Character Strengths in Work Organizations: New Findings and Practical Implications
Bibliographic record
Abstract
Character strengths are positive personal attributes, which are manifested in individuals’ behaviors, thoughts, and feelings (Peterson & Seligman, 2004). In 2004, Peterson and Seligman have identified 24 character strengths, which are valued over time, and across cultures and religions. In 2011, Seligman has presented these 24 character strengths as the cornerstones of all five components of the flourishing life. He proposed that by identification, exploration, and deployment of personal character strengths, individuals can experience positive emotions, engagement, positive relationships, meaning, and accomplishment (Seligman, 2011). Accordingly, character strengths were expected to be important contributors to human thriving in various life arenas, including at work. By using valid empirical methodologies, research on character strengths at work provides insights into connections, effects, and antecedents of individuals' character strengths deployment in the context of work, the mechanisms through which the use of character strengths at work operate, and ways in which they can be enhanced and developed. This research has accumulated over the years and the time is now ripe for an overview of what has been learned about character strengths at work. In this symposium we present the role of strengths use in the workplace by showing its personal and organizational benefits, provide practical implications on developing employee strengths use and how to do so. Thus, this symposium advances theory and research on workplace strength use, and positive organizational behavior as a field of study in management research (e.g. Cameron, Dutton, & Quinn, 2003). Such an integrative overview can be beneficial for researchers and practitioners, as it will point to key effects and processes related to character strengths at work, which should be considered in future research, as well as in managerial and self-oriented practices at work. Using Character Strengths at Work: Theoretical Framework and Related Evidence Presenter: Hadassah Littman-Ovadia; Department of Social Sciences and Psychology, Ariel U. Transactive Strengths Systems: Considering Strengths Use in Organizations as a Multilevel Construc Presenter: Marianne Van Woerkom; Tilburg U. Presenter: Maria Christina Meyers; Tilburg U. Presenter: Arnold Bakker; Erasmus U. Rotterdam Supporting Employees in the Use of their Strengths at Work: Exploring Boundary Conditions Presenter: Maria Christina Meyers; Tilburg U. The Role of Strengths Use in Leaders for Leaders and Subordinates Work Engagement and Strain Presenter: Claudia Harzer; Technical U. Darmstadt, Department of Psychology Presenter: Kathrin Rosing; U. of Kassel Well-Being and Performance at Work: Could Character Strengths be Part of the Solution? Presenter: Philippe Dubreuil; Business School, U. du Quebec a Trois-Rivieres, Canada Presenter: Claudia Harzer; Technical U. Darmstadt, Department of Psychology
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.020 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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 source (direct Gemma or distilled Codex), 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".