MétaCan
Menu
Back to cohort
Record W4300983399 · doi:10.1002/bse.3210

Toward holistic corporate sustainability—Developing employees' action competence for sustainability in small and medium‐sized enterprises through training

2022· article· en· W4300983399 on OpenAlexfundno aff
Sophia Schröder, Arnim Wiek, Steffen Farny, Philip Luthardt

Bibliographic record

VenueBusiness Strategy and the Environment · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSustainabilitySustainability organizationsCompetence (human resources)BusinessWorkforceSustainability scienceCorporate sustainabilityKnowledge managementManagementEconomicsEconomic growthComputer science

Abstract

fetched live from OpenAlex

Abstract To advance holistic corporate sustainability in small and medium‐sized enterprises (SMEs) requires employees to fully engage in sustainability efforts, which, in return, means to develop employees' action competence for sustainability. Little empirical evidence, however, exists on how to do this considering well‐known constraints SMEs face (time, expertise, resources). We present a transdisciplinary project that developed, delivered, and evaluated a sustainability training for the workforce of the Bohlsener Mühle, an SME that has pioneered corporate sustainability in Germany. The training was piloted for the business' apprentices and combined different learning modes to build participants' sustainable action competence. The pre‐post evaluation, supported by observations and qualitative interviews, revealed that employees' action competence for sustainability can be fostered through such trainings and is most effective if organizational factors that enable a corporate culture of sustainability are aligned. We conclude that a human‐centered and action‐oriented approach to training is needed to unleash the full potential of the workforce to advance corporate sustainability.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.135
GPT teacher head0.339
Teacher spread0.204 · 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 designTheoretical or conceptual
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

Citations33
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBusiness Strategy and the EnvironmentSame topicSustainability in Higher EducationFrench-language works237,207