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Record W2124885289 · doi:10.5267/j.msl.2013.09.025

Measuring the relative importance of strategic thinking dimensions in relation to counterproductive behavior

2013· article· en· W2124885289 on OpenAlexvenueno aff
Afsaneh Zamani Moghaddam, Faezeh Amirkamali

Bibliographic record

VenueManagement Science Letters · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicOrganizational Leadership and Management Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsRelation (database)Counterproductive work behaviorPsychologySocial psychologyMicroeconomicsBusinessComputer scienceEconomics

Abstract

fetched live from OpenAlex

The purpose of this paper is to explore the relative importance of strategic thinking dimensions in prediction of counter-productive behavior.The research method is based on a descriptive-Survey research.After collecting the questionnaires from 73 top managers and 110 staffs, the correlations between strategic thinking dimensions and counterproductive behavior were calculated.The relative importance method was used to calculate the relative weight of each dimension of strategic thinking in prediction of counterproductive behaviors.The results show that the strategic thinking of top managers is associated with their counterproductive behavior (correlation coefficient -0.38).Furthermore, The results of the Relative Importance Method indicate that the relative importance of each dimension of strategic thinking in prediction of counterproductive behavior is not the same.System perspective with 31.1% has the highest importance and hypothesis driven with 11.7% has the lowest weight.Intent focus, thinking in time and intelligent opportunism predict 14.1%, 13.3%, and 29.8% of counter-productive changes, respectively.

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)0.005
metaresearch head score (Gemma)0.036
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 score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.232
Teacher spread0.194 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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