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Record W3047529065 · doi:10.2308/jogna-19-014

The Right Stuff: Are Not-For-Profit Managers Really Different?

2020· article· en· W3047529065 on OpenAlexaff
Krista Fiolleau, Theresa Libby, Linda Thorne

Bibliographic record

VenueJournal of Governmental & Nonprofit Accounting · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicExperimental Behavioral Economics Studies
Canadian institutionsYork UniversityUniversity of Waterloo
Fundersnot available
KeywordsConscientiousnessExtraversion and introversionSocial psychologyLocus of controlAmbiguityEntitlement (fair division)PsychologyHarmPublic sectorProfit (economics)BusinessPublic relationsEconomicsPersonalityMicroeconomicsBig Five personality traitsPolitical scienceComputer science

Abstract

fetched live from OpenAlex

ABSTRACT In response to public pressure for accountability in the not-for-profit (NFP) sector, attempts have been made to adopt for-profit controls. These have generated mixed results. While many have argued that employees attracted to the NFP sector are “different,” little prior empirical evidence backs up this claim. To address this gap, we review the literature to identify claimed individual characteristics that might differ and use the survey method to examine whether these differences exist between the groups of responding managers working in the NFP and for-profit sectors. NFP respondents exhibit lower levels of narcissism, lower levels of entitlement, less extroversion, and a more externally oriented locus of control than their for-profit counterparts. In exploratory multivariate analysis, best predictors of NFP membership include extroversion, locus of control, conscientiousness, and moral reasoning. Rather surprisingly, the groups did not differ on altruism or tolerance for ambiguity. Implications for control system design are discussed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.027
GPT teacher head0.302
Teacher spread0.274 · 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.

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

Citations5
Published2020
Admission routes1
Has abstractyes

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