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.