Systematic profitability analysis of binary network marketing organizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it