TRACING THE EVOLUTION OF ECOMMERCE: HISTORICAL FOUNDATIONS, IMPACTS OF THE PANDEMIC, AND FUTURE DIRECTIONS
Bibliographic record
Abstract
Electronic commerce, or ecommerce, has been a topic of interest particularly in the last years. While ecommerce activities are notably facilitated today, it has gone through notably different forms since its first seeds were planted. The COVID-19 pandemic was a crisis for most businesses but also an opportunity for some, especially for those who were able to pivot or transform their businesses with respect to the new needs of consumers. With the positive impact of the pandemic on ecommerce sector observed especially as of the second quarter of 2020, its trend has surpassed the expectations. This overperformance came with numerous sceptical questions about the future of ecommerce. This study investigates the historical evolution, pandemic-induced transformations, and future trends of ecommerce. It conveys a compilation of milestones through systematic literature review with a focus on the practicality. This paper provides with a timeline of the ecommerce history to bridge its evolution in the last 80 years by visualizing the phases it has been through along with its anticipated trend in the upcoming years. The studied data reveals how COVID-19 accelerated ecommerce growth and highlights its implications for global retail. The growth of ecommerce may slow down but it will endure its growth as online shopping has already become an indispensable habit.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| 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".