Bibliographic record
Abstract
The impact of networking technologies on information systems (IS) and its auditing is growing dramatically. This growth is changing the nature of information systems in the modern organization, with special reference to e‐commerce. It would also be reasonable to infer that a corresponding effect is mounting on the information system's auditing function. This paper primarily stresses the identification of specific constructs which can contain the potential variables/critical success factors in audit engagement planning that contribute to the success/failure of audit engagement in e‐commerce‐centric technological scenario, and the same can be used to build a model for its empirical validity in future studies. The objective of this paper is to devise a model, based on the variables turned potential critical success factors to successfully perform audit engagement planning for the current state‐of‐the‐art e‐commerce technologies. The available literature is analyzed to identify appropriate candidates for factors that appear to materially affect the success of the e‐commerce audit resource planning function. Based on this model, an empirical examination, though not within the scope of this paper, is the next logical step in this direction to establish the validity of this model in the technologically complex e‐commerce milieu.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".