Perception of consumers regarding the use of data in the sharing economy
Bibliographic record
Abstract
Abstract Title Perception of consumers regarding the use of data in the sharing economy. Authors Laval Maeva & Noiraut Alexian Level Bachelor thesis, 15 ECTS credit Supervisor Venilton Reinert Examiner Hélène Laurell Keywords sharing economy, Y generation, consumers’ perception, and data Background As link between consumption of collaborative economy and perception of data had not yet been investigated; a more indepth analysis of consumer perception of data use by companies is needed. Purpose The purpose of this study is to understand the consumers’ perception about the use of their data by companies of the sharing economy. The goal is to see if they are aware of this phenomenon and if it would modify their behavior regarding the use of these online platforms. Research Question How does the generation Y perceive the fact that companies use their data in Sharing Economy businesses? Method A quantitative, qualitative research has been done for this study to understand generation Y's perception of the sharing economy regarding the use of data by companies. With this research, we focused on a survey but also on four focus groups composed of five persons: French students aged from 18 to 25 years old who travel often for both studies and leisure. We took people from different social backgrounds and have chosen different studies. We emphasize this research with previous theories about the consumer behavior with the buyer decision process in the sharing economy, the selfperception and the price-perception. Theoretical framework: Theories regarding perception on several aspects, from the individual values, to benefit going through price have been included as a basis of discussion in order to analyze our empirical findings. Findings and conclusion Through our research study, we have seen that the Y generation uses the sharing economy essentially for the economy benefit and the time saved but also for its social character and its environmental aspect. Data protection is a really concerning matter for Y generation but they don’t do much about it. Internet use is essential for young people so they accept, when they are aware, to give their data against the free usage of the platform. In addition, we have noticed that the main marketing application of data collection, customized advertising, are misconducted by companies and doesn’t reach users. Despite the awareness of users regarding the protection of data, this generation thinks that collaborative platforms are not so intrusive in their privacy regarding personal information request.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".