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Record W4312532380 · doi:10.55365/1923.x2022.20.30

The role of Creative Potential in the Project Management Process for the Implementation of the Company's Strategies

2022· article· en· W4312532380 on OpenAlexvenueno aff
Maryna Korsunska, Вероніка Буторіна, Kamran Abdullayev, Yuriy Kravtsov, Lesia Ustymenko

Bibliographic record

VenueReview of Economics and Finance · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegional Economic Development and Innovation
Canadian institutionsnot available
FundersErasmus+
KeywordsContext (archaeology)Process (computing)Relevance (law)Process managementQuality (philosophy)BusinessSubject matterManagement scienceKnowledge managementComputer scienceEngineeringEconomics

Abstract

fetched live from OpenAlex

This scientific study considers the issues of assessing the role of creative potential in the project management process to implement company strategies. The relevance of the subject matter is determined by the need to introduce creative solutions to improve the quality of company management in the context of implementing the selected management strategies. The purpose of this study is to determine the role of the company's creative potential upon managing the company's projects and expanding the potential opportunities for the implementation of its development strategy. The leading approach in this study is a combination of analytical and logical research methods of the subject matter. Identifying the role of creative potential in the functioning of a company, regardless of its sector, and setting the main aspects of innovative activity within the company in implementing creative solutions in its operation, constitutes the main results obtained in this study. Possibilities for further studies in this area are necessitating for in-depth researching options and prospects of introducing innovative solutions into the management processes of various companies, regardless of their sector, in the same way as studying the influence of such creative potential on companies growth and their transition to a new level of development strategy.

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)0.025
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.048
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.002
Science and technology studies0.0040.007
Scholarly communication0.0150.007
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.019
GPT teacher head0.265
Teacher spread0.246 · 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 designNot applicable
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

Citations16
Published2022
Admission routes1
Has abstractyes

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