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Record W2809935564 · doi:10.2495/st180151

A PROPOSED FRAMEWORK FOR MEASURING THE EFFECTIVENESS OF SOCIAL MEDIA: A STUDY OF IRISH TOURISM

2018· article· en· W2809935564 on OpenAlexaboutno aff
Colm Barcoe, Garvan Whelan

Bibliographic record

VenueWIT transactions on ecology and the environment · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
Fundersnot available
KeywordsTourismIrishPerformance indicatorContext (archaeology)Social mediaMarketingGovernment (linguistics)Agile software developmentScale (ratio)BusinessPublic relationsComputer sciencePolitical scienceEconomicsManagement

Abstract

fetched live from OpenAlex

Over the past five years, visitations of American holidaymakers to Ireland have grown exponentially owing to the online strategies of Tourism Ireland, a Destination Marketer (DMO) with a meagre budget which is extended by their understanding of best practices to maximise their monetary allowance.This suggested framework incorporates a range of Key Performance Indicators (KPIs) such as financial, marketing and operational that offer a scale of measurement from which the Irish DMO can monitor the success of each promotional campaign when targeting the US and Canada.These are presented not as final solutions but rather as suggestions based on empirical evidence obtained from both primary and secondary sources.This research combines the wisdom extracted through qualitative methodologies with the objective of understanding the processes that drive both emergent and agile strategies.The study extends the work relative to performance and examines the role of social media in the context of promoting Ireland to North America.There are two main themes that are identified and analysed in this investigation, these are the approach of the DMO when advocating Ireland as a brand and the benefits of digital platforms set against a proposed scale of KPIs, such as destination marketing, brand positioning and identity development.The key narrative of this analysis is to focus on the power of social media when capitalising upon marketing opportunities, operating on a relatively small budget.This will always be a relevant theme of discussion due to the responsibility of an organisation like Tourism Ireland operating under the restraints imposed by government funding.The overall conclusions of this research may help inform those concerned with the implementing of social media strategies develop clearer models of measurement when promoting a destination to North America.The suggestions of this study will benefit small and medium enterprises particularly.

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)0.025
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.007
Science and technology studies0.0090.025
Scholarly communication0.0160.009
Open science0.0030.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.258
Teacher spread0.243 · 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 designSimulation or modeling
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

Citations3
Published2018
Admission routes1
Has abstractyes

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