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Record W3122401407

Family Business in Tourism: State of the Art

2005· article· en· W3122401407 on OpenAlexaff
Donald Getz, Jack Carlsen

Bibliographic record

VenueSSRN Electronic Journal · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTourismHospitalityMarketingFamily businessBusinessProfit (economics)Profit maximizationEconomicsMicroeconomicsPolitical science
DOInot available

Abstract

fetched live from OpenAlex

A three-dimensional developmental model of the family firm is applied to the tourist industry.This model is evaluated through an overview of recent research on the themes and topics surrounding family businesses in tourism. These topics include the relatively high rate at which such family enterprises fail, the strategies used by families to counter or to adapt to the cyclical demand that characterizes the tourist industry, the degree to which family enterprises may be called entrepreneurial, and the challenges facing women who operate tourism and hospitality businesses.The roles that cultural values, location, and family values play in the success of family-operated tourism businesses are also considered. As a whole, the literature review suggests that not all elements in the developmental model are equally important.The fact that only a few family businesses in tourism are inherited or involve children renders the study of individual owners especially important.Also, because these businesses are often founded on the belief that the needs and preferences of the owner's family are paramount, normal expectations of growth and profit maximization are not likely to apply; indeed, only a few enterprises complete a full life cycle. (SAA)

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0010.004
Scholarly communication0.0060.004
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.006
GPT teacher head0.200
Teacher spread0.193 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations46
Published2005
Admission routes1
Has abstractno

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