Linking procedural memory with organizational learning through knowledge corridors
Bibliographic record
Abstract
Purpose This paper argues that the combination of factors that facilitate focal and peripheral vision represent two distinct types of knowledge corridors. While focal vision may help detect signals that relate to the current objectives of the firm, peripheral vision is directed to non-central issues that may provide signals that relate to emerging trends in the external environment. Ambidexterity vision refers to the tension between these two different business visions within the same organization. This paper aims to examine the significance of procedural memory to an organization’s ambidexterity vision, along with investigating the impact procedural memory has on organizational learning. Design/methodology/approach The research conducted in this study addresses the following two questions: Does the enhancement of procedural memory result in the development of superior ambidexterity vision? Does the simultaneous development of both focal and peripheral vision enhance organizational learning? These research questions are studied by conducting an empirical investigation involving data provided by 203 banking employees. These data are analysed using a structural equation modelling approach. Findings Analysis of the data provides support for the existence of a relationship between ambidexterity vision and organizational learning. Practical implications The findings suggest that the re-direction of managers’ perceptions to supporting and enhancing an ambidexterity vision is likely to result in an improvement in their ability to identify emerging trends, new and emerging customers and potential shifts in customer needs and tastes. Originality/value This work suggests that the re-direction of managers’ perceptions to supporting and enhancing an ambidexterity vision is likely to result in an improvement in their ability to identify emerging trends, new and emerging customers and potential shifts in customer needs and tastes.
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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 | 0.018 |
| 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 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".