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Record W4294238219 · doi:10.18280/ijsdp.170521

Impact of Innovation Types on Enterprises Sales Growth: Evidence from Kosovo

2022· article· en· W4294238219 on OpenAlexvenueno aff
Shaqir Elezaj, Ramiz Livoreka

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2022
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessMontenegroGovernment (linguistics)EntrepreneurshipEmpirical researchPrivate sectorProduct (mathematics)Transition countriesMarketingProcess (computing)Industrial organizationEconomic growthEconomicsFinanceRegional science

Abstract

fetched live from OpenAlex

This paper aims to analyze the impact of the types of innovations in increasing the sales of Kosovo's enterprises. The findings of this paper confirm the hypotheses that organizational innovations, product/process innovations and marketing innovations have a positive impact on increasing the level of sales. This empirical research was conducted with enterprises in Kosovo, so the main limitation of this research is the non-inclusion of enterprises of Albania, Montenegro, North Macedonia, Bosnia and Herzegovina, etc. The involvement of enterprises from these countries would provide a broader picture of the research issues for transition countries. This empirical research is of particular importance because it studies the impact of types of innovation on increasing the sales level of enterprises in transition countries. Kosovo suffer from weak enterprises sector and underdeveloped entrepreneurship sector. Without the development of an innovative system of the private sector and the entrepreneurial sector, Kosovo cannot overcome the transition process. Therefore, this paper could be a reference point for the government policies of Kosovo, which should support all enterprises in the development of all types of innovations.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.025
GPT teacher head0.276
Teacher spread0.251 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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