An Exploratory Study of the Formation and Impact of Electronic Service Failures1
Bibliographic record
Abstract
E-commerce service failures have been the bane of e-commerce, compelling customers to either abandon transactions entirely or switch to traditional brick-and-mortar establishments. Yet, there is a paucity of studies that investigates how such failures manifest on e-commerce websites and their impact on consumers. This paper, therefore, synthesizes extant literature on e-service and system success to arrive at a novel classification system that delineates e-commerce service failures into information, functional, and system categories, each with its own set of constituent dimensions. Extending expectation disconfirmation theory (EDT), we further distinguish among disconfirmed outcome, process, and cost expectancies as major consequences of e-commerce service failures. A theoretical model of e-commerce service failure classifications and their consequences was constructed together with testable propositions that relate the three failure categories to consumers’ disconfirmed expectancies. Finally, we explore the validity of our theoretical model based on descriptive accounts of actual occurrences of e-commerce service failures and their corresponding consequences. Consistent with our theoretical model, information and functional failures were found to be associated with disconfirmed outcome and process expectancies respectively. System failures, on the other hand, do not affect consumers’ disconfirmed expectancies, thereby contradicting our predictions. Post hoc analysis on constituent dimensions of information, functional, and system failures yielded additional insights on the preceding observations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".