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dissertation entitled

FACTORS RELATED TO THE COLLABORATIVE USE OF COMPUTER-
MEDIATED COMMUNICATION IN A GRADUATE COMMUNITY:
A STUDY OF ELECTRONIC MAIL

presented by

Seounghee Choi

has been accepted towards fulfillment
of the requirements for

Ph.D Educational Technology

degree in

 

 

 

 

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FACTORS RELATED TO THE COLLABORATIVE USE OF COMPUTER-
MEDIATED COMMUNICATION IN A GRADUATE COMMUNITY:
A STUDY OF ELECTRONIC MAIL
By

Seounghee Choi

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment Of the requirements
for the degree Of

DOCTOR OF PHILOSOPHY
Department Of Counseling, Educational Psychology
and Special Education

1996

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ABSTRACT

FACTORS RELATED TO THE COLLABORATIVE USE OF COMPUTER-
MEDIATED COMMUNICATION IN A GRADUATE COMMUNITY:
A STUDY OF ELECTRONIC MAIL

By

Seounghee Choi

Since the use Of computer-mediated communication systems has been
steadily increasing in the teaching and learning environments, this study
attempted to better understand factors related to the collaborative use of
electronic mail in one graduate community. The dissertation was based upon
aspects Of social learning theory. The researcher investigated whether personal
input factors (ability, motivation, and personality) and environmental input factors
(learning, support, and Opportunity to use) influence output factors (the amount
of use, collaborative use, and other purposes Of use Of e-mail).

Questionnaire was developed, pilot-tested, and mailed to the subjects.
One hundred eighteen students, international graduate students (3 = 79) and
doctoral students from three consecutive cohorts of a seminar class (Q = 39) in
the College of Education at Michigan State University, were the subjects in this
study. The research findings were as follows:

1. Ability was not a significant predictor of the use Of e-mail. Motivation
was a significant predictor of the use Of e-mail. External locus Of control as a
measure Of personality was a significant predictor of the number Of messages.

2. Prior Ieaming experience was not a significant predictor of the use of

e-mail. Support for using e-mail was a significant predictor Of collaborative use.

 

 

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Opportunity to use was a significant predictor Of the number Of messages and
other purposes Of use Of e-mail.

3. A positive relationship was found between ability and motivation,
between ability and personality, between motivation and personality, and
between prior learning experience and Opportunity to use the e-mail systems.

4. Statistically significant relationships were found between the set Of
personal factors and the set Of environmental factors and between the set of
input factors and the set Of output factors.

5. NO significant relationships were found between age, gender,
nationality, educational level Of the user, and amount of time at a certain
educational organization, and the output factors. However, significant
relationships were found between major and the amount Of use, collaborative
use, and other purposes Of use Of e-mail, and between the experience of using

e-mail and other purposes of use Of e-mail.

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ACKNOWLEDGEMENTS

A dissertation project is not a solitary endeavor. I would like to thank the
many individuals who made the completion Of this study possible.

First, I would like to extend my heartful thanks and gratitude to Dr.
Stephen Yelon, my advisor and committee Chairman. Since the beginning Of my
studies at MSU, he gave me advice, guidance, and encouragement on countless
occasions which led to my continuing the work. I would extend my appreciation
to Dr. Ann Austin, Dr. Joe Byers, and Dr. Patrick Dickson for serving as
members of my dissertation committee. I would also extend appreciation to Dr.
Jack Schwille for supporting this study and reviewing the instrument.

Very warm thanks to my family who have been supportive and patient
during the years Of my doctoral studies. TO my parents, Deungho Choi and
Soonim Hong whom I respect and love immensely. They instilled in me the
confidence and ethic to do my very best. To my parents-in-law, Seungchae
Kwon and Kyungja Kim for their love and support over the years. Many thanks
to my husband, Youngmin Kwon, and my children, Hoon and Jane, for their love,
patience, and encouragement.

Special thanks to my dear friends Dr. Yeon Hong Min and Joonghee Lee.
Dr. Min, my academic collegue, has provided tremendous encouragement and
energy so that I could work on my dissertation. To Junghee Lee, my friend, to

whom I am indebted for her caring and support.

 

 

 

 

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and earr

 

 

Last, but not least, I wish to sincerely thank you to the graduate students
who participate in this study. They were all so gracious and took the study as
seriously as did I. Valuable data have been gleaned as a result Of their sincere

and earnest involvement.

LIST OI
LIST OI

CHAPT'

l. INT

—ll"1—~lm

II. RE\

 

TABLE OF CONTENTS

Page
LIST OF TABLES ....................................................................................... iv
LIST OF FIGURES ...................................................................................... xi
CHAPTER
I. INTRODUCTION ................................................................................. 1
Background of the Study ................................................................. 1
The Problem ..................................................................................... 1
Definition Of Computer-Mediated Communication ........................... 4
The Rationale for This study ........................................................... 5
Adult Learners ...................................................................... 6
Collaborative Learning ......................................................... 8
Suggested Model ............................................................................ 12
Research Questions ....................................................................... 15
Organization Of the Dissertation ..................................................... 17
ll. REVIEW OF THE LITERATURE ........................................................ 18
Personal Inputs ............................................................................... 18
Ability ................................................................................... 19
Motivation ............................................................................. 20
Personality ........................................................................... 23
Environmental Inputs ...................................................................... 27
Prior Learning Experience ................................................... 28
Support ................................................................................ 29
Opportunity to Use ................................................................. 31
Outputs: Use Of the Systems .......................................................... 33
Amount Of Use ..................................................................... 34
Collaborative Use ................................................................... 34
Other Purposes Of Use .......................................................... 38

vi

 

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Ill. ME

 

 

I. ... JIIIE

 

IV. RE:

 

Research Hypotheses ....................................................................... 40

Hypothesis 1 .......................................................................... 41
Hypothesis 2 .......................................................................... 41
Hypothesis 3 .......................................................................... 41
Hypothesis 4 .......................................................................... 42
Hypothesis 5 .......................................................................... 42
Hypothesis 6 .......................................................................... 42
Hypothesis 7 .......................................................................... 42
III. METHODOLOGY .................................................................................. 43
Setting ............................................................................................... 43
The Sample ....................................................................................... 45
Methods ............................................................................................ 46
The Instrument .................................................................................. 48
Personal Variables ................................................................. 48
Environmental Variables ........................................................ 50
Output Variables .................................................................... 53
Background Information ......................................................... 54
Statistical Analysis ............................................................................ 55
Reliability Analysis ................................................................. 55
Multiple Regression Analysis ................................................. 55
Bivariate Correlations ............................................................ 57
Canonical Correlation Analysis ............................................... 57
Analysis Of Variance Of t-Test ................................................ 58
IV. RESEARCH FINDINGS ....................................................................... 59
Sample Characteristics ..................................................................... 59
Preliminary Evaluation of the Data ......................................... 60
Descriptive Statistics ........................................................................ 62
Independent Variables ........................................................... 63
Dependent Variables ............................................................. 76
Summary Of Descriptive Statistics .......................................... 80
Multiple Regression Analysis ............................................................ 81
Results Of Multivariate and Univariate Multiple
Regression ............................................................................. 82
Summary of Multiple Regression Analysis
Results .................................................................................. 88
Bivariate Correlations ....................................................................... 90
Canonical Correlation Analysis ........................................................ 92
Analysis Of Variance and t—Test Analysis ......................................... 95
Analysis Of Variance .............................................................. 95

vii

 

 

 

 

 

V. DIS

r:I_cn

 

t-Test Analysis ..................................................................... 103

Summary Of the Research Findings ............................................... 106
Results From the Open-Ended Questions ..................................... 109

V. DISCUSSION AND CONCLUSIONS .................................................. 111
Summary ........................................................................................ 1 1 1
Discussion of the Findings ............................................................. 113
Research Hypothesis 1 ....................................................... 113

Research Hypothesis 2 ....................................................... 115

Research Hypothesis 3 ....................................................... 117

Research Hypothesis 4 ....................................................... 117

Research Hypothesis 5 ....................................................... 118

Research Hypothesis 6 ....................................................... 119

Research Hypothesis 7 ....................................................... 120

Results From the Open-Ended Questions .......................... 122

Conclusions .................................................................................... 123
Independent Variables ........................................................ 123

Dependent Variables .......................................................... 124

Limitations ...................................................................................... 125
Implications for Practitioners .......................................................... 126
Implications for Theory ................................................................... 128
Recommendations for Future Research ......................................... 129

Final Words .................................................................................... 130

LIST OF REFERENCES ............................................................................ 131
APPENDIX A ........................................................................................... 141
APPENDIX B ........................................................................................... 149
APPENDIX C ........................................................................................... 150

viii

 

 

Table
Table
Table
Table

Table
Table
Table
Table

Table
Table
Table
Table
Table
Table
Table
Table

Table
Table
Table
Table
Table
Table
Table

Table
Table

Table

 

Table
Table
Table
Table

Table

Table

Table

Table

Table
Table
Table
Table
Table
Table
Table
Table

Table
Table
Table
Table
Table
Table
Table

Table
Table

Table

9599’.“

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10.
11.
12.
13.
14.
15.
16.

17.
18.
19.
20.
21.
22.
23.

24.
25.

26.

LIST OF TABLES

Page
Theoretical definitions of the personal input variables ............... 27
Theoretical definitions Of the environmental input variables ...... 33
Theoretical definitions of the output variables ........................... 4O
Breakdown of the international group of participants by
continent ................................................................................... 45
Breakdown of the doctoral seminar group Of
participants by cohort ............................................................... 46
Operational definitions and items addressing the personal
input variables .......................................................................... 51
Operational definitions and items addressing the
environmental input variables .................................................. 52
Operational definitions and items addressing the
output variables ....................................................................... 54
Characteristics Of the respondents .......................................... 61
Descriptive data for the ability scale ........................................ 63
Results Of the reliability analysis for the ability items ................ 64
Descriptive data for the motivation scale ................................. 65
Results of the reliability analysis for the motivation items ........ 66
Descriptive data for the personality scale ................................ 69
Results Of the reliability analysis for the personality items ...... 70
Information on the source and the effectiveness Of
learning e-mail ........................................................................... 73
Descriptive data for the support measure ................................ 74
Results Of the reliability analysis for the support items ............ 74
Descriptive information for access to the e-mail system .......... 75
Places where respondents used e-mail systems ..................... 76
Descriptive information for the amount-Of-use items ................ 77
Descriptive data for the collaborative use scale ....................... 77
Results Of the reliability analysis for the collaborative
use items .................................................................................. 79

Descriptive data for the other purposes of use scale ................ 78
Results of the reliability analysis for the other purposes

of use items 80
Summary Of descriptive statistics ............................................. 81

 

 

Table 1
Table I
Table I
Table .
Table 1
Table ‘
Table I

Table I
Table ;

Table I
Table I
Table I
Table .
Table I
Table 4

Table I
Table I

 

Table

Table

Table

Table

Table

Table

Table

Table
Table

Table
Table
Table
Table
Table
Table

Table
Table

27.

28.

29

30.

31.

32.

33.

34.
35.

36.
37.
38.
39.
40.
41.

42.
43.

Results Of the multivariate E—test of six independent variables
by the amount Of use (time and message) ................................
Univariate _E-test results for the two dependent variables

(time and message) ..................................................................
Results of the univariate regression analysis for six
independent variables by two dependent variables ..................
Results of the univariate regression analysis for

collaborative use Of e-mail
The t-test results for the six predictors Of collaborative

use of e-mail ..............................................................................
Results Of the univariate regression analysis Of other

purposes of use Of e-mail ...........................................................
The t-test results for the six predictors for other purposes

Of use of e-mail ..........................................................................
Correlations among the independent variables ........................
Canonical correlations between the personal input

factors and environmental input factors ...................................
Canonical correlations between input factors and

output factors ...........................................................................
Results of the analysis Of variance for age ..............................
Results Of the analysis of variance for major ...........................
Results of the analysis Of variance for nationality ....................
Results Of the analysis of variance for the amount Of time

at a certain organization ..........................................................
Results Of the analysis of variance for experience in

using e-mail .............................................................................
Results Of t—test for gender ......................................................
Results Of t—test for educational level Of the user ....................

83

83

84

86

87

87
91

93

94
96
98
99

101
102

104
105

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Figure

 

 

Figure 1.
Figure 2.
Figure 3.
Figure 4.
Figure 5.

LIST OF FIGURES

Page
Suggested model related to the use Of e-mail systems .............. 15
Personal input variables ............................................................. 19
Environmental input variables .................................................... 28
Variables Of outputs .................................................................... 33
Revised model based on the results related to the use Of
e-mail systems ........................................................................... 124

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CHAPTER I

INTRODUCTION

Background of the Study

Media and technology in education has taken many forms to improve
teaching-learning over the decades. Television and video, audio and printed
media, or computers are the means used by educators. The medium chosen for
an educational task may well affect the amount and kind of learning among the
members within an organization. More recently, these new technologies-
especially, electronic mailuhold much promise in improving education and, in
particular, for enhancing the productivity Of instructors and students involved in
research collaboration.

Vlfith the continued growth Of media and technology in education, the
educational organization will be challenged to exploit effective uses of such
technology for teaching-learning. One way to improve technology investment
and design decisions is to gain a better understanding Of how this new
phenomenon Of technology is being used and what factors are related to the
use of technology. Thus, this study will address the use Of technology by

particular members within a particular organization.
The Problem

For 20 years, computer-assisted instruction has been touted as a

revolution for education; nonetheless, it has not Changed the nature and depth

 

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Of the educational environment (Dirksen & Ostbye, 1989). The combination of
computer and telecommunication technologies has provided a communication
system called computer-mediated communication, which is different from any
form of computer-assisted instruction experienced previously and may become
more important than any other use Of computers in education. Computer-
mediated communication in education introduces new computer-mediated
learning; that is, there is interaction between the learner and the instructor, as
well as among the learners, whereas the traditional type of computer-assisted
instruction provides the learner with an immediate interaction between himself or
herself and the computer (Harasim, 1986). This technology has existed for
many years; however, interest in it has increased during the last five years.
Technical advances and reduced costs have contributed to this growth Of
interest and innovation (Waggoner, 1992). Almost all American research
universities and many comprehensive universities and liberal arts colleges are
already equipped with computer-mediated communication systems (Updegrove,
1991), and electronic mail is now being used by 6 to 8 million people in the
United States (D'Souza, 1992).

Computer-mediated communication in education is a new environment
with new attributes, and new approaches are required to understand, design,
and implement it (Beckvvith, 1987; Davie, 1988; Feenberg, 1986; Harasim, 1990;
Hart, 1987; Henri, 1988; Hodgson, 1989; McCreary & Van Duren, 1987).
Traditional computer-assisted instruction is a programmed and packaged
teaching and learning method. Computer-mediated communication, on the other
hand, allows for group communication between the instructor and the learner, as
well as among the learners themselves. Such interactive communication is
useful because it can support and amplify intellectual activity by facilitating the

sharing Of knowledge and understanding among individuals who are not at the

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3

same location or working at the same time. Thus, computer-mediated
communication as part of computer-assisted instruction has the potential to
increase and enhance the interaction between and among students and faculty
in a collaborative relationship (Bates, 1986; Dirksen & Ostbye, 1989; Downing,
Schooley, Matz, Nelson, & Martinez, 1988; D'Souza, 1991; Welsch, 1982). It
has been argued that computer-mediated communication methods could, in
principle, have a powerful role to play in enhancing Ieaming and teaching
between and among students and teachers in educational settings (Kaye, 1987;
Lewis 8 Hedegaard, 1993). The unique characteristics Of computer-mediated
communication—asynchronous, fast, cheap, and interactive—facilitate human
communications for learners and teachers regardless Of time or place.

Even though the benefits of computer-mediated communication
technologies seem to be clear to students and teachers (D'Souza, 1991, 1992;
Eslick, 1993; Rice, 1980), early researchers focused primarily on the effects Of
technical features or the effects Of computer-mediated communication on social
interactions in business settings (Kiesler, Siegel, & McGuire, 1984; Ku, 1992;
Nyce & Groppa, 1983). Recently, a number Of researchers have explored the
use Of computer-mediated communication for distance education (Carrier &
Schofield, 1991; Davie, 1988, 1989; Harasim, 1986, 1987, 1990; Mason & Kaye,
1989). However, little research has been conducted on the factors that influence
the use Of technologies in education. Just providing computer-mediated
communication technologies to students and teachers is not enough to
guarantee active and effective use Of the systems in educational environments.
Various listservs and discussion groups through computer-mediated
communication systems try to gain the participation Of users, but the existence Of
the systems is not sufficient to get users be active in the use Of computer-

mediated communication systems. For effective educational applications, it is

 

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critical that researchers examine the factors that influence the use of computer-
mediated communication systems, especially their collaborative use in
education, so that the results can be used to encourage the productive use Of
such systems. Hence, the researcher's main purpose in this study was to
investigate the factors that influence the use Of electronic mail, with special

attention to the collaborative use Of electronic mail in a graduate community.
Definition of Computer-Mediated Communication

Computerjmediate‘d communication can be defined as interactive
communication through computers (Culnan & Markus, 1987; Rice 8. Rogers,
1984). Computers connectedwith telephone lines, modems, and communication
software allow users to exchange, create, store, and distribute text files and

.u 5...”.—

databaseinformation. The general characteristics Of computer-mediated
communication are that it is asynchronous or nonsimultaneous, it is transmitted. -
quickly, and it is text-based (D'Souza, 1992). Electronic mail, electronic bulletin
boards, and computer conferencing are examples Of current computer-mediated
communication technologies. These examples are not separate systems and
may be‘interconnected, depending on the systems. I

Electronic mail (e-mail) is a kind Of mail service—that is, person-to-person
communication through text-based messages that are transmitted electronically.
Senders and receivers do not communicate at the same time and place. E-mail
can be transmitted in seconds or minutes, unlike printed correspondence, and,
in most cases, it contains no picture or sound components. E-mail provides a
very limited form Of group communication.

Computer conferencing is a more sophisticated version of e-mail for

group communication. It requires system software that can provide various

communication functions, for example, CONF ER, CoSy, EIES, and FORUM

 

 

 

5

(Cheng, Lehman, 8 Reynolds, 1991). The host computer has one mm of each
message from participants, and participants must be registered to share a file in
the host computer. In addition tO the one-tO-0ne communication Of e-mail,
computer conferencing Offers group communication for discussion and
conversation in a group. Individual users can join "conferences" on specific
topics Of interest containing the cumulative total Of messages sent to the
computer by the group members.

A bulletin board is an information system Offering an information-sharing
service for a special interest group. It is similar to computer conferencing,
except for the public nature Of the information in the bulletin board. Bulletin
board systems have become popular throughout the United States in recent
years (Steinfield, 1986b). Many list servers on the BITNET network, such as
EDTECH Mailing list and News group on USENET, are in the form Of a bulletin
board.

In this study, e-mail in computer-mediated communication was delimited
by defining e-mail as text-based messages transmitted from one computer to
another computer. E-mail was chosen because it is fairly common in
organizations, is easy to use, and usually allows other functions such as word
processing, information searching and retrieval, and electronic bulletin board. In
the following section, the rationale for this study is explained with respect to

adult and collaborative Ieaming.
The Rationale for This Study

Many researchers have pointed out that computer-mediated
communication systems are especially suited for higher educational
organizations because they are the premier information-processing

organizations (Balkovich, Lerman, & Parmelee, 1985; Kettinger, 1992; Rice &

 

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6

Case, 1983). Even though e-mail is now being used by 6 to 8 million people
(D'Souza, 1992) and educators are potential users of computer-mediated
communication technologies, little attention has been given to developing
applications for e-mail in educational settings (Cohen & Miyake, 1986; Davie,
1988; D'Souza, 1992; Laabs, 1993; Steinfield, 1986b). The rationale for this
study Of factors influencing the use of e-mail, with respect to adult Ieamers and

collaborative Ieaming, is described in the following paragraphs.
Adult Learners

Adult Ieamers are different from traditionally defined Ieamers (Knowles,
1984) and are typically Older than traditionally aged college students (Lucy,
1993). According to Knowles, the traditional pedagogical Ieaming model cannot
fully represent the adult educational process because adults have some different
characteristics from traditional students (Kegan, 1993). Adults work during the
day, are self-motivated to meet their professional needs, and are autonomous in
maintaining their study pace. Many adults work independently at their own pace
in a number of locations. They also need ongoing training in their skills and
knowledge to be able to cope with living in an information society. Knowles
(1984) viewed the adult learner as a mature, internally motivated, and
experienced student. Therefore, new delivery systems are required for adult
learners to participate in the higher educational process while they work during
the day, including Offering evening and weekend classes. Telecommunication
technologies, such as e-mail systems, can bring mature students to the learning
environment through collaborative or student-centered learning models.

Graduate-level educational communities in adult Ieamers were selected
because individual adult learners in a graduate community can actively

participate in the Ieaming process by acting as information providers rather than

 

 

 

7

as information consumers (Harasim, 1987; McCreary & Van Duren, 1987) and
individual adult Ieamers can study in their own time and place (Henri, 1988;
Kaye, 1992; Lewis & Hedegaard, 1993). A research paper may easily be edited,
transmitted, and reviewed by faculty and students at their convenience. E-mail
systems support adult learners in a graduate community, who, because Of other
important responsibilities involving their work and families, need to maximize
flexibility in their available time for Ieaming.

Therefore, to investigate the factors related to the use Of e-mail in a
graduate community, the researcher needed a graduate community whose
members are encouraged to use e-mail. VVIthin the graduate community, many
units, such as colleges, special Offices, and particular courses, encourage their
members to use e-mail. Two groups Of adult students who were encouraged tO
use e-mail were selected as targets Of the study because Of their availability and
convenience to the researcher: international graduate students and students
taking a graduate seminar class. The international graduate students were
strongly encouraged and supported to take part in email systems by a special
Office (Office of International Studies in Education) for communicating and
sharing discussion topics together or individually. Seminar students, on the
other hand, were supported and encouraged to use e-mail systems by their
classroom instructors for communicating with other students and instructors
regarding their class assignments and to further their academic life. Although
these two groups were varied within general purposes, they both had in common
the use of e-mail as a means of providing Opportunities for students and faculty
to share ideas and information, as well as for encouraging cooperative work in
education. Interaction with peers and faculty in both groups provides students

with active and effective Ieaming activities, enabling them to tap the combined

8

knowledge Of the group in a collaborative learning environment. Thus, all these

students were generalized to be part of one group.

Collaborative Learning

Computer-mediated communication holds significant advantages for
offering quality education and access to Ieaming resources to adult Ieamers who
work during the day, are self-motivated, and have autonomy (Mason, 1988)
because it can enhance and increase the collaborative or cooperative Ieaming
between and among students as well as faculty and students through rapid
communication (Harasim, 1987; Henri, 1988), regardless of where they are
located. Collaborative Ieaming refers to cooperating with other students and
teachers to experience a more satisfying Ieaming environment and to improve
individual as well as group performance (Slavin, 1983). Collaborative Ieaming
presumes that the learner is actively participating in the knowledge-making
process through interaction with others. The Ieamers are not passive recipients
Of knowledge but active workers involved in constructing knowledge through a
process Of discussion and interaction with Ieaming peers and experts. Thus,
knowledge is something that emerges through active dialogue, by formulating
ideas into words and building ideas and concepts through the reactions and
responses of others to these formulations. Instruction is perceived by the
learners and filtered, as well as modified, by their values, beliefs, and judgments.
Knowledge construction is an ongoing process that takes place in the leamers'
own minds (von Glaserfeld, 1987).

Many researchers (Dirksen & Ostbye, 1989; Downing et al., 1988;
D'Souza, 1991 ;Welsch, 1982) have reported that students who have the
Opportunity to collaborate with Classmates and instructors through computer-

mediated communication have more satisfying Ieaming environments. The

 

 

 

9

interactivity of computer-mediated communication Offers the possibility of greatly
accelerating exchanges between students and teachers, and of creating an
electronic classroom in which student can watch each other interact with the
teacher. F eenberg (1986) asserted that the technology of computer
conferencing is the first technology tO provide "effective electronic mediation Of
interactive communication in small groups" (p. 7).

Because of their text-based Characteristic, computer-mediated
communication systems can support normal Classroom activities such as
sending and receiving assignments, distributing handouts (Holden, 1992), and
carrying out joint writing projects (Davie, 1988). Written discourse through
computers also makes it possible to share some Of the spontaneity and flexibility
Of spoken conversation (Henri, 1988; Kaye, 1989) and to produce a new form of
collaborative writing (Davie, 1988; Feenberg, 1987, 1989). From this point, self-
motivated adult Ieamers can transmit their thoughts to their colleagues through
computers and wait for others to read them (Davis & Marlowe, 1986; D'Souza,
1991; Harasim, 1989). Writing through computers enhances students' creative
writing skill and increases their writing productivity. It also helps adult learners
work on projects with fellow classmates without meeting them (Davie, 1988;
Rimmershaw, 1992; Schriner & Rice, 1989), while working at their own place.

Schriner and Rice (1989) assessed the comparability Of computer
conferencing with collaborative Ieaming in writing courses. Students were able
to discuss their topic continuously with other students from whom they were
separated by time and place. The researchers investigated how students
formed a community and worked collaboratively through computer conferencing.
After students were comfortable with the use Of computer conferencing systems,
they were willing to share their opinions with group members and finally took on

responsibility for their writing. For example, students expressed their ideas to

 

 

the groc

their ide

1T

compute
commur
collabora

ERMQa

— — — ‘ —

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develop

 

 

 

10

the group members and elicited responses from other members by exchanging
their ideas through ongoing discussion. Schriner and Rice concluded that the
computer, far from making the Class more impersonal, created a close
community to help students control their Ieaming and to work together
collaboratively with their colleagues. The researchers argued that "rather than
talking alone to a computer, our students were talking through a computer to
larger worlds" (p.478). Finally, computer-mediated communication can help
develop a sense of academic community among adult learners.

The independence Of place and time in computer-mediated
communication allows adult learners to construct their knowledge at their own
pace and according to their unique cognitive learning styles, processing
capacities, and limitations. It promotes active and interactive Ieaming because it
provides a means for many people to weave together their ideas and
information, regardless Of when and where they contribute (Carrier & Schofield,
1991; Harasim, 1989; Kaye, 1989).

A number Of researchers who investigated the differences between
courses conducted through computer-mediated communication and those
conducted through face-tO-face meetings (Cheng, Lehman, & Armstrong, 1991;
Sydow, 1994) found that computer-mediated communication can enhance and
increase the interaction among students as well as between teacher and
students, leading to collaborative learning. For example, D'Souza (1991)
investigated the instructional benefits Of communicating and disseminating class
information and assignments via e-mail. One Of the treatment groups was
composed Of 22 students who received the traditional handout and an
assignment via e-mail, and the control group was composed Of 16 students who
received the handout and Classroom communication. There was no difference

between the treatment and control groups in their knowledge Of the course

 

   

11

content at the beginning of the course. However, D'Souza found significant
differences between groups on four performance scores (written assignments,
examinations, course project, and comprehensive final) at the end Of the course.
She concluded that e-mail facilitated the sharing Of data and aided in the
completion Of a joint project. The time-and-distance independence Of e-mail led
to greater communication among members Of the Class as well as between the
instructor and students. The members Of the treatment group also Claimed that
they exchanged their ideas on issues and class-related activities via e-mail
much better than they did in the traditional classroom setting. However, the
students' attitudes about active sharing and seeking Of information and playing
with ideas were crucial to the success Of computer-mediated communication.
The research discussed above indicated that because computer-mediated
communication is asynchronous, independent Of time and place, and written, it
allows great opportunities for adult Ieamers to have a collaborative or
cooperative Ieaming experience among themselves, as well as between
themselves and faculty. VVIth the widespread use Of computer-mediated
communication technology in education, the graduate community will be
challenged to exploit effective uses Of such technology for adult learning.
However, the research on computer-mediated communication has not resulted in
an integrated theory of computer communication in education that can aid in
explaining factors related to the use of computer-mediated communication for
adult learners. In the present study, the researcher formulated a conceptual
model regarding the factors related to the use of computer-mediated
communication, for an integrated theoretical approach in education. Further, the
factors suggested in the model were investigated with regard to the use Of
computer-mediated communication and finally the relationship among those

factors was investigated. The model is described in the following section.

 

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12

Suggested Model

Regardless Of the quality of the technology, a number Of factors may
influence the use Of technology. The use Of computer-mediated communication,
which enables asynchronous textual interaction through computer technology,
can be influenced by various factors. Previous communication researchers have
examined some of these factors, such as accessibility (Ku, 1992; Steinfield,
1986a; Vallee, Johansen, Randolph, & Hastings, 1974), personal style in using
media (Ku, 1992; Rice & Case, 1983; Vallee, et al., 1974), experience with
media (Ku, 1992; Steinfield, 1986a), and so on, in attempting to explain why
someone does or does not use computer-mediated communication technology.
For a theoretical approach in education, a model is suggested in this section to
investigate the factors related to the use Of computer-mediated communication
systems.

From the perspective Of social Ieaming theory, the behavior Of using a
specific medium is considered to be a function Of the person and the
environment. This social influence position is consistent with the social
information processing model Of media use Of Fulk, Steinfield, Schmitz, and
Power (1987) in organizational communication research. That is, the social
environment can affect an individual's attitudes and communication behavior.

Social learning theory is for prediction Of the behavior through "the
interaction of the individual and his or her meaningful environment " (Rotter,
1982, p.5). The distinction between the individual and the environment is one
that defines personal variables as a set of relatively stable characteristics and
defines environmental variables as the set of meaningful cues or stimuli to which
the individual is reacting. Social learning theorists believe that different people

will respond differently to the same situation because they mediate the stimulus

 

 

 

through
& Br0ph

 

persona
stimulus
persona
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comput
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Keller (‘
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13

through their own cognitive, behavioral, and environmental determinants (Good
& Brophy, 1990). Thus, behavior is seen as an end product Of a sequence of
personal cognitive transformation and interaction with the environmental
stimulus. The behavior of media use occurs within a relationship between
personal characteristics and environmental factors. TO understand users'
behavior and especially to understand why individuals respond differently to
computer-mediated communication systems, the factors that mediate each
individual's use Of the systems can be identified through the function of the
personal and environmental factors.

To understand human behavior in the context Of social Ieaming theory,
Keller (1983) specified the influence of personal and environmental factors in
terms Of effort, performance, and consequences. That is, output factors, such as
performance, effort, and consequences, are influenced by personal and
environmental inputs. Personal inputs, which are called instructional conditions,
include motivation, abilities or skills, and cognitive process, whereas
environmental inputs, which are called instructional methods, include
motivational, learning, and contingency design and management. Performance
means the actual accomplishment Of learning, such as acquiring knowledge
about the e-mail system; effort is the individual's engagement in actions aimed at
accomplishing the task, such as the active use Of technology; and consequences
are both the intrinsic and extrinsic outcomes that accrue to an individual, such as
satisfaction with the technology.

Baldwin and Ford (1988) identified the factors related to the transfer Of
training in a similar way to Keller's social Ieaming theory. In a training situation,
the learning process is explained in terms of training input factors, training
outputs, and conditions of transfer in the training. Even though Baldwin and

Ford's model aims at identifying factors that affect transfer of training, the model

 

can beg

incorpor]

personal-

  
  
  
  
  

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14

can be generalized to explain human behavior in the Ieaming process because it
incorporates Ieaming principles. Training input factors include training design,
personal characteristics, and the work environment. Training design includes
the learning principles, the sequencing Of training materials, and the relevance
Of the training content. Personal characteristics include ability or skill,
motivation, and personality factors. Work environmental characteristics include
climactic factors such as supervisory or peer support, as well as constraints and
Opportunities to perform Ieamed behaviors on the job. Training outcomes are
defined as the amount Of original learning that occurs during the training
program and the retention of that material after the program is completed.
Conditions Of transfer include both the generalization Of material Ieamed in
training and maintenance Of the Ieaming material over a period Of time.

The difference between the transfer process and understanding Of human
behavior is that a certain training procedure should be given to the individual for
measuring the outcome behavior and generalization in a training program. In
the model developed for this study, however, training procedure, whether it was
given to the individual or not, is considered as one Of input factors that affect
their behavior.

From the perspective of social learning theory, personal input factors as a
set of relatively stable characteristics include ability or skill for using technology,
motivation (perceptions about and attitudes toward technology), and personality.
Environmental factors as the set of meaningful cues or stimuli to which the
individual is reacting include prior Ieaming experience, support for using
technology, and opportunity to use technology. Output factor, that is, the use of
e-mail systems is specified into the amount Of use (time and messages),
collaborative use, and other purposes Of use Of e—mail. Thus, the use Of e-mail

systems may be explained by personal characteristics and environmental inputs.

 

 

 

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15

For the purpose Of this study, a model was developed to help investigate human
behavior in using a specific medium (Figure 1).

In addition to the above-mentioned input variables that were included in
the model, certain demographic variables might influence the use Of computer-
mediated communication systems. Various demographic variables were
included in this study because Of hints from previous research on the use Of
computer-mediated communication and because Of the logical relationship
between these variables and the use Of computer-mediated communication.
Specifically, age, gender, nationality, major, educational level of the user, the
amount of time at a certain educational organization, and the experience of
using e-mail were included in this study. However, the demographic variables
discussed above were not included as major components Of the model because
they might vary and change depending on the target organization. Based on

that model, the research questions will be detailed.
Research Questions

The researcher's main purpose in this study was to investigate the factors
that influence the use of e-mail, with special attention to the collaborative use of
e-mail in a graduate community. TO study these variables systematically, a
graduate community whose members are strongly encouraged to use e-mail was
selected. The following research questions were posed to guide the collection
of data for this study.

1. DO personal input factors (ability, motivation, and personality) influence the
amount Of use, collaborative use, and other purposes of use of e-mail in a
graduate community?

1.1. Does ability influence the amount of use, collaborative use, and other

purposes Of use of e-mail in a graduate community?

16

 

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Figure 1. Suggested model related to the use of e-mail systems

1.2. Does motivation influence the amount of use, collaborative use, and
other purposes of use of e-mail in a graduate community?
1.3. Does personality influence the amount of use, collaborative use, and
other purposes of use of e-mail in a graduate community?
2. DO environmental input factors (learning, support, and Opportunity to use)
influence the amount of use, collaborative use, and other purposes of use Of e-
mail in a graduate community?
2.1. Does the effectiveness prior learning experience influence the
amount of use, collaborative use, and other purposes of use of e-mail in
a graduate community?
2.2. Does support for using e-mail influence the amount of use,
collaborative use, and other purposes of use of e-mail in a graduate

community?

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17

2.3. Does opportunity to use influence the amount Of use, collaborative
use, and other purposes Of use Of e-mail in a graduate community?
3. Is there a relationship between pairs of personal input factors (ability,
motivation, and personality)?
4. Is there a relationship between pairs of environmental input factors
(Ieaming, support, and opportunity to use)?
5. Is there a relationship between the personal input factors and the
environmental input factors?
6. Is there a relationship between the input factors (ability, motivation,
personality, Ieaming, support, and Opportunity to use) and the output factors
(amount of use, collaborative use, and other purposes of use of e-mail)?
7. Are there mean differences between the categorical demographic variables
(age, gender, nationality, major, educational level of the user, amount Of time at
a certain educational organization, and experience of using e-mail) and the
output factors (amount Of use, collaborative use, and other purposes Of use Of e-

mail)?
Organization Of the Dissertation

Chapter II contains a review Of the literature as background for the study,
leading to the hypotheses that provide a framework for this study. The research
design, the subjects involved in the study, the methods employed in carrying out
the research, and the data-collection procedures are described in Chapter III.
The results Of the investigation and findings related tO the research hypotheses
are presented in Chapter N. Chapter V contains a summary Of the study, a
discussion Of the findings, conclusions drawn from the findings, limitations Of the

study, implications, and recommendations for future research.

 

T
sections
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CHAPTER II

REVIEW OF THE LITERATURE

The review of related literature is organized into the following three
sections drawn from the model described in Chapter I: (a) personal inputs,
including ability, motivation (perception and attitude), and personality; (b)
environmental inputs, including the effectiveness Of learning, support for using e-
mail, and opportunity to use; and (c) outputs, including the amount of use (time
and messages), collaborative use, and other purposes of use Of computer-
mediated communication systems. Theoretical definitions are also drawn in

each three section for relating to the methodology in Chapter III.
Personal Inputs

Individuals process and transfer information depending on their own
personality, ability, and motivation, and this affects their behavior (see Figure 2).
Individual differences in attitudes, perceptions, ability, and personality may have
more of an effect on the use Of computer-mediated communication systems than
any other input factors. Zmud's (1979) review of literature indicated that
individual differences may be strongly related to the use Of computers. Higher
levels of usage Of systems may be related to individual characteristics. Hill:
(1990) also asserted that computer-mediated communication may not be for
everyone and encouraged researchers to explore individual differences that may

influence the usage Of systems.

18

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Figure 2. Personal input variables

Ability

Keller (1983) defined ability as "what a person can do," whereas
motivation is "what a person will do" (p. 388). Ability is much more stable, or
consistent than motivation, and the measure Of general ability tends to be
consistently correlated with outcome performance. Ability in using computer-
mediated communication can include writing skills, reading skills, and typing
skills in English (Hiltz, 1984; Ku, 1992) because most communication in
computer-mediated communication technology is done through a computer
terminal and involves reading, writing, and typing texts in English.

A lack of competence in reading and writing skills in English may cause
some people to avoid this technology. The individuals perception of his or her
writing skill influences the amount of input because the user believes other
readers are evaluating that writing (Clarke, 1991). Because most Of information
is entered using a keyboard, lack of typing skill has also been mentioned as one
of major Obstacles to more widespread use Of computer conferencing (Vallee et
al., 1974); typing skill is related to the actual communication speed within the
system (Davie & Palmer, 1984). If a user has difficulty with fast and correct

typing, this could be a deterrent to using the systems. However, Hiltz's study

 

 

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mediate
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20

(1984) found that typing, reading, and writing skills has failed to predict the
usage of computer-mediated communication for a group Of scientists.

Ability also refers to knowledge about computer hardware and software
and about the specific computer-mediated communication systems the user
employs (Davie, 1989; Grabowski, Suciati, & Pusch, 1990). In computer-
mediated communication courses Offered for graduate students, the most difficult
problem students reported was a lack Of skill in connecting their modem to the
computer and the telephone line and in operating communication software
(Davie, 1989). Grabowski et al. (1990) found that knowledge of the computer
was one factor encouraging its use in their study, whereas Ku (1992) found that
knowledge Of the e-mail system did not increase the use Of e-mail but would help
people feel more comfortable with the system. Because computer-mediated
communication supports many technical functions—for example, forwarding,
editing, filing and copying messages, and retrieving informations—an individual
who has more knowledge Of computer-mediated communication systems would
be more comfortable using them. As a result, ability associated with using e-
mail—writing, reading, and typing skills in English and knowledge about
computers and e-mail systems-were included in this study to determine whether

ability influences the use of e-mail systems.
Motivation

Motivation has more variability than ability because it refers to the
direction and the magnitude Of behavior (Keller, 1983). Even though the term
"motivation" has been interpreted in many ways, it is defined for purposes Of this
study as a specific desire on the part Of a learner to learn the content of a
specific program (Noe & Schmitt, 1986). Motivation in computer-mediated

communication can be described as a desire and interest to use such systems,

 

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21

especially perceptions about and attitudes toward communicating with people.
People decide tO use computer-mediated communication systems according to
their experiences, perceptions, goals, and many other environmental variables.
Many researchers (Harasim, 1986; Hiltz, 1984; McCreary & Van Duren, 1987;
Riel 8 Levin, 1990) have pointed out that motivation is the key factor to consider
in designing and facilitating a computer Ieaming network. Hiltz (1984)
mentioned that one of the major factors that may influence the use Of e-mail is
an individual's motivation. Users need a strong interest in communicating,
exchanging information, and Obtaining new information about using the systems.
Perceptions about and attitudes toward computer-mediated
communication systems are hypothesized to have an effect on motivation to use
the systems. People's perceptions have a direct effect on their use Of computer-
mediated communication systems. Previous researchers (Hiltz, 1984; Rice &
Case, 1983; Steinfield 1986a) found that people who tend to become heavy
users Of e-mail systems have strong feelings about the benefits Of such a system
early on and they continue to use the system. User's perceptions Of e-mail's
appropriateness and benefits are associated with higher levels Of system usage.
If individuals perceive that it is easy or simple to use e-mail, they are more likely
to use computer-mediated communication systems (Kim, 1994; Steinfield,
1986a). In research about e-mail users' perceptions of computer-mediated
communication (Kim, 1994), e-mail users perceived computer-mediated
communication as freer, more frequent, and more equal than face-tO-face
communication. Furthermore, e-mail users regarded computer-mediated
communication as more useful, more effective, and more convenient than face-
tO-face communication for task-related communication, whereas they regarded
face-tO-face communication as more useful and more effective than computer-

mediated communication for non-task-related communication.

 

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22

According to Wlodkowski (1991), attitude influences human behavior and
Ieaming because it helps people make sense Of their world and gives direction
as to what behavior would be most helpful in dealing with that world. Attitude is
Ieamed; that is, it is acquired through processes such as experience and/or
instruction. Attitudes affect how people use computer technology (Howard,
1986), either enhancing or impeding the use Of computer-mediated
communication systems. In research on attitude in computer education,
attitudes toward computers appeared to be a primary component Of successful
instruction in computer education (Bear, Richards, & Lancaster, 1987; Loyd &
Gressard, 1984).

Loyd and Loyd (1985) Classified four types Of attitudes in using
computers: (a) anxiety or fear of computers, (b) confidence in one's ability to use
or learn about computers, (c) liking computers or enjoying working with
computers, and (d) perceived usefulness Of computers in present or future work.
They developed the Computer Attitude Scale (CAS) to measure attitudes in the
above-mentioned four areas, entitled Computer Anxiety, Computer Confidence,
Computer Liking, and Computer Usefulness. Using a sample Of 114 teachers
enrolled in microcomputer staff development courses, Loyd and Loyd found that
the CAS was reliable in measuring teachers' attitudes toward computers and
was effective in differentiating among teachers with different amounts Of
computer experience.

Bear et al. (1987) Claimed that, even though students' attitudes toward
computers and toward Ieaming about computers are important factors in the
success or failure Of computer education, few psychometrically sound
instruments for measuring attitudes toward computers have been developed. TO
address the lack of adequate measures of attitudes, they developed a computer

attitude scale which supported the validity Of their scale as a measure Of

r4

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23

computer attitudes. More researches on attitudes toward using computers, such
as Howard's comprehensive research (1986), have shown that attitudes toward
using computers can be measured validly and influence the use Of computers in
education. However, attitudes toward computer-mediated communication in this
study are different from the above-mentioned attitudes toward computers, in that
computer-mediated communication is connected to the communication process
with another person through a computer channel.

In recent research, Clarke (1991) developed the Computer-Mediated
Communication Apprehension Scale because some people cannot use
computer-mediated communication technologies because Of their attitudes, such
as apprehension or anxiety when using technology to communicate with another
person. His scale included three factors: confidence in using computer-
mediated communications, interest in communicating with others via the
computer, and concerns about the privacy Of computer-mediated
communication. His research also found that those people with positive
attitudes toward computer-mediated communication were likely to use e-mail
more Often for their communications. His computer-mediated communication
apprehension scale was included in this study because the scale represented
attitudes toward using computer-mediated communication technology and
exhibited strong enough internal consistency to measure the use Of computer-
mediated communication systems. As a result, perceptions about and attitudes
toward using e-mail as a measure Of motivation were included in this study tO

determine whether motivation influences the use Of e-mail.

Personality

Personality refers to the cognitive and affective structures maintained by

individuals to facilitate their adjustments to events, people, and situations

 

 

 

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24

encountered in life (Gough, 1976). Personality has more traits according to
researchers than the ones that follow. locus Of control, dogmatism, ambiguity
tolerance, extroversion/ introversion, need for achievement, risk-taking
propensity, and anxiety level. Of these traits, locus Of control as an aspect Of
personality might explain the usage of computer-mediated communication
because locus Of control is a stable personality trait that is likely to affect
individual motivation and ability tO learn new programs (Noe & Schmitt, 1986;
Rotter, 1966), and can be used to address communication behavior (Rubin &
Rubin, 1989).

Locus of control refers to a person's expectancy regarding controlling
influences on environments and their lives (Rotter, 1966). How some persons
regard an environment, like computer-mediated communication systems, may
differ from how others perceive and react to the same environment. "Internals"
perceive that the event or environment is contingent on their own behavior; that
is, they can control their environment. Internally controlled people tend to be
assertive and self-directed (Lefcourt, 1976; Levenson, 1974).

In contrast, "externals" believe that outcomes that occur in their lives are
beyond their personal control; therefore, they attribute the causes to luck, fate,
or the actions Of others (Levenson, 1974). Externally controlled people are
other- or chance-directed and powerless (Lefcourt, 1976). Rotter (1982) defined

the internal-external control as follows:

In general, internal-extemal control refers to the degree to which
the individual believes that what happens to him or her results from
his or her ovm behavior versus the degree to which the individual
believes that what happens tO him or her is the result Of luck,
chance, fate, or forces beyond his or her control. (p. 313)

In social learning theory, locus Of control is also related to self-efficacy,

which refers to the personal conviction that one can execute the behavior

required

level 01

   

 

25

required for successful performance (Bandura, 1977). Individuals with a high
level Of self-efficacy will control their efforts in order to cope with new situations.
In the same context, individuals who are internals believe that new
environments, such as new technology in the work setting, are contingent on
their own behavior and are, therefore, under their personal control. Because
internals believe they can control their environment, they take opportunities at
work that may increase the probability of improving their performance.

Locus Of control has been suggested as a means to understand the
nature Of learning processes in different kinds Of learning situations (Rotter,
1966) and further to investigate the use Of computer technologies (Bruning,
1992; Howard, 1986; Shneiderman, 1980). Locus of control has been reported
as influencing the use of computers (Howard, 1986) and internally controlled
people were more inclined tO be confident in using computer-mediated
communication (Bruning, 1992). As a result, locus Of control as a measure Of
personality was included in this study to determine whether locus Of control
influences the use of e-mail.

In addition to the above-mentioned personal input variables that were
included in the model, certain demographic variables might influence the use Of
computer-mediated communication systems. Various demographic variables
were included in this study because of hints from previous research on the use
of computer-mediated communication and because Of the logical relationship
between these variables and the use Of computer-mediated communication.
Specifically, age, gender, nationality, major, educational level Of the user, the
amount Of time at a certain educational organization, and the experience of
using e-mail were included in this study.

Age, gender, and educational level were included in this study because

they have been mentioned as influencing the use Of computer-mediated

 

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time a‘
studer

more

IeChr
sI’ste
Such
com
Usin
exp,

Slgr

Con
ark

TeS

26

communication (Bruning, 1992; Hiltz, 1984; Kerr & Hiltz .1982; McCreary 8. Van
Duren, 1987; Zmud, 1979). Kerr and Hiltz found that age was inversely related
to the use Of computer-mediated communication. They pointed out that younger
people may use computing technology more than Older people because younger
people have had more exposure to such technology. Kerr and Hiltz also found a
moderate relationship between the use of computer-mediated communication
and educational level, reasoning that more educated users feel less threatened
by system complexity.

Major was included in this study because some majors might encourage
students to use e-mail more than others for their specific content. The amount of
time at a certain educational organization was included because the more time
students are at a educational organization that encourages the use of e-mail, the
more likely they might be to use e-mail.

Individual experience with computer-mediated communication
technologies was included in this study because it may also affect the use Of the
systems (Ku, 1992; Steinfield, 1986a). The more experience students have with
such technologies, the more likely they are to use e-mail. Experience with
computers reduces anxiety about computers and increases confidence with
using computers (Clarke, 1991; Howard & Smith, 1986; Lucy, 1993). More
experience with computers, e-mail, and online services has been found to be
significantly related to a more positive attitude toward computers and vice versa.

The demographic variables discussed above were not included as major
components Of the model because age, gender, major, and nationality might vary
and change depending on the target organization. Nevertheless, previous
researchers have found that these demographic variables influence the use of

computer-mediated communication.

 

 

are the
Ability
as writ:
email 5
specific

Person.

 

to lacili

person

Table

\

Varie
\

Abili

Mot

Per

beh
Eng

27

In summary, ability, motivation (perception and attitude), and personality
are the personal input variables included in the model developed for this study.
Ability refers to the skills related to tasks involved in using e-mail systems, such
as writing, reading, and typing skills in English, and knowledge of computers and
e-mail systems. Motivation refers to the student's desire to use e-mail systems,
specifically perceptions about and attitudes toward using e-mail systems.
Personality refers to cognitive and affective structures maintained by individuals
to facilitate their adjustments to events. The theoretical definitions Of the

personal input variables are presented in Table 1.

Table 1. Theoretical definitions Of the personal input variables

 

 

Variable Theoretical Definition

Ability Skills related to the tasks involved in using e-mail

Motivation Student's desire to use e-mail systems, specifically
perceptions about and attitudes toward using e-mail
systems

Personality Student's cognitive structure related to adjusting to the

environments, specifically, the feeling Of controlling the
use of e-mail (locus Of control)

 

Environmental Inputs

In addition to personal factors, environmental factors influence individual
behavior in using computer-mediated communication, as shown in Figure 3.
Environmental factors include the effectiveness Of learning, support for using

systems, and Opportunity to use.

 

.3 .. ..

 

signifies
Efiectiv
comput
1986; I-
that We
“’um
succes
Colliers
aCaden
training
line as:
assisIaI

I
Ieaming
bUSIneS
8Than 3

28

 

Figure 3. Environmental input variables

Prior Learning Experiences

Prior learning experiences, such as workshops or training, may
significantly contribute to active and collaborative participation of users.
Effective orientation for using e-mail is a critical factor in the successful use of
computer-mediated communication technologies (Cheng et al., 1991; Harasim,
1986; Hart, 1987; Lucy, 1993; McCreary & Van Duren, 1987). Harasim found
that well-organized, face-tO-face training at the beginning of the use Of
computer-mediated communication technologies is a critical factor in the
success of the network. If users feel uncomfortable about computer
conferencing at the beginning, they may not want to use it later. For successful
academic computer conferencing, McCreary and Van Duren recommended
training before implementation and providing several forms Of printed and on-
line assistance during the academic conference. A lack Of training material or
assistance prevents potential users from employing the systems.

Nyce and Groppa (1983) suggested designing an effective and easy
learning program for nonusers who declined to use e-mail systems in their
business projects. Considering nonusers' complaint of being uncomfortable with
e-mail systems, Nyce and Groppa designed a better education program,

including an easy-tO-follow manual and online help, which enhanced progress in

 

the use
had lea
efiectiv

 

 

 

 

mechr
cmnnu
SUCCeE
educa‘
and a
mefol
group
chia

and w

facilitg

sUppo

fOund

29

the use of a management communication tool. As a result, how effectively users
had Ieamed to use e-mail was included in this study to determine whether

effectiveness of Ieaming influences the use Of e-mail.

Supgon

Usage Of computer-mediated communication systems will be increased if
the climate is supportive and encourages the use Of computer-mediated
communication. Riel and Levin (1990) examined the critical features of
successes as well as failures in designing electronic communities for comparing
educational activities among a university research group, a teachers' network,
and a students' network. After comparing the three groups, they determined that
the following features enhanced the success Of electronic communities: (a) a
group with which people can communicate, (b) a certain activity or a shared task
with a specific outcome, and (c) a coordinator who will facilitate group planning
and work.

The use Of computer-mediated communication systems has been
facilitated when users of the system were members of a network providing a
supportive relationship (Culnan, 1985; Hiltz, 1984; Steinfield, 1986a). Hiltz
found that the number Of persons on the system whom the user can contact
affects the use of e-mail. Her research was a nearly 10-year longitudinal study
of the use Of e-mail, using the Electronic Information Exchange System (EIES), a
computer conferencing system developed by Murray Turoff at the New Jersey
Institute Of Technology. The expectation Of increased communication with
existing colleagues led tO greater use Of EIES. Trust and Openness among
group members were seen as crucial to the success Of computer conferencing,
even though these qualities were not absolutely necessary for effective

computer-mediated communication among members.

 

 

 

 

30

Cohen and Miyake (1986) agreed that the organization of a
communication network influenced the use Of e-mail. For the active use of e-
mail, there needs tO be a shared goal or task or a certain activity on which users'
interests converge, so that users can expect relevant gains from such interaction
(Davie 8: Palmer, 1984). The mere presence Of a computer network does not
produce active communication in an educational environment. To facilitate
participation Of diverse users in the Intercultural Learning Network, Cohen and
Miyake designed the program in a progressive manner, starting with projects that
required loose coordination and that allowed participants to set their own
working schedules. Thus, activities or tasks on a computer-mediated
communication system were found to be an important factor in how much group
members participate in its use (Laabs, 1994), and how activities on the network
are organized is critical (Cohen & Miyake, 1986).

Many researchers (Feenberg, 1987; Harasim, 1986; Hardy, 1992; Kaye,
1987; Kerr, 1986; Laabs, 1994; Mason, 1988; McCreary 8. Van Duren, 1987;
Nyce & Groppa, 1983) have stressed the importance Of a moderator who is
knowledgeable about the system and can manage the discussion, if the purpose
Of the system focuses on group communication. Feenberg's (1987) "weaving
concept" and Romiszowski and de Hass's (1989) "moderator" suggest the
importance of a person who can manage the discussion in an electronic
community. The weaving concept in Feenberg refers to the fact that an
instructor can weave or summarize participants' contributions and return this
summary to the on-line discussion without face-to-face communication.
Instructors in computer-mediated communication systems have the role Of
summarizing the discussion, asking for clarifications, creating unity, and
ensuring that the discussion does not drift Off track. Computer-mediated

communication can be used effectively when a moderator sets up groups Of

31

participants as "conference members," establishes and names a file in the
control computer to store the discussions, and sometimes deletes irrelevant
messages.

For technical support, there must be someone who can answer questions
about computer-mediated communication technology (Cheng et al., 1991).
Computer-mediated communication technology is an evolutionary technology
that is more than an appendage to the computer. It allows users to use
computers in different ways, in a way to interact socially with others. When
users have questions or problems concerning hardware and software to operate
the systems, they want help which can eliminate extra efforts to learn their
communication systems. Thus, it has been recommended that a technical
person (Nyce & Groppa, 1983) or online help (Culnan, 1985) should be available
to promote use and tO handle users' questions.

For the purpose of this study, support for using e-mail was included in this
study to determine whether support for using e-mail influences the use Of e-mail.
Support for using e-mail included the following elements: (a) requiring or
encouraging the use Of e-mail in a Class or other works, (b) assisting in effective
communication through moderator or instructor, (C) providing technical support

and (d) providing a group with which the user can communicate.

Opportuniy to Use

Opportunity tO use pertains to whether the user has easy access to a
computer hooked up with a modem or must go elsewhere to use the system.
Access to the system frequently has been mentioned as a major determinant Of
usage of the system (Davie & Palmer, 1984; Grabowski et al., 1990; Jones,
Kirkup, Kirkwood, & Mason, 1992; Kaye, 1987; McConnell, 1990; Rice & Case,

1983; Steinfield, 1986a). Grabowski et al. found that no modem access was one

32

Of major factors inhibiting the use Of the systems. Easy access to the system is
crucial in ensuring the effective use Of computer-mediated communication
systems, preferably with each user having his or her own communication
system. mm the growing number Of computers and declining costs, ease Of
access should increase substantially in the future.

Rice and Shook (1988) used five measures Of terminal accessibility: (a)
physical distance from a terminal, (b) percentage Of time the terminal was being
used by others, (c) the number Of people sharing the terminal, (d) the general
difficulty Of access, and (e) the number Of minutes one usually had to wait to get
a terminal. Both physical distance and difficulty of access were significantly
correlated with usage Of electronic message systems, whereas the three other
accessibility variables were not related to usage. For the purpose of this study,
ease of access to e-mail systems cited frequently in the literature was included
to determine whether ease Of access to e-mail systems influences the use of e-
mail.

In summary, prior learning experience, support for using systems, and the
opportunity to use are the environmental input variables included in the model
developed for this study. The prior learning experience refers to how effectively
the knowledge needed tO use e-mail systems is conveyed. Support refers tO
physical and psychological support which can encourage the use Of e-mail.
Opportunity to use refers to users' access to e-mail systems. Theoretical

definitions Of the environmental input variables are presented in Table 2.

33

Table 2. Theoretical definitions of the environmental input variables

 

Variable

Prior Learning
Experience

Suppon

Opportunity to
Use

Theoretical Definition

Effectiveness Of prior learning experience to use e-mail

Physical and psychological support for using e-mail

Access to e-mail systems

 

Outputs: Use of the Systems

Conceptualization of the use Of computer-mediated communication refers

to the amount of use (time and messages), the amount of collaborative use, and

the amount Of other purposes Of use of systems (see Figure 4). Usage of

computer-mediated communication refers to how people use such

communication, as well as how much they use it. Amount Of usage alone cannot

lead to effective learning and better performance. Learning can be improved

when computer-mediated communication systems are employed tO increase

collaborative communication between teachers and students, and among

students.

 

Figure 4. Variables of outputs

34

Amount Of Use

The amount Of use refers to how much an individual uses computer-
mediated communication for all purposes. It can be conceptualized as the time
an individual spends online and the number Of messages an individual sends or
receives (Ku, 1992). Amount of use is a quantitative measure Of use, regardless
Of how an individual uses computer-mediated communication.

In previous research on the time individuals spend online, Rice and
Shook (1988) found that users in an aerospace company reported spending an
average of 38 minutes per day using the system. University administrators spent
roughly the same amount of time using their systems (Rice & Case, 1983).
Rafaeli (1986) found that 62% of users of a university electronic bulletin board
spent 5 to 15 minutes each time on the bulletin board. It is supposed that the
amount Of time spent online and the number Of messages sent and received
might vary, depending on the organization and when the research was
conducted. For the purpose Of this study, the amount of use Of e-mail included

the amount Of time spent online and the number of messages sent and received.
Collaborative Use

The main interest in this study was to identify factors related to the
collaborative use Of computer-mediated communication in education. In addition
to the amount of use, communication researchers have discovered that people
use computer-mediated communication technologies for a host Of purposes
(Rice, 1989; Rice & Case, 1983; Steinfield, 1986a). Research on how people
use computer-mediated communication has been conducted in administrative
and business settings. Rice and Case reported that computer-mediated

communication has been used for exchanging information, asking questions,

35

exchanging Opinions, staying in touch, generating ideas, making decisions,
exchanging confidential information, resolving disagreements, getting to know
someone, and bargaining/negotiating.

When examining the use Of computer-mediated communication in the
area of communication research, many researchers have assumed that
computer-mediated communication is appropriate for routine tasks as well as for
socioemotional uses. Rice (1980) and Heimstra (1982) asserted that computer-
mediated communication is appropriate for tasks that center on simple
information exchange but is inappropriate for interpersonal tasks. Kiesler et al.
(1984), however, showed the socioemotional use Of e-mail in their experiment.
Evidence continues to show that computer-mediated communication can be
used for emotional interactions. People use computer-mediated communication
to express their feelings and emotions through the text (Rice 8. Love, 1987;
Steinfield, 1985). Although there may be many ways to use computer-mediated
communication, depending on the research area and the specific environments,
this researcher focused on the collaborative use of e-mail in educational
settings. The collaborative use Of e-mail is more task-related including
completing class assignments, exchanging academic opinions, and asking
questions, while socioemotional uses affect the supportive climate encouraging
the collaborative use Of computer-mediated communication.

Interactivity as a unique Characteristic Of computer-mediated
communication is a powerful learning feature, which provides for the
collaborative use Of the medium in education (Black, Klingenstein, & Songer,
1995; Carrier & Schofield, 1991; Feenberg, 1987; Harasim, 1987; Henry, 1988;
Hiltz, 1990; Kaye, 1989; Lieb, 1990; Newman, 1990). Many researchers have
suggested that the greatest potential use of computer-mediated communication

in education is the collaborative or cooperative use, and it has been

36

recommended that collaborative Ieaming features be investigated. Collaborative
Ieaming means that both teacher and students are active participants in the
learning process (Slavin, 1983). The Ieaming process is supplied not only
through the teacher's instruction but also by students' interaction. Thus,
knowledge is not a prepackaged item to be delivered from teacher to students.
Rather, knowledge is constructed from shared ideas; there is active interaction
with others through computer-mediated communication systems. Much of the
learning content is supplied by students.

A number Of researchers have reported that the use Of computer-
mediated communication as a supplementary teaching tool can facilitate and
support the interaction among students as well as between teacher and students
(Downing et al., 1988; Dreher, 1984; D'Souza, 1991; Feenberg, 1987; Welsh,
1982). In Welsch's research on using e-mail as a teaching tool, he employed e-
mail to increase his access tO students' questions and answers. Homework and
progress reports were transmitted via e-mail between class sessions. A Class
bulletin board announced new assignments and directions, along with items of
general interest. Overall, students believed that the course was much improved
because Of the use of e-mail.

Downing et al. (1988) used e-mail to supplement traditional modes of
instructor-student interactions at the University Of Arizona. The results Of the
experiment suggested that students preferred tO use e-mail to interact with
faculty, that e-mail improved the quality Of instruction, and that it was easy and
relatively inexpensive to use e-mail in many classroom situations. Therefore,
computer-mediated communication can be used to supplement and significantly
improve the quality and frequency of instructor-student interaction. For example,
potential applications Of computer-mediated communication in educational

settings include: (a) replying to queries and requests from students regarding

 

so
all.
(Hr
ma

Iear

37

course content, (b) providing advice and guidance, (c) helping students solve
problems in understanding the subject matter Of a course, (d) serving as a
transmission medium for sending in homework and returning test results, (e)
discussing projects and work with a tutor, (f) bringing students together in
accordance with their interests and needs, and (g) encouraging team projects
and setting up self-help groups (D'Souza, 1992, p.23).

Explorative studies have been undertaken tO investigate the collaborative
use Of computer-mediated communication in adult Ieaming (Beckwith, 1987;
Davie, 1988; Harasim, 1986, 1987; Schriner & Rice, 1989). Beckwith suggested
a group problem-solving approach using the capability Of interactive
communication, whereas Davie suggested joint writing projects, in which drafts
Of papers are sent back and forth between students, and appropriate changes
are made. Students involved in joint writing projects have developed successful
strategies for vwiting together and have commented favorably on the experience.
Students can work with other students on a joint project over long periods of time
without leaving their computer terminals. Sydow (1994) also reported that group
communication among students via computers facilitated a task-specific project
that brought about collaborative learning. Because users have been found to
participate more equally in computer-mediated communication than in face-to-
face Classroom situations, network collaboration may support democratic
interaction (Harasim, 1986, 1987; Hardy, 1992).

Many studies have shown that computer-mediated communication
supports group interaction and creates collaborative or cooperative learning. It
allows the student to communicate with other students, not just with faculty
(Hiltz, 1986). Such communication encourages those with the same subject
matter interests to discuss and debate their ideas, thereby promoting meaningful

learning (Cohen & Riel, 1989; Harasim, 1990; Kaye, 1989; Riel, 1990; Schriner

 

Ta:

905

38

& Rice, 1989; Sydow, 1994). Students are likely to reflect on and process the

information they receive via e-mail without the constraints Of time and place.
For the purpose Of this study Of the collaborative use of e-mail in a

graduate community, collaborative use included the following elements:

- Doing class assignments or research projects with faculty or other classmates

(Davie, 1988; Downing et al., 1988; D'Souza, 1991; Mason & Kaye, 1990)

- Exchanging information with faculty, other classmates, and/or friends (Cohen &

Riel, 1989; Downing et al., 1988; D'Souza, 1991; Harasim, 1987; Kaye, 1989;

Rice & Case, 1983; Riel, 1990)

- Interacting with the group in which the user is involved (Hiltz, 1990; Mason &

Kaye, 1990)

Other Purposes Of Use

Computer-mediated communication technology provides other uses of
communication, in addition to the collaborative use. One Obvious use Of
computer-mediated communication is for navigating a tremendous number and
variety of information resources; otherwise, it would not be possible for learners
to gain easy access to those resources. Given the great number and
accessibility Of data bases online (Davis & Marlowe, 1986; Steinfield, 19863),
users do not need to spend a significant amount Of time researching materials in
libraries. Many data bases covering a wide variety Of subjects, such as the
Educational Resources Information Center (ERIC), are easily accessible online.
Technology also connects users to the outside world as they search for
information and navigate the network through Internet or World-\NIde-Web. The
rapid growth of Internet, as well as other network servers, is making it more

possible for learners to explore new ways to access useful information. Thus,

39

there is an abundance of information and resources that users can access and
employ in their academic study through computer technology.

For an experiment to explore the potential uses Of advanced computer
technology in higher education, the Athena Project at the Massachusetts
Institute of Technology makes libraries more accessible and provides
campuswide access to the course catalog, leading to a rich educational
environment (Balkovich et al., 1985). \NIth regard to teachers' use of
networking, Black et al. (1995) found that teachers use it for: (a) accessing
information such as that from ERIC, (b) using informational resources from
Gopher, (C) collecting information from all over the country, (e) encouraging
students to search library sources, and (e) accessing scientists or other
professionals for collaborative projects. For the purpose of this study Of the
other purposes Of use Of e-mail in a graduate community, other purposes Of use
included the following elements: (a) searching for information, (b) navigating
lntemet or World-Mde-Web, and (c) subscribing listservs.

In summary, the amount of use, collaborative use, and other purposes of
use of computer-mediated communication are the output variables included in
the model developed for this study. The amount Of use refers to how much an
individual uses e-mail for all purposes. Collaborative use refers tO the use of e-
mail for communicating and interacting between and among students and others,
to learn a particular idea or skill or to accomplish a particular learning task.
Other purposes Of use refers to the use Of e-mail for reading, finding or
searching for information. Theoretical definitions of the output variables are

presented in Table 3.

40

Table 3. Theoretical definitions of the output variables

 

 

Variable Theoretical Definition
Amount of Use How much a student uses e-mail systems for all
purposes
Collaborative Use Of e-mail systems for communicating and
Use interacting between and among students, faculty

and others, to learn a particular idea or skill or to
accomplish a particular learning task

Other Purposes Of Use Of e-mail systems for reading, finding or
Use searching for information

 

Research Hypotheses

The researcher's main purpose in this study was to investigate the factors
that influence the use of e-mail, with special attention to the collaborative use of
e-mail in a graduate community. The researcher first investigated whether
personal input factors (ability, motivation, and personality) influenced the amount
of use (time and messages), collaborative use, and other purposes Of use of e-
mail in a graduate community. Second, the researcher investigated whether
environmental input factors (learning, support, and opportunity to use)
influenced the amount of use (time and messages), collaborative use, and other
purposes Of use of e-mail in a graduate community. Last, the relationship
between and among input and output factors was investigated. The following

hypotheses were formulated to analyze the data collected in this study.

41

Hypothesis 1

Personal input factors (ability, motivation, and personality) can predict the
amount of use, collaborative use, and other purposes Of use Of e-mail in a
graduate community.

1.1. Ability can predict the amount Of use, collaborative use, and other

purposes Of use Of e-mail in a graduate community.

1.2. Motivation can predict the amount Of use, collaborative use, and

other purposes Of use Of e-mail in a graduate community.

1.3. Personality can predict the amount of use, collaborative use, and

other purposes of use Of e-mail in a graduate community.

Hypothesis 2

Environmental input factors (learning, support, and opportunity tO use)
can predict the amount Of use, collaborative use, and other purposes Of use Of e-
mail in a graduate community.

2.1. The effectiveness Of prior learning experience can predict the amount

of use, collaborative use, and other purposes Of use Of e—mail in a

graduate community.

2.2. Support for using e-mail can predict the amount Of use, collaborative

use, and other purposes of use Of e-mail in a graduate community.

2.3. Opportunity to use can predict the amount Of use, collaborative use,

and other purposes of use of e-mail in a graduate community.

Hypothesis 3

There is a relationship between pairs Of personal input factors (ability,

motivation, and personality) .

 

ar

50."

Oil

bit

he

Ie

 

 

 

42

Hypothesis 4

There is a relationship between pairs Of environmental input factors

(learning, support, and Opportunity to use).

Hypothesis 5

There is a relationship between the personal input factors (ability,
motivation, and personality) and the environmental input factors (Ieaming,

support, and opportunity to use).

Hypothesis 6

There is a relationship between the input factors (ability, motivation,
personality, learning, support, and Opportunity to use) and the output factors

(amount Of use, collaborative use, and other purposes of use of e-mail).

Hypothesis 7

There are mean differences between the categorical demographic
variables (age, gender, nationality, major, educational level of the user, amount
Of time at a certain educational organization, and experience Of using e-mail)
and the output factors (amount of use, collaborative use, and other purposes of

use of e-mail).

 

st
in
fart
ram
Elude

With!

CHAPTER III

METHODOLOGY

The researcher's main purpose in this study was to investigate the factors
that influence the use Of e-mail, with special attention to the collaborative use Of
e-mail in a graduate community. The methodology used in addressing the
research hypotheses is described in this section. Included are descriptions Of
the educational setting, the study sample, the research method, the instrument,

and data-analysis procedures.
Setting

TO systematically study the variables Chosen for this investigation, the
researcher needed a graduate community whose members are encouraged to
use e-mail. Vlfithin the graduate community, many units, such as colleges,
special Offices, and particular courses, encourage their members to use e-mail.
For example, Michigan State University provides registered students with the
Pilot e-mail system or other kinds Of e-mail systems. Colleges encourage
students to use e-mail, Internet, or World-Vlfide-Web. In this study, graduate-
level educational communities were selected because graduate education has
particular characteristics, such as frequent contacts between students and
faculty and among students. Interaction with peers and faculty provides
students with active and effective learning activities, enabling them to tap the

combined knowledge Of the group in a collaborative Ieaming environment.

43

44

Special Offices, such as the Office Of International Studies in Education at
Michigan State University, provide electronic networking for international
graduate students and intemationally—oriented faculty in the College Of
Education, which is called INT-ED. INT-ED is an e-mail listserv in the form Of a
bulletin board. TO promote communication among all Of the international
graduate students in the College Of Education, individual students are strongly
encouraged and supported to take part in that e-mail system because it allows
participants to communicate and share discussion topics together or individually.

Special courses, such as a seminar class in the Counseling, Educational
Psychology and Special Education Department in the College Of Education at
Michigan State University require its first year doctoral students to take a
seminar Class. It is strongly recommended that the students in this seminar
class use e-mail because it allows students many possibilities to communicate
with other students and instructors regarding their class assignments and to
further their academic life.

For this study, these two groups of students were selected as targets Of
the study: international graduate students and students taking the graduate
seminar Class. Both groups had similar characteristics—that is, they were full
time graduate students majoring in education at Michigan State University.
However, the international graduate students used English as a second
language and were supported by the Office of International Studies, whereas
some Of the seminar students used English as a first language and were
supported by their classroom instructors. One Objective of both groups is to
facilitate communication among students as well as between faculty and
students in the College Of Education. Although these two groups were varied
within general purposes, they both had in common the use of e-mail as one

means Of providing opportunities for students and faculty to share ideas and

 

 

for
the

IE1

Table

\
limb
Slide

45

information, as well as for encouraging cooperative work in education. Thus, all
these students were generalized to be part of one group. The Office Of

International Studies in Education endorsed this study.
The Sample

International graduate students and doctoral students from three
consecutive cohorts of a doctoral seminar class offered by the College Of
Education at Michigan State University were selected as participants in the
study. In this way, data could be collected on student members of an
educational group who were strongly encouraged to belong to and use an e-mail
system, to discover whether they used the system for collaborative learning
purposes and what factors seemed to be related to that use. Although this study
was primarily concerned with the factors influencing the collaborative use Of
e-mail in an educational setting, an attempt was made to identify the factors
affecting e-mail use that might be applicable to other educational organizations.

One group Of participants included all 109 international graduate students
enrolled in the College Of Education in Spring 1995. At time of study, three-
fourths Of these international students subscribed to INT-ED networking. Of
these 109 students, 60 were Asian, 22 were African, 13 were Middle Eastern, 7

were Canadian, 5 were Latin American, and 2 were European (see Table 4).

Table 4. Breakdown of the international group Of participants by continent

 

Continent
Middle Latin
Asia Africa East Canada America Europe
Number Of
Students 60 22 13 7 5 2

 

46

The other group of participants included 73 graduate students from three
consecutive cohorts Of a doctoral seminar, CEP 900 (1992, 1993, and 1994)
which is Offered by the College Of Education at Michigan State University. As
seen in Table 5, 28 Of these students were from the 1992 seminar class, 27 from
1993, and 18 from 1994. Seventeen students in the seminar group were
international students; thus, the doctoral seminar group comprised 56 students.
In total, 165 graduate students in the College Of Education at Michigan State

University made up the potential sample for this study.

Table 5. Breakdown Of the doctoral seminar group of

 

 

 

participants by cohort
Year
1992 1993 1994
Number Of students 28 27 18
Number Of seminar students 5 8 4
overlapping with the international
fiOUIL

 

Methods

The survey method was used in this study. Survey research is the most
basic method in the social sciences and can be used creatively to gather
information on both reactions to existing media and predictions Of future use
(Johansen, Miller, & Vallee, 1974; Kerr & Hiltz, 1982). Thus, for the purpose Of
this study, to collect data about factors related to the use Of a specific medium, a
survey was the most appropriate method to use. In the first phase, the
researcher developed questions regarding the input and output factors after

reviewing pertinent literature.

47

In the second phase, the questionnaire was reviewed for clarity, length,
and format by 12 graduate students taking classes regarding technology and
education at Michigan State University and four faculty members in the College
Of Education in a pilot test. Seven students responded that the length Of the
questionnaire was "just right," whereas five students said it was too long. Most
Of students (10) responded that the format of the questionnaire was easy tO
follow. In addition, information was gathered with regard to such aspects as item
wording, response options, and layout. The students and faculty also provided
feedback on the clarity and appropriateness of specific items. According to their
feedback, some items were reworded and rescaled.

TO assess the content validity Of the 10 questionnaire items concerning
the collaborative use of e-mail, three faculty members, including the researcher's
advisor, Checked these items. As a result, open-ended questions were added to
elaborate on the collaborative use Of e-mail.

In the third phase, after the instrument had been approved by the
University Committee on Research Involving Human Subjects (UCRIHS), it was
distributed by US. mail to 165 graduate students with a pie-stamped envelope
for return in April, 1995. TO encourage participation, an advertising message
was sent tO the target samples via e-mail, at the time the questionnaire was
mailed. The questionnaires were coded for the purpose Of checking the
respondents. Three weeks after the questionnaire was sent to the target
samples, a second mailing was sent to members of the target samples who had
not yet responded. In a month, 123 questionnaires were returned, for a
response rate of 75%. Among unrespondents, some of them were out Of country
or had wrong mail addresses and Characteristics Of others seemed to be similar

with respondents, in terms Of major, nationality, and educational level (master's

48

or doctoral). Thus, unrespondents might not make a difference on the results Of

research findings.
The Instrument

The instrument used in the study was a survey. According to the
suggested model, the questionnaire contained items concerning the personal
factors, environmental factors, and output factors of interest in the study.
Background information on the respondents was gathered, as well. A copy Of
the questionnaire can be found in Appendix A. The variables addressed in the

questionnaire and their Operational definitions are described below.
Personal Variables

The personal input variables addressed in this study were ability,
motivation (perception and attitude), and personality variables. Ability refers to
skills related to using e-mail systems. Motivation refers to the students'
perceptions about and attitudes toward use e-mail systems. Personality refers
to cognitive and affective structures maintained by individuals to facilitate their
adjustments to events. Questionnaire items pertaining to each personal input

variable are detailed in the following paragraphs.

Ability. Ability was measured by the knowledge necessary to use email
systems, which included the following skills: writing, reading and typing in
English. Participants responded to these three items using a 5-point Likert-type
scale (ranging from strongly agree to strongly disagree). Because ability also
means knowledge about computer hardware and software and about specific e-
mail systems employed by the user, the subjects were asked their perceptions Of

their own knowledge of computers and e-mail systems (from highly experienced

49

to inexperienced). Total scores were derived by adding all items. Higher scores

reflected better ability.

Motivation to use e-mail: Respondents were asked to indicate their
perceptions about the following aspects of using e-mail (usefull useless, fastl
slow, unnecessary/ necessary, difficult! easy, simple/ complex, comfortable/
uncomfortable, inefficient! efficient, convenient! inconvenient). They rated each
characteristics using a 5-point scale. These dimensions came from Ku's (1992)
perceived utility and ease Of use Of e-mail using a 7-pOint scale. However, it is
measured by asking respondents to rate using a 5-point scale. DeVellis (1991)
recommended that the number Of Options be reduced to as few as five.

\NIth regard to attitudes about use of e-mail, Clarke's (1991) Computer-
Mediated Communication Apprehension Scale was modified and rewarded
slightly to apply specifically tO use Of e-mail, rather than computer-mediated
communication technologies in general. The Computer-Mediated
Communication Apprehension Scale includes three dimensions: (a) confidence
in using computer-mediated communications, (b) interest in communicating with
others via the computer. and (C) concerns about the privacy Of computer-
mediated communication, which represented users' attitudes toward using a
computer-mediated communication technology. The scale is composed Of 20
statements to which subjects respond using a 5-point Likert-type scale (ranging
from strongly agree to strongly disagree); eight items measure confidence,
seven items measure interest, and five items measure concerns about privacy.
Clarke reported a coefficient alpha of .92 for the confidence dimension, .91 for
the interest dimension, and .87 for the concerns-about-privacy dimension. The
overall coefficient alpha was .94. Total scores were derived by adding the

responses to all items. Higher scores reflected greater motivation tO use e-mail.

50

Personality. Locus Of control was used as a measure Of personality
because locus Of control is a stable personality trait that is likely to affect
individuals‘ motivation and ability to use the computer for communication.
Levenson's (1974) scale was Chosen for use in this study because she
elaborated Rotter's locus Of control scale, especially the concept that externals
expect that fate, chance, or powerful others will control events. The scale was
constructed to measure belief in chance expectancies as separate from a
powerful-others orientation (Lefcourt, 1976). It is designed to determine the
relationship between one's expectancies for control and participation in new
environments and to be a conceptually clearer than Rotter's locus Of control
scale. The scale includes three dimensions: powerful-others control, internal
control, and chance control. It is composed of eight items representing each
dimension, to which participants respond using a 5-point Likert-type scale
(ranging from strongly agree to strongly disagree). Levenson reported the
reliability Of the instrument to be .77 for the powerful-others dimension, .78 for
the Chance—control dimension, and .64 for the internal-control dimension. Total
scores were derived by adding the responses to all items. Higher scores
reflected greater internal locus Of control. Operational definitions and items
addressing the personal input variables in the questionnaire are summarized in

Table 6.
Environmental Variables

The environmental input variables addressed in this study were the prior
learning experience, support for using e-mail systems, and opportunity to use.

The prior learning experience refers to how effectively the knowledge

51

Table 6. Operational definitions and items addressing the personal input
variables

 

 

Variable Operational Definition Items Scale
Ability Self-report of writing, reading, and 8(a,b,c), Low-Hi
typing skills in English, and degree 9, 10, (5-25)
Of knowledge Of computers and e-
mail systems
Motivation SeIf-report Of perceptions about and 11, Poe-Neg
attitudes toward using e-mail 12, (8-40)
Low-Hi
(20-100)
Personality Self-report Of how students perceive 13, Low-Hi
their control Of their lives (This may (24-120)

relate to controlling new technology
such as the e-mail environment and
usage)

 

needed to use e-mail systems is conveyed. Support refers to physical and
psychological support, which can encourage the use Of e-mail systems.
Opportunity tO use refers to users' access tO e-mail systems. Items pertaining to

each Of the environmental input variables are described below.

Prior learning experience: These questions concerned where the users
Ieamed to use e-mail and how effective that method was in helping them to use
e-mail. Respondents were asked to indicate their answer using a 5-point scale
(ranging from very effective to very ineffective). There was also a "not
applicable" category. Total scores were derived by adding the scores on all

items in this section.

52

Support: The respondents were asked whether they had a supportive
environment when they communicated with someone through e—mail. Eight
questions were asked, to which students indicated their agreement or
disagreement using a 5-point Likert-type scale (ranging from strongly agree to
strongly disagree). Total scores were derived by adding the scores on all items
in this section. Higher scores reflected users having more support for using e-

mail.

Opportunity to use: This variable addressed users' access to the e-mail
systems. Respondents were asked how easy it was for them to access to e-mail
systems (from very easy to very difficult). Higher scores on this item reflected
easier access to e-mail systems. Operational definitions and items addressing
the environmental input variables in the questionnaire are summarized in Table

7.

Table 7. Operational definitions and items addressing the environmental
input variables

 

 

Variable Operational Definition Items Scale
Prior learning Self-report of perceived prior learning 16, Low-Hi
experience experience to use e-mail (1 -20)
Support Self-report of the degree of help and 17, Low-Hi
encouragement for using e-mail (8-40)
Opportunity Self-report of ease of access to e-mail 15, Easy-Diffi.

to use systems (1 -5)

 

53

Output Variables

The output variables addressed in this study were amount of use,
collaborative use, and other purposes of use of e-mail. Amount of use refers to
the number of messages sent and received, and the amount of time an individual
spent online. Collaborative use refers to using e-mail for communicating and
interacting between and among students, as well as between students and
faculty, for learning a particular idea or skill or for accomplishing particular
Ieaming tasks. Other purposes of use refers to using e-mail for reading or

searching for information.

Amount of use: To measure the amount of use, students were asked
about the frequency with which they used e-mail, the amount of time they used

e-mail each week, and the number of messages sent and received each week.

Collaborative use: Respondents were asked about their collaborative use
of e-mail. To measure the collaborative use of e-mail, 10 types of usage were
listed; frequency of use was measured with a scale ranging from more than once
a day to never. Total scores were derived by adding the scores on all items.

Higher scores reflected more collaborative use of e-mail.

Other purposes of use: The respondents were asked about other
purposes of using e-mail, such as reading and searching for information. To
measure other purposes of use, five uses were listed and measured with a
frequency scale ranging from more than once a day to never. Total scores were
derived by adding the scores on all items. Higher scores reflected more other
purposes of use of e-mail. Operational definitions and items addressing the

output variables in the questionnaire are summarized in Table 8.

54

Table 8. Operational definitions and items addressing the output variables

 

 

Variable Operational Definition Items Scale
Amount of use Self-report of amount of time using e- 19,

mail and number of messages sent and 20, 21,

received
Collaborative Self-report of communicating and 22, Low-Hi
use interacting between and among (10-50)

students, faculty and others via e-mail,
to learn a particular idea or skill or to
accomplish a particular learning task

Other purposes Self-report of using e-mail for reading 23, Low-Hi
of use or searching for information (5-25)

 

Background Information

Background information on respondents' age, gender, educational level
(master's or doctoral), major, nationality, the amount of time at an organization
(from less than 1 year to more than 4 years), and experience with e-mail (from
less than 1 year to more than 4 years) was sought. The last part of the
questionnaire contained three open-ended questions. One of these questions
concerned how e-mail had been valuable to respondents' academic study.
Respondents also were asked whether they had used e-mail in ways they did
not find particularly helpful. Finally, respondents were asked for additional
comments they would like to make. Answers to the open-ended questions were

not analyzed on the computer, but they are discussed in Chapter IV.

 

Cron
multl
mar
Codi
‘Mm

alpha
mam

andz.
depend
tmpOrla
Variable
W983i
imad
fault

55

Statistical Analysis

To answer the research hypotheses, several different types of statistical
analyses were used on the data collected in the questionnaires. The statistical
procedures that were employed in analyzing the data for this study are explained

in the following sections.

Reliability Analysis

After the data are collected, reliability can be measured using the
Cronbach alpha coefficient. Reliability analyses were conducted on all of the
multi-item scales included in the questionnaire. The reliability coefficient of a
test refers to the consistency of the evaluation results. The Cronbach alpha
coefficient indicates the degree to which responses to individual items correlate
with the total score (Mehrens & Lehmann, 1984). In general, a high coefficient
alpha indicates that a scale has a good degree of homogeneity, and therefore

that the items are measuring the same construct.
Multiple Regression Analysis

Multiple regression analyses were conducted to examine Hypotheses 1
and 2, assessing the relationship between the independent variables and the
dependent variables. The results of this analysis indicated the relative
importance of each independent variable to the prediction of each dependent
variable. Lewis-Beck (1989) pointed out two advantages of using multiple
regression. The first advantage is that this technique offers a fuller explanation
of the dependent variable than does bivariate regression, as few phenomena
result from a single cause. The second advantage is that it makes the effect of a

particular independent variable more certain by removing the possibility that

 

liege

 

CONE

eaSe

56

distorting influences from the other independent variables are present. Two
univariate and one multivariate regression equation were calculated, each of
which represented hypothesized relationships among the variables. Each
equation was checked for possible violations of assumptions by using residual

scatterplots.

Assumptions

Many assumptions are not crucial in describing a data set. But if
inferences are to be made about the population from which the data were drawn,
satisfaction of those assumptions will substantially increase the number of useful
inferences. The following is a brief discussion of whether the variables in the

model meet those assumptions (Tabachnick 8 F idell, 1983, pp. 93-95).

1. Normality. In the population, the scores on the dependent variable are
normally distributed for each of the possible combinations of the levels of
the X variables.

2. Homoscedasticity. In the population, the variances in the dependent
variable for each of the possible combinations of the levels of the X
variables are equal.

3. Linearity. In the population, the relationship between the dependent
variable and an independent variable is linear when all other independent

variables are held constant.

Assumption of normality, homoscedasticity, and linearity can be examined
by inspecting a scatter plot of residuals between predicted dependent variable
scores and error of prediction. Violation of the preceding three assumptions can
be corrected by transforming the data. Although transformed variables will not

increase difficulties in interpretation, they do not always perform better than the

   

ilysgs

 

57

original ones. Fortunately, regression analysis is robust to many violations of
these three assumptions (Pedhazur, 1982; Tabachnick & F idell, 1983). The
researcher decided to check the variables for serious departures from the three
assumptions listed above. When data transformation was considered
necessary, the analysis was repeated for the transformed and original variable.
For multiple regression, multicollinearity may result in difficulties in
estimating regression statistics. Multicollinearity occurs when two independent
variables are perfectly, or nearly perfectly, correlated with each other, or when
one independent variable is perfectly correlated with the combination of other
independent variables (Lewis-Beck, 1980, pp. 58-62). Multicollinearity can be
detected by first producing a correlation matrix for all independent variables.
Correlation coefficients that are above .8 reveal redundant variables. Multiple
regression is then conducted, with each variable in turn serving as a dependent
variable and all others as independent variables. A high squared multiple

correlation indicates multicollinearity among independent variables.
Bivariate Correlaflms

To test Hypotheses 3 and 4, Pearson correlation tests were conducted to
determine the direction and magnitude of linear associations (Glass & Hopkins,
1984) among the independent variables (ability, motivation, personality, prior

learning experience, support for using e-mail, and opportunity to use).

Canonical Correlation Analysis

 

Canonical correlation analysis is for analyzing the relationship between
two sets of variables. To test Hypotheses 5 and 6, canonical correlation

analysis was performed to determine the relationship between the set of

r—.

pe
the

life
vari.

3f8‘

or a
inde;

inde;

 

 

58

personal input factors and the set of environmental input factors, and between

the set of input factors and the set of output factors.

Analysis of Variance and t-Test

An analysis of variance and a t-test were performed to find the mean
differences between the categorical demographic variables and the dependent
variables for Hypothesis 7. The purpose of an analysis of variance and a t-test
are to compare the means among different groups or levels to determine
whether the observed differences between them represent a chance occurrence
or a systematic effect. An analysis of variance compares groups which differ on
independent variables with two or more levels, whereas t-test compares one

independent variable with two levels.

 

the
e-n
per
of I
ma
en»
infll

DUI

the
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lest

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late
year:

We

CHAPTER IV
RESEARCH FINDINGS

The researcher's main purpose in this study was to investigate the factors
that influence the use of e-mail, with special attention to the collaborative use of
e-mail in a graduate community. The first step was to investigate whether
personal input factors (ability, motivation, and personality) influenced the amount
of use (time and messages), collaborative use, and other purposes of use of e-
mail in a graduate community. The second step was to investigate whether
environmental input factors (learning, support, and opportunity to use)
influenced the amount of use (time and messages), collaborative use, and other
purposes of use of e-mail in a graduate community.

The results of the data analyses are presented in this chapter. First, the
characteristics of the sample are described. Next, descriptive statistics including
reliability (of the each scale that was used in the study, are reported. Third, the
results of the statistical analyses that were used to examine each of the research

hypothesis are reported. Finally, the responses to the open-ended questions

are presented.

Sample Characteristics

The total number of respondents was 123, corresponding to a response
rate of 75%. As seen in Table 9, the respondents averaged approximately 34
years of age (mean = 33.9, id = 7); 43% (g = 53) were male and 57% (_n = 70)

were female. Most of the respondents were doctoral students ([1 = 97, 79%).
59

60

Sixty percent (51 = 74) of the respondents were in the Department of Counseling,
Educational Psychology, and Special Education, whereas 24% (_n = 30) were in
the Department of Teacher Education, 8% (g = 10) were in the Department of
Physical Education and Science, and 6% (g = 7) were in the Department of
Educational Administration. The nationalities of the respondents were as
follows: Asia (41%), United States (30%), Africa (14%), Middle East (7%), Latin
America (4%), Europe (2%), and Canada (2%). They had been at Michigan
State University an average of 4 years (g = 1.8) and had experience using e-

mail for an average of 3 years (g = 1.7).
Prelim_inarv Evaluation of the Data

Data were analyzed using SPSS for \Mndows (Norusis, 1993). Several
steps were taken to screen the data before analysis. As indicated by
Tabachnick and Fidell (1983), the first step is to inspect out-of-range values,
plausible means and dispersions, and variations for accuracy of input.
Frequencies were run for all variables to check for entry errors. Variables with
suspicious distributions were checked for accuracy of input. Five respondents
who did not use e-mail were excluded from further data analysis; therefore. 118
respondents were used.

The next step was to identify outliers. Outliers are cases with such
extreme values on one variable that they unduly affect the average value or the
variability of scores. To detect outliers, the scatter plot of each variable and
standardized residuals above 3 were checked (T abachnick & Fidell, 1983, p.74).
By using this criterion, two variables were found to have outliers: the number of
e-mail messages sent and received per week and the amount of time spent one-
mail per week. Frequencies were run a second time to ensure that outlying

cases were properly treated.

 

Table 9. Characteristics of the respondents

61

 

 

 

 

 

 

Attribute fl % Mean SD Range
Age 33.9 7.1 22-52
Gender
Male 53 43.1
Female 70 56.9
Total 123 100.0
Educational level
Master 25 20.3
Doctoral 97 78.9
Others 1 0.8
Total 123 100.0
Major
Counseling, Educational 74 60.2
Psychology & Special
Educafion
Teacher Education 30 24.4
Physical Education 10 8.1
& Exercise Science
Educational Administration 7 5.7
Others 2 1.6
Total 123 100.0
Nationality
Asia 50 40.7
USA 37 30.1
Africa 17 13.8
Middle East 9 7.3
Latin America 5 4.1
Canada 3 2.4
Europe 2 1.6
Total 123 100.0

 

Difso
deper.
U39)

mum,

62

 

 

 

 

Table 9 (Continued)
Attribute fl % Mean SQ Range

Years at MSU 3.59 1.84 1-6
less than 1 year 27 22.0
1 year 5 4.1
2 year 32 26.0
3 year 20 16.3
4 year 5 4.1
more than 4 years 34 27.5
Total 123 100.0

Experience using E-mail 3.07 1.69 0-6
no experience 5 4.1
less than 1 year 24 19.5
1 year 14 11.4
2 year 32 26.0
3 year 25 20.3
4 year 8 6.4
more than 4 years 15 12.2
Total 123 100.0

Descriptive Statistics

In this study, there were six independent variables (ability, motivation,
personality, prior learning experience, support, and opportunity to use) and three
dependent variables (amount of use, collaborative use, and other purposes of
use). Descriptive information on each variable and alpha coefficients for the

multi-item scales used in this investigation are provided below.

 

 

W33
Who
The
War

relic

63

Independent Variables

In this study, four scales (ability, motivation, personality, and support) and
two individual items (prior Ieaming experience and opportunity to use) were
treated as independent variables. Information on each of these variables is

discussed in the following paragraphs.

Ability

The ability scale included five items regarding skill in English and skill in
using computers. Descriptive information regarding ability is presented in Table
10. As shown in the table, the mean scores of the five items ranged from 1.40 to
5.00. The average score was 3.71 out of 5, with a standard deviation of .70.

The average score indicated that respondents had good ability for using e-mail.

Table 10. Descriptive data for the ability scale

 

Mean 3.71 Std Dev .70
Minimum 1.40 Maximum 5.00

 

To assess the degree of reliability of the ability scale, coefficient alpha
was calculated. The coefficient alpha for the five items was .78. Three subjects
who did not complete all of the items were excluded from the analysis (3 = 115).
The coefficient alpha obtained for ability indicated a moderately high degree of
internal consistency. Results of the reliability analysis for the ability items are

reported in Table 11.

TI

 

 

Not

C01
Sta

64

Table 11. Results of the reliability analysis for the ability items (N = 115)

 

 

Corrected
Item-Total
Item Mean SD Correlation
1. l have good typing skills in English 3.88 1.07 .56
2. I have good reading skills in English 4.28 .89 .67
3. I have good writing skills in English 4.07 1.04 .67
4. Using e-mail, do you consider 3.14 .81 .39
yourself:
5. Using computers, do you consider 3.24 .85 .47
yourself:

 

Note: Alpha = .78 (5 items)

Corrected item-total correlation: Correlates the item being evaluated with all the

scale items, excluding itself

Scale for items 1, 2, and 3: Strongly agree = 5, Agree = 4, Neutral = 3,

Disagree = 2, Strongly disagree = 1

Scale for items 4 and 5: A highly advanced user = 5, An advanced user = 4,
An intermediate user = 3, A beginner = 2, lnexperienced = 1

65
Motivation

The motivation scale included 28 items regarding perceptions about and
attitudes toward using e-mail. Descriptive information regarding motivation is
presented in Table 12. As shown in the table, the mean scores of the 28 items
ranged from 2.39 to 5.00. The average score was 3.91 out of 5, with a standard
deviation of .53. The average score indicated that respondents had positive

perceptions about and attitudes toward using e-mail.

Table 12. Descriptive data for the motivation scale

 

Mean 3.91 Std Dev .53
Minimum 2.39 Maximum 5.00

 

To assess the degree of reliability of the motivation scale, coefficient
alpha was calculated. The coefficient alpha for the 28 items was .93. Seven
subjects who did not complete all of the items were excluded from the analysis
(N = 111). The coefficient alpha obtained for the motivation scale indicated a
high degree of internal consistency. Results of the reliability analysis for the

motivation items are reported in Table 13.

66

Table 13. Results of the reliability analysis for the motivation items

 

 

(Ll = 111)
Corrected
Item-Total

Item Mean SD Correlation

1. useful I useless 4.59 .66 .60

2. fast I slow 4.49 .73 .44

3. necessary I unnecessary 4.19 .93 .42

4. easy / difficult 4.11 .90 .54

5. simple I complex 4.00 .96 .43

6. comfortable / uncomfortable 4.10 .93 .68

7. efficient/ inefficient 4.35 .72 .55

8. convenient I inconvenient 4.46 .76 .43

9. l have no fear of using e-mail to 4.06 .95 .68
communicate with other people

10. Using e-mail to participate in a group 3.12 .99 .55
discussion is very exciting to me

11. I enjoy sending messages to others 3.97 .92 .58
using e-mail

12. lfeelconfident in my ability to clearly 3.78 .98 .55
express ideas using e-mail

13. I am*not as good as others in using e- 3.27 1.11 .31
mail

14. I am calm and relaxed using e-mail to 3.66 .89 .71
share ideas with others

15. I use e-mail with confidence 3.87 .84 .73

67

 

 

Table 13 (Continued)
Corrected
Item-Total

Item Mean SD Correlation

16. It bothers me to think I probably will 3.37 1.00 .38
not know exactly who reads the e-
mail I send

17. It makes me nervous to use e-mail to 3.34 1.17 .56
communicate any important
information

18. In using e-mail to exchange valuable 3.45 1.07 .43
ideas, I am afraid my ideas might be
used without my permission

19. It makes me nervous to think that a 3.51 1.02 .45
lot of other people could read the e-
mail I send

20. I feel excited and enthusiastic 3.50 .92 .47
thinking about using e-mail to
communicate with others

21. I have the ability to be an effective e- 4.03 .80 .66
mail user

22. I am not very ggod at e-mail 3.72 .98 .60
communicating

23. Using e-mail to communicate with 4.12 .90 .63
others is just not worth the effort

24. If I had a choice, I would never use 4.33 .97 .60
e-mail to communicate with other
people

25. I feel insecure about my ability to use 3.98 .98 .61

e-mail*

68

 

 

Table 13 (Continued)
Corrected
Item-Total
Item Mean SD Correlation
26. The challenge of continuing to learn 3.56 .94 .32
to use e-mail is exciting
27. If given the opportunity, I use e-mail 4.13 .86 .53
28. I always feel someone is looking 4.07 .80 .36

over my shoulder monitoring every
message I send”

 

Note: Alpha = .93 (28 items)
* Item was reversed before analysis was conducted.

Scale for items from 1 to 8:
5 points for positive statement through 1 point for negative statement

Scale for items from 9 to 28: Strongly agree = 5, Agree = 4, Neutral = 3,
Disagree = 2, Strongly disagree = 1

Personaliy

The locus of control scale as a measure of personality included 24 items
regarding internal control, power control, and chance control. To assess the
degree of reliability of the personality scale, coefficient alpha was calculated.
The coefficient alpha for the 24 items was .82. Seven subjects who did not
complete all of the items were excluded from the analysis (N = 111). The
researcher eliminated three items whose item-total correlations were less than
.20. Some items had zero variances. After those three items were eliminated,
the coefficient alpha of the remaining 21 items on the personality scale
increased from .82 to .86. Descriptive information for the personality scale is

presented in Table 14. As shown in the table, the mean scores of 21 items

69

ranged from 2.55 to 4.65. The average score was 3.55 out of 5, with a standard
deviation of .44. Scores on the locus of control scale as a measure of

personality indicated that respondents were slightly internal.

Table 14. Descriptive data for the personality scale

 

Mean 3.55 Std Dev .44
Minimum 2.55 Maximum 4.65

 

The coefficient alpha obtained for the personality scale indicated a high
degree of internal consistency (.86). Results of the reliability analysis for the

personality items are reported in Table 15.

70

Table 15. Results of the reliability analysis for the personality items
(_=111)

 

 

Corrected
Item-Total

Item Mean SD Correlation

1. To a great extent my life Is controlled 3.67 .92 .54
by accidental happenings

2. I feel like what happens in my life is * 3.62 .90 .59
mostly determined by powerful people

3. When I make plans, I am almost 3.65 .79 .42
certain to make them work

4. Often there is no chance of protecting 3.51 .91 .55
my personal interest from bad
happenings

5. Men I get what I want, it's usually 3.90 .73 .52
because I am lucky

6. Although I might have good ability, I 3.07 1.06 .55
will not be given leadership
responsibility without appealing to
those In positions of power

7. I have often found that what Is going to 3.09 .85 .26
happen will happen

8. My life Is chiefly controlled by powerful 3.89 .76 .59
others

9. Whether or not I get into a car 3.61 .79 .46
accident Is mostly a matter of luck

10. People like myself have very little 3.44 .91 .56

chance of protecting our personal
interests when they conflict with those
of strong pressure groups

71

Table 15 (Continued)

 

 

Corrected
Item-Total
Item Mean SD Correlation

11 It's not always wise for me to plan too 3.63 .98 .52
far ahead because many things turn
out to be a matter of good or bad
fortune

12. Getting what I want requires pleasing 3.38 .86 .52
those people above me

13. Whether or not I get to be a leader 3.20 .97 .49
depends on whether I'm lucky enough
to be in the right place at the right
time

14. If important people were to decide 4.01 .80 .50
they didn't like me, I probably wouldn't
make many friends

15. I can pretty much determine what will 3.36 .90 .20
happen in my life

16. I am usually able to protect my 3.72 .58 .35
personal interests

17. Whether or not I get into a car 3.25 .78 .20
accident depends mostly on the other
driver

18. When I get what I want, it's usually 3.95 .70 .20
because I worked hard for it

19. In order to have my plans work, I 3.24 .87 .46
make sure that they fit in with the
desires 9f people who have power
over me

20. My life is determined by my own 3.75 .80 .32

actions

 

72

 

 

Table 15 (Continued)
Corrected
Item-Total
Item Mean SD Correlation
21. It's chiefly a matter of fate whether or 3.86 .83 .22
not I have a few friends or many

friends"'

 

Note: Alpha = .86 (21 items)
* Item was reversed before analysis was conducted.

Scale for items: Strongly agree = 5, Agree = 4, Neutral = 3, Disagree = 2,
Strongly disagree = 1

Prior Learning EQerience

The prior learning experience measure included four items regarding the
source of learning to use e-mail and the effectiveness of that learning. As
shown in Table 16, many users learned to use e-mail by themselves, using an
instruction sheet or manual (79%), and learning from friends showed very
effective (mean = 4.26). Reliability could not be measured because respondents
answered the items among four items which were applied to them. Overall,

respondents perceived the learning experience to have been effective.

73

Table 16. Information on the source and the prior learning experience
of e-mail

 

Mean of Number of

 

 

Source Effectiveness respondents Percent
workshop/class 3.11 18 15.2
myself using instructional manual 3.85 93 78.8
friends 4.26 74 62.7
others 4.00 12 10.1

Note : Mean = 4.05 _S_Q = .79 (4 items)

Scale for items: Very effective = 5, Effective = 4, Neutral = 3, Ineffective = 2,
Very ineffective = 1

Support

The support for using e-mail scale included eight items. To assess the
degree of reliability of the items dealing with support for using e-mail, coefficient
alpha was calculated. The coefficient alpha for the eight items was .52. Ten
subjects who did not complete all of the items were excluded from the analysis
(_h_l = 108). The researcher eliminated three items whose item-total correlations
were less than .20. After those three items were eliminated, the coefficient alpha
of the remaining five items addressing this variable increased from .52 to .59.
Descriptive information for the support measure is presented in Table 17. As
shown in the table, the mean scores of the five items ranged from 1.00 to 5.00.
The average score was 2.77 out of 5, with a standard deviation of .78. The
average score indicated that respondents did not have much help and

encouragement for using e-mail.

74
Table 17. Descriptive data for the support measure

 

Mean 2.77 Std Dev .78
Minimum 1.00 Maximum 5.00

 

The coefficient alpha obtained for the support items (.59) indicated a
moderate degree of internal consistency. Results of the reliability analysis of the

items assessing support for the use of e-mail are presented in Table 18.

Table 18. Results of the reliability analysis for the support items (_N_ = 108)

 

 

Corrected
Item-Total
Item Mean SQ Correlation
I use e-mail, because:
1. I am required to for class 2.54 1.34 .45
2. I am required to for other works 2.77 1.34 .25
3. there is someone who encourages me 2.98 1.10 .33
to use it
4. there is a person who can manage 2.90 1.08 .36
discussion by e-mail
5. there is a person who can help me 2.61 1.05 .32
about using e-mail systems, such as
file transfer

 

Note: Alpha = .59 (5 items)

Scale for items: Strongly agree = 5, Agree = 4, Neutral = 3, Disagree = 2,
Strongly disagree = 1

75

Opportunity to Use

The opportunity-to-use measure included one item regarding easy access
to the e-mail systems. Descriptive information regarding access to the e-mail
system is presented in Table 19. As shown in the table, the mean scores of
items addressing ease of access to the e-mail system ranged from 2.00 to 5.00.
The average score was 4.39 out of 5, with a standard deviation of .85.

Reliability could not be measured because the item addressing easy of access
to the e-mail systems comprised one item. The average score indicated that

respondents had very easy access to the e-mail system.

Table 19. Descriptive information for access to the e-mail system

 

 

Mean 4.39 Std Dev .85
Minimum 2.00 Maximum 5.00
Note:

Scale for items: Very easy = 5, Easy = 4, Neutral =3, Difficult = 2,
Very difficult =1

Specifically, 102 (86%) respondents said they had easy access to the e-
mail system; only 10 of them indicated it was neutral and just 6 of them said it
was difficult. Thus, level of access to the e-mail systems was not a concern for
the users. Most of the respondents were enrolled as full-time graduate students,
who had their own account and easy access to the e-mail system for their
academic study.

There was also an additional item regarding where they have access to e-
mail; and many respondents said they used e-mail systems at home (75.4%) or
at the laboratory (58.5%). Information regarding where respondents used e-mail

systems is presented in Table 20.

76

Table 20. Places where respondents used e-mail systems

 

 

Number of
Place respondents Percent
Home 89 75.4
Laboratory 69 585
Office 47 39.8
Others 9 7.6

 

Dependent Variables

In this study, two scales (collaborative use and other purposes of use)

and one individual item (amount of use) were treated as dependent variables.
Amount of Use

The amount-of-use measure included three items regarding the amount of
time respondents spent on e-mail and the number of messages sent and
received. Descriptive information regarding the amount-of-use items is
presented in Table 21. As shown in the table, the average amount of time they
used e-mail was 123 minutes per week, with a large range from 3 minutes to 600
minutes. The average number of messages sent and received also covered a
large range, from 1 to 70 and from 1 to 400, respectively (see Table 21). There
was also an additional item on frequency of checking e-mail; and respondents
checked e-mail almost every day (mean = 5.15 times per week). Reliability
could not be measured because items addressing amount of use asked the
subjects to write the amount of time and the number of messages, and

comprised single item.

77
Table 21 . Descriptive information for the amount-of-use items

 

 

Item Mean g Range_
1. Frequency of checking e-mail per week 5.15 2 1-7
2. Amount of time (minutes) using e-mail per 123.48 130.31 3-600
week
3. Number of messages received per week 58.58 69.35 1-400
4. Number of messages sent per week 12.40 13.80 0-70

 

Note:

Scale for item 1: the number of checking e-mail per week from 1 to 7
Scale for item 2: the amount of time using e-mail per week
Scale for items 3 and 4: the number of e-mail messages per week

Collaborative Use

The collaborative use of e-mail scale included 10 items. Descriptive
information for the collaborative use scale is presented in Table 22. As shown in
the table, the mean scores of the 10 items ranged from 1.00 to 4.00. The
average score was 2.12 out of 5, with a standard deviation of .62. The average
score indicated that respondents used e-mail for a collaborative purpose about

once a month.

Table 22. Descriptive data for the collaborative use scale

 

Mean 2.12 Std Dev .62
Minimum 1.00 Maximum 4.00

 

78

To assess the degree of reliability of the collaborative use scale,
coefficient alpha was calculated. The coefficient alpha for the 10 items was .77.
Fifteen subjects who did not complete all of the items were excluded from the
analysis (N = 103). The coefficient alpha obtained for the collaborative use
scale indicated a moderately high degree of internal consistency. Results of the
reliability analysis of the items assessing collaborative use are reported in Table

23.
Other Pugposes of I_J_sg

The other purposes of use scale included five items. Descriptive
information for this scale is presented in Table 24. As shown in the table, the
mean scores of the five items ranged from 1.00 to 4.40. The average score was
2.31 out of 5, with a standard deviation of .79. The average score indicated that

respondents used e-mail for other purposes about more than once a month.

Table 24. Descriptive data for the other purposes of use scale

 

Mean 2.31 Std Dev .79
Minimum 1.00 Maximum 4.40

 

To assess the degree of reliability of the other purposes of use scale,
coefficient alpha was calculated. The coefficient alpha for the five items was
.68. Four subjects who did not complete all of the items were excluded from the

analysis (N = 114). The coefficient alpha obtained for this scale indicated a
moderate degree of internal consistency. Results of the reliability analysis of the

other purposes of use items are reported in Table 25.

79

Table 23. Results of the reliability analysis for the collaborative use items
(N = 103)

 

 

 

Corrected
Item-Total
Item Mean fl Correlation
I use e-mail to communicate with:
1. faculty for class assignments 2.04 .91 .47
2. faculty on research projects 1.91 .90 .24
3. my advisor on my graduate program 1.96 .86 .37
4. faculty on other topics of interest to me 2.00 .85 .51
5. students on class assignments 2.00 1.01 .45
6. students on course related topics 1.93 .96 .51
7. students on other topics of interest to 2.21 1.12 .55
me
8. students or faculty at other universities 2.07 1.03 .64
for exchanging information
9. family or friends in the US. for 2.60 1.29 .41
exchanging information
10. family or friends outside the US. for 1.88 1.17 .23

exchanging information

 

Note: Alpha = .77 (10 items)

Scale for items: More once a day = 5, Once a day = 4, Once a week = 3,
Once a month = 2, Never = 1

80

Table 25. Results of the reliability analysis for the other purposes of use
items (N = 114)

 

 

 

Corrected
Item-Total
Item Mean fl Correlation

I use e-mail and networks for:

1. searching the library (such as MAGIC) 2.47 .99 .43

2. reading bulletin board 2.35 1.28 .37

3. joining listservs (such as EGSO) 2.64 1.38 .42

4. searching the Internet with GOPHER 2.21 1.09 .54

5. searching the World VWde Web (such 1.85 1.17 .44

as Mosaic)

 

Note: Alpha = .68 (5 items)

Scale for items: More once a day = 5, Once a day = 4, Once a week = 3’
Once a month = 2, Never = 1

Summagy of Descriptive Statistics

In this study, there were six independent variables (ability, motivation,
personality, prior learning experience, support, and opportunity to use) and three
dependent variables (amount of use, collaborative use, and other purposes of
use). Descriptive information for each variable and alphas for the multi-item

scales used in this investigation are summarized in Table 26.

81
Table 26. Summary of descriptive statistics

 

 

Variable Mean SD Alpha
Independent Variable
Ability 3.71 .70 .78
Motivation 3.91 .53 .93
Personality 3.55 .44 .86
Learning 4.05 .79
Support 2.77 .78 .59
Opportunity to use 4.39 .85
Dependent Variable
Amount of use
time 123.48 130.31
message 35.22 36.54
Collaborative use 2.12 .62 .77
Other purposes of use 2.31 .79 .68

 

Multiple Regression Analysis

Multiple regression analyses were conducted to examine Hypotheses 1
and 2. Hypothesis 1stated that personal input factors (ability, motivation, and
personality) can predict the amount of use, collaborative use, and other
purposes of use of e-mail in a graduate community. Hypothesis 2 stated that
environmental input factors (learning, support, and opportunity to use) can '
predict the amount of use, collaborative use, and other purposes of use of e-mail
in a graduate community. According to the model, two univariate equations and
one multivariate regression equation were calculated, each of which represented

hypothesized relationships among the variables.

82

Results of Multivariate and Univariate Multiple Regression Analyses

A series of regression analyses were run to investigate the best predictor
of the amount of use (time and messages), collaborative use, and other
purposes of use of e-mail in a graduate community. Mean scores on the amount
of use (time and messages), collaborative use, and other purposes of use of e-
mail were used as the dependent variables and mean scores on the six
independent variables were used as predictors. The results of the six-variable
regression analyses for prediction of each dependent variable are presented in

this section.

Multivariate Multiple Regression Analysis
for the Amount of Use

For prediction of the amount of use (time and messages), a multivariate
multiple regression analysis was performed between the amount of time and the
number of messages as dependent variables and ability, motivation, personality,
learning, support, and opportunity to use as independent variables. Multivariate
multiple regression was used to take into account the intercorrelations (g = .51)
among the dependent variables (time and messages) and simultaneously to test
the effects of the independent variables on the dependent variables. Results of
the evaluation of assumptions also led to transformation of the dependent
variables to reduce skewness and improve the normality of residuals.
Logarithmic transformations were used on the amount of time using e-mail and
the number of messages sent and received.

As shown in Table 27, the omnibus multivariate test was statistically

significant at less than the .05 level. This multivariate test was followed by two

83

univariate regression analyses (one for each dependent variable). The results

of the two univariate regression analyses are presented in Table 28.

Table 27. Results of the multivariate E-test of six independent variables
by the amount of use (time and message)

 

Test Name Value Approx E Hypoth. gr; Error 91 SkLof E

Pillais .27 2.74 12.00 206.00 .00
Hotellings .36 3.09 12.00 202.00 .00
VIIIIks .72 2.91 12.00 204.00 .00
Rays .26

 

Note: E-statistic for WILKS' lambda is exact

Table 28. Univariate E-test results for the two dependent variables (time
and message)

 

 

Variable Sq. Mul. Adj. _R-sq. Hypoth. Error Il_ll_§ E Sig. 0f E
3 ML

Time .18 .13 4.05 1.04 3.88 .00

Message .24 .19 6.09 1 .09 5.53 .00

 

Note: Univariate E-test with (6, 103) Q

As shown in Table 28, both of the regression models were statistically
significant [time: 5 (6, 103) = 3.88 ; message: E (6, 103) = 5.53]; and 13% and
19% of the total variance in each dependent variable were explained by the six
variables included in the model. The results of the regression analyses on six
independent variables on the two dependent variables are presented in Table

29.

84

Table 29. Results of the univariate regression analysis for six independent
variables by two dependent variables

 

Dependent Variable .. The Amatfl of Time

Covariate B Beta Std. Err t-Value Sig. I
Ability .18 .12 .18 1.02 .30
Motivation .61 .30 .24 2.47 .01
Personality -.30 -.12 .24 -1 .27 .20
Learning -.10 -.07 .14 -.76 .44
Support -.07 -.05 .12 -.60 .54
Opportunity .18 .14 . 14 1.27 .20

Dependent Variable .. The Number of Messages

Covariate 8 Beta Std. Err t-Value Sig. 1’
Ability .19 .11 .19 1.05 .29
Motivation .66 .30 .25 2.61 .01
Personality -.58 -.22 .24 -2.34 .02
Learning -.19 -.13 .14 -1.38 .16
Support -.10 -.07 .13 -.81 .41
Opportunity .32 .24 .14 2.24 .02

 

As shown in Table 29, motivation was a statistically significant predictor
(at the .05 level) for both the amount of time spent on e-mail and the number of
messages sent and received, after controlling for the other independent
variables in the regression equation. Personality and opportunity to use e-mail
were significant predictors only for the number of messages, not the amount of
time. However, ability, prior learning experience, and support for using e-mail
were not statistically significant predictors (at the .05 level) for either the amount

of time spent on e-mail or the number of messages sent and received.

85

Univariate Multiple Regression Analysis
for Collaborative Use

For prediction of collaborative use of e-mail, a univariate multiple
regression analysis was performed between collaborative use of e-mail as the
dependent variable and ability, motivation, personality, Ieaming, support, and

opportunity to use as the independent variables (see Table 30).

Table 30. Results of the univariate regression analysis for collaborative
use of e-mail

 

 

i Sum of Squares Mean Squares
Regression 6 1 1.94 2.99
Residual 106 31.14 .29
E = 6.77 Signif. E = .00

 

As shown in Table 30, the six-variable regression was statistically
significant [E (6, 106) = 6.77]; and 27% of the total variance in the dependent
variable was explained by the six variables included in the model. The
researcher computed the partial regression coefficients for those six predictors
to investigate which of them had a statistically significant relationship with the
dependent variable. The t-test results for the partial regression coefficients of

the six independent variables are presented in Table 31.

86

Table 31 . The t-test results for the six predictors of collaborative use
of e-mail

 

 

 

Variable B SE 8 Beta I Sig. T
Ability .02 .09 .02 .24 .80
Motivation .37 .13 .31 2.85 .00
Personality .12 .12 .08 .98 .32
Learning .13 .07 .17 1.81 .07
Support .24 .06 .30 3.65 .00
Opportunity .01 .07 .07 .22 .82
(Constant) -1 .74 .08

 

As shown in Table 31, both motivation and support for using e-mail were
statistically significant predictors at less than the .05 level. However, ability,
personality, prior learning experience, and opportunity to use were not

statistically significant predictors at the .05 level.

Univariate Multiple Regression Analysis
for Other Purposes of Use

For prediction of other purposes of use of e-mail, a univariate multiple
regression analysis was performed between other purposes of use of e-mail as
the dependent variable and ability, motivation, personality, Ieaming, support,
and opportunity as the independent variables. As shown in Table 32, the six-
variable regression was statistically significant [E (6, 106) = 6.15]; and 25% of
the total variance in the dependent variable was explained by the six variables

included in the model.

87

Table 32. Results of the univariate regression analysis of other
purposes of use of e-mail

 

 

df Sum of Squares Mean Squares
Regression 6 18.00 3.00
Residual 106 51.72 .48
E = 6.15 Signif. E = .00

 

The researcher computed the partial regression coefficients for those six
predictors to investigate which of them had a statistically significant relationship
with the dependent variable. As shown in Table 33, both motivation and
opportunity to use were statistically significant predictors at the .05 level. Ability,
personality, prior Ieaming experience, and support for using e-mail were not

statistically significant predictors at the .05 level.

Table 33. The g-test results for the six predictors for other purposes of
use of e-mail

 

 

 

Variable B SE 8 Beta I Sig. T
Ability .12 .12 .10 .98 .32
Motivation .34 . 16 .23 2.05 .04
Personality -.20 .16 -.1 1 -1.26 .20
Learning .05 .09 . 12 .59 .55
Support .12 .08 .23 1.42 .15
Opportunity .22 .09 .23 2.25 .02

(Constant) -.38 .70

 

88

 

Summagy of Multiple Regression Analysis Results

The results concerning each research hypothesis for which multiple

regression analysis was used are summarized in the following paragraphs.

Hypothesis 1.1:

Ability can predict the amount of use, collaborative use, and other
purposes of use of e-mail in a graduate community.

To answer Hypothesis 1.1, the researcher used a g-test of the partial
regression coefficient of ability on each of the three dependent variables. As
shown in tables, gwas 1.02 and 1.05 for the amount of use (time and messages,
respectively), 1 was .24 for collaborative use, and gwas .98 for other purposes of
use. The test results indicated that ability was not a significant predictor of the
amount of use, collaborative use, or other purposes of use of e-mail in a

graduate community.

Hypothesis 1.2:

Motivation can predict the amount of use, collaborative use, and
other purposes of use of e-mail in a graduate community.

To answer Hypothesis 1.2, the researcher used a g-test of the partial
regression coefficient of motivation on each of the three dependent variables.
As shown in tables, Iwas 2.47 and 2.61 for the amount of use (time and
messages, respectively), Iwas 2.85 for collaborative use, and t was 2.05 for
other purposes of use. The test results indicated that motivation was a
significant predictor of the amount of use, collaborative use, and other purposes

of use of e-mail in a graduate community.

 

89

Hypothesis 1.3:

Personality can predict the amount of use, collaborative use, and
other purposes of use of e-mail in a graduate community.

To answer Hypothesis 1.3, the researcher used a t-test of the partial
regression coefficient of personality on each of the three dependent variables.
As shown in tables, twas -1.27 and -2.34 for the amount of use (time and
messages, respectively), 3 was .98 for collaborative use, and g was -1.26 for
other purposes of use. The test results indicated that locus of control as a
measure of personality was a significant negative predictor only of the number of
messages, not the amount of time, collaborative use, or other purposes of use of

e-mail in a graduate community.

Hypothesis 2.1:

The prior Ieaming experience can predict the amount of use,
collaborative use, and other purposes of use of e-mail in a
graduate community.

To answer Hypothesis 2.1, the researcher used a g-test of the partial
regression coefficient of the prior learning experience on each of the three
dependent variables. As shown in tables, gwas -.76 and -1.38 (time and
messages, respectively) for the amount of use, gwas 1.81 for collaborative use,
and I was .59 for other purposes of use. The test results indicated that the prior
Ieaming experience was not a significant predictor of the amount of use,

collaborative use, or other purposes of use of e-mall in a graduate community.

Hypothesis 2.2:

Support can predict the amount of use, collaborative use, and
other purposes of use of e-mail in a graduate community.

To answer Hypothesis 2.2, the researcher used a g-test of the partial

regression coefficient of support on each of the three dependent variables. As

90

shown in tables, gwas -.60 and -.81 for the amount of use (time and messages,
respectively), 1 was 3.65 for collaborative use, and gwas 1.42 for other purposes
of use. The test results indicated that support was a significant predictor of
collaborative use, not other purposes of use or the amount of use in a graduate

community.

Hypothesis 2.3:

Opportunity to use can predict the amount of use, collaborative
use, and other purposes of use of e-mail in a graduate community.

To answer Hypothesis 2.3, the researcher used a g-test of the partial
regression coefficient of opportunity to use on each of the three dependent
variables. As shown in tables, gwas 1.27 and 2.24 for the amount of use (time
and messages, respectively), I was .22 for collaborative use, and gwas 2.25 for
other purposes of use. The test results indicated that opportunity to use was a
significant predictor of the number of messages and other purposes of use, not

the amount of time or collaborative use in a graduate community.
Bivariate Correlations

To test Hypotheses 3 and 4, Pearson correlation tests were conducted
among the independent variables (ability, motivation, personality, prior learning
experience, support for using e-mail, and opportunity to use). The correlation
coefficient for each pair of variables was tested for statistical significance. The

minimum significance level was set at .05. Results are shown in Table 34.

 

91

Table 34. Correlations among the independent variables

 

Ability Motivation Personality Learning Support Opportunity

Ability 1 .00

Motivation .57* 1 .00

Personality .37* .27* 1 .00

Learning .23* .34* -.02 1 .00

Support .00 .00 -.07 -.05 1 .00
Opportunity .42* .51* .13 .43* .00 1.00

 

* Significant at the .05 level.

Hypothesis 3:

There is a relationship between pairs of personal input factors
(ability, motivation, and personality).

Statistically significant correlations were found between all pairs of
personal input variables, as shown in Table 34. There was a positive
relationship at the .05 level between ability and motivation (I = .57), between
ability and personality (5 = .37), between motivation and personality (I = .27).

Hypothesis 4:

There is a relationship between pairs of environmental input
factors (prior learning experience, support for using e-mail, and
opportunity to use).

Only one statistically significant correlation was found between a pair of
environmental input variables (see Table 34). There was a positive relationship
at the .05 level between prior learning experience and opportunity to use the e-
mail systems (5 = .43). However, no relationship was found at the .05 level
between prior Ieaming experience and support for using e-mail, or between

support for using e-mail and opportunity to use the e-mail system.

92

Canonical Correlation Analysis

To answer Hypotheses 5 and 6, canonical correlation analysis was
performed to determine the relationship between a set of personal input factors
and a set of environmental input factors, and between a set of input factors and
a set of output factors. Pedhazur (1982) recommended that R} < .10 (i,e., less
than 10% shared variance) be treated as not meaningful (p. 727) and that

canonical loadings greater than .30 be treated as meaningful (p. 732).

Hypothesis 5:

There is a relationship between the personal input factors and the
environmental input factors.

A canonical correlation analysis was performed between the set of
personal input factors and the set of environmental input factors. The set of
personal factors included ability, motivation, and personality and the set of
environmental factors included prior Ieaming experience, support for using e-
mail, and opportunity to use e-mail.

The first canonical correlation was .56 (31% of variance) and lambda was
.66 (p < .001). The remaining two canonical correlations were effectively zero
(R3 = 2 & Rf = 0, respectively). The first canonical correlation, therefore,
accounted for the significant linkages between the two sets of variables. As
shown in Table 35, the variables relevant to the first canonical variate in the set
of personal variables were, in order of negative loadings, motivation and ability.
The set of environmental factors contained negative loadings for opportunity to
use and prior learning experience. Taken as a pair, lower motivation (-.95) and
less ability (-.74) tended to be related to less opportunity to use (-.96) and

ineffective learning experience (-.65).

93

Table 35. Canonical correlations between the personal input factors and
environmental input factors

 

First Canonical Variate
Correlation Coefficient

 

Personal Set

 

Ability -.74 -.34

Motivation -.95 -.80

Personality -.16 .18
Environmental Set

Learning -.65 -.30

Support -.03 -.05

Opportunity -.96 -.83
Hypothesis 6:

There is a relationship between the input factors and the output factors.

A canonical correlation analysis was performed between the set of input
factors and the set of output factors. The set of input factors included ability,
motivation. personality, prior Ieaming experience, support for using e-mail, and
opportunity to use. The set of output factors included amount of use,
collaborative use, and other purposes of use of e-mail.

The first canonical correlation was .61 (37% of variance); the second was
.41 (17% of variance). Lambda was .50 (p < .001). The remaining two
canonical correlations were effectively zero (R3 = 3 8. R3 = 0, respectively). The
two canonical correlations therefore, accounted for the significant linkages
between the two sets of variables. As shown in Table 36, the variables relevant
to the first canonical variate in the set of input variables were, in order of positive
loadings for motivation, opportunity to use, ability, prior learning experience, and
support for using e-mail. The set of output factors contained positive loadings

for other purposes of use, collaborative use, and amount of use. Taken as a

94
Table 36. Canonical correlations between input factors and output factors

 

 

 

 

First Canonical Variate Second Canonical Variate
Correlation Coefficient Correlation Coefficient
Inputs Set
Ability .64 .17 .01 .19
Motivation .88 .61 .02 .02
Personality .09 -.16 -.43 -.64
Learning .48 .08 -.18 -.55
Support .30 .26 -.63 -.72
Opportunity .74 .32 .28 .53
Outputs Set
TIme .62 .02 .29 .12
Message .68 .33 .60 .72
Collaborative .78 .47 -.56 -.88
Other Purposes .82 .46 .13 .14

 

pair, the first canonical variates indicated that those with motivation (.88),
opportunity to use (.74), ability (.64), effective Ieaming experience (.48), and
support for using e-mail tended to employ more other purposes of use (.82),
collaborative use (.78), and amount of use (message: .68; time: .62).

The second canonical variate in the set of input factors was composed of
negative loadings for support and personality, whereas the corresponding
canonical variate for the set of output factors was composed of positive loadings
for amount of use and negative loadings for collaborative use. Taken as a pair,
these variables suggest that less support for using e-mail (-.63) and external
locus of control (-.43) corresponded to more number of messages (.60) but less

collaborative use (-.56).

95

Analysis of Variance and g-Test Analysis

Analysis of variance and t-tests were performed to find the relationships
between the categorical demographic variables and the output factors for

Hypothesis 7.

Analysis of Variance

Analysis of variance was employed to determine whether there were

significant differences among group means with two or more levels.

4692

An analysis of variance was performed to determine whether students in
different age groups differ significantly in amount of use (time and messages),
collaborative use, and other purposes of use. The respondents averaged
approximately 34 years of age (mean = 33.9, pg = 7). Age was categorized as
22-28 years (25%), 29-33 years (29%), 34-41 years (29%), and 42-52 years
(16%). As shown in Table 37, no significant differences were found at the .05
level between age and amount of use [time : E (3, 107) = 1.51; message : E (3,
109) = .53], collaborative use [E (3, 110) = .73], or other purposes of use [E (3,
110) = .84].

96
Table 37. Results of the analysis of variance for age

 

. the amount of use (time)

Source gr Sum of Mean E E
Squares Squares Ratio Prob.

Between 3 78675.96 26225.32 1.51 .21

WIthin 107 185437674 17330.62

Total 110 193305270

. the amount of use (message)

Source d_f Sum of Mean E E
Squares Squares Ratio Prob.

Between 3 2166.61 722.20 .53 .66

WIthin 109 147748.89 1355.49

Total 112 149915.50

. collaborative use

Source gl_f Sum of Mean E E
Squares Squares Ratio Prob.

Between 3 .86 .28 .73 .53

Vlfithin 110 43.04 .39

Total 1 13 43.90

. other purposes of use

Source d_f Sum of Mean E E

Squares Squares Ratio Prob.
Between 3 1 .60 .53 .84 .47
Within 110 69.52 .63

Total 113 71.13

 

97
Ma] r

An analysis of variance was performed to determine whether students in
different majors differ significantly in amount of use (time and messages),
collaborative use, and other purposes of use. Major was categorized as
Counseling, Educational Psychology and Special Education (60%); Educational
Administration (6%); Physical Education and Exercise Science (8%); and
Teacher Education (24%). As shown in Table 38, significant differences were
found at the .05 level between major and amount of use [time : E (3, 107) = 2.56;
message : E (3, 109) = 2.87], collaborative use [E (3, 110) = 3.31], and other
purposes of use [E (3, 110) = 3.61].

Nationaligy

An analysis of variance was performed to determine whether there were
significant differences between nationality and amount of use (time and
messages), collaborative use, and other purposes of use. Nationality was
categorized as United States (30%), Asia (41%), Africa (14%), and other
countries (15%). As shown in Table 39, no significant differences were found at
the .05 level between nationality and amount of use [time : E (3, 100) = .62 ;
message : E (3, 102) = 1.21], collaborative use [E (3, 103) = .46], or other
purposes of use [E (3, 103) = 1.29].

98

Table 38. Results of the analysis of variance for major

 

. the amount of use (time)

Source _d_f Sum of
Squares
Between 3 13324.85
Within 107 185186244
Total 110 198508729

. the amount of use (message)

Source g Sum of
Squares
Between 3 1 1081 .69
WIthin 109 140020.88
Total 112 151102.57
. collaborative use
Source d_f Sum of
Squares
Between 3 3.65
WIthin 1 10 40.42
Total 113 44.07
. other purposes of use
Source g: Sum of
Squares
Between 3 6.40
WIthin 110 64.87

Total 113 71.28

Mean
Squares

44408.28
17307.12

Mean
Squares

3693.89
1 284.59

Mean
Squares

1.21
.36

Mean
Squares

2.13
.58

Ratio

2.56

Ra-tio

2.87

Ra_tio

3.31

Ra_tio

3.61

Prob.

.05

Prob.

..03

In

Prob.

.02

Prob.

.01

 

 

Table 39. Results of the analysis of variance for nationality

 

. the amount of use (time)

Source 91 Sum of
Squares
Between 3 34833.14
WIthin 100 185484038
Total 103 188967352

- the amount of use (message)

Source g: Sum of
Squares
Between 3 4866.13
Within 102 136717.00
Total 105 141583.14
. collaborative use
Source g Sum of
Squares
Between 3 .55
WIthin 103 40.96
Total 106 41.52
. other purposes of use
Source 1f Sum of
Squares
Between 3 2.44
\lVIthin 103 64.51

Total 106 66.95

Mean
Squares

11611.04
18548.40

Mean
Squares

1622.04
1340.36

Mean
Squares

.18
.39

Mean
Squares

.81
.62

Ratio

.62

Ra_tio

1.21

Ratio

Ratio

1.29

Prob.

.59

Prob.

.30

I11

Prob.

.70

In

Prob.

.27

 

100

Amount of Time at a Certain Organization

An analysis of variance was performed to determine whether there were
significant differences between the amount of time at a certain educational
organization and amount of use (time and messages), collaborative use, and
other purposes of use. The respondents had been at a certain educational
organization an average of 4 years (s_d = 1.8). The amount of time at a certain
educational organization was categorized as less than 1 year (22%), 1 year
(4%), 2 years (26%), 3 years (16%), 4 years (4%), and more than 4 years (27%).
As shown in Table 40, no significant differences were found at the .05 level
between the amount of time at a certain educational organization and amount
use [time : E (5, 107) = .83 ; message : E (5, 109) = 1.30], collaborative use [E
(5, 110) = 1.55], or other purposes of use [E (5, 110) = 1.62].

Qperience of Using E-mail

An analysis of variance was performed to determine whether there were
significant differences between the experience of using e-mail and amount of
use (time and messages), collaborative use, and other purposes of use. The
respondents had experience using e-mail for an average of 3 years (g = 1.7).
The experience of using e-mail was categorized as less than 1 year (19%), 1
year (11%), 2 years (26%), 3 years (20%), 4 years (6%), and more than 4 years
(12%). As shown in Table 41, no significant differences were found at the .05
level between the experience of using e-mail and amount of use [time : E (5,
107) = 1.62 ; message : E (5, 109) = 1.75] or collaborative use [E (5, 110) =
1.81]. However, a significant difference was found at the .05 level between the

experience of using e-mail and other purposes of use [E (5, 110) = 2.88].

Table 40. Results of the analysis of variance for the amount of time at a

certain organization

 

. the amount of use (time)

Source 91 Sum of
Squares
Between 5 74610.07
WIthin 107 191571212
Total 112 199032219

. the amount of use (message)

Source d_f Sum of
Squares
Between 5 8594.74
WIthin 109 143629.81
“Total 114 152224.56
- collaborative use
Source g Sum of
Squares
Between 5 4.89
VIIithin 1 10 66.40
Total 1 15 71.29
. other purposes of use
Source 1f Sum of
Squares
Between 5 2.93
VVithin 1 10 41 .41

Total 1 15 44.34

Mean
Squares

14922.01
17903.85

Mean
Squares

1718.94
1317.70

Mean
Squares

.97
.60

Mean
Squares

.58
.37

Ratio

.83

Ra—tio

1.30

Ratio

1.62

Ratio

1.55

Prob.

.52

9er.

.26

Prob.

.16

Prob.

.17

 

102
Table 41. Results of the analysis of variance for experience in using e-mail

 

. the amount of use (time)

Source g1 Sum of
Squares
Between 5 140350.08
Within 107 18499721 1
Total 112 199032219

. the amount of use (message)

Source d_f Sum of
Squares
Between 5 1 1318.96
WIthin 109 140905.60
Total 114 152224.56
. collaborative use
Source 9: Sum of
Squares
Between 5 3.38
WIthin 1 10 40.96
Total 1 15 44.34
. other purposes of use
Source _d_f Sum of
Squares
Between 5 8.26
WIthin 1 10 63.03

Total 115 71.29

Mean
Squares

28070.01
17289.45

Mean
Squares

2263.79
1292.71

Mean
Squares

.67
.37

Mean
Squares

1.65
.57

RaTtio

1.62

Ra-tio

1.75

Ra—tio

1.81

RaTtio

2.88

Prob.

.16

Prob.

.12

Prob.

.11

I11

Prob.

.01

 

103

t-Test Anal sis

A t-test was employed to determine significant differences between two

group means.
Gender

A g-test for independent samples of gender of the user was performed to
determine whether there were differences between genders and amount of use
(time and messages), collaborative use, and other purposes of use. The gender
of users was categorized as female (57%) and male (43%). As shown in Table
42, no significant differences were found at the .05 level between genders and
amount of use [time : E = 3.18 ; message : E = .04], collaborative use [E = .02],

or other purposes of use [E = 1.14].
EducafionalLevel

A g-test for independent samples of the educational level of the user was
performed to determine whether there were differences between the educational '
level and amount of use (time and messages), collaborative use, and other
purposes of use. The educational level of the user was categorized as master's
level (20%) and doctoral level (79%). As shown in Table 43, no significant
differences were found at the .05 level between the educational level and
amount of use [time : E = 3.50 ; message : E = 1.51], collaborative use [E_= .05],

or other purposes of use [E = .02].

Table 42. Results of g-test for gender

 

. the amount of use (time)

N. of
Variable Cases Mean
Female 63 132.46
Male 50 112.16

. the amount of use (message)

N. of
Variable Cases Mean
Female 63 33.55
Male 52 37.23

. collaborative use

N. of
Variable Cases Mean
Female 64 2.1 1
Male 52 2.1 1

. other purposes of use

N. of
Variable Cases Mean
Female 65 2.21
Male 51 2.43

SE of
Mean

18.83
15.56

SE of
Mean

4.34
5.42

SE of
Mean

.08
.08

SE of
Mean

.09
.11

I11

1.62

I11

.04

In

.02

I11

1.14

Prob.

.16

Prob.

.82

Prob.

.88

Prob.

.28

 

 

Table 43. Results of g-test for educational level of the user

 

. the amount of use (time)

N. of
Variable Cases Mean
Doctor 92 131.71
Master 21 87.38

. the amount of use (message)

N. of
Variable Cases Mean
Doctor 94 38.38
Master 21 21 .02

. collaborative use

N. of
Variable Cases Mean
Doctor 94 2.1 5
Master 22 1 .93

. other purposes of use

N. of
Variable Cases Mean
Doctor 94 2.37

Master 22 2.07

SE of
Mean

14.55
20.95

SE of
Mean

3.88
6.02

SE of
Mean

.13

SE of
Mean

.08
.16

I11

3.50

I‘l'l

1.51

I11

.05

In

.02

Prob.

Prob.

Prob.

.82

Prob.

.86

 

106

Hypothesis 7:

There are mean differences between the categorical demographic
variables (age, gender, nationality, major, educational level of the
user, amount of time at a certain educational organization, and
experience of using e-mail) and the output factors (amount of use,
collaborative use, and other purposes of use of e-mail).

No significant mean differences were found at the .05 level between age,
gender, nationality, educational level of the user, and amount of time at a certain
educational organization and the output factors (amount of use, collaborative
use, and other purposes of use of e-mail). However, significant differences were
found at the .05 level between major and amount of use, collaborative use, and
other purposes of use of e-mail, and between the experience of using e-mail and

other purposes of use of e-mail.
Summary of the Research Findings

The researcher’s main purpose in this study was to investigate the factors
that influence the use of e-mail, with special attention to the collaborative use of
e-mail in a graduate community. This study investigated whether personal input
factors (ability, motivation, and personality) and environmental input factors
(learning, support, and opportunity to use) influence the amount of use (time and
messages), collaborative use, and other purposes of use of e-mail in a graduate
community. This study further investigated the relationship between pairs of
input factors, the relationship between personal input factors and environmental
input factors, and the relationship between input factors and output factors.
Also, demographic variables were examined to determine whether there were

significant differences from output factors.

107

The findings for the seven research hypotheses are summarized as

follows:

1.

Some personal input factors (ability, motivation, and personality) could
predict the amount of use (time and messages), collaborative use, and
other purposes of use of e-mail in a graduate community. Ability was not
a significant predictor of the amount of use, collaborative use, or other
purposes of use of e-mail. Motivation was a significant predictor of the
amount of use, collaborative use, and other purposes of use of e-mail.
Extemal locus of control as a measure of personality was a significant
predictor of the number of messages.

Some environmental input factors (learning, support, and opportunity to
use) could predict the amount of use (time and messages), collaborative
use, and other purposes of use of e-mail in a graduate community. Prior
learning experience was not a significant predictor of the amount of use,
collaborative use, or other purposes of use of e-mail. Support for using e-
mail was a significant predictor of collaborative use. Opportunity to use
was a significant predictor of the number of messages and other purposes
of use of e-mail.

Statistically significant correlations were found between pairs of all
personal input variables (ability, motivation, and personality). A positive
relationship was found between ability and motivation, between ability
and personality, and between motivation and personality.

Only one statistically significant correlation was found between a pair of
environmental input variables (prior learning experience, support for using
e-mail, and opportunity to use). A positive relationship was found
between prior learning experience and opportunity to use the e-mail

systems. However, no relationship was found between prior learning

108

experience and support for using e-mail or between support for using e-
mail and opportunity to use the e-mail system.

Statistically significant relationships were found between the set of
personal factors (ability, motivation, and personality) and the set of
environmental factors (prior learning experience, support for using e-mail,
and opportunity to use). Users with lower motivation and less ability
tended to have less opportunity to use e-mail and ineffective Ieaming
experience.

Statistically significant relationships were found between the set of input
factors (ability, motivation, personality, prior learning experience, support
for using e-mail, and opportunity to use) and the set of output factors
(amount of use, collaborative use, and other purposes of use of e-mail).
The first canonical variates indicated that those with motivation,
opportunity to use, ability, effective learning experience, and support for
using e-mail tended to employ more other purposes of use, collaborative
use, and amount of use. The second canonical variates indicated that
less support for using e-mail and external locus of control corresponded
to more number of messages, but less collaborative use.

No significant relationships were found between age, gender, nationality,
educational level of the user, and amount of time at a certain educational
organization, and the output factors (amount of use, collaborative use,
and other purposes of use of e-mail). However, significant relationships
were found between major and the amount of use, collaborative use, and
other purposes of use of e-mail, and between the experience of using e-

mail and other purposes of use of e-mail.

109

 

Results From the Open-Ended Questions

To acquire specific information about how e-mail had been valuable to the
respondents' academic study, three open-ended questions were posed. More
than half of the respondents (g = 65) answered at least one of the three
quesfions.

Many students (g = 21) used e-mail as an information search through
library networking or listservs for their academic study. Collaborative use was
also employed in: (a) discussing topics of interest and work with faculty, (b)
communicating with advisor for guidance, (c) receiving information about the
subject-matter of a course, (d) serving as a medium of transmission for sending
in homework, (e) exchanging information with colleagues related on academic
study, (f) working on group projects in a class, and (g) communicating with
"world-class" researchers or professors at a distance.

However, respondents also complained about information overload of e-
mail because of subscribing listservs or reading a bulletin board. There were
too many messages coming in for them to keep up-to-date. The respondents
mentioned that they unexpectedly spent a lot of time reading a bulletin board or
other messages. Even though one respondent could handle an information
overload using by a special function (such as "digest" mode), this option did not
seem to be popular with other respondents.

The others complained about written communication of e-mail. In regard
to the text-based feature of computer-mediated communication, one of

respondents made the following comment:

1”.

110

E-mail is [a] convenient tool, but sometimes it will cause severe
problem if we don't use it carefully. Because writing a paper and
correct expression via e-mail is not a easy job. On the other hand,
interpreting others' mail and imagine mailer's tone and intention is
also difficult. We make take advantage of e-mail, but sometimes
we may need to "pay" back something.

However, the following comment from a respondent seems an appropriate

way to conclude this section:

E-mail has been [an] invaluable resource for me, since the
completion of my graduate programs on timely communication with
people at remote locations. I just wish I had the time to explore
more use of electronic communication.

CHAPTER V

DISCUSSION AND CONCLUSIONS

The present study was an attempt to investigate the factors that influence
the use of e-mail, with special attention to the collaborative use of e-mail in one
graduate community. This chapter contains a summary of the study and a
discussion of the findings related to the research hypotheses. The chapter also
contains conclusions drawn from the study findings, limitations and implications

of the study, and recommendations for future research.
Summary

The researcher‘s main purpose in this study was to investigate whether
personal input factors (ability, motivation, and personality) and environmental
input factors (learning, support, and opportunity to use) influence the amount of
use (time and messages), collaborative use, and other purposes of use of e-mail
in a graduate community.

A correlational design was used in this study; a group of subjects
(international graduate students and graduate students from a seminar class)
was measured on several variables (Campbell & Stanley, 1963). The power of
six independent variables was tested in predicting the amount of use (time and
messages), collaborative use, and other purposes of use of e-mail. The
independent variables were ability, motivation, personality, prior learning

experience, support for using e-mail, and opportunity to use e-mail.

111

 

112

One hundred eighteen students, international graduate students (g = 79)
and doctoral students from three consecutive cohorts of a seminar class (g = 39)
in the College of Education at Michigan State University, were the subjects in
this study. The respondents were from Asia, the United States, Africa, the
Middle East, Latin America, Europe, and Canada.

The analysis of data was divided into five phases. First, descriptive
statistics including reliability were employed. Reliability analyses were
conducted on all multi-item scales that were employed in the investigation. Next,
multiple regression analysis was conducted to investigate the relationship
between six independent variables and three dependent variables. This
analysis was used to test Hypotheses 1 and 2. Third, bivariate correlation was
conducted to determine whether there was a relationship between pairs of
independent variables (ability, motivation, personality, prior learning experience,
support for using e-mail, and opportunity to use). The Pearson correlation test
was used to address Hypotheses 3 and 4. Fourth, to test Hypothesis 5 and 6, a
canonical correlation analysis was performed to discover whether there was a
relationship between the set of personal input factors and the set of
environmental input factors, and between the set of input factors and the set of
output factors. Last, an analysis of variance and a _t_-test were performed to
determine whether there were mean differences between the categorical
demographic variables and the dependent variables for Hypothesis 7. The
findings regarding the research hypotheses are presented in the following

section.

 

113

Discussion of the Findings

The following discussion is organized around the seven research
hypotheses addressed in this study and the results from the open-ended

quesfions.

Reseflch Hypothesis 1

Personal input factors (ability, motivation, and personality) can predict the
amount of use, collaborative use, and other purposes of use of e-mail in a
graduate community.

The results indicated that some of the personal input factors could predict
the amount of use (time and messages), collaborative use, and other purposes
of use of e-mail in a graduate community. Ability, which refers to skills
associated with using e-mail, was not a significant predictor of using e-mail. The
researcher interpreted this finding to mean that they should be able to use e-mail
readily because subjects in this study were graduate students who had
competence in reading, writing, and typing English and using computers for their
academic study. Descriptive information supported the assumption that
respondents were good at typing, reading and writing English and had computer
knowledge. It was supposed that respondents had been exposed to computer
technology, such as word processing, in their previous academic life and that
most of them used computers for writing their research papers or searching for
information in the library.

Motivation was a significant predictor of the amount of use, collaborative
use, and other purposes of use of e-mail. Specifically, positive perceptions
about e-mail and positive attitudes toward e-mail were related to the amount of
use, collaborative use, and other purposes of use of e-mail. This finding is

consistent with previous research, in which it was found that users must be

114

highly motivated to use the systems (Harasim, 1986; Hiltz, 1984; McCreary &
Van Duren, 1987; Riel 8. Levin, 1990). Many researchers have emphasized that
an individual's motivation is one of the major factors that may influence his or her
use of e-mail. Users should have a strong desire and interest to use such
systems for communicating with other people.

External locus of control as a measure of personality variable was a
significant predictor of the number of messages in a graduate community. In this
study, externally controlled people, who perceive that their lives were beyond
their control or were influenced by chance or powerful others, used more e-mail
messages than internally controlled people, who perceive that the event or
environment was contingent on their own behavior; that is, they could control
their environment. The researcher interpreted this finding to mean that
extemally controlled people might use e-mail as a way to increase feelings of
control in their lives as consuming information from others rather than actively
participating in communication. Such people are called "lurkers" (Romiszowski
& de Hass, 1989), who only read messages and seldom respond to computer-
mediated communication. Further investigation of the difference between
sending and receiving messages supported this position because external locus
of control was statistically significant only with regard to receiving messages, not
sending messages. This finding was also consistent with those of research on
the use of video-cassette recordings (VCR) (Rubin, & Rubin, 1989). Rubin and
Rubin found that external locus of control was a predictor of frequency of VCR
use. Perhaps, externally controlled individuals might use VCRs more as a way
to increase feelings of control in their lives. Therefore, external users may
employ e-mail to consume information from others' messages rather than to take
part in interactive communication because these individuals feel other- or

chance-directed and powerless (Lefcourt, 1976).

 

115

In summary, ability was not a significant predictor of the amount of use,
collaborative use, or other purposes of use of e-mail. Motivation was a
significant predictor of the amount of use, collaborative use, and other purposes
of use of e-mail. External locus of control as a measure of personality was a

significant predictor of the number of messages.

Research Hypothesis 2

Environmental input factors (learning, support, and opportunity to use)
can predict the amount of use, collaborative use, and other purposes of
use of e-mail in a graduate community.

Some environmental input factors (learning, support, and opportunity to
use) could predict the amount of use (time and messages), collaborative use,
and other purposes of use of e-mail in a graduate community. Learning, which
refers to the prior learning experience to use e-mail, was not a significant
predictor of using e-mail. The researcher assumed that the task of Ieaming to
use e-mail was so simple that graduate students could learn it by themselves.
Descriptive information showed that many users had learned to use e-mail by
themselves using an instruction sheet or manual (79%) and that this learning
had been effective. This result may be interpreted in the context of the results
pertaining to the personal factor of ability. Respondents in this study were
graduate students who had enough skills to learn to use e-mail systems by
themselves, and these skills were effective for using e-mail. Therefore, the
relative differences in effectiveness of the prior learning experience did not make
a difference in terms of using e-mail in this study.

Support was a significant predictor of collaborative use. Specifically,
support for using e-mail, such as encouraging the use of e-mail in a class,
assisting in effective communication, or providing technical support, was related

to collaborative use. This finding is consistent with the results of previous

116

research, which indicated that users need to be encouraged to use computer-
mediated communication for class or to be provided with an effective course
design by the instructor for interactive computer conferencing (Davie & Palmer,
1984; Feenberg, 1987; Kerr, 1986).

In this study, there were varying findings about variables reflecting
opportunity to use e-mail. Opportunity to use, which refers to access to the e-
mail system, was a significant predictor of the number of messages and other
purposes of use, but not of the amount of time or collaborative use in a graduate
community. This means that easy access to e-mail systems was related to the
number of messages sent and received and other purposes of use, such as
seeking and reading information through networking. Easy access to e-mail
systems might make it easier for users to send or receive their messages and to
explore electronic networking. However, easy access appeared to have little
relationship on the amount of time spent online or collaborative use.

In summary, prior learning experience was not a significant predictor of
the amount of use, collaborative use, or other purposes of use of e-mail.
Support for using e-mail was a significant predictor of collaborative use.
Opportunity to use was a significant predictor of the number of messages and

other purposes of use of e-mail.

Resea_rch Hypothesis 3

There is a relationship between pairs of personal input factors (ability,
motivation, and personality).

Statistically significant correlations were found between pairs of all
personal input variables (ability, motivation, and personality). Specifically, a
positive relationship was found between ability and motivation, between ability

and personality, and between motivation and personality. This means that skills

117

associated with using e-mail were related to respondents' perceptions about and
attitudes toward using e-mail, as well as to respondents' expectancy regarding
controlling influences on environment and their lives. In addition, perceptions
about and attitudes toward using e-mail were related to expectancy regarding
controlling influences on environment and their lives. It is likely that the more
skills in using e-mail they have, the more they have positive perceptions about
and attitudes toward using e-mail; the more skills in using e-mail they have, the
more they feel they can control their environment and their lives; and the more
they have positive perceptions about and attitudes toward using e-mail, the more
they feel they can control their environment and their lives. Thus, pairs of all
personal input factors were related to each other in predicting the use of e-mail
because it might be that personal factors were defined as a set of relatively

stable characteristics in the individual.

Research Hypothesis 4

There is a relationship between pairs of environmental input factors
(Ieaming, support, and opportunity to use).

One statistically significant correlation was found between a pair of
environmental input variables (learning, support for using e-mail, and opportunity
to use). There was a positive relationship between learning and opportunity to
use. That is, those who had more effective learning experience also had easier
access to e-mail systems. \NIth regard to Ieaming experience—respondents had
Ieamed to use e-mail by themselves and this learning had been effective-easy
access to the e-mail systems may have allowed them to learn to use e-mail at
their convenience.

However, no relationship was found between the effectiveness of prior

learning experience and support for using e-mail or between support for using e-

118

mail and opportunity to use the e-mail system. This indicated that the prior
Ieaming experience was not related to whether subjects had support for using e-
mail, nor was support for using e-mail related to whether subjects had easy
access to e-mail system. Therefore, environmental input factors were less
related to each other in predicting the use of e-mail than were personal input
factors. The researcher interpreted this finding to mean that personal factors
were defined as a set of relatively stable characteristics, were more likely to be
instructional conditions, and could not be manipulated. Environmental factors,
on the other hand, were defined as a set of meaningful cues or stimuli to the
user which the individual is reacting, were more likely to be instructional

methods, and could be manipulated to get the expected outcomes.

Resga‘rch Hypothesis 5

There is a relationship between the personal factors (ability, motivation,
and personality) and the environmental factors (learning, support, and
opportunity to use).

Statistically significant relationships were found between the set of
personal factors and the set of environmental factors. Subjects with lower
motivation and less ability tended to have fewer opportunities to use e-mail and
ineffective prior learning experiences. It may be expected that those who have
negative perceptions about and attitudes toward using e-mail and a lack of
competence in skills associated with using e-mail have had ineffective prior

learning experiences and have difficulty gaining access to the e-mail system.

119

Research Hypothesis 6

There is a relationship between the input factors (ability, motivation,
personality, learning, support, and opportunity to use) and the output

factors (amount of use, collaborative use, and other purposes of use of e-
mail).

Statistically significant relationships were found between the set of input
factors and the set of output factors. The first canonical variates indicated that
those who had motivation, easy access to e-mail systems, ability to use such
systems, effective learning experiences, and support for using e-mail tended to
have a greater amount of use, collaborative use, and other purposes of use of e-
mail.

‘lhe result of the first canonical variate was expected in the model in this
study; that is, those who have positive perceptions about and positive attitudes
toward e-mail, access to the systems, skills associated with using e-mail,
effective learning experiences, and support for using e-mail might make greater
use e-mail, have more collaborative use, and employ more other purposes of
use. Only the personality variable was not included in the results of the first
canonical variate because the results for the second canonical variate showed
that externally controlled people might use e-mail messages more but engaged
in less collaborative use. The results for the second canonical variate also
showed that those who had less support for using e-mail might use e-mail
messages more but engaged in less collaborative use. These findings are
consistent with the results for Hypothesis 1; that is, externally controlled people
may employ e-mail to consume more information from others through e-mail

without participating in active and effective communication with others.

 

120

Research Hypothesis 7

There are mean differences between the categorical demographic
variables (age, gender, nationality, major, educational level of the user,
amount of time at a certain educational organization, and experience of
using e-mail) and the output factors (amount of use, collaborative use,
and other purposes of use of e-mail).

No statistically significant mean differences were found between some of
the demographic variables (age, gender, nationality, educational level of the
user, amount of time at a certain educational organization) and the output
factors. That is, certain demographic variables did not influence the amount of
use, collaborative use, or other purposes of use of e-mail.

Unlike Kerr and Hiltz's (1982) findings based on previous studies, but
similar to the findings of Steinfield (1986a), age was found not to be significantly
related to the use of e-mail. The notion of Kerr and Hiltz that age was inversely
related to the use of computer-mediated communication—younger people may
use computing technology more than older people because younger people
have had more exposure to such technology—did not seem to hold true for users
in this study who were highly educated (graduate students) and had good skills
in using e-mail. Despite the variations in their ages, the subjects in this study
were a very homogeneous group in that they were all graduate students in the
College of Education at Michigan State University.

The finding that educational level was not related to the use of e-mail may
be understood in a context similar to the findings regarding the age. The logic
behind the importance of education in Kerr and Hiltz's (1982) findings based on
previous studiesnthat better educated people understand the system and are
less threatened by learning new technologyumay be irrelevant when users are
all highly educated. This assumption was supported by the results pertaining to

the ability variable in Hypothesis 1. Ability was not a significant predictor of the

 

121

use of e-mail because the subjects in this study were graduate students all of
whom had competence in the skills necessary for their academic study.
Descriptive information supported that respondents were good at skills
associated with using e-mail.

Similar logic can be used to explain the lack of a relationship between
gender or nationality and the use of e-mail by a highly educated and
homogeneous group in one graduate community. As in Grabowski et al.'s
(1990) research, gender was not related to the use of e-mail by subjects who
were graduate students. Even though the nationalities of the subjects in this
study represented various countries, all of them were graduate students in the
College of Education who have competence in using e-mail.

Significant differences were found among subjects with various majors
with regard to the amount of use, collaborative use, and other purposes of use of
e-mail. However, these differences must be interpreted very cautiously because
more than half of the students in this study (60%) were from one major. One
group of subjects were international graduate students and the other group were
doctoral students from three consecutive cohorts of a seminar class in the
College of Education at Michigan State University. The latter group had the
same major and were also strongly encouraged to use e-mail in the class, even
though the major was defined as the department in this study which included
many submajors.

Similar to Kerr and Hiltz's (1982) findings based on previous studies, the
relationship that was found between experience of using e-mail and the other
purposes of use of e-mail may be interpreted as meaning that the more
experience students have, the more comfortable they are in doing information

search and navigation.

A]

 

122

In summary, no statistically significant mean differences were found
between some of the demographic variables (age, gender, nationality,
educational level of the user, amount of time at a certain educational
organization) and the output factors (the amount of use, collaborative use and
other purposes of use of e-mail). However, significant mean differences were
found between major and the amount of use, collaborative use, and other
purposes of use of e-mail and between the experience of using e-mail and the

other purposes of e-mail.

Results From the Open-Ended Questions

Many students used e-mail as an information search through library
networking or listservs for their academic study. This finding indicates that
computer-mediated communication makes available to students a tremendous
number and variety of information resources at their own convenient place, to
which learners might not otherwise gain easy access. This was the most
frequent type of the use of e-mail for their academic study. Collaborative use
was also employed as the researcher expected in this study: (a) doing class
assignments or research projects with faculty or other classmates, (b)
exchanging information with faculty, other classmates, and/or friends, and (c)
interacting with the group in which the user is involved.

However, respondents also complained about information overload and
written communication of e-mail. Users are often faced with information
overload (Hiltz & Turoff, 1985) because of subscribing listservs or reading a
bulletin board. Many of the messages were of no interest to users and make a
burden for them. If users can structure information according to their needs,
therefore eliminating uninteresting messages, they might be better able to

manage the information (Harasim, 1987; Hill: & Turoff, 1985). Because e-mail is

 

123

text-based and written communication, users cannot see or hear each other. In
various contexts, nonverbal behaviors, such as the feelings in vocal and facial
expressions, are important to express ideas and communicate each other.
These nonverbal behaviors are more richly communicative than the feeling
expressed written. Written communication may produce some adverse effects.
‘lhat is, the absence of cues such as facial expressions might result in
miscommunication or misunderstanding among users. Users might be more

careful for expressing their opinions, such as explaining exactly what they

intended to convey in their opinions.
Conclusions

‘lhe factors related to the use of computer-mediated communication may
vary from situation to situation, from individual to individual, and from one time to
another. It would be very difficult for a researcher to discover all of the factors
related to such use. In this study, however, an attempt was made to investigate
the factors influencing the use of e-mail in one graduate community, using the
model developed for this research. Conclusions are described in light of
independent and dependent variables employed in this study. The suggested

model was revised based on the following conclusions.

Independent Variables

As shown in Figure 5, for a relatively simple computer-mediated
communication task, such as e-mail within a graduate community:
1. Ability is not likely to predict the use of e-mail.
2. Motivation is likely to predict the use of e-mail.

3. External locus of control is likely to predict the number of messages.

 

124

  
        
   
   
 

   

~.
~. “‘3. M». w“
x R \“ O

x, “Q 3Q“.

  

 

it" 1"“ 5:. ='\
{‘3- (“\q:
s3: tars:
w. ‘5 ~
-.
-.
4

v
Massage. W'Mm“

Wr- :9: a.

   
    

    

    

 

Figure 5. Revised model based on the results related to the use of e-mail

systems

4. The effectiveness of prior learning experience is not likely to predict the use

of e—mail.

5. Support for using e-mail is likely to predict the collaborative use of e-mail.

6. Opportunity to use is likely to predict the number of messages and other

purposes of use of e-mail.

Dependent Variables

For a relatively simple computer-mediated communication task, such as

e-mail within a graduate community:
1. Amount of use (time and messages) is likely to be related to motivation.
The number of messages is likely to be related to the external locus of

control and to the opportunity to use.

125

2. Successful use of e-mail for collaborative purposes is likely to be related to
motivation and to support for using e-mail.
3. Other purposes of use of e-mail is likely to be related to motivation and to

the opportunity to use.
Limitations

The outcomes of this study need to be considered in light of the following
limitations:

The researcher surveyed participants' perceptions of e-mail only at one
time within one college of one university (the College of Education at Michigan
State University). Participants from other universities might have responded
differently, and surveying the sample more than once would have increased the
confidence that the responses were accurate representations of the participants'
perceptions. Thus, the findings from this study are applicable to one university

organization at one time.

 

Another limitation of the study is that it is technology and time bound. As
technology becomes more widely available and its use is expanded, the findings
might be different from what they were when this study was conducted.

The third limitation involves the statistical techniques employed in the
research. In this study, specific scales were chosen for the analysis. That is.
not all factors for each variable were used because of the relatively small
number of cases (N = 118). Ideally, regression analysis would require 20 times
more cases than variables (Tabachnick & Fidell, 1983). Some variables in this
study had several factors; including all of these factors in the study might have
resulted in more specific findings.

Finally, given the correlational nature of the design, it was not possible to

infer causality from the findings. That is, the findings concerning the use of e-

126

mail did not yield information about causal relationships among the six

independent variables.

Implications for Practitioners

Understanding the factors that might promote the use of computer-
mediated communication can be of direct value to those educational
practitioners who wish to encourage productive applications of such
communication. The implications drawn from the findings in this study are
discussed in this section.

The finding that motivation and support were significant predictors of the
use of computer-mediated communication can be applied to designing systems
to encourage people to use computer-mediated communication. The results of
this study showed that successful use of computer-mediated communication was
dependent on the user's positive perceptions about and attitudes toward using
e-mail and the support for using e-mail, such as encouraging the use of e-mail in
a class, assisting in effective communication, or providing technical support.
Therefore, Keller's motivational-design ARCS model might be applied to
implement the use of computer-mediated communication (Keller, 1987). The
ARCS model includes the following four elements: (a) attention, (b) relevance,
(c) confidence, and (d) satisfaction.

For obtaining and sustaining the student's Attention, which refers to
capturing the interest of students, students can be provided with effective
features-asynchronous, fast, cheap, and interactive—of computer-mediated
communication technology. Positive perceptions about and attitudes toward
using e-mail are crucial to the success in implementing a computer learning
network (Harasim, 1986; Hiltz, 1984; McCreary 8 Van Duren, 1987; Riel & Levin,

1990). For Relevance, which refers to meeting the personal needs of the

 

 

 

127

student to effect a positive attitude, some real benefits and interests of
computer-mediated communication should be given to users to encourage them
to use the medium to communicate with group members, before implementing
computer-mediated communication (Harasim, 1987; Kaye, 1987). For
Confidence, which refers to helping the students believe/feel that they will be a
success and control their success, teachers should assist in effective
communication among students or provide structured tasks and guidance.
Therefore, teachers should develop strategies to encourage collaborative use of
computer-mediated communication to achieve certain academic outcomes. One
strategy might be that instructors who lead discussions through computer-
mediated communication should avoid drifting off from the main point
(Romiszowski & de Hass, 1989). This creates in students an interest in knowing
where to find information of they need and where to put new contributions. For
Satisfaction, which refers to reinforcing accomplishment with rewards (internal
and external), students might increase their self-esteem and achievement as a
consequence of interacting with other people and from successfully completing a
meaningful learning activity through computer-mediated communication. This
kind of satisfaction may work as an external motivator and a further internal
motivator. Wlodkowski (1991) also presented a comprehensive list of strategies
for enhancing adult motivation to learn. This list could serve as a departure
point for empirical investigations aimed at adult learners.

Finally, many instructors in the university and the graduate community are
providing or encouraging the use of computer-mediated communication or other
new technology in order to increase interaction among students as well as
between students and instructors. However, the existence of a physical system
does not guarantee the productive use of media and technology in education.

Rather than thrusting educators and students indiscriminately into the use of

 

 

128

new technology, it could be helpful to investigate the most appropriate ways of
stimulating effective use between students and instructors using media and
technolgy. This exploratory study may help in future applications related to the
use of new technology and contribute general understanding of factors related to

the use of technology in education.
Implications for Theory

For an integrated theoretical approach, this study formulated a model to
investigate the factors related to the use of computer-mediated communication
systems. The model was formulated from the perspective of social Ieaming
theory which proposes that behavior is considered to be a function of the person
and the environment. The personal factors included ability or skill for using
technology, motivation (perceptions about and attitudes toward technology), and
personality. The environmental factors included prior learning experience,
support for using technology, and opportunity to use technology. The output
factor, that is, the use of e-mail systems was specified into the amount of use
(time and messages), collaborative use, and other purposes of use of e-mail.
Thus, it was supposed that the use of e-mail systems could be explained by
personal characteristics and environmental inputs.

The findings that some of personal and environmental factors were
significant predictors of the use of e-mail do suggest the theoretical support of
social learning theory. For prediction of the behavior, personal variables, such
as motivation and personality can predict the use of e-mail. Environmental
variables, such as support and opportunity to use can predict the use of e-mail.
Thus, this study validated the theoretical position that social Ieaming theory
could predict the human behavior through "the interaction of the individual and

his or her meaningful environment " (Rotter, 1982, p.5).

 

129

Recommendations for Future Research

Computer and telecommunications technologies have become more
pervasive, and the knowledge of how to incorporate computer technology into
education may be as important to general education as are reading and writing.
Hence, the following recommendations are made for future research:

1. The findings of this study seemed to suggest that success of using
computer-mediated communication technology for collaborative purposes in
education may be strongly related to motivation and to support. Considering the
importance of interaction and collaborative learning in education, it is imperative
that research be conducted to determine what kinds of motivational strategies
can be used to promote collaborative learning through this technology or what
kinds of support techniques can produce collaborative learning.

2. At this stage, little is known about ways to facilitate Ieaming through
computer-mediated communication. The features of this medium are suitable for
particular subjects, not all subject areas. Future researchers might focus on
teaching techniques (Davie, 1988, 1989) for particular subjects. such as
supportive environments and a variety of joint writing projects, to support
effective and active learning through computer-mediated communication. Which
subjects and subject content are appropriate for this technology? What kinds of
teaching techniques can be matched with the subject content?

3. In general, the findings supported using a multivariate approach to
predict how e-mail is used in an educational setting, taking into account various
factors among the independent and dependent variables. Considering the many
factors in each scale, future researchers should employ a multivariate approach

with more subjects.

 

130

4. Responses to the open-ended questions suggested the effect of
written communication aspect of computer-mediated communication. Computer-
mediated communication systems have begun to influence the Ieaming-teaching
process, by having students and teachers interact through computer terminals.
It is recommended that a study be conducted on the effects of a lack of
nonverbal behaviors on the effective use of computer-mediated communication.

5. Quantitative methodology is not sufficient to explain the factors
influencing the use of computer-mediated communication; qualitative methods
should also be used to gain a deeper and richer understanding of this
phenomenon. What kinds of tasks in education are influenced by user
characteristics? How do students use computer-mediated communication in

their academic study?
Final Words

Telecommunication technology in the next decade will have major impact
on educational environments. Well-designed technology can quantitatively and
qualitatively change the nature and depth of students' learning and achievement.
This medium does not replace the teacher but provides an alternative way for
teachers and students to meet, developing cooperative relationship and
individuality. Therefore it should be used "to create learning and teaching
environments that are more effective and exciting for at least some kinds of
materials, rather than merely trying to replicate the traditional classroom

electronically" (Hiltz, 1986, p. 104).

 

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APPENDICES

 

141
APPENDIX A

SURVEY OF THE USE OF ELECTRONIC MAIL

I. Background Information

 

1. My age is: (please write) years old
2. I am: (please check the blank) female male
3. My current status is: (please check the blank) doctoral program master‘s program

4. My major is: (please check the blank)

Counseling, Educational Psychology and Special Education
Educational Administration

Physical Education and Exercise Science

Teacher Education
Others (please specify)

 

5. My nationality is: (please write)

 

6. How long have you been at MSU? (please check the blank)

less than 1 year 1 year 2 years 3 years
4 years more than 4 years
7. Do you use e-mail? (please check the blank) Yes No

If yes, how long have you used e-mail?
less than 1 year 1 year 2 years 3 years

_ 4 Years more than 4 years

 

142

II. Skills

8. The following statements are general skills in using e-mail. How much do you agree or
disagree with the following statements? Please circle one of the letters which applies
to you.

strongly strongly
agree agree neutral disagree disagree
3) l have good typing skills in English SA A N D SD
b) I have good reading skills in English SA A N D SD
c) l have good writing skills in English SA A N D SD

9. Using e-mail, do you consider yourself: Please check the blank which applies to you.

inexperienced a beginner an intermediate
an advanced user a highly advanced user

10. Using computers, do you consider yourself: Please check the blank which applies to you

inexperienced a beginner an intermediate
an advanced user a highly advanced user

Ill. Personal Characteristics

11. The following statements are perceptions of using e-mail. Please circle one of the numbers
which applies to you.

E-mail is:
neutral

useful 5 4 3 2 1 useless
fast 5 4 3 2 1 slow
necessary 5 4 3 2 1 unnecessary
easy 5 4 3 2 1 difficult
simple 5 4 3 2 1 complex
comfortable 5 4 3 2 1 uncomfortable
efficient 5 4 3 2 1 inefficient

convenient 5 4 3 2 1 inconvenient

"’ E.

 

 

143

12. The following statements are about attitude about use of e-mail. How much do you agree
or disagree with the following statements? Please circle one of the letters which applies

to you.
strongly strongly
agree agree neutral disagree disagree
. I have no fear of using e-mail to communicate SA A N D so

with other people

. Using e-mail to participate in a group discussion SA A N 0 SD
is very exciting to me

. I enjoy sending messages to others using e-mail SA A N D so

. I feel confident in my ability to clearIy express SA A N D so
ideas using e-mail

. I am not as good as others in using e-mail SA A N 0 SD

. I am calm and relaxed using e-mail to share SA A N D so

ideas with others
. I use e-mail with confidence SA A N D SD

. It bothers me to think I probably will not know SA A N D SD
exactly who reads the e-mail I send

. It makes me nervous to use e-mail to SA A N D so
communicate any important information

. In using e-mail to exchange valuable ideas, I am SA A N D so
afraid my ideas might be used without my
permission

. It makes me nervous to think that a lot of other SA A N D so
people could read the e-mail I send

. I feel excited and enthusiastic thinking about SA A N D so
using e-mail to communicate with others

. I have the ability to be an effective e-mail user SA A N D so

. I am not very good at e-mail communicating SA A N D so

. Using e-mail to communicate with others is just SA A N D so

not worth the effort

. If I had a choice, I would never use e-mail to SA A N D SD
communicate with other people

 

144

strongly strongly
agree agree neutral disagree disagree
. I feel insecure about my ability to use e-mail to SA A N D SD
communicate with other people
. The challenge of continuing to learn to use SA A N D SD
e-mail is exciting
. If given the opportunity, I use e-mail SA A N D SD
. I always feel someone is looking over my SA A N D SD

shoulder monitoring every message I send

13. The following statements are perceptions of controlling future events. Please circle one of the
letters which applies to you.

strongly strongly
agree agree neutral disagree disagree
. Whether or not I get to be a leader depends mostly SA A N D SD
on my ability
. To a great extent, my life is controlled by accidental SA A N D SD
happenings
. I feel like what happens in my life is mostly SA A N D SD
determined by powerful people
. Whether or not I get into a car accident depends SA A N D SD
mostly on how good a driver I am
. When I make plans, I am almost certain to make SA A N D SD
them work
. Often there is no chance of protecting my personal SA A N D SD
interest from bad happenings
. When I get what I want, it's usually because I am SA A N D SD
lucky
. Although I might have good ability, I will not be SA A N D SD

given leadership responsibility without appealing to
those in positions of power

. How many friends I have depends on how nice 3 SA A N D SD
person I am

 

145

. I have often found that what is going to happen will
happen

. My life is chiefly controlled by powerful others

. Whether or not I get into a car accident is mostly a
matter of luck

. People like myself have very little chance of
protecting our personal interests when they conflict
with those of strong pressure groups

. It's not always wise for me to plan too far ahead
because many things turn out to be a matter of good
or bad fortune

. Getting what I want requires pleasing those people
above me

. Whether or not I get to be a leader depends on
whether I'm lucky enough to be in the right place at
the right time

. If important people were to decide they didn't like
me, I probably wouldn't make many friends

. I can pretty much determine what will happen in my
life

. I am usually able to protect my personal interests

. Whether or not I get into a car accident depends
mostly on the other driver

. When I get what I want, it's usually because I worked
hard for it

. In order to have my plans work, I make sure that
they fit in with the desires of people who have power
over me

. My life is determined by my own actions

. It's chiefly a matter of fate whether or not I have a
few friends or many friends

strongly strongly

agree agree neutral disagree disagree
SA A N D SD
SA A N D SD
SA A N D SD
SA A N D SD
SA A N D SD
SA A N D SD
SA A N D SD
SA A N D SD
SA A N D SD
SA A N D SD
SA A N D SD
SA A N D SD
SA A N D SD
SA A N D SD
SA A N D SD

 

 

146

IV. Environments for E-mail

If you do not use e-mail, please go to 26.
14. Where do you get access to e-mail systems? Please check all which apply to you.

Home Office Lab. Others: (please specify)

15. How easy is it for you to access e-mail systems? Please circle the numbers which applies
to you.

Very easy easy neutral difficult very difficult
5 4 3 2 1

16. Where did you Ieam to use e-mail and how effective was it for helping you Ieam to use
e-mail? Please circle the numbers which applies to you.

not very very
applicable effective effective neutral ineffective ineffective

. I took a workshop/class "A 5 4 3 2 l

. I Ieamed by myself using instruction NA 5 4 3 2 1
sheet/manual

. My friends taught me to use e-mail NA 5 4 3 2 1

. others (please describe briefly) NA 5 4 3 2 1

 

17. How much do you agree or disagree with the following statements?
Please circle one of the letters which applies to you.

I use e-mail, because: strongly strongly
agree agree neutral disagree disagree
. I am required to for class SA A N D SD
. I am required to for other works SA A N D SD
. I enjoy communicating with friends SA A N D SD
. I enjoy other e-mail activities (such as listservs) SA A N D SD
. I see it as an important professional skill to acquire SA A N D SD
. there is someone who encourages to use SA A N D SD
. there is a person who can manage discussion SA A N D SD
by e—mail
. there is a person who can help me about using SA A N D SD

e-mail systems, such as file transfer

IL

 

 

147

V. Use of E-mail

18. Think of atypical week during the most recent semester. In a typical week, how many days
do you check e-mail? Please circle numbers which apply to you.

1 2 3 4 5 6 7 days

19. In atypical week, how many minutes do you spend
sending and receiving messages?
Please write average time. minutes per week

20. In a typical week, how many messages do you receive?
Please write average number of messages. messages per week

21. In atypical week, how many messages do you send?
Please write average number of messages. messages per week

22. How often do you use e-mail for the following reasons? Please circle one of the numbers
which applies to you.
more
I use e-mail to communicate with: once once once once
a day a day a week a month never

. faculty for class assignments 5 4 3 2 1
. faculty on research projects 5 4 3 2 1
. my advisor on my graduate program 5 4 3 2 1
. faculty on other topics of interest to me 5 4 3 2 1
. students on class assignments 5 4 3 2 1
. students on course related topics 5 4 3 2 1
. students on other topics of interest to me 5 4 3 2 1
. students or faculty at other universities for 5 4 3 2 1

exchanging information

. family or friends in the US. for exchanging 5 4 3 2 1
information

. family or friends outside the US. for exchanging 5 4 3 2 1
information

. Others: (please describe) 5 4 3 2 1

 

 

 

148

23. How often do you use e-mail for the following reasons? Please circle one ofthe numbers
which applies to you.

more
once once once once
I use e-mail and networks for: a day a day a week a month never
. searching the library (such as MAGIC) 5 4 3 2 1
. reading bulletin board 5 4 3 2 1
. joining listservs (such as EGSO) 5 4 3 2 1
. searching the lntemet with GOPHER 5 4 3 2 1
. searching the Worid VVIde Web (such as Mosaic) 5 4 3 2 1
. others: (please describe) 5 4 3 2 1

 

VI. Comments

24. In the past week, can you think of ways in which e-mail has been particularly valuable to
your study? If so, please describe briefly.

25. In the past week, have you found yourself using e-mail in ways which you did not find
particularly worthwhile for your study? If so, please describe briefly.

26. Any additional comments that you would like to make?

 

 

149

APPENDIX 8

COVER LETTER ATTACHED TO THE SURVEY

Dear ;

I am Seounghee Choi, a doctoral student in the Counseling, Educational
Psychology and Special Education department at Michigan State University.

I am currently conducting research to examine factors that influence student's
use of e-mail. The enclosed questionnaire is to investigate the factors related to
the amount of usage and collaborative usage of e-mail. I would appreciate your
taking approximately 20 minutes of your time to complete the questionnaire.

This questionnaire serves as part of my doctoral dissertation project. Your
participation is of great importance to me. If you have any questions about the
study or the procedures, you can contact me at 337-8468 or at
21602CS@MSU.EDU. This study has been approved by my dissertation
committee at Michigan State University and endorsed by the Office of
International Studies in Education. Please read each statement carefully. You
are not asked to identify yourself, so all responses will remain anonymous.

Your participation is entirely voluntary. If you decide to participate and find that
you do not feel comfortable answering any questions, you are free to withdraw at
any time. Although no specific benefits can be guaranteed to you through this
participation, it is possible that you might benefit by the opportunities to think
about how you use e-mail. Also being aware of your e-mail use pattern may
help you use e-mail better and make you more aware of the advantages of using
e-mail.

I have enclosed a self-addressed, stamped return envelope for your
convenience and to facilitate a prompt response. Please send it to me or drop it
at my office (#238 Erickson Hall). Thank you for your cooperation in this
research endeavor. Your PROMPT RESPONSE will be greatly appreciated.

Sincerely,

Seounghee Choi
Graduate Student

 

150

APPENDIX C

UCRIHS LETTER OF APPROVAL

March 30, 1995

TO: Seoun hee Choi
3049 iber St. #7
E. Lansing, Mi 48823

RE: IRBf: 95-143
TITLE: FACTORS INFLUENCING THE COLLABORATIVE USAGE OF
COMPUTER-MEDIATED COMMUNICATION (E-RAIL)
REVISION REQUESTED: N/A
CATEGORY: 1-A,C
APPROVAL DATE: 03/29/95

The University Committee on Research Involving Human Subjects'(UCRIHS)
review Of this project is complete. I am pleased to adVise that the
rights and welfare Of the human subjects appear to be adequately
protected and methods to Obtain informed consent are apprOpriate.
lhegegorg, the UCRIHS approved this prOjeCt including any revision

is e a ove.

RENEWAL: UCRIHS approval is valid for one calendar year, beginning with
the approval date shown above. Investigators planning to
continue a project be and one year must use the green renewal
form (enclosed with t e original approval letter or when a
project is renewed) to seek ugdate certification. There is a
maximum Of four such expedite renewals ossible. Investigators
wishing to continue a prOject beyond the time need to submit it
again or complete reView.

REVISIONS: UCRIHS must review any changes in procedures involving human
subjects, rior to initiation Of t e change. If this is done at
the time 0 renewal, please use the green renewal form. TO
revise an approved protocol at any O her time during the year,
send your written request to the CRIHS Chair, requesting reVised
approval and referencing the project's IRB # and title. Include
in our request a description Of the Change and any revised
ins ruments, consent forms or advertisements that are applicable.

PROBLEMS/

CHANGES: Should either Of the followin arise during the course Of the
work, investigators must noti UCRIHS romptly: (1) problems
(unexpected Side effects, comp aints, e C.) involVing uman

subjects or (2) Changes in the research environment or new
information indicating greater risk to the human sub'eCts than
existed when the protocol was preViously reviewed an approved.

If we can be Of any future help, lease do not hesitate to contact us
at (517)355-2180 or FAX (517)3 6- 171.

, LI“

ravid E. Wright, P .D.
CRIHS Chair ‘

DEW:pjm

Sincerely,

  

CC: Stephen L. Yelon