Early sales of new technology products: a framework for comparing the sales cycle of competing start-up and large supplier firms
Bibliographic record
Abstract
The objective of this paper is to examine the theory and describe a methodology to compare the early sales of innovative technology products made by two samples: technology-based start-ups and large companies. The categories examined for the samples comparison include target buyer characteristics (size, type of business, distance from buyer and years in operation), first meeting with the buyer firm (method of introduction, department and power level of initiator), the buyer's perspective of the product offer (importance and value), the buyers involvement in product development, the relationship strength developed between the buyer and the seller firms, the buyer's purchase decision-making process and the resulting degree of buyer loyalty. Based on these factors, the author proposes hypotheses to reduce the early sales cycle duration and increase the buyer's loyalty. The intent is to offer a method for providing insights into the early buyer's view of the new product's sale cycles. Sellers currently facing the task of developing early sales for their new product could then adjust their investing, partnering, hiring, outsourcing and designing policies based on the results gathered from successful predecessors.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".