Purchase intention in an electronic commerce environment
Bibliographic record
Abstract
Purpose The purpose of this paper is to understand the integrated impact of the application of protection measures against identity theft on consumers’ synergistic perception of trust, the cost of products/services and operational performance (OP) – all of which in turn is postulated to contribute to purchase intention (PI) when shopping online. Design/methodology/approach In order to accomplish the specified aim, this study first conducted an experiment by involving the students from a university in Bangladesh. Then a survey was conducted to capture their opinion based on the previous experiment. Findings The study identified that in e-commerce, OP and trust have potential impact on pursuing consumers’ PI. Traditionally, price is always an issue in marketing; however, for e-commerce, this issue does not have direct impact on PI. Research limitations/implications The main limitation of this study is that a less established e-commerce example was utilized to conduct the experiment and survey for validating the model. Also, the study was conducted only in the context of Bangladesh and a student sample was utilized. Future studies can test the model in different contexts (particularly to verify the impact of privacy) by utilizing data from consumers. Practical implications This study has resolved a controversial issue by generating clear guidelines that the overall conjoint effect of OP, trust, and price on PI is neither negative nor neutral. Synergistically, the application of these controlling tools of identity theft can substantially enhance consumers’ trust, which is the single most predictor to pursue consumer PI. Originality/value This study has provided in-depth insight into the impact of different controlling measures in e-commerce PI. Practitioners have potential learning from this study that if consumers find the application of different controlling mechanisms against cybercrimes, particularly identity theft, enhancing the reliability, authenticity and security of transactions in this virtual medium, they do not mind paying a higher price. Such insights have not been provided by existing studies on this topic. Developing trust on e-commerce purchase is the driving force, not the price.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".