Internet Learning Volume 5, Number 1, Fall 2016/Winter 2017 | Page 25

Internet Learning
ers. The second article was a report on
the launch of a customer experience
academy for truck drivers, not scholarly
theory associated with customer experience
nor theory applied to educational
institutions, the nature of this study.
The results indicated that no significant
customer experience study applied to
the academy could be found.
Content Analysis
A
quantitative study, such as the
content analysis, allows variables
to be measured to determine
whether the hypothesis can be
generalized (Creswell, Clark, Gutmann,
& Hanson, 2003). This is a benchmark
study to ascertain the level of CX and
UX interactions and to make recommendations
on how to take the CLSER
website that is said to be in a default
CX mode, document it, and collect improvements
that can be purposefully
put back into the site, thus leading to
more customer advocates. H 1 : Using
CX theory applied to the CLSER website
design and interactions will show a
default design, but customers will lead
the next CLSER iteration to more purposefully
include CX interactions that
they deem necessary. Consequently, as
these customers continue to become
advocates, they can advance their professional
life through the development
and dissemination of research.
The CLSER website first went
online in early 2014 and was not modified
until after this study was completed
in early 2016. A coding book of definitions
operationalized the CLSER implicit
website messages and promises
by tying them to key words, frequency,
and prominence of words posted in the
site such as: assistance, honorarium, and
scholarship, words that indicated financial,
or help available, for example.
Steinhart (2010) studied both
implicit and explicit promises as they
related to product expectations. Explicit
promises are those the company
states about a product or service. “Implicit
promises, on the other hand, are
cues that lead to inferences about what
product performance should and will
be like”(Steinhart, 2010, p. 1710). While
this differentiation is important to note,
this study operationalized promises
tied to key words aboard the site into
one primary category of promises to
benchmark their existence and how
prominently they appeared.
Corpus Linguistics Content
Analysis software was selected as a basic
algorithmic tool to parse through
CLSER pages to examine the frequency
of terms that were operationalized
as promises. Such software can parse
only those pages on the site that belong
to the root CLSER (see the Appendix).
While this tool provided the frequency
data, like most such algorithmic text
analyzers, it cannot readily determine
the journalistic prominence of such
messages (Budd, 1964).
Budd argued that information
located more toward the front of newspapers
and on the top fold was the most
prominent or most likely to get read.
Thus, for this study, the CLSER website
promises made starting on its home
page and those terms found closest to
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