Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
The Generation Gaps of Smartphone Usage Behaviour in Taiwan
Kee Kuo Chen*, Shang-Hsing Hsieh** and Wen-Lai Shen***
To investigate generation gaps of smartphone usage behavior in Taiwan, a
questionnaire including 24 items has been designed and used to collect
required data from students and faculties of a University in Taiwan. Among the
total of 183 complete returned responses, 2.7% of which answered they have
not possessed a smartphone. After the validity of this scale has been
examined, usage behavior is classified into three patterns: entertainment, life
and work, and communication by exploratory factor analysis. Multivariate
analysis of variance shows that only entertainment behavior pattern has a
significant difference among generations. The findings from further analysis
are as follows: (1) the means of entertainment patterns can be separated into
two groups: group 1 which consists of Baby Boomers and Generation-X and
group 2 which consists of Generation-Y and Generation-Z. (2) item “roam in
Facebook or Twitter” has the highest score while the item “watch TV program”
has the lowest score among the seven entertainment behavior items, (3) these
same phenomena appeared in all generations. (4) item “dial and answer calls”
is the main function of smartphone for. (5) the results imply that the older
customers including baby boomers and Generation-X are hardly benefiting
from the entertainment pattern of possessing a smartphone. (6) the result also
shows an interesting fact that Generation-Z seems to be too young to care the
usage of cloud function in their smartphone.
1. Introduction
Smartphone usage has proliferated in recent years. Most of developed and developing
countries in the world, including Taiwan, have enjoyed the rapid deployment and high
penetration of smartphones. Knowing customer usage behavior pattern is very important to
making strategic marketing plans as well as for designing the next generation of devices for
firms. Business strategies such as mobile phone manufacturers, application developers, and
relevant stakeholders in the industry would greatly appreciate the information as they can be
used to determine their marketing strategies, and plans for the future directions.
Smartphones, nowadays, is one of the most popular intelligent consumer products in the
world. Smartphones that offer advanced capabilities, such as the ability to download apps
makes is an intelligent device just like a PC. However, smartphones involve hardware and
software (as a technological innovation), both have a low level of observability. Most of
smartphone users still learn how to utilize their smartphones effectively after their purchase.
Purchasing a smartphone is only a "means to an end", with the end being the attainment of
benefits from utilizing smartphone (Wilkie, 1990). After all, no matter how smart the
smartphone; it will not meet the expected benefits and effectiveness,
*Dr. Kee Kuo Chen, corresponding author, Department of Marketing and Logistics Management, Yu Da
University, Taiwan. Email: kkchen@ydu.edu.tw
**Dr. Shang-Hsing Hsieh, Department of Marketing and Logistics Management, Yu Da University, Taiwan. Email:
shhsieh@ydu.edu.tw
***Mr. Wen Lai Shen, graduate student, Department of Marketing and Logistics Management, Yu Da University ,
Taiwan. Email: lailai_shen@yahoo.com.tw
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
if it is not being utilized. To many customers, utilizing intelligent products effectively need to
take time and effort even new intelligent products are considered more autonomous than
before (Rijsdijk et al., 2007). The usage behaviors of smartphones would influence the
customers‟ post-purchase evaluation (Stokmans, 1998), which, in turn, would influence
repurchase intention. The subject of usage behavior will become even more important to the
field of marketing management than before because more and more intelligent products will
be used in the future.
Recently researchers have noted the issue and moved their attention to gaining an
understanding of customers‟ usage behavior of m-services following their initial adoption
(Stokmans, 1998; Park et al. 2011; San-Martin & López-Catalán 2013; Lu, 2014). Generation
is one of the major variables to segment consumer market. Consumer learning abilities
change with age. Despite the above developments, no empirical evidence currently exists on
whether generations will influence smartphone usage behavior pattern. This paper
hypothesizes that whether or not baby boomer and generation X who may be parents of
Millennials can use a smartphone as skillfully as the younger generation. This paper aims to
examine the different smartphone usage behavior between generations in Taiwan.
2. Literature Review
Smartphone usage behavior has been researched extensively in marketing and related fields.
Previous studies have found that smartphone users differ by orders of magnitude. Rahmati et
al. (2012) explore this variability to understand how users install and use native applications
in ecologically valid environments. Osman et al. (2011) present the result of a survey on the
attitude and behavior of consumers toward the various types of smartphone usages such as
application software, e-mail, Internet browsing, ringtones, and other mobile contents. Their
findings indicated that the “smartness” of smartphone has yet to be fully exploited, since
most of the usages are limited to core functionalities of mobile phone such as making phone
call and Short Message Service (SMS).
Researchers in technology acceptance have addressed the moderating role of age (e.g.,
Hur et al., 2014; Venkatesh and Davis, 2000). Blankenship (1998) and Zakaria (2001)
studied the factors that were related to computer use by instructors in teaching, and he found
that age was a statistically significant predictor of computer use in classroom instruction.
Most of the previous results empirically showed that majority of the smartphone users are
teenagers and younger adults (Wilska, 2003; Osman et al., 2012). Steenkamp et al. (1999)
and Tellis et al. (2009) found a significant negative effect of age on innovativeness.
Approximately 90% of smartphone owners in Malaysia are below 36 years of age, and 61%
of the respondents who agreed that hardware is important are male, whereas only 39% of
those who agreed on this are female (Osman et al. 2011). Approximately 80% of smartphone
owners in China are below 44 years of age (ProsperChina insightcenter, 2012), and in U.S.
more than half of smartphone owners are between the age of 12-34 and approximately 90%
of smartphone owners are below 36 years of age (Edison Research and Arbitron 2012).
Previous studies have also reported the generation gap among different generations (e.g.,
Edwards, 2012; Gafni and Geri, 2013; Perez, 2010). Falaki et al. (2010) characterized
intentional user activities – interactions with the device and the applications used – and the
impact of those activities on network and energy usage. The authors concluded that
smartphone usage was immensely diverse among users. However, there is a lack of
extensive studies exploring generation gaps for smartphone usage behavior.
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
3. The Methodology and Model
3.1 Hypotheses development
3.1.1 Usage behavior
Usage behavior starts from the second step of the expectancy confirmation theory that
customers accept and use that product or service. Smartphones are being adopted at a
phenomenal pace but little is known about how people use these devices until 2010.
Nowadays, smartphones can be used in all activities of communication, business, games,
news, instant messaging, entertainments, the latest social networking service, applications,
etc. Falaki et al. (2010) characterized two types of smartphone usage behavior: intentional
user activities – interactions with the device and the applications used – and the impact of
those activities on network and energy usage; and they concluded that there are immensely
diverse usage behavior among users. Osman et al. (2012) surveyed 1814 smartphone users
in Malaysia and reported the following usage patterns of smartphone: make a phone call,
check e-mail, business, entertainment, studying, browsing Web page, short message send,
instant messaging, GPS, read PDF/Word, others. Statista (2014) displayed activities children
carried out on smartphones in Great Britain 2014 on which taking pictures or films,
downloading apps, chatting with friends, playing games, social networking (facebook or
twitter), browsing online, watching videos, learning things, video chatting (skype or FaceTime)
and
watching
TV
and
“none
of
the
above”
were
included
(http://www.statista.com/statistics/293492/smartphone-usage-behavior-among-children-greatbritain/). Among these activities, only 2% of respondents selected the item “none of the
above”.
3.1.2 Generations
Members in the same generation share the same major cultural, political, and economic
experiences and have similar outlooks and values (Kotler et al., 2012). Generations have
been used as a moderator variable extensively in marketing and related fields. In addition to
evidence presented in literature review, it is found that many older persons in Taiwan
obtained their smartphone from their children as a gift as well as a second hand good passed
down by their children. Some persons, particularly for aged persons, possess their
smartphones before they can use smartphones better than cellphones they had before.
Consequently, this paper proposes a hypothesis that the smartphone usage is different
among generations.
3.2 Questionnaire development
On the basis of the items in Falaki et al. (2010), Osman et al. (2012) and in Statista (2014),
an initial pretest, nine graduates of a department of Marketing and Logistics Management
judged the set of all items. These students commented on unclear and ambiguous items and
came up with suggestions on how existing items might be improved. They also provided a
number of new items. Particularly, for the items included in the measure of usage behavior,
the initial list of 15 items, 12 items were edited, and 9 new items were added. Overall, this
resulted in a revised 24 items for the measure of usage behavior.
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
In the second stage, we refined the questionnaire by running two pretests with
respondents from a convenience sample of 43 undergraduates who were born after 1994,
and another convenience sample of 31 faculties of a University of Taiwan. The standard
deviation over these two different respondents is an indicator of how consistent different
respondents rate this item for the smartphones. The standard deviations of all items in the
measures of usage behavior are between 0.12 and 0.73. The values of F-test indicate that
equality of two standard deviations of each item was not rejected at a level of 0.05, for all
items. All items consisted of five choices by the following five-point scale: 1 = “never used,” 2
= “(averagely) less than one time usage per day,” 3 =“(averagely) among one to five times
usage per day,” 4 = “(averagely) among six to ten times usage per day,” and 5=” (averagely)
over ten times usage per day”.
For classifying respondents‟ age, a question consisted of four generation choices is
included, in accordance with the general definition of generation (Rosenburg, 2009), by the
following scale: 1 = “born before 1964 (baby boomers whose age older than 50),” 2 = “born
between 1965 to 1981 (Gen. X whose age between 33 to 49)” 3 = “born between 1982 to
1993 (Gen. Y whose age between 21 to 32),” and 4 = “born after 1994 age below 21.”
3.3 Data collection
Data collection took place from May to August, 2014. 625 undergraduates of Department of
Marketing and Logistics Management and 236 faculties at Yu Da University were invited to
participate in this study. Each of seven graduates was dispatched to survey students of the
Department at the University, and the authors mailed the questionnaire to each faculty. Each
respondent
received
an
email
containing
an
internet
address
at
http://www.mysurvey.tw/edit/questionnaire!list.htm and was asked to fill an online selfreported questionnaire located at the address. Among the total of 200 questionnaires, 17 of
which were returned incomplete, and only 5 copies out of the 183 completed questionnaires
answered that they had not possessed a smartphone. The profile of generation and gender
of completed copies are listed in Table 1.
The percentage of generations fit well the profile of surveyed respondents in the
University. A total of 53.4% of the respondents were female (see Table 1). The percentage of
the gender is small but not significant unfit for the demographics of the population we
surveyed. We believe these issues are only minor limitations to our study.
Table 1 Sample demographic characteristics (N=178)
Gender
n
(%)
Generation
Male
83 46.6
Baby Boomers
Female
95 53.4
Gen. X
Gen. Y
Gen. Z
Age
above 50
33 to 49
21 to 32
below 21
n
32
57
53
36
(%)
18.0
32.0
29.8
20.2
4. The Findings
4.1 Measure validation
An exploratory factor analysis was used to identify and purify the reliability of innovation
attributes and customer perceived risk scale. First, items with factor loading below .4 and
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
items strongly loading on more than one factor were excluded (Hair et al., 2006). Second,
Cronbach Alpha (α) and item-to-item correlations were calculated for each factor. Items that
increased Cronbach Alpha when deleted, were excluded.
After deleting a few items according to the criterion mentioned previously, Cronbach
Alphas (α) of all constructs are greater than 0.7, indicating an acceptable level of internal
consistency (Bagozzi and Yi, 1988). The significant factor loadings demonstrate convergent
validity, while the Cronbach alphas and construct reliabilities indicate satisfactory internal
consistency (Churchill 1979; Fornell and Larcker 1981). Convergent Validity of the measures
was tested by calculating the composite reliability (CR) of the constructs and the average
variance extracted (AVE) (Fornell and Larcker, 1981). The criteria of reliability and validity are
satisfying, as AVE is above .50 and CR is above .70. Table 2 reports the psychometric
properties of each measure.
In summary, smartphone usage behavior (UB) has been factored into three patterns
including entertainment factor (EN), in which seven items were involved, life and work factor
(LW), in which twelve items were involved, and communication factor (CM), in which four
items were involved. Attribute innovation has been factored into two determinants including
advantage and compatibility (AC), in which five items were involved, and complexity (CX), in
which four items were involved. Consumer innovativeness (CI) has been factored into three
determinants including novelty seeking (NS), in which six items were involved, independent
decision (ID), in which five items were involved, and reluctance to adopt new products (RA),
in which three items were involved. Finally, customer satisfaction has three items remaining.
Table 2 The measures of smartphone usage behavior patterns and their psychometric
properties
Behavior pattern
Items
Standardized
Factor
Loading
1 Entertainment
(EN)
α =0.804,
CR =0.923,
AVE=0.751,
2. Life and work
(LW)
α =0.711,
CR
=
0.85986 a
EN1
download games
from App
EN2 roam in Facebook or
Twitter
EN3 check into places
EN4 use Youtube to
watch movies
EN5 watch TV program
EN6 online information
search
EN7 listen music.
0.69094 b
0.68766
0.70766
0.68166
0.54576
0.50688
0.56237
0.89261
LW1
use
learning
software.
LW2
use
exercise
0.55945
0.59430
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
0.849 ,
AVE=
0.684
3. Communication
(CM)
α =0.747,
CR
=0.929,
AVE=
0.815
a
construct
loading, b item
loading
recording software
LW3
use
secretary
function
to
booking
or
registering a seat
LW4 use GPS or Google
Map.
LW5 read pdf 、Excel or
word files.
LW6 use cloud monitoring
system.
LW7 send or receive email
LW8 use calendar.
LW9 use Dropbox or
other
cloud
hardware system
LW10 online purchasing
LW11 read electronic
book
LW12 look up weather
0.59138
0.50319
0.48371
0.42351
0.61071
0.52049
0.53943
0.55781
0.61587
0.46065
0.73330
CM1 dial and answer
calls
CM2 use Line or Wechat
CM3 make photograph.
CM4 record voice or
video
0.52396
0.50789
0.63405
0.48731
4.2 Generation gaps analysis
After purification of measurements, we then performed a multivariate analysis of variance to
test the equalities of three smartphone usage behavior patterns among generations. This
analysis was conducted using SAS (9.3) PROC GLM model with the average score of items
within three usage behavior patterns as response score, respectively. The results are
presented in Table 2. Our hypothesis indicated that the equality of smartphone usage
behavior pattern of entertainment among generations was significantly disproven. Table 3
shows the differences of individual items among generations. It is found that the generation
gap of entertainment usage behavior resulted from a fact that p-values of all items are less
than 0.02. Scheffé‟s multiple-comparison procedures give more detailed information about
the differences among the means of these items across generations (Hair et al., 2006). The
profile of seven means is displayed in Figure 1 where the numbers of horizontal axis indicate
the items in the entertainment pattern. It is clear that the means of entertainment items of
baby boomers and Gen. X are totally less than those of Gen. Y and Gen. Z. This result
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
indicates that the means of entertainment items can be separated into two groups: group 1
which consists of baby boomers and Gen. X and group 2 which consists of Gen. Y and Gen.
Z. The findings also clearly show that item 2 (roam in Facebook or Twitter) has the highest
score while item 5 (watch TV program) has the lowest score among the seven entertainment
behavior items, and these same phenomena appeared in all generations.
The generation gap of life and work usage pattern was not accepted because only three
out of twelve items had a significant difference at a level of 0.05. The Gen. Z‟s scores in two
cloud related items ,Item 6 (use cloud monitoring system).and item 9 (use Dropbox or other
cloud hardware systems), are lower than those of Gen. X‟s and Gen. Y‟s scores, indicating
that the ages of Gen. Z are too young to care the usage of cloud function in their smartphone.
Another interesting fact is that the highest score amongst all items for baby boomers is the
item COM1 (dial and answer calls) indicating that “dial and answer calls” is the main function
of smartphone for this generation.
Table 2 Statistics for MANOVA
Pattern\Gen.
Baby Boomers
Entertainment
2.598
Live and work
2.440
Communication
3.703
Gen. X
3.012
2.614
3.954
Table 3 Statistics for MANOVA
Variable\Gen.
Baby Boomers
Entertainment pattern items
EN1 download games from App
2.225
EN2 roam in Facebook or Twitter 3.156
EN3 check into places
2.156
EN4 use secretary func3.000
tion to booking or
registering a seat
EN5 watch TV program
2.093
EN6 online information
2.968
search
EN7 listen music
2.562
Live and work pattern items
LW1 use learning software.
LW2 use exercise recording
software
LW3 use secretary function to
booking or registering a
seat
LW4 use GPS or Google Map
LW5 read pdf、Excel or word
files.
LW6 use cloud monitoring
system.
LW7 send or receive e-mail
LW8 use calendar
LW9 use Dropbox or other cloud
Gen.Y Gen.Z F-value p-value
3.584 3.571 12.05 <0.0001
2.805 2.526 1.69 0.170
4.000 4.109 2.11 0.101
Gen. X Gen.Y Gen.Z F-value p-value
2.883
3.623
2.857
3.142
3.622
4.169
3.396
3.622
3.397
3.812
3.187
3.562
12.39 <0.001*
5.58
0.001*
7.15 0.001*
3.52 0.016*
2.064
3.454
2.792 2.875 6.65
3.716 3.625 4.27
3.064
3.773 4.000 10.68 <0.001*
2.406
1.812
2.428
1.870
2.981 2.500 3.46
2.207 2.000 1.22
2.281
2.480
2.566 2.250
0.52 0.667
2.968
2.531
3.051
2.974
3.471 3.437
3.339 3.062
2.41 0.068
3.98 0.008*
1.781
1.454
1.452 1.250 1.92 0.127
3.062
3.093
2.343
3.454
3.142
2.688
3.188 3.062 1.13 0.337
3.094 3.000 0.06 0.980
2.849 2.375 1.19 0.414
<0.001*
0.006*
0.017*
0.302
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
hardware system
LW10 online purchasing
LW11 read electronic book
LW12 look up weather
Communication pattern items
CM1 dial and answer calls
CM2 use Line or Wechat
CM3 make photograph
CM4 record voice or video
*p-value < 0.05
1.687
2.125
3.187
2.168
2.428
3.233
2.603 2.187
2.679 2.186
3.226 3.000
4.406
4.093
3.687
2.625
4.337
4.259
4.090
3.129
4.113
4.509
4.169
3.207
4.375
4.750
4.187
3.125
4.94 0.002*
1.78 0.152
0.22 0.880
1.41
3.06
2.72
3.07
0.242
0.029*
0.046*
0.029*
Figure 1 Profiles of generation smartphone usage behavior patterns
5.Summary and Conclusions
To investigate generation gaps of smartphone usage behaviour in Taiwan, a questionnaire
including 24 items has been designed. A percentage of 2.7% of total 183 complete returned
respondents answered that they had not possessed a smartphone. This figure is larger than
those appeared in the literature indicating a high market penetration of smartphones in
Proceedings of 29th International Business Research Conference
24 - 25 November 2014, Novotel Hotel Sydney Central, Sydney, Australia, ISBN: 978-1-922069-64-1
Taiwan. After the validity of this scale being examined, usage behaviour is classified into
three patterns: entertainment, live and work, and communication. Multivariate analysis of
variance shows that only entertainment behaviour pattern has a significant difference among
generations. Furthermore, the result of Scheffé‟s multiple-comparison procedures indicates
that the means of entertainment patterns can be separated into two groups: group 1 which
consists of baby boomers and Gen. X and group 2 which consists of Gen. Y and Gen. Z. The
finding also shows that item 2 (roam in Facebook or Twitter) has the highest score while item
5 (watch TV program) has the lowest score among the seven entertainment behaviour items,
and these same phenomena appeared in all generations. And “dial and answer calls” is the
main function of smartphone for this generation. Our results imply the fact that older
customers including baby boomers and Gen. X are hardly the benefiting from the
entertainment pattern of possessing a smartphone. Another interesting fact is that the ages
of Gen. Z seems to be too young to care the usage of cloud function in their smartphone.
Our study confirms the results conducted by the previous researches (Edwards, 2012;
Gafni and Geri, 2013; Perez, 2010) that smartphone usage was immensely diverse among
users. The contribution of this paper is to identify that the three usage behaviour patterns
(entertainment, life and work, and communication) and discovered that the difference of
usage patterns is in the entertainment usage behaviour. However, the percentages of
smartphone usage behaviours among various generations reported by this paper seem
somewhat inconsistent with the results of the previous studies (e.g., Wilska, 2003; Osman et
al., 2012; ProsperChina insightcenter, 2012). The inconsistency may result from the fact that
our respondents focus only on the college students and faculties of a university in Taiwan.
Despite the above limitation, our results may provide important insights to smartphone
marketing strategist in the industry to improve the usage method of their smartphone for their
older customers.
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