Information technology and performance: Integrating data envelopment analysis and configurational approach
Bibliographic record
Abstract
While several studies claim that information technology (IT) improves business performance, others claim that the impact of IT on performance remains unclear. Based on data envelopment analysis (DEA), this paper empirically examines the relationship between IT factors, intermediate performance metrics, and business outcomes. It also advances a new conceptual perspective to investigate the relationship between IT investment and performance. We propose a theoretical framework based on network DEA models, considering multiple periods, multiple inputs and outputs to study and understand the influence of IT on performance. Using a sample of 86 firms from Asia, Europe, and the US, we measure information technology performance with network DEA models, advance an explanation of the relationship between IT and performance and compare this relationship by regions and industries. By integrating DEA and a configurational analysis, we also develop a set of configurations of IT performance to understand the differences by regions and industries. Our results show that: IT performance shows little regional difference, but significant industrial diversity. We found four configurations to capture industrial differences in IT performance, and found that the efficiency of IT operations rather than IT investments, was the main reason leading to an increase in business performance.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".