Selecting distribution channel strategies for non‐profit organizations
Bibliographic record
Abstract
Purpose The purpose of this paper is to better understand the selection of a distribution channel strategy for a non‐profit organization selling products or services to its end customers. Design/methodology/approach Two channel strategies are generally considered: an integrated channel where the non‐profit organization sells its products or services using its own selling departments or branches; and a decentralized channel where the non‐profit organization sells through a for‐profit retailer. The fundamental question is: how should a non‐profit organization select its distribution channel strategy under certain market conditions? Findings It was found that selecting a decentralized channel strategy results in an optimal retail price that is higher than that under an integrated channel strategy, which results in lower customer welfare under the decentralized channel. It was also found that a decentralized channel behaves as an integrated fully for‐profit channel. Thus, whether a non‐profit organization should choose an integrated or a decentralized channel when facing competition from an integrated or a decentralized fully for‐profit channel depends on its cost structure and the level of substitutability of the products or services offered by the two channels. Practical implications When competing with an integrated fully for‐profit channel, the non‐profit organization is better off using an integrated channel under strong competition or a decentralized channel under weak competition. When competing with a decentralized fully for‐profit channel, the selection is more complicated. It was found that a decentralized channel is the best choice if the price competition factor, where threshold value depends on the cost structure, is large. Originality/value Non‐profit organizations have a clear (perhaps increasing) need for distribution centers or retailers in order to reach people who need their products or services. Moreover, it has been reported that the interactions between for‐profit and non‐profit sectors continue to grow, thus increasing the forms of community involvement available to reach people. It is thus clear that additional research is needed to better understand the selection of a distribution channel strategy for a non‐profit organization selling products or services to its end customers, and also the related managerial implications.
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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 | 0.002 | 0.006 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".