A framework for research on e‐learning assimilation in SMEs: a strategic perspective
Bibliographic record
Abstract
Purpose This study aims to propose an integrative conceptual framework of e‐learning adoption and assimilation that is adapted to the specific context of small to medium‐sized enterprises (SMEs). Design/methodology/approach The literature on the state of e‐learning usage in SMEs and on the IT adoption and assimilation factors that can be specifically applied to e‐learning adoption and assimilation in this context are reviewed. These factors are then integrated within a research framework, and a set of 20 propositions formulated. Findings The paper identifies the technological, organizational and environmental factors that are likely to favor or hinder e‐learning adoption and assimilation in SMEs, as well as the interaction among these factors. Research limitations/implications The integrative framework and the 20 propositions that emanate from it constitute the conceptual foundation for a research program and hypotheses on the adoption and assimilation of e‐learning in SMEs. Practical implications This study offers managers a frame of reference to analyze their firm's situation before initiating an e‐learning program by highlighting key adoption and assimilation factors in the specific context of SMEs. Originality/value This study proposes an integrative conceptual framework of e‐learning adoption and assimilation that is adapted to the specific context of SMEs.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".