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Record W3110283866 · doi:10.5267/j.msl.2020.11.006

The effect of intellectual capital on competitive advantage: The mediating role of innovation

2020· article· en· W3110283866 on OpenAlexvenueno aff
Ulya Nawaf Obeidat, Bader Yousef Obeidat, Ala’aldin Alrowwad, Muhammad Turki Alshurideh, Ra’ed Masa’deh

Bibliographic record

VenueManagement Science Letters · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Capital and Performance Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual capitalCompetitive advantageRelational capitalStructural capitalHuman capitalBusinessIndustrial organizationCapital (architecture)Individual capitalEconomicsMarketingEconomic capitalEconomic growth

Abstract

fetched live from OpenAlex

The aim of this study is to explore the impact of intellectual capital on the achievement of competitive advantage in organizations. It also aims to explore the mediating impact of innovation on the relationship between intellectual capital and the achievement of competitive advantage. To meet the study’s goals, the researchers reviewed the relevant literature. The researchers also provide definitions for intellectual capital and innovation. They also identified the dimensions of intellectual capital (i.e. structural, human and relational capital). The study identified the types of innovation (incremental and radical innovation) and found that intellectual capital significantly influences on the achievement of competitive advantage. Moreover, the study found that innovation has a mediating influence on the relationship between intellectual capital and the achievement of a competitive advantage.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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

Citations112
Published2020
Admission routes1
Has abstractyes

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