Abstract
This research tells a story of age, aging, and evolving with mobile technologies in a single Canadian community. Using data from 2005 and 2012, we critically analyze seniors’ use of mobile technologies by applying Taylor’s information use environment. The article seeks to understand the influence of context in studying user behaviour vis-à-vis a) device ownership, b) communication practices, and c) technology preferences. Findings suggest that while the social rhetoric of seniors as adopters of mobile technologies (i.e., silver surfers) is premature, there is evidence of seniors leapfrogging older mobile devices and acquiring smartphones—with consequential complications for catching up to widening skills gaps. We also identify a variability of experiences within this generational group suggesting that there may be an additional digital divide among seniors.
Cet article porte sur l’âge, le vieillissement et l’évolution des technologies cellulaires dans une communauté canadienne spécifique. En recourant à la théorie de Taylor sur l’environnement d’utilisation d’information, nous effectuons l’ analyse critique de l’usage de technologies cellulaires par les aînés. Nous examinons le contexte influençant les comportements de ceux-ci en fonction : 1) de la possession d’appareils; 2) des pratiques communicationnelles; et 3) des préférences technologiques. Bien que la rhétorique selon laquelle les seniors sont des adeptes de technologies cellulaires soit prématurée, nos indications suggèrent que ceux-ci sont en train d’ignorer les vieux appareils cellulaires en faveur des téléphones intelligents. Nous identifions d’autre part une diversité d’expériences parmi ces aînés, ce qui nous porte à croire qu’il existe un fossé numérique entre eux.
The social consequences of the near ubiquity of mobile devices in North American cities are increasingly being studied (Baron, 2008; Campbell & Ling, 2008; Castells, Ferández-Ardéval, Qui, & Sey, 2007; Caron & Caronia, 2007; Chayko, 2008; Fortunati, 2002; Goggin, 2012; Ling & McEwen, 2010; McEwen & Schaeffer, 2012). However, data on how adoption of internet-enabled mobile phones (i.e., smartphones) has taken shape across different segments of the population, and how this is changing over time, is only just emerging. In the absence of such data, social rhetoric bolstered by people’s observations within their families offers descriptions of tech-savvy grandparents sharing photos on Facebook from their smartphones only offers anecdotal evidence. These stories sketch an unsubstantiated assumption that smartphone adoption is common to all age groups, and the motif of the “silver surfer” (a parody of the Marvel comic fictional superhero from the 1960s) as an older user of new media riding a wave of digital data has proliferated to suggest that seniors are actively embracing online tools (Curtis, 2014). But is this true or are these examples of exceptions to the rule? By interrogating the increasingly pervasive conceptualization of seniors, defined as people 60 years old and above, as adopters of information and communications technologies, we seek to analyze seniors’ experiences with, and beliefs about, mobile technologies over time.
Embracing a comparative approach, this article focuses on seniors’ use of mobile technologies and asks the following questions:
What insights can we gain into how the spread of mobile phones, and in particular smartphones, has affected seniors?
What challenges and opportunities arise as senior Canadians adopt mobile devices in their everyday lives?
How can we conceptualize seniors’ attempts to resolve problems related to mobile devices?
In this article, we describe seniors in East York, Toronto, to explore the issue of information/technology overload that this demographic has faced, and identify the resolution of problems as they relate to device ownership, communication practices, and technology preferences.
Using data collected by our research team from East York in 2005 and again in 2012—a seven-year period correlated with widespread mobile phone adoption in Toronto—we apply Taylor’s (1991) information use environment framework to understand the influence of context in studying user behaviour vis-à-vis 1) device ownership, 2) communication practices, and 3) technology preferences. What makes this research unique is that the analytical approach to structuring the research data allows us to assess insights into seniors’ information behaviour rooted in a deep consideration of the context within which seniors are living.
We use Taylor’s (1991) information use environments as a theoretical framework scaled to the analysis of a community. Although Taylor’s original exposition of the framework was applied to organizations, our use of the framework does not represent an extension of a micro-concept of an institution to a macro-concept to a community; rather, the community is an instantiation of the information use environments. Even though a community does not exist within four walls of a building as typified by traditional organizations, a community is a type of organization. We demonstrate how information use environments as a theoretical framework can be applied at a community level, while also exploring the opportunities and the challenges of extending the theory.
This article responds to Taylor’s (1991) call for researchers to continue to adapt the theory: “It is not graven in stone; indeed the author hopes it will spur discussion and improvement as a means of organizing what it is we know, and that it will stimulate further research” (p. 219). Although Taylor’s (1991) seminal paper on the framework is well cited, only a few examples of empirical studies (i.e., Agada, 1999; Edwards, 2012; Hersberger, Murray, & Sokoloff, 2006; Rosenbaum, 1996) extensively apply Taylor’s framework to data, and so the framework remains relatively unknown and is, therefore, ripe for consideration in the study of new and mobile media.
User studies of mobile media as a specific form of information and communications technologies (ICTs) have generally adopted perspectives from fields that include media studies (Sawchuk & Crow, 2012), science and technology studies (Shilton, 2009), urban sociology (Pain, Grundy, Gill, Towner, Sparks, & Hughes, 2005), social psychology (Bianchi & Phillips, 2005), and communications (Katz & Aakhus, 2002). Within the field of information studies, there is a long-standing tradition of investigating issues that arise from people’s interaction with technologies, such as archives, catalogues, barcodes, databases, search engines, e-readers, computers, and other devices that mediate users’ engagement with information. In particular, concepts from information studies offer lenses for understanding engagements with ICTs that centre around an ontological framing of devices as media intimately imbricated with information seeking, acquisition, processing, sharing, and use. In a sense, information scholars engage less in “user” studies where the focus is primarily on observations of what people do with technologies, but are concerned with observations of the infrastructure that underlie the experiences and outcomes of interactions with information. This infrastructure includes analyses of interaction locales, policies, user interfaces, documentation, data, power relations, information problems, descriptions of users, and, of course, consideration of technologies. Research taking this approach results in “information” studies, and the emergent human experiences can be understood within the frameworks of information behaviour, which are very useful for investigating communications phenomena.
The work of social construction of information theorists, such as Brenda Dervin, Tom Wilson, and Robert Taylor, stands out as efforts to craft uniquely information studies perspectives on how devices are incorporated into the lives of users. The sense-making theory forwarded by Dervin and others (Dervin, 1998; Dervin, Foreman-Wernet, & Lauterbach, 2003; Dervin & Nilan, 1986) provided an analytical model for investigating user-device interaction as needs-driven and highly situated within a given context. Sense-making positions users as beings who are constantly constructing and creating whatever they choose based on their needs within a given situation (within structural constraints). Wilson (1981, 1994) drew from Dervin’s focus on analyzing the context within which information behaviour is occurring, but he noted a tendency for Dervin to reduce the role of objects in the sense-making process and he refined the model by including information sources or intervening variables as loci for investigation. Taylor (1991) built on Dervin’s and Wilson’s work to define a structure within which user behaviours and devices could be understood. The result is Taylor’s information use environments (IUE) framework, described in his seminal article published in the book Progress in Communication Sciences in 1991.
With IUE, Taylor (1991) provides a useful analytical framework to understand the influence of context in studying user behaviour vis-à-vis devices, information seeking, and access to information. Since a device is a structuring element in terms of how people organize, the device has become a core element. Taylor (1991) asks what are the elements of setting that influence information behaviour and further outlines four general influences: 1) setting; 2) sets of people; 3) problems; and 4) resolution of problems.
By setting, Taylor refers directly to the nature of the physical context that people are operating within. Since Taylor applied his framework to organizations, researchers have shied away from attempting to employ IUE in other settings. However, Taylor encouraged the use of IUE in a variety of context types because he considered the setting fundamental to the ways people seek and use information. In this study, we consider the history of East York as a setting, with the physical infrastructure, services available, access to resources, and so on, as relevant to the analysis of user behaviour in the geographic area.
In IUE, sets of people refer to demographic and non-demographic characteristics of users, and analysis of these groups seeks to uncover differences within each set as they are related to information behaviour. In this study, seniors are a set of people who are analyzed and compared with other sets outside of the age range defined as seniors. We are interested specifically in issues of device ownership among seniors. Device ownership has historically been associated with access to information, as debates on the digital divide can attest (Morris & Brading, 2007; Friemel, 2016). The emergence of mobile phones in the mid-1990s and the widespread adoption of these devices over the past 15 years in Canada have made mobile phone device ownership an important metric to track when considering information access, given the uniquely high degree of personal attachment to these devices.
Taylor posits that an IUE has characteristic problems that the aforementioned sets of people are concerned about. We are interested in problems that emerge from, or are solved through, the use of mobile phones by sets of people in East York in 2005 and in 2012.
The last influence in IUE is called resolution of problems and is viewed in the sense-making tradition of uncovering what constitutes the resolution of problems to a given set of people. This perspective offers insights into users’ attitudes toward the benefits and costs of information, in our case mobile phones; what are their sentiments regarding choices and tradeoffs; and what do they determine as acceptable outcomes to problems brought about by and/or resolved through the use of mobile phones?
We employ Taylor’s information use environments as the guiding analytical framework in this research to explore:
Setting: What is the nature of the East York context within which seniors live and how has it changed from 2005 to 2012?
Sets of people: What distinguishes seniors from the general population in East York in 2005 and 2012? How has mobile phone device ownership changed over this period?
Problems: What are the characteristics of typical problems or tensions that seniors have to deal with? Did these problems change from 2005 to 2012?
Resolution of problems: What are seniors’ attitudes toward the benefits and costs of mobile phones, and in what ways have their sentiments changed from 2005 to 2012?
A natural consequence of using Taylor’s framework is that the research findings and discussion sections are integrated, and they are presented in a combined section.
With the growth and spread of ICTs, mobile technologies have become widely accepted and utilized. Moving into the twenty-firstcentury, the Telus Canadians and Technology survey (Wellman, Garofalo, & Garofalo, 2009) found that about one-quarter of Canadians (aged 13+) report that their mobile phones are the primary way to keep in touch with friends and family (28%) and to organize their social life (22%). Therefore, studying the social implications of mobile devices is no longer just a glimpse into the lives of the technological elites; rather, it is an examination of an important aspect of Canadian life.
Mobile research tends to focus on young people, often referred to as “digital natives” (Palfrey & Gasser, 2010; Nelissen & van den Bulck, 2017). Rare and somewhat limited studies describe seniors as lagging in their use of mobile phones (Smith, 2013) and indicate that seniors are considered a less desirable target demographic. This has perhaps contributed to less of an academic and industry focus on this older age group (Nikou, 2015). In developing a model of the senior digital literacy paradox, Schreurs, Quan-Haase, and Martin (2017) identify the media discourse that creates an atmosphere of doubt and self-doubt around older adults’ use of technologies.
Seniors in the United States have relatively low adoption of smartphones in comparison to other age groups; 27 percent of American seniors (aged 65+) own a smartphone (Pew Internet Centre, 2015). Problematically, much of the research and statistics on seniors’ mobile phone use is generated from the United States. However, seniors represent a growing proportion of the population: “The aging population is, therefore, becoming a social and political priority” (Martinez-Pecino, Lera, & Martinez-Pecino, 2012, p. 876). And the discourse of the “silver surfer,” of older people developing expertise using new technologies, is common and still proliferates (Edwards, Duffy, & Kelly, 2015; Selwyn, Gorard, Furlong, & Madden, 2003).
Other research on seniors and mobile phones sometimes contradicts the “silver surfer” narrative, contributing to a lack of clarity on the topic. The research largely falls within two broad categories: 1) adoption research to broadly understand how seniors are using mobile technologies (e.g., Conci, Pianesi, & Zancanaro, 2009); and 2) technological usability research to understand the limitations of current phone design and develop new technologies that are more “senior-friendly” (e.g., Baecker, Moffatt, & Massimi, 2012).
Much of the adoption research applies a technology acceptance model (TAM) to seniors’ mobile phone use. However, a focus on acceptance and use does not provide sufficient insight into understanding seniors and mobile technologies (Nikou, 2015). Research identifies the “how” and “why” of mobile phone use (Kubik, 2009), which is often tied to social support and security for seniors. Considering that health is a growing concern for an aging population, there is extensive literature focusing on seniors’ use of mobile technologies for health (Heart & Kalderon, 2013; Joe & Demiris, 2013; Varshney, 2014).
Technological usability research focuses on the technology itself to understand how to develop new technologies or adapt existing technologies to be more user-friendly for seniors who may have limited physical or cognitive abilities. Seniors have been less inclined to learn all the features of their technologies (Berenguer et al., 2017; Weilenmann, 2010); for example, few seniors are using mobile phones to text message or to go onto the internet (McMurtrey, Zeltmann, Downey, & McGaughey, 2011). However, seniors want to use smartphones with a variety of applications, rather than a basic mobile phone (Massimi, Baecker, & Wu, 2007). Seniors believe that the positive benefits of using technology, including smartphones, outweigh the costs as they recognize the need to use technologies in order to participate in society (Mitzner et al., 2010).
It is important to note that, in our review of the literature on seniors, there is a lack of definitional precision with regards to age cohorts. We try to clarify what is meant by the term “seniors” as we compare this group with others in our analysis. Pew Research (2014) categorizes the generations into the following categories: Silent Generation (born 1928–1945), the Baby Boomers (born 1946–1964), Generation X (born 1965–1980), and the Millennial Generation (born after 1980). Even among the senior population, divergent definitions are applied, as a generational definition provides less facility with an aging population. Haddon and Silverstone (1996) coin the term “young-elderly” to refer to people 60 to 75 years old, and Suzman, Willis, and Manton (1995) identify the “oldest old people” as those over 80 years old (as cited in Neves, Franz, Munteanu, Baecker, & Ngo, 2015). Kubik (2009) identifies “young old” as 57 to 75 year old and “old old” as those older than 75 years. Largely, an artificial divide between years does not do justice to recognizing the apparent similarities and differences between people who are on the border of the classification.
Curiously, baby boomers are the only generation consistently identified and categorized across the literature, as being born within the years 1946 and 1964. In comparison to younger cohorts, baby boomers generally use their mobile phones to make calls and less frequently engage in data downloads (Kumar & Lim, 2008). In addition, baby boomers are less critical of the level of service quality provided by their mobile service provider in comparison to younger generations (Petzer & De Meyer, 2011). Although the digital divide is still apparent (Hargittai, 2002, 2007; Wei & Hindman, 2011), the divide between young and old people is diminishing, with more affluent seniors using more technology (DiMaggio, Hargittai, Celeste, & Shafer, 2004; McMurtrey et al., 2011).
This study seeks to provide a deeper understanding of media use in a specific Canadian community, East York, in order to provide a comparative analysis of the same community over time. In a previous study of East York conducted in 2005, our research team found that people shifted from community-based groups to individualized networks (Wellman et al., 2006). In 2012, our team returned to this community to consider communication patterns that have emerged following the introduction and adoption of the mobile phone within Canadian society.
This article uses 350 surveys and 87 interviews from 2005 and 101 in-depth interviews from 2012, all of which were conducted in East York, as our core research instrument to understand the extent to which Canadian seniors have incorporated ICTs in their lives. The research design allows us to directly compare the East York residents’ stories about how they adopted, became familiar, and eventually incorporated different ICTs into their daily lives. As a result, the research team is uniquely positioned to investigate how mobile technology use, practices, and attitudes have changed over time.
The 2005 data come from NetLab’s Connected Lives research, where Barry Wellman and colleagues studied communication practices in East York. The 2012 data are from NetLab’s Networked Individuals research project. As the data are from the same research lab, we have implicit permission to use data from the 2005 study. For the 2005 research, a random sample of 350 English speaking adults over the age of 18 participated in a 32-page hand-delivered survey that included questions on ICT ownership and specific uses. The survey was executed between July 2004 and March 2005 with a response rate of 56 percent (Kennedy & Wellman, 2007). In addition, a 25 percent subsample of the survey respondents were interviewed in their homes—roughly 87 individuals. The interviews focused on participants’ daily work, leisure, household, relations, social networks, social routines, and ICT use (Hogan, Carrasco, & Wellman, 2007; Kennedy, Wellman, & Amoroso, 2011). The 2005 data analysis did not specifically focus on the relationship between age and media ownership and use; as a result, much of the historical data needed to be recoded. For example, smartphone ownership was determined based on whether respondents reported using BlackBerry or Palm devices for various functions, including searching for telephone numbers, making daily plans, and looking up email addresses.
Our 2012 data is part of a larger project that included research participants who are adult residents in East York. The participants were selected by using a commercially available list of 2,321 names and addresses of individuals residing in East York. These individuals are English speakers (although their first language might not be English) and are at least 18 years of age. An informational invitation letter was sent out to random households in East York. Graduate students from the University of Toronto were hired to conduct the interviews. Potential participants were offered a $50 gift card to show appreciation for their time. The semi-structured interviews lasted between 60 and 90 minutes. The interviews were typically conducted in the participants’ homes, but participants also had the option of meeting at a public location, such as a coffee shop, or at their workplace. We constructed a list of both closed- and open-ended questions to collect data on device ownership and participants’ use patterns. Sample questions included: What kinds of devices do you own? Do you ever watch TV or movies on your mobile device(s)? What do you use your cellphone/tablet for? The interviews were recorded, transcribed, and anonymized. The data were then iteratively hand-coded using NVivo and quantitatively analyzed using Stata. Considering the 2012 study was intended to elicit rich stories surrounding ICT use, and was not intended as a replication of the 2005 study, a survey instrument was not included. Comparisons between the 2005 and 2012 data were made based on patterns discerned from the 2005 survey and interviews, and the 2012 interviews.
We consulted demographic data from Statistics Canada (StatCan), the official national statistics agency, in order to build demographic profiles of East York in 2005 and 2012. Participants from both studies are grouped into six mutually exclusive age groups—under 40, 40–49, 50–59, 60–69, 70–79, and 80+—to understand a diverse range of age groups (see Table 1). Beyond the quantitative differences, this research is also focused on teasing out the qualitative differences observed among age groups. We classify seniors as those over 60 years of age. Although this differs from StatCan’s definition of seniors as those who are over 65 years of age, our classification affords a conversation about seniors and technology use outside of these previously defined boundaries and is aligned with other research in the area (e.g., Schreurs, Quan-Haase, & Martin, 2017).
Toronto is a city often described as a collection of neighbourhoods. East York represents one of Toronto’s well-known neighbourhoods located a few kilometres north-east of downtown. East York was a former borough of Toronto and in terms of municipal governance it was amalgamated into the city of Toronto in 1924. This categorization is important because, as a borough, well-maintained records exist for the neighbourhood. East York is also an ideal site for study because it mirrors the city’s historical growth by immigration; is primarily English-speaking; has municipal infrastructure comparable to other neighbourhoods; represents a wide range of ethnicities, socioeconomic statuses (residents are on the higher end of the city’s average for after-tax income, and residents are in slightly lower numbers of low income compared to the city’s average); has unemployment rates at the national average; and has household arrangements that are consistent with averages in the city (City of Toronto, 2012).
East York also has some defining characteristics that make it a distinct part of the City of Toronto. East York has historically had the highest percentage of seniors in Toronto, but the demographics are slowly changing with young families buying homes in this area (“Welcome to East York,” 2015). East York is a relatively social capital-rich neighbourhood and, as such, residents have strong ties with other residents. This means that at least some part of their communications are conducted offline, free of technology, and they often exchange social support when needed. Media use has previously been studied in this location in 1968 and 1978. We also have data to conduct a comparative analysis assessing the influence of communication technologies in 2005 and 2012.
Based on StatCan census data reported in 2006, East York had 9,065 residents (51% female). Compared to other neighbourhoods in Toronto, East York was home to a greater number of older Torontonians (2% more) than the average neighbourhood—particularly in the age range of 55–64 and those over 64. These figures were also higher than averages reported from census data in 2001; in 2006 there were 30 percent more women and 50 percent more men between 80 and 84. In terms of dwelling types in 2001, East York had a high number of single-family homes—58 percent compared to 24 percent as the average in other Toronto neighbourhoods. There were also considerably more independent-living seniors (defined by StatCan as adults over 65) as residents of private homes and in retirement homes in East York compared to other areas.
Based on StatCan census data reported in 2011, the neighbourhood of East York had 9,120 residents (51% female). Seniors aged 65 and over made up 15.5 percent of the population. Compared to other neighbourhoods in Toronto, East York was home to 7 percent more older Torontonians, which represents a 5 percent increase over the seven-year period. In terms of dwelling types in 2011, East York maintained the same higher than average number of single-family homes—59 percent compared to the 26 percent average for Toronto neighbourhoods, with 28 percent of seniors living alone in private homes and in retirement homes.
Based on our 2005 survey, older respondents were less likely to be engaged in any form of employment (i.e., full-time, part-time, self-employed); for example, 66.1 percent of those who were under 40 years of age, 74.2 percent of those who were between 40 and 49, and 69.2 percent of those who were between 50 and 59 reported being employed at the time of the survey (see Table 2). However, the percentage of respondents 60 and over who were employed drops significantly, to 29 percent of those between 60 and 69, 10.5 percent of those between 70 and 79, and none over 80 years of age being employed.
In terms of internet skills, seniors were less likely to report positively on our skill determination question on whether they knew how to download files from the internet in comparison to younger age groups. Internet file downloading ability is an often used functional ICT skill testing question and is reliably used in studies carried out by the Oxford Internet Institute and other research organizations. While more than 90 percent of those under the age of 40 reported knowing how to download files from the internet, only 75 percent of those between 60 and 69, and 71.4 percent of those between 70 and 79 reported knowing how to download files from the internet (see Table 3).
Based on our 2012 interviews, older respondents were less likely to hold any form of employment when compared to other age cohorts. Table 2 shows that 66.7 percent of those who were under 40 years old, 94.7 percent of those who were between 40 and 49, and 81 percent of those who were between 50 and 59 reported being employed. The same figures for those who were between 60 and 69, 70 and 79, and 80+, respectively, were 52 percent, 37.5 percent, and 9.1 percent. However, it is important to note that when compared to employment data from 2005, senior respondents in 2012 were employed in greater numbers. In other words, seniors in East York are increasingly present in the workforce since 2005 (see Table 2).
In terms of internet skills, seniors in 2012 continued to lag behind their younger counterparts in their ability to download files from the internet. While 100 percent of those under the age of 40 and 94.7 percent of those between 40 and 49 reported knowing how to download files from the internet, only 76 percent of those between 60 and 69, 62.5 percent of those between 70 and 79, and 36.4 percent of those over 80 years old reported knowing how to download files from the internet (see Table 3).
This finding takes on additional importance when we take into consideration that there are more seniors in the workforce in 2012 compared to 2005. Since on-the-job training is a core source of internet skill acquisition for post-secondary-school adults (van Dijk & van Deursen, 2014), with more seniors employed we might anticipate that internet skill levels would also increase, as there are more jobs with digital skill requirements in 2012 than there were in 2005. However, we did not observe an increase in seniors’ internet skills in the seven-year period. The data show that 75 percent of the respondents in their 60s in 2005 reported knowing how to download files from the internet; however, in 2012, only 62.5 percent of the respondents in their 70s knew how to download files from the internet. We could conjecture that the types of jobs seniors are increasingly employed in do not involve skills such as internet file downloads. This may be indicative of a broader trend and potentially a growing digital divide for this group, which would need to be investigated and substantiated in further research.
The period from 2005 to 2012 coincides with a time in urban North American cities when the introduction, adoption, and widespread use of technology brought people face to face with an information and communication problem described by Shenk (1997) as an “infoglut” or, as Toffler (1970) terms it, an “information overload.” Information overload simply refers to the notion of an individual receiving too much information (Eppler & Mengis, 2004). In an age when people are presented with an increasing number of sources of and access to information, people need to determine how to handle, process, and use the information efficiently: “Living in an ‘information society’, we are bombarded with information whether or not we actively seek it” (Edmunds & Morris, 2010, p. 17). While the concept of information overload is not new, the problem has been exacerbated by the widespread availability of the internet via smartphones.
Related to information overload is technology overload, which refers to “the cognitive and physical burden placed on human beings due to usage of multiple devices for everyday activities” (Grandhi, Jones, & Hiltz, 2005, p. 2291). In the time period between 2005 and 2012, there has been a rapid change in use of ICTs, specifically with regards to mobile technologies. Caron and Caronia (2007) argue, “By giving rise to new forms of interaction, this technology [smartphone] obliges us to rethink the cultural models of social encounters” (p. 4).
While there are certainly numerous benefits to using technology, including increased social support, interpersonal contact, and community commitment (Wellman, Quan-Haase, Witte, & Hampton, 2001), challenges and problems also arise. With countless information sources and an endless firehose of information that people are trying to make sense of in their daily lives, there is competition for attention across multiple devices. According to Bawden and Robinson (2009), “These technical advances have led to a much more rich and complex information environment…” (p. 181). In the past, people were able to overcome the challenge and limitations of face-to-face communication with the invention and adoption of writing, printing, telephony, and digital technologies (Caron & Caronia, 2007). Accordingly, the current “problem,” as defined by Taylor, for seniors in East York is how to effectively manage the changes in a complex information environment brought about by new and mobile technologies. As identified in the research below, different age groups are developing different resolutions to this problem; and the resolutions lie in the changing communication practices.
Emerging from the data, the aforementioned problem of information overload can be understood by examining the emergent themes of a) device ownership, b) communication practices, and c) technology preferences. In general, seniors indicated that they were dealing with the problem of information and technology overload in three ways: 1) they tended to own fewer devices and were less likely to own “smart” devices, in comparison to younger people; 2) neither basic mobile phones nor smartphones were highly integrated into their daily communication practices; and 3) they did not prefer mobile platforms over other forms of information access and did not value mobile phones as their most important devices. Below we argue that these findings can be attributed to the different communication needs of seniors as a Taylorian “set of people,” who are both shaped by the social-capital rich setting in which they are embedded and who are also engaged in specific life-stage experiences that shape their communication practices.
By device ownership, we refer to participants’ responses to the question “What kinds of devices do you have?” This question is less concerned with the distinction between use and ownership, since personal communications devices are rarely shared in East York, as identified in our 2005 research. Instead, we are interested in the extent to which the various age cohorts are adopting devices and the factors that motivate acquisition.
We begin with an analysis of the different patterns of device ownership based on what respondents reported having in 2005 and, later, in 2012. In 2005, ownership of basic mobile phones (i.e., mobile phones with no or limited internet-access capability) was widespread in East York, and these devices were being used by people 18 to 69 years old—with very little basic mobile phone use by seniors 70 years and older.
Table 4 shows that only the younger groups of respondents, arguably those who are the most tech savvy, reported owning smartphones, such as BlackBerry or Palm PDA devices. Smartphone users included: 24 percent of those under 40 years old, 16 percent of those 40 to 49 years old, 17 percent of those 50 to 59 years old, 6 percent of those 60 to 69 years old, and no smartphone users over 70 years old. Although low in numbers, smartphone use is noteworthy among those 60 to 69 years old, and given the significant level of ownership of basic mobile phones by those age 60 to 69 in 2005, it is interesting to witness how this age group’s mobile and smartphone ownership evolves in the 2012 data.
To explain the relatively low levels of smartphone ownership, we note that in 2005 smartphones were still a novelty item, as this year precedes the proliferation and wide-scale adoption of smartphones in Canada (Reed, 2010). That is not to imply that smartphones were not available for use in the early 2000s. Handspring (e.g., Palm Treo 600), Microsoft (Windows CE Pocket PC), and BlackBerry (e.g., BlackBerry 5810) all offered their version of now ubiquitous mobile internet devices around 2005, but Apple’s iPhone and Google’s Android were not released in Canada until 2008 and 2009, respectively. The low smartphone ownership in 2005 is most likely accounted for by the limited number of smartphones or smart devices on the market, and it is also unlikely that mobile phone users in 2005 were sufficiently familiar with the internet functions and corresponding data plan requirements of these new devices.
Patterns of device ownership among our 2012 respondents are summarized in Table 5 and Figure 2a. As anticipated, smartphone ownership was highest among the youngest respondents, as all respondents under the age of 40 reported owning smartphones. This is consistent with reports from studies elsewhere in the world that show high levels of smartphone adoption among young people (Nielsen, 2016). This forms a stark contrast with the 80+ age group, in which none of the members reported having smartphones—although 54.6 percent did have basic mobile phones. Among other motivations, it is possible to consider that younger respondents may be attempting to combat the problem of infoglut by owning more devices that are “smart” and, therefore, utilizing information management features of smartphones.
In addition, we witness significant changes in both basic mobile phone and smartphone ownership when comparing patterns in 2005 and 2012. On the one hand, there is increased basic mobile phone ownership during the seven-year interval in the 40-49 and 70–79 age groups. One explanation is that more people in these age cohorts began to use mobile phones for the first time during the period 2005 and 2012 and started with basic mobile phones (instead of smartphones). Similarly, among those aged 50–59 and 60–69, there is some evidence that some people were switching from basic mobile phones to smartphones, while others in the same groups also acquired mobile phones for the first time.
The data also show that for some groups there was very little change in mobile phone ownership—in other words, mobile phone ownership numbers appear to stagnate (see Figure 2b). For example, if we follow respondents who were in their 60s in 2005, it is likely that many of them would be in their 70s in 2012. Our data show that basic mobile phone ownership remained the same for this age group over the seven-year period—it was low to start with and showed no net gains or losses—but this group showed significant increases in smartphone ownership, as 31.3 percent of them reported owning smartphones in 2012. A possible interpretation of the data is that some seniors were catching up on mobile connectivity, and they did so by bypassing basic mobile phones and going straight to smartphones. In a sense, seniors may have been exhibiting the “leapfrog” effect, which refers to starting out with the newest technologies available as late adopters (Goldenberg & Oreg, 2007).
Another factor affecting people’s motivation to switch from basic mobile phones to smartphones is the mobile contract renewal system in Canada; in 2005, users were bound to their mobile phones via multi-year contracts, with high costs to upgrade before the date assigned by the service provider. By 2012, this changed as Canadian service providers encouraged users to upgrade to smartphones by providing incentives for individuals to get out of their current contracts. These incentives included heavily subsidized smartphones as well as a lower cost for data plans. These incentives, coupled with a much larger selection of smartphone devices, help to explain the increase in smartphone ownership among almost all of the age groups from 2005 to 2012.
Furthermore, basic mobile phone ownership was significantly lower among those over 70 years of age in 2005—about 25 percent below the youngest age group. In 2012, the difference was still 25 percent, but it was in the opposite direction; a higher proportion of older respondents owned basic mobile phones, while younger adults had migrated to smartphones. It appears as if basic mobile phones, with the core functionality of voice calling and sending text messages, were able to fulfill the needs of many seniors across the time period. Basic mobile phones are associated with instrumental uses, such as arranging to meet someone at a certain time, and are also used expressively, for example, to reach out to someone for social support by way of a call or text message (Ling & Yttri, 2002). These simpler devices are not well-suited to making more complex information searches or to accessing internet-based sources. When we compare the 2005 and 2012 data, we note that retired seniors have a reduced need for managing information glut. If we reflect on the influence of life stage and residential setting on device ownership, and if seniors are living in close proximity to each other near or within seniors’ residences (as in the case of East York by 2012), it is quite probable that basic mobile phones satisfy seniors’ instrumental and expressive needs. As a result, the majority of seniors are not motivated to migrate to smartphones.
We cannot compare 2005 and 2012 data regarding tablet devices because tablets were not commercially available in 2005. However, it is interesting to note that the 2012 data indicate a negative relationship between tablet ownership and age. Almost 78 percent of respondents under 40 years of age reported owning tablets. This number decreased to 57.9 percent for those between 40 and 49 years old, 33.3 percent for those 50–59 years old, 40 percent for those 60–69 years old, and 18.8 percent for those 70–79 years old. None of the 11 respondents over 80 years old reported owning a tablet (see Table 5). Furthermore, insignificant chi-square values for computer ownership in 2012 suggest there are no significant differences in computer ownership among different age groups.
Combined with the two previous findings, this suggests that the age- or generation-based digital divide is less about computer-based activities and instead, the generational digital divide is increasingly about owning mobile devices (mobile phones and tablets).
Examining communication practices, defined broadly as the habitual ways in which people understand and make themselves understood with others, offers a lens to analyze how mobile phones are integrated and embedded in daily life and expands the analysis beyond motivations of owning a mobile device.
In 2005, emergent information seeking practices on mobile phones were instrumental in nature. Not only was there limited adoption of smartphones across the generations, but people were also using their smartphones in limited functional ways. As indicated in Table 6, only 8.1 percent of the respondents used their smartphone to find the email address of a contact, 10.4 percent of people used their smartphone to find a phone number, and 11.9 percent of people used a smartphone to plan their day. Out of these respondents, close to 80 percent of those who used a smartphone for these instrumental purposes were under 50 years of age, as seniors were largely yet to adopt smartphones. Overall, in the early 2000s, people’s communication practices on smartphones were limited and were typically instrumental in nature.
By 2012, three communicative patterns emerge, related to mobile-ization, multiple media use, and function.
Mobile-ization: From 2005 to 2012, there was rapid growth in smartphone adoption (see Tables 4 and 5), which also corresponded with a shift to mobile-ization (Urry, 2003; Wellman, 2001). Mobile-ization refers to the pattern of people shifting to mobile devices for purposes that have traditionally been served by other devices. Interesting generational differences emerge in patterns of content consumption; rather than only having a television as a source of entertainment, almost all of the respondents under 40 years old used their mobile or tablet devices for this purpose. For example, when asked about the motivation to use a tablet to stream media content, a 35-year-old woman captures the convenience of using tablets when she remarked, “Depends what I am doing. If I’m going in the kitchen, I’d rather not carry my laptop to the kitchen, I take the Playbook with me.” This highlights a shift to mobile-ization in the youngest generation, as they often prefer to watch TV shows and movies on a mobile phone or tablet, rather than a television.
In comparison to those under 40, older respondents were less likely to use mobile phones or tablets to view media content. Specifically, seniors tend to be less mobile as they tend to leave their primary residence for large periods of time less regularly, and as a result, seniors do not have to switch to mobile devices for their communication practices. For seniors living in retirement homes, there is a greater likelihood of meeting each other face to face, as social connection with co-located others is facilitated by communal living (e.g., shared meal services, organized group activities), and instrumental coordination can take place on landlines or basic mobile phones.
Multiple media use: Beyond the communicative practices of how mobile technologies are used, an interesting finding of multiple media use has emerged where there are significant differences among age cohorts. The second screen experience refers to when an individual engages with media content on a device, typically a television, and also employs the use of a second device, typically a computer, phone, or tablet (see Holt & Sanson, 2014).
In 2012, younger respondents multi-tasked with multiple technologies more than seniors (Jeong & Fishbein, 2007; Wang & Tchernev, 2012; Brasel & Gips, 2011). Younger people, who were largely in the workforce and had more extensive social lives outside of the home, multi-tasked because in many aspects of their daily lives they reported feeling like they needed to stay connected to information that was changing. Multi-tasking practices that were banal to those under 40 years old (and to some people in their 50s) were uncommon among seniors: for example, browsing the internet and chatting while watching TV, texting while working on the computer, and searching for information while on the phone. However, this does not mean that seniors did not multi-task at all; rather they multi-tasked in more limited, but distinctive ways. For example, the most prolific response to the multiple media use question by seniors was talking on the phone while watching television.
In comparison, younger respondents tended to incorporate new media in their multi-tasking practices. New media refers to content that is available on-demand online and typically includes blogs, video games, and social media (Miller, 2008). Following the trend identified in 2012, we anticipate that multiple media use will continue to evolve, and we will begin to see what we call “multiple mobile media use,” whereby multiple media use will shift to mobile devices: for example, watching a TV show on a tablet while engaging with social media from a smartphone (Jacobson, 2016).
Despite popular views regarding seniors’ lack of ability or interest in multi-tasking (Jeong & Fishbein, 2007; Rohm, Sultan, & Bardhi, 2009), none of the participants who reported not multi-tasking raised cognitive capacity as the rationale for not using multiple devices at the same time. Instead, those who explicitly spoke about why they do not multi-task identified their desire to keep things separate:
I try to be separate, I don’t want to … Well, it’s good to do two things at a time, sometime[s] you have to. Let’s say you’re using the computer and someone calls you. Maybe you have to answer, but that is the only situation that you have to do that. Otherwise I prefer to use phone as phone and computer as computer. (71-year-old man)
Function: Another interesting finding is the different functions and values ascribed to mobile technologies—particularly mobile phones. Seniors tend to see their mobile phones only as communication devices (even if they have a smartphone), whereas younger generations view the smartphone as a communication, information, and entertainment device. As a communication device, the smartphone is used for phoning, emailing, texting, et cetera. As an information device, with access to the internet, smartphones enable users to seek information online. Interestingly, one of the most frequently reported uses of smartphones is looking for directions. Using smartphones as entertainment devices, many respondents under the age of 40 stream media content, such as TV shows and movies. Considering their mobile lifestyles, young people enjoy the convenience of mobile devices to engage with information and entertainment online as their smartphones easily travel with them. For example, a 46-year-old man describes his smartphone use:
I’m a photographer so I use it as a camera quite a bit … I own a pretty nice camera, but I don’t always have it with me. I’ve got pictures that I’ll like for the rest of my life off of my phone, you know, so that’s fine. I like birds, so on the main page I have the Peterson field guide to the birds. I’m a musician so this [mobile phone] has replaced things like electronic guitar tuners … I can get international radio stations off this, so if you want to listen to the northern Scotland weather and traffic, no problem, you’re right there.
The younger cohort views smartphones as also serving the functions of other devices. A 27-year-old man remarks, “All the things I do with my laptop I can do it here. I can check my email, I can talk … I can send text messages and all my contacts are here.” While a displacement effect may not be apparent, the mobile phone is used in ways that were previously only possible with other technologies. A smartphone may serve as a watch, camera, calculator, laptop, music player, gaming device as well as other non-technological functionalities, including a notepad, calendar, newspaper, and so forth.
As evidenced above, there are stark differences in the communication patterns of seniors and younger people: mobile-ization, multiple media use, and function. By considering the sets of people and setting as outlined by Taylor, we argue that this diverging pattern is due to the specific communication needs of the elderly, who have smaller egocentric networks (Cornwell, Laumann, & Schumm, 2008) than younger people. From our data, we note that most seniors, and particularly retired seniors, appear to have less of a need to juggle between multiple foci online and have fewer experiences of information overload and, as a result, many seniors have less of a need to use mobile technologies.
By technology preferences, we refer to people’s choices, uses, and purposes of mobile technologies in their day-to-day lives. This includes a consideration of whether people have a favourite technology; what tasks and activities they do with technologies, including examples of choosing one technology over another; as well as whether and how technology preferences change over time.
During the research design phase in 2004/2005, there was a focus on landlines, desktop computers, televisions, and laptops as the core communications technologies. As a result, we have minimal data regarding participants’ preferences for mobile technologies. Despite this limitation, we were able to retrieve some data from the interviews, which point to the emergence of a general pattern.
In 2005, the majority of households still had landlines; however, among East York residents 19–29 and 30–39 years old, mobile phones were gaining popularity. In 2007, (McEwen, 2010) conducted research on young adults (n = 173) aged 18 to 25 living in Toronto to examine the basic mobile phone practices (there were very few smartphones in use) and evaluate the role that basic mobile phones played in social networks. From the aggregated findings, McEwen (2010) developed a persona profile describing basic mobile phones as the preferred technology for this age group. On average participants were 18 years old and started using a mobile phone in grade nine or approximately 14 years of age. They used their mobile phone every day and engaged in various activities on it, such as speaking and texting or setting alarms, totalling between 10 and 25 times per day. When asked the question “Which would you be most upset about losing for one day?” they chose the mobile phone two-to-one over email access, social networking software (e.g., Facebook), and search engine access (see Figure 3). Apart from talking and texting, they also used their mobile phone as a replacement for a wristwatch, alarm clock, camera, music player, and “little black book.” They rarely used a landline even if one was available. They always had their mobile phone with them, never switched it off, and went to bed with their phone within arm’s reach (McEwen, 2010).
Similarly, the 2005 research in East York revealed interesting comparative qualitative data. Around that time frame, participants over 60 years old had markedly different views on technology preferences. Instead of the highly integrated use of the mobile phones (witnessed with those 18–25 years old described above), seniors tended to view mobile phones as reserved for emergency purposes only. For example, when asked about the different kinds of devices owned, a 69-year-old man remarked, “Oh yeah, telephone, we have a portable phone, and we have a cellphone. I use the cellphone mainly in the car and carry it as an emergency type thing.”
Younger respondents valued their smartphones significantly more than seniors (see Table 7). To them, smartphones were quasi-replacements for computers, portable music players, books, and other sources of entertainment. Evidence for this trend of using the mobile phone as a substitute for other devices is evidenced in McEwen’s data from 2007, and continued and expanded by 2012, when smartphones were more prevalent. Interestingly, the increase in the number of participants who selected the mobile phone as their preferred device also began to include older participants by 2012. For instance, a 35-year-old woman said, “My phone [is the most important], because it gives me everything I need … if the network is there.” Many younger respondents expressed not only their appreciation for, but also dependence on smartphone functions, such as GPS.
In 2012, respondents in their 50s and 60s still identified their computers (both desktops and laptops) as their most important device. Additionally, respondents in their 70s indicated that they valued their TV more than other devices because many participants in this age cohort explained that consuming entertainment was one of their main daily activities.
Although in 2005 many respondents in their 50s and 60s said they valued their mobile phones only as a device that would help them with emergency situations (e.g., a medical emergency), by 2012, East York residents aged 50–69 did not view mobile phones as primarily used for emergencies. Instead, this sentiment only persisted among the oldest seniors. For example, an 87-year-old woman explained that she kept her mobile phone on an armband because she was “prone to fainting or in a case [of] a heart attack.” This shift evidences that people have begun to embed mobile technologies into more aspects of their daily functions. Among the oldest respondents (over 80 years old), the landline telephone was their most important technology because they saw it as a “lifeline” through which they could quickly receive vital help.
Finally, the data points to an interesting pattern among baby boomers who, as a cohort, became seniors between 2005 and 2012. In 2005, baby boomers were between 41 and 59 years old. Subdividing this cohort, we can classify those who were 40–49 years old in 2005 as early boomers and those 50–59 years old as late boomers. Similarly, in the 2012 dataset, most early boomers were in the 50–59 age category and most late boomers in the 60–69 age category. In 2005, both early and late boomers owned devices in very similar proportions across all categories. However, by 2012 one in every three early boomers were adopting tablets, while none of the late boomers owned tablets (see Table 5).1 In addition, early and late boomers felt that their skills were comparable in 2005, but by 2012 late boomers reported that their internet skill levels remained the same as they were in 2005, while early boomers reported that their internet skill levels were increasing (see Table 3).
Over time, the early boomers who were generally still in the workforce in 2012 were exhibiting a propensity for adopting mobile media, such as tablets, and improving their internet skills in a similar fashion to younger cohorts in the dataset. Although baby boomers are largely treated as a definitive category in the literature, our data suggest that baby boomers are more appropriately separated into two categories: early boomers and late boomers. Therefore, we argue that the broad age range that defines the baby boomer category may be misleading with respect to mobile communication use; we encourage further research to investigate this split within the baby boomers category.
In this article, we embraced Taylor’s information use environments to better understand seniors’ mobile device use in the Toronto neighbourhood of East York. By outlining the setting, sets of people, problems, and resolutions across a time span, we were able to include factors—such as increases in the number of seniors living alone or in residences, seniors in the workforce, and seniors with digital skills—in the analysis to enrich the understanding of the findings. Taylor’s theoretical framework facilitated a comparative approach to the data and afforded us the ability to identify emergent patterns in device ownership, communication practices, and technology preferences. The application of this framework to community-level analysis and use of comparative data demonstrates that Taylor’s theoretical framework can be successfully extended to other settings.
We set out to gain insights into the effects of diffusion of mobile phones, and in particular smartphones, on seniors; the challenges and opportunities that arise as senior Canadians adopt mobile devices in their everyday lives; and seniors’ resolution of problems related to mobile devices. Our findings suggest that despite continued efforts to keep seniors connected using new technologies, the majority of seniors are still largely lagging behind other age groups. The media conceptions of the digitally savvy “silver surfers”—older people readily adopting and using new media—have not been fully actualized. In fact, within a single community, there exists a generational digital divide. Although this finding suggests that the marketing of the silver surfer was premature, there is evidence that over time early boomers are bringing the change that was anticipated—perhaps they are the first to find the wave. More effort needs to be made to bridge the generational gaps in technology use, with particular importance being given to differing intragenerational practices.
We also identified that seniors who are adopting mobile devices in their later years are leapfrogging older technologies and are using smartphones as their first mobile devices. There are consequences of not aging along with each new version of a technology, and there may be a greater learning curve for those seniors who did not have the benefit of beginning with basic mobile phones. A higher level of literacy is required for using newer models of smartphones, and more support may be needed as seniors adopt these devices. Mobile phone designers and application developers should take note of this pattern and consider developing devices with user interfaces appropriate for the increasingly aging population.
While mobile phones are not deemed to be the preferred technology by older seniors, we believe that this, in part, reflects the fact that information overload is less of a factor in the problems that seniors face on a day-to-day basis. This is important to consider given the focus in provincial budgets on how to effectively keep seniors connected to information that affects them. For example, initiatives are already being implemented for text message alerts from drugstores regarding prescription medication refills that are targeting seniors.2 If mobile phones are currently not the preferred devices for seniors, then initiatives like these will need to be re-envisioned in the short term. For the longer term, as more seniors adopt mobile devices, more mobile media literacy programs will need to be implemented to ensure success, which will also have a positive impact on increasing seniors’ skill levels so that they can be employed in a more diverse set of jobs.
We acknowledge that our study had some limitations: data from 2005 and 2012 did not always align in terms of questions asked in both studies, requiring us to seek secondary sources to fill the gaps; by electing to do an in-depth analysis in one community, we must be cautious with generalizations; and while our data collection spanned an exciting period for mobile media, data on smartphone use was limited in 2005. Despite these limitations, we believe that the research brings new insights to the Canadian communications landscape and offers a firm foundation for future scholarship.
Acknowledgements
We would like to acknowledge the support provided by the Social Sciences and Humanities Research Council Insight Grant. We also would like to thank our colleagues at NetLab for their support and collaboration throughout the research.
1. In 2012, there were more smartphones owned by late boomers than by early boomers, which is perhaps explained by upgrades yet to take place for early boomers, who still owned a relatively high percentage of basic mobile phones.
2. See Rexall Pharma Plus, Rexall auto refill, https://www.rexall.ca/services/rexall-auto-refill.
| Agada, John. (1999). Inner-city gatekeepers: An exploratory survey of their information use environment. Journal of the American Society for Information Science, 50(1), 74–85. Google Scholar | |
| Baecker, Ronald. M., Moffatt, Karyn, & Massimi, Michael. (2012). Technologies for aging gracefully. Interactions, 19(3), 32–36. Google Scholar | |
| Baron, Naomi S. (2008). Always on: Language in an online and mobile world. New York, NY: Oxford University Press. Google Scholar | |
| Bawden, David, & Robinson, Lyn. (2009). The dark side of information: Overload, anxiety and other paradoxes and pathologies. Journal of Information Science, 35(2), 180–191. doi:10.1177/0165551508095781 Google Scholar | |
| Berenguer, Anabela, Goncalves, Jorge, Hosio, Simo, Ferreira, Denzil, Anagnostopoulos, Theodoros, & Kostakos, Vassilis. (2017). Are smartphones ubiquitous?: An in-depth survey of smartphone adoption by seniors. IEEE Consumer Electronics Magazine, 6(1): 104–110. doi:10.1109/MCE.2016.2614524 Google Scholar | |
| Bianchi, Adriana, & Phillips, James G. (2005). Psychological predictors of problem mobile phone use. CyberPsychology and Behavior, 8(1), 39–51. Google Scholar | |
| Brasel, S. Adams, & Gips, James. (2011). Media multitasking behavior: Concurrent television and computer usage. Cyberpsychology, Behavior, and Social Networking, 14(9), 527–534. Google Scholar | |
| Campbell, Scott W., & Ling, Rich. (2008). Effects of mobile media. In J. Bryant & M.B. Oliver (Eds.), Media effects: Advances in theory and research (3rd ed.) (pp. 592–606). New York, NY: Routledge. Google Scholar | |
| Caron, André H., & Caronia, Letizia. (2007). Moving cultures: Mobile communication in everyday life. Montréal, QC: McGill-Queen’s University Press. Google Scholar | |
| Castells, Manuel, Fernández-Ardèvol, Mireia, Qiu, Jack Linchuan, & Sey, Araba. (2007). Mobile communication and society: A global perspective. Cambridge, MA: MIT Press. Google Scholar | |
| Chayko, Mary. (2008). Portable communities: The social dynamics of online and mobile connectedness. Albany, NY: State University of New York Press. Google Scholar | |
| City of Toronto. (2012). City of Toronto neighbourhood profiles. URL: http://www1.toronto.ca/City Of Toronto/Social Development, Finance & Administration/Neighbourhood Profiles/pdf/2011/pdf1/cpa58.pdf [March 20, 2017]. Google Scholar | |
| Conci, Mario, Pianesi, Fabio, & Zancanaro, Massimo. (2009). Useful, social and enjoyable: Mobile phone adoption by older people. In Human-computer interaction—INTERACT 2009 (pp. 63–76). Berlin, Heidelberg: Springer. Google Scholar | |
| Cornwell, Benjamin, Laumann, Edward O., & Schumm, L. Philip. (2008). The social connectedness of older adults: A national profile. American Sociological Review, 73(2), 185–203. Google Scholar | |
| Curtis, Sophie. (2014). Rise of the silver surfers, technology news. The Telegraph. URL: http://www.telegraph.co.uk/technology/news/10683749/Rise-of-the-silver-surfers.html [March 20, 2017]. Google Scholar | |
| Dervin, Brenda. (1998). Sense-making theory and practice: An overview of user interests in knowledge seeking and use. Journal of Knowledge Management, 2(2), 36–46. Google Scholar | |
| Dervin, Brenda, Foreman-Wernet, Lois, & Lauterbach, Eric. (2003). Sense-making methodology reader: Selected writings of Brenda Dervin. Cresskill, NJ: Hampton Press. Google Scholar | |
| Dervin, Brenda, & Nilan, Michael. (1986). Information needs and uses. Annual Review of Information Science and Technology, 21, 3–33. Google Scholar | |
| DiMaggio, Paul, Hargittai, Eszter, Celeste, Coral, & Shafer, Steven. (2004). From unequal access to differentiated use: A literature review and agenda for research on digital inequality. In K.M. Neckerman (Ed.), Social inequality (pp. 355–400). New York, NY: Russell Sage Foundation. Google Scholar | |
| Edmunds, Angela, & Morris, Anne. (2000). The problem of information overload in business organisations: A review of the literature. International Journal of Information Management, 20(1), 17–28. Google Scholar | |
| Edwards, Sheri L. (2012). Re-conceptualizing the information use environment: Enablers of and constraints to human information behavior in hospice care volunteerism in the southeastern Appalachian region. Unpublished doctoral dissertation. Knoxville, TN: University of Tennessee. Google Scholar | |
| Edwards, K., Duffy, R. M., & Kelly, B. D. (2015). The silver surfer: Trends of internet usage in the over 65 and the potential health benefits. Irish Medical Journal, 108(6): 171–174. Google Scholar | |
| Eppler, Martin J., & Mengis, Jeanne. (2004). The concept of information overload: A review of literature from organization science, accounting, marketing, MIS, and related disciplines. The Information Society, 20(5), 325–344. doi:10.1080/01972240490507974 Google Scholar | |
| Fortunati, Leopoldina. (2002). The mobile phone: Towards new categories and social relations. Information, Communication and Society, 5(4) 513–528. Google Scholar | |
| Friemel, Thomas N. (2016). The digital divide has grown old: Determinants of a digital divide among seniors. New Media & Society, 18(2), 313–331. Google Scholar | |
| Goggin, Gerard. (2012). Reading (with) the iPhone. In P. Snickars & P. Vonderau (Eds.), Moving data: The iPhone and my media (pp. 195–210). New York, NY: Columbia University Press. Google Scholar | |
| Goldenberg, Jacob, & Oreg, Shaul. (2007). Laggards in disguise: Resistance to adopt and the leapfrogging effect. Technological Forecasting and Social Change, 74(8), 1272–1281. Google Scholar | |
| Grandhi, Sukeshini A., Jones, Quentin, & Hiltz, Starr Roxanne. (2005). Technology overload: Is there a technological panacea? AMCIS 2005 Proceedings, 493, 2291–2297. Google Scholar | |
| Haddon, Leslie, & Silverstone, Roger. (1996). Information and communication technologies and the young elderly. LSE Research Online, University of Sussex, Falmer, UK. URL: http://eprints.lse.ac.uk/62450/ [March 20, 2017]. Google Scholar | |
| Hargittai, Eszter. (2002). Second-level digital divide: Differences in people’s online skills. First Monday, 7(4). URL: http://firstmonday.org/article/view/942/864 [March 20, 2017]. Google Scholar | |
| Hargittai, Eszter. (2007). A framework for studying differences in people’s digital media uses. In N. Kutscher & H. Otto (Eds.), Cyberworld unlimited (pp. 121–136). Wiesbaden, Germany: VS Verlag fur Sozialwissenschaften/GWV Fachverlage GmbH. Google Scholar | |
| Heart, Tsipi, & Kalderon, Efrat. (2013). Older adults: Are they ready to adopt health-related ICT? International Journal of Medical Informatics, 82, e209–e231. Google Scholar | |
| Hersberger, J. A., Murray Adam L., & Sokoloff, S. M. (2001). The information use environment of abused and neglected children. Information Research, 12(1), paper 277. Google Scholar | |
| Hogan, Bernie, Carrasco, Juan Antonio, & Wellman, Barry. (2007). Visualizing personal networks: Working with participant-aided sociograms. Field Methods, 19(2), 116–144. Google Scholar | |
| Holt, Jennifer, & Sanson, Kevin (Eds.) (2014). Connected viewing: Selling, streaming, & sharing media in the digital age. New York, NY: Routledge. Google Scholar | |
| Jacobson, Jenna. (2016). Networked spectators: Social media conversation & moderation at the Olympic opening ceremony. Online Information Review, 40(6), 746–760. Google Scholar | |
| Jeong, Se-Hoon, & Fishbein, Martin. (2007). Predictors of multitasking with media: Media factors and audience factors. Media Psychology, 10(3), 364–384. Google Scholar | |
| Joe, Jonathan, & Demiris, George. (2013) Older adults and mobile phones for health: A review. Journal of Biomedical Informatics, 46(5), 947–954. Google Scholar | |
| Katz, James E., & Aakhus, Mark. (2002). Perpetual contact: Mobile communication, private talk and public performance. Cambridge, MA: Cambridge University Press. Google Scholar | |
| Kennedy, Tracy L.M., & Wellman, Barry. (2007). The networked household. Information, Communication & Society, 10(5), 645–670. Google Scholar | |
| Kennedy, Tracy L.M., Wellman, Barry, & Amoroso, Julie. (2011). Can you take it with you? Mobility, ICTs and work-life balance. In J. Katz (Ed.), Mobile communication: Dimensions of social policy (pp. 191–210). New Brunswick, NJ: Transaction. Google Scholar | |
| Kubik, Sara. (2009). Motivations for cell phone use by older Americans. Gerontechnology, 8(3), 150–164. doi:10.4017/gt.2009.08.03.007.00 Google Scholar | |
| Kumar, Archana, & Lim, Heejin. (2008). Age differences in mobile service perceptions: Comparison of Generation Y and baby boomers. Journal of Services Marketing, 22(7), 568–577. doi:10.1108/08876040810909695 Google Scholar | |
| Ling, Rich, & McEwen, Rhonda. (2010). Mobile communication and ethics: Implications of everyday actions on social order. Nordic Journal of Applied Ethics, 4(2), 11–26. Google Scholar | |
| Ling, Rich, & Yttri, Birgette. (2002). Hyper-coordination via mobile phones in Norway. In James E. Katz & Mark Aakhus (Eds.), Perpetual contact: Mobile communication, private talk, public performance (pp. 139–169). Cambridge, MA: Cambridge University Press. Google Scholar | |
| Martinez-Pecino, Roberto, Lera, Maria J., & Martinez-Pecino, Magdalena. (2012). Active seniors and mobile phone interaction. Social Behavior and Personality, 40(5), 875–880. Google Scholar | |
| Massimi, Michael, Baecker, Ronald M., & Wu, Michael. (2007). Using participatory activities with seniors to critique, build, and evaluate mobile phones. In Proceedings of the 9th International ACM SIGACCESS Conference on Computers and Accessibility (pp. 155–162). ACM: Association for Computing Machinery. URL: http://dl.acm.org/citation.cfm?id=1296871 [March 20, 2017]. Google Scholar | |
| McEwen, Rhonda N. (2010). A world more intimate: Exploring the role of mobile phones in maintaining and extending social networks. Unpublished doctoral dissertation. Toronto, ON: University of Toronto. Google Scholar | |
| McEwen, Rhonda N., & Scheaffer, Kathleen. (2012). Orality in the library: How mobile phones challenge our understandings of collaboration in hybridized information centers. Library and Information Science Research, 34(2), 92–98. Google Scholar | |
| McMurtrey, Mark E., Zeltmann, Steven M., Downey, James P., & McGaughey, Ronald E. (2011). Seniors and technology: Results from a field study. Journal of Computer Information Systems, 51(4), 22–30. Google Scholar | |
| Miller, Vincent. (2008). New media, networking and phatic culture. Convergence: International Journal of Research into New Media Technologies, 14(4), 387–400. Google Scholar | |
| Mitzner, Tracey L., Boron, Julie B., Fausset, Cara Bailey, Adams, Anne E., Charness, Neil, Czaja, Sara J., Dijkstra, Katinka, Fisk, Arthur, D., Rogers, Wendy A., & Sharif, Joseph. (2010). Older adults talk technology: Technology usage and attitudes. Computers in Human Behavior, 26(6), 1710–1721. doi:10.1016/j.chb.2010.06.020. Google Scholar | |
| Morris, Anne & Brading, Helena. (2007). E-literacy and the grey digital divide: A review with recommendations. Journal of Information Literacy, 2(3): 13–28. Google Scholar | |
| Nelissen, Sara, & Van den Bulck, Jan. (2017). When digital natives instruct digital immigrants: Active guidance of parental media use by children and conflict in the family. Information, Communication & Society, doi:10.1080/1369118X.2017.1281993. Google Scholar | |
| Neves, Barbara Barbosa, Franz, Rachel L., Munteanu, Cosmin, Baecker, Ronald, & Ngo, Mags. (2015). “My hand doesn’t listen to me!” Adoption and evaluation of a communication technology for the “oldest old” (pp. 1593–1602). CHI 2015. ACM Press. doi:10.1145/2702123.2702430 Google Scholar | |
| Nielsen. (2016). Millennials are top smartphone users. URL: http://www.nielsen.com/us/en/insights/news/2016/millennials-are-top-smartphone-users.html [March 20, 2017]. Google Scholar | |
| Nikou, Shahrokh. (2015). Mobile technology and forgotten consumers: The young-elderly: Mobile technology and forgotten consumers. International Journal of Consumer Studies, 39(4), 294–304. doi:10.1111/ijcs.12187 Google Scholar | |
| Pain, Rachel, Grundy, Sue, Gill, Sally, Towner, Elizabeth, Sparks, Geoff, & Hughes, Kate. (2005). ‘So long as I take my mobile’: Mobile phones, urban life and geographies of young people’s safety. International Journal of Urban and Regional Research, 29(4), 814–830. Google Scholar | |
| Palfrey, John, & Gasser, Urs. (2010). Born digital: Understanding the first generation of digital natives. New York, NY: Basic Books. Google Scholar | |
| Petzer, D.J., & De Meyer, C.F. (2011). The perceived service quality, satisfaction and behavioural intent towards cellphone network service providers: A generational perspective. African Journal of Business Management, 5(17), 7461–7473. Google Scholar | |
| Pew Research Centre. (2015). U.S. smartphone use in 2015. URL: http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/ [March 20, 2017]. Google Scholar | |
| Pew Research. (2014). The generations defined. URL: http://www.pewsocialtrends.org/2014/03/07/millennials-in-adulthood/sdt-next-america-03-07-2014-0-06 [March 20, 2017]. Google Scholar | |
| Reed, Brad. (2010). A brief history of smartphones. PC World. URL: http://www.pcworld.com/article/199243/a_brief_history_of_smartphones.html [March 20, 2017]. Google Scholar | |
| Rohm, Andrew, Sultan, Fareena, & Bardhi, Fleura. (2009). The multitasking paradox: Media consumption among young consumers. Marketing Management, 18(6), 20–25. Google Scholar | |
| Rosenbaum, Howard. (1996). Structure and action: Towards a new concept of the information use environment. In Proceedings of the 59th Annual Meeting of the American Society of Information Science, 33, 152–156. Medford, NJ: Information Today. Google Scholar | |
| Sawchuk, Kim, & Crow, Barbara. (2012). “I’m G-Mom on the phone”: Remote grandmothering, cell phones and inter-generational dis/connections. Feminist Media Studies, 12(4), 496–505. Google Scholar | |
| Schreurs, Kathleen, Quan-Haase, Anabel, & Martin, Kim. (2017). The older adults digital literacy paradox: Aging, media discourse, and self-determination. Canadian Journal of Communication, 42(2), 359–377. Google Scholar | |
| Selwyn, Neil, Gorard, Stephen, Furlong, John, & Madden, Louise. (2003). Older adults’ use of information and communications technology in everyday life. Ageing and Society, 23, 561–582. Google Scholar | |
| Shenk, David. (1997). Data smog: Surviving the information glut. London, UK: Abacus. Google Scholar | |
| Shilton, Katie. (2009). Four billion little brothers? Privacy, mobile phones, and ubiquitous data collection. Communications of the ACM, 52(11), 48–53. Google Scholar | |
| Smith, Aaron. (2013). Smartphone ownership 2013. Pew Internet and American Life Project. URL: http://www.pewinternet.org/2013/06/05/smartphone-ownership-2013 [March 20, 2017]. Google Scholar | |
| Suzman, Richard M., Willis, David P., & Manton, Kenneth G. (Eds.). (1995). The oldest old. Oxford, UK: Oxford University Press. Google Scholar | |
| Taylor, Robert S. (1991). Information use environments. In B. Dervin & M.J. Voigt (Eds.), Progress in communication sciences (pp. 217–255). Norwood, NJ: Ablex Publishing. Google Scholar | |
| Toffler, Alvin. (1970). Future shock. New York, NY: Random House. Google Scholar | |
| Urry, John. (2003). Social networks, travel and talk. British Journal of Sociology, 54(2), 155–175. doi:10.1080/0007131032000080186 Google Scholar | |
| Van Dijk, Jan A.G.M., & van Deursen, Alexander J.A.M. (2014). Digital skills: Unlocking the information society. New York, NY: Palgrave Macmillan. Google Scholar | |
| Varshney, Upkar. (2014). Mobile health: Four emerging themes of research. Decision Support Systems, 66, 20–35. Google Scholar | |
| Wang, Zheng, & Tchernev, John M. (2012). The “myth” of media multitasking: Reciprocal dynamics of media multitasking, personal needs, and gratifications. Journal of Communication, 62(3), 493–513. Google Scholar | |
| Wei, Lu, & Hindman, Douglas Blanks. (2011). Does the digital divide matter more? Comparing the effects of new media and old media use on the education-based knowledge gap. Mass Communication and Society, 14(2), 216–235. Google Scholar | |
| Weilenmann, Alexandra. (2010). Learning to text: An interaction analytic study of how seniors learn to enter text on mobile phones. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1135–1144). ACM: Association for Computing Machinery. URL: http://dl.acm.org/citation.cfm?id=1753496 [March 20, 2017]. Google Scholar | |
| Welcome to East York. (2015). Toronto neighbourhood guide. URL: http://www.torontoneighbourhoods.net/neighbourhoods/east-york/east-york [March 20, 2017]. Google Scholar | |
| Wellman, Barry. (2001). Physical place and cyberspace: The rise of personalized networking. International Journal of Urban and Regional Research, 25(2), 227–252. Google Scholar | |
| Wellman, Barry, Garofalo, Amanda, & Garofalo, Vanessa. (2009). The internet, technology and connectedness. Transition, 39(4), 5–7. Google Scholar | |
| Wellman, Barry, Hogan, Bernie, Berg, Kristen, Boase, Jeffrey, Carrasco, Juan-Antonio, Côté, Rochelle, & Tran, Phuoc. (2006). Connected lives: The project. In P. Purcell (Ed.), Networked neighbourhoods: The connected community in context (pp. 161–216). London, UK: Springer. Google Scholar | |
| Wellman, Barry, Quan-Haase, Anabel, Witte, James, & Hampton, Keith. (2001). Does the internet increase, decrease, or supplement social capital? Social networks, participation, and community commitment. American Behavioral Scientist, 45(3), 436–455. Google Scholar | |
| Wilson, Thomas D. (1981). On user studies and information needs. Journal of Documentation, 37(1), 3–15. Google Scholar | |
| Wilson, Thomas D. (1994). Information needs and uses: fifty years of progress? In B. C. Vickory (Ed.), Fifty years of information progress: A Journal of Documentation review (pp. 15–51). London, UK: Aslib. Google Scholar |