Bibliographic record
Abstract
Purpose Leading organizations invest large amounts of time, energy, and financial resources in conducting employee surveys. Through Mercer Human Resource Consulting's work on more than 1,000 survey projects, ten key areas within the survey process have been identified that consistently stand out as potential stumbling blocks to survey success. The purpose of this paper is to make companies aware of these potential blocks, and show that by adopting best practices to avoid them, organizations can significantly improve the odds of conducting a successful survey. Design/methodology/approach According to Mercer Human Resource Consulting's What's Working™ research, upwards of 50 percent of employers in Sweden, Japan, Singapore, the USA, Brazil, Australia, Canada, the UK, and Ireland regularly conduct employee surveys. Employee engagement is more often the intended ultimate outcome of employee surveying. All the same, employee surveys often fail in their strategic aims. Through Mercer's work on more than 1,000 survey projects, ten key areas within the survey process have been identified that consistently stand out as potential stumbling blocks to survey success. Findings This article identifies the ten key stumbling blocks to employee survey success as: Project planning; Communication; Questionnaire design; Timing; Prioritization of issues; Engaging senior management; Data delivery; Follow‐up support; Monitoring and accountability, and Linking survey results to business outcomes. These stumbling blocks and methods of overcoming them are described. Originality/value It is becoming increasingly clear to organizations that employee engagement has a significant influence on organizational performance and can become a long‐term source of competitive advantage. An original connection is made between effective employee surveys and employee engagement, and best‐practice guidance is provided on ensuring survey success. Otherwise, a survey runs the risk of destroying rather than building employee engagement.
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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 | 0.084 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".