Avoiding Greenwashing in Event Marketing: An Exploration of Concepts, Literature and Methods
Bibliographic record
Abstract
Greenwashing, defined by the Oxford Dictionary as “disinformation disseminated by an organization so as to present an environmentally responsible public image” can cause multifarious problems for companies. The phenomenon of greenwashing has, however, not attracted much attention in the event marketing literature to date. The purpose of this paper is twofold. It first describes and analyses the specific characteristics and features of greenwashing in event marketing. It then seeks to identify the current fundamental approaches of how to avoid greenwashing in event marketing and to assess their potential. A two-step literature analysis with complementary search approaches served as a methodical framework. First, journals related to event marketing were screened for the keywords “greenwashing” and “greenwash”. Next, the general literature was consulted for the same keywords. The results clearly demonstrate that the subject of greenwashing has been widely neglected in the event literature. There appears to be no overall concept or approach that allows event actors to avoid greenwashing, albeit various individual initiatives exist. However, it also became clear that initiatives against greenwashing in event marketing can be developed and implemented in the short and long term, for example by integrating different stakeholders. Additional political and juridical efforts based on specific guidelines are also necessary to prevent greenwashing in the future. The study is the first one to provide a systematic approach to the topic of greenwashing in the context of event marketing, including relevant approaches for its avoidance. It can thus help practitioners to better detect and avoid greenwashing in the event industry and to guide similar research in the future.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".