Self-Efficacy in M-LearningJason HutchesonRunnin.docx

Self-Efficacy in M-Learning
Jason Hutcheson
Running head: 3Capella UniversityTable of Contents
Literature Review5
Self-Efficacy Theory5
Theoretical Foundations.5
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-
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
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.
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
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
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
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
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
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
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
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-
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
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.
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,
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
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
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
through the proposed research.References
Adams, C. M. (2014). Collective student trust a social resource
for urban elementary students. Educational Administration
Quarterly, 50(1), 135–159. doi:10.1177/0013161X13488596
Al-Awidi, H. M., & Alghazo, I. M. (2012). The effect of student
teaching experience on preservice elementary teachers’ self-

efficacy beliefs for technology integration in the UAE.
Educational Technology Research and Development, 60(5),
923–941.
Alenezi, A. R., Karim, A., Malek, A., & Veloo, A. (2010). An
empirical investigation into the role of enjoyment, computer
anxiety, computer self-efficacy and internet experience in
influencing the students’ intention to use e-learning: A case
study from Saudi Arabian governmental universities. Turkish
Online Journal of Educational Technology, 9(4), 22–34.
Ananou, T. (2014). Academic Honesty in the Digital Age
(Dissertation). Indiana University of Pennsylvania,
Pennsylvania.
Artino, A. (2012). Academic self-efficacy: From educational
theory to instructional practice. Perspectives on Medical
Education, 1(2), 76–85. doi:10:1007/s40037-012-0012-5
Bandura, A. (1986). Social Foundations of Thought and Action:
A Socialy Cognitive Theory. Englewood Cliffs, NJ: Prentice-
Hall.
Bandura, A. (1993). Perceived self-efficacy in congitive
development and functioning. Educational Psychologist, 28(2),
117–148.
Celik, V., & Yesilyurt, E. (2013). Attitudes to technology,
perceived computer self-efficacy and computer anxiety as
predictors of computer supported education. Computers &
Education, 60(1), 148–158. doi:10.1016/j.compedu.2012.06.008
Chen, J., & Usher, E. (2013). Profiles of the sources of science
self-efficacy. Learning and Individual Differences, 24, 11–21.
Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An
investigation of mobile learning readiness in higher education
based on the theory of planned behavior. Computers &
Education, 59(3), 1054–1064.
doi:10.1016/j.compedu.2012.04.015
Clinkenbeard, P. R. (2012). Motivation and gifted students:
Implications of theory and research. Psychology in the Schools,
49(7), 622–630. doi:10.1002/pits.21628
Cordova, J., Sinatra, G., Jones, S., Taasoobshirazi, G., &

Lombardi, D. (2014). Confidence in prior knowledge, self-
efficacy, interest and prior knowledge: Influences on conceptual
change. Contemporary Educational Psychology, 39, 164–174.
Creswell, J. (2009). Research Design (3rd ed.). Los Angeles:
Sage.
Davis, F. (1989). Perceived usefulness, perceived ease of use,
and user acceptance of information technology. MIS Quarterly,
13(3), 319–340.
Dinther, M., Dochy, F., & Segers, M. (2011). Factors affecting
students’ self-efficacy in higher education. Educational
Research Review, 6, 95–108.
Eteokleous, N., & Ktoridou, D. (2009). Investigating mobile
devices integration in higher education in cyprus: Faculty
perspectives. International Journal of Interactive Mobile
Technologies, 3(1), 38–48. doi:10.3991/ijim.v3i1.762
Field, A. (2013). Discovering Statistics Using IBM SPSS
Statistics (Third.). Los Angeles: SAGE Publications Ltd.
Fuegen, S. (2012). The impact of mobile technologies on
distance education. TechTrends: Linking Research and Practice
to Improve Learning, 56(6), 49–53.
Gao, Z., Lee, A. M., Xiang, P., & Kosma, M. (2011). Effect of
learning activity on students’ motivation, physical activity
levels and effort/persistence. ICHPER-SD Journal of Research,
6(1), 27–33.
Hong, J.-C., Pei-Yu, C., Shih, H.-F., Lin, P.-S., & Hong, J.-C.
(2012). Computer self-efficacy, competitive anxiety and flow
state: Escaping from firing online game. Turkish Online Journal
of Educational Technology, 11(3), 70–76.
Hoyt, W., Imel, Z., & Chan, F. (2008). Multiple regression and
correlation techniques: Recent controversies and best practices.
Rehabilitation Psychology, 53(3), 321–339.
Irby, T. L., & Strong, R. (2013). Agricultural education
students’ acceptance and self-efficacy of mobile technology in
classrooms. NACTA Journal, 57(1), 82–87.
Ismail, I., Bokhare, S. F., Azizan, S. N., & Azman, N. (2013).
Teaching via mobile phone: a case study on Malaysian teachers’

technology acceptance and readiness. Journal of Educators
Online, 10(1).
Knowles, M. (1970). The Modern Practice of Adult Education:
Androgogy vs. Pedagogy. New York, NY: Association Press.
Ktoridou, D., Gregoriou, G., & Eteokleous, N. (2007). Viability
of mobile devices integration in higher education: faculty
perceptions and perspective. Presented at the 2007 International
Conference on Next Generation Mobile Applications, Services
and Technologies, IEEE.
Liljestrom, A., Enkenberg, J., & Pollanen, S. (2013). Making
learning whole: An instructional approach for mediating the
practices of authentic science inquiries. Cultural Studies of
Science Education, 8(1), 51–86.
Male, G., & Pattinson, C. (2011). Enhancing the quality of e-
learning through mobile technology: A socio-cultural and
technology perspective towards quality e-learning applications.
Campus-Wide Information Systems, 28(5), 331–344.
Park, S. Y., Nam, M.-W., & Cha, S.-B. (2012). University
students’ behavioral intention to use mobile learning:
Evaluating the technology acceptance model. British Journal of
Educational Technology, 43(4), 592–605.
Pegrum, M., Oakley, G., & Faulkner, R. (2013). Schools going
mobile: A study of the adoption of mobile handheld
technologies in Western Australian independent schools.
Australasian Journal of Educational Technology, 29(1), 66–81.
Pi-Hsia Hung, Gwo-Jen Hwang, I-Hsiang Su, & I-Hua Lin.
(2012). A concept-map integrated dynamic assessment system
for improving ecology observation competences in mobile
learning activities. Turkish Online Journal of Educational
Technology, 11(1), 10–19.
Rossing, J. P. (2012). Mobile technology and liberal education.
Liberal Education, 98(1), 68–72.
Shank, D. B., & Cotten, S. R. (2014). Does technology empower
urban youth? The relationship of technology use to self-
efficacy. Computers and Education, 70, 184–193.
doi:10.1016/j.compedu.2013.08.018

Shree Ram, B., & Selvaraj, M. (2012). Impact of computer
based online entrepreneurship distance education in India.
Turkish Online Journal of Distance Education, 13(3), 247–259.
Stergiaki. (2013). Acceptance and usage of extensible business
reporting language: an empirical review. Journal of Social
Sciences, 9(1), 14–21. doi:10.3844/jssp.2013.14.21
Tabachnick, B., & Fiddell, L. (2013). Using Multivariate
Statistics. Upper Saddle River: Pearson Education, Inc.
Tan, P. I. J. (2012). Second career teachers: Perceptions of self-
efficacy in the first year of teaching. New Horizons in
Education, 60(2), 21–35.
Tella, A., Tella, A., & Adeniyi, S. O. (2011). Locus of control,
interest in schooling and self-efficacy as predictors of academic
achievement among junior secondary school students in Osun
State, Nigeria. New Horizons in Education, 59(1), 25–37.
Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User
acceptance of information technology: Toward a unified view.
MIS Quarterly, 27(2), 425–478.
Vogt, P. (2007). Quantitative Research Methods for
Professionals. Boston: Pearson Learning
Solution
s.
Wang, K. T., Fu, C.-C., & Rice, K. G. (2012). Perfectionism in
gifted students: Moderating effects of goal orientation and
contingent self-worth. School Psychology Quarterly, 27(2), 96–
108. doi:10.1037/a0029215

Y
ou may not spend much time thinking about logistics,
but it’s all around you. That’s because “logistics” is a
catchall term that covers a vast range of services and
capabilities—so vast that the total amount of money devoted
to all things logistical was nearly $1.5 trillion in the U.S. last
year, equivalent to 8.3 percent of the nation’s gross domestic
product (GDP), according to the Council of Supply Chain
Management Professionals.
That demonstrates the degree to which companies of all
sizes and in all industries rely on some aspect of logistics in
their business models, and making the right decisions in this
area “can determine the success or failure of an organization,”
says Paul Myerson, professor of supply chain management
at Lehigh University’s College of Business and Economics.
“These decisions have a huge impact, not only on cost and
service, but also on revenue, since poor service can result in
lost sales and damage to a business’s reputation.” And with
more and more companies expanding into global markets, the
stakes become even higher.

Transportation and inventory carrying costs account for
more than 95 percent of the enormous sum U.S. businesses
spend on logistics each year, while shipper-related costs and
logistics administration account for less than 5 percent. But
what’s signifi cant about the latter two categories is that they
present real opportunities to cut costs, boost effi ciency, and
improve ROI and bottom-line performance, especially for small
and medium-sized businesses.
If you’re wondering where to start, Myerson says the most
important advances for SMBs in this area in recent years are
the development of readily available, low-cost, sophisticated
technology, and the growth and accessibility of third-party
logistics providers (3PLs). “These developments give SMBs
access to the same capabilities as their larger competitors,”
he notes.
Whether you know it or not, logistics is a vital part of your
business. As it becomes more complex, look for partners
who can solve the problems you may not even see coming.
Your New Logistics Challenge:
GLOBALIZATION

I N C . B R A N D E D C O N T E N T / L O G I S T I C S
S1
When Inbound Logistics, an industry
trade publication, asked shippers of all
sizes about the greatest challenges they
faced last year, cutting transport costs
topped the list, at 63 percent. Business
process improvement (32 percent) and
improving customer service (31 percent)
also emerged as high priorities. SMBs
share those same concerns, of course,

but they also face additional challenges.
“One common challenge—a great
one to have—is rapid growth,” says
Jason Roberts, managing director of
Freightview, which provides revolutionary
technology solutions to help shippers
streamline their freight quoting, booking,
tracking, and reporting. Poor visibility
across carriers, competing against larger
competitors with warehouses closer to
customers, capitalizing on emerging global
opportunities, and navigating a morass
of rules and regulations—especially in

international markets—are others.
Enterprise organizations have been
harnessing technology to meet their
logistics challenges for many years,
especially transportation management
system (TMS) software. A TMS typically
manages four key logistics processes:
planning and decision-making to achieve
the most effi cient and economical
transportation solutions; plan execution;
follow-up, including shipment tracing,
customs clearance, invoicing, and other

administrative duties; and measurement
of key performance indicators (KPIs).
Most TMS solutions rely on electronic
data interchange (EDI), an aging
technology, and they require signifi cant
capital investment and in-house IT
resources. That puts TMS solutions
out of reach for many SMBs, especially
those doing fi ve to 25 shipments a day
of less-than-truckload (LTL) size. A new
generation of cloud-based technology
solutions that rely on application
programming interfaces (APIs) rather

than EDI are making it possible for many
SMBs to achieve the kind of cost and
productivity benefi ts that enterprise
organizations have been getting from EDI-
based TMS for years.
Roberts calls out three primary benefi ts
cloud-based solutions like Freightview
can provide to SMBs:
• Shippers can access all their freight-
shipping rates from all their carriers
and brokers in one place. “Instead of
going to their carrier websites one at

a time, they can instantly compare all
their different costs, servicing options,
and transit times. That makes it easy for
them to identify the right way to move
each shipment with the right balance
of low cost and quick delivery,” he
explains.
• By scheduling pickups and tracking
shipments in one place, a shipper’s
visibility isn’t diffused across multiple
websites. “When shippers need to track
a shipment or double-check a carrier
invoice, there’s one source for all of their

information.”
• When it comes time to negotiate with
carriers, shippers have the information
they need to get the best deal. “They
have data about their shipment
characteristics, lanes, and spending,”
Roberts points out. “They are well
positioned to collaborate with their
carriers to get the best possible
rates based on facts rather than
assumptions.”
Big companies with TMS software

already have those advantages, Roberts
acknowledges, but until now, they’ve
been out of reach for SMBs. “The costs
were too high, the implementations
took too long, and the software was
too hard to use,” he says. “Freightview
brings these features to SMBs via the
cloud with a low price point, quick
implementation—often the same day—
and the right set of features to help
without getting in the way.”
Growth Can
Pose Problems

The Price Is Right
S3
Businesses with more complex shipping needs, such
as a mix of LTL and full-truckload shipments, intermodal
transportation requirements, and others, or those just
looking for greater convenience and fl exibility might be better
served by a 3PL. This is robust outsourcing of the one-stop-
shopping variety, as 3PLs provide multiple logistics services
(transportation, warehousing, cross-docking, inventory
management, packaging, freight forwarding) integrated to best
meet their customers’ needs.

“Shippers using a 3PL gain access to a vast network of
resources and industry expertise that can save them time and
money,” says Greg West, vice president North America LTL at
C.H. Robinson, which was voted the No. 1 3PL by readers of
Inbound Logistics for the fi fth consecutive year in 2015.
“Using
a 3PL gives shippers the fl exibility to scale logistics according
to inventory needs, which is very important for businesses
with signifi cant seasonal swings in volume. At C.H. Robinson,
we are continuously improving every link in the supply chain
to optimize speed, effi ciency, and cost-effectiveness to benefi t
our clients.”
C.H. Robinson leverages scalable global technology

in its Navisphere® platform to help clients doing business
internationally bring all aspects of their supply chain together,
providing them with end-to-end shipment visibility and
reporting
across all regions where they do business. It also offers
Collaborative Outsourcing® as an additive approach to logistics
outsourcing, enabling businesses to add the resources and
integrated services they need to help drive desired outcomes.
This hybrid approach can improve supply chain performance for
the same or lower total landed cost, reduce capital investment,
increase insight into performance metrics, and speed up
response time to changing market conditions.
Several major new developments in global trade are

expected to play a signifi cant role in logistics in 2016,
affecting
nearly every business that imports or exports goods, says John
LaMancuso, chief sales and marketing offi cer at Livingston
International, North America’s leading customs brokerage and
trade compliance fi rm focused on simplifying the movement
of goods through international borders for more than 40,000
clients. “New trade agreements aim to open markets and
simplify trade processes, creating an unprecedented
opportunity for North American companies to expand their
business,” LaMancuso says.
One important development is the implementation of a new
U.S. Customs and Border Protection (CBP) system known

as the Automated Commercial Environment (ACE), which
requires every U.S. company conducting international trade to
submit forms electronically to a single source. When ACE goes
fully into effect in February, it will streamline and automate
existing manual processes by providing shippers with a single
portal where they can submit forms to CBE. “Paper will be
eliminated, and the international trade community will be able
to comply with U.S. laws and regulations more easily and
effi ciently,” LaMancuso says. “Businesses will have better
visibility with respect to release and inspection, shipment cycle
time will be reduced as mismatched information is identifi ed
earlier in the process, and turnaround for customs and agency

review will be faster.”
That streamlined process may prove even more benefi cial
to companies doing business internationally as several
pending trade agreements take effect. One is the Trans-Pacifi c
Partnership (TPP), which LaMancuso says is the biggest free-
trade deal in history. The 12 countries participating in the TPP
account for about 40 percent of global GDP, and they reached
an agreement in October after seven years of negotiations.
Now it must be ratifi ed by the governments of each country,
a process that could start in the U.S. next year. A separate
trade and investment agreement, the Transatlantic Trade and
Investment Partnership (T-TIP), being negotiated between
the U.S. and 28 European Union member countries would

increase access to European markets for U.S.-made goods
and services.
New trade agreements aim
to open markets and simplify
trade processes, creating an
unprecedented opportunity for
North American companies to
expand their business.
The Fast Route to Optimization
Seizing Global Opportunities
S5

3PL:
A third-party fi rm to which a variety of
logistics services are outsourced, such
as purchasing, inventory management
and/or warehousing, transportation
management, and order management.
Detention/demurrage:
Penalty charges assessed by a carrier
for holding transportation equipment
(such as trailers or containers) longer
than a stipulated period of time for
loading/unloading.
FOB (free-on-board) point:

Point at which ownership of freight
transfers from shipper to consignee
(the freight receiver).
FOB terms-of-sale:
Document stipulating who arranges
for transport and carrier, who pays for
transport, and the FOB point.
Freight bill-of-lading (BoL):
Document providing a binding
contract between a shipper and a
carrier for transportation of freight;
specifi es obligations of both parties,
and usually designates the consignee.
Freight forwarder:

Agency that receives freight from a
shipper and arranges transport with
one or more carriers; often used for
international shipping.
LTL:
Less-than-truckload shipment; priced
according to weight, commodity class,
and mileage within designated lanes.
TL/FTL:
Truckload/full truckload shipment,
where the shipper contracts an entire
truck for direct point-to-point transport
and pays a price per mile within
designated lanes, regardless of size of

shipment; less expensive than LTL.
A Crash Course on
Logistics Lingo
“To be successful, it is imperative
that companies understand how these
trade developments could impact their
business and do their due diligence to
prepare for them,” LaMancuso says.
“They should strategize business
opportunities and adapt their
business model to encompass trade
opportunities. It’s also important to
build a compliance strategy so that

importing and exporting processes and
documentation are in compliance with
the pending trade agreements’ rules
and regulations.”
While many SMBs might have the
strategic fi repower to capitalize on the
opportunities LaMancuso foresees,
the complex demands of regulatory
compliance can be overwhelming.
Fortunately, they can outsource many
of those responsibilities to third-party
providers. “Livingston International

simplifi es the complexities of importing
and exporting for its clients of all sizes,
giving them the freedom to focus on
growing their business,” he says. “Since
understanding trade regulation trends
and the latest news is important, we
educate SMB clients through weekly
webinars on all facets of trade, including
compliance, expansion into new regions,
trade agreements, duty recovery, and
regulations.” Livingston International
also provides innovative technology
solutions, such as its TradeSphere suite of

automation software.
Companies with a more narrowly
focused international business model
often can get the logistics help they need
from a third-party provider targeting
their specifi c industry vertical. Bongo
International, for example, focuses
on e-retailers looking to expand into
global markets. “Our goal is to make
every international transaction as easy
as possible for both the customer and
the retailer,” says Greg Sack, managing

director and co-founder of Bongo
Conquering Complexity
S7
The E-commerce
Advantage
The company offers two solutions,
Bongo Checkout and Bongo Export.
The fi rst is a fully outsourced cross-
border enablement (CBE) solution that
includes currency conversion, shipping
calculations, duty/tax calculations,

compliance, and payment. “With Bongo
Checkout, we provide a currency
conversion feed so the retailer can
display its website in more than 120
countries,” Sack explains. “This gives the
international customer a level of comfort
knowing that the site is set up to handle
international transactions.”
Bongo International takes over when
things move to the checkout stage,
displaying a calculation of landed costs in
the language detected in the customer’s
browser settings. “Customers are

then able to check out through Bongo
International, while the site retains the
look and feel of the retailer.” Orders
are screened for fraud then pushed to
the retailer with the domestic shipping
address of a Bongo export hub, where
goods may be repackaged to optimize
shipping weight and confi guration
before being processed and delivered to
international customers via FedEx.
Bongo Export is designed for retailers
that want to maintain merchant-of-record

status. Customers create a shopping basket
and select their destination country to
receive a shipping, duty, and tax quote. An
API calculates the landed cost and submits
it back to the website, and goods from
accepted orders are shipped to a Bongo
export hub, where they go through the same
process as Bongo Checkout orders.
As Rick Schreiber, partner,
manufacturing & distribution, at BDO USA,
observes, “Getting goods from point A
to point B is a critical component of any
company’s supply chain. An effi cient

transportation and logistics (T&L) system is
essential, as errors and ineffi ciencies in any
one component can quickly snowball and
become quite costly for a small business.
Beyond that, T&L is also a key ingredient
in overall customer satisfaction.” While
effi cient T&L remains a signifi cant challenge
for many SMBs, it’s one that technology
is making easier to meet. “Using software
that produces actionable analytics has
allowed companies to vastly improve T&L
effi ciencies. The most signifi cant advantage

to a well-thought-out T&L structure is being
able to provide the highest level of service at
all times,” Schreiber affi rms.
Customers are then able
to checkout through
Bongo International, while
the site retains the look
and feel of the retailer.
International. “In order to do that, we
provide end-to-end services that are
confi gurable to each retailer. This gives
our retailers the ability to create the
experience they feel will generate the

optimal conversion rate based on their
customers’ buying habits.”
Copyright of Inc. is the property of Mansueto Ventures LLC and
its content may not be
copied or emailed to multiple sites or posted to a listserv
without the copyright holder's
express written permission. However, users may print,
download, or email articles for
individual use.
Global Brand Architecture Position
and Market-Based Performance:
The Moderating Role of Culture
M. Berk Talay, Janell D. Townsend, and Sengun Yeniyurt
ABSTRACT
Companies expend vast resources to create product and brand

portfolios in the global marketplace. Yet knowledge of
the market-based performance implications of various positions
in a firm’s portfolio architecture is lacking in the litera-
ture. To further the understanding of managing brands in the
global marketplace, the authors develop a conceptual
framework based on the tenets of signaling theory, explore the
relationship between global brand architecture and
market-based performance, and consider how culture moderates
this relationship. The results of the analyses, from a
panel data set of 165 automotive brands operating in 65
countries from 2002 to 2008, reveal that global brands per-
form better in the marketplace than their nonglobal
counterparts. Cultural values indeed provide boundary
conditions
for this relationship, suggesting that alternative strategies for
some markets may be advisable.
Keywords: global brands, global brand architecture, culture,
signaling, panel data analysis
S
trategically managing the identity of brands and
products in a global environment is among the
most challenging activities for executives of com-

panies of all sizes. Choosing the markets to serve and
the means to serve them is a fundamental issue, and
immense resources are expended to implement strategies
to achieve performance objectives. The dynamics of
globalization and competition have caused multi-
national companies to evolve from parochial strategies
focused on individual markets toward more complex
portfolio management strategies that transcend national
boundaries. Developing the best range of focus for
brands and products goes beyond features and attrib-
utes; in the contemporary environment, it also includes
strategic geographic scope considerations.
The geographic range strategy that firms employ to man-
age their brands is characterized by a hierarchical struc-
ture of products and brands present in global markets;
that is, firms often employ a variety of options in their
branding strategies, from local/domestic branding to
branding with regional, multiregional, or global orienta-
tions. This phenomenon is referred to as a global brand
architecture (GBA) and specifically refers to the portfolio
of brands a firm controls on a continuum of geographic
scope and degree of consistency (Douglas, Craig, and
Nijssen 2001; Townsend, Yeniyurt, and Talay 2009).

Developing a rational GBA is a key element of a firm’s
overall international marketing strategy because it offers
a foundation to leverage its brands’ equity across mar-
kets, integrate acquired brands, and rationalize global
strategies (Douglas, Craig, and Nijssen 2001).
To develop and deploy a GBA effectively, it is important
to understand the market-based performance outcomes
derived from various options within the GBA. Although
the GBA is not a new concept, actual market-based per-
formance of brands at various positions in a GBA has
yet to be grounded with empirical support; that is, exist-
ing studies have not addressed whether global brands
actually perform better than single-country, regional, or
multiregional brands. This study expands the extant
M. Berk Talay is Associate Professor of Marketing, University
of
Massachusetts Lowell (e-mail: [email protected]). Janell D.
Townsend is Associate Professor of Marketing, Oakland
University
(e-mail: [email protected]). Sengun Yeniyurt is Associate Pro-
fessor of Marketing, Rutgers University (e-mail:
[email protected]
rutgers.edu). Seigyoung Auh served as associate editor for this

article.
Journal of International Marketing
©2015, American Marketing Association
Vol. 23, No. 2, 2015, pp. 55–72
ISSN 1069-0031X (print) 1547-7215 (electronic)
Global Brand Performance 55
56 Journal of International Marketing
research on global brands by investigating how GBA
affects a brand’s performance in the global marketplace.
It is likely that global brands perform better in different
markets because of the influence of national culture.
Different regions of the world seem to have varying
mechanisms through which global brand perceptions
and attitudes are formed, processed, and employed in
purchase decisions. Akdeniz and Talay (2013) find mul-
tifarious effects of culture as moderators of the relation-
ship between product signals and performance.
Research in the Chinese market has shown that global

retail brands influence customers through different
functional and psychological values (Swoboda, Penne-
mann, and Taube 2012). In Eastern European markets,
global brands have been found to be perceived as a pass-
port to global citizenship (Strizhakova, Coulter, and
Price 2008). Dimofte, Johansson, and Bagozzi (2010)
find support for the notion that ethnic cultures within a
country act as moderators of global brand quality per-
ceptions. Therefore, it is well established that national
culture plays a significant role in directly influencing
consumer financial decision making and also moderates
the impact of marketing efforts by the financial services
firm (Petersen, Kushwaha, and Kumar 2015).
Although culture has been addressed as an important con-
sideration from many perspectives, research on its role in
moderating the relationship between branding strategies
and market performance is lacking. To that end, we iden-
tify extrinsic boundaries based on cultural factors, which
can cause the performance of alternative strategic
approaches to vary in global markets. This enables us to
contribute to the global branding literature by extending
the understanding of how culture affects managerial deci-
sions, which will help improve opportunities for successful
GBA management. We suggest, and empirically demon-

strate, that the effects of culture are likely to change the
relationship between the signal sent by the brand’s posi-
tion in the GBA and the brand’s market-based perfor-
mance. Research from a broad array of disciplines has
posited culture as a measure of values and beliefs and has
widely viewed it as a precursor to the acceptance of global
brands in a country due to the advent of global consumer
cultures (Alden, Steenkamp, and Batra 2006). When in
doubt, consumers will choose products and brands that
have synergies with their values and beliefs, which are cor-
related with their cultural heritage.
Moreover, consumer attitudes toward global brands,
perceptions of their quality, and purchase likelihood
form the foundation of much of the global brand litera-
ture (Özsomer and Altaras 2008; Steenkamp, Batra,
and Alden 2003). Recent international marketing stud-
ies have focused on perceptions of global versus local
brands. Consumer attitudes toward global and local
products (Steenkamp and De Jong 2010), brand exten-
sions of global or local origin (Iversen and Hem 2011),
and perceived quality and global brand purchase likeli-
hood (Özsomer 2012) all underlie attempts to under-
stand the differences between global and local brands.

Psychological mechanisms that create differences in
attitudes toward global brands from developed versus
developing countries have also been of interest to
researchers (Alden et al. 2013; Guo 2013; Swoboda,
Pennemann, and Taube 2012). However, market-based
performance metrics are missing from the global brand-
ing literature. Although the aforementioned studies
related to global brand perceptions make an important
contribution, they all employ perceptual data derived
either from controlled experimental designs or from
survey-based studies. The current study, in contrast,
considers actual strategic brand deployment through
the GBA, rather than consumer perceptions of deploy-
ment. This is important because creating tangible mar-
ket performance is among marketing’s most fundamen-
tal contributions to the firm. Therefore, our research
fills a gap in the literature, in that we present actual
market-based data to assess the performance implica-
tions of the various geographic range options, as mod-
erated by national culture. Specifically, we provide mar-
ket share by brand as a means to evaluate global brand
performance because market share and its derivatives
are the most salient measures of market performance in
all marketing literature streams. In addition, as
Steenkamp (2014) notes, market share is a valued out-

come for global brands and is a topic Chabowski,
Samiee, and Hult (2013) suggest as an important foun-
dation for further research.
Overall, our study makes several contributions to the lit-
erature. We expand the research on local versus non -
local (Batra et al. 2000; Zhou, Yang, and Hui 2010) and
local versus global (e.g., Steenkamp and De Jong 2010)
dichotomies and demonstrate that brand multinational-
ity is a hierarchical continuum. We demonstrate the
actual market-based performance effects of the GBA
and illustrate that they depend on, along with many
other factors, a country’s cultural milieu.
The remainder of this article is structured as follows.
First, we present a framework based on a conceptualiza-
tion of global marketing strategies and actions (signals),
which lead to market-based performance outcomes for
brands (sales). Next, we develop hypotheses and present
a longitudinal econometric model to test them. Our

model is fit with a data set that has extensive temporal
and geographical coverage and is among the most com-
prehensive in the global brand strategy literature.
Specifically, we develop a longitudinal model utilizing
brand-level data for 165 automotive brands (e.g.,
Chevrolet, Mini) owned by 96 companies (e.g., General
Motors, BMW) from 18 countries, operating in 65 mar-
kets from 2002 to 2008. We present the results of our
analysis and conclude with a discussion of managerial
implications and limitations of the research.
LITERATURE REVIEW AND CONCEPTUAL
FRAMEWORK
Global firms often own many products and brands
that can vary in scope across and between national
markets. To manage these brands and products as
portfolios, firms are increasingly implementing unam-
biguous international brand architectures in which
ranges and geographic scope are employed as strategic
means of facilitating both brand consistency and dif-
ferentiation across international markets (Douglas,
Craig, and Nijssen 2001). This phenomenon seems to
be in response to the emergence of market segments
that transcend national boundaries (Hofstede, Wedel,

and Steenkamp 2002). That is, for a firm to be success-
ful in all the markets it serves, it may choose to vary
the mix of brands available in some countries and offer
only a certain number of brands in each of its markets.
For example, Toyota has managed its namesake brand
globally but has offered brands such as Lexus and
Scion as primarily domestic brands (ironically, in the
United States rather than Japan, its country of origin)
for a time before incrementally expanding into other
markets.
In line with the notion of managing brands geographi-
cally, GBA has four basic strategies: global, multi -
regional, regional, and domestic (Townsend, Yeniyurt,
and Talay 2009). This is reflected in the strategic orien-
tations taken by companies operating in the variety of
contexts in the global environment. While progressive in
nature, there are differences in characteristics and tactics
that enable firms to achieve these positions.
Global brands are present in all major market regions of
the world and employ an integrated approach to stan-
dardization across markets. Multiregional brands may
be present in several markets, across several continents,
but they do not have a centralized or standardized mar-

keting program across geographic markets; they are not
present in all triad markets (i.e., Asia, Europe, North
America). Regional brands are offered in multiple coun-
tries in one geographic region (Rugman and Collinson
2004). Morrison, Ricks, and Roth (1991) suggest that a
multiregional approach may actually provide the best
strategic balance between global and domestic brands
and optimize performance. In a study of global automo-
tive manufacturers, Schlie and Yip (2000) show that
most were following a regional strategy, with a few
moving toward a global orientation. Douglas and Craig
(2011) argue that firms should develop alternatives to
global strategies, such as semiglobal marketing strate-
gies, to maximize their performance.
Single-market brands are those sold in an individual
national market. Even in the age of globalization, single-
market brands still have a place in a firm’s GBA.
Although a single-market brand serves only one
national market, studies such as Kapferer (2002) suggest
there are means for these brands (usually but not always
domestic) to compete through local knowledge and
flexibility. Appealing to patriotism and ethnocentrism
are strategies these types of firms undertake (Klein

2002). Although there is reason to believe that brands
with more narrow geographic approaches may provide
attractive options for consumers, mounting evidence
seems to support the notion that global brands act as a
quality signal.
These positions in a GBA provide different signals in the
markets in which they do business. For example, global
brands have been established as signals of quality
(Steenkamp, Batra, and Alden 2003). Cues such as these
are used as a means to mitigate the effects of uncer-
tainty. Consumers may not know about the quality of a
product or brand, and they will use available informa-
tion in their process of evaluation. Consumer product
evaluation cues are either extrinsic (i.e., the cue is not
physically part of the product) or intrinsic (i.e., the cue
is a core product attribute) (Richardson, Dick, and Jain
1994). “Globalness” of a brand is therefore an extrinsic
feature that consumers interpret in their choice process
and is a reflection of the brand’s position in a GBA.
The GBA management process is conceptualized as the
practices employed in the implementation and monitor-
ing of global brand strategies for the brands in a firm’s
portfolio. The scope of brands in the GBA is thus a mea-

sure of the breadth of a brand’s global strategy. Alden,
Steenkamp, and Batra (2006) show that attitudes
toward consumption options are clustered along a con-
tinuum of local–hybrid–global orientations, and they
suggest that there are market-driven arguments to be
made for the role of alternative geographically based
strategies. Research has indicated that global dispersion
and geographic scope, coupled with local market
knowledge, facilitate the launch of brands globally
(Yeniyurt, Townsend, and Talay 2007). Furthermore,
GBA is an important strategic consideration of a brand’s
position and stage of internationalization (Townsend,
Yeniyurt, and Talay 2009). Steenkamp and De Jong
(2010) suggest that, because of the variety of attitudes
toward local and global products, it may be best for
firms to vary their branding and product portfolio
strategies across markets.
Our framework (Figure 1) provides a means to under-
stand how culture moderates the relationship between

GBA and market-based performance. Fundamentally, a
global company’s marketing program activities com-
prise the strategy, structure, and process undertaken by
the organization. These activities occur in a dynamic
environment, in which situational context can establish
boundary conditions (e.g., culture) that determine a var-
iation in relationships between brand strategies and
market-based outcomes. From a distribution perspec-
tive, a GBA is driven by the external environment,
which is broad and varied across geographic and cul-
tural boundaries (Douglas, Craig, and Nijssen 2001).
The central tenet of the framework we employ is the
contextual premise that market-based performance
returns on global brand marketing strategies will vary
on the basis of boundary conditions presented by extrin-
sic cues in the market environment. Essentially, in what
market contexts are brands at different positions in a
GBA likely to perform better?
Several extrinsic brand cues potentially affect the rela-
tionship between a brand’s position in GBA and its mar-
ket performance. In this research, we investigate the role
of cultural concepts and how they alter the effects of
global brand marketing strategies on market-based per-

formance. The most dominant cultural paradigm uti-
lized when analyzing and assessing culture is that of
Hofstede (1983), which forms the basis for a significant
proportion of the cross-cultural studies undertaken in
the literature. In light of the prominence of Hofstede’s
cultural dimensions, we use them in our analyses of cul-
ture’s role in the relationship between GBA and perfor-
mance. We believe that these dimensions will also mod-
erate the relationship between global brand marketing
strategies and market-based performance. The develop-
ment of the hypotheses that follow is based on the con-
ceptualization of GBA as a progressive set of categoriza-
tions ranging from single country to global.
Power Distance
Hofstede, Hofstede, and Minkov (2010, p. 61) define
power distance as “the extent to which the less power-
ful members of institutions and organizations within a
country expect and accept that power is distributed
unequally.” Low-power-distance cultures tend to be
egalitarian and attribute less importance to differences
in prestige, wealth, and status in their interpersonal
relationships. In contrast, high-power-distance cultures
emphasize prestige, wealth, and authority as crucial

factors in forming social classes as well as shaping the
relationships between them. Attaining and maintaining
prestige in such societies are important sources of per-
sonal satisfaction. People living in high-power-distance
cultures are sensitive to social norms and tend to
exhibit conformity to the norms of the classes with
Figure 1. Conceptual Framework
GBA
Global brands
Multiregional brands
Regional brands
Cultural Dimensions
Power distance
Individualism
Masculinity

Uncertainty avoidance
Market
Performance
Control Variables
Country of origin
Market commitment
Market size
Luxury
Local brand
GDP per capita
GDP growth rate
Population
Human development

which they are affiliated as well as those to which they
aspire. Therefore, relative to the other categories of
geographic scope in a GBA, brands higher up in the
GBA should be more important signals in high-power-
distance cultures because they might have a stronger
influence on increasing perceived quality and decreas-
ing perceived risk. Those brands could more strongly
symbolize power, prestige, wealth, and status (i.e., val-
ues that are more emphasized in high-power-distance
cultures), and such societies exhibit stronger motiva-
tions to follow and imitate their aspirational social
classes, which are more likely to consume global
brands. Therefore,
H1: Brands with a higher position in a GBA (i.e.,
with a broader geographic scope) exhibit bet-
ter (worse) market-based performance in
countries with a higher (lower) level of power
distance.

Individualism
The relative degree of individualism/collectivism exhib-
ited by a national culture is believed to act as a moder-
ator to the relationship between global brand marketing
strategies and market-based performance. People from
individualist cultures tend to view themselves as liber-
ated and distinct from others in their society. Primary
importance is given to the well-being of their immediate
family and themselves rather than to society as a whole.
In contrast, people from collectivist cultures feel that
they belong to a group and will be more likely to allow
the needs of the group to come before their own indi-
vidual needs.
Two important distinctions between individualist and
collectivist cultures make this dimension highly relevant
for our study: patriotism and consumer ethnocentrism.
Patriotism refers to love for and a sense of pride in one’s
own country, a sacrificial devotion to it, respect and loy-
alty to its people, and protection of it against outsiders
(Balabanis et al. 2001). Patriotic consumers regard pro-
tecting their country’s economic interests and support-
ing domestic producers as their duty and, thus, show
high intentions of buying domestic products and low

intentions of buying foreign products.
Consumer ethnocentrism, in contrast, refers to “the
beliefs held by consumers about the appropriateness,
indeed morality, of purchasing foreign-made products”
(Shimp and Sharma 1987, p. 280). Research has
shown that consumer ethnocentrism predicts, albeit
with varying precision among product categories, con-
sumers’ preferences to favor and purchase domestic
products over their foreign counterparts (Balabanis et
al. 2001). Ethnocentric tendencies have been found to
be better predictors of purchase of domestic versus for-
eign products than demographic and marketing-mix
variables (Herche 1994). The literature has also sug-
gested that collectivist cultures exhibit significantly
higher levels of patriotism and consumer ethnocen-
trism than individual ist cultures, and the people in
such cultures are more likely to subordinate their per-
sonal interests for the country’s welfare (Hofstede,
Hofstede, and Minkov 2010). Therefore, consumers
from individualist cultures would be more receptive to
brands with higher levels of the GBA, whereas those
from collectivist cultures would be more receptive to
brands from their home countries because this would

support the group to which they belong. We hypothe-
size the following:
H2: Brands with a higher position in the GBA (i.e.,
countries with a higher level of individualism
(collectivism).
Masculinity
Masculinity is among the cultural dimensions that
should make a difference as to the type of brand that
would be successful in a market. Masculine cultural val-
ues suggest assertiveness, achievement, and acquisition
of wealth as more important in a society (Hofstede,
Hofstede, and Minkov 2010). In masculine cultures,
successes are more important than caring for others or
improving the general quality of life for everyone in the
society (Hofstede 1983). People may demonstrate
achievement by having the latest and most prestigious
possessions, which is essentially a proxy for success and
reflects a given level of status. Thus, status purchases
and conspicuous consumption are more prevalent in
masculine cultures. Consumers in masculine cultures

tend to buy more expensive watches and real jewelry, fly
business class on leisure trips more frequently, and, most
notably for this study, are more likely to favor foreign
products and brands over their domestic counterparts
(Hofstede, Hofstede, and Minkov 2010). Indeed, one
rather ubiquitous way of demonstrating success and
achievement in highly masculine cultures is to purchase
global brands (De Mooij and Hofstede 2011) because
they signal power, prestige, wealth, and status. There-
fore, we expect the following:
with a broader geographic scope) exhibit better
(worse) market-based performance in countries
with a higher level of masculinity (femininity).
Uncertainty Avoidance
Uncertainty avoidance captures “the extent to which
people feel threatened by ambiguous situations and have
created beliefs and institutions that try to avoid these”

(Hofstede, Hofstede, and Minkov 2010, p. 418). This
dimension refers to the endeavors of certain cultures to
increase stability and predictability and to eschew ambi-
guity. Because of our focus on the variance in the inter-
pretation of different types of brands in different cul-
tural milieus, it is the most relevant cultural dimension
for this study. Consumers in high-uncertainty-avoidance
cultures are more risk averse and less tolerant of ambi-
guity. They tend to reduce aversion and ambiguity by
seeking and favoring credible signals.
Moreover, consumers in these markets tend to utilize mar-
keting information more frequently and intensely because
they are less sensitive to search costs and more willing to
collect and process information than consumers in low-
uncertainty-avoidance cultures. In this study, we propose
that consumers from high-uncertainty-avoidance cultures
will place more emphasis on brands as cues of product
quality and process information about them more
intensely than consumers from low-uncertainty-avoidance
cultures. This is because information search and process-
ing in the purchasing process should positively correlate
to a culture’s risk-aversion level (Dawar and Parker
1994). As a brand’s position increases in the GBA, it will
signal superior quality, higher customer demand, and

proven success in various parts of the world, all of which
decrease the perceived risk. Stated differently, we posit
that the effects of global brands will be stronger in high-
uncertainty-avoidance cultures because these cultures are
more sensitive to ambiguity.
countries with a higher (lower) level of uncer-
tainty avoidance.
METHODS
Sample Description
We investigate the relationship between a brand’s global
marketing strategy and its market-based performance in
the context of the global automotive industry for the
period 2002–2008 using a data set of 165 brands (e.g.,
Chevrolet, Mini) owned by 96 companies (e.g., General
Motors, BMW) from 18 countries, operating in 65
countries. This data set not only enables us to cover
approximately 90% of the global automotive industry
but also represents the most extensive temporal and spa-

tial coverage in the literature to date (Figure 2).
The automotive industry is relevant for this research
because of its economic and strategic importance, along
with its geographic scope. It has been experiencing robust
growth due to increasing global demand, particularly
from emerging markets; developed markets have been
relatively stagnant and, therefore, more competitive.
Vehicle production has more than doubled since 1975,
from 33 million to 73 million in 2007. Moreover, the
automotive industry is characterized by broad-based
research and development and extensive supply chain
management, with many marketing operations conducted
on a global scale (Talay, Dalgic, and Dalgic 2010).
The automotive industry is also rife with highly dynamic
market competition, with continual launches of new
products into existing markets and new brands and
products into new markets. The emergence of developing
countries as major players in the industry—as consumers
on the one hand and manufacturers and suppliers on the
other—as well as a convergence in demand characteris-
tics has created an even greater need to embrace the busi-
ness concepts associated with globalization. Further-
more, automobiles are highly image-conscious products

that typically include a substantial level of purchase
involvement and are relatively costly to dispose of after
purchase. We believe, therefore, that this highly turbu-
lent and global industry is a suitable context for analyz-
ing the performance of global brands.
Dependent Variable
This study examines the impact of cultural milieu on the
link between the GBA and market performance. Our
dependent variable is the market share of brand i in
country j in year t as proxy of market performance. This
performance criterion is widely used in the international
marketing literature (Guo 2013; Iversen and Hem 2011;
Swoboda, Pennemann, and Taube 2012), thus allowing
for comparisons between our study and other extant
works. Furthermore, both scholars and practitioners
consider this variable an important performance indica-
tor (Farris et al. 2006). However, market share of a
brand in a given country may not reflect performance in
terms of profit, return on investment, or shareholder

value. Market share figures for this study are extracted
from a data set containing 165 brands (e.g., Chevrolet,
Mini) from 18 countries, owned by 96 companies (e.g.,
General Motors, BMW), operating in 65 countries from
2002 to 2008. This proprietary data set was provided
by CSM Worldwide, which specializes in providing fore-
casting and market intelligence solutions to manufactur-
ers, suppliers, and financial organizations in the global
automotive industry. To control for the effects of skew-
ness in distribution and outliers in the data, we used the
natural logarithm of this variable in our analyses (e.g.,
Talay, Calantone, and Voorhees 2014).
Independent Variables
GBA. We use three dummy variables to denote global,
multiregional, and regional brands. Following Rugman
and Collinson (2004), who define a global brand as hav-
ing at least 20% of its sales in each of the three regions of
the broad “triad” of the European Union, North Amer-
ica, and Asia, we operationalize the position of a brand in
the GBA by its presence in three continents: Asia, Europe,
and North America. To be more precise, we identify a
brand as global if it has operations in all three of these

continents (e.g., Ford, Honda, Mercedes-Benz). If a brand
is present in multiple countries and continents but is not
a global brand, it is identified as multiregional (e.g., Seat,
Pontiac, Volga). Regional brands refer to brands that
have operations in only one continent, albeit in multiple
countries within the same continent (e.g., Dacia, Holden,
Morgan). Finally, if a brand is available in only one coun-
try, it is identified as a single-country brand, which we use
as the base case in the analyses.
Cultural Dimensions. To capture the effects of culture,
we utilize Hofstede, Hofstede, and Minkov’s (2010)
scores for the cultural dimensions of power distance,
individualism, masculinity, and uncertainty avoidance.
Using these scores individually rather than as a compos-
ite enables us to gain deeper insights about the varying
effects of each cultural dimension on the relationship
between a brand’s locus in the GBA and its market per-
formance.
Control Variables
While our conceptual and empirical models include a set
of factors that are plausibly related to the market share of

a brand i in country j in year t, we acknowledge that our
data set still has limitations. Indeed, there may be other
Figure 2. Markets Included in This Study
factors (e.g., marketing spending, distribution, financing
options) influencing the brand performances that we are
not able to observe because of the geographical breadth
and temporal depth of our data set. This raises the con-
cern that there may be unobserved heterogeneity in our
data because the factors that are not accounted for in our
model are correlated with those that are accounted for,
and this may lead to a bias in the estimated effects. There-
fore, we include a series of control variables, which may
affect the market performance of brand. Specifically, we
account for the effects of a brand’s commitment to a
country, the level of development of a market along with
its actual and potential size, whether a brand is a luxury
brand, and whether a brand is a local brand.

We operationalize market commitment as follows: the
models of brand i sold in country j in year t as a percent-
age of the entire set of models brand i offered in year t.
For example, BMW’s global product-market commit-
ment levels to the Malaysian and U.S. markets in 2005
was 81.8% and 90.9%, respectively, because it offered
only 9 models in Malaysia and 10 models in the United
States, of the 11 total BMW models available in 2005.
We also distinguish luxury brands and nonluxury brands
because they may be subject to different demand char-
acteristics (Dubois, Czellar, and Laurent 2005). The lit-
erature has shown that being “local” in a market can
change demand for a product both for better and for
worse (Batra et al. 2000). Therefore, in an attempt to
control for the effects of being a local brand, we also
include a dummy variable indicating whether a brand is
sold in its home country at year t.
We also include a brand’s national origin as a control
variable. Nationality is an important driver of brand
identity management strategies, and even brands with a
global approach are guided by the principles derived
from the national heritage of the brand. Therefore, we

include a variable for nationality in the model to control
for the effects of the variance in global orientations and
strategies observed by brands from different countries
(Chryssochoidis, Krystallis, and Perreas 2007). We oper-
ationalize country of origin using a set of seven dummy
variables for China, France, Germany, Italy, Japan, South
Korea, and the United States. Each dummy variable
equals 1 if the home country of the brand is the country
that dummy variable denotes, and 0 otherwise. Brands
from other countries establish the base condition.
Brands from these countries capture 138 of the 165
brands in our data set and make up 89.19% of all sales
during the 2002–2008 period.
We capture the level of development in a country with
three variables: gross domestic product (GDP) growth
rate, GDP per capita based on purchasing power parity
(PPP), and the Human Development Index. We measure
the potential size of the market in country i using the
country population at time t, whereas the actual market
size is operationalized as the total number of vehicles
sold in country i at time t. Similar to our dependent
variable, we used the natural logarithm of market size to
control for the effects of skewness in distribution and
outliers in the data.

Model Development
We have compiled a panel data set of country-level
annual sales composed of repeated observations of
brands. To test our hypotheses, we therefore employ ran-
dom-effects cross-sectional time-series models corrected
for serial correlation of errors (Wooldridge 2002).
Random-effects models rest on the assumption that ai
and xit are uncorrelated (Wooldridge 2000, p. 449). The
transformation of the data is conducted in such a way
that the variance–covariance matrix of the composite
error has a block diagonal pattern that requires general-
ized least squares estimation. This allows for an assess-
ment of the variation both within and between each
group of observations. The random-effects estimation is
more efficient than the alternatives because it uses a
weighted average of both types of estimators when we
assume zero correlation between the explanatory
variables and the composite error (Kennedy 2003, p.
307). The model we test has the following structure:
(1) yit = xitb + (ai + eit),
where ai ~ (a, s2a) and eit ~ (0, s2u). Because the random-

effects estimation method assumes that ai and xit are
uncorrelated, it is necessary to assess whether this
assumption is true. This is accomplished through com-
parison of the estimates obtained from the fixed-effects
model and the random-effects model (Hausman 1978).
The Hausman test assesses the null hypothesis that the
coefficients estimated by the efficient random-effects
estimator are equivalent to the ones estimated by the
consistent fixed-effects estimator. Performing the Haus-
man test yields a c2 statistic of 29.3 (d.f. = 19, p = .061).
The null hypothesis cannot be rejected, and the assump-
tion that ai and xit are uncorrelated can be made, indi-
cating that the random-effects model will not produce
biased coefficients. Thus, both theoretically and method-
ologically, random-effects estimation is an appropriate
specification for this data set.
We also checked for serial autocorrelation in errors as
suggested by Wooldridge (2002) using the xtserial routine
in Stata 13.0. Although several tests for serial autocorre-
lation in panel data models have been proposed, this rela-

tively new test requires fewer assumptions (Wooldridge
2002). The results of the Wooldridge test indicate an F(1,
2,924) = 164.559 with Prob > F = .0001. Therefore, the
null hypothesis is strongly rejected, indicating that there is
serial correlation in the composite error and implying that
(1) pooled ordinary least squares estimation will be inef-
ficient, and generalized least squares will be a better esti-
mation technique, and (2) an auto regressive (e.g., AR[1])
disturbance term should be included in the model. Fur-
thermore, the results obtained through the random-
effects estimation indicate a first-order serial correlation
(r) of .579. The modified Durbin–Watson test statistic =
.659 and the Baltagi–Wu locally best invariant test =
1.127 also confirm the need to correct for serial autocor-
relation. Therefore, we used the xtregar procedure in
Stata 13.0 to allow for first-order autoregressive correla-
tion among the disturbance terms.
Tables 1 and 2 present the descriptive statistics and pair-
wise Pearson correlations for the key variables, respec-
tively. A relatively higher mean value of a dummy
variable, which can range between 0 and 1, indicates
that our data set contains more observations of that
variable. Market size (i.e., the total annual sales of all
brands) varies significantly from 5,722 vehicles sold in a

country (Macedonia in 2002) to approximately 17 mil-
lion vehicles (the United States in 2005).
We tested for multicollinearity and found that both the
average and the maximum variance inflation factor val-
ues were less than 10, a commonly used cutoff value
(Koutsoyiannis 1977), which we attribute to the wide
spatial and temporal coverage in our data set. The highest
average and maximum variance inflation factor values in
the estimated model are 2.77 and 7.87, respectively.
Table 3 presents the results of our estimations. We find
that the market shares of global brands are 217%
[exp(1.153) – 1 ¥ 2.168] higher than those of brands
operating in single country (b = 1.153, p < .01), while
multiregional brands (b = .796, p < .01) and regional
brands (b = .663, p < .01) have approximately 121% and
94% higher market shares than their single-country
(baseline condition) counterparts, respectively. Overall,
we observe that the brands with higher positions in the
GBA tend to sell more than brands with lower positions.1
We found all of the estimated coefficients for the inter-
action effects between cultural dimensions and GBA lev-

els to be statistically significant and in the hypothesized
direction (i.e., positive), indicating support for our
hypotheses. That is, for any cultural dimension, the
magnitudes of the coefficients of the GBA levels are
ranked as global > multiregional > regional. This sug-
gests that being higher up in the GBA not only improves
the positive effects of individualism and masculinity on
market performance but also decreases the negative
impacts of power distance and uncertainty avoidance.
To further elucidate the moderating effects of cultural
milieus on the link between GBA position and market
performance, we conducted a spotlight analysis. This
technique uses basic statistics to analyze the simple
effect of one variable at a particular level of another
variable, continuous or categorical (Spiller et al. 2013).
The results of the spotlight analyses, which are consis-
tent with our findings, appear in Appendix B.
Table 1. Descriptive Statistics
Variable M SD
Market share 5.161 1.938
Global brand .690 .462

Multiregional brand .229 .420
Regional brand .038 .191
Power distance 56.283 2.886
Individualism 5.300 22.587
Masculinity 48.241 22.371
Uncertainty avoidance 68.234 21.512
China .026 .159
France .070 .255
Germany .254 .435
Italy .089 .285
Japan .194 .395
South Korea .045 .207

United Kingdom .031 .172
United States .239 .426
Market commitment 43.843 28.772
Market size 1.1 ¥ 106 2.3 ¥ 106
Luxury brand .296 .456
Local brand .044 .205
GDP per capita (PPP) 21,783.690 14,334.580
GDP growth rate 4.194 3.115
Population 8.9 ¥ 107 2.5 ¥ 108
Human development .805 .098
Ta

bl
e
2.
B
iv
ar
ia
te
C
or
re
la
tio
n
M
at
rix
o
f V
ar
ia
bl
es

Self-Efficacy in M-LearningJason HutchesonRunnin.docx

Self-Efficacy in M-LearningJason HutchesonRunnin.docx

Recommended

Recommended

More Related Content

Similar to Self-Efficacy in M-LearningJason HutchesonRunnin.docx

Similar to Self-Efficacy in M-LearningJason HutchesonRunnin.docx (20)

More from edgar6wallace88877

More from edgar6wallace88877 (20)

Recently uploaded

Recently uploaded (20)

Self-Efficacy in M-LearningJason HutchesonRunnin.docx