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Record W4294898287 · doi:10.34190/ecie.17.1.769

The Effect of Entrepreneur's Fear of Failure on firm's Entrepreneurial Orientation

2022· article· en· W4294898287 on OpenAlexaff
Merihan Attia, Iman Seoudi

Bibliographic record

VenueEuropean Conference on Innovation and Entrepreneurship · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsAssociation of Universities and Colleges of Canada
Fundersnot available
KeywordsProactivityEntrepreneurial orientationEntrepreneurshipFear of failureBusinessDimension (graph theory)Orientation (vector space)PsychologyScale (ratio)Business administrationMarketingSocial psychology

Abstract

fetched live from OpenAlex

This study focuses on the fear of failure (FF) in entrepreneurship and its effect on the entrepreneurial orientation (EO) as a strategic orientation in firms. There has been much research on EO as a predictor of organizations’ performance since it is considered a manifestation of the entrepreneurial direction inside the organization. Yet, the research on the relationships between individual-level variables and EO itself still has room for contribution, especially the internal aspects related to the entrepreneur or the manager. Therefore, this research aims to explore FF in entrepreneurship and its inhibiting and motivating roles. In addition, given that the firm is the unit of analysis in this study, this research explained how an entrepreneur’s FF affects the EO of the firm amidst the COVID-19 outbreak. The study administered a quantitative research design using a structured questionnaire. The results showed a positive influence of the entrepreneur’s fear of failure on the firm’s entrepreneurial orientation manifested in the firm’s innovativeness dimension. However, the other two dimensions that contribute to the firm’s EO representation (proactiveness & risk-taking) showed insignificant relationships with the entrepreneur’s FF.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.040
GPT teacher head0.255
Teacher spread0.215 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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