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Record W3106787732 · doi:10.1111/joms.12671

Narrow‐Framing and Risk Preferences in Family and Non‐Family Firms

2020· article· en· W3106787732 on OpenAlex
Hanqing Fang, Esra Memili, James J. Chrisman, Linjia Tang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Management Studies · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsFraming (construction)InternationalizationChinaEntrepreneurshipBusinessInvestment decisionsFamily businessMarketingFinancePolitical scienceInternational tradeBehavioral economics

Abstract

fetched live from OpenAlex

Abstract Building upon prospect theory’s concept of narrow‐framing, we explore family firms’ risk preferences across multiple decisions in corporate entrepreneurship. We argue that family firms’ decisions are less likely to be narrowly framed (more likely to be made as a group rather than in isolation) compared to non‐family firms. Examining the interaction between two risky decisions (internationalization and R&D investment) in two samples of publicly traded firms in the USA and China confirms our hypotheses. Family firms appear more likely than non‐family firms to diversify risk when making multiple decisions concerning corporate entrepreneurship. However, given inferior performance, risk taking across multiple decisions in family firms is positively related.

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.

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)0.000
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 score0.084
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.036
GPT teacher head0.253
Teacher spread0.217 · 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