Unveiling the Trends in E-Commerce and Online Consumer Behaviour: A Bibliometric Analysis from 2018-2023
Bibliographic record
Abstract
The online retail sector has undergone a remarkable surge, reshaping the global retail landscape and necessitating a nuanced comprehension of its intricate dynamics, particularly in understanding consumer behaviour. With e-commerce sales soaring to a monumental $5.7 trillion in 2022, the profound influence of consumer behaviour on market trends, business strategies, and policy formulation cannot be overstated. This study offers a comprehensive and diverse perspective by conducting an exhaustive bibliometric analysis of literature on online consumer behaviour, focusing on works published between 2018 and 2023. Leveraging the Bibliometrix R package and Biblioshiny user interfaces ensures the robustness of the analysis, which encompasses citation patterns, author collaboration dynamics, keyword frequencies, and international cooperation. By pinpointing influential articles, authors, journals, and emerging trends, the study identifies significant contributors, influential works, and fruitful collaborations, thereby illuminating the multifaceted nature of the e-commerce industry. The analysis underscores the pivotal role of interdisciplinary collaboration in fostering international partnerships and the growing significance of technological advancements. The findings of this study hold valuable insights for academics, industry professionals, and regulators, empowering them to devise effective strategies and foster sustainable e-commerce practices. By contributing significantly to the body of e-commerce research, this study has the potential to bolster consumer trust, enrich customer experiences, and steer future research endeavours in an industry characterised by rapid evolution.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.131 | 0.185 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".