The Hidden Power of 1:1 Meetings in a Relationship-Driven Organization
Bibliographic record
Abstract
Employee turnover remains one of the most persistent and costly challenges facing organizations, yet many retention strategies miss a crucial element: the everyday relationship between employees and their immediate supervisors. One of the most common leadership behaviours—recurring one-on-one (1:1) meetings—has received surprisingly little attention in both research and practice. This study explores whether the quality of these recurring 1:1 meetings, as perceived by employees, helps build stronger relationships with their managers and also reduce their intention to leave the organization. Data were collected through a cross-sectional survey of 145 employees at a North American service-based company. A new measurement tool, the Meeting Effectiveness Questionnaire (MEQ-6), was developed for this study to assess the perceived quality of recurring 1:1s. The survey also included a validated relationship quality scale (LMX-7) and a turnover intention item. Results showed that employees who experienced more effective 1:1s reported significantly stronger relationships with their supervisors. These relationships, in turn, were linked to much lower turnover intentions. Together, MEQ-6 and LMX explained 23% of the variance in turnover intention—consistent with meta-analytic benchmarks. To deepen these findings, 11 follow-up interviews were conducted. These conversations revealed why meetings matter—showing that high-quality 1:1s serve as relational anchors, while poor meetings erode trust and connection. Post hoc analysis tested a refined 3-item version of the MEQ (MEQ-3), showing even stronger alignment with relationship quality, though not a direct prediction of turnover—suggesting these meeting behaviours work indirectly through relational strength. The MEQ-6 represents a new, reliable tool for evaluating 1:1 meeting effectiveness. More importantly, the study offers a practical path forward: by investing in better 1:1 meetings, leaders can strengthen relationships, boost engagement, and lower voluntary turnover. Notably, the analysis showed that for every one-point improvement in meeting effectiveness (on a five-point scale), the odds of turnover intention drop by 41%. This makes 1:1 meetings a highly actionable and cost-effective tool for retention.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".