The contribution of human capital and resource‐based view to small‐ and medium‐sized tourism venture performance in Ghana
Bibliographic record
Abstract
Purpose Grounded in human capital theory and resource‐based view, this paper aims to examine the effect of the entrepreneur's human capital and the venture's resources on the performance of small‐ and medium‐sized tourism ventures (SMTVs) in Ghana. Design/methodology/approach The data were collected from 247 SMTVs, defined as tourism establishments employing less than 100 employees in the Western and Central regions of Ghana. Hypotheses derived from human capital and resource‐based theories were tested to assess the relationship between the theories and SMTV performance. Findings The study found a significant positive relationship between education, experience and performance. However, the hypothesised positive relationship between entrepreneurial family background and SMTV performance was inconsistent with prior studies. The findings with respect to the hypothesised relationship between venture resources and SMTV performance were mixed. Research limitations/implications The study suffers from industry‐specific, size‐specific and region‐specific limitations. Another limitation is the focus on human capital and venture resources as the determinants of tourism venture performance. Practical implications Knowing that education and experience per se impact on tourism venture performance, it behoves entrepreneurs in the tourism industry to endeavour to acquire the requisite education and experience. The finding has policy implications in the provision of tailor‐made training and incubation programs for SMTV entrepreneurs. Originality/value The study adds to the understanding of the unique nature of entrepreneurship in tourism by identifying the significance of human capital factors and venture resources on the performance of tourism ventures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".