Academic Exchange Quarterly   Spring  2004: Volume 8,  Issue 1

 

Factors Affecting Student Adoption of Online Education

 

Parbudyal Singh, Ph.D.

William Pan, Ph.D.

 

Parbudyal Singh is an Assistant Professor in the School of Administrative Studies, York University, Canada.  William Pan is a Professor at the University of New Haven, Connecticut.

 

Abstract

North American colleges and universities are increasing their use of online education.  While there is a large volume of literature on the reasons for administrators’ offering online education, there is less written on why students take such courses.  In this paper, using a sample of 101 graduate business school students, we examine the factors associated with the adoption of online education by students.  Implications for administrators are discussed.

 

Introduction

 

Online education is creating excitement among educators in colleges and universities in the United States and further afield.  For some it offers a way to reach a wider audience, including those not targeted by traditional higher-education institutions.  For others, it offers a new pedagogical tool that has the potential to transform the learning process.  And for others, as exhibited through the University of Phoenix Online Education model, it is perceived as an inexpensive way to grow their student population and revenues (Economist, 2002; Olsen, 2002).  While the reasons for implementing online courses and programs vary, a few common themes emerge from an examination of the literature: expanding access to under-served populations; alleviating classroom capacity constraints; capitalizing on emerging market opportunities - such as working adults - and, serving as a catalyst for institutional transformation (Aron, 1999; Berger, 1999; Eastman & Swift, 2001; Fornaciari, Forte, & Matthews, 1999; Oliver, 1999; Volery & Lord, 2000; Webster & Hackley, 1997).

 

The use of online education has grown significantly as a result of its real and perceived benefits (McGinn, 2000).  Urdan and Weggen (2000) state that revenues from Web-based training for online education are forecasted to climb from $550 million in 1998 to $11.4 billion in 2003.  John Chambers, CEO of Cisco, states that “education over the Internet is so big, it is going to make e-mail look like a rounding error” (Chambers, 1999).  The number of colleges and universities offering online education has also increased dramatically – from 93 in 1993 to 762 in 1997 (Hankin, 1999), including many established universities such as Duke, University of Baltimore, Colorado State University, University of Florida, New York University, University of Maryland, Massachusetts Institute of Technology, Ohio University, Pennsylvania State University, Stanford University, the University of Wisconsin, and the University of Tennessee (Eastman and Swift, 2001).  In 2000, U.S. universities offered over 54,000 courses online with an enrollment of over 1.6 million students (Driver, 2002).  A more recent estimate suggests that 2.2 million students enrolled in online courses in 2002 (Sausner, 2003).

 

Despite such rapid growth of online education on campuses, there is a paucity of related research in the business and management literature.  As Arbaugh (2000: 213) notes, “because Internet-based instruction is a relatively new means of communicating knowledge, research on this type of instruction is still in its infancy.”  Furthermore, there is a general lack of research on the reasons associated with the adoption of online courses by students not only in business studies but in other disciplines as well.  Rather, the focus has been on the reasons for implementation by administrators.  Thus, our main objective in this empirical study is to examine the factors associated with the use of online education by students within a School of Business.  This paper presents the results of our investigation.

 

Literature Review

 

A review of the extant research reveals that while there is a relatively large body of literature on the growth and use of online education by educators, there is less research on the factors associated with the adoption of online education by students.  Demographic variables, such as student and work status (full or part-time), seem to be among the most pertinent in explaining the use of online education by students.  For instance, one of the primary reasons for students’ taking online courses gleaned from the literature is that inflexible work schedules may not allow for participation in traditional 9-5 classes (Canzer, 1997; Donoho, 1998; Phillips, 1998).  That is, students working full-time generally do not have the flexibility to attend “in-class” sessions.  Student quality may also be a deciding factor.  As Volery and Lord (2001) argue, the brightest students may prefer to learn in an individual online environment rather than sharing their knowledge with less bright students in a traditional classroom setting.  Personal commitments, such as responsibilities to the family, measured by marital status and number of children, are also cited as important factors driving the use of online education by students (Berger, 1999; Roberts, 1998).  Apart from demographic variables, technological factors are also important.  For instance, having a computer at home and prior experience with computers and the Internet are important variables influencing student adoption of online courses (Volery and Lord, 2001).

 

One’s attitude towards a new technology is pivotal in the decision on whether or not to adopt it (DeLoughry, 1996).  Yet, student attitudes towards online education has attracted little, if any, attention in the empirical literature in business education, and is a distinctive contribution of this study to this emerging body of knowledge.  Thus, in this study, we will examine the relationships between the adoption of online education by students and the availability and use of appropriate technology; student demographics; and student attitudes towards online education.

 

Methods

 

Sample and Procedures

A questionnaire survey was distributed to 220 students enrolled in the MBA program of a university in the northeast United States.  The development of the questionnaire was guided by the extant literature on the subject, as well as by interviews with professionals associated with the delivery of online education.  One hundred and one usable surveys were returned (a response rate of approximately 46%).  The sample consisted of 51 males and 50 females.  Sixty-two of the respondents were married, 33 single, and 6 divorced; 74 students had no children; 86 were part-time students; 88 worked full-time; and 30 had taken an online course before.

 

The survey questions focused on four broad areas: a) if students were taking online courses, and if so, how many; b) technology; c) student demographics and other characteristics; and d) student attitudes towards online education.  The technology-oriented questions were classified into two parts: availability of appropriate technology, and the actual use of computer and Internet technology.

 

Courses Taken Online: There were four questions in this section: whether the student was currently taking any online course with the university (0 = no; 1 = yes); if so, how many (a continuous variable); whether the student was taking online courses with other institutions (0 = no; 1 = yes); if so, how many (a continuous variable).

 

Use of the Computer and the Internet: Four questions captured a respondent’s technological expertise: amount of time spent on the Internet at home, and at work (2 questions), and the amount of time spent on the computer at home and at work (2 questions).  These were continuous variables.  The alpha reliability of the scale was 0.60.

 

Student Characteristics: Demographic variables included: student status, with full-time students coded as 1, and part-time students coded 2; work-status (full-time, part-time, retired, and unemployed – coded 1, 2, 3 and 4, respectively); age group (coded into 8 categories, with 1 = under 20, 2 = 21-24, 3 = 25-29, … and 8 = 50 and over); sex (1 = male; 2 = female); marital status (1 = married; 2 = single; 3 = divorced; 4 = separated; and 5 = widowed); and, number of children (measured as a continuous variable.

 

Student Quality was measured as current GPA (as reported by respondent/student).

 

Availability of technology: A scale/index was developed to capture these questions as a single variable.  There were six questions that asked whether the respondent had any of the following: a computer at home; a computer at work; Internet at home; Internet at work; e-mail at home; and e-mail at work (all coded 0 = no; 1 = yes).  The alpha reliability of the scale was 0.65.

 

Attitude Towards Online Education: Four statements, using a Likert-type scale ranging from 1 = strongly disagree, to 7 = strongly agree, were used to capture the respondents’ attitudes towards online education: universities that use online education are more competitive; online education is of the same quality as education in class; there is more student effort in online classes; and, there are higher standards for online education.  The alpha reliability for the scale was 0.67.

 

Analyses

 

Basic descriptive statistics were used to assess the characteristics of the sample and ANOVAs were used to examine the key differences between students who were taking online classes versus those who were not; correlation analysis was utilized to assess the relationships among the variables in the study.

 

Results and Discussion

 

As Table 1 below reveals, students who enrolled in online courses differ significantly from “non-adopters” in three key categories: student status, age, and attitudes toward online education.  More specifically, more part-time and younger students in the sample take online courses, as well as those with more “positive” attitudes towards this new delivery medium.

 

 

Table 1: Descriptive Statistics and Differences (ANOVAs) on Key Factors

 

Variables

total sample

adopters

 

non-adopters

group difference

 

 

Student status

 

Work Status

 

Age Group

 

Sex

 

Marital Status

 

Number of Children

 

Current GPA

 

Tech. Availability

 

Tech. Use

 

Attitudes to Online Education

Mean   Std Dev

 

1.85          .36

 

1.25          .79

 

4.34          1.62

 

1.50          .50

 

1.47          .69

 

0.46    0.85

 

 

2.41          1.27

 

0.87   0.20

 

7.21          2.80

 

4.25   1.06

Mean  Std Dev

 

1.69        0.47

 

1.23        0.65

 

3.81        1.63

 

1.46        0.51

 

1.46        0.58

 

0.27        0.60

 

 

2.35        1.23

 

0.92        0.17

 

7.83        2.93

 

4.93    1.00

Mean    Std Dev

 

1.91        0.29

 

1.28        0.83

 

4.52        1.59

 

1.51          0.50

 

1.47        0.72

 

0.52        0.92

 

 

2.42          1.29

 

0.86        0.21

 

7.0           2.74

 

4.01    0.97

F-Ratio 

 

7.39***

 

0.08

 

3.84**

 

0.15

 

0.00

 

1.67

 

 

0.06

 

1.55

 

1.69

 

17.03***

 

N = 101; *** p < .01; ** p < .05; * p = .10

 

 

These results are also exhibited in the correlations (Table 2 below).  Taking an online course is correlated at statistically significant levels with student status (increases with part-time status); age (decreases as one gets older); and attitudes to online education (increases with positive attitudes).  Interestingly, taking online courses is not significantly associated with work status (part versus full-time), sex, marital status, number of children, and current GPA.

 

Table 2: Correlations

                1              2              3              4              5              6              7              8              9              10            11            12            13            14

1              -

2              96***      -

3              11            09            -

4              -00           -01           85***      -

5              -26***     -28***     09            07            -

6              -03           04            -07           -06           -46***     -

7              -19**       -22**       -07           -06           48***      -17*         -

8              -04           -08           21**        17            14            -06           10            -

9              -00           -00           -06           -09           -08           -03           04            11            -

10            -13           -14           -05           07            13            -00           16            -13           -33***     -

11            -03           00            -06           -13           -12           22**        -02           07            18*          -02           -

12            12            14            13            11            27**        -43***     08            -02           -03           -01           -12           -

13            13            13            -06           01            03            -02           11            -13           -09           11            -01           10            -

14            39***      38***      -06           -17*         -10           05            -01           -15           -20**       19**        21**        11            10            -

1= taking university Online course; 2 = No. of university online courses; 3 = taken other online course; 4 = no. of other online courses; 5= Student status; 6 = Work Status; 7 = Age Group; 8 = Sex; 9 = Marital Status; 10 = Number of Children; 11 = Current GPA; 12 = Tech. Availability; 13 = Tech. Use; 14 = Attitudes to Tech

n = 101; *** p < .01; ** p < .05; * p < .10

 

 

There are several implications for administrators.  First, as the literature suggests, we found that students attending the university on a part-time basis were more likely to adopt online courses in their educational programs.  Thus, it seems as if the flexibility offered by Web-based education is important in the adoption decision, and administrators should take advantage of this element in their marketing campaigns.  It should be noted, as well, that work status was not a statistically significant factor in explaining the adoption of online education.  We are not sure of the exact reasons for this in our study but it is possible that a large number of MBA students surveyed received tuition reimbursement for enrolled courses and that their employers do not offer funds for online courses.  Second, since students’ attitudes are associated with the adoption of Web-based education, administrators may want to survey potential students to capture perceptions of this pedagogical tool and develop profiles of potential students.  Again, this will help with marketing campaigns through the identification of target markets.  Finally, unlike much of previous research, we did not find work status, sex, family connections, and student quality to be significant variables.  Thus, it may be prudent for administrators to carefully assess the costs and benefits of promotional efforts that target these market segments.  The foregoing should, however, be tempered by the fact that a limitation of this study is its relatively small sample size.

 

Online education is here to stay.  Hopefully, this research will contribute to the knowledge base on this issue and help administrators improve their marketing and other operations.

 

References

 

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