Realising value from big data analytics: The process of affordance actualisation
Bibliographic record
Abstract
Recent developments in big data analytics (BDA) enable financial institutions to optimize their decision-making processes. However, many BDA projects fail due to a lack of understanding of the value related to the realisation process . Guided by affordance actualisation theory, this article uses a critical realist case study approach to examine BDA affordance actualisation in a Malaysian-owned regional bank. Data collection involved an online survey, semi-structured interviews, and archival reviews. Using four BDA affordances specific to the banking industry , along with contextual mechanisms shaping organisational actions, interactions, and structures, our study highlights the importance the process of building BDA capabilities to realise BDA value. By highlighting the iterative and dynamic nature of the affordance actualisation process, this research extends theoretical insights into how organisations build BDA capabilities to realise value.This multi-layered research provides practitioners in banking and other sectors with actionable insights to enhance BDA implementation and realise its potential value.
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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 | 0.034 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.017 | 0.027 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".