Finance in Family Business Studies: A Systematic Literature Review
Bibliographic record
Abstract
The aim of this paper is to examine how the family business literature and financial issues interact. It is carried out on 448 articles methodically selected from 24 management journals and 102 finance journals. After discussing the periodical development of literature, the study identifies the key research topics in the both fields and by crossing the results it identifies specific interaction trends. The classical financial theory cannot be applied to family businesses. The outcome of this research discloses that socioemotional wealth could contribute to determine a new perspective under which to examine the interplay between the family and the business. The schematic overview on the state of art let us reflect on the gaps that could be bridged, through a coherent advancing of financial studies in family business. The understanding that classical financial theory cannot be applied to family businesses and the discovery of peculiar family business dynamics can assure the continuity of the firm by defying the growth of firm’s value in term of the emotional components beyond the financial considerations. This review shows to researchers a wider scenario of the family business by leading to many challenges and gives an essential support to scholars in advancing a beneficial research.
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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.028 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".