The Role of E-Accounting Adoption on Business Performance: The Moderating Role of COVID-19
Bibliographic record
Abstract
In the last decade, information systems (ISs) have made dynamic developments in light of their ability to enhance the performances of businesses. In relation to this, an organization that is effectively and efficiently managed often displays optimum performance using financial systems such as electronic accounting (e-accounting). Thus, essentially, e-accounting is utilized for the automation of operational processes and for improving business efficiency and performance. More currently, e-accounting dynamic development has laid credence to the performance of businesses in a way that the influence cannot be exaggerated. Nevertheless, past studies evidenced that successful e-accounting depends on critical success factors, and hence this study primarily aims to conduct an evaluation of e-accounting using DeLone and McLean’s information system model (DM ISM) among firms in Jordan. More specifically, this study determines the influence of information quality, system quality, service quality, system usage, and user satisfaction on business performance. The current study adopted a quantitative method, applying a self-administered survey questionnaire for the purpose of data collection from 104 e-accounting users. This study employed partial least squares structural equation modeling (PLS-SEM) to validate the data, and based on the findings, system quality and information quality affect system use; service quality of e-accounting had no significant impact on use, but e-accounting use had a significant influence on the satisfaction of users. Moreover, e-accounting system use and user satisfaction positively influence business performance. This study is an extension of the current IS literature, particularly of those focused on determining the effects of e-accounting benefits. This study validated the proposed model in the context of Jordanian firms and contributes to both the literature on and practice of e-accounting. This study provided implications, limitations, and recommendations for future research.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".