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Record W2757355717 · doi:10.1080/09669582.2017.1374962

Trade-offs between dimensions of sustainability: exploratory evidence from family firms in rural tourism regions

2017· article· en· W2757355717 on OpenAlexaff
Andreas Kallmuenzer, William Nikolakis, Mike Peters, Johanna Zanon

Bibliographic record

VenueJournal of Sustainable Tourism · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSustainabilityTourismCorporate social responsibilityRural tourismBusinessExploratory researchSocial sustainabilityPublic economicsMarketingEconomicsTourism geographyEcologyPublic relationsSociologyGeographyPolitical science

Abstract

fetched live from OpenAlex

Family firms often pursue social and environmental sustainability, or corporate social responsibility (CSR) efforts that go beyond regulations. This is particularly true in nature-based industries. This study draws on socio-emotional wealth (SEW) and tourism literatures, as well as random utility theory, to disentangle the drivers of sustainability in rural tourism family firms (RTFFs). Informed by interviews, this study applied a novel choice-method survey, that brings understanding to the CSR payoffs and trade-offs between ecological, social and economic attributes in RTFFs. The results from 152 family firms in Western Austria show that after satisfying financial requirements, RTFFs are predominantly motivated by ecological and social considerations. The findings indicate that RTFFs obtain greater utility from positive ecological and social outcomes than additional financial profits, which the authors hypothesise is because of family-related SEW dynamics that enhances CSR. The findings from this study offer theoretical and practical insight into the motivations for proactive sustainability strategies among RTFFs.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.266
Teacher spread0.236 · 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 designQualitative
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

Citations120
Published2017
Admission routes1
Has abstractyes

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