Gratitude in the Workplace: Fostering Inclusive Organizations
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".