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Record W2761504281 · doi:10.1108/ijwbr-03-2017-0011

Where to visit, what to drink? A cross-national perspective on wine estate brand personalities

2017· article· en· W2761504281 on OpenAlexaff
Sussie Morrish, Leyland Pitt, Joseph Vella, Elsamari Botha

Bibliographic record

VenueInternational Journal of Wine Business Research · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicWine Industry and Tourism
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSophisticationAdvertisingPersonalityPersonality psychologyTourismMarketingExtraversion and introversionBig Five personality traitsSincerityBusinessPsychologySociologyGeographySocial psychology

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to illustrate how brand personality and its dimensions can be applied to wine tourism, and how a content analysis of the text taken from a wine estate’s website can be used to derive a snapshot of how brand personality is communicated. Design/methodology/approach The paper uses the text analysis software DICTION to identify the extent to which each estate’s website communicates the brand personality dimensions of excitement, competence, ruggedness, sincerity and sophistication, and then agglomerates the scores of individual estates within a region to overall scores for the country or wine region in which they are located. Findings Major findings are that the southern hemisphere producers, Australia, New Zealand and South Africa, communicate all five brand personality dimensions to a greater extent than do the northern hemisphere regions of Bordeaux and Napa. Furthermore, while the levels of brand personality communication may differ, all countries and regions seem to follow the same pattern, or stated differently, emphasize the same brand personalities as their international counterparts. Excitement is the main dimension communicated, and then sincerity. Ruggedness and competence are communicated to a lesser extent and sophistication is hardly communicated at all. Research limitations/implications The countries/regions selected for the study are among the most popular tourist destination wineries within five of the world’s prominent wine producing countries and regions. However, this selection is arbitrary and were also carefully chosen merely by the simplicity and convenience afforded by a Google search. The results are also an aggregation of the wineries within a region and does not give any indication of the brand personality of a single website for a winery with in a region, which might be very different from the aggregation. Practical implications Wine tourism is a big business for many wine estates as well as regional and national economies, generating huge potential for economic growth and job creation above and beyond the production and sale of wine. The paper offers a practical insight for wineries that want to portray themselves to the world and especially to their target customers. At a general level, the approach illustrated here provides a way for those who manage wine tourism at the national, regional and estate levels to gauge whether the personality of their brand is being communicated online as they intend it to be. Social implications Wine tourism is very social in nature, and the findings in this study offers a unique understanding of how customers could perceive their destination especially where they are looking to experience the wine estate among similar minded people. A wine estate marketer might wish to be conveying a personality of sophistication and competence, and then be informed by a study like this that the brand is instead being communicated as exciting and sincere. Originality/value The paper illustrates the use of powerful content analysis software, DICTION, to determine the extent to which this text specifically communicates dimensions of brand personality, and in broader terms gives a feel for the tone of text. Regular use of the technique helps wine marketing decision makers to track their own brand’s personality as well those of competitors over time.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.508
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0060.005
Open science0.0020.001
Research integrity0.0000.001
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 head0.087
GPT teacher head0.425
Teacher spread0.338 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

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