Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the impact of the Internet, and more specifically social media, on franchise business models. Design/methodology/approach A review of both franchising and Internet literatures enables the creation of a simple model that distinguishes between surface waves, highly visible innovations that influence a restricted set of franchise business models, and deep waves that have a broader and more long-lasting influence on all franchises. Findings The first Internet era had a surface wave, online selling, that impacted relatively few franchises, but the deep wave of the wide availability of information and training materials has had a broader and more sustained impact on franchise systems. Similarly, Web 2.0’s social element has created a surface wave, the shared economy for hotels and cars, that affects relatively few franchises, but the deep wave of user-rating Web sites and Apps promises to have a broader and more long-lasting influence. Research limitations/implications This paper enables researchers identify potential research topics, highlighting the need to determine the impact of social media on how consumers perceive quality and the influence this has on their ongoing behavior. Practical implications This paper helps practitioners understand how the Internet influences the competitive balance between franchised and non-franchised businesses. Hence, it will be of interest to any large organization that offers high quality decentralized products or services, as they typically either franchise or compete with franchised businesses. As well, for entrepreneurs considering investing in a franchise, this paper will help identify which business models are more sustainable in the face of Internet innovation. Originality/value The surface wave/deep wave model is a new approach to analyzing the long-term impact of the Internet on all decentralized businesses.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".