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Record W2341625722 · doi:10.1111/1748-8583.12097

Unpacking the black box: understanding the relationship between strategy, HRM practices, innovation and organizational performance

2016· article· en· W2341625722 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueHuman Resource Management Journal · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsMcMaster University
FundersMcMaster University
KeywordsUnpackingBusinessBlack boxKnowledge managementProcess managementOrganizational performanceOperations managementIndustrial organizationMarketingComputer scienceEngineering

Abstract

fetched live from OpenAlex

The links between HRM practices and organizational performance have received considerable research attention as significant contributors to sustained competitive advantage. However, the processes that link HRM practices and organizational performance are not fully understood. This study examines the relationships between skill‐enhancing, motivation‐enhancing and opportunity‐enhancing bundles of practices, innovation and organizational performance, and looks at the mediating effect of innovation over time at the workplace level. The results indicate that the temporal pathway from skill‐enhancing practices to innovation to organizational performance is positive and significant even after controlling for reverse causality. Strategic activity is also explored and is found to be a significant moderator. This is an indication of the importance of aligning strategy with HRM practices and innovation to achieve improved organizational performance outcomes.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.644
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.000
Scholarly communication0.0010.001
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 head0.132
GPT teacher head0.295
Teacher spread0.163 · 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