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Record W4384790202 · doi:10.26905/jmdk.v10i2.8997

Development of Innovation Capability Model: Analysis of e-CRM and Risk Perceptions of Covid-19 in Bogor City MSMEs

2022· article· en· W4384790202 on OpenAlexaboutno aff
Yanita Ella Nilla Chandra, Husnil Barry

Bibliographic record

VenueJURNAL MANAJEMEN DAN KEWIRAUSAHAAN · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessQuarter (Canadian coin)MarketingOrder (exchange)Variety (cybernetics)Scale (ratio)Industrial organizationGeographyFinanceComputer science

Abstract

fetched live from OpenAlex

The phenomenon of the commercial revolution 4.0 encourages MSME actors to comply with current trends in order that the goods produced may be competitive. Creative enterprise merchandise require innovation competencies with a view to produce advanced merchandise. This study takes a look at discusses improvement troubles withinside the MSME quarter that are despite the fact that restricted in conventional organization control, inadequate fine of human assets, manufacturing scale and strategies, low innovation functionality and confined get admission to to economic establishments, especially banking. The growth the variety of enterprise actors primarily based totally on facts from the Cooperatives Office the town of Bogor in 2021 with a mean growth of 6.141% of all forms of organizations isn't always matched with the aid of using trends the subject of innovation to assist enhance the advertising overall performance of SMEs the town of Bogor. Where in enhancing the capacity of innovation, it's miles anticipated to synergize with Customer Relationship Marketing or CRM such as numerous information, consumer involvement, long-time period partnerships and technology-primarily based totally trouble fixing in developing enterprise 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 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.046
GPT teacher head0.328
Teacher spread0.282 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueJURNAL MANAJEMEN DAN KEWIRAUSAHAANSame topicSMEs Development and Digital MarketingFrench-language works237,207