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Wearable Technologies: Possibilities in
Bringing Innovative Learning Experiences to
the Classroom
Unpublished manuscript
January 31, 2015
By: Amy Schmitz Weiss, Ph.D.,
Associate Professor of Journalism |
School of Journalism and Media Studies | San Diego State University
Technology plays a crucial role in higher education today.1
Specifically,
ICTs or Information and Communication Technologies 2
(i.e. computers, the
Internet, etc.) are used in many classrooms nowadays as tools to aid in basic
instruction (e.g. lecturing, class activities, homework assignments, etc.).3
Specifically in journalism and mass communication programs, ICTs are also
being used as a tool to aid in teaching news gathering and reporting skills vis-à-
vis digital still cameras, video cameras, audio recorders, laptops, etc., and as a
tool to aid in teaching content production vis-à-vis image processing, video
editing, web publishing, etc., to prepare students for the 21st
century newsroom.4
In fact, the 21st
century newsroom now reflects a digital environment
where new forms of ICTs (e.g. smartphones, tablets, etc.) are rapidly being
adopted for use in news reporting and gathering tasks in the newsroom.5
Some
individuals have defined this moment in time as one of radical transformation for
the journalism profession and academy where ICTs are changing the idea of how
journalism is taught and practiced.6
For example, the latest ICT being explored with in the newsroom is the

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wearable device.7
Wearable devices can be considered “devices that can be
worn by users, taking the form of an accessory such as jewelry, sunglasses, a
backpack, or even actual items of clothing such as shoes or a jacket”.8
These
kinds of devices can track and hold data about an individual and their daily life
ranging from how many steps they took in a given day to how much sleep they
get at night. Google Glass and various smartwatches are now being used and
experimented with by some reporters for shooting video as well as capturing
audio.9
As it relates to the classroom, wearable technology devices are not
unknown to the educational sector. The 2013 and 2014 Horizon Report identified
that wearable technology will become one of the next big technologies to be
adopted in higher education within the next three to five years.10
Currently,
universities around the world as well as in the United States have started to
experiment with the use of wearable devices for classroom instruction.11
Some
U.S. journalism programs and faculty now have Google Glass and are
experimenting with its use in classes.12
In fact, Robert Hernandez, a journalism
professor at the University of Southern California has a Google Glass class
underway this fall with students in an aim to create glass-centric software for
journalists.13
As journalism programs begin experimenting with wearable devices in the
classroom, some preliminary questions about this ICT must be explored before
its future possible implementation in the classroom. In general, how is the device

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perceived and adopted by the greater population of young adults? How do young
adults perceive its general use in higher education?
These preliminary insights can give journalism educators a better
understanding of how this innovation is perceived by young adults, how it can be
used in general for learning from a student’s perspective, and the likelihood of
the adoption of such technology by young adults.
The insights from this type of study can contribute to previous work by
scholars who have studied ICT adoption from the perspective of the educator14
and/or the effectiveness of an ICT in a specific program or class.15
It must be
noted that few studies have been conducted to understand the factors that make
students adopt an ICT, thus this study can also help to provide more insight to
educators from the learner perspective.16
Using the Diffusion of Innovations theoretical framework, this study
examines how the wearable device is perceived and adopted among young
adults based on an online census survey (n=29,940) that was conducted at a
southwestern university in April 2014.
Theoretical Background
Understanding the factors to ICT adoption can be explored in a variety of
ways. Scholars over the years have used the Theory of Reasoned Action,17
the
Theory of Planned Behavior18
as well as the Theory of Technology Acceptance19
to explore the attitudes, beliefs, and behaviors of ICT adoption at the individual
level. However, the Diffusion of Innovations theory is another approach to
examining this. This theory helps to provide a descriptive model of adoption of an

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ICT by its attributes and characteristics. The attributes and characteristics of an
innovation must also be considered aside from the adopter’s own beliefs or
behaviors. Thus, this study uses the Diffusion of Innovations theory because it
seeks to describe how young adults are adopting the wearable device and what
specific attributes and characteristics are driving such adoption.
According to Rogers, an innovation can be defined as “an idea, practice,
or object that is perceived as new by an individual or other unit of adoption.”20
It
can be defined as technology, which Rogers defines as “ a design for
instrumental action that reduces the uncertainty in the cause-effect relationships
involved in achieving a desired outcome.”21
Wearable devices can be considered a technology and innovation similar
to the desktop computer or smartphone. Wearable technology is a concept that
can be tied back to the early 1990s with the development of Ubiquitous
Computing where it was predicted that computers would weave themselves
seamlessly into daily life.22
The first scientist to experiment with wearable
devices was Steve Mann who created wearable computer experiments in the late
1990s that were somewhat big in size, slow in processing power and limited in
physical use.23
Since then, wearable technologies have become smaller in size,
run faster because of sophisticated microchips and processors, and can
withstand a variety of elements (e.g. rain, sunshine, etc.).24
According to research market firm, SSI, wearable technologies will
become popular in the mainstream market within the next two to three years.25
In
fact, a recent survey conducted by Harris Interactive of 2,577 U.S. adults, about

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46% said they would be interested in owning a wearable technology device.26
Another survey conducted late last year,27
identified that among U.S. adults
interested in purchasing a wearable technology device, 33% said they would be
interested in wearing a smartwatch, 22% said wearing the device in their clothing
and 19% said wearing glasses. Of those who already own a wearable technology
device, eight in 10 said that it has enhanced their life. Specifically, 61% felt more
informed, another 61% said it helped with their personal efficiency, 47% felt more
intelligent, and 37% said it helped with their career development.28
These statistics can be placed within the context of the natural adoption
curve that occurs with a new technology. According to Rogers, innovations will
go through an adoption curve in the market that consists of innovators at 2.5%,
then the early adopters at 13.5%, the early majority that will then represent
another 34% over time, the late majority at 34%, and laggards representing the
end of the adoption curve at 16%.29
Based on current market trends, wearable
technology can be considered at the early stage of Rogers’ adoption curve based
on the number of people who are currently adopting wearable devices.
Furthermore, within the Diffusion of Innovations theoretical framework,
Rogers states there are attributes of innovations that speed or impede diffusion
and how an individual may adopt an innovation. These include relative
advantage, compatibility, complexity, trialability and observability. Relative
advantage is that the innovation is perceived better than the idea that
supersedes it. This can be determined by economic profitability, social prestige
or other benefits. Compatibility is that an innovation is perceived consistent with

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the values, experiences and the needs of the adopter. Complexity is how the
innovation is perceived as relatively difficult to understand or use. Trialability is
that the innovation may be experimented with on a limited basis. Observability is
how the innovation is easily visible to others. 30
These attributes as Rogers has defined them have been adapted and
studied for decades in different contexts and theoretical models in organizational
behavior, social psychology and information science fields.31
As it applies to this study, several scholars in education and educational
technology32
have studied how students have adopted ICTs (e.g. wikis,
smartphones, blogs, social networking sites, e-learning systems, etc.) and their
attributes in the classroom setting. Several of these studies have shown how the
attributes of observability, compatibility, relative advantage and trialability of the
ICT can play a factor into a student’s likelihood to adopt and use a technology.
For example, observabilty can play a factor for students in how they
decide to adopt a technology based on what is happening in the social circle
around them. For example, Lai, Weng and Lei found in their research that
students’ adoption of technology (e.g.. social networking sites, video games,
wikis, blogs, smartphones, etc.) for learning purposes was connected to how
much their peers and teachers supported the technology in addition to their own
attitudes toward technology. Perceived usefulness and general ICT literacy skills
influenced adoption but had less predictive power.33
Another study identified that students will perceive and in turn, adopt new
media production technologies (e.g. web-ready media via audio, photo and

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video) for classroom and learning use based on the influence of significant others
(e.g. other students and instructors) who adopt new media production
technologies. Furthermore, the study identified that students acknowledged that
technology was important to their future careers and thus, were likely to adopt
new media production technologies as a result of seeing the value of such skills
for professional competency.34
Compatibility can also play a factor for technology adoption. Based on a
survey of students’ perceptions of an e-learning system and their intentions to
use the technology, findings showed that students accepted and adopted a
technology when it matched their learning expectations and when it was
compatible to their learning lifestyle.35
Relative advantage and understanding the uses and benefits of
technology can also play a factor into a student’s adoption of ICTs. A group of
researchers surveyed and interviewed university students from two different
disciplines (i.e. engineering and social work) and found that students adopted
general technologies such as websites, course learning management systems,
smartphone apps for learning purposes but didn’t adopt technologies such as
wikis, YouTube, blogs or social media as readily. To examine this difference, the
researchers identified in interviews with the students that they had a limited
understanding of knowing the potential benefits of using such technologies for
classroom use and thus only adopted those technologies they saw a direct
benefit and use from. Several students also stated that they looked to their peers
or instructors for guidance in technology adoption. The researchers posited that

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students might be using certain technologies in a formal learning context based
on what their instructors are doing with such technologies in the classroom.36
It can be concluded that the attributes of compatibility, relative advantage,
and observability as well as attitudes toward such ICTs (e.g. smartphone apps,
wikis, social networks, websites, etc.) can play a factor in technology adoption
among students as it relates to the classroom and learning experience.37
Research Questions
Research has shown that young adults are adopting ICTs for classroom
use and they are open to adopting new ICTs when their peers and teachers
adopt or perceive its usefulness as well as its compatibility in context to the
learning environment.38
Based on the past ICT scholarship, how are young adults
adopting the latest ICT - the wearable device? What attributes drive adoption or
not? How is this technology perceived for use in the educational setting? These
issues are addressed in the following research questions.
RQ1: What innovation attributes (e.g. relative advantage, compatibility,
complexity, trialability and observability) speed or impede diffusion of wearable
technology by young adults?
RQ2: What innovation attributes (e.g. relative advantage, compatibility,
complexity, trialability and observability) speed or impede diffusion of wearable
technology by young adults for educational purposes?
RQ3: What ways could young adults perceive wearable technology
devices being used in educational settings?

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Method
An online census survey39
was administered to college students 18 years
and older attending a southwestern university in April 2014 (n=29,940).
Responses were obtained from 454 young adults.40
Respondents were recruited
through an email invitation and sent by the campus registrar office to the entire
study body at the university.41
This study focuses on young adults because they
are the fastest adoption group of wearable technology devices than any other
age segment.42
The university population and not just journalism and mass
communication students were surveyed for this study in order to provide a
comprehensive and full picture about how young adults are adopting wearable
technologies. The insights garnered from this census survey can provide
journalism educators with preliminary information of the possible uses of this ICT
in general in the classroom.
Measurement. Respondents were asked about their device ownership
(e.g. smartphones, game consoles, laptops, etc.), their Internet usage,
perceptions and uses of wearable technologies. Questions in the survey were
based on existing survey instruments on innovation adoption of information
communication technologies (ICTs) and wearable technologies.43
Existing scales
from Moore and Benbasat44
were used for this study to measure Rogers’
innovation attributes of relative advantage, compatibility, complexity/ease of use
and trialability to determine perceptions for wearable technology adoption (see
Table 1).45
All scales had reliability above .80, which is considered very good
according to DeVellis.46
To make this study as replicable as possible, the

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observability scale was excluded from this study as Moore and Benbasat had
construct issues that resulted in low reliability and validity.47
The researcher used
another method by which to measure observability among the respondents.48
Table 1
Innovation Characteristics Scale Items by Moore and Benbasat
Relative Advantage
• Using a wearable tech device would enable me to accomplish tasks more quickly.
• Using a wearable tech device would improve the quality of the work I do.
• The disadvantages of using a wearable tech device would far outweigh the advantages. *
• Using a wearable tech device would improve my performance at school.
• Using a wearable tech device would give me greater control over my work at school.
• Using a wearable tech device would increase my productivity.
Compatibility
• Using a wearable tech device would be compatible with all aspects of my life.
• I think that using a wearable tech device would fit well with the way I like to work, play
and live.
• Using a wearable tech device would fit into my lifestyle.
Complexity/Ease of Use
• I believe that wearable tech device would be cumbersome to use.
• It would be easy for me to remember how to perform tasks using a wearable tech device.
• Using a wearable tech device would require a lot of mental effort.
• Using a wearable tech device would be frustrating.
• Using a wearable tech device would be clear and understandable.
• I believe that it would be easy to get a wearable tech device to do what I want it to do.
• Overall, I believe that a wearable tech device would be easy to use.
• Learning to operate a wearable tech device would be easy for me.
Trialability
• I’ve had a great deal of opportunity to try various wearable tech devices.
• I know where I can go to satisfactorily try out various uses of wearable tech devices.
• A wearable tech device was available to me to adequately test run its use.
• I was permitted to use a wearable tech device on a trial basis long enough to see what it
could do.
Note. *Item removed from scale due to low reliability. Observability scale was not included due to
construct issues from Moore and Benbasat. Observability was measured using another method.
See article for details.
The relative advantage scale consisted of 5 items with a reliability of α.=91.
The compatibility scale consisted of 3 items with a reliability of α=.90.
The complexity/ease of use scale consisted of 8 items with a reliability of α=.88.
The triability scale consisted of 4 items with a reliability of α=.87.

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Sample Demographics. About 63.7 of respondents were female and 36.3%
were male. The majority of the students were undergraduates (74.7%), 24.5%
were graduate students and less than one percent other.
Over 80% of respondents were aware of wearable technology devices.
However, only 16% owned a wearable technology device in comparison to 84%
that didn’t. Of those who owned a wearable device, frequency of its use varied.
Thirty-four percent used the device several times a day, 20.6% used it 3-5 days a
week, 14.7% said they used it less often, 10.3% said they used it once a day,
and 7.4% said every few weeks. The majority stated they owned a health or
fitness monitor (N=64), followed by smart glasses (N=2), smart watches (N=2),
mobile life tracker (N=1) and a people tracker device (N=1).
Results
In order to answer RQ1, students were first segmented into adopters or
non-adopters of wearable technology devices based on their ownership of such
devices and ANOVA analyses were run according to aforementioned innovation
attributes scales.
Table 2
Innovation Attributes by Adopter Category
_____________________________________________________________________________
Adopter Non-Adopter
Innovation Attributes Mean SD Mean SD F-ratio df Sig.
_____________________________________________________________________________
Relative Advantage 2.55 .85 3.06 .77 23.480 1, 417 .001
Compatibility 2.29 .79 3.12 .92 46.610 1, 416 .001
Complexity/Ease of Use 2.17 .59 2.72 .65 38.878 1, 392 .001
Trialability 3.32 1.12 4.07 .83 38.928 1, 402 .001
Note: Scale from 1-5 strongly agree to strongly disagree

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As shown in Table 2, of the respondents who have adopted a wearable
technology device, agreed that the innovation attributes of relative advantage
(M=2.55, SD=.85) F(1, 417) = 23.480, p = .001, compatibility (M=2.29, SD=.79)
F(1, 416) = 46.610, p = .001, ease of use/complexity (M=2.17, SD=.59) F(1, 392)
= 38.878, p = .001 and trialability (M=3.32, SD =1.12) F(1, 402) = 38,928, p =
.001 significantly showed to aid their adoption and use of such device in
comparison to non-adopters. For the innovation attribute of observability, 92% of
adopters were knowledgeable and exposed to wearable technology devices in
comparison to 86% of non-adopters.
Table 3
Reasons for Adoption of Wearable Technology Devices
________________________________________________
Reason %
_________________________________________________
Track physical activity 53.6
Usefulness 11.5
Trying out new gadgets 10.1
To be healthy 10.1
Other 10.1
For fun 4.3
(N) (69)
_________________________________________________
A majority of respondents who did adopt a wearable technology device
stated their reason was to use it to track physical activity (53.6%), followed by its
usefulness (11.5%), trying out new gadgets (10.1%), to be healthy (10.1%) and
for fun (4.3%) (see Table 3).

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Table 4
Reasons for Non-Adoption of Wearable Technology Devices
_____________________________________________________
Reason %
_____________________________________________________
Don’t have a need for item 45.9
Don’t know enough about it 21.3
Cost is too high 20.5
Have too many gadgets already 5.5
It’s a fad 4.2
Don’t like the way it looks 2.4
Don’t like the feel/weight
of the product .3
(N) (381)
____________________________________________________
Of those who didn’t adopt a wearable technology device, 45.9% said they
had no need for the item, followed by 21.3% said they didn’t know enough about
it, 20.5% cited cost, 5.5% said they have too many gadgets already, 4.2%
believe it’s a fad, 2.4% didn’t like the way it looks, and .3% didn’t like the
feel/weight of the product (see Table 4). However, 29.6% of respondents said
they would consider purchasing a wearable device in the future in comparison to
23.8% that wouldn’t. And 46.6% said they were not sure if they would make the
purchase in the future or not.
In order to answer RQ2, respondents were recoded into interested and
non-interested students based on their response whether they would use
wearable technologies in the classroom. ANOVA analyses were then run
according to Rogers’ innovation attributes.

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Table 5
Adoption/Rejection of Wearable Technology Devices for Educational Purposes by
Innovation Attributes
_____________________________________________________________________________
Interested Non-Interested
Students Students
Innovation Attributes Mean SD Mean SD F-ratio df Sig.
_____________________________________________________________________________
Relative Advantage 2.65 .68 3.46 .72 139.355 1, 418 .001
Compatibility 2.65 .84 3.48 .88 95.927 1, 417 .001
Complexity/Ease of Use 2.46 .63 2.84 .67 33.323 1, 392 .001
Trialability 3.84 .99 4.10 .80 7.893 1, 401 .01
Note: Scale from 1-5 strongly agree to strongly disagree
As shown in Table 5, interested students agreed that the innovation
characteristics of relative advantage (M=2.65, SD=.68) F(1, 418) = 139.355, p =
.001, compatibility (M=2.65, SD=.84) F(1, 417) = 95.927, p = .001,
complexity/ease of use (M=2.46, SD=.63) F(1, 392) = 33.323, p = .001 and
triability (M=3.84, SD=.99) F(1, 401) = 7.893, p = .01, do play a role in their use
and adoption of such device for educational purposes in comparison to non-
interested students. For the innovation attribute of observability, 88% of
interested students were knowledgeable and exposed to wearable technology
devices in comparison to 86% of non-interested students.
RQ3 examined how young adults perceive wearable technology use in
higher education. A majority of the respondents (59.6%) said they would be
interested to use one in their classes in comparison to 40.4% who said they
wouldn’t. Respondents had varied answers of the ways wearable technology
devices could be used in and outside the classroom.

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Table 6
Wearable Technology Device Use in Educational Settings
_______________________________________________________
Activity %
_______________________________________________________
Recording a class lecture 74.4 (n=338)
Note-taking 55.3 (n=251)
Taking photos during class related to topic 52.2 (n=237)
Gathering information for class topic 51.3 (n=233)
Taking photos related to outside class
activities 37.9 (n=172)
In-class assignments 37.2 (n=169)
Finding a relevant reading to the class topic 36.8 (n=167)
In-group activities 33.5 (n=152)
For field trips or outside class activities 31.5 (n=143)
Asking instructor questions 30 (n=136)
________________________________________________________
Note: Respondents had the option to select more than one option.
As shown in Table 6, 74% percent said the wearable device could be used
to record a class lecture, 55% said for note-taking, 52.2% said for taking photos
related to class topic, 51% said using the device for gathering class information,
37.9% said for taking photos outside the class, 37.2% said for in-class
assignments, 33.5% said using the device for in-group activities, 31.5% said for
field trips and 30% said for asking the instructor questions.
Conclusion/Discussion
This study shows that the adoption of wearable technologies by young
adults remains in a very early stage. Considering Rogers’49
adoption curve, we
are still in the innovator to early adopter stage. Furthermore, there is a greater
awareness of wearable technology devices, but less people who actually own
one. Both of these findings reflect a larger trend nationally and globally of this
kind of device and its adoption in the marketplace.50

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Of those who owned the device, many had several reasons for adopting it,
but the majority stated physical activity tracking as the main reason. For non-
adopters, the lack of necessity, lack of knowledge of the device, and cost were
the main reasons for not owning one. However, not all respondents were against
future adoption of such device since almost 30% were going to buy one in the
future and another 46% were uncertain.
As for classroom use, the majority of respondents saw the multimedia
capabilities (e.g. video, photo) as well as information gathering and storage
features of the wearable technology device as possible aids in their learning of
class materials.
Adoption of the wearable technology device among the young adults was
statistically significant to the innovation attributes of relative advantage,
compatibility, complexity/ease of use, trialbility and observability, reflecting
Rogers’51
diffusion of innovations adoption pattern.
Future research in this area could explore how such devices are being
used by young adults by conducting focus groups, in-depth interviews or having
self-tracking diaries charting the adoption process.
The findings from this study only reflect the perceptions of young adults
from one southwestern university and are not generalizable to the larger
population, but the study does provide insight into the early adoption of the
wearable technology device into the market. It also shows how young adults who
own these devices are using them for information gathering and utility purposes
albeit for physical fitness tracking. However, these purposes can be extended

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into the classroom setting. Thus, this study provides the start to an important
conversation in the academy about how innovations like the wearable technology
device can be explored for use in higher education.
Becker, Vlad and Simpson state that journalism and mass communication
programs need to adapt and innovate in order to maintain and grow enrollments
in the years and decades ahead if they want to be sustainable.52
Journalism and mass communication programs that adopt innovations
such as wearable technology devices before widespread use in society and the
journalism profession may help to prepare journalism students for the unknown
when they enter the workforce in the future.53
Also, as ICT adoption research has identified, educators who adopt such
innovations can have an impact on students’ general ICT adoption and influence
their students’ behaviors and perceptions of the adoption of such ICTs by how
they present and use such ICTs in the classroom.54
Journalism educators can
play a crucial role in this process by how they adopt an ICT like the wearable
device in the classroom and how their students react to such an ICT and
consider its use for the classroom but also the future newsroom they may work in.
It is through new ways of exploration and experimentation with such
innovations that higher education and for that matter, journalism and mass
communication programs can keep pace with the market, remain sustainable for
the future, and help in the creation of a new market of professionals who have
the latest skills to do their job, and are on the cutting-edge of innovation in order
to explore the possibilities of how work can be done in the 21st
century.

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Notes
1
Lee Bernard Becker, Tudor Vlad, and Holly Anne Simpson, “2013 Annual Survey of Journalism Mass
Communication Enrollments: Enrollments Decline for Third Consecutive Year,” Journalism & Mass
Communication Educator 69 (4 2014): 349-365, first published on October 27,
2014, doi:10.1177/1077695814555432; Maria Marron, “Transitions and Challenges,” Journalism &
Mass Communication Educator 68 (1 2013): 3–5, doi: 10.1177/1077695813475789 ; John Pavlik, “A
Vision for Transformative Leadership: Rethinking Journalism and Mass Communication Education for
the Twenty-First Century,” Journalism & Mass Communication Educator 68(3 2008): 211–22 doi:
10.1177/1077695813499561; Becker, Vlad and Simpson, “2012 Annual Survey of Journalism and Mass
Communication Enrollments: Enrollments Decline for Second Year in a Row.”
2
ICT is a technology that provides information through communication technologies such as computers,
the Internet, cell phones and video conferencing.
3
Laurence Johnson, Samantha Adams Becker, Victoria Estrada, and Martín, “Technology Outlook for
STEM+ Education 2013-2018: An NMC Horizon Project Sector Analysis,” The New Media Consortium,
2013, http://www.nmc.org/publications/2013-technology-outlook-stem (accessed January 30, 2014), 12;
Laurence Johnson, Samantha Adams Becker, Victoria Estrada, and Freeman, “NMC Horizon Report:
2014 Higher Education Edition,” The New Media Consortium, 2014,
http://redarchive.nmc.org/publications/2014-horizon-report-higher-ed (accessed November 8, 2014).
4
Andreas Veglis and Andreas Pomportsis, “Journalists in the Age of ICTs: Work Demands and
Educational Needs,” Journalism & Mass Communication Educator 69(1 2014): 70-71;
Carol Smith, Haeryon Kim, and James Bernstein, “Computer-Mediated Communication and Strategies
for Teaching,” Journalism & Mass Communication Educator, 48(1 1993): 80-83; William Smith,
“Computer-Mediated Communication: An Experimental Study,” Journalism Educator, 48(4 1994): 27-33;
John Russial, "Mixed Messages About Pagination and Other Skills," Newspaper Research Journal 16
(winter 1995): 60-70; Ann Auman, Frank Fee, and John Russial, "Noble Work, but Undervalued: The
Status and Value of Copy Editing in Journalism Schools," Journalism & Mass Communication Educator
57 (summer 2002): 139; Lewis A. Friedland and Sheila Webb, "Incorporating Online Publishing into the
Curriculum," Journalism & Mass Communication Educator (autumn 1996): 54-65; Jennifer Wood
Adams and Meussa L. Voynich, “The Use of Design Technology in U.S. Journalism Programs,”
Journalism & Mass Communication Educator (spring 2008):42-54; Cindy Royal, "Teaching Web Design
in Journalism and Mass Communications Programs: Integration, Judgment and Perspective,"
Journalism & Mass Communication Educator 59 (winter 2005): 400-14; Michael Huntsberger and Alan
Stavitsky, “The New "Podagogy": Incorporating Podcasting into Journalism Education,” Journalism &
Mass Communication Educator (winter 2007): 397-410.
5
Du and Thornburg, “The Gap between Online Journalism Education and Practice: The Twin Surveys.”
6
Pavlik, “A Vision for Transformative Leadership: Rethinking Journalism and Mass Communication
Education for the Twenty-First Century.” Jean Folkerts, “History of Journalism Education,” Journalism &
Communication Monographs 16 (4 2014): 227-299, first published on July 22,
2014 doi:10.1177/1522637914541379.
7
Stuart Dredge, “How Vice's Tim Pool used Google Glass to cover Istanbul protests,” The Guardian July
30, 2013, http://www.theguardian.com/technology/2013/jul/30/google-glass-istanbul-protests-vice
(accessed November 1, 2014); Alastair Reed, “Could Google Glass change the face of journalism?:
Journalism.co.uk, January 15, 2014
https://www.journalism.co.uk/news/could-google-glass-change-the-face-of-journalism-/s2/a555596/
(accessed November 1, 2014).
8
Laurence Johnson, Samantha Adams Becker, Victoria Estrada, and Martín, “Technology Outlook for
STEM+ Education 2013-2018: An NMC Horizon Project Sector Analysis,” The New Media Consortium,
2013, http://www.nmc.org/publications/2013-technology-outlook-stem (accessed January 30, 2014), 12.
9
Stuart Dredge, “How Vice's Tim Pool used Google Glass to cover Istanbul protests,” The Guardian July
30, 2013, http://www.theguardian.com/technology/2013/jul/30/google-glass-istanbul-protests-vice
(accessed November 1, 2014); Alastair Reed, “Could Google Glass change the face of journalism?:
Journalism.co.uk, January 15, 2014, https://www.journalism.co.uk/news/could-google-glass-change-the-
face-of-journalism-/s2/a555596/ (accessed November 1, 2014); Alastair Reed, “Smartwatches and the
digital future of news,” Journalism.co.uk, January 20, 2014,
https://www.journalism.co.uk/news/smartwatches-and-the-digital-future-of-news/s2/a555625/ (accessed
November 1, 2014).
10
Johnson, Becker, Estrada, and Martín, “Technology Outlook for STEM+ Education 2013-2018: An NMC
Horizon Project Sector Analysis.” ; http://cdn.nmc.org/media/2014-nmc-horizon-report-he-EN-SC.pdf
11
Johnson, Becker, Estrada, and Martín, “Technology Outlook for STEM+ Education 2013-2018: An NMC

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Weiss
19
Horizon Project Sector Analysis.” http://cdn.nmc.org/media/2014-nmc-horizon-report-he-EN-SC.pdf;
Laurence Johnson, Samantha Adams Becker, Victoria Estrada, and Freeman, “NMC Horizon Report:
2014 Higher Education Edition,” The New Media Consortium, 2014,
http://redarchive.nmc.org/publications/2014-horizon-report-higher-ed (accessed November 8, 2014).
12
Adam Offitzer, “How Journalism Professors Are Bringing Google Glass into the Classroom,” American
Journalism Review, April 10, 2014, http://ajr.org/2014/04/10/google-glass-journalism-new-perspective/
(accessed May 30, 2014). Some journalism programs include: DePaul University, Michigan State
University, Texas Christian University, Lehigh University, San Diego State University, among others.
13
Chris Gayomali, “Meet the USC Journalism Professor Leading Course on Google Glass,” Fast
Company, April 3, 2014, http://www.fastcompany.com/3028476/whos-next/meet-the-usc-journalism-
professor-leading-a-course-on-google-glass (accessed May 30, 2014)
14
Margaret DeFleur and Lucinda Davenport, “Innovation Lag: Computer-Assisted
Classrooms Vs. Newsrooms,” Journalism Educator 48 (2 1993): 26-36; Kevin Lee and Charles Fleming,
“Problems of Introducing Courses in Computer-Assisted Reporting,” Journalism Educator, 50 (3 1995):
23-34; Lucinda Davenport, Fred Fico, and Margaret DeFleur, “Computer-assisted Reporting in
Classrooms: A Decade of Diffusion and a Comparison to Newsrooms,” Journalism & Mass
Communication Educator, 57 (1 2002) 2-22; Kimberly Lawless and James Pellegrino, “Professional
Development in Integrating Technology into Teaching and Learning: Knowns, Unknowns, and Ways to
Pursue Better Questions and Answers,” Review of Education Research 77, 4 (2007): 575–614; Willam
Sugar, Frank Crawley, and Bethann Fine, “Examining Teachers’ Decisions to Adopt New Technology,”
Educational Technology & Society 7, 4 (2004): 201–13; Timothy Teo, “A Path Analysis of Pre-Service
Teachers’ Attitudes to Computer Use: Applying and Extending the Technology Acceptance Model in an
Educational Context,” Interactive Learning Environments 18, 1 (2010): 65–79; Carol Smith, Haeryon
Kim, and James Bernstein, “Computer-Mediated Communication and Strategies for Teaching,”
Journalism & Mass Communication Educator, 48(1 1993): 80-83.
15
Lee and Fleming, “Problems of Introducing Courses in Computer-Assisted Reporting.”; Patrick
Sutherland, “Diffusion of Courses with World Wide Web Features: Perceptions of Journalism and Mass
Communication Program Administrators,” Journalism & Mass Communication Educator, 58 (1
2003):384-395; Shelton Gunaratne and Byung Lee, “Integration of Internet Resources into Curriculum
and Instruction,” Journalism & Mass Communication Educator 51 (2 1996): 25-35; DeFleur and Lucinda
Davenport, “Innovation Lag: Computer-Assisted Classrooms Vs. Newsrooms.”; Davenport, Fico, and
DeFleur, “Computer-assisted Reporting in Classrooms: A Decade of Diffusion and a Comparison to
Newsrooms;” William Smith, “Computer-Mediated Communication: An Experimental Study,”
Journalism Educator, 48(4 1994): 27-33; John Russial, "Mixed Messages About Pagination and Other
Skills," Newspaper Research Journal 16 (winter 1995): 60-70; Ann Auman, Frank Fee, and John
Russial, "Noble Work, but Undervalued: The Status and Value of Copy Editing in Journalism Schools,"
Journalism & Mass Communication Educator 57 (summer 2002): 139; Lewis A. Friedland and Sheila
Webb, "Incorporating Online Publishing into the Curriculum," Journalism & Mass Communication
Educator (autumn 1996): 54-65; Jennifer Wood Adams and Meussa L. Voynich, “The Use of Design
Technology in U.S. Journalism Programs,” Journalism & Mass Communication Educator (spring
2008):42-54; Cindy Royal, "Teaching Web Design in Journalism and Mass Communications Programs:
Integration, Judgment and Perspective," Journalism & Mass Communication Educator 59 (winter 2005):
400-14; Michael Huntsberger and Alan Stavitsky, “The New "Podagogy": Incorporating Podcasting into
Journalism Education,” Journalism & Mass Communication Educator (winter 2007): 397-410.
16
Toby Hopp, “Subjective Norms as a Driver of Mass Communication Students’ Intentions to Adopt New
Media Production Technologies,” Journalism & Mass Communication Educator
68 (4 2013): 348– 364, doi: 10.1177/1077695813506993
17
Martin Fishbein and Icek Ajzen, Belief, Attitude, Intention and Behavior: An Introduction to Theory and
Research. (Reading, MA: Addison-Wesley, 1975).
18
Icek Ajzen, “From Intentions to Actions: A Theory of Planned Behavior,” in J. Kuhl and J. Beckmann
(eds.), Action-control: From Cognition to Behavior. (NY: Springer, 1985), 11-39;
Kiernan Mathieson, “Predicting User Intentions: Comparing the Technology Acceptance Model with the
Theory of Planned Behavior,” Information Systems Research 2 (1 1991): 173-191;
Shirley Taylor, S., and Peter Todd,“Understanding Information Technology Usage - a Test of Competing
Models,” Information Systems Research 6 (2 1995): 144-176; Shirley Taylor, S., and Peter Todd,
“Assessing IT Usage: The Role of Prior Experience,” MIS Quarterly 19 (4 1995): 561-570.
19
Fred D. Davis, “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information
Technology,” MIS Quarterly 13 (3 1989): 319-340; Fred D. Davis, Richard Bagozzi, and Paul Warshaw,
“User Acceptance of Computer Technology: A Comparison of Two Theoretical Models,” Management
Science 35 (8 1989):982-1003.
20
Everett Rogers, Diffusion of Innovations, Fourth Edition. (NY: The Free Press, 1995),11.

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Weiss
20
21
Rogers, Diffusion of Innovations, 12.
22
Mark Weiser, “The Computer for the 21
st
Century.” Scientific American Special Issue on
Communications, Computers, and Networks, (1991): 78-89.
23
Steve Mann, “Wearable Computing as Means for Personal Empowerment,” Keynote address presented
at the First International Conference on Wearable Computing, Fairfax, Virginia, May 12–13, 1998,
http://wearcam.org/icwckeynote.html (accessed January 30, 2014); Steve Mann and Hall Niedzviecki,
Hall, Cyborg: Digital Destiny and Human Possibility in the Age of the Wearable Computer (Toronto:
Doubleday, 2001).
24
Google launched its Google Glass prototype to the public in 2013, a pair of glasses that the user could
wear to view the Internet, get directions, take video and photos all from a small lens near their eye.
Although it remains in beta, latest figures place the number of Google Glass owners somewhere around
10,000.
25
Jason Kannon, “Wearable Technology Grows in Popularity,” SSI, September 18, 2013,
http://www.surveysampling.com/~/~/link.aspx?_id=D6BCAB8C95DC4E5AA23B9ADCD0099681&_z=z
(accessed January 30, 2014).
26
Larry Shannon-Missal, “Are Americans Prepared to Sport Wearable Tech?” Harris Interactive,
November 6, 2013,
http://www.harrisinteractive.com/NewsRoom/HarrisPolls/tabid/447/ctl/ReadCustom%20Default/mid/150
8/ArticleId/1312/Default.aspx (accessed January 30, 2014).
27
Will Oremus, “Survey: People Don't Really Want to Wear Computers on Their Face,” Slate.com,
November 25, 2013,
http://www.slate.com/blogs/future_tense/2013/11/25/citrix_wearable_technology_survey_adults_prefer_
smartwatches_to_google_glass.html (accessed January 30, 2014).
28
Joe McKendrick, “Google Glass is Only the Beginning of Cloud-Powered Wearable Technology, Survey
Suggests,” Forbes.com, June 5, 2013, http://www.forbes.com/sites/joemckendrick/2013/06/05/google-
glass-is-only-the-beginning-of-cloud-powered-wearable-technology-survey-suggests/ (accessed
January 30, 2014).
29
Rogers, Diffusion of Innovations, 262
30
Rogers, Diffusion of Innovations, 212-251.
31
Icek Ajzen and Martin Fishbein, Understanding Attitudes and Predicting Social Behavior (Englewood
Cliffs, NJ: Prentice-Hall, 1980); Martin Fishbein and Icek Ajzen, Belief, Attitude, Intention and Behavior:
An Introduction to Theory and Research. (Reading, MA: Addison-Wesley, 1975); Davis, “Perceived
Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology.”; Davis, Bagozzi,
and Warshaw, “User Acceptance of Computer Technology: A Comparison of Two Theoretical Models.”;
Viswanath Venkatesh, Michael Morris, Gordon Davis, and Fred Davis, “User Acceptance of Information
Technology: Toward a Unified View,” MIS Quarterly 27 (3 2003): 425-478; Ritu Agarwal and Jayesh
Prasad, “A Conceptual and Operational Definition of Personal Innovativeness in the Domain of
Information Technology,” Information Systems Research 9 (2 1998): 204-215; Gary Moore and Izak
Benbasat, “Development of an Instrument to Measure the Perceptions of Adopting an Information
Technology Innovation,” Information Systems Research, 2(3 1991): 192-222; Viswanath Venkatesh,
Fred Davis and Michael Morris, “Dead Or Alive? The Development, Trajectory And Future Of
Technology Adoption Research,” Journal for the Association of Information Systems, 8 (4 2007): 267-
286.
32
Jian-Lian Chen, “The effects of education compatibility and technological expectancy on e-learning
acceptance,” Computers & Education, 57 (2011): 1501–1511; Anoush Margaryan, Allison Littlejohn, and
Gabrielle Vojt, “Are digital natives a myth or reality? University students’ use of digital technologies,”
Computers & Education, 56 (2 2011): 429–440; Wilma Clark, Kit Logan, Rosemary Luckin, Adrian Mee,
and Martin Oliver, “Beyond Web 2.0: mapping the technology landscapes of young learners,” Journal of
Computer Assisted Learning, 25 (2009): 56–69l Chun Lai, Qiu Wang, and Jing Lei, “What factors
predict undergraduate students’ use of technology for learning? A case from Hong Kong,” Computers &
Education, 59 (2012): 569-579; Hopp, “Subjective Norms as a Driver of Mass Communication Students’
Intentions to Adopt New Media Production Technologies.”
33
Lai, Wang, and Lei, “What factors predict undergraduate students’ use of technology for
learning? A case from Hong Kong.”
34
Hopp, “Subjective Norms as a Driver of Mass Communication Students’ Intentions to Adopt New Media
Production Technologies.”
35
Chen, “The effects of education compatibility and technological expectancy on e-learning acceptance.”
36
Margaryan, Littlejohn, and Vojt, “Are digital natives a myth or reality? University students’ use of digital
technologies.”
37
Chen, “The effects of education compatibility and technological expectancy on e-learning acceptance.”;
Margaryan, Littlejohn, and Vojt, “Are digital natives a myth or reality? University students’ use of digital

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21
technologies.”; Clark, Logan, Luckin, Mee, and Oliver, “Beyond Web 2.0: mapping the technology
landscapes of young learners.”
38
Chen, “The effects of education compatibility and technological expectancy on e-learning acceptance.”;
Margaryan, Littlejohn, and Vojt, “Are digital natives a myth or reality? University students’ use of digital
technologies.”; Clark, Logan, Luckin, Mee, and Oliver, “Beyond Web 2.0: mapping the technology
landscapes of young learners.”; Lai, Wang, and Lei, “What factors predict undergraduate students’ use
of technology for learning? A case from Hong Kong.”; Hopp, “Subjective Norms as a Driver of Mass
Communication Students’ Intentions to Adopt New Media Production Technologies.”
39
Census survey means every student in the campus population was surveyed. The accuracy of the
intended group and the reliability is higher than a sampling survey which only covers a cross section of
the intended group the survey is intended for.
40
Despite this response rate from the census, the respondents are representative of the makeup of the
campus population by gender, enrollment level and ethnicity. Campus enrollment was 84%
undergraduate and 15% graduate. The ethnicity of the campus population was 36.7% were white,
29.5% were Hispanic, 13.3% were Asian/Pacific Islander, 5.4% were other, 3.7% were African
American and .2% were Native American/American Indian. The campus population was 56% females
and 44% males. Sixty-three percent of respondents were female and 36% were male. As for survey
respondents, 74.7% were undergraduates and 24.5% were graduates. The ethnicity of the survey
respondents was 41.7% were white, 24.5% were Hispanic, 18.1% were Asian Pacific Islander, 4.2%
were other, 3.9% were African American and 1% were Native American/American Indian.
41
To aid participation in the survey, the researcher provided an incentive of a random drawing to win
Apple or Android Gift Cards for those who completed the survey.
42
Johnson, Becker, Estrada, and Martín, “Technology Outlook for STEM+ Education 2013-2018: An NMC
Horizon Project Sector Analysis; Shannon-Missal, “Are Americans Prepared to Sport Wearable Tech?”
43
Gary Moore and Izak Benbasat, “Development of an Instrument to Measure the Perceptions of Adopting
an Information Technology Innovation,” Information Systems Research, 2(3 1991): 192-222;
Shannon-Missal, “Are Americans Prepared to Sport Wearable Tech?”; Kannon, “Wearable
Technology Grows in Popularity.”
44
Moore and Benbasat, “Development of an Instrument to Measure the Perceptions of Adopting an
Information Technology Innovation.”
45
Rogers, Diffusion of Innovations.
46
Robert F. DeVellis, Scale Development, Theory and Applications, 2
nd
edition (Thousand Oaks, CA:
Sage Publications, 2003).
47
Moore and Benbasat, “Development of an Instrument to Measure the Perceptions of Adopting an
Information Technology Innovation,” 203.
48
Rogers states that an innovation can be classified as computer hardware or equipment, and that it will
have higher observability and likelihood for adoption than computer software that is not as apparent to
observe or be exposed to. Wearable technology devices are physical objects such as glasses,
smartwatches, etc. that are visible in a social system. Thus, the visibility of such an innovation can be
answered by exposure to and knowledge/awareness of such innovation.
49
Rogers, Diffusion of Innovations.
50
Kannon, “Wearable Technology Grows in Popularity,”; Shannon-Missal, “Are Americans Prepared to
Sport Wearable Tech?”; Oremus, “Survey: People Don't Really Want to Wear Computers on Their
Face.”
51
Rogers, Diffusion of Innovations.
52
Becker, Vlad and Simpson, “2012 Annual Survey of Journalism and Mass Communication Enrollments:
Enrollments Decline for Second Year in a Row.”
53
Jane Singer, David Craig, Chris Allen, Virginia Whitehouse, Anelia Dimitrova and Keith Sanders,
“Attitudes of Professors and Students About New Media Technology,” Journalism & Mass
Communication Educator (summer 1996): 44.
54
Chen, “The effects of education compatibility and technological expectancy on e-learning acceptance.”;
Margaryan, Littlejohn, and Vojt, “Are digital natives a myth or reality? University students’ use of digital
technologies.”; Clark, Logan, Luckin, Mee, and Oliver, “Beyond Web 2.0: mapping the technology
landscapes of young learners.”; Lai, Wang, and Lei, “What factors predict undergraduate students’ use
of technology for learning? A case from Hong Kong.”; Hopp, “Subjective Norms as a Driver of Mass
Communication Students’ Intentions to Adopt New Media Production Technologies.”