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Record W1976914280 · doi:10.1504/ijev.2012.046520

Factors that affect small business performance in Canada

2012· article· en· W1976914280 on OpenAlexaboutno aff
Amarjit Gill, Nahum Biger

Bibliographic record

VenueInternational Journal of Entrepreneurial Venturing · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsProsperitySmall businessAffect (linguistics)RecessionMarketingBusinessOrder (exchange)FeelingElectronic businessEconomicsFinanceBusiness modelEconomic growth

Abstract

fetched live from OpenAlex

The importance of small business performance cannot be ignored. Small business enterprises encourage expansion and prosperity of the Canadian economy and have helped to secure a more stable position for Canada in the economic recession. This paper seeks to extend the findings of Robson and Bernard (2007) and Okpara and Wynn (2007) related to barriers to small business growth. This study utilised survey research (a non-experimental field study design). Small business owners from Western Canada were surveyed in order to gather information. Subjects were asked about their beliefs and feelings regarding factors that affect the performance of their small businesses. The findings of this paper indicate that lack of financing, market challenges, and regulatory issues are perceived as factors that negatively affect the performance of small business in Canada. The results also show that sales level of small firms (‘past success’) has positive impact on small business performance in Canada. This paper brings empirical evidence to the studies of factors affecting small business performance. In addition, it offers useful insight into the non-financial measures of success. The findings may be useful for the Canadian governments and small business management advisors.

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 score0.029
Threshold uncertainty score0.995

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 communication0.0000.000
Open science0.0000.000
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 head0.061
GPT teacher head0.217
Teacher spread0.156 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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