The Effect of Application Ambidexterity on Firm Agility
Bibliographic record
Abstract
Firm agility is quickly becoming an essential capability for companies to effectively compete in hypercompetitiveenvironments. At the same time, firms are using applications that enable close integration coupled with an increased ability toreconfigure processes. Such applications should manifest a balance between integration and reconfigurability. This is theconcept of applications ambidexterity. The IS literature has selectively focused on either integration or reconfigurability. Byfocusing on only one characteristic we have been unable to understand the IT - agility relation. Research suggests thatintegration and reconfigurability alone cannot explain the true nature of the IT - agility relation. This conceptual paperattempts to understand the complementary effects of integration and reconfigurability on firm agility. It contributes to theliterature by theorizing the role of IT application ambidexterity on firm agility through the mediating variables of knowledgeexploration, knowledge exploitation and process adaptability.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".