Internet usage in the academic environment: the technology acceptance model perspective.

Alshare, Khaled ; Grandon, Elizabeth ; Miller, Donald 等

ABSTRACT

This study examined the impacts of perceived ease of use (PEOU), perceived usefulness (PU), and perceived Internet content (PIC) on students' usage of the Internet. Additionally, it investigated the impacts of these variables on usage of the Internet as moderated by gender, educational background, income, computer users' classification, and self-reported measure of computer knowledge. We modified the original technology acceptance model (TAM) and created a theoretical model to better understand the hypothesized relationships. To validate the research model, we collected data from 170 students at a regional Midwestern university. The results showed that PEOU and PU, but not PIC, were significant factors in influencing usage of the Internet. Additionally, gender was the only significant moderator. PEOU affected usage of the Internet more strongly for female students than it did for male students.

INTRODUCTION

Although much research has been conducted on Internet adoption in business environments (e.g. Tan & Teo, 1998; Stanfield & Grant, 2003; Teo & Pian, 2004), few have examined the adoption of the Internet in academic environments. Educational institutions, especially colleges and universities, are trying to take advantage of the decreased costs that delivering course content over the Internet may provide (Karelis, 1999; Valentine, 2002). As Lundgren and Nantz (2003) mentioned, about 500,000 courses were available on the Internet in 2003. However, before starting a project of this nature, educational institutions need to understand factors that motivate and determine Internet usage among students. The study intends to address this research gap by focusing on establishing the factors that influence Internet usage by college students.

A large percentage of the research on technology adoption/usage has used Davis's (1989) technology acceptance model (TAM). In his parsimonious model, Davis stated that perceived ease of use (PEOU) and perceived usefulness (PU) are the most salient beliefs in determining individual acceptance intention and behavior. Lederer et al. (2000) summarized sixteen articles published from 1991 to 1999 in leading MIS journals that tested the TAM model for different technologies (e.g. ATM, e-mail, Netscape, Access, Internet, Word, and Excel). TAM has been shown to explain a significant amount of the variance in intentions to use a technology and/or actual use of a technology. Among the studies that focused on TAM, most have followed Davis's (1989) assumption that external variables (such as gender, experience, and other demographic variables) influence technology adoption/usage through beliefs about PEOU and PU. These external variables have been considered to have a direct influence on PEOU and PU. In this study, however, we are extending the unified model proposed by Venkatesh et al., (2003) which introduced some external variables (such as gender, age, experience, and voluntariness) as moderators between the hypothesized antecedents of intention (such as performance expectancy, effort expectancy, social influence, and facilitating conditions) and behavioral intention. Additionally, we included perceptions of Internet content (PIC) as another antecedent of Internet usage. In other words, we speculated that external variables may affect the direction and/or strength of the relations between PEOU, PU, and PIC and technology usage.

The paper is organized as follows. In the next section, we briefly review related studies, propose a theoretical model for the factors that influence Internet usage, and present the hypotheses to be tested. Then, we describe the research method and report the results. The last section presents conclusions, implications, limitations, and future research questions.

LITERATURE REVIEW OF CONSTRUCTS AND HYPOTHESES

Perceived Ease of Use (PEOU) and Perceived Usefulness (PU)

The technology acceptance model (TAM) proposed by Davis (1989), is a well-established model of IT adoption and use. TAM theorizes that PEOU and PU are the key determinants of computer usage. It suggests that external variables indirectly influence the decision to use technologies through their impact on PEOU and PU. PEOU is defined as "degree to which a person believes that using a particular system would be free of effort" (Davis, 1989, p. 320). PU is defined as the "degree to which a person believes that using a particular system would enhance his or her performance" (Davis, 1989, p. 320). TAM has been tested in numerous studies (Adams et al., 1992; Hendrickson et al., 1993; Igbaria et al., 1997; Riemenschneider et al., 2003; Subramanian, 1998; Szajna, 1994) and shown to explain a reasonable amount of the variance in actual use of the technology. For example, Davis (1989) found that PU was significantly correlated with self-reported current usage (r=0.63) and self-predicted future usage (r=0.85). PEOU was also significantly correlated with current usage (r = 0.45) and future usage (r = 0.59).

The application of TAM in the academic environment has not been investigated as thoroughly. Mccloskey (2003-2004) applied TAM to test whether PEOU, PU, and security concerns influenced electronic commerce participation among college students. Seyal et al. (2002) developed a model to test whether PU, PEOU, and other variables determine Internet usage among college academics. In both cases, PU and PEOU were found to be significant predictors of the technology in question. In this study, and based on the importance of Internet usage/adoption among college students as mentioned earlier, we focused on PU and PEOU to explore their direct impact on Internet usage. Thus, we stated the following hypotheses:

H1: There will be a positive correlation between perceived ease of use and Internet usage.

H2: There will be a positive correlation between perceived usefulness and Internet usage.

Perceptions of Internet Content

Internet content has been widely studied, especially in research that has focused on how web site content influences its success (Torkzadeh & Dhillon, 2002; Scheffelmaier & Vinsonhaler, 2002-2003; Palmer, 2002). As Huizingh (2000) defined it, "content refers to the information, features, or services that are offered in the web site" (p. 123). Many studies have also considered web site content as one of the most important web site design characteristics (Liu & Arnett, 2000; Ranganathan and Ganaphaty, 2002; Ranganathan & Grandon, 2002). To the best of our knowledge, perceptions of Internet content, which we defined as positive or negative feelings, and their relationship with Internet usage have not yet been studied.

A different stream of research has focused attention on exploring the pathological side of the Internet based on the content to which Internet users are exposed (Armstrong, 2001; Boschert, 2001; and Grohol, 2003). Soule et at. (2003) defined the term "Internet addiction" to explain the "various technological disorders described by psychologists" (p. 64). Some of these disorders included cybersexual addiction, net compulsion, and information overload, all of which are related to Internet content. It seems natural to think that some people may perceive Internet content as a threat to their mental health and may try to avoid its usage, while others may have a positive perception toward it and will continue or start to use it. Thus, we proposed the following research hypothesis:

H3: There will be a positive correlation between positive perceptions of Internet content and Internet usage.

The Impact of Moderator Variables

In his original model, Davis (1989) suggested that external variables indirectly influence technology usage through perceptions of ease of use and usefulness. Based on this assertion, many studies have explored the direct effect of external variables on PEOU and PU and examined the role of PEOU and PU as mediators in the relationship between the external variables and technology usage. For instance, Alshare et al. (2004) explored the direct effect of external variables (gender, age, income level, educational background, student classification, and self-reported measures of computer knowledge) on computer usage and their indirect effect through PEOU, PU, computer literacy, and negative attitude toward computers. They found that the relations between gender and PU; age and computer literacy; and income and negative attitudes were significant. Income was the only external variable that influenced computer usage directly. Gefen and Straub (1997) found that gender had a significant impact on PEOU and PU but no direct effect on usage behavior. In a similar line of inquiry, Agarwal and Prasad (1999) reported that PEOU and PU "fully mediated" the effects of individual differences (level of education, tenure in workforce, prior similar experience, participation in training, and role with regard to technology) on intention of users to use a new information technology. Contrary to Agarwal and Prasad's (1999) findings, Hubona and Burton-Jones (2003) reported that perceived ease of use and perceived usefulness "partially mediated" the influences of individual differences (level of education, employment category, and length of time since first use) on e-mail usage. In other words, they found that these external variables directly and indirectly influenced usage of e-mail.

The studies cited above followed Davis's assumption that the external variables influence technology usage through beliefs of PEOU and PU. There are few exceptions that introduced moderator variables in the original TAM. As mentioned earlier, Venkatesh et al., (2003) tested whether gender, age, experience, and voluntariness affect the relationship between the antecedent of technology adoption and its usage. The results showed that gender and age were significant moderators between performance expectancy, effort expectancy, and social influence and behavioral intention. Additionally, they found that experience was a significant moderator between effort expectancy and social influence and behavioral intention. In another study conducted by Venkatesh and Morris (2000), the authors explored the relationship between PEOU, PU, and subjective norm with the intention to use a system for data and information retrieval and studied how these relationships changed by considering gender as moderator. They found that men's technology usage decisions were more strongly influenced by their perceptions of usefulness, while women were more strongly influenced by perceptions of ease of use.

From a theoretical view point, there is reason to expect that computer knowledge (very good to excellent vs. poor to good) and income (high vs. low) will influence Internet usage differently (Alshare et al., 2004). There is also some evidence to think that student background (business vs. non-business) will influence the use of the Internet in a different way. It seems that business students have more computer-related classes to take than non-business students which, in turn, may influence Internet usage. For example, in a survey conducted by Hindi et al. (2004) to AACSB schools, it was found that 100 percent of respondent schools required at least one computer literacy course, 60 percent required a second computer literacy course, and 14 percent required a third computer literacy course for all business majors. These percentages may differ from non-business students nation wide.

In this study, we extended the aforementioned research and proposed that gender, computer user classification, educational background, self-reported measure of computer knowledge, and income can be considered as moderator variables in the relationship between PU, PEOU, PIC, and Internet usage. We proposed the following set of hypotheses:

H4a: Perceived ease of use will influence Internet usage more strongly for women than it will for men.

H4b: Perceived usefulness will influence Internet usage more strongly for men than it will for women.

H4c: Perceived Internet content will influence Internet usage more strongly for women than it will for men.

HH5a: Perceived ease of use will influence Internet usage more strongly for heavy users than it will for light users of computers.

H5b: Perceived usefulness will influence Internet usage more strongly for heavy users than it will for light users of computers.

H5c: Perceived Internet content will influence Internet usage more strongly for heavy users than it will for light users of computers.

H6a: Perceived ease of use will influence Internet usage more strongly for students with business majors than it will for students with non-business majors.

H6b: Perceived usefulness will influence Internet usage more strongly for students with business majors than it will for students with non-business majors.

H6c: Perceived Internet content will influence Internet usage more strongly for students with business majors than it will for students with non-business majors.

H7a: Perceived ease of use will influence Internet usage more strongly for students with very good-excellent computer knowledge than it will for students with poor-good computer knowledge.

H7b: Perceived usefulness will influence Internet usage more strongly for students with very good-excellent computer knowledge than it will for students with poor-good computer knowledge.

H7c: Perceived Internet content will influence Internet usage more strongly for students with very good-excellent computer knowledge than it will for students with poor-good computer knowledge.

H8a: Perceived ease of use will influence Internet usage more strongly for students with high-income levels than it will for students with low-income levels.

H8b: Perceived usefulness will influence Internet usage more strongly for students with high-income levels than it will for students with low-income levels.

H8c: Perceived Internet content will influence Internet usage more strongly for students with high-income levels than it will for students with low-income levels.

RESEARCH FRAMEWORK

The Proposed Model

Based on the previous analysis, we proposed the following theoretical model.

[FIGURE 1 OMITTED]

Sample and Data Collection

Data were obtained from a survey questionnaire, which was administered to college students at a regional Midwestern University during Fall 2003. In addition to asking questions concerning demographic variables such as gender, age, educational background, students' classification, and income level, the questionnaire requested information about Internet usage, PEOU, PU, and PIC. The construct of Internet usage was defined in terms of number of hours devoted to the Internet. PEOU and PU were taken directly from Davis' (1989) scale and customized to measure Internet adoption. We used 5 questions to measure each of the constructs PEOU and PU. Due to the lack of previous scales to measure PIC (positive or negative perceptions of Internet content), we created a scale with 4 items. Survey participants responded to statements using a 5-point Likert scale ranging from strongly disagree to strongly agree. SPSS was used to compute frequencies, means, percentage, ANOVA, a reliability test (Cronbach coefficient), Pearson correlation, and discriminant analysis.

Factor analysis (Principal component, with Varimax rotation) was performed to confirm that the items loaded according to the proposed model. According to Hair et al. (1998), the acceptable value for factor loading for a sample size of 150 is 0.45. Thus, items with loading less than 50 percent were dropped from further analysis. Appendix A presents the factor analysis results. Appendix B shows the final items and descriptions that were used in the computations. The Cronbach's coefficient was used to determine reliability of questionnaire items. Table 1 shows the values of alpha. The reliabilities for PEOU and PU are comparatively high for an exploratory study (Nunnally, 1978).

DATA ANALYSIS

During Fall 2003, the questionnaire was distributed to 300 college students at a regional Midwestern university. One hundred seventy students returned the survey. This represented a response rate of 57 percent. A summary of frequency distributions for relevant variables is presented in Table 2.

Forty-eight percent of the sample was males. The vast majority of students was under 30 years old (traditional students) (90 percent). Seventy-one percent of the participants were majoring in business. Eighty-one percent of the students were full-time students. Sixty-one students reported that their monthly family incomes were high. Only five students (3 percent) reported that they did not have computers at home. More than 54 percent of the students stated that their knowledge about computers was very good to excellent, while six percent reported that their knowledge was poor to fair. Approximately, 40 percent indicated that their knowledge about computers was good. This should be of no surprise, since more than one-half of the students used computers over two hours per day. Only three students did not have e-mail accounts. Thirty-six percent of the students used the Internet for more than two hours per day, mostly for class-related activities and communication (e-mail). Shopping on line was reported to be the least-used activity on the Internet (22 percent). However, 49 percent of students used the Internet for entertainment activities.

The majority of students had access to the Internet either from school or home (90 percent and 88 percent respectively). Sixty-two percent had Internet access from homes of friends; 47 percent had access at work. Only 7 percent of the participants reported that they used these computer shops to access the Internet. The vast majority of students indicated that the availability of the Internet was good to excellent; only 4 percent felt that the availability of the Internet was fair to unacceptable. The majority of students reported that the cost of the Internet was fair. Twenty percent felt it was expensive or very expensive, and 16 percent considered the Internet to be inexpensive or very inexpensive.

THE RESULTS OF THE STUDY

The results of the study are divided into two sections. The first section discusses the relationships between the usage of the Internet and PEOU, PU, and PIC. The hypotheses (H1-H3) were tested using the Pearson correlation procedure. The second section analyzes the effect of moderator variables such as gender, educational background (business vs. non-business), income (high vs. low), classification of computer users (heavy vs. light), and a self-reported measure of computer knowledge (very good-excellent vs. poor-good) on the relationship between PEOU, PU, and PIC and usage of the Internet. The hypotheses (H4a-H8c) were tested using ANOVA.

Based on Pearson correlations (Table 3), the first two hypotheses (H1-H2) were accepted, but the third hypothesis was rejected. Thus, PEOU and PU were significant factors affecting Internet usage. While PEOU was the most influential variable that affected Internet usage, PIC was not a significant factor.

To measure the power of the above three variables (PEOU, PU, and PIC) in predicting (classifying) the students into two groups of Internet users (heavy and light users), a discriminant analysis (Huberty, 1994) was conducted. The dependent variable, Internet usage, was measured as a dichotomous variable: light users of the Internet (those who used the Internet less than or equal to 2 hours a day) and heavy users (those who used the Internet more than 2 hours a day) (Soule et al., 2003). The set of three independent variables corresponded to PEOU, PU, and PIC. Each independent variable represented the average of its respective items.

The independent variables were considered simultaneously in the analysis. Thus, the discriminant function was computed considering all of the independent variables, regardless of the discriminating power of each one. The Wilk's lamba was significant (Chi-square = 8.059, p<0.018) indicating that overall the three factors differentiated among the two groups (light users and heavy users). By using the cut-off value for factor loading of 0.3 suggested by Hair et al. (1998), only two variables (PEOU and PU) showed significant values (see Table 4). The rank of importance, given by the value of the loading, involved "perceived ease of use" followed by "perceived usefulness."

The Impacts of Moderators Variables on Internet Usage

In this section, hypotheses (H4a-H8c) were tested using ANOVA. This approach was consistent with prior research (Pearson et al., 2002-2003 Winter; Baron and Kenny 1986). Only significant results of ANOVA are reported.

To use ANOVA analysis, we converted the three factors (PEOU, PU, and PIC) into categorical variables. We divided the average of response scales into three groups: the median, below the median, and above the median. Therefore, each factor of PEOU, PU, and PIC had three levels. Gender, educational background (business vs. non-business), income level (low vs. high), self-reported level of knowledge about computers (very good--excellent vs. poor--good), and computer users' classification (light vs. heavy) were considered moderators between PEOU, PU, and PIC and usage of the Internet. Then, we evaluated the moderator effect of these variables by adding the interaction term between each of them and PEOU, PU, and PIC.

All hypotheses (H4a-H8c) were rejected with the exception of H4a. As a result, educational background, income, self-reported level of knowledge about computers, and computer users' classification were not significant moderators in the relationships between PEOU, PU, and PIC and usage of the Internet. On the other hand, gender was a significant moderator for the relationship between PEOU and usage of the Internet (PEOU*gender F=3.803, p<0.05). For each subgroup (male and female), regression analysis was used to check the direction and strength of the relationship between PEOU and Internet usage (H4a). The direction of the relationship is given by the sign of beta coefficient, while the strength is given by the absolute value of the same coefficient. The sign of beta coefficient was the same for both groups; however, beta for the female subgroup (0.839) was greater than the beta coefficient for the male subgroup (0.061). Thus, PEOU affected usage of the Internet more strongly for female students than it did for males. Therefore, H4a was supported.

DISCUSSION AND CONCLUSION

This study examined the effect of PEOU, PU, and PIC on students' usage of the Internet. In addition, it investigated the impacts of these variables on usage of the Internet as moderated by gender, educational background, income, computer users' classification, and self-reported computer knowledge.

PEOU, PU, but not PIC, significantly affected Internet usage. PEOU was the most influential factor in affecting students' usage of the Internet. Additionally, gender was the only significant moderator. Students' usage of the Internet was affected by PEOU and PU. Therefore, educators need to reinforce these two concepts (ease of use and usefulness) especially when deciding to teach online classes. Institutions need to make certain that students are willing to take online courses and are ready to embark on distance learning using the Internet. This might be accomplished by showing students how the Internet can be used as valuable source of information. Additionally, instructors might ask students to use the Internet more frequently and demonstrate how easy it is for them to find the desired information.

Even though PIC was found to be a valid and reliable construct, it did not significantly affect students' usage of the Internet. This outcome should be of interest to instructors. It appeared that students considered Internet content as a trusted source for class-related activities. However, other studies found that Internet content was a significant factor in affecting consumer buying/selling behavior (Huizingh, 2000; Ranganathan and Ganapathy, 2002). The role of instructors becomes more important to show students the correct way for obtaining quality information on the Internet. One explanation for not having PIC as a significant factor could be the fact that it may have been perceived to be something other than what was defined in this study. Another possible explanation is that the Internet could be considered by many students to be the most convenient way of finding information for class-related activities; therefore, students may not have much concern about the actual content.

The other significant finding was the fact that PEOU influenced usage of the Internet more strongly for female students than it did for male students; this confirmed results from previous research (Venkatesh & Morris, 2000). However, gender did not seem to impact how PU influenced Internet usage. It was believed that males would be significantly influenced by PU, as it impacted their Internet usage as reported by other research (Venkatesh & Morris, 2000). Once again, previous studies targeted consumers, while this study targeted college students. It is a fair assumption that both male and female students considered the Internet as useful to them, at least for primary class-related activities. Thus, gender was not a significant moderator in the relationship between PU and Internet usage.

Other significant findings of this study were that educational background, income, Internet users' classification, and self-reported computer knowledge did not impact how PEOU, PU, and PIC influenced Internet usage. These findings were not anticipated. The authors initially believed that PEOU, PU, and PIC would influence Internet usage more strongly for students with higher incomes, who were business majors, heavy users of computers, and more knowledgeable about computers. This expectation was not supported by the data. However, computer users' classification and self-reported knowledge about computers were found to be direct predictors of Internet usage (F= 104.9, p< 0.01; F=7.475, p< 0.01 respectively). Therefore, the more frequently students use computers, the more likely they are to use the Internet. Also, with greater knowledge about computers, there is a greater likelihood that students will utilize the Internet to a greater extent.

The findings of this study are also helpful to business leaders, since students represent future employees. The Internet becomes an essential tool for selling, buying, and advertising. These findings provide employers with information about significant factors that might influence capabilities of future employees. Organizations may decide to establish training workshops for new employees to educate them about the Internet and reinforce the importance of PEOU, PU, and PIC. Future research might include more factors that might influence students' usage of Internet such as social influence and perceived behavioral control and more moderators such as age, experience, availability, and cost of the Internet. Another direction for future research is to conduct a comparison between graduate and undergraduate students. A third plausible direction is to develop a similar study of respondents in different countries to test other factors that might impact students' usage of the Internet, such as culture and government regulations.

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Khaled Alshare, Emporia State University Elizabeth Grandon, Emporia State University Donald Miller, Emporia State University

Table 1: Reliability Analysis

Construct Cronbach Coefficient

Perceived Ease of Use (PEOU) (4 items) 0.85
Perceived Usefulness (PU) - (4 items) 0.85
Perceived Content (PIC) - (3 items) 0.67

Table 2: Frequency Distributions of Key Variables (n = 170)

 No. of
Variable Respondents Percent (%)

Gender:
 Male 81 47.60
 Female 89 52.40

Educational
 Business 120 70.58
 Non-business 50 29.42

Family monthly income:
 Low (<=24, 000 per year) 66 38.80
 High (over 24, 000 per year) 104 61.20

Having a computer at home:
 Yes 165 97.10
 No 5 2.90

Having e-mail accounts:
 Yes 167 98.20
 No 3 1.80

Knowledge about computers:
 Very good - Excellent 93 54.70
 Good 67 39.30
 Poor - Fair 10 6.00

Using computer per day:
 0.1 - 1 hour 24 14.10
 1.1 - 2 hours 54 31.80
 2.1 - 3 hours 35 20.60
 more than 3 hours 57 33.50

Computer user classification:
 Light users (<= 2 hours per day) 78 45.90
 Heavy users (> 2 hours per day) 92 54.10

Access to Internet:
 Home 150 88.23
 Work 80 47.05
 Schools 153 90.00
 Friends 106 62.35
 Computer Shops 13 7.06
 Other 4 2.35

Internet cost:
 Expensive - very expensive 34 20.00
 Fair 108 63.53
 Cheap - very cheap 28 16.47

Internet availability:
 Fair - Unacceptable 6 3.53
 Good 28 16.47
 Very good - excellent 136 80.00

Usage of the Internet per day:
 0.1 - 1 hour 50 29.41
 1.1 - 2 hours 59 34.71
 2.1 - 3 hours 26 15.29
 more than 3 hours 35 20.59

Internet user classification:
 Light users (<= 2 hours per day) 109 64.11
 Heavy users (> 2 hours per day) 61 35.89

Internet applications usage:
 Class related activities 109 64.12
 Communication 105 61.76
 Entertainment 84 49.41
 Other activities 60 35.29
 Selling/buying 38 22.35

Table 3: Correlations between Main Constructs and Internet Usage

Factor Pearson Coefficients

Perceived Ease of Use 0.266 *
Perceived Usefulness 0.205 *
Perceived Content 0.046

*. p<0.05;

Table 4: Structure Matrix

Factors Factor Loadings

Perceived Ease of Use (PEOU) 0.997
Perceived Usefulness (PU) 0.703
Perceived Content (PIC) 0.240

Appendix A
Rotated Component Matrix (Factor Analysis)

Items Component

 1 2 3

PU3 0.839 0.279 0.047
PU2 0.830 0.290 0.019
PU1 0.797 0.295 -0.063
PU4 0.688 0.176 -0.092
PEU1 0.264 0.806 -0.027
PEU2 0.184 0.804 0.060
PEU3 0.307 0.777 0.004
PEU4 0.494 0.670 -0.044
PC1 -0.101 -0.146 0.870
PC2 (inv.) -0.106 0.015 0.862
PC3 (inv.) 0.263 0.405 0.562

Appendix B

Significant Items Considered in the Final Analysis

Construct Item Description

Perceived Ease PEU1 Learning to use the Internet would be
of Use (PEOU) easy for me

 PEU2 I would find it is easy to get the
 Internet to do what I want it to do

 PEU3 I would find the Internet easy to use

 PEU4 It would be easy for me to become
 skillful at using the Internet

Perceived PU1 Using the Internet would increase my
Usefulness (PU) productivity

 PU2 Using the Internet would enhance my
 effectiveness in my career

 PU3 I would find the Internet useful in my
 career

 PU4 Using the Internet would make my
 communication with others more efficient

Perceived PC1 The information provided by the Internet
Internet is reliable
Content (PIC)
 PC2 (inv.) I question the quality of the information
 provided by the Internet

 PC3 (inv.) The credibility of the information
 provided by the Internet is a concern
 for me