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Work and Personal Determinants of Mobile-Technology Adoption and Use Among Community-Dwelling Older Adults: A Mixed-Methods Approach

Special Issue: Technology, Work, and Inequality. Volume 3, Issue 3: Autumn 2022. DOI: 10.1037/tmb0000088

Published onAug 01, 2022
Work and Personal Determinants of Mobile-Technology Adoption and Use Among Community-Dwelling Older Adults: A Mixed-Methods Approach
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Abstract

Mobile phones play a key role in information and communication technology, as they ease communication and interaction for users. However, older adults are often described as being on the wrong side of the digital divide, as they are less likely to adopt and use such technology. While research abounds on technology adoption and use among older adults, prior research has often examined this demographic as a homogenous group, however inequalities among older adults exist driven by socioeconomic backgrounds. Furthermore, researchers have called for understanding the mechanisms behind technology adoption and use among older adults so that we can start to bridge the digital divide. We sought to address gaps in the current literature by integrating the motivational theory of lifespan development and the theory of digital inequality in examining the inequalities and motivations that drive mobile technology adoption, use, and proficiency. We used a mixed method approach to collect data from 67 community-dwelling older adults that consisted of semistructured interviews and survey assessments. Results suggest that inequalities in income and occupation at/prior to retirement are associated with categories of mobile technology proficiency. Results further suggest that the majority of participants employ compensatory secondary control strategies which facilitate lower levels of mobile technology adoption and use. Implications for research and practice are discussed.

Keywords: mobile technology, older adults, motivation, inequality, digital divide

Acknowledgements: The authors would like to thank Shelby Davis, Kennedy Hammonds, Adalin McDaniel, Molly Simmons, and Melissa Sorensen for their time and effort in assisting with the coding of interview data in the study presented.

Disclosures: The authors have no conflicts of interest to disclose. These research findings have not been previously presented, however, the research rationale was presented at the 5th Biannual Aging and Work Small Group Meeting in St. Gällen, Switzerland in November 2019.

Data Availability: Data for this study are available to other researchers through the first author’s open science framework profile (https://osf.io/j2mx9; Burch, 2022). Data include numerically coded interview data as well for entire populations. Mobile phones play a key role in ICT (Ezoe et al., 2009), easing communication and interaction for all age demographics. Indeed, the versatility and rapid advancement of mobile phone technology have made mobile phones indispensable in activities of daily living (e.g., Katz, 2006). However, there are some populations and age demographics with which technology, as survey and demographic data; interview protocol with full list of interview questions, and data codebook.

Open Science Disclosures: The data are available at https://osf.io/j2mx9

Open Access License: This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC-BY- NC-ND). This license permits copying and redistributing the work in any medium or format for noncommercial use provided the original authors and source are credited and a link to the license is included in attribution. No derivative works are permitted under this license.

Correspondence concerning this article should be addressed to Katrina A. Burch, Department of Psychological Sciences, Western Kentucky University, 1906 College Heights Boulevard #22030, Bowling Green, KY 42101, United States. Email: Katrina.burch@wku.edu


It has been noted that two of the major challenges in today’s society are the technological revolution and the rapidly increasing age of global populations (e.g., Martinez-Pecino et al., 2012). Information and communications technology (ICT) are highly influential, and have become more so with the COVID-19 pandemic and the rapid shift to virtually working and schooling from home for entire populations. Mobile phones play a key role in ICT (Ezoe et al., 2009), easing communication and interaction for all age demographics. Indeed, the versatility and rapid advancement of mobile phone technology have made mobile phones indispensable in activities of daily living (e.g., Katz, 2006). However, there are some populations and age demographics with which technology, rather than leading to social inclusion, may expand social exclusion through little to no use of such technology. Specifically, older adults are at the center of the so-called digital divide, which is defined as inequalities regarding access, intensity, and nature of ICT use (Anderson & Perrin, 2017).

Researchers note that while older adults are more digitally connected than in decades past, there remains a notable digital divide between younger and older adults, especially those who are economically disadvantaged (Anderson & Perrin, 2017). Indeed, technology use holds many attractions for seniors, such as increased autonomy (Chaffin & Harlow, 2005); increased connectivity to loved ones (Chen & Schulz, 2016); and increased levels of empowerment and self-efficacy (Hill et al., 2015). What’s more, researchers note that lack of technology use puts elderly adults at a disadvantage, impeding their ability to function independently, such as performing everyday tasks (e.g., grocery shopping, doctor’s visits; Czaja et al., 2006). Furthermore, while the use of technology in elderly populations have often been pessimistically portrayed, there is evidence that suggests that older adults would like to use new technologies (e.g., mobile phones) as a means of staying active and engaged with society (Kurniawan, 2008).

There is a growing body of research which examines technology adoption and use of older adults (e.g., Davis, 1989; Rogers, 2003; Venkatesh et al., 2003). As noted by Hargittai et al. (2019), research has primarily focused on bridging the digital divide for older adults to enhance social connectivity and obtain information and resources from the internet. However, gaps in our understanding of technology use among older adults still exist. In particular, little is understood about individual differences in the adoption and use of mobile technology in older adults. For example, recent research has sought to examine individual differences in the breadth of internet use (Leukel et al., 2021), and internet skills (Hargittai et al., 2019). However, prior research primarily uses large online/paper-and-pencil surveys which do not allow for understanding the differences and motivations behind technology use because participants are studied in aggregate rather than emphasizing the understanding of individual differences provided by qualitative, contextually rich data. Magsamen-Conrad and Dillon (2020) note that technology adoption and use is often measured by survey-based research which does not allow for the capturing of individual differences which can help researchers better understand the decision-making process involved in adoption and use. Indeed, van Dijk (2006) argues that the lack of qualitative research is problematic in that we need to be able to understand the precise mechanisms (e.g., motivations, skills, and usage access in settings where interpersonal relations and particular cultures dominate) in adoption and use of (mobile) technology in “bridging” the so-called digital divide. In addition, researchers argue that those older adults least likely to use technology (e.g., older adults from lower socioeconomic and educational backgrounds) are often described as a homogenous group (Neves et al., 2018), although it is recognized that there are differences in their background and experiences. Unfortunately, the individual differences in motivation to adopt and use mobile technology in older adults with lower socioeconomic and more disadvantaged backgrounds is yet to be explored.

Therefore, we seek to address theoretical and methodological gaps in literature by examining individual differences and motivations behind mobile technology adoption and use in a largely economically disadvantaged sample of community-dwelling older adults in Southcentral Kentucky. Specifically, we seek to integrate the motivational theory of lifespan development (Heckhausen & Schulz, 1995; Heckhausen et al., 2010) with the theory of digital inequality (DiMaggio et al., 2004; van Dijk, 2006) to emphasize and understand the motivational strategies employed by community-dwelling older adults in mobile technology adoption and use. We do this through a qualitative (interview), mixed-method approach using thematic analysis and correlation analyses in an underserved sample of economically disadvantaged older adults.

We feel that this is an important area of consideration given older adults represent an exponentially growing demographic that aim to benefit from technological advancements, potentially more so, than their younger counterparts. However, as mentioned, older adults are disadvantaged in their use of mobile technology. Indeed, it has been postulated that seniors who are less affluent or have lower levels of educational attainment are more disadvantaged than their more affluent or well-educated counterparts when it comes to technology adoption and use (Anderson & Perrin, 2017). Importantly, DiMaggio et al. (2004) note that “inequality in access to and use of information is a systematic source of social inequality,” (p. 53). In considering community-dwelling older adults in an economically disadvantaged area, we also have to consider the sociodemographic and work backgrounds that may contribute to inequalities in the lack of adoption and use of mobile technology. We first discuss the Motivational Theory of Lifespan Development and the Theory of Digital Inequality in understanding technology adoption and use among older adults. We then integrate discussions of inequality in social and work backgrounds that may help us to understand the motivations behind mobile technology adoption and use. We conclude our literature review with the integration of theory in examining the aforementioned.

The Motivational Theory of Lifespan Development

The motivational theory of lifespan development posits that people seek to exercise personal agency, in other words, people seek to exercise the ability to initiate and direct actions toward the achievement of defined goals (Heckhausen & Schulz, 1995; Heckhausen et al., 2010), referred to as primary control capacity. To maintain primary control, people employ two strategies: primary control strategies are used to shape individuals’ environments consistent with their needs, and when that is not possible, secondary control strategies are used to alter goals to fit environmental demands (Heckhausen & Schulz, 1995). Primary and secondary control strategies can be further distinguished by whether they are selective or compensatory. Shane and Heckhausen (2019) note that “selective primary, selective secondary, and compensatory primary control strategies facilitate goal engagement processes … [while] compensatory secondary control strategies help individuals to disengage from a goal” (p. 114). Selective primary control strategies involve behavioral and cognitive efforts in attaining goals. When goals cannot be obtained easily, individuals will engage in selective secondary (i.e., for goal pursuit) and compensatory primary (i.e., for goal adjustment) control strategies (Shane & Heckhausen, 2019).

Aging is associated with depleting resources, making goals more difficult to attain. While primary control striving is stable over an individuals’ lifespan (i.e., the motivation to attain goals, overcome obstacles, and maintain a positive self-concept), primary control capacity declines as individuals age (Shane & Heckhausen, 2019). Therefore, older adults increasingly use secondary control strategies which facilitate goal adjustment and/or disengagement.

In particular, Heckhausen et al. (2010) suggests that as individual’s age, their goals shift from long-term extrinsic goals to more short-term intrinsically oriented goals. However goals are likely to shift due to factors associated with age. For example, research by Ng and Feldman (2009a, 2009b) suggests that self-efficacy decreases with age. Self-efficacy (Bandura, 1977) refers to an individual’s belief in their ability to execute a plan of action. Research suggests that age-related factors are negatively associated with self-efficacy (e.g., Bausch et al., 2014; Kanfer & Ackerman, 2004; Kooij et al., 2008). Furthermore, Maurer (2001) argues that self-efficacy for learning and skill development decreases with age, which is likely associated with the notion that older adults perceive that key abilities required for learning decline with age (Salthouse, 2012), including their ability to adopt and use mobile technology. This notion adheres to the (false) adage, you cannot teach an old dog new tricks. Indeed, there is evidence that suggests that older adults would like to use new technologies as a means of staying active and engaged with society (Kurniawan, 2008); but motivation is key to learning (Tyler et al., 2020).

Understanding the control strategies older adults employ in mobile technology adoption and use can shed light on better communication and intervention techniques.

The Theory of Digital Inequality

Older adults are more likely to have grown up and developed without the use of many of today’s technology, which likely negatively influences their self-efficacy with learning and using mobile technology. The theory of digital inequality suggests that there are many differences associated with technology use among individuals (DiMaggio et al., 2004). The theory of digital inequality posits that categorical inequalities between groups of people in society (e.g., young vs. old; affluent vs. nonaffluent) leads to unequal distribution of resources in accessing and using ICT. Resources refer to material (e.g., having a computer or mobile smart phone; financial), mental (e.g., motivations, cognitive ability), and social resources (e.g., social support; technical support). Essentially, the theory of digital inequality would suggest that discrepancies in resources among categorical groups explains technology adoption and use in those groups.

van Dijk (2006) posits that technology adoption and use is reliant on the process of access. Access refers to microdeterminants that influence an individual’s ability to make use of ICT. According to the cumulative and recursive model of access (van Dijk, 2006), motivational access (i.e., desire to adopt, purchase, use, or learn technology) leads to material access (i.e., obtaining or possessing technology), which then contributes to skills access (i.e., learning the technology), which finally contributes to actual use and proficiency (i.e., being able to fully use technology). The process model of access (van Dijk, 2006) also includes a feedback loop; for example, when a new technological innovation is released (e.g., iPad), the process of access repeats.

The process begins with motivational access, so to understand the digital divide, and the categorical inequalities that contribute to understanding mobile technology adoption and use, we must understand how motivations (control strategies) in goal attainment interact with, and influence, other levels of access (i.e., material access) in the aforementioned process. van Dijk (2006) notes that motivational access comprises both a social/cultural and mental/psychological component. While a social/cultural explanation in motivational access in considering technology adoption and use might be that technology “does not appeal [to] low-income and low-educated people” (Katz & Rice, 2002); mental/psychological explanations are grounded in technology anxiety and technophobia (van Dijk, 2006). van Dijk (2006) notes that technology anxiety and technophobia are major barriers to technology adoption and use for seniors. Furthermore, material access and skills access may be limited when considering socioeconomically disadvantaged older adults.

Examining Inequality Among Older Adults

As mentioned, prior research often treats older adults as a homogenous group on the wrong side of the digital divide (Neves et al., 2018). However, among older adults, there are additional divides that may influence material and skills access, as well as motivations in adopting and using mobile technology. Specifically, social inequalities among older adults may help researchers and practitioners alike, to understand the facilitators of, and barriers to, adopting and using technology. Traditional indicators of socioeconomic status include education, income, and occupational status which influence social inequalities among older adults in their adoption and use of technology.

Inequalities among older adults, in particular, are likely driven by concomitant factors, in other words, social and work inequalities are inextricably linked. Specifically, older adults likely were employed in one of two distinct labor market segments, the primary (core) sector and the secondary (peripheral) sector (Bidwell et al., 2013). In the primary sector, employees benefited from long-term relationships with employers that were likely associated with steady wage increases as well as health insurance and old age pensions (Doeringer & Piore, 1971; Jacoby, 1997). Primary sector jobs are characterized by high wages, good-working conditions, job stability, and good opportunities for training and promotion. However, those employed in the secondary (or peripheral) sector were not provided the same level of benefits; this sector was primarily comprised of women, racial minorities, and low-skilled workers (Doeringer & Piore, 1971; Jacoby, 1997). Secondary sector jobs are characterized by less opportunities for training, low wages, poor working conditions, high turnover, and less opportunities for advancement and union membership. The absence of retirement or pension plans for low-skilled workers as compared to higher skilled workers influences, and likely widens, the digital divide among older adults as some are more likely to be economically disadvantaged compared to their more affluent counterparts.

Lower income older adults are likely drawing social-security benefits (in the United States), which may be their only source of income. Older adults with lower incomes likely have less education, and thus retired from occupations where earning potential was limited. As such, education, income, and occupational status as indicators of social inequality are linked to technology use and adoption because of their impact on material and skills access. Indeed, research supports that socioeconomic status leads to subdivides within digitally divided populations (i.e., older adults; Cotten et al., 2016).

Integrating Theory in Examining Mobile Technology Adoption and Use in Older Adults

Integrating the motivational theory of lifespan development with the theory of digital inequality for older adults would suggest that the key to understanding mobile technology use and adoption for older adults lies in their use of control strategies. According to the motivational theory of lifespan development, control strategies are the motivational strategies that adults use in goal setting. As stated, selective secondary control strategies facilitate goal engagement, while compensatory primary control strategies facilitate goal adjustment (Shane & Heckhausen, 2019). Specifically, selective secondary control strategies includes intrinsic volitional strategies which enable individuals’ to commit to goal pursuits (i.e., increasing the perceived value and ability to achieve the goal; Shane & Heckhausen, 2019). Compensatory primary control strategies, on the other hand, involves finding alternative ways to attain a goal and getting help from external resources. This suggests that bridging the digital divide for older adults must include persuading older adults to perceive the value of, and recognizing and believing in their ability to, adopt, learn, and use mobile technology, while being open to receiving help from others in order to attain the goal of mobile technology adoption and use. In particular, compensatory primary control strategies are necessary for older adults to address and/or prevent competence gaps, such as adopting and using newer technologies. Indeed, recent research supports that older adults will invest the time necessary to adopt and learn newer technologies “if they perceive the value in the technology, have positive attitudes related to technology readiness, and have confidence that they have the cognitive abilities needed to acquire the necessary skills,” (Sharit et al., 2021, p. 1).

However, integrating the motivational theory of lifespan development and the theory of digital inequality would suggest that older adults with less resources in accessing, adopting, and using technology (e.g., less affluent, less education) may engage in compensatory secondary control strategies. Compensatory secondary control strategies are self-protective strategies that facilitate goal disengagement enacted in a way that minimizes threats to one’s self-esteem (Shane & Heckhausen, 2019). In other words, older adults may reason that they do not need to adopt and use newer technology because they’ve been able to “get by” without it. This only contributes to a widening of the digital divide between older and younger adults, and among older adults who differ in sociodemographic and economic backgrounds. Research suggests that the adoption and use of technology for older adults provides an important tool which enhances autonomy and social connection (Chaffin & Harlow, 2005; Chen & Schulz, 2016). Therefore, while some older adults may believe they can “get by” without learning and mastering newer technology, research would suggest that the health and social benefits may outweigh older adults’ skepticism (Chaffin & Harlow, 2005; Chen & Schulz, 2016).

Please see Figure 1, for a representation of the integration of the motivational theory of lifespan development with the theory of digital inequality in considering mobile technology adoption and use for older adults. Specifically, this integrated model suggests that, according to the theory of digital inequality, motivational access (desire to have) leads to material access (physical access); however, the use of compensatory primary or secondary control strategies may enhance this association or buffer it. In other words, the use of compensatory primary control strategies may strengthen the association between one’s desire to have mobile technology and their material or physical obtainment of said technology; while the use of compensatory secondary control strategies may inhibit this association.

Figure 1

Integrated Model Depicting Technology Adoption and Use for Older Adults.

Note. Compensatory primary and secondary control strategies (motivational theory of lifespan development) are depicted as moderators of the relationship between motivational and material access (theory of digital inequality) in conceptualizing mobile technology adoption and use by older adults.

The Present Study

We sought to understand mobile technology adoption and use in community-dwelling older adults using an integrated theoretical framework to understand motivations in mobile technology adoption and use. Specifically, mobile phone technology advances rapidly, and in an ever-changing technology landscape, mobile phones in particular have the capacity to be leveraged to enhance community-dwelling older adults’ mobility and independence through the use of apps that support activities of daily living (e.g., rideshare, grocery delivery). Research supports that managing activities of daily living are important indicators of enhanced quality of life and healthy aging for older adults (Molzahn et al., 2010). However, as mentioned, older adults, particularly those who are less affluent, are on the disadvantaged side of the ever-widening digital divide. Researchers note that while the literature on older users of technology is growing, there are few studies focused specifically on mobile technology use (e.g., smartphones; Mohlman & Basch, 2021). In addition, much of prior research have utilized survey methodology with relatively large samples, treating older adults as a homogenous group when indeed, they differ significantly in terms of educational, occupational, and socioeconomic background (Neves et al., 2018).

Therefore, we examined mobile technology adoption and use in community-dwelling older adults in Southcentral Kentucky using a combination of semistructured interviews and survey techniques. Recent census statistics indicate that nearly 17% of the total population of Kentucky is age 65 or older (United States Census Bureau, 2019); this is projected to increase to approximately 26% by 2030 (Institute for Aging, 2015). Therefore, as the population ages, the need to understand mobile technology adoption and use among older adults has become necessary in order to help close the digital divide. Indeed, understanding the motivations of older adults in the adoption and use of mobile technology could help researchers and practitioners alike to determine how best to facilitate mobile technology proficiency through training efforts that are tailored to older users (Roque & Boot, 2018).

Semistructured interviews are an appropriate method when considering understanding motivations behind, and barriers to, mobile technology adoption and use as they allow for structured questions that can be clarified through follow-up questions. Participants were able to describe their current living and care situation, as well as describe their motivations behind using and/or adopting mobile technology. Motivations behind mobile technology adoption and use could then be coded and categorized. Survey techniques were used in order to collect demographics and occupational history, as well as to assess proficiency in using mobile technology. Given prior research, we formulated the following research questions as they pertain to the motivational strategies older adults use when considering the adoption and use of mobile technology:Research Question 1: What motivational strategies (compensatory primary control or compensatory secondary control) do older adults use when considering the adoption and use of mobile technology? Research Question 2: What motivational strategies (compensatory primary control or compensatory secondary control) do older adults use when considering the adoption and use of assistance of daily living apps?

Furthermore, based on prior research we formulated the following hypotheses as they pertain to sociodemographic differences in the adoption and use of mobile technology:Hypothesis 1: Education and current income level will be positively associated with the use of mobile technology such that those who report a higher education level and a higher current income level will be more likely to currently have and use mobile technology. Hypothesis 2: Occupation will be positively associated with the use of mobile technology such that those whose occupation before retirement required more knowledge-based, rather than skill-based work, will be more likely to currently have and use mobile technology.

Method

Participants

Participants consisted of 67 community-dwelling older adults recruited from seven senior centers located in Southcentral Kentucky. A senior center is a community-based center for older adults that are usually locally funded. Senior centers allow a communal space for older adults to gather, fulfilling social, physical, emotional and intellectual needs. Senior center patrons in Southcentral Kentucky come from a variety of backgrounds. While many patrons were natives of Kentucky, a subset of the sample had decided to retire in Kentucky to be close to family and because the low cost-of-living allows older adults to stretch their incomes. While the majority of participants were retired (N = 58), seven participants reported being currently employed in some fashion. For example, one patron who volunteered to participate also assisted with cooking meals at the senior center part-time for a modest wage. Another patron who volunteered had retired from her prior occupation but was currently working as a part-time manager of a community center.

As of the 2018 census statistics for the state of Kentucky, 36.4% of the population is over 50. Furthermore, 8.8% of the population of Kentucky over 60 are classified as persons of color. The average income for the state of Kentucky for those age 65+ is approximately $25,500, with only 44.7% of the population of older adults (60+) reporting having a retirement income. The average social security income for older adults (age 60+) in Kentucky is approximately $9,900. Therefore, participant demographics for the study sample are representative of the population of older adults in Kentucky.

Participants were prescreened to ensure that they were not experiencing excessive cognitive decline (i.e., dementia or Alzheimer’s). Participants ranged in age from 49 to 91, with 90% of the sample identifying as older than 60 (Mage = 72.7, standard deviation [SD] = 8.63). The majority of participants identified as female (70.8%), White (78.5%), with 69.7% of the sample indicating having a high-school diploma (or equivalent) or greater. Approximately 26% (N = 17) of participants indicated having some high-school education, with 4.5% (N = 3) having only a junior high education (up to 8th grade). The majority of participants were divorced, widowed, or separated (58.2%), with 30% of the sample indicating that they were currently married, and 12% of the sample indicating that they were single and have never been married. The overwhelming majority of participants (73%) reported having an annual household income of less than $25,000 and being retired (89.2%). In addition, the majority of participants reported retiring from production-related occupations (31%), and service-related occupations (27.6%). Please see Table 1 for full sociodemographic breakdown.

Materials

Standardized interview protocols were developed in an effort to assess participation in social activities; knowledge and use of computers, internet, and mobile phones/apps; motivation in learning and using mobile application technologies; personal competence; mobility and/or mobility limitations; and sociodemographic characteristics, including gender, age, occupation before retirement, work experience, socioeconomic level, marital status, and educational background. Interviews were conducted between January and March, 2020.

Measures

Cognitive decline was assessed utilizing the short form of the Mini-Mental Status Examination (MMSE; Folstein et al., 1975). The MMSE consisted of 21 items that required participants to verbally respond and recall information, and each item correct was scored with 1 point. Participants were required to score at least 17 points or above, which would indicate that participant is not likely experiencing issues around cognitive decline.

Mobile technology use was assessed utilizing the Mobile Device Proficiency Questionnaire, a previously validated and reliable measure (MDPQ; Roque & Boot, 2018). The MDPQ contains eight subscales: mobile device basics (nine items), communication (nine items), data and file storage (three items), interests (eight items), calendar (three items), entertainment (five items), privacy (four items), and troubleshooting and software management (five items). All items contained the stem, “Using a mobile device, I can …,” and were assessed along a 5-point Likert-type ability scale ranging from 1 (never tried) to 5 (very easily). Subscales were summed and averaged such that higher mean subscale scores indicated greater proficiency with mobile phone technology. Reliabilities were assessed using Cronbach’s α, and ranged from 0.88 to 0.97 for all subscales.

Demographics included age, sex, race, education, marital status, five-digit home zip code, annual income, and work history and retirement. The reported demographic information was used to index inequalities in the sample. Specifically, education contained the following response categories: less than high school, some high school, high school graduate (or GED), some college (or vocational school), 2-year college degree (associate’s), 4-year college degree (bachelor’s), and graduate degree or beyond. Education was dummy-coded such that those participants who had a high school degree or less were given a value of 0, while those who had some college (or vocational education) or more were given a value of 1. Current income contained the following response categories: under $25,000, $25,000–$49,999, $50,000–$74,999, $75,000–$99,999, and $100,000 or more. Income was dummy-coded such that participants who responded that their household income was greater than $25,000 were given a value of 0, while those who responded that their income was less than $25,000 were given a value of 1. Finally, occupation was categorized with the following response options: management, business, financial operations; sales and related occupations; construction trades and related work; production occupations; professional and related occupations; office and administrative work; installation, maintenance, and repair occupations; transportation and material moving operations; service occupations; farming, fishing, forestry; and education. Occupation was dummy-coded such that those participants who responded that their occupational industry prior to retirement fell into the following categories was given a value of 0 (nonprofessional): sales and related occupations; construction trades and related work; production occupations; installation, maintenance, and repair occupations; transportation and material moving operations; service occupations; and farming, fishing, forestry. Participants who responded that their occupational industry prior to retirement fell into the following categories was given a value of 1 (professional): management, business, financial operations; professional and related occupations; office and administrative work; and education. This coding scheme was developed by the research team.

Procedure

The research was approved by the university institutional review board and was performed in accordance with relevant ethical standards. All participants were interviewed by two researchers in a quiet room. One researcher prepared audio-recording equipment and interviewed the senior participant, while the second researcher took notes. All participants provided signed and recorded verbal consent to the interview and survey process. Initially, the two researchers introduced themselves to the senior participants, thanked them for participating, and then collected consent signatures. Prior to the interviews, all participants were prescreened to ensure they were not experiencing cognitive decline using the MMSE screening questionnaire. All participants were read the same script prior to verbally completing the MMSE, and research assistants were trained in administering and scoring the MMSE prior to interacting with participants.

All interviews were audio-recorded to allow researchers to clarify notes. Following interviews, participants completed a survey questionnaire to assess computer and mobile device proficiency, as well as to report their demographics and work history. Participation was voluntary, and volunteers were paid $30 cash for participating in an hour-long interview and 30-min of questionnaires. Research assistants were available while participants were filling out questionnaires to answer questions and assist participants if necessary.

Data Preparation

Research questions one and two were approached through deductive-driven thematic analysis. Thematic analysis involves understanding written content through the use of themes and patterns within the data (Braun & Clarke, 2012). Using the motivational theory of lifespan development, preidentified themes included the use of compensatory primary and secondary control strategies. Interviews underwent a two-step coding process whereby four coders were trained in the coding process. After undergoing training, coders independently coded in teams of two (each team independently coded approximately 34 interviews each). Interviews were analyzed into clauses, and these clauses were then synthesized into categories, themes, and subcategories. Coding teams met independently with a third party trained in the coding process to discuss any discrepancies in coded information. Once coders achieved an intercoder reliability of 0.8 or greater, the teams then switched and recoded the additional 34 interviews from the other coding team. Again, coding was not complete until coders achieved an intercoder reliability of 0.8 or greater. Categories and themes were then numerically coded so that correlation analyses could be performed. We were primarily interested in current mobile phone adoption and use, so participants were asked if they had a cell phone, and if so which type (flip phone, or smartphone). In addition, we were primarily interested in the use of applications to assist with activities of daily living (e.g., rideshare, grocery delivery), and therefore we specifically asked participants to describe their motivations in adopting and using app-based technology to assist with their activities of daily living. These motivations were then coded into compensatory primary control strategies (i.e., willingness to learn if provided training and support) and compensatory secondary control strategies (i.e., not interested due to expense and/or lack of need, not interested because of hesitation around ability to learn).

Quantitative data were used to test Hypotheses 1 and 2. Specifically, we examined bivariate zero-order correlations of survey data to test the significance of the associations. Data for this study are available to other researchers through the first author’s open science framework profile (https://osf.io/j2mx9; Burch, 2022). Data include numerically coded interview data as well as survey and demographic data, interview protocol with full list of interview questions, and data codebook.

Results

Results are presented to address the aforementioned research questions and organized first by descriptive analyses and hypothesis testing, and then by analysis of semistructured interview data. Specifically, we first report findings related to the motivations that older adults employ in adopting and using mobile technology (Research Question 1). We then present findings related to the motivations that older adults employ in adopting and using mobile applications to assist with activities of daily living (Research Questions 2).

Descriptive Analyses and Hypothesis Testing

Characteristics of the sample are presented in Table 1, with means, SDs, and bivariate correlations presented in Table 2. As can be seen by Table 2, income level was significantly associated with more proficiency in the following mobile phone categories: basics (r = 0.31, p < .05), communication (r = 0.46, p < .05), data and file storage (r = 0.40, p < .05), interests (r = 0.42, p < .05), calendar (r = 0.33, p < .05), privacy (r = 0.36, p < .05), and troubleshooting (r = 0.28, p < .05). However, education was not significantly associated with proficiency in any of the mobile phone categories, therefore, partial support was found for Hypothesis 1. Specifically, the pattern of correlations for the sample indicate that those participants with more income are more likely to self-report more proficiency with mobile phone technology. A pattern of significant correlations in mobile phone technology was also found for participants who reported retiring from a professional occupation. Specifically, prior professional occupation was significantly associated with more proficiency in the following mobile phone categories: communication (r = 0.33, p < .05), data and file storage (r = 0.28, p < .05), and interests (r = 0.29, p < .05), providing partial support for Hypothesis 2. Of note for the sample, income and occupation were significantly correlated (r = 0.56, p < .05), indicating that those participants who reported retiring from a professional occupation also indicated an annual household income of $25,000 or more. These results suggest that more affluent community-dwelling older adults in Southcentral Kentucky are more likely to report mobile phone adoption and use, which is consistent with prior research.

Table 1
Sample Characteristics

Demographics

N

Percent

M (SD)

Age

72.7 (8.6)

Female

46

68.7%

Ethnic/racial

 White

51

78.5

 Black

12

18.5

 Native American

2

3.1

Marital

 Single

8

11.9

 Married

20

29.9

 Divorced, widowed, separated

39

58.2

Education

 Less than high school

3

4.5

 Some high school

17

25.8

 High school grad

24

36.4

 Some college

12

18.2

 2-year college

4

6.1

 4-year college

3

4.5

 Graduate school or beyond

3

4.5

Household income

 Under $25,000

46

73

 $25,000–$49,999

12

19

 $50,000–$74,999

1

1.6

 $75,000–$99,999

1

1.6

 $100,000+

3

4.8

Mobile phone type

 None

10

14.9

 Flip phone

28

41.8

 Smartphone

29

43.3

Table 2
Means, Standard Deviations, Correlations, and Reliabilities

Variable

M

SD

1

2

3

4

5

6

7

8

9

10

11

12

13

14

1. Age

72.7

8.63

2. Sex

0.03

3. Education

−0.08

−0.17

4. Income

0.04

−0.16

0.18

5. Occupation

−0.02

−0.03

0.56*

0.39*

6. Motive

0.02

−0.16

0.11

−0.15

−0.18

7. Basics

3.03

1.48

−0.33*

0.12

0.05

0.31*

0.23

0.17

(0.88)

8. Communication

2.15

1.86

−0.39*

−0.03

0.21

0.46*

0.33*

0.11

0.86*

(0.97)

9. Data

1.48

1.62

−0.34*

−0.09

0.20

0.40*

0.28*

0.04

0.69*

0.84*

(0.94)

10. Interests

2.06

1.92

−0.42*

−0.05

0.24

0.42*

0.29*

0.10

0.80*

0.95*

0.82*

(0.97)

11. Calendar

2.02

1.86

−0.48*

−0.10

0.09

0.33*

0.15

0.62*

0.69*

0.71*

0.70*

(0.88)

12. Entertainment

2.13

1.76

−0.51*

0.02

0.10

0.24

0.24

0.10

0.76*

0.84*

0.64*

0.86*

0.72*

(0.90)

13. Privacy

2.29

1.80

−0.44*

0.001

0.14

0.36*

0.21

0.03

0.76*

0.85*

0.73*

0.90*

0.75*

0.89*

(0.90)

14. Troubleshoot

2.21

1.72

−0.54*

−0.02

0.14

0.28*

0.14

0.08

0.74*

0.82*

0.75*

0.84*

0.77*

0.82*

0.85*

(0.91)

Note. Reliabilities reported along the diagonal. Motive was coded as 0 (compensatory secondary control) and 1 (compensatory primary control). *p<.05

Motivations in Adopting and Using Mobile Technology

Participants were asked their motivations behind learning how to use mobile phone technology (if they did not currently use smartphones). If participants currently used smartphones, they were asked what their motivations were in using a smartphone. Motivational responses were coded as compensatory primary control strategies or compensatory secondary control strategies. Frequencies and summary statistics are reported in Table 3.

Table 3
Frequencies for Motivational Responses in Mobile Technology Adoption and Use

Type of phone

Motivation

CSC

CPC

N

%

N

%

No mobile phone

7

18.9

2

3.1

Flip phone

23

35.9

5

7.8

Smart phone

14

21.9

13

20.3

Total

44

68.8

20

31.3

Note. 64 total responses were used to calculate the percentages. CSC =compensatory secondary control; CPC=compensatory primary control.

Compensatory Primary Control Strategies

As mentioned, compensatory primary control strategies involve finding alternative ways to attain a goal and getting help from external resources. Less than half of the participants (20/64 coded responses; 31.3%) reported using compensatory primary control strategies in willingness to learn and/or currently using mobile technology. Specifically, of the participants who currently had no mobile phone or a flip phone, only seven (7/37-coded responses; 18.9%) reported using compensatory primary control strategies in willingness to learn how to use smart mobile-based technology. For example, one participant mentioned the desire to get a smartphone so that they could be more connected:

[I’d like to be able to use mobile technology to] communicate better and learn [to do more], but that’s hard because [I don’t have anyone to teach me].

Of the participants who currently had a smartphone, 13 (13/27-coded responses; 48.1%) reported using compensatory primary control strategies in why they adopted and used them. For example, one participant mentioned that her grandson bought her smartphone for her and it allows her to be connected and informed:

[I use my smartphone for] everything … I can check on my kids, [use Facebook], and [get information from Siri]. I love Siri, and I use Siri [a lot]. [But I would like to learn more]. I know the iPhone will do a lot, but I can’t get it right. My granddaughter tries to [teach me, but we get frustrated with each other].

Another participant mentioned a willingness to learn more if there was a class she could take:

I use [my smartphone] but I’m not the best, [I have to ask my grandchildren for help]. I would love to [take a] class and be shown some things so that when my grandkids try to say, “Let me show you Grammy,” [I can say] I got it, I’m good. I like learning new things.

Compensatory Secondary Control Strategies

As mentioned, compensatory secondary control strategies are self-protective strategies that facilitate goal disengagement enacted in a way that minimizes threats to one’s self-esteem (Shane & Heckhausen, 2019). In other words, older adults may reason that they do not need to adopt and use new technology because they’ve been able to “get by” without it. More than half the participants reported using compensatory secondary control strategies in willingness to learn and/or currently using mobile technology. Specifically, of the participants who currently had no mobile phone or a flip phone, most (30/37-coded responses; 81%) reported using compensatory secondary control strategies in willingness to learn how to use mobile-based technology. For example, one participant mentioned that they had a flip phone and that was all they needed. When asked if they would consider upgrading to smart mobile technology they provided the following response:

If I thought I needed it, [but] I want to be smart without the phone. [I enjoy the news, but I’m able to watch it on the television], and I’m happy with that. [I have everything I need].

Another participant mentioned that they preferred the simple life:

[I] just stay with what I know. Just the simple stuff for me.

Another participant who indicated that they would not upgrade to a smartphone indicated their projected use could not justify the cost:

I wouldn’t use [a smart phone] enough [to justify] the expense of it. It’s more technology than I need.

Of the participants who currently had a smartphone, 14 (14/27-coded responses; 51.8%) reported using compensatory secondary control strategies in their willingness to learn new mobile-based technology. The most common reason provided was that participants were able to do what they needed and they did not need to know more. For example, participants mentioned the following:

I’ve got Google, I’ve got messaging and messenger, and [I don’t need much else].

I’ve functioned for most of my life without [mobile phones], [and] I’ve gotten along well.

Another participant with a smart phone mentioned that they were displeased with how fast mobile technology was changing and they did not care to keep up:

I [use my phone to check the] weather … I will check Google or ask Siri [for information]. I check the stock market. I check my number of steps. I [use my phone often]. [But I have a lot of hesitation in using newer technology … [because] it changes so fast … I don’t need [to know more].

Motivations in Adopting and Using Apps to Assist With Activities of Daily Living

Participants were asked their motivations behind adopting and using apps to assist with activities of daily living, regardless of their current mobile phone status (no phone, flip phone, smart phone). In particular, we were interested if participants would be interested in enhancing their mobility and independence through the adoption and use of grocery-delivery apps, rideshare apps, and medication management apps. Again, participants’ motivational responses were coded as primary control strategies or secondary control strategies. Interestingly, the majority of participants were aware of grocery-delivery (72%) apps, while a little less than half of the participants were aware of ridesharing (48.1) and medication management (42%) apps. Of the participants who reported using mobile app technology, the following apps were reported as primarily used: entertainment and games (25.4%); social media (29.9%); communication (28.4%); information (3%); and daily living (e.g., banking; 6%). Frequencies and summary statistics are reported in Table 4.

Table 4
Frequencies in Motivational Responses for App Adoption and Use

Grocery

Ride share

Medication Management

CSC

CPC

CSC

CPC

CSC

CPC

Type of phone

N

%

N

%

N

%

N

%

N

%

N

%

No mobile phone

2

4.2

0

0

1

2.2

0

0

1

2.2

0

0

Flip phone

11

22.9

11

22.9

14

31.1

8

17.8

16

35.6

6

13.3

Smart-phone

12

25.0

12

25.0

13

28.9

9

20.0

13

28.9

9

20.0

Total

25

52.1

23

47.9

28

62.2

17

37.8

30

66.6

15

33.3

Note. Grocery apps = 48 coded responses; rideshare apps = 45 coded responses; medication management apps = 45 coded responses. CSC = compensatory secondary control; CPC = compensatory primary control.

Compensatory Primary Control Strategies

When asked about grocery delivery apps, a little less than half of the participants with coded responses reported using compensatory primary control strategies in adopting and using grocery-delivery apps in assisting with activities of daily living (23/48, 47.9%). Of note, these participants also all reported using a flip phone or smartphone for mobile technology. One participant who reported using a smart phone indicated that they would use grocery delivery apps, “If I got to where I could not go by myself.” Another participant mentioned interest if someone could train them to use a grocery delivery app:

Yes, I would be interested in learning [if training were available].

Another participant who was aware of grocery delivery apps mentioned the following:

[Grocery delivery apps] would be nice. You can order what you want and pay for it and then it’s at your door. [I’d like to] try that.

About one-third of participants indicated using compensatory primary control strategies when considering the use of rideshare apps to assist with activities of daily living (17/45; 37.8%). While participants were less aware these apps existed, they reported interest in using them if their mobility was limited:

[I haven’t used those apps but] if I couldn’t drive [anymore], yes, [I would use them].

Another participant indicated that they would love to learn to use ridesharing apps but they would need training:

Yeah, I hear about [rideshare apps], but I wouldn’t know how to [use] them. I’m open and willing to learn.

Finally, approximately one-third of participants also indicated using compensatory primary control strategies when considering the use of medication management apps to assist with activities of daily living (15/45; 33.3%). Again, while fewer participants indicated awareness of medication management apps, many indicated a willingness to learn.

[Medication management apps] would be neat. [They] would come in handy, [especially if the phone] would let you know if you took [your pill] or not. [I’d like training] and more knowledge.

Compensatory Secondary Control Strategies

When presented with information on apps to assist with daily living and the motivations in using grocery delivery, rideshare, and medication management apps, in particular, more than half of the participants indicated using compensatory secondary control strategies in adopting and using such technology. Again, the common theme was that this technology was less likely needed. Specifically, 52.1% (25/48) reported using compensatory secondary control strategies when considering adopting and using grocery delivery apps. Other participants mentioned that they know of grocery delivery apps but would find no use for them:

[If I couldn’t get to the store] I’d just find someone to take me.

I have no use for [grocery delivery apps]. When my daughter goes to the grocery I go with her. She’s really good about bringing me along.

Another participant indicated that they knew about grocery delivery apps, but they enjoy the store:

Yeah, but I like to go to the grocery store, [so] I wouldn’t use anything like that.

In considering the use of rideshare apps to assist with activities of daily living, nearly two-thirds of participants reported using compensatory secondary control strategies (28/45; 62.2%). One participant indicated that if they needed to get somewhere, they would rely on family and friends:

If I needed to, I would call somebody and let them drive one of [my three vehicles to get me where I needed to go].

In considering the use of medication management apps to assist with activities of daily living, again two-thirds of participants reported using compensatory secondary control strategies (30/45; 66.6%). One participant indicated that she prefers to manage her own medications:

I prefer to [manage my medications] because I don’t take that much. Just three at night and three in the morning and I know what they are. So far so good.

Other participants mentioned that their local pharmacies offer automatic refills and that is all they need:

[My local pharmacy] does pretty good. [The pharmacist] keeps up with me.

I get my medication at Walmart so they keep track of what I need.

Other participants mentioned that they used pill-boxes, and that method was working fine for them:

I’ve got a container … that’s got a box for every day of the week, [and that’s what I use].

Well, I do sometimes forget my noon medicine but I do my own pill boxes.

Well see, I have a two-week pill box. I wouldn’t need an app for that.

Discussion

The goal of the present study was to investigate mobile technology adoption and use among community-dwelling older adults. We accomplished this through the integration and application of the motivational theory of lifespan development and the theory of digital inequality in understanding individual differences in motivational, material, and skills access. First, we examined the influence of current income, prior education, and occupation on proficiency with mobile technology. Results suggest that education (high school or less than high school compared with education beyond high school) was not significantly associated with indicators of proficiency with mobile technology among our sample of community-dwelling older adults in Southcentral Kentucky. However, unsurprisingly results do suggest that current income level and occupation at or prior to retirement are significantly associated with indicators of proficiency with mobile technology. Specifically, older adults whose annual household income exceeds $25,000 per year and those who have or are retired from occupations that were more professional in nature are more proficient with mobile technology. In considering the sample demographics, much of the sample, 31% and 27.6%, respectively, were employed in production and service occupations prior to retirement. Production and service-oriented jobs, now, are more technology-focused. However, prior to the 1990s, digital technology was not as present in production and service-oriented occupations (Cortada, 2004), even then the advancement of digital technology in manufacturing and service was not what it is now. Now production- and service-oriented occupations are being revolutionized by machine learning (e.g., artificial intelligence), requiring greater skill set and comfort with digital technological advances.

These results suggest that among a seemingly homogenous sample of older adults (community-dwelling residents of Southcentral Kentucky), there are differences in materials and skills access likely driven by economic and work history advantages and disadvantages. Therefore, even among older adults who are often portrayed on the wrong-side of the digital divide, subdivides are present.

Next, we utilized semistructured interviews to understand the motivations behind mobile technology adoption and use, specifically, whether older adults used compensatory primary or compensatory secondary control strategies and how these motivational strategies influenced their technology adoption and use, and willingness to learn. We did so by first examining motivations in mobile technology adoption and use, then by examining the motivations in adopting and using apps to assist with activities of daily living. Results revealed that the majority of community-dwelling older adults in our sample employ compensatory secondary control strategies in both mobile technology adoption and use, as well as the adoption and use of apps to assist with activities of daily living. Older adults are a demographic that may likely benefit the most from technology adoption and use, especially considering the ongoing COVID-19 pandemic which has led to an ever-increasing digital world, yet many participants reported that they have “gotten along fine” without the use of mobile technology and that mobile technology offers more than they need. Participants also reported that they do not need apps to assist with their activities of daily living because they are either getting around themselves or are able to rely on family and friends, as well as their pharmacies in assisting them without technology intervention. Interestingly, many of these same participants report concerns with physical (44.8%), general health (16.4%), and cognitive (14.9%) decline as well as concerns with aging, and thus are more likely to benefit from the increase in mobility and independence offered by the use of mobile technology and apps to assist with activities of daily living. Recent research suggests that smartphone technology can be used to detect postural stability in older adults which can help assess fall risk, a leading cause of injury-related death in older adults (Hsieh et al., 2019). As mentioned, research further suggests that older adults who are able to manage their independence and mobility through control in their activities of daily living report better quality of life and more successful aging outcomes (Molzahn et al., 2010). The use of mobile technology for older adults provides an avenue for managing activities of daily living and maintaining independence and social connections.

While fewer older adults in our sample employed compensatory primary control strategies, those that did demonstrated a clear understanding of how mobile technology can enhance their lives, as well as a willingness to learn. Many participants demonstrated excitement in the possibility of being able to learn to use their mobile technology more effectively, as well recognizing how different apps to assist with activities of daily living can enhance their quality of life, commenting that many have an awareness of such apps and would like to learn how to use them. One participants quote is particularly resonant, “[I would like] things that will help me stay out of a nursing home longer; that’s my fear.” When asked what scares this participant about nursing homes, they responded that, “I’ve been independent all my life and worked all my life, it would be difficult to be dependent on someone else.” Indeed, many participants also emphasized that if they had help or training, they feel they could be more successful in mobile technology adoption and use.

Theoretical Contributions and Practical Implications

In integrating the motivational theory of lifespan development with the theory of digital inequality, we have provided evidence to support that understanding the association between motivational access and materials access through motivational strategies that older adults use in later life is necessary to understanding mobile technology adoption and use. In particular, the literature on the digital divide is abundant, and spans many disciplines, for example, computer science, gerontology, sociology, and psychology. However, this literature must incorporate theoretical contributions and empirical findings across the spectrum of disciplines in order to continue to advance our understanding of technology adoption and use among seniors so that intervention efforts and policies can be designed that would allow those older adults who are less technologically savvy the material and skills access they need to be successful users of technology, thus bridging the divide. Indeed, targeting motivational strategies to enhance compensatory primary control will better facilitate goal engagement and adjustment in technology use and adoption among older adults.

In practice, intervention efforts should be aimed at training programs that take into account adult learning theories. Older adults demonstrate a desire and willingness to learn, yet research suggests, as do our interviews, that older adults are concerned with their ability to learn (Kurniawan, 2008). Some participants commented that they would be interested in a training program to learn how to better use mobile technology, but the trainer would need a lot of patience. Repetition and hands on practice can facilitate crystallized intelligence (Hering et al., 2017). In addition, policies aimed at providing senior centers (a community resource for community-dwelling older adults) the resources necessary to implement local technology training programs may be beneficial in starting to bridge the divide for community-dwelling older adults.

Furthermore, technology companies can do more to assist in bridging the digital divide for older adults. Mobile technology not only changes on a daily basis, but many mobile interfaces are challenging for older adults to see and manipulate (Cisco, 2010). Indeed, one participant commented that “maybe technology companies should make it a little easier for us seniors to download stuff and get the apps and things [we need]. [It’s challenging when it’s difficult to keep up].” This quote highlights that mobile technology proficiency is associated with many hassles and frustrations for older adults. With interventions aimed at training, cost (to enhance material access), and more older adult–friendly interfaces, mobile technology adoption and use will reap more benefits.

Limitations and Future Research

We examined the use of motivational strategies in understanding technology adoption and use among community-dwelling older adults, as well as the economic disadvantages derived from social and work inequalities in material and skills access. We did this through a mixed-methods study whereby we conducted semistructured interviews and collected survey data on mobile phone proficiency and demographics with a sample of 67 community-dwelling older adults in Southcentral Kentucky. In doing so, we sought to understand the contextual differences in mobile technology adoption and use among older adults while recognizing that they are not a homogenous group which may illuminate the subdivides in digital access and use for this demographic. Our data were collected prior to the beginning of the COVID-19 pandemic (January–the first week of March, 2020). While we had hoped to conduct more interviews and collect more survey data from our sample who were largely without the digital means to collect survey-based data online, unfortunately we had to end our data collection early. Importantly, we recognize that quantitative analyses rely on sufficient sample sizes in order to more confidently model that data of interest, and thus with a sample of 67 older adults, we chose to investigate proficiency and social and work inequalities via correlations rather than through regression-based analyses which may help us to understand the predictors and incremental variance provided in the outcomes of interest. Future research should examine how social and work history factors interact in understanding technology adoption and use among older adults.

While we sought to increase our sample size in order to have the power to detect the effects of interest in the quantitative portion of our study, we reached thematic saturation with our sample of older adults at approximately 30 interviews. However, we conducted more interviews in an effort to collect as much data as possible (interviews were conducted prior to participants filling out survey measures, and often research assistants assisted participants in filling out the paper-and-pencil surveys and were on hand to clarify and answer questions). While there is very little guidance on sample size justification for semistructured interviews, a review of qualitative interview studies suggests that in most cases, a sample of 20–30 is sufficient for reaching data saturation (Marshall et al., 2013).

Furthermore, our data were cross-sectional in nature, and while the semistructured interviews provide contextual value where it is often lacking in survey-based research, we recognize that longitudinal methods that track technology adoption, use, and proficiency among older adults would provide valuable information that is lacking from the current body of literature on older adults and technology. However, researchers note that given the rapidity with which mobile technology changes, a cross-sectional approach is best as it provides a snapshot of technology adoption and use for the point in time (Zhou et al., 2012).

It is also worth noting that while many participants reported using compensatory primary control strategies, recognizing the value of mobile technology adoption and use, many participants did not. Some participants specifically mentioned cost while others referred to the hassle and frustration with the use of mobile technology considering its continuous advancement. It is possible that the cost and frustration of using mobile technology outweigh the benefits of using technology for some. While there is a large body of research discussing the benefits of technology use for older adults, future research should seek to understand the boundary conditions of these benefits given the aforementioned points on cost and frustration of use.

In addition, while we did not examine resources as a barrier to material access to technology, we recognize that resources may serve as a precursor to motivational access or a moderator of the association between motivational and material access, especially for older adults who have not grown up with mobile technology. In contrast, lower income younger adults who make less than $30,000 per year are more likely to have a mobile phone, and are more likely to only have access to the internet through their mobile phone rather than multiple devices as compared to their more affluent counterparts (Anderson & Perrin, 2017).

Conclusion

In conclusion, older adults, while primarily treated as a homogenous group, demonstrate inherent inequalities that likely preclude them from fully engaging in mobile technology adoption and use. These inequalities are likely driven by socioeconomic factors derived from income and occupational type prior to retirement (i.e., lower income and less professional work history are associated with less proficiency in mobile technology). Furthermore, motivational access in understanding technology adoption and use among older adults may be conceptualized and understood through targeting older adults’ compensatory strategies. Specifically, compensatory primary control strategies support goal adjustment; those who use compensatory primary control strategies in their adoption and use of mobile technology recognize that mobile technology may enhance their lives but also recognize that they may need to seek help to learn such technology. Our results support that many older adults use compensatory primary control strategies, demonstrating a willingness to learn technology as well as a recognition of how technology can enhance their independence and mobility.

Supplemental Materials

https://doi.org/10.1037/tmb0000088.supp


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