Personalizing videos to improve fundraising: evidence from reward-based crowdfunding
Bibliographic record
Abstract
Purpose Drawing on integrated insights from signaling theory and the cognitive theory of multimedia learning, this study investigates the effect of video personalization on crowdfunding performance. “Video personalization” is defined as information presented in a video in a way that is designed to promote the feeling of being and interacting with others. This study also aimed to examine the moderating effects among various signals of video personalization. Design/methodology/approach This study constructs a theoretical model of how video personalization affects crowdfunding performance through an integrated theory lens. This study measures several signals of video personalization, namely, first-person wording (FPW), second-person wording (SPW), asking questions and talking to the camera. The direct and moderating effects of video personalization on crowdfunding performance are examined by using 2,858 crowdfunding projects on Kickstarter. Findings This study revealed that using SPW, asking questions and talking directly to the camera positively impact crowdfunding performance, while talking to the camera attenuates the positive effect of using SPW and asking questions with respect to funding amounts. Originality/value This study contributes to the literature on resource mobilization in crowdfunding by examining how video personalization impacts resource mobilization in a crowdfunding setting. The findings extend signaling theory by broadening its boundaries. This advance is accomplished by integrating insights from cognitive science into signaling theory. This study also contributes to cognitive theory in multimedia learning by identifying novel ways to personalize videos and by broadening that work to a novel empirical context, entrepreneurship.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".