Scholars’ Mine
Scholars’ Mine
Masters Theses
Student Theses and Dissertations
Spring 2016
Factors influencing the adoption of smart wearable devices
Factors influencing the adoption of smart wearable devices
Apurva Adapa
Follow this and additional works at: https://scholarsmine.mst.edu/masters_theses
Part of the Technology and Innovation Commons
Department:
Department:
Recommended Citation
Recommended Citation
Adapa, Apurva, “Factors influencing the adoption of smart wearable devices” (2016). Masters Theses.
7492.
https://scholarsmine.mst.edu/masters_theses/7492
This thesis is brought to you by Scholars’ Mine, a service of the Missouri S&T Library and Learning Resources. This
work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the
permission of the copyright holder. For more information, please contact scholarsmine@mst.edu.
FACTORS INFLUENCING THE ADOPTION OF SMART WEARABLE DEVICES
by
APURVA ADAPA
A THESIS
Presented to the faculty of the Graduate School of the
MISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY
In Partial Fulfillment of the Requirements for the Degree
MASTER OF SCIENCE IN INFORMATION SCIENCE AND TECHNOLOGY
2016
Approved by
Dr. Fiona Fui Hoon Nah, Advisor
Dr. Keng L. Siau
Dr. Richard Hall
Copyright 2016
Apurva Adapa
All Rights Reserved
iii
ABSTRACT
This study aims to examine the factors and issues in adoption of smart
wearable devices. Wearable devices have many functions to offer which make them
very useful in our daily lives. However, factors influencing the adoption of these
devices are not well understood. This research explores the inhibiting and contributing
factors influencing the adoption of wearable devices by employing the laddering
approach. Qualitative data were collected through in-depth interviews using the
laddering technique in order to understand these factors. Wearable devices that were
examined include the Smart Glass (Google Glass) and the Smart Watch (Sony Smart
Watch 3). After the participants had the opportunity to try out these two devices, the
factors that are most important to them in deciding whether to adopt or not to adopt
these devices were laddered. The use of the laddering technique with the Means-End
Chain approach not only offers a greater understanding of the factors influencing the
adoption of wearable devices, but also reveal the relationships among these factors
and any meaningful associations with self (i.e., the user). This research has advanced
our understanding on the adoption of wearable devices and provide some insights into
the key design criteria to better fit users’ needs.
iv
ACKNOWLEDGEMENT
Foremost, I would like to express my heartfelt gratitude to my advisor, Dr.
Fiona Fui-Hoon Nah, who has given me the freedom to explore a research topic that is
not only of interest but is also passionate to me while providing continuous guidance
and encouragement throughout the learning and research process of this master’s
thesis. Also, I would like to thank all the people who willingly took part in my study
for their valuable time and insights. Furthermore, I would like to thank Dr. Keng Siau
and Dr. Richard Hall for being part of my thesis committee and taking time to review
my work. I would also like to thank all my lab mates and faculty for all the help and
suggestions, especially Samuel Smith for being the second coder during the data
analysis.
v
TABLE OF CONTENTS
ABSTRACT
………………………………………………………………………………………………….. iii
ACKNOWLEDGEMENT ………………………………………………………………………………..
iv
LIST OF ILLUSTRATIONS…………………………………………………………vi
SECTION
1. INTRODUCTION ……………………………………………………………………………………..
1
1.1 CHALLENGES FOR WEARABLE TECHNOLOGY ACCEPTANCE
…….
1
1.2 RESEARCH APPROACH …………………………………………………………………..
2
1.3 THESIS ORGANIZATION………………………………………………………………….
2
2. THEORETICAL FOUNDATION AND LITERATURE SURVEY …………………
3
2.1 MODELS OF TECHNOLOGY ADOPTION …………………………………………
3
2.2 STUDIES ON ADOPTION OF WEARABLE TECHNOLOGY ………………
5
3. RESEARCH METHODOLOGY………………………………………………………………….
8
4. DATA COLLECTION ……………………………………………………………………………..
10
4.1 DATA COLLECTION PROCEDURES ………………………………………………
10
5. ANALYSIS AND RESULTS
…………………………………………………………………….
14
5.1 INTER-RATER/CODER RELIABILITY
…………………………………………….
16
5.2 MAJOR FINDINGS ………………………………………………………………………….
34
5.2.1. Smart Glasses
…………………………………………………………………………..
34
5.2.2. Smart Watches …………………………………………………………………………
35
5.2.3. Student and Working Groups
……………………………………………………..
36
6. LIMITATIONS AND FUTURE RESEARCH
……………………………………………..
38
7. CONCLUSIONS AND IMPLICATIONS
……………………………………………………
39
REFERENCES ………………………………………………………………………………………………
41
VITA ……………………………………………………………………………………………………………
43
vi
LIST OF ILLUSTRATIONS
Figure 5.1: Ladder
…………………………………………………………………………………………..
14
Figure 5.2: Google Glass Contributing Factors – Student Group …………………………..
19
Figure 5.3: Google Glass Contributing Factors – Working Group …………………………
21
Figure 5.4: Google Glass Inhibiting Factors – Student Group ……………………………….
23
Figure 5.5: Google Glass Inhibiting Factors – Working Group ……………………………..
25
Figure 5.6: Smart Watch Contributing Factors – Student Group ……………………………
27
Figure 5.7: Smart Watch Contributing Factors – Working Group ………………………….
29
Figure 5.8: Smart Watch Inhibiting Factors – Student Group
………………………………..
31
Figure 5.9: Smart Watch Inhibiting Factors – Working Group
………………………………
33
1. INTRODUCTION
This section begins with a discussion of the current state of wearable
technology in the market followed by the motivation for conducting this research. The
main research question and a brief outline of the proposed research approach are also
presented. The section closes with an outline of this thesis along with its main
research contributions.
1.1 CHALLENGES FOR WEARABLE TECHNOLOGY ACCEPTANCE
Wearable technology is often talked about and is hyped these days. Wearable
devices are everywhere and are as commonly used as mobile phones. Wearable
technology was the cover story for the September 2014 issue of Time magazine. Since
the first ever wearable device—the Bluetooth headset, which debuted in 2000—
wearable devices seem to have finally arrived in the mainstream market. Wearable
technology has experienced a lot of challenges before getting the big break into the
mainstream market and being accepted by people. Some very common challenges
include battery life, display, privacy, etc. As the technology continues to evolve, some
challenges have been overcome while new ones have arisen. All in all, wearable
technology has always seen a hesitation when being adopted by people, which brings
us to our research question: What are the factors that influence the adoption of smart
wearable devices? While some factors contribute to the adoption of wearable
technology, others inhibit the adoption, i.e., some features or factors make users want
to adopt smart wearable technology, while others make them not want to adopt smart
wearable technology. As such, the primary research question is a two-fold question:
What are the contributing factors for adoption of smart wearable devices, and what
are the inhibiting factors for adoption of smart wearable devices?
2
1.2 RESEARCH APPROACH
The important aspect of this research is to identify the factors that influence a
user’s decision to adopt or not to adopt smart wearable devices. This was done using
the qualitative approach in order to gather very rich data from the subjects using in-
depth interviews. While the primary goal of this research is to identity the factors that
influence a user’s decision to adopt or not to adopt wearable devices, it is also useful
to understand the underlying values behind each of these factors. In order to identify
these values, the laddering methodology was adopted. The factors elicited from the
users were laddered by asking why a factor is important to them. Each factor is
laddered to a consequence and its respective values. Thus, we are not only able to
gather factors that influence the user’s decision to adopt or not to adopt smart
wearable devices, but also substantiate the value offered by each factor to the users.
1.3 THESIS ORGANIZATION
The rest of the thesis is organized into six sections as follows.
Section 2 reviews technology adoption models and studies on wearable
technology adoption in the literature.
Section 3 describes the research methodology along with the reasons for the
choice of the research approach.
Section 4 explains the data collection procedures and the interview process.
Section 5 describes how the data was parsed and analyzed, and presents the
results in the form of hierarchical value maps.
Section 6 discusses limitations of this research along with future scope and ideas
intended as a guide to future research.
Finally Section 7 concludes the thesis with a summary of the research and its
results, as well as the implications.
3
2. THEORETICAL FOUNDATION AND LITERATURE SURVEY
2.1 MODELS OF TECHNOLOGY ADOPTION
To lay the foundation for our research, we drew upon three established models
found within the literature that are related to the acceptance of technology:
Technology Acceptance Model (TAM), Innovation Diffusion Theory (IDT), and
Unified Theory of Acceptance and Use of Technology (UTAUT). The Technology
Acceptance Model (TAM) was developed in 1989 and has seen an ample number of
applications within technology acceptance research, and thus, has received significant
empirical support. TAM is centred around two core constructs—perceived ease of use
and perceived usefulness. Perceived ease of use is defined as “the degree to which the
user expects the target system to be free of effort” [1, p.26]. Perceived usefulness is
defined as the user’s “subjective probability that using a specific application system
will increase his or her job performance within an organizational context” [1, p.26].
Although TAM has seen relatively few applications in the context of wearable device
acceptance due to the novelty of this research area, the very nature of TAM allows it
to be “capable of explaining user behaviour across a broad range of end-user
computing technologies and user populations” [1, p.34]. Furthermore, TAM has been
“applied to a diverse set of technologies, contexts and users” [2, p.428], including the
context of fairly recent technological advancements such as smartphones [3, 4]. For
our research on the acceptance of wearable devices, the two core constructs of TAM –
perceived ease of use and perceived usefulness – are particularly relevant.
Furthermore, perceived ease of use falls in line with one of Dvorak’s [10] outlined
elements for the acceptance of wearable technology. Hence, we expect the constructs
in TAM to be highly relevant and appropriate for our research.
4
Roger’s Diffusion of Innovation (DOI) theory was developed in 1962 to
understand and explain how a product or idea is perceived and adopted by different
social groups [5], which coincides with the nature of our research. DOI has been
applied to a variety of technology adoption research, ranging from factors that drive
mobile commerce [6] to social media usage [7]. DOI theory encapsulates a number of
constructs: (1) relative advantage, (2) compatibility, (3) complexity, (4) trialability,
and (5) observability. The first construct, relative advantage, refers to the gains that
result from the adoption of an innovation. Since one of our research goals is to
determine if there are any new characteristics of smart wearable technologies that
significantly influence their rate of adoption, the construct of relative advantage is
expected to be relevant, not to mention that it is similar in concept to perceived
usefulness in TAM. In addition, it is worthwhile to take complexity into account, as
previous technology adoption research has found complexity to be a contributing
factor. Furthermore, complexity is related to perceived ease of use, a core construct of
TAM, which we have previously discussed.
The Unified Theory of Acceptance and Use of Technology (UTAUT) was
developed in 2003 for the purpose of measuring the variety of perceptions held within
the information technology innovation context [8]. The core constructs used in
UTAUT are (1) performance expectancy, (2) effort expectancy, (3) social influence,
and (4) facilitating conditions. While all of these constructs are applicable for our
research, the first three are particularly relevant. For instance, performance
expectancy and effort expectancy tie in closely with the core constructs in TAM i.e,
perceived usefulness and perceived ease of use respectively. In our research, these
two constructs will be referred to as the benefits of using a smart wearable device and
the effort required to use a smart wearable device. Social influence is also expected to
5
play an important explanatory role in our research since previous literature on
wearable technology [9] has indicated support for this construct.
2.2 STUDIES ON ADOPTION OF WEARABLE TECHNOLOGY
Along with the technology adoption models discussed above, there are some
recent studies that focus on wearable devices and their adoption. Among the studies
on technology acceptance and technology adoption, very few studies focus on
wearable technology adoption. This thesis is one of the first scholarly attempts to
understand the adoption of smart wearable technology.
The term wearable or wearable technology has been defined in several ways in
the literature. In the book, “Moving Wearables into the Mainstream: Taming the
Borg,” authored by Joseph Dvorak [10] who has over 10 years of experience in
wearable technology and design, he specified some of the most important
characteristics and design elements for wearables to be widely adopted. He identified
five broad elements that affect the acceptance of wearable technology in the
mainstream: wearability, ease of use, compelling design, functionality, and price.
Rhodes [11] identified three important criteria for wearable devices: portable
while being operational, needing minimum manual input, sensitive to the user’s
attention even when not actively used. Mann [12] identified three desirable
characteristics for wearable systems: situated physically such that the user and others
consider it part of the user, controlled by the user, and having negligible operational
delays. Based on Feiner’s [9] study, he stressed the importance of a wearable being
mobile and that “mobility breeds collaboration”. The other criteria he deems
important are appearance/attractiveness, comfort, optically transparent, and
inexpensive. As such, several researchers have identified various criteria for wearable
devices. In this research, we will use a qualitative approach that is grounded in data to
6
identify a comprehensive set of factors influencing wearable device adoption and
compare the factors that we have identified with those in the literature.
A wearable technology should yield user experience that allows the user to be
only minimally aware of the system in order to stay focused on the task at hand. The
basic capabilities specified by Dvorak [10] in order to achieve positive user
experience with wearable technology are flexibility, adaptability in the user interface,
environmental and situational awareness, compelling applications, intelligence, and
low operational inertia (OI) design. OI refers to the resistance a device, service, or
system imposes against its use due to the way it is designed.
Studies which focus on the social aspects of wearables have found that gender
and culture play a crucial role in the acceptance of wearable devices as well. In the
study, Perception of Wearable Computers for Everyday Life by General Public:
Impact of Culture and Gender on Technology conducted by Duval and Hashizume
[13], they found that although there are some common interests across gender and
cultures, there are also significant divergence such as system autonomy. Feiner [9]
considers social acceptance of a wearable device as a major influence of the tracking
accuracy. He says this because devices that track data are not yet fully accepted by
everyone. So the accuracy of this tracking data is debatable as it’s gathered from very
few devices. He further adds that what we share or what information we release to be
tracked may also depend upon social protocols.
Another key challenge for wearable technology is battery life [14]. Wearables
are expected to be small and thin, but there is hardly any space left for the inclusion of
a larger battery. Longevity of the battery ensures availability of the device when
needed for longer use.
7
Billinghurst and Starner [15] indicated that a wearable device should satisfy
three goals: they must be mobile, they must augment reality, and they must provide
context sensitivity in order to have some practical value in the real world. Wearable
devices need to become more intimately connected to our daily lives while combining
collaboration, computation, and context sensitivity in order to enhance personal
productivity. All the computational needs/functions should be carried out by these
devices in order for the human brain to focus more on creativity. This can be achieved
by augmenting artificial intelligence over human intelligence and by making
information gathering and filtering more natural like a daily activity of life.
In another study entitled “An Acceptance Model for Smart Watches:
Implications for the Adoption of Future Wearable Technology” by Kim and Shin
[16], they developed an extended Technology Acceptance Model by integrating the
original model with key psychological determinants of smart watch adoption. Their
findings show that the psychological determinants, affective quality and relative
advantage of smart watches, were found to be associated with perceived usefulness,
whereas mobility and availability contributed to greater perceived ease of use of the
technology.
Many of the above studies make use of technology adoption theories and
models which we have discussed in the earlier part of this section.
8
3. RESEARCH METHODOLOGY
This study is explorative and inductive in nature. This qualitative research
made use of rich data that were collected through in-depth interviews with students
and staff of a Midwestern university. To understand contributing and inhibiting
factors in the adoption of smart wearable devices, this research utilized a grounded
theory approach in order to analyze the rich data gathered from the interviews and
organize them into a map that shows the important factors influencing the adoption of
a smart wearable device. In order to discover and understand the fundamental values
of factors influencing the adoption of wearable devices, we utilized the laddering
methodology [17]. The methodology can also identify attributes of products that are
of value to consumers or users. These attributes can be laddered to produce or identify
consequences offered to the user. The consequences are the functional benefits of an
attribute as viewed by the user. These consequences are in turn laddered to identify
the personal values of the user, which determine their attitude toward the product. The
laddering methodology is similar to means-end theory [18] in that the attributes can
be seen as “means” while the values can be seen as “ends”. However, it is also
different because means-end theory focuses more on the importance of each
consequence, while laddering methodology focuses on the importance of the attribute-
consequence-value linkages. The laddering methodology explains attributes as
product characteristics that are easily identifiable by individuals. These attributes have
one or more consequences which are the perceived functional benefits or issues
associated with a product. Every consequence reinforces personal values or emotional
benefits that are important to the individual. Understanding the linkages is necessary
in order to substantiate the factors that influence user’s decision to adopt wearable
devices. The laddering methodology can best answer the research question because
9
the methodology is based on the “argument that consumer choice reflects a
relationship between product attributes, the consequences of selecting the product,
and desired end-states, or values.” [19]
10
4. DATA COLLECTION
Data was collected from 25 individuals from a Midwestern university. Out of
the 25 individuals, 15 were students—both graduate and undergraduate—and 10 were
university staff. The first group that comprises students is called the student/non-
working group and the second group that comprises university staff is called the
working group. Two sets of user groups were interviewed in order to check if the
consequences, values, and linkages differ between the students who have little to no
income versus the staff who work full time and earn a steady income. In analyzing the
data, both sets (working group and student group) were analyzed independently and
their differences are highlighted and discussed.
4.1 DATA COLLECTION PROCEDURES
In order to capture two different types of smart wearable devices, the Google
Glass and Sony SmartWatch 3 were utilized during the in-depth interviews. The
Google Glass was used to represent heads-up and head-mounted displays, while the
Sony SmartWatch 3 with fitness tracking abilities was used to represent smart
watches. In the data collection process, each in-depth interview consisted of five
parts. It began with the demonstration of one device, either Google Glass or the Sony
SmartWatch 3. We alternated the order of device demonstrations between
participants; if one participant had Google Glass demonstrated first, the next
participant would have the Sony SmartWatch 3 demonstrated first. The
demonstration included a video demo, which was a YouTube video created by the
manufacturer of the device that demonstrated the usage and basic functionality of the
device, followed by a live demonstration by the researcher specifying all the hardware
and software specifications of the device. After the demos, the subject could use and
gain experience with the device by trying out the device for five minutes. The first
11
part ended with two questions which asked the participant to specify the top three
factors that would make them want to adopt the device and also the top three factors
that would make them not want to adopt the device. The same procedure was
followed for the second device along with the same questions.
The next part of the data collection process was to elicit distinctions between
the devices. According to the laddering methodology, [17] at least two eliciting
distinction methods must be used when utilizing the laddering technique. Eliciting
distinctions help in probing more meaningful differences between products in order to
ladder them later. These differences that are mentioned by the user provide a basis to
ask more “Why is it important to you?” questions thus building more ladders. The
two eliciting distinction methods used in this paper are triadic sorting and preference
consumption differences. [17]
During the triadic sorting phase of eliciting distinctions, the subject was
presented with three visual cards. Each visual card contained a picture of its
respective device, i.e., Sony SmartWatch 3, Google Glass, and a smartphone. Hence,
each visual card represented a specific device. The smartphone was included during
this phase to provide the subject with a reference for a common smart device; every
subject indicated familiarity with a smartphone. The subject was then asked to
compare the devices and state their differences (see below for the sample questions).
These differences may affect the subject’s level of interest to adopt one or more of the
devices.
Researcher
“I would like to ask you to tell me some important ways in which the
smartphone differs from Google Glass and Sony SmartWatch.”
12
“I would like to ask you to tell me some important ways in which Google
Glass differs from the smartphone and Sony SmartWatch.”
“I would like to ask you to tell me some important ways in which Sony
SmartWatch differs from Google Glass and the smartphone.”
In the preference-consumption differences phase of eliciting distinctions, the
subject was asked to place the visual cards with the device pictures in the order which
they would prefer to adopt, with 1 being the most likely to adopt and 3 being the least
likely to adopt. Next, the subject was asked to place the cards in the order of their
usage, with 1 being the most used device and 3 being the least used device.
Throughout the process, the subject was asked to think out loud and express their
thoughts on why they chose to place the device pictures in the order of their choosing.
The subject was also asked open-ended questions to encourage elaborations (see
sample questions below). Doing so provided the researcher an opportunity to gain
further insight on factors that influence the subject’s decision to adopt the wearable
devices.
Researcher:
Why do you prefer 1 over 2?
Why is 3 least preferred?
Which one do you like the most? Which one do you use the most? If they’re
different, why?
All of the factors were then gathered and rated by the subject based on
importance using a 5-point scale, i.e. 1 = not important at all, 2 = slightly important, 3
= moderately important, 4 = very important, and 5 = extremely important.
After each of the factors were rated, the researcher began the laddering
process. Starting with the highest-rated factors, the subject was asked why a particular
13
factor is important to them. If necessary, the subject was asked multiple open-ended
questions until they stated a value which was linked to the initial attribute. With the
identification of this value, the laddering process for the attribute was completed.
14
5. ANALYSIS AND RESULTS
Qualitative data from 25 participants was parsed and the laddering technique
was applied to form several attribute-consequence-value linkages. Each attribute-
consequence-value linkage is called a ladder. For example, an illustration of the ladders
linking an attribute, Tech Novelty (lowest level), a consequence, Professionalism
(middle level), and two values, Respect and Image (highest level) shown in Figure 5.1.
Figure 5.1: Ladder
In other words, Tech Novelty is the attribute mapping to the consequence,
Professionalism, which maps to the values, Respect and Image. The blue box
represents the first level or the attribute level, and the green box represents the top
most value level. The rest that are presented in the intermediate level(s) are the
consequence(s). Each linkage is a relation that can be positive or negative. A positive
relation is denoted by a (+) sign and a negative relation is denoted by a (-) sign. In this
example, Tech novelty positively influences Professionalism i.e., the newness of the
technology increases professionalism, and Professionalism increases Respect and
Image. These ladders were formed from the interview data where the interviewer
Value
Consequence
Attribute
15
asked ‘why is it important’ question for every element that the subject mentioned as
an important factor.
All such ladders were parsed and the factors that were similar in meaning were
combined into one common abstract concept to capture the overall meanings or
concept. A map was generated for each category or group of data, i.e., one map for
each set of contributing and inhibiting factors for each smart wearable device (Google
Glass and Sony Smart Watch) and with different maps for the two user groups,
student and working groups. The following are the 8 maps generated to summarize
the findings from this research:
1. Google Glass Contributing Factors – student group
2. Google Glass Contributing Factors – working group
3. Google Glass Inhibiting Factors – student group
4. Google Glass Inhibiting Factors – working group
5. Smart Watch Contributing Factors – student group
6. Smart Watch Contributing Factors – working group
7. Smart Watch Inhibiting Factors – student group
8. Smart Watch Inhibiting Factors – working group
A map is simply an aggregation of all relevant ladders in three layers –
attributes, consequences and values. For example, the first map titled “Google Glass
Contributing Factors – student group” is a representation of all the smaller ladders
mentioned by the student group on contributing factors to adoption of Google Glass
smart wearable technology.
Reading a map is fairly straight-forward. As mentioned previously, it has 3
layers, the first or bottom layer has the attributes or factors, the second layer has
consequences, and the third or top most layer has values. In this research, all the
16
attributes are colored blue and the values colored green to facilitate identification of
attributes, consequences, and values. The relations as mentioned before are denoted as
(+) if it is a positive relation and (-) if it is a negative relation.
The 25 subjects who participated in this study fell in two different categories –
student group and working group. The student group consisted of 15 subjects who
were 9 male and 6 female undergraduate and graduate students in the age group of 19-
25 years. The working group consisted of 10 subjects who were 4 male and 6 female
university staff in the age group 26-50 years old. The data collected from the subjects
was analyzed separately for the two groups (student and working) in order to
understand the differences in the values between the two groups.
Also, during the data analysis stage, a second coder was hired to analyze the
data independent of the researcher’s analysis in order to gather a more complete
perspective in laddering and forming the maps. After both the coders analyzed the raw
data, any differences between them (e.g., categorizing and naming of elements) were
resolved through consensus among the two coders.
5.1 INTER-RATER/CODER RELIABILITY
As the data is qualitative in nature, having a second coder to analyze the data
helped to make the results more reliable as some of the thoughts mentioned by the
subjects in the interviews could be interpreted differently. Hence there was a second
coder for data analysis.
As mentioned earlier, having more than one coder helped to improve the
reliability of the results. For example, when a subject from the student group was
asked about the contributing factors to the smart wearable device – Smart Watch – the
screen size and the hardware design of the device were noted as important factors in
deciding whether or not to adopt the device. These factors fall in the attribute level of
17
laddering and the subsequent interview conversation centered around why these
factors were important. While analyzing this data, the first coder inferred that the
factor in the case of screen size should be called the Display while the factor, Design
should be left as is. But according to the second coder’s interpretation, both the
factors, screen size and design, can be clubbed into one factor called Form Factor
(refer to Figure 5.5). The term, form factor, gathers both factors about design
considerations very well which the subject was trying to explain. So after some
discussion and taking into consideration what other subjects who mentioned the same
factors meant, we decided upon the term, Form Factor.
Another such example in the coding process relates to the factor GPS. The
first coder combined both GPS feature and the accuracy of the feature in the same
factor labelled GPS and hence it was not possible to differentiate between subjects’
frustration or satisfaction with the GPS of the Google Glass, due to the inaccuracy of
the maps and other navigation functionality provided by the GPS. So both the coders
decided that GPS accuracy in itself can be considered a factor which the subjects
linked to satisfaction that in turn, increased the usefulness of the device, which in turn
increased the value for money – a personal value for the subject. (see to Figure 5.1)
Having a second coder and coming to a consensus for some terms was very
helpful because the data was in the form of raw concepts generated from the
interviews. When more than one subject indicated a factor, they might make use of
different words or phrasings while referring to the same thing. Consolidating and
standardizing the raw concepts into a generic form was necessary as they facilitated
the forming and generation of the maps using these abstract and generic concepts and
resulted in more meaningful and reliable results. The maps that were generated from
this research were shown next.
18
Figure 5.2 is a map representing all the ladders (attribute-consequence-value
linkages) mentioned by the respondents, in the student category, when asked about the
contributing factors for Google Glass. The contributing factors for Google Glass as
mentioned by the student group are Brand, Functionality, Hands-free, Compatibility,
Interface, GPS, GPS Accuracy, Voice Recognition Accuracy, Technology Novelty,
Messaging and Social Media Apps, Look and Feel. The concept, brand, refers to the
meaning associated with the brand of the smart wearable device, in this case Google,
that was found to be an important contributing factor to adopt a smart wearable
device. Brand was also a factor mentioned only by the student group but not the
working group.
Participant 5 mentioned the following:
“If it’s a brand I love, like Google, I automatically assume they come up with
something exciting… something I’m gonna like”
Another participant, Participant 6 stated the following:
“I’m more likely gonna buy it, if it’s a known brand”
And when asked why, Participant 6 said “Then I know it has good buyer
support, so I can use it longer without having to spend more money on a new one”
All the attributes or factors in blue are linked to one or more consequences
which are linked to the values in the top most layer, colored green. The values that
were found in the “Google Glass Contributing Factors – student group” were Value
for money, Interest/Passion, Family Value, Belonging and Image. All the relations in
this map are positive relations.
