Demographic Variables Related to On-Line Purchasing by University Students: Preliminary Findings
Robert Marsh, Department of Management, Georgia Southern University,
P.O. Box 8152, Statesboro, GA 30460; phone: 912.681.5216; fax: 912.681.0710;
e-mail: gsi21154@gsaix2.cc.gasou.edu
Thomas Case, Department of Management, Georgia Southern University,
P.O. Box 8152, Statesboro, GA 30460; phone: 912.681.5205; fax: 912.681.0710;
e-mail: tcase@gasou.edu
O. Maxie Burns, Department of Management, Georgia Southern University,
P.O. Box 8152, Statesboro, GA 30460; phone: 912.681.5087; fax: 912.681.0710;
e-mail: maxburns@gasou.edu
Demographic Variables Related to On-Line Purchasing by University Students: Preliminary Findings
Abstract
There is growing evidence that business-to-consumer e-commerce is
increasing and becoming mainstream. A large number of studies have addressed
e-tailer initiatives to stimulate online sales. Even more have focused on
consumer demographic variables related to online buying, but do not directly
address one of the most “wired” and prolific groups of Internet users,
university students. Previous
e-commerce consumer demographics studies suggest that college students who are
more knowledgeable about computers and the Internet as well as those use that
the Internet most frequently are more likely to make online purchases. Previous
studies also suggest that male college students are more likely to be make
Internet purchases than female college students and that disposable income
level is directly related to Internet purchasing. Initial results of survey
data collected from 190 undergraduate and MBA students tend to be consistent
with these predictions. Further, more powerful, tests for our predictions that
are currently underway are identified.
Demographic Variables Related to On-Line Purchasing by University Students: Preliminary Findings
There is growing evidence that the Internet is having a significant impact on consumer shopping behaviors. Internet shopping has entered the mainstream, it is no longer a novelty. Major brands and retail outlets such as J.C. Penney, L.L. Bean, Macy’s , Sears, Spiegal, and the Gap, have set electronic shop alongside established pioneers like Amazon.Com, CDnow, and Virtual Vineyards. And, URLs are everywhere including product labels, toothpaste tubes, and TV commercials.
Consumer comfort levels with Internet shopping also seem to be increasing. Anxiety about online transaction security is diminishing among consumers and the variety of goods and services that they are purchasing on-line is broadening (Direct Marketing, 1999; Kruger, 1999). Online shoppers tend to be affluent (Holstein, Thomas, and Vogelstein, 1998) and more than half of the Web population has been using the Internet for over a year (Levy, 1998). Regular Internet users, as a group, hold 60% of the U.S. population’s buying power (Discount Store News, 1999), and a significant percentage of this group (nearly 40%) report that they prefer to shop online rather than visit local stores. According to Kotkin (1998), consumers are generally interested in reducing the amount of time that they spend shopping in a mall and over the next 15 years, non-store retail formats (catalog, phone, TV, and online) are expected to expand to account to more than 50% of all retailing.
Consumer acceptance of online shopping is driven in part by perceived benefits. According to Burke (1997), the most salient benefits of Internet shopping for consumers include convenience, product information, customized products and services, and an enhanced shopping experience. Burke’s assertion is consistent with findings reported by Jarvenpaa and Todd (1997) and Then and DeLong (1999).
Consumer comfort, confidence, and convenience are encouraging patterns of acceptance for online shopping which exceed former projections (Greer and O’Kenner, 1999).In response, merchants are enhancing Website content to influence the purchasing decisions of online shoppers.
E-tailers are using a number of ingenious approaches to entice online shoppers with product information, personalized recommendations, and other Web-enabled approaches. For example, shoppers can preview music CDs by downloading digitized sound clips. You can download and use trial versions of software for a month or two. On most auto-makers’ sites, potential buyers can immerse themselves in panoramic interior views of the latest models. And, sites employing collaborative filtering software, e-tailers stimulate on-line impulse buying by informing the buyers of specific product buyers about other products also purchased by previous buyers of that product (Fortune, 1999).
Shopping bots, available at price comparison Websites such as Bottom Dollar, mySimon, PriceScan, and Shopper.com, can provide added assurance to prospective buyers that they are not paying too much for a specific product (Turner, 1999). Additional assurance about acceptable prices is also often available at auction sites such as eBay. An increasing number of sites also enable prospective buyers to compare competing products from multiple vendors on features/specifications in addition to price; this enables customers to gather product research information faster and more easily than they can by leafing through print catalogs, visiting local bricks and mortar outlets, or even visiting individual vendor Websites.
Online product research product does not necessarily translate into online sales. In many instances, consumers do online research only to close the deal in local retail outlets after seeing and touching the products on their short lists. Because of this, an increasing number of merchants now view the Internet as a tool for bringing more customers into their retail stores and as a means for carrying inventories online that are not carried in their stores (Kruger, 1999).
A considerable number of studies have focused on the types of products that are purchased online. Books, computers, computer software, music, and travel services are consistently identified as selling the best over the Internet by such studies. Some studies have also considered the demographics of online shoppers and buyers. Among the demographic variables considered are gender (Greer and O’Kenner, 1999; Retail On-Line , 1998; Then and DeLong, 1999; Tweney, 1999); age (e.g., Dietz, 1999; Gupta, 1995); and personality factors/individual differences (Jones and Vijayasarathy, 1998). Other variables identified as being correlated with on-line shopping and purchasing include computer knowledge and use, Internet knowledge and use, and Web “veteran” vs. newcomer status (CyberAtlas, 2000a).
Several studies have reported that men are more likely than women to make purchases online (e.g. Tweney, 1999). However, the gap between male and female on-line buyers is narrowing over time, largely because women are among the fastest growing group of Internet users and is expected to close over the next two to three years as they become seasoned Web veterans (CyberAtlas, 2000a, 2000b; Tweney, 1999.)
Some studies have indicated that Internet users over 25 years of age are more likely than users 18 to 25 to research product information online but to actual make purchases in retail outlets (Dietz, 1999; Retail On-Line, 1998). However, senior citizens are projected to account for increasingly higher percentages of total consumer online spending in the years ahead (CyberAtlas, 2000b). Teenagers are also expected to demonstrate increases in on-line purchases over the next few years. Research indicates that most teens prefer going online to watching television and prefer chatting online to talking on the telephone; as they get older, the amount of time that they spend online increases (CyberAtlas, 2000b).
According to one study, eighty-seven percent of college students are on-line and represent the most active group on the Internet (CyberAtlas, 2000b). In spite of being a major on-line force, little research has focused on the online shopping and buying patterns of college students. While the disposable income of college students may inhibit online buying (relative to older groups), online shopping and product research might be very common among college students.
Several predictions about the online purchasing behaviors of college students are suggested by the findings of the research cited above. These include:
1) College students with considerable computer knowledge/experience will be more likely to make online purchases than those without such knowledge/experience.
2) College students with considerable Internet knowledge/experience will be more likely to make online purchases than those without such knowledge/experience. This and the previous prediction are consistent with findings indicating that on-line buyers are more likely to be “veterans” than “newcomers” (e.g., CyberAtlas, 2000a).
3) Male college students are more likely than female college students to make online purchases. This is consistent with research results indicating that buyers are more likely to be male than female (e.g. Tweney, 1999)
4) Student disposable income will be related to making online purchases. This prediction is consistent with findings that the average on-line buyer has an annual income of more than $50,000 (e.g. Holstein et al., 1998).
5) More highly educated college students (upperclassmen and graduate students) are more likley to make online purchases than will less educated college students (underclassmen). Because most college students are an extremely active group of Internet users, upperclassmen and graduate students are likely than underclassmen to be Internet “veterans” and thereby, online buyers (e.g. CyberAtlas, 2000a).
In order to test these predictions, an 87-item survey instrument was developed. It was first administered in print form to MBA students enrolled in a required IT Management course during Spring Semester 1999. Extra credit was used to encourage the students to complete the survey. Responses were obtained near the end of the semester and were anonymous; no attempt was made to associate a particular form with a particular student. Nor was there an attempt to identify which students completed the forms (other than that needed to ensure the appropriately awarding of extra credit points).
During Summer and Fall Semester 1999, the survey instrument was converted into an HTML document suitable for display on the World Wide Web. Response options were converted into either radio buttons, check boxes, text fields, or text areas depending on the nature of the question. The “submit data” button at end of the document invokes a Perl script that takes the data from the form and processes it into an appropriate format so that it can be added to the log files. As each respondent submits the data, a new line containing all the information is added to the log the files. Each line is a delimited text field containing all the information for one completed form in the order specified.
Two identical on-line surveys were created for data collection during Fall Semester 1999. MBA students taking the IT Management course were directed to one URL to complete the survey while undergraduate students in one section of MGNT 4135 (Management Information Systems) and two sections of CISM 2130 (Introduction to Computer Information Systems) were directed to a second URL. As was the case for the Spring Semester 1999 respondents, Fall Semester respondents were offered extra course credit to complete the survey. As in the case of the print survey, no attempt was made to identify or associate any of the data with any individual respondent (students were directed to e-mail their instructor upon completing the survey and providing the time/date of completion).
After all the data was collected, the log files were downloaded as text files and then imported into an Excel spreadsheet as a delimited text file. The undergraduate and graduate log files were merged into a single spreadsheet. This spreadsheet file was then combined with another that contained the MBA data collected during Spring Semester 1999. The resulting spreadsheet was then imported into SPSS 10 to facilitate data analysis relevant to testing our predictions
A total of 190 survey responses were obtained. These were almost evenly distributed among MBA and undergraduate students. The sample was also almost evenly divided by gender.
Numerous items on the survey focused on respondent online purchasing and shopping behaviors. For example, respondents were asked how frequently they used the Internet to find product information (the five-point response scale ranged from “not at all” to “daily”) . They were also asked to indicate on five-point scales (from “none” to “more than 25”) how many Internet purchases they made in the past year, and how many they had ever made. Respondents also indicated the value of the most expensive item they had purchased as well as the average price of the purchases they had made; the five-point response ranges for these items were from “less than $10” to “more than $500”. A section of the questionnaire enabled respondents to identify the types of items they had purchased (books, clothing, gifts, travel services, etc.) and the type(s) of electronic transactions that they concerned. Respondents who had not made an on-line purchase were asked to identify the reasons why they had not.
The demographic variables related to this paper’s predictions were measured on various scales. Gender options were (1=male and 2=female). Self-perceived computer knowledge was measured by a five-point scale (from 1=no knowledge to 5=expert). Self=perceived Internet knowledge was measured by a similar five-point scale. Internet use was measured by a five-point scale (where 1=do not use to 5=daily use). Education was measured by a five-point scale (1=some high school, 5=post graduate) while income was assessed by a six-point set of ranges (where 1=less than $15,000 and 6=more than $100,000).
The relationships among these demographic variables and the total number of Internet purchases (ever) is summarized in the following correlation matrix.
Variable |
No. of Purchases |
Gender |
Computer Knowledge |
Internet Knowledge |
Internet Use |
Education |
|
No. of Purchases |
|
|
|
|
|
|
|
Gender |
-.170* |
|
|
|
|
|
|
Computer Knowledge |
.194* |
-.184* |
|
|
|
|
|
Internet Knowledge |
.196* |
-.168* |
.677** |
|
|
|
|
Internet Use |
.251** |
-.081 |
.165* |
.251** |
|
|
|
Education |
.169* |
-.083 |
.284** |
.031 |
-.012 |
|
|
Income |
.178* |
-.176* |
.167* |
-.131 |
-.014 |
.412** |
*significant at the .05 level
**significant at the .01 level
The correlations are generally in line with our predictions. The number of Internet purchases made by this sample of college students was found to be correlated with computer knowledge, Internet knowledge, and Internet use. These results are consistent with predictions 1 and 2. Consistent with predictions 4 and 5, the total number of online purchases was correlated with education and income.
Prediction 3 was also supported by the survey responses. The significant negative correlation between total Internet purchases and gender indicates a relationship between being male and total number of online purchases. The correlations among gender and the other demographic variables suggest that males are more likely than females to report higher levels of computer knowledge, Internet knowledge, and income.
As might be expected, computer knowledge is highly correlated with Internet knowledge and Internet use. Computer knowledge was also correlated with and income.
Follow-up analyses of the data are currently underway. To further test our predictions, ANOVA and MANOVA comparisons of the total Internet purchases and the other survey items related to online shopping and purchasing are being conducted for the following independent variables: gender, computer knowledge, Internet knowledge, Internet use, and education standing. Median splits will be use to classify respondents as being high or low in computer knowledge, Internet knowledge, and Internet use prior to these comparisons. Education standing will be broken out as a comparison of undergraduate and graduate (MBA) student.
Because of the inter-correlations among the demographic variables, a series of regression analyses are planned to tease out the demographic variables that best predict online purchasing by each of the following groups: all college students, MBA students (only), and undergraduate students (only). These analyses should assist in the identification of moderator variables.
Overall, findings from the initial analyses performed on survey responses collected from MBA and undergraduate students seem to consistent with predictions derived from previous demographic studies that focused on gender (e.g. Tweney, 1999), computer knowledge, Internet knowledge, and Internet use (e.g. CyberAtlas, 2000a). Our results support the notion that in spite of being one of the fastest growing groups of Internet users, women, to date, are less likely to make online purchases than men. Our initial results seem to be consistent with previous findings indicating that online buying is related to computer and Internet knowledge and use.
Future investigations should address the question of the extent to which university students as a group are representative of all Internet users and online purchasers. They should also address how changes in the online shopping and purchasing behaviors of this group across time.
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