Subjective Knowledge, Perceived Risk, and Information Search when Purchasing a Franchise: A Comparative Exploration from Australia
Bibliographic record
Abstract
Franchising has long been a method by which organizations seek to expand and facilitate local market development. However, franchising as a growth strategy can often be hampered by lack of suitable franchisees. To mitigate this shortage, some franchisors have engaged in recruiting franchisees internally from the ranks of their employees in addition to the traditional approach of recruiting franchisees externally. Predominantly franchisees are individuals rather than corporations and thus purchasing a franchise should most commonly be characterized as a consumer acquisition. To explore the relationship between subjective knowledge, perceived risk, and information search behaviors when purchasing a franchise qualitative interviews were conducted with franchisees from the restaurant industry. Half of these respondents were externally recruited having never worked for the franchisor and half were internally recruited having previously been employees of the franchisor. The external recruits expressed a strong desire to own their own business and engaged in extensive decision-making processes with significant information search when purchasing their franchises. Contrastingly, the internal recruits expressed a strong desire to be their own boss and engaged in limited, bordering on habitual decision-making processes with negligible information search when acquiring their franchises. The results reveal that differences in subjective knowledge and perceived risk appear to significantly impact the extent of information search between these two groups. A model of the relationship between subjective knowledge, perceived risk and information search in the purchasing of a franchise is developed that reconciles these findings. The findings also have practical implications for franchisors’ franchisee recruiting efforts which are integral to their capacity to develop local markets.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 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".