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Most commonly used devices.
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As people possess increasing numbers of information devices, situations where several devices are combined and used together have become more common. We present a user study on people's current practices in combining multiple information devices in their everyday lives, ranging from pragmatic tasks to leisure activities. Based on diaries and interv...
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... Apple devices, while the remaining 10 had a more balanced mixture of devices from different manufacturers. In addition to their personal devices, seven participants also had devices provided by their employers. Not surprisingly, the most commonly used devices by the participants were smartphones, computers, tablets, and home media centers ( Fig. ...
Citations
... Particular emphasis will be placed on examining tablets as a specific type of mobile device, as they are particularly popular among the age group of young children (Rideout & Robb, 2020). In addition, the use of other mobile and digital technologies will be considered where appropriate, as the use of different information devices is often interconnected (Jokela et al., 2015). ...
This cumulative doctoral thesis examines the role of mobile devices in young children's information behavior from different perspectives. On the one hand, it explores whether information-related activities are part of young children's use of mobile technologies. On the other hand, it investigates whether aspects of children's information behavior play a role in parents' and children's perceptions of mobile device use. The first study presented in this thesis gains exploratory insight into young children's use of mobile devices through interviews with parents of families with children aged one to six years. Based on a secondary analysis of the interview data, the second study examines how parents perceive and mediate young children's use of mobile devices and discusses how this might influence children's information behavior. By applying a uses and gratifications approach, the third study investigates what customer reviews for a tablet for children reveal about the use of this device and expectations of the families. Using a multi-method approach, the fourth study places a particular focus on the inclusion of children's perspectives and investigates how children aged four to six years use mobile devices and whether aspects related to children's information behavior play a role in families' perceptions of this use. Overall, the results show that mobile devices can clearly play a role in young children's information behavior, although their potential for children's information discovery is not always prominent in parents' and children's perceptions. With these findings, this work makes an important contribution to addressing existing research gaps regarding young children's information behavior in general as well as in the specific context of mobile device use.
... Currently, the Internet is broadly used for working and leisure connections with various electronic devices [62]. In terms of preventing problematic gaming use, keeping away from smart phones, tablet computers, and desktops is not a plausible solution. ...
Gaming is a popular but possibly problematic activity among college students. To distinguish gamers with potential problematic gaming behaviors (PGB) is crucial to mental health staff. Two studies were conducted that aimed to explore portraits of gamers with PGB in college campuses. The first study selected 20 college students, diagnosed with problematic gaming behaviors, from a longitudinal dataset and semi-structured interviews were conducted for a systematic description of long-term PGB. The second study selected four personas with the richest coding data of internet addiction and depression from 20 gamers. The profiles and life experiences of the personas showed changing processes of gaming motives and push–pull–mooring effects across the years. “Loss of purpose in life” and “desperate to escape from stress or boredom in the real world” were the important push effects. Mooring effects revealed their addiction or depression symptoms and the process of developing the addiction. The dynamics of “push”, “pull”, and “mooring” effects were clearly indicated in the results suggesting PGB might be a long-term coping strategy and a consequence of depression and loneliness. Dealing with depression and finding real-life goals could help PGB gamers to change the dynamics of their gaming motives and push–pull–mooring effects. The results may help develop interventions for gamers with problematic gaming behaviors.
... Moreover, an early qualitative study into multi-device use at work observed three key developments: "specializationthe allocation of specific roles of each device; parallelismthe coordination of devices for simultaneous use, and fragmentation -the division of data across multiple devices" (Santosa & Wigdor, 2013, p. 63). In a similar vein, a more recent observational study suggests that multi-device use can be organized into sequential and parallel use, with parallel use either being related, unrelated, or exhibiting some form of resource lending, such as sharing a tablet screen onto a laptop, or using a phone as a TV remote (Jokela et al., 2015). The 'kit' of devices users carry therefore determines what types of activities they can engage in, and have become an influencing factor for their lifestyle (Dearman & Pierce, 2008;Oulasvirta & Sumari, 2007); kit choices have been found to depend on device capabilities, screen size, and portability (Nguyen et al., 2021). ...
... The smartphone's utility to users and its contribution to the kit they carry seems to stay invariable across different assemblages of devices and contexts of use. This is important for the idea of resource lending discussed previously (see section 2.2; Jokela et al., 2015;. The smartphone appears to be the key tool lending resources to other devices and increasing the capability of assemblages for users. ...
Using multiple devices at the same time is becoming increasingly common in the daily lives of users, be it for work or for leisure. This paper presents in situ qualitative and quantitative evidence of multi-device use from a dataset of over 200h of first-person and interview recordings (n = 41). We discuss three different ‘patterns’ of multi device use (work, leisure, mixed use) and illustrate the user experience in detail with three participant journeys. We find that the smartphone was always ‘in the mix’; we did not observe multi-device use without the smartphone, or isolated use of other devices. Overall, we suggest that looking at transitions between activities users engage in rather than devices they use is more effective to understand multi-device use. Based on this analysis, we highlight issues around the patterns and experiences of multi-device use in everyday life and provide recommendations for design and further research.
... Users have started possessing and using multiple devices. The situations in which multiple devices are combined and used together have become more common (Jokela et al., 2015). For example, digital content, such as digital music and video, can be seamlessly streamed or downloaded on multiple devices. ...
Based on a theoretical perspective of the optimum stimulation level (OSL), we investigate how the cross-media usage (multi-device and multi-app usage) of individual users influences their exploration of new music in a music-streaming platform. We also examine whether gender moderates the link between cross-media usage and new music exploration behavior. We analyze survey responses from 1116 college students in China, and our findings show a significant and positive effect of multi-device usage on new music exploration. By contrast, multi-app usage does not have any statistically significant effect. The results also indicate that being a woman positively moderates the relationship between multi-app usage and new music exploration behavior. Our study contributes to the understanding of exploratory user behavior in a new media context by linking the OSL theory to digital music consumption.
... This echoes Dearman et al. 's finding [22] that data access is challenging to manage with multiple devices. Other studies further unpack use-cases and practices, such as the spectrum of sequential-parallel and related-unrelated use of multiple devices, and how users choose between them [41]. Other work identifies key roles of multiple devices and workflows [61], or how attention switches in multi-device setups [57]. ...
... Complementing this categorization of physical device configurations, from survey responses we synthesized five patterns -and pattern combinations -of common multi-device usage (expanding on [41]): integration (e.g., device capabilities complementing each other), cloning (e.g., facilitating collaborative sharing), expanding (e.g., increasing screen real estate), partitioning (e.g., for unrelated parallel tasks), and migrating (e.g., continuing interrupted tasks later on another device). We contextualize these patterns by explaining motivations, perceived values, and challenges behind using multiple devices, and discuss how within fragmented workflows there are not only states, but also transitions -that is, combinations of patterns -such as the sequential flow from integration to partitioning pattern. ...
... Prior research revealed patterns and key behaviors in multi-device usage (e.g., [30,41,61]). Grudin observed that people tend to partition tasks and information between multiple monitors [30]. ...
To better ground technical (systems) investigation and interaction design of cross-device experiences, we contribute an in-depth survey of existing multi-device practices, including fragmented workflows across devices and the way people physically organize and configure their workspaces to support such activity. Further, this survey documents a historically significant moment of transition to a new future of remote work, an existing trend dramatically accelerated by the abrupt switch to work-from-home (and having to contend with the demands of home-at-work) during the COVID-19 pandemic. We surveyed 97 participants, and collected photographs of home setups and open-ended answers to 50 questions categorized in 5 themes. We characterize the wide range of multi-device physical configurations and identify five usage patterns, including: partitioning tasks, integrating multi-device usage, cloning tasks to other devices, expanding tasks and inputs to multiple devices, and migrating between devices. Our analysis also sheds light on the benefits and challenges people face when their workflow is fragmented across multiple devices. These insights have implications for the design of multi-device experiences that support people's fragmented workflows.
... However, sequential patterns in interaction with non-robotic devices are still frequently used [123], in contrast to interaction with robots. For example, a study by Jokela el al. [73] analyses 123 real-life interactions in non-dyadic systems containing multiple devices, they can demonstrate that 37% of users (compared to only 15% in HRI) interact with multiple devices in a sequential manner. ...
... The final aspect for further inquiry relates to what kind of tasks are used in non-dyadic interaction. As Jokela et al. [73] notes, the type of the task influences the changing of interaction modules (e.g., switching devices or adding another device to their parallel usage of multi devices). Examining what interaction principle might be effective for certain kinds of tasks (e.g., simple or complex execution) could be explored in the future research. ...
Going beyond dyadic (one-to-one) interaction has been increasingly explored in HRI. Yet we lack a comprehensive view on non-dyadic interaction research in HRI. To map out 15 years of works investigating non-dyadic interaction, and thereby identifying the trend of the field and future research areas, we performed a literature review containing all 164 publications (2006-2020) from the HRI conference investigating non-dyadic interaction. Our approach is inspired by the 4C framework, an interaction framework focusing on understanding and categorising different types of interaction between humans and digital artefacts. The 4C framework consists of eight interaction principles for multi-user/multi-artefact interaction categorised into four broader themes. We modified the 4C framework to increase applicability and relevance in the context of non-dyadic human-robot interaction. We identify an increasing tendency towards non-dyadic research (36% in 2020), as well as a focus on simultaneous studies (85% from 2006-2020) over sequential. We also articulate seven interaction principles utilised in non-dyadic HRI and provide specific examples. Last, based on our findings, we discuss several salient points of non-dyadic HRI, the applicability of the modified 4C framework to HRI and potential future topics of interest as well as open-questions for non-dyadic research.
... Consumers tend to increasingly own additional smart devices that could provide complementary sensing and logging capabilities (Westcott et al., 2019). This is particularly important since many apps are now synchronized across multiple devices that a person may own (Årsand et al., 2015;Liu et al., 2017) and people sometimes use multiple devices simultaneously (Jokela et al., 2015). That said, integrating data from multiple devices is a topic that all mobile sensing research (not just app usage research) will need to tackle. ...
App usage data provide some of the most psychologically rich information one can collect using mobile sensing methods. Here, we discuss how data from the applications ( “apps”) people use to enhance the functionality of their mobile devices can advance research in all subdisciplines of psychology. First, we describe prior psychological work on app usage behavior. Next, we provide a detailed guide for researchers interested in working with app usage data. Specifically, we discuss different ways to 1) collect app usage data (e.g., usage logs, screenshots), 2) categorize individual apps and app categories, 3) analyze app usage data (e.g., considering app adoption, usage quantities, sequences, within-app behavior), and 4) enrich app usage data (e.g., using web APIs, experience sampling). We conclude by discussing technical and ethical challenges posed by app usage research, as well as an outlook on the future of app usage on new kinds of mobile devices.
... The diary study involved 13 of the 37 participants. The diary study allowed for in situ data collection about the participant's daily work habits, technology arrangements and uses, and provided further insight into participants' practices in naturalistic settings over a longer period of time (Grinter and Eldridge, 2001;Jokela et al., 2015). Since the work day of an MKW involves frequently shifting social and spatial environments (Ciolfi and de Carvalho, 2014), it makes direct researcher observation difficult. ...
We theorize mobile knowledge workers' uses of digital and material resources in support of their working practices. We do so to advance current conceptualizations of both 'information infrastructures' and 'digital assemblages' as elements of contemporary knowledge work. We focus on mobile knowledge workers as they are (increasingly) self-employed (e.g., as freelancers, entrepreneurs, temporary workers, and contractors), competing for work, and collaborating with others: one likely future of work that we can study empirically. To pursue their work, mobile knowledge workers draw together collections of commodity digital technologies or digital assemblages (e.g., laptops, phones, public WiFi, cloud storage, and apps), relying on a reservoir of knowledge about new and emergent means to navigate this professional terrain. We find that digital assemblages are created and repurposed by workers in their infrastructuring practices and in response to mobility demands and technological environments. In their constitution, they are generative to both collaborative and organizational goals. Building from this we theorize that digital assemblages, as individuated forms of information infrastructure, sustain stability and internal cohesion even as they allow for openness and generativity.
... The study indicates that distributed interfaces with clear spatial references to one another can provide users with lower cognitive load in interaction [4]. Today, how to relieve cognitive load has a great significance in a multi-devices environment where switching devices during tasks burdens users with additional cognitive efforts [5]. ...
... In particular, a relative complexity of the washing machine's interface and its combination of various types of physical buttons, knobs, and digital UI components make the washing machine a good starting point for exploring the possible application of this paper's topic to cross-device interaction in a home environment. Secondly, washing machines often enable a parallel use [5] of other devices. Using a washing machine is often accompanied by other tasks simultaneously, like cooking and cleaning, and its relatively simple functionality clears a path to an initial understanding of the parallel use in cross-device interaction scenarios. ...
Multiple distributed interfaces in cross-device interaction increase users’ cognitive load when switching from one device to another. A clear spatial reference and physical affordances can help reduce additional cognitive efforts users pay in a multi-device system. However, increasing physical affordances may adversely affect users’ cognitive process in comprehending the desired interaction. By providing physical affordances in sequential order, interfaces can limit the number of physical affordances at one affordance for each step of the interaction. When assisted by digital contents, these sequential physical affordances can better communicate possible actions of an interface. Tangible Dial, an integrated control interface system with a shape-changing dial and digital displays, was designed and tested with users to validate the principle of spatial references and sequential affordances in cross-device interaction.
... Nearly 81% of the US Internet users engage a second device, most favorably mobile, while watching TV. 18 Academic research is being developed to conceptualize multi-screen usage and understand related consumer behavior (e.g., Blake 2017; Jokela et al. 2015;Rooksby et al. 2015). Social media companies, such as Facebook, have poured significant resources into understanding why and how consumers use multiple devices during content consumption, particularly since most second-screen activities are related to social activities, such as texting friends or visiting social networks. ...
Producing compelling film content profitably is a top priority to the long-term prosperity of the film industry. Advances in digital technologies, increasing availabilities of granular big data, rapid diffusion of analytic techniques, and intensified competition from user-generated content and original content produced by subscription video on demand platforms have created unparalleled needs and opportunities for film producers to leverage analytics in content production. Built upon the theories of value creation and film production, this article proposes a conceptual framework of key analytic techniques that film producers may engage throughout the production process, such as script analytics, talent analytics, and audience analytics. The article further synthesizes the state-of-the-art research on and applications of these analytics, discuss the prospect of leveraging analytics in film production, and suggest fruitful avenues for future research with important managerial implications.
