The Connect Framework: A Conceptual Proposal for Bridging Educational Transformation and Marketing Strategy
Bibliographic record
Abstract
Traditional marketing strategies emphasizing mass communication and short-term conversion have become increasingly inadequate in an era characterized by personalization, emotional resonance, and sustained trust-building. As technological capabilities expand, consumer expectations now demand relationship-based marketing approaches. In response to these shifts, the CONNECT methodology—originally developed in 2019 to support international student success—offers a structured seven-stage framework adaptable to modern marketing contexts. This paper reinterprets CONNECT—Cultivate Trust, Optimize Pathways, Nurture Motivation, Network Strategically, Engage Continuously, Collaborate with Stakeholders, and Track & Transform—through the lens of contemporary marketing theory and practice. By integrating current academic literature (2020–2025), case analyses, and conceptual modeling, this study positions the CONNECT framework as a robust, consumer-centric, and emotionally intelligent marketing strategy. The proposed conceptual model offers pathways for operationalization, empirical validation, and practical application across diverse marketing environments.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".