Social media usage in hotel human resources: recruitment, hiring and communication
Bibliographic record
Abstract
Purpose – The purpose of this study is to explore the use and non-use of social media (SM) by North American hotels for human resource (HR) activities. Design/methodology/approach – This exploratory study used an online survey and a sampling frame of 1,711 North American hotels with 300 or more rooms, excluding economy properties. With a response rate of 17.1 per cent and a defined population, data were weighted to reflect the midscale, upscale and luxury market classes. Findings – Slightly more than half of North American hotels use SM for HR activities. Higher service level hotels are related to SM HR use generally; midscale properties report higher usage for internal communication. Use of SM in hotel HR is more focused on marketing versus recruitment activities. Research limitations/implications – The generalizability and, therefore, implications are limited to North American hotels, midscale or higher with 300 or more rooms. Future research should complement this broad-based study by delving more deeply into rationale for HR communication over hiring functions for SM and its overall adoption for HR in the hospitality industry. Practical implications – This study provides an understanding of how SM is being used and its perceived usefulness across a variety of HR activities. The findings will inform the application of SM for hotel HR purposes. Originality/value – This is the first empirical study about SM and HR practices in the North American hotel industry.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".