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Record W2810874324 · doi:10.1108/ijebr-12-2017-0503

Entrepreneurs’ individual-level resources and social value creation goals

2018· article· en· W2810874324 on OpenAlexaff
Steven A. Brieger, Dirk De Clercq

Bibliographic record

VenueInternational Journal of Entrepreneurial Behaviour & Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEntrepreneurship Studies and Influences
Canadian institutionsBrock University
Fundersnot available
KeywordsValue (mathematics)Social capitalOriginalityHofstede's cultural dimensions theoryEntrepreneurshipSocial entrepreneurshipPerspective (graphical)Context (archaeology)Resource (disambiguation)Empirical researchSample (material)BusinessSociologyKnowledge managementSocial scienceQualitative research

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to provide a better understanding of how the interplay of individual-level resources and culture affects entrepreneurs’ propensity to adopt social value creation goals. Design/methodology/approach Using a sample of 12,685 entrepreneurs in 35 countries from the Global Entrepreneurship Monitor, it investigates the main effects of individual-level resources – measured as financial, human and social capital – on social value creation goals, as well as the moderating effects of the cultural context in which the respective entrepreneur is embedded, on the relationship between individual-level resources and social value creation goals. Findings Drawing on the resource-based perspective and Hofstede’s cultural values framework, the results offer empirical evidence that individual-level resources are relevant for predicting the extent to which entrepreneurs emphasise social goals for their business. Furthermore, culture influences the way entrepreneurs allocate their resources towards social value creation. Originality/value The study sheds new light on how entrepreneurs’ individual resources influence their willingness to create social value. Moreover, by focussing on the role of culture in the relationship between individual-level resources and social value creation goals, it contributes to social entrepreneurship literature, which has devoted little attention to the interplay of individual characteristics and culture.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.097
GPT teacher head0.368
Teacher spread0.271 · 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

Citations91
Published2018
Admission routes1
Has abstractyes

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