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Record W1842621406

Organizational Performance and Complementarity in Human Resources Management Practices

2003· article· en· W1842621406 on OpenAlexaffabout
Jacques Barrette, Jules Carrière

Bibliographic record

VenueSSRN Electronic Journal · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsComplementarity (molecular biology)RemunerationOrganizational performanceBusinessHuman resource managementCLARITYStaffingHuman resourcesOrganizational effectivenessKnowledge managementMarketingEconomicsManagementComputer science
DOInot available

Abstract

fetched live from OpenAlex

For more than ten years, much published material has argued that human resource management (HRM) can play a major role in improving organizational performance. Several researchers claim that to exert a significant impact on organizational performance, HR practices need to be integrated or complementary with each other. However, the concept of complementarity suffers from a lack of operational clarity and has been essentially approached from a statistical standpoint that has limited our understanding of the architecture of the overall system of HR practices. On the other hand, several authors assert that the complementarity of HR practices cannot be studied outside its organizational context, especially in the industrial sector. They argue that differences in the nature of activity between service organizations and manufacturing companies are likely to have implications on which practices are adopted and how these practices impact the human and corporate performance of the organizations in question.In its first phase, this study proposes an operational definition of the concept of complementarity that can be used to select which practices to include in an organization’s HRM system. This complementarity has been defined as “the set of practices originating from various areas of HRM activity whose combined application can be rationally justified and empirically demonstrated to have a synergistic effect on organizational performance in a given sector.” Thus, on the basis of this definition, the authors developed a number of “complex items,” incorporating HRM practices from four major operational areas : staffing, remuneration, training and performance assessment. Each of these combinations embodies a link of complementarity between practices, and the additional impact of each combination is our way of measuring its complementarity. This study has the dual purpose of first verifying the hypothesis that “the more practices from different areas of HRM are complementary, the more they will improve organizational performance” (H. 1) and, second, that “it is likely that the impact of complementary practices will vary depending on whether the organizations concerned belong to the manufacturing or service sector” (H. 2).The items to measure organizational performance come from a previous study. These data were derived from questionnaires completed by 177 Canadian firms and the internal reliability varies from .77 to .90. To measure the degree of complementarity between HRM practices, 22 items were developed with an internal reliability of .84. The data were obtained from 238 manufacturing companies and 325 service organizations. The results corroborated Hypothesis 1, indicating that the complementarity of HR practices was responsible for a significant increase in productivity/efficiency, competitive positioning and client acquisition/growth. The results also corroborated Hypothesis 2, showing that, when the two different economic sectors are compared in terms of dependant variables, a higher degree of complementarity is particularly associated in service companies with increased productivity and efficiency, better competitive positioning and a greater number of clients and increased market share. In the case of manufacturing companies, the results indicate that the higher degree of complementarity has particular impact on the first two factors. The results are discussed in the light of current research and the limitations of the research are presented.

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)0.012
metaresearch head score (Gemma)0.042
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 score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.042
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.007
Science and technology studies0.0020.011
Scholarly communication0.0060.005
Open science0.0010.011
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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

Citations1
Published2003
Admission routes2
Has abstractyes

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