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Record W4392755628 · doi:10.33423/jabe.v26i1.6864

The Role of Intellectual Capital on Hospital Performance: Evidence at Facility-Level

2024· article· en· W4392755628 on OpenAlexvenueno aff
Mark Chun, Michael P. Seagraves, Charla Griffy‐Brown, Doug Leigh

Bibliographic record

VenueJournal of Applied Business and Economics · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual capitalBottleneckConstruct (python library)ProductivityAffect (linguistics)BusinessCapital (architecture)Sample (material)Social capitalQuality (philosophy)Structural capitalIndividual capitalIndustrial organizationMarketingKnowledge managementHuman capitalPsychologyOperations managementFinancial capitalEconomicsEconomic growthFinanceSociologySocial scienceComputer science

Abstract

fetched live from OpenAlex

Based on the social capital theory, this study argues that intellectual capital, defined as knowledge and capabilities within the organization, significantly affect hospital performance. This study examines the impact of intellectual capital on four key hospitals’ performance metrics, i.e. quality, productivity, length of stay, and satisfaction. Using a sample of 34 hospital facilities’ operational reports to construct hospital performance and individual-level survey of 143 individuals across these 34 facilities to construct intellectual capital during 2018, this study finds that intellectual capital significantly increases employee productivity and reduces patient stay length. This study contributes to the literature by providing evidence that intellectual capital plays an important role in reducing bottleneck for hospitals to meet increasing demand in healthcare services.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.894
Threshold uncertainty score0.445

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 head0.017
GPT teacher head0.192
Teacher spread0.175 · 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 designOther design
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
Published2024
Admission routes1
Has abstractyes

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