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Record W2952402835 · doi:10.1080/00208825.2019.1703376

Dynamic capabilities and environmental sustainability for emerging economies’ multinational enterprises

2020· article· en· W2952402835 on OpenAlexaff
Pervaiz Akhtar, Subhan Ullah, Saman Hassanzadeh Amin, Gaurav Kabra, Sarah Shaw

Bibliographic record

VenueInternational Studies of Management and Organization · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEmerging marketsMultinational corporationSustainabilityMacroBusinessOrder (exchange)Micro levelGovernment (linguistics)Macro levelIndustrial organizationEconomic systemEconomicsEconomic impact analysis

Abstract

fetched live from OpenAlex

The purpose of this study is to enhance our understanding of how macro (country)—level dynamic capabilities (DC), such as government environmental policies, legal and market requirements, and technological advances, and micro (firm)—level DC, such as organizational size, culture, and managerial characteristics, are related to emerging economies multinationals’ environmental sustainability policies and practices. Limited studies explore linkages between macro-and micro-level DC and environmental sustainability, which urge emerging economies’ multinationals to reconsider their environmental policies and practices in order to compete with enterprises from developed countries. We develop a theoretical framework and offer propositions about the fundamental links between macro and micro DC and emerging economies environmental sustainability efforts. The propositions can be empirically tested in subsequent studies using country-level and firm-level data to examine the interactions between macro-and micro-level capabilities, in relation to sustainable policies and procedures, for multinationals in emerging economies.

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)0.000
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 score0.364
Threshold uncertainty score0.642

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.007
GPT teacher head0.223
Teacher spread0.216 · 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

Citations54
Published2020
Admission routes1
Has abstractyes

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