A PROPOSED FRAMEWORK FOR MEASURING THE EFFECTIVENESS OF SOCIAL MEDIA: A STUDY OF IRISH TOURISM
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".