Using university websites for student recruitment: A study of Canadian university home pages examining relationship marketing tactics and website usability
Bibliographic record
Abstract
Using the World Wide Web university websites hold considerable power for reaching prospective students via a cost-effective means, and, in fact, some view the Internet as the great equalizer among institutions. Unlike traditional forms of marketing and promotion, the quality of a website does not have to be limited by the size and budget of the institution. On the Internet the price of accessing the medium and distributing the message can be equal for all. Therefore, in terms of marketing activities, university websites have the potential to remove the disparity between what larger universities are able to accomplish and what smaller institutions would like to achieve. Establishment of a website provides no guarantee that visitors, and specifically prospective students, will find what they seek within a reasonable time frame. Therein rests the dilemma for institutions. Failure to locate desired information or difficulty negotiating the connections might, and likely do, lead prospective students to exit a site. A content analysis of Canadian University websites during the fall of 2006 found the level of usability was fair to good, while the level of relationship marketing content was only moderate to fair. Websites were compared to standard usability guidelines established through prior research. Content was assessed for relationship building capacity using prior research into student expectations for website content. Categories were established according to Kotler's (1992, 1996) Five Levels of Relationship Marketing theory. The analysis found a significant negative correlation between usability and relationship marketing content. That was interpreted to mean institutions performed well at one or the other aspect of their websites, but not both. There was also a significant correlation between website usability and institutional size and operating budget, illustrating that perhaps the Internet is not the great equalizer many believe it to be.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".