A two-stage SEM-artificial neural network analysis of the organizational effects of Internet of things adoption in auditing firms
Bibliographic record
Abstract
This paper examines the role of vision as a mediating variable of the relationship between organizational factors and IoT adoption in audit firms in the US. Using a combination of analyses based on structural equation modeling (SEM) and artificial neural network (ANN) technology as the primary research methodology. Seven hypotheses were accepted, including one related to the impact of vision on IoT adoption. In general, all accepted hypotheses had a positive effect on IoT adoption. In addition to the direct positive impact of vision on IoT technology adoption, the magnitude of that effect varied depending on the context of each hypothesis. Drawing evidence from the results, this study demonstrates that vision was a partial mediating variable in the relationship between the organizational factor and IoT adoption. As a result, the model can help audit firms adopt IoT technology successfully. On the other hand, it makes essential recommendations for implementing IoT technology in light of the role that vision plays as a mediating variable in this model. The Technology-Organization-Environment (TOE) framework and Diffusion of Innovation theory (DOI) are combined with the vision to improve model predictive power.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".