Entrepreneurial Initiative Selling within Organizations: Towards a More Comprehensive Motivational Framework
Bibliographic record
Abstract
abstract We develop and test a motivational framework to explain the intensity with which individuals sell entrepreneurial initiatives within their organizations. Initiative selling efforts may be driven by several factors that hitherto have not been given full consideration: initiative characteristics, individuals' anticipation of rewards, and their level of dissatisfaction. On the basis of a survey in a mail service firm of 192 managers who proposed an entrepreneurial initiative, we find that individuals' reported intensity of their selling efforts with respect to that initiative is greater when they (1) believe that the organizational benefits of the initiative are high, (2) perceive that the initiative is consistent with current organizational practices (although this effect is weak), (3) believe that their immediate organizational environment provides extrinsic rewards for initiatives, and (4) are satisfied with the current organizational situation. These findings extend previous expectancy theory‐based explanations of initiative selling (by considering the roles of initiative characteristics and that of initiative valence for the proponent) and show the role of satisfaction as an important motivational driver for initiative selling.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".