Linking peripheral vision with relational capital through knowledge structures
Bibliographic record
Abstract
Purpose Peripheral vision (PV) or side vision refers to that which is visible to the eye while being outside of its central area of focus. PV enables organisms to detect movement and potential threats in their environment. The purpose of this paper is to contribute to the understanding of the concept of PV in the business environment, as well as its relationship with knowledge structures in the form of technology knowledge and absorptive capacity. The relative importance and significance of technology knowledge and absorptive capacity as mediators between “relational capital” (RC) and “PV” are also examined. Design/methodology/approach The paper reports an empirical investigation involving 125 employees from the banking sector. Data collected was statistically analysed using PLS-graph software version 03.00. Results of the data analysis show relationships uncovered in the existing literature. Findings The creation of RC by employees from the banking sector relies to a large extent on managers’ ability to perceive, analyse and understand activity that is often outside the focus of their attention. Practical implications Managers who explicitly value their RC have a wider vision of their environment. In turn, a wider understanding of the activity in the environment drives the strengthening of the organisation and its RC. Originality/value PV can have a direct impact on the organisation’s appetite for the development of its technology knowledge base, thus contributing to enhance the firm’s absorptive capacity as well as the extent, quality and value of its RC.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".