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Record W4386964027 · doi:10.30564/jsbe.v6i4.5874

The Impact of Leadership on the Growth of ICT Companies

2023· article· en· W4386964027 on OpenAlexaff
Mitra Madanchian, Hamed Taherdoost

Bibliographic record

VenueJournal of Sustainable Business and Economics · 2023
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsBecton Dickinson (Canada)University Canada West
Fundersnot available
KeywordsInformation and Communications TechnologyBusinessICTSProductivityMarketingPublic relationsEconomicsEconomic growthPolitical science

Abstract

fetched live from OpenAlex

Nowadays, one of the major purposes of any company is to survive in the competitive business markets. This purpose would be achievable through improving and developing growth in most cases. Prior studies suggest that leadership is a vitally important factor in achieving growth in any company. Some researchers studied leadership topics as one of the significant motivations for developing and improving the growth of a company and agreed that understanding the role of leadership and its effect on the growth of the company is influential. In all countries, Information Communication Technology (ICT) companies are recognized as significant growth drivers by leaving considerable impacts on that. According to the literature, growth is a determining factor for the productivity of any company. Based on the literature of the ICTs, weak and insufficient leadership skills are considered as the main factors that cause failure in achieving the planned growth and development of ICT. Consequently, the goal of this study is to clarify how leadership affects the development of ICT enterprises.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0000.001
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 head0.036
GPT teacher head0.224
Teacher spread0.188 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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