Lean Startup Practices: Operationalizing the Technological Business Planning Process in an Academic Environment
Bibliographic record
Abstract
The generation of innovation in the academic environment involves the development of technology/product, technological transfer and business, which together make up the Technological Business Planning Process (TBPP). This process can be divided into three stages: world of technology (initial stage), world of transition from technology to product and business (intermediate stage), and world of business (final stage). In this context, there is the Lean Startup (LS) methodology, which comprises a set of potential practices to facilitate the operationalization of these stages of development. However, the existing literature is incipient and lacks guidance on the LS practices with the greatest potential to contribute to each of these stages, which constitutes a theoretical gap. Thus, this research aims to identify the LS practices most used by researchers-entrepreneurs in the different stages of the Technological Business Planning Process. The methodological approach used was the case study in an important Brazilian public university. In this context, nine innovation projects in the process of generating technological business were analyzed. The results show four main contributions: i) the practices contributed mainly to the intermediate phase of the TBPP; ii) BMC and MVP were considered the most important practices to operationalize TBPP; iii) the LS practices contributed significantly to the knowledge management between team projects; and iv) the combined implementation of the practices highlighted the benefits for TBPP. This study contributes to technology innovation management in the academic environment and provides some gaps that can be developed in future works in technological projects context.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".