Differences in Characteristics of Online versus Traditional Students: Implications for Target Marketing Iryna Pentina Concha Neeley
ABSTRACT. This study provides insight for educators and administrators into differences between students enrolled in Web-based and traditional classes as online learning enters the growth stage of its product life cycle. We identify characteristics that differentiate online students from those who prefer traditional education methods in order to offer more effective marketing techniques for attracting and retaining online students. Results of the study suggest that students in traditional classes have higher perceptions of performance and financial risk than their online counterparts. Social character was another important factor determining the choice of traditional over online learning. Recommendations to assist administrators in increasing enrollment in their online programs are presented. doi:10.1300/J050v17n01_05 [Article copies available for a fee from The Haworth Document Delivery Service: 1-800-HAWORTH. E-mail address: Website: © 2007 by The Haworth Press, Inc. All rights reserved.]
Iryna Pentina, PhD, is a doctoral candidate affiliated with the Department of Marketing and Logistics, University of North Texas, P.O. Box 311396, Denton, TX 76203. She holds a doctorate in History and Theory of Pedagogics from Kharkov State Pedagogical University (E-mail:
[email protected]). Concha Neeley, PhD, is Assistant Professor, Department of Marketing and Hospitality Services Administration, Central Michigan University, 108A Smith Hall, Mt. Pleasant, MI 48859 (E-mail:
[email protected]). Journal of Marketing for Higher Education, Vol. 17(1) 2007 Available online at http://jmhe.haworthpress.com © 2007 by The Haworth Press, Inc. All rights reserved. doi:10.1300/J050v17n01_05
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KEYWORDS. Distance education, online learning, Web-based courses
INTRODUCTION Recent business research confirms that companies with more educated and creative human resources provide higher customer value and derive better financial performance (Day 1994; Prahalad and Hamel 1990; Hunt and Morgan 1995). This emphasis on knowledge as an advantagebuilding resource is one of the major drivers of increasing university enrollments (O’Donoghue 2002). In response to increasing enrollment, shrinking public budgets, and opportunities offered by highly developed information technology, more universities are incorporating online classes in their curricula. This type of educational delivery assists universities in dealing with increasing number of students, and in the long run is expected to decrease instruction-related costs. According to the 2004 Sloan Survey of Online Learning (2004), based on responses from over 1,100 U.S. colleges and universities, online enrollments continue to grow at an increasing rate (expected 24.8% in 2004, up from 19.8% in 2003), with 2.6 million students learning online in the fall of 2004. The majority of schools surveyed consider online education critical to their long-term strategy (53.6%), and a large number (40.7%) of academic leaders believe that students are at least as satisfied with online courses as they are with face-to-face offerings (Sloan Survey 2004). While the advantages of online learning are abundant, certain concerns are also being voiced. They focus on quality issues, such as faculty control over the curriculum, class size, and the tradeoffs of self-paced, learnercontrolled process compared with same-time, same-place interchange (Kriger 2001). Other challenges include inadequate skills in operating technologically mediated teaching and learning environment, lack of face-to-face communication with the teacher, and unsatisfied need of belonging to a group of fellow students (Clark 2000). Faced with changes in demand from students, and increasing competition from corporate teaching institutions and virtual universities, conventional universities are adjusting their marketing practices and developing services, promotions, and price schedules to appeal to the evolving student market (O’Donoghue 2002). In this context, choosing the right strategy to effectively target prospective students is of primary importance. Currently, in order to appeal to students who might be interested in online instruction, advertisements
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(both online and in other media) emphasize convenience, economy of time, and flexibility. While this (mostly intuitive) approach appears to be logical, in our opinion, its appeal is too general, and does not necessarily address the potential impact of more narrow needs of students such as quality of education, perceptions of risk, or social needs. At the stage when online learning is no longer a “new product,” a shift is warranted in promotional strategies and tactics, from generic appeals to addressing more specific needs of current and potential students. This paper attempts to explore differences in the individual characteristics of students who choose online over traditional delivery methods, with the purpose to suggest more refined marketing approaches. It is important for educators to understand these differences in order to attract and encourage retention of online students. The paper is structured in the following manner. First, we present arguments grounded in existing marketing and education literature and resulting hypotheses. Next, we describe the method of our research followed by a presentation of the results of hypotheses testing. A discussion of recommendations based on these results is provided. Finally, limitations of the study are offered. LITERATURE REVIEW AND HYPOTHESES Demographic Characteristics While designing appropriate market segmentation and targeting strategies, it is important to consider various demographic characteristics of potential customers. Analysis of the existing literature suggests that the profile of a “typical” online student has not remained stable over the past decade. For example, studies in the early 1990s found that online learners were older females who had both work and family responsibilities (Hazel and Dirr 1991; Robinson 1992), and were “place-bound” with a majority living 100-200 miles from the closest campus (Gibson and Graff 1992). In contrast, more recent research (Liviertos and Franks 1996) suggests that online students are more “time-bound” than “place-bound,” a characteristic that can also be applied to contemporary traditional students (Latanich, Nonis and Hudson 2001). Latanich, Nonis and Hudson (2001) legitimately question the relevance and validity of previous findings to the demographic characteristics of current online students. According to their study, there are no significant differences in age, gender, and employment between the two groups (Latanich, Nonis and Hudson 2001).
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Another study (Clayton 2001) supports these findings, reporting that today’s online students do not differ from the traditional on-campus students in terms of average distance they live or work away from campus. The author concludes that today’s online students simply reflect the demographics of the general student population (Clayton 2001). Based on these findings, we hypothesize the following: Hypothesis 1 (H1). There are no significant differences in age, gender, and income level between online and traditional students. Hypothesis 2 (H2). There is no significant difference in time required to reach campus between online and traditional students. Diffusion of Innovation The concept of the Product Life Cycle (PLC, Figure 1) is instrumental in assessing the stage of adoption of the online method of class delivery, which in turn will suggest some characteristics of the adopters. According to the PLC theory, any new product or service adoption follows four stages: introduction to the market, market growth, market maturity, and sales decline. At each stage, characterized by specific competitive situation, profits, and market size, a different marketing strategy should be used. Each of the four stages also differs in the amount and characteristics of customers who adopt the new product/service (innovation), and continue to use it in the future. The process by which an innovation is communicated through certain channels over time among the members of a social system is called diffusion of innovation (Rogers 1995). FIGURE 1. The Sequence and Proportion of Adopter Categories
Source: Modified from Schiffman and Kanuk 2000.
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It has been estimated (using normal distribution approximation) that at the early Introduction stage, only a fraction of the customers who eventually adopt the product–the so-called Innovators–buy the first models. Innovators are venturesome individuals that are guided by personal expertise and confidence, and enjoy being on the cutting edge. In the later Introduction stage, the new product/service is diffused among the Early Adopters who rely on the information provided by the Innovators’ use of the new products to make their own adoption decisions. They are opinion leaders who make judicious and well-informed decisions. During the Growth stage, the Early Majority group that generally takes a long time to fully adopt an innovation joins Innovators and Early Adopters. The Maturity stage of the PLC attracts the mass market of the Late Majority that adopts a new product under peer pressure, or for financial reasons. Finally, more skeptical or traditionalistic Laggards are the last to adopt the innovation (Schiffman and Kanuk 2000). While online learning can certainly be considered an innovation with a potential to change the “education industry” in a profound way, recent data suggest that around 20% of total educational services (including corporate training and “pure” virtual universities alongside with online courses offered by traditional institutions) are now provided online (Bocchi, Eastman and Swift 2004). This, along with increasing competition, and variability and diversity of e-learning offerings, provide strong indication that online education is entering the Growth stage of the PLC. This, in turn, suggests that the potential adopters of this education method in the next several years will be the Early Majority category. Therefore, contrary to existing views (Latanich, Nonis and Hudson 2001) that consider online students to be innovators, we argue that innovativeness is instrumental in identifying consumers who are most likely to pioneer in new product purchases, that is, innovators and early adopters (Rogers 1995), and would not be a distinguishing characteristic of today’s Early Majority segment of online students. Hypothesis 3 (H3). There will be no significant difference in degree of innovativeness between online and traditional students. Time Pressure and Time Management The traditional advantages of time, place, and sequence flexibility associated with online learning were intended to reduce the pressure resulting from lack of time for those working full time and supporting
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families. Common promotional appeals by universities use advantages of taking classes online such as saving time on commuting, self-paced and not schedule-bound learning, and any time–any place access to courses. These appeals are intended to attract the customers who, due to various circumstances, cannot attend regular classes on campus. This flexible approach requires a lot of self-organizing and ability to manage one’s time. Previous research, however, has shown that today’s online students do not differ from the traditional on-campus students in terms of average distance they live or work away from campus, nor in their marital or employment status (Clayton 2001). It has also been shown that the perception of lack of time is as characteristic of traditional students as of their online counterparts (Liviertos and Franks 1996). Hypothesis 4 (H4). There will be no significant difference between online and traditional students in time pressure and time management characteristics. Social Character Inner-directedness and other-directedness are the two social character types proposed by Riesman (1950) to distinguish between people who turn to their own inner values and standards for guidance in their behavior, and those who depend upon the people around them to give direction to their actions (Kassarjian 1962). Inner-directed persons are thought to be driven by their need for accomplishment while other-directed persons are motivated by the need for approval from others. Since online learning emphasizes self-directed and self-controlled approach, this trait appears to be discriminant between the two types of students. Prior research showed inconclusive results while investigating a similar concept of locus of control and its relation to the two types of students (Latanich, Nonis and Hudson 2001). Because online learning is self-directed, we hypothesis that those students who choose online courses are more likely to display inner-directed character; thus: Hypothesis 5 (H5). Online students will be more inner-directed than traditional students. Motivation Motivation, defined as “the ability to be self-starting or self directed” (Robbins 2001) has been found to be higher for online student populations
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(Latanich, Nonis and Hudson 2001), supporting the idea that students who do not receive regular supervision need to be more goal-oriented to succeed. Students may be intrinsically or extrinsically motivated in their selection of class delivery mode. Persons who are intrinsically motivated engage in activities for their own sake rather than as a means to an end; whereas, extrinsically motivated are more motivated by the end result than how it is attained (Holbrook 1986). Extrinsically motivated persons have a clear goal in mind when engaging in an activity. Likewise, we posit that students who are motivated by the end result and have a clear goal of completing the course will be more likely to pursue the online delivery method. Students who are more motivated by the activity itself, in this case learning, will be more likely to choose traditional mode of delivery in which they must attend classes. Hypothesis 6 (H6). Online students will be more extrinsically motivated than traditional students. Risk Perception Types and levels of consumer perceived risk have been extensively utilized in marketing for market segmentation, positioning, and designing pre- and post-purchase risk-reducing strategies (Bauer 1960; Shimp and Bearden 1982; Warwick and Mansfield 2003). Generally, when evaluating consumer needs, five major types of risk are assessed: financial, performance, social, psychological, and physical (Schiffman and Kanuk 2000). We believe that two types of risk are most relevant to the evaluation of online classes: financial (risk that the product will not be worth its cost), and performance (risk that the product will not meet the expectations). Due to the intangible and inconspicuous nature of online learning, we are not considering other types of risk. Previous studies have indicated that online learners have higher propensity to take risks (Latanich, Nonis and Hudson 2001), and that performance risk issues (concerning academic offerings, quality of professors, degrees offered) and financial risk issues (concerning tuition costs, availability of financial aid, and scholarships) were the top issues for traditional students and their parents in their college choice (Warwick and Mansfield 2003). Because of the intangible nature of online courses and the unknowns involved in taking these types of courses, we believe that those persons
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more willing to enroll in such courses would have a higher tolerance for risk. Based on this logic and previous findings, we hypothesize: Hypothesis 7 (H7). Perception of performance and financial risk will be lower for online students compared with traditional students. According to existing risk-related marketing literature, one of the ways to reduce perceived risk is by providing trial opportunities (Shimp and Bearden 1982). The more experience a customer has with the product/service, the more familiar it becomes, and the less are its perceived risk. Therefore, we suggest: Hypothesis 8 (H8). Previous experience with online learning is positively related to the choice of online class. Performance Singh and Pan (2004) suggest that “student quality” (measured by grades) can be a deciding factor determining the choice of online delivery method because the more talented students may prefer to learn in an individual environment rather than sharing their knowledge with less bright students in a traditional setting. The logical extension to this statement would be to predict that online students will perform better (get higher grades) than their traditional counterparts. Also, consistent with our Hypotheses 5 and 6, which posited stronger self-directedness and goal-orientation of online students, it is logical to expect online students to perform better. Hypothesis 9 (H9). Performance will be higher for online students than for traditional students.
METHOD Data were collected using an online survey from a convenience sample of undergraduate business administration students at a major southwestern AACSB-accredited university. Three groups of students from different sections (online, daytime traditional, and evening traditional) of a core Foundations of Marketing course provided responses for extra
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credit. The total sample size of 278 students was obtained from the following: Group 1. Out of 220 undergraduate students enrolled in an online (WebCT-based) section of the Foundations of Marketing course, 158 usable responses were received, for a 72% response rate. Group 2. Out of 110 undergraduate students enrolled in an evening section of a traditional (face-to-face) Foundations of Marketing course, 67 usable responses were obtained, for a 61% response rate. Group 3. Out of 95 undergraduate students enrolled in traditional (face-to-face) daytime section of a Foundations of Marketing course, 53 usable responses were received, for a 56% response rate. Students were informed of the survey by their instructors during class for traditional sections and via e-mail for online classes in the middle of the semester following midterm exams. The URL of the survey was provided and extra grade points were rewarded to those students whose unique IDs appeared in the survey database by the deadline. All the students were assured of the confidentiality of the information they provided. The 77-item online survey included measures for age, gender, marital status, employment, household income, distance from campus, and time it takes to reach campus, performance (the student’s midterm grade in the class), previous experience with online classes, innovativeness (measured using the scale developed by Leavitt and Walton 1975; alpha = 0.845), time management and time pressure (measured using the scales previously prepared and tested by Lumpkin and Darden 1982; alpha = 0.843 and 0.791), social character (measured using the scale previously developed and tested by Kassarjian 1962; alpha = 0.723), intrinsic/ extrinsic motivation (measured using the scale developed by Holbrook 1986; alpha = 0.675), and perception of performance risk and financial risk (measured using the scales developed by Shimp and Bearden 1982; alpha = 0.712 and 0.967). Student’s choice of online versus traditional method of class delivery was determined by actual enrollment in traditional versus online section of the course. The reliability coefficients provided above were computed for the independent variables using Cronbach’s alpha. All measures (with the exception of intrinsic/extrinsic
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motivation) provided acceptable reliability coefficients (> 0.70) as suggested by Nunnally (1978). ANALYSIS The demographics of the sample showed that the proportion of female students was 62% in the daytime class, 54% in the night class, and 68% in the online class. The average age for all three classes was 25.1 years (25.0 for day, 26.3 for night, and 24.7 for online classes (p = 0.222). These numbers are reflective of the general undergraduate student population numbers (56% female, 23.4 years average age, according to the National Center of Education Statistic). This makes our sample representative of the general U.S. student population in age and gender, and contributes to the external validity of our findings. In Table 1 results from the two hypotheses (H1 and H2) related to demographic variables and driving time to campus are provided. H1 stated that there will be no significant difference in demographics for online and traditional students. The one-way ANOVA (Table 1) results support this hypothesis showing insignificant F-values for all differences between the means for all the three groups. H2 posited no difference in the time required to reach campus between online and traditional students. The F-value of 1.509 is nonsignificant (p = 0.220) for all three groups, thus supporting H2. H3 was also supported, indicating that though the mean of innovativeness is higher for the online class than for either of the traditional classes, this difference is not statistically significant (see Table 2). Results of testing H4 are also presented in Table 2. They support the statement TABLE 1. Mean Values (⫾SD), and ANOVAs for Demographic Variables Class Type
N
GenderFemale (%)
Age (Years)
Drive to Campus (min.)
Income ($1,000)
Day
53
62.2
25.0 ⫾ 6.4
20.5 ⫾ 14.7
40.5 ⫾ 19.3
Night
67
58.2
26.3 ⫾ 7.0
25.4 ⫾ 18.7
36.5 ⫾ 17.6
Online
158
67.7
24.7 ⫾ 4.5
25.8 ⫾ 17.4
38.7 ⫾ 18.2
Total
278
64.4
25.1 ⫾ 5.6
24.7 ⫾ 17.3
38.5 ⫾ 18.2
F
1.772
1.498
1.509
0.067
Significance
0.184
0.222
0.220
0.796
59
0.233
3.4200
Significance
Combined
3.3816 3.4491 1.4290
278
Online
Mean
F
120 158
Traditional
N
Class Type
0.46727
0.45995
0.47593
SD
Innovativeness
0.02802
0.03659
0.04345
SE
0.904
0.0150
3.9041
3.9114
3.8944
Mean
1.15626
1.17921
1.13017
SD
SE
0.06935
0.09381
0.10317
Time Management
0.909
0.0130
3.8621
3.8565
3.8694
Mean
TABLE 2. Innovativeness, Time Management and Time Pressure
0.93156
0.92853
0.93938
SD
SE
0.05587
0.07387
0.08575
Time Pressure
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that online and traditional students are similar in both time pressure and time management characteristics. H5 stated that online students will be more inner-directed than traditional students. This statement was marginally supported (F = 2.937, p = 0.088, see Table 3). H6 was not supported (see Table 3). On the contrary, the students of the traditional evening class showed higher extrinsic motivation (goal orientation) than both daytime and online students at a marginal significance level (p = 0.063). The hypothesized higher perceived financial and performance risk of traditional students (H7) was supported (see Table 4). A significant difference was also found between the number of online classes previously taken for online and traditional students supporting H8 (see Table 5). Supporting H9, online students showed significantly higher performance than traditional students on the midterm exam (see Table 5). DISCUSSION Results of the study support the idea that online learning has entered its growth stage, characterized by increased adoption rate and diminishing TABLE 3. Social Character and Intrinsic/Extrinsic Motivation Class Type
N
Traditional Online Combined F Significance
120 158 278
Social Character
Intrinsic/Extrinsic Motivation
Mean
SD
SE
Mean
SD
SE
0.5898 0.6136 0.6033 2.9370 0.088
0.12216 0.10831 0.11488
0.01115 0.00862 0.00689
4.1844 4.2872 4.2428 2.3700 0.125
0.54648 0.55536 0.55291
0.04989 0.04418 0.03316
TABLE 4. Financial and Performance Risk Class Type Traditional Online Combined F Significance
N 120 158 278
Financial Risk
Performance Risk
Mean
SD
SE
Mean
SD
SE
4.6306 3.4451 3.9568 20.895 0.000
2.33025 1.99028 2.21899
0.21272 0.15834 0.13309
5.1917 4.4573 4.7743 15.8470 0.000
1.65474 1.41596 1.56380
0.15106 0.11265 0.09379
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TABLE 5. Number of Online Classes Taken and Performance Class Type
N
Day Night Online Combined F Significance
53 67 158 278
Number of Online Classes Taken Mean
SD
SE
0.79 0.72 2.63 1.82 172.35 0.000
1.261 1.042 1.217 1.507
0.173 0.127 0.097 0.090
Performance Mean 3.03 2.79 3.14 3.03 18.89 0.000
SD
SE
0.410 0.454 0.471 0.477
0.076 0.075 0.050 0.038
innovativeness status. This fact explains the dynamics of its target market demographics: Online students are not different from traditional students in their marital status, income, education, or distance to campus. In our sample, the majority of students in all three types of classes had at least 3 years of college education; the average age was not significantly different; and the employment status showed no statistically significant difference among groups. This implies that the current promotional messages aimed at full-time working women with children, over 35 years old (the “typical” online student a short while ago) may no longer be effective. Another important change has occurred in the perception of time pressure: Online students are no more pressed for time than regular daytime students. Therefore, messages promoting “anytime–anyplace,” “not schedule-bound,” “no commuter hassle” learning may not be the decisive argument in students’ choice of online education. In other words, segmenting the student market into those who are time- and place-bound (potential online students) and those who are not (potential traditional students) is not valid. Promoting convenience and flexibility is not a differentiation strategy any longer. This may explain why students who are better organized (high on time management) and more sensitive to time pressure are as likely to choose online as traditional classes. The same is true for more goal-oriented students. Apparently, other benefits beyond convenience, flexibility, and control need to be considered in planning targeting strategies. Our data suggest that the greatest difference between online and traditional students is their perception of risk. This suggests that those students choosing traditional classes may perceive higher risk involved in taking an online class such as not fulfilling its learning objectives or not being worth the tuition. This finding (which, essentially, emphasizes the higher concern for quality for those who choose to take classes
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on campus) is supported by other research. Warwick and Mansfield (2003), for example, in their study of evaluative criteria used by students and parents in college choice, report that the most important criterion for both students and parents was the academics of the institution (which comprised quality of faculty, quality of majors of interest, and overall academic reputation), the second most important being tuition costs and financial aid. Higher sensitivity to financial risk on the part of traditional students can be accounted for by the existence of federal Higher Education Act and its “50 percent rule,” which often prohibits online students from receiving federal student aid. This makes online education less affordable, and, consequently, less attractive for lowerincome students. Our data further concur with Warwick and Mansfield (2003) who found friendly atmosphere to be the third most important criterion of college choice for both students and their parents. In support of this finding, traditional students in our sample tend to be more other-directed in their social character, which indicates that college atmosphere and social life are an important factor for those who choose to study in a traditional classroom setting. Capraro, Patrick and Wilson (2004) found that attractiveness of social life (characteristics of the people and experiences to be found at a school) is at least as important as quality of education in influencing a candidate’s choice. They hypothesize that emergence of Generation Y in the college candidate pool (who are more concerned with a balance between work and relaxation) may be a factor introducing the social character variable in the education decision making process (Capraro, Patrick and Wilson 2004). The finding that previous experience with taking online classes differentiates those students choosing online instruction suggests that experience with this type of education delivery reduces the perception of associated risks, both performance and financial. Therefore, providing students in traditional classes with an opportunity to try Web-based instruction might improve chances of their using such classes in the future. As a result of the online method of education delivery entering the growth stage of its product life cycle, the natural process of its adoption by a wider potential market requires changes in promotional and marketing strategies and tactics. Positioning should shift from emphasizing convenience and flexibility alone to quality, reputation, convenience, and flexibility. Other differentiation and positioning approaches may include providing individualized programs, tailored to match the needs of each student (one-on-one marketing). This advice is in accord with
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the accepted marketing theory, which postulates that at the growth stage characterized by intense competition, advertising should shift from educating customers about the benefits of the generic product to brand and reputation building (Schiffman and Kanuk 2000), in our case emphasizing quality and uniqueness of a particular program, differentiating it from other online programs. We also believe that risk-reducing strategies will be well placed at this stage. Some of them may include the following (Harrison-Walker 2001): Effectively using word of mouth–conducting and publishing satisfaction surveys, maintaining forums and feedback sites, encouraging advice and communication among the current and potential students; providing a trial opportunity–encouraging increased use of Internetbased material and assignments in on-campus classes; and individualizing and customizing learning–providing potential students with specialized information about what options are available to them individually, and improving point-of-contact communication through advising efforts.
LIMITATIONS AND FUTURE RESEARCH Our results are based on a relatively small sample of business students concentrated in a large university in the South. Although their demographic characteristics are similar to those of undergraduate students nationwide, our findings can be generalized with caution. Some universities may have university-specific differentiation among daytime, evening, and online teaching modes (e.g., based on financial incentives), which would make student population not amenable to motivational segmentation. Additionally, extrapolating our conclusions to universities that offer a disproportionately larger share of online classes and attract students from a wider national and international population may not be suitable. Replications of the findings are needed before any large-scale conclusions can be made. It is not feasible in a single study to capture all variables that might explain the choice of one delivery method over the other. Our purpose was to test the characteristics suggested by previous research as important in explaining the differences between online and traditional students. Future research should attempt to identify additional variables that may help explain the choice between online and traditional education delivery methods.
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RECEIVED: 01/19/06 ACCEPTED: 04/16/06 doi:10.1300/J050v17n01_05
