Exploring the walled garden theory: An empirical framework to assess pricing effects on mobile data usage
Bibliographic record
Abstract
This paper performs an exploratory study of mobile usage patterns over three years (2013–2015) in the context of pricing practices such as zero-rating. In recent years, there has been heated ongoing debate regarding whether offering different pricing plans, such as zero-rated services and applications, might slant user behavior toward certain content on the Internet. Our study gathers empirical measurements of mobile application usage to address this research question. We shed light on this issue by performing an exploratory analysis of the effects of different data plans and connection types on mobile data usage, as well as measuring quantitative and qualitative pricing effects of zero-rating on mobile data usage. First, we perform a longitudinal exploratory study using data collected from the MySpeedTest application. We analyze differences in usage behavior between the top five most used applications in the United States (US) and South Africa (ZA), comparing usage on different connection types (Wi-Fi vs. cellular) as well as for devices on different data plans (prepaid vs. postpaid limited monthly data cap vs. uncapped). Our findings show that US users consume slightly more cellular data than Wi-Fi data for most of the US top five most used applications, while South African users generally prefer Wi-Fi connections (with the notable exception of Facebook). Further, US users on postpaid plans display much higher average monthly mobile data usage than those on prepaid plans, while South African users on prepaid plans generally display much higher usage than those on postpaid plans. Next, we perform a deeper analysis into the possible behavioral effects of zero-rating in South Africa. We find in one case that zero-rating WhatsApp on Cell-C's network increases overall usage of the application, regardless of connection type. In the case of zero-rating Twitter on MTN network, we observe increased mobile data usage of the zero-rated application during and immediately after the promotion, but not in the long term. Some of our results yield striking patterns, yet point to the need for richer datasets to confirm these initial findings. Finally, to gain further insights into the user motivations behind our empirical observations, we implement a mobile-based survey among a randomly selected group of individuals in South Africa and Kenya. We observe that use of zero-rating services is actually quite low among respondents. Further, zero-rating seems to serve more as a popular method for data conservation—an effort toward which respondents show a strong dedication—than as a walled garden that would otherwise discourage users from venturing beyond zero-rated applications.
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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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".