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1 – 2 of 2Tyler Skinner, Steven Salaga and Matthew Juravich
Using the lens of upper echelons theory, this study examines the degree to which National Collegiate Athletic Association athletic department performance outcomes are associated…
Abstract
Purpose
Using the lens of upper echelons theory, this study examines the degree to which National Collegiate Athletic Association athletic department performance outcomes are associated with the personal characteristics and experiences of the athletic director leading the organization.
Design/methodology/approach
The authors match organizational performance data with athletic director and institutional characteristics to form a robust data set spanning 16 years from the 2003–04 to 2018–19 seasons. The sample contains 811 observations representing 136 unique athletic directors. Fixed effects panel regressions are used to analyze organizational performance and quantile regression is used to analyze organizational revenues.
Findings
The authors fail to uncover statistically significant evidence that athletic director personal characteristics, functional experience and technical experience are associated with organizational performance. Rather, the empirical modeling indicates organizational performance is primarily driven by differentiation in the ability to acquire human capital (i.e. playing talent). The results also indicate that on average, women are more likely to lead lower revenue organizations, however, prior industry-specific technical experience offsets this relationship.
Originality/value
In opposition to upper echelons research in numerous settings, the modeling indicates the personal characteristics and experiences of the organization's lead executive are not an economically relevant determinant of organizational performance. This may indicate college athletics is a boundary condition in the applicability of upper echelons theory.
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Keywords
Di Kang, Steven W. Kirkpatrick, Zhipeng Zhang, Xiang Liu and Zheyong Bian
Accurately estimating the severity of derailment is a crucial step in quantifying train derailment consequences and, thereby, mitigating its impacts. The purpose of this paper is…
Abstract
Purpose
Accurately estimating the severity of derailment is a crucial step in quantifying train derailment consequences and, thereby, mitigating its impacts. The purpose of this paper is to propose a simplified approach aimed at addressing this research gap by developing a physics-informed 1-D model. The model is used to simulate train dynamics through a time-stepping algorithm, incorporating derailment data after the point of derailment.
Design/methodology/approach
In this study, a simplified approach is adopted that applies a 1-D kinematic analysis with data obtained from various derailments. These include the length and weight of the rail cars behind the point of derailment, the train braking effects, derailment blockage forces, the grade of the track and the train rolling and aerodynamic resistance. Since train braking/blockage effects and derailment blockage forces are not always available for historical or potential train derailment, it is also necessary to fit the historical data and find optimal parameters to estimate these two variables. Using these fitted parameters, a detailed comparison can be performed between the physics-informed 1-D model and previous statistical models to predict the derailment severity.
Findings
The results show that the proposed model outperforms the Truncated Geometric model (the latest statistical model used in prior research) in estimating derailment severity. The proposed model contributes to the understanding and prevention of train derailments and hazmat release consequences, offering improved accuracy for certain scenarios and train types
Originality/value
This paper presents a simplified physics-informed 1-D model, which could help understand the derailment mechanism and, thus, is expected to estimate train derailment severity more accurately for certain scenarios and train types compared with the latest statistical model. The performance of the braking response and the 1-D model is verified by comparing known ride-down profiles with estimated ones. This validation process ensures that both the braking response and the 1-D model accurately represent the expected behavior.
Details