Why Attend Tradeshows? A Comparison of Exhibitor and Attendee’s Preferences
Bibliographic record
Abstract
Tradeshows and conventions continue to thrive in the twenty-first century, both for information exchanges and direct selling, but numerous forces have meant changes in show operation and in participants’ selection criteria for attendance. A study of more than 2,500 tradeshow exhibitors and attendees document a clear bifurcation in the reasons for attendance in these two groups. Exhibitors are primarily focused on business and contact development, whereas participants seek a unique experience and are motivated heavily by educational goals. Successful tradeshows will need to satisfy both of these complementary sets of goals. The effects of social media and mobile technology on tradeshows are noticeable but still in flux, as many shows increasingly use virtual methods for information exchange and contact development. Environmental sustainability has become important to both exhibitors and attendees, and budgetary constraints continue to be an issue. Not only are there differences in relative preferences of exhibitors and attendees, but subgroups within each category also show different tradeshow criteria, based on age, frequency of tradeshow visits, career stage, and their technology readiness.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".