How Does Big Data Analytics Shape Human Heuristics Adaptation in Strategic Decision-Making? A Perspective of Environmental Uncertainty Contingencies
Bibliographic record
Abstract
As big data analytics (BDA) has increasingly influenced strategic decision-making, researchers and practitioners are continuously debating the roles of humans versus machines in making decisions. Our multiple-case analysis examines how BDA shapes decision makers’ adaptation of heuristics in response to different dimensions of environmental uncertainty (i.e., complexity versus dynamism). Contrary to prior literature that suggests that BDA supplants human heuristics or impedes humans from adapting their heuristics, our findings underscore that BDA shapes the adaptation of heuristics through three distinct modes: alternative-reorienting, cue-patching, and relation-conditioning. Specifically, BDA shapes heuristics adaptation through the hybrid mode of cue-patching and relation-conditioning when environmental complexity is high and through the alternative-reorienting mode when environmental dynamism is high. However, when environmental complexity and dynamism are both high, the uncertainty in the environment may render BDA less effective, and substantial business acumen is required to adapt heuristics further. In addition, our findings reveal a pinning mechanism of BDA—that is, by keeping one component of human heuristics unchanged, a fixed point of comparison is created for evaluating the changes to other components of the heuristics. This study contributes to the literature by theorizing how BDA shapes heuristics adaptation and adds value to strategic decision-making in uncertain environments.
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How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".