A multi-sector comparison of relational learning and information and communication technologies adoption
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper analyses the nature of the impact of relational learning on the adoption of information and communication technologies (ICT) and vice versa. The authors investigate the implementation of ICT through a relational learning process by means of an empirical investigation of 203 small- and medium-sized enterprises in the Spanish telecommunication and the Spanish construction industries (107 firms from the telecommunications sector and 96 from the construction sector). In the analysis, it is proposed that the relational learning process can be structured into three phases (transfer, transformation, and harvesting of knowledge). Structural equation modelling reveals that, in order to implement these relational learning phases, companies need to provide and support ICT as prior steps and this is most significant for those in the telecommunications sector. These results have implications for managers when they come to formulating policies and making a choice as to the organisational capabilities to target in order to ensure the effective adoption of ICT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it