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Record W4283785370 · doi:10.55482/jcim.2022.32900

Individual Perceptions of Institutional Uncertainty: Evidence from India

2022· article· en· W4283785370 on OpenAlexvenueno aff
Elizabeth L. Rose, Nandini Lahiri

Bibliographic record

VenueJournal of Comparative International Management · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBureaucracyOptimismAffect (linguistics)BusinessCompliance (psychology)State (computer science)Institutional theoryPerceptionScale (ratio)Institutional changeAccountingPublic relationsPolitical sciencePublic administrationEconomicsPsychologySocial psychologyManagementPolitics

Abstract

fetched live from OpenAlex

The nature of a location’s institutional environment affects businesses and individual citizens, and the extent to which people trust institutions may affect regulatory compliance. We investigate institutional trust among three groups in India: founders of entrepreneurial ventures, second (or later) generation managers of family firms, and salaried employees. Rather than treating the institutional environment as monolithic, we consider six components that represent policy, implementation, and security: central and state governments, bureaucracy, judiciary, army, and police. Based on large-scale, questionnaire-based data, we find evidence that the antecedents of trust differ across both the three groups of respondents and the six aspects of the institutional environment. Institutional environment, entrepreneurs, family firms, India, emerging markets, trust, optimism, uncertainty

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.295
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), 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
Published2022
Admission routes1
Has abstractyes

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