Configurations for Achieving Organizational Ambidexterity with Digitization
Bibliographic record
Abstract
Organizational ambidexterity refers to the capability of businesses to balance the pursuit of radical innovation simultaneously with incremental innovation. It echoes the popular notion that to thrive well in a competitive economy, businesses need to balance their exploration of new markets and products with exploitation or balance operational efficiency with flexibility. Digital technologies have become central to enabling organizational ambidexterity. The analysis reveals how the three dimensions of digitization efforts—IT implementation spending, IT training, and actual IT usage—should be combined with specific internal and external factors to develop greater ambidexterity. Two of these complementary factors are either a centralized organizational structure or a strong supplier and partner network—the first a likely channel for cross-organizational knowledge transfer and the second for interfirm knowledge transfers. However, determining which combinations are useful also depends on the size of the business and competitiveness of markets. Large businesses, or those in more competitive sectors, derive a slightly greater advantage from digitization than small firms or those in less competitive sectors. These findings are useful for policy makers tasked with subsidy allocation to industry sectors and managers when allocating investment spending for digitization.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".