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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.

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.

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

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

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

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).

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

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.

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.

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

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.

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

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

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

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,

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’

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.

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

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.

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

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.

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).

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

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

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

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

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.

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

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

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

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

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

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

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.

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

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

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.

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”.

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

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

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

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?

Chapter 3 – Methodology

This research was conducted to answer the following question: In a higher education

institution, does self-reported organizational culture type intersect with self-reported

perceptions of technology acceptance? If so, how? This research employs a quantitative

approach with a descriptive design to identify this intersection. Subjects were part of a single

university population.

Population

This is a case study conducted at a single institution of higher learning. This small, private,

Catholic, urban university consists of approximately 1176 undergraduate students, 621 graduate

students, and 681 law students dispersed across 6 Schools (with names similar to The College,

The School of Business, The School of Law, The School of Leadership, The School of Science

and Math, and The School of Religious Studies). At the time of the study, there were 100 full-

time faculty members, and 136 adjunct faculty members among the 6 schools. Administrators

(faculty who are committed to the administrative duties that are a necessary part of running the

day-to-day operations of the campus) numbered 3 and are included within the full-time faculty

count. Finally, there are 282 full-time support staff (non-faculty members who accomplish

administrative tasks, such as IT support staff, Financial Aid office staff, and non-faculty library

staff) distributed among these schools and in the student services area. Students were not a part

of this survey. This put the total survey group at 443 potential respondents.

Instrument

A single survey was used containing two combined instruments that have been used in

previous research projects. The instruments were: 1) the Organizational Culture Assessment

Instrument (OCAI, Appendix B), and 2) the Technology Assessment Model (TAM, Appendix

C).

The OCAI gathers data through numerically weighted responses to four statements (A,

B, C and D – each representing a culture type) in each of six categories of organizational

culture. Each set of four statements has one-hundred points divided among the statements. All

“A” statements are added and divided by the number of responses to an item to get a mean

score. The “B,” “C,” and “D” statements are calculated in the same way. This generates the

mean scores for each of the four culture types. These culture types: “Clan,” “Adhocracy,”

“Market,” and “Hierarchy” constitute four variables that are used in an analysis with the TAM

variables as described later in this chapter. In addition, the culture type with the highest mean

score is used as the variable to denote the individual respondent’s “Dominant Culture”.

Aggregate means are calculated to represent each School’s “Dominant Culture”. This variable

is measured against six other basic demographic variables described later in this chapter.

The TAM items contain six statements, which result in the variables of Perceived

Usefulness (PU), Perceived Ease of Use (PEU), and one numerical value for Actual Usage

(AU) for each of two technologies, for a total of twenty-six responses. PU and PEU employ a

seven-point Likert scale (with values from 1 to 7, 7 being a strong agreement with the statement

and 1 being a strong disagreement with the statement). This produces a single answer for each

respondent based on the means of the responses to each facet. The AU is the number of hours a

person reports for each of two target technologies. The mean of these numbers is also

calculated. These results each produce interval data (positive whole integers).

Anonymous demographic data gathered includes age, gender, School affiliation, position

(faculty full-time vs. faculty part-time, for example), rank (if faculty), and highest degree

earned. The “Dominant Culture” variable is used with each of the six other variables in the

analysis as described later in this chapter.

Procedure

For the purpose of privacy, this researcher was not allowed access to an email list for

direct solicitation of responses. Instead, he prepared a message to relay to faculty, staff, and

administration via email by way of the Chair of the Faculty Forum (the head of the governing

body that serves as the collective voice of full-time and adjunct faculty in dealing with matters

with the Administration) and the Director of Marketing. Potential respondents were told that the

surveys would be administered during a two-week period from mid-to-late April 2010. Adjunct

faculty received an email from the Chair of the Faculty Forum inviting them to participate in

the online survey. To take the online survey, respondents read a brief introduction to the

instruments and answered basic demographic questions in addition to the two surveys (see

Appendix A). Respondent privacy was assured through statements prior to taking the survey

(Appendix D), and no names or email addresses were requested or gathered through the survey

website.

The research sought to receive a 10% or higher response rate from the on-campus

subjects (full-time faculty, staff and administrators) and an approximate 10% response rate

from adjunct faculty. However, this response rate requirement could in some cases be construed

as arbitrary in light of research by Holbrook, Krosnick and Pfent (2007) who suggest that

surveys with a low response rate (less than 25%) are only minimally less accurate than those

with higher response rates. In the final analysis, this researcher did not deem the low response

rate to be a critical detrimental factor of the research (this is further addressed in Chapter 5).

Seven variables are distilled from the data collected: the three variables from the TAM,

including Perceived Usefulness, Perceived Ease of Use and Actual Usage, and the four culture

types from the OCAI. Using a Pearson r inter-correlation, these seven variables are compared to

one other.

In addition, the OCAI variables’ highest mean score, as representative of a single

respondent’s “Dominant Culture,” is distilled. This is the first of the seven demographic

variables analyzed against the TAM variables - using ANOVAs - to see if there are any

demographically significant results. Any resulting significance then undergoes a Bonferroni

correction post-hoc analysis. The Bonferroni post-hoc analysis is used to determine the specific

significance of three or more items in an among-group analysis of variance analysis. Bonferroni

corrections are used on all of the demographic variables except gender, since that variable has

only two possible variables within the group. A p-value of less than 0.05 with Bonferroni

correction suggests significant variance. For example, if it is found that there is a p-value of

less than 0.05 between School Rank and Dominant Culture, the Bonferroni correction might

point to Associate Professors as having a significant correlation with a particular “Dominant

Culture.”

Details of survey items, response rates, means of school scores on Culture Type, results

of the Pearson r, ANOVAs of demographic responses, and Bonferroni correction on two

significant findings are found in Chapter Four.

Chapter 4 - Results

This study is designed to find out what overlap exists, if any, in self-reported results on a

single survey among two main variables: Organizational Culture Type and the Technology

Acceptance Model. Demographic information was gathered as an ancillary to investigate whether

these variables contribute to an understanding of the dynamics of an organizational culture. Two

instruments were merged into a single survey administered to respondents at a small Catholic

University in the southeastern United States.

The first instrument, the Organizational Culture Assessment Instrument, measures the

variables of the Clan Culture, Adhocracy Culture, Market Culture, and Hierarchy Culture. This

researcher took the highest mean of self-reported culture type among the four cultures to establish

the Dominant Culture variable. The second instrument, based on the TAM, measures the

variables of Perceived Usefulness (PU), Perceived Ease of Use (PEU), and Actual Usage (AU) of

the individual respondents. The investigator desired to receive 40 usable responses from the

potential pool of 443 in the university population. However, it was not a central concern if this

number could not be reached, in light of what Holbrook et. al. (2007) suggest about sample size.

Procedure

An online survey was created with six sections using the survey tool found at

http://www.surveymonkey.com. The first section contains the user agreement to take part in the

survey. Respondents need to check a box indicating agreement to the terms of the survey to

move on to the next section. The second section contains six demographic items including age,

gender, highest degree obtained, position within the university, faculty rank (if full-time), and

main school affiliation (these demographic questions were used in ANOVAs with the TAM as

representative of different contributing aspects to Organizational Culture. The third section

contains two parts:

1) Twenty-four items relating to the TAM: twelve items based on the school’s

learning management system (Blackboard) and twelve items based on the

school’s main student and faculty information and grading system

(WebAdvisor). The twelve items measure Perceived Usefulness and Perceived

Ease of Use for each technology. The online survey tool scrambled these

twenty-four items for each survey participant, but the results for the investigator

appear in the order they were entered into the electronic survey.

2) One item for each of the two technologies asks for a numerical estimate of

actual time usage on Blackboard and actual time usage on WebAdvisor (this

constitutes the data for the third TAM construct Actual Usage).

The fourth section of the survey is the Organizational Culture Assessment Instrument. It

contains the six categories of four statements that require respondents to distribute one-hundred

points among the statements. Respondents weigh their responses based on the proximity to their

current views on their organizational culture. The fifth section is an open-ended comment box to

collect user responses with regards to the survey itself. Respondents are not required to enter any

information in this box and the survey can be completed without it. Table 1 summarizes the

survey questions for type of information needed, type of question administered, number of

questions within the section, and type of calculation needed from the respondent for the result to

be a valid response.

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.

Section Summary Question Type Number of Questions

Calculation Needed

1 User agreement Single answer indicated via checkbox

1 n/a

2 Basic demographics Single answer indicated via

radio button 6 User choice

TAM (Perceived Usefulness & Perceived

Ease of Use)

Likert scale 1-7 indicated via radio button

12 User choice

3b TAM Actual Usage

(Blackboard) Number input 1 User choice

3c TAM Actual Usage

(WebAdvisor) Number input 1 User choice

4 OCAI Number input 6 100 divided into the four

culture variables

5 Survey comments Open-ended text input 1 None

Data Collection

Access to surveys was available for ten days between the days of April 13and April 23,

2010. The university’s Director of Marketing sent out an email to faculty (both full-time and part-

time) and staff email lists. The email contained a link to the online survey that was prepared for

this study. The survey was prepared so that respondents were required to answer every question

before the survey allowed them to proceed to the next set of questions. In all, there were six

screens of survey questions to answer. Before the end of the survey period, the researcher, with

the assistance of the Director of Marketing, followed up with all full-time and part-time faculty

and staff through two subsequent emails.

Findings

This section presents the results of the analysis conducted on the research question in

this study. The results are presented in three sub-sections. Sub-section one (sample sizes for the

research) discusses how the sample sizes were obtained for the main research question and the

six additional demographic questions on the survey. Sub-section two (Main Results) contains the

substantive analyses for mean scores by school on culture type; means, standard deviations,

inter-correlations, and reliabilities of the seven variables (Four Culture Types variables and the

three TAM variables) used in this research are shown. Sub-section three (the demographic

variables) summarizes ANOVA results for Dominant Culture as it compares with the six

demographic items on the survey in relation to the TAM variables, including an analysis of a

post-hoc analysis performed on two variables related to school affiliation.

Sample Sizes for the Research

Seven variables were employed for this analysis. Table 2 shows all of the data collected

from the online surveys. Eighty-one participants initially accessed the online survey. However,

only sixty-eight respondents returned usable data (84% response rate). Thirteen people filled out

the demographic data (indicated in items 2 through 7 in Table 2) but then exited the survey.

Some respondents only completed the twenty-four TAM questions and did not fill out the

Dominant Culture items. In total, thirty-nine responses could be used in the analysis of the

Organizational Culture Items. For each of the six demographic categories, there were sixty-eight

useful responses for analysis.

For the Dominant Culture category, calculated by finding the highest mean among the

four categories of responses for each individual respondent, nineteen respondents (49%) could

be categorized as part of the Clan culture. Four respondents (10%) considered themselves part of

the Adhocracy culture. Six respondents (15%) indicated they were in a Market culture, and the

remaining ten respondents (26%) placed themselves in the Hierarchy culture.

Over half of the respondents were from the Baby Boomer generation, with thirty-five out

of sixty-eight (51%) indicating that they were born between 1946 and 1964. Generation X

(1965-1980) was the next largest group with nineteen responses (28%). Generation Y (1981 or

later) followed with nine responses (13%). Only five respondents (7%) were from the oldest

generation, the Silent Generation (born before 1946).

Gender was also one of the basic demographic variables collected. Making up a large

majority of the sample, forty-four out of sixty-eight (65%) respondents indicated that they were

female. The remaining twenty-four (35%) indicated male.

As far as School affiliation, the Leadership School made up a majority of the responses

with twenty-three out of sixty-eight (34%). This was followed by The College with fifteen

respondents (22%), the Business School with twelve (18%), Science and Math with seven

respondents (10%), the Theology School returned a total of six respondents (9%), and the Law

School was the smallest with five respondents (7%).

Level of education was another variable collected. The distribution of self-reported most

recent degrees counted thirty-three (49%) having obtained their Doctorates, twenty-six (38%)

indicating they had a Master’s degree, and nine (13%) indicating obtaining a Bachelor’s degree.

Among faculty indicating their position, there were forty-eight respondents. Twenty of

the sixty-eight survey respondents noted they were staff. Of the forty-eight, twenty-six (54%)

were full time faculty (non-administration), thirteen (27%) indicated they were adjunct faculty,

and the remaining nine (19%) considered themselves as administration.

A sizeable number of the respondents reported that they were faculty at the University.

Among those twenty-six who indicated they were full-time faculty, seven (27%) held the rank of

professor, eight (31%) indicated they were of the associate professor rank, ten (38%) were at the

rank of assistant professor, and one (4%) indicated a rank of lecturer or instructor.

Table 2 Number of Subjects per Category

Research Variable Category Cell Size (n) Total 1. Dominant Culture Clan 19

(Highest mean of the four culture types) Adhocracy 4 Market 6

Hierarchy 10 39 2. Generation 1981 or Later 9 1965 to 1980 19 1946 to 1964 35 Before 1946 5 68 3. Gender Male 24 Female 44 68 4. School The College 15 Business 12 Law 5 Leadership 23 Science and Math 7 Theology 6 68 5. Degree Bachelor's 9 Master's 26 Doctoral 33 68 6. Position in the university Full Time 26 Adjunct 13 Administration 9 Staff 20 68 7. Rank if full time faculty Full Professor 7

Associate Professor 8 Assistant Professor 10 Lecturer/Instructor 1 n/a 42 68

There is an important item to note from the responses for Dominant Culture in the

Organizational Culture Assessment Instrument. It was found that two subjects of the forty-one

who answered the survey completely had identical scores for at least two of the four possible

Dominant Cultures. Therefore, both were eliminated from the subsequent analyses.

Consequently The n-size for the dominant culture research variable was thirty-nine.

Table 3 shows the mean score for each school with regard to the culture type variables.

Totals for the means were rounded, but added up to close to 100 for each school. The aggregate

mean score for the six schools comprising the university was 32.20. This indicates that the

university is a moderately strong Clan Culture. The Hierarchy Culture mean score was 27.27.

Adhocracy Culture was 21.05 with Market Culture being weakest at a mean score of 19.48 for

the forty-one total responses. A look at each individual school, however, shows widely varying

results from the university mean score. The College, which had six respondents, returned the

highest mean score in the Hierarchy Culture variable (31.39). The next highest mean was the

Clan Culture (28.06). Market Culture was next (23.47) with Adhocracy returning the weakest

mean (17.98). With eight responses, the Business School followed the same order as The

College with a higher than aggregate Hierarchy Culture mean score (38.77). Clan Culture was

second highest (26.13). Market Culture (17.92) and Adhocracy Culture (17.19) shared very

similar mean scores. The Law School had only one valid respondent from the five who

originally responded. Mean scores are reported here for that respondent, but were not used in

any subsequent analyses. With the majority of responses at seventeen, the Leadership School

returned the Clan Culture as its highest mean score (30.48). The other three variables followed

with nearly identical mean scores of Adhocracy Culture (24.43), Market Culture (22.55), and

Hierarchy Culture (22.54). The Science and Math School also responded. Five total respondents

returned equally strong mean scores in Clan (32.67) and Hierarchy (32.33) Cultures. This was

compared to weak mean scores for Adhocracy (19.67) and Market (15.33) Cultures. Finally, the

Theology School’s four respondents returned an extremely high mean on the Clan Culture

variable (57.71). It also returned a correspondingly extremely low mean in the Market Culture

(6.04). Adhocracy Culture (24.17) was above the aggregate mean. while Hierarchy Culture

(12.08) was substantially lower than the aggregate mean score.

Table 3 Mean scores by School on Culture Type School Clan Adhocracy Market Hierarchy 1. The College (n=6) 28.06 17.08 23.47 31.39

2. Business (n=8) 26.13 17.19 17.92 38.77

3. Law (n=1) 30.50 12.67 30.50 26.33

4. Leadership (n=17) 30.48 24.43 22.55 22.54

5. Science/Math (n=5) 32.67 19.67 15.33 32.33

6. Theology (n=4) 57.71 24.17 6.04 12.08

Total (n=41) 32.20 21.05 19.48 27.27

Table 4 shows the means, standard deviations, inter-correlations, and reliabilities of the

seven main variables measured by the survey. ANOVAs were run on all variables against each

other using the data from the thirty-nine valid respondents producing the results below.

Perceived Usefulness had a mean score of 5.12 on a scale of 7. It has a significant

positive correlation with both Perceived Ease of Use and Actual Usage with the p value for PEU

at less than 0.05, and a p value for AU at less than 0.01. This means that respondents' positive

perception of usefulness corresponds both with their perception that the software is easy to use

and, correspondingly, use it for more hours during a work week.

Perceived Ease of Use returned a mean score of 5.13 on a scale of 7, almost identical to

the mean of the PU variable. However, outside of the correlation with PU, PEU does not

correlate significantly with any of the other five variables in this study.

The thirty-nine respondents recorded a mean of 5.78 hours of usage for the two

softwares studied. Outside of its correlation with PEU, that, as stated above, had a p value of less

than 0.01, there were no other significant correlations to the other variables.

None of the four cultural variables returned significant correlations with the three TAM

variables. However, a significant correlation was found among the cultural variables.

For the thirty-nine respondents, the Clan Culture returned the highest mean score, 32.97

with a standard deviation of 15.97. It has no significant correlation with either the Adhocracy or

Market Culture variables. However, a p value of less than 0.01 was returned with a negative

correlation of -0.38. This means that the more people report that they favor the statements

associated with Clan Culture, the less they favor statements on the Hierarchy Culture.

The Adhocracy Culture returned the second highest mean score of 29.12. The standard

deviation is lowest among the four Culture Type variables at 10.91. Adhocracy Culture has no

significant correlations with the Clan or Market Cultures. However, it returned a p value of less

than 0.01 with a negative correlation of -0.58 with the Hierarchy Culture variable. This means

that the more likely a respondent is to choose a statement for the Adhocracy culture, the less

likely they are to choose a statement for the Hierarchy culture.

The Market Culture returned the lowest mean of 19.10 and a standard deviation of

14.55. The Market Culture variable has no significant correlations with any of the other

variables.

The Hierarchy Culture variable returned a mean score of 23.31 with a standard deviation

of 14.94. As stated above, Hierarchy Culture has a significant negative correlation with both the

Clan and Adhocracy Culture variables.

Cronbach’s Alpha was used to determine validity of the collected data and returned valid

scores for every variable except the Actual Usage variable. For the purposes of this research, all

of these results are at an acceptable level to validate the data.

Table 4 Means, Standard Deviations, Intercorrelationsa, and Reliabilitiesb of the Seven Main Variables Used (Including Three TAM Variables and Four OCAI Variables) Variables Mean sd n (PU) (PEU) (AU) (Clan) (Adhoc) (Mkt) (Hier) 1. Perceived Usefulness 5.12 1.23 39 0.97 2. Perceived Ease of Use 5.13 1.12 39 0.87** 0.95 3. Actual Usage 5.78 6.05 39 0.32* 0.29 0.48 4. Clan Culture 32.47 15.97 39 0.03 -0.14 0.003 0.84 5. Adhocracy Culture 29.12 10.91 39 -0.09 -0.05 0.25 -0.06 0.76 6. Market Culture 19.10 14.55 39 -0.15 -0.09 -0.11 -0.66 -0.11 0.83 7. Hierarchy Culture 23.31 14.94 39 0.19 0.27 -0.07 -0.38** -0.58** -0.19 0.75 * p < 0.05 ** p < 0.01 a. Pearson's r b. Cronbach's Alpha Coefficient along the diagonal

It can be seen that two Alphas exceed the 0.85 recommendation (meaning their construct

is highly dependable), four reside between 0.7 and 0.85 (indicating a satisfactory alpha), and one

falls below 0.7 (Actual Usage - AU). However, since AU is only a two-item measure (one

question for total hours spent using Blackboard and one question for total hours spent using

WebAvisor), it is not expected that this item will return an acceptable Cronbach’s Alpha

Coefficient. If more items on the survey measured AU, it would have positively affected the

reliability measure. Therefore, it is unfair to judge that the two-item measure is unreliable since

these items measure the actual usage of two administrative technologies employed at the

university studied. This researcher can conclude, therefore, that Cronbach’s Alpha Coefficient

has been used in the appropriate manner given the focus and purpose of this study.

The Demographic Variables

Some significance was found among the seven variables within both TAM and OCAI. In

addition, this researcher determines that using ANOVAs to compare the demographic variables

(including the Organizational Culture “Dominant Culture” variable as a single variable) and the

three TAM variables with the Bonferroni correction, where significance was found, is

appropriate. ANOVA, the Analysis of Variance, determines whether two groups of data have

significant distributive variance between them. In this case, if significance is found between

TAM and the demographic data, the Bonferroni post-hoc is used to discover where the general

significance lies. However, Bonferroni might not point to any source of significance. These

results appear in Tables 5, 6, and 7 with Tables 6 and 7 detailing the results of Bonferroni Post-

Hoc analyses.

As seen in Table 5, for the main research question regarding the intersection between

TAM and Organizational Culture, the results show that Perceived Usefulness (PU) (F=1.561,

p>0.05), Perceived Ease of Usage (PEU) (F=2.622, p>0.05), and Actual Usage (AU) (F=0.208,

p>0.05) are not significantly related according to the Dominant Culture. In addition, ANOVAs

were run on all of the demographic variables. However, it should be noted that PEU had a p-

value very close to 0.05 with a returned values of 0.066. Though not significant, it is the closest

value of significance between the two main theoretical constructs of TAM and OCAI.

For the demographic of generation, there is no significance with PU (F=1.386, p>0.05),

PEU (F=1.746, p>0.05), or AU (F=0.784, p>0.05). Generation seems to have no significant

bearing on technology acceptance.

For gender, PU is significant (F=5.150, p<0.05), but the Bonferroni post-hoc test is

impossible to use because it is not appropriate with fewer than three categories inside of a single

variable. Also, PEU is significant (F=10.283, p<0.05) but, again, a post-hoc could not be

calculated. AU is not distributed according to gender (F=3.092, p>0.05). These findings seem to

agree with other, more detailed studies that found that women tend to accept technology at a

higher rate than men do. However, gender as a factor on PU and PEU was not an initial source

of interest for this researcher.

For those who noted their School affiliation, significant differences exist. PU is

distributed according to school (F=2.419, p<0.05), but the Bonferroni test (Table 6) shows that

no school scores significantly higher on the Perceived Usefulness construct than any other

school. PEU is not distributed according to school (F=1.599, p<0.05), although AU is distributed

according to school (F=2.914, p<0.05). Again, the Bonferroni post-hoc (Table 6) shows that no

school perceives significantly higher AU than any other school.

Significance is not found with any of the remaining demographic variables. A

respondent’s academic credentials also returns results that are not significant with PU (F=0.842,

p>0.05), PEU (F=0.045, p>0.05), and AU (F=3.021, p>0.05). A respondent’s faculty rank is not

significant: PU (F=0.965, p>0.05), PEU (F=0.045, p>0.05), and AU (F=0.631, p>0.05) all return

scores that show no significance. Finally, the status of a respondent within the institution does

not have a significant relationship with PU (F=1.159, p>0.05), PEU (F=0.995, p>0.05), or AU

(F=0.072, p>0.05).

Table 5 Results of ANOVA Analyses for Perceived Usefulness, Perceived Ease of Use, and Actual Usage on the Seven Demographic Variables Collected Research Question Variable F-statistic p-value 1. Dominant Culture Perceived Usefulness 1.561 0.216 Perceived Ease of Use 2.622 0.066 Actual Usage 0.208 0.890 2. Generation Perceived Usefulness 1.386 0.255 Perceived Ease of Use 1.746 0.166 Actual Usage 0.784 0.507 3. Gender Perceived Usefulness 5.150 0.027 Perceived Ease of Use 10.283 0.002 Actual Usage 3.092 0.083 4. School Perceived Usefulness 2.419 0.046 Perceived Ease of Use 1.599 0.174 Actual Usage 2.914 0.020 5. Degree Perceived Usefulness 0.842 0.436 Perceived Ease of Use 0.045 0.956 Actual Usage 3.021 0.056 6. Position Perceived Usefulness 0.965 0.415 Perceived Ease of Use 0.045 0.987 Actual Usage 0.631 0.598 7. Full Time Perceived Usefulness 1.159 0.337 Perceived Ease of Use 0.995 0.417 Actual Usage 0.072 0.990

Table 6 shows the results of a post-hoc analysis using the Bonferroni Analysis. This is

done because the ANOVA performed on the variables of Perceived Usefulness (PU) and School

Affiliation was valued at less than 0.05 showing significance. The post-hoc analysis is useful

because these two variables may return a significant difference among the schools, thus

identifying one school as significantly distinct from the others inside of this result.

It is important to note that none of the analyses return a p-value of less than 0.05, the

level of significance. In fact, all values return a 1.0, meaning there is no significant difference at

all in the reporting of scores for PU. Some of the Law school p-values were close to the 0.05

threshold, but this data would probably not be useful since there is only a single valid Law

School respondent.

Similarly, Table 7 shows the results of a post-hoc analysis using the Bonferroni analysis.

This is done because the ANOVA performed on the variables of Actual Usage (AU) and School

Affiliation was valued less than 0.05, showing significance. As with the PU post-hoc, there are

similar results that had no significance. Most of the values are at 1.0 with many Law school

numbers coming close to the p-value of 0.05 but not crossing the threshold. In this case, other

values are considerably less than 1.0, but still not close to the 0.05 value to point to any

significant School differences.

Table 6 Results of Bonferroni Analysis for Perceived Usefulness for the Six Schools

Category (I) Category (J) Mean Difference p-value The College Business 0.662 1.000 Law 1.739 0.139 Leadership -0.029 1.000 Science and Math -0.416 1.000 Theology 0.364 1.000 Business The College -0.663 1.000 Law 1.076 1.000 Leadership -0.692 1.000 Science and Math -1.078 1.000 Theology -0.300 1.000 Law The College -1.739 0.139 Business -1.076 1.000 Leadership -1.768 0.087 Science and Math -2.155 0.070 Theology -1.380 1.000 Leadership The College 0.029 1.000 Business 0.692 1.000 Law 1.768 0.087 Science and Math -0.387 1.000 Theology 0.393 1.000 Science and Math The College 0.416 1.000 Business 1.078 1.000 Law 2.155 0.070 Leadership 0.387 1.000 Theology 0.780 1.000 Theology The College -0.364 1.000 Business 0.300 1.000 Law 1.375 1.000 Leadership -0.393 1.000 Science and Math -0.780 1.000

Table 7 Results of Bonferroni Analysis for Actual Usage for the Six Schools

Category (I) Category (J) Mean Difference p-value The College Business 0.133 1.000 Law 3.133 1.000 Leadership -4.410 0.202 Science and Math 0.633 1.000 Theology -1.533 1.000 Business The College -1.333 1.000 Law 3.000 1.000 Leadership -4.544 0.261 Science and Math 0.500 1.000 Theology -1.667 1.000 Law The College -3.133 1.000 Business -3.000 1.000 Leadership -7.544 0.072 Science and Math -2.500 1.000 Theology -4.667 1.000 Leadership The College 4.410 0.202 Business 4.544 0.261 Law 7.544 0.072 Science and Math 5.044 0.433 Theology 2.877 1.000 Science and Math The College -0.633 1.000 Business -0.500 1.000 Law 2.500 1.000 Leadership -5.044 0.433 Theology -2.167 1.000 Theology The College 1.533 1.000 Business 1.667 1.000 Law 4.667 1.000 Leadership -2.877 1.000 Science and Math 2.167 1.000

Chapter 5 - Discussion

Summary of Findings

The original intent of this research is to explore the relationship between two main

variables: Organizational Culture and Technology Acceptance. The first variable is found within

the Competing Values Framework and its Organizational Typology of four cultures. The second

variable consists of three facets of the TAM. Analysis of the collected data shows no significant

relationship in the Pearson r analysis between these two main variables.

However, analyses into the demographic nature of technology acceptance return

significant data. This data indicates a significant relationship between Gender with Perceived

Usefulness and Gender with Perceived Ease of Use. Further, a relationship was found in the data

between self-identified School affiliation with Perceived Usefulness and School affiliation with

Actual Usage of software. However, the Bonferroni post-hoc analysis did not reveal the source

of this significance. Also, the fact that the School of Theology has a high mean score for Clan

Culture with a correspondingly low mean score for Market Culture stands out.

These significant results, and the rest of the results published in the tables, are detailed

and analyzed in the following sections. They are discussed in terms of the strengths and

deficiencies of the data presented. Each of the tables in the analyses is open to various

interpretations. It is important to take a brief look at the reliability of this study before

proceeding to an analysis of the data.

Descriptive and Reliability Analyses

In order to assess the internal consistency of the items measuring each respective

construct, Cronbach's alpha coefficient was used. Cronbach’s alpha posits that at least half of the

internal variability of the questions used on a survey should account for the measurement of the

underlying construct. This equates to a Cronbach's alpha of approximately 0.7071 (this number

squared equals the approximate minimum acceptability of the alpha at around 50%). However,

the literature recommends that an Alpha of 0.85 or higher represent a reliable test, while those

that fall between 0.7 and 0.85 are satisfactory. In fact, Rosenthal and Rosnow (1984) state that

“for purposes of clinical testing, reliability coefficients of approximately .85 or higher may be

considered as indicative of dependable psychological tests, whereas in experimental research,

instruments with much lower reliability coefficients may be accepted as satisfactory” (p. 50).

Data Analysis

The construction of the demographic section found in Table 1, a list of the five survey

sections, is open to question. Instructions on how to complete the survey may not have been

explicit enough when noting the time it would take to complete the survey. The two pieces of

technology are used by faculty and staff in two different ways. Blackboard is primarily used by

faculty for course delivery and management. Staff has little to no use for, or experience with,

Blackboard. WebAdvisor serves as a purely administrative function where faculty find course

enrollment lists, and enter official grades for the work done in Blackboard. The functional

difference between the two pieces of software might also have been analyzed for significance.

In future research, the researcher will make two adjustments including: 1) the focus will be

exclusively on either the faculty or the staff (more than likely on faculty), and 2) the appropriate

institutional software must be in regular use by all survey respondents.

Table 2, reporting the number of subjects per category, clearly shows that sixty-eight

respondents completed all of the demographic data at the beginning of the survey (a 15%

response rate), but only thirty-nine (a 9% response rate) went on to completely evaluate the

statements on Organizational Culture, which required a bit more thought and a more

sophisticated, nuanced response. Having respondents allot one-hundred points among four

statements may have reduced the desire to complete the survey. Of the five Law School

respondents, only one went on to finish the survey, so no analysis of the school was possible.

This fact points again to the necessity of making instructions clearer for the OCAI items and

reiterating the importance of responding to those particular survey items. In the opinion of the

researcher, the questions do not pose difficult mathematical concepts for the respondents, but

they certainly do make respondents take a little time in being accurate with responses. In the

future, the researcher may offer an incentive of a small raffle gift given to a random respondent

who completes the entire survey. However, as stated previously, Holbrook et. al. (2007) note

that a low response rate does not necessarily mean a less accurate representation of the overall

population.

Table 3, mean scores by School on Culture Type, contains, in the opinion of the

researcher, the richest data with which to launch future qualitative studies on Culture Type. The

mean scores of the total respondents point to an organization that is internally focused (since

the Clan and Hierarchy scores were higher on the internal/external continuum) and leaning

toward flexibility in innovation (since the Clan and Adhocracy scores were higher on the

flexibility/control continuum). This university can be generally characterized as family-like,

where employees and teamwork are valued. Awareness of basic Culture Types aids the design

of technology acceptance regimes that draw on the strengths and values of an institution.

Literature on OCAI use is still evolving, but there is still little on organizational culture in

higher education, since the OCAI tends to focus on for-profit corporate leadership development.

The data reported in Table 4, the inter-correlations, reinforces the relationships between

Perceived Usefulness, Perceived Ease of Use, and Actual Usage found in other studies noted in

Chapter 2. The Cronbach’s alphas attained were expected, except for the Actual Usage, which

is accounted for because only two responses per respondent were taken. This low score on the

alpha coefficient cannot be remedied in this instance since Actual Usage, a rough estimate of

weekly hours of usage, can only be reported in one way.

The negative correlation between the Hierarchy Culture and both the Clan and

Adhocracy cultures is worth some thought. It might be interpreted to mean that those

respondents who felt their Culture was too hierarchical had a strongly adverse reaction to the

idea of flexibility posed by the statements for the Clan and Adhocracy Cultures. In any case,

the data from this table serves as a springboard for future research into how different

organizational cultures might best be served by technology acceptance programs put together

by administrators in higher education.

The ANOVAs found in Table 5 for the seven demographic variables produced

interesting, but perhaps ultimately inconclusive results. The p-value for Gender for both

Perceived Usefulness and Perceived Ease of Use were statistically significant. Yet, there are

more rigorous studies that point to the gender gap in technology. This result was outside of the

interest of the researcher, which is also why there was nothing contained in the literature review

about this. More of interest was the School Affiliation means and the statistically significant p-

values of both Perceived Usefulness and Actual Usage. This significant result is more than

likely the place where this researcher will continue to work, since it can be generalized that

school affiliation (and, by inference, its organizational culture) within a larger institution has

some bearing on how people perceive the usefulness of technology. Since staff was not parsed

out of this analysis, it is not known whether the separation would have produced separate

significant results. In the future, the researcher will more than likely work with the faculty and

administration in addition to working with the inter-relationships between the two groups. This

will be done to describe how organizations incentivize the processes contained within

technology acceptance through organizational culture change.

The Bonferroni post-hoc analysis found in Tables 6 and 7 serve to reinforce the need for

further research, since neither analysis produced a specific source within the separate schools

that accounts for the significance of the data found in Table 5. These two analyses are an

enticing challenge for the researcher. They point to the need for survey design that might

produce a clearer picture of the intersections of the two main variables studied in this project.

Implications for the subject institution

This study investigates the relationship between self-reported organizational culture and

self-reported perceptions of using technology. It was carried out primarily through the

researcher’s anecdotal experience of working with professors in higher education for nearly

twenty years. The researcher observed that some workplaces value and implement technology

to a sophisticated degree, while other workplaces either passively reject technology use and

change, or actively seek to reject changes.

In the literature review, the researcher found that many of the TAM studies point to and

find potentially important antecedents to the TAM model. It was in this spirit that research was

conducted to see if Organizational Culture might also be an antecedent. Before discussing how

the results might be interpreted for the institution where the surveys were collected, this

researcher would first like to make some observations about the instruments used: the OCAI

and the TAM related questions.

The Organizational Culture Assessment Instrument has been a useful conceptual

framework for this researcher. The instrument is not intended to be used in a dichotomous

fashion, and this is one reason why the data does not reveal significance among the most

important variables as reported in Chapter 4. Instead, the instrument is designed to help

organizations focus on fine-tuning structural strengths, while addressing deficiencies and needs

among weaker facets of organizational culture. The data from this research (and the findings of

School affiliation, if nothing else) hint that there is value in using the instrument as part of a

change program geared toward greater technology acceptance in higher education. The results

hint that there exist subcultures within the main culture of this particular university, and that

School affiliation and gender may contribute to the type of culture found within the separate

Schools, though the data does not directly suggest this.

The TAM items proved an extremely reliable measure, as the Chronbach’s alpha

confirmed; this was especially true with the ANOVAs, which confirmed the strong

relationships between PU and PEU on AU. The results of comparing the means of these facets

of TAM, however, did not end up being as important to this study as originally intended. The

simplest and oldest model of TAM was chosen for its robustness as a reliable measure and to

parallel internal study at the university in which it was administered. In these respects, the TAM

did not disappoint. The model is valid and work by other researchers continues to explore the

complexities of the model, especially as it pertains to groups,national, and regional cultures, as

well as organizational cultures.

To reiterate, the most compelling finding was the descriptive statistic of the means of the

Organizational Culture Assessment Instrument. The School of Theology’s mean of the Clan

Culture (57.71) and the Market Culture (6.04) were in sharp contrast to the other four schools

(again, the Law School could not be compared since there was only one valid response). This

suggests that the School of Theology has a culture that is unique among the other five schools,

seeing itself as strongly Clan centered and strongly Market averse. In fact, all the schools

reported higher means on Clan and Hierarchy Cultures, which share the common facet of being

internally focused. It can be generalized that this particular university gets its drive and energy

from within. This factor, coupled with the significance of the School Affiliation ANOVA with

Perceived Usefulness and Perceived Ease of Use, strongly hints that with further investigation, a

stronger statistical relationship might be uncovered between Organizational Culture and

Technology Acceptance.

For this university, it might be a good idea for each school to first develop its technology

implementation policies based on the values of the school. Each school has its own motto,

which, it is assumed here, is closely connected with the vision and mission statement of that

particular school. Because of this, it can be assumed, and the data from this study suggests, that

each school places a different value on each of the two continua found in the OCAI. The way

technology is implemented varies slightly from school to school, based on these cultural

values,missions,vision statements.

If this researcher were on a university-wide technology implementation task force, he

would want to meet with the chief educational officers (CEO) of the university and each school,

so that commonalities and differences could be discussed,reviewed, and appropriate measures

taken for each school. A follow-up with each CEO would lay the framework for integrating

technology acceptance and use among members of the school through many of the remedies

suggested by Kay’s (2005) literature review.

Implications for Organizational Cultures

Focusing on the bigger picture, the results of these calculations point to the

effectiveness of the Organizational Culture Assessment Instrument. At the very least, the OCAI

is an excellent diagnostic tool that can and should be used in a consistent and planned manner,

often within an organization that seeks to engineer cultural change. The OCAI is useful in an

educational setting to distinguish the sub-cultures that exist within the institution. Data on

School affiliation shows that there is a relationship with Technology Acceptance.

This could mean that Organizational Change Directors are able to chart change courses

that are consistent with the expectations of the participants in certain Culture Types.

Technology policies in the Theology School, for example, do not necessarily have to

correspond with those of the other Schools inside the same university, because the view of the

culture has much to do with the subjects being studied and the research being done at that

School. Outcomes for the six Schools studied are obviously different, and it is worthwhilefor

each School to use a variation of the OCAI (in line, one would hope, with educational

organizational culture) to help to refine and develop all of their policies, including technology

implementation. OCAI is an excellent tool for both formative and summative assessment of any

particular culture, and this researcher highly recommends the simplicity and robustness of the

tool.

Implications for Educational Leaders

Like organizational change directors, educational leaders have the task of moving their

stakeholders ever forward to meet the constantly changing challenges ahead. The OCAI is but

one useful tool in helping to define the roles of all participants within a particular culture. The

OCAI, as suggested by the dissident response taken from the survey, might be fine-tuned or

retooled to fit more closely with educational imperatives not found in corporations or other

businesses. Because of this consideration, this researcher has included some questions for

further research specifically pertaining to the retooling and modification of OCAI to make it

coincide with educational rather than private business imperatives.

Recommendations for Practice

Preparing the literature review for this study was a stimulating experience. Higher

education is caught in a dilemma: change technology implementation at an ever-quickening

pace or risk obsolescence. It is a stark prediction, but one that is bearing out in the literature

(Kamenetz, 2010) and affecting the mainstream of thought. Dissertations like thisare also at

risk, because the body of available research grows and changes at an exponential rate. This

researcher knows that many of the studies cited in this dissertation have already been replaced

by fresher, peer-reviewed research whose findings both support and contradict earlier studies. It

is becoming truer that even dissertations are obsolete as soon as they are published, even before

they are published, and this researcher includes this dissertation in that statement.

Knowing the exigencies of time and task, educational leaders must balance the current

culture of the institution with the current technological realities, and with the technologies on

the horizon, quickly heading toward the institution. It is not a task that is easy to undertake and,

at times, it is impossible to anticipate the consequences of moving either too slowly, and risk

losing students, or too quickly, and risk losing experienced faculty. Cultural change and

technological change do go together, to the extent that both must be invented and re-invented at

a faster rate. There is less time to make critical decisions about the direction the institution

needs to go. There is also less time to make changes if things are perceived to be working in a

less than satisfactory manner. It is an exciting, but challenging time to be an educational leader.

Recommendations for Future Research

In constrast to the most obvious findings of this exploratory study, the researcher

believes there are factors of perceived organizational culture that are linked to factors of

technological change. The evidence of several TAM studies using the structural equation

modeling method to predict the impact of extraneous factors on TAM, though not used in this

research, point to these possibilities. Some questions for further studies, based on this and

earlier comments, include the following:

• To what extent do internally focused cultures (Clan and Hierarchy) inhibit or

nurture technology acceptance?

• To what extent do externally focused cultures (Market and Adhocracy) inhibit or

nurture technology acceptance?

• To what extent do more controlled cultures (Hierarchy and Market) inhibit or

nurture technology acceptance?

• To what extent do less controlled cultures (Clan and Adhocracy) inhibit or

nurture technology acceptance?

• What leadership characteristics help or hinder technology acceptance within

each of these cultures?

• How can OCAI be modified to be more attuned to educational culture, as

opposed to corporate or business culture?

• Can OCAI be used to distinguish between educational culture and corporate or

business culture?

Limitations of the Study

It is doubtful, but not out of the question, to determine whether the findings here can be

generalized to any population outside of this study, primarily because the sample size was

small, even in light of Holbrook et. al's (2005) statement on sample size. It was anticipated that

the low response rate might mean that a simple descriptive analysis would have to be carried

out. Results reported on a small sample size must be evaluated with this in mind, while also

considering that small response rates do not necessarily mean inaccurate data. This researcher

might, in future, visit faculty offices to solicit paper responses to the survey, if the survey

period is producing a lower than expected response rate. The School of Theology, for example,

only had four respondents with usable data, 10% of the valid respondents to the survey.

However, The School of Theology has fewer than fifteen full-time faculty members, so this is

more than a 25% response rate for that particular entity. The Law School only returned one

survey that for analyses, which could not, ultimately, be used. There are thirty-two full-time

faculty members in the Law School. These variations are difficult to control in an invited

survey, but a physical follow-up where response rates are low may increase the rate of return.

As an aside, based on the exploratory nature of this study, the researcher performed a

parallel study with a similar survey, but with members of social networking sites as random

participants invited to take the survey. The researcher ultimately decided that though the study

and resultant calculations returned results that differed from the case study of the university, it

could not serve as a reliable measure for any single university as a culture, primarily because

respondents’ identities could not be reliably authenticated. E-mail addresses were neither

solicited nor supplied by any of the respondents. The data still exists, but the researcher has not

analyzed it.

One anonymous participant at the university that served as the focus of this study,

commented that the Organizational Culture Assessment Instrument was not valid for

Educational Organizational Culture (emphasis added), though the respondent did not justify

this assertion. Since this was a purely quantitative study and data submission was kept

anonymous, no qualitative or richer data could be obtained from the comment; however, the

comment has been noted and this researcher intends to do more research that aligns the

Organizational Culture Assessment Instrument with higher education, including collection of

qualitative research in future studies.

Lastly, the OCAI is not intended to be dichotomous. Out of convenience, this researcher

used the highest mean to define a single respondent’s Dominant Culture to carry out a

quantitative analysis. A richer data set obtained through personal interviews with respondents

would yield richer findings. Nevertheless, this research was intended primarily to be

exploratory in nature. This researcher hopes to use some of the same variables in later research

that will help co-construct antecedents to technology acceptance in higher educational

institutions.

Organizational Culture and Technology Acceptance in the 21st

Century

Today, there exist equivalent social networking tools that can be used to support and

expand the “Tell Us Your Passion” activity first described in Chapter 2 of this dissertation. These

are contingent on whether the organization values and gives incentives for the individual’s

awareness, usage, and communication of the utility of the tools to colleagues. As an example, one

such social media tool is Twitter. Twitter is a website that gives users the ability to "micro-blog"

(as described above from the term "blog") using only 140 characters to send a message to

"followers," or people who have chosen to subscribe to the messages that are sent out by the

micro-blogger via the Twitter website.

"Tell Us Your Passion" could be regarded as roughly equivalent to the "What are you

doing?" prompt found at Twitter. These two activities are based on the same sound pedagogical

purpose: people learn best through the personalization of a learning activity. However, how

would an individual operate in an organizational culture that seeks to use the most appropriate

technologies for an activity? Where would the individual learn, for example, that Twitter has the

potential to yield an exponentially higher quality result than a 3x5 index card? How would this

individual know that by invoking what is of interest to an individual through this activity in 2010

through Twitter, it will simultaneously generate a springboard of conversation between and

among that individual’s followers on Twitter, who can, in turn, send the message out to their

followers (called a “retweet”)?

What type of organizational culture, if any, helps individual educators see how using

Twitter for this particular activity has the potential to result in both a rapid spread of information

pertinent to the individual’s social network, and a rapid organization of action to respond to such

information according to the context? In fact, Twitter users, like this researcher, already

understand that such deliberate action from an experienced teacher and administrator can be done

because of awareness of how to build a community of people with similar interests. The challenge

of Twitter’s 140 character limit ,necessitates succinct messages to explain oneself to a constantly

forming and reforming constellation of groups of peers. They, in turn, respond in some

predictably unpredictable ways. This researcher did not learn of these possibilities using Twitter

within the context of any organization, but instead through his personal commitment to learning

about and adopting the most significant technologies that assist learning. Was it something in the

institutional cultures in which he worked that led to this personal commitment? Did the

organizational culture contribute, either directly or indirectly, to this researcher’s personal views

on how things should work at an institution of higher learning?

We are now entrenched in the second decade of the new millennium. However, teaching

and learning with online technologies, such as Twitter, remains in “catch-up” mode. Academic

organizations, especially those related to higher education, continue to drag their feet when it

comes to using and applying emerging technologies. Private enterprise models are built partially

to be models of technological efficiency. Public higher education still seems quite the opposite.

This researcher, at heart an optimist, wonders when higher education will end the older, slower

practices that promote stability, while carefully and cautiously accepting change. He doubts that

this practice is sustainable, as it has been for the past two hundred years.

Self-aware educational organizational cultures ready to invent and reinvent themselves,

their missions, their visions, and their ways of interacting within and throughout their

organizations in a timely manner are those cultures that survive. The ones that wither and die are

the ones that hold on to the way things used to work; the ones ignoring, at their peril, the social,

political, economic, and psychological waves that technology change portends will join the ranks

of the forgotten.

This researcher does not predict the end of the world for higher education. He knows that

in as little as twenty years, it will look radically different, unimaginably different. He knows,

however, that it will not look anything like it does today, and asks whether the educational leaders

of today even have a glint of this understanding. Is change in organizational culture the key to the

survival of higher education in an ever-changing technological reality? Even in the face of the

results of this research, this researcher still insists on the answer: Yes.

APPENDIX A - Survey: Demographic Questions

Here are the questions as they appeared on the survey administered at

http://www.surveymonkey.com. School names remain anonymous for the purpose of reporting

this research.

Instructions: Answer all questions. Your name is NOT required. Please do not write it anywhere

on this survey. Your answers are anonymous. Please answer all questions.

1. Age

2. Gender

3. Highest Degree Obtained

a. BA

b. MA

c. PhD/EdD

4. Position at the University

a. Full-time faculty

b. Adjunct faculty

c. Administration

d. Staff

5. If Full-time faculty, RANK

a. Full professor

b. Associate professor

c. Assistant professor

d. Lecturer/Instructor

6. School you are affiliated with:

a. School 1

b. School 2

c. School 3

d. School 4

e. School 5

f. School 6

APPENDIX B - Survey: Organizational Culture

Assessment Instrument (OCAI)

Instructions: The OCAI consists of six questions. Each question has four alternatives. For each of

the six questions, read each of the four statements. Divide 100 points among these statements

depending on the extent to which each alternative is similar to your own organization. Give a

higher number of points to the alternative that is most similar to your organization. For example,

in question 1, if you think alternative A is very similar to your organization, alternatives B and C

are somewhat similar, and alternative D is hardly similar at all, you might give 55 points to A, 20

points each to B and C, and 5 points to D. Just be sure that your total equals 100 for each of the

six questions.

You will supply two answers to each of these six questions. Answer each question for what your

organization is like now (under the NOW column). Also answer for what you would prefer your

organization to be in five years in order to be more successful than now (under the PREFERRED

column).

1. Dominant Characteristics

a. The organization is a very personal place. It is like an extended family. People

seem to share a lot of themselves.

b. The organization is a very dynamic and entrepreneurial place. People are willing

to stick their necks out and take risks.

c. The organization is very results oriented. A major concern is with getting the job

done. People are very competitive and achievement oriented.

d. The organization is a very controlled and structured place. Formal procedures

generally govern what people do.

2. Organizational Leadership

a. The leadership in the organization is generally considered to exemplify

mentoring, facilitating or nurturing.

b. The leadership in the organization is generally considered to exemplify

entrepreneurship, innovating or risk taking.

c. The leadership in the organization is generally considered to exemplify a no-

nonsense, aggressive, results-oriented focus.

d. The leadership in the organization is generally considered to exemplify

coordinating, organizing, or smooth-running efficiency.

3. Management of Employees

a. The management style in the organization is characterized by teamwork,

consensus, and participation.

b. The management style in the organization is characterized by individual risk-

taking, innovation, freedom, and uniqueness.

c. The management style in the organization is characterized by hard-driving

competitiveness, high demands, and achievement.

d. The management style in the organization is characterized by security of

employment, conformity, predictability and stability of relationships.

4. Organization Glue

a. The glue that holds the organization together is loyalty and mutual trust.

Commitment to this organization runs high.

b. The glue that holds the organization together is commitment to innovation and

development. There is an emphasis on being on the cutting edge.

c. The glue that holds the organization together is emphasis on achievement and

goal accomplishment. Aggressiveness and winning are common themes.

d. The glue that holds the organization together is formal rules and policies.

Maintaining a smooth-running organization is important.

5. Strategic Emphases

a. The organization emphasizes human development. High trust, openness, and

participation persist.

b. The organization emphasizes acquiring new resources and crating new

challenges. Trying new things and prospecting for opportunities are valued.

c. The organization emphasizes competitive actions and achievement. Hitting

stretch targets and winning in the marketplace are dominant.

d. The organization emphasizes permanence and stability. Efficiency, control and

smooth operations are important.

6. Criteria of Success

a. The organization defines success on the basis of the development of human

resources, teamwork, employee commitment, and concern for people.

b. The organization defines success on the basis of having the most unique or

newest products. It is a product leader and innovator.

c. The organization defines success on the basis of winning in the marketplace and

outpacing the competition. Competitive market leadership is the key.

d. The organization defines success on the basis of efficiency. Dependable delivery,

smooth scheduling, and low-cost production are critical.

APPENDIX C - Survey: TAM Questions

Note: these questions were scrambled on the online version.

Instructions – For each statement below, indicate your level of agreement or disagreement to

the statement by circling the corresponding number (1 for complete disagreement to 7 for

complete agreement).

1 – completely disagree 2 – strongly disagree 3 – disagree 4 – neutral

5 – agree 6 – strongly agree 7 – completely agree

XXXXX= Blackboard and Web Advisor

Statements of Perceived Usefulness (PU)

1. Using XXXXX enables me to accomplish job tasks more quickly.

2. Using XXXXX improves my job performance.

3. Using XXXXX increases my job productivity.

4. Using XXXXX enhances my effectiveness on the job.

5. Using XXXXX makes it easier to do my job.

6. I find XXXXX useful in my job.

Statements of Perceived Ease of Use (PEU)

1. Learning to operate XXXXX was easy for me.

2. I find it easy to get XXXXX to do what I want it to do.

3. My interactions with XXXXX are clear and understandable.

4. I find XXXXX to be flexible to interact with.

5. It was easy for me to become skillful at using XXXXX.

6. I find XXXXX easy to use.

Statement of Actual Usage (AU):

Usage Volume:

Please specify (estimate) how many hours each week you normally spend using

XXXXX: _____ hours.

APPENDIX D - Survey: Consent Statement

Description of Project – This project is the dissertation project of Larry Davies, EdD candidate

in Educational Leadership at St. Thomas University. The project is seeking to explore the

relationships between Organizational Culture and Technology Acceptance and Usage.

Statement of Confidentiality – Your answers are anonymous. All data will be published in the

dissertation that constitutes a requirement for Mr. Davies to obtain his accreditation.

Explicit Statement of Consent – By answering these questions, you agree that you understand

the questions are used purely for research purposes only. By clicking “I accept” you agree to the

above statement terms of this online survey.

Contact Information – [email protected]

Statement of Risks/Benefits – One of the main benefits of this study might be to improve the

technology training and dissemination at xxx University now and into the future. Other data

obtained might be used to diagnose remedies for organizational culture change either now or into

the future. There are no personal or professional risks to you for answering these questions.

APPENDIX E - Email Announcements to Solicit

Participants

1st email

Dear xxxx community,

I am conducting a short survey on organizational culture and technology acceptance at xxx

University. This research has the potential to benefit technology training at xxx University. I

would greatly appreciate if you would be kind enough to complete this survey at the link below

by April 23, 2010. It should take about 10 to 15 minutes of your time. Your responses are

completely anonymous, and there is no risk to you for completing it.

http://www.surveymonkey.com/s/9FRRHRP

This research is being done by me, Larry Davies, in the School for Leadership Studies at St.

Thomas University as part of my Ed.D. in Educational Leadership. I thank you in advance for

your help and participation.

2nd email

Dear xxxx community,

Thank you so far to those who have completed my survey. This notice is for those who have not

yet done so.

I am conducting a short survey on organizational culture and technology acceptance at xxxx

University. This research has the potential to benefit technology training at xxxx University. I

would greatly appreciate if you would be kind enough to complete this survey at the link below

by April 23, 2010. It should take about 10 to 15 minutes of your time. Your responses are

completely anonymous, and there is no risk to you for completing it. Please make sure you

answer all questions, otherwise I am not able to use your responses for my analysis.

http://www.surveymonkey.com/s/9FRRHRP

This research, approved by the Institutional Research Board at STU, is being done by me, Larry

Davies, in the School for Leadership Studies at St. Thomas University as part of my Ed.D. in

Educational Leadership. I thank you in advance for your help and participation.

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Abstract
Acknowledgments
Dedication
Table of Contents
List of Figures
List of Tables
Chapter 1 – Introduction

Overview
Technology and Organizational Culture
The Intersection

Chapter 2 – Literature Review

Introduction
Learning and Technology
Organizational Paradigms
Organizational Andragogy
Innovation and Acceptance
The Diffusion of Innovation
Technology Acceptance
The Competing Values Framework (CVF)
Competing Values Framework Research
Variations of the Technology Acceptance Model
Modified TAM Research
TAM in Organizational Culture
TAM in Academia
Other Studies Relevant to the Research Problem
Personal Learning With and Through Technology
Statement of the Problem

Chapter 3 – Methodology

Population

Chapter 4 - Results

Procedure
Data Collection
Sample Sizes for the Research
The Demographic Variables

Chapter 5 - Discussion

Summary of Findings
Descriptive and Reliability Analyses
Data Analysis
Implications for the subject institution
Implications for Organizational Cultures
Implications for Educational Leaders
Recommendations for Practice
Recommendations for Future Research
Limitations of the Study
Organizational Culture and Technology Acceptance in the 21st Century

APPENDIX A - Survey: Demographic Questions
APPENDIX B - Survey: Organizational Culture Assessment Instrument (OCAI)
APPENDIX C - Survey: TAM Questions

Statements of Perceived Usefulness (PU)
Statements of Perceived Ease of Use (PEU)
Statement of Actual Usage (AU):
Usage Volume:

APPENDIX D - Survey: Consent Statement
APPENDIX E - Email Announcements to Solicit Participants
REFERENCES