Predicting SMEs Willingness to Adopt ERP, CRM, SCM & E-Procurement Systems
Bibliographic record
Abstract
The attention of software vendors has moved recently to Small to Medium-sized Enterprises (SMEs) offering them a vast range of Enterprise Systems (ES) including ERP, CRM, SCM and E-Procurement systems, which were formerly adopted by large firms only.From reviewing the literature on the adoption and diffusion of Information Systems' (IS) innovations, the question of 'why some SMEs choose to adopt ES while seemingly similar others facing the same market conditions do not' is still under-studied.This paper intends to fill this gap by developing a model that can be used to predict which SMEs are more likely to become adopters of ES.Using direct interviews, data was collected from 102 SMEs located in the Northwest of England.Logistic regression was used to analyse the data.Results reveal that factors influencing SMEs' adoption of ES are different from factors influencing SMEs' adoption of other previously studied IS innovations.SMEs were found to be more influenced by technological and organisational factors than environmental factors.Moreover, results indicate that firms with a greater perceived relative advantage, a greater ability to experiment with ES before adoption, a greater top management support, a greater organisational readiness and a larger size are predicted to become adopters of ES.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".