Digital Product Innovation Within Family Firms: A Construal Level Perspective
Bibliographic record
Abstract
Digital product innovation (DPI) is critical for the survival of firms, especially those operating in traditional industrial-age industries. While research has started to investigate digital innovation in family firms (FFs) considering them as a monolithic group, we still lack a more nuanced perspective that considers heterogeneity among FFs with respect to DPI and what drives such variance. Drawing on construal level theory to explain the risk behavior and goal time horizon of FF owner-managers, we propose and find that the presence of later family generations in control positively influences DPI in FFs, while the presence of a family CEO is detrimental to DPI. Furthermore, we propose that these relationships are moderated by the size of the top management team (TMT), finding that a larger TMT weakens the positive relationship between later generations in control and DPI. We base our analysis on a longitudinal sample of 103 FFs in the automotive, industrial engineering, and pharmaceutical sectors observed from 2013 to 2020. This first empirical study applying construal level theory to the family business literature has important implications for the FF digital innovation literature and for FF owner-managers interested in achieving DPI.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".