The effect of technology on learning during the acquisition and development of competencies in technology‐intensive small firms
Bibliographic record
Abstract
Purpose The purpose of this paper is to consider whether the characteristics of a technology affect the type of learning mode used for acquiring abilities related to specific competencies. While technological competencies have a direct impact on firm performance for technology‐intensive start‐ups, few if any of these firms posses all the prerequisite competencies required for a given technology‐product‐market paradigm as the firm enters or remains over time in that market. Consequently, high tech entrepreneurial firms must learn, acquire and develop competencies initially and in response to the changing requirements of industry standard products. Design/methodology/approach The paper includes a study of all 35 high‐tech start‐ups in the semiconductor silicon industry using primary and secondary source data. Findings The characteristics of a technology affect which of ten different learning methods are chosen by a firm to acquire a competence. The study finds that risk, uncertainty, status, pervasiveness, observability, disruptiveness, and centrality are technological characteristics that influence the learning modes that are selected by a firm. Originality/value This is the first study to focus on the impact of technological characteristics on learning methods used. Practical and theoretical value in determining under what technological circumstances a learning method should be used to acquire and develop skills with a new technology.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".