Citation
Using a policy process model to examine the campus-wide laptop computing programs of two universities

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Title:
Using a policy process model to examine the campus-wide laptop computing programs of two universities
Creator:
Clark, J. Anne
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Language:
English
Physical Description:
xii, 330 leaves : ; 28 cm

Subjects

Subjects / Keywords:
Laptop computers ( lcsh )
Universities and colleges -- Data processing ( lcsh )
Universities and colleges -- Computer networks ( lcsh )
Genre:
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )

Notes

Bibliography:
Includes bibliographical references (leaves 311-330).
General Note:
School of Education and Human Development
Statement of Responsibility:
by J. Anne Clark.

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|University of Colorado Denver
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Auraria Library
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All applicable rights reserved by the source institution and holding location.
Resource Identifier:
50726861 ( OCLC )
ocm50726861
Classification:
LD1190.E3 2002d .C52 ( lcc )

Full Text
1
USING A POLICY PROCESS MODEL TO EXAMINE THE
o
\
CAMPUS-WIDE LAPTOP COMPUTING PROGRAMS
OF TWO UNIVERSITIES
by
J. Anne Clark
B. A., St. Cloud State University, 1971
M.A., University of CaliforniaChico, 1987
A thesis submitted to the
University of Colorado at Denver
in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Educational Leadership and Innovation
2002


This thesis for the Doctor of Philosophy
degree by
J. Anne Clark
has been approved
Rodney Muth

Brent G. Wilson


Clark, J. Anne (Ph.D., Educational Leadership and Innovation
Using a Policy Process Model to Examine the Campus-wide Laptop
Computing Programs of Two Universities
Thesis directed by Professor Michael J. Murphy
ABSTRACT
Employing effective adoption and implementation processes for
information technology (IT) is a concern on many campuses. This
investigation examined two universities' processes for adoption and
implementation of campus-wide laptop computing.
Primary data collection methods were interviewing and document
review. Findings were revealed through the integrated use of a chronology of
significant events and a policy process model, which was synthesized by the
researcher for particular application to information technology issues. As a
result of this study, adjustments were recommended to the policy process
model to improve its descriptive and analytic utility.
Both case universities, Alpha University (AU) and Kappa University
(KU), successfully integrated campus-wide laptop computing into teaching
and learning. This success was attributable, in large part, to extensive faculty
development programs that were provided within an environment that
allowed faculty professional autonomy in deciding how and when to utilize
laptop technology.
AU and KU had substantially different levels of available resources to
support laptop computing. AU employed a financing model that generated
sufficient funds for on-going support of the project, and AU resources were
augmented by grant funds. KU had fewer resources. Its financing model
created significant institutional debt and was, at the time of this study, being
replaced by another model.
in


Differences between the AU and KU cases were strongly related to
elements of the policy contexts in which each of these two universities
developed and implemented their laptop computing policies. The AU policy
context was characterized by long-standing consensus-based leadership.
Further, the AU mission was continuously focused on instructional
technology for over a decade. In contrast, KU's policy context was
characterized by directional shifts in both leadership approach and policy
agenda. KU had four presidents in less than a decade, with a dramatic shift
away from authoritarian leadership. Instructional technology rather abruptly
became an academic priority. Implementation difficulties were attributable
primarily to KU's insufficient resources, combined with discontinuity of
leadership and institutional mission
This study contributed to knowledge of adoption and implementation
processes for campus-wide laptop computing. The study also revealed that a
policy process model provides an effective lens through which to examine
university IT processes.
IV


DEDICATION
I dedicate this thesis to my son, Michael Clark, for his technical and
personal support throughout my doctoral studies.


ACKNOWLEDGEMENT
My thanks to my committee chair, Michael Murphy, and my committee
members, Jana Everett, Rodney Muth, and Brent Wilson, for their valuable
guidance and recommendations.


CONTENTS
Tables......................................................xii
CHAPTER
1. INTRODUCTION...............................................1
Purpose of the Study....................................3
Statement of the Problem................................4
Research Questions......................................7
Significance of the Study............................. 8
Definition of Terms....................................10
Limitations of the Study...............................12
2. REVIEW OF THE LITERATURE..................................14
The Policy Sciences and Policy Analysis................14
Policy Process Terminology......................17
Policy Process Characteristics..................20
Policy Process Models...........................23
The Policy Process and University Information Technology ...27
Use of a Policy Process Model for This Study...........32
The Current U. S. Higher Education Environment.........37
Focus on Accountability and Productivity........37
Demographic and Enrollment Trends...............39
Financial Challenges............................41
Changing Institutional Planning and Decision
Processes.......................................43
Information Technology and U.S. Higher Education.......47
IT Planning, Implementation, and Budgeting......47
IT as a Strategic Investment....................55
Expanding Access Through Distance Education.....60
Using Information Technology to Improve Pedagogy ..63
Faculty Professional Development and Recognition for
Integration of Technology.......................66
Campus-wide Laptop Computing...........................69
vii


Integrating Laptop Technology into Teaching and
Learning.........................................71
Student Issues and Impacts.......................73
Faculty Issues and Impacts.......................76
User Training and Technical Support:.............79
Computing Hardware and Software..................81
IT Infrastructure and Instructional Facilities...83
Financial Issues.................................86
Summary................................................88
3. RESEARCH METHODOLOGY.......................................90
Qualitative Approach...................................90
Case Study Methodology...........................92
Use of the Literature............................93
Role of the Researcher...........................94
Selection of Participant Institutions..................95
Data Collection Strategies and Protocols...............96
The Interviews...................................98
Document Collection and Review..................100
Informant Follow-up Information.................102
Data Organization and Categorization.................103
Data Organization in a Chronology of Significant
Events..........................................103
Data Categorization Using a Policy Process Model.110
Discussion of the Findings............................114
4. PRESENTATION OF FINDINGS FOR THE ALPHA UNIVERSITY
CASE............................................:.........115
The Informants........................................116
Data Collection and Organization......................119
Findings Categorized by Phases of the Policy Process
Model.................................................120
Phase I: Agenda Setting.........................120
Phase II: Problem Definition and Analysis.......123
Phase HI: Identification of Solution Criteria...126
Phase IV: Designing Policy Solution Alternatives.129
Phase V: Evaluation of Alternative Policy Solutions ... 134
viii


Phase VI: Authoritative Adoption of Preferred Policy
Solution................................................138
Phase VII: Acquisition (Pre-implementation).............140
Phase VIII: Implementation..............................145
Phase IX: Monitoring and Evaluation of Outcomes.........151
Phase X: Continuation, Adaptation, Expansion,
Reduction, or Termination...............................155
Findings Related to the Characteristics of a Policy Process.... 161
Contextual..............................................162
Iterative...............................................165
Linked to Resources.....................................166
Multi-discipline and Multi-method.......................167
Problem-oriented........................................168
Values-oriented.........................................169
Findings on Policy Process Participants.......................170
Findings on Facilitating and Inhibiting Events and Actions .. 172
Facilitating Events and Actions.........................172
Inhibiting Events and Actions...........................179
Summaiy of Findings...........................................183
5. PRESENTAION OF FINDINGS FOR THE KAPPA UNIVERSITY
CASE...............................................!........187
The Informants................................................188
Data Collection and Organization..............................192
Findings Categorized by Phases of the Policy Process
Model.........................................................193
Phase I: Agenda Setting.................................193
Phase II: Problem Definition and Analysis...............195
Phase HI: Identification of Solution Criteria...........197
Phase IV: Designing Policy Solution Alternatives........199
Phase V: Evaluation of Alternative Policy Solutions ...201
Phase VI: Authoritative Adoption of Preferred Policy
Solution................................................204
Phase VH: Acquisition (Pre-implementation)..............206
Phase VHI: Implementation...............................213
Phase IX: Monitoring and Evaluation of Outcomes.........223
Phase X: Continuation, Adaptation, Expansion,
Reduction, or Termination...............................228
IX


Findings Related to the Characteristics of a Policy Process ....234
Contextual.......................................234
Iterative........................................237
Linked to Resources..............................238
Multi-discipline and Multi-method................240
Problem-oriented.................................242
Values-oriented..................................242
Findings on Policy Process Participants................243
Findings on Facilitating and Inhibiting Events and Actions ..245
Facilitating Events and Actions..................245
Inhibiting Events and Actions....................249
Summary of Findings....................................253
6. DISCUSSION, CONCLUSIONS, AND
RECOMMENDATIONS...........................................259
Discussion related to Research Questions One and Two...260
Alpha University.................................260
Kappa University.................................269
Comparison of Alpha University and Kappa
University.......................................281
Conclusions......................................288
Summary of Conclusions for Research Questions
One and Two............................................291
Discussion and Conclusions for Research Question Three.292
Utility of the Synthesized Policy Process Model..292
Recommended Adaptations to the Policy Process
Model............................................295
Recommendations for Further Research...................299
APPENDIX
A: LETTER REQUESTING UNIVERSITY PARTICIPATION IN
THE STUDY ..................................^.......302
B: LETTER REQUESTING INDIVIDUAL PARTICIPATION IN
THE STUDY .............................................303
C: INFORMED CONSENT LETTER.
304


D: INTERVIEW QUESTIONS..................307
E: SAMPLE CHRONOLOGY WITH FICTITIOUS
EVENT...................................309
F: LETTER REQUESTING REVIEW OF THE CHRONOLOGY
OF SIGNIFICANT EVENTS...................310
REFERENCES..................................311
XI


TABLES
Table
2.1 Synthesized Policy Process Model..................................35
3.1 Steps in Developing a Chronology of Significant Events...........105
3.2 Format of the Chronology of Significant Events ..................107
4.1 Alpha University Informant Data..................................117
4.2 Policy Process Model Phase I ....................................120
4.3 Alpha University Agenda Setting .................................121
4.4 Policy Process Model Phase II ...................................124
4.5 Alpha University Problem Definition and Analysis.................125
4.6 Policy Process Model Phase HI ...................................127
4.7 Alpha University Identification of Solution Criteria ............128
4.8 Policy Process Model Phase IV ...................................130
4.9 Alpha University Identification of Policy Solution Alternatives .131
4.10 Policy Process Model Phase V ....................................135
4.11 Alpha University Evaluation of Policy Solution Alternatives......136
4.12 Policy Process Model Phase VI ...................................138
4.13 Alpha University Authoritative Decision to Adopt Preferred
Solution.........................................................139
4.14 Policy Process Model Phase Vn ...................................140
4.15 Alpha University Acquisition (Pre-implementation) ...............141
4.16 Policy Process Model Phase Vni ..................................145
4.17 Alpha University Implementation..................................147
4.18 Policy Process Model Phase IX ...................................152
4.19 Alpha University Evaluation of Outcomes .........................152
4.20 Policy Process Model Phase X .................................. 155
4.21 Alpha University Program Continuance, Adjustment, or
Termination......................................................157
4.22 Significant Alpha University Policy Context Events and Actions ... 163
4.23 Alpha University Multidisciplinary Methods and Approaches .......168
4.24 Alpha University Group and Individual Participants by
Institutional Sector ............................................171
4.25 Events and Actions Perceived to Facilitate Campus-wide Laptop
Computing at Alpha University ................................. 173
xii


4.26 Events and Actions Perceived to Inhibit Campus-wide Laptop
Computing at Alpha University .................................180
5.1 Kappa University Informant Data ................................190
5.2 Policy Process Model Phase I ...................................193
5.3 Kappa University Agenda Setting ................................194
5.4 Policy Process Model Phase II ..................................196
5.5 Kappa University Problem Definition and Analysis ...............196
5.6 Policy Process Model Phase III .................................198
5.7 Kappa University Identification of Solution Criteria ...........198
5.8 Policy Process Model Phase IV ..................................200
5.9 Kappa University Identification of Policy Solution Alternatives.201
5.10 Policy Process Model Phase V ...................................202
5.11 Kapp a University Evaluation of Policy Solution Alternatives ...203
5.12 Policy Process Model Phase VI ........................:.........204
5.13 Kappa University Authoritative Decision to Adopt Preferred
Solution.......................................................205
5.14 Policy Process Model Phase VII .................................206
5.15 Kappa University Acquisition (Pre-implementation)...............207
5.16 Policy Process Model Phase VIII ................................213
5.17 Kappa University Implementation ................................214
5.18 Policy Process Model Phase IX ..................................224
5.19 Kappa University Evaluation of Outcomes ........................225
5.20 Policy Process Model Phase X ...................................228
5.21 Kappa University Program Continuance, Adjustment, or
Termination ...................................................229
5.22 Significant Kappa University Policy Context Events and Actions .. 235
5.23 Kappa University Multidisciplinary Methods and Approaches ......240
5.24 Kappa University Group and Individual Participants by
By Institutional Sector........................................244
5.25 Events and Actions Perceived to Facilitate Campus-wide Laptop
Computing at Kappa University .................................246
5.26 Events and Actions Perceived to Inhibit Campus-wide Laptop
Computing at Kappa University..................................250
6.1 New Policy Process Model Phase Combining Phases
m, IV, and V...................................................297
6.2 New Policy Context Category ....................................298
xiii


CHAPTER 1
INTRODUCTION
While working as a planning consultant for a university client in 1997,
I developed an interest in this university's policy of requiring all students to
lease a laptop computer. After discussing the laptop computing initiative
with several faculty and administrators of this University, I became
particularly interested in learning about the decision and implementation
processes that were being used by this and other universities to implement
campus-wide laptop computing programs. An online listing of higher
education institutions requiring laptop computers and a conference for laptop
computing universities held at Wake Forest University (NC) in January 1999
provided an opportunity for me to collect information on decision and
implementation processes for campus-wide laptop computing at ten
institutions.
Coursework and guided readings in my educational policy and
administration Ph. D. program introduced me to the theory and applications
of the policy process with reference to public policy issues (deLeon, 1988;
Kingdon, 1984; Lasswell, 1971; Lindblom, 1980; May & Wildavsky, 1978;
Quade, 1982; Wildavsky, 1979). Review of the policy process literature
revealed an emerging application of a policy process approach to
organizational and institutional policy issues (Easton, 1965; Palumbo &
l


Calista, 1990; Weimer & Vining, 1992). The literature also includes
discussions of the policy process as applied to university policy issues
(Baldridge, Curtis, Ecker, & Riley, 1978; Fincher, 1987; Gill & Saunders, 1992;
VanVught, 1997).
During the spring of 1999,1 conducted a preliminary investigation
involving ten colleges and universities that require students to have laptop
computers (Zeglen & Clark, 1999). The goal of this preliminary investigation
was to gain insight into the decision and implementation processes
universities were employing relative to laptop computing programs and to
determine whether these processes could be described and analyzed in terms
of a policy process model.
Responses to a survey and informal interviews revealed that decision
and implementation processes for campus-wide laptop computing initiatives
included some consistent elements, but also varied from one institution to
another. My preliminary investigation revealed that some elements of a
policy process approach were evident, such problem analysis. Other policy
process elements, such as comparison of alternative policy solutions, were not
identified. None of the ten institutions that I surveyed identified all of the
policy process elements I had distilled from the literature, but most identified
at least several.
After reviewing the survey and interview data, it was apparent that
my survey for this preliminary research had been only minimally effective in
collecting the information that I was seeking. I concluded that, in order to
2


understand more fully the characteristics and elements of an institution's
decision and implementation approaches for campus-wide laptop computing,
an in-depth investigation would be required.
As an outgrowth of my study of information technology in educational
environments, my involvement in university information technology
consulting, my study of policy process literature, and my experience in
conducting the preliminary investigation of ten institutions, I developed (in
collaboration with Marie E. Zeglen) an initial policy process model for
analyzing campus-level information technology processes (Zeglen & Clark,
1999). Dr. Zeglen and I presented this model at the International Conference and
Annual Meeting of the Society for College and University Planning in Atlanta, July
1999. The paper and presentation focused on the need for an effective
information technology decision and implementation approach that is:
adaptable to the context of a particular university's environment, responsive
to a range of stakeholders' needs and perspectives, and able to produce
alternative solutions that are actionable within the institution's resource limits
(Zeglen & Clark). Subsequently, I have incorporated aspects of this policy
process model into analyses conducted for university clients. This work has
informed my thinking in defining a policy process model for this study.
Purpose of the Study
The purpose of the study is to understand how universities adopt,
implement, and evaluate complex information technology policies such as
3


campus-wide laptop computing. The study uses a policy process model to
guide an examination of adoption and implementation processes for laptop
computing at two universities. In an extensive review of the literature, I
found no research studies that reference a policy process model to examine
university decision and implementation processes for significant information
technology issues. This study is intended to contribute to the literature
addressing campus-level information technology planning and
implementation processes.
This study is designed as an investigation of adoption and
implementation processes for campus-wide laptop computing, referencing
particular characteristics and elements of a policy process model.
Specifically, this study analyzes how two selected universities successfully
developed and implemented their campus-wide laptop computing initiatives.
The study uses a policy process model synthesized by the researcher for
particular application to higher education information technology issues.
Based on the research results, this synthesized model may be amended to
reflect more closely the processes of the two universities studied.
Statement of the Problem
Among the most significant challenges universities currently confront
are the difficulties involved in integrating continuously advancing
information technologies into core academic and administrative processes
(Foster & Hollowell, 1999; Green, 1999). Information technology (IT) issues
4


impact every aspect of academic and institutional life, including student
recruitment and retention, faculty rewards and promotion, funding and
budgeting; capital planning, libraries, and business functions (Katz, 1999;
Kling, 1996; Massy, 1995; Oblinger & Rush, 1998). Information technologies
offer universities unprecedented opportunities as well as significant potential
challenges and difficulties (Green 1998; Katz, 1999; Miller, 2000; Zemsky &
Massy, 1995).
A new higher learning industry is rapidly evolving in the current
knowledge-based economy (Carr, 2000; Heeger, 2000). Corporations and for-
profit educational organizations are offering technology-based degree
programs, certificates, and life-long learning opportunities to diverse
populations of learners (Katz, 1999). Some funding, particularly private and
corporate, is being channeled away from traditional higher education
institutions toward emerging education providers (Zemsky & Massy, 1995).
In a rapidly changing higher education environment that includes significant
competition from these new programs, traditional institutions are confronting
the challenge of utilizing information technology resources efficiently and
effectively (Blustain, Goldstein, & Lozier, 1999; Duderstadt, 1999; Farrington,
1999).
Budgeting and funding for information technology are an on-going
challenge for traditional universities. Higher education institutions are
devoting significant and increasing levels of resources to IT. In the 2000-2001
academic year, U.S. colleges and universities allocated a record $3.3 billion for
administrative and academic hardware and software (Olsen, 2001a, p. A53).
5


Technology projects can be major resource investments, whose success or
failure can impact an institution's overall financial health (Foster &
Hollowell, 1999; Peebles, 2000). Funding the escalating IT demands of an
institution while simultaneously supporting other on-going academic and
administrative needs is a continual challenge for most colleges and
universities.
The serious challenges of efficient and effective integration of
information technologies require appropriate decision and implementation
processes (Bernbom, 1999; Twigg, 1999). Traditional university decision and
implementation processes are often perceived to be inadequate for addressing
university information technology issues. The five-year planning cycle
common in higher education (Steinbach, 1995) may be too long for
information technology, considering the rapid pace of technological change
(Fougere & Olinsky, 1990) and the continual escalation of student and faculty
expectations for access to the latest technologies (Tapscott & Caston, 1993).
The prevalent university decision processes, when utilized for information
technology issues, are frequently perceived to be contentious, frustrating,
unworkably slow, and generally ineffective (Olsen, 2001b). Support for this
assertion is abundant in the literature.
Kenneth Green's study, The 1998 National Survey oflnformation
Technology in Higher Education, reports that colleges and universities are
struggling with IT planning, that many institutions have not developed IT
plans, and that IT planning issues are significant technology concerns.
Blustain et al. (1999, p. 60) discuss a range of IT pitfalls including: failure of
6


senior administrators to provide guidelines and parameters for planning;
failure to consider the perspectives of students; and failure to specify resource
requirements. Heeger (2000) discusses frequent instances of institutions
rushing into use of new technologies without recognizing the many
implications for the institution as a whole (p. 8).
In summary, this study examines the difficulties that colleges and
universities face in attempting to effectively integrate information
technologies. The study then attempts to identify and explicate the elements
and characteristics of information technology decision and implementation
processes, as exemplified by two universities that have developed and
implemented campus-wide laptop computing programs.
Research Questions
The broader question, which sets a context for the research questions,
is: How do universities go about the process of adopting, implementing, and
evaluating an innovative information technology policy? This study is
focused on a particular IT issue that a growing number of universities are
addressing: how to plan and implement a policy requiring all students to
have a laptop computer. Three research questions are addressed in this study.
1. What actions, events, and approaches characterize the
processes of two universities in their adoption,
implementation, and evaluation of a campus-wide laptop
computing policy?
7


2. Did particular elements of the campus-wide laptop
computing policy processes facilitate or inhibit the laptop
computing programs at the case universities?
3. Are there adaptations to the synthesized policy process
model used in the study that would make the model more
useful as a descriptive and analytical tool for the laptop
policy processes of the case universities?
As researcher, I anticipate that the synthesized policy process model
used to guide this investigation will provide an effective framework through
which the significant events, actions, participants, and outcomes of a campus-
wide laptop computing policy may be thoroughly examined. I further
anticipate that this investigation will support an in-depth discussion of
specific aspects of laptop policy development and implementation that the
participants believe facilitated or inhibited the policy process. In addition, I
anticipate that this study may reveal specific ways in which the synthesized
policy process model could be adjusted in order to provide a more useful
framework for future investigations of university information technology
policy processes.
Significance of the Study
As information and learning technologies are increasingly integrated
into both the academic and administrative core processes of universities,
significant IT policy and program decisions are being made by higher
education institutions (Green, 1999; Kaufman & Lick, 2000; Luker, 2000;
Oblinger & Rush, 1998). However, effective IT decision and implementation
8


processes are currently insufficiently developed in the university setting
(Blustain et al., 1999; Katz, 1999). Analysis of effective IT decision and
implementation cases is worthy of research, as the results may contribute to a
better understanding of how universities could further develop their
approaches to IT planning, decision making, and program implementation.
Campus-wide laptop computing is a significant campus information
technology issue for a number of reasons (Zeglen & Clark, 1999):
1. A growing number of post-secondary institutions are
currently considering the adoption of campus-wide
laptop computing.
2. Adopting a campus-wide laptop computing program
significantly impacts a university's students, faculty, and
staff, as well as external stakeholders.
3. A campus-wide laptop computing program has
significant and wide-ranging academic, fiscal, and
facilities implications.
The design of this study may be used as a reference point in the
design of subsequent case studies to determine how other laptop computing
policies were developed and implemented. The results that emerge may serve
as a basis for other qualitative or quantitative studies designed to further
understanding of how universities can effectively address IT issues.
Results may contribute to improved university IT decision and
implementation processes. If the study reveals meaningful correspondence
9


between effective adoption and implementation practice and the policy
process model, university leaders may wish to reference such a policy process
model when designing their institutional approach to campus-wide laptop
computing or other complex IT issues that have significant institutional
impacts.
Definition of Terms
The following terms were defined for use in this study:
Campus-wide laptop computing: An institutional program
that provides all faculty and students with continuous,
individual access to a laptop computer and the network
infrastructure to utilize the laptop computer for multiple
academic purposes
Higher education institution: An accredited educational
organization that provides post-secondary courses and
programs
Information technology: Computer-based and computer
network-based technology utilized for business,
communication, teaching, and learning purposes
(abbreviated as IT)
Instructional technology: Faculty and student use, for
purposes of teaching and learning, of multimedia
equipment and materials; computer equipment and
software applications; networked resources; and other
electronic devices
10


Laptop computer: A portable microcomputer, weighing 3 to
6 pounds, designed to be used conveniently by an
individual in a variety of environments; synonymous with
notebook computer
Planning: Institutional activity that attempts to influence
the future through aligning ideas, actions, and resources
with preferred outcomes; synonymous with policy making
and decision making
Policy: A set of decisions and actions designed to achieve
specific objectives through a defined course of action
within a particular institution or environment
Policy characteristics: Qualities of the phases and actions in
the policy process
Policy implementation: The phase of the policy process by
which a program or initiative is put into practice and
adapted to the realities of a particular institutional setting
Policy making: The phases and actions involved in
developing a policy intended for implementation in a
specific environment; synonymous with decision making
and planning
Policy process: the entire array of events and actions
involved in the development, implementation, and
evaluation of a specific policy solution (synonymous with
policy analysis)
Policy process model: A particular model of the policy
process comprised of specific phases, processes, and
actions as described or synthesized by a policy author
11


Policy solution alternative: A solution (typically a program,
project, or initiative) being considered to address a policy
problem.
Program: The actions or steps that must be taken to achieve
or implement a policy; synonymous with initiative
Limitations of the Study
This study is limited to data collected from two laptop computing
universities. Ten informants participated in the study from one university,
and twelve informants participated from the second university.
The primary data collection strategy was focused interviews, with
document review and follow up questions as secondary data collection
strategies. Therefore, the data analyzed in the study are limited to data the
informants believed to be relevant and accurate. The accuracy and
inclusiveness of the data were addressed in follow-up questions with
informants.
The study was designed as an in-dept investigation of the processes
that two universities used in developing and implementing effective campus-
wide laptop computing policies. The findings and implications of the study
are not, however, intended to prescribe a step-by-step methodology for
campus-wide laptop computing. Rather the findings and conclusions are
intended to contribute to an understanding of the multiple issues and
12


processes involved in implementing a complex information technology
program such as campus-wide laptop computing.
13


CHAPTER 2
REVIEW OF THE LITERATURE
The literature review for this study evolved during the course of my
doctoral studies, in particular, my emphases on faculty development and
policy and planning for information technology in higher education. The
review expanded as I conducted research to support consulting projects for
higher education client institutions and developed papers for presentation at
meetings of professional organizations. The functions of the literature in this
study are to provide background and context for the study, to frame the
multiple issues addressed in the study, to support the conceptual framework
and of the study, and to contribute to interpretation of the findings. The
literature review is organized by major topics, including the policy sciences
and policy analysis; the current U.S. higher education environment;
information technology and higher education; and campus-wide laptop
computing.
The Policy Sciences and Policy Analysis
The philosophical foundations of the policy sciences can be traced to
the thesis of John Dewey, Henry James, and other early 20th century
pragmatists, that empirical data and rational advice and procedures should
have a direct effect on social problems (deLeon, 1988, p. 18). The early theory
and techniques of the policy sciences were formulated in the 1920s and 1930s
14


at the University of Chicago under Charles E. Merriam (Brewer & deLeon,
1983, pp. 8). Harold D. Lasswell, formally educated as a political scientist
under Merriam, was the first to explicate a new discipline, the policy sciences,
with a focus on analysis and resolution of social policy issues (Lewin &
Shakun, 1976, p. 3).
In 1951, Lasswell and Daniel Lerner published The Policy Sciences, a
work that summarized contributions made in the field up to that date. This
work conceptualized the policy sciences approach as distinct from the social
sciences as a whole, as well as distinct from applied social science and
political science. Lasswell conceptualized the policy sciences approach as
multidisciplinary, acknowledging the contributions of philosophy, history,
economics, political science, law, sociology, and psychology, as well as the
biological and physical sciences (Brewer & deLeon, 1983, p. 9). Lasswell
developed this concept of the policy sciences as a contextual approach to
social problems that seeks knowledge of the whole problem, but does not
expect to develop completely successful solutions (deLeon, 1988, p. 23).
For approximately twenty years, between 1950 and 1970, Lasswell and
Myres McDougal nurtured the discipline of policy sciences at Yale University
through their research, writing, and training programs. The policy sciences
were not, however, taken up and extended by other scholars during these
two decades of the "behavioral revolution," when the social sciences were
focused primarily on value-free, objective approaches (Brewer & deLeon,
1983, p. 9).
15


In 1971, Lasswell published A Pre-View of Policy the Sciences, a
handbook summary of what he perceived as the essential elements of the
approach. The journal Policy Sciences, initiated under the sponsorship of The
Rand Corporation and edited by Edward S. Quade, was a significant event in
the evolution of the discipline. Also at this time professional training and
degree-granting programs in the policy sciences were established at the Rand
Graduate Institute, Duke University, University of Michigan, and Harvard
University. Academic policy science programs received generous support
from foundations such as the Ford Foundation to encourage the education of
professionals capable of analysis of complex problems (Gill & Saunders 1992,
P-6).
According to Brewer & deLeon (1983), policy research, analysis, and
training were professional fads during the 1970s. While many policy analyses
were conducted during this decade, most neglected the basic elements and
humanistic aspects of the policy sciences approach as explicated by Lasswell
(p. 9). For example, in the early 1970s efforts were made to mold the study of
policy formation to reflect planning, management, and evaluation techniques
such as PPBS (planning, programming, and budgeting systems) and
management by objectives (Fincher 1987, p. 284). PPBS was adopted by the
federal government, but it was found to be inappropriate and was
discontinued. The highly specialized, technical features of approaches such as
PPBS were not sensitive to historical, cultural, social, or political aspects of
institutional and public policy (Dror, 1967; Gill & Saunders, 1992, p. 5).
Noting the lack of success with applications of PPBS at the University of
California, Balderston & Weathersby (1973) asserted that PPBS was
16


particularly inappropriate for planning and management in higher education
(p- 34).
Two disciplinary approaches, operations research (systems analysis)
and economics, made contributions to the solution of some public policy
problems, and as a result, much of the public policy literature came to use the
terms systems analysis and policy analysis interchangeably (deLeon, 1988, p.
24). Dror (1967) and others challenged this trend with the assertion that
systems analysis and economics approaches were not, in themselves,
sufficient to deal with many significant elements of public policy. Lasswell's
concept of the policy sciences as broadly multidisciplinary, rather than
narrowly defined by methods such as PPBS and systems analysis, was carried
forward by some policy theorists and practitioners. In 1971, Dror reflected
Lasswell's work in his design for the policy sciences, emphasizing the need
for a multidisciplinary approach that included both the normative and
behavioral sciences (Lewin & Shakun, 1976, p. 3). In 1988, deLeon asserted,
"the multidisciplinary plank of the policy sciences platform rests securely, if
not necessarily concretely" (p. 29).
Policy Process Terminology
The term policy analysis has been widely used in the literature.
According to Patton & Sawicki (1986), the term policy analysis was probably
first used by Charles Lindblom in 1959 to describe a comparative analysis that
utilizes both quantitative and non-quantitative methods and recognizes the
17


interaction of values and policy (p.17). Numerous writers have used the term
policy analysis to refer collectively to the phases, steps, and actions involved
in the policy process. The term policy analysis commonly, but not invariably,
refers to the development of policy as well as its implementation and
evaluation. The range of variation and parallels in various definitions of
policy analysis are evident in the following definitions, listed in chronological
order.
the systematic investigation of alternative policy options
and the assembly and integration of evidence for and
against each option... involves a problem-solving
approach, the collection and interpretation of information,
and some attempt to predict the consequences of
alternative courses of action. (Ukeles, 1977 p. 223)
a process of relating objectives to resources through social
interaction and intellectual cogitation. (Wildavsky, 1979 p.
404) .
formulates the policy problem as a whole, specifying goals
and other values, soliciting and evaluating alternative
solutions, and identifying the solution that best
corresponds to the formulated values. (Lindblom, 1980, p.
15)
the separation of policy issues into smaller, more
manageable problems for purposes of interpretation and
implementation ... then reassembling the parts into a
functional or organized unit for purposes of interpreting,
analyzing, and developing implementation strategies.
(Fincher, 1987 p. 283)
18


a decision making tool and means for guiding informed
decision making when goal conflicts exist within an
organization and its environment. (Gill & Saunders, 1992
p. 6-7)
client-oriented advice relevant to public policy decisions
and informed by social values. (Weimer & Vining, 1992,
p.l)
a systematic process for reducing issues or problems to
actionable solutions. (Zeglen, 1996 p. 3)
This study uses the term policy process, rather than the term policy
analysis, to describe the full range of activities involved in developing,
implementing, and evaluating an institutional policy. Other policy process
terms used in this study, as defined by various authors are:
Policy agenda: the list of issues or problems to which
officials are paying serious attention at any given time.
(Kingdon, 1984, pp. 3-4)
Agenda setting narrows this set of issues or problems to
those that are actually the focus of attention. (Kingdon,
1984, pp. 3-4)
Policy goals: the values that participants seek to promote.
(Weimer & Vining, 1992, p. 217)
Policy problem: A condition or issue for which appropriate
solutions are sought and implemented. (Brewer & deLeon,
1983; Rochefort & Cobb, 1990)
19


Policy environment: the constraints within which the
analysis is to be performed, including problem and values.
(Patton & Sawicki, 1986, p. 15)
Policy methods: systematic procedures for attacking specific
problems. (Patton & Sawicki, 1986, p. 38)
Values: individuals' or groups' views, convictions and
opinions, held deliberately and tenaciously or even
casually, are embodiments of the values they hold.. .which
lead to actions. (Leung, 1985, p. 23)
Policy Process Characteristics
The six characteristics of the policy process most widely found in the
literature are discussed here: contextual, iterative, linked to resources, multi-
method, problem-oriented, and values-oriented.
Contextual. According to Brewer & deLeon (1983), contextual refers
to understanding the relationship between the parts and the whole of a
problem and having a clear appreciation of past, present, and future events as
they interact and change (p. 15). DeLeon (1988) describes policy analysis as
contextual in nature. He asserts that if policy decisions are made without
recognition of the complex contextuality of a problem, policy solutions may
cause unintended, regrettable outcomes. Wildavsky (1979) refers to
contextuality in his assertion that organizational considerations are an
essential and integral part of policy analysis (p. 6). Wildavsky also notes that
20


programs resulting from policy implementation become part of the evolving
policy context (p. 24).
Iterative. According to DeLeon (1988, p. 36) "the policy process
operates as a series of iterative stages and feedback loops" (p. 36). VanVught
(1997) also describes policy analysis as an iterative process in which each step
forces reconsideration of the previous ones until policy decisions are made (p.
392). Wildavsky (1979) asserts that in the policy process one problem often
succeeds and replaces another, requiring a new iteration of the process. Gill &
Saunders (1992) discuss several other policy authors (Lindblom, 1959;
Quade, 1982; Stokey & Zeckhauser, 1978) who have described the iterative
nature of policy analysis. Bardach (2000) states that in the course of a policy
process, iteration is continuous (p. xv).
Linked to Resources. Wildavsky (1979) discusses the particular
importance of relating resources, both material and intellectual, to objectives
in the process of policy analysis (p. 10). He asserts that objectives depend on
resources, in that resources limit objectives. Policy analysis considers
resources and objectives, means and ends, together through the process of
comparing programs (p. 17). Similarly, Weimer & Vining (1992) note that
policy analysis is the art of the possible and, therefore, resource constraints,
including budgetary, infrastructure, and personnel, are of central importance
in policy design (p. 215).
21


Multi-discipline and Multi-method. Lasswell (1951) conceptualized
the policy science orientation as multidisciplinary, cutting across existing
disciplinary specializations (p. 3). Wildavsky (1979) discusses a number of
approaches used in policy analysis including qualitative political theory for
refining the picture of where to go; quantitative modeling for systematizing
guesswork on how to get there; microeconomics for aligning expectations
with limited resources, and macro-organization theory for understand the
need to correct errors (p. 3). Fincher (1987) notes that a policy analysis may
be historical, comparative, longitudinal or developmental, sociological,
legalistic, political, economic, technological, or a mixture of these and other
possibilities (p. 284). A policy process typically utilizes a range of methods
and approaches from various disciplines (Fincher, 1987, p. 284; Weimer &
Vining, 1992, p. 244; Wildavsky, 1979, p. 3; Yanow, 2000, p. 5).
Problem-oriented. Policy analysis is explicitly problem-oriented
(deLeon, 1988, p.29). The policy problem that is to be addressed is the central
focus in Brewer & deLeon's (1983) model of the policy process. Rochefort &
Cobb (1990) discuss the increasing importance of problem definition in the
study of policy making, and problem definition is specified as a distinct phase
in a number of policy process models (VanVught, 1997; Weimer & Vining,
1992; Zeglen, 1998).
Values-oriented. An emphasis on human values was an essential
element of the policy sciences approach as defined by Lasswell (1971) in A
22


Pre-View of the Policy Sciences. Nagel & Neef (1979), in their discussion of the
role of values in policy analysis, note that policy analysis involves seeking to
achieve or maximize given values or goals (p. 147). Leung (1985) gives
examples of values as an integral element in the definition of policy
objectives, the design of policy strategies, and the evaluation of policy
outcomes (p. 30). Weimer & Vining (1992) assert that, in the policy process,
specifying goals and objectives requires overt recognition of values (p. 213).
These six policy process characteristics constitute a dimension of the
policy process model synthesized for this study. These characteristics
contribute an additional, non-linear element to the model.
Policy Process Models
LasswelTs initial definition of the policy sciences emphasized process
and led to the formulation of models of the policy process. In 1956, Lasswell
defined an early version of the policy process in his discussion of the policy
phases of intelligence, promotion, prescription, invocation, application,
termination, and appraisal (deLeon, 1988, p. 29; Lasswell, 1956). May &
Wildavsky (1978) described a policy process that includes: agenda setting,
issue analysis, implementation, evaluation, and termination. Brewer and
deLeon (1983) based their policy process explication on a series of stages:
initiation, estimation, selection, implementation, evaluation, and termination.
No one model of the policy process is universally accepted, but the
literature does generally acknowledge that phases of the policy process are
23


appropriate organizing categories. Policy process phases as organizing
categories is evident in the historical evolution of the field through a focus on
one policy process phase after another, beginning with problem recognition,
moving on to program formulation, to evaluation research, and to
implementation research (deLeon, 1988, p. 34).
Lasswell (1956) defined a sequence of functional stages that he called
the decision process. For each stage of this decision process he outlined
pertinent questions, tasks, strategies and example hypotheses (Lasswell,
1977). Building on Lasswell's work, a number of scholars, educators, and
practitioners have explicated policy process models (Brewer & deLeon, 1983;
Kingdon, 1984; MacRae & Wilde, 1979; Quade, 1982; Stokey & Zeckhauser,
1975; Wildavsky, 1979). These models delineate steps or phases of the policy
process with associated guiding questions or actions. Some models describe
phases up to and including the development or selection of a policy, while
others include post-policy selection phases including policy implementation,
evaluation, and continuation or termination phases (Patton & Sawicki, 1986;
Starling, 1979). Most policy process models follow a general pattern of three
basic policy phases: policy formulation, policy adoption, and policy
implementation (Carlucci, 1990, p. 150). In examining various policy process
models, it is helpful to understand that each of the policy phases is closely
tied to the other phases and that the phases are likely to occur iteratively
rather than in strict linear succession (deLeon 1988, p. 36).
Policy process models that contain the same elements may not place
them in the same order. For example, Weimer & Vining (1992) assert that the
24


setting of goals and objectives should not be the first step in a policy process
(p. 4). Wildavsky (1979) asserts that goals should not be taken as initial
givens, but rather goals and means should be chosen simultaneously. Quade
(1982) , however, places objectives as the first element in a policy process. He
also notes, however, that a complete and thorough early inquiry into
objectives may not be possible (p. 45).
Four policy process models are briefly outlined here that, among the
four, include all the phases defined in the literature: Brewer & deLeon (1983),
Carlucci (1990), VanVught (1997), and Weimer & Vining (1992). Three of the
four models discussed address only the policy formation phases of a policy
process (VanVught, Weimer & Vining, Zeglen, 1996). The Brewer & deLeon
(1983) model includes, in addition to policy formulation and selection, a
policy implementation phase, and a policy evaluation phase. A final phase is
defined in which the policy may be changed, scaled back, or terminated.
DeLeon, (1988) asserts that the policy formation and selection phases are
closely related, and the policy implementation and evaluation phases are
likewise closely related (p. 30). Carlucci's model addresses primarily the
acquisition and implementation phases.
Brewer & deLeon (1983) present a model of the complete policy
process, adapted and simplified from Lasswell (1956). Brewer & deLeon
define six basic phases through which policies and programs pass over time:
initiation, estimation, selection, implementation, evaluation, and termination.
The termination phase of this model recognizes the possibility of new
25


problems emerging from policy implementation, creating the need for an
iteration of the policy process to address these new problems (p. 20).
Carlucci (1990) developed a policy implementation model that
expands the implementation phase of the policy process to include an
acquisition phase that follows the adoption phase. Carlucci's model is
particularly applicable to implementation of policy innovations related to
technology since the successful implementation of technical innovations
typically involves the acquisition of tools, techniques, equipment, and
methods (pp. 148-152).
VanVught (1997) enumerates five policy process phases: model the
problem, choose goal, specify constraints, craft policy alternatives, and
evaluate impacts. The evaluation phase in this model is placed prior to
policy implementation. VanVught's discussion does not emphasize
implementation or address a post-implementation evaluation or iteration
phase (pp. 395-399)
Weimer & Vining (1992), similar to VanVught, do not emphasize
policy selection, implementation, or evaluation. Policy processes are grouped
into four phases: Problem analysis; information gathering; solution analysis;
and communication. Of the five models outlined here, only the Weimer &
Vining approach identifies information gathering as a separate phase. The
other models imply that information is gathered and used in each phase of
the policy process., p. 205),
26


The Policy Process and University
Information Technology
This study applies a policy process model, synthesized from the
models of several policy authors. The synthesized policy process model is
intended to be particularly applicable for institutional information technology
policy issues.
The explication of the policy process has evolved primarily in the
arena of public policy issues and solutions. However, some theorists and
practitioners have discussed the policy process in relationship to policy-
making within organizations and institutions. A few writers have focused
specifically on the policy process within higher education institutions. As do
numerous other authors, these writers tend to use the terms policy analysis
and policy process synonymously.
Fincher, in his 1973 work The Purpose and Functions of Policy in Higher
Education, recommended a policy process model for universities in which
decisions, plans, and programs may be evaluated, modified, and
implemented. Fifteen years later, however, Fincher (1987) described the state
of policy analysis in higher education as promising but not highly
sophisticated (p. 285). More recently, several authors have discussed policy
analysis within higher education institutions.
According to Gill and Saunders (1992) an interdependent relationship
exists between university policy analysis and university planning. Policy
analysis is used as an approach to thoroughly research issues; these issues
27


then shape institutional goals and objectives (p. 29). Gill and Saunders
differentiate between institutional planning and policy analysis in that
planning usually involves relatively large-scale concerns, while policy
analysis is more focused on specific issues or questions (p. 33).
VanVught (1997) describes two specific functions of policy analysis in
a university setting. The first function is as a means to support university
presidents and administrators by providing advice on opportunities and
threats. A second function is the exploration of possible outcomes of
alternative sets of decisions and actions (p. 326). VanVught also discusses
relevant policy analysis elements including: study of both internal and
external arenas, diagnosis of problems, design of policy solutions, and
facilitation of policy implementation (p. 337).
Zeglen (1998) outlines a similar policy analysis model for use within
higher education institutions. The phases of this model include: problem
clarification; development of alternative solutions; evaluation and
comparison of alternative actions; definition of implementation options; and
presentation of policy recommendations (p. 3). Zeglen emphasizes the value
of using multiple methods to systematically compare alternative policy
solutions in order to provide decision-makers with recommendations for
effective action within a specific institutional environment (p. 5).
As discussed earlier in this review of the literature, information
technology issues are becoming increasingly critical to higher education
institutions and have become integral into most institutions' strategic and
28


academic goals. A thorough review of the literature, however, did not reveal
works addressing policy analysis or the policy process in relationship to
university information technology decision-making or implementation
Zeglen & Clark (1999), in a presentation to the Annual Meeting of the
Society for College and University Planning, discussed utilizing a policy
process approach to university information technology issues. Zeglen &
Clark point out that a typical approach to information technology initiatives
and projects in higher education institutions follows a project management
model, focusing primarily on implementation. A project management
approach often addresses the technical aspects of implementation outside the
context of a broader institutional policy process. These authors discuss the
appropriateness of a policy process approach as an alternative to traditional
project management for institutional information technology projects in
higher education institutions (p. 8). The advantages of a policy process
approach, according to these authors, include: incorporating the expertise of a
wide range of stakeholders and experts into defining a viable policy;
considering the environmental context of the policy; analyzing several
competing alternative solutions simultaneously; and considering resources
simultaneously with solutions.
Zeglen & Clark also find a close relationship between several
characteristics of a policy process and the characteristics of information
technology projects. Firstly, a policy process includes on-going consideration
of the resources needed to implement alternative policy solutions.
Technology projects typically involve commitment of significant capital,
29


operating, physical, and human resources (Blumenstyk, 1998; Foster &
Hollo well, 1999; Peebles, 2000). Secondly, a policy process is, by definition,
iterative. Continual evolution and change are characteristic of the
information technologies, necessitating iterative planning processes for any
institution utilizing these technologies on an on-going basis. Thirdly, a policy
process includes attention to human values. According to Hughes (2001),
technology is malleable in that it is shaped by people whose values may serve
to significantly delay, prevent, or enhance technology implementation (p. 18).
A close relationship may also be identified between implementation
processes for information technology projects and the implementation phase
of a policy process. Zeglen & Clark emphasize that information technology
project s require significant attention to implementation, and a project
management approach to such projects typically focuses primarily on
implementation. The implementation phase of the policy process has been
the most recent phase to receive the attention of policy process scholars.
Since 1975 a new subfield of policy analysis has focused on the role of
implementation. Implementation research has challenged the assumption that
this phase is merely a process in which professionals carry out (or do not
carry out) the intentions of policy makers (Palumba & Calista, 1990). Bardach
(1977) and Yin (1984) define implementation as a multistage process,
including a pre-implementation phase of technical testing and organizational
acceptance.
Rogers' (1995) model for the dissemination of innovations focuses on
the adoption of technological innovations by individuals and organizations.
30


Yin, Heald and Vogel (1977) include as technical innovations artifacts,
materials, computer systems, and analytical methods and practices (p. 3).
Early diffusion research generally stopped short of investigating
implementation (Rogers, 1995). Rogers' framework, grounded in behavioral
science research, has been widely used as a conceptual framework for
studying the implementation of technical innovations within organizations
(Hanson, 1998, p. 32).
Carlucci (1990) links Rogers' dissemination of innovation framework
with policy analysis and references Rogers (1983) in discussing
implementation as an element of the policy process (p. 151). Carlucci defines
a pre-implementation, or acquisition, phase that follows the adoption
decision in a policy process. He asserts that acquisition paves the way for
implementation in that "successful implementation never takes place without
successful acquisition" (p. 158). Determining funding and defining
parameters of the innovation to be adopted are part of the acquisition phase.
When acquisition is complete, the institution has made an irreversible
commitment to implement the technical innovation (p. 158).
Carlucci (1990) notes that in educational organizations implementation
is often guided by the discretion of street-level bureaucrats. In the case of
educational institutions, these street-level bureaucrats would be the faculty
and technical professionals. The work of Rogers and Carlucci, taken together,
provide a rationale for the use of a policy process model (with both an
acquisition and an implementation phase) for studying implementation of
technological innovations in educational organizations.
31


Use of a Policy Process Model for This Study
My previous study of the policy sciences literature led me to
investigate the appropriateness of using a political conceptual framework and
a policy process model for this study. The work of two particular scholars,
taken together, provided a rationale for using a political model for the study
of higher education institutional processes.
A notable political sciences theorist, David Easton (1965), describes
political interactions as "predominantly oriented toward the authoritative
allocation of values" (p. 50). He discusses educational organizations,
business firms, trade unions, political parties, and churches as social
subsystems with their own internal political systems, which he defines as
"parapolitical systems" (p. 52). According to Easton, authoritative allocations
of values are made within these parapolitical systems. Easton states that
because each of these social subsystems, "contains sets of activities that can be
designated as its political system, the conceptual structures developed to
examine societal political systems have relevance for understanding the
operations of parapolitical systems" (p. 56). He further contends,
Examination of the structures and processes related to the
authoritative allocation of values in organizations and
other groups can be quite helpful in shedding new light on
the structures and processes of the more inclusive societal
political system, (p. 51)
The work of a second scholar, J. Victor Baldridge, supports the use of a
political model for the study of higher education institutions. From 1970 to
32


1975 the Stanford University Center for Research and Development in
Teaching conducted a study of the American college and university titled The
Stanford Project on Academic Governance, directed by Baldridge. One of the
major conclusions of this study was that a political model of decision making
is useful for understanding academic governance. Baldridge first introduced
this concept in 1971 in his work, Power and Conflict in the University. The
model was modified and further explicated in Policy Making and Effective
Leadership (Baldridge, Curtis, Ecker, & Riley, 1978) in which the authors
argue that, while "the political model is not a substitute for the bureaucratic
and collegial models of academic decision making ... it is a strong contender
for interpreting academic governance" (p. 44). According to Baldridge et al.,
The political model assumes that complex organizations
may be studied as miniature political systems, with
interest group dynamics and conflicts similar to those in
city, state, and other political environments. The political
model has several stages, all of which center around the
university's policy-formation processes ... [for] policy
decisions that bind the organization to important courses
of action, (p. 34)
Baldridge et al. describe the actual decision making processes within
universities as "fluid and complex" and argue, "a political model offers an
appropriate analytical model for describing and mapping the political events
around organizational decisions" (p. 39). They outline the academic
organization's political system as
a complex social structure that generates multiple
pressures; there are many sources and forms of power and
33


pressure that impinge on decision makers; there is a
legislative stage that translates these pressures into policy,
and there is a policy execution phase that generates
feedback and potentially new conflicts, (p. 41)
The legislative and policy execution phases discussed in this work,
correspond to the decision and implementation processes of a typical policy
process model and to the processes defined in a policy process model
synthesized for this study.
A policy process model for use in this study was synthesized, based
principally on the Brewer and deLeon model as a framework (1983,p. 20) that
incorporates the entire policy process. The synthesized model was expanded
and explicated with concepts and elements from other authors (Bardach,
2000; Carlucci, 1990; Kingdon, 1984; McKay, 1985; Palumbo & Calista, 1990;
Patton & Sawicki,1986; Rochefort & Cobb, 1994; VanVught,1997; Weimer &
Vining, 1992; Wildavsky, 1979). The phases are understood to be generally
sequential, although iteration of actions from prior phases is anticipated in
any policy process. Likewise, later phases may precede earlier ones
(Wildavsky; Bardach). Further, not all steps are necessarily significant for
analyzing every problem (Bardach). The model synthesized for this study is
presented in Table 2.1.
This synthesized policy process model is used in this study as a means
of categorizing the events and actions of the universities in their adoption and
implementation of a campus-wide laptop computing policy. Use of the
model serves to assure that all relevant aspects of the universities' processes
34


Table 2.1: Synthesized Policy Process Model
Policv Process Phase Example Actions
I. Agenda Setting 1 --Acknowledge crisis or significant event 2- -Accumulate information, data, and resources in a specific knowledge area 3- Examine environmental factors / forces, trends, public opinion, perspectives of leaders 4- Discuss priorities
II. Problem Definition and Analysis 5- Explore, describe, and frame the problem 6- Assemble evidence 7- Quantify the problem 8- Model the problem 9- Anaiyze the problem
III. Identification of solution criteria (referencing values, goals, and constraints) 10- Frame the issues 11- Explore values of stakeholders and subpopulations 12- Formulate generalized goals 13- Address value conflicts 14- Specify analysis scope and methods 15- Establish technical criteria 16- Establish financial criteria 17- Establish political acceptability 18- Establish administrative viability
IV. Identification, design, or synthesis of policy solution alternatives (programs) 19- Review current policies 20- -Explore concepts, claims and possibilities 21 -Explore/design solutions 22 Identify impacts 23- Outline implementation scenarios
V. Comparison of alternative policy solutions (programs) 24- Pursue contacts and information sources 25- Identify and organize relevant data, facts, and theories 26- Debate, compromise, bargain, accommodate 27- Apply pre-established criteria 28- Evaluate technical feasibility 29- Explore value acceptability 30- Predict impacts of alternatives 31- Estimate how alternatives meet goals and constraints 32- Anticipate future constraints 33- Formulate and present recommendations
35


Table 2.1: Synthesized Policy Process Model (Continued)
VI. Authoritative adoption of preferred policy solution (program) 34- Select preferred alternative 35- Decide to adopt the solution 36- Communicate adoption decision to stakeholders
VII. Acquisition (Pre- implementation) 37- ldentify relevant organizational considerations 38- Formulate guidelines 39- Specify incentives and resources 40- Set up standards and schedules 41 -Assign responsibilities 42- Develop criteria for solution success 43- Contract for the technology 44- Install the technology
VIII. Implementation 45- Determine best practices 46- Provide user education for tools, techniques, equipment, and methods 47- Provide user opportunities 48- Describe potential pitfalls 49- Establish performance indicators
IX. Monitoring and Evaluation of Outcomes 50- Review pre-established criteria 51- Measure achievement of intended outcomes 52- Describe or measure unintended outcomes 53- Compare expected and actual performance levels 54- Conduct cost-oriented analysis 55- Identify cause/responsibility for performance discrepancies 56- Place findings within larger context of initial problem
X. Continuation, Adjustment, or Termination 57- Determine costs, benefits, and consequences if continuation planned 58- Determine costs, benefits, and consequences if reduction or termination is planned 59- Amelioration as necessary 60- Investigation of new problems created by continuation, adjustment, or termination
are examined, including contextual aspects that were described by informants
as significant to the policy process. In addition to ten phases of a policy
36


process, the model includes six policy process characteristics that provide
additional perspectives on the policy process. It was not anticipated that the
universities would or should employ every action outlined in the model, but
rather that the model would provide a comprehensive matrix of possible
actions with which to examine the data.
The Current U. S. Higher Education Environment
Higher education institutions are experiencing multiple pressures
including demands for accountability from policy makers and the public,
changing demographic and enrollment trends, and significant financial
challenges (Matier, Sidle, & Hurst, 1995, p. 76). In response to these
challenges some institutions are redefining their relationships to their
external environments, crafting more specific missions, and engaging in
strategic planning processes.
Focus on Accountability and Productivity
The enduring, broad goals of higher education have remained
relatively unchanged over the past decades. Falk and Carlson (1995)
summarize these broad goals as developing literacy, preparation for
citizenship, career preparation, and developing cultural or multicultural
perspectives (p. 5). Students and parents expect a higher education
experience that provides not only these essential aspects of a general
37


education, but also introduces inquiry-based learning (Boyer Commission on
Educating Undergraduates in the Research University, 1998, p. 12).
The demand for institutional accountability for these broad goals, as
well as for more specific goals, is increasingly evident (Boyer Commission on
Educating Undergraduates in the Research University, 1998; Graves,
Henshaw, Oberlin, & Parker, 1997; Witherspoon, 1996). For example, the
public is asking colleges and universities to meet specific goals, such as
providing affordable, quality undergraduate programs that students can
complete in four years (Pew Higher Education Roundtable, 1994). As another
example, policy makers are focusing on the need for higher education
institutions to assure access for diverse populations with a wide range of
educational needs (Matier et al., 1995, p. 75.)
Interest in accountability for measurable outcomes is apparent at
institutional and program levels as well as for individual faculty and
students. State policy makers in many states are focusing on establishing
institutional performance criteria, and some states have developed funding
models based on performance criteria (Mingle & Lenth, 1989; Taylor,
Meyerson, & Massy, 1993). Institutions themselves and some programs are
surveying alumni, employers, and community members in an effort to
evaluate their own effectiveness in achieving specific outcomes (Dolence,
Rowley, & Lujan, 1997). The teaching performance of individual faculty
members is routinely assessed at most colleges and universities by asking
students to evaluate the teaching effectiveness of faculty and of the courses
they teach. Students in some programs and disciplines are being asked to
38


demonstrate their cumulative learning and skills in capstone projects and
courses during their last semesters or through compiling portfolios of their
work.
Some higher education leaders and faculty have resisted such
accountability efforts, protesting that the intrinsic value of a quality education
is, at least in part, intangible and cannot be measured (Vinsonhaler,
Vinsonhaler, Bartholome, Stephens, & Wagner 1995). However, these
objections are becoming less common in the current competitive.higher
education environment that places increasing importance on career
preparation and keeping pace with global, economic, and technological
change (Massy & Zemsky, 1995).
Demographic and Enrollment Trends
Changing demographics are creating more demand for access to
higher education (Daniel, 1997). Increasing numbers of adult learners are
enrolling in higher education courses and programs and increasing numbers
of recent high school graduates are pursuing post-secondary education.
Adults are retraining for new careers and seeking degrees at all levels in
unprecedented numbers (Brown & Duguid, 1996). For the fall semester 2000,
The Chronicle of Higher Education Almanac (Evangelauf, 2001, p. 20) reported
that 37.3 percent of all higher education students were over the age of 24, and
10.7 percent were over the age of 39. Simultaneously, the number of high
school graduates has risen in many states and regions. Nationwide, a steep
39


increase in high school graduates is expected until year 2008 (WICHE, 1998).
Overall, the percentage of high school graduates enrolling in higher
education has increased consistently (Macunovich, 1997). This trend is
evidenced by the statistics for the decade between 1985 and 1995, when
higher education enrollments of high school graduates increased by nearly 18
percent despite a dip in the total number of high school graduates. Between
1973 and 1993, college enrollment among high school graduates increased by
30 percent.
These increases in adult as well as high school graduate participants
will contribute to substantial enrollment demands on higher education in the
coming decade. The National Center for Education Statistics has projected an
overall increase of up to 20 percent in higher education enrollments between
2000 and 2011. Enrollment patterns, however, are not consistent across all
higher education institutions but vary by institution type, between private
and public institutions, and among geographic regions (Evangelauf, 2001).
Some institutions in the Western U.S. report being nearly overwhelmed by
enrollment demands (Paulien & Associates, 2001). Other institutions,
particularly in rural areas, are struggling to attract students in sufficient
numbers to avoid terminating programs or closing campuses (Paulien &
Associates, 1999).
Increasing enrollments in higher education reflect the need of
individuals for meaningful employment in the current knowledge-based
economy as well as the national need for a productive workforce (Falk &
Carlson, 1995). Economic trends contributing to increased demand for higher
40


education include globalization, the pace of technological innovation, and
acceleration of knowledge accumulation (Quinn, 2001, p. 3).
Financial Challenges
From 1985 to 1995, U.S. higher education faced unprecedented
financial challenges related to rising costs and declining support (Lissner &
Taylor, 1996; McPherson & Schapiro, 1995). A study of the financial health of
the American higher education sector conducted in 1994 by the Commission
on National Investment in Higher Education (1994) reported a fiscal crisis in
American higher education in which costs and demands were rising far faster
than funding. This study described the financial condition as unsustainable
and recommended increased public funding for higher education institutions.
State funding for public colleges and universities, however, either
decreased, leveled off, or failed to meet increasing operating costs in most
states during this period (Phillips, Morell, & Chronister, 1996). Higher
education's share of state appropriations declined in most western states
between 1985 and 1995 and appropriations per full-time equivalent student
also declined (WICHE, 1998). Although the late 1990s brought somewhat
improved state funding in many states, a trend continues toward lower
percentages of public institutional revenues being provided from state
appropriations (Phillips et al., p. 9). For the year 1999,36 percent of states
reported reducing or slowing the pace of growth of higher education
appropriations (Evangelauf, 2001). For the fiscal year 2000-2001 state
41


spending on higher education grew by the smallest rate in five years, and in
thirteen states higher education was not appropriated sufficient new funds to
keep pace with inflation (Schmidt, 2002, p. A20).
Several solutions to this weakening financial condition have been
recommended and pursued by various colleges and universities. Many
public as well as private institutions have substantially increased their tuition
and fees. Average tuition at public four-year colleges rose 22 percent in the
five-year period between 1995 and 2000 (Evangelauf, 2001).
The Commission on National Investment in Higher Education (1994)
recommended that institutions establish new coalitions with corporations and
other public and private organizations in order to both avoid a fiscal crisis in
higher education and to meet the workforce needs of the business sector.
Many institutions have pursued collaborations with businesses and other
organizations and report benefits such as grant funding, technical equipment
and software donations, and collaboratively funded capital projects (de la
Garza, Landrum, & Samuels, 1997).
Funding has also been available to some institutions over the past
decade for information technology development. A study conducted for the
academic year 1998-1999 by the National Association of State Universities and
Land-Grant Colleges reported that sixty-six percent of the institutions
surveyed had received state funds for information technology initiatives.
Forty-five percent received federal funds, and forty-three percent received
private donations. Twenty-six percent had corporate partners that financed IT
42


initiatives such as smart classrooms and conferencing facilities (Olsen, 1999,
p. A23). Graves et al. (1997) warn, however, that unless institutional IT
infrastructure and technical support staff are sufficient, a grant-funded
project may fail or funds may be terminated.
Voluntary giving to both public and private higher education
institutions has risen substantially over the past decade (Heeger, 2000p. 8).
Between 1994 and 1999 voluntary giving to public and private institutions
7
overall rose by 82 percent (Evangelauf, 2001). Private and corporate entities
have made significant contributions to the acquisition and integration of
information technologies at many higher education institutions. Notable
examples during the past decade are National Science Foundation grants for
IT infrastructure, corporate donations of hardware and software, and
foundation grants for the development of technology-based curriculum
(Twigg, 2002). Funding from these sources does not come to an institution,
however, without significant expenditures of faculty and staff time in
preparing funding proposals, as well as monitoring, managing, and reporting
for grant-funded projects.
Changing Institutional Planning
and Decision Processes
University decision, or policy, processes have traditionally been
unique to the higher education sector, as compared to the corporate sector
and other public sector institutions (Baldridge et al, 1978; Dill, 1984; Graves
et al., 1997; Levinson, 1989; Meyer & Rowan, 1978). Because many university
43


decisions are directly related to a university's basic missions of teaching and
research, many important decisions are typically made by academic
departments, faculty senates, or individual faculty. Hardy et al. (1983)
describes these types of decisions as decision by professional judgment (p. 412).
Some areas of university decision making are typically reserved for
administrative leaders, while many other decisions emerge from collective
processes. These may be described respectively as decision by administrative
fiat and decision by collective choice (Dill, 1984, p. 76; Hardy et al., 1983,
p. 417).
This pattern of distributed decision making has been variously
described as decentralized (Perkins, 1973, p. 156), collegial (Chaffee, 1983, p. 15)
and shared governance (Gappa, 1993, p. 75). Other writers on academic
governance have coined various descriptive terms including: collegium
(Millett, 1962, p. 83) and organized anarchy (Cohen & March,
1974, p. 3). Weick (1976) describes universities as loosely coupled systems (p. 2)
in which actors in one part of the system are not necessarily tightly integrated
with those in another part. Balderstron and Weathersby (1973) concluded
that the informal collegial structures that evolve within higher education
institutions are often more significant than the formal governance structures
(p. 34).
Baldridge (1983) outlines three major models of higher education
governancebureaucratic, collegial, and politicaland suggests that all three
commonly co-exist within an institution. Similarly, Chaffee (1987) asserts that
44


many forms of decision-making function simultaneously within a single
organization (p. 30). These various traditional governance models and
decision processes attracted very little scrutiny from outside the academy for
many decades preceding the economic recession of the mid-1980s (House,
1994).
During the 1970s, higher education enrollments were stabilizing and
revenues declined. Colleges and universities began to reallocate resources
and became selective about growth areas. During the 1980s, institutions
began to define specific missions and goals in response to resource shortfalls,
changing characteristics of students, and rising expenditures for computing
and scientific equipment (Norris & Poulton, 1991, p. 45). Science and
technology expenditures increased further in the 1990s and enrollment trends
continued to change. Norris and Poulton accurately predicted that the 1990s
would be an era of retrenchment and reallocation, bringing new
opportunities for collaborative partnerships as well as a competitive
education marketplace (Quinn, 2001).
Thus economic, demographic, and technological changes have created
significant challenges for higher education institutions (Kennedy, 1995;
Lissner & Taylor, 1996; Zemsky & Massy, 1995), bringing the decision
processes of universities under new scrutiny by the public, legislators, and
governing bodies (Ruppert, 1997). Some critics of decentralized decision
processes in higher education have recommended alternative decision
approaches and tools adapted from the corporate sector (Keller, 1983;
Seymour, 1992).
45


In response to changes in the external environment, internal
challenges, and the scrutiny of policy makers and the public, many colleges
and universities have been defining their institutional missions more
specifically and developing institutional objectives based on these missions.
Many institutions are engaging in strategic planning (Dolence et al., 1997) in
order to define an optimal alignment of the institution within its
environment, provide direction for the institution as a whole, and define
constraints within which the institution must operate (p. 14).
The structure and parameters of strategic planning processes are often
outlined by institutional leaders and may involve high levels of participation
throughout the entire institution, or may involve primarily executives only.
Some institutions open the strategic planning process to the wider campus
community through consultation and communication, while others wait until
the process is complete to share the plan beyond the executive level (Dunn,
1998, p. 17). While a trend toward inclusive strategic planning is emerging,
processes vary with institutional type and culture (Dolence et al., 1997).
Institutional strategic plans may establish guidelines for other
regularly occurring planning that addresses administrative and operational
activities (Norris & Poultin, 1991, p. 8). In particular, many institutions are
becoming progressively more attentive to aligning their regular budget
processes with the institutional mission, goals, and strategic plan (Dolence et
al., 1997). Two examples of institutional endeavors that require close
alignment with institutional strategic planning are the development and
management of complex collaborations with external entities (Quinn, 2001)
46


and the integration of information technologies into academic and
administrative core processes (Leach & Smallen, 2000; Peebles, 2000).
Information Technology and
U. S. Higher Education
Society and the economy are in a phase of rapid technological change
in which the basic ways information is generated and transmitted are being
significantly altered. As with other technological changes of similar
magnitude, specific future applications and outcomes of information
technology innovations in are largely unpredictable (Heeger, 2000; Hughes,
2001, p. 16). Despite this unpredictability, higher education institutions are
closely mirroring society and the economy in their pervasive integration of
information technologies. As these technologies are becoming increasingly
more central to the core processes of higher education institutions, the ways
in which work is done in the academy is being fundamentally altered (Batson
& Bass, 1996, p. 47; Quinn, 2001). While most college and university leaders
are aware that information technologies are not a panacea for institutional
problems, they do tend to define information technology as a mission critical
factor (Graves et al., p. 449; Hughes, 2001).
IT Planning, Implementation,
and Budgeting
As information technologies are increasingly integrated into both
academic and administrative institutional processes, multiple significant IT
47


decisions are regularly being made by most higher education institutions
(Kaufman & Lick, 2000; Luker, 2000; Green, 1999). Universities face
significant challenges and pitfalls in designating responsibility for planning,
funding, and implementing current and projected IT infrastructures and
applications that will serve a broad range of institutional constituents and
functions (McClure, 1997)
Particular characteristics of the information technologies themselves
may confound decision making and planning. New technologies become
available rapidly and continuously, with major redefinition of technologies
occurring every three to five years (Saxena, 2000). Universities, however,
cannot wait for information technologies to stabilize; significant decisions
must be made as to which technologies will best support immediate as well
as long-term objectives. Universities are attempting to take advantage of
what is currently available while confronted with continual technological
change and the difficulty of anticipating future applications (Alvarez, 1996;
Hughes, 2001).
Planning. In a recent publication discussing the impacts of IT on
colleges and universities, Katz (1999) described the condition of IT planning
and implementation.
The complex interactions among campus citizens,
resources, technologies and practices befuddle analysis
and decision making and foster either inaction or reluctant
48


action based on recommendations that are poorly
understood, (p. 44)
Katz further emphasized that campus IT planning is frequently ineffective
because the governance and organizational structures necessary for making
decisions related to IT are not well developed in most colleges and
universities (p. 45). Gilbert (1996b) reported that IT planning is often
fragmented.
Most colleges and universities have one or more groups
assigned to some task associated with planning the
institution's future with respect to information technology.
Most often each group focuses on a piece of the overall
picture, and there is no single forum for bringing together
all those who are needed for an understanding of overall
trends and for supporting major advances, (p. 10)
IT issues and projects have wide-ranging impacts that cross the
traditional institutional boundaries between academic disciplines and
administrative functions. Selecting and implementing appropriate
information technologies requires collaboration among individuals and units
with academic, technological, and administrative expertise (Kling, 1996;
Oblinger & Rush, 1998; Wilder, 1999). New planning groups, either
permanent or ad hoc, may be needed to enable dialogue across disciplines
and institutional roles and agendas. Gilbert (1996b) asserts that no planning
group, no matter how inclusive, can be certain of making all the right
decisions, but detrimental decisions may be avoided by including key
stakeholders in discussions leading to institution-wide IT strategies (p. 16).
49


Because the technology resources of an institution are typically shared
across the campus among academic departments and administrative units,
collaborative planning is essential. Faculty and staff involvement in
institution-wide IT planning is critical to effective IT integration. Faculty
should be involved in setting priorities and in making the many decisions
which effect technology integration throughout the institution. Such a
participatory approach tends to minimize any resistance to technology and
contribute to the active participation of faculty and staff in technology
integration (Hughes, 2001, p. 18; Resmer, Mingle, & Oblinger, 1995, p. 19).
A comprehensive institutional technology plan addressing long-range
strategic plans as well as immediate incremental implementation is widely
recommended (Cartwright, 1996; Graves et al., 1997). A comprehensive
campus wide technology plan should address infrastructure elements such as
telecommunications, networking, and telephony as well as computing,
media, and library functions and processes for both existing and emerging
technologies. Specific timelines and milestones should be set, responsibilities
assigned and costs delineated. Establishing standards for minimal levels of
student and faculty access and training is also essential.
A number of educational organizations have published guidelines for
organizing and conducting institutional IT planning. The American
Association for Higher Education's Teaching, Learning, and Technology
Roundtable (www.aahe.org) provides a model for organizing campus-wide
technology planning. The National Center for Technology Planning
(www.nctp.com) is a source of technology planning guidelines as well as
50


sample technology plans from a variety of higher education institutions.
EDUCAUSE (www.educause.edu) is a national association whose mission is
to promote the intelligent use of information technology in higher education.
In support of this mission EDUCAUSE sponsors policy and advocacy
initiatives, conferences, seminars, institutes, professional development,
publications, information services, alliances, and awards programs.
Green and Gilbert (1995) assert that the successful integration of
information technologies may require organizational structure changes
within the institution. Common structural changes include the establishment
or reorganization of a central IT service unit, integration of the academic and
administrative computing functions into one unit, integration of library,
media, and computing functions, and creation of an institutional technology
officer position (Graves et al., 1997; Green, 1999, p.21).
Green (1999) reported that 58% of institutions surveyed had a chief
information technology officer. At over half these institutions, both academic
and administrative computing functions reported to this technology officer
(p. 21). Graves (1997) described the institutional technology officer, or CIO, as
primarily a technology strategist rather than a technologist. He views this
position as a management and advisory role responsible for identifying and
analyzing IT-enabled institutional opportunities (p. 448). Specific areas of
responsibility of a chief information officer include spending appropriately to
assure that IT can continue to support the institutional mission; maintaining
the value of the IT infrastructure; assuring a sufficient number of adequately
trained and compensated staff; and delivering effective services efficiently
51


(Leach & Smallen, 2000). An experienced higher education chief information
officer noted that a primary challenge for CIOs is contending with the short
life cycle of technology, given that the pace of decision-making in higher
j education is typically slow (Olsen, 2001b).
It is widely recommended that information technology planning
should be an integral part of institutional strategic planning (Cartwright,
1996; Massy, 1995; Saxena, 2000). In 1997 Graves et al. (1997) reported that
many institutions, however, had not linked information technology to their
institutional missions (p. 432). More recently, Green reported in The Campus
Computing Survey (1999) that a growing number of colleges and universities
report having strategic and financial plans for information technology.
Institutions reporting a strategic IT plan had increased from 48 percent in
1998 to 60 percent in 1999. Institutions reporting a financial IT plan rose from
37.3 percent in 1998 to 44.9 percent in 1999 (p. 7). Green cautions, however,
that the data may overstate the number of institutions with thorough
strategic IT plans that include financial considerations. Saxena (2000) shares
the perspective that a good institutional IT strategic plan should include cost
factors, as well as identify current technology resources, determine IT goals
that align with the institutional mission and direction, and define time
frames. With an effective strategic plan as a foundation, long-term and short-
term strategies can then be developed for funding, acquiring, and
implementing IT resources to achieve institutional goals (p. 22).
52


Implementation. In the recent past, responsibility for implementation
of IT initiatives and projects was typically assigned solely to a university's
central IT service unit. IT organizations most often employ project
management approaches borrowed from the corporate sector. These
approaches often do not place importance on communication with planning
teams and constituents. In instances where IT planning is relatively isolated
from institution-wide IT planning, leaders may give little direction, set few
parameters, and establish no structure or process for communication. In
instances of insufficient communication among those responsible for initial IT
decision making and those responsible for implementation, unnecessary
expenses and wide-spread frustration have often resulted (Blustain et al.,
1999; Saxena, 2000). Recent trends toward organizational restructuring such
as creation of an institutional technology officer position and efforts toward
inclusive campus-wide technology planning may increase communication
among planners and implementors and help to reduce inefficiencies in IT
implementation (Graves et al., 1997).
Costs and Budgeting. Institutional expenditures for information
technology have increased steadily over the past decade. For the academic
year 2000-2001 American colleges and universities spent 13 percent more on
information technology than the previous year, while overall institutional
budgets rose by an average of 3 percent. Including outside services such as
hardware warranties, total higher education IT spending was estimated to be
nearly $4.4 billion for the 2000-2001academic year (Olsen, 2001a, p. A53).
53


Institutional commitment for technology should come from the highest
levels of administration to ensure long-term funding support (Saxena, 2000,
p. 23) in order to refresh and replace technology as it becomes obsolete.
Training and infrastructure costs also require significant on-going funding.
Budgeting for technology as a yearly line item expense is far preferable to
funding with one-time allocations. Technology support services and
equipment replacement and upgrades should be integrated into the operating
budget (Massy, 1995). Cartwright (1996) noted, however, that many higher
education institutions were funding technology with one-time budget
allocations or special appropriations.
More recently Green, (1999) reported a trend toward longer range IT
budget planning and increasing budget allocations. He reported that in 53
percent of institutions surveyed academic computing budgets rose from the
previous year, and at 54 percent of institutions the administrative computing
budgets rose from the previous year (p. 17). Green also reported that
institutions were addressing IT budget issues with a variety of strategies. The
most commonly reported strategies included requiring all students to pay an
IT-computer fee, reorganizing central IT operations, making greater use of
student assistants, and outsourcing Internet access to commercial providers
(p. 16).
Analyzing the relative costs and benefits of various information
technology projects and applications is an important issue on most campuses
(Green, 1999). A survey of liberal arts colleges, conducted annually since
1991 (Leach & Smallen, 2000), focuses on determining the actual costs of
54


providing IT core services to a campus community. The study defined the
core services as: computer repair, helpdesk, network services, administrative
information systems, instructional support, student support, Web support,
installation-replacement, distance learning, training, and planning-
management. Average per student IT expenditures for providing these core
services has risen steadily from approximately $750 in 1991 to approximately
$1,275 in 1998.
During the 1990s, information technology was widely touted as a
means of reducing costs through increasing instructional and institutional
productivity (St. John, 1994; Zemsky & Massy, 1995). Other writers have
more recently asserted that IT has added to costs, increasing the financial
pressures on institutions (Graves et al., 1997). The short-term increases in IT
expenditures are well documented. Longer-term productivity benefits have
not yet been clearly demonstrated (Carr, 2001, May 11). Measuring increases
in productivity related to information technologies has proven difficult for
public and private sector organizations. Traditional cost-benefit analyses for
information technology investments are problematic due to the intangible
nature of benefits and the inexperience of many institutions in determining
the costs of academic activities (Daniel, 1997, p. 13; Willcocks, 1994, p. 4).
IT as a Strategic Investment
Graves et al. (1997) assert that IT resources are not merely an
institutional expense, but are also an investment in the competitive standing
55


of the institution and its productivity (p. 445). Strategic aspects of IT
investment that are commonly discussed are the competitive advantage of IT
resources in attracting and retaining students and faculty, and extending
access to include new markets, thereby increasing revenues.
Providing equitable access to sufficient IT resources for all students is a
significant concern for many institutions and programs. Recent high school
graduates are accustomed to having access to sophisticated information
technology resources in their schools and at home (National Center for
Education Statistics, 2000). These students come to a campus with high
expectations for access to computing resources in dormitories, laboratories,
libraries, and study areas. Not only recent high school graduates, but also
returning adult students who are employed in the corporate sector are
expecting much higher levels of technology than many higher education
institutions have been accustomed to providing. Student expectations are
pressuring colleges and universities to provide technologies that were
previously provided only by larger universities with large IT budgets
(Saxena, 2000).
Institutions with exemplary IT resources are taking full advantage of
this asset in their recruiting strategies. Each of the institutions that are listed
on the annual 100 Most Wired Campuses list capitalizes on this coveted
designation in both their Internet and print public relations materials
(Bernstein, Caplan, & Glover, 2000, para. 2).
56


Institutions are experimenting with innovative approaches to assuring
that their students have sufficient access to IT resources. Some institutions are
requiring students to bring their own computers to campus so that
institutional resources can be redirected toward providing core IT services
and infrastructure (Zeglen & Clark, 1999, p. 14). Another emerging option for
institutions is replacing desktop PC's with information appliances supported
by powerful servers that provide access to software applications and
computing power through a centralized computing. This alternative to
equipping computing laboratories with more expensive personal computers
is intended to save substantial costs for service and maintenance of PCs as
well as for the equipment and software itself. Some institutions are installing
wireless networks within buildings and in areas between buildings so that
laptop computer users need never be disconnected from the campus network
and Internet (Young, 2000). Some institutions or programs within institutions
are providing or requiring laptop computers or personal digital assistants for
all students and faculty (Carr, 2001, May 18).
Institutions are also utilizing information technologies to make
administrative processes more convenient for students through online
registration, financial services, and student records systems (Graves et al.,
1997). In addition many colleges and universities are utilizing Internet-based
courses to provide campus-based students with more options for taking
courses at times when they are not offered in an on-campus, face-to-face
section. At smaller institutions that have limited ability to offer a full range of
academic courses and programs, students have access to specialized courses
and are able to complete programs and degrees more quickly. An additional
57


strategic objective of integration of information technologies into
administrative processes is increased efficiency and productivity (Lissner &
Taylor, 1996). Nationwide, colleges and universities are installing enterprise-
wide administrative software applications for information storage and
retrieval functions including personnel, finance, and academic records and
processes (Green & Gilbert, 1995).
Attracting and retaining faculty is another strategic aspect of IT
investments. Faculty whose research interests require powerful computing
systems are being recruited by institutions with the most advanced
computing environments (Barone & Hagner, 2001, p. 5). Similarly, faculty
whose disciplines rely on sophisticated computing applications such as
graphics intensive multimedia are likely to consider these as essential IT
resources to support their teaching and professional advancement. Faculty
increasingly conduct a significant amount of academic scholarly and
professional work collaboratively, using a full range of telecommunications
media including synchronous and asynchronous Internet communication and
analysis (Batson & Bass, 1996, p. 45). These activities require not only
powerful computing hardware and appropriate software, but also high-speed
data transfer capacity and a robust campus network infrastructure. An
institution that provides this level of IT resources has a competitive
advantage in attracting and retaining both research and teaching faculty.
The emergence of a new "education industry" (Heeger, 2000, p. 8) is
creating a competitive environment in which corporations and other
organizations are infringing on what was previously the sole domain of
58


traditional colleges and universities. These competing providers are also
meeting needs such as corporate training that were traditionally not served
by colleges and universities (Miller, 2000). An increasing number of
institutions are viewing distance education programs as a strategic
investment through which they are able to serve previously underserved
populations and increase institutional revenues (Graves et al., 1997). To this
end, many colleges and universities have, over the past decade, invested
significant funds to develop and sustain interactive video distance learning
initiatives based on a remote-group teaching model. Graves, et al. note that
the space, equipment, personnel, and other resources needed for courses
delivered via interactive video networks are scarce and thus not scalable to
the point of profitability (p. 436). Daniel (1997) suggests that while this
delivery model may serve new markets, it has high opportunity costs and
thus may not increase overall revenues (p. 15). Learning productivity,
defined as teaching more students with the same number of faculty without
compromising quality, is difficult to assess due to the complexity of
associating specific costs with specific units of output for both traditional and
technology-based courses (Haberaecker, 1992, p. 130),
Nevertheless, various strategies to increase revenues by teaching more
students per faculty member with lower capital and operating costs are being
enthusiastically pursued by many colleges and universities. Internet-based
courses and programs are being developed that, unlike interactive video
network courses, do not require large initial investments for equipment, high
fees for connectivity, nor substantial technical support. Another strategy is
teaching larger core classes using collaboratively developed high quality
59


multimedia course materials. These courses may be accessed by both
campus-based and distance students. Efforts in this strategy are being
funded by multiple-year grants to numerous institutions by the Pew
Charitable Trust (Twigg, 2002). Whether or not distance delivery is less costly
to provide than on-campus delivery has not yet been clearly established in
the literature.
Graves et al. (1997) assert that in order for IT to be truly cost effective
in higher education, the prevailing contact-hour-lecture model of instruction
will have to be fundamentally restructured (p. 434). Given the on-going
financial challenges U.S. colleges and universities are facing, some higher
education scholars have concluded that the current model of higher
education is no longer economically viable. They have proposed establishing
a new instructional model, similar to the British Open University, in order to
meet the learning needs of a nation that is experiencing dramatic
technological and economic change (Daniel, 1997).
Expanding Access
Through Distance Education
Higher education has been delivered by methods other than face-to-
face interaction for several decades by means of correspondence, broadcast
television, audio and video recording and, more recently, interactive video
networks (Graves et al., 1997, p. 435; Witherspoon, 1996, p. 5). Evidence
suggests that distance educationthe delivery of instruction over a distance to
individuals located in one or more venues(Phipps, & Merisotis, 1998) is
60


playing an increasing role as part of U.S. higher education (National Center
for Education Statistics, 1999, p. 1). A nationally representative survey of
postsecondary distance education, based on data for the 1997-1998 academic
year, found that approximately half of all two-year and four-year higher
education institutions offered or planned to offer distance education courses
in the next three years (National Center for Education Statistics, 1999, p. iii).
A common institutional goal related to distance education is the
improvement of learning productivity by teaching more students
simultaneously without sacrificing instructional quality (Witherspoon, 1996).
Another closely related goal is the improvement of individual students'
productivity by decreasing the time it takes to complete courses and degrees,
without sacrificing content (Gilbert, 1996a, p. 9). In addition, many
institutions want to provide learning opportunities through distance learning
for students who are not able to attend classes on-campus. In an effort to
achieve these goals, many institutions are currently offering or investigating
the possibilities for Internet-based instruction as an alternative or addition to
other distance delivery strategies (National Center for Education Statistics,
1999). Some institutions are undertaking these ventures individually, some as
collaborative arrangements of various types with other institutions or
corporations (Camevale, 2000; Witherspoon, 1996).
An Internet-based delivery model is thought to have several significant
advantages over the interactive video network model. Probably most
important, the Internet-based model is likely to reduce costs (Daniel, 1997, p.
15) since data networks scale much more economically than do interactive
61


video networks (Graves et al., 1997, p. 436) and do not require the use of
physical classrooms. Another significant advantage is that Internet-based
courses provide collaborative communication and multimedia capabilities,
not available though interactive video networks, which may enhance
educational effectiveness (Roccetti & Salomoni, 2001, p. 27). In addition,
Internet-based education is available for the individual learner to access at
convenient times at home or in the work environment.
Internet-based delivery systems are increasingly the choice of working
adult students, career changing students, and professional students pursuing
continuing education credits and degrees (Miller, 2000). Through Internet-
based courses, students are gaining access to programs at all post-secondary
levels, from introductory general education to specialized graduate degrees
(Heeger, 2000, p.9). Several factors are playing a significant role in the
increasing participation of students in Internet-based courses and programs.
Internet access from home and work is becoming available to more people;
networking and multimedia technologies continue to evolve, allowing faster
access to high quality, interactive educational programming and resources;
and, the price of computer hardware and software is declining.
Simultaneously, federal restrictions are being reduced for traditional higher
education institutions related to their Internet-based education programs
(EDUCAUSE, 2001; National Center for Education Statistics, 1999, p. 7).
Not all faculty and students want to participate in or support their
institutions' Internet-based education programs, however. Caplan (2000)
concluded that, given options, students prefer the human interaction of
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classroom instruction over distance learning technologies (p. 146). A number
of online learning programs have not attracted the large numbers of
enrollments anticipated. (Blumenstyk, 2001, p. A-29; Mangan, 2001, p. A31).
Research on the effects and outcomes of online learning for particular student
populations and disciplines is still limited, and faculty on many campuses are
concerned about the appropriateness and effectiveness of online delivery of
courses and entire programs (Rosenberg, 2001, p. 44). Twigg (2000) discusses
another faculty concern with online programs, the question of who owns
online courses and course materials. Noble (1998) contends that online
learning programs are a manifestation of a trend toward the
"commoditization of instruction," in which universities are being
transformed into markets for instructional products, such as copyrighted CD-
ROMs, courseware, and Web-sites (Sect. 1, Para. 3).
Using Information Technology
to Improve Pedagogy
Information technologies are widely assumed to be a means to
improve teaching and learning and to meet changing educational objectives
(Deden, 1998). Quinn (2001) asserts that as the pace of knowledge production
increases and information becomes obsolete more quickly, the primary task of
higher education is shifting from the dissemination of knowledge to
development of students' skills in research, analysis, and synthesis. To
achieve this task, new approaches to teaching and learning are needed such
as direct discovery and interdisciplinary approaches to real world problems
(p. 30). Batson and Bass (1996) state, "Substantial affinity exists between
63


newer pedagogical emphases and the capabilities of information
technologies"
(p- 43).
Uses of information technology to enhance teaching began for many
institutions in the 1980s with the innovations of a minority of faculty early
adopters. Subsequently, instructional technology in the classroom has
evolved to program-wide and, in some cases, institution-wide applications
(Roth & Sanders, 1996). The Campus Computing Project, an annual study of IT
in higher education conducted since 1989, shows that use of IT in college
instruction has consistently risen over the past decade (Green, 1999). Sloan
(2000) asserts that, while technology changes constantly, the primary use of
technology in education, to enhance learning, has not changed since
pioneering efforts with instructional technology (para. 2).
The needs of higher education students are changing with changing
student demographics. Higher education students are increasingly more
diverse in age, cultural characteristics, and specialized learning needs (Falk &
Carlson, 1995, p. 28). In addition, students exhibit a range of learning styles,
developmental levels, and life experiences that impact learning outcomes.
Integration of technology into teaching is a means to address the needs of a
variety of higher education students (Kling, 1996; McPherson & Shapiro,
1995). More project-based, active learning strategies and other new
instructional models allow learning to be more easily customized for
individuals and particular groups through technology applications (Batson &
Bass, 1996; Massy, 1995). Green (1999) predicts the future predominance of a
64


hybrid learning model in which technology supplements the content and
discourse that have been the core of traditional of higher education courses
(p. 4).
The integration of information technologies is occurring in synchrony
with new teaching methodologies. As faculty incorporate information
technologies into their teaching, learning may become more collaborative,
contextual, and interactive (Batson & Bass, 1996; Massy, 1995, Resmer et al.,
1995, p. 5). The relationship between teachers and students may change if
emphasis shifts from teachers and what they are teaching to students and
what they are learning (Spender, 1995; Watters, Marshall,& Alexander, 1998,
p. 50). The use of instructional technology may not, however, significantly
change teaching and learning unless fundamental questions are asked about
the use of technology (Graves et al., 1997, p. 439). According to Falk and
Carlson (1995), an effective multimedia instructional approach considers the
educational problem, instructional goals, the context and setting, and
characteristics of learners (p. 35).
Information technology resources are widely viewed as indicators of
program and institution quality. The quality of higher education programs
and institutions is increasingly being measured not just by library volumes
and faculty credentials, but also in terms of Internet access, software
applications and network connections. A 1998 study conducted by the Boyer
Commission on Educating Undergraduates in the Research University,
Reinventing Undergraduate Education, recommends the creative use of
information technology as one of ten critical ways to improve undergraduate
65


education (p. 25). The report advises institutions to take a careful and
systematic approach to using technology in ways that enrich teaching rather
than substitute for it. The assumption that technology resources are essential
tools for educational programs is also reflected in the accreditation standards
of regional associations and professional organizations. Examples are the
accreditation criteria of the North Central Commission on Accreditation and
School Improvement (http: / /www.ncacihe.org) and the American Bar
Association (2001b) standards for accreditation of law schools that now
include accreditation criteria related to information technology resources.
Faculty Professional Development and
Recognition for Integration of Technology
According to an annual study of information technology in higher
education (Green, 1999), the single most important issue on U. S. campuses is
assisting faculty efforts to integrate technology into instruction, (p. 3). The
difficulties and challenges of effectively incorporating information
technologies into teaching and learning should not be underestimated
(McCollum, 1998b). The literature increasingly presents accounts of faculty
difficulties resulting from lack of training and technical support, inadequate
or unavailable equipment, and insufficient network infrastructure (Bumiske
& Monke, 2001). An annual study of higher education faculty, Overview of the
1998-99 Faculty Norms, (HERI, 2000) reported that 67 percent of college and
university faculty found keeping pace with information technology to be
stressful for them during the past two years, ranking above other sources of
66


stress such as research and publishing; teaching load; and the review and
tenure process (para. 6).
In an effort to meet the challenge of faculty development for the
integration of technology in teaching, many colleges and universities are
establishing technology development centers staffed by technical and
instructional technology specialists to work with faculty members in
incorporating current and emerging technologies into teaching and learning
(Hughes, 2001). Most faculty development programs for instructional
technology offer hands-on opportunities to learn to use various types of
equipment and software applications and to create their own multi-media
learning materials. The best programs do not, however, merely train faculty
to use computers and multimedia in specific ways, but also provide
opportunities for faculty to understand how various uses of learning
technologies support teaching objectives. (Gilbert, 1996b; Graves et al., 1997,
p. 438; Loader, 1993; Spender, 1995). A comprehensive faculty development
program requires a major commitment of time, energy, and funds as well as
adequate technical support. An effective faculty development program is,
therefore, likely to be supported at the institution level, rather than the
department level (Gilbert, 1996b; Spender, 1995).
The problem that faculty face in balancing their teaching, research, and
publication efforts is exacerbated by the demands of utilizing information
technology. Overall the culture of higher education traditionally values
faculty research activity above teaching activity (Bensimon, 1996; Boyer
Commission on Educating Undergraduates in the Research University, 1990).
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Thus some faculty are reluctant to become involved in the time-intensive
activity of developing new ways of teaching with technology when such
involvement limits their time for research and publication, upon which
promotion typically depends (Gilbert, 1996b, p. 11; Green, 1999, p. 8).
Faculty who already have tenure may be more likely to devote time to using
technology in teaching than are younger faculty who are struggling to meet
research and publication goals (Brown, 1998; Kiernan, 2000, p. A45).
Both younger faculty and faculty with tenure who are utilizing
electronic media in teaching and research activities are expressing concern
about intellectual property rights for their scholarly work, including course
materials, placed on the Internet for use by students and other scholars (The
Node Learning Technologies Network, 1999). Some institutions, such as MIT,
are taking the lead by working with faculty to develop intellectual property
policies that protect faculty while promoting electronic access to scholarly
resources (Massachusetts Institute of Technology, 2001). Green (1999) notes,
however, that only 28 percent of institutions surveyed report having
developed intellectual property policies for scholarly materials in digital
formats, with the majority of institutions not yet addressing this issue, which
is of significant importance to many faculty (p. 8).
An annual study of information technology in higher education
(Green, 1999) revealed that few institutions formally recognize and reward
faculty for their investments in IT and instruction as part of the review and
promotion process (p. 7). While some institutions are attempting to revise
their faculty recognition guidelines to acknowledge innovative involvement
68


with information technologies in teaching, the wider academic culture has not
moved toward such recognition. This reality may significantly limit
recognition for faculty within their own academic disciplines (Graves et alv
1997, p. 441).
Campus-Wide Laptop Computing
In the early 1980s a few small colleges and universities including
Stevens, Drew, Clarkson, Drexel, Bently, and New Jersey Institute of
Technology, began to require students to own computers (Biros, 1998, p. 64;
Burg & Thomas, 1998, p. 22). The predominant higher education model,
however, for providing students with access to information technology
resources has been equipping and staffing general use computer laboratories
and, in many instances, additional departmental student computer
laboratories. Beginning in the mid-1990s, a small but increasing number of
colleges and universities have adopted an alternative means of assuring
access to computing and electronic network resources, requiring or strongly
advising students to buy or lease their own desktop or laptop computer.
A recent survey found that, for the 2000-2001 academic year,
approximately five percent of colleges and universities required their
students to have their own laptop or desktop computers, an increase of one
percent over the previous year (Olsen, 2001a, p. A53). Of one hundred
institutions on the Most Wired Campuses list for the year 2000,11 percent
required their students to own computers (Bernstein et al., 2000).
69


Many small liberal arts institutions were among the first to require
laptop computers (Burg & Thompson, 1998, p. 22). More recently, some large
public universities began requiring freshmen to have their own laptop or
desktop computers. The University of North CarolinaChapel Hill, and the
University of CaliforniaDavis have implemented laptop requirements
(Olsen, 2001c). In addition to institutions that require every student in every
program to have a computer, many more institutions require a computer for
students in some programs but not others. Business education programs and
professional preparation programs such as law schools are increasingly likely
to require computers (The Node Learning Technologies Network, 1999). Of
184 law schools accredited by the American Bar Association, 10.3 percent
require students to have a computer (American Bar Association, 2000a). An
online list of U. S. and Canadian colleges and universities that have campus-
wide or program-specific laptop computing initiatives is maintained and
frequently updated by Dr. Ray Brown of the Associated Colleges of Central
Kansas (http://www.acck.edu/~arayb/NoteBookList.html).
Higher education institutions are developing campus-wide laptop
computing programs as a means to provide their students and faculty with
access to information and learning technology resources (Grier & Bryant,
1998). The primary advantage of requiring laptop rather than desktop
computers is portability, which facilitates computer and network-based small
group collaboration and in-class instructional activity.
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Institutions give a variety of reasons for adopting campus-wide laptop
computing. Common objectives include: assuring student preparation for
technology-intensive work environments; gaining institutional strategic
advantage in student and faculty recruitment; meeting institutional fiscal
constraints; promoting pedagogy and curriculum change; and assuring
equitable access to global communications and academic resources (Conger,
1999; The Node Learning Technologies Network, 1999; Thomas et al., 1998).
Integrating Laptop Technology
Into Teaching and Learning
A widely accepted principle of technology and instruction is that
technology is an enabler to pedagogical improvement, but does not ensure
such improvement (Candiotti & Clarke, 1998, p. 37; Thomas, et al., 1998, p.
36). A number of institutions have asserted that pedagogy and curriculum
reform is an objective or preferred outcome of their laptop computing
initiatives. Several institutions that recognized a need for pedagogical reform
perceived the adoption of laptop computing as an opportunity for faculty and
programs to examine how and what they teach (Holleque & Cartwright,
1997). A guideline to pedagogical reform followed by a number of laptop
institutions including Mayville State University (ND) and Floyd College (GA)
is the Seven Principles of Good Practice in Undergraduate Education (Chickering
& Gamson, 1987; Graham, Cagiltay, Lim, Craner & Duffy, 2001). The
University of Minnesota, Crookston (UMC) reported that curriculum revision
was an essential part of its transition to a laptop computing environment.
Simultaneous with implementation of the laptop requirement, UMC courses
71


were redesigned with more emphasis on self-directed and interactive
learning. An institutional goal was also set to integrate measurable learner
outcomes into all courses within five years of implementing the laptop
program (Sargeant, 1997).
Several institutions that have implemented campus-wide laptop
computing reported making pedagogical and curricular change a priority
prior to implementation, but others had not made curricular change a high
priority until after implementation. An example is Clayton College and State
University that adopted universal computing in 1997. Over the following
two years a multi-faceted faculty development program was conducted, with
the goal of transforming a faculty-centered, lecture based academic culture
into a learner-centered environment in which the use of computing was
central to teaching and learning (Zeglen & Clark, 1999, p. 18).
For individual faculty, integrating laptop technology into teaching
practices is often an evolutionary process beginning with email and course
web pages, moving into use of real-world data or archival resources, and
evolving to analytical and problem solving projects (Oblinger & Rush, 1998,
p. 101). Most laptop computing institutions are attempting to encourage
integration of the technology by providing faculty with support, training, and
incentives while giving them the option to participate or not to participate in
whatever ways they choose (Brown, 1998).
72


Student Issues and Impacts
Implementation of universal computing has multiple impacts on
students' academic and campus-life experiences and expectations including
costs of participation and effect on career marketability. The increases in
student applications, enrollments, and retention subsequent to adopting
laptop computing reported by a number of laptop institutions may indicate
an overall positive student response to laptop initiatives. Such institutions
include University of MinnesotaCrookston, Valley City State University
i
(ND), and Wake Forest University (NC), (The Node Learning Technologies
Network, 1999).
Students at laptop institutions report that the most important issue for
them is the increased financial burden (The Node Learning Technologies
Network, 1999). Most institutions are sharing the costs of laptop computing
with their students through increased tuition or technology fees (Holleque &
Cartwright, 1997). At the University of MinnesotaCrookston, one of the
first institutions to require laptop computers, tuition and fees increased by 25
percent for the first year of implementation (Young, 1997). At Valley City
State University and Mayville State University students pay an annual fee of
$850. Wake Forest University, a private liberal arts institution, finances its
program by charging students an additional $3,000 tuition yearly. Sonoma
State University offers students a variety of financial arrangements for
acquiring computer access including: purchase outright; obtaining a loan to
purchase; using financial aid; or borrowing a computer from a pool of loaner
equipment (Arminana, 1998). Federal financial aid may be a source of
73


funding for a laptop computing program. At institutions where every
student is required to lease or purchase a computer, students become eligible
to include the cost of the computer as an educational expense for purposes of
financial aid (The Node Learning Technologies Network, 1999).
Many institutions have surveyed the perspectives of their students on
increased technology fees before implementing universal computing, and
most report that students are willing to pay the addition fee or tuition
because they believe that a universal computing environment will
significantly enhance their educational experience (Deden, 1998, p. 62). Not
all students readily accept the idea of increased tuition or fees for campus-
wide laptop computing. An example of objection to increased student costs is
Wake Forest University where some students initially protested the tuition
increase of $3,000 year (Young, 1997).
Both the U. S. Air Force Academy and the Georgia Institute of
Technology list preparation of students for computer-intensive work
environments as a primary rationale for implementing universal computing.
Students at these and other laptop universities support this objective as
revealed by their responses on surveys conducted by their institutions. The
major advantage they perceive in laptop programs is increased career
preparation and marketability (The Node Learning Technologies Network,
1999). At the University of MinnesotaCrookston, 90 percent of student
survey respondents said the laptop program had helped them develop the
technology skills they will need in their careers (Young, 1997).
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The capacity for computing any time, anywhere is also seen as a major
academic boon by students (Oblinger & Rush, 1998, p. 91). When the campus
network is robust and network connections pervasive across the campus,
student options for accessing networked resources and communications are
most dramatic (Brown, 1999). Access to the campus network from off-
campus locations is considered essential by most students and is provided or
accommodated by means of modem banks or Internet service providers by
many laptop computing institutions.
Students at laptop institutions typically expect the computers they
have been required to obtain will be extensively used throughout their
academic programs. In addition to increased academic applications, students
expect to be able to register, access grades and other records, receive
academic advising, find campus jobs, and obtain course materials
electronically (Thomas et al., 1998). Many laptop institutions are providing
extensive information over the campus network such as class and faculty
directories; class rosters and schedules; event notices; and institutional
policies (Pinheiro, 1995).
As the primary constituents of a universal computing initiative,
students have been included in planning for laptop initiatives on some
campuses. Given the diversity of student populations and perspectives on
most campuses, student focus groups and surveys have been utilized at some
institutions as a means of incorporating student perspectives and needs in
planning and implementation processes. The University of Colorado at
Boulder surveyed student opinion when considering adoption of a laptop
75


requirement. Based primarily on student responses, policy makers decided
against requiring every student to have a laptop computer. Because
approximately 75 to 80 percent of students already owned computers, a
decision was made to focus on assisting students who do not own a computer
in obtaining one, rather than requiring all students to have a laptop (Zeglen &
Clark, 1999, p. 24).
Faculty Issues and Impacts
Faculty participation in a laptop initiative is widely acknowledged as a
critical factor in the success of the program. Primary faculty concerns
typically include time requirements, training and support, and incentives and
rewards (The Node Learning Technologies Network, 1999).
The interest and involvement of faculty is essential to the ultimate
success of the initiative (Oblinger & Rush, 1998, p. 102). Not all institutions
involve faculty in planning for the adoption and implementation of a laptop
computer requirement. One institution reported that a decision to implement
universal computing was made by executive mandate of a new president
with little prior planning and no input from faculty. In addition, no faculty
development efforts preceded implementation. Faculty resistance to laptop
computing was reported to be high at this institution (Zeglen & Clark, 1999,
p. 20). In contrast, institutions where faculty were included in planning for
the laptop computing, report widespread faculty support for the initiative
(Sargeant, 1997; Candiotti & Clarke, 1998).
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Many laptop computing institutions have provided laptop computers
to faculty one or two semesters prior to implementing the student
requirement. During this time period faculty training and development
opportunities were provided to acquaint faculty with hardware and software
usage and to allow planning for course-specific uses of laptop computing
(Haden, 2001; The Node Learning Technologies Network, 1999).
In order to successfully integrate technology into the curriculum, new
faculty skills beyond typical existing expertise are required. Even when the
assistance of technical and instructional support professionals is available,
faculty may need to become familiar with principles of instructional design,
use of various software applications, and functions of technical systems in
order to capitalize on the potentials of various technologies (Resmer et al.,
1995, p. 21). A comprehensive faculty development program requires the
commitment of significant resources and is therefore most effectively
supported at the institution rather than the department level (Spender, 1995).
One of the greatest challenges institutions face in implementing a
laptop requirement is providing faculty development that offers the right
rewards and incentives to faculty while actually improving teaching and
learning (Burg & Thomas, 1998, p. 24). Grants and funding that allow faculty
to pursue professional and curriculum development are a common incentive.
At University of MinnesotaCrookston, Valley City State University, and
University of Denver, grant funds are made available to faculty (Conger,
1999). Such funds are commonly used for a variety of activities including:
attending conferences, purchasing software or equipment, or hiring technical
77


assistants. Another common incentive is to provide release time from
teaching to allow faculty to undertake projects related to laptop
implementation. This type of faculty incentive supports real choices for
faculty in determining the best ways to integrate technology within their own
discipline and courses (The Node Learning Technologies Network, 1999).
Laptop computing institutions have reported varying levels of success
for methods of faculty development. Institutions that did not consult faculty
in planning faculty development programs have found that time was wasted
creating materials and programs that did not meet faculty needs, and
expectations. For example, faculty rarely used the video taped programs
designed by one university to teach the use of specific software applications.
This university later surveyed faculty to determine their preferences and
began to design materials and sessions to meet specific identified needs
(Zeglen & Clark, 1999, p. 21). The most effective faculty development
programs for the integration of information technology meet faculty needs
using approaches that appeal to faculty. Faculty have frequently reported
that they prefer discipline-specific, just-in-time learning opportunities
facilitated by staff with expert knowledge applied to projects that they will
actually use with their classes (Candiotti & Clarke, 1998, p. 38; Deden, 1998,
p. 63).
Developing the skills necessary for integrating laptop computing in
teaching requires a significant time commitment from faculty (The Node
Learning Technologies Network, 1999) who must decided among conflicting
demands on their time for teaching, research, publication, and service.
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Successful implementation of laptop computing may be negatively impacted
by faculty promotion policies. Research done at the International Center for
Computer Enhanced Learning at Wake Forest University revealed that senior
faculty (who have already attained tenure and an established place within
their departments) and new faculty (who are not yet fully invested in a life
time career as faculty) are most likely to be heavily involved in technology
implementation. Faculty who are in the midst of a tenure and promotion
process are less likely to be heavily involved (Brown, 1998).
In surveying nine campuswide computing institutions, Gier (1998)
found consensus at all institutions that pedagogical applications of
technology should be recognized for purposes of retention, tenure, and
promotion. Only one of the nine institutions, however, recognized the
development of software products as valid research. Further, none of the
institutions surveyed had revised their tenure criteria to reflect their laptop
computing policies. Some institutions, however, had modified their student
evaluation forms to reflect the use of technology by asking students to
evaluate faculty use of technology in teaching.
User Training and
Technical Support
Laptop institutions typically require that students participate in some
initial training in the use of their laptops and software (The Node Learning
Technologies Network, 1999). At the University of North CarolinaChapel
Hill incoming students participate in a laptop orientation program (Olsen,
79


2001c). At University of MinnesotaCrookston students are required to
participate in proficiency modules for productivity software. Training on
software upgrades is a continual process. Further technology learning may
be provided by special introductory courses, as part of the normal
curriculum, or by optional training sessions for particular technology skills
and applications (Thomas et al., 1998, p. 37).
The need for technical support typically increases significantly with
the adoption of laptop computing. The more frequently hardware and
software standards are changed, the more technical support and user training
are required (Brown, Burg & Dominick, 1998, p. 35). Having sufficient
technical staff is critical to the success of a laptop computing initiative
(Candiotti & Clarke, 1998, p. 40). Institutions are using a variety of
approaches to manage the costs of technical support. Standardization of
hardware and software requires fewer technical support staff and allows staff
to develop expertise in one set of technologies (Saxena, 2000). A number of
laptop universities employ students with technology skills to assist users
(Gier, 1998). Some have also placed student assistants in classrooms to
provide assistance to faculty during class sessions (The Node Learning
Technologies Network, 1999). Some institutions have added technical
support staff and others have reorganized their computer support units or
reallocated positions (The Node Learning Technologies Network). Technical
support may also be available from hardware and software vendors.
Effective academic and administrative computing systems should be
integrated or coordinate; however, academic and administrative computing
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technical support and user training may be effectively provided by the same
computing services unit or by separate units. Technical support may be
organized on a centralized or decentralized model, or a combination of the
two approaches. In a centralized support model, all support is provided from
one central unit. Decentralized support places technology experts within
each school and department (Dominick & Goff, 2000).
Computing Hardware and Software
Most colleges and universities implementing campus-wide laptop
computing require one standard computing platform, specific hardware
model, and a standardized suite of software, primarily to ease the burden of
hardware and software support (Brown et al., 1998; LeBlanc, & Teal, 1998, p.
67). Other advantages of standardization include: cost effectiveness in
purchasing and service, the option of faculty and students offering technical
help to their peers, user convenience, and simplifying help desk services
(Sargeant, 1997).
Some laptop institutions establish minimum hardware and software
specifications and allow students and faculty to select any hardware brand
and model and software applications that meet these minimum
specifications. While allowing more options for users, this approach does not
provide the same efficiencies of service and support and economies of scale as
programs that specify identical hardware and software for all users (Brown et
al., 1998). Some laptop institutions successfully maintain a multi-platform
81


computing environment (Biros, 1998). Georgia Technical University is
developing platform-independent courseware by using a web-based interface
(LeBlanc & Teal, 1998, p. 67).
Another alternative utilized at some laptop institutions is establishing
preferred computer hardware and software for which maximum user support
and training is provided. Minimal or no support and training are provided
for non-preferred hardware and software. This approach allows faculty or
students who have specific needs or preferences for other computing
platforms and applications to deviate from the standard while the institution
still benefits from the efficiencies and economies of scale of standardization
(Dominick & Goff, 2000).
Even when institutions specify the brand and model of hardware and
the software applications required, several different generations of hardware
and software are likely to be used simultaneously because hardware and
software companies are continuously changing and upgrading their products
(The Node Learning Technologies Network, 1999). If an institution phases in
its laptop requirement by requiring succeeding entering classes to own
computers, each entering class of students is likely to have a different
generation of equipment and software (Dominick & Goff, 2000). Providing
faculty with laptop computers one or more semesters in advance of the
student requirement may result in faculty having different hardware and
software than students. Most laptop computing programs reevaluate
hardware and software requirements annually and provide for upgrading or
replacing hardware and software to assure that students and faculty have
82


access to up-to-date computing resources (LeBlanc & Teal, 1998, p. 69).
Differences in upgraded or replaced hardware and software tend to be less
significant when the platform and manufacturer remain the same.
An important consideration for some institutions in choosing an
approach to hardware and software requirements is the number of students
who are already required to own a computer by their particular program or
major or who already own a compute by choice. At the University of
FloridaGainsville, most third and fourth year students were already covered
by a departmental computer requirement, which varied from one program to
another. As a result the choice of implementation model at Florida was to set
minimum hardware and software standards and allow students to select the
platform, brand, model, and software they prefer (McCollum, 1998a).
IT Infrastructure and
Instructional Facilities
Institutions requiring laptop computers typically create a technology-
intensive campus as part of the campus-wide laptop computing program to
provide the university community with access to networked digital resources
and electronic communication capabilities (Haden, 2001, p. 12; Thomas et al.,
1998, p. 35). With implementation of laptop computing the number of
connections to the campus network and traffic over the network increase
significantly. The network should be readied to accommodate up to a ten
fold growth in email traffic, Internet use, and file sharing (Brown et al., 1998).
Many laptop institutions have extensively upgraded their campus data
83


network infrastructure and instructional facilities to support the laptop
initiative (Thomas et al., 1998, p. 35). The University of Minnesota,
Crookston developed a five-year plan to build an instructional technology
center, reconfigure classrooms to provide network connectivity at each seat,
upgrade the local area network, and add dial-in access (Sargeant, 1997). At
Sonoma State University infrastructure upgrades included: upgrades of
discipline specific and general use computing labs; a separate network
backbone for student computing; improved modem access; and multimedia
presentation equipment in classrooms (Arminana, 1998).
Appropriately designed and equipped classrooms are widely
considered to be essential for full implementation of campus-wide laptop
computing. Faculty at laptop institutions typically require students to use the
laptop computers in some, but not all, class sessions; however, the option to
do so is preferred by most faculty. Accommodating this preference for
flexibility requires that all or most classrooms be designed to allow each
student to access the campus network and Internet resources via a laptop
computer.
A number of laptop computing institutions including Seton Hall
University, Wake Forest University, and University of Denver have equipped
most classrooms with a data port and electrical connection at every seat
(Conger, 1999). Additional data ports and electrical outlets have been
installed in libraries, study areas, and informal gathering areas. Such a
campus-wide laptop computing environment may provide as many as two or
three network drops per student (The Node Learning Technologies Network,
84


1999). Other institutions such as Clayton College and State University are
building technology-intensive teaching facilities to accommodate new
methods of teaching and learning with technology (Paulien & Associates,
1998) . Some campuses have installed or are studying the possibilities for
wireless local area networks to meet some networked computing needs and
decrease the need for access to data ports (Conger, 1999; Weiser, 1998).
Decisions on how many classrooms will be equipped with a port at every seat
should be made keeping in mind the progress of wireless technology (Brown,
1999) .
Cohen and Castner (2000) emphasize that the design of effective
instructional environments begins with the needs of students and faculty
rather than with the technology itself. This planning principle may be
overlooked by institutions that are renovating instructional space to
accommodate campus-wide laptop computing. For example, fixed student
tables and instructor multimedia podiums have been installed in classrooms
at many laptop institutions to accommodate data and electrical wiring. This
configuration limits faculty options for arranging furniture to accommodate
small group work. In a study at Canisius College, Cohen and Castner (2000)
found that faculty preferred to be able to rearrange seating in 70 percent of
their class sections. This same survey revealed that 56 percent of faculty who
used a computer for presentation preferred that there be no permanently
installed equipment podium because of the inflexibility imposed by a fixed
podium (p. 8).
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Financial Issues
Although a primary reason given for implementing laptop computing
at many institutions is eliminating the costs of maintaining student computer
labs (Candiotti & Clarke, 1998; Olsen, 2001a), many laptop computing
institutions are continuing to provide some student computing labs (Thomas,
et al., 1998). For example, at Sonoma State University between 1995 and 1998
new computing labs accounted for 22% of technology expenditures and
computing lab upgrades accounted for 23% of expenditures (Armiana, 1998).
Most students who own their own computers still make substantial use of
campus general use or academic department computing labs for printing,
accessing specialize hardware and software, and accessing academic and
technical assistance (LeBlanc & Teal, 1998, p. 66). While expenditures for
student computing labs may decrease with campus-wide laptop computing,
expenses for user training, faculty development, and infrastructure are
significant. A laptop institution may realize cost savings over time, assuming
that the campus infrastructure would be upgraded with or without the laptop
program, but savings in the short term are unlikely (The Node Learning
Technologies Network, 1999).
Total cost of implementing campus-wide computing are in the millions
of dollars (McCollum, 1998a). Over a four year period West Virginia
Wesleyan College spent almost $5 million on its laptop computing program,
including $1.5 million to upgrade the campus data network infrastructure
(Carlson, 2001). With expenditures of this magnitude, institutions must
develop financial planning models for providing a steady flow of resources
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for ongoing implementation of the laptop program. Many institutions are
developing funding models which draw on a variety of funding sources
including: administrative restructuring and streamlining; redirection of
existing technology funds; increased tuition or fees; direct expenditures by
students; and partnerships with corporate entities (Blumenstyk, 1998; Resmer
et al., 1995, p. 11).
Institutions are obtaining funding for network infrastructure and
capital projects such as renovation or construction of technology-enhanced
learning environments from federal and state sources, private donors,
organizations, as well as corporate donors and partners (Brown et al., 1998;
The Node Learning Technologies Network, 1999). The most widespread and
visible corporate partnership program for laptop computing is IBM's
ThinkPad University project (Burg & Thomas, 1998, p. 24). In 1993 the
University of MinnesotaCrookston was the first institution to adopt this
partnership program which, in addition to hardware and software for every
student, offers planning, management, and support assistance for
implementation of laptop computing.
Wake Forest University and Seton Hall University have developed
income-producing ventures that provide consulting services for other
colleges and universities related to laptop computing. Wake Forest, in
partnership with IBM, established the International Center for Computer
enhanced Learning.
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