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Record W7020043766

Innovation as a critical success factor in small businesses in Prince Edward Island

2011· article· en· W7020043766 on OpenAlexaboutno aff

Bibliographic record

VenueIslandScholar (University of Prince Edward Island) · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicReligious Tourism and Spaces
Canadian institutionsnot available
Fundersnot available
KeywordsCritical success factorSmall businessExploitSuccess factorsCompetitive advantageMainlandDisadvantageBest practice
DOInot available

Abstract

fetched live from OpenAlex

In a fast paced global economy, the competitive advantage in business rests with those that are the most flexible and best able to exploit the challenges and opportunities presented. In this environment, small businesses operating from a small island could be perceived as being at a competitive disadvantage vis-à-vis their mainland competitors. The following research however, while indicative and not representative, suggests that small businesses operating on and from a small island can successfully compete in a global environment by flexing their entrepreneurial innovation skills. My research examines locally owned and operated small businesses in the fish processing sector in Prince Edward Island, one of the more traditional sectors on the small Canadian island province. The research examines whether businesses in this sector are innovative, what types of innovative practices they employ, and whether they consider being innovative as key to their success. A qualitative approach to the research was chosen and face-to-face interviews were conducted during the summer and fall of 2009. This study suggests that small businesses in this sector are innovative as evidenced by the development of new products, new markets, niche markets, and new production technologies. While all businesses considered these innovative practices key to their success, they also rated other factors such as location, education/experience and family as important or more important to their success.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
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
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.023
GPT teacher head0.267
Teacher spread0.244 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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