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EURASIA Journal of Mathematics Science and Technology Education ISSN: 1305-8223 (online) 1305-8215 (print)
2017 13(7):4009-4020 DOI 10.12973/eurasia.2017.00769a
© Authors. Terms and conditions of Creative Commons Attribution 4.0 International (CC BY 4.0) apply.
Correspondence: You-Te Lu, Southern Taiwan University of Science and Technology, Taiwan.
The Relationship between Motivation, the use of
Mobile Devices and Satisfaction with life for Older
Farmers
You-Te Lu Southern Taiwan University of Science and Technology, TAIWAN
Yi-Hsing Chang Southern Taiwan University of Science and Technology, TAIWAN
Tien-Wen Sung Fujian University of Technology, CHINA
Received 30 May 2016 ▪ Revised 30 November 2016 ▪ Accepted 11 March 2017
ABSTRACT
In terms of functionality, today’s mobile devices allow users to surf the Internet, monitor e-mail, watch and share
videos and pictures, interact on social-networks and utilize a large array of software-driven applications. Much
research concerns motivation and satisfaction in the school system, but there is little empirical evidence of how
these factors affect older farmers. While mobile technologies and social media have changed the value and
importance of human connections, it is necessary to understand the interaction between motivation and
satisfaction with life for older famers. This study determines the relationships between motivation, the use of
mobile devices and satisfaction with life for older farmers. Key factors are operationalized using scales that are
widely used and tested. A survey is distributed to participators and a multiple regression is used to determine
whether positive motivation for the use of the Internet and mobile devices predicts the scale for the satisfaction
with life. This study contributes to related subjects by determining factors that could be optimized with a view to
enhancing learning and satisfaction with life for old farmers.
Keywords: motivation, mobile technology, satisfaction with life, old farmers
INTRODUCTION
Overview
Agriculture is the backbone of most Chinese economies, especially in Taiwan, where it
accounts for 1.88 percent of the nation’s gross domestic product. However, Taiwan’s
agriculture sector is facing problems such as an aging farmer population, shortage of business
managers and the gap between education and employment. As the aging problem inhibits the
adoption of new technologies and the use of new knowledge, mobile learning could open the
door for a new type of learning for seniors.
Lifelong learning is broadly defined as learning that is pursued throughout life at any
time and in any place. In other words, an adult’s learning activities play a notable role in the
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pursuit of knowledge. Horrigan (2016) pointed out that the majority of Americans feel that
they are lifelong learners and that they participate in activities that include the use of
technology to learn more about a personal interest. Although those with more education and
higher incomes are more likely to engage in lifelong learning, technology assets are strongly
tied to the possibility that adults engage in learning activities. With a rapidly aging farming
population, any attempt to improve the welfare of older farmers is vital.
Adults with technology access tools, such as mobile devices, are also more likely to be
lifelong learners and to use the Internet to pursue knowledge. There is a strong sense that
people feel more comfortable when they continue to learn, in order to stay relevant in a
changing environment. In terms of learning and technology, new means of communications
could translate learning into a happier life. Therefore, with the Internet and mobile
technologies providing possible access to information and the general mobility of knowledge,
mobile devices allow farmers to gain instant access to useful information. There is much
research about motivation and satisfaction in the school system, but there is little empirical
evidence of how these factors affect older farmers. While mobile technologies and social media
have changed the value and importance of human connection, it is necessary to understand
the interaction between motivation and satisfaction with life for older famers.
THEORETICAL BACKGROUND
Older Farmers
Agriculture is one of the most hazardous occupations in many regions and older farmers
are often considered to be a “special needs population that needs recognition and attention”
(Hernadez-Peck, 2001). Although agriculture is a major industry in the majority of countries,
the share of the population that works in agriculture is declining as countries develop. In
State of the literature
As the aging problem inhibits the adoption of new technologies, mobile learning could open the
door for a new type of learning for seniors.
With a rapidly aging farming population, any attempt to improve the welfare of older farmers is
vital.
It is necessary to understand the interaction between motivation and satisfaction with life for
older famers.
Contribution of this paper to the literature
The intention to adopt new technology is positively related to users’ needs and the proliferation
of mobile learning has created a wealth of learning opportunities for seniors.
Motivation and the use of mobile devices are understandably correlated with satisfaction with
life levels for older farmers.
Older farmers who are confident in using information communication technologies to interact
with others have good quality of life.
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particular, the proportion of older farmers is significant and is growing. According to O’Neill
(2014), 12.1 per cent of Asian farmers are over 55. An agricultural holder is defined as the
person who exercises management control over the agricultural holding and makes major
decisions concerning the use of resources. The average proportion of Asian agricultural
holders who are over the age of 55 is 28.5 percent. Therefore, older farmers over the age of 55
are the subjects for this study.
Motivation and the use of Mobile Devices
Motivation refers to factors that engage goal-directed behavior for the needs that drive
individuals and explain what people do (Pezzulo, Van Der Meer, Lansink & Pennartz, 2014;
Redman, 2016). Mobile devices are any devices that are carried on the person the majority of
the time, such as a smartphone, a tablet, or a hand-held device. A mobile device is also capable
of communication via the Internet Hoffmann, 2015). The key factors that define mobile
learning are mobility and the ability of users to access the Internet for learning purposes,
without being tied to a location (Wu et al., 2012; Hoffmann, 2015).
Six aspects of learning with mobile devices that might be motivating were proposed at
the IADIS International Conference on Mobile Learning in 2007: control over learners’ goals,
ownership, fun, communication, learning-in-context and continuity between contexts. The
authors suggested that using mobiles for learning is likely to be highly motivating. (Jones &
Issroff, 2007). With respect to the definition of the terminology of motivation (Chang &
Villegas, 2008; Stafford, Stafford, & Schade, 2001), mobile devices have several functions that
lead consumers to use them, such as (1) short text mail, (2) communication with friends, (3)
taking photos and uploading them, (4) playing games, (5) listening to music and (6) mobile
nets. Chang & Villegas (2008) listed six motivation factors for the use of mobile phones, as
Figure 1. The Motivation Factors for the use of Mobile Phones （Chang & Villegas, 2008）
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shown in Figure 1. It is seen that mobile devices are a new multidimensional communication
technology that are used to enrich learners’ knowledge, from the user standpoint. Indeed, the
fact that this technology supports learners in defining their own interests and ways of
accessing further learning opportunities is crucial.
The use of Mobile Devices and Satisfaction with life
According to Saeednia & Nor (2013), Maslow’s hierarchy gives the most accurate
description of human motivation. Specific factors, such as safety and esteem, have been proved
to have the greatest correlation with satisfaction with life. Leung & Matanda (2013) showed
that self-determined motivation mediates the relationships between the use of technology and
satisfaction with life.
However, the use of mobile devices could be a significant predictor of negative influence
on the users (Salehan & Negahban, 2013).Mobile learning might not give sufficient importance
to what it is that makes a learning activity valuable, in that it does offer a way to extend the
support of interactions in everyday life and personal satisfaction with life. Sharples et al.,
(2007) also proposed a theory of learning for the mobile age that emphasizes lifelong learning
activity. “A theory of mobile learning must take account of the ubiquitous use of personal and
shared technology.” (p. 224). In other words, mobile technologies and the new conceptions of
learning are a personally managed lifelong activity.
Four elements must be incorporated into the design of a mobile framework (Liu et al,
2008). As shown in Figure 2, these four elements are (1) an analysis of requirements and
constraints, (2) mobile learning scenario, (3) the design of the technology environment and (4)
Figure 2. The Design Framework for Mobile Learning (Liu et al., 2008)
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the design of learner support services. It is emphasized that an understanding of user needs
and the factors that influence learning is essential to the design of mobile learning activity.
This is an activity-oriented design framework that places emphasis on supporting the learners
in their goal to acquire knowledge and skills that could enhance their satisfaction with life.
Many studies have explored the relationship between the use of mobile devices and
measures of satisfaction with life. A relationship has been suggested whereby cell phone use
increases subjective well-being or happiness. However, if the use of mobile devices is
negatively related to the adoption of technology and positively related to anxiety, then it may
have an indirect, negative influence on satisfaction with life （Lepp, Barkley, & Karpinski,
2014). Satisfaction with life was defined by Shin and Johnson (1978) as referring to “a
judgmental process in which individuals assess the quality of their lives on the basis of their
own unique set of criteria” (Pavot & Diener, 1993, p.164).
Adult Learners and Technology
Studies have indicated that the perceived usefulness of technology affects the user’s
intention to adopt mobile learning that might be perceived as valuable to adult learners, such
as learning opportunities (Tan et al, 2014). The technology provides a shared learning space
for single learners and for groups. Most importantly, for learners who use an interactive
technology with online help systems, there is a shared understanding. However, learning
approaches that use mobile technology with knowledge resources have become important
tools for the delivery of educational resource (Sharples, 2007; Paulins, balina, & Arhipova,
2014).
Tang et al (2012) identified several factors that impact older adults’ learning using mobile
technology. Firstly, if a task list is provided for the learning process using mobile devices, older
adults are very motivated (Goal setting). Secondly, older adults’ motivation to learn to use a
mobile device is positively related to their perceived need to use a mobile phone (Perceived
needs). Thirdly, older adults’ motivation for learning to use a mobile device is influenced by
their understanding of technology (Exposure to technology). Finally, older adults who are
highly motivated to learn are generally found to experience more successes and greater
satisfaction with their learning outcomes (Tang et al, 2012).
METHODOLOGY
Research Design
This study determines the motivational factors that contribute to participation in mobile
learning by older farmers. Previous and current research and unpublished interview data that
is presented in this paper shows that there is reason to suspect that the use of mobile devices
and Satisfaction with life are related. Because the capabilities of mobile devices are expanding,
there is a need to study older farmers’ adoption of mobile devices. The two main research
questions (RQs) are: (1) What is the relationship between motivation (Goal setting, Perceived
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needs, Exposure to technology) and the use of mobile devices? and (2) What is the relationship
between the use of mobile devices and Satisfaction with Life. The following hypotheses (H1 =
RQ1; H1 = RQ2; see below) are proposed:
(RQ1 – H1). Mobile Device Use (MDUse) has a positive relationship with Goal Setting
and a positive relationship with Perceived Need. Exposure to Technology is negatively related
to the use of Mobile Devices.
(RQ2 – H1). The use of mobile devices is positively related to Satisfaction with Life.
Population and Sample
The target population for this paper is limited to the members of the Tainan City Anna
District Farmer’s Association. A total of 107 of the 130 surveys were returned. Two of the 107
surveys were not completed, so were not useable. A total of 105 surveys were used for the
analysis.
Instrumentation
A survey was completed during class in a school of continuing education by all adults
who consented to participate. The survey composed several sections: (1) demographic
information, (2) the Satisfaction with Life Scale (SWLS; Diener et al., 1985), (3) questions about
the use of mobile devices (Lepp et al., 2013) and (4) Positive attitude towards the Internet
(Shillair et al., 2015).
Data Collection
Demographic information included questions about sex, age and educational level. Less
than elementary school was 1, high school graduate is 2 and college graduate is 3. The SWLS
contains five statements about general satisfaction with life (i.e., subjective well-being) using
a 5-point Likert scale from ‘‘Strongly Disagree’’ to ‘‘Strongly Agree’’. Higher scores on this
measure indicate greater satisfaction with life, with a score of 20 representing the neutral point
on the scale (i.e., equally satisfied and dissatisfied).
Questions about the use of mobile devices stated the following: “As accurately as
possible, please estimate the total amount of time you spend using your mobile phone each
day. Please consider all uses, except listening to music” (Lepp et al., 2013). For instance, the
total amount of time that older adults spend with mobile devices includes calling, texting,
using Facebook, e-mailing, sending photos, gaming, surfing the Internet, watching videos and
all other activities that use mobile devices.
Key factors were operationalized using scales that are widely use and tested. Full
variables and Cronbach’s alpha levels are given in Table 1. Satisfaction with life indicators
include questions developed by Diener, Emmons, Larsen & Griffin (1985), and the perceptions
of positive attitude towards the Internet from Hiltz and Johnson (1990) and modified by
Shillair et al (2015) are also listed in Table 1.
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Data Analysis
Descriptive statistics are used to examine the demographic data. Pearson correlations
between the main variables of interest were examined prior to the path analysis and two
Multivariate Analysis of Variances (MANOVAs) were conducted to determine the influence
of sex and the interaction between sex and the use of mobile devices and the SWLS.
Based on the review of previous research, the hypothesis was that motivation works as
a mediated moderator between the use of mobile devices and the level of satisfaction with life.
RESULTS
Sample Demographics
The sample in this study includes 105 randomly selected farm household members
from the Tainan City Anna District Farmer’s Association in Taiwan. The overall response rate
for this research was 82.3% (n = 107). The total useable response rate was 80.7% (n = 105).
Descriptive statistics for all the major variables are presented in Table 2. On average,
participants reported spending 150.15 (SD = 112.00) minutes per day using their mobile
devices. The mean GS score was just above 3.05 (SD = .42) and the mean PN score was just
above 3.05 (SD = .3). The mean ET score was 2.1 (SD = .2). The hypothesized correlations
Table 1. Questions and Variables
Variables Questions Source and alpha levels
Satisfaction with Life
Scale
Five point scale (1=strongly disagree to 5=strongly
agree)
Diener, Emmons, Larsen &
Griffin, 1985
Alpha = .889
In most ways my life is close to ideal
The conditions for my life are excellent
So far, I have gotten the important things I want in life
I am satisfied with my life as a whole
If I could live my life over, I would change almost nothing
Positive Attitude
towards the Internet
Five point scale (1= strongly disagree to 5=strongly
agree) “Please tell me how much you agree or disagree
with the following statements: Using the Internet has:
Hiltz and Johnson, 1990
Shillair, et al., 2015
alpha= .97
Made it easier for me to reach people
Contributed to my ability to stay in touch with people I
know
Made it easier to meet new people
Made it easier to get information that I
need
Increased the quantity of my communication with others
Made me feel less isolated
Helped me connect with my friends and family
Increased the quality of my communication with others
Is useful to me
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between the major variables are in the expected direction and statistically significant (p < .05)
and the correlations between MDUse and SWL are statistically significant (p < .05).
Regression Analysis
Table 3 shows the relationships between positive motivation, the use of mobile devices
and the satisfaction with life scale. The table shows that the correlation between positive
motivation and mobile device use is a medium .217 and that here is a negative correlation
between satisfaction with life and other variables.
A multiple regression was conducted to determine whether a positive motivation
towards the Internet and the use of mobile devices predicts satisfaction with life scale. Using
the enter method, a positive motivation towards the Internet and the use of mobile devices
explain a significant amount of the satisfaction with life scale (F(2, 102))=13.138, p < .000), with
an R2 adjusted value of .189.
Figure 3 shows the mediation moderation regression analysis. If the use of mobile
devices is viewed as an independent variable and satisfaction with life as a dependent variable,
the positive motivation towards the Internet on satisfaction with life levels has an effect that
changes through the satisfaction with life scale.
Table 2. Descriptive statistics for the major variables in the mobile devices data set (N = 105)
Variable Mobile Devices
M SD Min Max
MDUse 131.4 64..40 60.00 240.00
GS 3.05 .42 2.0 4.5
PN 3.05 .3 2.1 3.8
ET 2.1 .2 0.0 3.0
SWLs 14.08 3.45 5 25
Age 70.5 9.09 55 85
Education 1.86 .790 1 3
Note. MDUse = minutes per day, GS = goal setting score, PN = perceived needs score, ET = exposure to
technology score. SWLs = Total Satisfaction with Life Scale score.
Table 3. Correlations between Positive Motivation, MDUse and SWLs
Measure Positive Motivation Satisfaction with life
Positive Motivation -
.217* -
Satisfaction with life -.336** -.369** -
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
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Discussion
Aside from factors such as the aging of the agricultural labor force and the transfer of
the agricultural labor force to nonagricultural sectors, one of the issues that Taiwanese farmers
face is a lack of information and technical knowledge regarding learning approaches and
improved methods of farming that can significantly affect yields. This study uses a
quantitative research survey: a questionnaire was distributed to 130 older farmers in a Tainan
Farmer Organization in Taiwan. Motivation factors were identified using principal
components analysis. The conceptual framework was tested using correlation analysis and
Multivariate Analysis of Variances (MANOVAs). Statistically significant relationships are
observed between motivation and the use of mobile devices. The findings enable the
implementation of strategies to enhance older farmers’ learning opportunities and satisfaction
with life.
Motivation and the use of mobile devices are understandably correlated with
satisfaction with life levels for older farmers. Although this model explains only 19% of the
variance, it does show that Internet adaptation is an important factor for older people, who
would gain benefits from mobile technologies with networking. This research suggests that
older farmers who are confident in using information communication technologies to interact
with others have good quality of life. This supports the theory that “mobile learning must take
account of the ubiquitous use of personal and shared technology” (Sharples et al., 2007). It also
supports the conclusion that the intention to adopt new technology is positively related to
users’ needs, which implies that the nature of an innovation is the most powerful predictor for
the use of technology and satisfaction with life (Li, 2014).
In terms of functionality, today’s mobile devices allow users to surf the Internet, e-mail,
watch and share videos and pictures, interact on social-networks and utilize a large array of
software driven applications. The proliferation of mobile technology and online learning has
created a wealth of learning opportunities for learners. According to Negahban & Chung
Figure 3. Mediation Moderation Analysis
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(2015), mobile devices create a social image in the society for the users. The use of mobile
devices increases users’ perception of the fit between functionalities of their needs, which leads
to greater satisfaction with life.
Limitations and Suggestion for Future Research
It is not easy to accurately measure the levels of satisfaction with life and the positive
motivation towards the Internet, especially for older adults. Most of the participants only use
their own mobile devices for basic functionality, such as making calls. The main reason for not
exploring beyond basic features, such as apps, is the lack of knowledge about the services that
are provided via the Internet. As a result, participants are reluctant to use their mobile devices
freely. They also stated that they needed to acquire a better understanding of modern
technologies and the accompanying learning resources. Therefore, older farmers are
motivated to achieve a successful and enjoyable learning experience, which leads to
satisfaction through lifelong learning.
However, further research is necessary, using a greater number of participants, in order
to further clarify the relationship between motivation and learning experience and to
determine how the motivational factors can be manipulated to increase older farmers’ desire
to learn to use mobile devices more generally.
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