The impact of absorptive capacity on the ex-post adoption of agile methods: The case of Extreme Programming model
Bibliographic record
Abstract
Agile development methods have emerged to overcome some of the process and product-related problems associated with traditional models. They are believed to be lightweight, people focused, adaptive and allow better information systems development (ISD) performance. Nevertheless, they require a significant capacity of absorbing new set of skills, knowledge and mindset changing. When using agile methods IS developers are faced with a challenge to quickly assimilate the mindset of these new methods and develop the ability to recognize information and apply it in context. This paper reports on two ex-post ISD project implementation. We integrate a central construct in the dynamic capability theory - absorptive capacity to explain agile method adoption and usage. The findings show that absorptive capacity, indeed, plays an important role in adopting and using agile method-Extreme Programming model. The implications of these findings for both researchers and practitioners are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".