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Record W2034650407 · doi:10.1108/17582951311325890

Planning tourism through sporting events

2013· article· en· W2034650407 on OpenAlexaff
Angelo Presenza, Lorn Sheehan

Bibliographic record

VenueInternational Journal of Event and Festival Management · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSport and Mega-Event Impacts
Canadian institutionsDalhousie University
Fundersnot available
KeywordsTourismOriginalityProduct (mathematics)MarketingPortfolioNew product developmentPopulationPosition (finance)PerceptionValue (mathematics)BusinessPsychologyGeographySociologySocial psychology

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to build on the concept of using a population or portfolio of events to help rejuvenate or redefine the strategic position of a destination. The aim is to gain a general understanding of the local community outlook towards a process of repositioning the tourism product based on a portfolio of sporting events. Design/methodology/approach A quantitative research design using a case study approach examined resident attitudes in a beach community of south Italy. In total, 740 questionnaires were received and a cluster analysis was used to study the 11 statements about residents’ perceptions of tourism development and sport events. Findings The findings reveal that resident attitudes towards tourism development are strongly related to their perceptions of their degree of involvement in the setting of strategy and the direction of development. The results also support previous beliefs about increasing interest in the sport tourism product and that sporting events are viewed as important drivers of tourism destination development. The research reveals the presence of different resident attitudes and the cluster analysis is helpful in finding homogeneous groups of residents within the destination. Originality/value There is limited understanding of the degree to which the local community fits into the plans of a city's pro‐growth agenda and the role that a tourism strategy based on sport events can have. This is particularly true in southern Italy where the classical sun, sea and sand (3S) tourism model is in severe crisis and new ways of development are urgently required.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.344

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.031
GPT teacher head0.363
Teacher spread0.333 · 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 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

Citations45
Published2013
Admission routes1
Has abstractyes

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