Configurations of Innovations across Domains: An Organizational Ambidexterity View
Bibliographic record
Abstract
How do firms balance explorative and exploitative innovation for superior firm performance? While most prior studies have approached this issue by focusing on technology‐related innovation, the role of balancing exploration and exploitation in other important organizational domains, i.e., marketing, and the interaction effect of ambidexterity across different domains have been overlooked. This study contributes to this line of research by investigating how firms simultaneously balance exploration and exploitation across two critical domains, namely technology innovation and market innovation. The study distinguishes four types of configurations: market leveraging (technology exploration and market exploitation), technology leveraging (technology exploitation and market exploration), pure exploitation (technology exploitation and market exploitation), and pure exploration (technology exploration and market exploration). From an organizational ambidexterity perspective, the current work investigates whether and how these different combinations exert distinctive effects on firm performance. Specifically, the article posits that (a) technology exploration and market exploitation complement each other, and (b) technology exploitation and market exploration also complement each other, such that both market leveraging and technology leveraging strategies have positive effects on firm performance. The article also maintains that such positive relationships are fully mediated by differentiation and low cost advantages. Conversely, it is argued that (c) technology exploration and market exploration conflict with each other, and (d) so do technology exploitation and market exploitation, such that both pure exploration and pure exploitation have negative effects on firm performance. Hypotheses were tested using survey data collected from 292 manufacturing and service firms in China. The results supported most of the hypotheses, except that pure exploration demonstrated no significant relationship with firm 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".