Finding your Totem: unveiling the effects of a positive technology intervention on employees’ well-being and perceived team effectiveness with self-determination theory
Bibliographic record
Abstract
Virtual work presents numerous benefits in contemporary workplaces yet concurrently might pose substantial challenges, notably impacting employees’ well-being and team dynamics. This study investigated the Totem activity, a digital gamified team exercise designed to promote employees’ strengths. Drawing on Self-Determination Theory (SDT), it is proposed that the intervention positively influences employees’ well-being and perceived team effectiveness by enhancing need satisfaction and autonomous motivation. Using an experimental design, our study examined the impact of the activity on 58 teams (n = 395) and compared it with a wait-list control group of seven teams (n = 67). The data were gathered pre- and post-intervention for both groups, with the experimental group answering a third questionnaire 3 weeks post-intervention (n = 202). Multilevel analyses revealed that the experimental group displayed notable increases in all the studied variables post-intervention compared to the control group. Longitudinal analyses using a latent change score model showed that variations in need satisfaction during the Totem activity predicted changes in work motivation, psychological well-being, and team effectiveness across 3 weeks. This study shows that a digital strengths-based team intervention like Totem is an affordable, scalable, and self-directed way to support employees’ psychological needs and, thus, overcome challenges associated with virtual work.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 0.000 |
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.
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".