English 205Masterworks of English LiteratureHANDOUTSCritica.docx

English 205:
Masterworks of English Literature
HANDOUTS
Critical Approaches to Literature
Plain text version of this document.
Described below are nine common critical approaches to the
literature. Quotations are from X.J. Kennedy and Dana
Gioia’s Literature: An Introduction to Fiction, Poetry, and
Drama, Sixth Edition (New York: HarperCollins, 1995), pages
1790-1818.
· Formalist Criticism: This approach regards literature as “a
unique form of human knowledge that needs to be examined on
its own terms.” All the elements necessary for understanding
the work are contained within the work itself. Of particular
interest to the formalist critic are the elements of form—style,
structure, tone, imagery, etc.—that are found within the text. A
primary goal for formalist critics is to determine how such
elements work together with the text’s content to shape its
effects upon readers.
· Biographical Criticism: This approach “begins with the simple
but central insight that literature is written by actual people and
that understanding an author’s life can help readers more
thoroughly comprehend the work.” Hence, it often affords a
practical method by which readers can better understand a text.
However, a biographical critic must be careful not to take the
biographical facts of a writer’s life too far in criticizing the
works of that writer: the biographical critic “focuses on
explicating the literary work by using the insight provided by
knowledge of the author’s life.... [B]iographical data should
amplify the meaning of the text, not drown it out with irrelevant
material.”
· Historical Criticism: This approach “seeks to understand a
literary work by investigating the social, cultural, and
intellectual context that produced it—a context that necessarily

includes the artist’s biography and milieu.” A key goal for
historical critics is to understand the effect of a literary work
upon its original readers.
· Gender Criticism: This approach “examines how sexual
identity influences the creation and reception of literary works.”
Originally an offshoot of feminist movements, gender criticism
today includes a number of approaches, including the so-called
“masculinist” approach recently advocated by poet Robert Bly.
The bulk of gender criticism, however, is feminist and takes as
a central precept that the patriarchal attitudes that have
dominated western thought have resulted, consciously or
unconsciously, in literature “full of unexamined ‘male-
produced’ assumptions.” Feminist criticism attempts to correct
this imbalance by analyzing and combatting such attitudes—by
questioning, for example, why none of the characters in
Shakespeare’s play Othello ever challenge the right of a
husband to murder a wife accused of adultery. Other goals of
feminist critics include “analyzing how sexual identity
influences the reader of a text” and “examin[ing] how the
images of men and women in imaginative literature reflect or
reject the social forces that have historically kept the sexes
from achieving total equality.”
· Psychological Criticism: This approach reflects the effect that
modern psychology has had upon both literature and literary
criticism. Fundamental figures in psychological criticism
include Sigmund Freud, whose “psychoanalytic theories
changed our notions of human behavior by exploring new or
controversial areas like wish-fulfillment, sexuality, the
unconscious, and repression” as well as expanding our
understanding of how “language and symbols operate by
demonstrating their ability to reflect unconscious fears or
desires”; and Carl Jung, whose theories about the unconscious
are also a key foundation of Mythological Criticism.
Psychological criticism has a number of approaches, but in
general, it usually employs one (or more) of three approaches:
1. An investigation of “the creative process of the artist: what is

the nature of literary genius and how does it relate to normal
mental functions?”
2. The psychological study of a particular artist, usually noting
how an author’s biographical circumstances affect or influence
their motivations and/or behavior.
3. The analysis of fictional characters using the language and
methods of psychology.
· Sociological Criticism: This approach “examines literature in
the cultural, economic and political context in which it is
written or received,” exploring the relationships between the
artist and society. Sometimes it examines the artist’s society to
better understand the author’s literary works; other times, it
may examine the representation of such societal elements within
the literature itself. One influential type of sociological
criticism is Marxist criticism, which focuses on the economic
and political elements of art, often emphasizing the ideological
content of literature; because Marxist criticism often argues that
all art is political, either challenging or endorsing (by silence)
the status quo, it is frequently evaluative and judgmental, a
tendency that “can lead to reductive judgment, as when Soviet
critics rated Jack London better than William Faulkner, Ernest
Hemingway, Edith Wharton, and Henry James, because he
illustrated the principles of class struggle more clearly.”
Nonetheless, Marxist criticism “can illuminate political and
economic dimensions of literature other approaches overlook.”
· Mythological Criticism: This approach emphasizes “the
recurrent universal patterns underlying most literary works.”
Combining the insights from anthropology, psychology, history,
and comparative religion, mythological criticism “explores the
artist’s common humanity by tracing how the individual
imagination uses myths and symbols common to different
cultures and epochs.” One key concept in mythlogical criticism
is the archetype, “a symbol, character, situation, or image that
evokes a deep universal response,” which entered literary
criticism from Swiss psychologist Carl Jung. According to Jung,
all individuals share a “‘collective unconscious,’ a set of primal

memories common to the human race, existing below each
person’s conscious mind”—often deriving from primordial
phenomena such as the sun, moon, fire, night, and blood,
archetypes according to Jung “trigger the collective
unconscious.” Another critic, Northrop Frye, defined archetypes
in a more limited way as “a symbol, usually an image, which
recurs often enough in literature to be recognizable as an
element of one’s literary experience as a whole.” Regardless of
the definition of archetype they use, mythological critics tend to
view literary works in the broader context of works sharing a
similar pattern.
· Reader-Response Criticism: This approach takes as a
fundamental tenet that “literature” exists not as an artifact upon
a printed page but as a transaction between the physical text and
the mind of a reader. It attempts “to describe what happens in
the reader’s mind while interpreting a text” and reflects
that reading, like writing, is a creative process. According to
reader-response critics, literary texts do not “contain” a
meaning; meanings derive only from the act of individual
readings. Hence, two different readers may derive completely
different interpretations of the same literary text; likewise, a
reader who re-reads a work years later may find the work
shockingly different. Reader-response criticism, then,
emphasizes how “religious, cultural, and social values affect
readings; it also overlaps with gender criticism in exploring
how men and women read the same text with different
assumptions.” Though this approach rejects the notion that a
single “correct” reading exists for a literary work, it does not
consider all readings permissible: “Each text creates limits to
its possible interpretations.”
· Deconstructionist Criticism: This approach “rejects the
traditional assumption that language can accurately represent
reality.” Deconstructionist critics regard language as a
fundamentally unstable medium—the words “tree” or “dog,” for
instance, undoubtedly conjure up different mental images for
different people—and therefore, because literature is made up

of words, literature possesses no fixed, single meaning.
According to critic Paul de Man, deconstructionists insist on
“the impossibility of making the actual expression coincide with
what has to be expressed, of making the actual signs [i.e.,
words] coincide with what is signified.” As a result,
deconstructionist critics tend to emphasize not what is being
said but how language is used in a text. The methods of this
approach tend to resemble those of formalist criticism, but
whereas formalists’ primary goal is to locate unity within a text,
“how the diverse elements of a text cohere into meaning,”
deconstructionists try to show how the text “deconstructs,”
“how it can be broken down ... into mutually irreconcilable
positions.” Other goals of deconstructionists include (1)
challenging the notion of authors’ “ownership” of texts they
create (and their ability to control the meaning of their texts)
and (2) focusing on how language is used to achieve power, as
when they try to understand how a some interpretations of a
literary work come to be regarded as “truth.”
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ii
Copyright 2010: Lawrence Bennett Davies
v
Abstract
This research project was performed as a preliminary
investigation to determine the relationship
between organizational culture types and technology acceptance
in an institution of higher education.
Thirty-nine respondents out of a single population of 443
eligible participants returned usable data.
Using an online survey, subjects responded to a series of short
demographic questions. They
were then asked to respond to statements about how they
perceived the usefulness and ease of use of
two types of software found within the institution. Finally, they
were asked to respond to a series of
statements about the organizational culture at their institution.
The data collected were analyzed using a

series of ANOVAs and a Pearson r correlation.
Results showed no significant correlation between the two
variables. However, demographic
data for the cultural means returned significant results. Some
ANOVAs showed significant differences
in the means of two demographic categories, though a
Bonferroni post-hoc analysis to isolate the cause
behind the significance was inconclusive. The results suggest
that there is a connection between
subjects’ affiliation with their particular school and their
attitudes toward the usefulness and usage of
technology.
vi
If you want to build a ship, don’t drum up people to collect

wood and don’t assign them tasks and work,
but rather teach them to long for the endless immensity of the
sea.
Antoine de Saint-Exupéry
vii
Acknowledgments
I know I am going to forget someone somewhere, but I will at
least try to cover all those who
helped in some way as I went through this process.
First and foremost, I thank my committee members, Dr. Booker,
Dr. Cingel and Dr. Thomas for
their patience during the process and their unwavering
assistance.
This research also required assistance from the following
individuals and organizations:
- The Institutional Review Board at St. Thomas University,
headed by Dr. Gary Feinberg, who
approved the instruments;

- Assistance from Dr. Ken Johnson & Dr. Jerry Weinberg,
statistics professors at St. Thomas
University, and Dr. Morris Knapp statistics professor at Miami
Dade College;
- Dr. Richard Bagozzi, who has contributed extensively to TAM
models and research, and had
many suggestions on where to follow up with further TAM
research;
- Dr. Risa Blair, Dr. Maria Sevilliano, and Dr. Marcia Williams
who read and commented on
early drafts of the research design and conceptual frameworks;
- Dr. Larry Rubin who always said what I needed to hear at the
most appropriate time;
- My colleagues in the march to finish this dissertation: Dr.
Judith Gray, Dr. Donovan
McFarlane, and Dr. Lynn Kendrick. May we all see the fruits
borne from our hard work;
- Dr. Jason Nolan for being Dr. Jason Nolan.
viii
Dedication

To my wife, Atsuko, and my daughter, Ayaka:
大変お待たせしました。
To my late parents, Janice (1931-2008) and Stuart (1928-2010):
Thanks for providing me with a ticket to this show.
ix
Table of Contents
Abstract
...............................................................................................
............................................ v
Acknowledgments....................................................................
..................................................... vii
Dedication
...............................................................................................
..................................... viii
Table of Contents
...............................................................................................
............................ ix
List of Figures
...............................................................................................
............................... xiii
List of Tables
...............................................................................................

................................ xiv
Chapter 1 – Introduction
...............................................................................................
.................. 1
Overview
...............................................................................................
...................................... 1
Technology and Organizational Culture
..................................................................................... 1
The Intersection
...............................................................................................
........................... 2
Chapter 2 – Literature Review
...............................................................................................
......... 4
Introduction
...............................................................................................
.................................. 4
Learning and Technology
............................................................................. ..................
............ 4
Organizational Paradigms
...............................................................................................
............ 8
Organizational Andragogy
...............................................................................................
......... 10

Innovation and Acceptance
...............................................................................................
........ 12
The Diffusion of Innovation
...............................................................................................
...... 12
x
Technology Acceptance
...............................................................................................
............. 14
The Competing Values Framework (CVF)
............................................................................... 16
Competing Values Framework Research
.................................................................................. 18
Variations of the Technology Acceptance Model
.................................................................... 23
Modified TAM Research
...............................................................................................
........... 24
TAM in Organizational Culture
...............................................................................................
. 26
TAM in Academia
...............................................................................................

..................... 30
Other Studies Relevant to the Research Problem
..................................................................... 33
Personalization in Learning With and Through Technology
.................................................... 36
Statement of the Problem
...............................................................................................
........... 37
Chapter 3 – Methodology
...............................................................................................
.............. 38
Population
...............................................................................................
.................................. 38
Instrument
...............................................................................................
.................................. 39
Procedure
...............................................................................................
................................... 40
Chapter 4 - Results
...............................................................................................
......................... 42
Procedure
...............................................................................................
................................... 42

Data Collection
...............................................................................................
.......................... 44
Findings..................................................................................
................................................... 45
Sample Sizes for the Research
...............................................................................................
... 45
The Demographic Variables
...............................................................................................
...... 53
xi
Chapter 5 - Discussion
...............................................................................................
................... 59
Summary of Findings
...............................................................................................
................. 59
Descriptive and Reliability Analyses
........................................................................................ 59
Data Analysis
...............................................................................................
............................. 60
Implications for the Subject Institution
..................................................................................... 63

Implications for Organizational Cultures
.................................................................................. 65
Implications for Educational Leaders
....................................................................................... 66
Recommendations for Practice
...............................................................................................
.. 66
Recommendations for Future Research
.................................................................................... 67
Limitations of the
Study.................................................................................... ..
...................... 68
Organizational Culture and Technology Acceptance in the 21st
Century ................................. 69
APPENDIX A - Survey: Demographic Questions
....................................................................... 72
APPENDIX B - Survey: Organizational Culture Assessment
Instrument (OCAI) ...................... 74
APPENDIX C - Survey: TAM Questions
.................................................................................... 77
Statements of Perceived Usefulness
(PU)............................................................................. 77
Statements of Perceived Ease of Use (PEU)
......................................................................... 77

Statement of Actual Usage (AU):
......................................................................................... 78
Usage
Volume:..................................................................................
.................................... 78
APPENDIX D - Survey: Consent Statement
................................................................................ 79
Description of Project
...............................................................................................
............ 79
xii
Statement of Confidentiality
...............................................................................................
.. 79
Explicit Statement of Consent
..............................................................................................
79
Contact Information
...............................................................................................
............... 79
Statement of Risks/Benefits
...............................................................................................
... 79
APPENDIX E - Email Announcements to Solicit Participants
.................................................... 80

REFERENCES
...............................................................................................
.............................. 82
xiii
List of Figures
Figure 1 – The Organizational Culture Type
Matrix………………………………………17
Figure 2 – The Technology Acceptance Model at its Earliest
Inception…………………..23
xiv
List of Tables
Table 1 – Survey Sections Indicating Summary of the
Information, Type of Question Administered,
Number of Questions Given within the Section and Type of
Calculation Needed From the Result of the
Question………………………………………………..44
Table 2 – Number of Subjects per
Category………………………………………………..48

Table 3 – Mean Scores by School on Culture
Type………………………………………………..50
Table 4 – Means, Standard Deviations, Intercorrelationsa, and
Reliabilitiesb of the Seven Main Variables
Used (Including Three TAM Variables and Four OCAI
Variables) ………………………………………52
Table 5 – Results of ANOVA Analyses for Perceived
Usefulness, Perceived Ease of Use, and Actual
Usage on the Seven Demographic Variables
Collected………………………………………………..55
Table 6 – Results of Bonferroni Analysis for Perceived
Usefulness for the Six Schools……………………57
Table 7 – Results of Bonferroni Analysis for Actual Usage for
the Six Schools……………………………58
1
Chapter 1 – Introduction
Overview
This chapter introduces the two main theoretical frameworks
that constitute this study: a)
organizational culture types and b) technology and the

Technology Acceptance Model. It presents
a brief background on each framework, and introduces
theoretical foundations based on anecdotal
evidence to establish the need to look at the correlation between
the two theoretical frameworks.
It then explores the type of data that will be collected utilizing
surveys, and suggests a method of
analysis.
Technology and Organizational Culture
Given the pace and change of technology, interest in the use of
technology is an ever-
increasing, quickly evolving area of research. According to
Moore’s Law, first reported by Moore
(1965), it is known that technology increases exponentially year
by year.. Moore theorized that
computing power would double every eighteen months to two
years. Data ranging from the mid-
1950s to today shows that Moore’s Law holds true. Moore’s
Law is now a generally accepted
theory when it comes to understanding the hardware that drives
changes in technology.
Another accepted theory is that every higher education
institution has a unique set of

values and norms of operation that constitute its organizational
culture. The theoretical concept of
organizational culture is not new, and has arisen from
interdisciplinary studies of management,
sociology, psychology, educational psychology, human resource
development, anthropology, and
social psychology (Cameron & Quinn, 2003).
2
The Intersection
Using these two theoretical frameworks, this study examines
the effects of the pace and
change of technology on organizational cultures at institutions
of higher education. This research
project was based on the examination of how these institutions
address, accept, plan, and
implement remedies to best utilize these rapidly changing and
emerging advances. Anecdotal
evidence suggests that institutions of higher education are
increasingly employing technology to

support learning, but that there is variation among each
institution’s approach.
The relationship between organizational culture and the value
placed on technology
integration and change rarely keeps pace with the strictly
physical changes in computer power.
However, there is already anecdotal evidence that an
institution’s organizational type has an
effect on the pace and depth with which it embraces change
(Bagozzi, 2007; Venkatesh, Morris,
Davis, & Davis, 2003). Whether this change is grounded in
technology, teaching methods,
training, or the construction and reconstruction of day-to-day
information infrastructures that are
the glue of the university, change does happen at the pace that
is most comfortable for the
individual institution. Though not the subject of this study, the
pace and depth of change are
different if examined within individual institutions.
This research study explores the relationship between
organizational culture and the
acceptance and implementation of adaptation to new
technological advances. First, it examines

data (collected via an online survey and from individuals at a
specific institution of higher
education) defining educational organizational cultures into one
of four culture types. It also
collects data about technology acceptance via the same online
survey. Finally, some basic
descriptive demographic data collected are compared with the
organizational culture data to
determine significance in difference between the two.
These three data sets are then examined for correlations. A
statistically significant
correlation between the data sets may have implications for
higher educational institutions and
3
their intended role in implementing technology acceptance.
It is hoped that this study will help inform proponents of
technology in education of the
factors that influence an institution to accept and embrace
innovative and rapid changes in their
technological educational infrastructure.

4
Chapter 2 – Literature Review
Introduction
This chapter presents the historical and theoretical frameworks
upon which this research
is based, and reviews literature in the two main areas that are
the subject of this research. It also
traces the history of educational organizations and their ongoing
challenge to foster and sustain
technology use among all stakeholders. It culminates in posing
the main problem that emerges
from this history.
This study starts by reviewing literature stemming from and
including Cameron and
Quinn's (1999) Competing Values Framework (CVF), and the
corresponding four culture types
that serve as the dependent variable in this study. The study
then reviews literature on the three

facets of Davis’ (1989) Technology Acceptance Model, which
constitute three separate
independent variables of this study. The review of the literature
helps solidify and situate the
significance of these two main areas of the research.
Learning and Technology
While studying to obtain a Master's degree in Teaching English
to Speakers of Other
Languages during September of 1993, this researcher
participated in an activity that was among
the typical community building events employed at that
institution of higher education. Receiving
a 3x5 index card, each aspiring teacher was charged with a
simple assignment: “Tell Us Your
Passion." This was done to invoke what was interesting to the
group at the time; the results would
be used as a springboard for conversation and to develop
affinity among the group. This was a
deliberate action on the part of the faculty, who were well-
versed on how to build a community of
learners. The small space on the card presented a challenge; a
response would have to be succinct.

5
It had to explain the researcher’s “self” to this newly formed
group of peers.
The cards were posted and a variety of styles and approaches
were used. Some students
drew pictures of classrooms, ideal learning spaces, or ambitious
projects they wanted to pursue
after graduation or had already accomplished. Others filled the
card's one side with words,
meticulously explaining their philosophy of life, their latest
books read, their latest travel
experiences, and their hopes for what to do after graduation.
The researcher in this study took a
short time before posting a card with only two words that
expressed what was at the center of his
attention: "The Internet." When other classmates and faculty
read this, it was - at that time -
beyond most of their comprehension. No one understood the
term or its implications in that
context. Subsequent efforts to explain the coming revolution
that this technology portended - and
especially its impact on higher education - fell mostly on blank
stares and disinterest. The class

went on to study and deliberate on what "good teaching and
learning" entails.
Fifteen years later, even this researcher could not anticipate the
impact that technology
would have on the world nor some of the major issues that
would connect education to
technology; neither could he anticipate or hypothesize, the
impact that technology would have on
his life and interests. In the five years between 1994 and 1999,
the impact of technology on
education came into focus as technologies bred faster computers
with greater processing power.
One major issue that began to emerge in higher education
during the mid-1990s was the
challenge of educating ever-growing numbers of students using
the new Internet medium while
keeping faculty and staff up to date on developments in
technology. In the early 2000s, anecdotal
evidence suggested that institutions were trying to address how
to juggle various competing
software packages with the needs of they users. This included
the demands of individuals and
small factions of faculty and staff. All of these separate,
piecemeal systems were slowly

converging into single enterprise systems that promised
scalability, ease of content delivery, and
ease of administration. However, there was still a lack of
connection between administrative
decisions and faculty adaptation. The phenomenon of skeptical
faculty wariness towards such
6
systems also emerged anecdotally. Not much research was
conducted at that time to understand
these emerging phenomena.
Only five years later, a 2005 October headline proclaimed:
"Blackboard Buys
Competitor (WebCT) for $180M" (Clabaugh, 2005), then to
May of 2009 "Blackboard Buys
Angel Learning" (Clabaugh, 2009). These two acquisitions by
Blackboard, Inc., a Washington,
D.C.- based company with over 1000 employees, made it the
largest private company supplying
Internet technology to institutions of higher education. The
technology used by most learning

institutions as part of their Internet-based solution to provide
"good teaching and learning" online
is known by several acronyms; most prevalent among these are
Managed Learning Environments,
Learning Content Management Systems, Content Management
Systems, or Learning
Management Systems (also known as LMS, which will be used
henceforth). The Southern
Association of Colleges and Schools (SACS, 2000), the main
institution for granting accreditation
to schools in the Southeast United States, notes that online or
distance learning “both support[s]
and extend[s] the roles of educational institutions. Increasingly,
(distance education programs) are
integral to academic organization, with growing implications
for institutional infrastructure”
(p.2).
Blackboard's new acquisition brought 1,900 new clients (many
in higher education) from
Canadian-Based WebCT to its then base of more than 1,800.
Blackboard’s subsequent acquisition
of Angel, a company founded by Indiana University/Purdue
University that was mostly employed
by Community Colleges in the United States, also increased the

company’s client base.
This enabled Blackboard to have direct access to close to 5,800
schools, government
agencies, and corporate customers worldwide (Clabaugh, 2009).
These acquisitions were of
particular interest to this researcher who spent almost four years
(2004 to 2007) as a Blackboard
LMS administrator at a small, private university in South
Florida. In January 2008, he moved on
to a public college and became an Angel administrator at a
college that had just migrated from
WebCT LMS to Angel LMS.
7
Once again, he found himself at the center of the controversy
that set the higher education
community abuzz. Faculties and administrators falsely
perceived Blackboard, a privately held
company, to be the only LMS available to higher education. In
addition to K-12 institutions, the
military and major world governments were interested in online
education. However, there were

open source LMSs available, including Moodle, founded by an
Australian educator, and Sakai,
led by an American consortium. According to the Open Source
Initiative, Open Source
(http://www.opensource.org) is “a development method for
software that harnesses the power of
distributed peer review and transparency of process” (2010,
Homepage). As of this writing, the
Justice Department of the United States has been looking into
protests of monopolistic practices
from former Angel clients (Rupar, 2009) and others who saw no
alternative LMS as a result of
the Angel acquisition. The Blackboard acquisition of Angel,
though, had already been finalized.
These events raised the following questions: Why did a single
company have such
influence over so many learning institutions? Why did this
cause such a stir? Would skeptical
faculty resist administrative fiat once again? What had
administrations been trying to do to
persuade, coerce, or otherwise bring faculty to embrace and use
the technologies that Blackboard
delivered? Surely, there must already be some research on
higher education that addresses some

of the issues that lead to purchasing and using a Learning
Management System such as
Blackboard or Moodle, and getting faculty to “buy into” the
process of using such software?
These questions seemed important to explore, especially as this
researcher continued to
move away from teaching and toward technology administration
in higher education. With a solid
base of experience as an instructor in higher education, and an
emerging base of experience as a
technology administrator, this researcher had been dealing with
the tensions that exist within
organizations between faculty and administrators. He was able
to see the same issue from two
different and very important perspectives. He noticed that each
institution he worked for had a
different set of values and emphasis on process. He theorized
that these differences, especially
when it came to technology implementation, might have
something to do with the cultural
8

makeup of the organization. It was time to examine the nature
and makeup of organizational
cultures.
Organizational Paradigms
Some groundwork had already been laid, and dozens of separate
studies on technology
implementation had been completed by 2006. The most
prominent of these studies was a 2006
literature survey. In the survey of sixty-eight journal articles
focusing on the introduction of
technology to pre-service teachers, Kay (2006) isolated ten
strategies that had been employed to
some extent in higher educational institutions to get faculty and
students better acquainted with
technologies such as the Blackboard Learning Management
System, and to exploit the emerging
technologies of the Internet. Those strategies identified
included institutions delivering a single
technology course (one course that could be used to deliver a
course); offering mini-workshops;
integrating technology in all courses; modeling methods to use
technology; using multimedia;
fostering collaboration among pre-service teachers, mentoring

teachers and faculty; practicing
technology in the field; focusing on education faculty; focusing
on mentor teachers; and
improving access to software, hardware, and/or support.
Kay's (2006) most significant finding was that most of these
published studies suffered
from a lack of comprehensive research methodology; there were
poor data collection instruments,
vague sample and program descriptions, small samples, an
absence of statistical analysis, or weak
anecdotal descriptions of success. Kay (2006) found that most
institutions had few systemic
operating modalities, and fewer researchers showed any rigor in
looking at or uncovering such
modalities. The institutions examined in these studies seemed to
have a vague notion regarding
the importance of addressing technology policy. Few had taken
any substantial or measurably
proactive steps to implement anything that added value to the
educational experience, whether
online or face-to-face. There were no programs that seemed to
be planned systematically. Most of
these remedies for infusing technology throughout courses and
curricula were treating symptoms

9
without trying to define the problem. In this researcher’s mind,
most of these programs were
unaware that a problem needed defining.
It seemed that institutional decisions for technology
implementation continued to
contribute to the resistance of faculty to embrace and integrate
technology into their curricula.
Was it something about the organizational culture of these
institutions that might have a bearing
on this? Did organizational culture contribute to needed
technology policy formation and
implementation? These questions had, to some extent, been
placed into a socio-cultural historical
context. In fact, there were already some studies that looked at
how organizational culture
impacts technology acceptance. Upon this basis, this researcher
began a search for clues to link
the two constructs.
One early clue pointed to educational paradigms. Paradigms,

according to
Dictionary.com, are “a set of assumptions, concepts, values, and
practices that constitute a way of
viewing reality for the community that shares them, especially
in an intellectual discipline”.
Before Blackboard's Angel acquisition, Craig (2007) looked at
institutional-level LMS-centered
thinking, and noted that emerging Web 2.0 technologies meant
that institutions would need to
begin looking beyond the LMS paradigm (described below). The
term "Web 2.0" was attributed
to DiNucci (1999) and is characterized by websites that foster
collaboration and enhanced social
networking activities, such as Delicious, a social bookmarking
website, and WordPress, a web-
publishing platform. In the world of online presence, Web 2.0
companies are being created at an
ever-increasing pace.
Craig (2007) claimed that the Learning Management System
enterprise paradigm was
mired in the pre-1999 mentality that the Internet could be used
as a single, central entity to
control student's "good learning and teaching". This paradigm
included the notion that learning

with technology would happen most effectively through a
course designer, a course developer,
and instructor-lead paradigm. The traditional effective way to
teach was a linear, top-down view
of transmitting knowledge and information with little
consideration given to the learner or to
10
learning. Organizational cultures of higher education seemed, at
best, to be largely unaware how
to address these claims. There seemed little evidence that they
were planning a move away from
this paradigmatic trench. Web 2.0 was considered a more
socially based, collaborative, and
“disruptive” technology (in the sense that it got in the way of
traditional approaches of using
technology in education) that Craig (2007) claimed higher
education needed to understand and
incorporate. Perhaps there was a philosophical disconnect in
higher education vis-à-vis the
emerging learning theories of the early 1990s. One of these
learning theories was ready to explain

these organizational and technological disconnects. Proponents
of the theory of Constructivism
began to move to the forefront since the theory partially
explained the impact of culture and
social groups on how individuals construct meaning in the
context of their communities
(Jonassen, 1993; Jonassen & Welsh, 1993; Jonassen, Peck &
Wilson, 1999; Wilson, 1997 Wilson
& Ryder, 1996).
Organizational Andragogy
Based primarily on the writings of educational psychologist Lev
Vygotsky (1978),
Constructivism posits that younger learners construct their
knowledge within the context of the
language, culture, and history of their social and cultural
groups. Furthermore, children make
sense of the world by being “scaffolded” by their peers who
have higher thought processes via the
“Zone of Proximal Development” (p. 86). This social learning
continues to a lesser extent into
early adulthood, and exists throughout a person’s life.
Constructivist theory is based largely on informal learning.
Seen through the lens of

Vygotsky’s Constructivist views, the LMS paradigmatic
difference is described, quite simply, as
the "sage on the stage" versus the "guide on the side" (King,
1993). The instructor-as-sage
transmits knowledge through lectures and tests for
understanding through formal assessments,
such as multiple choice tests and quizzes. Students study the
same thing at the same time in the
same order. The instructor-as-guide spends less time
transmitting information through lectures,
11
and more time helping students work to form better questions on
problems posed, collaborating in
groups to present ideas in project-based scenarios, and looking
to their peer group for affirmation
of their learning. Students study things that interest them with
guidance from their peers and their
instructor.
In Web 2.0 parlance, Constructivism is the "pull" versus the
Instructionist "push," with

Instructionism defined as the act of “transmitting facts to
passively receptive students” (Shabo,
1997). In Craig’s (2007) view of the old “sage” paradigm,
information is “pushed” en masse
down to students via the LMS in a linear order that is pre-
chosen by the instructor for the student
to “study”. The student is then quizzed or tested (usually by
multiple choice, true/false or
matching question types), and the score is taken as a reflection
of learning. In the new
Constructivist-based “guide” paradigm, an outcome is stated, a
situation is given, and supporting
information is available to students in a non-linear format for
them to “pull” from as needed.
Alternative assessments, such as a portfolio of work or the
presentation of a collaborative project,
are taken as a reflection of learning and, in many cases, marked
against a pre-determined rubric
that indicates to students how their work will be evaluated by
the instructor.
As more research articles touting Constructivist paradigms
appeared in the literature in
the late 1990s, it seemed clear that a re-examination of
institutional culture’s impact on the use of

technology was in order, since individual success is not
necessarily translated to institution-wide
success. Constructivists, after all, base many of their views on
placing individuals within a socio-
cultural-historical context for their thoughts, actions, and
transmissions of knowledge. For
Constructivists, culture is inherent at some level in the decision
to implement curricula and to use
available technological tools of the time (Jonassen, Peck &
Wilson, 1999).
The evidence of the need for an educational paradigm shift has
been mounting since the
late 1990s in higher education, but is also found in the
administrative views on the schooling of
younger students. Project Tomorrow (2009) reported that many
K-12 schools in the USA actually
impede students’ usage of technology by blocking access to
websites, limiting students’
12
technology usage in the classroom, and imposing rules to
further limit technology, though many

students report that they make ample use of the social
networking tools and mobile technologies
afforded to them at home. Students report that in addition to
taking tests online, they play
educational games; use social networking sites such as MySpace
and Facebook for collaborative
endeavors; plan, produce, and direct audio and video projects;
create presentations; and chat with
their friends about schoolwork through instant messaging
services available on the Internet or via
mobile phones (Project Tomorrow, 2009). This report draws
further attention to those who
suspect that there might be a generational and organizational
gap in technology acceptance and
intention to use. Therefore, this researcher had to find a
theoretical basis to explain why different
organizational cultures vary in their approach and pace when
accepting and implementing
technology (as reported above by Kay, 2006) given the
developments in technology and
technology-based learning that were happening during the late
1990s to mid-2000s.
Innovation and Acceptance
Craig’s (2007) assertions on the need for a paradigm shift are

important to note, mainly
because of his theoretical underpinnings. One of the influential
bases of Craig's (2007) study
appears in a 2003 publication of Rogers' work (started in the
early 1960s) on the Diffusion of
Innovations (DoI). It is important to understand DoI as it relates
to technology acceptance and is
worth a brief overview here.
The Diffusion of Innovation
According to Rogers (2003), the Diffusion of Innovation (DoI)
is characterized by five
stages. In the first stage, knowledge, the individual learns of the
innovation but has little to no
information about what the innovation means. Nevertheless, the
individual's learning of the
innovation triggers an interest to find out more about the
innovation. This stage is the fulcrum on
which the other stages depend.
13
In the second stage, persuasion, the individual looks for

information based on interest, in
order to be convinced that the innovation adds value. Once the
individual determines thatthere is
value in pursuing the knowledge gained from the innovation, the
third stage, decision, comes into
play.
In decision, the individual spends time looking at the value
added merits and demerits of
the innovation. The individual uses this as a means to make the
subsequent decision of acceptance
or rejection. The allotted time for this stage is unpredictable; it
can be instantaneous or occur after
long deliberation. Once the individual makes the decision, the
next stage occurs.
The fourth stage, implementation, is not necessarily a full
acceptance or rejection of the
innovation; as the individual employs the innovation, he/she
actively seeks more information
about the innovation, which depends on context.
Implementation may take place, but is not
necessarily an all-or-nothing proposition. This stage is subject
to continuous revision and
reimplementation, and its pace and depth depending on the
subject.

Finally, in confirmation, the individual has found the merits and
value added by the
innovation. Moves to integrate and implement the innovation
begin while the individual pays
attention to avoiding and ignoring those parts of the innovation
found to be unusable in the
particular context. Confirmation is implementation with trust
and wary discrimination.
As theorized by Rogers (2003), these five stages have been
defined and refined for
individuals since he began his first studies in the 1940s.
However, transference of DoI to
technology acceptance did not occur in any significant manner
until the late 1980s. Even then,
technology acceptance literature only looked at the level of the
individual. As reported below, it
was becoming obvious to this researcher where an identifiable
gap was forming: no research was
being conducted that looked beyond the individual response.
Research could, and should, be
conducted at the level of the organizational culture, as a group.
The research could then look at
the group’s relationship to technology acceptance. The
conclusion is to find a model of

technology acceptance that can be used as a theoretical basis for
carrying out research that
14
explores the links of technology acceptance and organizational
culture. The Technology
Acceptance Model provides the first construct.
Technology Acceptance
On the heels of DoI came research from Davis (1986, 1989) and
Davis et al. (1989) and
the Technology Acceptance Model (TAM). In brief, the classic
TAM explains an individual's
perceived usefulness (PU), perceived ease of use (PEU),
attitude toward usage, and actual use
(AU) of innovative technology. With over 700 research studies
referencing TAM (Bagozzi,
2007), the instrument itself created a paradigm shift of sorts,
and has come to serve as a reliable
and valid instrument linking these four factors. Organizations
have employed TAM to find ways
of addressing the rapid changes in physical technology as it

relates to organizational reactions to
those changes. It is an industry standard to identify where lack
of remedies exist. Many
subsequent research studies have tried to modify TAM with
extraneous pre-conditions for the
facets of TAM; these are addressed below.
Interestingly, Bagozzi (2007) identifies two critical gaps that
emerge from this body of
research including: 1) acceptance on the individual level has
been looked at as a terminal
behavior, not as an interim behavior (p. 245), and 2) acceptance
does not necessarily correspond
to intention to use (p. 246). In other words, TAM may not help
to account for the last three stages
of DoI. This points to the need for further study on why this
model has weaknesses when
explaining the latter stages of DoI, and strengthens the case that
there are pre-conditions that feed
into the earlier facets of TAM (Bagozzi, 2007).
More important to this study is Bagozzi's (2007) assertion that
"technology acceptance
research has not considered group, cultural or social aspects of
decision making and usage very

much" (p. 247, emphasis added). He further posits the notions
of individual intention versus
collective intention, and notes that he and his colleagues have
begun studies in this area. Bagozzi
(2007) identifies this as an important hole in research that
remains unaddressed in the literature.
15
As stated above, Kay (2006) and Craig (2007) note that
institutions are incorrectly
serving the technological needs of their faculty and students.
Bagozzi (2007) points to the
shortcomings of current research on technology acceptance and
intention to use technology at the
group level. However, it was Ball (2005) who first looked at the
impact of organizational culture
on innovation acceptance and adoption, and may hold the key to
understanding: 1) why
institutions move at the pace they do to accept, adapt, and adopt
technology in support of sound
teaching and learning practices, and 2) how research could
diagnose and change organizational

culture's impact on the pace and breadth of this acceptance,
adaptation, and adoption.
Ball (2005) limited his study to the organizational culture of
business schools, but his
findings are noteworthy. Building on Cameron and Ettington's
(1988) organizational culture
model and Cameron and Quinn's (1999) "Competing Values
Framework," Ball found evidence to
predict the impact of organizational cultural on technology
acceptance. In brief, he notes that so-
called "hierarch[ical]" organizational cultures featuring high
internal social control, short-term
vision, and top-down management styles are those least likely
to accept, adapt to, and adopt the
changing demands of technology. On the other hand, "adhoc"
organizational cultures featuring
high external individual control, long-term vision, and bottom-
up, collaborative management
styles are those most likely to accept, adapt, and adopt
technological advancements. In both cases,
Ball (2005) notes that even though his research looked at
organizational culture's effect on the
acceptance, adaptation, and adoption of innovative technology,
he only studied the individual's

role in technology implementation after the organizational
culture had made its intentions known.
One of his main recommendations for further research includes
looking more closely at the
organizational cultural level of technology implementation
based on the type of organizational
culture. This recommendation has become the focus of this
dissertation.
In heeding Ball's (2005) call for research, Powell (2008)
completed preliminary
investigations to learn how smaller institutions embrace
innovation and accept technological
advances (in this case, how they adopt a new Learning
Management System). However, his
16
study, like Ball's (2005), did not adequately classify the
organizational culture by typology, nor
its overall impact on acceptance and intention.
It is clear to this researcher that there is a specific need to
examine the link between
organizational culture and its connection to the rate and pace of

technology acceptance. The
Organizational Culture Assessment Instrument (OCAI)
(Cameron & Quinn, 1999) is an
instrument designed to assess the organizational culture
typology in particular, and includes a
simple to score, reliable, valid instrument with which to assess
organizational culture. Ball’s
(2005) use of Cameron and Quinn’s (1999) Competing Values
Framework and the accompanying
OCAI aided this researcher.
The Competing Values Framework (CVF)
The Competing Values Framework is one of a handful of
frameworks proposed during
the last twenty years that classifies organizational cultures into
typologies for the purpose of
helping to plan and implement organizational change. CVF
helps to determine the effectiveness
of an organization and its leaders through typographical
categorization. In the CVF, four
identified organizational culture types are placed into
quadrants. Each side of the quadrant
represents the opposites of two major cultural process
dimensions situated on two intersecting

continua (see Figure 1). Lying on the vertical y-axis, one
continuum contains flexibility and
discretion, which points to a culture's ability to change, adapt,
and be organic; the other
continuum contains stability and control, which points to a
culture's ability to be self-sustaining.
Lying on the horizontal x-axis, the next continuum looks at the
balance between internal focus
and integration, which includes harmony inside of the
organization, and external focus and
differentiation, which includes elements of the organization that
need to look externally for
energy and effectiveness (Cameron & Quinn, 1999). This matrix
is illustrated in Figure 1.
17
Figure 1: The Organizational Culture Type Matrix
Source: Cameron and Quinn (1999)
From this framework, Cameron and Quinn (1999) developed the
Organizational Culture
Assessment Instrument (OCAI) to diagnose organizational

culture types. The OCAI contains two
sections of six identical questions situated within the CVF. The
first set assesses the current state
of the culture, while the second set identifies the preferred state
of the culture to help researchers
determine both current culture types, preferred culture types,
and the competing values that make
up the difference. The OCAI is comprised of six main questions,
each containing a topical
heading. Dominant Characteristics looks at the organizational
atmosphere of the workplace;
Organizational Leadership asks to characterize the leaders;
Management of Employees looks
specifically at management styles within the organization;
Organization Glue attempts to list
specific attributes of the organization that hold it together;
Strategic Emphases asks respondents
to list the priorities of the workplace; Criteria of Success
surveys how the organization defines
success (Cameron & Quinn, 1999).
18

The results determine four types of organizational cultures: the
Hierarchy Culture,
characterized by a high level of control and stability, and
possessing an internal focus; the Clan
Culture, characterized by a high level of flexibility and
discretion, and possessing an internal
focus; the Adhocracy Culture, the opposite of the Hierarchy,
characterized by a high level of
flexibility and discretion, but with an external orientation; and
the Market Culture, driven by high
control with an outward focus (Cameron & Quinn, 1999).
Competing Values Framework Research
Recent studies use the CVF and related frameworks to explore
the co-relational impact of
organizational culture on various facets of social interaction.
Taken as a whole, these studies
suggest correlations between national culture and organizational
culture, predicative correlation
of balanced culture type to job satisfaction, organizational
culture acceptance and implementation
of innovation, an organizational culture’s propensity toward
social responsibility, and the
correlation between the organizational culture and the types of

leaders that function best inside of
it. These studies provide the theoretical basis for this study.
Übius and Alas (2009) used the CVF as a guide to study over
6000 individuals from eight
different countries, all of whom worked in some type of
enterprise culture. Using surveys
translated into several languages, they gathered and analyzed
data using linear regression
analysis. They suggest that these four organizational culture
types significantly and positively
impact a corporation’s social responsibility with regard to
addressing social issues, regardless of
national origin. This means that organizational culture can
transcend the national culture of the
organization’s home country. They further suggest that the
market culture does not have a
significant impact on the social responsibility of respecting the
interests of agents, meaning that
this particular organizational culture type has one particular
attribute that distinguishes it from the
other three culture types. Finally, they suggest that different
cultures favor different

19
organizational cultures types; for example, in Estonia and
Finland, the clan culture is prevalent; in
China, market and adhocracy cultures are dominant. Übius and
Alas (2009) focused on the entire
organizational culture, but there are studies that look at facets
within a particular culture type.
This study also uses linear regression to predict the impact of
organizational culture on other
factors. Based on this study, this researcher understands the
need for research that has the
possibility of exposing a correlation between variables related
to organizational culture. Similar
studies help emphasize the importance of further research in
this area.
In their study of almost 300 mid-to-upper level corporate
managers, Belasen and Frank
(2007) affirm that organizational culture types have a strong
impact on the types and abilities of
leaders found in each culture. Using detailed surveys, they
employed multi-dimensional scaling
models to test the degree of fit of managers within their CVF
models. They then employed

LISREL to examine the relationships between managerial traits
and their roles. Belasen and
Frank (2007) define these types of leaders as: the latent
analyzer leader, who functions best in a
hierarchical culture; the latent motivator leader, who works best
in the clan culture; the latent task
master leader, who works most effectively in the market
culture; and the latent vision setter
leader, who finds the adhocracy culture most suitable. This
finding can mean that it is not the
overall culture that matters, but the types of leaders that are
found inside a particular organization
that have the most impact on implementation of innovations,
including the use of technology.
This study affirms that there are correlations that can be found
by doing research using the CVF
as a theoretical basis, but it limits itself to attributes of
leadership development within an
organization and, thus, is unable to explore the implications
beyond the individual level.
In a mixed quantitative-qualitative case study of a firm in the
forestry industry, Crespell
and Hansen (2008) suggest that an organizational culture
balanced between the four types within

the CVF has a positive effect on job satisfaction and
organizational commitment, which, in turn,
positively affects productivity, efficiency, and safety. The study
used a survey with follow-up
interviews, and was analyzed using descriptive statistics and
correlation analysis. The t-test
20
statistic was employed to compare the mean scores of the
respondents. A Pearson-r was used to
look at the correlation between variables. Further analysis of
the data suggests that employees in
this study also state that they are satisfied with the level of
innovation that is encouraged within
their organizational culture. Crespell and Hansen’s (2008) study
confirms, to some extent, that
Frank’s assertions about leadership have merit.
However, there is evidence that this might not be the case. The
use of the Pearson-r in
this study to determine correlation is of great interest to this
researcher, and contributed to the

decision to employ the same quantitative analysis. This
researcher decided that interviewing was
not a suitable research method. Interviewing might be employed
in future research where a more
predicative study could utilize interviews to get a richer picture
of the correlations that this
researcher wants to establish.
Using a modified version of CVF, Mallak and Lyth (2009)
surveyed almost 4000
employees at a large multi-national corporation. Their data
pointed primarily to the significance
that any organizational culture that is not centered among the
four culture types (this particular
culture came down heavily in the realm of a clan culture) has
frequent and severe communication
issues within the organization. This subsequently disrupts many
facets of production. If leaders
are influential, then perhaps it only occurs when it is most
difficult to establish a dominant culture
type. This brings up another important question: Does it
necessarily have to be the leaders who
most influence the organization’s culture? Some studies do not
support this notion. Mallak and
Lyth’s (2009) research was designed to address three issues.

However, only the third issue, “to
identify relationships between culture and outcomes including
job satisfaction, organizational
performance, and quality system attributes,” is of interest to
this researcher (p. 29). Methods for
data analysis included employing descriptive statistics. In
addition, post-hoc co-relational
analyses were used to determine how variables behaved with
respect to each other, allowing an
analysis of unanticipated relationships among the variables.
Mallak and Lyth’s (2009) study
contributed to theoretical underpinnings of this researcher’s
project; namely, the notion that there
21
is a marked difference in some findings between the levels of
managers and mainstream
employees. This pointed this researcher to study the overlying
impact of organizational culture on
another variable, and to establish a clear relationship between
those variables. Although this
researcher ruled out interviewing as a data collection method,

there is interesting data from
studies that employed interviewing.
In their in-depth, multi-method, though mostly qualitative,
study of four math teachers at
disparate higher education institutions, Adamy and Heinecke
(2005) posit that without
organizational support, any single individual’s attempt toward
innovation in technology is short-
lived. Though the CVF was not used in this study, the findings
are rooted in a similar framework.
They conclude that organizational culture has three aspects to
satisfy if the individual innovator is
to succeed. This includes how the organization allocates and
uses its technological resources, how
the organization interacts with key players and stakeholders
within the organization, and the
overall influence of the organizational culture on technological
innovation and integration.
This study only serves to highlight the dynamic between
leadership and the other
members of the organization. In which direction does influence
go, and how quickly does it flow
from one entity to another? Employing their theoretical
framework of analytical induction to

gather and evaluate rich data sets, the researchers suggest a
strong link between the degree of
technology acceptance and positive attitudes toward technology
as influenced by the culture of
the organization. This seems conclusive with their assertion that
“individual innovative behavior
is certainly a condition for technology diffusion in teacher
education, but this behavior does not
exist in a vacuum” (Adamy and Heinecke, 2005, p. 254).
Although Kimbrough and Componation (2009) did not
specifically use the Competing
Values Framework, they employed a similarly grounded
framework in their study of the
relationship between organizational culture and enterprise risk
management. In a methodology
similar to what this researcher proposes to employ, they used a
two-part instrument to establish
the correlation, surveying 2000 mid-to-upper level executives
after an initial pilot survey. Their
22

two instruments were set up in such a way that correlations
were easily calculated. They suggest
that there is a significant positive correlation between the two
variables. Indeed, most of the
executives surveyed reported that their organizational culture is
a driving force in the acceptance
and implementation of enterprise risk-management policies. The
main implication of Kimbrough
and Companation’s (2009) work is that organizational culture is
a force, but it does not
specifically note how it becomes a force. The other implication
that is relevant to this research is
that organizational culture is the driving force behind decision
making throughout the
organization.
Kirkhaug (2009 constructed a values-based framework similar
to CVF to look at a single
Norwegian company’s hierarchical organizational culture. The
study used a mixed-method that
featured an initial quantitative survey and interviews with some
of the respondents to obtain
richer information about their replies. Results suggest that
“affective commitment and group
coherence correlated positively with perception of values among

employees… [and]…loyalty
toward immediate superiors was significantly negatively
correlated with perception of values” (p.
317). A strong relationship between the values of the
organizational culture to group coherence,
and a possible conflict with leadership types based on the
organizational culture type can be
inferred from this study. This was an interesting study because
leadership was negatively
correlated to values, which contradicts extant studies.
These sample studies suggest that there is conflicting evidence
over where the need for
organizational culture change intersects with technology. Yet, it
is clear from these works that
there may be an impact on technology acceptance. It is not the
purpose of this research to find out
where cultural influence originates, but research suggests that
organizational culture may have a
significant impact on change within the culture, whether it be in
product development, project
work, or - as is in the interest of this researcher - technology
acceptance and integration.

23
Variations of the Technology Acceptance Model
The origin of the Technology Acceptance Model rests with
Davis (1986, 1989) and Davis
et al. (1989). TAM posits five measurable actions, including an
individual's perceived usefulness
of technology (PU), perceived ease of use (PEU), attitude
toward technology, behavioral
intention to use, and actual use (AU) of the technology.
Figure 2: The Techonolgy Acceptance Model at its Earliest
Inception
Source: Davis (1989)
The TAM defines Perceived Ease of Use as ‘‘the degree to
which a person believes that
using the system will be free of effort,” and Perceived
Usefulness as ‘‘the degree to which a
person believes that use of the system will enhance his or her
performance’’ (Davis, 1986, p.
320). Actual use is a simple quantitative construct. Intention to
Use is not part of this study and

so is not addressed here.
There have been many modifications and adaptations of the
TAM, and several studies
24
compare or use these modified versions of the basic model. As
reported by Bagozzi (2007),
below are samples of recent pertinent studies from over 700
studies that have been conducted
since the inception of the TAM. This researcher chose those
most closely related to theproposed
study and a rationale for choosing them is given.
Several studies propose and test hypotheses that look at the
antecedents of the TAM, i.e.
factors outside of TAM that explain the relationships found
within the traditional model. Many of
the studies show varying degrees of correlation among
variables. Most of the studies found
positive correlations between the antecedents and the traditional
TAM variables, with some
exceptions noted below. Although a variety of antecedent

variables are employed, none of them
have a bearing on the research proposed by this study, except to
the extent that all agree that there
are possible extraneous factors to account for the preliminary
stages of the TAM.
Modified TAM Research
Sun and Zhang (2006) examined several different acceptance
models with historical
significance to TAM through a literature review of sixty-nine
studies that employ various models
of the TAM as a theoretical basis. They propose that all models
need to be viewed through the
lense of three factors: organizational factors (including
volunteerism and task/profession),
technological factors (including individual vs. group, purpose,
and complexity), and individual
factors (including intellectual capability, cultural background,
gender, age and experience). All of
these are moderating antecedents on user technology
acceptance. This study serves as a reminder
that the TAM itself does a good job of explaining acceptance,
but does not address the factors that
influence the antecedents to acceptance. The most important
implication of Sun and Zhang’s

(2006) work to this research is their first conclusion that “this
study suggests that research on
moderating factors [of technology acceptance] is of great value”
(p. 71).
Yi, Jackson, Park, and Probst (2006) modified the TAM with
two other frameworks when
surveying 220 healthcare professionals. Using Cronbach’s alpha
to test for reliability, and
25
LISREL to construct a correlation matrix, their findings
supported earlier studies that examined
Perceived Usefulness and Perceived Ease of Use. PEU had a
significant effect on PU. Many
studies report both positive and negative correlations among
various TAM facets. This study,
however, was situated within the healthcare industry, which
may raise issues of suitability for
applying the four culture types to higher education.
Lee, Kim, Rhee, & Trimi (2006) used a simplified TAM with a
small sample of object-

oriented programmers to test the efficacy of the TAM in
predicting actual usage. They found
some significance with their relationships. One-hundred fifty-
four members of the Association of
Information Technology Professionals (AITP) were surveyed on
the added variables of support
and innovativeness in addition to traditional TAM variables.
Cronbach’s alpha was used to test
for reliability of the data. A covariance matrix using LISREL
was constructed in which PU and
PEU were strongly correlated to AU, even with the non-
traditional variables included in the
model. This study is one of many that conclude that extraneous
factors add substantively to the
TAM. Because of this study and others, many researchers see an
opportunity; the TAM itself
needs to be modified and tested to construct a more accurate
model.
When reviewing literature specifically related to research in the
health fields, Holden and
Karsh (2010) examine over twenty studies that investigate the
relationships between variables
found inside two important descendents of the TAM. TAM2
(Venkatesh & Davis, 2000) adds

subjective norm as an influence on both Perceived Usefulness
and Intention to Use. Image, job
relevance, output quality, and results demonstrability are added
as influences on Perceived
Usefulness. The Unified Theory of Acceptance and Use of
Technology simplified TAM by
identifying performance expectancy, effort expectancy, social
influence, and facilitating
conditions as antecedents to Intention to Use; PEU, PU, and
Attitude were eliminated from the
model. These modified Technology Acceptance Models aid in
understanding the functions of the
five main facets of the TAM and continue to garner widespread
support as a basis for further
studies. Holden and Karsh (2010) offer a salient question for
further research that is of particular
26
interest to this researcher: "What are group-level characteristics
that affect relationships in
TAM?” (p. 168). Looking at the various TAM models that
Holden and Karsh (2010) identify,

even as late as 2008, this researcher’s challenge is to be certain
that the original TAM is
considered a valid enough model on which to base a study. This
researcher decided that more
connections in the literature between the TAM and
organizational culture are worth further
investigation to see if they employ the basic TAM model or use
one of its evolutionary kin.
TAM in Organizational Culture
Many recent studies have used the traditional, unmodified TAM
to investigate extraneous
factors that contribute to Perceived Ease of Use, Perceive
Usefulness, and Actual Usage within
organizational cultures. Burton-Jones and Hubona (2006)
hypothesize that system experience,
level of education, and age are prerequisite, contributing factors
to the TAM. Using a
questionnaire completed by 125 staff and professional
employees at a large government agency in
the eastern United States, the researchers suggest that these
external variables have a greater
effect on usage of technology than on attitudes toward
technology and the usefulness of
technology. Their methodology was to isolate data on usage

frequency and usage volume. This
study establishes the notion that there exist external
contributing factors to TAM that influence
actual usage. Though this researcher does not specifically
separate volume and frequency (in fact,
frequency will not be surveyed), he is encouraged by this study
that his underlying assumptions
about contributing factors may be properly grounded.
Using a multiple case study method, Tarafdar and Vaidya
(2006) surveyed four Indian
financial service firms (characterized culturally as follows -
Pioneer, those that tried a new
technology soonest; Advanced, the early mainstream technology
adopters; Late, those firms that
resisted acceptance but eventually understood and implemented
change; and Laggard, firms that
never implemented or implemented catastrophically late). Their
basic findings suggest that
company leadership has a strong influence on all factors of the
TAM. Descriptions of the Pioneer
27

culture map to an adhocracy culture, while descriptions of the
Laggard culture describe
management and key stakeholders as wholly uninterested and
opposed to the implementation of
new technologies. This study connects TAM to organizational
culture and supports some research
studies in organizational culture already noted.
Ahmed, Daim, and Basoglu (2007) investigated the significance
of information
technology (IT) with the TAM. They specifically examined IT
planning (how an organization
decides to use technology), IT implementation (when it decides
it needs to be used) and IT
diffusion (the pace at which usage is implemented) and their
relationships, focusing on
organizational differences between managers and employees.
Forty-four respondents in a single
organization filled out a questionnaire, which included
questions concerning the three facets of IT
and the interrelationship among them. Regression analysis on
each factor shows that all three IT
parameter relationships are significant, including the perceived
organizational cultural differences

between management and staff. Like Tarafdar and Vaidya’s
(2006) study suggests, organizational
culture, both management and staff, intersect to some degree
with technology acceptance.
Hur’s (2007) TAM modification, the Sport Web Acceptance
Model, helped determine
that perceived ease of use, usefulness, enjoyment, and
trustworthiness are potential mediating
variables in accepting a sports website. The research consisted
of 337 respondents to Hur’s
modified survey and added the predictors of sport involvement,
psychological commitment to a
team, perceived enjoyment, and perceived trustworthiness. This
study also supports extraneous
factors to TAM.
Alexander (2008) looked at ethnic identity, especially among
African-Americans, as a
contributing factor to technology acceptance. He further divided
two classic TAM variables into
sub-variables. Perceived ease of use was divided into trait (or
generalized) efficacy and state (or
task-specific) efficacy. Perceived usefulness was divided into
symbolic utility and functional
utility. From 257 total survey respondents, he suggests that

there is a positive correlation between
identity to each of the two general variables. It seems more
apparent in this study that extraneous
28
factors play a role in at least some facets of TAM.
Bueno and Salmeron (2008) added to the TAM the factors of
Top Management Support
(managers who agree with and champion technology
acceptance), Communication (dissemination
of information about when and how to employ new
technologies), Training (specific formal and
informal programs created with a particular organization to
address technology change),
Cooperation (the ability of both management and staff to work
together to implement change),
and Technological Complexity (the learning curve of a new
piece of technology, high or low).
They surveyed ninety-one workers in nine companies across
various industries who were
implementing an Enterprise Resource Planning (ERP) system
within their organizations. They

suggest that each of the factors they studied contribute
significantly and positively, and that
successful ERP system implementation depends on the
individual’s response to PEU, PU, and
AU within the TAM. This study points out that pre-requisites to
technology acceptance and
measurement of their significance post difficult methodological
challenges for researchers. The
discipline of the study of management did not reduce this
significance.
Magni and Pennarola (2008) used extraneous theoretical
frameworks from the discipline
of management studies, applying both Leader-Member
Exchange (LMX) - a manager as an
interface between the organization and the individual employee
- and Team-Member Exchange
(TMX) - the relationship of an individual with his or her
teammates - to a more recent version of
the TAM (as expressed by Ventkatesh et al., 2003). In their data
analysis, they found strong links
between Perceived Ease of Use and Perceived Usefulness with
TMX, LMX on Perceived
Usefulness, organizational support on PEU, and a positive
significance on PEU with commitment

to use technology. This study points even more strongly in the
direction of looking above the
individual level of technology acceptance into the organization
as a whole and its impact on
individual choice.
Perceived Credibility, Perceived Enjoyment, and Social Norm
were added to TAM’s
influence in an online banking study in Malaysia (Amin, 2009).
In one province of Malaysia, a
29
survey was administered randomly to 120 bank customers who
were new to an online banking
system. Using linear regression, the researcher concludes that
there are positive correlations
among internal factors of the TAM including PEU on PU. He
also suggests that external factors
positively influenced PEU, PU, and AU; those factors were
acceptable extraneous contributors to
the TAM model.
Taft (2007) preceded Amin (2009) in examining extraneous

factors to the TAM in regard
to online banking. She surveyed 173 undergraduate and
graduate students at a campus in the
southeastern United States. Her modifications included factors
of eBanking, perceived ease of use
(PEUEB), eBanking computer self-efficacy (EBCSE), locus of
control (LOC), and prior training
in eBanking (PTEB). Using regression analysis to assess the
extent of the relationships among the
variables, she found significant positive relationships between
EBAU and EBCSE, EBAU and
PTEB, and EBCSE and PEUEB. There were many other studies
reviewed from the management
and business disciplines, but these few studies illustrate the
breadth of extant research.
Wang, Shu, and Tu (2008) examined the impact of
organizational culture on
“technostress.” As defined by Weil and Rosen (1997),
technostress is ‘‘any negative impact on
attitudes, thoughts, behaviors, or psychology caused directly or
indirectly by technology” (p.
3003). Using a modified version of the TAM, and a different
organizational culture framework
(high and low centralization juxtaposed with high and low

innovation), Wang et al. (2008)
surveyed 951 employees in eighty-six Chinese organizations
based in Xi’an, Shenzhen, Chengdu,
Taiyuan, Beijing, Shijiazhuang, and Shanghai, covering the
manufacturing, financial, IT, service
industries, and government agencies. Using a multiple analysis
of variance followed by a
Scheffe’s test (for pair-wise comparisons), they found a strong
correlation between organizational
culture types and the negative effects of technology based on
organizational culture types.
Cultures with low centralization and low innovation (Culture
Type I) score lowest on the
technostress scale. This analysis offers more evidence for
culture type's impact on technology
implementation.
30
Using a different set of frameworks, and a methodology
employing an exploratory multi-
case study research design, Desouza, Dombrowski, Awazu,
Baloh, Papagari, and Jha (2009)

looked at several factors important to the innovation process in
an organization to indicate that
organizational cultures can be characterized as either “brittle”
or “robust”. These factors included
idea generation (the ability of the organization to define and
provide a place for ideas to occur),
idea mobilization (how accepted ideas get dispersed throughout
the organization), advocacy
(specific people within the organization who are charged with
the task of idea mobilization),
screening (an idea evaluation process is in place),
experimentation (supporting resources exist,
and the process for implementing the idea is existent and
sanctioned), commercialization (the idea
is publicized and outside stakeholders are involved in further
refinement of the idea), and
diffusion and implementation (an open system of feedback is
established to further disseminate
the idea with further errors viewed as feedback loops for further
refinement). Though neither the
Competing Values Framework, nor the Technology Acceptance
Model were part of this work, it
does suggest that organizational culture may have a significant
influence on technology adoption

and implementation.
All of the studies discussed above point out the significance of
organizational culture as it
relates to technology acceptance. However, none of the studies
focused specifically on higher
education. Do organizational cultural factors in technology
acceptance affect higher education?
Are there studies that explore the link? It may be best to answer
this question by reviewing
studies of the TAM in higher education.
TAM in Academia
Park (2007) sampled 628 university students in Korea using a
modified TAM2 model to
suggest that there is a strong link between TAM and behavioral
intention of students with regard
to eLearning. Subjective norm, the social way of viewing how
things usually work, also has a
strong influence on TAM, pointing to an organizational
imperative to support technology
31

implementation vehemently. From the analysis, he concludes
that organizations should be certain
technology is viewed in a positive light and that eLearning
support systems are robust and
ubiquitous. Park’s (2007) recommendations were contained in
one of several studies that came to
this same conclusion and that link TAM with organizational
culture is some way.
Roca, Chiu, and Martinez (2006) investigated eLearning
continuance intention (the
intention expressed by respondents as to whether they would
take more eLearning courses after
their experience with a single course) using a modified version
of TAM. They collected 184
responses from a web-based survey given to students who had
taken at least one online course
through the United Nations System Staff College, or through the
International Training Centre of
the International Labour Organization. Using LISREL to
perform a structural equation model
(SEM) test on the constructs, they found that those who
confirmed they would continue to take
eLearning courses scored high on PU and PEU. The quality of
the information to support these

courses was also significant. Though this study does not
specifically link culture and the TAM, it
points to a well-developed organizational infrastructure that
supports the use of technology much
as Bueno and Salmeron’s (2008) study concludes.
However, there are studies with findings that contradict those
discussed above. Using a
modified TAM model called the Motivation Acceptance Model
(MAM), Siegel (2008) examined
four facets, including perceived usefulness, perceived
organizational support, perceived ease of
use, and attitude toward LiveText (a web-based ePortfolio
construction software). Surveying
fifty-nine adjunct and full-time professors at a large
southeastern university in the United States,
and using Cronbach’s alpha, regression, t-tests, and descriptive
statistics, he found that perceived
organizational support does not significantly influence any of
the other facets, including faculty
attitude toward the software, faculty liking of the software, or
faculty finding the software useful.
This study hints at the absence of a direct link in higher
education between faculty and support

staff in fostering technology acceptance.
Two additional studies support some of the findings above
though they are of lesser
32
significance to the study proposed here. Nevertheless, they are
worth noting. Although they are
not the basis of the current study, they contain analyses
regarding the intersecting factors that may
contribute to the results of the study in an indirect way.
Shen and Eder (2009) used a modified TAM in their
investigation of virtual worlds
(usually three-dimensional worlds, such as SecondLife, which
are avatar-based and have
sophisticated communication interfaces). Using the antecedents
of computer playfulness,
computer self-efficacy, and computer anxiety, they surveyed
seventy-seven undergraduate and
continuing studies students at the business school in a
university in the United States. Using
SmartPLS to analyze the measurement model (factors), and the
structural model (path analysis),

they found that Perceived Usefulness mediates through
Perceived Ease of Use. Virtual worlds are
gaining a strong interest as of this writing, but are not
considered in this study primarily because
students, not faculty and support staff, who make up a
significant part of virtual world
populations; this population is not relevant to this study.
Flosi (2008) adds to the traditional TAM variables of Perceived
Ease of Use and
Perceived Usefulness by including concern for privacy and
security, implementation time, faculty
computer anxiety, social influence, and facilitating conditions
when examining their impact on a
Learning Management System. Surveying faculty at two large
universities in Texas, 283 surveys
were tabulated and analyzed using multiple regression analysis.
It was found that Perceived
Usefulness and Perceived Ease of Use do not influence the use
of course management software
(in both cases the LMS was WebCT). This study was based on
faculty use of WebCT, an LMS
that has ceased to exist because of a buyout by Blackboard.
Nevertheless, technology changes

rapidly. Some may claim that any study of the WebCT LMS,
though obsolete by the standards of
WebCT, should not be considered valuable data. Though this
researcher disagrees with the
assertion, data from studies where the institution still uses the
WebCT system are not considered.
33
Other Studies Relevant to the Research Problem
There are additional factors outside of both the Competing
Values Framework and the
Technology Acceptance model that are worth brief discussion.
These factors have significant
relevance in this researcher’s decision to focus on the research
problem. These are noted
primarily for their contribution to how they influence this
researcher in framing the problem
statement. Below is an overview of studies that contribute to
this framing.
Some of the key factors found in the literature review
above,that are relevant to this study

include: 1) the individual's dominant sub-culture within a larger
educational organization, 2) the
impact of an individual’s gender, 3) the impact of an
individual’s education, 4) the significance of
an individual’s position within the university, 5) the
individual’s employment status, 6) the
individual’s school affiliation, and 7) the individual’s age.
While all of these are certainly relevant
factors in technology acceptance, this researcher decided to
focus on only two of these: the age of
the respondent and the dominant sub-culture in which the
respondent resided.
Viewing research data through the lens of the respondent’s age
is of great interest to this
researcher. Literature that divides age into four different social
generations, as defined and cited
below, has become ubiquitous. An overview of many studies
shows there is a perceptible gap
between education and technology (Riedel, 2009) and the use of
technology among these four
generations(LexisNexis Technology Gap Survey, 2009). This
researcher pondered the question:
Dothe perceptions of technology and acceptance of change
based on the differences among the

four generations contribute to the make-up of the organizational
culture as a whole? An
exploratory investigation into the literature of generations was
deemed necessary. This would
help this researcher judge to what extent it would be worth
analyzing the generational impact on
organizational culture. Strauss and Howe (1992) defined those
born between 1965 and 1980 as
“Generation X,” or “GenX,” and those born between 1981 to the
present as “Generation Y,” or
“GenY”.
34
In two studies related to the concept of generations and
technology acceptance, these two
generations indicate they prefer to use eTexts, which were
usually electronic versions of a
textbook, versus physical textbooks (Baston, 2009; Harrison,
2009). Students report their
preference for obtaining knowledge from a professor versus the
Internet (Robertson, 2009), with

many who were born after the “Boomer” (Strauss & Howe,
1992) generation (those born between
1946 and 1964) stating a preference for the latter.
Another factor that may have relevance to the study of the
generational factor is the
continuing dearth of adequate technology training for
stakeholders (Alvord, 2008; Kay, 2006),
which might be rooted in rejection of change by those in the
pre-GenX and GenY generations.
Extant research highlights the continuing resistance, or outright
rejection of technologies such as
cell phones and smart-phones, on the part of K-12
administrators (Manzo, 2009) to post-graduate
curricula administrators (Nagel: 2009a, 2009b). The data in
these studies hint at generational
decision-making, where the Boomers and the “Silent” (Strauss
& Howe, 1992) generation (those
born before 1946) are the main decision makers. These trends in
administrative decision-making,
where earlier generations reject technological change at a
higher rate than later generations, have
alarmed prominent scholars. From Brigham Young University
(Jarvik, 2009) to Harvard's
Business School (Pierce, 2009), the relevance of higher

educational institutions is being
questioned, as this sort of regressive decision-making continues
unabated. In fact, for some
progressive advocates of technological change (the 21st Century
Skills advocates), there is an
organized backlash among the more vocal critics on the other
side of the argument (Sawchuk,
2009). This fed into the question: Were earlier generations
having a stronger impact on
organizational culture than later generations?
The rapid emergence of the Social Networking software age
(Lavenda, 2008), and its
related institutional implications is also connected to
generational impact. Shirky (2008) notes
that all large institutions must come to terms with two major
facts that have become apparent
since the advent of the Internet: information sharing and
coordinating responses to events is
35
easier. The implications do not hint at incremental change,
however. Instead, Shirky (2008) notes

that advances in information sharing and event response are
happening exponentially. These
advances are notable to the extent that the author characterizes
it as a narrowing of the gap
between intention and action, hinting at new developments in
Diffusion of Innovation studies
cited earlier (Rogers, 2003). Most importantly, and perhaps of
some relevance to the generation
gap, Shirky notes that "social tools (such as those categorized
as "Web 2.0") don't create
collective action - they merely remove the obstacles to it" (p.
159). This researcher observes that
the implied removal of obstacles is a type of innovation. This
generated the idea that studying
generational acceptance of innovation in the present as a timely
and much needed task.
Shirky infers that the post-Boomer generations (GenX and
GenY), as stated by Rogers
(2003), are quick to learn about, be persuaded by, and decide to
use, implement, and confirm
using the tools of technology en masse. Further, there is an
emerging understanding that
technological innovations happen fast, and technology changes,
exponential in nature, require

almost exponential decisions on where, when, and how fast to
accept, adapt, and adopt. Shirky’s
implication is that the post-Boomer generations at least
instinctively understand this.
Another significant dialogue continues in the literature
surrounding the concept of
generations. Prensky (2001a, 2001b) states that these new
generations (GenX and GenY) process
information differently and, thus, can be dubbed "digital
natives." Some of the emerging
empirical data summarized in the Project Tomorrow report
(2009) support the “digital native”
concept. However, at the institutional level, organizational
cultures have a large and foreboding
task: they need to change and they need to concurrently balance
the generational impact on their
organizations as they change. There is a possibility that it is the
older generations (the Silent and
Boomer generations), whom Prensky has dubbed the “digital
immigrants,” who continue to lay
obstacles to the irreversible changes that will occur with or
without their cooperation.
However, although there is the possibility of a generational
impact on organizational

culture, there is little research that directly addresses this
question. In the end, this researcher
36
decided that generations could be analyzed in an ancillary way
and would not constitute the focus
of the research. In fact, this research explores the seven
previously mentioned key variables. The
focus remains centered on cultural typology and its possible
impact on technology acceptance. He
discovered that there is limited research that characterizes
organizational culture's "typology" as it
pertains to innovation and technology acceptance, so the
generational study, though seemingly
worth pursuing, is worth only secondary consideration.
One area of interest to the researcher is exploring how to help
institutions of higher
education stay current with technological advances. This study
is directed at the stakeholders
involved in the process of keeping the organization current,
especially the staff and faculty

charged with this somewhat foreboding and, perhaps, elusive
task. The research findings may
reveal why different institutions embrace innovation at different
speeds. There is already
anecdotal evidence that some institutions move quickly while
others move at a slower pace, or
deliberately and systematically do what they can to avoid
innovation and technology acceptance.
This study seeks to illustrate the elements of the intersection of
organizational culture and
technology acceptance. This study could also be characterized
as preliminary exploratory work
that will eventually lead to future research on how to best
implement technology innovation in
higher education.
Personal Learning With and Through Technology
The beginning of this chapter related the "Tell Us Your
Passion" exercise. This was done
to highlight the following conclusion based on the review of the
literature on organizational
culture and technology acceptance found earlier in this chapter.
The 3x5 card completed in 1993
is an excellent activity with an excellent technology. It was a
great learning experience because

the educational institution culture valued the personalization of
learning and the best practice in
technology that complimented the activity. In this case, the best
technology was a 3x5 paper
index card. In 2010, this type of personalization of learning is
better served with a myriad of tools
37
now available on the Internet. Why isn’t it?
Statement of the Problem
The question, “why isn’t the personalization of learning being
enhanced by the current
technologies available?” points this researcher to the
formulation of the problem that needs to be
investigated: What intersection exists, if any, between the type
of organizational culture reported
by a respondent and that individual’s stated perceptions of
technology acceptance in an
institution of higher education? If it exists, is it possible to
illustrate the intersection?

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