Next-Generation Entrepreneurs and Succession: An Exploratory Study of Modes and Means of Managing Social Capital
Bibliographic record
Abstract
Relationships and connectivity play an enhanced role in most models of the new economy. For many firms, strategic advantage resides in the social capital (or relational wealth) they are able to nourish and maintain. This important asset is accumulated over time and not easily traded or transferred. For family firms with long-term continuity goals, the transfer and management of this largely intangible asset are a most significant activity. This research is based on interviews of next-generation entrepreneurs in 18 different firms. It contributes to the family business and more general management literature by identifying different ways in which relational wealth is transferred, created, and managed. Four different modes of transferring social capital emerged from the data: unplanned, sudden succession; rushed succession; natural immersion; and planned succession and deliberate transfer of social capital. Additionally, seven means of managing social capital emerged: deciphering existing network structures, deciphering the transactional content of network relationships, determining criticalities, attaining legitimacy, clarifying optimal role, managing ties through delegation and division of labor, and striving for optimal network configuration and reconstituting network structure and content. This paper concludes with a series of propositions for further research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| 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".