Interrogating the Economic, Environmental, and Social Impact of Artificial Intelligence and Big Data in Sustainable Entrepreneurship
Bibliographic record
Abstract
ABSTRACT Artificial intelligence and big data are increasingly being integrated into sustainable entrepreneurship practices. Yet, conventional literature often neglects to critically examine their economic, environmental, and social implications. We conducted a systematic literature review to understand when, how, and for whom artificial intelligence and big data in sustainable entrepreneurship generate value. Our findings suggest that the three dimensions of sustainability—economic, environmental, and social—should be examined through a tri‐level impact prism: the immediate efficiency or transparency gains firms report; the hidden or temporally deferred costs that accumulate; and—notably—the distributional consequences that determine who reaps the benefits and who inherits the burdens. Direct benefits can evolve into costs over time and, if neglected, may reinforce injustices that rebound and erode future gains. Whether the broader trajectory settles on the virtuous or vicious side of that loop depends on five boundary conditions: organizational capabilities, technological maturity, socio‐cultural values, sectoral and regulatory context, and temporal dynamics. Our study advances theory by extending the triple‐bottom‐line lens into a reflexive impact‐by‐cost framework—one that foregrounds rebound effects and justice considerations, injects power, path dependency, and distributional conflict into socio‐technical transition debates, and recasts contingency and dynamic capabilities theories around shifting cost and justice configurations.
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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 | 0.035 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".