The impact of intragroup relationship conflict on intention to re-enroll
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Scholars have identified several predictors of learner satisfaction, but little research addresses the impact of intragroup conflict in a virtual learning context. The purpose of this paper is to investigate the potentially deleterious effects of perceived intragroup relationship conflict on virtual learners’ intention to re-enroll. Design/methodology/approach Data were systematically collected from virtual learners using an online questionnaire and then analyzed by multiple regression models. Findings The results show that emotional expressiveness is an antecedent to perceived intragroup relationship conflict, and the relationship is moderated by individuals’ perceived enjoyment of computer-mediated communication. Virtual learners with a higher emotional expressiveness (i.e. extraverts) experience higher perceived relationship conflict, which in turn, lowers their intention to re-enroll. Research limitations/implications The study confirms the antecedent and consequence of perceived intragroup relationship conflict in a virtual learning context. Practical implications Educational institutions and businesses may use three proposed strategies to deal with intragroup relationship conflict. Originality/value The study contributes to the limited knowledge on how to effectively manage virtual learning interactions by educational institutions and businesses.
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.
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 | 0.002 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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 it