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Record W2908709076 · doi:10.1108/ijebr-07-2018-0465

Government institutional support, entrepreneurial orientation, strategic renewal, and firm performance in transitional China

2019· article· en· W2908709076 on OpenAlexaff
Chengli Shu, Dirk De Clercq, Yunyue Zhou, Cuijuan Liu

Bibliographic record

VenueInternational Journal of Entrepreneurial Behaviour & Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsBrock University
Fundersnot available
KeywordsGovernment (linguistics)ReputationBusinessEntrepreneurial orientationStructural equation modelingOriginalityEntrepreneurshipIndustrial organizationProactivityMarketingValue (mathematics)ChinaTransition economyEconomic systemEconomicsMarket economyFinanceManagementPsychologySociologyPolitical science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to examine how entrepreneurial orientation (EO) and strategic renewal (as a critical dimension of corporate entrepreneurship) might transmit government institutional support and thereby enhance firm performance in a transition economy. Design/methodology/approach Multi-respondent data were collected from 230 Chinese-based firms. The hypotheses were tested with structural equation modeling, in combination with a bias-corrected bootstrap method, to assess the significance of the theorized direct and indirect relationships. Findings Government institutional support enhances EO and strategic renewal individually, yet EO also fully mediates the relationship between government institutional support and strategic renewal. Moreover, strategic renewal fully mediates the relationship between EO and firm financial performance, and it partially mediates the relationship between EO and firm reputation. Originality/value This study contributes to entrepreneurship literature by testing an organization-level model of entrepreneurial phenomena in established firms that identifies EO and strategic renewal as two distinct mechanisms through which government institutional support in a transition economy can enhance organizational effectiveness, which entails the firm’s financial performance and reputation. In doing so, this study provides an extended understanding of how EO and strategic renewal might influence a firm’s financial and nonfinancial outcomes in different ways.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.030
GPT teacher head0.286
Teacher spread0.256 · 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 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

Citations167
Published2019
Admission routes1
Has abstractyes

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