The Imitator's Dilemma: Why Imitators Should Break Out of Imitation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Imitation and innovation are two primary R&D approaches that firms follow in technology development, especially in R&D‐intensive industries. That imitation and innovation share R&D resources and investments gives rise to what is coined in this article as the imitator's dilemma. The imitator's dilemma tells a story of why firms should break out of imitation‐oriented R&D and move toward innovation‐oriented R&D in order to sustain their innovation output and profit performance. This article contributes to the technology and innovation management literature by illuminating the imitator's dilemma both theoretically and empirically. To this end, this study develops and tests hypotheses to investigate the influence of a firm's imitation activity on its innovation output and profit performance, which represent a gap in the current literature. A longitudinal research design is followed on an unbalanced panel dataset between 1991 and 2010 from a sample of 227 firms in three R&D‐intensive manufacturing industries in the United States, including computer, semiconductor, and pharmaceutical. The results of this research reveal a dilemma for imitators. Imitation activity can generate positive returns in terms of a firm's innovation output and return on assets ROA (a measure of short‐term profits). However, these returns are unsustainable. Excessive levels of imitation activity within the firm results in negative returns in terms of its innovation output and ROA. Additionally, any level of imitation activity, low or high, negatively impacts a firm's Tobin's Q (a measure of long‐term corporate valuation). Accordingly, this article makes novel contributions to the technology and innovation management literature by explaining the imitator's dilemma and how firms may effectively manage it.
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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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it