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Gratitude in the Workplace: Fostering Inclusive Organizations

2019· article· en· W2966277896 on OpenAlexaff
Lauren Rachel Locklear, Sharon Sheridan, Ryan Fehr, Olivia Amanda O’Neill, Xueqi Wen, Mark G. Ehrhart, Robert Eisenberger, Hooria Jazaieri, Tae‐Yeol Kim, Deog Ro Lee, Steven W. Whiting

Bibliographic record

VenueAcademy of Management Proceedings · 2019
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsGratitudeFeelingPsychologySocial psychologyPublic relationsPolitical science

Abstract

fetched live from OpenAlex

The field of organizational psychology is currently in what some scholars have referred to as an affective era (Judge, Weiss, Kammeyer-Mueller, & Hulin, 2017) and have called for examinations of discrete emotions at work. In response to these calls, the subject of gratitude has recently received attention in management research, but there is still little knowledge of the functions of gratitude in the workplace. Therefore, the purpose of this symposium is to provide understanding of gratitude in organizations by exploring its antecedents and outcomes. Specifically, this symposium demonstrates the many ways gratitude can enhance the employment experience for individuals and can foster inclusive organizations. This symposium consists of four papers that explore the significance of gratitude as it is felt and expressed between employees and their coworkers, supervisors, and organizations. Specifically, the first paper investigates the relative contribution of feelings of gratitude toward the organization in the employee-organization relationship as compared to alternative theoretical explanations. The second paper examines the influence of attributions on employee expressions of gratitude toward their supervisors. The third study examines which characteristics of work events lead employees to feel and express gratitude toward coworkers. Finally, the last paper explores the manifestation and functions of gratitude culture at multiple levels of analysis. Together, these papers advance understanding of the positive impact that gratitude can have on employees and their organizations. We believe that this symposium showcases the importance of gratitude in organizations and will motivate future research in this area. The Employee-Organization Relationship: Contributions of Gratitude and Indebtedness Presenter: Xueqi Wen; U. of Houston Presenter: Robert Eisenberger; U. of Houston-Main Campus Presenter: Tae-Yeol Kim; China Europe International Business School Presenter: Deog Ro Lee; Seowon U. How Subordinates’ Attributions Influence Feelings of Pride, Felt Gratitude, and Expressed Gratitude Presenter: Sharon Sheridan; U. of North Dakota Examining Antecedents of Gratitude Expressions in the Workplace Presenter: Lauren Rachel Locklear; U. of Central Florida Presenter: Mark G. Ehrhart; U. of Central Florida Presenter: Steven Whiting; U. of Central Florida The Social Functions of Gratitude in Organizations: A Multi-Method Study Presenter: Olivia Amanda O'Neill; George Mason U. Presenter: Hooria Jazaieri; Northwestern Kellogg School of Management

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.003
Scholarly communication0.0040.004
Open science0.0010.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.340
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2019
Admission routes1
Has abstractyes

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