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Record W3006042031 · doi:10.1108/jabs-02-2019-0041

Managerial skills and performance in small businesses: the mediating role of organizational climate

2020· article· en· W3006042031 on OpenAlexaff
Gholamhossein Mehralian, Mohammad Peikanpour, Maryam Rangchian, Hamed Aghakhani

Bibliographic record

VenueJournal of Asia Business Studies · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Leadership and Management Strategies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsStructural equation modelingBusinessContext (archaeology)Affect (linguistics)MarketingConceptual modelOriginalityOrganizational performancePharmacySet (abstract data type)Knowledge managementBusiness administrationPsychologySocial psychology

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to develop a conceptual model to determine whether organizational climate (OC) mediates the effect of managerial skills (MSs) on business performance in small businesses, such as pharmacies. Design/methodology/approach The model proposed in this research was tested using separate questionnaires specifically designed for managers, employees and clients. The data set consists of responses from 301 managers, 470 clients and 328 employees from community pharmacies in Tehran, capital of Iran, which were analyzed using structural equation modeling. Findings Although the results indicated no significant direct relationship between MSs and pharmacy performance (PP), they also confirmed that having a context-appropriate set of MSs can positively affect PP via the mediating effect of OC. Originality/value This is the first study investigating how MSs improve performance in retail pharmacies. Although this research focuses specifically on small businesses in the pharmaceutical industry, it nevertheless contributes to the literature by showing the importance of OC.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.216
Teacher spread0.198 · 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

Citations30
Published2020
Admission routes1
Has abstractyes

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