Digital Marketing Effects of Clubhouse on Crowdfunding in the Context of COVID-19
Bibliographic record
Abstract
In the marketing mix, promotion is mentioned as using the communication channels available to present and market the product or service at hand. In recent years, social media has risen as an influential marketing communication channel in digital space. Apart from end-to-end direct messengers and video communication in times of the COVID-19 pandemic, the social media channel Clubhouse offers an audio-only experience. The current research lacks analysis of the potential influence of the hyped social network. Due to the novelty of the channel and the absence of text messages as well as visual stimuli, questions regarding the impact that usage of this social media channel might have on crowdfunding, a means of rising popularity in alternative financing, have arisen. The study builds upon the media richness theory of Daft and Lengel as well as the channel expansion theory of Carlson and Zmud. Besides literature research, explorative expert interview analyses were applied to answer the research question at hand. The main findings include different approaches to foster the opportunities of Clubhouse for marketing crowdfunding campaigns in line with insights about the user group of Clubhouse as well as development options for the platform.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".