Exploring the relationship between robot employees' perceptions and robot-induced unemployment under COVID-19 in the Jordanian hospitality sector
Bibliographic record
Abstract
The topic of robots is gaining traction in both academic discourse and popular media due to their growing prevalence in the hospitality sector. The hospitality industry is likely to encounter practical challenges in adopting the increased use of robots. The primary objective of the present research was to conduct an empirical investigation into a theoretical framework that explores how employees view robot-caused unemployment, with particular attention given to their perceptions of robot adoption. The study utilised structural equation modelling to analyse data obtained from 401 service employees in Jordan through online questionnaires. The findings indicate that the perception of robot-induced unemployment among employees is significantly influenced by their social skills, perceived risk, awareness, and trust in using service robots. The present study established a theoretical framework for investigating user perceptions of robot-induced unemployment within the particular setting of hospitality robots. The findings offer valuable insights for guiding future development and research efforts in the hotel service robot industry as well as informing marketing strategies for hotel managers. Ultimately, these efforts may contribute to the sustainable growth of service robots and associated sectors, including the hotel service sector.
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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 | 0.003 | 0.007 |
| 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) | 0.002 | 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 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".