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Record W3160498824 · doi:10.1002/bse.2808

How do small businesses pursue sustainability? The role of collective agency for integrating planned and emergent strategy making

2021· article· en· W3160498824 on OpenAlexaffabout
Christopher Luederitz, Guido Caniglia, Barry A. Colbert, Sarah Burch

Bibliographic record

VenueBusiness Strategy and the Environment · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsUniversity of WaterlooWilfrid Laurier UniversityMcGill University
Fundersnot available
KeywordsSustainabilityCLARITYBusinessFutures studiesCraftAgency (philosophy)Sustainability organizationsTheory of planned behaviorSocial sustainabilityStructuringPublic relationsMarketingProcess managementEconomicsManagementControl (management)SociologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Abstract Research on sustainability strategy in large corporations has shown that carefully planned strategies can address environmental and social concerns. However, we still lack clarity on how small businesses form sustainability strategies. Assuming that small businesses can—or should—carefully plan strategies is inappropriate considering that such organizations often lack the needed resources, foresight, and formalized decision‐making structures. Building on the study of two craft breweries in Canada and Germany, we detail how a combination of planned and emergent actions enables owners, employees, and external stakeholders to jointly form strategic sustainability orientation. Developing these findings into an integrated activity‐based model, we show the need to move beyond the dichotomy between planned and emergent strategizing. We contribute a human‐centered perspective to the sustainability strategy literature and suggest that research should take the role of people in small businesses more seriously as here interpersonal relationships and collective agency are central in forming strategic sustainability orientation.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
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 head0.012
GPT teacher head0.210
Teacher spread0.197 · 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.

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

Citations46
Published2021
Admission routes2
Has abstractyes

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