Attending virtual academic conferences: the roles of financial support from universities and researchers’ career stage
Bibliographic record
Abstract
Universities generally provide financial support, such as travel grants or conference subsidies, for researchers to attend academic conferences, and motivations for attendance vary across different career stages. Amid tightening financial constraints for academic institutions, rising travel costs, and the improved infrastructure of virtual academic conferences (VACs), VACs offer a cost-effective means to enhance inclusiveness. Drawing on Conservation of Resources (COR) theory, we examine whether lack of financial resources motivates academics to attend VACs and whether this relationship is stronger for early career scholars. Using pre- and post-conference survey data from 197 attendees of two large academic management conferences, we conducted regression analyses to test our hypotheses. Our study contributes to equity, diversity, and inclusion research by empirically demonstrating that VACs can level the playing field for underprivileged academics lacking financial support. We extend COR theory by showing that academics facing resource loss use VACs to regain valuable resources, addressing the underexplored process of resource acquisition. Furthermore, we advance VAC literature by applying a resource conservation lens, moving beyond the dominant technology acceptance perspective, and by providing the first time-lagged evidence that intention to attend VACs predicts actual attendance. Findings offer theoretical and practical implications for fostering inclusivity in academic conferences.
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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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".