Team microfranchising as a response to the entrepreneurial capacity problem in low-income markets
Bibliographic record
Abstract
Purpose – Drawing on a qualitative study of youth microfranchising in the Tanzanian computer sales, service, and training sector, the purpose of this study is to identify the challenges and advantages of a team-based approach to owning and operating a microfranchise business in the context of a least developed country. However, disadvantaged entrepreneurs typically still lack a critical mass of specialized technical skills and general managerial skills to manage a differentiated and competitive microenterprise business. A team-based approach to microfranchising can allow for combining specialized skills among more than one business owner; however, the potential risks and opportunities of team-microfranchising have not been studied. This study makes a contribution toward filling this gap by identifying five challenges and five advantages of team microfranchising which provide guidance for future research and practice. Design/methodology/approach – Qualitative data (interviews, observation and archival documents) were analyzed from an in-depth case study of youth microfranchising in the Tanzanian computer sales, service and training sector. Findings – Results revealed that microfranchise businesses in sectors that require multiple complementary higher-level skills are suited to a team microfranchise approach. Findings suggest that the greater the limitations on franchisee skills and the more pronounced the lack of public goods and institutions, the greater the potential for team microfranchising to overcome the entrepreneurial capacity constraints and institutional voids in low-income market contexts. Further, team-based microfranchises may be able to compete more effectively in sectors where economies of scale are not a significant factor, such as service industries and small-scale niche manufacturing. Also identified are five potential challenges and five areas of opportunity for practitioners seeking to implement a team-microfranchise approach. Research limitations/implications – The current study examined microfranchising among teams of youth in the Tanzanian computer sales and service sector. Further research could examine team microfranchising among other demographic groups in different sectors and the different regulatory, institutional and cultural contexts of other regions and countries. Social implications – If developed effectively in the right contexts, the team-based approach to microfranchising can potentially double the job-creation impacts of microfranchising ventures. Originality/value – This study is the first to assess the viability and boundary conditions of a team-based approach to microfranchising.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".