Experimental study of the influence of social ... - Wiley Online Library

Japanese Psychological Research 2010, Volume 52, No. 3, 244–255 Special issue: Cognitive aging studies for quality of life

doi: 10.1111/j.1468-5884.2010.00434.x

Is learning a family matter?: Experimental study of the influence of social environment on learning by older adults in the use of mobile phones1 KENJI MORI* and ETSUKO T. HARADA2 Hosei University

Abstract: It is necessary to investigate the ways in which designs for the use of artifacts created by information technology (IT) can improve the quality of life of older adults who use these devices. The present research investigated how older adults learned to use mobile phones in daily life; it focused on the effects that families may exert on the acquisition of this kind of knowledge. A 3-week experiment was conducted with 11 users who had no previous experience using mobile phones. For half of the participants, their household comprised only a spouse; the households of the remaining participants comprised three generations, including children and grandchildren. The results showed that learning to use mobile phones was facilitated in participants living in households with larger families, particularly those living with younger generations. The findings suggest that differences in users’ social environments create differences in the levels of use of the technology. The results are discussed in terms of the effects of family structure on older adults learning new technology. jpr_434

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Key words: older adults’ learning, family structure, social support, mobile phones.

Most advanced countries are experiencing rapid societal changes. In Japan, this includes a large increase in the population of older adults, with the proportion of people aged 65 years and older reaching 22.2% in 2009 (Ministry of Internal Affairs and Communications, 2009). Another change taking place in Japan involves the increasing sophistication of information technology (IT), which, in turn, has led to increasing dependency on IT-based equipment in our daily lives and social systems. For example, significant growth trends are evident in digital networking related to communications and access to information, the digitization

of the public space, and the continuing development of novel home electronics featuring advanced technology. Ideally, all members of society are expected to adapt to such changes, but in reality older adults have difficulties with this; they particularly feel increased pressure from societal requirements to adapt to new technologies. A variety of studies have begun to address the relationships between the sophistication of IT and the ageing of society. Dickinson and Gregor (2006) argued that few studies have demonstrated actual improvements in older adults’ well-being through computer use.

*Correspondence concerning this article should be sent to: Kenji Mori, Faculty of Social Sciences, Hosei University, Aihara-cho, Machida 194-0298, Japan. (E-mail: [email protected]) 1

Part of the data in this article was presented at the 5th Annual Conference of the Society of Japanese Cognitive Psychology and at the Cognitive Ageing Conference 2008.

2

The second author is now at University of Tsukuba.

© 2010 Japanese Psychological Association. Published by Blackwell Publishing Ltd.

Is learning a family matter?

However, others (Adachi, 2004; Czaja & Lee, 2007) observe plausibly that IT artifacts have significant potential for improving the daily lives of older adults. Additionally, a recent study, using questionnaire data, suggests that those older adults who are unable to use new technologies are indeed disadvantaged in communication and information gathering (Mizuno, 2006). Furthermore, Cutler (2006) pointed out that the technology in any country can affect its citizens’ views of older people and that the design of novel equipment should be examined with this in mind. Ideally, technology should be aimed at improving the quality of life and at benefitting society at large. To these ends, it is necessary to investigate methods of creating effective user-centered designs for IT-based artifacts for older adults. Previous research has focused on the reasons underlying the difficulties older adults exhibit in using technology (Aula, 2005; Czaja, 1996; Harada & Akatsu, 2003; Mead, Batsakes, & Fisk, 1999; Morrell & Echt, 1996) and on identifying the factors that can reduce the difficulties older adults show in adapting to new technologies (Czaja, Charness, Fisk, Hertzog, Nair, Rogers, & Sharit, 2006; Smither & Braun, 2001). Various research has provided results consistently showing that user characteristics such as sex, age, education, and income are related to computer use in Europe (Peacock & Künemund, 2007), the USA (O’Brien, Olson, Charness, Czaja, Fisk, Rogers, & Sharit, 2008), and Japan (Inagaki & Gondo, 2005). Also, several studies have focused on various characteristics of technology and the interactions between users and these characteristics (O’Brien et al., 2008; Ogawa, Gondo, & Inagaki, 2006). However, little research has addressed the problems that are related to the ways in which people learn to use technologies in their daily lives. In the field of engineering, skilled use of novel artifacts tends to be considered as something that is acquired through reading instruction manuals and/or taking training courses. However, when we actually attempt to use artifacts we often begin to question how much we have learned through these formal means.

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Are there other ways to learn the use of novel artifacts? Alternative kinds of learning processes have rarely been studied, even with research involving younger adults. Casual observation, as well as several field studies, suggests that even younger adults rely heavily on social networks and/or social support in order to learn IT-based skills in workplace environments (Nojima & Sakatani, 1992). If the same learning processes that occur in the workplace also occur in daily life, then, broadly speaking, our social environment, which can be expanded to include family, friends, community, and other sources, may also influence the learning process and the acquisition of technological skill. Considering that older adults appear to have more difficulty in individually using IT-based equipment (Harada, Mori, & Taniue, 2010), the influence of the social environment may have a disproportionate role in facilitating skill acquisition in older (versus younger) adults. Although older adults do not always interact in a community in ways that younger adults might, many studies have found that they are typically not isolated from society and interact with many people (Antonucci & Akiyama, 1987). Cody, Dunn, Hoppin, and Wendt (1999) investigated computer training programs, and revealed that social support and connectivity with others improved the likelihood that the older adult participants would continue in the program; in this analysis, it was also found that social connectivity was positively correlated with diversity in computer use ability. This study suggests that the social environment may influence the learning process in older adults with regard to adapting to new technology. As Aula (2005) pointed out, older people who are retired cannot always ask for computer assistance from younger colleagues; the social situation for older adults differs from that for younger adults. Because the family unit is one of the largest factors in providing social support for older adults (Antonucci & Akiyama, 1987; Shanas, 1979), the effect of the family structure may also contribute to an older adult’s acquisition of technological skills. Thus, a major factor for © Japanese Psychological Association 2010.

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K. Mori and E. T. Harada

older adults in learning to use technologies may be whether or not they live with the family members of younger generations, even though little research has directly examined empirically either how older adults learn to use new technologies/tools in their daily lives, or how family members of older adults affect their learning to use new technology. Support for this hypothesis is found in a recent study (Mori & Harada, 2007) in which questionnaire data revealed that family structure influenced older adults’ subjective ratings of how well they view their use of mobile phones. Specifically, this research indicated that the presence of family members such as grandchildren in the household of older adults strongly affected their learning to use mobile phones. The present research builds on these findings. It examines whether living in the same household with younger generations, including grandchildren, influences seniors’ use of the type of IT that is prevalent in society. In this research, mobile phones are the devices selected to represent new technology. A 3-week consecutive experiment was conducted to investigate how older adults learn to use mobile phones in daily life and how this learning was affected by family structure. This research was conducted as part of a study that included four usability tests, seven regular interviews and questionnaires, and an investigation of daily use patterns of two different groups of older adults, namely those who live in three-generation households and those who live in husband-andwife-only households.

Method Recruitment Pre-study questionnaire. A pre-study questionnaire was administered to 289 older adults (age 65–91 years, mean 70.8 years; 178 male, 111 female). The respondents were members of the Silver Human Resource Center in Tokyo, which aims to provide opportunities for work and social participation for senior citizens.The questionnaire was administrated prior © Japanese Psychological Association 2010.

to the 3-week experiment, and consisted of demographic information (e.g. age, sex, and the number of family members living with the participants) and experience with technology (e.g. mobile phones, computers, and automated teller machines). Participants. Eleven participants were older adults who reported no previous experience using mobile phones in the pre-study questionnaire. They were categorized into two groups based on reported family structure. The “spouse” group consisted of six participants (three male, three female) living in a household consisting only of two people, a participant and a spouse, with a mean age of 70.8 years (SD = 3.72 years). The “grandchildren” group consisted of five participants (three male, two female), with a mean age of 71.2 years (SD = 3.87 years), living in a household with grandchildren. The two groups were yokedcontrolled by age, sex, education, and experience with personal computers, as shown in Table 1. All participants in the grandchildren group lived together with their spouse, married children, and two grandchildren; Table 2 presents the ages of the grandchildren and the number of mobile-phone users in the grandchildren group. The grandchildren of participants 2 and 3 didn’t have a mobile phone. Also, none of the spouses of the participants in the grandchildren group had a mobile phone.

Experimental design A mixed factorial design was employed with family and trial (a repeated-measures variable) as independent variables.The family factor consisted of two levels: spouse group and grandchildren group. The level of the trial factor differed depending on variables (4 to 7).

Procedure All participants agreed to use a mobile phone for 3 weeks. Use was free of any charge, but participants consented to having phone use monitored by researchers. In addition, they agreed to meet with researchers seven times


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Table 1 Yoked control of participants Pair number

Sex

Group

Age (years)

School years (no.)

1

Male

2

Male

3

Male

4

Female

5

Female

6

Female

Spouse Grandchildren Spouse Grandchildren Spouse Grandchildren Spouse Grandchildren Spouse Grandchildren Spouse Grandchildren

78 78 67 67 73 72 69 68 70 71 68 –

11 8 16 16 12 11 12 12 11 12 12 –

Experience with using computers Frequent user Frequent user Frequent user Nonuser Past user Frequent user Occasional user Occasional user Occasional user Past user Nonuser –

Note. Experience with using computers was controlled by group. Participant 6 did not have a control pair.

Table 2 The age of grandchildren and the numbers of mobile phone users in the grandchildren group Pair number

1 2 3 4 5

Sex

Age of grandchildren (years)

Male Male Male Female Female

19 6 8 9 19

and and and and and

17 4 6 5 17

Number of mobile phone users Spouse

Child

Grandchild

Total

0 0 0 0 0

0 2 2 2 2

1 0 0 1 2

1 2 2 3 4

Table 3 Data collection procedure Place At experimental room

At participant’s home

Task

Times

Interval (days)

Questionnaire: Subjective rating for the use functions Interviews Phone logs Usability testing Writing a diary

6 7 6 4 21

3 or 4 3 or 4 3 or 4 7 Every day

during the 3-week period. During this period, four usability tests (every 7 days), seven interviews and questionnaires (every 3–4 days) were conducted. Additionally, participants kept a diary that detailed their mobile phone use. After the first usability test was conducted, the participants were taught how to use basic functions such as turning the phone on/off, dialing/ answering a call, charging the battery and setting the phone to silent mode. Furthermore,

the participants’ phone logs were collected by experimenters using data synchronization software. Table 3 shows the timing of these tasks. The participants were questioned individually; each session took 60–90 min. The participants were paid for participation following the guidelines of the Silver Human Resource Center. The experiment started after each participant signed a consent form. Privacy protection applied to all the data gathered. © Japanese Psychological Association 2010.

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The target device A mobile phone designed for older users (Fujitsu, F882iES). Instruments Questionnaire. The participants were given the same 19-item questionnaire six times over the course of the experiment, starting with the second session, that is, 3 days after they received the mobile phone. The questionnaire was designed to measure the participants’ subjective assessment of use of various mobile phone functions and consisted of items related to the 19 typical functions of a mobile phone: answering calls, charging the battery, turning the phone on/off, making calls from the phonebook, dialing calls, using call records to make calls, setting the phone to silent mode, storing phone numbers in the phonebook, redialing calls, reading text messages, replying to text messages, sending new text messages, setting a ringer melody, taking photos, adjusting the earpiece volume, setting alarms, attaching data to emails, viewing websites, and making video phone calls. For each question, participants had to answer using a 0–2-point scale (from 0 indicating “I don’t use this function” to 2 indicating “I frequently use this function”). Interviews. The interviews with participants focused on their reported use of the mobile phone in their daily life. The participants used their diaries to aid in their responses to the interviews. Additionally, other interviews regarding family size were also conducted in one of the seven sessions. Within each of the two groups (spouse, grandchildren) family size could vary, as it was defined by the number of family members either living in the same household as the participant or living in separate households. These interviews were recorded on a digital voice recorder (Sanyo, ICR-S300RM). Phone logs. The frequency of calls and text messages, as well as the number of stored phone numbers and photos, was recorded using data synchronization software. Data that could © Japanese Psychological Association 2010.

not be collected using this software (e.g. setting silent mode, key press volume, ringer melody, and wallpaper displays) were analyzed and recorded manually. These data were collected by experimenters at six different times (i.e. trials) during the 3-week period of study. The participants confirmed in the interviews that people other than themselves had not set these functions on the mobile phones. Other instruments. Usability tests were conducted four times, spaced 1 week apart; they measured a participant’s ability to operate his/her mobile phone. The participants were requested to keep a record regarding any new knowledge acquired regarding the use of their phones, as well as daily notes including questions and comments about the phones. However, these data were not used for the analysis in this paper.

Results Although various data were acquired in this experiment, in this paper we report only those results from the data that could be used for quantitative analysis. Relevant data included the assessment of the participants’ social environment, their subjective ratings, as well as objective data documenting mobile phone use. Some qualitative analysis regarding the learning experience of individual participants and/or interactions between participants and their families will be discussed elsewhere. Social environment of participants First, Fisher’s exact test revealed a significant difference in the number of persons living with participants between groups (p < .001); more persons living with participants were found in the grandchildren group than the spouse group. Next, a one-way analysis of variance (ANOVA) between groups (spouse, grandchildren) was determined using four dependent measures: family size (including persons not living in the same household), number of mobile phone users in the household, and number of mobile phone users living together in a household and


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Table 4 Environment of participants Variable

The number of persons living together*** Family size including persons not living together The number of mobile-phone users living together** The number of mobile-phone users living together and not living together†

Spouse group

Grandchildren group

Mean

SD

Mean

SD

1.00 5.33 0.17 3.33

0.00 3.33 0.37 2.21

5.00 9.40 2.40 7.80

0.00 3.88 1.02 4.02

F

3.06 20.29 4.45

Note. The numbers of persons living together was analyzed using Fisher’s exact test and the other three variables were analyzed using ANOVAs. **p < .01. ***p < .001. †p < .10.

living separately in different households (note: the spouse group has family members living separately in other households). As shown in Table 4, family size did not differ significantly between the spouse and the grandchildren groups. In contrast, these data indicate that the two groups differed significantly with regard to the number of mobile phone users living in the same household, F(1,9) = 20.29, p < .01; more phone users were found in the grandchildren than the spouse group. Moreover, in the spouse group, there was only one participant whose spouse had a mobile phone. Unlike the results for family size, these ANOVAs found a tendency toward significance for total numbers of mobile phone users, F(1,9) = 4.45, p < .10. In summary these results showed differences in measures of the social environment, such as the number of persons living with participants and the number of mobile phone users in the two groups, verifying the experimental manipulation. Subjective rating of usage In order to assess the levels of mobile phone use, a 2 (family group) ¥ 6 (trial) mixed factorial ANOVA was conducted using the total subjective rating score (i.e. over all 19 functions; the Cronbach’s alpha was .89). Figure 1 shows the total phone use score as a function of each trial, that is, the six data collection times, as a function of family structure, that is, group. There was a significant main effect for group, F(1,9) = 6.02, p < .05. The grandchildren group (M = 20.23, SD = 4.77) had significantly higher

ratings than the spouse group (M = 12.28, SD = 5.78). In addition, a main effect for trial was also found, F(5,45) = 26.63, p < .001. Multiple comparisons (LSD) showed significant changes between successive trials (p < .05); exceptions to this included days 10–14 and days 17–21. However, these effects were qualified by a significant trend of interaction between group and trial, F(5,45) = 2.27, p < .10. According to the results of simple main effect analysis, the scores were significantly higher in the grandchildren group than in the spouse group on days 3, 17 and 21 (p < .05). In other words, no significant family differences were found on days 7, 10 and 14. A simple main effect for trial was also significant, revealing a gradual increment in total use scores, with a stagnation period for learning around days 10–14 in the spouse group (day 3 was lower than days 10, 14, 17 and 21; day 7 was lower than days 10, 17 and 21; day 14 was lower than days 17 and 21, p < .05). In contrast, there was no stagnation period for learning at the middle stage, that is, days 10–14 in the grandchildren group (day 3 was lower than days 14, 17 and 21; day 7 was lower than days 14, 17 and 21; day 10 was lower than days 14, 17 and 21; day 14 was lower than days 17 and 21, p < .05). One interpretation of this finding is that group differences at the initial stage (at day 3 of the trial) reflect rapid learning of basic functions in the grandchildren group, whereas later differences (at days 17 and 21) reflect learning of advanced functions in this same group. © Japanese Psychological Association 2010.

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Analysis of objective data First, in an analysis of the objective data, the participants’ phone logs, detailing the total frequency of calls and text messages, and the number of stored phone numbers and photos over 3 weeks, were tested using ANOVAs. As shown in Table 5, there were no significant differences for calls, stored phone numbers, and sending text messages between the two groups. In contrast, the results showed significant differences for the frequency of receiving text messages, F(1,9) = 4.11, p < .10, and the number of photos stored, F(1,9) = 3.59, p < .10, with the grandchildren group using these functions more frequently than the spouse group. Because the frequency of using text message functions in the grandchildren group showed a large standard deviation, we also compared whether or not the text message function (sending and receiving) was used in the two groups. In the spouse group, only one of six participants used the text message functions, while four of five participants of the grandchildren group used text messaging (Fisher’s exact test, p = .08). The participants who used this function in the grandchildren group sent and received text messages from 3 to 111 times. Next, Fisher’s exact test was used to compare changes in the internal settings for

the mobile phone over the 3-week period between the spouse and the grandchildren group. The following functions were examined to check for any changes during the experiment: silent mode, key press volume, ringer melody, and wallpaper display. The total number of functions changed was also analyzed. As shown in Table 6, there was no significant difference for each setting, but the total data indicated a significant difference (p < .05). More specifically, the majority of participants in both groups set their phone to silent mode. However, the participants in the grandchildren group sometimes used the other three functions, whereas those in the spouse group did not use them at all.

Discussion The results showed that, relative to participants in the spouse group, participants in the grandchildren group used functions such as receiving text messages, taking photos, and changing internal settings more frequently. These findings suggest that the differences in the family structure, as the social environment, of these participants lead to differences in their levels of technology use. In terms of the social environment, the analysis of the environment in which the participants lived indicated significant

Use of 19 functions (score)

35.0 30.0 25.0 20.0 15.0 10.0 Spouse group Grandchildren group

5.0 0.0 3 days after

Figure 1

7 days

10 days

14 days

17 days

Transition of total scores of subjective rating for the usage functions.

© Japanese Psychological Association 2010.

21 days


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Table 5 Use of functions based on phone logs Variable

Spouse group

Frequency of making calls Frequency of answering calls The number of phonebooks entries stored Frequency of sending text message Frequency of receiving text message† The number of stored photos†

Grandchildren group

F

Mean

SD

The number of users

Mean

SD

The number of users

18.00 6.67 2.50

9.26 3.73 3.25

6 6 3

26.20 8.80 5.00

7.11 10.76 5.40

5 5 4

2.15 0.17 0.73

1.00

2.24

1

12.80

18.35

4

2.85

0.83

1.86

1

15.60

24.00

4

4.11

3.00

3.46

3

9.00

5.90

5

3.59

Note. Logarithmic transformation was used for variables of text message. This table shows raw data for all variables. †p < .10.

Table 6 The number of participants who changed the internal settings on the mobile phone Group

Set Not set

Silent mode

Key push volume

Ringer melody

Wallpaper

Total*

S

GC

S

GC

S

GC

S

GC

S

GC

4 2

4 1

1 5

3 3

0 6

2 4

0 6

2 4

5 19

11 9

Note. The two participants who used the ringer melody and the wallpaper were not the same participants. GC = grandchildren group. S = spouse group. *p < .05.

differences in the number of persons living together in the same household, as well as in the number of family members who reported using mobile phones. At the same time, there was no significant difference between the two groups in total family size (i.e. including extended family members). Taken together, these data indicate that the two groups differ primarily in the number of mobile phone users they encounter on a daily basis.With regard to learning profiles, subjective evaluations revealed a significant difference between the two groups, suggesting a rapid learning of basic functions and learning of advanced functions in the participants in the grandchildren group. In addition, analysis of objective data showed that the participants in the grandchildren group were more likely to use advanced functions, such as receiving text messages, taking photos, and changing internal settings, than those in the spouse group. In summary, because the participants in the grand-

children group live together in a household with younger generations, they appear to have more opportunities for learning how to use mobile phones than do the participants in the spouse group. Based on the data gathered from phone logs, the data covering frequency of calls showed that both groups used their mobile phones often. We assume that the people close to the participants in the two groups interact by phone with comparable frequency; this assumption was confirmed by data showing no significant group differences in the number of stored phone numbers. That is, the factor of family structure did not result in differences in the number of calls made and received, possibly reflecting the fact that there were no significant differences in total family size between the two groups. Taking these findings into account, it is interesting that there were group differences only in the frequency of receiving text © Japanese Psychological Association 2010.

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messages, as participants in the grandchildren group received more text messages than those in the spouse group. Because, generally, most users of the mobile phones had learnt the use of text messages, the participants in the grandchildren group were able to receive text messages more frequently than the participants in the spouse group. And it seems natural to presume that receiving such contact was advantageous for the participants in the grandchildren group in learning to use the mobile phone, or at least the use of the text message function. More interestingly, there were also significant differences in the functions that are not always used for communication: taking photos and changing internal settings. This suggests that learning how to use mobile phones can be facilitated not only by the desire to have communicative interactions with family members, but that this learning is also facilitated by the social environment of users, such as family structure, which may be mediated by environmental factors that affect the possibility of receiving effective social support for learning novel artifacts. As Docampo Rama (2001) said, “it is not unusual to see grandchildren teach their grandparents how to use a video recorder, mobile phone, computer, or Internet (p. 1).” It is common for people to ask a person who seems to understand certain equipment to explain the use of this equipment. In other words, when the participants live in a household consisting of only the participant and a spouse, learning to use the mobile phone is likely prove to be relatively difficult, unless other effective support is provided. This study has several limitations, however. One concerns the nature of learning. Although our results revealed differences in learning performance between the two groups, details of the learning process itself remain unclear. Nevertheless, our results lead us to infer that the participants in the grandchildren group learned advanced functions through interaction with their families. This learning seems to be particularly related to functions such as text messaging, storing phone numbers, storing photos, and use of the other three internal settings. Assuming this inference is justified, it suggests that © Japanese Psychological Association 2010.

there was a strong family influence on participants in their daily lives. Mori (2007) conducted semi-structured interviews with 17 mobile phone users in two groups based on use levels, and revealed that the participants in the highuse group recognized that people around them were using mobile phones in a wider variety of ways than those in the low-use groups. Considering this result, there appears to be two possible roles played by the individuals surrounding the participants, particularly the young adults who live with older users. The first role is that of a model user of technology, namely an individual who demonstrates sophisticated usage of technology. This role may motivate older users to use more advanced functions because their attention is directed to the existence of these functions; in addition, it can underscore the merits of using these types of functions. The other role is that of a continuous giver of direct and timely support for the learning of operations. This role is different from the former because this is not limited only to persons using the same device as the learners. Although people who already have the same type of equipment seem to play this role of continuous operation supporter, there is also a possibility that nonusers may be potent providers of support for this learning. For instance, grandchildren who are still small and do not yet have a mobile phone can facilitate the learning of operations. This is because older adults are less likely to resolve their difficulties with new IT-based equipment than are younger people, who tend to overcome use problems by themselves (Harada et al., 2010). Actually, all support for the learning of operations that was reported in the participants’ diaries during this experiment was provided by family members living together with the participants, including a 6-year-old grandchild who didn’t have a mobile phone.This result also indicated that there were some difficulties in obtaining help from people outside the household. The large variability in the frequency of sent and received text messages in the grandchildren group (3 to 111 times) may also be indicative of individual differences in the


learning strategies of older adults, which are not entirely accounted for by either family structure or the presence of grandchildren. There might be individual differences in the possibility of getting these two kinds of support. Moreover, it is difficult to judge whether very young children or preschool-aged children, who are assumed to be nonusers of mobile phones and computers, can effectively teach their grandparents how to use these devices. Again, however, it is clear from the results that there were certain group differences related to family structure as one of the strongest environmental factors on older adults’ learning of technology. This process of learning facilitation by young family members needs more rigorous examination in the future. Furthermore, it is necessary to examine whether there are differences in the roles as supporters of older adults’ learning between children and grandchildren. Note that the two groups of participants did not differ in the use of basic mobile phone functions, functions that were taught on the first day of the experiment. It might be important to emphasize that the participants’ ability to make phone calls and to use the silent mode could be a result of the intervention of this experiment itself. This remains a possible interpretation, because we do not have comparative data based on the performance of people who did not participate in the experiment. When we attempt to create methods to support older adult users who live only with a spouse, this effect might be important and should be investigated more fully. A second limitation of this study involves the variation in family structure selected for examination in this research. Because this research continued for several weeks, incurring high experimental costs, we limited our number of groups to two: households with children and grandchildren versus those without. This comparison followed from a hypothesis developed in previous research (Mori & Harada, 2007). However, it would be useful to examine other types of family structures that involve older adults. In particular, it would be helpful to assess skill learning in older adult users who live alone. This is because data collected from

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previous questionnaires shows that these older adults actually use mobile phone functions more than older adults who live with only a spouse; moreover, there was no difference in levels of use between older adults who lived alone and those who lived with grandchildren. As this report has shown, there are positive learning effects for older adults who live in three-generation households, and the advantage in mobile phone use by older adults who live alone seems rather contradictory to the present research; for this reason it should be investigated more thoroughly. Finally, there is a question of generalization. Might these results be biased by the fact that the object used in the study was a mobile phone? Do these data represent a general phenomenon found with many different kinds of IT-based artifacts? It is possible that the characteristic of mobile phones as a communication tool could create differences in learning between the grandchildren group and the spouse group. However, considering that there were also significant differences in the use of functions that are not necessarily communicative (photos and internal settings), additional research investigating the use of other IT-based artifacts is necessary. The presence of a social environmental factor that facilitates skill acquisition of operating a mobile phone suggests that there is a difference in learning opportunities with respect to IT-based artifacts that are associated with the social environments of the older adult users, such as family structures. What is necessary is greater attention to, and discussion of, ways to increase the availability of social or environmental support systems for an aging society. In fact, the number of older adults who live with grandchildren continues to decrease, meaning that currently the real problem entails figuring out how to provide effective social support to older users who are living only with a spouse (30% of total households with elderly people in Japan). That is, this segment of the population in particular will require support that enables them to keep pace with changing technologies and/or gain some value from new technology, such as an informational care system at home © Japanese Psychological Association 2010.

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(Ogata, Harada, Shimoebisu, Nambu, Akatsu, & Taniue, 2003). Possible resolutions to this problem may involve the development of better designed artifacts and social support environments. Related to this is the need to develop artifacts that are more readily usable by older adults within certain social niches. Future usability studies should be conducted with a focus, not only on the interaction between the user and the equipment, but also on interactions between the user, the equipment, and his/her social environment. It would also be possible to undertake a systematic psychological study of older adults’ learning to use IT-based artifacts, relating to various personal characteristics as a recipient of personal and social support, as well as the factors that users can effectively learn from others. Applying cognitive psychology to real life situations, particularly focusing on learning to use new technology, could extend research in a fruitful way and pertain to many aspects of cognitive ageing and psychology.

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