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Record W4417492866 · doi:10.21275/sr251212084745

Navigating the Labyrinth: An Analysis of Consumer Rights in Subscription-Based Service Models

2025· article· W4417492866 on OpenAlexaboutno aff
Raja Kumar Murugesan

Bibliographic record

VenueInternational Journal of Science and Research (IJSR) · 2025
Typearticle
Language
FieldSocial Sciences
TopicEuropean and International Contract Law
Canadian institutionsnot available
Fundersnot available
KeywordsConsumer protectionBusiness modelMultitudeConsumer-to-businessConsumer Bill of RightsNegotiationSharing economyCompetition (biology)Consumer lawRevenue

Abstract

fetched live from OpenAlex

The subscription economy has experienced explosive growth, fundamentally altering consumer-business relationships across a multitude of sectors. Projections indicate the market will reach over $1.5 trillion by 2033, driven by consumer demand for convenience and business demand for predictable revenue (Grandview Research, n.d.). However, this paradigm shift has been accompanied by a significant erosion of consumer rights, facilitated by deceptive business practices such as "dark patterns," opaque terms, hidden fees, and intentionally difficult cancellation processes, collectively known as "subscription traps?. This paper investigates the critical legal and regulatory responses to these challenges. Through a comparative legal analysis, it examines the evolving frameworks in the United States, the European Union, the United Kingdom, and Canada. Key regulatory interventions include the U.S. Federal Trade Commission's "Click-to-Cancel" rule, California's influential Automatic Renewal Law (ARL), the EU's Consumer Rights Directive, and the UK's proposed Digital Markets, Competition and Consumers Bill. The analysis reveals a clear global trend toward strengthening consumer protections by mandating transparency, requiring express consent, and simplifying cancellation procedures. This paper concludes that these developments represent a crucial rebalancing of power in the digital marketplace, forcing businesses to move beyond mere compliance toward more ethical, consumer-centric models. The findings have profound implications for business strategy, regulatory enforcement, and the future of digital commerce.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0010.005
Scholarly communication0.0010.002
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.092
GPT teacher head0.468
Teacher spread0.376 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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