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Record W2110541224 · doi:10.1002/hrm.21527

Internal Commitment or External Collaboration? The Impact of Human Resource Management Systems on Firm Innovation and Performance

2013· article· en· W2110541224 on OpenAlexaff
Yu Zhou, Ying Hong, Jun Liu

Bibliographic record

VenueHuman Resource Management · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsMcMaster University
FundersRenmin University of ChinaNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsAmbidexterityGroup cohesivenessBusinessModerationKnowledge managementHuman resource managementHuman resourcesResource (disambiguation)Work systemsIndustrial organizationManagementWork (physics)Computer sciencePsychologyEconomicsEngineering

Abstract

fetched live from OpenAlex

Abstract Complementing previous research that showed a positive effect of general human resource management (HRM) systems on general firm performance, this article undertakes an integrative approach to compare the main effects and examine the interaction effects of two particular HRM systems on influencing firm innovation and performance. Using data from 179 organizations in China, we found that both the commitment‐oriented system, which emphasized internal cohesiveness, and the collaboration‐oriented system, which was intended to build external connections, contributed to firm innovation and, subsequently, bottom‐line performance. We also found an attenuated interaction between the two HRM systems in predicting firm innovation. We employed a mediated‐moderation path model to extricate the relationships. Results suggested that organizations that implemented both HRM systems to promote innovation might face ambidexterity challenges. Ideas for future research and practical implications 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 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)0.004
metaresearch head score (Gemma)0.018
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 score0.004
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.026
GPT teacher head0.278
Teacher spread0.251 · 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

Citations172
Published2013
Admission routes1
Has abstractyes

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