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Record W2030837798 · doi:10.1108/14626001011041229

Is there a relationship between information technology adoption and human resource management?

2010· article· en· W2030837798 on OpenAlexaffabout
Wendy R. Carroll, Terry H. Wagar

Bibliographic record

VenueJournal of Small Business and Enterprise Development · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Strategy and Innovation
Canadian institutionsSaint Mary's UniversityUniversity of Prince Edward Island
Fundersnot available
KeywordsGeneralizability theoryRestructuringBusinessHuman resource managementNova scotiaOriginalityKnowledge managementHuman resourcesValue (mathematics)Resource (disambiguation)Investment (military)MarketingFunction (biology)Industrial organizationManagementEconomicsQualitative researchPsychologyComputer science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to investigate whether the adoption of information technology is associated with human resource management and organizational restructuring. Design/methodology/approach SMEs in Nova Scotia were visited and complete data from 130 firms were obtained. Findings The rate of IT adoption varies noticeably among SMEs in Nova Scotia, with less than 10 per cent being high adopters. IT adoption was strongly associated with employer size, organizational restructuring and investment in human resource management. Research limitations/implications The data are cross‐sectional and the generalizability of the results may be limited. While it was found that HRM and IT were strongly related, it was whether they are associated with higher employer performance was not examined. Practical implications The results suggest that IT may play a role in enhancing the human resource function. Originality/value There has been little research exploring the link between HRM and IT adoption, particularly among small firms. This paper fills some of the gaps.

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)0.001
metaresearch head score (Gemma)0.013
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 score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.022
GPT teacher head0.221
Teacher spread0.200 · 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

Citations33
Published2010
Admission routes2
Has abstractyes

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