This document provides a template and guidance for writing an academic essay. It discusses key elements like introducing the topic in the first paragraph without labeling it, using headings to organize longer papers, writing clear body paragraphs with citations, and formatting tables, images, and a references list. It also provides examples of different heading levels and discusses developing self-efficacy through goal setting, feedback, and group work to enhance learning.

1. 1
3
Title of Your Essay
Your First and Last Name
Ashford University
Course Code: Name of Course (e.g. ENG 122: English
Composition II)
Instructor Name
Due Date
Title of Your Essay
Start the first paragraph here, which should introduce your
reader to the subject you are writing about, as well as your
particular position or claim. Do not label it “Introduction.”
Instead, simply write your paper’s title as a level 1 heading:
bold, centered, with title case. To learn more about what an
introduction should include and what to include in a conclusion,
please see the following resource: Introductions & Conclusions.
You can use this template to help you format your paper. For
longer papers, include level headings.
The Writing Process
Spend time planning your paper. A good practice is to
brainstorm ideas and decide how to express the main idea or
thesis, this is part of entering the scholarly conversation. Once
you have a rough idea of what you want to discuss or argue,
create an outline or list to help you organize the evidence you
plan to present.
Writing the Body Paragraphs
Each paragraph of your assignment should be clear and easy to

2. follow. We have several good resources to help you write a
strong paragraph, such as How to Write a Good Paragraph page
and the Integrating Research page.
Level 2 Heading (Bold, Flush Left, Title Case)
Level headings can be placed anywhere in your paper as a way
to classify or organize your paper into sections. There are five
levels; the first three levels are more commonly used in
graduate-level assignments, whereas levels four and five may be
used in longer papers (e.g., dissertation). Use level 2 headings
to break level 1 into categories, level 3 to break up level 2, and
so on. Do not move to a lower level of heading if there is only
one section. As a general rule, check with your instructor about
formatting expectations.
Level 3 Heading (Bold, Flush Left, Italic, Title Case)
Likewise, to show your readers where the paragraph begins
when using a level 3 heading would be a great idea.
Level 4 Heading (Bold, Indented, Title Case). In longer papers,
you may use a level 4 heading to create even further
subsections. In this case, note that the paragraph begins directly
after a period on the same line as the level 4 heading. The level
5 headings follow the exact same conventions, only they would
also be italicized.
Using Citations Correctly
In addition to being well-written, each paragraph should include
an in-text citation to all information summarized, paraphrased,
or quoted from outside sources. The Writing Center provides
many resources to help you follow correct citation style and
gives lessons and examples of how to paraphrase and cite
sources. The Introduction to APA 7th Edition page is a good
place to start.
Using Tables, Graphs, Images, and Appendices
For some papers and reports, you may choose to add a table,
graph, or image
within the body of the draft. Or you may choose to include an
appendix at the end of your paper. These can help to provide a
visual representation of data or other information that you wish

3. to relay to your reader. Follow this guidance to understand
when and how to use these features.
References
Author's Last Name, Initials. (Publication Year). Title of
article. Title of Periodical, volume #(issue #), pp–pp.
http://doi:xx.xxxxxxxxxx
Author's Last Name, Initials. (Publication Year). Title of book.
Location: Publisher.
Author's Last Name, Initials. (Publication Year). Title of digital
book. http://www.xxxxxxx.xxxxxxxx
Author's Last Name, Initials. (Publication Year). Title of book
with DOI. https://doi:xxxxxxx
Editor's Last Name, Initials. (Ed.). (Publication Year). Title of
anthology. Location: Publisher.
Producer’s Last Name, Initials (Producer), & Director’s Last
Name, Initials (Director). (Year). Title of motion picture
[Motion picture]. Country of Origin: Studio.
**For help formatting your reference page, please see our
Formatting Your References List page.
Self-Efficacy in M-Learning
Jason Hutcheson
Running head: 3Capella UniversityTable of Contents
Literature Review5
Self-Efficacy Theory5
Theoretical Foundations.5

4. Intentional Development of Self-Efficacy.7
Self-Efficacy in Learning9
Role of Self-Efficacy in Andragogy.9
Relationship between Self-Efficacy and Academic
Achievement.10
Integration of Self-Efficacy in Learning Design.12
Self-Efficacy in Technology Acceptance14
Technology Acceptance Modeling.14
Mobile Technology Acceptance.16
Methodology and Approach16
Methodology and Rationale17
Research Methodology Analysis.17
Methodology Selection Rationale.18
Population and Sample19
Sample Recruitment Strategy19
Instrument19
Conclusion20
Abstract
Technology has become engrained into daily life. The most
prominent technology today is mobile technology. Through
mobile “smart” phones, tablets, and laptops, the modern
population is connected through mobile technology;
everywhere, all of the time. However, many of the benefits of
mobile technology have not translated into the educational
environment. This represents a problem for both the education
and the information technology industries. In order to
effectively address this problem, researchers need to understand
the challenges of integrating mobile technology in the course
room and determine the drivers influencing the acceptance of
mobile technology. Existing literature has indicated a
relationship between self-efficacy and the acceptance of mobile
technology in the course room. However, the degree of
correlation between learner self-efficacy and the acceptance of
mobile technology has not yet been determined. This paper
analyzes the existing literature concerning the role of self-

5. efficacy in mobile learning (m-learning) and presents the
foundation for research concerning the relationship between
self-efficacy and mobile technology acceptance.
Self-Efficacy in M-Learning
Existing literature has identified value in the integration of
mobile technology in the course room with respect to the
promotion of collaboration (Fuegen, 2012; Liljestrom,
Enkenberg, & Pollanen, 2013; Pegrum, Oakley, & Faulkner,
2013; Shree Ram & Selvaraj, 2012). Still, mobile technology
for education remains underutilized. Existing literature
extensively discusses the challenges associated with
transitioning to an m-learning enabled environment (Cheon,
Lee, Crooks, & Song, 2012; Eteokleous & Ktoridou, 2009;
Ktoridou, Gregoriou, & Eteokleous, 2007; Male & Pattinson,
2011; Rossing, 2012). Chief among the challenges for
transitioning to m-learning is the acceptance of mobile
technology in learning, which lends to the importance of
identifying and classifying key determinates for mobile
technology acceptance.
This paper analyzes the existing literature concerning self-
efficacy in order to assess the role of self-efficacy in m-
learning. The paper begins by analyzing the theoretical
foundations of self-efficacy and how self-efficacy can be
developed. This is followed by an analysis of the role of self-
efficacy in learning, especially concerning andragogy and how
self-efficacy is engaged in support of learning design. Then the
paper evaluates the role of self-efficacy in technology
acceptance for both general technology and mobile technology.
The paper concludes in the analysis and selection of a research
methodology, sampling strategy, and instrument to address the
research question.Literature ReviewSelf-Efficacy Theory

6. Theoretical Foundations. The theoretical foundations of self-
efficacy are rooted in Bandura’s (1986) Social Cognitive
Theory. Social Cognitive Theory seeks to define human
behavior through the personal interaction of individuals with
their environment: defining the concept of self-efficacy and the
relationship of self-efficacy toward task engagement. Self-
efficacy addresses an individual’s belief that he or she can
accomplish what he or she set out to accomplish (Bandura,
1986). Through this definition, self-efficacy presents as a
strong influence toward task engagement. Through self-
efficacy, individuals analyze and determine their perceived
ability to accomplish a task against the perceived difficulty of
the task. This implies that individuals with low domain self-
efficacy are unlikely to engage in tasks which are perceived to
be moderately or highly difficult. The uncertainty that drives
low self-efficacy can be confused with a lack of self-
confidence.
Although closely related, self-efficacy and self-confidence are
two very distinct concepts. Self-efficacy is distinguished from
self-confidence primarily through self-efficacy’s domain
specific relevance and specific regard toward defined tasks
(Bandura, 1986). Where self-confidence presents as a general
concept, self-efficacy relates to specific task engagement. In
consideration of this distinction, an individual may have
varying degrees of self-efficacy in reference to several
different, but similar, tasks. Therefore, efficacy in learning
mathematics is distinctly different from efficacy in learning
history. This domain specific nature of self-efficacy enables
specific engagement toward cognitive development.
Self-efficacy theory establishes the role of self-efficacy within
cognitive development. Bandura (1993) asserts that self-
efficacy strongly influences cognitive development through
cognitive, motivational, affective, and selection processes. This
influential effect of self-efficacy on cognition and affection
enforces self-efficacy’s influence on task engagement though
the influence of intellectual and emotional responses.

7. Similarly, the effect of self-efficacy on motivational processes
implies influences toward task sustainment. Additionally, in
consideration of self-efficacy’s effect on selection processes,
self-efficacy presents an influential role in task selection:
affecting selection between simple or difficult tasks. However,
the role of self-efficacy before and after initial task engagement
are distinctly different.
The relationship between self-efficacy and performance presents
in a cyclic nature. While initial performance is only moderately
influenced by self-efficacy, subsequent performances are
strongly influenced by self-efficacy (Bandura, 1993). This
cyclic relationship between self-efficacy and performance
indicates that repeated failures will negatively impact task self-
efficacy and subsequently influence decisions to further engage
in failed tasks. However, likewise, this relationship indicates
that repeated success will positively impact task self-efficacy
and subsequently encourage repeated task engagement. This
relationship supports development learning approaches which
engage in increasingly difficult tasks in order to promote task
self-efficacy.
In addition to internal factors, self-efficacy is also influenced
through external interactions. Tan (2012) determined that self-
efficacy is strongly influenced by perceptions of individual
performance compared to the performance of both peers and
mentors. As individuals engage in new tasks, their perceptions
of success are derived in comparison to the performance of
others. Therefore, performance which consistently aligns with
peers and matures towards the performance levels of mentors
positively influences self-efficacy. Consequently, in
understanding the role of self-efficacy in cognitive
development, how can educators engage the intentional
development of self-efficacy toward the enhancement of
learning activities?
Intentional Development of Self-Efficacy. One method of self-
efficacy development lies is goal definition. According to
Artino (2012), self-efficacy is enhanced through the

8. establishment of clear and specific goals. With this in mind,
educational practices, such as definition of learning objectives
and provision of grading rubrics, work to build self-efficacy.
However, Clinkenbeard (2012) expands on this concept of goal
definition: asserting that self-efficacy is promoted through
student involvement in the definition of goals. Therefore, the
definition of goals alone is not sufficient to actively develop
self-efficacy. Self-efficacy development is best served when
students are engaged to help establish learning goals. This
concept of self-efficacy development aligns with Knowles
(1970) concept of adult learning which asserts that adults seek
learning which is practically relevant within their lives.
A second method of self-efficacy development is associated
with goal difficulty. Artino (2012) asserts that self-efficacy is
enhanced through the encouragement of challenging goals. This
indicates that the more challenging the goal, the better then
influence on self-efficacy. However, if a goal is too
challenging, failure to meet that goal can actually damage self-
efficacy. Clinkenbeard (2012) provides clarification that tasks
should be established with an optimal difficulty. Under this
premise, goals are defined which will challenge students, but
are not so difficult as to impose likely failures. This concept of
goal development reinforces the engagement of increasingly
difficult tasks in support of self-efficacy development.
Another method of self-efficacy development promotes quality
communication between student and teacher. Artino (2012)
presents that the provision of honest feedback is productive to
the development of self-efficacy toward learning. Again,
however, Clinkenbeard (2012) expands on this concept:
asserting that feedback needs to be presented in a positive
manner. However, the two independent assessments of
feedback are not mutually exclusive. Synthesized, these
assessments assert the delivery of feedback which is both honest
and positively presented.
A fourth method of self-efficacy development involves the
engagement of group activities. Artino (2012) and

9. Clinkenbeard (2012) both agree that self-efficacy in learning is
enhanced through the engagement of managed group activities.
Through these group activities, Artino asserts, “teachers can use
other students as models to demonstrate how to successfully
complete a learning task” (p. 83). By working in groups,
learners are able to vicariously experience task completion and
can experience in positive peer pressure to engage in tasks
themselves.Self-Efficacy in Learning
Role of Self-Efficacy in Andragogy. The role of self-efficacy
in andragogy is directly related to the self-directed nature of
andragogical learning. According to Knowles (1970), adult
learning asserts the maturation of learner engagement toward
self-directedness. In his seminal work on andragogy, Knowles
describes the distinctions between the effective learning
approaches of adults and children. However, Knowles asserts
that andragogy should not be considered as the antithesis of
pedagogy, and that the selection of andragogical and
pedagogical instructional methods should relate to student
topical maturity rather than age (p. 59). As students mature in
their understanding of a topic, engagement of self-directed
learning activities become more appropriate. However, self-
directed learning requires persistence to persevere through
difficult tasks without external motivation.
Self-efficacy and self-directed learning intersect in the
engagement of difficult tasks. Gao, Lee, Xiang, and Kosma
(2011) concluded that self-efficacy directly influences
engagement in vigorous activity and persistence. Therefore, as
self-efficacy is enhanced, individual engagement in vigorous
activity and persistence are also increased. In the role of self-
directed learning, this indicates that development of self-
efficacy indirectly enables engagement in self-directed learning.
Furthermore, as e-learning primarily engages self-directed
learning approaches, development of self-efficacy in support of
e-learning is highly supported. However, self-efficacy does not
address all elements of e-learning.
E-learning has received large degrees of criticism for

10. heightened susceptibility to plagiarism and issues of academic
honesty. However, the influence of self-efficacy does not
extend into concerns of academic honesty. Ananou (2014)
determined that, although students perceived cyber-plagiarism
as a significant concern, student self-efficacy is not related to
self-reported cyber-plagiarism. In consideration of these
findings, concern can be raised regarding the balance in
developing self-efficacy and reducing the likelihood of cyber-
plagiarism. While self-efficacy may not reduce cyber-
plagiarism, it apparently doesn’t support to counter cyber-
plagiarism either: indicating that cyber-plagiarism does not
derive from concerns of non-performance. However, additional
research is required to fully investigate this phenomenon as the
reliability of self-reported plagiarism is questionable
considering that the participants have no incentive to self-
incriminate. Regardless, the relationship between self-efficacy
and andragogy is well established and presents strongly in
correlation to academic achievement.
Relationship between Self-Efficacy and Academic Achievement.
The role of self-efficacy in the improvement of academic
achievement is built on the effects of social cognition on the
learning process. Higher educational programs that are
grounded in a basis of social cognitive theory demonstrate
improved success in academics (Dinther, Dochy, & Segers,
2011). As social cognitive functions define human behavior
(Bandura, 1986), programs which seek to develop specific
academic behavior are enabled through the influence of
individual social cognitive constructs. In consideration of this
research, engagement of social cognitive development activities
support the improvement of academic achievement. These
social cognitive functions, as defined by Bandura (1986),
include identification, vicarious learning, and self-efficacy.
Furthermore, additional research further supports the
relationship between self-efficacy and academic achievements.
As education evolves with society, self-efficacy development
becomes increasingly beneficial to goals of academic

11. achievement. Tella, Tella, and Adeniyi (2011) concluded self-
efficacy to have a direct influence on academic achievement:
indicating that this influence is stronger in the context of self-
directed learning. This research confirms the suggestion that
self-efficacy positively influences academic achievement, and
justifies recent efforts to integrate self-efficacy development in
the educational environment. Furthermore, as educational
programs continue to migrate toward online and mobile learning
platforms, this andragogical role of self-efficacy becomes
increasingly important.
The effect of self-efficacy on academic achievement operates in
conjunction with other variables. Cordova, Sinatra, Jones,
Taasoobshirazi, and Lombardi (2014) classified students into
three categories, demonstrating varying degrees of self-efficacy
in combination with prior knowledge and interest. The results
of their research indicate a highly complex relationship between
self-efficacy and academic achievement. While students with
low self-efficacy, prior knowledge, and interest correlated
directly with lower academic achievement, students with higher
self-efficacy, prior knowledge, and interest were divided
between low and high academic achievement (p. 172). These
results indicate that the influence of self-efficacy on academic
achievement is affected by other factors. Although self-efficacy
may be a good predictor of academic achievement, other
factors, including prior knowledge and interest, have either a
mediating or moderating effect on this relationship. This
integrated relationship of various social cognitive constructs
with academic achievement becomes clearer through analysis of
the relationships between constructs.
Integrated relationships between constructs, or covariance, can
distort the perceived relationship between self-efficacy and
performance. Hong, Pei-Yu, Shih, Lin, and Hong (2012)
identified a negative correlation between self-efficacy and
anxiety. This relationship between self-efficacy and anxiety
indicates that the direct influence of self-efficacy may be
weaker than the study perceives in consideration of the

12. mediating effect that self-efficacy could have on the
relationship between anxiety and performance. Although the
research of Hong et al. does not address this mediation effect,
hierarchical regression analysis could be employed to better
understand the distinct relationships present. Although this
research gap is not the focus of this study, it presents an
opportunity for future research which should be explored
further. Regardless, the effect of self-efficacy on academic
achievement is still largely supported in existing research and
justifies investigation regarding how self-efficacy can be
integrated into learning design.
Integration of Self-Efficacy in Learning Design. The active
development of social cognitive attributes demonstrates a
positive enhancement in learning. Adams (2014) determined
that active development of collective trust in students directly
influenced academic achievement. This research demonstrates
the indirect influence of active cognitive development on
learning. Therefore, the active development of core learning
capabilities enables learning beyond the standard distribution of
information and knowledge. By developing learning capability,
students become more adept and efficacious in the learning
process and are better equipped to engage in learning across
multiple disciplines. One method of active self-efficacy
development is presented through supervised mastery
experiences.
In alignment with the cyclic relationship between performance
and self-efficacy, student teaching experience presents positive
influences on self-efficacy in pre-service teachers. Al-Awidi
and Alghazo’s (2012) evaluation of pre-service teaching
experience identified that engaging in student teaching
enhanced both self-efficacy and future performance. This
research clarifies the relationship between self-efficacy and
performance, and demonstrates the effect of active self-efficacy
development on performance. Furthermore, this research
implies that the engagement of practical application
instructional techniques advances self-efficacy and

13. subsequently advances learning. However, the non-
experimental nature of this research precludes the experiences
of those student teaching participants whom did not continue
into the role of pre-service teachers.
Experimental research presents a more holistic insight into the
relationship between practical application and self-efficacy
development. Through experimental research, Chen and Usher
(2013) evaluated the effect of mastery experiences on self-
efficacy development through the analysis of self-efficacy both
before and after participation in mastery experiences. They
concluded that mastery experiences provide a powerful source
for self-efficacy development (Chen & Usher, 2013). Mastery
experiences provide opportunities for students to work through
problems in a supervised environment: eliminating feeling of
inadequacy, producing successful performances, and building
self-efficacy. Interestingly, although mastery experiences
produce consistent results across multiple student bases, some
students presented a heightened development of self-efficacy.
Not all students benefit from active self-efficacy development
equally. Exposure to multiple sources of self-efficacy
development enhances self-efficacy development in some
students. Highly adaptive students draw from multiple sources
of efficacy development simultaneously (Chen & Usher, 2013).
Therefore, to effectively engage self-efficacy development in
learning, educators need to 1) provide multiple sources of self-
efficacy development simultaneously, and 2) maintain
awareness of how students respond to varied activities:
identifying students which are less adaptive and adapting
learning activities to accommodate student needs. While
supervised practical application is a powerful efficacy building
tool, unsupervised practical application, especially in group
settings, may actually be harmful to self-efficacy.
Opportunities for supervised practical application provide an
immensely valuable resource in the development of self-
efficacy and the promotion of task engagement. Discrepancies
in early performance, especially in persons with low levels of

14. cognitive self-worth, can negatively impact self-efficacy
(Wang, Fu, & Rice, 2012). However, discrepancies are not
restricted to failed task execution and can include lower degrees
of success in comparison to peers or other self-established
success criteria (p. 97). As people judge personal performance
in comparison to peers, students that fall behind are likely to
experience negative self-efficacy even in the engagement of
practical application exercises. Therefore, it is properly
managed self-efficacy development which has demonstrated
positive results in the application of learning.Self-Efficacy in
Technology Acceptance
Technology Acceptance Modeling. With the increasing use of
technology to enable and enhance education activities, it is
important to understand the role of self-efficacy in the use of
technology enabled learning, or e-learning. In their 2010 study
regarding the role of enjoyment, computer anxiety, computer
self-efficacy, and internet experience toward intent to engage in
e-learning, Alenezi, Karim, Malek, and Veloo determined that
computer self-efficacy had significant influence on student
intention to engage in e-learning (p. 32). This research
provides an important link between self-efficacy and the
acceptance of technology in the learning environment,
indicating mobile self-efficacy as likely to influence the use of
mobile technology.
Self-efficacy indirectly influences technology acceptance
through the influence of perceived ease of use. While computer
self-efficacy is not a direct determinate of technology
acceptance, it does influence perceived ease of use. Similarly,
computer anxiety and attitudes toward using technology also
influence perceived ease of use (Venkatesh et al., 2003; Celik &
Yesilyurt, 2013). In fact, Celik and Yesilyurt (2013)
determined that self-efficacy and anxiety significantly influence
teacher attitudes toward computer supported education.
Through these indirect relationships, technology developers,
organizational leaders, and educators can improve technology
acceptance through programs which build user groups’ self-

15. efficacy and reduce the anxiety and negative stereotypes of
computer use. Understanding these intertwining relationships is
necessary in designing and marketing new technologies.
Furthermore, these relationships do not represent unidirectional
influence. As self-efficacy influences technology acceptance,
technology engagement further builds self-efficacy.
Not only does self-efficacy influence technology acceptance,
but technology engagement reflectively influences self-efficacy.
In a study conducted by Shank and Cotton (2014), technology
enabled learning demonstrated direct influences on multiple
domains of self-efficacy; technological, mathematics/science,
academic, and general. Therefore, the successful engagement of
technology produces improved efficacy in the learner’s ability
to subsequently engage that same technology in the future. This
aligns with Bandura’s (1993) presentation of the cyclic nature
between performance and self-efficacy, and future supports the
concept of presenting mastery experiences with increasing
difficulty. Therefore, engagement of simple, unrelated tasks
may be necessary while integrating technology into the
classroom in order to build technology self-efficacy to the point
necessary to recognize the full educational benefit of the
technology.
Mobile Technology Acceptance. Despite the findings of early
technology acceptance research, research specific to mobile
technology acceptance has determined direct relationships with
predictors which have been defined as indirect by the TAM.
For example, research conducted by Park, Nam, and Cha (2012)
specifically evaluates mobile technology acceptance in relation
to previously identified indirect influences of technology
acceptance. The study determined attitude toward mobile
learning as the primary direct construct in predicting the
acceptance of mobile technology in an educational environment
(p. 602). Furthermore, Irby and Strong’s (2013) research,
concerning mobile technology acceptance among agriculture
students, determined self-efficacy as a direct determinate of
mobile technology acceptance (p. 84). The assertion of attitude

16. and self-efficacy as direct determinates of mobile technology
acceptance run contrary to the assessment of Venkatesh et al.
(2003) of both attitude and self-efficacy as indirect
determinates, and implies a deviation in acceptance
relationships concerning mobile technology.Methodology and
Approach
This research will use a quantitative methodology with a non-
experimental approach. The quantitative methodology provides
the opportunity to investigate the phenomenon from an
objective perspective, adding credibility to Bandura’s (1986)
self-cognitive theory (Creswell, 2009). With a multiple
regression research design, the research will evaluate the
relationship between mobile self-efficacy and mobile
technology acceptance, clarifying the existence and strength of
the relationship (Creswell, 2009).
The existing literature concerning technology acceptance
maintains strong support for quantitative research. In their
seminal works on technology acceptance, both Davis (1989) and
Venkatesh et al. (2003) engage quantitative research toward the
development and refinement of survey instruments designed to
evaluate technology acceptance constructs. Furthermore,
research has engaged these surveys in combination with various
statistical techniques to study and validate technology
acceptance theory (Eteokleous & Ktoridou, 2009; Alenezi,
Karim, Malek, & Veloo, 2010; Ismail, Bokhare, Azizan, &
Azman, 2013; Irby & Strong, 2013). The continued engagement
of the academic community in the quantitative study of
technology acceptance demonstrates an implied acceptance of
the propriety in using quantitative research methodologies to
evaluate this topic. However, not every quantitative
methodology aligns with every research question related to
technology acceptance.
Practically, the topic of technology acceptance addresses two
primary concerns: predicting the acceptance of a technology
within a population, and explain the factors which are
influencing the acceptance of a technology within a population.

17. Both concerns are associated with analyzing the relationships
between variables. Vogt (2007) asserts that, while the terms
regression and correlation are often used interchangeably,
regression analysis is regularly associated with predictions and
correlation analysis is regularly associated with explanations of
existing relationships. Therefore, the alignment of research
towards a correlation technique, two-tailed t test, or a
regression technique, hierarchical regression analysis, is highly
dependent upon the research objectives, as either methodology
is appropriate for technology acceptance research.Methodology
and Rationale
Research Methodology Analysis. In the analysis of existing
relationships, the two-tailed t test provides a quality
correlational analysis technique. The two-tailed t test
independently analyzes the relationship between defined
variables (Vogt, 2007). The strength of this statistical analysis
technique is that it directly analyzes the relationship between
two variables, and clearly demonstrates the presence, or
absence, of a relationship. However, the two-tailed t test does
not analyze the strength of the correlation in terms of how much
variance is explained by the relationship, or the effects of
covariance (Tabachnick & Fidell, 2013). Therefore, while the
two-tailed t test is appropriate for determining the presence of
relationships, this technique does not quantify the effect of that
relationship.
In determining predictors for relationships, hierarchical
regression analysis provides a quality regression analysis
technique. Hierarchical regression analysis engages a multi-
step analysis process to analyze the degree of variance in a
defined construct which is explained by multiple other
constructs (Tabachnick & Fidell, 2013). The strength of this
statistical analysis technique is that it analyzes relationship
strength and covariance. However, hierarchical regression
analysis engages complex statistical analysis and requires the
underlying data sets to align with assumptions of normality,
homogeneity, and multicollinearity (Fields, 2013). Therefore,

18. this technique is most readily engaged in the analysis of
multiple independent variables in conjunction with one or more
dependent variables.
Methodology Selection Rationale. The proposed research
question most directly aligns with hierarchical regression
analysis, which readily analyzes the effects of covariance
(Tabachnick & Fidell, 2013). However, Hoyt, Imel, and Chan
(2008) claim that the presence of covariates does not, itself,
justify the use of hierarchical regression analysis, and that the
use of this technique is designed specifically to address the
identification or validation of mediator variables. With this
consideration, the alignment of the research topic with
hierarchical regression analysis is not merely related to the
presence of covariates, but with the emphasis of the research
topic to validate the moderating relationship of the covariates.
Therefore, hierarchical regression analysis is most capable of
analyzing the relationship between self-efficacy and mobile
technology acceptance in consideration of the moderating
effects of effort expectancy and performance
expectancy.Population and Sample
The population for this research will be undergraduate students.
Undergraduate students represent a population of learners which
are capable of understanding and representing survey response
which will address the constructs of self-efficacy, effort
expectancy, performance expectancy, and behavioral intent to
use. This research will use the SurveyMonkey Audience service
which will provide a sample frame of undergraduate students
for participation in the survey. This sampling approach will
provide a sample of 384 participants, which is similar to
samples used in other recent research regarding mobile
technology acceptance (Irby & Strong, 2013), aligns with the
sampling design, and is supported through power analysis using
the GPower3 software.Sample Recruitment Strategy
To support the recruitment of research participants, the
researcher will coordinate with the survey distribution service
regarding timelines, survey distribution requirements, and

19. population restrictions. Then, the researcher will assess and
approve the distribution of the survey instrument. The survey
service will randomly distribute the survey instrument within
the sample frame. Participants will complete the survey via the
survey distribution service, and the survey service subsequently
provides participant survey responses to the
researcher.Instrument
This research will engage a modification of Venkatesh, Morris,
Davis, and Davis’s (2003) survey instrument developed in
support of the Unified Theory of Acceptance and Use of
Technology (UTAUT). The original instrument has been widely
accepted and used in support of technology acceptance research
(Pi-Hsia Hung, Gwo-Jen Hwang, I-Hsiang Su, & I-Hua Lin,
2012; Stergiaki, 2013; Alenezi, Karim, Malek, & Veloo, 2010;
Eteokleous & Ktoridou, 2009). The specific modification that
will be engaged by this study was modified by Irby and Strong
(2013) to specifically address the acceptance of mobile
technology, and presented acceptable reliability coefficients of;
performance expectancy = .92, effort expectancy = .91,
behavioral intention = .97, and self-efficacy = .95.Conclusion
Where the role of self-efficacy is well defined in support of
learning, the role of self-efficacy in the engagement of m-
learning is less clear. While self-efficacy has been designated
as an indirect determinate for technology in general (Venkatesh,
Morris, Davis, & Davis, 2003), specific research regarding
mobile technology indicates a relationship between self-efficacy
and mobile technology acceptance in the course room (Irby &
Strong, 2013; Alenezi, Karim, Malek, & Veloo, 2010;
Eteokleous & Ktoridou, 2009; Ismail, Bokhare, Azizan, &
Azman, 2013). However, the degree of correlation between
learner self-efficacy and the acceptance of mobile technology
has not yet been determined. This represents a gap in the
existing literature regarding the integration of mobile
technology in the educational environment and addresses the
recommendation for future research provided by Irby and Strong
(2013) to research the effect of self-efficacy on mobile

20. technology acceptance (p. 85).
4
This paper analyzes the existing literature concerning self-
efficacy and its role in m-learning. The paper evaluated the
theoretical foundations of self-efficacy and methods for the
intentional development of self-efficacy. Then the paper
assessed the role of self-efficacy in learning and the
relationship between self-efficacy and academic achievement.
Finally, the paper appraised the role of self-efficacy in
technology acceptance and the distinctions in the existing
literature regarding mobile technology acceptance. This
disconnect in the existing literature regarding the role of self-
efficacy in technology and mobile technology acceptance
produces the core research problem which will be addressed
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