The adoption of business-to-consumer commerce for small and medium enterprises growth
Bibliographic record
Abstract
This study aimed to address the underexplored area of the adoption of Business-to-Consumer (B2C) Commerce by Small and Medium Enterprises (SMEs). In addition, this study specifically focused on factors influencing B2C adoption by SMEs, its impact on marketing performance, and potential strategies for optimization. Recognizing the scarcity of quantitative studies on digitization's impact on SMEs, this study emphasized the need for a systematic understanding of these enterprises’ responses to e-commerce adoption. In line with the Technology-Organization-Environment (TOE) framework, the primary focus was on the continuous evaluation and optimization of e-commerce platforms, including AI integration, within core marketing strategies. Based on customer tech-savviness in the environmental dimension, adapting e-commerce strategies ensured a comprehensive approach in the evolving technological landscape. While providing valuable insights, several limitations, such as context-specific findings and potential response bias due to self-reported data were also identified. Consequently, future investigations were advised to include comparative studies between e-commerce-adopting and conventionally operating organizations, as well as explore perspectives of e-commerce users and consider industry-specific variations. This was pertinent because investigating e-commerce implementation in emerging technologies and platforms could offer insights into the dynamic landscape of digital business. In conclusion, this study contributed to the cognition of B2C Commerce adoption in SMEs, offering practical insights and strategic recommendations for leveraging technology to enhance marketing performance and overall business growth.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".