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Automating Data Envelopment Analysis in Python: Functional Comparison with XlDEA/XIDEA and Methodological Assessment of Second-Stage Inference
| dc.creator | Taco Arias, Marlon Stalin | en |
| dc.date | 2026-06-30 | |
| dc.date.accessioned | 2026-08-03T16:43:31Z | |
| dc.date.available | 2026-08-03T16:43:31Z | |
| dc.identifier | https://imjeta.org/index.php/IMJETA/article/view/Automating-Data-Envelopment-Analysis-in-Python | |
| dc.identifier | 10.67294/j87y4t65 | |
| dc.identifier.uri | http://bdigital2.ula.ve:8080/xmlui/654321/26933 | |
| dc.description | This study evaluated whether a reproducible Python workflow can strengthen Data Envelopment Analysis in industrial efficiency studies when compared with spreadsheet-based tools such as XlDEA/XIDEA. A methodological, documentary, and computational comparative design was applied. The study examined two implementation environments: a reproducible Python workflow and spreadsheet-based analysis tools. Data were collected through a structured comparison matrix that assessed methodological coverage, automation and scalability, reproducibility and auditability, and second-stage inferential robustness. The analytical procedure reviewed input-oriented CCR estimation, bootstrap inference, Tobit modeling on inefficiency, truncated regression with double bootstrap, and automated report generation. The main result indicates that Python provides a more scalable and auditable architecture for repeated analysis, especially when monthly data, multiple decision-making units, and standardized outputs are required. However, spreadsheet tools remain useful for exploratory applications because they offer greater initial accessibility for non-programming users. The study concludes that Python is preferable for production-grade efficiency analysis, while truncated regression with double bootstrap should guide future second-stage inference when contextual determinants of efficiency are analyzed. | en |
| dc.format | application/pdf | |
| dc.language | en | |
| dc.publisher | Grupo Afronta C.A. | en |
| dc.relation | https://imjeta.org/index.php/IMJETA/article/view/Automating-Data-Envelopment-Analysis-in-Python/20 | |
| dc.rights | Copyright (c) 2026 Marlon Stalin Taco Arias (Author) | en |
| dc.rights | https://creativecommons.org/licenses/by-nc-nd/4.0 | en |
| dc.source | International Multidisciplinary Journal of Emerging Technologies and Applications; Vol. 1 No. 3 (2026); 79-94 | en |
| dc.source | 3135-6214 | |
| dc.subject | Data Envelopment Analysis | en |
| dc.subject | Python Workflow | en |
| dc.subject | Technical Efficiency | en |
| dc.subject | Bootstrap Inference | en |
| dc.subject | Truncated Regression | en |
| dc.title | Automating Data Envelopment Analysis in Python: Functional Comparison with XlDEA/XIDEA and Methodological Assessment of Second-Stage Inference | en |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:eu-repo/semantics/publishedVersion |
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