Fostering Service Excellence through Listening: What Hospitality Managers Need to Know
Bibliographic record
Abstract
Amid the “noise” created by increased use of computers and other technology, the ability to listen takes on increasing importance for hospitality employees. Listening is essential in the course of delivering personal, customized service. A survey of eighty-three hospitality managers found the highest agreement with the statement that effective listening is vital to business success. For hospitality organizations, that success is tightly linked to the quality of service produced by employees. At the same time, survey respondents gave their lowest agreement to the statement that most members of their organization listen well. Listening is the foundation of two organizational processes essential to service delivery, one involving the accurate exchange of information and the other facilitating the development of strong relationships. Employees who are good listeners have a willingness to listen and an awareness of their own listening ability (although that may be overestimated). While developing listening competencies is not easy, it is possible for managers to improve their service employees’ listening abilities through modeling effective listening and offering training that is then augmented in the workplace—all the while improving service delivery. In addition to the rapid pace of the hospitality industry and interference from technology, one other barrier to effective listening is the diversity of employees and guests. Not only cultural differences, but also gender and age differences influence listening styles and effectiveness.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.025 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".