GUFI Overview

Bonnie, David

doi:10.2172/1422987

Cited by 2 publications

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“…It is observed that the admission processes, in-house activities, course evaluation parameters, teaching and learning styles often are positively biased towards students exhibiting these personality traits as compared to their counterparts with traits of being shy, reserved, cold or aloof. Consequently, being on the opposite continuum of these so-called favorable traits, especially on the side of introversion, is often looked upon as a social stigma (Bonnie, 2018; Schmitt, 2017). Likewise, being low on agreeableness is often critiqued in terms of political, untrustworthy and ruthless behavior, thereby, causing a preferential bias towards individuals exhibiting high agreeableness (Toegel and Barsoux, 2012).…”

Section: Introductionmentioning

confidence: 99%

Prioritizing personality diversity: a commitment and performance based perspective

Garg

Anand

Vakeel

2023

IJEM

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PurposeUsing the affect theory of social exchange, this study investigates the mediating role of students' affective commitment between their personality traits (extraversion and agreeableness) and academic performance.Design/methodology/approachThis research employs mixed-method study, that is exploratory text analysis using 123 responses followed by a survey of 300 responses among the management students to test the proposed model.FindingsResults reveal a direct positive association of extraversion and agreeableness with students' affective commitment towards their academic institution. Additionally, negative indirect effects of affective commitment were found between the two personality traits and academic performance.Originality/valueThe study highlights both positive and negative outcomes of so-called favorable personality types of extraversion and agreeableness, thereby, building a prima facie case for promoting personality diversity in management institutions.

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Section: Introductionmentioning

confidence: 99%

Prioritizing personality diversity: a commitment and performance based perspective

Garg

Anand

Vakeel

2023

IJEM

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EMPRESS: Accelerating Scientific Discovery through Descriptive Metadata Management

2022

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High performance computing scientists are producing unprecedented volumes of data that take a long time to load for analysis. However, many analyses only require loading in the data containing particular features of interest and scientists have many approaches for identifying these features. Therefore, if scientists store information (descriptive metadata) about these identified features, for subsequent analyses they can use this information to only read in the data containing these features. This can greatly reduce the amount of data that scientists have to read in, thereby accelerating analysis. Despite the potential benefits of descriptive metadata management, no prior work has created a descriptive metadata system that can help scientists working with a wide range of applications and analyses to restrict their reads to data containing features of interest. In this paper, we present EMPRESS, the first such solution. EMPRESS offers all of the features needed to help accelerate discovery: it can accelerate analysis by up to 300 ×, supports a wide range of applications and analyses, is high performing, is highly scalable, and requires minimal storage space. In addition, EMPRESS offers features required for a production-oriented system: scalable metadata consistency techniques, flexible system configurations, fault tolerance as a service, and portability.

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GUFI Overview

Cited by 2 publications

References 0 publications

Prioritizing personality diversity: a commitment and performance based perspective

Prioritizing personality diversity: a commitment and performance based perspective

EMPRESS: Accelerating Scientific Discovery through Descriptive Metadata Management

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