CHAPTER 2 OLDER ADULTS` HEDONIC AND EUDAIMONIC WELL

Doctoral Thesis
Older Adults’ Affective and Cognitive Factors and
Information and Communication Technology
By
Jia ZHANG
09D55075
Supervised by
Associate Professor Hiroyuki UMEMURO
Department of Industrial Engineering and Management
Graduate School of Decision Science and Technology
Tokyo Institute of Technology
November 2012
ABSTRACT
The purpose of this dissertation was to investigate the significance of affective and
cognitive factors, such as well-being, cognitive abilities and computer attitudes that
contribute to the use of new technologies of older people in order to identify those
relationships that are relevant to sustaining digital engagement in older age and to
provide independent and meaningful life to older people.
Three studies were conducted to detect the relationships between older adult’s
ICT use and their affective and cognitive factors. The objective of the first study was
to observe whether older adults' hedonic and eudaimonic well-being during ICTs are
associated with their experiences in daily life activities. The results indicated that
older adults' well-being in ICT activities was significantly correlated with that in
corresponding daily life activities. In some activity pairs, the relations were
moderated by perceived usefulness. Hedonic and eudaimonic well-being can be
predicted by different dimensions of computer attitudes. The second part of this
dissertation was focusing on understanding the influencing factors of the dynamic
changes in the process when older people adopt a new technology. Preliminary
results showed two dimensions of computer attitudes increased when older adults
started to use technology products or services. On the other hand, significant
declines of three dimensions of computer attitudes were observed when older adults
stopped to use technological products. For non-computerized products, no changes
on computer attitudes were observed. With regard to cognitive abilities, no
consistent pattern was observed. The third stream of this research focused on
tracking the new technology adoptions of older people in a longer-term and to check
the relationships of innovativeness and computer attitudes in older adults’ subsegments. The results showed that older people who adopted more new technology
products/services held more positive attitudes toward computers. On the other hand,
older adults who discontinued using more new technology products/services held
more positive attitudes toward computer than those who stopped using fewer new
technologies.
To conclude, in the study on affective factors, hedonic and eudaimonic wellbeing in ICT usage was two important and different perspectives when investigating
user experiences in ICT. In addition, older adults’ hedonic and eudaimonic wellbeing in ICT usage were associated with that in their corresponding daily lives. In
the study on dynamic changes and cognitive factors, I concluded that computer
attitudes were found to have relations with dynamic changes in technology usage, in
addition to usage status that has been reported in previous studies. In the last study
on older adults’ innovativeness and computer attitudes, older people who adopted
more new technology products/services held more positive attitudes toward
computers. Thus, it is necessary to subdivide the group into sub-segments when
older people are research subjects.
Finally, all these findings are discussed in terms of the design of ICT systems
to improve well-being of older adults in ICT usage and make them better utilize
technology in their lives.
ACKNOWLEDGEMENTS
I would like to acknowledge a few of special persons here who have inspired,
supported, and encouraged me over these years.
First of all, I would like to express my greatest gratitude to my supervisor,
Associate Professor Hiroyuki Umemuro for his invaluable advice, guidance,
patience, supports and encouragements during my three and half years in Affective
lab. I deeply appreciate the opportunity he provided me to pursue an academic
career in the Tokyo Institute of Technology. I am also thankful to the doctoral
committee, consisting of Professors Takao Enkawa, Masaaki Muraki, Sulin Chung
and Hirotaka Aoki for their helpful comments.
I would like to thank all those who contributed towards the completion of this
work, to all members at Umemuro Laboratory form which I felt supported until the
end. I would like to thank for former doctoral student Ramon Solves Pujol and
doctoral student Waratta Authayarat for their encouragement and support during my
research life. Special thanks to Xiuzhu Gu for her constructive suggestions on my
research and encouragement.
My great appreciation goes to all persons who contributed to our data collection.
I am grateful to all older adults participating in the surveys. I also thank to Professor
Don Bouwhuis and other anonymous reviewers for their constructive advices on my
research papers.
Finally, I want to express my gratitude to my parents and my husband
Shuang Liu. Without their strong support, I would not have been able to take the
challenge to study in Japan for my doctoral degree.
TABLE OF CONTENTS
CHAPTER 1 INTRODUCTION ................................................................................. - 1 1.1
RESEARCH BACKGROUND ........................................................................................ - 1 -
1.1.1
Affective factors and use of new technologies ............................................................................. - 2 -
1.1.2
Cognitive factors and ICT use ...................................................................................................... - 5 -
1.2
OBJECTIVES OF THE DISSERTATION .................................................................... - 6 -
1.3
STRUCTURE OF THE DISSERTATION..................................................................... - 8 -
References ................................................................................................................................... - 11 -
CHAPTER 2 OLDER ADULTS’ HEDONIC AND EUDAIMONIC WELL-BEING
IN INFORMATION AND COMMUNICATION TECHNOLOGY ....................... - 14 2.1
INTRODUCTION .......................................................................................................... - 14 -
2.2
WELL-BEING STUDIES IN ICT CONTEXT ............................................................ - 14 -
2.2.1
Current situation on well-being studies in ICT context .............................................................. - 15 -
2.2.2
The connection between ICT and daily life experiences ............................................................ - 17 -
2.3
METHOD ........................................................................................................................ - 19 -
2.3.1
Participants ................................................................................................................................. - 19 -
2.3.2
Procedure .................................................................................................................................... - 19 -
2.3.3
Measurements ............................................................................................................................. - 19 -
2.4
RESULTS ........................................................................................................................ - 24 -
2.4.1
Well-being .................................................................................................................................. - 25 -
2.4.2
Well-being and activities correlated ........................................................................................... - 27 -
2.4.3
Predictors of ICT well-being ...................................................................................................... - 31 -
2.5
DISCUSSION .................................................................................................................. - 34 -
I
2.6
CHAPTER SUMMARY................................................................................................. - 37 -
References ................................................................................................................................... - 40 -
CHAPTER 3 DYNAMIC CHANGS OF TECHNOLOGY USAGE AND
COMPUTER ATTITUDES AND COGNITIVE ABILITIES OF JAPANESE OLDER
ADULTS
- 43 3.1
INTRODUCTION .......................................................................................................... - 43 -
3.2
METHOD ........................................................................................................................ - 46 -
3.2.1
Participants ................................................................................................................................. - 46 -
3.2.2
Procedure .................................................................................................................................... - 50 -
3.2.3
Measurements ............................................................................................................................. - 50 -
3.3
RESULTS ........................................................................................................................ - 54 -
3.3.1
Computer Attitudes ..................................................................................................................... - 54 -
3.3.2
Cognitive abilities ....................................................................................................................... - 55 -
3.4
DISCUSSION .................................................................................................................. - 60 -
3.5
CHAPTER SUMMARY................................................................................................. - 62 -
References ................................................................................................................................... - 64 -
CHAPTER 4 LONGITUDINAL STUDY ON RELATIONSHIPS BETWEEN
TECHNOLOGY ADOPTION AND COMPUTER ATTITUDES OF OLDER
PEOPLE
- 66 4.1
INTRODUCTION .......................................................................................................... - 66 -
4.2
METHOD ........................................................................................................................ - 68 -
4.2.1
Participants ................................................................................................................................. - 68 -
4.2.2
Procedure .................................................................................................................................... - 68 -
4.2.3
Measurements ............................................................................................................................. - 69 -
4.3
4.3.1
RESULTS ........................................................................................................................ - 71 Innovativeness ............................................................................................................................ - 71 -
II
4.3.2
Computer attitudes ...................................................................................................................... - 72 -
4.4
DISCUSSION .................................................................................................................. - 74 -
4.5
CHAPTER SUMMARY................................................................................................. - 77 -
References ................................................................................................................................... - 78 -
CHAPTER 5 CONCLUSIONS .................................................................................. - 80 5.1
RESEARCH OUTCOMES ............................................................................................ - 80 -
5.1.1
Well-being and older adults’ ICT usage ..................................................................................... - 80 -
5.1.2
Cognitive factors and older adults’ dynamic changes of new technology adoption ................... - 81 -
5.1.3
Older adults’ innovativeness and cognitive factors .................................................................... - 82 -
5.2
RESEARCH IMPLICATIONS ..................................................................................... - 82 -
5.2.1
Applications of affective factors in ICT ..................................................................................... - 82 -
5.2.2
Applications of cognitive factors in ICT .................................................................................... - 85 -
5.3
LIMITATIONS ............................................................................................................... - 86 -
5.3.1
Limitation of the sample ............................................................................................................. - 86 -
5.3.2
Limitation of the methodology ................................................................................................... - 87 -
5.4
FUTURE STUDIES ........................................................................................................ - 88 -
References ................................................................................................................................... - 90 APPENDIX A Hedonic and eudaimonic well-being questionnaire ..................................... - 92 APPENDIX B Test-retest questionnaire of thePersonally Expressive Activities Questionaaire
(PEAQ) ...................................................................................................................................... - 103 APPENDIX C Technology usage and computer attitudes questionnaire ......................... - 114 -
III
List of tables
Table 2-1 Numbers of participants who reported on well-being for each activity and pairs of
activity .......................................................................................................................................... - 25 Table 2-2 Hedonic and eudaimonic well-being differences across daily life activities and
corresponding ICT activities; HWB = hedonic well-being; EWB = eudaimonic well-being; ..... - 26 Table 2-3 Differences in hedonic and eudaimonic well-being between pairs of daily life and ICT
activities; HWB = hedonic well-being; EWB = eudaimonic well-being; .................................... - 26 Table 2-4 Pearson's correlation coefficients of hedonic (HWB) and eudaimonic (EWB) well-being
for news reading activities that showed a significant correlation between the daily life activity and
its ICT counterpart ; ..................................................................................................................... - 28 Table 2-5 Pearson's correlation coefficients of hedonic (HWB) and eudaimonic (EWB) well-being
for chatting activities that showed a significant correlation between the daily life activity and its
ICT counterpart ; .......................................................................................................................... - 29 Table 2-6 Pearson's correlation coefficients of hedonic (HWB) and eudaimonic (EWB) well-being
for shopping activities that overall showed no significant correlation between the daily life activity
and its ICT counterpart, but are now divided between high and low perceived usefulness ......... - 30 Table 2-7 Pearson's correlation coefficients of hedonic (HWB) and eudaimonic (EWB) well-being
for mailing activities that overall showed no significant correlation between the daily life activity
and its ICT counterpart, but are now divided between high and low perceived usefulness ; ....... - 31 Table 2-8 Final multiple regression models of ICT eudaimonic (EWB) and hedonic well being
(HWB); IPU=ICT perceived usefulness; DEWB= daily life eudaimonic well-being; DHWB= daily
life hedonic well-being; CI =confidence interval; ........................................................................ - 33 Table 3-1 Participant numbers in each year ................................................................................. - 46 Table 3-2 Participation statuses of all registered participants from 2003 to 2009 ....................... - 47 Table 3-3 Numbers of dynamic changes when older adults adopted or discontinue technology
products ........................................................................................................................................ - 52 Table 3-4 Means and standard deviations of computer attitudes when older adults adopted
technology products ..................................................................................................................... - 54 Table 3-5 Means and standard deviations of computer attitudes when older adults discontinued
using technology products............................................................................................................ - 55 Table 3-6 Means and standard deviations of cognitive abilities when older adults adopted
technology products ..................................................................................................................... - 56 -
IV
Table 3-7 Means and standard deviations of cognitive abilities when older adults discontinued
technology products ..................................................................................................................... - 57 Table 3-8 Means and standard deviations of GCA when older adults adopted technology products . 58 Table 3-9 Means and standard deviations of GCA when older adults discontinued technology
products ........................................................................................................................................ - 59 Table 4-1 Medians of the annual adoption and discontinuance numbers among four technology
groups ........................................................................................................................................... - 72 Table 4-2 Means and standard deviations of computer attitudes among four groups of older people 74 -
V
List of figures
Figure 1-1 Structure of the dissertation….…………………………………………......- 10 Figure 3-1 Definition of dynamic changes……………………………...………...……- 50 Figure 4-1 Means and standard deviations of computer attitudes among four groups of
older people ……………………………...…………………………………..…...……- 73 -
VI
CHAPTER 1
1.1
INTRODUCTION
RESEARCH BACKGROUND
Information and communication technologies (ICTs) are becoming indispensable for
people of all countries and ages. Today, it is hard to find a corner in our lives that is
not affected by technology. We are surrounded by it at home, commuting, at work,
and in our leisure time.
Meanwhile, globally, the number of persons aged 60 or over is expected almost
to triple, increasing from 737 million in 2009 to 2 billion by 2050. In the more
developed countries, 21% of the population is already aged 60 years or over and that
proportion is projected to reach 33% in 2050. In developing countries as a whole,
even though just 8% of the population is today aged 60 years or over, that proportion
will more than double by 2050, reaching 20% that year (UN Population division
DESA [UNPD DESA], 2009).These unprecedented trends would heavily affect
societies, companies and the development of technologies. In Japan, there is a larger
number of older persons than younger persons, and the number of senior households
without any nonsenior household members surpassed 9.5 million in 2009 (Yamada,
2009).
According to the research of Usui (2011), the current generation of older adults
is healthy and places more emphasis on self-reliance and quality of life. They
command a larger disposable income and national financial assets than their
previous generations did. They are willing to consumer goods and services that
promote independent living. Innovations in telecommunications technology and
equipment, telecare and telematics, consumer electronics, robotics, and other hightech engineering address new demands arising out of the social and cultural changes
surrounding their living environments. New technologies can also improve older
adults’ quality of life and support their independence by providing access to online
services (for example, shopping, banking), online information and unique
possibilities of communication with friends and family members. There is a
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potential for active aging, which changes the current assumptions about the aged
dependency ratio.
Although it is the fact that new ICT is becoming necessary for our daily lives,
not all older adult can use it freely and enjoy the benefits brought by those new
technologies. This leads to the question about why some older people feel reluctant
to use new ICT technologies. Is it that they do not have the skill to use? Or is it that
they do not have an enjoyable engagement? Is it that they dislike being connected
with others? Or is it that it doesn’t give them anything valuable? Those questions
have in common that psychology is the discipline that studies them. In other words,
all these issues are related to psychological characteristics of users, rather than the
technologies. In a world, the technology use will become more and more common.
In order to develop support systems to support the independency of older age, it is
essential to understand the relationship between the use of new technologies and
psychological characteristics including psychosocial and cognitive factors.
1.1.1
Affective factors and use of new technologies
The research on well-being has emerged as an important area of study, focusing
on positive psychological functioning and positive subjective experience in the new
millennium (Seligman & Csikszentmihalyi, 2000). However, theorists have found
the issue of well-being to be complex and controversial. There are various types of
well-being, such as emotional, mental, psychological and social (Wyller et al., 2003;
McDaid, Curran & Knapp, 2005; Diener, Suh, Lucas & Smith, 1992; Keyes, 1998),
but they have not necessarily been properly defined or specified in terms of a
measurement methodology. Argyle (1992) suggested that there are two ways for
people to define happiness; one is defined in terms of positive emotions, like joy,
happy, while the other is defined as satisfaction with life or satisfaction with specific
aspects of life, such as marriage or job. Ryan and Deci (2001) distinguished theories
of well-being into two basic types: hedonic and eudaimonic views. They argued that
‘happiness’ appears to reflect relatively short-term, situation-dependent affective
expressions which is considered as ‘hedonic aspect’, while ‘life satisfaction’ turns to
reflect long-term, more stable cognitive evaluations, which is called as ‘eudaimonic
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aspect’. In other words, a happy person enjoys positive emotions while perceiving
his or her life to be meaningful. This definition does not only concern to a single
moment, but also to a generalized aggregate of one’s experiences.
Hedonism, as a view of well-being, has been expressed in many forms, varying
from pleasures of the body to pleasures of the mind. Aristippus, a Greek philosopher
from the fourth century BC, taught that the goal of life is to experience the
maximum amount of pleasure, and that happiness is the totality of one’s hedonic
moments. In the 1970s, Kraut (1979) suggested that hedonic enjoyment refers to the
positive affect that accompanies acquiring material objects one wishes to possess or
engaging in activities one wants to experience. Among the many ways to evaluate
this concept, subjective well-being (SWB; Diener, 1984) is most widely used in
hedonic psychology research.
From the eudaimonic perspective, Aristotle’s theories of well-being emphasize
perfection or virtue. However, some ancient eudaemonists, for example the
Epicureans, denied that eudaimonia consists of perfection. They arguably agreed
with Aristotle that well-being involves the fulfillment of our natures as human
beings, but they believed that we fulfill our natures by achieving pleasure (Haybron,
2008).
In contemporary research, the term eudaimonia has many different
interpretations. Waterman and his colleagues (1993; 2008) suggested that
eudaimonia occurs when people’s life activities are most congruent with deeply held
values, and they are fully engaged. Under such circumstances, people feel intensely
alive and authentic. Eudaimonic experience of an activity occurs when there is an
unusually intense involvement in an undertaking, such as a feeling of intensely being
alive, a feeling of being complete or fulfilled while engaged in an activity, an
impression that this is what the person was meant to do and a feeling that this is who
one really is. Self-determination theory (SDT; Ryan & Deci, 2000) is another
perspective that has embraced the concept of eudaimonia, or self-realization, as a
central definitional aspect of well-being. SDT posits three psychological needs—
autonomy, competence and relatedness—and theorizes that fulfillment of these
needs is essential for psychological growth. In addition, Ryff and colleagues’ six-
-3-
dimension psychological well-being model (PWB; Ryff, 1989; Ryff & Keyes, 1995)
is widely adopted in this research area. They have explored the question of wellbeing in the context of developing a lifespan theory of human flourishing. Although
the research on well-being and hedonic experience is rather complicated, Ryan and
Deci (2001) argued that because of the theoretical and practical importance, wellbeing research is probably best conceived as a multidimensional phenomenon that
includes aspects of both the hedonic and eudaimonic conceptions of well-being.
Some researchers have conducted trials to investigate the positive side of
product using feelings, such as pleasure and enjoyment, in usability engineering
(Jordan, 2000; Brandtzæg, Følstad & Heim, 2005). However, the existing models of
user experience in human-computer interaction that incorporate hedonic aspects
such as pleasure are rare and often overly simplistic (Hassenzahl, 2005). To
complement the traditional usability concepts, Hassenzahl (2004) proposed the
hedonic attributes of interactive products, which can be subdivided into
identification and stimulation. Despite using the word ‘hedonic’, Hassenzahl
considered hedonic attributes to emphasize individuals’ psychological well-being,
and fulfill the needs of competence/personal growth and relatedness/self-expression,
which were intensively discussed by the eudaimonic theorists above. Slegers, van
Boxtel and Jolles (2008) argued that anticipated psychological benefits of the
Internet, such as competence and connectivity with family members, are hard to
quantify and measure with currently available well-being instruments. Therefore, it
is necessary to consider both hedonic and eudaimonic aspects of well-being when
studying older adults’ ICT usage.
Why is it important to study well-being? The benefits of well-being at the
individual level are very significant. Lyubomirsky, King and Diener (2005) found
that people with higher level of well-being are more likely to be more healthy,
sociable, active and long-lived. Since well-being is so important to our society and
since technology seems to pervade almost all areas of our lives, the study of the
different effects of technology on our well-being would seem fundamental.
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1.1.2
Cognitive factors and ICT use
(1) Cognitive abilities
Cognitive abilities are the basic elements of cognition that refers to perception,
working memory, decision and so on. Older peoples’ physical, mental activities have
all been shown to affect cognitive abilities; also conversely, cognitive and physical
decreasing can also limit access to activities. Just as physical capabilities and
limitations change with age, so do cognitive abilities. When studying on older
people’s technology usage, it is important to understand their specific cognitive
capabilities and limitations. A useful way to think about the full range of human
cognitive abilities is to categorize those into fluid intelligence and crystallized
intelligence. Fluid intelligence is those abilities needed in unfamiliar, rapidly
changing situation, which included perceptual speed, working memory, spatial
ability and environmental support. Crystallized intelligence represents the sum of
knowledge that one has gained through a lifetime of formal education and life
experience (Pak & McLaughlin, 2010).
The difficulties older adults encounter when using technologies may eventually
compromise an independent lifestyle, which is a primary goal of many older
individuals (Slegers et al., 2008). Everyday tasks that are essential to independent
functioning, but which have become more and more technology-driven, may
become too difficult for older adults to perform autonomously. It is therefore
important to evaluate and improve the usability of everyday technological systems to
accommodate the needs of older users. Czaja et al. (2006) demonstrated that both
fluid and crystallized intelligence were significant independent predictors for
breadth of computer use and use of the Internet when controlling for age, education,
computer self-efficacy and computer anxiety. Fluid intelligence was the only
significant predictor of experience with computers, including experience with input
devices, proficiency with basic computer operations and proficiency with computer
applications, while crystallized intelligence was predictive breadth of World Wide
Web experience.
In a wider ICT context, the same findings also pertain to the use of everyday
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technology in older persons. Slegers, Boxtel and Jolles (2009) examined the
relationship between cognitive abilities and performance in the use of different
technological everyday devices. The results indicated that cognitive flexibility and
perceptual speed were significant predictors for most of the task performances, such
as programming a phone number in the memory of the phone, or playing song
number four from this CD. Only the use of an alarm-clock, a ticket vending machine
and a microwave oven was not significantly predicted by any cognitive abilities,
which possibly is because that all participants would have mastered the usage with
these electronic devices, and all this knowledge has transferred to crystallized
intelligence.
(2) Computer attitudes
A few studies have investigated the attitude towards computers of older people
and computer use. In general, it seems that as older adults have more use
experiences, their attitudes are more positive toward computer (Czaja & Sharit,
1993). Computer use may effectively improve attitudes toward computer among
older people (Smith, 2005; Lagana, 2008). Furthermore, the relationships between
computer attitudes and personal attributes, such as age, gender, were analyzed to
thoroughly understand the attitude of older adult toward computers (Dyck & Smither,
1994). Besides the studies about computer attitudes of older people and computer
use, Umemuro (2004) claimed that computer attitudes can be predictors of the usage
of various computerized products. From the literature review, it is not difficult to
find that there are many studies on computer attitudes of older people in HCI field,
also many studies on technology diffusion among older people and segmentation of
older customers in marketing research. However, there are few studies using the
diffusion theory to segment older people and to study the differences of computer
attitudes among various groups of older adults longitudinally.
1.2
OBJECTIVES OF THE DISSERTATION
The central theme of this dissertation is to investigate affective and cognitive
-6-
factors, such as well-being, cognitive ability and computer attitudes that contribute
to the use of new technologies among older people in order to identify those
relationships that are relevant to assisting digital adoption in older age. Thus, there
are overall two objectives in this dissertation. First, to establish an affective
approach to study older people’s technology use experiences: to clarify the definition
of well-being, study the relationship between ICT technologies and daily lives of
older adults. As Umemuro (2009) proposed, affective and cognitive factors are to
focus on two different aspects of human, and influence each other. Thus, efforts
should be made on development of affectiveness study and implication into existing
usability and user study. Second, to establish a cognitive approach to older adults’
digital experiences study: to consider longitudinal changes of older adults’
technology adoption and their changes on cognitive factors. To fulfill these
objectives, the themes are focused in the dissertation as follows.
I.
To propose a theoretical structure of well-being for ICT usage of older adults. In
particular, to examine whether older adults' hedonic and eudaimonic well-being
during ICT activities are associated with their experiences in daily life activities
and to investigate whether perceived usefulness, daily life well-being and
computer attitudes can be used to predict hedonic and eudaimonic well-being in
ICT usage. To derive essential characteristics of well-being in older people’s
digital use for further investigations as well as for improving the design of
future digital systems and products.
II.
To propose a method to analyze dynamic change of adoption and
discontinuance in technology usage, and then to explore the adoption and
discontinuance of technological products and services by older adults, as well as
to clarify relationships among older adults’ computer attitudes, cognitive
abilities and dynamic usage changes in a longitudinal aspect by using seven
years collective data set.
III. To understand the nature of “innovativeness” of older people in terms of
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technology adoption, and to subdivide older people by the adoption and
discontinuance levels of technology products and services, and to investigate
whether there are differences in older adults’ computer attitudes across segments
and to discover possible reasons or contributing factors. Also, to discuss older
people’s behaviour patterns and how to use when designing a new technology
product/service for them.
1.3
STRUCTURE OF THE DISSERTATION
The dissertation is organized into three main parts in five chapters. The first
part covered theories and histories on older people’s technology usage and its
relations with affective and cognitive factors through concerned literatures (Chapter
1). The second part presents older adults’ technology usage studies by examining the
affective factors, i.e., hedonic well-being and eudaimonic well-being, and the
cognitive factors, i.e., the attitudes toward computer usage and cognitive abilities of
older adults in Japan (Chapters 2-4). The third part is the conclusions of the
dissertation (Chapter 5). To clearly elucidate this, the structure of the dissertation
was illustrated in Figure 1-1.
Chapter 1 set the view point for the dissertation by providing relevant research
backgrounds. The central theme of the dissertation was affective and cognitive
factors of older people’s technology usage. This chapter identified existing problems
and formulated the objectives of the dissertation. It also briefly stated the structure
of the dissertation.
Chapter 2 focused on hedonic and eudaimonic well-being. A pilot study on
older adults’ well-being in ICT was presented to introduce a well-being
measurement to well fit to the study in ICT contexts. Then the correlation of wellbeing between older adults’ ICT and daily life experiences was testified.
Chapter 3 explored dynamic changes of technology usage which is an
-8-
unexplored topic in gerontology research. The definition of dynamic changes of
technology usage was given. Whether there were significant changes of computer
attitudes and cognitive abilities when the adoption or discontinuance of technology
products occurred were investigated.
Chapter 4 paid attention on the older adults’ technology use changes in long
term. In this study, older adults’ new technology adoption can be considered as a
way of measuring their innovativeness. Then, older adults’ sub-segments were
investigated in relation with older people’s technology acceptance behavior by their
innovativeness.
Chapter 5 concluded the study with a summary of key outcomes. This final
chapter also presented the contributions of research in this dissertation as well as the
implications of future system design. Limitations on this dissertation and expected
future studies were also discussed.
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Chapter 1
Research background and Objectives
Cognitive factors
Affective factors
Chapter 3
Chapter 2
Hedonic and eudaimonic wellbeing of older people in ICT
Dynamic
changes/computer
attitudes/cognitive
ability
Chapter 5
Conclusions and Implications
Figure 1-1 Structure of the dissertation
- 10 -
Chapter 4
Innovativeness/
computer attitudes
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CHAPTER 2
OLDER ADULTS’ HEDONIC AND
EUDAIMONIC WELL-BEING IN INFORMATION AND
COMMUNICATION TECHNOLOGY
2.1
INTRODUCTION
New information and communication technology (ICT) has been rapidly
introduced in the daily lives of the general population. Children and young adults
can enjoy the fun and convenience introduced by the Internet, social networking
services and 3G telecommunication networks. However, older adults may not be as
comfortable with technology as young people (O’Hara, 2004). Some social
researchers have highlighted the fact that the information society is also an aging
society (Bernard & Phillips, 2000). To promote ICT to older adults and help them be
comfortable with new technologies, it is important for us to understand older adults’
well-being in the ICT context.
Although there is substantial literature on Internet use and older adults' wellbeing, ambiguous definitions of well-being and various methodologies have been
employed (Joshanloo & Ghaedi, 2009). Well-being has been represented by
psychological constructs such as life satisfaction, depression, psychological wellbeing, locus of control and self-efficacy (Shapira, Barak & Gal, 2007; Chen &
Persson, 2002; Straka & Clark, 2000; Karavidas, Lim & Katsikas, 2005; White et al.,
2002). Also, a wide variety of well-being measurements have been used. These
inconsistencies have caused researchers confusion regarding this topic, and it is
difficult to draw general conclusions across the studies. It is reasonable to speculate
that not all these psychological constructs are appropriate for ICT research purposes,
and certain measurements of well-being may be more suitable than others in
measuring ICT use. Therefore, it is important to review the definition of well-being
and clarify corresponding measurements in this research context.
2.2
WELL-BEING STUDIES IN ICT CONTEXT
- 14 -
2.2.1
Current situation on well-being studies in ICT context
There are contradictory viewpoints on the relationship of well-being and ICT
usage. Based on numerous observations in a variety of cultures and settings,
researchers have suggested that ICT can affect the well-being of the elderly
significantly and positively. The use of the Internet by older adults enables them to
maintain a social network and social contact in their homes (Blit-Cohen & Litwin,
2004).
Chen and Persson (2002) compared older Internet users and non-users by using
a multi-dimensional well-being scale and a survey to measure the hours spent on the
Internet. Their findings showed that significant correlations between hours of
Internet use and the dimensions of well-being ‘personal growth’ and ‘purpose of life’
for which users had significantly higher scores than non-users, suggesting that ICT
can affect the well-being of the elderly significantly and positively. In KoopmanBoyden and Reid’s (2009) study, almost same results were repeated. Participants
were classified into Internet users and non-users based on survey results. Well-being
was assessed using general as well as domain-specific questionnaires of well-being.
Gender, age, education, household composition, income, and work were significant
predictors of Internet/e-mail usage. Significant positive relationships were also
found between Internet/e-mail usage and well-being. Karavidas, Lim and Katsikas
(2005) conducted a study of older people in south Florida, a southern state in the
United States, found that usage of computer technology had a positive impact on life
satisfaction, with the benefits including increased independence, maintenance of
social networks, and access to health-related information. Other researchers
substantiated that computer usage improves the well-being of older adults using
intervention studies, such as introducing computer training (Shapira, Barak & Gal,
2007; Straka & Clark, 2000; White et al., 2002). These findings thus indicate that
use of computers and Internet can have a positive effect on well-being of older
people.
In contrast, other researchers hold completely different opinions. Kraut et al.
(1998) argued that Internet use may result in loss of social contact, depression and
- 15 -
loneliness among younger adults. In results reported by Jackson et al. (2004),
Internet use had no effect on psychological well-being and social involvement. By
meta-analysis of forty previous studies in this field, Huang (2010) claimed that
Internet use exerted a small negative effect on psychological well-being. In
gerontology studies, some researchers (White et al, 2002; Slegers, van Boxtel, &
Jolles, 2008) examined the psychosocial impact of Internet training and Internet
access in residential older people. Participants either received computer and Internet
training or were included in control groups. Several well-being measures were taken
at baseline. After the weeks of training intervention, follow-up tests were conducted.
The results showed that no significant difference was found in the scores between
baseline and follow-up measurement on any of the mood and well-being related
scores for either group.
Although the benefits of ICT use seem obvious to older adults, most of these
studies were hampered by methodological problems such as small sample sizes, lack
of control groups and compound results with training/support effect. Therefore,
Dickinson and Gregor (2006) stated that there is no empirical evidence to support
the assertion that computer use alone has a positive effect on well-being among
older adults. In this study, empirical intervention studies were criticized for
overlooking the effect of increased interaction between participants and training and
support which they received in such interventions (the long-observed Hawthorne
effect). Thus, who provided the support and the format of the support and the extent
to which this mediates the effects of ICT on well-being and mood needs further
investigation.
Well-being can be considered at four interrelated levels of analyses: the
instance or event, the activity, the individual and groups (Waterman, Schwartz &
Conti, 2008). Although measurements such as life satisfaction scales, PWB scales
and SWB scales have been widely acknowledged as valid and used in previous
studies on the relationship between well-being and ICT usage, it is noteworthy that
those scales are generally viewed as assessments of well-being at an individual level.
For example, DeNeve (1999) suggested that SWB is determined to a substantial
degree by genetic factors and is relatively stable across the lifespan. Thus, it is
- 16 -
reasonable to infer that these measurements at an individual level are not capable of
detecting well-being related to using ICT products. Evidence from a number of
researchers have supported this inference. For example, Slegers, van Boxtel and
Jolles (2008) carried out an intervention study that examined the causal relationship
between computer use and measures of various aspects of well-being. No effects of
computer and Internet usage on life satisfaction and other well-being were found
among older adults in a one-year period. To empirically measure older adults’ wellbeing in ICT usage, appropriate measurements at the activity level should be
employed.
At the activity level, well-being can be measured by aggregate diary data using
the experience sampling method (ESM; Csikszentmihalyi, Larson & Prescott, 1977)
across activities, or by the Personally Expressive Activities Questionnaire (PEAQ).
The PEAQ was developed by Waterman (1993) as a global evaluation of well-being
related to specific activities and has been used in several studies (Waterman, 1993;
2004).
2.2.2
The connection between ICT and daily life experiences
ICT has many unique features and its own characteristics that our daily life
does not have. Nevertheless, most ICT applications are developed to facilitate our
daily life activities. For example, online shopping lets people to do their shopping
without going outside. E-mail is a substitute for postal mail that helps people keep in
touch with their friends easily and economically. As discussed by Dickson and
Gregor (2006), it is important to compare computer use with a similar activity that
requires a comparable level of training or no training. Connectivity between ICT use
experience and daily life experience is important, but there are few studies that have
addressed this. Therefore, it is essential to examine the relationship of well-being in
ICT usage and that in daily life.
Among many variables that may influence ICT usage, perceived usefulness and
ease of use are two especially important determinants, with usefulness having a
significantly greater correlation with ICT usage behavior than ease of use (Davis,
1989). When people believe that using a particular ICT application will enhance
- 17 -
their efficiency and make their lives more convenient, they may experience greater
well-being by using ICT. Therefore, perceived usefulness should be considered
when studying the connection between well-being in daily life and that in ICT usage.
To explore more about well-being in ICT usage, predictors of two kinds of
well-being are also proposed. It is a widely shared assumption that individuals’
attitudes are useful in understanding and predicting their behavior. For example, a
person with positive attitudes towards computers may be more likely to choose to
use computers and enjoy it more than those with very negative attitudes. A few
studies have been conducted on the relationship between computer attitudes and
well-being. In Karavidas, Lim and Katsikas's research (2005), self-efficacy helped to
increase participants' overall life satisfaction. Beas and Salanova (2006) reported
that computer attitudes have a negative relationship with psychological well-being
indicators (e.g., job-related anxiety and depression). Thus, computer attitudes, as a
multidimensional scale, can be used as predictors of well-being in the ICT context.
The purpose of this study was to investigate the relationship between older
adults' well-being in daily life activities and their corresponding ICT activities. This
relationship was investigated across activities to find whether perceived usefulness
influences the relationship in different ways. To further understand the two types of
well-being in ICT, perceived usefulness in ICT, daily life well-being and computer
attitudes were used to predict hedonic and eudaimonic well-being in ICT usage.
Hypothesis 1 was that older adults’ well-being in ICT usage is correlated with
their well-being in daily life. This correlation was expected to be moderated by their
perceived usefulness of the activity. That is, if older adults consider an ICT activity
useful, then their well-being in the daily life activity influences their well-being in
the ICT parallel activity. If they consider an ICT activity useless, then their wellbeing in daily life should have no relation with their well-being in the ICT activity.
Hypothesis 2 was that perceived usefulness of ICT, daily life well-being and
computer attitudes are predictors of both hedonic and eudaimonic well-being in ICT
usage. However, the two kinds of ICT well-being are expected to be predicted by
different dimensions of computer attitudes. Self-efficacy can significantly predict
eudaimonic well-being in the ICT context, whereas dimensions related to affect (e.g.
- 18 -
interest and anxiety) are predictors of hedonic well-being in ICT activities.
2.3
METHOD
2.3.1
Participants
The survey was carried out between July and August 2010. Older adults over
age 60 volunteered to participate in this research in response to advertisements in
regional newspapers. They were all Japanese residents living in the Tokyo
metropolitan and suburban areas. We distributed 166 questionnaires and received 82
valid responses (49.39% valid response rate). Eighty-two participants were aged
between 61 and 86 years (M = 72.91, SD = 5.36). Of the 82 participants, 43 were
males (age: M = 73.74, SD = 5.13) and 39 were females (age: M=72.0, SD=5.52).
2.3.2
Procedure
Five daily activities and corresponding ICT activities were selected. The
criterion for selection was that the older adults knew all the activities and their ICT
counterparts. These five pairs of activities were shopping and online shopping,
newspaper reading and online news reading, chatting and online chatting, playing
games and playing online games, and writing postal mail and writing email. A
questionnaire investigating hedonic and eudaimonic well-being, perceived
usefulness and computer attitudes was mailed to participants. They were asked to
complete the questionnaire at their own pace and send it back to the investigators.
2.3.3
Measurements
The questionnaire consisted of three parts. The first part was the PEAQ, used to
investigate hedonic well-being and eudaimonic well-being of participants in
provided activities. The original questionnaire (Waterman, 1993) comprised two
scales with six question items in each to probe the degree of hedonic well-being and
eudaimonic well-being when participants were doing their personally salient
activities.
Because the original statements in the PEAQ do not evoke appropriate
responses from Japanese older adults, the degree of statements was changed from
- 19 -
intensively positive to positive. For example, in the original questionnaire, the
statement “This activity gives me my greatest pleasure” was changed to “This
activity gives me great pleasure”. To validate this modification, an additional
questionnaire-based study was conducted with a different set of participants from
this study. To evaluate the correlation between responses to the original PEAQ and
the modified edition, 26 Japanese adults (21–76 years old, M=33.8, SD=18.6,
females 23%) were asked to evaluate four activities, two ICT activities and two
daily activities, using both the original PEAQ and the modified edition. The
correlations between the original and modified editions were 0.92 (p<0.001) for
hedonic well-being and 0.88 (p<0.001) for eudaimonic well-being, suggesting that
the two editions were measuring the same dimensions.
Furthermore, one-week test-retest reliabilities were also analyzed for the
modified edition. Among the 26 participants, 23 returned the questionnaire one week
after the first test. The correlations between the first and second tests were 0.82 (p <
0.001) for hedonic well-being and 0.89 (p < 0.001) for eudaimonic well-being.
These results suggested that the modified edition had sufficient test-retest reliability
when compared with 0.80 (p < 0.0001) for hedonic well-being and 0.78 (p < 0.0001)
for eudaimonic well-being reported in the original study (Waterman, 1993).
Hedonic well-being measures the degree to which an individual has positive
affect that accompanies engaging in daily and ICT activities he/she wants to
experience. Items on hedonic well-being comprise statements as “When I engage in
this activity I feel quite satisfied”, “This activity gives me my strong sense of
enjoyment”, “When I engage in this activity I feel good”, “This activity gives me
great pleasure”, “When I engage in this activity I feel a warm glow” and “When I
engage in this activity I feel very happy”. Participants responded to each question
item of the modified PEAQ on a seven-point Likert scale from 1 (strongly disagree)
to 7 (strongly agree). The score of hedonic well-being dimension was the average
score of six question items of hedonic well-being section in modified PEAQ ranged
from 1 to 7. Cronbach's alphas for hedonic well-being in five pairs of activities
ranged from 0.94 to 0.98.
Eudaimonic well-being examines the degree to which an individual experiences
- 20 -
feelings of intensely alive or authentic, a feeling of being complete or fulfilled when
engaged in an activity. Items on eudaimonic well-being comprise statements as
“This activity gives me the great feeling of really being alive”, “When I engage in
this activity I feel intensely involved”, “This activity gives me the strong feeling that
this is who I really am”, “When I engage in this activity I feel that this is what I was
meant to do”, “I feel quite complete or fulfilled when engaging in this activity” and
“I feel a special fit or meshing when engaging in this activity”. Participants
responded to each question item of the modified PEAQ on a seven-point Likert scale
from 1 (strongly disagree) to 7 (strongly agree). The score of eudaimonic well-being
dimensions was the average score of six question items of eudaimonic well-being
section in modified PEAQ ranged from 1 to 7. Cronbach's alphas for eudaimonic
well-being were 0.95 to 0.98.
The second part was the Perceived Usefulness Scale taken from Davis's two
six-item scales for assessing user-perceived usefulness and perceived ease of
computer usage (1989). Perceived usefulness measures the degree to which a person
believes that doing a certain activity or using a particular system would enhance his
or her performance in daily life. For the present study, only the perceived usefulness
section was adopted, and the descriptions of some items were modified to adapt to
non-computer usage activities. For example, the original statement “Using _______
would make it easier to do my job” was changed to “Doing ________ would make it
easier to live my life”.
The perceived usefulness dimension has 6 question items. Items on the
dimension comprise such statements as “Doing ________ would make it easier to
live my life”, “Doing ________ would enable me to accomplish tasks in my life
more quickly”, “Doing ________ would improve my performance in my life”,
“Doing ________ would increase my productivity in my life”, “Doing ________
would enhance my effectiveness in my life” and “I would find doing ________
useful in my life.” Participants responded to each item on a seven-point Likert scale
from 1 (strongly disagree) to 7 (strongly agree). The score of perceived usefulness
was the average score of six question items in this section ranged from 1 to 7. In this
study, Cronbach's alphas of perceived usefulness for the five pairs of activities
- 21 -
ranged from 0.91 to 0.97.
To probe participants' computer attitudes, the last part of the questionnaire
included the Attitudes Toward Computers Questionnaire (ATCQ; Jay & Willis,
1992) and Computer Anxiety Scale (CAS; Loyd & Gressard, 1984). The ATCQ is a
35-item multidimensional scale for assessing seven dimensions of attitudes toward
computers: comfort (the feelings of comfort with computers and their use), selfefficacy (the feelings of competence with computers), gender equality (the belief
that computers are important to both men and women), control (the belief that
people control computers), dehumanization (the belief that computers are
dehumanizing), interest (the extent to which one is interested in learning about and
using computers) and utility (the belief that computers are useful). Participants
responded to items on a five-point Likert scale from 1 (strongly agree) to 5 (strongly
disagree).
Each subscale consists of five (comfort, efficacy, gender equality, control and
interest) or six (dehumanization and utility) items; that is one item in each of
dehumanization and utility also belongs to one of the subscales. The average score
of the corresponding responses to the five or six items belonging to each subscale
were calculated and used as the score for the sub-dimension. Cronbach's alphas for
the ATCQ were comfort (0.70), self-efficacy (0.74), gender quality (0.86), control
(0.57), dehumanization (0.76), interest (0.81) and utility (0.61).
The comfort dimension has 5 question items: “I feel comfortable with
computer (R)”, “computers make me nervous”, “I don’t feel confident about my
ability to use a computer”, “computers are confusing” and “computers make me feel
dumb”. The average score of this dimension ranges from 1 to 5.
The self-efficacy dimensions has 5 question items: “ I know that if I worked
hard to learn about computers, I could do well (R)”, “computers are not too
complicated for me to understand (R)”, “I think I am capable of learning t use a
computer (R)”, “ I think I am capable of learning to use a computer (R)”, “given a
little time and training, I know I could learn to use a computer (R)”. The average
score of this dimension ranges from 1 to 5.
The gender equality has 5 question items: “using computers is more important
- 22 -
for men than for women”, “more women than men have the ability to become
computer scientists”, “using computer is more enjoyable for men than it is for
women”, “working with computers is more for women than men”, “women can do
just as well as men in learning about computer (R)”. The average score of this
dimension ranges from 1 to 5.
The control dimension has 5 question items: “computers will never replace the
need for working human beings (R)”, “our world will never be completely run by
computers (R)”, “people are smarter than computers (R)”, “people will always be in
control of computers (R)” and “soon our lives will be controlled by computers”. The
average score of this dimension ranges from 1 to 5.
The dehumanization dimension has 6 question items: “computers turn people
into just another number (R)”, “the use of computer is lowering our standard of
living (R)”, “computers control too much of our world today (R)”, “computers are
making the jobs done by humans less important (R)”, “computers are dehumanizing
(R)” and “soon our lives will be controlled by computers (R)”. The average score of
this dimension ranges from 1 to 5.
The interest dimension has 5 question items: “learning about computer is a
worthwhile and necessary subject (R)”, “reading or hearing about computers would
be (is) boring”, “I don’t care to know more about computers”, “computers would be
(are) fun to use (R)” and “learning about computers is a waste of time”. The average
score of this dimension ranges from 1 to 5.
The utility dimension has 6 question items: “life will be (is) harder with
computers”, “everyone could get along just fine with computers”, “it is not
necessary for people to know about computers in today’s society”, “computers are
too fast”, “people will always be in control of computers (R)” and “computers make
the work done by people more difficult”. The average score of this dimension ranges
from 1 to 5.
The CAS is part of a 30-item, three dimensional computer attitudes scale that
represents the dimension of feelings of anxiety or fear related to computers.
Computer Anxiety Scales consists ten question items. Items on the Computer
Anxiety Subscale comprise statements as “computers do not scare me at all (R)”,
- 23 -
“computers usually make me feel nervous and uncomfortable”, “It is not scary to
hear other people talk about computers (R)”, “I feel hostility towards computers”, “It
is not a bother with taking computer training courses (R)”, “computers make me
unhappy”, “It is comfortable to take computer training course (R)”, “when I use
computers, I feel calm (R)”, “using computers makes work easy (R)” and “using
computers makes me scary and confused”. Revered items were noted by (R).
Participants were asked to respond to each item on a four-point Likert scale from 1
(strongly agree) to 4 (strongly disagree). The score of CAS was the average score of
ten corresponding question items ranged from 1 to 4. Cronbach's alpha for the CAS
was 0.84.
Given that the content of the ATCQ and CAS are overlapping, four composite
variables were employed based on the results of factor analysis on the ATCQ and
CAS by Czaja and colleagues (2006). Because the Likert scales for items of the
ATCQ and CAS were different, the z score was computed for each item. The four
composite variables were general computer attitudes, anxiety, self-efficacy and
interest. The general computer attitudes score was the average of the
dehumanization (reversed scored), utility and control scales. The anxiety scale was
the average of scores of the CAS and reversed comfort scale. Self-efficacy and
interest were left as separate variables.
2.4
RESULTS
Table 2-1 summarizes the number of participants who reported their well-being
in each activity. To calculate the correlations between daily life activities and their
ICT counterparts, the numbers of participants doing both activities are also shown.
Because there were only 11 respondents who played both traditional games and
online games, this pair of activities was eliminated from the following analysis. The
software used for data processing in this study was PASW Statistics 18.
- 24 -
Table 2-1 Numbers of participants who reported on well-being for each activity and pairs of
activity
2.4.1
Activity
n
Shopping
80
Online shopping
39
Shopping and online shopping
38
Newspaper reading
82
Online news reading
37
Newspaper and online news reading
37
Chatting
82
Online chatting
69
Chatting and online chatting
69
Playing games
48
Playing online games
14
Playing games and online games
11
Writing postal mail
76
Writing email
60
Writing postal mail and email
55
Well-being
Paired sample t-tests were conducted to examine the differences in hedonic
well-being and eudaimonic well-being across all activities.
Significant differences between the valances of hedonic and eudaimonic wellbeing older adults were found in all eight activities (Table 2-2). The effect sizes of
the differences in shopping and in newspaper reading were moderate, while all other
effect sizes were small.
- 25 -
Table 2-2 Hedonic and eudaimonic well-being differences across daily life activities and
corresponding ICT activities; HWB = hedonic well-being; EWB = eudaimonic well-being;
HWB
Scale
EWB
Cohen
n
t
M
SD
M
SD
D
Shopping
80
5.17
1.15
4.44
1.06
8.36***
0.65
Online shopping
39
3.82
1.26
3.44
1.17
3.43**
0.31
News paper reading
82
4.84
0.92
4.34
1.08
6.43***
0.50
Online news reading
37
4.15
1.20
3.82
1.16
2.94**
0.28
Chatting
82
5.26
1.19
4.82
1.18
7.88***
0.37
Online chatting
69
4.53
1.29
4.16
1.40
5.31***
0.28
Writing postal mail
76
5.17
1.30
4.95
1.19
3.03**
0.18
60
4.66
1.31
4.39
1.25
3.79***
0.22
Writing email
*p<0.05; **p<0.01; ***p<0.001.
Table 2-3 Differences in hedonic and eudaimonic well-being between pairs of daily life and ICT
activities; HWB = hedonic well-being; EWB = eudaimonic well-being;
Daily activities
Scale
Shopping
News reading
Chatting
Writing postal mail
ICT activities
n
M
SD
M
SD
t
Cohen
d
HWB
38
5.06
1.07
3.80
1.27
4.93***
1.07
EWB
38
4.32
1.05
3.42
1.18
3.97***
0.80
HWB
37
4.79
1.02
4.15
1.20
4.53***
0.57
EWB
37
4.27
1.17
3.82
1.16
3.11**
0.38
HWB
69
5.37
1.17
4.53
1.29
5.89***
0.68
EWB
69
4.92
1.17
4.16
1.40
5.67***
0.59
HWB
55
5.07
1.40
4.59
1.31
2.04*
0.35
55
4.90
1.30
4.31
1.23
2.85**
0.46
EWB
*p<0.05; **p<0.01; ***p<0.001.
- 26 -
Next, paired sample t-tests were conducted to examine the differences in wellbeing between daily life activities and ICT activities in pairs. The data of
participants who answered both activities in those pairs were used in this section:
shopping and online shopping (n=38); newspaper reading and online news reading
(n=37); chatting and online chatting (n=69); and paper mail and email (n=55).
Significant differences in hedonic well-being and eudaimonic well-being were found
between the daily life activity and its corresponding ICT activity in the shopping pair,
news-reading pair and chatting pair. A significant difference was also found in
eudaimonic well-being in the writing postal mail email pair, while there was no
significant difference in hedonic well-being (Table 2-3). Overall, older adults felt
greater hedonic and eudaimonic well-being in daily life activities compared with the
ICT counterparts. There were rather large differences in means for the two types of
well-being between shopping and online shopping, while the difference was
moderate in the news-reading pair and chatting pair. The difference in eudaimonic
well-being for the writing postal mail email pair was also moderate.
In order to compare hedonic and eudaimonic well-being in daily life and in ICT
usage, hedonic and eudaimonic well-being scores were pooled with four pairs of
activities. A two-way repeated-measure ANOVA was also conducted on well-being
by 2 types of well-being x 2 contexts. Significant main effect of daily life and ICT
was found (F(1, 332) = 204.68, p <0.001), and significant main effects of hedonic
and eudaimonic well-being was also found (F(1,332) = 5.14, p = 0.02). No
interaction was discovered between two ways. Post-hoc tests were conducted. Wellbeing in daily life was significantly higher than that in ICT usage (F(1, 334) =
202.36, p < 0.001), the scores of hedonic well-being was significantly higher than
eudaimonic well-being (F(1,334) = 3.35, p = 0.05)
2.4.2
Well-being and activities correlated
A series of correlation analyses on well-being scores were conducted between
daily life activities and their ICT counterparts. The participants in this section were
those who had experiences on both daily life activities and their corresponding ICT
activities.
- 27 -
Table 2-4 Pearson's correlation coefficients of hedonic (HWB) and eudaimonic (EWB) wellbeing for news reading activities that showed a significant correlation between the daily life
activity and its ICT counterpart ;
Newspaper reading
HWB
Newspaper
reading
Online news
reading
EWB
Online news reading
HWB
HWB
1.00
EWB
0.79**
1.00
HWB
0.72**
0.58**
1.00
EWB
0.68**
0.72**
0.83**
EWB
1.00
n=37, **p<0.01
As a result of correlation analyses, two different types of activities appeared.
One group was pairs of activities that showed a significant correlation between the
daily life activity and its counterpart. They were newspaper reading and online news
reading, and chatting and online chatting. The other group was two pairs of activities
for which no correlation was found in well-being scores between the daily activity
and corresponding ICT activity. They were shopping and online shopping, and
writing postal mail and email. For all activities, hedonic well-being and eudaimonic
well-being for the same activity were significantly correlated.
Hedonic well-being in newspaper reading was correlated with that in online
news reading, and eudaimonic well-being in this pair was also correlated.
Eudaimonic well-being in online news reading was correlated with hedonic wellbeing in newspaper reading, and hedonic well-being in online news reading was
correlated with eudaimonic well-being in newspaper reading (Table 2-4). Hedonic
well-being in chatting was significantly correlated with that in online chatting, and
eudaimonic well-being in this pair of activities was also correlated. Eudaimonic
well-being in online chatting was significantly correlated with hedonic well-being in
chatting face to face, and hedonic well-being in online chatting was significantly
correlated with eudaimonic well-being in chatting as well (Table 2-5).
- 28 -
Table 2-5 Pearson's correlation coefficients of hedonic (HWB) and eudaimonic (EWB) wellbeing for chatting activities that showed a significant correlation between the daily life activity
and its ICT counterpart ;
Chatting
HWB
Online chatting
EWB
HWB
HWB
1.00
EWB
0.90**
1.00
HWB
0.54**
0.60**
1.00
EWB
0.48**
0.64**
0.91**
EWB
Chatting
Online chatting
1.00
n=69, **p<0.01
On the other hand, there were no correlations between hedonic well-being
scores and eudaimonic well-being scores for the daily life activity and ICT
corresponding activity in the shopping pair and writing mail pair for the entire
sample. Therefore, the sample was divided into two subgroups for further analysis.
Perceived usefulness was used as the criterion for grouping. The first group was
defined as the participants whose perceived usefulness scores were greater than four
(the center value of the score), and will be referred to as the High Perceived
Usefulness (HPU) group. They considered ICT activity useful. The other group was
defined as participants whose perceived usefulness scores were equal or less than
four and will be referred to as the Low Perceived Usefulness (LPU) group. They
perceived that ICT activity is less useful.
- 29 -
Table 2-6 Pearson's correlation coefficients of hedonic (HWB) and eudaimonic (EWB) wellbeing for shopping activities that overall showed no significant correlation between the daily life
activity and its ICT counterpart, but are now divided between high and low perceived usefulness
Shopping
HWB
EWB
Online shopping
HWB
EWB
Perceived high usefulness (n=25)
Shopping
Online
shopping
HWB
1.00
EWB
0.76**
HWB
0.37
0.57**
1.00
EWB
0.24
0.46*
0.80**
1.00
1.00
Perceived low usefulness (n=13)
Shopping
Online
shopping
HWB
1.00
EWB
0.80**
1.00
HWB
-0.72**
-0.54
1.00
EWB
-0.58*
-0.26
0.81**
1.00
*p < 0.05, **p<0.01
In the online shopping ‘Useful’ group (high perceived usefulness), hedonic
well-being in online shopping was significantly correlated with eudaimonic wellbeing in shopping. No significant correlations in hedonic well-being or eudaimonic
well-being scores between shopping and online shopping were found. On the other
hand, in the online shopping ‘Less useful’ group (low perceived usefulness), hedonic
well-being in online shopping was negatively related to that in shopping. No
significant correlation in eudaimonic well-being scores between online shopping and
shopping was found (Table 2-6).
- 30 -
Table 2-7 Pearson's correlation coefficients of hedonic (HWB) and eudaimonic (EWB) wellbeing for mailing activities that overall showed no significant correlation between the daily life
activity and its ICT counterpart, but are now divided between high and low perceived usefulness ;
Postal mail
HWB
E-mail
EWB
HWB
EWB
Perceived high usefulness (n=41)
Postal mail
E-mail
HWB
1.00
EWB
0.94**
1.00
HWB
0.31*
0.38*
1.00
EWB
0.41**
0.49**
0.92**
1.00
Perceived low usefulness (n=14)
Postal mail
E-mail
HWB
1.00
EWB
0.94**
1.00
HWB
0.10
0.29
1.00
EWB
-0.22
-0.08
0.81**
1.00
*p <0.05,**p<0.01
In the email ‘Useful group’, eudaimonic well-being was significantly correlated
between daily and ICT activities. No significant correlation was found in hedonic
well-being between writing postal mail and writing email. In the email ‘Less useful’
group, no significant correlation was found in well-being scores between writing
postal mail and writing email (Table 2-7).
2.4.3
Predictors of ICT well-being
Older adults’ daily life well-being, perceived usefulness of ICT and computer
attitudes were examined as predictors of ICT well-being. In this study, well-being
scores of participants were measured in four pairs of daily life and their
corresponding ICT activities. The scores for ICT hedonic well-being (IHWB), ICT
eudaimonic well-being (IEWB), ICT perceived usefulness (IPU), daily life hedonic
- 31 -
well-being (DHWB), and daily life eudaimonic well-being (DEWB), representing all
activities, were calculated. IHWB, IEWB and IPU were the sum of hedonic wellbeing scores, the sum of eudaimonic well-being scores and the sum of perceived
usefulness in the four ICT activities, respectively. DHWB was the sum of hedonic
well-being scores, and DEWB was the sum of eudaimonic well-being scores in four
corresponding daily activities.
Daily life well-being (DHWB and DEWB), four composite computer attitude
variables and IPU were employed as independent variables to predict IEWB by
stepwise regression. Because of the high correlation between DHWB and DEWB (r
= 0.90, n = 77, p < 0.001), two models, one with DHWB and the other with DEWB
as the independent variable, were built. Besides the daily life well-being variables,
both final models included IPU and self-efficacy (Table 2-8). IPU, DEWB and selfefficacy significantly predicted IEWB. IPU resulted in a significant increment in R2
of 0.901, while DEWB resulted in a significant increment in R2 of 0.012. Selfefficacy also resulted in a significant increment in R2 of 0.005.
In a similar way, IPU, DHWB and self-efficacy also significantly predicted
IEWB. IPU resulted in a significant increment in R2 of 0.901, while DHWB resulted
in a significant increment in R2 of 0.005. Self-efficacy also resulted in a significant
increment in R2 of 0.003.
Next, IHWB was predicted with daily life well-being (DHWB and DEWB),
four composite computer attitude variables and IPU as independent variables by
stepwise regression. Because of the high correlation between DHWB and DEWB,
two models were also examined, one with DHWB and the other with DEWB. Beside
the daily life well-being variables, both final models included IPU (Table 2-8). IPU
and DHWB significantly predicted IHWB. IPU resulted in a significant increment in
R2 of 0.910; DHWB resulted in a significant increment in R2 of 0.006. In a similar
way, IPU and DEWB significantly predicted IHWB. IPU resulted in a significant
increment in R2 of 0.910; DEWB resulted in a significant increment in R2 of 0.004.
- 32 -
Table 2-8 Final multiple regression models of ICT eudaimonic (EWB) and hedonic well being
(HWB); IPU=ICT perceived usefulness; DEWB= daily life eudaimonic well-being; DHWB=
daily life hedonic well-being; CI =confidence interval;
Variable
B
95% CI
EWB: R2=0.92; F(3,73)=280.03, p<0.001
IPU
0.83***
[ 0.77, 0.89]
DEWB
0.16**
[ 0.07, 0.25]
Self-efficacy
0.43*
[ 0.04, 0.83]
Constant
-2.02
[-3.78,-0.27]
EWB: R2=0.91; F(3,73)=255.46, p<0.001
IPU
0.84***
[ 0.77, 0.90]
DHWB
0.11*
[ 0.02, 0.20]
Self-efficacy
0.41*
[ 0.00, 0.82]
Constant
-1.37
[-3.34, 0.61]
HWB: R2=0.92; F(2,74)=412.74, p<0.001
IPU
0.90***
[ 0.85, 0.96]
DHWB
0.11*
[ 0.03, 0.19]
Constant
-1.34
[-3.03, 0.43]
HWB: R2=0.91; F(2,74)=404.63, p<0.001
IPU
0.90***
[ 0.84, 0.96]
DEWB
0.09*
[ 0.01, 0.18]
Constant
-0.84
[-2.49, 0.82]
n=77; * p<0.05; **p<0.01; ***p< 0.001
These results of regression analysis suggest that hedonic and eudaimonic wellbeing, along with perceived usefulness of ICT, can predict hedonic and eudaimonic
well-being when older adults use ICT. For eudaimonic well-being, computer selfefficacy might also predict older adults’ ICT well-being.
- 33 -
2.5
DISCUSSION
Oron-Gilad and Hancock (2009) posited that the trend in human factor
development is from ergonomics to hedonomics. Umemuro (2009) suggested that
technology should be designed considering affective experiences it might provide its
users. Therefore it is important to understand positive experiences in ICT usage from
a more comprehensive view, including both hedonic and eudaimonic aspects of
well-being. In addition, to improve older adults’ well-being, it is necessary to
connect well-being in their daily lives with well-being in the ICT context.
The results indicated that in all pairs of daily and ICT activities, older adults'
well-being in daily life activities was greater than their well-being in the ICT
counterparts. Though many studies have shown that by using ICT older adults' wellbeing can be improved, it is nevertheless true that older adults still consider the
Internet as second place to the real world in accomplishing daily tasks (Fallows,
2004).
Then, the scores between hedonic and eudaimonic well-being in all activities
were compared. Our intention was to compare the valence (or intensity) of hedonic
affects with that of eudaimonic feelings when older adults were engaging in ICT
activities. The result showed that older adults’ hedonic well-being was greater than
their eudaimonic well-being. However, Converse and Presser (1986) argued that the
Likert scale confounds extremity (a dimension of attitudinal position, i.e. a person
hold an extreme position with little feeling) with intensity (how strongly a position
is felt, i.e. a person hold a middle road position with considerable passion). It is
admitted that the compounded effect were more complex to explain in this study,
because the descriptions of well-being items were comparative degree. Then,
Albaum (1997) suggested that composite scores do not reflect the intensity
dimension well. Although we conducted as the intensity (or valence) comparison,
the results should be explained with caution. And it is suggested that to investigate
this question, participants’ predisposition and how strongly the person feels about
the answer should be measured respectively.
One of the major findings of this study was the correlation of well-being
- 34 -
between ICT activities and daily life counterparts. In online news reading and online
chatting, well-being in the ICT context was significantly correlated with that in the
daily life counterpart. On the other hand, in online shopping and email, the relation
between older adults' well-being in ICT activities and that in daily activities was
moderated by their perceived usefulness of the ICT activities. In either case, older
adults' hedonic and eudaimonic well-being in ICT usage were correlated with their
daily life activities.
These findings may help researchers review the relationship between computer
usage and older adults’ well-being from a new perspective. To improve the wellbeing of older adults in ICT usage, it is necessary to make use of their daily life
experiences, for example, by appropriately designing systems that can better
simulate and connect with their daily life experiences.
An unexpected finding was the different correlation patterns across various
activities. One possible explanation is that complexity of ICT activities and
participants’ perceived usefulness might influence the relationship of well-being
between ICT usage and daily life. Therefore, in ICT activities that are less
complicated, such as online news reading and online chatting, older adults might be
able to connect their well-being in daily life directly with that in ICT usage,
whatever their perceived usefulness. Meanwhile, in more complicated ICT activities,
such as online shopping and writing email, only older adults who perceive ICT
activity as useful might be able to connect their well-being in ICT with that of daily
life.
The other unexpected result of this study was related to online shopping. For
older adults who perceived that online shopping is not useful, there was a negative
correlation between hedonic well-being in online shopping and that in daily life
shopping. A possible explanation for this is that those who perceive online shopping
as useful can enjoy not only the fun but also the convenience of such shopping.
Those who perceive online shopping as useless, on the other hand, could be very
concerned about the risks involved. The more they experience well-being from
shopping, the more concerned they are about online shopping. As one participant
explained, ‘It is risky to shop online by just checking catalogs’.
- 35 -
It is predictable that younger adults’ well-being in ICT activities is also
connected with their daily life experiences. There are two ways to influence an
individual’s perception of usefulness. The first is that both perceived usefulness and
perceived ease of use exist in the model, ease of use influences one’s perception of
usefulness. Writing email is this case. For younger adults, writing email is not as
difficult as for older adults. Younger adults’ well-being in writing email might be
correlated with their well-being in writing postal mail without the mediation of
perceived usefulness. The second way is that only perceived usefulness exists in the
model, and there is no relation between perceived usefulness and ease of use. In this
situation, perceived usefulness could influence the relationship of well-being
between ICT and daily life, no matter how difficult the activity is. From the negative
relationship we discovered in online shopping, it can be referred for younger adults
that the correlation is mediated by perceived usefulness.
To further analyze the property of well-being in ICT usage, multiple stepwise
regressions were calculated with IPU, computer attitudes and daily life well-being as
independent variables. The results showed that IHWB could be predicted by IPU
and daily life well-being. In addition, IEWB was predicted by IPU, daily life wellbeing and self efficacy. This finding suggests that older adults’ daily life well-being
can be used to predict their well-being in ICT usage, though perceived usefulness of
ICT activities was the dominant predictor of ICT well-being.
Another finding is that besides ICT usefulness and daily life well-being, selfefficacy predicted IEWB, but did not predict hedonic well-being. A possible
explanation is that older adults with higher self-efficacy can use ICT without
obstacles or frustrations, facilitating increased competence in ICT use. Competence,
considered one factor of IEWB, leads to an increase in IEWB. Because eudaimonic
well-being is closely related to achievement, competence in using ICT for a longer
term might have a significant influence on such well-being. Because IHWB focuses
only on affect aspect of well-being in the short term, self-efficacy might have a
smaller influence on the short term periods. This suggests that hedonic and
eudaimonic well-being in the ICT context are two different aspects of well-being. It
is also true for younger adults. Hedonic and eudaimonic well-being in ICT can be
- 36 -
predicted by different dimensions of computer attitudes. However, younger adults
meet less usage problems when they are engaging in ICT activities, so effect size of
self-efficacy might be not as big as for older adults. Meanwhile, other attitudes
might be included in ICT eudaimonic well-being regression model. In our study,
interest and anxiety were hypothesized as predictors of IHWB. The reason they did
not remain in the final regression model could be the multicollinearity between IPU
and these two dimensions of computer attitudes.
2.6
CHAPTER SUMMARY
The objectives of this research were to investigate whether hedonic and
eudaimonic well-being in daily life activities correlates with those in ICT
corresponding activities, and investigate whether perceived usefulness, daily life
well-being and computer attitudes can predict both types of well-being in ICT. To
conclude, the model of ICT hedonic and eudaimonic well-being is suitable for
studying users’ positive experiences in ICT context. Although hedonic and
eudaimonic well-being are overlapped, they are two different empirical concepts and
can be predicted by different variables. In addition, older adults’ hedonic and
eudaimonic well-being in ICT usage were associated with that in corresponding
daily life. The implications of this study include that it is important to consider
different aspects of positive experiences of user when new technology products are
designed, and it is helpful to make use of older adults’ daily life experiences to
promote their ICT experiences.
This study represents a preliminary effort to examine the correlation of wellbeing in daily life activities and ICT activities. The following issues are limitations
that should be addressed in future research.
First, only results from older adults who were engaged in both daily life activity
and ICT activity were used in the analyses. It is likely that the sample was
representative of only a part of older adults who are familiar with ICT, that is, those
who are high-functioning volunteers with positive attitudes towards research. Thus,
it may not be appropriate to generalize the results to the older population in general,
- 37 -
especially to those who have less experience with new ICT activities. In addition,
because young people may be different from older adults in terms of ICT usage, it
would be of value to conduct a comparative study to examine whether such
relationships also exist for younger generations, and whether they are related to age
differences between older adults and young people.
Second, hedonic well-being and eudaimonic well-being were highly correlated.
Instead of asking participants to recall their experiences, it might be more
appropriate to study real time experiences. If ratings of well-being had been made
when participants were actually engaged in the different activities, the correlation
between hedonic and eudaimonic well-being might have been different. In addition,
prior studies have indicated that the two types of well-being can be distinguished in
terms of their associations with a substantial number of measures. In this research,
although some predictors of well-being in ICT usage were proposed, other variables
should be examined in future studies. Karavidas et al. (2004) have reported that selfrealization values and activity importance are more useful variables in predicting
eudaimonic well-being compared with hedonic well-being, and should be employed
in future research.
Third, although this research demonstrated that older adults' well-being in ICT
activities is connected with their well-being in daily activities, a causal relationship
between them was not clear. Further research should be done to determine the causal
relationship of well-being between daily life activities and ICT activities.
Finally, perceived usefulness was revealed as a predictor of ICT well-being in
this study. However, there still remains the issue of perceived usefulness of nonfunctional ICT activities such as games. Although the activity pair of games and
online games was excluded from the analysis in this study, whether perceived
usefulness would still determine well-being related to online games is questionable.
Nowadays computer games can be an effective way to enhance older adults’ wellbeing. Telecare systems are frequently used for game playing by older people. In
addition, advanced play devices are heavily promoted for older people as a way to
get physical exercise. These ‘fun’ technologies may be considered different in nature
from more practical ICT uses such as those investigated in this study. Whether the
- 38 -
same predictors still hold for games and play or if new variables should be proposed
should be studied in future research.
- 39 -
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CHAPTER 3
DYNAMIC CHANGS OF TECHNOLOGY
USAGE AND COMPUTER ATTITUDES AND
COGNITIVE ABILITIES OF JAPANESE OLDER
ADULTS
3.1
INTRODUCTION
Technological
advancements
have
become
wide-spread,
and
their
implementation into products of everyday use is accelerating. Now more and more
information and communication technology products and services are appearing in
our lives. Bank offices reduce local branch offices often to automatic teller machines
(ATM); public transport installs automatic ticket machines for distributing tickets.
Cruise control and navigation systems are being introduced in the car. The
introductions of such new technologies can be perceived as challenges. Meanwhile,
the developments of technology may make some functions easier to use, also for
older people (Tacken et al., 2005). With the development of technology, older adults
start making use of those products inevitably.
Although technology has the potential to improve the lives of older adults by
increasing their independence in daily life and enhancing communication with their
family, older adults are often considered not to be capable of adapting those fastdeveloped technologies. Therefore, supporting the elderly in making use of new
technologies has become increasingly important.
Meanwhile, the process of adoption and discontinuance of a technology for
older adults is more complex than it was estimated. When a new technology product
is launched, older adults may hesitate to try it. Or even if they ever tried, they may
stop using it soon. These might be due to the willingness or ability of older adults to
adopt the product. Gradually with the penetration of the product among population,
older adults may also be influenced by their friends, family members, or
participation in a training course, and then they may restart using it. On the other
hand, however, with the decline of their physical or cognitive abilities, older adults
may stop using the product again. It is a dynamic process for older adults to adopt a
- 43 -
new technology. In many of previous studies, researchers have not focused on this
dynamic process very much and studied technology usage by older people as states,
assuming that older adults are the same with younger adults; once they start using a
technology, they continue using it. In order to understand the nature, causes, and/or
influencing factors of dynamic changes of technology usage by older adults, longterm investigations are essential.
In this study, therefore, older adults’ dynamic changes of using technology were
investigated. In this study dynamic change refers to a specific time point when older
adults started or stopped to use a certain technology product. Dynamic changes of
using, i.e. adoption or discontinuance of a product were recognized by comparing
the participants’ reports of daily usage of technologies for every two consecutive
years.
One factor likely to influence the dynamic change of technology usage is
people’s attitudes towards it. Older adults’ computer attitudes have been studied in
relation to computer and other technology products usage. Rogers and her
colleagues (2005) claimed that when older adults received new information about
computer, their attitudes toward computer might increase. Umemuro and Shirokane
(2003) demonstrated a positive attitude was a reliable predictor of actual computer
usage in the long term of one year. However, relationships between attitudes and
dynamic changes in usages of technologies have seldom been studied. One reason
could be that most of studies were conducted in a rather short period, such as several
weeks or a year. It is difficult to observe dynamic changes happen. Therefore, if the
data are collected in a longer period such as years, it might become possible to track
the dynamic changes in usage of technology products by older adults.
Other possible factors that are thought to influence technology usage are
cognitive abilities. Cognitive abilities are the basic elements of cognition that refers
to perception, working memory, decision and so on. Just as physical capabilities and
limitations change with age, so do cognitive abilities. When studying on older
people’s technology usage, it is important to understand their specific cognitive
capabilities and limitations. Cognitive abilities are a multifaceted construct, and
considered to change with aging. Findings from Umemuro’s (2004) study suggested
- 44 -
that some of cognitive abilities are important predictors of computer use. Among
cognitive abilities that have been studied, spatial ability, associative memory,
perceptual speed, and also a cognitive style of field independence have been
reported to relate with the usage and learning of computer skills.
In spatial abilities, there are two different yet similar factors: spatial
visualization and spatial orientation. Visualization is the ability to manipulate or
transform the image of spatial patterns into other arrangements. Orientation is the
ability to perceive spatial patterns or to maintain orientation with respect to objects
in space. Spatial orientation requires only mental rotation of the configuration, while
visualization requires both rotation and performing serial operations. Pak (2001)
suggested that spatial ability is important in the performance of computer-based
tasks because of menu hierarchies that require users to navigate to the desired
information.
Associative memory is the ability to recall one part of a previously learned but
otherwise unrelated pair of items when the other part of the pair is presented. This
memory involves the storage and retrieval of information form intermediate term
memory. Umemuro3 claimed that associative memory is considered to play an
important role in remembering interface presentations and user goals in association
with operation procedures.
Perceptual speed involves primarily the temporal parameters of a visual search
through a field of specified elements. Perceptual speed is also considered to be
important for usage of technologies including computer, because some new
technologies such as computers often present large amounts of information to users
and also require visuomotor control in their operation.
Field independence appears to be related to the cognitive ability called
“flexibility of closure”. It is the ability to hold a given visual percept or
configuration in mind so as to dissemble it from other well defined perceptual
material.
The purpose of this study was to investigate the adoption and discontinuance of
technological products and services by older adults for a long term of years. This
study also aimed to clarify relationships among older adults’ computer attitudes,
- 45 -
cognitive abilities and usage changes. Based on the above arguments, two
hypotheses were derived for this study:
Hypothesis 1 was that if older adults start adopting a technology product, their
computer attitudes increase; if older adults stop using a technology product, their
computer attitudes decrease.
Hypothesis 2 was that if older adults start adopting a technology product, their
cognitive abilities increase; if older adults stop using a technology product, their
cognitive abilities decrease.
3.2
METHOD
3.2.1
Participants
Older adults aged over 60 years old residing in Tokyo metropolitan area and its
suburban area in Japan participated in this investigation voluntarily. They
participated in this research in response to recruitment advertisements on local
newspapers. Total number of participants was 121. Of these, 70 were male (age: M =
74.37, SD = 4.98) and 51 were female (age: M = 72.91, SD = 5.98). The participant
numbers per year were listed in Table 3-1. Also, for each participant, his/her
participation status was shown in Table 3-2 by year.
Table 3-1 Participant numbers in each year
Year
Chiba
Tokyo
Total
2003
33
23
56
2004
22
39
61
2005
14
39
53
2006
35
53
88
2007
29
59
88
2008
33
26
59
2009
31
65
96
- 46 -
Table 3-2 Participation statuses of all registered participants from 2003 to 2009
Year
2003
2004
2005
2006
2007
2008
2009
C501
○
○
○
○
○
○
○
C502
○
○
○
○
○
○
○
C503
○
○
○
○
○
○
○
C504
○
×
×
×
×
×
×
C505
○
○
×
○
○
○
○
C506
×
×
×
○
○
○
○
C508
○
×
×
○
×
○
○
C509
○
×
×
○
×
○
○
C510
○
○
○
○
○
○
○
C512
○
○
×
×
○
×
×
C513
○
×
×
×
×
×
×
C515
○
○
○
○
○
○
○
C517
○
×
○
○
×
×
×
C518
○
○
○
○
○
○
○
C520
○
×
○
○
○
○
○
C521
○
×
×
○
○
○
○
C522
○
○
○
○
○
×
×
C523
○
○
×
○
×
×
×
C524
○
○
○
○
○
○
○
C525
○
×
×
×
×
×
×
C526
×
○
×
○
○
○
○
C527
○
○
×
×
×
×
×
C528
×
×
×
○
○
○
×
C530
○
×
○
○
×
×
×
C531
○
○
○
○
○
○
○
C601
×
○
○
○
○
○
○
C602
○
×
×
○
○
○
○
C603
○
×
×
○
○
○
○
C604
○
○
×
○
○
○
×
C605
○
×
×
×
×
×
×
C606
○
×
×
×
×
×
×
C607
○
×
×
○
○
○
○
C608
○
○
×
○
×
×
×
C609
○
○
×
×
×
×
×
C610
○
×
×
○
×
×
×
C611
○
×
×
×
×
○
○
C612
○
×
×
○
○
○
○
C701
×
○
×
○
×
×
×
ID
- 47 -
C702
×
○
×
○
○
○
○
C703
×
○
×
○
○
○
○
C704
×
○
×
○
×
×
○
C705
×
×
○
○
○
×
×
C706
×
×
×
○
○
○
○
C707
×
×
×
○
○
○
○
C801
×
×
×
×
○
○
○
C802
×
×
×
×
○
○
×
C901
×
×
×
×
×
×
○
C902
×
×
×
×
×
○
○
C903
×
×
×
×
×
○
×
C904
×
×
×
×
×
○
○
C905
×
×
×
×
×
○
○
T501
○
×
×
×
×
×
×
T502
○
○
○
○
○
○
○
T503
○
○
○
×
×
×
×
T505
×
○
×
○
○
○
○
T506
○
×
×
×
×
×
×
T507
○
○
×
○
○
○
○
T508
×
×
×
○
○
○
○
T509
○
×
×
×
×
×
×
T510
○
○
○
○
○
○
○
T511
○
×
×
○
○
○
×
T512
○
×
×
○
○
○
○
T514
○
×
×
×
×
×
×
T515
○
○
○
○
×
×
×
T516
○
×
×
×
×
×
×
T517
×
○
×
○
○
○
○
T518
○
○
○
○
○
○
○
T519
○
○
○
○
○
○
○
T520
○
○
×
×
×
×
×
T521
○
×
×
○
○
○
○
T522
○
○
○
○
○
○
○
T523
○
○
○
○
○
○
○
T524
×
×
×
○
○
○
×
T525
○
○
○
○
○
○
○
T526
×
×
○
×
×
×
×
T527
○
○
○
○
×
○
○
T528
×
○
×
○
○
×
○
T529
○
×
○
×
×
×
×
T530
○
○
○
○
○
○
○
T531
×
×
×
○
○
○
○
- 48 -
T532
×
○
×
×
×
×
×
T533
○
○
○
○
○
○
○
T534
×
×
×
○
○
○
○
T601
×
○
×
○
○
×
○
T602
×
○
×
×
×
×
×
T603
×
○
×
×
○
×
○
T604
×
○
○
×
○
○
○
T605
×
○
○
○
○
○
×
T606
×
×
○
○
○
○
×
T607
×
○
×
○
○
○
○
T608
×
○
×
○
×
○
×
T609
×
○
○
○
○
×
×
T610
×
○
×
×
×
○
○
T611
×
○
○
○
○
×
○
T612
×
○
×
×
×
×
×
T613
×
○
×
○
○
×
○
T614
×
○
×
×
×
×
×
T615
×
○
×
○
○
×
×
T616
×
○
×
○
○
×
○
T617
×
○
○
○
○
×
○
T618
×
○
○
○
○
×
×
T619
×
○
×
×
○
×
×
T620
×
×
×
×
×
×
○
T621
×
○
×
○
×
×
×
T622
×
○
×
○
○
×
○
T623
×
○
×
×
×
×
×
T625
×
×
○
○
○
×
×
T626
×
×
×
○
○
×
○
T627
×
×
○
×
×
×
×
T628
×
×
×
○
○
×
○
T630
×
×
○
○
○
×
○
T701
×
×
○
○
○
×
○
T702
×
×
○
○
○
×
○
T703
×
×
○
○
×
×
×
T704
×
×
○
○
×
×
×
T705
×
×
○
○
○
×
○
T707
×
×
○
×
×
×
×
T708
×
×
○
×
×
×
×
T715
×
×
○
○
×
×
×
T806
×
×
×
×
○
×
○
T808
×
×
×
×
○
×
×
*C000 = participants in Chiba; T000 = participants in Tokyo;
- 49 -
○ = participated; × = not participated.
3.2.2
Procedure
A questionnaire probing their technology usage and computer attitudes, which
was originally developed by the Center for Research and Education on Aging and
Technology Enhancement (CREATE; Czaja et al., 2006) and then modified by the
investigators to match the goal of this study, was sent to the participants every year
since 2003 until 2009. They were asked to complete the questionnaire with their
own paces and then send it back to the investigators. The participants were also
invited to participate in on-site investigation of cognitive abilities which was held
every year. The number of participants agreed to participate in the investigation
varied year to year. The data were collected and inputted by Umemuro Laboratory.
3.2.3
Measurements
Technology usage was assessed by a questionnaire asking participants their
daily usage experiences of products and services based on modern technologies.
They were: touch screen type automatic teller machine (ATM), car cruise control,
car navigation system, mobile phone, computer, computerized catalog in a library,
copier, fax, home security system, digital camera, video camera, video game, video
player/recorder (VCR), DVD player, DVD recorder, ticket vending machine,
answering machine, microwave oven, self-service gas station and IC card.
○
○
×
×
Adoption
Discontinuance
○
○
×
×
Frequent dynamic changes
Fig. 3-1 Definition of dynamic changes
If participants answered the same questionnaire in two consecutive years, it was
- 50 -
considered as one valid case. In Table 3-2, for instance, C508 participated in 2003,
2006, 2008 and 2009. Only the results between 2008 and 2009 were used as one
valid case. All other years’ results were not used.
C501 participated in this
investigation every year since 2003 to 2009, so six valid cases were considered. In
total, 272 cases were recognized with 121 participants. Dynamic changes of usage,
i.e. adoption or discontinuance of a product were recognized by comparing the daily
usage of technologies listed in the questionnaire for every two consecutive years, as
shown in Figure 3-1. Because more participants answered computer attitudes
questionnaire than cognitive abilities questionnaire, the numbers of participants were
listed into two parts. The valid case numbers for computer attitudes were listed in
Table 3-3, and the case numbers for cognitive ability questionnaire were listed in
Table 3-8, Table 3-9.
Participants’ computer attitudes were investigated using the Attitudes Toward
Computers Questionnaire (ATCQ; Jay & Willis, 1992). ATCQ is a 35-item
multidimensional scale to assess seven dimensions of participants’ computer
attitudes: comfort, self-efficacy, gender-equality, control, dehumanization, interest
and utility. Participants responded to items on a five-point Likert scale from 1
(strongly agree) to 5 (strongly disagree). The scores of those items marked by (R)
were calculated with reversed scores. Because the numbers of items were not equal
across all dimensions, the average score of participants’ responses to corresponding
question items in each dimension was calculated, and the average was used as the
score of the dimension of ATCQ.
The comfort dimension has 5 question items: “I feel comfortable with
computer (R)”, “computers make me nervous”, “I don’t feel confident about my
ability to use a computer”, “computers are confusing” and “computers make me feel
dumb”. The average score of this dimension ranges from 1 to 5.
The self-efficacy dimensions has 5 question items: “ I know that if I worked
hard to learn about computers, I could do well (R)”, “computers are not too
complicated for me to understand (R)”, “I think I am capable of learning t use a
computer (R)”, “ I think I am capable of learning to use a computer (R)”, “given a
little time and training, I know I could learn to use a computer (R)”. The average
- 51 -
score of this dimension ranges from 1 to 5.
Table 3-3 Numbers of dynamic changes when older adults adopted or discontinue technology
products
Products
Adoption
number
Discontinuance
number
Answering machine
19
27
Mobile phone
18
13
Copier
10
15
Car cruise control
13
16
Car navigation system
12
6
Fax
12
10
Microwave oven
10
10
Computerized catalog in a
library
24
21
VCR
19
37
Video camera
16
18
ATM
12
16
Home security system
15
11
Self-service gas station
21
16
Video game
9
17
Digital camera
28
9
DVD player
39
22
DVD recorder
18
10
Ticket vending machine
29
17
IC card
49
15
Computer
24
7
The gender equality has 5 question items: “using computers is more important
for men than for women”, “more women than men have the ability to become
computer scientists”, “using computer is more enjoyable for men than it is for
women”, “working with computers is more for women than men”, “women can do
just as well as men in learning about computer (R)”. The average score of this
dimension ranges from 1 to 5.
- 52 -
The control dimension has 5 question items: “computers will never replace the
need for working human beings (R)”, “our world will never be completely run by
computers (R)”, “people are smarter than computers (R)”, “people will always be in
control of computers (R)” and “soon our lives will be controlled by computers”. The
average score of this dimension ranges from 1 to 5.
The dehumanization dimension has 6 question items: “computers turn people
into just another number (R)”, “the use of computer is lowering our standard of
living (R)”, “computers control too much of our world today (R)”, “computers are
making the jobs done by humans less important (R)”, “computers are dehumanizing
(R)” and “soon our lives will be controlled by computers (R)”. The average score of
this dimension ranges from 1 to 5.
The interest dimension has 5 question items: “learning about computer is a
worthwhile and necessary subject (R)”, “reading or hearing about computers would
be (is) boring”, “I don’t care to know more about computers”, “computers would be
(are) fun to use (R)” and “learning about computers is a waste of time”. The average
score of this dimension ranges from 1 to 5.
The utility dimension has 6 question items: “life will be (is) harder with
computers”, “everyone could get along just fine with computers”, “it is not
necessary for people to know about computers in today’s society”, “computers are
too fast”, “people will always be in control of computers (R)” and “computers make
the work done by people more difficult”. The average score of this dimension ranges
from 1 to 5.
Cognitive abilities and cognitive style were measured with the sections selected
form the Kit for Factor-referenced Cognitive Ability Tests (Ekstrom, French,
Harman & Derman, 1976) developed by Educational Testing Service. Five test
batteries were selected and applied: spatial visualization, spatial orientation,
associative memory, perceptual speed and field independence.
Spatial visualization measures the ability to manipulate or transform the image
of spatial patterns into other arrangements. Paper folding test (Ekstrom et al., 1976)
was adopted for testing visualization. Spatial orientation assesses the ability to
perceive spatial patterns or to maintain orientation with respect to objects in space.
- 53 -
Cube comparisons test was selected for orientation test. Associative memory
measures the ability to recall one part of a previously learned but otherwise
unrelated pair of items when the other part of the pair is presented. Picture-number
test was used for associative memory test. Perceptual speed involves primarily the
temporal parameters of a visual search through a field of specified elements.
Number comparison test was used to test perceptual speed. Field independence
assesses the ability to hold a given visual percept or configuration in mind so as to
dissemble it from other well defined perceptual material. Hidden figures test (French,
Ekstrom & Price, 1968) was used to examine field independence.
3.3
RESULTS
3.3.1
Computer Attitudes
A series of paired t-tests were conducted to investigate whether there were
significant differences in computer attitudes between the two consecutive years
when usage changes of technology products occurred. The results were summarized
in Tables 3-4 and 3-5.
Table 3-4 Means and standard deviations of computer attitudes when older adults adopted
technology products
Product
Variable
n
Before
adoption
After
adoption
M
SD
M
SD
t
Copier
gender equality
10
3.00
0.41
3.26
0.55
-2.41*
Computerized catalog in a
library
gender equality
24
3.08
0.56
3.24
0.53
-2.39*
Digital camera
interest
28
3.67
0.51
3.84
0.49
-3.06**
*p < 0.05, **p < 0.01
As seen in Table 3-4, older adults’ computer attitudes increased significantly
when older adults started to use some technology products or services. For example,
older adults’ interest increased significantly after they started using digital camera.
- 54 -
When older adults started to use copier and computerized catalog in a library, their
gender equality also increased significantly.
Table 3-5 Means and standard deviations of computer attitudes when older adults discontinued
using technology products
Product
Variable
n
Before
discontinuance
After
discontinuance
M
SD
M
SD
t
Home security system
utility
11
3.76
0.35
3.52
0.32
2.95*
Digital camera
interest
9
3.64
0.53
3.44
0.58
2.68*
DVD recorder
control
10
3.90
0.37
3.60
0.31
3.31**
*p < 0.05, **p < 0.01
On the other hand, significant declines of computer attitudes were observed
when older adults stopped to use some technological products (Table 3-5). When
older adults stopped using home security system, their computer attitudes of utility
declined significantly. Older adults’ interest toward computer dropped significantly
when they stopped using digital camera. Also when they stopped using DVD
recorder, their computer attitudes of control declined.
For other technology products, i.e. ATM, car cruise control, car navigation
system, mobile phone, computer, fax, video camera, video game, VCR, DVD player,
ticket vending machine, answering machine, microwave oven, self-service gas
station and IC card, significant changes in computer attitudes were not observed.
3.3.2
Cognitive abilities
A series of paired t-tests were also conducted to investigate whether there were
significant differences in cognitive abilities between two consecutive years when
usage changes of technology products occurred. The results were summarized in
Table 3-6 and Table 3-7.
- 55 -
Table 3-6 Means and standard deviations of cognitive abilities when older adults adopted
technology products
Product
Mobile phone
Variable
associative
memory
spatial
visualization
Microwave oven
After
adoption
M
SD
M
SD
6
14.33
11.89
10.00
12.59
3.08*
7
21.29
4.65
23.86
6.28
-2.47*
4
9.00
3.19
6.63
3.17
3.45*
n
spatial orientation
Copier
Before
adoption
t
Microwave oven
field independence
4
12.00
8.68
8.50
7.55
5.42*
Computerized catalog in a
library
perceptual speed
8
55.13
6.85
58.50
6.44
-4.47**
ATM
field independence
5
14.00
4.06
10.90
5.66
3.82*
IC card
associative
memory
12
18.08
5.20
23.00
6.62
-3.34**
IC card
spatial orientation
12
13.00
11.08
4.92
8.11
3.11*
*p < 0.05, **p < 0.01
As seen in Table 3-6, older adults’ spatial visualization, spatial orientation and
field independence declined significantly when older adults started using microwave
oven, mobile phone, IC card and ATM. Older adults’ associative memory and
perceptual speed increased significantly when they started using copier, IC card and
computerized catalog in a library. On the other hand, as seen in Table 3-7, when
older adults stopped using copier, computerized catalog in library, video
player/recorder and ATM, their cognitive abilities such as spatial visualization,
spatial orientation and field independence increased significantly. It should be
noticed that when older adults started to use IC card, their associative memory
ability decreased, while their spatial orientation ability increased. However, two
dimensions of older adults’ cognitive abilities increased significantly when they
adopted microwave oven. Therefore, it was difficult to give a meaningful conclusion
based on the results.
- 56 -
Table 3-7 Means and standard deviations of cognitive abilities when older adults discontinued
technology products
Product
Copier
Computerized catalog
in a library
VCR
ATM
Variable
spatial
visualization
field
independence
field
independence
spatial
orientation
n
Before
discontinuance
After discontinuance
t
M
SD
M
SD
5
3.80
3.72
7.55
3.55
3.05*
8
11.84
8.86
15.28
8.60
2.83*
15
8.22
6.46
11.72
7.27
2.85*
5
6.20
7.56
9.60
8.17
3.47*
*p < 0.05.
The way of calculating each cognitive ability score was different from others.
Z-scores were calculated for all cognitive abilities. Duo to the inconsistent changes
of cognitive abilities on products such as IC card, general cognitive ability (GCA)
was pooled by summing up all z-scores of cognitive abilities. A paired t-test of GCA
was conducted before and after dynamic changes of all products/services usage
(Before: M = 0.00, SD = 0.98; After: M = 0.00, SD = 1.02). There was no difference
on GCA when dynamic changes happened, t(101) = 0.22, p = 0.82. Then, paired ttests were conducted to compare GCA of older adults before and after their adoption
of each technology product. The results were summarized in Table 3-8 and Table 3-9.
As seen in Table 3-8, older adult’s cognitive abilities decreased significantly
when they started to use answering machine. For other products and services, no
significant difference was discovered. Although the differences were found on two
dimensions of cognitive abilities when using IC card, the results didn’t repeat. As
seen in Table 3-9, no significant difference was discovered before and after older
adults stopped using technology products and services. To summarize the results
above, with regard to cognitive abilities, no consistent pattern could be observed
when usage changes of technologies occurred.
- 57 -
Table 3-8 Means and standard deviations of GCA when older adults adopted technology
products
Before adoption
Product
After adoption
n
t
M
SD
M
SD
Answering machine
9
0.09
1.21
-0.10
1.15
3.69**
Mobile phone
6
-0.15
1.25
-0.22
1.43
0.62
Copier
7
-0.13
0.94
-0.01
0.95
-2.36+
Car cruise control
4
0.36
0.29
0.68
0.26
Car navigation system
6
0.18
0.96
0.07
0.95
0.60
Fax
6
0.53
1.03
0.59
1.08
-0.60
Microwave oven
4
0.03
0.87
-0.21
1.01
2.47
Computerized catalog in a
library
8
0.26
0.73
0.32
0.70
-0.47
VCR
7
0.10
1.11
0.01
0.79
0.43
Video camera
2
-1.00
0.07
-0.96
0.72
-0.12
ATM
5
0.03
0.82
-0.19
0.54
0.90
Home security system
8
-0.15
0.80
0.05
0.61
-0.62
Self-service gas station
9
-0.01
0.93
-0.03
0.81
0.14
Video game
3
0.10
1.19
0.32
0.90
-1.02
Digital camera
9
-0.08
0.84
-0.33
0.86
2.05+
DVD player
13
-0.11
1.02
-0.16
1.27
0.40
DVD recorder
5
-0.29
0.85
-0.60
1.09
2.65+
Ticket vending machine
8
0.44
1.07
0.21
0.99
2.12+
IC card
12
0.00
1.12
-0.01
0.88
0.08
Computer
2
1.24
0.94
1.27
1.11
-0.27
*p < 0.05; +p < 0.10
- 58 -
1.15
Table 3-9 Means and standard deviations of GCA when older adults discontinued technology
products
Before adoption
Product
+
After adoption
n
t
M
SD
M
SD
Answering machine
8
0.28
1.22
0.05
1.41
1.74
Mobile phone
4
-0.27
1.53
-0.23
1.87
-0.24
Copier
5
-0.67
1.00
-0.43
0.90
-2.65+
Car cruise control
5
-0.20
0.12
1.41
-1.85
Car navigation system
1
-
-
-
-
-
Fax
5
0.35
0.82
-0.06
0.93
1.97
Microwave oven
2
0.98
0.65
0.75
0.24
0.80
Computerized catalog in a
library
8
-0.04
0.90
-0.07
1.15
0.26
VCR
15
-0.05
0.85
-0.10
1.10
0.40
Video camera
3
-0.02
0.76
-0.10
1.37
0.24
ATM
5
-0.28
0.83
-0.13
1.00
-1.21
Home security system
4
0.17
0.91
-0.09
0.66
1.04
Self-service gas station
5
-0.81
1.05
-0.79
0.98
-0.91
Video game
6
-0.45
1.21
-0.48
1.13
0.22
Digital camera
2
0.18
0.47
0.25
0.45
-4.30
DVD player
5
-0.59
0.83
-0.65
0.71
0.51
DVD recorder
4
0.43
0.71
0.42
0.69
0.06
Ticket vending machine
7
0.52
0.91
0.48
1.17
0.29
IC card
5
0.50
1.25
0.64
1.01
-1.15
Computer
2
1.01
0.70
1.24
0.94
-1.35
p < 0.10
- 59 -
1.15
3.4
DISCUSSION
This study investigated dynamic technology usage changes of older adults over
years, and relations of computer attitudes and cognitive abilities with these changes.
Computer attitudes were found to have relations with dynamic changes in
technology usage, as well as with usage status that has been reported in previous
studies. The results confirmed attitudes as major influential factor on technology
adoption of older people. Explicitly, the dimensions of gender equality, utility,
control and interest were found to have relations with dynamic changes of
technology adoption. These results underscore the diversity of attitude changes
among different products. Jay and Wills (1992) mentioned that self efficacy and
comfort are two attitude dimensions targeted by the training program. In this
research, dynamic changes were supposed to have occurred in their daily life
environment and not necessarily with a help from interventions. This might be the
reason that there were no changes observed on these dimensions.
Another important result was that significant changes in computer attitudes
were observed only for the computerized technology products; when older people
started or stopped to use non-computerized products, there were no significant
attitude changes observed. One possible explanation is that the complexity of
operation of these two groups of products and services are quite different. Even if
some technologies are somehow implemented using computer-based technologies, if
older adults don’t have to use complicated commands and menu to control those
products, they will not consider them related with computer. Then their computer
attitudes might not appear to be significantly different even if they started or stopped
to use non-computerized or “somehow computerized” technologies.
In terms of cognitive abilities, some differences were discovered on several
dimensions of cognitive abilities when dynamic changes of technology adoption
happened, but general cognitive abilities didn’t show significant difference. Hunter
(1986) explained that general cognitive ability is usually measured by summing
across tests of several specific aptitudes, usually verbal aptitude, quantitative
aptitude, and sometimes technical aptitude. He claimed that it is general cognitive
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ability and not specific cognitive aptitudes which predicts job performance.
Therefore, we concluded that cognitive abilities did not show clear relations with
dynamic changes of technology usage, while they have been reported to have some
relations with usage status in previous literature.
One possible explanation for this could be that cognitive abilities are agerelated multifaceted variables, and thus older adults’ cognitive abilities might change
regardless of their changes in usage of certain technology products and services. A
useful way to think about the full range of human cognitive abilities is to categorize
those into fluid abilities and crystallized intelligence (Pak & McLaughlin, 2010).
Fluid abilities are those abilities needed in unfamiliar, rapidly changing situation,
which included perceptual speed, working memory, spatial
ability and
environmental support. Crystallized intelligence represents the sum of knowledge
that one has gained through a lifetime of formal education and life experience. The
fluid intelligence shows moderate to large age-related differences between younger
and older adults. Unlike fluid intelligence, crystallized knowledge continues to
increase with age. Although some knowledge of ICT use might become crystallized
intelligence with practice (such as, the knowledge how to use particular software or
mobile phone), others will remain reliant on those abilities that deteriorate with age
(such as working memory, attention and perceptual speed). Systems to support
technology usage of older people should therefore focus on supporting this fluid
intelligence.
Meanwhile, it is interesting to study these two topics by comparison with
younger participants. Roger (2003) suggested that Individuals tend to expose
themselves to ideas that are in accordance with their interests, needs and existing
attitudes. Also, Morris and Venkatesh (2000) claimed that younger workers found
attitude toward using a new technology to be more salient than older workers at the
initial technology adoption. We expect that the results we have got in this study will
also appear in younger population. In particular, when younger adults adopt a new
computerized product, their computer attitudes may also increase. Meanwhile, when
they discontinue using a computerized product, their computer attitudes may
decrease as well. However, it is should be aware that with the development of
- 61 -
computer and Internet technology, how people conceive those technologies may
change accordingly. In addition, Curran and Meuter (2005) suggested that multiple
factors need to be considered when studying technologies and that the salient factors
may vary among technologies and their stages in the adoption process. In order to
inspect the differences among technologies, it is suggested to examine people’s
attitudes of each new technology respectively, and explore whether there are any
differences between younger and older adults on the product attitudes and adoptions.
In terms of cognitive abilities, younger adults’ cognitive abilities, especially fluid
abilities, remain constant in a long period of time, thus it is predictable that no
relationship should be discovered between cognitive ability and dynamic changes of
technology usage in younger population. This result is different from what we have
got from older population.
Another point we need to pay attention is that the numbers of valid sample of
cognitive abilities were rather small due to the limited number of participants who
agreed to participate in the measurement sessions. Thus it is still possible that we
could not simply observe any statistically significant results, even if there existed
some patterns. The results of this study thus should be interpreted with caution. In
order to make this point clear, further investigations with larger sample and longer
period should be pursued.
Finally, in this study, all examples of technology products and services were
analyzed as a whole and not categorized into groups by nature. As seen in the
discussion on non-computerized and computerized products and services in the
previous section, some nature of the products could be important in order to better
understand the adoption and discontinuance of usage of older adults. Further
analysis with this viewpoint should also be pursued in future work.
3.5
CHAPTER SUMMARY
This is an original study that proposes the dynamic changes of technology
usage of older adults and that examines the relationships between older adults’
computer attitudes, cognitive abilities and the dynamic changes. The results showed
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that computer attitudes of older adults could be related to some extent to their
dynamic changes of technology usage. These relationships discussed above imply
that the computer attitudes are predictors for future technology usage, as discussed
by White and Weatherall (2000). It also might imply that computer attitudes may be
predictors of dynamic changes of new technology usage. However, cognitive
abilities were not found consistent relationship with dynamic changes. Docampo
Rama et al. (2001) mentioned that usage experience with one interface type might
improve certain cognitive abilities that were necessary to use it. The current study
does not give consistent result; therefore further study is required to give insights
into this issue.
The following issues are limitations that should be addressed in future research.
Firstly, significant results were only observed with computerized technology
products, like digital camera, DVD recorder and computerized catalogue in a library.
So, it is important to categorize technology products and services in terms of
operation complexity and penetration situation when doing the similar study.
Secondly, in this study, all participants were older adults came from a similar region
and were of a single ethnic group. Therefore, to further generalize and confirm the
findings of this study, research with younger people with a variety of characteristics
needs to be pursued. Finally, since the cognitive ability test was conducted as an
optional on-site investigation, the numbers of valid sample on cognitive abilities
were small. Thus, a larger number of participants need to be examined in the future
research to allow for refinement of findings.
- 63 -
References
Czaja, S. J., Sharit, J., Charness, N., Fisk, A. D., & Rogers, W. (2001). The Center
for Research and Education on Aging and Technology Enhancement (CREATE):
A program to enhance technology for older adults. Gerontechnology, 1(1), 50-59.
Curran, J. M., & Meuter, M. L. (2005). Self-service technology adoption: comparing
three technologies. Journal of Sevices Marketing, 19(2), 103-113.
Docampo Rama, M., De Ridder, H., & Bouma, H. (2001). Technology generation
and age in using layered user interface. Gerontechnology, 1(1), 25-40.
Ekstrom, R. B., French, J. W., Harman, H. H., & Derman, D. (1976). Manual for kit
of factor-referenced cognitive test. Princeton: Educational Testing Service.
French, J. W., Ekstrom, R. B., & Price, L. A. (1968). Kit of Reference Tests for
Cognitive Factors. Princeton: Educational Testing Service.
Humter, J. E. (1986). Cognitive ability, cognitive aptitudes, job knowledge, and job
performance. Journal of Vocational Behavior, 29(3), 340-362.
Jay, G. M., & Willis, S. L. (1992). Influence of direct computer experience on older
adults’ attitude toward computers. Journal of Gerontology: Psychological
Sciences, 47(4), 250-257.
Morris, M. G., & Wenkatesh, V. (2000). Age differences in technology adoption
decisions: implications for a changing work force. Personnel psychology, 53,
375-403.
Tacken, M., Marcellini, F., Mollenkopf, H., Ruoppila, I., & Szeman, Z. (2005). Use
and acceptance of new technology by older people. Findings of the international
MOBILATE survey: ‘Enhancing mobility in later life’. Gerontechnology, 3(3),
126-137.
Pak, R. (2001). A further examination of the influence of spatial abilities on
computer task performance in younger and older adults. Proceedings of the
Human Factors and Ergonomics Society 45th Annual Meeting, 45(22), 15511555.
Rogers, W. A., Stronge, A. J., & Fisk, A. D. (2005). Technology and aging. Reviews
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of Human Factors and Ergonomics, 1(1), 130-171.
Umemuro, H. (2004). Computer attitudes, cognitive abilities, and technology usage
among older Japanese adults. Gerontechnology, 3(2), 64-76.
Umemuro, H., & Shirokane, Y. (2003). Elderly Japanese computer users: assessing
changes in usage, attitude, and skill transfer over a one-year period. Universal
Access in the Information Society, 2(4), 305-314.
White, J. & Weatherall, A. (2000). A grounded theory analysis of older adults and
information technology. Educational Gerontology, 26(4), 371-386.
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CHAPTER 4
LONGITUDINAL STUDY ON
RELATIONSHIPS BETWEEN TECHNOLOGY
ADOPTION AND COMPUTER ATTITUDES OF
OLDER PEOPLE
4.1
INTRODUCTION
In the past decade, computer and information technologies have developed at
an unprecedented rate. Now information and communication technology has become
an integral component of work, healthcare, communication and entertainment. At the
same time, the world population is aging. With the development of technology, older
adults start making use of those products inevitably. Therefore, supporting the
elderly in making use of new technologies has become increasingly important.
Roger’s innovation diffusion theory (2003) is a well-known and widely used
theory to explain the process in which an innovation is introduced to members of a
social system over time. Innovation diffusion theory posits that the individuals in
social system do not all adopt an innovation at the same time. Rather, they adopt in
an over-time sequence. Thus individuals can be classified into adopter categories on
the basis of when they first begin using a new idea. Innovativeness describes the
degree to which an individual is relatively earlier in adopting new ideas than other
members of a system. The classifications of members of a system are on the basis of
their similar degree of innovativeness. Thus, five ‘adopter’ categories (indicating the
stage of adoption of the technologies) that follow an S-shaped curve were identified:
innovators (small numbers of early users), early adopters (more users in this
category), early majority (many users in this category), later majority (some users in
this category), and laggards (a small number not using the technology). To measure
innovativeness, Midgley and Dowling (1978) suggested determining how many of a
specified list of new products a particular individual has purchased at the time of the
survey.
In technology research, Goldsmith (2001) found that those who scored higher
on innovation were associated positively with more hours of Internet use, greater
- 66 -
Internet purchasing. However, older adults were considered to be lagged behind the
general population in adopting new technologies. Rogers and her colleges (2005)
suggested that older adults tend not to be early adopters. Eastman and Iyer (2004)
found that older adults used more Internet, they are not necessarily innovative. In
contrast, Moschis (2003) claimed that older adults are heterogeneous than younger
age groups. Although some of older adults tend to lag behind the general population
in adopting new technologies, there is increasing recognition that older adults in
early old age can be active, productive and engaged members of society. Their postretirement years are often seem as presenting opportunities to take up new activities,
engage in new learning opportunities. Rose and Fogarty (2010) conducted a study
about the segmentation of mature consumers aged over 55. The results showed that
even within the restricted age range of a mature consumer population, the trends of
segments apparent in general population can also be observed. In order to break
older people into sub-segments, it is necessary to subdivide older people and
investigate on the traits of those subgroups.
A few studies have investigated the attitude towards computers of older people
and computer use. In general, it seems that as older adults have more use
experiences, their attitudes are more positive toward computer (Czaja & Sharit,
1993). Computer use may effectively improve attitudes toward computer among
older people (Smith, 2005; Lagana, 2008). Furthermore, the relationships between
computer attitudes and personal attributes, such as age, gender, were analyzed to
thoroughly understand the attitude of older adult toward computers (Dyck & Smither,
1994). Besides the studies about computer attitudes of older people and computer
use, Umemuro (2004) claimed that computer attitudes can be predictors of the usage
of various computerized products. From the literature review, it is not difficult to
find that there are many studies on computer attitudes of older people in HCI field,
also many studies on technology diffusion among older people and segmentation of
older customers in marketing research. However, there are few studies using the
diffusion theory to segment older people and to study the differences of computer
attitudes among various groups of older adults longitudinally.
The purpose of this study was to subdivide older people by the adoption and
- 67 -
discontinuance levels of technology products and services, and to investigate
whether there are differences in older adults’ computer attitudes across segments.
Based on the literature above, the following hypotheses are proposed:
Hypothesis 1 was that older adults who adopted more new products and
services have more positive computer attitudes than those who adopted less new
products and services.
Hypothesis 2 was that older adults who discontinued more new products and
services have more negative computer attitudes than those who discontinued less
new products and services.
4.2
METHOD
4.2.1
Participants
Older people aged over 60 years old participated in this investigation
voluntarily in response to advertisements on regional newspapers. They are residing
in Tokyo metropolitan area and its suburban area. The total number of participants
was 121. The results from 23 participants who only participated in the investigation
once during 2003 to 2009 were eliminated. For example, C504 only participated in
this study in 2003. The detailed information was shown in Table 3-2. Four
participants didn’t answer computer attitudes scales completely; the corresponding
results were also removed. Therefore, valid responses of 94 participants were
received. Ninety four participants were aged between 63 an 85 years (M = 73.1, SD
= 4.81). Of the 94 participants, 60 were males and 34 were females.
4.2.2
Procedure
A questionnaire probing their usage of various technologies and computer
attitudes was sent to the participants every year since 2003 until 2009. They were
asked to complete the questionnaire with their own paces and then send it back to
the investigators. The data were collected and inputted by Umemuro Laboratory.
The adoption and discontinuance were determined by comparing the daily usage of
technologies listed in the questionnaire between the first year and the last year they
- 68 -
answered the questionnaire. There was the case that an older adult frequently started
and stopped using a product during 2003 and 2009, then the first time he started and
the last time he dropped the product were recognized and other changes in between
were removed in the remaining analyses. For example, in Table 3-2, C508
participated in 2003, 2006, 2008 and 2009. The answers in 2003 and 2009 were
compared and all other years’ results were eliminated.
4.2.3
Measurements
The test batteries used in this study were adapted from those developed by the
Center for Research and Education on Aging and Technology Enhancement
(CREATE) (Czaja, et al., 2001) and modified by the authors. The questionnaire
consisted of two parts: technology usage and computer attitudes.
Technology usage was assessed by a questionnaire asking participants their
experiences and possession of various technological products and services. These
were: answering machine, mobile phone, CD player, copier, car cruise control, car
navigation system, facsimile (fax), microwave oven, computerized catalog in a
library, video camera, automatic teller machine (ATM), home security system, selfservice gas station, console game, digital camera, DVD player, DVD recorder, ticket
vending machine in train station, IC card, computer, LCD TV, high-definition TV,
handheld game console, MP3 music player, digital money integrated in mobile
phone, and mobile TV.
To probe participants' computer attitudes, the Attitudes Toward Computers
Questionnaire (ATCQ; Jay & Willis, 1992) was adopted. The ATCQ is a 35-item
multidimensional scale for assessing seven dimensions of attitudes toward
computers: Comfort, Efficacy, Gender Equality, Control, Dehumanization, Interest
and Utility. Participants responded to items on a five-point Likert scale from 1
(strongly agree) to 5 (strongly disagree). The scores of those items marked by (R)
were calculated with reversed scores. Because the item numbers were not equal in
all dimensions, the average score of participants’ responses to the corresponding
question items in each dimension was calculated, and the average was used as the
scores for the sub-dimension of ATCQ.
- 69 -
The comfort dimension has 5 question items: “I feel comfortable with
computer (R)”, “computers make me nervous”, “I don’t feel confident about my
ability to use a computer”, “computers are confusing” and “computers make me feel
dumb”. The average score of this dimension ranges from 1 to 5.
The self-efficacy dimensions has 5 question items: “ I know that if I worked
hard to learn about computers, I could do well (R)”, “computers are not too
complicated for me to understand (R)”, “I think I am capable of learning t use a
computer (R)”, “ I think I am capable of learning to use a computer (R)”, “given a
little time and training, I know I could learn to use a computer (R)”. The average
score of this dimension ranges from 1 to 5.
The gender equality has 5 question items: “using computers is more important
for men than for women”, “more women than men have the ability to become
computer scientists”, “using computer is more enjoyable for men than it is for
women”, “working with computers is more for women than men”, “women can do
just as well as men in learning about computer (R)”. The average score of this
dimension ranges from 1 to 5.
The control dimension has 5 question items: “computers will never replace the
need for working human beings (R)”, “our world will never be completely run by
computers (R)”, “people are smarter than computers (R)”, “people will always be in
control of computers (R)” and “soon our lives will be controlled by computers”. The
average score of this dimension ranges from 1 to 5.
The dehumanization dimension has 6 question items: “computers turn people
into just another number (R)”, “the use of computer is lowering our standard of
living (R)”, “computers control too much of our world today (R)”, “computers are
making the jobs done by humans less important (R)”, “computers are dehumanizing
(R)” and “soon our lives will be controlled by computers (R)”. The average score of
this dimension ranges from 1 to 5.
The interest dimension has 5 question items: “learning about computer is a
worthwhile and necessary subject (R)”, “reading or hearing about computers would
be (is) boring”, “I don’t care to know more about computers”, “computers would be
(are) fun to use (R)” and “learning about computers is a waste of time”. The average
- 70 -
score of this dimension ranges from 1 to 5.
The utility dimension has 6 question items: “life will be (is) harder with
computers”, “everyone could get along just fine with computers”, “it is not
necessary for people to know about computers in today’s society”, “computers are
too fast”, “people will always be in control of computers (R)” and “computers make
the work done by people more difficult”. The average score of this dimension ranges
from 1 to 5.
4.3
RESULTS
4.3.1
Innovativeness
The penetration rate of each product/service was calculated by year. Because
the investigation period is from 2003 to 2009, some of the products appearing in the
product list in 2003 were not new in 2009. In order to measure older adults’
innovativeness, products’ penetration rates among older people were used to
categorize technology products. If the penetration rate is high, it means the product
has been widely adopted, and it is not a new product among older people.
Roger (2003) assumed that adopter distribution is normal, and used the mean
and the standard deviation to divide a normal adopter distribution into several
categories. In this study, the same way was adopted to categorize technology
products. The first group was named as ‘emerging group’ including the
products/services whose penetration rates were below 16%, including car cruise
control, video camera, home security system, console game, handheld game console,
MP3 music player, digital money integrated with mobile phone, and mobile TV. The
second group was named as ‘early group’ including technologies whose penetration
rates were between 16% and 50%, including car navigation system, computerized
catalog in a library, self-serviced gasoline station, DVD player and DVD recorder.
The third group was named as ‘mature group’ including products that the penetration
rates were between 50% and 84%, including answering machine, copier, mobile
phone, CD player, digital camera, personal computer, LCD TV and high-definition
TV. The last group was called as ‘obsolete group’ which included technologies that
- 71 -
the penetration rates were above 84%, such as fax, microwave oven, ATM, ticket
vending machine in train station and IC card.
The annual adoption number and discontinuance number were defined as the
average number of technologies a participant either started or stopped per year.
Because the product numbers were not equal across groups, the annual adoption
number and discontinuance number were divided by the product numbers in each
group, and multiplied 6.5 (26/4). To test the normality, Kolmogorov-Smirnov test
was conducted. The results showed that they were not normal distribution. As a
result of Friedman test, the numbers of adoption among groups were varied
significantly (χ2 = 38.66, p < 0.001). Post-hoc tests by Wilcoxon signed-rank test
were conducted. There were significant differences in the adoption number between
emerging group and early group (Z = -4.43, p < 0.001), emerging group and mature
group (Z = -5.18, p = 0.006), emerging group and obsolete group (Z = -4.57, p =
0.001). The results didn’t show any significant difference in the numbers of
discontinuance across groups. The medians of adoption and discontinuance numbers
are shown in Table 4-1.
Table 4-1 Medians of the annual adoption and discontinuance numbers among four technology
groups
Adoption
Discontinuance
Penetration rate
Median
min
max
Median
min
Max
Emerging
< 16%
0.00
0.00
0.85
0.00
0.00
1.63
Early
>= 16%, < 50%
0.43
0.00
1.95
0.00
0.00
2.60
Mature
>= 50%, <84%
0.54
0.00
3.25
0.27
0.00
1.63
Obsolete
>= 84%
0.43
0.00
2.60
0.00
0.00
5.20
4.3.2
Computer attitudes
In this study, older adults’ innovativeness is not only defined by how many new
- 72 -
products they adopted, but also by how many new products they discontinued using.
In order to investigate whether there are any differences in computer attitudes
between older people who are more innovative and older people who are less
innovative, participants were subdivided into four groups according to adoption and
discontinuance levels of technologies of which the penetration rates were below
50%. The adoption and discontinuance numbers were not following normal
distribution, thus medians were used for dividing participants into groups with either
high or low adoption/discontinuance numbers. The median for adoption was 0.50
and the median for discontinuance was 0.33.
Table 4-2 summarizes the means and standard deviations of computer attitude
scores by the groups of high/low adoption and discontinuance numbers. H1 and H2
were tested by conducting a series of Two-factor ANOVAs (2 adoption level × 2
discontinuance level) on the scores of computer attitude subscales as dependent
variables. Significant main effects of adoption level were found for comfort (F (1,
90) = 9.99, p = 0.002), interest (F (1, 90) = 8.86, p = 0.004), and utility (F (1, 90) =
8.02, p =0.006). People with higher adoption number showed higher attitude scores
in these dimensions. The results were in Figure 4-1. Thus, H1 was partially
supported.
Fig. 4-1 Means and standard deviations of computer attitudes among four groups of older
people
On the other hand, there were significant main effects of discontinuance level
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were found in comfort (F (1, 90) = 4.80, p = 0.03) and interest (F (1, 90) = 6.50, p =
0.01). People with higher discontinuance number showed higher scores for these
attitude dimensions. Therefore, H2 was denied.
There were no significant main effects of adoption and discontinuance level on
other subscales of computer attitudes. No significant interaction between adoption
level and discontinuance level were found.
Table 4-2 Means and standard deviations of computer attitudes among four groups of older
people
Low adoption
Computer attitude
dimensions
High adoption
Discontinuance
n
M
SD
n
M
SD
Low
20
3.00
0.61
31
3.40
0.48
High
10
3.27
0.70
33
3.67
0.51
Low
20
3.36
0.58
31
3.75
0.37
High
10
3.70
0.66
33
3.98
0.47
Low
20
3.25
0.41
31
3.58
0.48
High
10
3.34
0.63
33
3.64
0.49
Low
20
3.35
0.48
31
3.39
0.38
High
10
3.48
0.57
33
3.65
0.42
Low
20
3.22
0.25
31
3.30
0.43
High
10
3.34
0.53
33
3.25
0.54
Low
20
3.67
0.37
31
3.76
0.39
High
10
3.89
0.44
33
3.79
0.40
Low
20
2.68
0.48
31
2.48
0.43
High
10
2.48
0.65
33
2.38
0.47
Comfort
Interest
Utility
Efficacy
Gender equality
Control
Dehumanization
4.4
DISCUSSION
The purpose of this study was to investigate whether there are relationships
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between older adults’ computer attitudes and older people’s innovativeness on
technology adoptions. Twenty-six technology products/services which appeared in
the market in recent decade were employed in this study. To stratify older adults’
innovativeness, twenty-six technologies were divided into four groups according to
the penetration rates. The results showed that among four groups, older people
adopted fewer technology products/services of which penetration rates were below
16%. Meanwhile, no significant difference was found on older adult’s
discontinuance of technologies across groups. This result confirmed that it is less
likely for older people to start using technologies which are not popular among them,
such as car cruise control, handheld game console. On the other side, from the trend,
it is not difficult to tell that older people have been gradually adopting new
technology products/services from 2003 to 2009. As suggested by Rogers and her
colleagues (2005), for those technologies with high penetration rates, sometimes
adoption of a technology is not really the choice of the user, but the increasing
prevalence of the technology makes its use by people of all ages inevitable.
One of the most significant findings of this study was that older people who
adopted more new technology products/services held more positive attitudes toward
computers. There is clearly a need to consider the heterogeneous nature of the older
population, within older people population, the trends apparent in general population
can be observed. Whilst many older people – particularly in their ‘early old age’ are
extremely fit and able, others can experience hearing loss, mobility impairments,
physical and/or cognitive decline – particularly in their later years. Clearly there are
implications for how older people with differing levels of physical and cognitive
ability and mobility use technologies, and this is certainly the case for uses of
technologies in these contexts. Therefore, it is necessary to subdivide the group into
sub-segments when older people are research subjects. Furthermore, the different
attitudes held by older people in each segments toward computer provide evidence
upon which interaction or graphic design strategies can be developed to instruct the
system design and increase the rate of diffusion of new technologies among older
people.
Another significant result was that older people who discontinued using more
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technology products/services held more positive attitudes toward computer than
those who stopped using fewer technologies. This result was opposite to our
hypothesis. One possible explanation could be that older people with more positive
attitudes toward computer may try new technologies actively; meanwhile they might
stop using some of these products/services according to their personal preferences or
the decreasing prevalence of technology. Thus, the large number of technology
discontinuance might reflect that older people try to adopt technology
products/services actively, despite the fact that they give up during this process.
It is meaningful to investigate this topic among younger population. However,
it should be cautious that there are two types of new technology product adoptions.
One is adopting an innovative technology product which an individual never used
before, such as buying a PC in 1990s. The other one is upgrading the product you
have been using, such as purchasing a next-generation cell phone. In this study, we
investigated older adults’ technology adoptions in early 2000s, so the results were
proper to be considered as the first category. Ogletree and Williams (1990) claimed
that computer ownership is linked to more positive computer attitudes. Therefore, it
is not difficult to foresee that younger adults who adopted more new technology
products/services held more positive attitudes toward computers at the same time
when the study was conducted. As tech-based products are evolving as technologies
continue to improve, nowadays an individual adopt a new product by replacing his
old one. In the case of upgrading, Huh and Kim (2008) claimed that post-adoption
behavior plays a more critical role in develop users’ intention to upgrade than their
early innovativeness. Therefore, no relation between younger adults’ technology
adoption and their computer attitudes should be expected if we conduct the
investigation now with the same questionnaire.
Meanwhile, younger adults meet less using problems than their older
counterparts. Younger people discontinue using technology products/services
because of their personal preferences or the decreasing prevalence of technology.
Therefore, it is different from the results we got for older population. No correlation
should be discovered between attitudes toward computer and technology
discontinuances of younger adults.
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4.5
CHAPTER SUMMARY
This study represents a preliminary effort to examine the segmentation of older
people by technology adoption and the differences in older adults’ attitudes toward
computers among segments. European Commission (EC; 2011) noted a failure to
consider ageing when designing mainstream products and a distinct lack of industry
awareness about older users’ capabilities. Even when assistive technologies are
developed to help vulnerable groups, they note that a lack of interoperability can
hamper uptake. So, with the population ageing at an increasing rate, the need for
more consideration of technologies that will work across all age ranges, as well as
those that will work well with older people, is becoming increasingly important.
These findings contribute to our understanding of older people’s behavior patterns
and can be beneficial when designing a new technology product/service for older
people.
The following issues are limitations that should be addressed in future research.
The data in this study were collected in Japan. However, different countries have
different approaches with the technology in general and with the news in particular.
It is important that future work would bring more focus to the culture aspect of
technology adoption with larger sample sets in multiple countries. Additionally,
social factors, such as influences from one’s friends, family support, are also
important on technology adoption of older people, which should be addressed in the
future study. Finally, as discussed above, when older people adopt technologies,
there should be significant differences in the discontinuance of technology usage
between younger adults and older people, thus age differences should be examined
in the future research to allow for refinement of findings.
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References
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Czaja, S. J., Sharit, J. S., Charness, N., Fisk, A. D., & Rogers, W. (2001). The Center
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Eastman, J. K., & Iyer, R. (2004). The elderly’s uses and attitudes towards the
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Huh, Y. E., & Kim, S. H. (2008). Do early adopters upgrade early? Role of postadoption behavior in the purchase of next-generation products. Journal of
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attitudes and aptitude. Sex Roles, 23, 703-712.
Roger, E. M. (2003). Diffusion of Innovations. New York: Free Press.
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CHAPTER 5
5.1
CONCLUSIONS
RESEARCH OUTCOMES
The purpose of this dissertation was to investigate affective and cognitive
factors, such as well-being, cognitive ability and computer attitudes that contribute
to the use of new technologies in older people in order to identify those relationships
that are relevant to providing older people independent and happy lives. This study
centered in the field of human factors and focused on how affective and cognitive
factors affect different kinds of technology interactions of older people in Japan. It
was the aim of this study to develop theories and find out facts that might be useful
for improving the development and design of ICT systems and devices. Building
from the concepts of well-being, computer attitudes and cognitive abilities, this
dissertation explored the possibilities of using those factors for supporting older
adults on technology adoption. Many sound conclusions were elicited that the
relationships between well-being, cognitive abilities and use of new technologies
might have interactive dynamics and are reciprocal for older people.
Overall, this dissertation results indicated that affect and cognitive factors
should attract more attention for technology design of older adults. New ICT and
other technologies that take into account both affect and cognitive factors can
become much easier for older adults to adopt, and facilitate older people’s daily lives.
This dissertation showed that affect and cognitive factors, which can be interesting
for digital device development and design, may include hedonic and eudaimonic
well-being, computer attitudes, cognitive abilities. However, other psychological
factors, beyond the scope of this dissertation, have potential to also be relevant, and
could be explored in further researches.
5.1.1
Well-being and older adults’ ICT usage
From the affective aspect, according to the pilot study on older adults’ hedonic
and eudaimonic well-being, it yielded several major outcomes on well-being of
older people in their ICT use and daily lives. First, we acquired a modified valid
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measurement of hedonic and eudaimonic well-being in ICT for older adults. Second,
with respect to the connection of daily life experiences and ICT usage, we
formulated a hypothesis based on the mediation of perceived usefulness. The results
indicated that for online news reading and online chatting, older adults' well-being
associated with ICT activities was significantly correlated with well-being
associated with corresponding daily life activities. In online shopping and writing
email, the relation between older adults' well-being in ICT activities and their wellbeing in corresponding daily activities was moderated by perceived usefulness.
Finally, the differences between hedonic and eudaimonic well-being was also
confirmed by regression results. Perceived usefulness and daily life well-being are
important predictors of both hedonic and eudaimonic well-being in ICT usage,
whereas self efficacy is a unique predictor of eudaimonic well-being in ICT usage.
To conclude, hedonic and eudaimonic well-being in ICT usage were two important
and different perspectives when investigating user experiences in ICT. In addition,
older adults’ hedonic and eudaimonic well-being in ICT usage were associated with
that in corresponding daily life.
5.1.2
Cognitive factors and older adults’ dynamic changes of new
technology adoption
From cognitive aspects, the studies on the relationship between the use of new
technologies and cognitive factors were conducted in order to develop new ICTs to
facilitate digital lives of older people. In this part, we aimed at eliciting relations of
dynamic changes in technology usage and changes of computer attitudes and
cognitive abilities. A series of paired t-tests were conducted to investigate whether
there were significant changes of computer attitudes and cognitive abilities when the
adoption or discontinuance of technology products occurred. Preliminary results
showed two dimensions of computer attitudes, i.e. gender-equality and interest,
increased when older adults started to use technology products or services. On the
other hand, significant declines of three dimensions of computer attitudes, i.e.
interest, utility and control, were observed when older adults stopped to use
technological products. For non-computerized products, no changes on computer
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attitudes were observed. With regard to cognitive abilities, no consistent patterns
were observed. In this investigation on technology usage of older adults over years,
computer attitudes were found to have relations with dynamic changes in technology
usage, in addition to usage status that has been reported in previous studies. On the
other hand, cognitive abilities did not show clear relations with usage changes, while
they have been reported to have some relations with usage status.
5.1.3
Older adults’ innovativeness and cognitive factors
The next theme was intended to study the trends of various technology
adoptions in long-term and to discuss the necessity of sub-dividing older adults into
sub-segments in respect of their innovativeness. A series of two-way ANOVA were
conducted to compare older adults’ computer attitudes across sub-segments which
were divided by their innovativeness. Older people who adopted more new
technology products/services held more positive attitudes toward computers. The
findings from this study support the claim that older people are heterogeneous. Even
within older people population, the trends apparent in general population can be
observed. On the other hand, older adults who discontinued using more new
technology products/services held more positive attitudes toward computer than
those who stopped using fewer new technologies.
5.2
RESEARCH IMPLICATIONS
5.2.1
Applications of affective factors in ICT
Some suggestions are made for studying older adults’ well-being in ICT use.
Because the existing models of user experience in human-computer interaction that
incorporate hedonic aspects such as pleasure are rare and often too simplistic, we
would suggest that when studying positive experiences of ICT usage, both hedonic
and eudaimonic well-being should be taken into consideration to display a
completed picture of user experiences in both short-term and long-term. Hedonic
well-being describes the affect aspects of an individual’s positive experiences when
engaging with technology, while eudaimonic wellbeing measures the extent to which
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a person is fully functioning with ICT. By including two aspects of older adults’
positive use experiences, researchers can not only understand the emotion reactions
older adults may have when adopting a new technology, but also understand the long
psychological impacts of technology usage on older adults. The results could help
researcher to develop new systems to fulfill special psychological requirements of
older adults, and to provide them comfortable aging lives with technology.
Ryff (1989) suggested that the criteria of well-being generated are diverse and
extensive. There is not clear definition on to what degree, well-being should be
defined and measured. It should be discussed on what proper measurements on
activity level are and what survey method should be adopted in this research context.
In this dissertation, the PEAQ was used as the measurement. The PEAQ was
developed to measure the well-being in the personal salient activities of people. In
Waterman’s (1993) paper, the purpose of the PEAQ is to obtain a range of activities
differing in their levels of reported personal expressiveness and hedonic enjoyment.
And personal expressiveness occurs when there is an unusually intense involvement
in an undertaking, or a feeling of a special fit or meshing with an activity that is not
characteristic of most daily tasks. In this study, our participants were older adults,
and ICT activities are difficult to be considered as personal salient activities for them.
Before we did this investigation, a focus group was held to ask their positive feelings
in their hobby and in ICT activities, the results showed that older adults had positive
psychological experiences during using or learning ICT, such as “I am enjoyable
when I wrote email with my friends”, but they seldom used intensive emotion words
to describe this kind of feelings. On the other hand, they mentioned that by using
ICT, they felt connected with their family members and the development of their
skills. Although older adults didn’t express extreme positive feelings during ICT
usage, they developed their skills of operating computer, advanced their purpose in
living by connecting with family members. Therefore, older adults experienced the
feeling of self-realization though the fulfillment of personal potentials such as the
development of one’s skills, the advancement of one’s purposes in living, but with
less intensity. To accurately describe the positive feelings when older adults are
doing ICT related activities, it is suggested that using words, such as ‘very’ and
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‘quite’ describe the intensity of the emotions. Superlative degree should be avoided
in expressions.
Also, duo to the broad definition of well-being, it is suggested to use other
existing measurements on hedonic and eudaimonic well-being to valid these
concepts in ICT context. Hedonic well-being describes emotional aspects of positive
experiences. Bradburn (1969) consider that positive and negative affect is somehow
independent, and happiness is the balance between positive and negative emotion.
Also, by eudaimonic view, under some conditions, to have greater well-being
ultimately, an individual experiences rather than avoids negative feelings (Parrott,
1993). Therefore, it is recommended that measuring both positive and negative
affect when doing the similar research. For example, Bradburn’s (1969) Affect
Balance Scale (ABS), computer emotion scale (Kay & Loverock, 2008) are
suggested. Meanwhile, there are many measurements for eudaimonic well-being. In
this study, we used the PEAQ which treated eudaimonic well-being as one
dimension variable. Ryff and Signer (1998) presented a six multidimensional
construct termed psychological well-being (PWB) to measure this kind of human
positive functioning. Ryan and Deci (2000) proposed self-determination theory
(SDT) embraced the concept of eudaimonia. SDT posits three basic psychological
needs – autonomy, competence, and relatedness, and the fulfillments of these three
psychological needs will relate to one’s positive functioning, and lead to personal
growth. Thus, it is necessary to discuss which dimensions should be included into
eudaimonic well-being in ICT context, and to make questionnaire correspondingly
in the future study.
To improve the well-being of older adults in ICT usage, it is necessary to make
use of their daily life experiences. Dickinson and Gregor (2006) stated that there is
no empirical evidence to support the assertion that computer use alone has a positive
effect on well-being among older adults. Meanwhile, most ICT applications are
developed to facilitate our daily life activities. Therefore, It may also be suggested
that well-being in ICT should be studied in the context of people’s daily life. By
doing so, designers may make use of older people’s abundant live experiences to
improve the use experiences of older adults.
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5.2.2
Applications of cognitive factors in ICT
For the cognitive factors, suggestions were also made.
First, we would suggest that computer attitudes significantly influence older
people’s adoption of new technology. To ensure that technologies are easier to be
adopted by older adults, it is important for researchers to promote computer attitudes
of older adults in practice. As discussed by Czaja and Sharit (1998), computer
attitudes are influenced by direct technology use experiences, it might be an
effective way to provide older adults opportunities of training, or to offer them
necessary support when they start using a new technology to sustain their digital
engagement.
Second, as not all functional abilities decline in all people, and the rate of
decline varies widely across individuals, it is important to remember that even if
people who aged over 65 are generally being called “older adults”, they are a
heterogeneous group, which chronological age can only serve as a marker for agerelated changes and functional capabilities. As an effective variable to measure older
people how early they would like to adopt new technology, the number of new
technology adoption also considered as innovativeness has been successfully used to
subdivide older adults into sub-segments. This intergroup variability speaks to the
importance of conducting user testing with target user populations. To ensure that
technologies are usable by older adults, designers must be aware of age-related
changes in abilities and minimize the demands on those abilities imposed by the
technology. At the same time, designers may benefit from an understanding of intact
abilities that may be used to improve the performance of older adults.
Finally, longitudinal study was used in the dissertation on technology
penetration rates and the changes of older adults’ adoption. The results observed in
this dissertation showed that older adults’ technology usage and the products they
used are also changing across time. In order to gain the completed picture of older
adults’ needs and experiences, it is necessary to use this methodology on aging and
technology study.
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5.3
LIMITATIONS
There are several limitations in this dissertation, which can be categorized into
two aspects, the samples and the methodology.
5.3.1
Limitation of the sample
Due to sampling and experiment methods, some limitations of the sample
should be taken into consideration.
First, the participants in this dissertation were volunteers, thus volunteer effect
is a major concern when interpreting the results we have gained. Volunteers may not
be representative of the overall older population. In Chapter Two, only results from
older adults who were engaged in both daily life activity and ICT activity were used
in the analyses. It is likely that the sample was representative of only a part of older
adults who are healthy, and are familiar with ICT, that is, those who are highfunctioning volunteers with positive attitudes towards research. In Chapter Three,
the study was based on the data collected from older adults who had the dynamic
changes. Although there were over 100 participants in this study, the numbers of
valid sample were rather small due to the limited number of participants who
actively adopted and stopped using technologies. Thus, it may not be appropriate to
over generalize the results to the older population in general, especially to those who
have less experiences with new ICT activities.
Also, generation differences (or age differences) should be taken into account
when doing the similar study. Because young people may be different from older
adults in terms of ICT usage, it would be of value to conduct a comparative study to
examine whether the results we got in this dissertation also exist for younger
generations, and whether they are related to age differences between older adults and
young people. Thus, generation differences should be examined in the future
research to allow for refinement of findings.
Finally, all the participants in this dissertation were living in Japan. However,
different countries have different approaches with the technology in general and
with the news in particular. It is important that future work would bring more focus
- 86 -
to the country or development differences of technology adoption with larger sample
sets in multiple countries. Additionally, social factors, such as economic
development, technology prevalence, family composition are also important on
technology adoption of older people, which should be addressed in the future study.
5.3.2
Limitation of the methodology
The study of hedonic and eudaimonic well-being in Chapter Two and the study
of dynamic changes in Chapter Three and the study of older adults’ innovativeness
in Chapter Four were based on testified questionnaires and mature methods of
statistical analysis. The methodology should be considered as adequate. However, it
is still necessary to pay attention on some limitations of questionnaire survey.
First of all, according to the relationships discussed previously, well-being in
ICT, computer attitudes and cognitive abilities may be predictors for future
technology adoption. For instance, in Chapter Two, the study demonstrated that
older adults' well-being in ICT activities is connected with their well-being in daily
activities. In Chapter Four, older adults who actively used technology obtained
higher computer attitudes. However, no evidence of the existence of causality or
mutual relationships was discovered. Further research should be done to give
insights into this issue.
Secondly, ICT includes many technology products and services. In order to
fully understand the relationship between older adults and ICT usage, it is necessary
to categorize technologies in terms of operation complexity and prevalence. For
example, in Chapter Two, in online news reading and online chatting, older adults
might be able to connect their well-being in daily life directly with that in ICT usage.
Meanwhile, in online shopping and writing email, only older adults who perceive
ICT activity as useful might be able to connect their well-being in ICT with that of
daily life. In Chapter Three, all examples of technology products and services were
analyzed as a whole and not categorized into groups by nature. However, some
characteristics of the products could be important in order to better understand the
adoption and discontinuance of usage of older adults.
Finally, according to the traits of different variables, methodology adjustment
- 87 -
should be considered. Different from cognitive factors which are rather stable within
a period, affective factors fluctuated easily. Also, most people report having positive
affect most of the time (Diener & Lucas, 2000). It might be more appropriate to
study real time experiences with methods such as experience sampling method
(ESM; Csikszentmihalyi, Larson & Prescott, 1977). If ratings of well-being had
been made when participants were actually engaged in the different activities, the
correlation between hedonic and eudaimonic well-being might have been different.
5.4
FUTURE STUDIES
Corresponding to these limitations, future studies are also suggested, all the
limitations hopefully can be improved. The study of hedonic and eudaimonic wellbeing was made as a pilot one, which aimed to provide the first results quickly, to
tackle with older adults’ well-being in ICT. Thereafter we plan to make a larger scale
survey to bring sound conclusions based on reliable data from more participants and
a number of ICT activities in the near future. Hassenzahl (2002) suggested that it is
important
to
better
understand
user
experience,
its
determinants
and
situational/personal mediation and to validate this understanding. In order to fully
understand the model of hedonic and eudaimonic well-being, it is necessary to
compare those two types of well-being with other positive experience variables,
such as satisfaction, pleasure and appealing.
Further research should establish to what extent and what types of use of new
technology can indeed improve cognitive abilities in healthy older people. In
addition, the impact of new technology usage on well-being still needs further
investigation, as the results are contradictory and research is limited to measuring
computer and computerized products and services, but does not include other
technologies.
Additionally, the longitudinal study on technology use and computer attitudes
and cognitive abilities could be extended. First, long-term surveys should be
conducted continuously for technology adoption to bring out sound conclusions. We
should also collect objective data related to older adults and technology usage in
- 88 -
social scale to investigate this topic by objective data and a bigger scope. In addition
to improvement of research in the dissertation, it would also be worth investigating
in more depth on the relations between use of ICT and different aging processes and
mental health disorders, which is suggested by van der Wardt, Bandelow and
Hogervorst (2012). Further study should explore the effective support of those
cognitive abilities that deteriorate with age but are essential for the adoption of new
technologies.
In addition, all studies limited their focus on the use of computers and the
Internet. The use of other new technologies, such as the use of telecare system,
robots or game and entertainment systems has not been considered, and therefore
their effect on well-being still has to be examined. Future randomized, controlled
intervention studies for different types of aging processes (with and without
pathologies) using different formats of support should therefore include a broader
range of ICT use to further examine the relationship and its direction between
wellbeing and use of new technologies.
- 89 -
References
Bradburn, N. M. (1969). The structure of psychological well-being. Chicago:
Aldine.
Csikszentmihalyi, M., Larson, R., & Prescott, S. (1977). The ecology of adolescent
activity and experience. Journal of Youth and Adolescence, 6(3), 281-294.
Czaja, S. J., & Sharit, J. (1998). Age differences in attitudes toward computers.
Journal of Gerontology: Psychological sciences, 53(5), 329-340.
Dickinson, A., & Gregor, P. (2006). Computer use has no demonstrated impact on
the well-being of older adults. International Journal of Human-Computer
Studies, 64(8), 744-753.
Diener, E., & Lucas, R. E. (2000). Subjective emotional well-being. In M. Lewis, &
J. M. Haviland (Eds.), Handbook of Emotions (pp. 325-337). New York:
Guilford.
Hassenzahl, M. (2002). The effect of erceived hedonic quality on product
appealingness. International Journal of Human-Computer Interaction, 13(4),
479-497.
Kay, R. H., & Loverock, S. (2008). Assessing emotions related to learning new
software: The computer emotion scale. Computer in Human Behavior, 24(4),
1605-1623.
Parrott, W. G. (1993). Beyound hedonism: motives for inhibiting good moods and
for maintaining bad moods. In M. Wegner, & J. W. Pennebaker (Eds.),
Handbook of Mental Control (pp. 278-305). Englewword Cliffs: Prentice-Hall.
Ryan, R. M. & Deci, E. L. (2000). Self-determination theory and the facilitation of
intrinsic motivation, social development, and well-being. American Psychologist,
55, 68-78.
- 90 -
Ryff, C. D. (1989). Happiness is everything, or is it? Explorations on the meaning of
psychological well-being. Journal of Personality and Social Psychology, 57(6),
1069-1081.
Ryff, C. D., & Singer, B. (1998). The contours of positiv human health.
Psychological Inquiry, 9, 1-28.
Van der Wardt, V., Bandelow, S., & Hogervorst, E. (2012). The relationship
between cognitive abilities, well-being and use of new technologies in older
people. Gerontechnology, 10(4), 187-207.
Waterman, A. S. (1993). Two conceptions of happiness: contrasts of personal
expressiveness (eudaimonia) and hedonic enjoyment. Journal of Personality and
Social Psychology, 64(4), 678-691.
- 91 -
APPENDIX A Hedonic and eudaimonic well-being questionnaire
「日常生活の様々な活動に見いだす価値」
質問紙
※この質問紙は皆さんが日常生活の中で行う様々な活動について、皆さんが
どのような価値を見いだすことができるかを調査するためのものです。
※1０の「活動」についてお尋ねします。まず日頃その「活動」をなさった経
験があるかを質問します。もしも答えが「いいえ」でしたら、指示の通りに
次の「活動」まで飛ばしてください。
※ 「程度」をお尋ねする質問には、あなたのお考えに当てはまる程度を、
７段階の尺度に 1 をつけてお答えください。
会員番号
- 92 -
質問 1．ショッピング
（実際に店に出かけて行う買い物）
1－1．ショッピングをされたことはありますか？当てはまる番号に 1 をつけて下さい。
1．はい→ 引き続き以下の質問にご回答下さい。
２．いいえ→ 次のページに進んで下さい。
1－２．ショッピングはお好きですか？当てはまる番号に 1 をつけて下さい。
1．はい
２．いいえ
当てはまらない
１－３．あなたにとってショッピングをしている時、あ
るいはすることは、下記の項目にどの程度当ては
まりますか？
右の尺度の当てはまる程度に 1 をつけて下さい。

これをしている時に満足感を感じる。

私にとって楽しいと感じる活動だ。

これをしている時はおおいに楽しんでいる。

これをしていると気分がいい。

これをしている時に喜びを感じる。

これをしていると心のぬくもり・暖かさを感じる。

これをしている時は幸せを感じる。

自分が真に活き活きしていると感じる。

これをしている時は熱心に没頭している。

これをしている時の私は真に自分らしいと思う。

これこそが私のやりたかったことだと感じる。

とても充実した満たされた気持ちになる。

自分にぴったり合っていると感じる。

これをすると生活の中の色々な用事を素早く片付ける
られるようになる。

生活をする上での自分の能力の向上につながる。

これをすると生活の生産性が向上する（同じ時間でよ
り多くのことが出来たり生み出したりできるようにな
る）

これをすると生活のひとつひとつの用事をより効果的
に出来るようになる。

これをすると私の生活に便利になる。

これをすることは私の生活に役立つ。
- 93 -
全く当
てはま
らない
ほとん
ど当て
はまら
ない
あまり
あては
まらな
い
当てはまる
どちら
とも言
えない
やや
当て
はま
る
まあま
ああて
はまる
とても
当ては
まる
質問２．オンライン・ショッピング
（インターネットなどでの通信販売）
２－1．オンライン・ショッピングをされたことはありますか？当てはまる番号に 1 をつけて下さい。
1．はい→ 引き続き以下の質問にご回答下さい。
２．いいえ→ 次のページに進んで下さい。
２－２．オンライン・ショッピングはお好きですか？当てはまる番号に 1 をつけて下さい。
1．はい
２．いいえ
当てはまらない
２－３．あなたにとってオンライン・ショッピングをし
ている時、あるいはすることは、下記の項目にど
の程度当てはまりますか？
右の尺度の当てはまる程度に 1 をつけて下さい。

これをしている時に満足感を感じる。

私にとって楽しいと感じる活動だ。

これをしている時はおおいに楽しんでいる。

これをしていると気分がいい。

これをしている時に喜びを感じる。

これをしていると心のぬくもり・暖かさを感じる。

これをしている時は幸せを感じる。

自分が真に活き活きしていると感じる。

これをしている時は熱心に没頭している。

これをしている時の私は真に自分らしいと思う。

これこそが私のやりたかったことだと感じる。

とても充実した満たされた気持ちになる。

自分にぴったり合っていると感じる。

これをすると生活の中の色々な用事を素早く片付ける
られるようになる。

生活をする上での自分の能力の向上につながる。

これをすると生活の生産性が向上する（同じ時間でよ
り多くのことが出来たり生み出したりできるようにな
る）

これをすると生活のひとつひとつの用事をより効果的
に出来るようになる。

これをすると私の生活に便利になる。

これをすることは私の生活に役立つ。
- 94 -
全く当
てはま
らない
ほとん
ど当て
はまら
ない
あまり
あては
まらな
い
当てはまる
どちら
とも言
えない
やや
当て
はま
る
まあま
ああて
はまる
とても
当ては
まる
質問３．新聞やテレビのニュース
３－1．新聞やテレビでニュースをご覧になったことはありますか？当てはまる番号に 1 をつけて下さい。
1．はい→ 引き続き以下の質問にご回答下さい。
２．いいえ→ 次のページに進んで下さい。
３－２．新聞やテレビでニュースを見るのはお好きですか？当てはまる番号に 1 をつけて下さい。
1．はい
２．いいえ
当てはまらない
３－３．あなたにとって新聞やテレビでニュースを見て
いる時、あるいは見ることは、下記の項目にどの
程度当てはまりますか？
右の尺度の当てはまる程度に 1 をつけて下さい。

これをしている時に満足感を感じる。

私にとって楽しいと感じる活動だ。

これをしている時はおおいに楽しんでいる。

これをしていると気分がいい。

これをしている時に喜びを感じる。

これをしていると心のぬくもり・暖かさを感じる。

これをしている時は幸せを感じる。

自分が真に活き活きしていると感じる。

これをしている時は熱心に没頭している。

これをしている時の私は真に自分らしいと思う。

これこそが私のやりたかったことだと感じる。

とても充実した満たされた気持ちになる。

自分にぴったり合っていると感じる。

これをすると生活の中の色々な用事を素早く片付ける
られるようになる。

生活をする上での自分の能力の向上につながる。

これをすると生活の生産性が向上する（同じ時間でよ
り多くのことが出来たり生み出したりできるようにな
る）

これをすると生活のひとつひとつの用事をより効果的
に出来るようになる。

これをすると私の生活に便利になる。

これをすることは私の生活に役立つ。
- 95 -
全く当
てはま
らない
ほとん
ど当て
はまら
ない
あまり
あては
まらな
い
当てはまる
どちら
とも言
えない
やや
当て
はま
る
まあま
ああて
はまる
とても
当ては
まる
質問４．インターネットでニュースを読む
４－1．インターネットでニュースをお読みになったことはありますか？当てはまる番号に 1 をつけて下さい。
1．はい→ 引き続き以下の質問にご回答下さい。
２．いいえ→ 次のページに進んで下さい。
４－２．インターネットでニュースを読むのはお好きですか？当てはまる番号に 1 をつけて下さい。
1．はい
２．いいえ
当てはまらない
４－３．あなたにとってインターネットでニュースを読
んでいる時、あるいは読むことは、下記の項目に
どの程度当てはまりますか？
右の尺度の当てはまる程度に 1 をつけて下さい。

これをしている時に満足感を感じる。

私にとって楽しいと感じる活動だ。

これをしている時はおおいに楽しんでいる。

これをしていると気分がいい。

これをしている時に喜びを感じる。

これをしていると心のぬくもり・暖かさを感じる。

これをしている時は幸せを感じる。

自分が真に活き活きしていると感じる。

これをしている時は熱心に没頭している。

これをしている時の私は真に自分らしいと思う。

これこそが私のやりたかったことだと感じる。

とても充実した満たされた気持ちになる。

自分にぴったり合っていると感じる。

これをすると生活の中の色々な用事を素早く片付ける
られるようになる。

生活をする上での自分の能力の向上につながる。

これをすると生活の生産性が向上する（同じ時間でよ
り多くのことが出来たり生み出したりできるようにな
る）

これをすると生活のひとつひとつの用事をより効果的
に出来るようになる。

これをすると私の生活に便利になる。

これをすることは私の生活に役立つ。
- 96 -
全く当
てはま
らない
ほとん
ど当て
はまら
ない
あまり
あては
まらな
い
当てはまる
どちら
とも言
えない
やや
当て
はま
る
まあま
ああて
はまる
とても
当ては
まる
質問５．おしゃべりをする
（お友達やご家族と直接会って話す）
５－1．おしゃべりをしたことはありますか？当てはまる番号に 1 をつけて下さい。
1．はい→ 引き続き以下の質問にご回答下さい。
２．いいえ→ 次のページに進んで下さい。
５－２．おしゃべりはお好きですか？当てはまる番号に 1 をつけて下さい。
1．はい
２．いいえ
当てはまらない
５－３．あなたにとっておしゃべりをしている時、ある
いはおしゃべりをすることは、下記の項目にどの
程度当てはまりますか？
右の尺度の当てはまる程度に 1 をつけて下さい。

これをしている時に満足感を感じる。

私にとって楽しいと感じる活動だ。

これをしている時はおおいに楽しんでいる。

これをしていると気分がいい。

これをしている時に喜びを感じる。

これをしていると心のぬくもり・暖かさを感じる。

これをしている時は幸せを感じる。

自分が真に活き活きしていると感じる。

これをしている時は熱心に没頭している。

これをしている時の私は真に自分らしいと思う。

これこそが私のやりたかったことだと感じる。

とても充実した満たされた気持ちになる。

自分にぴったり合っていると感じる。

これをすると生活の中の色々な用事を素早く片付ける
られるようになる。

生活をする上での自分の能力の向上につながる。

これをすると生活の生産性が向上する（同じ時間でよ
り多くのことが出来たり生み出したりできるようにな
る）

これをすると生活のひとつひとつの用事をより効果的
に出来るようになる。

これをすると私の生活に便利になる。

これをすることは私の生活に役立つ。
- 97 -
全く当
てはま
らない
ほとん
ど当て
はまら
ない
あまり
あては
まらな
い
当てはまる
どちら
とも言
えない
やや
当て
はま
る
まあま
ああて
はまる
とても
当ては
まる
質問６．インターネットでおしゃべりする
６－1．インターネットでおしゃべりをしたことはありますか？当てはまる番号に 1 をつけて下さい。
1．はい→ 引き続き以下の質問にご回答下さい。
２．いいえ→ 次のページに進んで下さい。
６－２．インターネットでおしゃべりをするのはお好きですか？当てはまる番号に 1 をつけて下さい。
1．はい
２．いいえ
当てはまらない
６－３．あなたにとってインターネットでおしゃべりを
している時、あるいはすることは、下記の項目に
どの程度当てはまりますか？
右の尺度の当てはまる程度に 1 をつけて下さい。

これをしている時に満足感を感じる。

私にとって楽しいと感じる活動だ。

これをしている時はおおいに楽しんでいる。

これをしていると気分がいい。

これをしている時に喜びを感じる。

これをしていると心のぬくもり・暖かさを感じる。

これをしている時は幸せを感じる。

自分が真に活き活きしていると感じる。

これをしている時は熱心に没頭している。

これをしている時の私は真に自分らしいと思う。

これこそが私のやりたかったことだと感じる。

とても充実した満たされた気持ちになる。

自分にぴったり合っていると感じる。

これをすると生活の中の色々な用事を素早く片付ける
られるようになる。

生活をする上での自分の能力の向上につながる。

これをすると生活の生産性が向上する（同じ時間でよ
り多くのことが出来たり生み出したりできるようにな
る）

これをすると生活のひとつひとつの用事をより効果的
に出来るようになる。

これをすると私の生活に便利になる。

これをすることは私の生活に役立つ。
- 98 -
全く当
てはま
らない
ほとん
ど当て
はまら
ない
あまり
あては
まらな
い
当てはまる
どちら
とも言
えない
やや
当て
はま
る
まあま
ああて
はまる
とても
当ては
まる
質問７．ゲーム
（囲碁、将棋、麻雀、トランプなど友人と直接集まって遊ぶ）
７－1．ゲームをされたことはありますか？当てはまる番号に 1 をつけて下さい。
1．はい→ 引き続き以下の質問にご回答下さい。
２．いいえ→ 次のページに進んで下さい。
７－２．ゲームはお好きですか？当てはまる番号に 1 をつけて下さい。
1．はい
２．いいえ
当てはまらない
７－３．あなたにとってゲームをしている時、あるいは
することは、下記の項目にどの程度当てはまりま
すか？
右の尺度の当てはまる程度に 1 をつけて下さい。

これをしている時に満足感を感じる。

私にとって楽しいと感じる活動だ。

これをしている時はおおいに楽しんでいる。

これをしていると気分がいい。

これをしている時に喜びを感じる。

これをしていると心のぬくもり・暖かさを感じる。

これをしている時は幸せを感じる。

自分が真に活き活きしていると感じる。

これをしている時は熱心に没頭している。

これをしている時の私は真に自分らしいと思う。

これこそが私のやりたかったことだと感じる。

とても充実した満たされた気持ちになる。

自分にぴったり合っていると感じる。

これをすると生活の中の色々な用事を素早く片付ける
られるようになる。

生活をする上での自分の能力の向上につながる。

これをすると生活の生産性が向上する（同じ時間でよ
り多くのことが出来たり生み出したりできるようにな
る）

これをすると生活のひとつひとつの用事をより効果的
に出来るようになる。

これをすると私の生活に便利になる。

これをすることは私の生活に役立つ。
- 99 -
全く当
てはま
らない
ほとん
ど当て
はまら
ない
あまり
あては
まらな
い
当てはまる
どちら
とも言
えない
やや
当て
はま
る
まあま
ああて
はまる
とても
当ては
まる
質問８．オンライン・ゲーム
（囲碁、将棋、麻雀、トランプなどインターネット上の相手と遊ぶ）
８－1．オンライン・ゲームをされたことはありますか？当てはまる番号に 1 をつけて下さい。
1．はい→ 引き続き以下の質問にご回答下さい。
２．いいえ→ 次のページに進んで下さい。
８－２．オンライン・ゲームはお好きですか？当てはまる番号に 1 をつけて下さい。
1．はい
２．いいえ
当てはまらない
８－３．あなたにとってオンライン・ゲームをしている
時、あるいはすることは、下記の項目にどの程度
当てはまりますか？
右の尺度の当てはまる程度に 1 をつけて下さい。

これをしている時に満足感を感じる。

私にとって楽しいと感じる活動だ。

これをしている時はおおいに楽しんでいる。

これをしていると気分がいい。

これをしている時に喜びを感じる。

これをしていると心のぬくもり・暖かさを感じる。

これをしている時は幸せを感じる。

自分が真に活き活きしていると感じる。

これをしている時は熱心に没頭している。

これをしている時の私は真に自分らしいと思う。

これこそが私のやりたかったことだと感じる。

とても充実した満たされた気持ちになる。

自分にぴったり合っていると感じる。

これをすると生活の中の色々な用事を素早く片付ける
られるようになる。

生活をする上での自分の能力の向上につながる。

これをすると生活の生産性が向上する（同じ時間でよ
り多くのことが出来たり生み出したりできるようにな
る）

これをすると生活のひとつひとつの用事をより効果的
に出来るようになる。

これをすると私の生活に便利になる。

これをすることは私の生活に役立つ。
- 100 -
全く当
てはま
らない
ほとん
ど当て
はまら
ない
あまり
あては
まらな
い
当てはまる
どちら
とも言
えない
やや
当て
はま
る
まあま
ああて
はまる
とても
当ては
まる
質問９．手紙をやり取りする
（葉書や便せんなど、郵便で送る手紙）
９－1．手紙をやり取りしたことはありますか？当てはまる番号に 1 をつけて下さい。
1．はい→ 引き続き以下の質問にご回答下さい。
２．いいえ→ 次のページに進んで下さい。
９－２．手紙をやりとりすることはお好きですか？当てはまる番号に 1 をつけて下さい。
1．はい
２．いいえ
当てはまらない
９－３．あなたにとって手紙をやりとりしている時、あ
るいは手紙をやりとりすることは、下記の項目に
どの程度当てはまりますか？
右の尺度の当てはまる程度に 1 をつけて下さい。

これをしている時に満足感を感じる。

私にとって楽しいと感じる活動だ。

これをしている時はおおいに楽しんでいる。

これをしていると気分がいい。

これをしている時に喜びを感じる。

これをしていると心のぬくもり・暖かさを感じる。

これをしている時は幸せを感じる。

自分が真に活き活きしていると感じる。

これをしている時は熱心に没頭している。

これをしている時の私は真に自分らしいと思う。

これこそが私のやりたかったことだと感じる。

とても充実した満たされた気持ちになる。

自分にぴったり合っていると感じる。

これをすると生活の中の色々な用事を素早く片付ける
られるようになる。

生活をする上での自分の能力の向上につながる。

これをすると生活の生産性が向上する（同じ時間でよ
り多くのことが出来たり生み出したりできるようにな
る）

これをすると生活のひとつひとつの用事をより効果的
に出来るようになる。

これをすると私の生活に便利になる。

これをすることは私の生活に役立つ。
- 101 -
全く当
てはま
らない
ほとん
ど当て
はまら
ない
あまり
あては
まらな
い
当てはまる
どちら
とも言
えない
やや
当て
はま
る
まあま
ああて
はまる
とても
当ては
まる
質問 1０．電子メールをやりとりする（携帯電話やインターネットなどの電子メール）
1０－1．電子メールをやりとりしたことはありますか？当てはまる番号に 1 をつけて下さい。
1．はい→ 引き続き以下の質問にご回答下さい。
２．いいえ→ 次のページに進んで下さい。
1０－２．電子メールをやりとりすることはお好きですか？当てはまる番号に 1 をつけて下さい。
1．はい
２．いいえ
当てはまらない
１０－３．あなたにとって電子メールをやりとりしてい
る時、あるいはすることは、下記の項目にどの程
度当てはまりますか？
右の尺度の当てはまる程度に 1 をつけて下さい。

これをしている時に満足感を感じる。

私にとって楽しいと感じる活動だ。

これをしている時はおおいに楽しんでいる。

これをしていると気分がいい。

これをしている時に喜びを感じる。

これをしていると心のぬくもり・暖かさを感じる。

これをしている時は幸せを感じる。

自分が真に活き活きしていると感じる。

これをしている時は熱心に没頭している。

これをしている時の私は真に自分らしいと思う。

これこそが私のやりたかったことだと感じる。

とても充実した満たされた気持ちになる。

自分にぴったり合っていると感じる。

これをすると生活の中の色々な用事を素早く片付ける
られるようになる。

生活をする上での自分の能力の向上につながる。

これをすると生活の生産性が向上する（同じ時間でよ
り多くのことが出来たり生み出したりできるようにな
る）

これをすると生活のひとつひとつの用事をより効果的
に出来るようになる。

これをすると私の生活に便利になる。

これをすることは私の生活に役立つ。
- 102 -
全く当
てはま
らない
ほとん
ど当て
はまら
ない
あまり
あては
まらな
い
当てはまる
どちら
とも言
えない
やや
当て
はま
る
まあま
ああて
はまる
とても
当ては
まる
APPENDIX B Test-retest questionnaire of thePersonally Expressive Activities Questionaaire (PEAQ)
日常の様々な活動のもつ価値についての研究（第 1 回調査）
研究へのご協力のお願い
この調査の目的は、日常の様々な活動が皆さんの生活にどのような価値を与えているかについて考察することです。調査の信頼性を維持するため
に、同じ様な内容を異なる質問で伺っている場所があります。また、同じ質問紙に 1 週間の間隔を空けて 2 回お答え頂きます。1 回の調査にかかる
時間は約 10 分です。
この質問紙は 2 つの部分から構成されています。第 1 部では皆さんの背景情報を伺います。第 2 部では、日常での 4 つの活動について評価をし
て頂きます。前述のように、ひとつの活動が 2 回現われ、異なる質問項目で評価をして頂きます。
記入済みの質問紙は、あなたにこの調査への参加をお願いした私達の研究室のメンバーに返却して下さい。
皆様の回答は私達の研究室以外には決して開示致しません。またこの調査結果は研究目的にのみ用いられ、研究結果を公開する時には統計的
に処理された代表的な値だけが報告されます。皆さんの個人を特定できる情報は決して開示されません。
この調査についてご不明の点は、下記までいつでもお問い合わせ下さい。
ご協力をよろしくお願い致します。
連絡先：
〒152-8552 東京都目黒区大岡山 2-12-1-W9-67
東京工業大学 大学院社会理工学研究科 経営工学専攻
准教授 梅室 博行
博士課程 張嘉
〒152-8552 東京都目黒区大岡山 2-12-1 W9-67
Tel 03-5764-2246 Fax 03-5734-2246
E-mail： [email protected]
- 103 -
Part 1: あなた自身についての質問
1．性別
２．年齢
□女性
□男性
＿＿＿＿歳
- 104 -
Part 2
あなたにとってオンライン・ショッピングをしている時、あるいはすることは、下記の項目にどの程度当てはまりますか？
項目
私はこの活動をしている時、大抵のほかの活動をしているときよりも満足を感じる
この活動は私に最高に楽しいという感覚を与える
私はこの活動をしている時、良い気分だ
この活動をしている時、私はもっとも大きな喜びを感じる
私はこの活動をしているときぬくもりを感じる
私はこの活動をしている時、大抵のほかの活動をしているときよりも幸せを感じる
私はこの活動をしている時、本当に生きているのだという感覚を最も強く感じる
私はこの活動をしている時、大抵のほかの活動をしているときよりも熱心に打ち込んでいると感じる
私はこの活動をしいる時、これが本当の私であるという感覚を最も強く感じる
私はこの活動をしている時、あたかも自分がこの活動をするように運命づけられていたように感じる
私はこの活動をしている時、大抵のほかの活動をしているときよりも充実している、あるいは満たされている
と感じる
この活動は特に私にぴったり合う、あるいは調和していると感じる
- 105 -
全く当
てはま
らない
ほ と ん
ど当て
は ま ら
ない
あまり当
てはま
らない
どちらと
も言え
ない
やや当
てはま
る
まあまあ
あては
まる
とても当
てはま
る
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
あなたにとって誰かとおしゃべりをしている時、あるいはすることは、下記の項目にどの程度当てはまりますか？
項目
これをしている時に満足感を感じる
私にとって楽しいと感じる活動だ
これをしている時はおおいに楽しんでいる
これをしていると気分がいい
これをしている時に喜びを感じる
これをしていると心のぬくもり・暖かさを感じる
これをしている時は幸せを感じる
自分が真に活き活きしていると感じる
これをしている時は熱心に没頭している
これをしている時の私は真に自分らしいと思う
これこそが私のやりたかったことだと感じる
とても充実した満たされた気持ちになる
- 106 -
全く当て
は まらな
い
ほ と んど
当てはま
らない
あまり当
ては まら
ない
どちらと
も言えな
い
やや当
てはまる
まあまあ
あてはま
る
とても当
てはまる
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
あなたにとって電子メールをやりとりしている時、あるいはすることは、下記の項目にどの程度当てはまりますか？
項目
私はこの活動をしている時、大抵のほかの活動をしているときよりも満足を感じる
この活動は私に最高に楽しいという感覚を与える
私はこの活動をしている時、良い気分だ
この活動をしている時、私はもっとも大きな喜びを感じる
私はこの活動をしているときぬくもりを感じる
私はこの活動をしている時、大抵のほかの活動をしているときよりも幸せを感じる
私はこの活動をしている時、本当に生きているのだという感覚を最も強く感じる
私はこの活動をしている時、大抵のほかの活動をしているときよりも熱心に打ち込んでいると感じる
私はこの活動をしいる時、これが本当の私であるという感覚を最も強く感じる
私はこの活動をしている時、あたかも自分がこの活動をするように運命づけられていたように感じる
私はこの活動をしている時、大抵のほかの活動をしているときよりも充実している、あるいは満たされてい
ると感じる
この活動は特に私にぴったり合う、あるいは調和していると感じる
- 107 -
全 く 当
てはまら
ない
ほとんど
当ては
まらない
あまり当
てはまら
ない
ど ちらと
も 言 え
ない
やや当
てはまる
まあまあ
あては
まる
とても当
てはまる
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
あなたにとって新聞を読んでいる時、あるいは読むことは、下記の項目にどの程度当てはまりますか？
項目
これをしている時に満足感を感じる
私にとって楽しいと感じる活動だ
これをしている時はおおいに楽しんでいる
これをしていると気分がいい
これをしている時に喜びを感じる
これをしていると心のぬくもり・暖かさを感じる
これをしている時は幸せを感じる
自分が真に活き活きしていると感じる
これをしている時は熱心に没頭している
これをしている時の私は真に自分らしいと思う
これこそが私のやりたかったことだと感じる
とても充実した満たされた気持ちになる
- 108 -
全 く当 て
はまらな
い
ほとんど
当てはま
らない
あまり当
てはまら
ない
どちらと
も言えな
い
や や 当
てはまる
まあまあ
あ ては ま
る
とても当
てはまる
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
あなたにとって誰かとおしゃべりをしている時、あるいはすることは、下記の項目にどの程度当てはまりますか？
項目
私はこの活動をしている時、大抵のほかの活動をしているときよりも満足を感じる
この活動は私に最高に楽しいという感覚を与える
私はこの活動をしている時、良い気分だ
この活動をしている時、私はもっとも大きな喜びを感じる
私はこの活動をしているときぬくもりを感じる
私はこの活動をしている時、大抵のほかの活動をしているときよりも幸せを感じる
私はこの活動をしている時、本当に生きているのだという感覚を最も強く感じる
私はこの活動をしている時、大抵のほかの活動をしているときよりも熱心に打ち込んでいると感じる
私はこの活動をしいる時、これが本当の私であるという感覚を最も強く感じる
私はこの活動をしている時、あたかも自分がこの活動をするように運命づけられていたように感じる
私はこの活動をしている時、大抵のほかの活動をしているときよりも充実している、あるいは満たされて
いると感じる
この活動は特に私にぴったり合う、あるいは調和していると感じる
- 109 -
全く当て
はまらな
い
ほとんど
当ては
まらない
あまり当
てはまら
ない
どちらと
も言えな
い
やや当
てはまる
まあまあ
あてはま
る
とても当
てはまる
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
あなたにとってオンライン・ショッピングをしている時、あるいはすることは、下記の項目にどの程度当てはまりますか？
項目
これをしている時に満足感を感じる
私にとって楽しいと感じる活動だ
これをしている時はおおいに楽しんでいる
これをしていると気分がいい
これをしている時に喜びを感じる
これをしていると心のぬくもり・暖かさを感じる
これをしている時は幸せを感じる
自分が真に活き活きしていると感じる
これをしている時は熱心に没頭している
これをしている時の私は真に自分らしいと思う
これこそが私のやりたかったことだと感じる
とても充実した満たされた気持ちになる
- 110 -
全く当て
は まらな
い
ほ と んど
当てはま
らない
あまり当
ては まら
ない
どちらと
も言えな
い
やや当
てはまる
まあまあ
あてはま
る
とても当
てはまる
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
あなたにとって新聞を読んでいる時、あるいはすることは、下記の項目にどの程度当てはまりますか？
項目
私はこの活動をしている時、大抵のほかの活動をしているときよりも満足を感じる
この活動は私に最高に楽しいという感覚を与える
私はこの活動をしている時、良い気分だ
この活動をしている時、私はもっとも大きな喜びを感じる
私はこの活動をしているときぬくもりを感じる
私はこの活動をしている時、大抵のほかの活動をしているときよりも幸せを感じる
私はこの活動をしている時、本当に生きているのだという感覚を最も強く感じる
私はこの活動をしている時、大抵のほかの活動をしているときよりも熱心に打ち込んでいると感じる
私はこの活動をしいる時、これが本当の私であるという感覚を最も強く感じる
私はこの活動をしている時、あたかも自分がこの活動をするように運命づけられていたように感じる
私はこの活動をしている時、大抵のほかの活動をしているときよりも充実している、あるいは満たされて
いると感じる
この活動は特に私にぴったり合う、あるいは調和していると感じる
- 111 -
全く当て
はまらな
い
ほとんど
当ては
まらない
あまり当
てはまら
ない
どちらと
も言えな
い
やや当
てはまる
まあまあ
あてはま
る
とても当
てはまる
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
あなたにとって電子メールをやりとりしている時、あるいはすることは、下記の項目にどの程度当てはまりますか
項目
これをしている時に満足感を感じる
私にとって楽しいと感じる活動だ
これをしている時はおおいに楽しんでいる
これをしていると気分がいい
これをしている時に喜びを感じる
これをしていると心のぬくもり・暖かさを感じる
これをしている時は幸せを感じる
自分が真に活き活きしていると感じる
これをしている時は熱心に没頭している
これをしている時の私は真に自分らしいと思う
これこそが私のやりたかったことだと感じる
とても充実した満たされた気持ちになる
- 112 -
全く当て
はまらな
い
ほとんど
当ては
まらない
あまり当
てはまら
ない
ど ちら と
も 言 え
ない
やや当
てはまる
まあまあ
あ ては
まる
とても当
てはまる
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
1
２
３
４
５
６
７
以上で終了です。
ご協力ありがとうございまし
- 113 -
APPENDIX C Technology usage and computer attitudes
questionnaire
リサーチ・メイト
2009 年度 年次調査
技術の利用と健康に関する継続調査
質問紙
※この質問紙は皆さんが生活する上での健康状態や技術との関わ
りを毎年継続的に調査するためのものです。
※できるだけすべて解答して頂きたいのですが、もしもお答えに
なりたくない質問があったらとばして下さって結構です。
会員番号
- 114 -
健康に関する質問
※あなたにもっとも当てはまるものに 1 をして下さい。
1. あなたの現在の健康状態は？
□良くない
□あまり良くない
□まあまあ
□良い
□大変良い
2. 同年代の方と比べて、健康だと思いますか？
□良くない
□あまり良くない
□まあまあ
□良い
□大変良い
3. 今の健康状態にどのくらい満足していますか？
□満足していない □あまり満足していない □どちらともいえず
□大変満足
□満足
４. 今まで健康問題によって不都合を感じた事がどのくらいありますか？
□全くない □ごくたまにある □たまにある □よくある □常にある
5．下に日常生活の中の色々な活動事項が書かれています。あなたの現在の
健康状態限界を感じるもの、その頻度について、あなたに当てはまる欄
に 1 を記入して下さい。
よく辛いと た ま に 辛 い 全 く 辛 い と
感じる
と感じる
感じない
a. 激しい運動（ランニング、重いもの
を運ぶ、水泳など）
b. 穏やかな運動（机を動かす、掃除機
をかける、ボーリング、ゴルフなど）
c. 食事を運ぶ
d. 階段を数段昇る
e. 階段を一段昇る
f. かがむ、しゃがむ
g. 1 キロメートル以上歩く
h. 数百メートル歩く
i. 百メートル歩く
j. お風呂に入る、着替える
6. 今何人でお住まいですか?
□ひとりで □夫婦で □家族で(子供と) □家族で(親と) □家族で(その他)
- 115 -
生活の中の技術に関する質問
この質問では、あなたの生活の中での技術との関わり方を知ることを目的と
しています。下の当てはまるものに 1 をつけてください。
1. 下記のものであなたが一度でも使ったことのあるもの全てに 1 をつけて下
さい。
□ 留守番電話
□ 携帯電話
□ CD プレーヤー
□ コピー機
□ 自動車のクルーズコントロール(自動速度調整)
□ 自動車のナビゲーションシステム(カーナビ)
□ ファックス(複合機も含む)
□ 電子レンジ
□ 図書館のコンピュータ目録
□ ビデオ
□ ビデオカメラ
□ 銀行自動支払機（ＡＴＭ）
□ ホームセキュリティーシステム
□ セルフサービスのガソリンスタンド
□ テレビゲーム (Wii, プレーステーションなど)
□ デジタルカメラ
□ DVD プレーヤー・ブルーレイプレーヤー (視聴)
□ DVD レコーダー・ハードディスクレコーダー・ブルーレイレコーダ
ー(録画)
□ 駅の切符の自動販売機
□ IC カード(鉄道の Suica・Pasmo、電子マネー Edy・Waon など)
□ コンピュータ(パソコンなど)
□ 薄型テレビ(液晶・プラズマなど)
□ ハイビジョンテレビ(地上波ディジタルなど)
□ 携帯型ゲーム機 (NintendoDS, PSP など)
□ 携帯型音楽プレーヤー (iPod など)
□ 携帯電話での買い物の決済 (おさいふケータイ など)
□ 携帯電話でのテレビの視聴 (ワンセグ)
- 116 -
1-2. 下記のものであなたが日常的に使っているもの全てに 1 をつけて下さい。
□ 留守番電話
□ 携帯電話
□ CD プレーヤー
□ コピー機
□ 自動車のクルーズコントロール(自動速度調整)
□ 自動車のナビゲーションシステム(カーナビ)
□ ファックス(複合機も含む)
□ 電子レンジ
□ 図書館のコンピュータ目録
□ ビデオ
□ ビデオカメラ
□ 銀行自動支払機（ＡＴＭ）
□ ホームセキュリティーシステム
□ セルフサービスのガソリンスタンド
□ テレビゲーム (Wii, プレーステーションなど)
□ デジタルカメラ
□ DVD プレーヤー・ブルーレイプレーヤー (視聴)
□ DVD レコーダー・ハードディスクレコーダー・ブルーレイレコーダ
ー(録画)
□ 駅の切符の自動販売機
□ IC カード(鉄道の Suica・Pasmo、電子マネー Edy・Waon など)
□ コンピュータ(パソコンなど)
□ 薄型テレビ(液晶・プラズマなど)
□ ハイビジョンテレビ(地上波ディジタルなど)
□ 携帯型ゲーム機(NintendoDS, PSP など)
□ 携帯型音楽プレーヤー (iPod など)
□ 携帯電話での買い物の決済 (おさいふケータイ など)
□ 携帯電話でのテレビの視聴 (ワンセグ)
- 117 -
2. 下記のもので、あなた自身が持っているもの全てに 1 をつけて下さい。
□ 留守番電話
□ 携帯電話
□ CD プレイヤー
□ コピー機
□ 自動車のクルーズコントロール(自動速度調整)
□ 自動車のナビゲーションシステム(カーナビ)
□ ファックス(複合機も含む)
□ 電子レンジ
□ ビデオ
□ ビデオカメラ
□ テレビゲーム (Wii, プレーステーションなど)
□ デジタルカメラ
□ DVD プレーヤー・ブルーレイプレーヤー (視聴)
□ DVD レコーダー・ハードディスクレコーダー・ブルーレイレコーダ
ー(録画)
□ コンピュータ(パソコンなど)
□ 薄型テレビ(液晶・プラズマなど)
□ ハイビジョンテレビ(地上波ディジタルなど)
□ 携帯型ゲーム機(NintendoDS, PSP など)
□ 携帯型音楽プレーヤー (iPod など)
- 118 -
3. コンピュータ(パソコン・業務用すべて含む)を使ったことがありますか？
□はい
□いいえ
→いいえを選んだ場合は、10 ページ「コンピュータの考え方
に関する質問」までとばして下さい。
4. あなたのコンピュータ使用歴にあてはまるものに 1 をつけて下さい。
□ ６ヶ月以内
□ ６ヶ月以上 1 年以内
□ 1 年以上３年以内
□ ３年以上５年以内
□ ５年以上
5. あなたの過去３ヶ月での、コンピュータ最高使用回数は何回ですか？
□ 数ヶ月に 1 回
□ 毎月
□ 週一回
□ 週数回
□ 日常ほぼ毎日
6. ここ３ヶ月コンピュータを使いましたか？
□はい
□いいえ
はい、のひとはどのくらい使いますか？
□ 週に 1 時間以下
□ 週に 1 時間以上５時間以下
□ 週に５時間以上 1０時間以下
□ 週に 1０時間以上 1５時間以下
□ 週に 1５時間以上
いいえ、のひとは、いつごろコンピュータを使いましたか？
□ 6 ヶ月以内
□ 6 ヶ月以上 1 年以下前
□ 1 年以上３年以下前
□ ３年以上５年以下前
□ ５年以上前
7. 今まで World Wide Web（「ウェブページ」または「インターネットのホー
ムページ」）を使った事がありますか？
□全くない
→「全くない」の方は 9 ページの質問 12. までとばして下さい。
- 119 -
□めったにない
□あまりない
□何回かある
□頻繁にある
8. 一週間に何回くらい World Wide Web を利用しますか？
□1 時間以内
□1 時間以上５時間以内
□５時間以上 1０時間以内
□1０時間以上 1５時間以内
□1５時間以上
9. Web を使いはじめてどのくらいの期間になりますか？
□６ヶ月以内
□６ヶ月以上 1 年以内
□1 年以上３年以内
□３年以上５年以内
□５年以上
10. どのようにして Web の使い方を習得しましたか？（あてはまるもの全て
に 1 をつけて下さい。）
□試行錯誤、自分自身で。
□家族に教わって
□友人に教わって
□Web の使い方の本を読んで。
□Web の使い方教室に参加して。
□Web 上の指示に従って。
□テレビもしくはビデオを見て。
□その他（
）
- 120 -
11. 以下のもので、あなたが World Wide Web で日頃やる事、もしくはやった
事のあるもの全てに 1 をつけて下さい。
□ 電子メール
□ 掲示板
□ 投資情報を見つける
□ 銀行(オンラインバンキング)
□ (個人の)旅行情報を見つける
□ (仕事の)旅行情報を見つける
□ ホテル・飛行機の予約
□ 天気予報を見る
□ ゲームをする
□ 仕事の情報を共有する
□ メーリングリストに参加する
□ ショッピングする・オークションでものを買う
□ ニュースを読む
□ スポーツ情報を見つける
□ 趣味の情報を見つける
□ 医学情報を見つける
□ Web の使い方の情報を見つける
□ 法律の情報を見つける
□ 図書の検索
□ 場所の情報を見つける（美術館・ホテルなど）
□ 地図を見る
□ テレビの番組表を調べる
□ 最近のニュース記事を探す
□ 物を売る（広告を出す）
□ 政党などの組織情報を見る
□ 政府の情報を見る
□ オンラインマガジンを見る
□ 他の人の情報を探す（メールアドレス・電話番号など）
□ 百科事典の情報を見つける
□ 地域活動の情報を見つける
□ 地域サービスの情報を見つける
□ 交通機関情報を見つける
□ 商品宅配サービスの集荷・配送の依頼
□ 宗教の情報を見る
□ 社会活動の情報を見る
□ そ
の
他
（
）
12. チャット(chat, メッセンジャーなど)を使ったご経験がありますか？
- 121 -
□全くない
□ほんの少し
□多少
□かなり
□専門家
□何のことかわからない・よく知らない
13. 電子掲示板 (BBS) を使ったご経験がありますか？
□全くない
□ほんの少し
□多少
□かなり
□専門家
□何のことかわからない・よく知らない
14. ソーシャルネットワークサービス(SNS; ミクシィなど)を使ったご経験が
ありますか？
□全くない
□ほんの少し
□多少
□かなり
□専門家
□何のことかわからない・よく知らない
15. オンラインゲームを使ったご経験がありますか？
□全くない
□ほんの少し
□多少
□かなり
□専門家
□何のことかわからない・よく知らない
16. ご自分のブログ(weblog または blog)をお持ちですか？
□持っている
□持っていない
□何のことかわからない・よく知らない
- 122 -
コンピュータの考え方に関する質問
※この質問は、あなたがコンピュータについてどのように思っているかを知
るためのものです。次の文をその通りと思うか違うと思うか、あなたの感じ
方に一番近いものに 1 をして下さい。
1. コンピュータは快適と感じる。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
2. コンピュータを使うことは女性よりも男性にとって重要なことだ。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
3. コンピュータがあるからといって、人間の仕事がなくなる事はない。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
4. 男性よりも女性の方がコンピュータ科学者になる能力がある。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
5. コンピュータを学ぶことは必要で価値のあることだ。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
6. コンピュータは人間をただの数字のように扱う。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
7. コンピュータを使うことは、私達の生活標準を下げることになる。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
8. コンピュータは私達の生活の多くをコントロールし過ぎている。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
9. コンピュータのことを聞いたり、読んだりするのはつまらない。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
10. コンピュータを頑張って学べば、私にも習得できると思う。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
11.女性よりも男性の方がコンピュータを使うのを楽しむことができる。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
12. コンピュータは人間の仕事の価値を下げてしまう。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
- 123 -
13. コンピュータは、私を神経質にする。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
14. コンピュータは生活を難しくする。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
15. コンピュータについてもっと知りたいとは思わない。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
16. 男性より女性のほうが、コンピュータを使った仕事に向いている。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
17. コンピュータを使うのは楽しいと思う。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
18. コンピュータを使う自信がない。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
19. 女性も男性と同じくらいにコンピュータを学ぶ能力がある。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
20. 人はみなコンピュータなんかなくてもやっていける。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
21. コンピュータは非人間的だ。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
22. コンピュータは理解できないほど複雑というわけではない。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
23. 世界の全てがコンピュータで回るわけではない。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
24. 自分はコンピュータを勉強するのに向いているほうだ。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
25. 現代社会においてコンピュータについて知ることは、人々にとって必要
ではない。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
26. 人間はコンピュータより賢い。
- 124 -
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
27. コンピュータは速すぎる。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
28. これからも常に人間がコンピュータを適切に管理していく。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
29. 自分はコンピュータを学ぶ能力がある。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
30. コンピュータを学ぶのは時間の無駄である。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
31. コンピュータは私を混乱させる。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
32. コンピュータは人間の仕事をより難しくする。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
33. すぐにわれわれの生活はコンピュータに支配される。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
34. コンピュータは私を無口にさせる。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
35. 勉強する機会と時間があれば、私もコンピュータをマスターできる。
□強く同意する □同意する □どちらでもない □同意しない □全く同意しない
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※あなたの感じに最も近いものに 1 をつけて下さい。
1. 私はコンピュータが全くこわくない。
□強く同意する
□同意する
□同意しない
□全く同意しない
2. コンピュータを使うことは、私を神経質にさせる。
□強く同意する
□同意する
□同意しない
□全く同意しない
3. 他の人がコンピュータについて話すのを聞いても、怖くない。
□強く同意する
□同意する
□同意しない
□全く同意しない
4. コンピュータに対して敵意を感じる。
□強く同意する
□同意する
□同意しない
□全く同意しない
5. コンピュータの講座を受講するのは苦痛ではない。
□強く同意する
□同意する
□同意しない
□全く同意しない
6. コンピュータは私を不快にさせる。
□強く同意する
□同意する
□同意しない
□全く同意しない
7. コンピュータ講座は心地よい。
□強く同意する
□同意する
□同意しない
□全く同意しない
8. コンピュータを使おうとすると沈んだ気持ちになる。
□強く同意する
□同意する
□同意しない
□全く同意しない
9. コンピュータを使って仕事をするのは快適だ。
□強く同意する
□同意する
□同意しない
□全く同意しない
10. コンピュータを使うのは不安で、混乱する。
□強く同意する
□同意する
□同意しない
□全く同意しない
ありがとうございました
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