Effects of Marketing Theories and Customer Relationship Management on Small Colleges
Bibliographic record
Abstract
The purpose of this study is to examine the impact of marketing options on college selection, and of CRM tools on marketing efficiency. Five theories of marketing options and efficiency, including Customer Relationship Management, will undergird this study. The problem herein is that small colleges in particular must determine ways to market themselves, optimize use of technology, and increase enrollment in order to compete with other post-high school options. The purpose of this study is to examine the impact of marketing options on college selection, and CRM tools on marketing efficiency, using selected theories of marketing options and efficiency as theoretical framework. This study will be conducted utilizing qualitative methodology; information-gathering tools used will be interviews and questionnaires. Data interpretation will be through thematic analysis influenced by elements of transcendental phenomenology. The participants will be approximately 20 currents and former administrators/faculty from small colleges (fewer than 1000 students) from schools in the Mid-Atlantic region, and approximately 400 students enrolled in those schools. The administrators will be interviewed; the students will answer questionnaires. All inquiries are drawn from research questions that reflect the problem, purpose and theoretical framework of this study.
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".