Systematic profitability analysis of binary network marketing organizations
Bibliographic record
Abstract
Purpose This paper seeks to introduce a systematic approach to simulating a given binary network marketing (NM) growth topology in a definite society of people. Design/methodology/approach The study represented a binary plan network by its binary rooted tree, where each node represents a down‐line distributor of the root. The paper sought to find a cost function which would identify which existing node is most eligible to attract the new node. Using a survey strategy, the paper introduced some effective criteria, where cost function and design systematic algorithms were introduced, in order to simulate an NM growth topology. According to the designed algorithms, the paper modified a currently used compensation plan of the Questnet Company. Findings The comparison results indicate that the modified plan improves the efficiency by 6 percent, in the sense of profitability for the costumers, and also penetrates the market in 80 percent of trials. Research limitations/implications The paper did not find any currently proposed simulation for binary NM plans. So, in order to introduce the systematic approach, new criteria were obtained based on survey strategy. Practical implications Network marketing organization designers need such a systematic method to arrange their strategies according to the prediction of the network's growth topology. Originality/value The paper presents a novel idea for designing analytical simulation tools for NM plans verification. As far as the authors are aware, this is the first systematic method to propose binary compensation plans.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".