Bibliographic record
Abstract
Subject area The subject areas are social entrepreneurship and marketing in social enterprises. Study level/applicability This study is applicable to undergraduate or MBA-level courses; possibly executive programs as well. Case overview Farm Shop was established in 2012 as a not-for-profit trust, with an aim of developing a distribution platform for poor, rural communities across sub-Saharan Africa so that smallholder farmers could get the farm inputs and services needed to increase their productivity and income. Attempting to reach scale, this social enterprise is in the process of building a micro-franchise network. Unlike franchises in industrialized countries where the franchisor starts with a vetted and replicable turnkey business, Farm Shop was created from scratch. After prototyping the shop concept and validating the business model in Kiambu County of Kenya, Farm Shop has 10 fully operational shops and is keen to start its growth phase, aiming to have 120 shops in its network within the next 12-18 months. It is only at that point that break-even will be achieved. Recognizing the key role of marketing in Farm Shop’s growth efforts, the founders are now focused on finalizing their go-to-market (GTM) strategy. Having initiated and measured the results of a number of marketing activities over the past six months, it is now time to decide which of these activities should be incorporated into their micro-franchise system. The management team knows that to provide advice, training and quality products to farmers, they first needed to develop awareness, interest and desire for what Farm Shop has to offer, not to mention the need to gain the farmers’ trust. Fundamentally, farmers needed to be convinced that Farm Shop can help them improve their productivity and income. Expected learning outcomes The study enables to gain an overall understanding of the range of challenges and opportunities associated with establishing a micro-franchise in an emerging market context; to gain a better understanding of social marketing, including the four types of behavioral influence it attempts to achieve and the similarities and differences between social and commercial marketing; to introduce the “theory of change” concept, providing a framework for understanding how and why change will occur; to introduce the concept of business models and explore the differences between “traditional” and “social entrepreneurship” business models; to understand how a competitive advantage is created; to introduce basic marketing concepts and the GTM concept and its role and application in a business model for a new social enterprise and to understand how marketing contributes to the social enterprise’s strategic goals and sustainability, thereby gaining an understanding of how “social marketing” is differentiated from commercial marketing. Supplementary materials Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes. Subject code CSS 3: Entrepreneurship.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.402 | 0.123 |
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".