationalization and language strategy become a two‐way, co‐evolutionary process. A key aspect is the role of management in shifting the firm from a reactive to a more proactive stance on language strategy. A case study is used to contextualize and illustrate the co‐evolutionary process over the long term. Case data demonstrate the constant adoption and adaptation of coping mechanisms that feed into language strategy as internationalization unfolds. Managerial Summary: This article links the internationalization process of firms with the exposure to multilingualism and the development of language strategy. We outline how internationalizing firms may utilize a range of language coping mechanisms—such as the adoption of a common corporate language—to handle multilingualism. These feed into the development of language strategy. The case of Fazer, the Finnish bakery, confectionary, and catering firm, provides an illustration of how language strategy co‐evolves over time as internationalization proceeds—in Fazer's case, many decades. Fazer's experience also demonstrates the importance of management and changes in top management in ensuring a more proactive language strategy is adopted and enforced. Adequate allocation of resources and a link to performance management were found to be critical in supporting strategic implementation.

Despite the growing prevalence of work-life support (WLS) practices in companies, there is a lack of theoretical and empirical clarity on their benefits to organizational performance. It is also unclear if the organizational performance effects of WLS practices vary based on an organization's internal and external environments. The dual objective of this paper is to investigate whether WLS practices relate to customer-focused outcomes and, if so, under which conditions WLS practices yield benefits. Drawing on contingency theory, we examine how the boundary conditions of internal firm characteristics (e.g., percentage of top management team [TMT] members with children) and external environmental factors (e.g., gender egalitarianism of the country) moderate the relationship between WLS practices and customer satisfaction. We shed light on these issues by examining multisource, longitudinal data collected over three years from a multinational corporation operating in 27 countries. The results show that both percentage of TMT members with children and gender egalitarianism of the country strengthen the relationship between WLS practices and customer satisfaction. The findings provide insights into the circumstances when WLS practices provide performance benefits for firms and the translatability of these benefits from one country to another.

Studies addressing breast cancer risk factors have been looking at trends relative to age at menarche and menopause. These studies point to a downward trend of age at menarche and an upward trend for age at menopause, meaning an increase of a woman's reproductive lifespan cycle. In addition to studying the effect of the year of birth on the expectation of age at menarche and a woman's reproductive lifespan, it is important to understand how a woman's cohort affects the correlation between these two variables. Since the behavior of age at menarche and menopause may vary with the geographic location of a woman's residence, the spatial effect of the municipality where a woman resides needs to be considered. Thus, a Bayesian multivariate structured additive distributional regression model is proposed in order to analyze how a woman's municipality and year of birth affects a woman's age of menarche, her lifespan cycle, and the correlation of the two. The data consists of 212,517 postmenopausal women, born between 1920 and 1965, who attended the breast cancer screening program in the central region of Portugal.

Joint models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique clinical studies where longitudinal outcomes are recorded alongside event times. Those two processes are often linked and the two outcomes should thus be modeled jointly in order to prevent the potential bias introduced by independent modeling. Commonly, joint models are estimated in likelihood-based expectation maximization or Bayesian approaches using frameworks where variable selection is problematic and that do not immediately work for high-dimensional data. In this paper, we propose a boosting algorithm tackling these challenges by being able to simultaneously estimate predictors for joint models and automatically select the most influential variables even in high-dimensional data situations. We analyze the performance of the new algorithm in a simulation study and apply it to the Danish cystic fibrosis registry that collects longitudinal lung function data on patients with cystic fibrosis together with data regarding the onset of pulmonary infections. This is the first approach to combine state-of-the art algorithms from the field of machine-learning with the model class of joint models, providing a fully data-driven mechanism to select variables and predictor effects in a unified framework of boosting joint models.

This article introduces a new procedure for clustering a large number of financial time series based on high-dimensional panel data with grouped factor structures. The proposed method attempts to capture the level of similarity of each of the time series based on sensitivity to observable factors as well as to the unobservable factor structure. The proposed method allows for correlations between observable and unobservable factors and also allows for cross-sectional and serial dependence and heteroscedasticities in the error structure, which are common in financial markets. In addition, theoretical properties are established for the procedure. We apply the method to analyze the returns for over 6000 international stocks from over 100 financial markets. The empirical analysis quantifies the extent to which the U.S. subprime crisis spilled over to the global financial markets. Furthermore, we find that nominal classifications based on either listed market, industry, country or region are insufficient to characterize the heterogeneity of the global financial markets. Supplementary materials for this article are available online.

Economic inequality has a robust negative effect on a range of important societal outcomes, including health, wellbeing, and education. Yet, it remains insufficiently understood why, how, and by whom unequal systems tend to be perpetuated. In two studies we examine whether psychological mindsets adopted by the wealthy and the poor in their micro-social transactions act to perpetuate or challenge inequality. We hypothesized that occupying a wealthier socioeconomic position promotes the pursuit of self-interest and contributes to inequality maintenance; poorer socioeconomic position, on the other hand, should promote the pursuit of fairness and equality restoration. In Study 1, participants completed an ultimatum game as proposers after being primed to believe they are wealthier or poorer, offering money to either poor or wealthy responders. As expected, the wealthy pursued their self-interest and the net effect of this behavior contributes to the maintenance of inequality. Conversely, the poor pursued fairness and the net effect of this behavior challenges inequality. In Study 2, participants were responders deciding whether to accept or reject unfair distributions. Compared to the wealthier, the poorer challenged inequality by rejecting unequal offers. The links between micro-social processes and macro-societal inequality are discussed.

The practices and norms of public budgeting have often been seen as a brake on the flexibility needed of government organisations. This remains true despite historically significant financial management reforms designed around budgetary devolution. Seeing flexibility as operating along two dimensions – devolution and discretion – this paper revisits the underlying features of traditional public budgeting to develop a taxonomy of six generic ‘budget rules’. By isolating key properties of budget control, the paper uses two of the more prominent rules – annuality and purpose – to illustrate how the rules interact to generate control capacity, as well as the scope for rule variability in promoting increased flexibility.

This paper introduces a spatial panel quantile model with unobserved heterogeneity. The proposed model is capable of capturing high-dimensional cross-sectional dependence and allows heterogeneous regression coefficients. For estimating model parameters, a new estimation procedure is proposed. When both the time and cross-sectional dimensions of the panel go to infinity, the uniform consistency and the asymptotic normality of the estimated parameters are established. In order to determine the dimension of the interactive fixed effects, we propose a new information criterion. It is shown that the criterion asymptotically selects the true dimension. Monte Carlo simulations document the satisfactory performance of the proposed method. Finally, the method is applied to study the quantile co-movement structure of the U.S. stock market by taking into account the input–output linkages as firms are connected through the input–output production network.

A fundamental role of financial reporting is to provide information useful in forecasting future cash flows. Applying up-to-date time series modelling techniques, this study provides direct evidence on the usefulness of quarterly data in predicting future operating cash flows. Moreover, we show that the predictive gain from using quarterly data is larger for asset-heavy industries and industries with higher levels of earnings smoothness. This study contributes to the accounting literature by examining the usefulness of quarterly financial statements in predicting the realization of future cash flows. Our results help fill the gap in knowledge on quarterly financial statements and provide new insights on why the frequency of financial reporting matters. In addition, our findings have important policy implications for the ongoing debate over interim reporting requirements in multiple jurisdictions around the world.

CONTEXT: When errors occur in clinical settings, it is important that they are recognised without defensiveness so that prompt corrective action can be taken and learning can occur. Cognitive dissonance - the uncomfortable tension we experience when we hold two or more inconsistent beliefs - can hinder our ability to respond optimally to error. OBJECTIVES: The aim of this paper is to describe the effects of cognitive dissonance, a construct developed and tested in social psychology. We discuss the circumstances under which dissonance is most likely to occur, provide examples of how it may influence clinical practice, discuss potential remedies and suggest future research to test these remedies in the clinical context. METHODS: We apply research on cognitive dissonance from social psychology to clinical settings. We examine the factors that make dissonance most likely to occur. We illustrate the power of cognitive dissonance through two medical examples: one from history and one that is ongoing. Finally, we explore moderators at various stages of the dissonance process to identify potential remedies. RESULTS: We show that there is great opportunity for cognitive dissonance to distort judgements, delay optimal responses and hinder learning in clinical settings. We present a model of the phases of cognitive dissonance, and suggestions for preventing dissonance, reducing the distortions that can arise from dissonance and inhibiting dissonance-induced escalation of commitment. CONCLUSIONS: Cognitive dissonance has been studied for decades in social psychology but has not had much influence on medical education research. We argue that the construct of cognitive dissonance is very relevant to the clinical context and to medical education. Dissonance has the potential to interfere with learning, to hinder the process of coping effectively with error, and to make the accepting of change difficult. Fortunately, there is the potential to reduce the negative impact of cognitive dissonance in clinical practice.

There is much education and training devoted to the avoidance, early detection and mitigation of errors in the ED. Despite this, errors remain a common occurrence and at times contribute to adverse events. Patients bear the bulk of this burden, but staff also suffer. This article provides 12 tips to help trainees cope in a productive way after making an error.

Applied statisticians and pharmaceutical researchers are frequently involved in the design and analysis of clinical trials where at least one of the outcomes is binary. Treatments are judged by the probability of a positive binary response. A typical example is the noninferiority trial, where it is tested whether a new experimental treatment is practically not inferior to an active comparator with a prespecified margin δ. Except for the special case of δ = 0, no exact conditional test is available although approximate conditional methods (also called second‐order methods) can be applied. However, in some situations, the approximation can be poor and the logical argument for approximate conditioning is not compelling. The alternative is to consider an unconditional approach. Standard methods like the pooled z‐test are already unconditional although approximate. In this article, we review and illustrate unconditional methods with a heavy emphasis on modern methods that can deliver exact, or near exact, results. For noninferiority trials based on either rate difference or rate ratio, our recommendation is to use the so‐called E‐procedure, based on either the score or likelihood ratio statistic. This test is effectively exact, computationally efficient, and respects monotonicity constraints in practice. We support our assertions with a numerical study, and we illustrate the concepts developed in theory with a clinical example in pulmonary oncology; R code to conduct all these analyses is available from the authors.

Guanxi social networks are part of the fabric of Chinese society and central to every aspect of Chinese life including work. The relationship between guanxi and cronyism has been researched and discussed by scholars in supervisor–subordinate guanxi (SSG) studies. However, SSG cannot explain the full extent of cronyism in Chinese management, which usually encompasses a network of actors including a supervisor, a subordinate, a third party (called ‘leader’) who has a higher ranking than a subordinate, and possibly an intermediary between a leader and a supervisor in the same organization. Consequently, this paper develops a new construct leader–follower guanxi (LFG) to explain cronyism in Chinese management. LFG is defined as the existence of direct particularistic (ingroup) ties associated with a particular set of differentiated behavioral obligations based on social norms between a leader and a follower in the same organization. We examine the relationship between LFG and cronyism in Chinese organizations and propose that LFG will be positively associated with cronyism. We then use Chinese ‘face’ theory (mianzi and lian) to illustrate how LFG engenders cronyism in Chinese management. We assert that LFG serves as an invisible hand of cronyism in Chinese organizations. Finally, we consider how to develop leadership and HR practices that prevent cronyism in Chinese organizations.

Baumeister and Kilian (Journal of Business and Economic Statistics, 2015, 33(3), 338–351) combine forecasts from six empirical models to predict real oil prices. In this paper, we broadly reproduce their main economic findings, employing their preferred measures of the real oil price and other real-time variables. Mindful of the importance of Brent crude oil as a global price benchmark, we extend consideration to the North Sea-based measure and update the evaluation sample to 2017:12. We model the oil price futures curve using a factor-based Nelson–Siegel specification estimated in real time to fill in missing values for oil price futures in the raw data. We find that the combined forecasts for Brent are as effective as for other oil price measures. The extended sample using the oil price measures adopted by Baumeister and Kilian yields similar results to those reported in their paper. Also, the futures-based model improves forecast accuracy at longer horizons.

The consequences of reduced fertility and mortality on the age distribution are an issue for most developed countries, but especially for the ‘Asian tiger’ economies. We use functional data analysis forecasting techniques to project the population of Hong Kong. Our projections include error estimates that allow for forecasting error as well as exogenous variations of fertility and migration numbers. We separate out the effects of pure demographic shifts from projected behavioural changes in labour force participation.This enables us to look at the kinds of changes in labour force participation that would be required to offset the aging effects that we estimate.

Bivariate copula regression allows for the flexible combination of two arbitrary, continuous marginal distributions with regression effects being placed on potentially all parameters of the resulting bivariate joint response distribution. Motivated by the risk factors for adverse birth outcomes, many of which are dichotomous, we consider mixed binary‐continuous responses that extend the bivariate continuous framework to the situation where one response variable is discrete (more precisely, binary) whereas the other response remains continuous. Utilizing the latent continuous representation of binary regression models, we implement a penalized likelihood–based approach for the resulting class of copula regression models and employ it in the context of modeling gestational age and the presence/absence of low birth weight. The analysis demonstrates the advantage of the flexible specification of regression impacts including nonlinear effects of continuous covariates and spatial effects. Our results imply that racial and spatial inequalities in the risk factors for infant mortality are even greater than previously suggested.

This study explores the relationship between corporate social responsibility (CSR), financial misstatements and SEC enforcement actions. We find that firms with higher CSR are less likely to receive SEC enforcement actions for financial misstatements. Drawing on insights from stakeholder theory and the reputational literature, we identify two channels underpinning this relationship: (i) firms with higher CSR are less likely to engage in financial misstatements and (ii) the reputational effect of CSR reduces the likelihood of SEC enforcement actions. We find empirical evidence consistent with both channels.

Drawing on arguments from institutional theory, this study examines how social norms—specifically, local religious social norms—affect the motivational impact of equity‐based incentives. We test our model using longitudinal data on local religious norms, CEO equity incentives, and firm value. Consistent with our theoretical predictions, we find that local religious social norms attenuate the impact of CEO option incentives upon firm value. Furthermore, we find that the attenuating impact of local religious social norms increases with managerial discretion. These findings provide valuable insight for human resource professionals aiming to design compensation contracts for employees that are aligned with firm goals. Our findings also contribute to research on the motivational effect of equity incentives by demonstrating the importance of considering the social context in which executives are embedded.

Research Summary: With respect to entry mode decisions, psychic distance may play two contradictory roles. On one hand, the transaction cost perspective is concerned with opportunism. Psychic distance magnifies the threat of opportunism, which encourages higher levels of control by foreign firms. Conversely, the new internalization perspective emphasizes the role of complementary assets controlled by local entities. Distance increases the need to access local knowledge, which encourages firms to seek joint ventures. By adopting an experimental approach and measuring managers' a priori perceptions of distance, this article contributes to the literature by providing a more sophisticated approach for discriminating between these effects. The results indicate that distance magnifies the need for firms to access complementary assets; however, distance does not appear to magnify the threat of opportunism. Managerial Summary: This article explores the role that psychic distance (i.e., differences across countries in culture, language, religion, etc.) plays in how firms enter foreign markets. Specifically, do they prefer wholly owned subsidiaries (WOS) or do they prefer to form joint ventures (JV) with local players? One perspective argues that firms are concerned about potential partners in a foreign market taking advantage of them; thus, they will prefer the greater control a WOS offers. Conversely, firms may simply recognize that these differences put them at a disadvantage and prefer a JV as a way to gain local knowledge. Our experiments indicate that the latter explanation dominates. When entering very different countries, managers seem to prefer JVs in order to access key local knowledge.

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

Social influence has been shown to create significant unpredictability in cultural markets, providing one potential explanation why experts routinely fail at predicting commercial success of cultural products. As a result, social influence is often presented in a negative light. Here, we show the benefits of social influence for cultural markets. We present a policy that uses product quality, appeal, position bias and social influence to maximize expected profits in the market. Our computational experiments show that our profit-maximizing policy leverages social influence to produce significant performance benefits for the market, while our theoretical analysis proves that our policy outperforms in expectation any policy not displaying social signals. Our results contrast with earlier work which focused on showing the unpredictability and inequalities created by social influence. Not only do we show for the first time that, under our policy, dynamically showing consumers positive social signals increases the expected profit of the seller in cultural markets. We also show that, in reasonable settings, our profit-maximizing policy does not introduce significant unpredictability and identifies "blockbusters". Overall, these results shed new light on the nature of social influence and how it can be leveraged for the benefits of the market.

Abstract A privately informed seller seeks to liquidate a portfolio to raise cash. Each asset’s liquidity thus depends on the impact of its sale on the value of the entire portfolio. We demonstrate the importance of cross-signaling and derive sufficient conditions for a liquidity “pecking order” that determines the order of sale. For assets backed by a common pool, liquidity naturally aligns with seniority. Finally, we extend the portfolio liquidation game to consider security design and demonstrate the optimality of pooling securities and selling senior tranches or debt secured by the pool, with retention increasing in asset quality or informational asymmetry.

A Bayesian technique with analyses of within-person processes at the level of the individual is presented. The approach is used to examine whether the patterns of within-person responses on a 12-trial simulation task are consistent with the predictions of ITA theory (Dweck, 1999). ITA theory states that the performance of an individual with an entity theory of ability is more likely to spiral down following a failure experience than the performance of an individual with an incremental theory of ability. This is because entity theorists interpret failure experiences as evidence of a lack of ability which they believe is largely innate and therefore relatively fixed; whilst incremental theorists believe in the malleability of abilities and interpret failure experiences as evidence of more controllable factors such as poor strategy or lack of effort. The results of our analyses support ITA theory at both the within- and between-person levels of analyses and demonstrate the benefits of Bayesian techniques for the analysis of within-person processes. These include more formal specification of the theory and the ability to draw inferences about each individual, which allows for more nuanced interpretations of individuals within a personality category, such as differences in the individual probabilities of spiraling. While Bayesian techniques have many potential advantages for the analyses of processes at the level of the individual, ease of use is not one of them for psychologists trained in traditional frequentist statistical techniques.

This paper considers the estimation and inference procedures for the case of a logistic panel regression model with interactive fixed effects, where multiple individual effects are allowed and the model is capable of capturing high-dimensional cross-section dependence. The proposed model also allows for heterogeneous regression coefficients. New Bayesian and non-Bayesian approaches are introduced to estimate the model parameters. We investigate the asymptotic behaviors of the estimated parameters. We show the consistency and asymptotic normality of the estimated regression coefficients and the estimated interactive fixed effects when both the cross-section and time-series dimensions of the panel go to infinity. We prove that the dimensionality of the interactive effects can be consistently estimated by the proposed information criterion. Monte Carlo simulations demonstrate the satisfactory performance of the proposed method. Finally, the method is applied to study the performance of New York City medallion drivers in terms of efficiency.

We develop a new technique to estimate vector autoregressions with a common factor error structure by quantile regression. We apply our technique to study credit risk spillovers among a group of 17 sovereigns and their respective financial sectors between January 2006 and December 2017. We show that idiosyncratic credit risk shocks propagate much more strongly in both tails than at the conditional mean or median. Furthermore, we develop a measure of the relative spillover intensity in the right and left tails of the conditional distribution that provides a timely aggregate measure of systemic financial fragility and that can be used for risk management and monitoring purposes.

This article introduces a new procedure for analyzing the quantile co-movement of a large number of financial time series based on a large-scale panel data model with factor structures. The proposed method attempts to capture the unobservable heterogeneity of each of the financial time series based on sensitivity to explanatory variables and to the unobservable factor structure. In our model, the dimension of the common factor structure varies across quantiles, and the explanatory variables is allowed to depend on the factor structure. The proposed method allows for both cross-sectional and serial dependence, and heteroscedasticity, which are common in financial markets. We propose new estimation procedures for both frequentist and Bayesian frameworks. Consistency and asymptotic normality of the proposed estimator are established. We also propose a new model selection criterion for determining the number of common factors together with theoretical support. We apply the method to analyze the returns for over 6000 international stocks from over 60 countries during the subprime crisis, European sovereign debt crisis, and subsequent period. The empirical analysis indicates that the common factor structure varies across quantiles. We find that the common factors for the quantiles and the common factors for the mean are different. Supplementary materials for this article are available online.

A country's total fertility rate (TFR) depends on many factors. Attributing changes in TFR to changes of policy is difficult, as they could easily be correlated with changes in the unmeasured drivers of TFR. A case in point is Australia where both pronatalist effort and TFR increased in lock step from 2001 to 2008 and then decreased. The global financial crisis or other unobserved confounders might explain both the reducing TFR and pronatalist incentives after 2008. Therefore, it is difficult to estimate causal effects of policy using econometric techniques. The aim of this study is to instead look at the structure of the population to identify which subgroups most influence TFR. Specifically, we build a stochastic model relating TFR to the fertility rates of various subgroups and calculate elasticity of TFR with respect to each rate. For each subgroup, the ratio of its elasticity to its group size is used to evaluate the subgroup's potential cost effectiveness as a pronatalist target. In addition, we measure the historical stability of group fertility rates, which measures propensity to change. Groups with a high effectiveness ratio and also high propensity to change are natural policy targets. We applied this new method to Australian data on fertility rates broken down by parity, age and marital status. The results show that targeting parity 3+ is more cost-effective than lower parities. This study contributes to the literature on pronatalist policies by investigating the targeting of policies, and generates important implications for formulating cost-effective policies.

CEO incentives have been the subject of great interest for human resource scholars. We explore the institutional context within which the CEO makes sense of their incentives. Our theory suggests that CEO equity incentives interact with institutional norms to influence foreign market entry choices. Specifically, we argue that CEOs will weigh the risk bearing created by equity incentives, along with the consequences of legitimacy loss, when deciding whether to deviate from institutional norms when internationalizing. In doing so, we advance human resource literature by demonstrating that CEO responses to incentives are influenced by institutional norms and that CEOs' decisions to deviate from institutional norms are shaped by their incentives. We find support for our framework in the analysis of the stake taken by acquirers in 4,184 cross‐border acquisitions.

As colleagues and collaborators, we reflect on the work and legacy of Peter Carnevale, currently professor at the University of Southern California, and recipient of the 2002 Jeffrey Z. Rubin Theory-to-Practice Award of the International Association for Conflict Management (IACM). We review Carnevale’s main contributions, including his work on time pressure and surveillance, strategies for mediation, emotions in negotiation, and the use and integration of distinct methods for studying conflict and negotiation. We share personal anecdotes from our time as PhD students and collaborators with Peter Carnevale, and we touch on lessons learned for doing science and mentoring the next generation.

A growing body of research on moral markets—sectors whose raison d’être is to create social value by offering market solutions to social and environmental issues—has offered critical insights into the emergence and growth of these sectors. Less is known, however, about why some firms enter moral markets while others do not. Drawing from research on market entry, organizational identity, and social movements, we develop a theory that highlights the potential of organizational identity to explain variation in entry into moral markets. We then expand our framework by theorizing about contingencies that alter the shape of the relationship between organizational identity and market entry: the flexibility of the organizations’ identity, the type, and orientation of the social movement supporting the moral market, and the mode of market entry (de novo vs de alio). Finally, we discuss the contributions of our framework and opportunities for its extension.

Relatively little is known about how the composition of individual conflict management styles affects group functioning. This is unfortunate because, specifically in short-term project groups, this conflict management style composition may be pivotal given the strong task focus rather than establishing norms to guide or manage conflict. Therefore, we examined whether conflict style asymmetry within short-term project groups affects the link between intragroup conflict and the performance of groups. Data were collected among short-term project groups and the results suggest that asymmetry in both forcing and the problem-solving conflict management styles moderates the negative effect of task, relationship, and process conflicts on the performance of groups. We offer a discussion of the implications of these findings.

The outcome of the 2016 U.S. Presidential election was a big surprise to many, as the majority of polls had predicted the opposite outcome. In this two-stage cross-sectional study, we focus on how Democrats and Republicans reacted to this electoral surprise and how these reactions might have influenced the way they allocated resources to each other in small groups. We find that, before the election, Republicans showed greater in-group favoritism than Democrats, who treated others equally, regardless of their political affiliation. We then show that Democrats experienced the election outcome as an ego shock and, in the week following the election, reported significantly higher levels of negative emotions and lower levels of self-esteem than Republicans. These reactions then predicted how individuals' decided to allocate resources to others: after the election, Republicans no longer showed in-group favoritism, while Democrats showed out-group derogation. We find these decisions when the tables were turned can be partially explained by differences in participants' state self-esteem.

Agricultural expansion drives biodiversity loss globally, but impact assessments are biased towards recent time periods. This can lead to a gross underestimation of species declines in response to habitat loss, especially when species declines are gradual and occur over long time periods. Using Cold War spy satellite images (Corona), we show that a grassland keystone species, the bobak marmot (Marmota bobak), continues to respond to agricultural expansion that happened more than 50 years ago. Although burrow densities of the bobak marmot today are highest in croplands, densities declined most strongly in areas that were persistently used as croplands since the 1960s. This response to historical agricultural conversion spans roughly eight marmot generations and suggests the longest recorded response of a mammal species to agricultural expansion. We also found evidence for remarkable philopatry: nearly half of all burrows retained their exact location since the 1960s, and this was most pronounced in grasslands. Our results stress the need for farsighted decisions, because contemporary land management will affect biodiversity decades into the future. Finally, our work pioneers the use of Corona historical Cold War spy satellite imagery for ecology. This vastly underused global remote sensing resource provides a unique opportunity to expand the time horizon of broad-scale ecological studies.

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.