What drives mobile MOOC's continuous intention? A theory of perceived value perspective
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 The purpose of the study is to identify drivers of mobile massive open online course (MOOC) continuous intention (CI) through the lenses of customer perceived value (CPV) theory. Consumer choice is successfully explained by CPV, but lesser attention is given to linking the theory to the mobile MOOC context whereby the majority of theories have adopted approaches like technology acceptance model (TAM), unified theory of acceptance and use of technology (UTAUT), etc. Design/methodology/approach A mix-method approach was employed. Study A (qualitative), explores context-specific perceived value (PV) dimensions using nethnographic analysis of 627 learner reviews on Corsera app. Study B (quantitative), collects primary data by administering a questionnaire based on dimensions, and 231 responses were then analysed using structural equation modelling. Findings The results revealed that three context-specific PVs (i.e. pedagogy, interface and content) have a positive and significant impact on CI. Pedagogy value is a chief driving force of mobile MOOC CI followed by content value. Ubiquity value demonstrated insignificant impact. Practical implications The findings provide insights for MOOC apps and their developers for formulating better value propositions for ensuring sustainable business which may result in gaining a higher share in the growing mobile learning market. Originality/value This study bridges an important gap in mobile MOOC literature by providing a novel approach to investigating what drives mobile MOOC CI through the lenses of CPV theory. It is the first study investigating mobile MOOC CI through the specific CPV dimensions identified by employing a mixed-method approach. The study formulates a conceptual framework that may serve as a foundation for future research on mobile MOOCs for which literature is relatively scant.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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 it