Towards a more sustainable digital economy: a holistic understanding of giving consumers rights to control their information
Bibliographic record
Abstract
Consumer data forms the backbone of the digital economy, and its importance has increased dramatically since the COVID-19 pandemic hit. Nevertheless, governmental privacy regulations and firms in North America do little to protect consumer data from being overly used and misused. The emerging consumer movement in information privacy protection suggests that consumers no longer wish to remain silent about their privacy violations but rather advocate for and take action to protect their fundamental privacy rights. Consumers’ privacy-protective behaviors, which are detrimental to the sustainable growth of the digital ecosystem, are being increasingly leveraged to safeguard consumers against potential intrusions. Following the European Union’s General Data Protection Regulation (GDPR) that came into effect in 2018, some U.S. states and Canadian provinces have started revising their privacy legislation to give consumers greater control over their personal information. The implications of this initiative on consumer decision-making pertaining to privacy and on organizational outcomes have yet to be ascertained. In this dissertation, I investigate how, why, and under which conditions informing consumers about their rights to information control affects their privacy-protective behaviors and downstream marketing outcomes for firms. To do that, I apply a multi-method approach that includes text mining and text analysis into consumers’ social media posts, a field experiment, and eight online experiments to examine 155,021 consumers from 2019 to 2022. This research aims to shift the contemporary discussion from the unchecked market power of businesses in the digital economy to the emergence of consumer movements in the privacy domain. Further, this research enriches the literature on consumer behavior and decision-making in the privacy domain and guides policymakers and businesses in devising suitable policies for a sustainable digital economy.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".