Family Business in the Digital Age: The State of the Art and the Impact of Change in the Estimate of Economic Value
Bibliographic record
Abstract
Throughout the review of the most relevant literature on family businesses and business valuation, this work pursues a twofold purpose: to explore the possible evolutionary scenarios of family businesses in the era of digitalisation, highlighting their role and purpose; and to determine the valuation approaches that may be applied to them, also in light of the different role that intangible assets deriving from their digitalisation may assume. Therefore, after a description of the most relevant changes related to the digital transformation of the FB, the focus will be set on their valuation, paying special attention to the choice of the most appropriate methodology for “grasping” the aforementioned changes. Family businesses, in fact, due to their distinctive traits and the various estimation opportunities, require a dynamic business valuation process that has to be projected into the future and suitable for estimating those intangible assets that strongly characterise these types of companies, inasmuch as they are related to the implicit components that are strongly connected to the ownership and are a result of knowledge, strategic adaptability, and product innovation, and their possible impact on the risks and their expected flows. Thus, throughout a systematic literature review, the study provides, on the one hand, a clearer representation of the state of the art of the FB valuation in the digital age; on the other hand, it highlights the characteristics and peculiarities of “novel” FBs whose valuation needs to be conducted with due care.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".