Examining the effects of organizational readiness dimensions and extrinsic motivation on the continuance intention to use e-learning innovations
Bibliographic record
Abstract
The purpose of this study was to examine the effects of key organizational readiness dimensions and extrinsic motivation on the teaching staff’s intention toward the continued voluntary use of e-learning innovations post COVID-19 pandemic. These effects have not received considerable focus in the extant e-learning literature. To mitigate this lack, an integrated model encompassing dimensions from several organizational readiness frameworks and a motivational theory was developed. The model postulated these dimensions as direct determinants of the e-learning innovations continuance intention. A structured questionnaire-based survey was conducted to empirically assess the developed model. The intended population for this survey was composed of teaching staff at a Saudi higher education institution characterized by a wide adoption of e-learning innovations during the pandemic. The 233 valid responses obtained from this population were analyzed using the structural equation modeling method. The results indicated that only two organizational readiness dimensions (i.e., teaching staff readiness and administrative support) and extrinsic motivation were significant positive drivers of the continuance intention to use e-learning innovations. According to these findings, the study emphasizes that the key e-learning stakeholders should develop effective policies and procedures that reinforce the roles of the examined dimensions in promoting such continuance intention, which represents a crucial indicator for the successful implementation of the adopted innovation.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".