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Record W4416598531 · doi:10.1017/bpp.2025.10008

Nudging and boosting reasonable use of public products: two experiments from China

2025· article· en· W4416598531 on OpenAlexaff
Shuwei Zhang, Zibing Zhang, Shuang Li

Bibliographic record

VenueBehavioural Public Policy · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsInstitute on Governance
FundersInstitute of Psychology, Chinese Academy of SciencesNational Natural Science Foundation of ChinaSun Yat-sen UniversityFundamental Research Funds for the Central UniversitiesChinese Academy of SciencesNational Office for Philosophy and Social Sciences
KeywordsBoosting (machine learning)ReplicateCorporate governanceNudge theoryDefaultChoice architecture

Abstract

fetched live from OpenAlex

Abstract Behavioral instruments have unique advantages in certain governance contexts for the reasonable use of public products. Drawing on bounded rationality, we compare two major behavioral instruments – nudging and boosting – and experimentally test their effectiveness in promoting reasonable use of public products. We select the default option (nudging) and future orientation (boosting) as specific instruments. In Study 1, we conduct a laboratory experiment and find that (1) both the default option and future orientation reduce free electricity usage; (2) the immediate effect of the default option is greater than that of future orientation, but its delayed effect is smaller; and (3) the combination strategy is more effective than any single intervention. In Study 2, we conduct a field experiment targeting reasonable use of public toilet paper and basically replicate the results of the laboratory experiment. These findings reinforce our confidence in the effectiveness of nudging and boosting and suggest the possibility of bridging behavioral science with governance theory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.970

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.137
GPT teacher head0.382
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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