Strategic Direction and Sustainable Development in Franchising Organizations: A Conceptual Study
Bibliographic record
Abstract
Franchising is viewed as a significant industry in contributing to the global economic development by generating an average of $3.7 billion in annual franchise sales from 40 countries and provides over 18 million jobs opportunities. Due to the expansion of franchise business markets in Malaysia, the number of franchise outlets keeps increasing more than doubled within six years, with total 10720 outlets in 2010 to 23140 outlets in 2016. To intensify the continuance of this expanding process, franchise entrepreneurs need to conserve a sustainable creative value in undergoing the challenging of the business environment. The limitations of internal and external factors such as core capability, government support and other related dimensions affect the stability of franchise organization. This paper pursues to explore the related existing literature and underpinning theories that reinforce the sustainable development of franchise business. Most of the franchise models are captured in the different analysis of dimensions and strategies on sustainable development goals. The objective of this study is to conceptualize the strategic direction practices and propose a theoretical framework of sustainable franchising that can help the continuance of franchise business. Therefore, by supporting and participation of stakeholders, entrepreneurs, academicians, researchers, and governments, future analysis can develop and expose a sustainable model of franchising.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".