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Record W2997981614 · doi:10.1177/0312896219895065

In the driving seat: Executive’s perceived control over environment

2019· article· en· W2997981614 on OpenAlexaff
Gavin M. Schwarz, Karin Sanders, Dave Bouckenooghe

Bibliographic record

VenueAustralian Journal of Management · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Knowledge Management
Canadian institutionsBrock University
Fundersnot available
KeywordsControl (management)Upper echelonsInformation processingPerceived controlInformation processing theoryTask (project management)BusinessTest (biology)CognitionKnowledge managementPerceptionMarketingPsychologyComputer scienceManagementStrategic managementSocial psychologyEconomicsCognitive psychology

Abstract

fetched live from OpenAlex

This study investigates executives’ perceived control over their environment. Drawing on managerial cognition and upper echelons theory, we test a model that specifies perceived control over environment as made up of organizational routines (i.e. information processing capability and decision comprehensiveness) and executive understanding of performance (i.e. organizational effectiveness and organizational slack). Findings from a scenario study of 46 executives in 14 pharmaceutical firms show perceived control over the internal environment can be explained by information processing capability, and the interactions between organizational routines and resources. Perceived control over the external task environment can be explained by information processing. This difference accounts for the extent to which executives perceive that they can control their environment, adding to the more traditional view focused on the requirements for a strategic fit to environment. JEL Classification: M10, M12, L20

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)0.008
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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