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Record W4394895983 · doi:10.5267/j.uscm.2024.2.002

Improving co-creation strategies and competitive strategies to achieve business performance

2024· article· en· W4394895983 on OpenAlexvenueno aff
Edwin Aristiawan, Sucherly Sucherly, Sulaeman Rahman Nidar, Umi Kaltum

Bibliographic record

VenueUncertain Supply Chain Management · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsCompetitive advantageBusinessService (business)The InternetInformation and Communications TechnologyIndustrial organizationProcess managementMarketingComputer science

Abstract

fetched live from OpenAlex

The foundation of this study is the findings from implementing co-creation and competitive strategies to increase the operation efficiency of wholesale service companies of Information Communication Technology (ICT) of network, internet and infrastructure in Indonesia. It is necessary to review journal papers that discuss business performance in wholesale service companies of ICT of network, internet, and infrastructure. The study aims to find out whether a co-creation strategy and competitive strategy can drive business performance. The research method uses a quantitative approach using data collected using 54 random samples from a population and the data were analyzed using SEM-PLS. Results showed that both co-creation and competitive strategy affected business performance, but the competitive strategy was dominant. The insights gathered from this study should help wholesale service companies of ICT continually enhance their co-creation and competitive strategies, which will boost their overall 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 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)0.005
metaresearch head score (Gemma)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0080.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.013
GPT teacher head0.283
Teacher spread0.270 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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