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Record W4391062662 · doi:10.5267/j.uscm.2024.1.002

Company performance model of wholesale carrier service companies in Indonesia: Company capability, co-creation strategy, and external business environment

2024· article· en· W4391062662 on OpenAlexvenueno aff
Edwin Aristiawan, Sucherly Sucherly, Sulaeman Rahman Nidar, Umi Kaltum

Bibliographic record

VenueUncertain Supply Chain Management · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessIndonesianNegotiationService (business)Information and Communications TechnologyMarketingGoods and servicesStructural equation modelingProcess managementIndustrial organizationComputer scienceEconomics

Abstract

fetched live from OpenAlex

The goal of the study was to assess how Indonesian ICT companies that provide wholesale carrier services can manage the external business environment and improve their performance with the help of their co-creation strategy and corporate skills. Using partial quadratic structural equation modeling (PLS-SEM) to evaluate the research hypothesis, the study concentrated on 45 Indonesian ICT companies offering wholesale carrier services. The outcomes demonstrated how the company's co-creation strategy and capabilities increased its effectiveness and greatly improved its capacity to negotiate the external business climate. According to these results, enhancing organizational capacities and putting co-creation techniques into practice should be Indonesian ICT enterprises' top priorities if they want to improve performance and successfully navigate the external business environment. Involving consumers, users, or other stakeholders in the design and development of digital goods or services is known as the co-creation strategy. It can happen in a lot of different ways, such hackathons, online platforms, workshops, or prototype sessions.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.021
GPT teacher head0.245
Teacher spread0.224 · 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.

Study designSimulation or modeling
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

Citations2
Published2024
Admission routes1
Has abstractyes

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