Taking a Page from Retail: Secret Shopping for Academic Libraries
Bibliographic record
Abstract
Abstract Objective – The University Libraries at the University of North Carolina at Greensboro (UNCG) sought to gain feedback on the customer service experience beyond satisfaction surveys. After reviewing a variety of methods, it was determined to conduct a mystery or secret shopper exercise, a standard practice in the retail and hospitality world. Methods – Two mystery shopper assessments were conducted in 2010 and 2012. Students were recruited from a Hospitality Management class to serve as the secret shoppers. “Shoppers” completed a rating sheet for each encounter based on customer service values established by the Libraries. Data was analyzed and presented to staff. Results - Initial findings were generally quite positive but indicated that we could improve “going the extra mile” and “confirming satisfaction.” As a result, we developed training sessions for public services staff which were delivered during summer 2011. A LibGuide that included training videos was created for public services student employees who were required to view the videos and provide comments. In addition, we developed more specific public service standards for procedures such as answering the telephone, confirming satisfaction, and referring patrons to other offices. The Secret Shopper assessment was administered again in spring 2012 to see if scores improved. The results in the second study indicated improvement. Conclusions - The mystery shopper exercises provided the UNCG University Libraries with the opportunity to examine our services and customer service goals more closely. Conducting the mystery shopper study identified several areas to address. We realized we needed more clearly defined standards for staff to follow. We saw that we needed to discuss what “going the extra mile” means to us as an organization. We also needed to develop a scalable training method for student employees.
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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.017 |
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".