An Action Research Case Study of the Facilitators and Inhibitors of E-Commerce Adoption
Bibliographic record
Abstract
This research has studied an established Irish retail business as it takes its first tentative steps into the e-commerce arena. Although the adoption of e-commerce is widely studied in the academic world, only a small percentage of these studies focus on the Small to Medium size Enterprise (SME) retail sector. SMEs account for 97% of Irish companies and employ up to 800,000 people (Chamber of Commerce Ireland, 2006). Whilst examining the SME’s adoption of e-commerce, the factors that affected the adoption process were specifically identified and understood. This was achieved by conducting an action research case study. Action research merges research and practice thus producing exceedingly relevant research findings. The case study commenced in August 2003 and concluded in August 2004. It consisted of three distinctive action research cycles. The researcher worked in the SME throughout the research process, and had been employed there for the previous 5 years. This chapter demonstrates how the research was undertaken, and also discusses the justification, benefits and limitations of using action research. The research concluded that the adoption of e-commerce within the SME sector tends to be slow and fragmented, the presence of a “web champion” is paramount to the success of the project and Internet adoption is faster with the recognition of a business need. It also supported the evidence that an SME is more likely to adopt e-commerce when the SME owner has a positive attitude to IT.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".