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Record W2606662333 · doi:10.1108/jaoc-04-2016-0023

Eco-control change and environmental performance: a longitudinal perspective

2017· article· en· W2606662333 on OpenAlexaff
Jean‐François Henri, Marc Journeault, Carl Brousseau

Bibliographic record

VenueJournal of Accounting & Organizational Change · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSeriousnessScope (computer science)OriginalityEnvironmental changeScale (ratio)Sample (material)Perspective (graphical)Control (management)Value (mathematics)Environmental resource managementBusinessAccountingMarketingClimate changePsychologyEconomicsSocial psychologyGeographyManagementPolitical scienceComputer scienceEcologyStatisticsMathematics

Abstract

fetched live from OpenAlex

Purpose The aim of this longitudinal study is to quantitatively examine the impact of changes in the mix of eco-controls. More specifically, the purpose of this study is twofold. First, it investigates the nature of change occurring in eco-controls by analyzing three attributes of change, namely, direction of change, scope of change and scale of change. Second, this study investigates the impact of changes in eco-controls by examining to what extent the three attributes of change specifically explain environmental performance. Design/methodology/approach Longitudinal survey approach is used to collect data from a sample of manufacturing firms at two points in time. Findings The results suggest three main conclusions: changes leading to more (less) importance devoted to eco-controls within the organization contribute positively (negatively) to environmental performance; concerted changes on all aspects of the mix of eco-controls contribute more to environmental performance than piecemeal changes on specific aspects of the mix; and the aspect which contributes to environmental performance is not the scale of that change but the mere presence of a credible signal which reflects the seriousness of the intentions. Originality/value This paper contributes to management accounting change literature by breaking down the nature of change of management control practices in attributes (direction, scope and scale) and examining their specific impact on performance.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.006
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.006
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.224
Teacher spread0.203 · 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 teacher head, 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

Citations25
Published2017
Admission routes1
Has abstractyes

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