Business Administration - Research Publications
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ItemNo Preview Availablebamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)(JOURNAL STATISTICAL SOFTWARE, 2021-11)
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ItemGender equality in the Finance industry: The challenging ‘last mile’(ANZAM, 2021-12-03)Organisational efforts to increase gender equality in leadership have had limited success, and Australia’s ranking in the global gender equality index has slipped since 2006. We call this challenging stage in gender equality efforts the ‘last mile’. To understand what hinders or assists organisational efforts in the ‘last mile’ we conducted an in-depth study of the diversity efforts in a business unit of a large Australian bank. Interviews, focus groups and archival data analyses demonstrated that the bank’s gender equality performance was better than their institutional field, and the workforce was more optimistic about gender equality outcomes than executives. Ironically, these illusions of progress hindered the bank’s ability to travel the ‘last mile’.
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ItemExact confidence limits after a group sequential single arm trial(John Wiley and Sons, 2021-05-10)Group sequential single arm designs are common in phase II trials as well as attribute testing and acceptance sampling. After the trial is completed, especially if the recommendation is to proceed to further testing, there is interest in full inference on treatment efficacy. For a binary response, there is the potential to construct exact upper and lower confidence limits, the first published method for which is Jennison and Turnbull (1983). We place their method within the modern theory of exact confidence limits and provide a new general result that ensures that the exact limits are consistent with the test result, an issue that has been largely ignored in the literature. Amongst methods based on the minimal sufficient statistic, we propose two exact methods that out‐perform Jennison and Turnbull's method across 10 selected designs. One of these we prefer and recommend for practical and theoretical reasons. We also investigate a method based on inverting Fisher's combination test, as well as a pure tie‐breaking variant of it. For the range of designs considered, neither of these methods result in large enough improvements in efficiency to justify violation of the sufficiency principle. For any nonadaptive sequential design, an R‐package is provided to select a method and compute the inference from a given realization.
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ItemStatistical model building: Background "knowledge" based on inappropriate preselection causes misspecification(BMC, 2021-09-29)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.
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ItemA spatial panel quantile model with unobserved heterogeneity(Elsevier BV, 2021-10)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.
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ItemThe Predictive Ability of Quarterly Financial Statements(MDPI, 2021-09)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.
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ItemThe behavioral agency model: Revised concepts and implications for operations and supply chain research(Wiley, 2021-10-01)ABSTRACT The COVID 19 crisis and geopolitical ruptures of recent years have highlighted the importance of operations and supply chain management (OSCM) to firms and society. How do OSCM executives make decisions under uncertainty, and how do they balance the competing needs of various stakeholders? The behavioral agency model (BAM), which has been widely used in the management literature, focuses on the executive as the unit of analysis, like the behavioral science research in which it is embedded; by contrast, much of the supply chain risk management research has examined risk at the level of the firm. We review BAM literature and its core constructs, refine its original predictions, identify OSCM executive decision contexts that could take advantage of BAM, and highlight research opportunities using BAM. We aim to provide a platform for further risk research applying BAM in the domain of OSCM executive decision‐making.
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ItemBayesian Effect Selection in Structured Additive Distributional Regression Models(INT SOC BAYESIAN ANALYSIS, 2021-06)
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ItemBayesian variable selection for non-Gaussian responses: a marginally calibrated copula approach(WILEY, 2021-09)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.
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ItemCEO early-life disaster experience and corporate social performance(WILEY, 2021-11)Abstract Research Summary Despite an extensive upper echelons literature on how CEOs' prior experiences influence firm behavior, we know little about the influence of traumatic experiences early in CEOs' lives. Drawing on post‐traumatic growth theory, we describe how traumatic experiences early in CEOs' lives influence corporate social performance. Our theory points to the asymmetric impact of CEO early‐life trauma on responsible and irresponsible corporate social performance and to two boundary conditions: CEO age at the time of the traumatic event and the severity of the event. We develop and test our arguments in the context of large‐scale disasters experienced early in the CEO's life. Our findings advance strategic management research on the relationship between CEO experiences and firm outcomes. Managerial Summary We consider how traumatic experiences in childhood shape CEO cognition and values and, therefore, firm behavior. Our findings suggest that CEOs who have had to deal with traumatic early‐life events may gain psychological strength from such experiences and that their psychological growth informs firm conduct. Specifically, our findings indicate that experience of trauma early in the CEO's life is positively associated with corporate social performance. The implication is that boards aspiring to enhance this aspect of corporate performance may wish to consider the early‐life experiences of prospective CEOs. While early‐life experiences are unlikely to feature on a prospective CEO's résumé, the typical selection process for senior executive appointments is well placed to unearth executives' life histories.