SUCCESSION PLANNING AND BUSINESS CONTINUITY IN FAMILY-OWNED ENTERPRISES IN LAGOS STATE, NIGERIA
Bibliographic record
Abstract
This study investigates the effects of succession planning on the business continuity of family owned businesses in Lagos state, Nigeria. The study adopted a cross-sectional survey design and computed a sample size of three hundred and sixty-eight (368) from The Nigerian Association of Small and Medium Enterprises NASME database of registered family businesses of eight-thousand three hundred and ninety six registered family-owned businesses in Lagos state using the Cochran sample size formula. The senior staff and owners of family-owned businesses in Lagos state were purposively selected to fill out the structured questionnaires of the study. The questionnaires were adapted from previous studies and validated via a pilot study conducted in the Oluyole industrial area of Ibadan southwest, Oyo state, Nigeria. The study adopted SPSS version 25 for the descriptive statistics and Smart PLS version 4.0 for the inferential statistics to analyse the data. The study's findings revealed that succession planning influences 30.2% of business continuity, while the remaining 69.8% can be explained by the other exogenous variables different from business continuity. The study concludes that succession planning positively influences the business continuity of family-owned businesses in Lagos State, Nigeria.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".