No more Netflix and Chill? : the impact of value co-creation in video streaming business models on willingness to pay, loyalty and purchase intention : a qualitative and quantitative study on the Canadian market
Bibliographic record
Abstract
Value co-creation (VCC) has been explored in several different settings but remains unexplored in the field of video streaming business models. This dissertation addresses the existing gap by employing the findings of VCC from other contexts to online video streaming services with the example of Netflix in the Canadian market. The purpose of this research is to understand the impact of VCC in video streaming business models on consumers’ willingness to pay (WTP), loyalty (expressed as repurchase intention) and purchase intention. A multi-stage and mixed method approach is used to overcome previous challenges of researching VCC empirically and overcoming an incomplete conceptualization of VCC. A qualitative study established the Net-flix movie Black Mirror: Bandersnatch as an appropriate example of VCC which can be dis-played as a scenario description with screenshots. A pilot study refined the scenario description as well as the statements used for the manipulation in the following main study. The experi-mental design allows comparing the impact of VCC on Netflix users and non-users presenting them randomly with a VCC and no VCC scenario. The main study also controls for the moder-ating effect of usage as well as the recent phenomenon of binge-watching. An impact of VCC on WTP and repurchase intention for users could be shown, as well as the impact on WTP and purchase intention for non-users, whereas a moderating effect of usage could not be found. However, given the size of the sample and its nature, further research should explore VCC in video streaming services.
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 | 0.003 | 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 | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".