Introduction

Approximately seventy-five percent of US higher education students are classified as “nontraditional” (Choy, 2002). Nontraditional status is defined as a “student who does not enter postsecondary enrollment in the same year that he or she completed high school, attends part time for at least part of the academic year, works full time, is considered financially independent from a legal guardian, has dependents other than a spouse, is a single parent, or has a General Educational Development (GED) test score” (NCES, 2003). Nontraditional students are an underserved population who have b een afforded fewer opportunities to get a postsecondary education due to competing work and family responsibilities. The projected enrollment growth for higher education, however, is the result of nontraditional students attending college in record numbers. In contrast, a recent national report projects that the number of high school students expected to graduate and attend college within the same year is expected to decrease through 2015 (Western Interstate Commission for Higher Education [WICHE], 2008).

Online Degrees

To satisfy nontraditional students’ growing demands for flexible course scheduling in higher education, distance education has been employed as a means to accommodate their needs. Ninety percent of four-year, public institutions offer distance education, with the majority of courses delivered fully online (Allen & Seaman, 2005). Online instruction has experienced a compounded annual growth rate of 19% since 2002 (Allen & Seaman, 2010). A recent survey of over 300 institutions reported that over 4.6 million students are enrolled in online courses across the nation, with the majority of those courses offered by large, public institutions (Allen & Seaman, 2010). The current economic crisis has only served to increase the demand for online courses and degree programs; consequently, universities Presidents recognize student preferences for online courses (Allen & Seaman, 2010). Academic leaders, in response to online enrollment increases, include online education as a critical component of their universities’ strategic plans and report that it will be necessary for sustained growth (Allen & Seaman, 2010).

Surprisingly, growth in the number of institutions offering online degree programs is relatively small in comparison to that of online course offerings. Online degree programs increased minimally from 31% in 2004 to 35% in 2006 (Allen & Seaman, 2007). Allen and Seaman (2007) suspect that those who intend to offer online education are currently doing so and few others will develop online degree programs. A potential cause for the slow expansion from online courses to online degree programs is the perception that online degrees are inferior to traditional degrees (Maki & Maki, 2002; Parry, 2009; Young, 2000). Traditional educators fear that the lack of face-to-face interaction will impair student learning (Altschuler, 2001). Some find online courses highly susceptible to fraud, thereby devaluing the degree (Peabody, 2001; Symonds, 2001). Skeptics doubt the validity of the distance education research that demonstrates that online and traditional education produce comparable student learning outcomes (Shea, 2007b).

Despite the reservations about online education quality, the literature consistently demonstrates that student learning outcomes are similar for traditional and online courses and degree programs. Bernard and colleagues (2004) reported that online students had a slight advantage over traditional students. In a meta-analytic study of over 400 universities, Russell (2005) found no significant differences between the effectiveness of the traditional and online delivery mode. Moreover, the US Department of Education’s (DOE, 2009) meta-analytic study concluded that online education was effective for undergraduate and graduate students because online students achieve similar learning outcomes in comparison to students in traditional classes. Additionally, the DOE analyses demonstrated that online students may have an advantage over traditional students in that online learners spent more “time on task” than did traditional students.The DOE (2009) attributed improved learning in online courses to differences in content, pedagogy, and time on task.

Faculty Acceptance of Online Degrees

If students achieve similar learning outcomes as those enrolled in traditional degree programs, then what barriers prevent the adoption of online degree programs? The most commonly cited factor inhibiting the development of online degree programs is faculty acceptance of online education (Stith, 2000; Ulmer, Watson, & Derby, 2007). Higher education generally sets the pace for educational innovation and it is expected that those in higher education would adapt to meet the demands of a changing educational climate. Contrary to these expectations, approximately 95% of faculty members believe that traditional lecture is the most effective means to achieve student learning outcomes (Blin & Munro, 2008). Since faculty are content area experts, traditional interaction with students affirm their identity as such and thus make them resistant to alter their role of the “sage on the stage” to that of a “facilitator” or “coach”, as necessitated in online courses.

Faculty also report a number of other factors related to their resistance to online education. Yang and Cornelius (2005) note that faculty were concerned that their traditional courses were not compatible with online education. Several studies suggest that the amount of time required to transition traditional courses to the online format may be a critical obstacle (Arbaugh, 2000; Hartman, Dzuiban, & Moskai, 2000; Shea, 2007b). Others have reported that faculty were concerned about compensation issues (Hartman et al., 2000; Shea, 2007b), inadequate training (Arbaugh, 2000; Pankowski, 2004), and lack of administrative support (Bower, 2001; Yang & Cornelius, 2005).

Faculty acceptance thus far has been defined as a professor’s willingness to teach an online course. Little research has examined faculty perceptions of the legitimacy and value of online education. Allen & Seaman (2007; 2010) surveyed academic leaders about their perceptions of faculty acceptance of the legitimacy and value of online education. While most academic leaders accept the merits of online education, only 33% felt that their faculty had positive opinions about the delivery mode in 2006, which remained relatively unchanged at 30% in 2009. Identifying factors that lead to faculty acceptance of online education are of central importance to achieving the strategic goals of universities and meeting increasing student demands for online degrees. The cooperation of faculty is essential for the development and implementation of quality online degree programs as faculty members are responsible for curriculum development and the nature and quality of teaching.

Technology Acceptance Model Applied to Academic Settings

The Technology Acceptance Model (TAM) is based on the theoretical notions of Fishbein and Ajzen’s (1975) Theory of Reasoned Action, which purports that an individual’s beliefs influences her attitudes, which in turn influences her behavioral intentions. Davis (1989) adapted this model to provide explanatory determinants of information technology (IT) acceptance and use in business settings. Davis (1989) predicts intent to use technology based on an individual’s ease of use and perceived usefulness of the technology. In the TAM, intent to use technology is determined directly by ease of use and indirectly by perceived usefulness as moderated by ease of use (Agarwal & Prasad, 1999; Venkatesh, 1999). Perceived usefulness has demonstrated direct effects on intent to use technology as well (Venkatesh & Morris, 2000). In addition, ease of use and usefulness directly affect attitudes toward technology, which in turn indirectly affect intent to use technology (Davis, 1989).

The TAM has been used in IT research to explain user acceptance and a wide range of technology based behaviors (Lee, Cheung, & Chen, 2005). Despite the TAM’s proven capability of explaining technology acceptance, the model has not been broadly applied outside IT and business. The few studies that have extended the TAM beyond IT have found that the inclusion of additional variables is necessary to add explanatory power (Davis, Bagozzi, & Warshaw, 1992; Hsu & Lu, 2004; Kim, 2008). Several variables have been related to the TAM such as intrinsic motivation (Roca & Gagne, 2008; Venkatesh & Davis, 1996), facilitating conditions (Cheung, Chang, & Lai, 2000), and competence (Roca & Gagne, 2008; Venkatesh & Davis, 2000).

Application of the TAM to acceptance of online education has been examined on very few occasions. Martinez-Torres and colleagues (2008) analyzed an extended version of the TAM with university students. Ease of use, perceived usefulness, enjoyment, competence, facilitating conditions (i.e., student support services), and intent to use e-tools were examined using structural equation modeling (SEM). The findings revealed that enjoyment, perceived usefulness, ease of use, and previous e-tool use predicted students’ intentions to use e-tools (Martinez-Torres et al., 2008).

A study conducted with university faculty found that ease of use and perceived usefulness variables were predictive of the intent to teach online classes, but most of the variance was accounted for by perceived usefulness (Gibson, Harris, & Colaric, 2008). In addition, Kim (2008) used SEM to examine factors affecting faculty acceptance of online education using an extended version of the TAM. Ease of use, perceived usefulness, online teaching experience, facilitating conditions (i.e., faculty support and training), competence, and intent to teach online courses were analyzed. Ease of use and perceived usefulness accounted for 80% of the variance in intent to teach online courses. Facilitating conditions and prior online teaching experience accounted for 34% of the variance in ease of use; whereas, online teaching experience and competence accounted for 20% of the variance in perceived usefulness (Kim, 2008). Together, these findings demonstrate that an extended version of the TAM may help predict faculty intent to teach online.

The Current Study

The present study extends the TAM’s theoretical framework by including variables demonstrated to add predictive power to the model in educational settings. Specifically, the study examines the original explanatory variables included in the Davis (1989) TAM (i.e., ease of use and perceived usefulness), and includes motivation orientation, online teaching experience and facilitating conditions, which have been shown to enhance the TAM. Moreover, the current study also investigates faculty perceptions of the value and legitimacy of online education, as assessed by Allen and Seaman (2007; 2010). Based on previous research and theory, it was predicted that: 1) Ease of use and perceived usefulness will predict intent to teach online. 2) Online teaching experience will aid the TAM in predicting intent to teach online. 3) Facilitating conditions will improve the TAM’s prediction of intent to teach online. 4) Motivation orientation will assist the TAM in predicting intent to teach online. 5) The extended TAM, including facilitating conditions and motivation orientation, will predict intent to teach online. 6) The extended TAM, including facilitating conditions and motivation orientation, will predict interest in online degree programs. 7) The extended TAM, including facilitating conditions and motivation orientation, will predict the value and legitimacy of online education variables.

Method

Participants

The sample includes six college-level administrators (i.e., Department Chairs, Assistant Deans, and Associate Deans) and 121 faculty members employed at a large, public, open-enrollment university. Demographic data show that there are comparable numbers of men and women and more than half of the participants were members of the College of Humanities and Social Sciences. Less than half of the participants had taught hybrid or online courses (see Table 1 for Participant Characteristics).

Table 1. Participant Characteristics

Variable		n	%
Age
	21-30	3	2
	31-40	27	21
	41-50	34	28
	51-60	35	29
	Over 61	23	19
Gender
	Female	60	49
	Male	63	51
College
	Business	25	21
	Humanities and SOS	63	52
	Public Service	12	10
	Science and Tech	18	15
	University College	3	3
Job Title
	Visiting Professor	2	2
	Lecturer	31	27
	Assistant Professor	37	32
	Associate Professor	25	21
	Professor	16	14
	Department Chair	3	3
	Assistant Dean	1	1
	Associate Dean	2	2
Web-enhanced Course
	Yes	99	82
	No	21	18
Hybrid Course
	Yes	43	36
	No	75	64
Online Course
	Yes	46	39
	No	72	61
Interest in Online
	Not interested	27	25
	Somewhat	36	34
	Interested	15	14
	Very Interested	29	27

Note. N = 127. Age data were missing for 5 participants, gender for 4 participants, college for 6 participants, job title for 7 participants; taught web-enhanced for 10 participants, taught hybrid for
9 participants; taught online for 9 participants; and interested in teaching online for 20 participants.
SOS = Social Sciences; Tech = Technology.

Design and Procedure

Participants received an e-mail invitation to complete an online survey using Survey Monkey (surveymonkey.com, Portland, OR), which was approved by the university internal review board. Those interested in participating clicked on a link to the Survey Monkey website, reviewed the consent form, and completed the brief online survey. Participants were free to withdraw from the survey at any time and did not receive any compensation for their participation.

Measure

A survey was developed to investigate faculty acceptance of online education, faculty motivation orientation, and intent to teach online. The survey included 44 items that measured demographics, computer use, computer management system (CMS) experience, tool use, ease of use, perceived usefulness, faculty motivation orientation, degree program interest, faculty acceptance of online degrees, and intent to teach online (see the Appendix for the Faculty Acceptance Survey).

Demographic subscale. A four-item subscale measured age, gender, college, and rank.

Experience with computer management system (CMS). A nine-item subscale assessed faculty experience with the CMS. The items examined previous use of the CMS in various delivery modes, comfort with the CMS, and confidence in using the CMS.

A Principle Component Analysis (PCA) with Varimax rotation was performed on the ease of use, perceived usefulness, facilitating conditions and motivation orientation subscales. Items with factor loadings that exceeded .50 were included in the factor (see Table 2 for the factor loadings and coefficient alphas).

Table 2. Factor Loadings for Principle Component Analysis with Varimax Rotation for Ease of Use, Perceived Usefulness, and Facilitating Conditions

Scale		α	AVE	Loading
Ease of Use		.63	58%
	Easy to find online resources			.79
	Easy to become skillful			.88
	Easy to use CMS			.60
Perceived Usefulness		.95	56%
	Educational technology is useful			.71
	Online teaching is compatible			.92
	Appropriate for degree content delivery			.81
	Effective for student learning			.91
Facilitating Conditions		.82	73%
	Internal training and development			.88
	External training and development			.91
	Course peer evaluation			.77

Note. AVE = Average Variance Extracted.

Ease of use. Three items derived from Davis’ (1989) TAM measured ease of use using a four point response scale ranging from 1 (not at all easy to use) to 4 (very easy to use). A PCA was performed on the items and a single factor emerged, which accounted for 58% of the variance.

Perceived usefulness. Five items derived from Davis’ (1989) TAM measured perceived usefulness using a four point response scale ranging from 1 (not at all) to 4 (very much). A PCA was performed on the items and two factors were identified: the first factor consisted of four items, which accounted for 56% of the variance and a second factor consisted of a single item and accounted for an additional 20% of the variance. Analysis of internal reliability for the five items was .78, but internal reliability improved dramatically if the single item (i.e., online teaching will impact my teaching effectiveness) that loaded onto the second factor was deleted.

Facilitating conditions. Three items assessed facilitating conditions for online teaching and used a four point response scale ranging from 1 (not interested) to 4 (very interested). A PCA was performed and the items loaded on a single factor that accounted for 73% of the variance.

Faculty motivations for teaching online courses. A nineteen item subscale assessed motivation orientation for online teaching using a four point response scale that ranged from 1 (not motivated) to 4 (very motivated). PCA yielded a two factor solution (intrinsic and extrinsic motivation dimensions) that accounted for 52% of the total variance (see Table 3 for the factor loadings and coefficient alphas).

Faculty motivations for teaching traditional courses . Motivation orientation for traditional teaching was measured with 14 items using a four point response scale ranging from 1 (not motivated) to 4 (very motivated). A PCA was performed and a three factor solution emerged: an intrinsic motivation factor and two extrinsic motivation factors (flexible schedule and unconfident), which accounted for 66% of the variance (see Table 4 for the factor loadings and coefficient alphas).

Faculty acceptance of the legitimacy and value of online education. Three items measured faculty acceptance of online education by examining perceived prestige of online degrees, perceived opportunities for online graduates in the workforce, and perceived opportunities for online graduates to attend graduate school. Participants could either agree or disagree with the statements. Reliability analysis demonstrated that internal consistency of the items was good, α = .75.

Faculty intent to teach online. Three items examined faculty members’ intent to teach online. The items assessed interest in teaching online, interest in offering online degree programs, and the number of online courses interested in teaching in a given semester. Analysis of the items demonstrated good internal reliability, α = .88.

Table 3 . Factor Loadings for Principle Component Analysis with Varimax Rotation of Faculty Motivations to Teach Online Courses Scale

Scale Item	Intrinsic	Extrinsic
Teaching responsibilities	.05	.81
Research responsibilities	-.01	.77
Service responsibilities	.13	.85
Home responsibilities	.01	.72
Scheduled at inconvenient times	.04	.65
Scheduled at inconvenient locations	.22	.57
Commuting issues	.26	.53
Comfortable with CMS	.48	.51
Enjoy teaching online	.79	.28
Confident with teaching abilities	.64	.37
Student evaluations will improve	.57	.05
Students desire online courses	.70	.19
Students learn more in online courses	.79	-.00
I am more responsive	.71	-.13
I am more motivated	.81	.09
Prefer online interaction	.77	-.02
Prefer online grading	.61	.36
Easier to teach online	.57	.20
Financial incentive to teach online	.42	.38

Note. Factor loadings greater than .50 and clearly aligned on a single factor are boldfaced .
Coefficient alphas for the scales are: Intrinsic, α = .91; Extrinsic, α = .84.

Results

A series of hierarchical regression analyses were performed to predict the intent to teach online, interest in offering online degree programs, and faculty acceptance variables using ease of use, perceived usefulness, online teaching experience, facilitating conditions, and online versus traditional motivation for instruction. Menard (1995) states that hierarchical regression is most appropriate when theory drives the order of predictor entry into the model. The ease of use and perceived usefulness factors were entered into step one of the model and step two and three involved the inclusion of facilitating conditions and motivation orientation, respectively. Significant F change demonstrated that the inclusion of additional predictors into the model improved the variance accounted for in each of the criterion. Regression statistics for the various models tested are described below.

Table 4. Factor Loadings for Exploratory Factor Analysis with Varimax Rotation of Faculty Motivations to Teach Traditional Courses Scale Scale Item	Intrinsic	Schedule	Unconfident
Schedule is flexible	-.15	.78	.12
Like the commute	.12	.83	-.03
Convenient times	.18	.89	.03
Convenient locations	.18	.89	-.02
Not comfortable with CMS	.17	.05	.87
Unconfident with online	.12	.05	.84
Student evaluations will suffer	.08	-.06	.58
Students desire traditional	.43	.12	.27
Enjoy traditional courses	.57	.34	-.22
Prefer traditional interaction	.81	.09	.04
Students learn more	.90	.16	.19
I am more responsive	.93	.12	.15
I am more motivated	.91	.17	.18
Easier to teach traditional	.61	.18	.14

Note. Factor loadings that exceed .50 are boldfaced.
Coefficient alphas for the scales are: Intrinsic, α = .90; Schedule, α = .88; Unconfident, α = .65.

The TAM and Intent to Teach Online

Overall, the model significantly accounted for 8.4% of the variance in intent to teach online, F(2, 79) = 3.5, p < .05. Results indicated that perceived usefulness did not significantly predict intent to teach online, F(1, 79) = .03, p = .88, but ease of use was predictive of intent to teach and independently accounted for 28.9% of the variance (β = .54, p < .01).

Previous Online Teaching Experience, the TAM, and Intent to Teach Online

Regression analysis indicated that 7.8% of the variance was accounted for by ease and usefulness, F(2, 76) = 3.2, p < .05, but online teaching experience provided no predictive benefits to the TAM, F(3, 75) = 2.3, p = .09.

Facilitating Conditions, the TAM, and Intent to Teach Online

The TAM accounted for 9.3% of the variance in intent to use, F(2, 75) = 3.9, p < .05. When facilitating conditions was added into the model, it accounted for an extra 22.3% of the variance in intent to teach online, F(3, 74) = 11.4, p < .0001. Together, facilitating conditions and the TAM accounted for 31.6% of the variance in intent to teach online. Regression analyses indicated that ease of use (β = .46, p < .01) and facilitating conditions (β = .58, p < .0001) significantly predicted intent to teach online.

Online Motivation Orientation, the TAM, and Intent to Teach Online

The TAM significantly predicted intent to teach online, accounting for 11.4% of the variance, F(2, 47) = 3.0, p < .05. Adding the online motivation factors accounted for 26.7% more of the explained variance in intent to teach online, F(4, 45) = 6.9, p < .0001, for a total of 38.1% of the variance. Results demonstrated that ease of use (β = .42, p < .05), intrinsic motivation (β = .53, p < .05), and extrinsic motivation (β = .60, p < .01) significantly predicted intent to teach online.

Traditional Motivation Orientation, the TAM, and Intent to Teach Online

The original TAM accounted for 8.5% of the variance, F(2, 64) = 3.0, p < .05, and the model that included the traditional motivation factors accounted for an additional 24.8% of the variance, F(5, 61) = 6.5, p < .0001; therefore, the extended model accounted for a total of 33.3% of the variance in intent to teach online. Ease of use (β = .56, p < .01) and intrinsic motivation (β = -.61, p < .001) independently predicted intent to teach online.

The Extended TAM and Intent to Teach Online

The extended model, including facilitating conditions and the online motivation factors accounted for 46.5% of the total variance in intent to teach online. Ease of use, usefulness, and extrinsic motivation to teach online independently predicted intent to teach online. Moreover, regression analyses showed that ease of use, facilitating conditions, and intrinsic motivation to teach traditional courses significantly predicted intent to teach online (see Table 5 for the extended TAM regression analyses).

Table 5. Hierarchical Regression Analyses for Intent to Teach Online Predicted from the TAM, Facilitating Conditions, and Motivation Orientation for Online and Traditional Instruction

	Variables	Online Motivation				Traditional Motivation
		β	R2	ΔR2	F		β	R2	ΔR2	F
Step 1 Step 1
	Ease of Use	.56**					.54**
	Usefulness	-.32	.11	.09*	3.0*		-.22	.09	.09*	3.0*
Step 2
	Ease of Use	.48*					.45*
	Usefulness	-.40					-.25
	Facilitating Conditions	.49**	.29	.24***	6.3***		.61***	.32	.24***	10.1**
Step 3
	Ease of Use	.42*
	Usefulness	-.41
	Facilitating Conditions	.17**
	Intrinsic Online	.32
	Extrinsic Online	.55**	.43	.14***	6.6***
	Intrinsic Traditional						-.46***
	Schedule						-.10
	Unconfident						-.31	.47	.14**	8.7***

Note. * p < .05. ** p < .01. *** p < .001.

The Extended TAM and Interest in Offering Online Degree Programs

Hierarchical regression analyses using the extended TAM, which included facilitating conditions and either online motivation or traditional motivation, accounted for 51.4% and 55.3% of the variance in interest in offering online degree programs, respectively. Regression analyses showed that facilitating conditions, intrinsic motivation to teach online, and intrinsic motivation to teach traditional courses predicted interest in online degrees (see Table 6 for the hierarchical regression analyses).

The Extended TAM and Faculty Acceptance of the Legitimacy and Value of Online Education

A series of hierarchical regression analyses predicted perceived prestige, employment opportunities, and graduate school opportunities using the extended TAM. The findings are presented below.

The extended TAM and perceived prestige of online degrees.

The original TAM did not significantly predict perceived prestige, but the extended TAM with online motivation orientation accounted for a total of 36.3% of the variance in perceived prestige, F(5, 36) = 4.1, p < .01. Also, results showed that online intrinsic motivation significantly predicted perceived prestige of online degrees (β = -.19, p < .05). Neither the original nor extended TAM model with traditional motivation orientation predicted perceived prestige of online degrees (see Figure 1 for descriptive data about faculty perceptions of the prestige of online degrees).

Table 6. Hierarchical Regression Analyses for Interest in Offering Online Degree Programs Predicted from the TAM, Facilitating Conditions, and Motivation Orientation for Online and Traditional Instruction

	Variables	Online Motivation				Traditional Motivation
		β	R2	ΔR2	F	β	R2	ΔR2	F
Step 1
	Ease of Use	.23				.26
	Usefulness	-.12	.02	.02	.57	-.23	.03	.03	1.0
Step 2
	Ease of Use	.10				.14
	Usefulness	-.25				-.29
	Facilitating Conditions	.65***	.38	.36***	10.0	.76***	.47	.44**	18.7***
Step 3
	Ease of Use	.04
	Usefulness	-.30
	Facilitating Conditions	.45**
	Intrinsic Online	.70**
	Extrinsic Online	-.02	.43	.13**	9.7***
	Intrinsic Traditional					-.36**
	Schedule					.06
	Unconfident					-.12	.55	.08**	12.4**

Note. * p < .05. ** p < .01. *** p < .001.

The extended TAM and perceptions of online graduates’ opportunities for employment.

The original TAM did not predict perceptions of job opportunities, but the extended model with online motivation orientation factors accounted for 27.6% of the variance, F(5, 35) = 2.7, p < .05, and the extended TAM with traditional motivation orientation factors accounted for 21.4% of the variance in faculty perceptions of online graduates’ employment opportunities, F(6, 62) = 2.8, p < .01. Additionally, intrinsic motivation to teach traditional courses significantly predicted employment opportunity perceptions (β = .35, p < .01). Figure 1 depicts descriptive data about faculty perceptions of online graduates’ job opportunities.

The extended TAM and perceptions of online graduates’ opportunities to attend graduate school.

Neither the TAM nor the extended TAM with online or traditional motivation orientation factors predicted faculty perceptions of online graduates’ opportunities to attend graduate school (see Figure 1 for descriptive data about faculty perceptions of online students’ graduate school opportunities).

Discussion

Substantial research has used the TAM to explain technology use in a variety of settings (Argawal & Prasad, 1999; Lee et al., 2005; Martinez-Torres et al., 2008; Venkatesh & Davis, 2000). However, critical reviews of the TAM have revealed that additional predictors such as intrinsic motivation, enjoyment, competence, technology experience, and facilitating conditions bolster the TAM’s explanatory capabilities (Davis et al., 1992; Gibson et al., 2008; Kim, 2008; Martinez-Torres et al., 2008; Venkatesh & Davis, 2000). However, few studies have explained technology use by higher education faculty using the TAM (Gibson et al., 2008; Kim, 2008).

Figure 1.
Prestige = Do you think an online degree is as prestigious as a traditional degree?
Work = Do you think that students who complete online degrees will have the same
opportunities in the workforce as students who complete traditional degrees?
Graduate School = Do you think that students who complete online degrees will have the
same opportunities to attend graduate school as students who complete traditional
degrees?

In the present study, results demonstrated that the TAM predicted faculty members’ intent to teach online, supporting hypothesis 1. Further analyses revealed that perceived usefulness did not predict intent to teach online; whereas, ease of use was independently predictive of intent to teach online. These findings are inconsistent with previous TAM research in which perceived usefulness independently predicted intent to teach online, while ease of use had less predictive power (Gibson et al., 2008; Kim, 2008).

Online teaching experience and the TAM has predicted ease of use and intent to teach online (Kim, 2008). The current study did not replicate those results or find support for hypothesis 2. Surprisingly, neither comfort with the CMS nor having taught online had a direct or indirect effect on intent to teach online. These results may be due to faculty members lacking confidence in their online teaching abilities or experiencing fear from the risks associated with online instruction (e.g., lack of departmental support or faculty acceptance).

Consistent with previous research, facilitating conditions enhanced the predictive power of the TAM (Cheong, Park, & Hwang, 2004; Kim, 2008; Venkatesh & Davis, 1996; 2000), which supports hypothesis 3. Universities often underestimate the need for faculty training and support to transition from traditional to online instruction (Alavi & Gallupe, 2003). Furthermore, Muilenberg & Berge (2001) cited lack of faculty training and support as common barriers to the adoption of online education. The findings in the current study demonstrate the need for universities to prioritize faculty training to encourage adoption of online instruction.

Davis and colleagues (1992) added intrinsic and extrinsic motivation to the TAM as predictors of intent to use technology. The present study extends previous research by examining online motivation factors that assist the TAM in predicting intent to teach online. Both intrinsic and extrinsic motivation to teach online improved the explanatory capability of the TAM, which supports hypothesis 4. Extrinsic motivation to teach online was the strongest predictor of intent to teach online, followed by intrinsic motivation, and then ease of use. These data support previous research suggesting that intrinsic motivation is an important factor in explaining technology use (Davis et al., 1992; Martinez-Torres et al., 2008). Findings suggest that faculty who intend to teach online courses are motivated not only because they prefer the schedule flexibility afforded online instructors, but because they also enjoy teaching online, prefer online communication with students, and are more motivated and responsive in their online classes. Additionally, faculty who intend to teach online find it easy; they are confident in their teaching abilities and believe that online students learn more in this delivery mode.

Examination of the TAM and motivations for teaching traditional courses revealed that the extended model was superior in explaining faculty members’ intent or reluctance to teach online. Specifically, findings demonstrated that intrinsic motivation to teach traditional courses was the strongest independent predictor of reluctance to teach online; conversely, ease of use predicted intent to teach online. Put simply, faculty who find the CMS easy to use intend to teach online, but instructors who enjoy traditional courses, are more motivated and responsive in traditional courses, and believe that students learn more in traditional courses are those reluctant to teach online. Clearly, these findings provide unique insights into how motivation to teach in traditional formats deters faculty from embracing online teaching. To encourage reluctant faculty members to teach online, it may be necessary to demonstrate that online instruction can be a rewarding experience.

Facilitating conditions and motivation orientation predicted intent or reluctance to teach online over and above what was predicted by the TAM alone, which supports hypothesis 5. Results showed that faculty who desired flexible schedules and found online teaching easy and useful intended to teach more online courses in a given semester. In comparison, faculty members who were motivated to teach traditional courses intended to teach online only if they believed that online instruction was easy and necessary support was provided. However, instructors who did not plan to teach online were those who enjoyed traditional courses, were motivated and responsive in traditional courses, and believed that students learned more in traditional courses. The current study validates previous studies which explained intent to use technology using the TAM and intrinsic motivation to teach online (e.g., Davis et al., 1992; Martinez-Torres et al., 2008) and broadens the research to include an explanation for faculty resistance to online education by using motivation to teach traditional courses as a predictor of intended behavior.

Intention to use technology and sustained use of technology requires substantial commitment from the user. Current technology use has been predicted by intention to use technology in several studies (Davis et al., 1992; Martinez-Torres et al., 2008; Venkatesh & Bala, 2008; Venkatesh & Davis, 2000). However, these studies have examined how current technology use is moderated by intent to use. These models differ significantly from the model used in this study in two ways: 1) the extended TAM directly predicted use and 2) use was defined by a long-term commitment to use technology. Hypothesis 6 investigated whether the extended TAM predicted faculty members’ interest in offering online degree programs. Developing and maintaining an online degree program requires faculty to commit to teaching several online courses in a strategic manner each semester. The results provided support for hypothesis 6 with intrinsic motivation to teach online as the strongest predictor of interest in offering online degree programs, followed by facilitating conditions. Intrinsic motivation has been proven a stronger motivator than extrinsic motivation for faculty participation in online education (Bonk, 2001; Maguire, 2005; Parker, 2003). In prior research, intrinsic motivation and facilitating conditions are moderated by perceived usefulness and intentional use in explaining current technology use (Davis et al., 1992; Martinez-Torres et al., 2008). The current research indicates that faculty who enjoy online instruction, are more motivated in online courses, prefer online interaction with students, and believe that students learn more in online courses are interested in offering online degree programs. Moreover, faculty who are interested in receiving internal and external training and peer evaluations of their courses are also interested in offering online degrees in their disciplines. In contrast, faculty members who are intrinsically motivated to teach traditional courses are not interested in offering online degree programs.

Given that the extended TAM predicted intent to teach online and interest in offering online degree programs, it was further used in hypothesis 7 to explain faculty members’ perceptions of the value and legitimacy of online education. Using the extended TAM, intrinsic motivation to teach online was the strongest predictor of prestige perceptions. In particular, faculty members who were intrinsically motivated to teach online thought that the online and traditional degrees were equally prestigious.

The extended TAM was also predictive of faculty members’ perceptions of job opportunities for online and traditional degree graduates wherein intrinsic motivation to teach traditional courses was the strongest predictor of work opportunity perceptions. Specifically, faculty who enjoyed teaching traditional courses and believed that students performed better in traditional courses thought that traditional and online graduates would not have equal opportunities for employment in the workforce. Participants’ perceptions are consistent with studies that have examined businesses’ hiring practices, which have found that 4% of hiring managers were willing to accept a candidate with a fully online degree in comparison to 27% who were willing to accept graduates who completed both traditional and online courses and 96% who were willing to accept traditional graduates (Adams, 2008; Adams & DeFleur, 2006; Adams, DeFleur, & Heald, 2007).

The extended TAM did not predict faculty perceptions of online and traditional degree students’ opportunities to attend graduate school; therefore, the data provided only partial support for hypothesis 7. Adams (2008) found that very few graduate schools accept online degree students. Online degree graduates have less than a 10% chance of admittance into graduate schools (Adams, 2008; Defleur & Adams, 2004). Research also suggests that those who offer online bachelor degrees are reticent to admit online graduates into graduate level programs (Defleur & Adams, 2004). As the descriptive data shows (see Figure 1), participants in the current study differ little from faculty nationwide; there is a perceptual disparity in academia that online education is inferior to traditional education (Giannoni & Tesone, 2003; Yick, Patrick, & Costin, 2005; Stith, 2000; Ulmer, Watson, & Derby, 2007) and that perception is not only held by academics, but those in businesses as well (Adams, 2008).

Conclusions

Dedicated and committed faculty are essential to the success of online education and the extended TAM can be used to identify those faculty members. The extended TAM was predictive of faculty intent to teach online courses and interest in offering online degree programs, but findings were inconclusive related to faculty’s perceptions of the merits of online instruction. Factors such as image and subjective norms, which were included in the most recent TAM might have proved useful in explaining faculty perceptions of the legitimacy and value of online education and should be included in future studies (Venkatesh & Bala, 2008). Further, those institutions interested in offering online degree programs should provide sufficient technical support and development opportunities for faculty as facilitating conditions was consistently predictive of technology use, as was intrinsic motivation to teach online. Finally, faculty members who are intrinsically motivated to teach traditional courses are resistant to online education. It may behoove institutions to focus online adoption efforts on faculty who are amenable to online education.