The impact of business intelligence on electronic commerce growth: The mediating role of customer behavior analytics
Bibliographic record
Abstract
This paper investigates the influence of Business Intelligence (BI) technologies on the growth of electronic commerce, with a specific emphasis on the mediating impact of Customer Behavior Analytics (CBA). Grounded in the Theory of Planned Behavior (TPB). The paper sample comprised 330 customers who use the Amazon website in Jordan. A survey was utilized to gauge the impact of business intelligence on electronic commerce growth: the mediating role of customer behavior analytics. 282 questionnaires were returned for statistical analysis, adopting the quantitative approach, and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The outputs explain that both positive personal attitudes and BI adoption have a significant and positive effect on electronic commerce growth. Moreover, these factors contribute to enhancing CBA, which improves the ability to identify and capitalize on market opportunities in the digital environment. In theory, this study advances TPB by incorporating BI into models of entrepreneurial development. Practically, it offers smart strategies for nurturing a positive entrepreneurial attitude, widening the availability of BI tools, as well as developing electronic commerce ecosystems driven by BI that enhance operations and fuel the success of e-commerce websites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".