How do firms’ challenges influence technological adoption and innovativeness? A systemic perspective on team configuration, innovation and technology adoption during crises in Latin America
Bibliographic record
Abstract
Purpose To address specific challenges, some firms rapidly adopt new technological innovations; however, the effectiveness of these adoption practices remains understudied. The purpose of this study is to examine the implications of challenges faced by firms during crises, investigate the relationship between functional areas involved in addressing these challenges and the types of innovations and technologies adopted and investigate the impact of adopting these innovations and technologies on firms’ innovativeness during crises in Latin America, specifically Colombia. Methods The authors tested the hypotheses using a survey of 207 organizations and analyzed the data with structural equation modeling. Findings The findings of this study indicate that while the challenges faced by companies determine the types of technologies adopted during crises, they do not influence the choice of implemented innovations. Nevertheless, the choice of innovations and technologies implemented during crises significantly impacts firms’ overall innovativeness. Originality This study develops the Calyx empirical model of crisis, team configuration, innovation and technology adoption in emerging nations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".