Table · dataset · 2026
Replication data and code for: Modelling hunters' perceptions, cognitive-affective image and loyalty in Sierra Morena driven-hunt tourism
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Description
<h3 dir="ltr"><b>RESEARCH PROTOCOL for the Cognitive-Affective-Conative (CA) Mediation Model's Hunting Tourism</b></h3><p dir="ltr"><br></p><p dir="ltr">This dataset and analysis code accompany a study examining whether the cognitive-affective(-conative) model of destination image — widely validated in cultural, sun-and-beach, ecotourism, and adventure-tourism contexts — extends to hunting tourism, a specialised and socially controversial activity where image formation has not previously been tested under this framework.</p><p dir="ltr">Survey data were collected from 288 hunters and participants at eleven driven hunts (monterías) held on hunting estates across Sierra Morena, Córdoba province, Spain, between October 2025 and February 2026.
A structural equation model (estimated in JASP/lavaan, MLR estimator, FIML for missing data) specifies hunting-specific motivation (MOT) as a predictor of a second-order cognitive image of the hunting experience (CIH) — itself built from hunting effectiveness, infrastructure and accessibility, and service quality and hospitality — which in turn predicts satisfaction (STS) and, jointly with satisfaction, destination loyalty (LYL).</p><h4 dir="ltr">All four hypothesised structural paths were supported: motivation significantly predicted cognitive image, though with a small effect size; cognitive image strongly predicted satisfaction and, to a lesser extent, loyalty directly; and satisfaction strongly predicted loyalty, partially mediating the effect of cognitive image on loyalty.
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The comparatively weak motivation→image path is interpreted as a self-selection effect: unlike conventional tourism samples, driven-hunt participants are overwhelmingly experienced hunters (roughly three-quarters with over a decade of experience), leaving little variance for motivation to explain in how the experience is subsequently evaluated. Service quality and hospitality emerged as the strongest first-order driver of cognitive image, ahead of infrastructure/accessibility and hunting effectiveness — pointing destination managers toward socio-cultural and hospitality investment over motivational marketing aimed at an already-committed visitor base.</h4><p dir="ltr">The deposit includes the anonymised survey dataset, the R script (lavaan-based) replicating the structural model, the SPSS codebook, a study-area map, and a README documenting the full data-to-model workflow, to support independent replication of the reported structural paths, fit indices, and reliability statistics.</p>
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Where it is published
- DOI doi.org/10.6084/m9.figshare.33306465.v2 ↗
DOI / persistent id · from figshare com
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from figshare com
Topics
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- Astronomy & Astrophysics · Chemistry · Computer Science & AI · Earth & Environmental Science · Economics & Finance · Engineering · Humanities · Life Sciences · Medicine & Health · Ocean & Atmospheric Science · Psychology & Behavioral Science · Social Science · Tourist behaviour and visitor experience
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Provenance · 1 source records, 19 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| figshare | oai:figshare.com:article/33306465 | 4 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].anzsrc:field:350806 | mapping · figshare com | vocabulary-mapper@1.0.0 | keywords['Tourist behaviour and visitor experience'] |
| concepts[field].local:field:astronomy | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:chemistry | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:economics-finance | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:engineering | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:humanities | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:medicine-health | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:ocean-atmospheric | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:psychology-behavioral | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[field].local:field:social-science | mapping · figshare com | connector:figshare_com@1.0.0 | |
| concepts[modality].local:modality:image | enrichment · figshare com | keyword-concept-rules@1.0.0 | title+description (75%) |
| description | source · figshare com | connector:figshare_com@1.0.0 | /metadata/dc/description |
| license | source · figshare com | connector:figshare_com@1.0.0 | /metadata/dc/rights |
| publication_date | source · figshare com | connector:figshare_com@1.0.0 | |
| title | source · figshare com | connector:figshare_com@1.0.0 | /metadata/dc/title |