The individual performance outcome behind e-commerce
Bibliographic record
Abstract
Purpose While most e-commerce studies focus on the understanding of online customer behaviour, mainly adoption and purchase behaviours. The purpose of this paper is to examine the relationship between e-commerce and individual performance. The authors test the role of systems, information and service quality in e-commerce use and user satisfaction. Trust may become an important aspect for a consumer’s decision making, based on this the authors identify the effect of the role of trust on e-commerce use, user satisfaction and its impact on individual performance. This research has theoretical and managerial implications, since the protagonism of e-commerce is increasing in both academia and industry. Design/methodology/approach The authors apply a research model that integrates information systems success dimensions and user behaviour in the form of trust. The empirical approach was based on an online survey questionnaire of 437 individuals from Portugal. Findings The results reveal that overall quality and overall trust are important to explain use and user satisfaction in the context of e-commerce, which further leads to individual performance. The findings indicate that a higher level of use and user satisfaction increase individual performance. Originality/value The authors integrate information systems success dimensions and overall trust to understand the significance of e-commerce individual performance. The authors expect the results to enrich the understanding of the importance of considering both technological and behavioural factors to increase the success of e-commerce.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".