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Record W4388315849 · doi:10.5267/j.uscm.2023.8.017

The effect of intellectual capital towards sustainability of business performance mediated by eco-product innovation & external learning: The Indonesian bottled drinking water industries

2023· article· en· W4388315849 on OpenAlexvenueno aff
Daniel D. Kameo, Harijono Harijono

Bibliographic record

VenueUncertain Supply Chain Management · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsnot available
Fundersnot available
KeywordsCompetitor analysisIntellectual capitalBottled waterBusinessStructural equation modelingProduct innovationIndustrial organizationSustainabilityIndonesianProduct (mathematics)Competitive advantageMarketingBridging (networking)New product developmentKnowledge managementComputer science

Abstract

fetched live from OpenAlex

This study developed and empirically corroborated a structure explicitly propositioning eco-product innovation and external learning, correspondingly bridging the link between intellectual capital and sustainability performance. The theory of dynamic capabilities and knowledge-based view proposed eco-product creation and external education as mediators that developed a new conceptual model. This study utilized the Partial Least Square-Structural Equation Modelling (PLS-SEM method) to examine one hundred sixty-two Indonesian bottled drinking industries, exposing their attentiveness to cooperate in the study. More essentially, the study unraveled new justifications by scrutinizing external learning's role as more influential than eco-product innovation. Based on the practical viewpoint, intellectual capital helped exchange knowledge and information with distributors, suppliers, and competitors. Thus, the complete assimilation and transformation of their understanding could improve business 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.

How this classification was reachedexpand

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)0.004
metaresearch head score (Gemma)0.001
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 score0.496
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
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 head0.011
GPT teacher head0.225
Teacher spread0.214 · 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 teacher head, 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

Citations10
Published2023
Admission routes1
Has abstractyes

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