Old but Sold? Innovation Through Tradition Strategy for Export and the Role of Family Involvement
Bibliographic record
Abstract
ABSTRACT Despite the notable body of research, the family firm (FF) internationalization literature has overlooked the role of innovation strategies in explaining FFs' export performance. We focus on the innovation through tradition (ITT) strategy—specifically, the degree to which a firm leverages its firm‐specific, mature (i.e., past) knowledge in the innovation search and recombination process. This strategy is particularly relevant for FFs as it can have ambivalent effects on export performance. On the one hand, it may ease the liability of foreignness; on the other, it could exacerbate it, creating tensions that firms must carefully navigate. Drawing from the socioemotional wealth perspective, we argue that FFs will be more prone to adopt an ITT strategy. However, the extent to which they will be able to reap the advantages or suffer the constraints of such a strategy for export depends on the particular type of family governance. Specifically, we contend that family managers will have a positive moderating effect on the relationship between the degree to which a firm leverages firm‐specific mature knowledge and export intensity thanks to their direct involvement and operational control over innovation activities. Conversely, family owners lacking this direct involvement will have a negative moderating effect on this relationship. Our analyses, based on a global longitudinal sample of 134 listed firms in the automotive and pharma/biotech industries observed from 2008 to 2020, support our hypotheses. Our results contribute to the nexus of the FF internationalization and FF innovation literature streams, the ITT research in FFs, and the broader internationalization literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".