REFINING THE SERVICE ORIENTATION SCALE (SOS-22) FROM INSIDE THE CANADIAN LODGING SECTOR
Bibliographic record
Abstract
Purpose – The purpose of this study is to validate and refine, as appropriate, the Service Orientation Scale in the unique context of the Canadian lodging sector, while exploring demographic differences expressed by respondents. Design – The study is based on Groves’ 34-item service orientation scale developed for the hospitality industry. Online self-administration questionnaires were completed by 348 hospitality employees. Methodology/Approach – Confirmatory factor analysis revealed extensive loading issues in Groves’ three-factor model, while also surfacing problems with item inclusion in the four-factor model presented by Kim et al. (2003). Subsequent exploratory factor analysis led to the creation of an improved 22-item service orientation scale (SOS-22). Findings – This research significantly refines the multidimensional employee service orientation scale into a scale that balances the detail of the dimensions with the parsimony of the scale design. The richness of the construct is maintained as the measures span four dimensions: organizational support, service under pressure, customer orientation, and customer relations. As recruiting and retaining employees in the hospitality industry remains a major challenge, the SOS -22 model can be used to improve employee-organization fit at the recruitment stage and help organizations find talent that will improve the customer experience and achieve organizational goals. Originality of the research – The paper demonstrates improved modelling of the service orientation scale (SOS-22) over past iterations that struggles with replication with results both valid and reliable. This research uncovers novel results in the lodging sector of the hospitality industry, while surfacing demographics differences in service orientation, both by gender and job level, missing from earlier studies.
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.
How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".