Cellulographics : A novel smartphone user classification metrics

Despite the worldwide surge in smartphone use, there are no classification metrics based on its use. In this article, a comprehensive concept called ‘Cellulographics’ is introduced for characterization of smartphone users, which includes behavioral classification based on user characteristics like smartphone experience (SE), smartphone use skill (SUS), smartphone internet experience (SIE), smartphone use periods (SUP), smartphone screen time (SST), smartphone use frequency (SUF), smartphone use activities (SUA), and smartphone use location (SUL). This concept can be applied to any field of study without limitations, where smartphone


Introduction
Market segmentation is the key component of marketing (Smith, 1956). Companies strive to understand the customers and their needs to serve and to develop their marketing strategies (Cooil et al., 2008). Typically, marketers use geographic, demographic, psychographic, and behavioral variables for segmentation ( Fig. 1). Geographic segmentation is the oldest type of segmentation (Tynan & Drayton, 1987), followed by demographics, which can be traced from John Graunt's quantitative analyses of the "Bills of Mortality" published in 1662 (Smith & Keyfitz, 2013;Timaeus, 2014). But the term 'Demography' was first coined by a Belgian statistician, Achille Guillard in 1855. The behavioral segmentation became popular in 1960 when researchers proposed classifications based on brand loyalty (Cunningham, 1956), benefits (Haley, 1968), usage (Twedt, 1964), etc. Later in the 1970s, Demby (1971) proposed psychographic segmentation. Since the beginning scientists believed that conventional segmentations were inadequate for designing a marketing strategy (Yankelovich, 1964). Therefore, a few more classifications like Technographics (El-Gohary & Eid, 2013) and Webographics (Grossnickle & Raskin, 2001) emerged. These researchers emphasized that customers have been migrating to the online environment and that websites were important consumer interfaces.

CellulographicsÓ: the new metrics
The traditional segmentations are eventually becoming obsolete because consumers are migrating to smartphones for their daily online activities. Smartphones are versatile, portable, and accessible round the clock (Budiu, 2015). The processing power of present-day's smartphones surpasses that of desktops in the past, thus, allowing users to perform practically any task with ease. In terms of users, the Worldwide market share of mobile phones (54.98%) is far greater than desktops (42.54%) and tablets (2.47%) (Statcounter.com, 2022), and Worldwide, smartphone subscription is expected to grow to 7216 million users by 2026 (Statista, 2021). Excluding tablets, only mobile devices generate 54.4% of website traffic globally (Statista. com, 2022b). Similarly, 80% of social media browsing occurs through smartphones, it's even higher for specific platforms like Facebook (95.1%), Twitter (86%), LinkedIn (60%), etc. (Broadbandsearch. net, 2022). Mobile commerce contributes significantly (72.9%) to worldwide e-commerce retail sales (Statista.com, 2022a). From the above discussion, it is evident that smartphones are emerging as versatile devices enabling the user to perform various activities, yet there are no classification metrics based on smartphone usage to date. To cover this gap 'Cellulographics' has been proposed and defined as below.

Definition
Cellulographics is a term developed for behavioral classification of smartphone users based on diverse characteristics such as smartphone experience (SE), smartphone use skill (SUS), smartphone internet experience (SIE), smartphone use periods (SUP), smartphone screen time (SST), smartphone use frequency (SUF), smartphone use activities (SUA), and smartphone use location (SUL). These terms are explained below (Fig. 2).

Smartphone experience (SE)
It is the total number of years an individual has been using a smartphone. This criterion is crucial because the length of usage, familiarity, compatibility of the innovation with past experiences, existing values, needs, expertise, background, and prior knowledge can either inhibit or promote smartphone use (Al-Ghaith et al., 2010;Dey et al., 2013;Maes et al., 2006;Taylor & Levin, 2014).

Smartphone use skill (SUS)
It is an individual's self-assessment of the ability and proficiency to use the smartphone. Due to the differences in culture, social environment, personal characteristics, technological context, etc., information communication and technology (ICT) skills may vary among users (Haenssgen, 2018;Vimalkumar et al., 2020). Researchers have revealed that mobile phone efficacy, skills, and competence can affect the extent of technology use, perception, motivation, and impact (Campbell & Kwak, 2010;Dey et al., 2013;Liu et al., 2014). For example, specific smartphone skills are essential to access mobile financial services (Kiconco et al., 2020).

Smartphone internet experience (SIE)
It is an individual's internet experience through a smartphone. Marketers consistently attempt to provide a seamless mobile internet experience to the users (Asunmaa et al., 2002). Several advantages including mobility, lightweight, long battery life, instant-on capability, high-definition touch screen, and interactivity are leading to higher smartphone dependency and gratifications (Leung & Zhang, 2016). Uninterrupted internet access through smartphones encourages mobile lifestyle to receive directions while navigating unfamiliar locations, fill dead time by paying a bill or messaging while waiting for public transport, perform time-critical tasks like online meetings or using the internet for entertainment, or playing games (Gilbert & Han, 2005). The user engagement has reached a level where researchers have warned that pedestrian and driver behavior can be described as risky due to their mobile internet use (Byington & Schwebel, 2013;Maier et al., 2020). However, the type of smartphone internet experience is dependent on the efficiency and skill of the user (Liu et al., 2014;Turgut & Kursun, 2020).

Smartphone use periods (SUP)
It is the time of the day when the user indulges in smartphone use, like in the morning (6:01−12:00 h), afternoon (12:01−18:00 h), evening (18:01− 0:00 h), and night (01:00 −6:00 h) (MAEN). Throughout the day, the use of mobile touch screen devices is interspersed within our daily activities (Toh et al., 2019), but researchers believe that differences may exist in total daily duration, a number of  uses, and usage length across the day. Past studies revealed that the number of phone uses in the afternoon and evening differs from both in the morning and at night (Andrews et al., 2015) or least phone use was observed from midnight to early morning (Deng et al., 2019). Individuals indulged in excessive use to fill "empty moments" while commuting, lectures, and mornings/evenings at home (Oulasvirta et al., 2012). Some authors classified users based on their usage pattern and activities at different use periods like "night communicators", "evening learners", etc. (Zhao et al., 2016).

Smartphone screen time (SST)
It is the time measured in hours or minutes per day, spent by an individual on smartphone. Researchers believe that screen time is one of the effective methods for determining the degree of technology usage . These days, even preschool children have screen time of more than an hour per day and mobile phones contribute to more than 90% (Susilowati et al., 2021). Digital-screen time exposure is now an important criterion (Kaur et al., 2021) and researchers link it with health , problematic smartphone use (Horwood et al., 2021), mobile stickiness (Hsu & Tang, 2020) working memory abilities (Toh et al., 2021), customer experience (McLean et al., 2018), etc.

Smartphone use frequency (SUF)
It is the number of user sessions in a specific period or the number of times an individual checks his or her smartphone. SUF is also one of the important criteria to access technology usage  and previous studies indicate that SUF is closely associated with problematic smartphone use (Elhai et al., 2018), smartphone addiction (Andrade et al., 2020), rumination and boredom proneness , poor common executive function (EF) but enhanced shifting-specific abilities (Toh et al., 2021), etc. Recent studies have recommended examining SUF under their future research directions (Gentina & Rowe, 2020). Some researchers calculated frequency, duration, and occurrence of smartphone use sessions and found that on average participants had 24 sessions of 7 minutes each per day and the occurrences of sessions were more on weekdays than weekends (Deng et al., 2019).

Smartphone use location (SUL)
It is the location from which an individual uses the smartphone or accesses the internet through it, such as home, office, leisure place, etc. The biggest advantage of a smartphone is mobility (Cilliers et al., 2018), but social context and location influence users' interaction with their phones (Do et al., 2011). It is quite logical that individuals will use smartphones more when they are idle or waiting in a queue at a bus stop or a food shop or for coordination while traveling than in cinema or library. Further, phone engagement sessions are longer in the comfort of home than at work (Heitmayer & Lahlou, 2021). In this context, different researchers have tried to propose a locational taxonomy, but they lack business context (Zheng et al., 2010) or are too raw about private contexts (Liao et al., 2007). However, locational categories (shopping, movie and shows, work and education, recreation and amusement, food and drink, and sports and exercise) proposed by Exler et al. (2016) are quite balanced.

Smartphone use activities (SUA)
A smartphone is a versatile device, it can be used for a variety of work or leisure activities (Leung & Zhang, 2016). Based on typical daily media and technology usage, researchers have enlisted various types of smartphone activities (Cheever et al., 2014;, however, the one proposed by Elhai et al. (2016) is quite comprehensive and includes a total of 11 activities, which include voice/video calls, email, texting/instant messaging, internet/websites, social networking sites, games, music/podcasts/radio, watching video/tv/movies, taking pictures or videos, maps/navigation and reading books/magazines.

Concluding remarks
This article introduces a new classification metric called 'Cellulographics' based on smartphone use. This concept applies to any field of study without limitations, where smartphone use is involved. For example, medicine (health issues due to smartphone use like insomnia, disturbance or complications in sleep quality, physical and mental fatigue, auditory illusions, ocular issues, muscle pain and stiffness, musculoskeletal ergonomic issues, daily dysfunctions, etc.), psychology (smartphone addiction, stress, anxiety, mood swings, irritability, tolerance, nomophobia, fear of missing out, textiety, textaphrenia, ringxiety, smartphone dependencies like, feeling lost and lonely in the absence of smartphones, anxiously waiting to send or receive messages, uneasiness when they are unable to view messages, seeking attention or sensation, depression, impulsive behavior, pain intolerance, aggression, withdrawal, cognitive issues, etc.), sociology (phubbing, child neglect, child's smartphone use and problematic parenting by parents excessively indulged in smartphone, choosing smartphone use over personal interactions, intentional use of smartphones in potentially dangerous situations like driving or walking in traffic, seeking reassurance from friends and family, establishing online relationships, work-life-conflict, etc.), business management (user experience and engagement, online shopping, smartphone advertising, online reviews, social media, virality, consumers' purchase intention, mobile wallets, financial transactions through smartphones like blockchain, cryptocurrencies, mobile banking, ticket bookings, work-related smartphone use, brand loyalty, use of smartphone for work and non-work tasks, electronic service quality, electronic word of mouth, mobile marketing, content generated by smartphone users, etc.), computers (human-computer interactions, technology adoption, information search and dissemination, clickthrough behavior and data quality on smartphones, smartphone multitasking, smartphone operating systems, trust, security and privacy behaviors of smartphone users, etc.). Moreover, the concept is very flexible to be coupled with device-related characteristics like hardware or software specifications of smartphone or service provider characteristics like Internet connection speed, data plan, network quality, etc.