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Record W4385973888 · doi:10.5267/j.uscm.2023.7.016

An empirical study of critical success factors in implementing knowledge management systems (KMS): The moderating role of culture

2023· article· en· W4385973888 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueUncertain Supply Chain Management · 2023
Typearticle
Languageen
FieldComputer Science
TopicOrganizational and Employee Performance
Canadian institutionsnot available
Fundersnot available
KeywordsOrganizational cultureKnowledge managementBusinessEmpirical researchProcess (computing)Service (business)MarketingInformation technologyModerationPublic relationsPsychologyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

This research focuses on the moderating effect of culture on the relationships between KMS and other variables affecting KMS in the service industry. The effects of a number of variables on KMS were examined via analysis and hypothesis testing. These variables included culture; people; process; strategy; and technology. The results show that culture and people have a substantial impact on KMS's performance, emphasizing the need of cultivating a supportive company culture and empowering employees. Furthermore, strategy and technology were shown to be critical in allowing effective knowledge management practices in the service industry. The research also investigates the moderating impacts of culture on these linkages, demonstrating that culture modulates the impact of process, technology, and strategy on KMS. However, it was shown that the interplay between culture and people did not substantially alter the link between people and KMS. These results provide useful insights for firms looking to improve their knowledge management methods, underlining the need to take culture into account and aligning it with strategic goals and technology solutions. While the study adds to our understanding of knowledge management in the service industry, further research is needed to investigate other elements and situations. Overall, this research has practical significance for firms looking to enhance their knowledge management activities and overall organizational performance.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.023
GPT teacher head0.317
Teacher spread0.294 · 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