京都大学 / 0048 / 新制・課程博士 / 博士(経済学) / 甲第18763号 / 経博第514号 / 新制||経||273(附属図書館) / 31714 / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 西山 慶彦, 准教授 奥井 亮, 講師 末石 直也 / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DGAM

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Personality, as a frequently used predictor of job performance, has often been criticized for its low criterion-related validity when compared to cognitive tests and some other predictors. The present study investigated incremental validity of narrow-trait personality to distinguish predictive from non-predictive facets of Conscientiousness and Extraversion dimensions. In addition, some intermediate mechanisms that may link the two personality dimensions with the criterion, such as different types of person-environment (P-E) fit and job involvement variables, were tested as well. The institution's job performance scale, NEO-PI-3 personality scale, person-organization (P-O) fit, needs-supplies (N-S) fit, demands-abilities (D-A) fit, job involvement questionnaire (JIQ), and demographic measures were administered in an online survey to 295 professional and civil service employees of a midsize Midwestern university. The sample was predominantly female and Caucasian with a mean age of 45.8 years and a median length of current employment of 5.1 years. Both personality dimensions were positively related to overall job performance. Conscientiousness was a stronger predictor of task performance, whereas Extraversion was related more consistently to contextual performance. In stepwise multiple regression analyses containing facets of personality dimensions as predictors of overall job performance, Competence emerged as the only facet of Conscientiousness, and Warmth and Assertiveness as the only facets of Extraversion that accounted for a meaningful amount of variance in the criterion. The use of narrow-trait personality to predict overall job performance enhances criterion-related validity of the construct and renders it a more efficient predictor of job performance than global-trait personality. Among the potential mediators, P-O and D-A fit partially mediated the personality-performance relationship providing evidence for the importance of perceptions of congruence in values and the ability to meet demands of the job. Current results are considered in light of limitations. Implications for theory, research, and practice, as well as future research directions are discussed.

Modern large data sets inevitably contain outliers that deviate from the model assumptions. However, many widely used estimators, such as maximum likelihood estimators and least squared estimators, perform weakly with the existence of outliers. Alternatively, many statistical modeling approaches have matrices as the parameters. We consider penalized estimators for matrix-valued parameters with a focus on their robustness properties in the presence of outliers. We propose a general framework for robust modeling with matrix-valued parameters by minimizing robust loss functions with penalization. However, there are challenges to this approach in both computation and theoretical analysis. To tackle the computational challenges from the large size of the data, non-smoothness of robust loss functions, and the slow speed of matrix operations, we propose to apply the Frank-Wolfe algorithm, a first-order algorithm for optimization on a restricted region with low computation burden per iteration. Theoretically, we establish finite-sample error bounds under high-dimensional settings. We show that the estimation errors are bounded by small terms and converge in probability to zero under mild conditions in a neighborhood of the true model. Our method accommodates a broad classes of modeling problems using robust loss functions with penalization. Concretely, we study three cases: matrix completion, multivariate regression, and network estimation. For all cases, we illustrate the robustness of the proposed method both theoretically and numerically. / Statistics

Surface-crack configurations are among the most important crack problems in the aerospace industry. The residual strength of a surface-cracked component is complicated by three-dimensional variation of the stress-intensity factor around the crack front and plastic deformations, which vary from plane stress at the free boundary, to nearly plane-strain behavior in the interior. In 1973, a two-parameter fracture criterion (TPFC) was developed to analyze fracture behavior of surface-crack configurations. Estimates were made around the crack front for fracture initiation—the critical parametric angle. Recently, NASA developed the Tool for Analysis of Surface Cracks (TASC) software that predicts critical location. This thesis is the application of the TPFC with the TASC critical angles using an equation developed from the TASC software. The TPFC was applied to three materials: a brittle titanium alloy, a ductile titanium alloy, and a ductile 301 stainless steel. The TPFC with the TASC critical angles correlated fracture behaviors well.

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Woven structures are steadily emerging as excellent reinforcing components in dualphase composite materials subjected to multiaxial loads, thermal shock, and aggressive reactants in the environment. Metallic woven wire mesh materials display good ductility and relatively high specific strength and specific resilience. While use of this class of materials is rapidly expanding, significant gaps in mechanical behavior classification remain. This thesis works to address the mechanics of material knowledge gap that exists for characterizing the behavior of a metallic woven structure, composed of stainless steel wires on the order of 25 microns in diameter, and subjected to various loading conditions and stress risers. Uniaxial and biaxial tensile experiments, employing Digital Image Correlation (DIC) as a strain measurement tool, are conducted on woven wire mesh specimens incised in various material orientations, and with various notch geometries. Experimental results, supported by an ample analytic modeling effort, indicate that an orthotropic elastic constitutive model is reasonably capable of governing the macro-scale elasticity of the subject material. Also, the Stress Concentration Factor (SCF) associated with various notch geometries is documented experimentally and analytically, and it is shown that the degree of stress concentration is dependent on both notch and material orientation. The Finite Element Method (FEM) is employed on the macro-scale to expand the experimental test matrix, and to judge the effects of a homogenization assumption when modeling metallic woven structures. Additionally, plasticity of the stainless steel woven wire mesh is considered through experimental determination of the yield surface, and a thorough analytic modeling effort resulting in a modified form of the Hill yield criterion. Finally, mesoscale plasticity of the woven structure is considered, and the form of a multi-scale failure criterion is proposed and exercised numerically.

No / The study is based on a previously proposed methodology for multiaxial fatigue life assessment of injection-molded components (called ‘Through Process Modeling’ (TPM)). The present contribution focuses on stress concentration effects induced in notched samples. Purely macroscopic approaches are unable to capture the different mechanical responses of variably injected parts with the same shape. A high interest of the present method is to take into account the difference of fiber orientation resulting from the process. After briefly reminding the TPM method, it will be shown that good lifetime estimations are obtained for laterally injected samples, from a fatigue criterion identification based on longitudinally injected ones.