Assessing Faculty Attitudes towards Online Instruction: A Motivational Approach

David J. Prottas
Adelphi University

Catherine M. Cleaver
American Association of University Professors, Adelphi University Chapter

Deborah Cooperstein
Adelphi University


There continues to be a lack of congruence in the attitudes of faculty and administrators with respect to online or distance education. The authors developed and administered a questionnaire to assess pertinent attitudes and perceptions of full and part-time faculty (n= 421) toward online instruction at their private university in a U.S. Middle Atlantic State.  Responses to thirty-five items were subjected to exploratory factor analysis with four factors emerging labeled as technical resources, self-efficacy, strategic alignment, and contextual suitability. Differences were found based on a number of demographic variables including experience with online instruction, being part-time, working in a professional school, gender, and years teaching. Relationships among factors are also explored and practical implications discussed.


Technological, economic, and demographic changes have provided both opportunities and challenges for college and university students, faculty, and administrators. The explosion of technologies is seen by many as enabling high quality education to be made available to a broader range of students in a more convenient and cost effective manner.  Others are concerned that the technologies will be used to develop and market low quality substitutes for traditional education. Conversations about the appropriate roles of traditional and non-traditional forms of instruction are taking place on campuses around the world. The conversations are taking place in colleges and universities in which academic leaders believe the outcomes of online instruction are the same or superior to those of traditional face-to-face instruction and where the majority of faculty disagree with that assessment with these differences in perceptions remaining consistent over time (Allen, Seaman, Poulin, & Straut, 2016).

The emergence of non-traditional technologically facilitated forms of instruction has naturally lead to a proliferation of discussion and research as to its relative effectiveness as well as the attitudes of administrators, students, and faculty towards them (Johnson, et al., 2011; Lloyd, Byrne, & McCoy, 2012;  Muilenburg & Berge, 2001; Seok, DaCosta, Kinsell, & Tung, 2010; Shachar & Neumann 2010; Stewart, Bachman, & Johnson, 2010; Tanner, Noser, & Totaro, 2006; Ulmer, Watson, & Derby, 2007; Wilkes, Simon, & Brooks, 2006).

Effective strategic leadership is frequently seen as a series of ongoing conversations through which employees are engaged (Groysberg & Slind, 2012).  If administrators view online education as integral to an institution’s strategy it is necessary to understand and address the motivational factors that would influence their faculty’s willingness to support that effort. The current research is intended to contribute to further understanding of the factors that influence faculty attitudes in several ways:

  1. Develop measures of attitudinal constructs that would have motivational effects;
  2. Analyze differences in the attitudinal constructs related to individual demographic factors (age, experience teaching and gender) and organizational demographic factors (such as employment status and discipline) on attitudes;
  3. Examine relationships among these attitudinal constructs. 

Literature Review

There have been a number of studies that examined relationships between individual demographic factors and attitudes towards online instruction. For example, faculty have been found to have less favorable attitudes towards online instruction than students (Seok et al. 2010; Tanner et al., 2009; Wilkes, Simon, & Brooks, 2006). Additionally, faculty with greater experience teaching online and exposure to related technologies have more positive attitudes (Hall, 2013; Johnson et al., 2011; Lloyd, Byrne, & McCoy, 2012; Seok et al., 2010; Ulmer, Watson & Derby, 2007) as do female faculty (Lloyd et al., 2012; Seok et al., 2010). These studies reported responses to individual items rather than constructing scales.  It is worth noting that even though Ulmer et al. (2007) concluded that those who had more experience with online instruction had overall views that were more favorable they, paradoxically, expressed greater agreement with one item: “I believe distance education causes the quality of education to go down.”

Other researchers attempted to create measures of relevant constructs using exploratory factor analysis (EFA). Muilenburg and Berge (2001) and Lloyd et al. (2012) asked faculty to rate the severity of different barriers.  They specified sixty four barriers and from responses identified ten factors: administrative structure; organizational change; technical expertise, support, and infrastructure; social interaction and program quality; faculty compensation and time; threat of technology; legal issues; evaluation/effectiveness; access; and student-support services. Lloyd et al. (2012) specified 22 possible barriers and from responses identified four factors: interpersonal barriers, institutional policy barriers, training and technology barriers, and cost/benefit analysis barriers. They reported women perceived fewer barriers on all four dimensions than did men. Those who had never taken or taught online courses perceived greater interpersonal barriers than those who had more experience with online instruction.

Bolliger and Wasilik (2009) developed an instrument to assess the satisfaction of faculty who had taught online courses and identified three primary factors; student, instructor, and institution. Stewart et al. (2010) analyzed responses to items that might motivate faculty to teach online and items that might motivate them to teach traditionally. They also measured participant experience with, and perceived ease of use and usefulness of certain educational technologies, and intention to teach online. They found that the ease of use of the educational technologies, facilitating conditions, and extrinsic advantages of online were positively related to intention to teach online whereas the intrinsic advantages of teaching traditionally were negatively related with perceived usefulness; the intrinsic advantages of online were however, unrelated to intentions to teach online. Faculty in their study viewed online instruction as having less value and legitimacy than traditional instruction with respect to prestige, impact on employment opportunities, and opportunities to attend graduate school.


This research was undertaken in a single university located in the U.S. in a Middle Atlantic state. The university is classified by the Carnegie Foundation as a medium sized (approximately 8,000 students) four-year and above primarily non-residential institution which is majority undergraduate. The undergraduate instructional program is balanced between Arts & Sciences and professional programs (Business, Education, Nursing, Psychology and Social Work) with an undergraduate profile that is full-time, selective and with a high transfer-in. The graduate instructional program is doctoral with a preponderance of professional programs. The majority of the students at the University are undergraduate (a ratio of 2.1 based on full-time equivalents). However, there are marked differences across units. The ratio in the Arts & Sciences was 29.2 whereas the ratio among the five professional schools was 0.76.

The authors asked all full- and part-time faculty (n = 322 and n = 639) to voluntarily and anonymously complete the surveys with paper-and-pencil and on-line alternatives provided. A total of 421 useable surveys were returned (for a response rate of 44%). As some participants did not provide all information, including demographic information, the response rates for subgroups such as full or part time faculty are presented as minimums: full-time, 67% part-time, 28%.

The mean age of the respondents was approximately 51.9 years (sd = 12.18) and the mean years of experience teaching at the college level was 14.36 (sd = 11.33).  The majority of participants were full-time faculty (54.5%) with most of those holding the rank of Associate or Full Professor (63.4%) and Assistant Professors accounting for 28.7%.  Other full-time faculty such as Clinical and Visiting Professors accounted for 7.9%. The majority of the participants were male (54.7%). The organizational demographic composition of the sample was representative of the population as a whole.

The majority of subjects (67%) reported they had not taught any online courses (synchronous, asynchronous, full on online, or blended). In subsequent analysis, participants were classified into three categories: with no experience (67%); those with limited experience (one to ten classes, 28%), and those with greater experience (11 to 130, 5%)


The authors asked participants to choose among Likert-type response options to thirty five items (Table 1).  Participants were asked about their “attitude towards teaching through an online format (whether synchronous or asynchronous) those courses that you usually teach in a traditional classroom setting to the students that you usually teach” at the university. Items were intended to assess beliefs or attitudes that would be related to online instruction at the specific university, as opposed to higher education in general, and with reference to the students at that university, as opposed to college students in general.

The thirty-five items were intended to assess four distinct constructs: self-efficacy with respect to online instruction; strategic alignment, technical resources, and contextual suitability. While some prior research (e.g., Lloyd et al., 2012; Muilenburg & Berge, 2001) asked participants to evaluate barriers, the decision was made in this research to develop items which were primarily positive in tone. Asking faculty to evaluate aspects that were presented as negative might have been seen by faculty members, as well as administrators, as representing a deliberate attempt by the researchers to bias the responses by encouraging the expression of negative attitudes towards online instruction. The majority of items (28) were phrased in a manner that a person who endorsed them would most likely have a favorable attitude towards online instruction with endorsement of the other seven likely to be associated with a negative attitudes towards online instruction.

The support available to the faculty was assessed by eight items shown in Table 1. The university maintains a support unit which is described as “a vital part in the promotion of new technologies, pedagogies and professional development for faculty” and areas in which it specializes is “blended and online teaching, podcasting, media production, plagiarism prevention and detection, video conferencing, and the shift in pedagogy resulting with the use of technology in the classroom.”  For purposes of this research the unit will be identified as the Faculty Support Unit (FSU). Seven items made specific reference to the FSU. Subjects were provided 5-point Likert-type response options (Strongly Disagree, Disagree, Neither Agree/Disagree, Agree, Strongly Agree and Unsure/No opinion for six items and, for two items that asked about degree of helpfulness of the FSU, Almost no help, Little help, Moderately helpful, Somewhat helpful, Extremely helpful and Unsure/No opinion). For purpose of scoring each participant’s responses, not sure/no opinion responses were coded as representing the midpoint response and items were scored and averaged such that higher values expressed greater perceived technical support. The Cronbach alpha coefficient of internal reliability was .88.

Self-efficacy was assessed by six items shown in Table 1. Subjects were provided 5-point Likert-type response options (Strongly Disagree, Disagree, Neither Agree/Disagree, Agree, Strongly Agree and Unsure/No). Participants’ scores were calculated as above. The Cronbach alpha was .81.
Strategic alignment was assessed by six items shown in Table 1. Participants’ scores were calculated as above. The Cronbach alpha was .84. Contextual suitability was assessed by six items shown in Table 1. Participants’ scores were calculated as above. The Cronbach alpha was .84. Demographic variables included gender, academic unit, the number of online courses that the subject had taught during their careers and academic rank (including full- or part-time status) and age and years of experience teaching at the college level (within ranges of years).


The 35 items which assessed attitudes related to online instruction were subjected to an exploratory factor analysis with Varimax rotation in order to determine if it was possible to create measures of underlying constructs. The Kaiser (1960) rule by which all factors with Eigenvalues greater than 1 should be retained is commonly used and is the default for SPSS. However, it has been shown to produce spurious results and frequently overstates the number of factors (Hayton, Allen & Scarpello, 2004; Horn, 1965).  Therefore a parallel analysis (PA) using the procedure developed by Hayton et al (2004) was conducted which produced a five factor solution. The loadings of each of the thirty five items are also shown in Table 1. EFA can produce factors that are artifacts of question phrasing rather than representing differences in the underlying constructs; for example, reverse coding items alone might produce a different factor loading.  Only three reversed scored items loaded on the fifth column. Their content was examined and interpreted as relating substantively to contextual suitability. The Cronbach alphas of each of the four resulting scales (.88, .81. .84, and .84) were well above the Nunnally’s (1978) benchmark of .70.
As shown in Table 2, age and years teaching were strongly correlated (r = .55, p < .001) and both were negatively related to the attitudes. The relationships seemed somewhat more pronounced between years teaching and support (r = -.17, p < .01), self-efficacy (r = -.18, p < .001), strategic alignment (r = -.19, p < .001) and contextual suitability (r = -.26, p < .001). As shown in Table 2, the results were similar when the authors entered years in teaching with other demographic variables support (β  = -.13, p < .05), self-efficacy (β = -.13, p < .05), strategic alignment (β  = -.15, p < .01) and contextual suitability (β = -.20, p < .001).

The authors also found significant differences between full-time and part-time faculty with the part-time faculty reporting more favorable attitudes (support, r = .13, p < .01; strategic alignment, r = .17, p < .01; self-efficacy, r = .18, p < .001; contextual suitability, r = .24, p < .001) although when entered into a regression with other demographic variables the β’s remained statistically significant only for self-efficacy (β = .16, p < .01) and contextual suitability (β = .17, p < .01).

There were differences between men and women. Women perceived lower levels of technical resources (r = -.13, p < .01 and β = -.13, p < .05) and strategic alignment (r = -.07, p = .19 but β = -.08, p < .05).

The authors found differences between faculty in the Arts & Sciences and those in professional schools.  Professional school faculty reported less uncertainty (r = .11, p < .05) and higher levels of technical resources (r = .12, p < .05 and β = .12, p < .05) and contextual suitability (r = .17, p < .001 and β = .19, p < .001). Post hoc analysis, however, showed that faculty in the schools of nursing and education primarily accounted for these differences.  

There were differences in attitudes based on a faculty member’s prior experience teaching online courses with the number of courses taught being positively related to technical resources ( r = .15, p < .01), strategic alignment (r = .12, p < .01), self-efficacy ( r = .30, p < .001) and contextual suitability (r = .21, p < .001). However, as the distribution of the number of courses taught was far from normal, two dummy variables were created for limited online experience (eg., having taught from one to ten courses online) and substantial online experience (e.g, having taught more than ten online courses) and those were entered in the hierarchical regressions with other demographic control variables. The analysis remained significant only with respect to self-efficacy (limited online teaching, β = .24, p < .001 and substantial online teaching β = .31, p < .001) and contextual suitability (limited online teaching, β = .10, p < .05 and substantial online teaching β = .19, p < .001).     

Finally, the authors conducted a hierarchical regression with contextual suitability as the dependent variable. Each of the demographic variables was entered as the first step with the technical resources, strategic alignment, and self-efficacy entered in the second step. As shown in Table 3, the inclusion of the three attitudinal variables increased the R2 from .16 to .59 and the betas for all three were significant (technical resources, β = .12, p < .01; self-efficacy, β = .57, p < .001; strategic alignment, β = .17, p < .001). The betas for tenure (β = -.09, p < .05) and discipline (β = .08, p < .05) remained significant but none of the other demographic variables did. With the addition of those variables in the second step, status and the dummy variables for prior online experience ceased to be statistically significant.

In the evaluation of technical support, the authors found significant differences among faculty based on demographics. Faculty who were full-time and had taught for more years perceived lower levels of technical support, strategic alignment, self-efficacy, and contextual suitability than did adjunct and less experienced faculty.

The faculty’s perceptions of the adequacy of the technical resources provided to them to develop and deliver a quality course should have two effects on their motivation. Expectancy theory would suggest that the greater they evaluated the support, the more likely they would view their efforts as leading to successful courses (expectancy) and the more likely to expend effort (Beehr, Glazer, Fischer, Linton, & Hansen, 2009; Biggs, Brough, & Barbour, 2014; Boswell & Boudreau, 2001; Boswell, 2006; Boswell, Bingham, & Colvin, 2006; Schneider, et al., 2003; Vroom, 1964). Based on strategic alignment, the provision of these resources would be evaluated by the faculty as a concrete communication by the organization that it desires and values the creation of such courses (Biggs et al., 2014; Boswell, 2006; Schneider et al., 2003). Self-efficacy theory (Bandura, 1997) posits that an individual’s sense of self-efficacy would be related to their expending effort. Self-efficacy was originally conceptualized as a generalized assessment of the self. However, the concept has been expanded to include finer grained self-assessments with respect to specific domains of tasks, including teaching (Klassen, & Chui, 2010; Klassen, & Tze, 2014).


Online experience seemed unrelated to perceptions of the university’s technical resources and strategic alignment. In contrast, the finding that teaching online courses was generally positively related to attitudes regarding the suitability of online instruction and self-efficacy was logical and consistent with prior research (Hall, 2013; Johnson et al., 2011; Lloyd, Byrne, & McCoy, 2012; Seok et al., 2010; Ulmer, Watson and Derby, 2007). One explanation is that all faculty perceive similar structural characteristics, but that those who see that online instruction is suitable for the objectives of the students and the institution decided to expend the time and energy to acquire the necessary skills and abilities which had a positive effect on their sense of self-efficacy. The cross sectional design does not allow the authors to establish causality although, in most cases, it is unlikely that faculty would be mandated to teach online with that mandate leading them to acquire the needed skills and abilities and through those experiences coming to believe that online instruction was more contextually suitable.

The results of the hierarchical regression indicated that faculty perceptions of (i) online instruction as being integrated with the university’s overall strategy, (ii) level of technical support afforded to them, and (iii) their own abilities with respect to online instruction were each positively related to their perceptions of the contextual suitability of online instruction in their university. Once those attitudinal variables were included in the model, the prior experience teaching online courses ceased to be statistically significant and the explanatory power of demographic variables such as years teaching and discipline became less. 

In a study of 111 faculty who had taught at least one online course, Ray (2009) reported that 51% said that converting a course from face-to-face to totally online was hard or very hard. In our study 67% of our sample agreed or strongly agreed that “It would be a tremendous amount of work on my part to convert an existing course that I teach traditionally into an online alternative of equal quality.”        

The authors had expected there to be differences in attitudes between faculty in the Arts & Sciences and faculty in the professional schools because the professional schools have a greater proportion of graduate than undergraduate students with graduate students perhaps being viewed as  more mature and disciplined and more likely to succeed in an online environment. The authors also thought that faculty might view the subject matter of the professional schools as being more conducive to online instruction. Post hoc analysis found that professional school faculty from Education and Nursing exhibited markedly more favorable attitudes.

Study Limitations

There are of course limitations to the study. First, the within-organization design raises questions about the ability to generalize the findings to other institutions although the authors believe the advantage of being able to make the within-organization comparisons was valuable.     



The current research depicts a university at which faculty are unlikely to embrace online instruction. Consistent with prior research, they appear skeptical that it would “work” from the perspective of student learning, faculty enjoyment of teaching and faculty workload, or institutional objectives with respect to enrollment. It suggests the gap that Allen et al. (2016) reported between the views of administrators and faculty as to whether online instruction “works” for students is extant at this institution. The study suggests that, to the extent administrators want faculty to work enthusiastically towards increasing online activity it may need to better explain the role of online instruction in the overall strategy of the university, provide evidence that such instruction would work from the perspectives of the different stakeholders (students, faculty, and administration), and provide the technical and other pedagogical supports to make faculty feel more confident in their abilities to efficiently and effectively change from a traditional to a more technologically enhanced modality.


Allen, I. E., Seaman , J., Poulin, R., & Straut, T. T.  (2016). Online report card Tracking online education in the United States (Report) [PDF]. Babson Survey Research Group.

Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.

TA Beehr, S Glazer, R Fischer, LL Linton, CP Hansen Antecedents for achievement of alignment in organizations. Journal of Occupational and Organizational Psychology 82 (1), 1-20

Biggs, A., Brough, P., & Barbour, J. P. (2014). Strategic alignment with organizational priorities and work engagement: A multi-wave analysis. Journal of Organizational Behavior, 35(3), 301-317

Bolliger, D. U., & Wasilik, O. (2009). Factors influencing faculty satisfaction with online teaching and learning in higher education. Distance Education, 30(1), 103-116.

Boswell, W. R. (2006). Aligning employees with the organization’s strategic objectives: out of ‘line of sight’, out of mind. International Journal of Human Resource Management, 17(9), 1489–1511.

Boswell, W. R., Bingham, J. B., & Colvin, A. J. S. (2006). Aligning employees through “line of sight”. Business Horizons, 49(6), 499-509.

Boswell, W. R., Boudreau, J. W. (2001). How leading companies create, measure and achieve strategic results through "line of sight." Management Decision, 39(10), 851-859.

Groysberg, B., & Slind, M. (2012). Leadership is a conversation. Harvard Business Review,
90(6), 76-84.

Hall, O.B. (2013) Assessing faculty attitudes towards technological change in graduate management education. MERLOT Journal of Online Learning and Teaching, 9 (1), 39-51.

Hayton, J. C, Allen, D. G., & Scarpello, V. (2004). Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Organizational Research Methods, 7, 191- 205.

Horn, J. L. (1965). A rationale and test for the number of factors in factor analysis. Psychometrika, 32, 179-185.

Johnson, T. E., Meilin, V. B., Andaverdue, S., Galindo, A. M., Madrigal, K., & Kupcyznaki, L.  (2011). Faculty perception of online instruction and student learning. Journal of Educational Technology. 8(1), 12-19. 

Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141-151.

Klassen, R. M., & Chui, M. M. (2010), Effects on teachers’ self-efficacy and job satisfaction: Teacher gender, years of experience, and job stress. Journal of Educational Psychology,102(3), 741–756

Klassen, R. M., & Tze, V. C. (2014). Teachers' self-efficacy, personality, and teaching effectiveness: A meta-analysis. Educational Research Review, 2, 1259-76.

Lloyd, S. A., Byrne, M. M., & McCoy, T. S. (2012). Faculty-perceived barriers of online education.  MERLOT Journal of Online Learning and Teaching, 8(1), 1-12.

Muilenburg, L., & Berge, Z. L. (2001). Barriers to distance education: a factor-analytic study. American Journal of Distance Education, 15(2), 7-22.

Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill.

Ray, J. (2009). Faculty perspective: Training and course development for the online classroom. MERLOT Journal of Online Learning and Teaching, 5(2), 263-276.

Schneider, B., Godfrey, E. G., Hayes, S. C., Huang, M., Lim, B., Nishii, L. H., & Ziegert, J. C. (2003). The human side of strategy: Employee experiences of strategic alignment in a service organization. Organizational Dynamics, 32(2), 122-141.

Seok, S. DaCosta, B., Kinsell, C., & Tung, C. K. (2010). Comparison of instructors' and students' perceptions of the effectiveness of online courses.  Quarterly Review of Distance Education, 11(1), 25-36.

Shachar, M., & Neumann, Y. (2010). Twenty years of research on the academic performance differences between traditional and distance learning: Summative meta-analysis and trend examination. MERLOT Journal of Online Learning and Teaching, 6(2), 318-334.

Stewart, C., Bachman, C., & Johnson, R. (2010). Predictors of faculty acceptance of online education. MERLOT Journal of Online Learning and Teaching, 6(3), 597-616.

Tanner, J. R., Noser, T. C., & Totaro, M. W. (2009). Business faculty and undergraduate students' perceptions of online learning: A comparative study. Journal of Information Systems Education, 20(1), 29-40.

Ulmer, L. W., Watson, L. W., & Derby, D. (2007). Perceptions of higher education faculty members on the value of distance education. The Quarterly Review of Distance Education, 8(1), 59-70.

Wilkes, R. B., Simon, J. C., & Brooks, L.D. (2006). A comparison of faculty and undergraduate students’ perceptions of online courses and degree programs. Journal of Information Systems Education, 17(2), 131-140.
Vroom, V. H. (1964). Work and motivation. New York: Wiley.

Online Journal of Distance Learning Administration, Volume XIX, Number 4, Winter 2016
University of West Georgia, Distance Education Center
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