Moving Beyond Market Research: Demystifying Smartphone User Behavior in India
11 Sep 2017-Vol. 1, Iss: 3, pp 82
TL;DR: It is observed that Indian users spend significant time with their smartphones after midnight, continuously check notifications without attending to them and are extremely conscious about their smartphones’ battery.
Abstract: Large-scale mobile data studies can reveal valuable insights into user behavior, which in turn can assist system designers to create better user experiences. After a careful review of existing mobile data literature, we found that there have been no large-scale studies to understand smartphone usage behavior in India -- the second-largest and fastest growing smartphone market in the world. With the goal of understanding various facets of smartphone usage in India, we conducted a mixed-method longitudinal data collection study through an Android app released on Google Play. Our app was installed by 215 users, and logged 11.9 million data points from them over a period of 8 months. We analyzed this rich dataset along the lines of four broad facets of smartphone behavior -- how users use different apps, interact with notihcations, react to different contexts, and charge their smartphones -- to paint a holistic picture of smartphone usage behavior of Indian users. This quantitative analysis was complemented by a survey with 55 users and semi-structured interviews with 26 users to deeply understand their smartphone usage behavior. While our first-of-its-kind study uncovered many interesting facts about Indian smartphone users, we also found striking differences in usage behavior compared to past studies in other geographical contexts. We observed that Indian users spend significant time with their smartphones after midnight, continuously check notifications without attending to them and are extremely conscious about their smartphones’ battery. Perhaps the most dramatic finding is the nature of mobile consumerism of Indian users as shown by our results. Taken together, these and the rest of our findings demonstrate the unique characteristics that are shaping the smartphone usage behavior of Indian users.
Citations
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TL;DR: This review paper focuses on studies that have used smartphone sensing for the well-being of young adults and proposes taxonomies motivated from human science literature, which enable to identify important study areas.
Abstract: Over the years, mobile phones have become versatile devices with a multitude of capabilities due to the plethora of embedded sensors that enable them to capture rich data unobtrusively. In a world where people are more conscious regarding their health and well-being, the pervasiveness of smartphones has enabled researchers to build apps that assist people to live healthier lifestyles, and to diagnose and monitor various health conditions. Motivated by the high smartphone coverage among young adults and the unique issues they face, in this review paper, we focus on studies that have used smartphone sensing for the well-being of young adults. We analyze existing work in the domain from two perspectives, namely Data Perspective and System Perspective. For both these perspectives, we propose taxonomies motivated from human science literature, which enable to identify important study areas. Furthermore, we emphasize the importance of diversity-awareness in smartphone sensing, and provide insights and future directions for researchers in ubiquitous and mobile computing, and especially to new researchers who want to understand the basics of smartphone sensing research targeting the well-being of young adults.
17 citations
03 Sep 2018
TL;DR: In this paper, the authors uncover broad, latent patterns of mobile phone use behavior and reveal five generic phone use profiles which describe at least 10% of the participants each: limited use, business use, power use, and personality-and externally induced problematic use.
Abstract: Not all smartphone owners use their device in the same way. In this work, we uncover broad, latent patterns of mobile phone use behavior. We conducted a study where, via a dedicated logging app, we collected daily mobile phone activity data from a sample of 340 participants for a period of four weeks. Through an unsupervised learning approach and a methodologically rigorous analysis, we reveal five generic phone use profiles which describe at least 10% of the participants each: limited use, business use, power use, and personality- & externally induced problematic use. We provide evidence that intense mobile phone use alone does not predict negative well-being. Instead, our approach automatically revealed two groups with tendencies for lower well-being, which are characterized by nightly phone use sessions.
16 citations
17 Dec 2020
TL;DR: Zhang et al. as discussed by the authors performed a qualitative and quantitative user study by adopting a mixed-methods approach to understand the objective car sharing user behavior from different dimensions, e.g., spatial, temporal, and demographic.
Abstract: Qualitative and quantitative user studies can reveal valuable insights into user behavior, which in turn can assist system designers in providing better user experiences. Car sharing (e.g., Zipcar and car2go), as an emerging App-based online shared mobility mode, has been increasing dramatically worldwide in recent years. However, to date, comprehensive user behavior in car sharing systems has not been investigated, which is essential for understanding their characteristics and promotion roadblocks. With the goal of understanding various facets of user behavior in online car sharing systems, in this paper, we performed a qualitative and quantitative user study by adopting a mixed-methods approach. We first designed an attitude-aware online survey with a set of qualitative questions to perceive people's subjective attitudes to online car sharing, where a total of 185 participants (68 females) completed the survey. Next, we quantitatively analyzed a one-year real-world car sharing operation dataset collected from the Chinese city Beijing, which involves over 68,000 unique users and over 587,850 usage records. We dissected this attitude-free dataset to understand the objective car sharing user behavior from different dimensions, e.g., spatial, temporal, and demographic. Furthermore, we conducted a comparative study by utilizing one-year data from other two representative Chinese city Fuzhou and Lanzhou to show if the obtained findings from Beijing data may be generalizable to other cities having different urban features, e.g., different city size, population density, wealth, and climate conditions. We also do a case study by designing a user behavior-aware usage prediction model (i.e., BeXGBoost) based on findings from our user study (e.g., unbalanced spatiotemporal usage patterns, weekly regularity, demographic-related usage difference, and low-frequency revisitation), which is the basis for car sharing service station deployment and vehicle rebalancing. Finally, we summarize a set of findings obtained from our study about the unique user behavior in online car sharing systems, combined with some detailed discussions about implications for design.
14 citations
21 Jul 2021
TL;DR: The theory and practice of designing a diversity-aware platform for social relations is presented, which allows like-minded individuals to pursue similar interests or diverse individuals to complement each other in a complex activity.
Abstract: Diversity-aware platform design is a paradigm that responds to the ethical challenges of existing social media platforms. Available platforms have been criticized for minimizing users' autonomy, marginalizing minorities, and exploiting users' data for profit maximization. This paper presents a design solution that centers the well-being of users. It presents the theory and practice of designing a diversity-aware platform for social relations. In this approach, the diversity of users is leveraged in a way that allows like-minded individuals to pursue similar interests or diverse individuals to complement each other in a complex activity. The end users of the envisioned platform are students, who participate in the design process. Diversity-aware platform design involves numerous steps, of which two are highlighted in this paper: 1) defining a framework and operationalizing the "diversity" of students, 2) collecting "diversity" data to build diversity-aware algorithms. The paper further reflects on the ethical challenges encountered during the design of a diversity-aware platform.
13 citations
Cites background from "Moving Beyond Market Research: Demy..."
...Collecting smartphone data requires insights into local practices, as the use of smartphones among young people differ across countries (Mathur et al. 2017; Meegahapola and Gatica-Perez 2021)....
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06 May 2021
TL;DR: In this paper, the authors present a detailed analysis that uncovered novel usage patterns, such as extensive usage of directional gestures, reliance on voice and external keyboard for text input, and repurposed explore by touch for single-tap.
Abstract: People with vision impairments access smartphones with the help of screen reader apps such as TalkBack for Android and VoiceOver for iPhone. Prior research has mostly focused on understanding touchscreen phone adoption and typing performance of novice blind users by logging their real-world smartphone usage. Understanding smartphone usage pattern and practices of expert users can help in developing tools and tutorials for transitioning novice and intermediate users to expert users. In this work, we logged smartphone usage data of eight expert Android smartphone users with visual impairments for four weeks, and then interviewed them. This paper presents a detailed analysis that uncovered novel usage patterns, such as extensive usage of directional gestures, reliance on voice and external keyboard for text input, and repurposed explore by touch for single-tap. We conclude with design recommendations to inform the future of mobile accessibility, including hardware guidelines and rethinking accessible software design.
12 citations
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TL;DR: A large-scale deployment-based research study that logged detailed application usage information from over 4,100 users of Android-powered mobile devices is described, which finds that despite the variety of apps available, communication applications are almost always the first used upon a device's waking from sleep.
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645 citations
"Moving Beyond Market Research: Demy..." refers background in this paper
...Predominant App Usage Hours American* 4pm - 8pm [7] 12am - 4am Lowest App Usage Hours American* 4am - 8am [7] 8am - 12pm Mean inter-session duration for Communication apps Korean 26....
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25 Jun 2012
TL;DR: FALCON uses contexts such as user location and temporal access patterns to predict app launches before they occur, and provides systems support for effective app-specific prelaunching, which can dramatically reduce perceived delay.
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358 citations
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26 Apr 2014
TL;DR: This paper presents the first large-scale analysis of mobile notifications with a focus on users' subjective perceptions, and derives a holistic picture of notifications on mobile phones by collecting close to 200 million notifications from more than 40,000 users.
Abstract: Notifications are a core feature of mobile phones. They inform users about a variety of events. Users may take immediate action or ignore them depending on the importance of a notification as well as their current context. The nature of notifications is manifold, applications use them both sparsely and frequently. In this paper we present the first large-scale analysis of mobile notifications with a focus on users' subjective perceptions. We derive a holistic picture of notifications on mobile phones by collecting close to 200 million notifications from more than 40,000 users. Using a data-driven approach, we break down what users like and dislike about notifications. Our results reveal differences in importance of notifications and how users value notifications from messaging apps as well as notifications that include information about people and events. Based on these results we derive a number of findings about the nature of notifications and guidelines to effectively use them.
303 citations
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