Valuing the Institution: An Expanded List of Factors Influencing Faculty Adoption of Online Education


Madhavan Parthasarathy
University of Colorado Denver
Madhavan.Parthasarathy@ucdenver.edu

Marlene A. Smith
University of Colorado Denver
ma.smith@ucdenver.edu

Abstract

We find that faculty consider their self-interests, those of their students, and the value to their institution when deciding whether to adopt online education. Our sample of business school faculty at a public urban university suggests that faculty who perceive online education as contributing to a desirable image for the business school, and that online education allows the school to meet changing market needs, are more likely to deliver one or more of their courses online. Because our study includes two populations of faculty—those who have taught online and those who have not—we are able to statistically measure the link between perception and behavior using multiple discriminant analysis (MDA). The statistically significant relationship between institutional considerations and adoption of online education provides university administrators with a new glimpse of what motivates faculty to participate in online education.

Introduction

This paper is concerned with the adoption of online MBA courses by faculty in a large publicly-funded university and AACSB-accredited business school.  This general topic has been studied extensively (e.g., McKenzie, Mims, Bennett, & Waugh, 2000; Parker, 2003; Zhen, Garthwait, & Pratt, 2008), and several variables (e.g., time to deliver a course online, time and geographic flexibility) have been identified as influencers in the adoption decision (see especially Parker, 2003 and Maguire, 2005 for recent literature reviews). 

This exploratory study differs from much of the existing literature in two significant ways.  We propose several new motivators/inhibitors of the faculty decision to adopt online education: perceived student misconduct and faculty concerns for the institution. Little empirical attention has been paid to the latter, perhaps because the literature has mostly concluded that intrinsic self-interests are the predominant factors influencing the adoption decision (e.g., Betts, 1998; Schifter, 2002). Even so, it is plausible, for example, that faculty may not adopt online education, despite its many perceived advantages to themselves and their students, if they believe that it doesn’t lend itself to the department, school, or institution’s long-term goals.

Second, faculty attitudes towards online education adoption may not always converge with the actual decision to adopt online course delivery.  This article studies the actual act of adoption of online education by faculty, as opposed to their attitudes towards such a decision, and identifies the variables that influence this actual decision.  With few exceptions (e.g., Betts, 1998; Schifter, 2002) the vast majority of the literature in this field does not establish a direct empirical link between opinions and behavior (e.g., Black 1992; Dooley & Murphrey, 2000; Giannoni & Tesone, 2003; Bruner, 2007) and even fewer (Zhen, Garthwait, & Pratt, 2008 is a notable exception) use multivariate methods in which the strength of a particular independent variable must be proved against others in the model.

We begin by intermingling our hypothesis development with a review of the recent literature.  Next, we describe the data collection efforts, the method used, and the results.  Lastly, in the discussion section, the implications and contributions of the study and its limitations are presented.  

Theory and Hypotheses

When the opportunity to teach an online course presents itself to faculty, they likely consider several issues before determining the relative advantage of such a decision.  Past research devoted to online education adoption has generally focused on intrinsic and extrinsic motivating (or inhibiting) factors (e.g., Betts, 1998; Schifter, 2002; Parker, 2003).  For purposes of presentation, we categorize potential adoption factors into three groups: those related to the self-interests of the faculty, those related to faculty concerns for their students, and faculty concerns for their institutions. The variables, and direction of their influence on the adoption decision, are now considered in detail.

Faculty-related factors

Self-interest considerations are important to those who consider adopting a new service, in this instance, faculty adoption of online course delivery.  Among the many factors that motivate faculty participation in online education, we focus on three that have received widespread scrutiny in the literature: instructor preparation time, class delivery issues, and schedule flexibility.

Teacher Preparation Time

This is time devoted to developing and maintaining materials required to teach the class effectively.  Cavanaugh (2005), for example, finds that uploading materials to online platforms, changing dates of quizzes, communicating with students, updating multiple choice exams using web-based tools, and familiarizing students with an online course create additional time burdens for faculty teaching online. To the extent that a faculty member perceives there to be more class preparation required online (e.g., O’Quinn and Corry, 2002), she may be hesitant to adopt online classes.  Hence:

H1:  Faculty members who perceive greater preparation time for online courses as opposed to on-campus courses will be less apt to adopt online education.

Class Delivery Issues

One key area where online and on-campus courses differ is in the actual delivery of the course.  Interaction is often accomplished via online forums in which faculty read each student response and type appropriately, an activity that has generated faculty concern about the time needed to deliver a course online (White, 2000). Indeed, Lazarus (2003) found that the biggest time commitment to teaching online is participation and grading of online discussions. Because email is the primary form of communication between faculty and students in web-based courses, faculty have expressed concerns about the increased time commitment required of emailing individual students in online courses (White, 2000). The research that has addressed both the actual, measured increase in online class delivery time and faculty perceptions of it (e.g., Betts, 1998; Rockwell, Schauer, Fritz, & Marx, 1999; Schifter, 2002; Bender, Wood, & Vredevoogd, 2004; Cavanaugh, 2005), prompts the following hypothesis:

H2:  Faculty members who perceive greater course delivery burden in the online medium as opposed to the traditional classroom will be less apt to adopt online education.

Teacher Schedule Flexibility

The online medium affords greater flexibility for faculty than traditional teaching environments as faculty do not have to arrive at a certain time to a certain place to teach the class.  The faculty member does not have to be in the same city, country, or for that matter, continent, as long as he is able to access the online platform.  Flexibility in time is assured in that the faculty member is committed to teach at a given time only by design and at his convenience (e.g., synchronous chat room office hours).  The time and place flexibility offered by the online medium is one of online education’s great advantages to faculty (e.g., McKenzie et al., 2000; Rahman, 2001; Schifter, 2002; Parker, 2003; Cavanaugh, 2005).  Therefore, we propose that:

H3:  Faculty members who perceive online courses as affording greater schedule flexibility than on-campus courses will be more likely to adopt online education.

Student-related factors

Faculty may also consider student-related variables when making a decision to adopt online education.  For example, faculty may be sensitive to the fact that online education provides students with greater scheduling flexibility, and yet they may be worried about whether quality of the education delivered to them can be maintained. Based on the literature, we identify three student-related variables and propose a new one.

Course Quality

Those who have not added online education to their teaching repertoire may have a vague sense that quality suffers when courses are delivered online (Black, 1992; Betts, 1998). For example, Rahman (2001) found that faculty who had taught online believed that effective learning is possible with the new medium and, conversely, that detractors felt that something fundamental is lost without face-to-face contact. Maguire (2005) reports a concern about “…lack of standards for an online course,” Wilson (2001) about “…quality instruction,” and Landstrom (1995) that distance education “… courses are not as rigorous as regular courses.” Presumably, faculty who believe that the online format limits the quality of education that can be delivered to their students will view online education as less desirable, and thus:

H4:  Faculty members who perceive online courses are of lower quality than on-campus courses will be less likely to adopt online education.

Student Schedule Flexibility

As with faculty, online education has the promise to provide geographic and time flexibility to students. Online students have fewer concerns about commuting to campus (McKenzie et al., 2000), conflicts with other courses, and so forth. Adult working students can travel as work demands while remaining connected to their instructor and peers via the internet (e.g., Parker, 2003). Faculty members who are especially sensitive to student scheduling needs are hence more likely to teach online (Rockwell et al., 1999; Wolcott & Betts, 1999).  This thought is captured in the following hypothesis.

H5:  Faculty who perceive online courses as affording greater schedule flexibility to students than on-campus courses will be more likely to adopt online classes.

Student Slacking 

Betts (1998) and Schifter (2002) studied faculty concerns about expectations students may have about online education. Faculty might perceive that students expect online courses to be easier than traditional delivery methods (Sellani & Harrington, 2002; Bruner, 2007). Unrealized expectations of easier online courses might explain lower student retention rates online than on-campus (e.g., Williams, Duray, & Reddy, 2006), which can contribute to conflict between faculty, administrators, and students (Sellani & Harrington, 2002). Faculty wonder whether the online environment encourages plagiarism and cheating (e.g., O’Quinn & Corry, 2002; Sinn 2004).  Faculty who are especially sensitive to these issues may be hesitant to adopt online classes and thus:

H6:  Faculty who feel that students are more likely and able to slack off in the online environment as opposed to the on-campus environment will be less likely to adopt online education.

Student Conduct

Our final student-related variable came from a brainstorming session of the faculty group involved with the selection of variables for this study; to the best of our knowledge, it has not been tested in the literature. This variable considers whether student behavior is more abrupt and rude in the online environment. Several of us had experienced outlandish student behavior that probably would not have occurred in a face-to-face environment, presumably related to Rovai and Barnum’s (2003) observation that, “courses taken at a distance can be impersonal, superficial, misdirected, and potentially dehumanizing…” (p. 57). Faculty who are particularly sensitive to such behavior may be dissuaded from teaching online because of the associated aggravation that misconduct may invoke.  Hence,

H7:  Faculty who perceive deviant student behavior to be more frequent in the online environment as opposed to the on-campus environment will be less likely to adopt online education.

Institution-related factors

Along with self and student interests, faculty may consider broader strategic issues relating to their institution.  Rahman (2001) found that faculty who adopted online education believed that doing so supported the university’s mission statement. Bruner (2007) investigated whether online education would “… fundamentally change the nature of this college…” and “…bring positive attention to our college.” Maguire’s (2005) recent review of the literature provides only vague attributions to institutional issues, and proposes instead that self-interests of faculty are paramount to their decision to adopt online course delivery.

We propose two new institutional variables that faculty members may consider while deciding whether or not to adopt an online class, namely whether the new medium of course delivery is contemporary and hence enhances the reputation of the institution as a progressive institution, and if the new medium caters to current market trends.  These constructs are now individually discussed.

Enhancing Institutional Image 

In an increasingly competitive world, some educational institutions strive to position themselves as progressive institutions that adopt new and upcoming forms of education and technology (Dooley & Murphrey, 2000). Bruner (2007) reported that faculty wishing to contribute to an institution’s reputation are more likely to adopt behaviors consistent with that image.  As online education is currently one key indicator of an institution’s willingness and ability to adapt to changing educational delivery methods, we hypothesize that:

H8:  Faculty who perceive that online courses contribute to a progressive institutional reputation will be more likely to adopt online education.

Responding to Market Trends

Market trends in education are constantly changing.  In business schools, for example,  market and industry demand have prompted distance and hybrid course delivery,  company-specific education, executive education, accelerated degrees, and country-specific business courses (e.g., marketing in China).  Online education is especially desired by full time working adults attending school part-time who value time and geographic flexibility (Rahman, 2001; Parker, 2003). Online delivery of courses may help the institution attract a wider audience (e.g., Woolcott, 1997; Bruner, 2007) such as international students who can add an important perspective to the educational experience. Hence:

H9:  Faculty who perceive that online courses enhance the reputation of the institution as one that responds quickly to market trends will be more likely to adopt online education.

Method

Data Collection

The population consisted of 60 business school faculty at a large public urban university located in the western U.S.  A short questionnaire was distributed as an email attachment with a reminder sent one week later.  Almost all of the respondents printed out the survey, completed it by hand, and placed the completed questionnaire in the mailbox of the contact person. Forty-five responses were received, 12 from online-education adopters and 33 from non-adopters, resulting in a very high response rate of 75%.  Twenty-five of the 45 respondents (55.6%) were tenure-track faculty, nine (20%) were non-tenure-track, and the remainder marked “other” or did not answer. (We collected no further demographic information because the researchers were colleagues of the respondents and feared compromising the anonymity guaranteed as a condition of participation, as mandated by the university’s human subjects research guidelines.)

Instrument construction and validation

A committee of six business school faculty members monitored and oversaw the instrument development, its administration, and the data analysis.  The committee consisted of some faculty who taught online and others who did not. During extended rounds of discussion and consultation, every committee member made suggestions for improvement or change. The process of developing the questions and validating them amongst the committee members took over a month before the committee was satisfied with the quality of the questions.  Thus, face validity was achieved. 

To meet the objective of creating a short questionnaire designed to reduce respondent tedium, each construct was measured with only one or two questionnaire items. All items were measured using 7-point Likert scales which ranged from 1 = “strongly disagree” to 7 = “strongly agree”.  Table 1 summarizes the constructs, their reliabilities if applicable, and their other metric properties. The questions used for each construct are also shown in Table 1.  Preceding the Likert scales in the survey was a clear statement asking faculty to respond to each item “compared to an equivalent on-campus offering” as applicable.

As shown in Table 1, constructs were measured with two items each with the exception of student and faculty schedule flexibility, course comprehensiveness, and course rigor. We initially presumed that course comprehensiveness and course rigor were both indicators of course quality (hypothesis H4), but reliability analysis and a further reading of the questions revealed that the two questions were indeed measuring different things. Hence these two questions were broken down into single-item constructs and we report separate results for course rigor (H4a) and course comprehensiveness (H4b):

H4a:  Faculty members who perceive that online courses are less rigorous than on-campus courses will be less likely to adopt online education.

H4b:  Faculty members who perceive that online courses are less comprehensive than on-campus courses will be less likely to adopt online education.

Given the exploratory nature of this study, it was concluded that single-item measures, while not ideal, were nevertheless of value in understanding online education adoption.

As shown by the reliability statistics in Table 1, our two-item constructs show good to very good reliability with Cronbach’s Alpha ranging from 0.76 for Student Slacking to 0.91 for Progressiveness. In this sample, respondents believe that it takes longer to prepare and deliver an online course relative to on-campus offerings, as indicated by averages for these variables well above 5 on the 7-point scale. They also understand the inherent benefit of schedule and geographic flexibility to both faculty (mean = 5.51, s = 1.39) and students (mean = 6.13, s = 0.89).

Table 1

Construct Descriptive Statistics, Item Wording, and Instrument Reliabilities

Likert-Scaled
Construct/Item(s)a

Number of Items

 Mean

Std.
Dev.

Cronbach Alpha

Teacher Prep Time

  1. Preparation time for an online class is greater.
  2. The total amount of time devoted to preparing for class is greater online.

2

5.38

1.46

0.88

Teacher Course Delivery

  1. It takes more time to teach an online class
  2. It requires less effort to teach an online class. (reverse coded)

2

5.33

1.42

0.77

Teacher Flexibility
Online teaching provides greater schedule flexibility to teachers.

1

5.51

1.39

-

Course Rigor
The nature of the online medium makes the learning experience less rigorous for students.

1

4.25

1.51

 -

Course Comprehensiveness
Course content is less comprehensive in an online environment.

1

4.04

1.68

-

Student Flexibility

1

6.13

0.89

-

The online environment provides greater schedule flexibility to students.

 

 

 

 

         

Student Slacking

  1. Students are more likely to “slack off” in an online class.
  2. The time that students expend in online courses is lower.

2

4.19

1.41

0.76

Student Conduct

  1. Inappropriate student conduct is more likely to occur in an online class.
  2. Students are more liable to be rude or discourteous in an online setting.

2

4.32

1.45

0.84

Progressiveness

2

4.39

1.50

0.91

  1. The Business School needs more online courses to stay contemporary.
  2. Online courses allow the Business School to be a progressive educational institution.

 

 

 

 

 

Market Trends

  1. Online education is what a growing segment of our customer base desires.
  2. A growing segment of our customers find value in our online courses.

2

5.14

1.19

0.90

a  Respondents were asked to gauge each of the faculty and student-related questions “compared to an equivalent on-campus offering.”

Data Analysis and Results

When the Business School administration decided to encourage faculty to adopt online education, the dean’s office distributed an invitation to all faculty to participate in online education. Thus, faculty themselves made the selection, presumably based on their expectations. Since we theorize a distinction between adopters and non-adopters of online education based on perceptions of faculty, student, and institutional factors, we used multiple discriminant analysis (MDA) to test the model.  MDA is an appropriate statistical technique when the dependent variable is categorical and the independent variables are continuous.  The technique identifies how well independent variables can collectively predict membership in the dependent classification (Hair, Black, Babin, Anderson, & Tatham, 2006).  In this study, the categorical a priori dependent variable distinguishes non-adopters from adopters.  The independent variables are the faculty related factors, (namely teacher preparation time, teacher course delivery issues, teacher schedule flexibility), student-related factors (course rigor, course comprehensiveness, student schedule flexibility, student conduct, and student slacking), and institution-related factors (progressiveness, and market trends).   

Results of the analysis are presented in Table 2.  The multivariate discriminant function was significant at the 1% level since p = 0.003, confirming the ability of the independent variables to collectively discriminate between non-adopters and adopters. The predictive validity of the discriminant function was also assessed by comparing the overall hit ratio (82.2%) with the proportional chance criterion (60.8%) and a maximum chance criterion of 73.3%.  The maximum chance criterion measures the percentage of respondents in the larger of the two discriminant groups, and represents the minimum hit rate to establish predictive validity.  As a rule of thumb, the classification accuracy reflected in the overall hit ratio should be at least 25 percent higher than the proportional chance criterion for adequate predictive power (Hair et al., 2006).  The hit ratio in this study (82.2) far exceeded the classification percentage of 76.1 (25 percent greater than 60.8), thereby reflecting both significant and substantive levels of classification accuracy.

Table 2  

Results of Multiple Discriminant Analysis

 

 

Group means

 

 

Hypothesis

Independent variable

 

Non-adopters (n=33)

Adopters (n=12)

F-statistic

Discriminant loadings

H1

Teacher Prep Time

5.23

5.79

1.34

0.45

 

H2

Teacher Course Delivery

5.09

6.00

 3.37**

0.28

 

H3

Teacher Flexibility

5.30

6.08

 2.88**

0.26

 

 H4a

Course Rigor†

4.52

3.52

4.09*

-0.31

 

 H4b

Course Comp-rehensiveness†

4.12

3.83

0.26

-0.08

 

H5

Student Flexibility

6.00

6.50

 2.87**

0.26

 

H6

Student Slacking

4.37

3.71

1.94

-0.21

 

H7

Student Conduct

4.17

4.75

1.44

0.18

 

H8

Progressiveness

 

4.03

5.40

8.67*

0.45

 

H9

Market trends

 

4.85

5.94

8.80*

0.45

 

Multivariate significance level: 0.003
Percentage correctly classified (hit ratio): 82.2
Maximum chance criterion: 73.3
Proportional chance criterion: 60.80

* p < 0.05, ** p < 0.10
a Correlation between discriminating variables and the canonical discriminant function
†Greater values for course rigor and comprehensiveness imply lower values for the construct

As Table 2 indicates, three variables—course rigor, institutional progressiveness and market trends—were highly significant with p < 0.05. Course delivery time and teacher and student schedule flexibilities were marginally significant with p < 0.10.  The other variables—teacher preparation time, course comprehensiveness, student slacking, and student conduct—were insignificant in determining faculty adoption of online courses. 

Particularly noteworthy is that both institutional variables were highly significant.  Faculty members who felt that teaching an online class would help position the business school as a progressive institution that responds in a timely manner to market trends were highly likely to adopt online education.  Indeed, these were the two strongest variables in the analysis. Thus, we find in favor of hypotheses H8 and H9.

Among the faculty-related variables, effort required to deliver a course online was marginally significant, but it was in a direction opposite to what was proposed, contradicting hypothesis H2; adopters perceived online education delivery time to be significantly greater than non-adopters as shown by the group means in Table 2.  Some plausible explanations as to why this might be so are discussed in the next section. Similar to Zhen et al. (2008), the data are not consistent with our first hypothesis since teacher preparation time was not a statistically significant determinant of online course adoption.  Instructors do not perceive online education as requiring more prep time than an equivalent on-campus offering, and thus this is not a determinant of the adoption decision.  Much to our surprise, we found only weak support for hypotheses H3 and H5 regarding schedule flexibility. Faculty and student schedule flexibility, while marginally significant at the 10% level, were not as strong as the predictors concerning the institution, both of which were significant at the 5% level.

The results were mixed regarding our two course quality variables. Course rigor was, as expected, a barrier to adoption; i.e., those who viewed the online platform as hampering course rigor (a higher value for this variable implies less rigor) were less likely to adopt. Thus, we find in favor of hypothesis H4a. On the other hand, course comprehensiveness is not a statistically significant factor influencing adoption (H4b). Hence concerns about rigor, but not coverage, can influence the decision to teach online. 

The data failed to support either H6 or H7 regarding students slacking off or engaging in misconduct. Inappropriate student behavior was not considered important enough to influence the actual adoption decision.

Discussion

The results of the study are noteworthy in both the significance and insignificance of the variables studied.  For example, faculty schedule flexibility has been repeatedly mentioned in the literature as being critical in a faculty member’s consideration of adoption of online education.  Yet, according to our results, when it comes to actual adoption of online education (as opposed to attitudes towards adoption), these variables assume only marginal importance when viewed in the wider context of other, competing concerns.  Two of the most prominent of these competing variables are institutional factors: the desire by faculty members to help the school position itself as a progressive institution and as one that responds quickly to market trends. Others have reported administrators focus on the value of distance education for the institution (e.g., Wolcott, 1997), but our work is the first to find empirical evidence that faculty may share similar concerns. The notable statistical strength of the institutional variables in our study contributes a new perspective about faculty adoption of online courses, one that has not received empirical scrutiny in the literature. In particular, although a larger sample size taken from the same population might find some of our statistically insignificant variables to be significant, that larger sample would be expected to find continued significance of the institutional constructs.

While we predicted that respondents who perceived delivery time and effort to be greater in the online context would be less likely to adopt online education, the opposite was found to be true (although this finding was only marginally significant).  Why would those who perceived that online teaching involved greater delivery burden be more likely to adopt online education?   One plausible explanation is that these individuals were perhaps more likely to have investigated the pros and cons of online education and, based on their own research or consultations with other informed faculty, had formed more realistic expectations of actual online teaching.  These individuals would thereby enter the decision process with the knowledge that online education typically involves greater delivery effort, and would have come to terms with that fact. Both adopters and non-adopters agree that that it takes longer to deliver a course online than a traditional class (see Table 2). However, the non-adopters don’t know how much effort is really needed. An analogous observation can be made about instructor preparation time, since the group mean for adopters exceeds that of non-adopters for this variable (Table 2). However, in this case, the influence of instructor prep time is not a statistically significant predictor of the adoption decision. Contrarily, we suspect that both adopters and non-adopters have equally-good understandings of how online courses would impact instructor and student scheduling flexibility, for example—little or no additional research should be required to make informed decisions about the obvious. Instead, our results suggest that instructors who more highly value their own needs for schedule flexibility are more likely to adopt.

The literature does not provide well-defined constructs for course quality. We found that there can be two distinct aspects of course quality: rigor and comprehensiveness. However, only one of these, course rigor, contributes to the decision to teach online. Faculty who perceived that online courses were less conducive to course rigor than traditional classroom delivery were less likely, as predicted, to adopt online education.  Our results imply that faculty value educational rigor highly, and may consider rigor to be more important than personal or student convenience, for example, when it comes to a decision regarding online instruction. This bodes well for the quality of online education.

We found that inappropriate student conduct, in the form of student misbehavior or students slacking off, did not influence the decision to teach online. Perceptions of negative student conduct or student tendencies to slack off online are not issues perceived to be serious enough to warrant non-adoption of online courses.

Implications

Implications for Practice

The implications of this paper for deans and online course administrators are many and varied.  In addition to focusing on the advantages to faculty offered by online courses, administrators are advised to also stress strategic issues that will affect the institution.  In our sample, faculty display an interest in promoting the business school as a progressive institution that responds to the latest market trends via their willingness to offer courses online, and hence administrators will do well to promote this facet of online education. The key is to frame the message to faculty from a personal, student, and strategic perspective when presenting the advantages of online education.

When using experienced online faculty to mentor others (Wolcott, 1997), faculty who have not experienced the online medium may well hear about student misconduct online. Our results suggest that stories of this sort should not dissuade potential adopters. Advocates of online education can provide information about handling student online misconduct without fear of turning away a new group of online instructors.

Even though potential adopters believe that online courses are more time-consuming to deliver, they don’t know how much more time awaits them. Administrators should be forthright about course delivery issues and be prepared to counterbalance them, perhaps with course downloads, additional pay, or student assistance.

Implications for Future Research

Betts (1998), Meyer (2002), and Schifter (2002), among others, classify faculty motivation to participate (or not) in online education into intrinsic (e.g., intellectual challenge, lack of release time) and extrinsic (e.g., teaching awards, merit pay) factors. Our study suggests that there might be additional motivators that are less immediate and that accrue to faculty through the institution. Such factors might be labeled indirect intrinsic or extrinsic motivators in that faculty believe that when the institution benefits, so do they. Indirect intrinsic motivators might include the belief that an institution viewed as more contemporary will be more likely to attract benefactors to fund endowed chairs, similar to Bruner’s (2007) pondering whether distance education might “…bring additional ministry opportunities.” Perhaps a school that responds to market trends will attract better students for faculty to teach.  Do faculty believe that being associated with a “leading edge” university increases chances of being awarded federal research grants, an indirect extrinsic motivator? Or (dare we say it?) are faculty concerns for the institution purely altruistic? Although our study has identified the importance of indirect institutional motivators in faculty decisions to adopt online education, it has not provided a more detailed understanding of answers to the “why” question—and thus opens new ground for future research.

We have found that faculty are very concerned about the quality of education delivered online. We discovered, albeit serendipitously, two dimensions of course quality—rigor and comprehensiveness—only one of which influences the online adoption decision. This begs the question of whether there is more to the concept of course quality than we have uncovered in this study. 

Study Limitations

Readers should consider this an exploratory study because it suffers from some notable limitations.  First, in spite of a high response rate, the data set includes only 45 respondents, limited by the overall size of the faculty in the school.  Second, due to measurement limitations imposed by the committee, the number of questions per construct was limited to either one or two.  Finally, the survey respondents were business school faculty teaching in the MBA program at a public urban university. The university studied in this sample is relatively new and is shadowed (with regard to reputation and budget) by more established schools within the same state system; thus this institution and some of its faculty have assumed a “we try harder” attitude which may be reflecting their willingness to try new things like online education to further the image of the institution. We encourage researchers to adapt our study for application to different types of faculties, schools, departments, institutions, and cultures to more fully understand the conditions under our findings generalize to other populations.

Acknowledgments

We thank members of the faculty committee who participated in the development of the survey described in this article: Betty Conner, Woody Eckard, Marlena Fiol, Deborah Kellogg, and Ed O’Connor.

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Meyer, K.A. (2002). Does policy make a difference? An exploration into policies for distance education. Online Journal of Distance Learning Administration, 5(4). Retrieved from http://www.westga.edu/~distance/ojdla/winter54/meyer54.htm.

O’Quinn, L., and Corry, M. (2002). Factors that deter faculty from participating in distance education. Online Journal of Distance Learning Administration, 5(4). Retrieved from  http://www.westga.edu/~distance/ojdla/winter54/Quinn54.htm.

Parker, A. (2003). Motivation and incentives for distance faculty. Online Journal of Distance Learning Administration, 6(3). Retrieved from http://www.westga.edu/~distance/ojdla/fall63/parker63.htm.

Rahman, M. (2001). Faculty recruitment strategies for online programs. The Online Journal of Distance Learning Administration, 4(4). Retrieved from http://www.westga.edu/~distance/ojdla/winter44/rahman44.html.

Rockwell, S.K., Schauer, J., Fritz, S.M., and Marx, D.B. (1999). Incentives and obstacles influencing higher education faculty and administrators to teach via distance. Online Journal of Distance Learning Administration, 2(4). Retrieved from http://www.westga.edu/~distance/ojdla/winter24/rockwell24.html.

Rovai, A. P., & Barnum, K. T. (2003). On-line course effectiveness: An analysis of student interactions and perceptions of learning. Journal of Distance Education, 18(1), 57-73.

Schifter, C. (2002). Perception differences about participating in distance education. Online Journal of Distance Learning Administration, 5(1). Retrieved from http://www.westga.edu/~distance/ojdla/spring51/schifter51.html.

Sellani, R.J., and Harrington, W. (2002). Addressing administrator/faculty conflict in an academic online environment. Internet and Higher Education, 5, 131-145.

Sinn, J. (2004). Electronic course delivery in higher education: Promise and challenge. The Journal of Technology Studies, 30(1), 39-45.

White, C. (2000). Learn online: Students and faculty respond to online distance courses at Grant MacEwan Community College. T.H.E. Journal, 27(9), 66-68, 70.

Williams, E.A., Duray, R., & Reddy, V. (2006). Teamwork orientation, group cohesiveness, and student learning: A study of the use of teams in online distance education. Journal of Management Education, 30(4), 592-616.

Wilson, C. (2001). Faculty attitudes about distance learning. Educause Quarterly, 2, 70-71.

Wolcott, L. L. (1997). Tenure, promotion, and distance education: Examining the culture of faculty rewards. The American Journal of Distance Education, 11(2), 3-18.

Wolcott, L.L., and Betts, K.S. (1999). What’s in it for me? Incentives for faculty participation in distance education. The Journal of Distance Education, 14(2), 34-49.

Zhen, Y., Garthwait, A., and Pratt, P. (2008). Factors affecting faculty members’ decision to teach or not to teach online in higher education. Online Journal of Distance Learning Administration, 11(3). Retrieved from http://www.westga.edu/~distance/ojdla/fall113/zhen113.html.


Online Journal of Distance Learning Administration, Volume XII, Number II, Summer 2009
University of West Georgia, Distance Education Center
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