The purpose of this article is to analyze the market segments of students enrolled in undergraduate online business courses at a regional state university. By understanding the defining characteristics of these students, universities may be able to more effectively recruit and retain students in these market segments. A survey of undergraduate online students was conducted and analyzed to determine the various market segments being served, and a predictive model was prepared that incorporates key independent student variables that can forecast student demand for courses and degree programs online.
With the concept of distance education, a paradigm shift has occurred with the university now traveling to the student instead of the student traveling to the campus. This paradigm, however, requires more flexibility and decentralization (Sherry, 1996). The concept of strategy development for the university and the individual colleges is to provide educational services for their students. Some authors believe that a first mover advantage in online course development will provide them with new or expanded markets in the product life cycle curve (see for example Burnside, 2001; clayton, 2000; Schofield, 1999; and Willis, 2000), while others view it as a necessity in order to maintain market share (Willis, 2000). The current state of affairs in online education can be summarized as follows: "if you do not develop online courses, then someone else will". Therefore, many universities believe they are being swept along with this tide of events (clayton, 2001; Kidwell, Mattie & Sousa, 2000 and Oblinger, 2000)
One universal question is to determine if strategy follows product development, or if product development follows strategy (Farrington & Bornak, 2001 and Hezel & Dominguez, 1999). The rationale for this question is at the heart of online course development. If one selects to construct the strategy first, then determining the target market segmentation is critical as well as the demographics and characteristics of the market segment. The reverse would be to construct the online courses over a period of time and then ascertain the market(s) that is/are purchasing the online courses, and evolve a strategy.
Market segment identification is critical for the college in Grafting a strategy and to design a business model that allows for successful implementation and execution of the strategy (Kidwell, Mattie & Sousa, 2000; Morrison & Rossman, 2003; and Oblinger, 2000). The business model presents information to the administrators on the economic viability of their strategy. Therefore, it is imperative to select the type of segment that the college plans to service. Alternate educational strategies will vary based on the segment identified by the college that it wishes to serve, and the demographics as well as the behavioral characteristics will vary according to the market segment strata selected (Oblinger, 2000).
The demographic profiles of online students and traditional students have begun to be reported in literature in determining market segments for strategy formulation. Demographics allow investigators to compare and contrast traditional classroom student data to distance education student data. Although the results of such descriptions vary, the stereotype of an older part-time student has gained widespread acceptance, and persists for consumers of distance education (clayton, 2001). Preston and Booth (2002) and S/ulc (1999) claim that distance education demographics have become clearer and a detailed portrait is emerging. Moreover, some would argue that the distance education market is becoming homogeneous, while others would counter that it is very heterogeneous (Morrison & Rossman, 2003 and Oblinger, 2000). Does a "typical learner" exist in the traditional and online consumer market? At best, the data collected on demographics and behavioral characteristics is ambiguous (Peters, 2001). To be successful when implementing a strategy for an undergraduate online program, colleges within the university need to develop demographic and characteristic analysis of the market segment they wish to serve.
The purpose of this study is to examine the demographic and characteristic aspects of students enrolled in undergraduate online business courses. To help quantify the importance of demographics on demand, a dynamic empirical model is employed. The predictive model reveals that several segmentation variables significantly impact online demand.
Capturing student data has been a hallmark of universities since their inception (clayton, 2001; Richards, 1997 and S/ulc, 1999). Demographic data can help formulate a successful university educational strategy (Oblinger, 2000). However, when a university and its colleges determine that online education will become a component of its educational portfolio then strategy refinement for the university and individual colleges is required. According to Kidwell, Mattie & Sousa, 2000 and Carr (2000), market segmentation, demographics and behavioral characteristics are key criteria for strategy refinement.
Table 1 summarizes the learner segments paradigm with respect to demand classifications. A changing environment for education has resulted in emerging market segments above and beyond traditional college students. These new segments create market opportunities for colleges and universities.
Diaz (2000), Gibson and Graff (1992) and Thompson (1998) agree that online students are usually older, have higher grade point averages, and accomplish more college credit hours as well as degree programs. Szulc (1999) contends the literature of case studies, evaluations and dissertation research illustrates that distance education students are slightly different than traditional students by being older, have more professional working experience, as well as families and careers. However, Clayton (2001) challenge this "older part-time" student stereotype through demographic surveys conducted. Richards (1997) estimates that forty percent of the student population in higher education can now be classified as non-traditional.
Demographics impact attitude toward distance education (Peters, 2001). Student learning and outcomes are highly correlated to attitude (Clayton, 2001). Behavioral characteristics analysis assists in determining attitudes of consumers who are more favorably attracted to distance education.
Students who exhibit the following common characteristics: higher levels of self-motivation and self-organization, excellent time management skills, confidence utilizing the computer, maturity, enjoy class discussion and analysis, goal driven, independent learning style, disciplined, student/educational preparation, and isolation aversive, are more successful distance education students (Diaz and Cartnal, 1999; Diaz, 2002; Gibson, 1998; Gibson and Graff, 1992; Oblinger, 2000; and Sherry, 1996). Behavioral characteristics provide more refined information to administrators concerning the market segment that the college serves or wishes to serve with their online courses.
Educators are trying to forecast which segments of the student market are attracted to distance education and who will be successful. Demographic information and behavioral characteristics provide greater knowledge of the market segment and future segments to which the individual colleges wish to market their distance courses.
This study focuses on students enrolled in four undergraduate business courses at a Division II regional university located in the southwest US. The online courses and programs at this university began in 1997. The university now offers a complete online MBA program; however, no undergraduate online degree is offered at this time. Thus, the current online courses serve as alternative outlets for taking selected required classes. The fact that there is a limited number of online class offerings provides an opportunity to gauge emerging market segments for undergraduate business education.
The data for this manuscript were collected in February 2003 from four online courses: Two undergraduate economics courses, and two undergraduate marketing courses. A standardized online survey form was utilized; students were asked to participate, but not required to do so. The final sample had 180 unique undergraduate respondents.
THE RESEARCH MODEL AND HYPOTHESES
Based on the research cited above, the following model is posited: Desire to take college courses or programs online (D) is a function of: age (A), marital status (MS), pc literacy (PC), children at home ©, the number of hours per week the student works (W), grade point average (GPA), income (I), gender (G), and distance from campus (DST). Specifically,
(1) D = f(A, MS, PC, C, W, GPA, I, G, DST).
The dependent variable (D) was measured using a Likert scale response assessing student desire to complete courses and/or degrees online. Two of the four courses in this study had other oncampus sections available; thus, students had the ability to choose which learning method best suited their situation. The other two courses were offered only online during this particular semester, and are required courses in their respective majors.
The result was a student sample consisting of both opt-in online learners, and some who did not have a choice at the time. This guarantees a variety of responses in the dependent variable, and avoids having an in-grown sample. Several hypotheses follow:
Age: Szule (1999), Block (2003), Diaz (2000), and others note that online students tend to be older than traditional-age students. These students are more likely to be employed, as well as married and/or with children. Age was measured with an ordinal scale with 10-year increments above the traditional college category of 18-24. We thus hypothesize:
H1: Age is positively related to the desire to enroll in online courses and programs.
Marital Status: Szulc (1999) demonstrated that online students are likely to be married and to have families. Given the increasing age of college students (Richards, 1997), and the reported average age at this university (age 27), it stands to reason that a large number of these students will be married. Thus, for this binary response variable we hypothesize:
H2: Being married is positively related to the desire to enroll in online courses and programs.
PC Literacy: Gerlich and Neely (2005) found PC literacy to be an important factor in the enrollment of, and satisfaction with, online courses. This is intuitive, because a high degree of computer usage is expected in the online course. This variable was measured using a self-ranking of computer proficiency, yielding an ordinal variable with interval characteristics. Based on the prior research at the university, we thereby hypothesize:
H3: PC literacy will be positively related to desire to enroll in online courses and programs.
Children at Home: Szulc (1999) and O'Malley and McCraw (1999) show that online students are likely to be older than traditional students, and more likely to have families. The presence of children, along with marital and employer responsibilities, add many demands to students. Online courses thus become an attractive option. We thus hypothesize:
H4: Having children at home is positively related to the desire to enroll in online courses and programs.
Work Hours Per Week: O'Malley and McCraw (1999), Block (2003), and others show that college students are more likely to work than before, and also work more hours than before. This reflects an underlying shift in the traits of college students in general. Preliminary research showed that a vast majority of the student sample worked, and 85-percent worked 21 or more hours per week. This variable was assessed with an ordinal response question, broken into 10-hour increments. We thus hypothesize:
H5: The number of hours worked per week is positively related to the desire to enroll in online courses and programs.
GPA: Diaz and Cartnal (1999), Diaz (2002), Gibson (1998), and others demonstrate that the most successful distance learning students are those with the most maturity, discipline, and drive. These traits are also those of students in general with the highest grade point averages. This variable was assessed with an ordinal scale using a standard 4-point GPA range, broken into 0.5 increments. We hypothesize the following:
H6: Grade Point Average (GPA) is positively related to the desire to enroll in online courses and programs.
Income: Although Szulc (1999) showed that distance students tended to be older and have professional experience, which leads to the conclusion that they would have higher incomes than "traditional" students, the students polled in this study worked 20-40 hours per week, and were not employed in high-paying jobs. Online courses allowed them to earn their degrees, while at the same time freeing up more sizeable periods of time during which they could work. We thus hypothesize the following:
H1: Income will be negatively related to desire to enroll in online courses and programs.
Gender: Block (2003) and others report that there are more females enrolled in college, as well as in online courses. Given that women are nearly as likely to work as are men, and are often caring for children either in a married- or single-parent family, the convenience of taking courses and programs online is attractive. Thus, we hypothesize the binary response:
H8: Gender is positively related to the desire to enroll in online courses and programs.
Distance: Gerlich and Neely (2005) indicated that the student's physical distance from campus indicated their demand for online courses and programs. The nature of the nearby community and adjacent region certainly influence this finding. The university is located in a small bedroom community of 12,000 people, and is 15 miles from a town of 175,000. The outlying area is very rural, with scattered small towns. The entire region is approximately the size of Ohio, and has about 360,000 residents. We hypothesize the following:
H9: Distance from campus is positively related to the desire to enroll in online courses and programs.
A multiple regression was performed on the data. The results are presented in Table 2 below. The results above indicate that the number of hours worked per week (W), the presence of children at home (C), and distance (DST) from the university are significant predictors of the desire to enroll in online courses and programs. We thus retain H4, H5, and H9, while rej ecting the remainder. None of the variables had a correlation coefficient above 0.5, suggesting that murticollinearity is not a problem.
There are numerous studies in the literature outlining the various market segmentations and the strategies to capture these segments in online education. One of the surprises that we discovered in our research is that several of the national studies concerning the demographics of online students report that the students are older (35) and seeking online courses for career development. Our research, however, suggests that many of our online students fit the demographic profile of the traditional college age (18 - 24) student. This corresponds to recent investigations of public universities in the literature, which look somewhat like our university.
Furthermore, this supports the concept that we are providing a service instead of a degree online. This is a valid strategy in retention efforts due to a key demographic characteristic occurring on our campus as well as other campuses across the United States. Students are working many more hours. Students view higher education as being more expensive with validation of this view by current costs incurred, and the drive by students to retain the least amount of debt possible before graduation.
What was interesting in our research is the cost of online courses did not appear to have any significance. This can be interpreted that we may be under-pricing our product, but the strategy of the organization (university) would dictate this final pricing determination. In the literature many universities price their online courses the same as their traditional courses ("chalk and talk"). The consumer segmentation the university wishes to sell to in the market will have a bearing on the infrastructure costs and capital required to compete in that market.
MARKET SEGMENTS FOR ONLINE BUSINESS COURSES
The results reported above indicate that the university at which this study was conducted is serving multiple market segments. Prior to this study, no effort has been made to determine the various market segments, nor has there been an effort to develop strategies to reach them. Online courses have grown in popularity of their own accord, not the result of any concentrated marketing strategy.
The following market segments have emerged as distinct groups of customers that are currently enrolled in online business courses at the university:
Segment 1: Adults with families and careers. The results indicated that the presence of children as well as number of hours working were significant predictors of online course demand. The combination of these two variables results in a market segment likely to be older than traditional college age, with the added responsibilities of career and family.
Segment 2: Single working parents. While marital status itself was not a significant predictor of online course demand, it is possible to conclude that for the reasons listed with the market segment above that single working parents would also be a viable segment. Anecdotal evidence provided by students in the authors' courses suggests that single parents are not at all uncommon in the online class. The absence of a spouse makes the appeal of online learning even greater than for those who are married.
Segment 3: Traditional-age college students with jobs. This is probably the most surprising finding, for it includes on-campus as well as off-campus students. Anecdotal evidence gathered from faculty at the university suggests some regrets that online courses are being taken by students living in on-campus dormitories. While this may lead to dismay with academic traditionalists, it should be noted that the students participating in this study were highly likely to work 20-40 hours per week. Thus, regardless of whether they live at home, off-campus with friends, or on-campus, online courses offer a time-saving convenience that allows them to simultaneously work toward their degree, and be gainfully employed to pay for the costs of being in college.
Segment 4: Distance learners. This segment is the one that was assumed from the beginning by most universities entering the online arena. The region in which this university is located is one of great geographic distance between cities. The university's primary market consists of about 350,000 people, of which a little over 225,000 live within 25 miles of the campus. The remaining persons live up to 130 miles from campus. Furthermore, residents throughout the state, which may be up to 700 miles away, can enjoy in-state tuition and be able to take courses from home (without any travel costs, relocation costs, etc.)
These four segments can often be served equally well by one general course and/or program offering, yet each segment has characteristics that make them unique. The takeaway from this study, though, is that by identifying these segments, marketing programs can be built to try to penetrate each segment.
Understanding why current students enroll in courses and programs defines the market. Identifying and capitalizing upon the market allows the college and the university to recruit and retain its customers (students). In many respects the data developed from the research confirms the investigators' beliefs and is congruent with the literature on demographics and behavioral characteristics of the online undergraduate student market segment.
In other aspects of the research findings the data was somewhat surprising. What becomes apparent is that the online undergraduate college of business student at this university is subsumed in the larger, traditional student population. The online undergraduate business student takes a combination of traditional courses and online courses. Whether this is the outcome of online course supply limitations; due to the lack of resources, costs, expenses and so on or the strategic decision process is an important criterion to recognize. However, the literature and our research illustrate that the traditional student is older, married, non-residential, works more hours, and more likely to be female than male. In addition, the enrollment in online courses is rising, which supports distance education as a viable industry, although there have been some shakeouts (Carnevale and Olsen, 2003; and Clayton, 2000).
The college of business is using online courses as a retention strategy in order to provide flexibility for the traditional undergraduate business student. The research results confirm that the college is accomplishing this objective of retention. Incorporating a retention strategy for online course development has clearly defined the strategy and the components to employ for the college while
In the final analysis when colleges in a university understand individual backgrounds and experiences of the online student market segment this will afford greater knowledge of those who succeed and those who fail. It is hoped that this knowledge gained will contribute to a strategy that addresses the needs of online students and produces higher success rates. The success rate of online students translates into financial achievement for the colleges and the institution. The value of continuous and constant demographic and behavioral characteristics research of the online student market segment allows the colleges to attract and retain their customer group (students) by developing the correct strategy and remaining close to the mission of the university. If the college and the university are clear as to the strategic policy problem or issue that they are trying to resolve and the market segment (students) they serve with distance education, then the ability to develop strategy and policy is greatly enhanced and enriched.
Block, S. (2003). More students must earn while they learn. USA Today., April 4, 2003.
Burnside, R.M. (2001). Å-learning for adults: who has the goods? Technology Source, (http://ts.mi vu.org/default. asp? show=article&id=882).
Carnevale, D. & F. Olsen (2003). How to succeed in distance education. The Chronicle, 49(49), 25-26.
Carr, S. (2000). As distance education comes of age, the challenge is keeping the students. Chronicle of Higher Education, 46(23), 4-16.
Clayton, M.A. (2001). Who's online? a look at demographics of online student populations. Presented to the VCongress on the Americas, Puebla, Mexico.
Clayton, M. (2000). Click n' learn. Christian Science Monitor, 92(185), 15.
Diaz, D.P. (2000). Comparison of student characteristics, and evaluation of student success in an online health education course. Villenova University. (http://www.LTSeries.com/LTS/pdfjiocs/dissertn.pdf).
Diaz, D.P. (2002). Online drop rates revisited. Technology Source, (http://ts.mivu.org/de fault.asp?show=article&id=981).
Diaz, D.P. & R.B. Cartnal (1999). Students' learning styles in two classes: online distance learning and equivalent oncampus. College Teaching, 47 (4), 130-135.
Farrington, G. & S. Bronack (2001). Sink or swim? THE Journal, 28(10), 42-47.
Gerlich, R. Nicholas andBenNeely (2005). An assessment of the effectiveness of West Texas A&M University's online teaching program in serving market needs, Working Paper, West Texas A&M University.
Gibson, C.C. (1998). The distance learner's academic self-concept. In C. Gibson (Ed.), Distance Learners In Higher Education: Institutional Responses For Quality Outcomes, Madison, WI: Atwood, 65-76.
Gibson, C.C. & A.O. Graff (1992). Impact of adults' preferred learning styles and perception of barriers on completions of external baccalaureate degree programs. Journal of Distance Education, 7(1 ), 39-51.
Hezel, Richard T. & P. Sculz-Dominguez (2001). Strategic planning in e-learning collaborations. Education at a Distance, 15(40), 1-4.
Kidwell, Jill, Mattie, J. & M. Sousa (2000). Prepare your campus for e-business. Educause Quarterly, 23(2), 23-25.
Morrison, J. & P. Rossmand (2003). The future of higher education: an interview with Parker Rossman. Technology Source, (http://ts.mivu.org/default.asp?s0 how=article&id=l041).
Oblinger, D.G. (2000). The nature and purpose of distance education. Technology Source, (http://ts.mi vu.org/default. asp? show=article&id=647).
O'Malley, J. & H. McCraw (1999). Student perceptions of distance learning, online learning and the traditional classroom. Online Journal of Distance Learning Administration, 2(4), 7-16.
Peters, L. (2001). Through the looking glass: student perceptions of online learning. Technology Source, (http://ts.mi vu.org/default. asp? s0 how=article&id=907).
Preston, J. & L. Booth (2002). An e-commerce model for teaching online, (http : //ipfw.edu/as/tohe/2002/Papers/preston.htm).
Richards, T. (1997). Educating in a time of challenging student demographics. Technology Source, (http ://ts .mi vu. org/de fault, asp? show=article&id=544).
Schofield, J. (1999). Back to school online. Maclean's, 112(36), 68-71.
Sherry, L. (1996). Issues in distance learning. Internationaljournal of Educational Telecommunication, 1 (4), 337-365.
Szulc, P. (1999). Reassessing the assessment of distance education courses. THE Journal, 27(2), 105-111.
Thompson, M.M. (1998). Distance learners in higher education. In C. Gibson (Ed.), Distance Learners In Higher Education: Institutional Responses For Quality Outcomes, Madison, WI: Atwood, 9-24.
Willis, B. (2000). Distance education's best kept secrets. Technology Source, (http ://ts .mi vu. org/de fault, asp? show=article&id=673 ).
Joshua J. Lewer, West Texas A&M University
R. Nicholas Gerlich, West Texas A&M University
Terry Pearson, West Texas A&M University…