e‐Marketing Ireland: cashing in on green dots
Bibliographic record
Abstract
Purpose The purpose of this paper is to analyze an integrated marketing model that includes operations in the real and virtual worlds. Design/methodology/approach The authors selected a marketing campaign conducted by a real world enterprise (Tourism Dublin) and examined the virtual world business (Virtual Dublin) model through that lens. Findings At the “slope of enlightenment” stage of the Gartner technology hype cycle, it is found that Second Life offers value for its business clients who understand the use of an immersive virtual experience as part of a strategic marketing program. Practical implications The paper shows that strategic use of a simulation that provides an immersive experience, such as the virtual exploration of a tourist destination, as part of an integrated marketing program can deliver tangible results and add value to a marketing campaign. Social implications With a range of products and services that were previously inaccessible before purchase, consumers can “try before they buy” in a virtual environment such as Second Life. Originality/value To the authors’ knowledge, this is the first case study to examine the business model of a company operating in Second Life (a virtual world) that sells the value of an immersive customer experience as an important part of an integrated marketing communications program.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
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".