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Record W3127026029 · doi:10.1108/vjikms-06-2020-0110

Intellectual capital stocks and flows: examining the mediating roles of social capital and knowledge transfer

2021· article· en· W3127026029 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

VenueVINE Journal of Information and Knowledge Management Systems · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsIntellectual capitalKnowledge transferSocial capitalHuman capitalKnowledge managementEmbodied cognitionIndividual capitalBusinessStructural capitalOrganizational learningKnowledge value chainMediationSocial reproductionFinancial capitalEconomicsSociologyComputer scienceEconomic growth

Abstract

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Purpose This study aims to develop and empirically test a “stocks and flows”-based model of intellectual capital (IC) that examines how human-embodied knowledge (i.e., human capital) can be transformed into organisational non-embodied knowledge (i.e., organisational capital) through the mediating roles of social capital and the knowledge management (KM) process of knowledge transfer. Design/methodology/approach A structural model was developed and empirically tested using a survey data set of 295 questionnaires collected from the “knowledge-intensive” pharmaceutical manufacturing industry in Jordan. Findings Empirical results revealed that each of human capital, social capital and knowledge transfer has a positive and significant effect on organizational capital. In particular, knowledge transfer emerged as having the strongest effect. Social capital, on the other hand, emerged as having a positive and significant effect on knowledge transfer. Mediation analysis revealed that while human capital significantly affects organizational capital, such an effect is partially and significantly mediated by each of social capital as well as knowledge transfer. Practical implications This study provides senior managers in pharmaceutical manufacturing firms with valuable insights pertaining to the development of their IC, in terms of how to exploit their knowledge stocks (i.e. human-embodied knowledge and organizational non-embodied knowledge) through managing knowledge flows between them. This was shown to be significantly leveraged by the mediating roles of social capital as well as knowledge transfer. Originality/value This study provides important theoretical and empirical contributions to the extant literature in a number of ways. It provides better understanding of the intricate linkages among IC dimensions, and how these play complementary roles in organizational capital development. It has also provided important empirical evidence highlighting the vital mediating roles of social capital and knowledge transfer in facilitating knowledge flows, which aid in transforming human-embodied knowledge stocks into organizational-embodied ones.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.333
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.014
GPT teacher head0.217
Teacher spread0.203 · 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