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Record W7112579965

Strategies for Sustaining Small Business Beyond 10 Years

2025· article· W7112579965 on OpenAlexaboutno aff

Bibliographic record

VenueScholarWorks (Walden University) · 2025
Typearticle
Language
FieldBusiness, Management and Accounting
TopicHospitality and Tourism Education
Canadian institutionsnot available
Fundersnot available
KeywordsSmall businessThematic analysisCompetitive advantageStrategic planningFace (sociological concept)Qualitative researchKey (lock)Sustainable businessSocial business
DOInot available

Abstract

fetched live from OpenAlex

Small business owners in the restaurant industry face financial, operational, and competitive challenges that often lead to failure. Identifying practical solutions to these setbacks is critical to empowering entrepreneurs and fostering sustainable strategies for long-term success. Grounded in Lazear's jack-of-all-trades theory, the purpose of this qualitative multiple case study was to explore effective business planning strategies used by small restaurant owners to stay competitive and sustainable beyond the first 10 years of operation. The participants were five small restaurant owners in Western Canada, each with over 10 years of experience. Data were collected through semistructured interviews, public records, and organizational documents. Thematic analysis revealed key strategies: consistent industry benchmarketing, staff training, and adopting technologies like point-of-sale systems. Other strategies comprised strategic planning, adaptability, and customer relationship management. Recommendations include conducting periodic business evaluations, prioritizing employee development, and leveraging technology to enhance operations. The implications for positive social change include the potential to implement business planning strategies that improve small business sustainability, thereby fostering employee well-being, supporting local economies, and utilizing local suppliers.

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 categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
Scholarly communication0.0030.005
Open science0.0010.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.216
Teacher spread0.205 · 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 designNot applicable
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
Published2025
Admission routes1
Has abstractyes

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