Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 it