Replicate or adapt? Franchising and organizational routines
Bibliographic record
Abstract
Purpose The purpose of this paper is to introduce strategy as a factor that explains when franchisors – through the franchisees they select – seek to replicate routines exactly versus allow local adaptation of routines. Design/methodology/approach Combined archival and survey data from 248 US and Canadian franchisors actively seeking franchisees were used to test hypotheses via structural equation modeling. The robustness of results was comprehensively explored. Findings As hypothesized, results suggest that franchisors pursuing plural form strategies select franchisees with traits that foster replication, such as prior managerial experience and the desire to become multi-outlet franchisees. Those franchisors pursuing turnkey strategies seek franchisees who exhibit a willingness to experiment and adapt. In contrast to expectations, plural form franchisors were more likely to seek franchisees with local market knowledge. Originality/value Strategy influences whether franchisors select franchisees who will replicate versus adapt organizational routines. The authors introduce strategy as a factor affecting the extent to which routines are replicated exactly versus adapted locally. For franchising research, they challenge prior theory by explaining why franchisors invest in franchisee selection rather than waiting for the best franchisees to self-select into franchising.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".