Development of Sustainable Business Model: A Conceptual Framework for the Financial Sector to Obtain Successful ERP
Bibliographic record
Abstract
The present study identifies the key antecedent factors for accomplishing the adoption stage of enterprise resource planning (ERP) systems in their business operations. Four potential antecedent factors of adoption were derived from the literature, and data were obtained from a sample of 200 executives of the banking organizations of the financial sector across India. A structural equation modeling (SEM) technique was used to examine the complex relationships between antecedents and the adoption decision. The authors survey the literature to discover and classify critical success factors that are potentially applicable to financial sector. Four broad parameters namely, strategic, organizational, technological, and environmental (SOTE) has been identified for the efficacious development of ERP system in the financial sector of India. 11 dimensions has emerged as significant one for the financial sector. The results show that most of the success factors found in the literature apply to the industry. Nevertheless, distinct differences were found as well. Some factors, such as innovativeness of the implementation strategy, formulation of sound business plan, integration within the departments, adaptation capability of the system etc. would become the key instruments for successful implementation of ERP in the financial sector. The findings can help the executives of the financial sector to focus their attention, priorities, resources and leadership on managing the success factors that have been established to be critical for achieving ERP project implementation and ultimately, leading to the development of the sustainable business model.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".