Surveying FIFA instructors' behavioral intention towards the
Multimedia Teaching Materials
M. Armenteros a,*, Shu-Sheng Liawb, M. Fernández a, R. Flores Díaz c, R. Arteaga
Sánchez d
a Journalism and Audiovisual Communication Department, Carlos III University,
C/Madrid 133, 28903 Getafe, Spain
b General Education Center, China Medical University, 91 Shiuesh Road, Taichung 404,
Taiwan, ROC
c Statistics Department, Carlos III University, C/Madrid 133, 28903 Getafe, Spain
d Department of Financial Economics and Accounting, University of Huelva, Pza.
Merced 11, 21002 Huelva, Spain
Abstract
Instruction delivered via multimedia applications is changing the way elite football
refereeing instructors teach the Laws of the Game. Although e-learning applications are
popular, there is minimal research on instructors’ attitudes toward these teaching
materials. The purpose of this research is to explore instructors’ behavioral intentions
towards the Multimedia Teaching Materials usage. Accordingly, a questionnaire based
on an integrated theoretical framework has been delivered to 100 instructors from
different FIFA federations. Three main streams of research provide the basis for this
framework: the Technology Acceptance Model, the Motivational Model and the Social
Cognitive Model, combined with other variables used in educational technology
acceptance research. Perceived usefulness, followed by perceived enjoyment, perceived
ease of use and the multimedia instruction quality explain most of the instructors’
behavioral intention to use the Multimedia Teaching Materials.
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Keywords
Multimedia Teaching Materials, Technology Acceptance Model, Motivational Model,
Behavioral Intention, Refereeing, FIFA.
1. Introduction
Professional Training and Development of FIFA referees with New Technologies
The use of information and communication technologies as teaching tools has spread
into many different areas. This technology-based teaching across organizations has been
termed e-learning, which can be designed, developed, and delivered via computer and
Internet-based applications (Clark & Mayer, 2011; Horton, 2006).E-learning provides
employees with an opportunity to learn in a self-paced and interactive way, regardless
of where they are and when they are available.
Among FIFA employees, instruction delivered via all electronic media such as the
Internet or hypertext/hypermedia documents is changing the way elite football referees
teach and learn the Laws of the Game and related matters. FIFA has established a global
structure with regional instructors to conduct a continuous follow-up of the daily
training of referees and assistant referees at national and international level, and 209
member associations around the world benefits from specialized referee training courses
conducted by FIFA. Through the Member Association Referee Courses (MA Referee
Courses), FIFA develops regional and international refereeing courses and seminars for
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its six federations (AFC, CAF, CONCACAF, CONMEBOL, OFC and UEFA) in order
to teach in several areas: technical, methodological (uniformity on the interpretation of
Laws of the Game), fitness (to improve the quality of the physical instructors) and
administrative (refereeing organization development programs).
The Refereeing Assistance Programme (RAP) focuses on the education of referee
instructors and technical experts actively involved in the instruction of member
associations’ top referees and assistant referees both before and after the courses they
deliver. The RAP has two main activities: FUTURO III courses for referee instructors
and member association courses for top referees and assistant referees (Fédération
Internationale de Football Association, 2010) and allows courses to be held almost on a
daily basis all over the world, ensures that the match officials in all member associations
have adequate preparation. Therefore the RAP instructors constitute the elite of FIFA’s
professional training and development chain. They teach other instructors who will then
teach international referees all over the world in order to achieve uniformity when
applying the Laws of the Game.Since 2005, multimedia interactive materials (from now
on Multimedia Teaching Materials, MTM) have been produced by FIFA’s Refereeing
Department and Carlos III University’s research group TECMERIN. Both instructors
(as teachers) and referees (as learners) have worked on content production while
multimedia experts have integrated this content into applications according to
instructional design principles and guidelines developed from empirical experiments
(Mayer, 2005). These materials allow the students and instructors to interact with
different media types and forms (texts, images, videos, animations, sounds) and provide
immediate feedback to user decisions. The main MTM applications are:
Trivia Game (Armenteros, Benítez & Curca, 2010): This model allows instructors and
referees to assess their knowledge with a computer-based quiz. It loads 20 random
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questions from over 1,500 questions stored in a database. The player must choose the
level of difficulty (beginner or advanced) and answer the questions in a 15 minute
session. Each time the player chooses an answer the application gives immediate
feedback and tells the player if her/his answer is right or wrong. After answering 20
questions, a report appears with the selected answers, the correct and the incorrect ones.
Interactive Video Test (Armenteros, Benítez & Sillero, 2009): This consists of an
evaluation test with 20 questions about game situations that appear in video clips from
several football tournaments. For each clip, the application shows a window where the
student must answer whether a foul has been committed or not, as well as indicate the
gravity of the infraction (no card, yellow card or red card). After doing the same task for
the 20 clips, a report appears with the selected answers, the correct and the incorrect
ones.
Analysis of Match Situations: Once the student has worked with several Video Test
Sessions, he can obtain more information about the match situations with a
complementary application: the Analysis of Match Situations. This application shows
each clip accompanied by several tabs. When selected, each tab opens a window with
extra information regarding the decision the referee should apply, the reasons why this
decision is correct, the considerations to take into account when such a match situation
occurs, and the reference to the Laws of the Game where more content can be
consulted.
Teaching Material (Armenteros & Curca, 2008): This application presents the content of
the Laws of the Game book in a hypermedia format. Apart from text and static images
(that are present in the book), the Teaching Material provides examples of different
gaming situations with animations and videos, as well as a self-evaluation with
immediate feedback for each law consulted.
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The purpose of delivering these materials to instructors is to allow them to develop their
courses by showing different refereeing situations through videos, animations and
images, as well as allowing the instructors to evaluate learners’ knowledge and
judgments in real time through several auto evaluation and quiz systems.
Some of these materials have already been evaluated among referees as a learning tool
(Armenteros, Benítez & Curca, 2010), but nothing has been done as of yet regarding
instructor experiences with MTM as a teaching tool. This research aims to achieve
deeper knowledge about instructor behavioral intentions to use MTM. To achieve the
research objectives, the Technology Acceptance Model (TAM) (Davis, 1989), the
Motivational Model (MM) (Davis, Bagozzi & Warshaw, 1992) and the Social Cognitive
Model (Bandura, 1977), combined with other variables used in educational technology
acceptance research (Liaw, Huang & Chen, 2007; Lee, Cheung & Chen, 2005) have
been employed.
The paper is structured as follows: we begin with a review of the literature as a way of
introducing TAM, MM, SCT and other variables employed in previous research on
educational technology. We then develop the acceptance model and present our
hypotheses, followed by a description of our data gathering technique. We show the
results of our statistical analysis and reevaluate the hypotheses. Finally, we discuss the
results and the model’s limitations, and offer ideas for future lines of investigation.
2. Literature Review
Although research about e-learning acceptance has been carried out extensively, there
are no studies regarding the acceptance and behavioral intention of this kind of
technology among FIFA instructors.
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The purpose of new technology usually is improving performance conditions and
productivity in a workplace (Yoo, Han & Huang, 2012). Unfortunately, the opportunity
for better performance is lost when the technology is rejected by the user. Thus, user
acceptance is the main factor determining the failure or success of a technology (Davis,
1993).
When trying to understand the user’s behavior toward information technologies, several
theoretical models have been developed. Venkatesh, Morris, Davis & Davis (2003)
bring together the main influential models concerning IT usage and its acceptance
(Table 1).
Table 1. Near here
This study employs a model based on the Theory of Acceptance Model with the aim of
investigating the factors behind the behavioral intention to use Multimedia Teaching
Materials. The model provides a stable and secure way to predict user acceptance of a
wide range of new technologies. Over time educational technology researchers have
extended Theory of Acceptance Model generally by introducing factors from related
models. In this respect, core constructs from the Motivational Model (Liaw et al., 2007)
and Social Cognitive Theory (Sánchez & Hueros, 2010) have been used.
2.1. Technology Acceptance Model (TAM)
Davis’ Technology Acceptance Model (from now on TAM) (Davis, 1989) is designed
to predict and explain why a particular system may be unacceptable and in this way
identify which changes have to be made in that system. It provides a basis for tracing
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the impact of external factors on internal beliefs and intentions (Davis, Bagozzi &
Warshaw, 1989). Originally (Fig. 1), this model showed the latent causal relationships
that exist between perceived usefulness, perceived ease of use, external variables,
attitude towards system use, behavioral intention and actual system use.
Fig 1.Near here
Perceived usefulness (PU) measures how people believe their productivity and
effectiveness can be improved as a result of using a technology (Davis, 1989).
Perceived ease of use (PEU) is the degree to which people believe using a particular
system will involve no additional physical or mental effort. Davis (1989) suggests
perceived ease of use directly affects perceived usefulness, but not vice versa. If a user
works more productively because of a technology that is perceived as easy to use, the
user will also perceive that the technology is useful.
External variables would also have an influence on behavioral intention indirectly
through perceived ease of use and perceived usefulness. Regarding the comparison
between TAM and TRA, Davis et al., (1989) state that variables such as system design
characteristics, user characteristics (including cognitive style and other personality
variables), task characteristics, nature of the development or implementation process,
political influences or organizational structure would fall into this category. Although
the primary interest in their study was to examine the ability to predict and explain user
behavior with TAM, they included external variables in their model explaining that one
of its purposes was also to provide a foundation for studying the impact of external
variables on user behavior.
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Attitude is the degree to which a user is interested in a system, and has a direct effect on
the intention to use that system in the future.
Behavioral intention (BI) is the major determinant of usage behavior and the main
factor determining a system’s success. In Davis’ original TAM, behavioral intention is
supposed to be affected by attitude towards use, as well as by direct and indirect
perceived usefulness and perceived ease of use effects.
As a result of the use of this model, Davis et al., (1989) concluded that the effect of
attitude was only modest in predicting technology acceptance and that individuals may
use a technology even if they did not have a positive attitude toward technology per se,
as long as it is perceived to be useful and/or easy to use in ways that enhance
productivity. In this respect, Mathieson (1991), cited in Teo & Noyes (2011), suggested
that eliminating attitude would not significantly lower the predictive capability of the
TAM. Thus, TAM excludes the attitude construct in order to explain Behavioral
Intention more parsimoniously.
This model, both as Davis (1989) proposed and with some variations, has been applied
in different environments in recent decades: electronic mail systems, graphic edition
software (Davis, 1989), electronic commerce (Pavlou, 2003), virtual worlds (Fetscherin
& Lattemann, 2008) and streaming music consumption (Deniz, 2010). Some research
states that TAM is a valid model when extended to multimedia and e-learning context
(Saadé, Nebebe & Tan, 2007). It has been applied to measure user acceptance of new
technology in educational contexts, such as Moodle (Sánchez & Hueros, 2010)
computer introduction in classrooms (Teo, Lee & Chai, 2008), web-based collaborative
learning systems (Liaw et al., 2007) and interaction modes in learning environments
(Davis & Wiedenbeck, 2001).
2.2 Motivational Model
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The Motivational Model states that system acceptance depends both on extrinsic and
intrinsic motivation.
Extrinsic Motivation
Motivation is concerned with related behavior that arises in response to something apart
from its own sake, such as reward or recognition means the decision to use a particular
technology or system is determined partially by calculation of the benefits. Both
perceived usefulness and perceived ease of use have been labeled as extrinsic
motivations (Teo, Lim & Lai, 1999).
Nevertheless, there can be other kinds of factors that affect the attitude and intentions to
use a system. As new information technologies tend to use multiple media and rich
graphical interfaces, individual interaction with the technology becomes an increasingly
engaging experience (Agarwal & Karahanna, 2000).Following Agarwal & Karahanna it
is necessary to pay attention to non instrumental variables.
Intrinsic Motivation
In this regard, the decision to use a system could also be determined by intrinsic
motivation, which refers to the fact of doing an activity for its own sake: the activity
itself is interesting, engaging and satisfying (Lee et al., 2005).In a review of TAM,
Davis, Bagozzi & Warshaw (1992) took into account the Motivational Model, and
added perceived enjoyment (PE), stating that intrinsic motivations are also predictors of
behavioral intention to use a system.
Perceived enjoyment (PE) is defined as the degree to which the activity of using a
technology is perceived to be enjoyable in its own right apart from any performance
consequences that may be anticipated (Davis, et al., 1992). Some other researchers have
also hypothesized the perceived enjoyment as a factor determining the use of IT
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(Igbaria, Schiffman & Wieckowski, 1994), especially with systems related to hedonic
activities (Chesney, 2006; Van der Heijden, 2004). In regard to educational technology,
this variable has been examined by Teo & Noyes (2011), Liaw et al., (2007) and Lee et
al., (2005).
2.3 Social Cognitive Theory
Social Cognitive Theory (Bandura, 1977) is an empirically validated model of
individual behavior. It states that environmental influences (such as social pressure),
cognitive and other personal factors (such as personality or demographic characteristics)
and behavior are reciprocally determined.
Compeau & Higgins (1995) apply this model to computer utilization through the
parsimonious examination of the variable perceived self efficacy (PSE). These authors
define perceived self efficacy as people's judgments of their capabilities to organize and
execute courses of action required to attain designated types of performances. This
model is concerned not with the skills one has but with judgments of what one can do
with whatever skills one possesses (Bandura, 1986, cited in Compeau & Higgins, 1995).
2.4 External variables
As previously stated, TAM has also been used to assess the role of external variables,
such as system features and user characteristics, as determinants of the behavioral
intention through the key beliefs (perceived usefulness and perceived ease of use).
Some of these external variables added to the TAM model are:
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Multimedia instruction (MI): This is concerned with the instructor’s attitudes towards
the possibility of explaining concepts and processes by pictures (both static and
dynamic), as well as the affordance of assessing their pupils’ knowledge in real time
through quizzes and auto-evaluations. The implementation of these features in the
design of teaching materials involves higher costs than does development of traditional
materials such as textbooks. Is it really worth producing MTM? Do instructors value
them in a significant way? As for the quality of e-learning environment, instructors
believe that both system satisfaction and multimedia instruction are vital factors (Liaw
et al., 2007).
Previous experience with IT (PET): This is concerned with the previous use people have
made of technological applications and tools. Previous experience can be a significant
predictor of behavior toward a system (Taylor & Todd, 1995).
3. Research model and hypotheses
This work proposes a tool to measure FIFA instructors’ acceptance and behavioral
intentions towards MTM. It also aims to offer ideas to study football instructors’
acceptance and use intention towards multimedia teaching material in order to improve
the MTM which are currently being developed for Futsal and Beach Soccer Referees.
3.1 Design
Based upon the foregoing theories, we propose our research model in Figure 2.
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Fig 2. Near Here
Some variables considered in previous research related to educational technology have
been taken into account for our purposes. Specifically, apart from perceived usefulness
and perceived ease of use (hypothesized to be the main factors determining user
acceptance and behavioral intention by Davis (1989), we introduce an intrinsic variable
(perceived enjoyment), a variable from the social cognitive theory (self efficacy), a
variable regarding the system characteristics (multimedia instruction) and a variable that
deals with instructor’s characteristics (experience in IT use, labeled as external
variables).
3.1.1 Perceived usefulness
In regard to MTM, perceived usefulness reflects the degree to which instructors believe
that using MTM can make them work more efficiently, easily and with better results.
Apart from being the main influence on system use according to Davis (1989) and
Davis et al., (1992), perceived usefulness was a great predictor of the intention to use elearning technology among instructors (Liaw et al., 2007), academicians (Tarcan, Varol
& Toker, 2010) and pre-service teachers (Teo et al., 2008). Thus, we propose that:
H1: The perceived usefulness (PU) of Multimedia Teaching Materials influences
positively and directly the behavioral intention (BI) to use the system.
3.1.2 Perceived Ease of Use
As far as MTM is concerned, perceived ease of use is the degree to which instructors
believe working with MTM does not require additional physical or mental effort while
allowing them to reach their teaching goals in an easy way.
As hypothesized in the original TAM model, perceived ease of use of learning
technologies had a direct positive effect on perceived usefulness (Teo et al., 2008), and
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had a mediating impact on the intention to use a system through perceived usefulness
among academicians (Tarcan et al., 2010).We then propose:
H2: The perceived ease of use (PEU) influences positively and directly perceived
usefulness (PU).
3.1.3 Perceived Enjoyment
Perceived enjoyment is concerned with how instructors believe teaching with MTM is a
satisfying, exciting and enjoyable activity.
When taking into account intrinsic motivation variables such as perceived enjoyment, it
has been found that this variable plays a significant role in the user’s technology
acceptance. Davis and colleagues’ findings (Davis et al., 1992) indicate that people’s
intentions to use computers in the workplace are influenced mainly by their perceptions
of how useful the computers are for improving their job performance, and secondarily
by the degree of enjoyment they experience in using the computers per se. In addition,
both perceived usefulness and perceived enjoyment fully mediate the effects on usage
intentions of perceived ease of use. Similarly, and with respect to educational
technology, Lee et al., (2005) showed that both perceived usefulness and perceived
enjoyment played an important role in affecting student attitude and intention to use
internet-based learning media. In the context of the MTM, we can therefore postulate a
positive relationship between perceived enjoyment and intention to use MTM. Thus, the
third hypothesis is stated as follows:
H3: The perceived enjoyment (PE) of Multimedia Teaching Materials influences
positively and directly the behavioral intention (BI) to use the system.
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When compared with other research results, relationships between perceived enjoyment
and other variables are different depending on the context (Venkatesh, 2000).
Venkatesh found that enjoyment in different systems (an interactive online help desk
system, a multimedia system for property management and a PC-based environment for
a company payroll application) affected directly the perceived ease of use. That
relationship got stronger with increasing direct experience with the target system over
time. Similarly, Teo et al.,(2011) found that perceived enjoyment had a significant
direct influence on intention to use technology, as well as on perceived usefulness and
perceived ease of use among pre-service teachers. That encourages us to propose:
H4: The perceived enjoyment (PE) of Multimedia Teaching Materials influences
positively and directly Perceived Usefulness (PU).
H5: The perceived enjoyment (PE) of Multimedia Teaching Materials influences
positively and directly Perceived Ease of Use (PEU).
While Teo et al., (2011) and Venkatesh, Speier & Morris (2002) state that perceived
enjoyment influences perceived ease of use, other researchers state that it is perceived
ease of use which influences perceived enjoyment among undergraduate students (Lee
et al., 2005) and internet users (Teo et al., 1999).
3.1.4 Multimedia Instruction
Multimedia instruction (MI) tries to assess how the interplay between media richness
and immediate feedback improves instructor’s intention to use teaching materials.
Precisely, it could identify instructors’ beliefs about showing different match
management situations and referees’ techniques by means of videos, animations, images
and sounds, as well as testing their pupils with applications such as Video-test, Trivia
and Auto-evaluation.
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According to Liu, Liao & Pratt (2009) materials that implement media richness are
more accepted among students, since they stimulate perceived usefulness and
concentration. In the study by Liaw et al., (2007) instructors considered that multimedia
instruction is a vital factor determining the quality of an e-learning environment. It is
also a predictor of perceived enjoyment. These results encourage us to propose:
H6: The quality multimedia instruction (MI) of MTM influences positively and
indirectly Behavioral Intention (BI) through perceived usefulness (PU).
H7: The quality multimedia instruction (MI) of MTM influences positively and directly
the perceived enjoyment (PE).
3.1.5 Previous experience with IT
In the context of MTM, Previous experience with IT (PET) labels the instructors’
previous experience in working with hypermedia and multimedia systems, such as
Power Point, or different platforms (PC, Mac, iPhone).
Regarding previous experience with technology, Taylor & Todd (1995) stated there
may be differences between experienced and inexperienced users in the relative
influence of various determinants of IT usage, such as ease of use and perceived
usefulness. Users without experience may focus first on ease of use. With experience,
users may overcome concerns about ease of use and may focus their attention on
perceived usefulness. This suggests that the path from ease of use to a positive attitude
will be stronger for inexperienced users, while the path from perceived usefulness to a
positive attitude will be stronger for experienced users.
As MTM are relatively new among the teaching materials that instructors use, it is
presumed that previous experience with hypermedia and similar technologies can
influence the way they regard their easiness and usefulness. We consider that more
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experienced instructors will value MTM as easier to use and more useful. The
consequent hypotheses are:
H8: Previous experience with technology (PET) influences positively and directly
perceived ease of use (PEU).
H9: Previous experience with technology (PET) influences positively and directly
perceived usefulness (PU).
3.1.6 Perceived Self-efficacy
Perceived self-efficacy represents intructors’ perceptions of their ability to use these
materials in the accomplishment of their courses. In regard to MTM, it represents
intructors’ perceptions of their ability to use multimedia technology in the
accomplishment of a task.
As far as perceived self-efficacy is concerned, Albion’s study (2001) showed that selfefficacy was the key to explaining teachers’ use of technology for teaching in the
classroom. According to the study by Liaw et al (2007), instructors have highly positive
attitudes toward e-learning that included perceived self-efficacy, enjoyment, usefulness,
and behavioral intention of use. When understanding instructors’ behavioral intention to
use e-learning environments, perceived usefulness is the greatest contributor (56%) and
perceived self-efficacy has a contribution of 21%.
As far as self efficacy is concerned, we expect that instructors who feel confident when
teaching with the MTM will consider these materials more useful. Thus we have:
H10: Perceived self-efficacy (PSE) influences positively and directly behavioral
intention (BI) to use MTM.
3.1.7 Behavioral Intention
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Behavioral intention (BI): This is the dependent variable and the one we wish to
measure, i.e., the degree to which FIFA football instructors will want to use MTM.
A summary of the hypotheses is provided in Figure.3.
Fig 3. Near here
4. Research method
4.1- Development of instruments
Data were collected through a questionnaire administered to instructors among the
different FIFA federations. The first part of the questionnaire gathers information about
participants, such as sex, age and years of experience as an instructor.
The second part has 43 items where participants are asked to indicate their perceptions
and beliefs toward MTM. For these 43 questions a numerical ten-point scale was used.
We found that scale clear enough to show the results regarding the contrasts between
variables, and that it is especially intuitive for those surveyed, who usually see this type
of range of punctuation in other questionnaires.
Before the questionnaire was distributed, a provisional version was developed in order
to test the validity and accuracy of the questions. This questionnaire was used during a
FIFA course in Cuba with five international instructors and it was concluded that the
items were well understood and that they did not lead to different interpretations.
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The questions were based mainly on items Davis employed in his first TAM (1989, for
Perceived Usefulness and Perceived Ease of Use variables) and items obtained from
similar research (for MI, PSE, PET and PE, in Liaw et al. 2007). These items were
adapted both to specific features of MTM and to our knowledge about the use
instructors make of these materials.
4.2- Sample and data collection
A total of 100 instructors from the six FIFA confederations were invited to participate
in the research. Questionnaires were sent to each confederation’s coordinator, who
distributed, collected and sent them back to the research group without personal data of
those surveyed (so as to guarantee respondents’ anonymity ). A total of 92 out of 100
questionnaires were finally collected, and 88 of them were marked as valid. The other 4
were rejected because of incomplete or incorrectly stated answers.)
As a method for assessing the collected data, a multiple linear regression model was
applied. Through regression it is possible to find the relationships between independent
variables PEU, PSE, PE, PU, Mi and PET and the dependent variable (BI). These
variables are defined as aggregates of the multiple sub-variables defined in Annex A.
We performed independent regressions to test the individual influence, and we
aggregate different groups of these variables to test their joint influence. As a result, we
obtained two different models which together explain a large part of the behavior
intention we intended to study.
5. Analysis and Results
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The Cronbach’s alpha reliability test on instructors’ behavioral intention toward
Multimedia Teaching Materials was to be highly accepted (α = 0.960). The high alpha
reliability gives a support for questionnaire content reliability (Annex B).
When modeling the sub-variables as a function of the explanatory variables, it can be
seen that only four of the 38 (Var32, Var50, Var70 and Var73) give rise to a p-value
which is less than 0.05 in the estimation of the regression coefficient (Table 2).
Table 2. Near Here
Inside every group of variables (PET, PEU, PSE, PE, PU and MI) every sub-variable
contained essentially the same information, so it was more practical to obtain the
appropriate measures by grouping the sub-variables in “global” variables (1-PET, 2PEU, 3-PSE, 4-PE, 5-PU y 7-MI) where the repeated information was erased.
The values of the global variables then become the averages of the values of the
corresponding sub-variables , with one exception: sub-variables 26, 27 and 32 model a
question concerning a negative feature of the tests, so when computing the average we
modify them by taking (10-Variable 26), (10-Variable 27) and (10-Variable 32).
Once we defined the new global variables, we test their influence on the behavior. First,
individual linear regressions were performed, whose coefficients and significance are
resumed in Table 3.
Table 3. Near Here
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All the p-values are smaller than 0.01, which indicates a strong relation between BI and
any of the other variables, when these are taken separately. Regression coefficients,
which show the magnitude of the dependence, are also shown in Table 3.
To obtain a more complete model, we undertake a multiple regression of the aggregate
dependent variable with respect to the aggregate independent variables. However, if we
perform the regression with regard to all the independent variables, some
multicollinearity appears. The results of this estimation are shown in Table 4, and from
there it must be inferred that the only variable which influences BI in an independent
way is PU (p-value=0.00), the perceived usefulness. So the user, taking into account the
utility that he/she perceives, carries out behavior which does not depend on the
influence of the other variables.
Table 4. Near Here
To obtain a precise model, and complete the information contained in the variable PU,
we have tested some different groups of variables which may help to predict future
behavior (BI). After a number of estimations which combined different groups of
variables (and whose results are available upon request) we obtained two different
regressive models which avoid in a significant way the multicollinearity problem; the
first of them models BI in terms of PE and PU, while the second takes PU, PEU and MI
as explanatory variables. As we see below, the two models are complementary. The first
one is:
1) BI= 2-7796+0.152PE +0.573PU + e
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The global p-value of the multiple regression is 0.000 here, while the p-values for the
correspondent coefficients are 0.055 (PE) and 0.000 (PU); this offers a good level of
significance both globally and individually. Moreover, the coefficient of determination
is 0.503, and so more than half of the behavior of BI is explained by these variables.
The behavior intention grows rapidly in terms of the perceived usefulness, and a bit
slower in terms of the enjoyment which the learning method provides to the user.
2) BI= 4.422+0.237PU+0.161MI+0.547PEU+

The global p-value turns out again to be 0.000, and the individuals ones 0.030, 0.048
and 0.079, each of them below the level 0.1. The coefficient of determination is 0.359,
so these variables explain an important part of BI. Note that again PU gives rise to a
stronger increasing of the behavior intention, while the effect of the perceived ease of
use and the multimedia instruction, though smaller, is still significant.
The regression of PU on MI and PEU, which is again significant, gives rise to a
coefficient of determination of 0.462. In this way we can infer that more than half of the
information of BI, which linearly depends on MI and PEU, does not depend on PU, and
hence is uncorrelated with it.
The two proposed models, considered together, offer an accurate estimation of which
are the relevant variables that influence the behavior intention, and how they do it.
Although this research proposes behavioral intention (BI) as the dependent variable,
with the other ones being labeled as independent variables, the literature review has led
us to propose some hypotheses that attempt to discover possible influences from
perceived enjoyment (PE), previous experience with technology (PET) and multimedia
instruction (MI) on variables that are not behavioral intention (BI).
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Thus, we have carried out several individual regressions so as to see the influence of
perceived enjoyment (PE) on perceived usefulness (PU) (on the one hand) and on
perceived ease of use (PEU) (on the other hand); the influence of previous experience
with technology (PET) on perceived usefulness (PU) and, separately, on perceived ease
of use (PEU); and finally, the influence of multimedia instruction (MI) on perceived
enjoyment (PE).
Perceived enjoyment’s (PE) influence on perceived usefulness (PU) and perceived ease
of use (PEU) are estimated through two regressive models: PU=0.504PE+4.280
PEU=0.690PE+1.733.
For the first model, both the intercept and the slope are significant (p-value=0.000) and
the correlation coefficient is 0.64. For the second, the slope is significant at any level
(p-value=0.000), the intercept is thus for
=0.05 (p-value=0.025): and the correlation
coefficient is 0.655.
Regarding the previous experience with technology’s (PET) influence on perceived
usefulness (PU) and on perceived ease of use (PEU), we obtain the next two regressive
models:
PU=0.1741PET+7.339
PEU=0.429PET+4.356
Again here the two slopes are significant at any level (p-value=0.000), and the
correlation coefficients are respectively 0.246 and 0.457.
Finally, the multimedia instruction’s influence on perceived enjoyment is assessed
through the following model:
PE=0.538MI+4.220.
22
The correlation coefficient is 0.566 and the p-values are 0.That means there is a high
influence of multimedia instruction on perceived enjoyment.
6. Discussion
When joining PU and PE according to the first regressive model (BI= 2.779+0.152PE
+0.573PU +
), more than half of the Behavioral Intention is explained by these two
variables. Therefore, H1 and H3 are true. Behavioral Intention towards the use of MTM
would grow mainly because of the Perceived Usefulness of the system and, to a lesser
extent, the Perceived Enjoyment. This is consistent with previous studies regarding the
influence of perceived usefulness and perceived enjoyment on behavioral intention
(Davis, 1989; Davis et al., 1992; Liaw et al., 2007, Tarcan et al., 2010 and Teo et al.,
2011).
From the second regressive model (BI= 4.422+0.237PU+0.161MI+0.154PEU+
), one
of the TAM’s key beliefs, PEU, and an external variable, MI, explains as well part of
the Behavioral Intention. Additionally, more than half of the information provided by
MI and PEU doesn’t affect Behavioral Intention through PU. Thus, direct influence of
PEU and MI on behavioral intention is found. This is not consistent with previous
research, where perceived ease of use influences behavioral intention indirectly through
perceived usefulness (Davis, 1989; Tarcan et al., 2010), and multimedia instruction
influences behavioral intention via perceived usefulness (Liu et al., 2009). As a result of
this, H2 and H6 are not true, since PEU and MI influence positively and directly
Behavior Intention.
On the analysis between variables, next influences have been found: PE has a
significant influence on PU, and on PEU (as in Venkatesh, 2000 and Teo et al, 2011).
23
Thus, H4 y H5 are true. It has also been found a high influence from MI on PE, as in
Liaw et al. (2007), which means that H7 is also true.
Finally, although previous studies discovered a direct influence on PET on PEU and on
PU (Taylor & Todd, 1995) it has been found low influence from PET on PEU and very
low influence from PET on PU regarding the Multimedia Teaching Materials. Neither
PSE influences directly Behavioral Intention, as in Albion (2001) and Liaw et al.
(2007). Thus, H8, H9 and H10 are rejected.
These results help us to understand instructors’ behavioral intentions towards MTM, which
facilitates the creation of appropriate materials. Nevertheless, learners’ perceptions towards
MTM as learning materials may also give valuable guidelines for the design of further
multimedia interactive materials. Therefore, future research should explore the behavioral
intention towards MTM from the learners’ point of view. It could also be relevant to apply this
study to other collectives, such as the futsal and beach soccer instructors, who are also using
multimedia interactive materials as a teaching tool. 7. Conclusions
When developing a new teaching tool for instructors, it is necessary to assess the
reasons for instructors’ acceptance of this tool and their future intentions to use it.
Specifically, understanding their perceptions toward teaching technologies is a crucial
issue for enhancing teaching performance.
The main reason for FIFA instructors’ intention to use MTM in the future is their
perceived usefulness. The user, from the perceived utility, carries out behavior that does
not depend on how the other variables perform. PU is the only one that provides a
significant amount of information that is not offered by others. This influence is
followed by the perceived enjoyment, the perceived ease of use and the multimedia
instruction quality. A meaningful direct influence from either previous experience with
24
technology or self-efficacy has not been found. A summary of the results is shown in
Figure 5. In summary, MTM should be developed with these guidelines in mind:
usefulness, enjoyment, ease of use and multimedia content.
Fig 4. Near here
The results of this research clearly confirm that instructors are willing to use MTM to
aid their teaching activities as long as they perceive them as useful, enjoyable, easy to
use, and whenever the material is multimedia.
25
Annex A:
Questions.
Var.
Name
00
Age
01
Sex
02
Years of experience as an instructor
Previous experience with Technology
10
What is your experience using different platforms such as Mac, PC, Blackberry or iphone?
11
What is your experience using Internet?
12
What is your experience using Power Point?
13
What is your experience using the computer as a teaching tool?
14
What is your experience using MTM ?
Perceived Ease of Use
20
I find it easy to use MTM.
21
Planning and organizing MTM classes is easy.
22
I can use video in my classes in an easy and intuitive way.
23
The design of the graphic interface of the MTM materials is clear and understandable.
24
The use of MTM in classes requires little effort.
26
25
The MTM technologies allow me to achieve my objectives in a simple way.
26
Working with MTM in class is rigid and inflexible.
27
MTM technology often behaves unexpectedly.
Perceived self-efficacy
30
I feel sure of myself when using MTM in teaching.
31
I am comfortable using MTM.
32
I feel nervous before teaching when using multimedia technology.
Perceived enjoyment
41
I enjoy using a multimedia environment such as Power Point or Internet in teaching.
42
I enjoy using MTM as a teaching tool.
43
Using MTM as a teaching tool is satisfying.
44
Using MTM as a teaching tool is fun.
45
Using MTM as a teaching tool is positive.
46
Using MTM as a teaching tool is interesting.
47
Using MTM as a teaching tool is exciting.
Perceived usefulness
50
My work as an instructor would be difficult to carry out without MTM.
51
Using MTM provides me with a large degree of control over my work.
52
MTM offers a useful tool for carrying out the tasks established in the courses.
53
Using MTM improves performance in my work.
54
Using MTM helps me save time in the teaching process.
55
Using MTM I can carry out more work than would be possible without it.
56
Using MTM cuts down on the time I dedicate to unproductive teaching activities.
57
Using MTM increases my effectiveness in work.
58
Using MTM improves the quality of the work which I carry out.
59
Using MTM makes it easier to carry out my work.
Behaviour intention
60
I intend to keep using MTM for class support.
61
I intend to use MTM in my classes as much as possible.
27
62
I want to keep using MTM as opposed to stop using it.
63
I am going to talk about the positive aspects of using MTM in my classes.
64
I am going to recommend using MTM in class to other instructors.
Multimedia Instruction
70
I like using video as a multimedia instruction resource.
71
I like using photography as a multimedia instruction resource.
72
I like using graphics as a multimedia instruction resource.
73
I like using animation as a multimedia instruction resource.
74
I like using the interactive self-evaluation as a multimedia instruction resource.
Annex B
Variables
10
10
11
1
12
13
14
20
21
22
23
24
25
0.426
0.417
0.307
0.22
0.294
0.151
0.198
0.122
0.066
0.19
1
0.659
0.744
0.592
0.594
0.465
0.495
0.473
0.157
0.484
1
0.757
0.645
0.497
0.404
0.561
0.394
0.14
0.547
1
0.796
0.652
0.488
0.622
0.468
0.148
0.547
1
0.677
0.52
0.546
0.499
0.202
0.572
1
0.803
0.656
0.686
0.388
0.752
1
0.642
0.732
0.519
0.779
1
0.609
0.365
0.736
1
0.364
0.743
1
0.411
11
12
13
14
20
21
22
23
24
25
Variables
1
26
27
30
31
32
41
42
43
44
45
46
10
0.116
0.202
0.252
0.195
-0.140
0.274
0.200
0.020
0.109
0.149
0.116
11
-0.048
0.117
0.459
0.503
0.036
0.493
0.491
0.188
0.317
0.404
0.422
12
-0.074
0.095
0.531
0.489
0.085
0.567
0.533
0.272
0.420
0.470
0.395
13
-0.041
0.086
0.548
0.527
0.206
0.558
0.617
0.289
0.412
0.506
0.536
14
0.051
0.127
0.581
0.532
0.318
0.471
0.628
0.385
0.395
0.477
0.533
20
0.173
0.204
0.650
0.731
0.348
0.591
0.785
0.411
0.503
0.602
0.679
21
0.125
0.191
0.642
0.704
0.322
0.525
0.754
0.425
0.488
0.593
0.625
22
-0.005
0.157
0.615
0.581
0.340
0.664
0.766
0.399
0.512
0.572
0.603
23
0.269
0.279
0.568
0.687
0.440
0.554
0.701
0.363
0.496
0.581
0.653
24
0.139
0.011
0.376
0.365
0.054
0.395
0.377
0.598
0.279
0.319
0.330
25
0.059
0.294
0.819
0.876
0.409
0.762
0.861
0.447
0.500
0.721
0.723
26
1
0.674
-0.013
0.066
0.352
-0.023
0.048
0.015
0.187
0.028
0.067
1
0.182
0.237
0.443
0.211
0.245
0.066
0.047
0.216
0.273
1
0.868
0.303
0.675
0.740
0.395
0.429
0.605
0.577
1
0.329
0.710
0.770
0.426
0.472
0.661
0.626
1
0.275
0.330
0.068
0.143
0.208
0.306
1
0.666
0.334
0.418
0.585
0.571
1
0.550
0.584
0.828
0.863
1
0.321
0.492
0.538
1
0.629
0.544
1
0.822
27
30
31
32
41
42
43
44
45
46
1
28
Variables
60
61
62
63
64
70
71
72
73
74
10
0.171
0.129
0.220
0.273
0.088
0.119
0.249
0.206
0.159
0.067
11
0.438
0.406
0.184
0.384
0.249
0.415
0.170
0.084
0.192
0.211
12
0.323
0.301
0.140
0.253
0.130
0.349
0.193
0.091
0.243
0.171
13
0.264
0.318
0.049
0.200
0.124
0.398
0.270
0.156
0.337
0.227
14
0.298
0.293
0.038
0.171
0.116
0.365
0.278
0.223
0.369
0.278
20
0.490
0.443
0.197
0.362
0.246
0.479
0.281
0.253
0.320
0.259
21
0.491
0.444
0.221
0.365
0.315
0.444
0.137
0.143
0.289
0.207
22
0.506
0.510
0.237
0.258
0.372
0.552
0.205
0.274
0.487
0.405
23
0.472
0.444
0.205
0.335
0.353
0.501
0.174
0.193
0.317
0.361
24
0.320
0.252
0.381
0.338
0.334
0.294
0.090
0.166
0.257
0.200
25
0.581
0.586
0.278
0.465
0.416
0.437
0.166
0.252
0.419
0.411
26
0.087
-0.008
0.074
0.060
-0.012
0.089
-0.095
-0.075
-0.098
-0.136
27
0.181
0.255
0.183
0.248
0.258
0.203
-0.087
0.017
0.067
0.039
30
0.519
0.484
0.241
0.443
0.369
0.327
0.318
0.294
0.379
0.305
31
0.583
0.535
0.265
0.474
0.368
0.397
0.195
0.220
0.321
0.355
32
0.098
0.142
0.077
0.141
0.128
0.108
0.056
0.136
0.185
0.195
41
0.455
0.403
0.274
0.415
0.386
0.407
0.252
0.340
0.525
0.438
42
0.628
0.644
0.245
0.414
0.385
0.583
0.246
0.282
0.485
0.409
43
0.386
0.479
0.356
0.259
0.291
0.396
0.174
0.215
0.350
0.357
44
0.572
0.330
0.088
0.211
0.146
0.455
0.216
0.173
0.311
Variables
45
10
46
470.648
0.606
0.073
50 0.624
0.685
0.100
51 0.233
0.287
0.248
52 0.441
0.454
0.287
53 0.414
0.551
-0.059
54 0.604
0.693
-0.036
55 0.326
0.344
0.145
56 0.306
0.351
-0.141
11
47
0.575
0.269
0.488
0.051
0.128
0.288
0.275
0.363
0.349
0.134
0.564
0.241
0.323
0.246
12
50
13
51
0.224
0.144
0.296
0.437
0.009
0.312
-0.063
0.429
0.262
0.378
0.194
0.391
0.296
0.277
0.265
0.553
0.234
0.225
0.191
0.380
0.213
0.104
0.238
0.181
14
52
20
53
0.310
0.644
0.524
0.491
-0.033
0.656
0.064
0.579
0.163
0.636
0.326
0.318
0.269
0.750
0.398
0.439
0.189
0.729
0.273
0.541
0.472
0.451
0.423
0.481
0.515
0.280
0.235
0.375
0.488
0.602
0.111
0.479
0.070
0.479
0.239
0.289
0.537
0.403
0.228
0.370
0.354
0.516
0.308
0.014
0.249
0.183
0.4805
0.448
0.416
0.394
0.471
0.600
0.388
0.271
0.366
0.396
0.568
0.515
0.105
0.508
0.378
0.691
0.486
21
57
0.493
0.219
58
0.392
59
0.525
0.017
0.529
0.396
-0.093
0.421
0.010
0.546
-0.016
0.416
0.189
0.198
0.066
0.249
0.152
0.012
0.064
0.025
0.179
0.172
0.060
0.286
0.333
0.430
0.251
0.428
0.275
0.189
0.236
0.449
0.405
0.415
0.204
0.267
0.400
0.285
0.015
0.426
0.145
0.307
0.182
0.577
0.329
0.467
0.433
0.456
0.511
0.441
0.207
0.538
0.418
0.354
0.619
0.481
0.368
0.408
0.567
0.564
0.531
-0.047
0.581
0.156
0.639
0.456
0.677
0.444
0.363
0.427
0.576
0.238
0.475
0.405
0.573
0.503
-0.028
0.443
0.076
0.583
0.492
0.412
0.181
0.412
0.221
0.408
0.119
0.278
0.318
0.491
0.275
0.057
0.271
0.234
0.201
0.490
0.258
0.134
0.301
0.261
0.216
0.279
0.278
0.311
0.269
0.256
-0.093
0.275
-0.074
0.219
0.241
0.330
0.304
0.416
0.351
0.435
0.258
0.272
0.483
0.487
0.417
-0.054
0.362
0.143
0.309
0.443
0.572
0.356
0.501
0.351
0.582
0.259
0.297
0.502
0.665
0.425
0.018
0.422
0.236
0.379
0.588
0.471
0.694
0.442
0.625
0.230
0.543
0.518
0.237
0.201
0.257
0.466
0.041
0.215
0.276
0.258
0.194
-0.006
0.439
0.413
0.228
0.154
0.524
0.606
0.236
0.258
0.383
0.180
0.181
0.219
27
59
30
60
0.382
0.165
0.469
-0.045
0.546
0.064
0.218
0.229
0.596
0.091
0.678
-0.124
0.515
-0.002
1 0.048
0.814
31
61
0.468
0.118
1 0.490
0.538
32
62
0.174
-0.171
0.152
0.668
0.474
1 0.255
0.685
41
63
0.381
0.063
0.532
0.576
10.418
0.735
0.362
0.410
0.232
0.404
0.313
0.240
0.293
0.421
0.306
0.496
0.320
42
64
0.632
0.098
0.433
0.540
0.547
0.533
0.293
0.233
0.441
0.520
0.448
0.695
0.517
0.433
43
70
0.368
0.353
0.187
0.295
0.308
10.581
0.571
0.363
71
0.704
-0.120
0.290
0.340
0.325
0.347
0.127
0.373
10.287
0.744
0.328
0.566
0.269
0.574
0.446
0.567
0.491
0.363
0.266
44
10.343
0.391
0.245
45
72
0.676
0.137
0.484
0.550
0.658
0.596
0.476
0.247
0.733
0.147
0.423
0.626
0.616
0.616
0.640
0.301
0.749
0.579
0.801
1 0.651
0.573
73
0.506
1 0.690
0.685
1
0.061
0.311
0.442
0.492
0.505
0.403
0.316
0.439
0.624
1
0.458
0.1460
0.338
0.375
0.257
0.047
0.216
0.175
0.440
1
0.282
1
0.665
0.644
0.628
0.461
0.288
0.411
0.574
0.548
1
0.586
0.444
0.645
0.363
0.544
0.636
0.575
1
0.797
0.456
0.357
0.579
0.714
0.830
1
0.611
0.386
0.534
0.640
0.751
1
0.404
0.663
0.593
0.488
1
0.291
0.317
0.394
1
0.739
0.578
1
0.740
22
23
24
25
26
46
47
50
54
55
56
57
58
74
51
52
53
54
55
56
57
58
59
0.270
0.342
0.405
0.466
-0.090
0.131
0.420
0.159
0.258
0.570
1
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