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Record W4387343575 · doi:10.1108/itp-12-2022-0944

Knowledge management as an asset for operational processes in marginal healthcare centers

2023· article· en· W4387343575 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.

Bibliographic record

VenueInformation Technology and People · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsHealth careKnowledge managementKnowledge sharingBusinessLeverage (statistics)Structural equation modelingAsset (computer security)Process managementComputer scienceEconomics

Abstract

fetched live from OpenAlex

Purpose This research paper aims to explore the added value of knowledge management (KM) and its antecedents for innovation and organizational performance (OP) in marginal healthcare organizations. Design/methodology/approach Using insights from the resource-based view and knowledge-based theory of the firm, the model explains the effects of technology capabilities (TC) and organizational culture (OC) on the KM process, process innovation (PIN), administrative innovation (AIN) and OP. The authors used partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to analyze data collected from 168 healthcare practitioners in Cameroon using a survey. Findings The authors reveal that TC and OC positively impact some KM components. Knowledge sharing (KS), knowledge acquisition (KA) and responsiveness to knowledge (RK) influence PIN, while only PIN and KA influence OP. FsQCA provided several configurations that lead to high OP within healthcare centers. As a result, the results are adaptable to any healthcare center that wishes to set up one or more KM processes. Research limitations/implications Given that the results will help the health workforce make concerted decisions about medical care, the authors contribute significantly to the definition and optimization of KM in healthcare by implementing various processes and policies to ensure the continued existence of high-quality and outstanding healthcare systems. The KM propositions will enable healthcare centers to: (1) improve the quality of patient care through collegiality in medical practice; (2) optimize processes in the patient care chain; and (3) leverage knowledge gained though knowledge sharing among the medical team. The propositions open up avenues for future research in addition to providing practical implications for healthcare center practitioners. Originality/value This study sheds new empirical light on the relationships between KM antecedents and processes, innovation and OP in healthcare centers. This research is one of the few to examine the relationship between TC, OC, KM processes, innovation and OP in developing countries. This paper aims to fill this gap and inform future research concerning KM in the healthcare sector. Further, this study goes beyond testing the PLS-SEM approach's hypotheses by applying fsQCA to provide practical and comprehensive knowledge on how to increase the efficiency of a healthcare center through KM.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.901
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.016
GPT teacher head0.276
Teacher spread0.260 · 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