An In-depth Analysis of Autonomous Motivation: The Role of Social Media in Gaining Millennial's Support for Charitable Causes
Bibliographic record
Abstract
The purpose of the present study is to expand upon the tenets of Self-Determination Theory within a context of social media (SM). Specifically, we are assessing the impact of dimensions of autonomous motivation on Millennials’ support for charitable causes, in the social media domain. It has been said that ‘Millennials’ (those born after 1980) will be the most influential generation since the Baby Boomers. They are socially aware and civic minded and engaged in helping societal causes. Furthermore, the relationship the Millennial shares with arguably the most influential form of modern technology, social media, is truly groundbreaking. Social media has proven itself to be a powerful tool, not only for businesses, but also for society as a whole. The total sample consisted of 592 participants from two separate studies: Study 1 (CURE Foundation Denim Night Party in support of breast cancer awareness) and Study 2 (Dans la rue/Five Days for the Homeless charity to raise awareness for youth homelessness). Results indicated that integrated extrinsic motivation significantly predicted online-, cause-, and event-related behaviour intentions, while intrinsic motivation to know and experience stimulation significantly predicted all three behaviour intentions. Both the managerial and theoretical implications of this study are addressed herein, as well as future research avenues.
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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 | 0.002 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".