The effect cloud accounting adoption on organizational performance in SMEs
Bibliographic record
Abstract
The study challenges the previous literature and assumes the digital vision as a proxy of intention among Technological, Organizational, and Environmental (TOE) factors and investigates the influence of TOE factors on cloud accounting adoption among Small and Medium Enterprises (SMEs). Furthermore, the effect of cloud accounting adoption on the organization's performance as measured by the balanced scorecard was evaluated. The data collection strategy employed an online survey of owners and managers using snowball methodology, in which the survey was automatically introduced to the respondents most likely to find relevant. The data was validated prior to SEM analysis. Seven of eight hypotheses were accepted, including the two hypotheses about the impact of the digital vision on cloud accounting adoption and the hypothesis about the impact of cloud accounting adoption on balanced scorecard-measures organizational performance. Despite the importance of the statistically significant factors in the study model, the digital vision was the most affected by the organizational readiness factor. The findings contribute to the TOE model by challenging the previous literature and assumption of digital vision as a proxy of intention among TOE factors. Future studies should use the TOE framework more caution if the intention is assumed to be a mediating variable.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".