How learning and legitimacy goals influence inter-firm imitation in R&D investment decisions
Bibliographic record
Abstract
Much research has examined the drivers of firms’ R&D investments. However, many questions remain with respect to the role of R&D as a learning target and as a means of achieving legitimacy, particularly in the context of imitative R&D strategy. We develop a theory that integrates different explanations of why firms engage in imitation, highlighting efficiency-enhancing learning and legitimacy and focusing on firms’ R&D investment decisions. We argue that deviations in firm performance from social aspiration levels determine the salience of learning and legitimacy goals. Specifically, as performance moves from lying below to being above social aspiration levels, organizations gradually shift from a primary focus on learning vicariously from others’ R&D investments toward a focus on mimicking them to maintain legitimacy. An analysis of a sample of 2,081 Spanish manufacturing firms, as well as an online experiment with 863 participants from the manufacturing industry largely support our hypotheses. JEL CLASSIFICATION: D22, M10, O32
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".