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Record W2519837832 · doi:10.1002/cjas.1372

Establishing socially responsible workplaces: Need perceptions and institutional forces acting on MSE owners in Tanzania

2016· article· en· W2519837832 on OpenAlexvenueno aff
Katharina Poetz

Bibliographic record

VenueCanadian Journal of Administrative Sciences / Revue Canadienne des Sciences de l Administration · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsTanzaniaNormativeLeverage (statistics)Corporate social responsibilityInstitutional theoryPerceptionBusinessSustainabilitySustainable developmentSocial responsibilityPublic relationsMarketingEconomicsPolitical scienceManagementLawSocioeconomicsPsychology

Abstract

fetched live from OpenAlex

Abstract Small businesses are increasingly expected to contribute to sustainable development. Drawing on needs theory and institutional theory, this paper investigates drivers of and barriers to socially responsible workplaces that can fulfill the existence, relatedness, and growth needs of people working in these organizations. The findings generated from an in‐depth study of small enterprises in Tanzania indicate that owners’ and employees’ changing needs contribute to workplace creation but nevertheless often remain unfulfilled. The resulting tensions pose a challenge for sustainable development in Africa's emerging economies. This is discussed with regard to solutions that leverage the normative common ground between traditional values and Western notions of corporate social responsibility (CSR), taking into account the pressures exercised by regulatory and mimetic institutional forces. Copyright © 2016 ASAC. Published by John Wiley & Sons, Ltd.

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)0.003
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.096
GPT teacher head0.318
Teacher spread0.222 · 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 designQualitative
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

Citations1
Published2016
Admission routes1
Has abstractyes

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