Is Even Live Sports Vulnerable in the Digital Age?

I was struck today by a Washington Post story by Cecilia Kang and Will Hobson that considers the potential impact of new apps like Periscope (owned by Twitter) and Meerkat on the highly lucrative live-sports media sector.

Noting that its “live” aspect has largely protected televised sports from piracy and other digital-age erosions of control, Kang and Hobson suggest that this protection now faces a challenge from these new apps:

Just hold a smartphone up to a television to record and stream what’s airing, and suddenly piracy is easier than ever. That stunning recognition arrived this past weekend when droves of boxing fans skipped the $100 pay-per-view fee and watched the much-anticipated match between Floyd Mayweather Jr. and Manny Pacquiao free Saturday evening. Dozens of live streams of the fight were available through Periscope, and even though the app shut down 30 illegal streams, users gloated about their ability to watch…

“One of the challenges now is just the logistics of managing the takedown process. . . . We’re talking about real-time activity. Taking something down in half an hour may be half an hour too late,” said Douglas Masters, an intellectual property lawyer at Loeb & Loeb in Chicago.

Though they point out that “[m]edia firms say the onus should be on Periscope and similar apps to police themselves,” Kang and Hobson also note that:

Twitter, which owns Periscope, required HBO and Showtime, who co-produced the pay-per-view telecast, to alert it to illegal streams of the Mayweather-Pacquiao fight. Only then did it take down those accounts.

The article also makes clear that, while these apps have yet to significantly impact their business, major sports content providers are beginning to take seriously the potential impact of this emerging “live streaming” trend.  And, given the huge amounts of money involved, I’m pretty sure they’ll be putting increasing pressure (including legal action, as necessary) on these new live streaming apps and, in the case of Periscope, it’s corporate owner Twitter.  As the article suggests, Twitter is increasingly involved in content hosting and other business arrangements with cable networks and sports leagues, and will probably not want to jeopardize these relationships by being too closely tied to technologies that seriously threaten the latter’s revenue stream.

That being said, the introduction and expanding use of these live streaming apps are yet another development suggesting that the massive financial power of live sports media may finally be experiencing at least a little of the disruption and revenue erosion the rest of the traditional media industry has already been grappling with in the digital age.

Other efforts to move in this general direction include:

  • Dish Network’s Sling TV streaming service, which includes ESPN and other sports channels (but no local broadcast stations), and charges only $20/mo.  It reportedly had technical problems supporting the massive streaming demand  generated by Final Four basketball games, which suggests that it may need to either reduce its (probably already thin) margins to support higher streaming demand, increase its monthly rate, or find ways to charge extra for some sporting events.
  • Verizon’s new “customized TV” channel-tiering option, which allows non-sports fans to avoid the high cost of sports programming, and has already faced a legal challenge from ESPN, which claims the move violates the two companies’ contract.

Verizon and cable operators reportedly pay more than $6/mo. per subscriber to carry ESPN.  While offering tiers or a la carte options can free non-sports fans of this financial burden (plus whatever margin operators add to their own cost), Michael Nathanson, of MoffettNathanson Research estimates that ESPN would cost a whopping $36.30 per month in an a la carte world.

As someone who hasn’t watched a lot of sports since I was a teenager, I’d be OK with this model, though some of my friends and colleagues might not be very happy with it.  But perhaps we can all agree that some shaking up of the status quo in sports programming is a potentially healthy development, especially if it keeps more dollars in consumers’ pockets and less flowing to an industry marked by substantial cartelization and monopoly power (in both professional and college sports).

 

Primary takeaways

  • Digital inequality shows larger impacts on youth academic performance as compared to time spent on screens.

  • Digital skills play a significant role in mediating unstructured online engagement (social media use, playing video games, browsing the web) and youth academic, social, and psychosocial development.

  • Unstructured online engagement and face-to-face social interaction are complementary and continuously interact to create and enhance youth capital outcomes.


A familiar story: concerns of screen time

Today’s discussions of adolescent well-being have coalesced around a clear narrative: teenagers spend too much time online, and their academic performance, mental health, and social lives are deteriorating as a result. A steady stream of academic papers, books, and op-eds, alongside a growing number of policy proposals––school phone bans, age-gated social media use, restrictive screen-time limits––rest on the same underlying claim, aligning with a contemporary, digitized version of the displacement hypothesis:

Screen time, particularly the unstructured, free-time spent on social media, gaming, watching video content, or browsing the web, is said to displace the productive face-to-face activities that build adolescents into capable adults.

The implied and often practiced solution is restriction. In response, this dissertation tested this claim directly, and placed it within the broader context of adolescence.

Across three years, I followed 653 Michigan adolescents from early through late adolescence: in grades 8 or 9 (survey one, 2019) to grades 11 or 12 (survey two, 2022). Notably, these students, studied over time, were part of a broader pooled sample of 5,825 students across the same eighteen highschools. The study window captured the year before and the year after the peak of the COVID-19 pandemic and related lockdown orders, functioning as an unprecedented stress test for theories of adolescent social, academic, and digital life and, importantly, as a benchmark to compare the effects of pandemic-related change and inequality to those effects from screen time alone.

Across four studies of adolescents, consisting of six cross-sectional and longitudinal analyses, findings are not consistent with the displacement narrative, nor the broader concerns about the time youth spend on screens.

Findings are, however, consistent with something the current public and (most) academic discussions have largely overlooked or ignored: the gaps and inequalities that determine whether adolescents can access and use the internet meaningfully in the first place.

What the displacement hypothesis overlooks

Displacement and related research and policy concerning the time young people spend online assumes a “zero-sum” model of adolescent day-to-day time. An hour online is an hour not spent studying, reading, sleeping, or interacting face-to-face (i.e., time spent on more productive or developmentally “better” activity).

Indeed, this makes sense logically. However, as an empirical claim, this model requires time spent online to behave differently from all other ways adolescents allocate time; it must produce uniquely negative outcomes and be inherently harmful across digital contexts, rather than the typical mix of trade-offs corresponding to, and often overlooked among any other social or developmental context.

Yet, online time does not differ from other youth activity. Instead, I find it has a mix of pros, cons, and even some “uniquely digital” benefits which youth utilize for social and academic gains. When I compared unstructured digital media use against traditional face-to-face interaction and activities, both produced similar patterns: some negative associations with academic outcomes, some null, and some positive.

Trade-offs within traditional face-to-face activity (for example, social time with friends and family, or time spent in after-school extracurriculars) are treated as ordinary developmental experiences that must be experienced for the betterment of development. The identical trade-offs involving digital time tend to be overlooked or ignored, and online engagement is perceived as altogether harmful.

A growing body of evidence, including this dissertation, do not support that distinction. Indeed, the developmental context is routinely misread, leaving out the context of the experiences and time spent on digital, as well as face-to-face activities, interactions, existing inequalities, and changes inherent to development. As such, I proposed a novel framework to understand these contexts:

Digital capital exchange

Rather than treating screen time as a unified harm, this dissertation advances an exchange”-based framework, grounded in James Coleman’s theories of youth capital and digital inequality scholarship, particularly following Eszter Hargittai, Jan van Dijk, and Alexander van Deursen (see this list of all dissertation references for full works).

The core proposition is that adolescents’ online engagement is not an alternative to developmental activity but another, albiet modern domain through which young people accumulate and mobilize online resources––particularly digital skills––that work alongside existing social networks and experiences to be exchanged for human capital (measured as: academic achievement, aspirations, STEM interest) and social capital (peer networks, community participation, extracurricular involvement).

Online time is not the mechanism; instead, it is digital skills that I find to be the most vital component in youth capital exchange and enhancement. Unstructured online engagement contributes to online skills; those skills, accumulated and mobilized alongside existing peer, family, and community networks, translate into the outcomes researchers and parents care about, i.e., academic achievement, aspirations, and face-to-face interaction and social networks.

This digital capital framework treats online and in-person contexts as complementary rather than antagonistic, and it situates adolescents’ digital lives within the structural conditions––connectivity quality, device reliability, autonomy of use––that determine whether exchange can occur at all.


Methods (in brief)

Paper-and-pencil surveys were administered to students in classrooms at two time-points: spring 2019 (N=2,876) and spring 2022 (N=2,949), across the same eighteen predominantly rural Michigan schools, grades 8–12. Official, nationally-ranked standardized reading, writing, and math test scores (PSAT 8/9, PSAT 10, SAT; College Board) were then anonymously linked to students’ survey responses with the help of participating districts.

Cross-sectional path analyses modeled pooled and wave-specific samples (pooled N=5,825); two-wave cross-lagged panel models tested reciprocal, longitudinal relationships on the 653 students who completed both surveys. Multi-group analyses of the cross-lagged panel models compared relationships between girls (N=345) and boys (N=308). All longitudinal models included time-invariant socioeconomic covariates as well as time-varying covariates to reduce omitted-variable bias.

Key findings: an overview

To summarize, to the best of my ability, eight chapters across 376 pages, I present two primary findings:

First: digital inequality predicted larger and more consistent declines in human capital than screen time did.

Unreliable home internet and technology maintenance problems––experiencing and/or dealing with broken or outdated devices and software, restrictive school-issued hardware, issues with connecting to or maintaining internet access––decreased youth GPA and standardized test achievement. And, these effect sizes were substantially larger than any negative direct effect from unstructured digital media use.

Across all four empirical studies, digital inequality emerged as the most substantial predictor of academic and developmental decline.

Second: digital skills mediated the relationship between online time and adolescent academic and social outcomes.

Unstructured digital media use, particularly online gaming and web browsing, predicted higher internet and social media skills for adolescents, which in turn predicted stronger academic achievement and self-efficacy (human capital), and social interaction and extracurricular participation (social capital). The positive indirect effect of screen time through skills offset or exceeded any small negative direct effects across several outcomes (supporting our existing peer-reviewed work: Hales & Hampton, 2025, and which you can read more about here).

These exchange processes were amplified when peer and family networks were modeled alongside digital skills, consistent with the premise that online and offline contexts operate together rather than in competition. The effect was not universal: social media skills amplified rather than offset a negative association with consistency of interest, one of the two subscales of grit. The exchange framework describes a contextual and conditional, domain-specific mechanism, not a blanket defense of time spent online.

Implications

If digital inequality, and not screen time, is the primary predictor of adolescent academic and developmental decline, and still warrants concern regarding access quality and experience even with the broader adoption of digital devices across the United States, the current policy emphasis on restriction is pointed at the wrong target. The evidence supports a different set of priorities.

Stable, reliable home (fast) broadband should be treated as an educational prerequisite rather than a consumer amenity. Unreliable connectivity exerted larger downward pressure on human capital than any measure of screen time, and that pressure intensified during the pandemic-era reliance on digital infrastructure. Technology maintenance, device repair, replacement, technical support, and the flexibility to install software and explore the web autonomously, matters as much as initial access, and school-issued devices that restrict autonomous use appear to hinder skill accumulation rather than support it.

Restrictive parental mediation of internet use was negatively associated with grit and self-efficacy at magnitudes comparable to the positive contributions of face-to-face activity. This challenges the assumption that digital restriction functions protectively. Instructive mediation, teaching adolescents to verify information, navigate platforms critically, and mobilize online resources toward meaningful ends, is the posture the data supports.

Finally, the technical skill-building that occurs through gaming, self-directed exploration, and deep web use is skill-building, not wasted time. Closing the persistent gender gap in these domains likely requires legitimizing technical play for girls, rather than restricting it for everyone.

None of the above is an argument that screen time is benign. It is an argument that screen time is the wrong focus, particularly when studied mostly in isolation. Context matters substantially, whether that is time spent on other activities during adolescence, the period of adolescence itself, digital inequality, resources gained from such online use, and how all such factors interact. The factor that predicts whether a given adolescent can convert online engagement into capital outcomes is structural: access, infrastructure, skills, and the autonomy to use them. These factors are distributed unevenly, and its uneven distribution, not hours logged, is what separates adolescents who thrive from those who fall behind.

The full dissertation is available through Michigan State University’s ProQuest archive, or see the embedded full-text PDF below. I’m happy to share papers, preprints, or the underlying framework with anyone interested and working in this area––don’t hesitate to reach out via my contact form. Thanks for reading.

Is Even Live Sports Vulnerable in the Digital Age?