Impact of SME Manager's Behavior on the Adoption of Technology
Bibliographic record
Abstract
It has been acknowledged that firms must resort to technology in order to acquire the flexibility needed to meet the challenges posed by the globalization of the economy. The successful adoption of new technology has thus become a matter of survival for companies. Several studies have already highlighted the importance of the manager's behavior in the process of adopting technology. The Stevenson model suggests that this behavior may be situated anywhere on a continuum ranging from the characteristics of the administrator, at one extreme, to those of the entrepreneur, at the other. Using this model, we conducted a study of senior managers to analyze their behavior in the decision to adopt a new technology. A questionnaire was mailed to 450 of them and in-depth interviews were conducted among 54 others. Our findings show that in the process of adopting a new technology, half the managers adopted a behavior closer to that of an administrator. The other half acted more like entrepreneurs. This seems to have a major impact on the success of the adoption since we found that managers with an entrepreneurial style were not as successful as those who adopt an administrative style.
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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 | 0.002 | 0.015 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".