Implementing Big Data Analytics in Small Firms: A Situated Human Practice Approach
Bibliographic record
Abstract
Abstract This study identifies and addresses an important gap in the nascent literature on big data analytics, using a longitudinal case study to investigate the implementation and application of big data analytics into a small firm specialized in transport logistics. Our research is rooted in Practice Theory, considering the implementation of new technologies in organizations as a result of multiple social negotiations, interpretations, and interactions. Our findings indicate the importance and centrality of human factors in decision‐making and operational implementation, technology representing only a means to a clearly specified and collectively assumed objective. Big data analytics adoption and use in the case‐study firm represents a gradual process, with each stage justified by the need to solve the problems caused by heavy and unpredictable road traffic. This approach validates the entrepreneurial effectuation model, which defines a firm's strategy as a fragmented but continuous effort to find and implement effective solutions to the market challenges encountered.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 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".