International Journal of Communication 19(2025) A Critical View on the Domestication of Data
National University of Singapore, Singapore
While the term “big data” may have faded from public discourse, large-scale data collection and processing continue to underpin contemporary economic and social structures. Against this backdrop, this article critically examines the domestication of data—how individuals understand, integrate, and internalize data in everyday life. Drawing on domestication theory and empirical research conducted across nine towns and cities in England, this article argues that data now function as a part of epistemic (and moral) infrastructure. Central to this process is the presumed facticity of data, a tacit belief in data’s epistemic legitimacy and reality-value that subtly reconfigures self-understanding, everyday practices, and moral reasoning. Foregrounding reflexive engagement with data but also its centrality to everyday practice, this article contributes to theoretical debates on datafication and the lived experience of data systems.
Keywords: big data, datafication, data value, domestication theory, practice, moral agency
Jun Yu: [email protected]
Date submitted: 2024-09-26
Recent years have been marked by a pervasive shift toward datafication—the process of transforming life processes into streams of data inputs for computer-based processing—influencing everything from economic and social structures to intimate human interactions (Flensburg & Lomborg, 2023). Although the once-popular term “big data” has lost some traction lately (Pentzold & Knorr, 2024), the significance of large-scale data practices in contemporary society has not diminished; on the contrary, data mining and use have scaled up, driven by reduced costs for data collecting and processing alongside the proliferation of digital platforms and devices from which data are taken.
The process of data becoming deeply embedded in the social and economic fabric is often taken for granted or deemed “inevitable” (Kelly, 2017). This development is frequently supported by industry- and innovation-driven rhetoric that frames datafication as a natural and unquestionable progression, epitomized by the well-known metaphor of data as the “new oil” (The Economist, 2017), alongside terms like “data explosion” and “data as infrastructure” (Nolin, 2019). Central to such framings is the conceptualization of data as a valuable resource awaiting exploitation (Cohen, 2019; Couldry & Yu, 2018). This view serves as part of broader sociotechnical imaginaries (Jasanoff & Kim, 2015) that shape public understanding, influence policy debates, and legitimize prevailing data practices. In response, critics have deconstructed the naturalization of data(fication) through analyses of policy documents (Nolin, 2019), corporate and international organization reports (Couldry & Yu, 2018), marketing rhetoric (Beer, 2019), popular media (Pentzold & Knorr, 2024), high-profile memoirs (Knorr, Wolter, & Pentzold, 2024), and educational discourses (Yu & Couldry, 2022). Moreover, commentators argue that innovation rhetoric is often intentionally vague and aligns predominantly with corporate interests rather than with balanced societal needs (Richards, 2015).
Yet, a critical gap remains in how discourses around data permeate everyday language, perceptions, and eventually the practices of ordinary individuals—those most directly affected by data developments. Limited attention has been paid to the perspectives of “ordinary, non-expert folks’ thoughts and feelings” about their interactions and engagement with data (Kennedy, 2018, p. 18). While the public diffusion of technology may initially be shaped by business, governmental, and journalistic discourses (Barbrook, 2007), it is crucial to examine how these discourses operate and take shape within mundane vernacular contexts, particularly given that datafication involves not only economic but also deeply social relations (Couldry & Mejias, 2019). That is, data are not only collected, processed, and circulated but also narrated, legitimized, and internalized. This article addresses this gap by examining the domestication of data, arguing that individuals not only reflect on, adopt, or resist data use but also actively participate in its epistemic and moral legitimation, which gives it a place in everyday life. It aims to show how data appears as a social fact (Durkheim, 1895/1982) shaping social practices and moral reasoning in the long term.
Responding to Kennedy’s (2018) call, recent studies have explored how individuals engage with data and data-related practices, including self-tracking, personalization, algorithmic curation, and data-driven decision making. Some have closely examined how people reflexively engage with data practices (Das, 2024; Kennedy, Elgesem, & Miguel, 2015; Lomborg, Reutter, & Vilaza, 2024), adopting various tactics ranging from resistance and adaptation (Hartley & Schwartz, 2020) to resignation (Draper & Turow, 2019). Occasionally, these studies map a spectrum of user sentiments and practices informed by particular imaginaries (e.g., Bucher, 2017, on “algorithmic imaginary” and Beer, 2019, on “data imaginary”). While broadly aligned with this body of literature, this article explores not only discursive and reflexive engagements with data but also how these engagements materially translate into everyday practices, bringing a semblance of order and orientation to everyday life. Indeed, technologies gain traction through the interplay of symbolic-discursive resources and material routines.
To explore these processes, the article draws on the domestication framework developed by Roger Silverstone (1994) and his colleagues (Silverstone & Haddon, 1996; Silverstone, Hirsch, & Morley, 1992). Rather than limiting the framework to a narrowly conceived “home” environment or focusing solely on individual-driven domestication efforts, however, this article applies its conceptual core more flexibly to the processes through which data becomes integrated into everyday life and reconfigures the surrounding social worlds—worlds constituted through symbolic understandings and validated everyday practices (Couldry & Hepp, 2016). This more expansive application is justified for two reasons. First, domestic spaces and everyday lives have now become networked nodes within broader market and sociotechnical networks (Berker, 2023, p. 29), blurring the boundary between domestic and public/commercial spheres. Second, the domestication of “data” may be more directly managed by market forces, since tensions between individuals and data are often pre-emptively addressed by commercial systems that continuously monitor user behavior and tailor outputs to reinforce adoption and profitability (Zuboff, 2019). That is, market-driven adaptations have become more deeply implicated in the domestication process.
Understanding data’s cultural significance thus requires moving beyond purely technical or macro-discursive analyses, and beyond accounts that describe individuals as merely “taming” technology. Instead, attention should be given to how everyday meaning-making and lived experience in relation to data take shape—experiences often overshadowed today by a “fascination with the data traces” (Livingstone, 2019, p. 176). The domestication framework is particularly useful for this purpose, exploring how engagement with data translates into practical action, while remaining attentive to the enduring influence of technological power. Indeed, this framework has shown considerable adaptability (Hartmann, 2020): its strength lies not in a rigid, sequential model but in illuminating a multi-phased, negotiated relationship between people and technological power.
Drawing on qualitative research conducted across England, this article argues that data function first as a resource for instrumental purposes, but also as a deeply embedded epistemic and moral infrastructure reshaping self-understanding, social practices, and potentially ethical reasoning. It begins by outlining the domestication framework, then examines how participants interpret and respond to data, focusing particularly on its instrumental role. The discussion subsequently turns to what I call the presumed facticity of data, exploring how this tacit framework reshapes engagement with social worlds. A subtle yet crucial distinction emerges between the centrality of data and reliance on it: rather than suggesting that data inherently undermines or radically transforms agency and practice, the article shows how both are consciously recalibrated from the bottom up to accommodate and center data. People’s perception of their social worlds thus pivots around this presumed facticity, sustained through processes of second-order observation (Esposito & Stark, 2019) and the multivalence of data itself (Fiore-Gartland & Neff, 2015; Gerlitz, 2016). The final section explores how these dynamics might influence broader moral frameworks. Instead of offering definitive conclusions, however, it opens a discussion on how data practices are not simply adopted but actively implicated in an ongoing reconfiguration of social and moral landscapes.
Research Operationalization
Between 2016 and 2019, I conducted 46 semistructured interviews and think-aloud sessions across nine towns and cities in England. The research was conducted in person in the towns and cities where the participants resided. These qualitative data were initially gathered for my doctoral research on social media algorithms and social solidarity. Given its topical relevance, however, it also provides valuable insights into participants’ discourses, perceptions, and everyday engagements with data. Consequently, these insights have been re-coded and re-analyzed, using a thematic and interpretative approach to explore the domestication of data practices.
Participants exhibited various coping strategies and attitudes toward data that were contextually specific, often varying significantly even within individual practices. Approaches differed, depending on the platforms used, types of data involved, and personal backgrounds—aligning with existing literature (Kennedy et al., 2015; Turow, Hennessy, & Draper, 2015). Additionally, factors such as age, occupational background, intensity of social media use, and prior awareness of data mining appeared influential. For this reason, the re-coding and analysis strategy aligned with emerging scholarship on data reflexivity, which demonstrates how vernacular and relational practices concerning data and data infrastructures are shaped by social relationships and networks, personal beliefs, and institutional affiliations that evolve dynamically throughout life (Cotter et al., 2024; Das, 2024).
Importantly, however, the primary aim here is neither to provide a rigorous empirical analysis nor to establish causal relationships or definitive correlations between participants’ socioeconomic, cultural, or occupational backgrounds and their data-related attitudes or behaviors. Instead, the empirical insights presented serve chiefly as illustrative examples that ground and enrich the theoretical discussion. Accordingly, rather than cataloguing a range of attitudes and views already well-established in the literature (see e.g., Hartley & Schwartz, 2020; Kennedy et al., 2015), the analysis centers on how practices are restructured around data.
Domestication
Initially developed to study media technologies like television, the domestication of technology framework provides a valuable analytical lens for examining how technologies transition from novel, external entities into embedded and normalized components of everyday life. It emphasizes that integrating technology into daily routines is neither linear nor pre-determined, but an ongoing, dynamic negotiation in which individuals actively adapt technologies to fit their lifestyles, values, and domestic environments.
This negotiation unfolds through four interconnected stages: appropriation, objectification, incorporation, and conversion. Appropriation refers to how people envision the value of a technology; objectification describes the symbolic and spatial positioning of technology within daily life; incorporation entails integrating technology into routines, reshaping temporal and spatial organization; and conversion signals the projection of symbolic or social meanings beyond the individual context. In practice, these stages are neither strictly sequential nor mutually exclusive (Bakardjieva, 2006), so this article does not follow a rigid, step-by-step analysis. Nevertheless, applying the domestication framework to data specifically highlights how data attains cultural significance. This aligns closely with the concerns outlined above, underscoring how data practices are neither neutral nor merely technical but carry epistemic and moral significance precisely because domestication theory reveals how individuals’ active decision making around data evidences such significance. Crucially, the theory clarifies how data’s centrality does not imply dependence on or passive submission to technological dominance. As established in the extant literature, individuals display varied levels of engagement, critically navigating and actively shaping their interactions with data and technological systems. The focus, therefore, shifts from technological dominance to how everyday life is reorganized in ways that meaningfully integrate data and often center practices around it.
A particular strength of the domestication framework lies in its attention to everyday practices, notably captured in the objectification and incorporation stages. By foregrounding “practice,” domestication aligns with the perspective that social life is structured through interconnected, institutionally embedded routines, lending coherence and stability to human activity (Couldry, 2012; Schatzki, 2002). This approach positions data not as a series of isolated interactions between individuals and technology but as part of socially recognized practices that reflect and reinforce broader structures. Data become integral to everyday life not only instrumentally but morally, influencing judgements about appropriateness, acceptability, and meaningfulness within daily routines. For instance, at the appropriate stage, individuals evaluate whether specific data practices align with their values or pose ethical dilemmas. During objectification, personal data can become symbolically entwined with identity, extending its significance beyond mere utility. Incorporation stages prompt practical and ethical considerations, such as whether allowing data to guide leisure activities is ethically acceptable, leading individuals to articulate distinct moral positions.
However, applying the domestication framework specifically to data requires clarification. Traditional domestication studies typically examined discrete, tangible media technologies, such as television, whose functionality and spatial placements were fairly readily observable. By contrast, data in its unprocessed form (i.e., before it is presented to us as metrics or recommendations) remain intangible and largely invisible. Individuals may thus perceive themselves not as directly domesticating data, but as interacting through more tangible interfaces and infrastructures, such as laptops, apps, platforms, and smart devices that render data accessible and actionable.
Against this background, this article posits that what becomes embedded in routines and meanings are both the material devices and software, and the epistemic legitimacy and reality-claims embedded in data outputs generated by those systems. This helps explain why sets of data may become affectively charged objects, as Kennedy and Hill (2018) suggest. Consequently, I propose a two-layered understanding of domestication: first, the domestication of data-related devices and interfaces (e.g., apps, devices, and platforms); and second, the domestication of data itself as an epistemic and moral resource. As this article shows, the two layers are not simply parallel; they are reciprocal, each shaping and reinforcing the other within everyday routines. The former is typically more visible and device-oriented, while the latter relates to the internalizing of data as a meaningful and somewhat authoritative reference point. This internalization is actively contested and integrated into practices of self-understanding, interaction, and moral reasoning.
Navigating Individual Data Perceptions
By the late 2010s, when the fieldwork was conducted, discussions surrounding data, algorithms, and dataveillance had already entered mainstream discourse. Debates about the potential and implications of data extended well beyond expert communities (Beer, 2019), becoming a key cultural reference point fueled by academic and popular texts as well as documentaries like The Great Hack (Amer & Noujaim, 2019), which examined the Cambridge Analytica scandal. Consequently, everyday conversations and personal accounts reflected diverse and often conflicting understandings of data’s role and significance.
Taking a bottom-up approach to exploring these perceptions illuminates not only their diversity but also their deep embeddedness in everyday life. As anticipated, my research participants held varied and frequently ambivalent views about data, expressing critical sentiments alongside more favorable or utilitarian perspectives. This ambivalence mirrors the broader discursive landscape of the time and aligns with established insights that technological developments are shaped by a tapestry of competing imaginaries and contested meanings (Mansell, 2012; Markham & Tiidenberg, 2020). Concerns about surveillance, control, and privacy coexisted with acknowledgements of efficiency gains and societal benefits—often within the same accounts.
Some participants willingly shared personal data, especially with public services like the National Health Service (NHS), believing that this could serve the public good. Such decisions were framed through their “individual ethical barometers,” echoing the account of an IT analyst from Kennedy et al.’s (2015) study, who supported data mining as beneficial to society (p. 283). On a personal level, one research participant, Nick,[2] also viewed data as positively transformative. Unemployed at the time of the interview and relying on LinkedIn for job seeking, he valued the platform’s data-based recommendations for uncovering unexpected career options: “I’m having an interview for a chef job tonight … I’ve never thought of working as a chef, but LinkedIn suggested I try out for this position.”
At the other end of the spectrum, research participants also expressed discomfort or pessimism. Elsa, a design company director, found hypertargeted ads following her online purchases intrusive and unsettling, reflecting desires even she herself had not explicitly acknowledged. Her unease contrasts with a welfare rights officer in Kennedy et al.’s (2015) study, who perceived such personalization of ads as “fair game.” These contrasts highlight how interpretations of data’s utility and implications are indeed deeply shaped by differing social positions, experiences, and moral frameworks.
Whether seen positively (as aiding self-understanding, advancing social good, or “fair game”) or negatively (as invasive or mind-reading), these reflections reveal an epistemic shift wherein data increasingly holds intrinsic authority. Even when met with skepticism or discomfort, data outputs were rarely dismissed outright, suggesting an underlying assumption of epistemic legitimacy—even when interpretations felt ambiguous or dismaying. The response of one participant, Anthony, situated at the more extreme end of the spectrum, exemplifies this:
if I find it [his social media feed] boring, I wouldn’t still go as far as to do something to make it more interesting because that’s futile … What it shows is what it is [participant’s emphasis] … if it’s boring, then that’s what I am now.
As a manager on a computer science research project in London, Anthony had technical familiarity with data processing. Yet, his attitude was neither strictly analytical nor resigned. Though framed in neutral terms, it underscored his tacit acceptance of data’s subtle authority in revealing and shaping self-perception, echoing the participant Nick’s belief in the value of LinkedIn’s data-driven nudges. This orientation aligns with “dataism” (van Dijck, 2014), a worldview in which data are seen as reflective, rather than merely representational, of reality, acting as a medium of self-knowledge and collapsing boundaries between the self and its digital abstraction (data). This sense of epistemic legitimacy also surfaced among users of self-tracking apps and smartwatches. One participant, Joe, for example, critiqued the desire for excessive control through data: “With apps and tracking devices, we want to control even more in our life that actually shouldn’t be [controlled].” While critical of data commodification and the control associated with data extraction, and resonant with the critique that data primarily benefits corporations over individuals (Zuboff, 2019), Joe’s comment revealed an implicit belief in data’s ability to uncover dimensions of the self that have otherwise been unknowable.
Across these examples and varying views, participants converged on what I term the presumed facticity of data—a covert belief that data reflects, rather than represents, reality, without the need for interrogation. This presumed facticity reflects a mundane assumption that data simply reveals what already exists—bypassing questions of how that data are produced or structured. It rests on epistemic legitimacy, referring to the credibility granted to data as a valid form of knowledge.
Scholars have effectively challenged the notion that data offers a “higher form of intelligence and knowledge … with the aura of truth, objectivity, and accuracy” (boyd & Crawford, 2012, p. 663), pointing out that data are always socially constructed, contingent, and open to manipulation (Gitelman & Jackson, 2013). However, these critiques were rarely echoed in participants’ everyday discourses and reflections. Errors were typically seen as technical glitches rather than evidence requiring deeper interpretive processes. That data originate from tangible actions and behaviors—clicks, posts, movements, and interactions—further reinforced its empirical validity, even as participants largely overlooked how the complexity of human actions is flattened into data outcomes (Esposito & Stark, 2019).
To clarify, presumed facticity, unlike “dataism,” does not entail full trust in data or offer emotional reassurance; on the contrary, participants regularly questioned the veracity, meanings, and implications of data outputs. However, such doubts seldom severed the perceived link between data and reality, underscoring data’s deep entanglement in everyday routines, often through ambient, continuous collection. The sleep-tracking app example given by one participant, Martin, exemplifies this: he valued insights into his unconscious activities, such as “what had happened [while sleeping]—how long the walk [he’d] taken, how many footsteps [he] made, calories burnt.” He embraced these insights and described his device as a kind of technological prosthetic. Similarly, another participant, Olivier, even referred to his smart devices as his “external brain,” viewing the data stored within them as an extension of “[his] own knowledge.”
Here, the domestication of devices and the domestication of data do not merely coexist; rather, they form an interdependent relationship within routines. On one hand, the continuous appropriation and seamless objectification of tracking devices—such as physically carrying and regularly using smartphones and smartwatches—reinforces the legitimacy and symbolic value of the data they produce. On the other hand, the epistemic legitimacy granted to data outputs motivates further appropriation of the devices themselves (e.g., as an “external brain”). This reciprocal dynamic creates a mutually reinforcing cycle in which routine interactions with data devices can enhance the epistemic legitimacy of data, which in turn encourages continued engagement with these devices. Through such quiet, repetitive practices of integration, data’s role as an authoritative resource becomes deeply entrenched in everyday self-understanding and practices.
This tacit acceptance of data underscores its subtle yet powerful influence in shaping self-understanding and informing actions. Even Scarlett, an energy policy consultant in her late twenties and one of the most skeptical participants, reflected this logic during a think-aloud session. Asked to review a week’s worth of her Instagram activity—such as posts she liked, ads shown, and recommended contents—while verbalizing her actions and thoughts, she remarked that when the output (feed) did not align with her self-image or her recollection of recent activity: “I must’ve done something to cause this” (her emphasis). Her response, like Anthony’s (though less overtly), illustrated data’s persistent epistemic legitimacy despite doubts. Crucially, participants were not passive in their engagement but actively interpreted and questioned data outputs (Bolin & Velkova, 2020), resisting deterministic accounts of technology (Baym, 2015). However, amid skepticism, data remained a reference or validation point, revealing an ambivalent yet enduring relationship.
While these varied attitudes and accounts were likely shaped differently by personal backgrounds (Kennedy et al., 2015) and emotional and affective investments in data (Kennedy & Hill, 2018), underlying them was a pervasive sentiment that data are something we must “live with.” Participants’ experiences and actions were entangled within a broader fabric where data and data technologies are intricately woven into everyday life (Couldry & Hepp, 2016), and individuals must constantly reconfigure their actions in response to systems that collect, process, and display data. Yet, it is important not to read this as data molding behavior, but as signifying a conceptual shift where data are central to organizing meaning and value in everyday life. Domestication processes embed data into social worlds, assigning it a meaning based on its perceived epistemic legitimacy and presumed facticity.
However, unlike earlier media technologies with relatively stable and clearly defined functions, data resist straightforward appropriation and objectification. With television, for instance, domestication involved spatial placement within the home and distinct roles such as entertainment, information, or family bonding (Silverstone et al., 1992, pp. 19–21). In contrast, data are abstract, ubiquitous, multisourced, and multivalent—simultaneously monetized, personal, predictive, and behavioral (Gerlitz, 2016). This polysemy complicates efforts to incorporate data into a coherent domestic narrative or assign it stable symbolic meaning, often shifting attention toward tangible digital devices and immediate interfaces rather than the data itself. As mentioned above, some participants viewed data positively, while others experienced it as intrusive or unsettling. Even individuals comfortable sharing data for public-good purposes like healthcare expressed uncertainty or discomfort about its subsequent use, contrasting this sharply with more tangible forms of contribution (e.g., donating platelets) where outcomes are visibly apparent.
Paradoxically, however, this difficulty in appropriation and objectification further amplifies data’s power. Precisely because data resists complete understanding or ownership, it remains external to the self, even when derived directly from one’s own behaviors and thus presumed to be factual. This ambiguity of data as simultaneously intimate and external often led participants to attribute perceived problems or discomfort to technological devices or the companies behind them rather than questioning data’s foundational role. This mirrors the previously discussed reciprocal dynamic: tangible devices become the primary objects of domestication, symbolically embedding data into routines, even as data itself remains elusive and authoritative. In this sense, engaging with data comes to resemble individuals’ relationship to institutional decisions, such as government policies or court rulings, which are often neither fully trusted nor entirely understood yet are nonetheless treated as social facts (Durkheim, 1895/1982, p. 59). Such realities are perceived as existing independently of individual will, exerting influence by appearing as given aspects of social life.
What sets data partially apart from traditional social facts is its diffuse and embedded nature. Unlike laws or official rulings whose authority derives from formal institutions and their socially legitimated status, data’s epistemic legitimacy stems not from institutional authority conferred by the devices or the companies behind them, but from its everydayness—its ambient, corporeal, and ubiquitous presence. This diffuse quality sustains its presumed facticity, embedding data deeply within daily routines, which serves as a performative scaffold through which individuals stabilize the meaning of data-driven outputs, even in the face of acknowledged uncertainty and contingency. This is illustrated by Anthony’s remark, noted above, that “if it’s boring, then that’s what I am now.” Over time, such beliefs form the micro-foundations for treating data as a social fact, subtly shaping routines, expectations, and standards of normality and appropriateness. Like laws shaping behavior not just through coercion or punishment but by establishing normative benchmarks and standards (McAdams, 2015), data influences perceptions of what is reasonable or appropriate, not necessarily by imposing its interpretation and constraining choices but by quietly integrating itself into the background architecture of everyday life.
As these examples demonstrate, individuals’ relationship with data remains complex and fraught. My participants actively negotiated their positions through a blend of trust, skepticism, appropriation, and resistance, indicating that their engagement could not be reduced to a binary of compliance or rejection. Here, the double-layered domestication perspective proves particularly valuable, revealing how the tangible, routine use of devices (first layer) both reinforces and complicates the internalization and symbolic authority of data itself (second layer). As the following section shows, recognizing data’s normalization as a diffuse social fact raises important questions about internalization—specifically, how data increasingly guide not just how individuals come to know the world, but how they act within it. This sets the stage for a closer examination of how epistemic legitimacy is materially sustained through everyday practice.
Reconditioning Everyday Life
In this section, I build on the notion of presumed facticity to explore how data’s epistemic authority is enacted through material routines and practices, rather than rooted solely in belief or trust. Through repeated interactions with both data devices and outputs, individuals learn to navigate the opacity, authority, and symbolic weight of data, which becomes embedded in both the infrastructure and the rhythms of everyday life.
The subtle yet pervasive influence of data is particularly illuminated by the domestication framework’s emphasis on incorporating technologies into everyday routines. Individuals may adopt data-driven technologies for convenience, but it is through context-specific practices that data are rendered meaningful and actionable, even when appropriating it per se remains challenging. Martin, for example, used sleep data to regulate broader health and nutritional routines, as shown above, while another participant, Melody, employed health and calendar apps to structure fitness and nutritional goals, driven by a desire to “sort [herself] out” via self-tracking. These cases illustrate how data becomes practically meaningful within broader practices of self-care and personal improvement, where participants incorporate data as “raw material” for informed decision making (Pantzar & Ruckenstein, 2017).
Such examples reflect a wider trend among participants who used data in pursuit of concrete aims. For instance, Olivier “unliked” Australia-related social media pages on returning to the United Kingdom to prioritize local relevance. He first engaged with the platform’s settings (device domestication) to realign the data flowing back at him (data domestication), thus tailoring data to an evolving life situation. This dual process clarifies the dynamics at play. Data devices like smartphones and interface structures access and mediate engagement, becoming symbolically—and sometimes physically—embedded in routines. Meanwhile, data, as an epistemic object, are appropriated and made meaningful through social practices. Here, the practice-oriented dimension of the domestication framework intersects fruitfully with practice theory (Schatzki, 2002), foregrounding how life is organized through materially embedded, routinized, and socially recognized actions. While Silverstone and his collaborators did not explicitly adopt the language of practice theorists, their emphasis on mundane practices resonates strongly with these theoretical perspectives.
Integrating data as a diffuse social fact into routines and practices then creates a sociotechnical backdrop against which everyday life unfolds. Once again, the centrality of data does not imply blind dependence or trust. Instead, individuals structure or rearticulate activities around data—much as they previously did with the media (Couldry, 2012)—even when their engagement is inconsistent or consistently reflexive. Objectification and incorporation processes are not always smooth or conscious, partly because of the presence of the data double (Haggerty & Ericson, 2003), a datafied approximation of the self existing alongside embodied experience. Participants frequently referred to this double, explicitly or implicitly, without using the exact term, as an unsolicited yet persistent companion. For instance, one participant, Elsa, described data-based metrics as akin to “a helpful friend you didn’t want”—useful yet intrusive. She chose, however, not to alter the privacy settings, recognizing that even such an act would “tell those businesses something about [her]self.” Her decision reflects an awareness that the data trail is diffuse yet consequential, an extension of the self that cannot be easily disentangled.
Similarly, as mentioned, Olivier considered data and data devices as an “external brain,” alongside several others, managing functions traditionally reliant on memory or social norms, such as remembering birthdays or managing appointments. The participants in this strand thus regarded the data double as ordinary and even beneficial, normalizing ambivalence and reinforcing the belief that convenience justifies data exchange, echoing several participants in a study by Turow et al. (2015). This ambivalence—structuring life around data despite doubts—has become one of the key strategies of the incorporation process and reflects broader tensions in contemporary data-related practices, echoing Esposito and Stark’s (2019) observation that data outputs (e.g., rankings, ratings) are routinely consulted despite acknowledged inaccuracies (p. 11). Initially designed to reduce uncertainty, data outputs frequently generate new ambiguities, shifting attention from reliability to consequences. Their power, therefore, lies not so much in actual objectivity as in functioning as second-order observations—judgements shaped by perceptions of others’ valuations.
On social media and many data platforms, however, these second-order signals often appear to directly reflect first-order realities because of their felt proximity, immediacy, and personal relevance, being closely linked to, and perceived as stemming from, one’s own interactions and behaviors. Unlike traditional second-order observations, such as wine scores or university rankings, that are valued primarily because others also recognize their authority (Esposito & Stark, 2019), data outputs from social media and data platforms—metrics, recommendations, and algorithmically curated content—are often considered meaningful in and of themselves, precisely because they feel personally generated and empirically grounded. The legitimacy of these data outputs often arises from the surrounding sociotechnical conditions rendering them visible, comparable, and actionable in specific ways (Fiore-Gartland & Neff, 2015)—conditions which, in the case of social media and data platforms, are still institutionally processed and shaped by algorithmic systems, platform architectures, and commercial logics (Marres & Gerlitz, 2016). It is this collapse of the perceived immediacy of one’s own data traces into the invisible mediation of data infrastructures that grants data its compelling force and deepens its affective grip, reinforcing the presumption of its facticity even when, like rankings and ratings, it is externally constructed and curated.
This perhaps helps to explain why most participants, despite differing views, consistently highlighted the importance of “responsible data use,” balancing the benefits of data with a desire for autonomy. Sonia succinctly summarized this sentiment: “We should be able to have all the cool stuff data can do for us, but with real control over our information,” implying that the core issue resides not with data itself, but with institutional asymmetries governing its circulation and use.
This tension was not merely abstract but manifested in daily acts of negotiation, as uncertainty remained a recurring theme. Participants like Gisle attempted to “trick the system” by refreshing feeds or removing unwanted content from his Instagram feed, hoping to influence his digital exposure. Others relied on interpretive strategies. William, a tech journalist, was well aware that his social media feeds were algorithmically curated, yet he described anomalies as “non-threatening”—quirks in a largely “self-directed” digital environment. He employed what Eslami et al. (2015) call folk theories—experience-based explanations that help users render opaque systems intelligible and manageable. For William, these folk theories were not mere cognitive shortcuts, but practical tactics (partially) grounded in habitual routines of scrolling, clicking, interpreting, and meaning-making. These acts helped to stabilize his sense of epistemic control and embedded algorithmic presence more deeply into his everyday rhythms. Such actions together reveal a recurring impulse to make opaque systems “work better”—a form of domestication where ambiguity is accepted and normalized through ongoing coping practices.
Some others expressed more resigned attitudes. Lisa reflected: “If you want to use the apps or technology, you have to accept that.” Her stance reflects an “affective resignation” (Draper & Turow, 2019), where datafication is seen as inevitable in the face of feelings of diminished individual control. Yet, even this resignation was not passive—it constituted a form of negotiated acceptance, as noted by Turow et al. (2015), in which participants acknowledged the terms of data infrastructures while seeking to retain a semblance of autonomy. Such negotiations constitute another modality of objectification and incorporation, whereby individuals accommodate structural opacity not through adaptation, critique, or withdrawal, but through selective consent and routinized pragmatism.
Taken together, these examples reveal that participants did not necessarily navigate data and algorithmic opacity by demanding transparency or understanding, but by sustaining a sense of order and continuity within their daily routines. Neither data nor technological devices need to be fully trusted or understood to be influential; they only need to be actionable and sufficiently responsive to fit in with and support ongoing practices. This pragmatic orientation plays a key role in further domesticating data, embedding it within habitual patterns while diminishing the need for epistemic certainty. It is also this routinized integration, rather than reflexivity alone, that assists individuals in appropriating and objectifying data, assigning meaning to it, and determining its role within their everyday lives. Through domestication, participants accommodated data systems into their lived environments, even when their workings remained opaque. Meanwhile, data become more deeply domesticated—not just acquiring a place symbolically or spatially, but through the ongoing labor of making it “work” in practice, grounded in its diffuse presence and perceived legitimacy.
These varied forms of engagement reflect distinct phases of domestication through which data mediate not only representation but also the very operative logics by which individuals interpret and organize everyday life (Couldry & Hepp, 2016). Through objectification and incorporation, data are woven into routines and normalized. Then, through conversion, as explored in more detail in the next section, data practices acquire symbolic significance, projecting shared identities, values, and aspirations. Together, these processes demonstrate how data can recondition what is seen as appropriate, valuable, or legitimate in contemporary life.
Data as Moral Compass
From its early formulations, domestication theory has highlighted the moral struggles individuals face when integrating new technologies into the household’s moral economy (Silverstone, 2006, p. 236), pointing to tensions that go beyond mere practical concerns of adoption and adaptation. These tensions signal a potential remaking of the meanings, values, and norms associated with a technology as it becomes entangled with an individual’s identity and social relations. Building on this foundation, I offer a preliminary exploration of how data may function as a moral compass, guiding decisions, shaping ethical identities, and offering frameworks of worth that complement or reconfigure traditional sources of moral reflection. The focus here shifts to how data become objectified and incorporated as a moral resource: a means through which individuals understand what it means to be “good,” “responsible,” or “worthwhile.”
“I get wrong recommendations as they [social media platforms] don’t have enough data about me and make the wrong conclusions about who I am … I’d rather let everything [about myself] be recorded 100%,” explained Ethan, one of the participants. Frustrated by inaccurate LinkedIn recommendations, he chose to voluntarily provide more data about himself to correct the system. Given that he is a paralegal accustomed to working within procedural systems, this move could be read as a form of solutionism. At the same time, however, it reveals how individuals learn to align with valuation systems by participating more fully in them, bypassing concerns like surveillance or data extraction. Here, data become a medium of moral alignment: to be seen as a competent professional, one must become fully datafied, aligning self-presentation with system legibility.
This dynamic is more sharply illustrated in another participant’s case. Liz curated her digital presence on LinkedIn and Twitter to signal her achievements and materialize her aspirations: “Putting the data will make it happen,” she said. Such a comment reveals a belief in data’s performative capacity, that making something data-visible actualizes its moral and personal legitimacy. This affective engagement with data signals how data become part of a projected moral identity, not simply what one is, but who one ought to become. In this sense, data participates in shaping the values that constitute and define our ethical selves (Taylor, 1989).
Of course, not all engagements with data were enthusiastic. Anthony, as seen above, passively accepted a dull social media feed as a reflection of himself, treating data outputs as reflective inevitabilities. Similarly, another participant, Emily, exemplified reliance on wellness apps more than on expert medical advice. Despite her postgraduate critical training in sociology, she still found app-based data more helpful, not necessarily because it was more accurate but because it offered a sense of specificity and personal relevance that institutional sources seemed to lack. Still, in both cases, data become part of the default framework for moral reasoning, not because it is ineluctably more trustworthy but because it appears more actionable or responsive than conventional alternatives (even though such responsiveness may reflect a kind of false intimacy, where the illusion of personalization replaces nuanced judgment).
This logic also extends to interpersonal and relational domains. One participant, Archie, who worked for a data tech start-up, viewed “likes” and comments on social media as insignificant—or, as he put it, “just data”—but noted that his girlfriend treated them as affective gestures and even signs of care. For her, data-based engagement held moral weight; for him, it was negligible. This divergence illustrates how data practices encode relational expectations and symbolic responsibilities, even when their meanings are contested. As Gerlitz (2016) notes, such data outputs are designed to be flexible, open to multiple interpretations of value. This dynamic reflects the conversion phase of domestication, where technologies and associated practices assume symbolic and moral roles beyond their functional utility and individual conception. Practices of care or attentiveness may be rearticulated as they become nested within broader sociotechnical networks where platform interfaces translate such gestures into data that intersect with other operationalized standards like visibility or responsiveness. Within these systems, failure to sustain visible signals of care might risk relational misinterpretation.
“Sharenting” provides another case in point. Some participants who were parents rationalized their social media posting habits (about their babies) not only as documentation but also (despite well-documented concerns, e.g., Lipu & Siibak, 2019) as responsible parenting—an ethical commitment to record and perform care. These practices often extended beyond social media platforms to include ancillary technologies such as smart baby monitors and wearable sleep trackers, producing instrumental data that were sometimes cross-referenced with more representational forms of sharing. The iterative coupling of device handling and epistemic validation of outputs reappears here, reinforcing the integration of data into everyday parenting routines. Thus, as data-related practices become associated with being a “good parent,” “good partner,” or “competent person,” they undergo conversion, embedding data into shared norms of care, productivity, and social worth and entangling quotidian self-regulation with normative judgements.
The same embeddedness could also enable tactical forms of resistance. Yet, even when participants expressed discomfort with data collection, as in the case of Elsa’s unease with hypertargeted ads, they often rationalized their continued participation in terms of perceived benefit or fairness. Their moral calculus did not center on data extraction per se, but on whether the outcomes felt proportionate, legitimate, or valuable. While conceptions of fairness vary (Kennedy et al., 2015), the broader logic aligns with Espeland and Sauder’s (2007) argument that quantification reshapes what counts as valuable. Here, data assign moral worth by making specific aspects of life legible, comparable, and actionable. Although no participant explicitly claimed that their datafied self was more authentic or trustworthy, many implicitly revolved their thinking around data to affirm, (re)evaluate, and justify their decisions.
These patterns suggest a quiet yet significant shift in how moral agency is exercised. Unlike earlier sources of ethical orientation, from religious doctrine and cultural traditions to introspective dialogue (Taylor, 1989), data seemingly offer a mode of moral calibration that privileges measurement over meaning and proximity over depth. As discussed, it demands less introspection but promotes self-regulation through data outputs. As these quantified modes of ethical reasoning gain prominence, the space for ambiguity, relational nuance, or imaginative moral reasoning might be reshaped, if not sidelined. The risk is not that data dictate who we are, but that it channels how we come to know and guide ourselves. In this regard, data function not just as an informational scaffold, but also as an affective and normative infrastructure. As seen in Emily’s and Archie’s cases, value judgements and ethical decisions increasingly emerge from within datafied environments (even though they may be layered with existing moral codes). Being “good” becomes entangled with participating in data and platform logics: sharing, optimizing, responding, and recording.
This is far from suggesting a wholesale replacement of traditional moral frameworks or asserting a definitive status for data as a moral infrastructure. As history shows, ideas of value and morality have evolved through slow and gradual recalibration. Yet, as data mediate more of the everyday practices through which moral meaning is enacted and interpreted, its growing role as a moral compass must be acknowledged. The focus here is not on normative assessments of data collection or ethics policy, but on data’s cultural significance as a medium for redrawing the contours of moral selfhood. William’s reflection captures this shift:
In the days of RSS feeds I tried to organize everything and read through everything, but now I just trust the sorting algorithms … My reasoning is, if it’s [particular piece of news or information] important enough it’ll appear somewhere else, if not that’s fine … technologies aren’t perfect but neither are us humans.
This illustrates a moral repositioning: trust in technological systems is naturalized and justified not by their perfection or even enhanced efficiency, but by their perceived equivalence to human fallibility. Delegating the labor of judgment to algorithmic and data infrastructures not only reorganizes attention but reshapes what is perceived as worth knowing and, by extension, worth caring about.
While data-driven decisions are often considered as complementing traditional moral reasoning, this section offers a preliminary conceptual mapping: an effort to theorize how data become a moral infrastructure in everyday life. The domestication framework, particularly its conversion phase, clarifies how such infrastructures acquire shared cultural meanings, guiding identity, responsibility, and ethical conduct in diffuse yet powerful ways. As data become entangled in how individuals assess value, perform accountability, and articulate ethical selfhood, its role in shaping moral life must be taken seriously, not only as a technical or behavioral issue but also as a cultural and ethical transformation in the making.
Conclusion: The Paradox of the Data Subject
This article has argued that data, far from being a purely instrumental tool, has become an epistemic and moral infrastructure subtly yet pervasively reshaping how individuals navigate everyday life. Through domestication theory, and informed by empirical insights, we see how individuals appropriate, objectify, incorporate, and convert data into meaningful practices and expressions of identity, care, and value. My research participants engaged reflexively with data and data systems, while also normalizing and routinizing their presence, even amid ambivalence and uncertainty.
The domestication of data reveals a central paradox. On one hand, individuals use data to assert control, enhance efficiency, and perform moral selfhood. On the other hand, these very practices deepen their entanglement with data, constructing data as indispensable micro-infrastructures of everyday life (Berker, 2023, p. 29) and reinforcing their condition as data subjects. What emerges is not merely a set of data-informed behaviors but a mode of being increasingly oriented around data as a social fact that shapes perception, conduct, and ethical understanding.
While proponents of data analytics often claim that data enable more rational and informed decision making, the findings here suggest a more ambivalent reality. Data can provide useful feedback, but it cannot fully capture the complex and relational dimensions of moral life. Overreliance on data risks rendering moral decisions more transactional, privileging measurable outcomes over introspection, dialogue, and the capability to dwell in uncertainty. Although my participants often distrusted data platforms or the companies behind them, they continued to accept data outputs as meaningful or “good enough.” This acceptance marks a shift: data fill epistemic and ethical gaps, offering orientation where introspective or communal frameworks may falter. However, this may limit moral imagination, as quantified outputs increasingly shape what is visible, knowable, and valuable (Badiou, 2008, cited in Gerlitz, 2016).
This process is far from totalizing. Participants displayed multiple negotiation tactics: limiting data disclosure, developing folk theories, or reinterpreting their digital traces. Domestication here denotes an ambivalent process of coping and sometimes contestation. Nevertheless, dominant cultural scripts—particularly within Western, individualized contexts—tend to valorize optimization, self-analysis, responsibilization, and visibility (Henrich, 2020), nudging individuals toward self-datafication. This entanglement of empowerment and obligation constitutes the paradox of the data subject. In striving to become good parents, caring partners, or competent workers, individuals increasingly perform these identities through data, which thus becomes both a medium and a marker of virtue—a proxy for value and an infrastructure of moral becoming.
Beyond extending the empirical application of domestication theory, this article contributes to its conceptual development in three ways. First, it shifts the locus of domestication from discrete, home-based devices to intangible data, reflecting a context in which the boundary between “home” and the outside world is increasingly blurred. This enables the theory to address the evolving dynamics through which individuals integrate data into everyday routines. Second, it theorizes a reciprocal dynamic between data and material devices, showing how routine engagements create a feedback loop that enables the domestication of data—even without full trust or understanding—by foregrounding its actionable quality. Third, it extends the conversion phase by illustrating how data become embedded in the moral and affective fabric of everyday life, guiding ethical self-assessment and enabling moral calibration. While domestication theory has long acknowledged value struggles (between the moral economy of the household and the capitalist market), this article broadens its scope by theorizing how individuals incorporate data into their ethical frameworks through everyday negotiations, rather than merely responding to external imposition.
Certain limitations should be acknowledged. This research draws on fieldwork conducted in England and thus reflects imaginaries and practices shaped by specific Western configurations of selfhood, morality, and technology. As Milan and Treré (2019) remind us, however, data imaginaries are not universal but embedded in particular historical and cultural contexts. Future research should explore how the domestication of data unfolds in non-Western and non-Anglophone settings, where alternative ethical frameworks may yield different modes of engagement.
Although my fieldwork predated the widespread adoption of next-generation data collection and processing tools like generative AI, this does not diminish the article’s broader theoretical contribution. In fact, the theoretical lens developed here may prove even more relevant in an age of generative systems, which foreground efficiency and productivity by freeing time otherwise spent on reading, reasoning, and interrogation, as implied in Apple’s recent commercial for “Apple Intelligence” (Apple, 2024). The aim is not to provide an up-to-the-minute empirical snapshot, but to develop a theoretical lens for understanding enduring processes of meaning-making and valuation. Moreover, moral frameworks evolve gradually through ongoing recalibrations rather than technological rupture alone. This article, then, offers a way to apprehend how data become embedded in routines and in moral reasoning itself: a process likely to persist and intensify as new technologies continue to emerge.
In this light, the concluding provocation is this: as individuals seek agency, orientation, and recognition through data, they may simultaneously reinforce the very infrastructures that shape and constrain those desires. Data no longer merely support social life but configure its terms. Recognizing the epistemic and moral dimensions of datafication is therefore both a sociological and an ethical imperative.
References
Amer, K., & Noujaim, J. (Directors). (2019). The great hack [Documentary film]. New York, NY: The Othrs.
Apple. (2024, November 4). Apple Intelligence | Catch up quick | MacBook Pro [Video file]. YouTube. Retrieved from https://www.youtube.com/watch?v=BK8bnkcT0Ng
Bakardjieva, M. (2006). Domestication running wild: From the moral economy of the household to the mores of culture. In T. Berker, M. Hartmann, Y. Punie, & K. Ward (Eds.), Domestication of media and technology (pp. 62–79). Maidenhead, UK: Open University Press.
Barbrook, R. (2007). Imaginary futures. London, UK: Pluto Press.
Baym, N. K. (2015). Personal connections in the digital age (2nd ed.). Cambridge, UK: Polity.
Beer, D. (2019). The data gaze. London, UK: SAGE Publications.
Berker, T. (2023). Domestication as user-led infrastructuring. In M. Hartmann (Ed.), The Routledge handbook of media and technology domestication (pp. 29–41). Abingdon, UK: Routledge.
Bolin, G., & Velkova, J. (2020). Audience-metric continuity? Approaching the meaning of measurement in the digital everyday. Media, Culture & Society, 42(7–8), 1193–1209. doi:10.1177/0163443720907017
boyd, d., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. doi:10.1080/1369118X.2012.678878
Bucher, T. (2017). The algorithmic imaginary: Exploring the ordinary affects of Facebook algorithms. Information, Communication & Society, 20(1), 30–44. doi:10.1080/1369118X.2016.1154086
Cohen, J. E. (2019). Between truth and power. New York, NY: Oxford University Press.
Cotter, K., Ritchart, A., De, A., Foyle, K., Kanthawala, S., McAtee, H., & Watson, T. X. (2024). If you’re reading this, it’s meant for you: The reflexive ambivalence of algorithmic conspirituality. Convergence, 30(6), 1893–1918. doi:10.1177/13548565241258949
Couldry, N. (2012). Media, society, world. Cambridge, UK: Polity.
Couldry, N., & Hepp, A. (2016). The mediated construction of reality. Cambridge, UK: Polity.
Couldry, N., & Mejias, U. A. (2019). The costs of connection. Stanford, CA: Stanford University Press.
Couldry, N., & Yu, J. (2018). Deconstructing datafication’s brave new world. New Media & Society, 20(12), 4473–4491. doi:10.1177/1461444818775968
Das, R. (2024). Data reflexivity as work-in-progress: A relational, life-course approach to people’s encounters with datafication. Convergence, 30(6), 1939–1958. doi:10.1177/13548565241270889
Draper, N. A., & Turow, J. (2019). The corporate cultivation of digital resignation. New Media & Society, 21(8), 1824–1839. doi:10.1177/1461444819833331
Durkheim, É. (1982). The rules of sociological method (S. Lukes, Ed.). New York, NY: Free Press. (Original work published 1895)
The Economist. (2017, May 6). Data is giving rise to a new economy. Retrieved from https://www.economist.com/briefing/2017/05/06/data-is-giving-rise-to-a-new-economy
Eslami, M., Rickman, A., Vaccaro, K., Aleyasen, A., Vuong, A., Karahalios, K., … Sandvig, C. (2015). “I always assumed that I wasn’t really that close to [her]”: Reasoning about invisible algorithms in news feeds. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI ’15), Apr, 153–162. https://doi.org/10.1145/2702123.2702556
Espeland, W. N., & Sauder, M. (2007). Rankings and reactivity: How public measures recreate social worlds. American Journal of Sociology, 113(1), 1–40. doi:10.1086/517897
Esposito, E., & Stark, D. (2019). What’s observed in a rating? Rankings as orientation in the face of uncertainty. Theory, Culture & Society, 36(4), 3–26. doi:10.1177/0263276419826276
Fiore-Gartland, B., & Neff, G. (2015). Communication, mediation, and the expectations of data: Data valences across health and wellness communities. International Journal of Communication, 9, 1466–1484.
Flensburg, S., & Lomborg, S. (2023). Datafication research: Mapping the field for a future agenda. New Media & Society, 25(6), 1451–1469. doi:10.1177/14614448211046616
Gerlitz, C. (2016). What counts? Reflections on the multivalence of social media data. Digital Culture & Society, 2(2), 19–38. doi:10.14361/dcs-2016-0203
Gitelman, L., & Jackson, V. (2013). Introduction. In L. Gitelman & V. Jackson (Eds.), “Raw data” is an oxymoron (pp. 1–14). Cambridge, MA: MIT Press.
Haggerty, K. D., & Ericson, R. V. (2003). The surveillant assemblage. British Journal of Sociology, 51(4), 605–622. doi:10.1080/00071310020015280
Hartley, J. M., & Schwartz, S. A. (2020). Trust, disconnection, minimizing risk and apathy: A compass of coping tactics in datafied everyday lives. MedieKultur: Journal of Media and Communication Research, 36(69), 11–28. doi:10.7146/mediekultur.v36i69.121182
Hartmann, M. (2020). (The domestication of) Nordic domestication? Nordic Journal of Media Studies, 2(1), 47–57. doi:10.2478/njms-2020-0005
Henrich, J. (2020). The WEIRDest people in the world. New York, NY: Farrar, Straus and Giroux.
Jasanoff, S., & Kim, S.-H. (2015). Dreamscapes of modernity: Sociotechnical imaginaries and the fabrication of power. Chicago, IL: University of Chicago Press.
Kelly, K. (2017). The inevitable. New York, NY: Penguin Books.
Kennedy, H. (2018, March 29). How people feel about what companies do with their data is just as important as what they know about it. LSE Impact Blog. Retrieved from https://blogs.lse.ac.uk/impactofsocialsciences/2018/03/29/how-people-feel-about-what-companies-do-with-their-data-is-just-as-important-as-what-they-know-about-it/
Kennedy, H., Elgesem, D., & Miguel, C. (2015). On fairness: User perspectives on social media data mining. Convergence, 23(3), 270–288. doi:10.1177/1354856515592507
Kennedy, H., & Hill, R. L. (2018). The feeling of numbers: Emotions in everyday engagements with data and their visualisation. Sociology, 52(4), 830–848. doi:10.1177/0038038516674675
Knorr, C., Wolter, M., & Pentzold, C. (2024). Whistleblower memoirs: Deconstructing data consultants’ insider stories. Social Media + Society, 10(1), 1–10. doi:10.1177/20563051231224730
Lipu, M., & Siibak, A. (2019). “Take it down!”: Estonian parents’ and pre-teens’ opinions and experiences with sharenting. Media International Australia, 170(1), 57–67. doi:10.1177/1329878X19828366
Livingstone, S. (2019). Audiences in an age of datafication: Critical questions for media research. Television & New Media, 20(2), 170–183. doi:10.1177/1527476418811118
Lomborg, S., Reutter, L. M., & Vilaza, G. N. (2024). Dreaming with data: Cultivating critical consciousness in datafied living research. Convergence, 30(6), 2042–2058. doi:10.1177/13548565241288454
Mansell, R. (2012). Imagining the Internet. Oxford, UK: Oxford University Press.
Markham, A. N., & Tiidenberg, K. (Eds.). (2020). Metaphors of Internet. New York, NY: Peter Lang.
Marres, N., & Gerlitz, C. (2016). Interface methods: Renegotiating relations between digital social research, STS and sociology. The Sociological Review, 64(1), 21–46. doi:10.1111/1467-954X.12314
McAdams, R. H. (2015). The expressive powers of law: Theories and limits. Cambridge, MA: Harvard University Press.
Milan, S., & Treré, E. (2019). Big data from the South(s): Beyond data universalism. Television & New Media, 20(4), 319–335. doi:10.1177/1527476419837739
Nolin, J. (2019). Data as oil, infrastructure or asset? Three metaphors of data as economic value. Journal of Information, Communication and Ethics in Society, 18(1), 28–43. doi:10.1108/JICES-04-2019-0044
Pantzar, M., & Ruckenstein, M. (2017). Living the metrics: Self-tracking and situated objectivity. Digital Health, 3, 1–10. doi:10.1177/2055207617712590
Pentzold, C., & Knorr, C. (2024). When data became big: Revisiting the rise of an obsolete keyword. Information, Communication & Society, 27(3), 600–617. doi:10.1080/1369118X.2023.2227673
Richards, N. (2015). Intellectual privacy. New York, NY: Oxford University Press.
Schatzki, T. R. (2002). The site of the social. University Park: Pennsylvania State University Press.
Silverstone, R. (1994). Television and everyday life. London, UK: Routledge.
Silverstone, R., & Haddon, L. (1996). Design and the domestication of information and communication technologies: Technical change and everyday life. In R. Mansell & R. Silverstone (Eds.), Communication by design (pp. 44–74). Oxford, UK: Oxford University Press.
Silverstone, R., Hirsch, E., & Morley, D. (1992). Information and communication technologies and the moral economy of the household. In R. Silverstone & E. Hirsch (Eds.), Consuming technologies: Media and information in domestic spaces (pp. 115–131). London, UK: Routledge.
Silverstone, R. (2006). Domesticating domestication: Reflections on the life of a concept. In T. Berker, M. Hartmann, Y. Punie, & K. Ward (Eds.), Domestication of media and technology (pp. 229–248). Maidenhead, UK: Open University Press.
Taylor, C. (1989). Sources of the self. Cambridge, MA: Harvard University Press.
Turow, J., Hennessy, M., & Draper, N. (2015). The tradeoff fallacy: How marketers are misrepresenting American consumers and opening them up to exploitation. Annenberg School for Communication. Retrieved from https://repository.upenn.edu/handle/20.500.14332/2185
Van Dijck, J. (2014). Datafication, dataism and dataveillance: Big data between scientific paradigm and ideology. Surveillance & Society, 12(2), 197–208. doi:10.24908/ss.v12i2.4776
Yu, J., & Couldry, N. (2022). Education as a domain of natural data extraction: Analysing corporate discourse about educational tracking. Information, Communication & Society, 25(1), 127–144. doi:10.1080/1369118X.2020.1764604
Zuboff, S. (2019). The age of surveillance capitalism. New York, NY: Public Affairs.
Copyright © 2025 (Jun Yu). Licensed under the Creative Commons Attribution Non-commercial No Derivatives (by-nc-nd). Available at https://ijoc.org.
[1] The author would like to thank the anonymous reviewers for their invaluable comments on earlier versions of this manuscript, and Professor Christian Pentzold and Dr. Charlotte Knorr for organizing this special issue. The author also thanks Professor Myria Georgiou for her insights on recent domestication studies, and Professor Nick Couldry for his guidance and inspiration in social theory during the analysis of the data used in this manuscript.
[2] All research participants’ names are pseudonyms.