Analysis of Student and Faculty Enrollment and Attitudes in Online Education Innovation, Study Guides, Projects, Research of Innovation

Download Analysis of Student and Faculty Enrollment and Attitudes in Online Education Innovation and more Study Guides, Projects, Research Innovation in PDF only on Docsity! Innovation Diffusion & Online Education 1 Running Head: Roger’s Five Attributes of Innovation Diffusion and Online Education. Rogers’ Five Main Attributes of Innovation on the Adoption Rate of Online Learning Truman Do PSOC7200 Master of Arts Organizational Change Hawaii Pacific University Publication Date: 8/15/2008 Innovation Diffusion & Online Education 2 Certification Page The Professional Paper submitted by this student has been reviewed and is deemed to have met the Professional Paper (PSOC 7100/PSOC 7200) requirement for Hawaii Pacific University's, College of Professional Studies Graduate Program. Student Name: Truman Do Title of Professional Paper: Roger’s Five Attributes of Innovation Diffusion and Online Education 8/15/08 Gerald Glover, PhD Date Professor of Organizational Change Innovation Diffusion & Online Education 5 D: Survey Instrument for students 165 E: Survey Instrument for faculty members 170 F: Analysis of program of study for students 175 G: Analysis of program of study for faculty members 176 H: Analysis of future online course enrollments for students 177 I: Analysis of future online course enrollments for faculty 178 J: Analysis of online taken by students 179 K: Analysis of online taught by faculty members 180 Innovation Diffusion & Online Education 6 List of Tables Table Page 1 Frequency data of students who have taken online courses 75 2 Frequency data of students who have not taken online courses 82 3 Frequency data of faculty members who have taught 89 online courses 4 Frequency data of faculty members who have not taught 96 online courses 5 Frequency data of student and faculty member samples 103 6 Summary of findings 141 Innovation Diffusion & Online Education 7 List of Figures Figure Page 1 The research process 13 2 Students who have taken online courses 104 3 Students who have not taken online courses 113 4 Faculty members who have taught online courses 122 5 Faculty members who have not taught online courses 131 6 Student and faculty members samples 140 Innovation Diffusion & Online Education 10 Chapter One – Introduction Importance of Research Area Introduction. Every since 1995, Hawaii Pacific University (HPU), like many other post-secondary educational institutions across the country, has been integrating the online education component to its degree programs. A few years ago, HPU began to offer the Master of Arts in Organizational Change entirely online. Recently, it has offered prospective students the opportunity to pursue the business degree at the undergraduate level entirely online. There are various reasons for the increased offering of online education at this school. Three groups of stakeholder who have been creating the growth in online education are: school administrators, faculty members, and students. School administrators have been pushing for more online courses to reduce overhead costs that associated with rent, utility, insurance and janitorial services, etc. The national average instructor to student ratio was 20-to-1 per class, and this ratio was enforced by the limitation of a classroom size, faculty resistance to grading larger numbers of tests and papers, and the desire for smaller classes and individual attention (Housel & Bell, 2001). Likewise, faculty members have desired to teach online courses due to perceived advantages. One advantage cited was the ability to measure and monitor student accountability and participation in ways that were not feasible with traditional campus- based courses (Glover, 2006). And others, they like the flexibility of conducting classes while in other localities. Innovation Diffusion & Online Education 11 Students’ interest in online learning is quite similar to the faculty members. They want the flexibility to take classes online without leaving their home or terminating their present employment. In addition, the cost associated traditional education, such as travel would be reduced or eliminated (Glover, 2006). It is critical to measure the attributes that contribute to the adoption rate of online education by students and faculty members. The innovation diffusion model conceptualized by Dr. Everett Rogers has been applied by researchers to study the diffusion of new educational programs (Rogers, 2003). The advantages of online education perceived by faculty members may not be true as perceived by students. School administrators must carefully and intelligently craft an online education strategy that will benefit both stakeholders. In order for online education to be sustainable, two things must happen. Faculty members must want to teach in an online environment. Likewise, students must want to learn in an online environment. It is imperative that both stakeholders perceive the attributes of online education to be better than traditional campus-based education. Identification of Problem Statement of the Research Problem. The objective of this research project is to investigate the influence of Rogers’ five attributes of innovation diffusion on the adoption rates of online education by faculty members and graduate students in the College of Professional Studies at Hawaii Pacific University. First hypothesis: the attributes of relative advantage, compatibility, complexity (simplicity), trialability and observability of online learning are positively related to students who have taken online courses, in the College of Professional Studies. Innovation Diffusion & Online Education 12 Second hypothesis: the attributes of relative advantage, compatibility, complexity (simplicity), trialability and observability of online teaching are negatively related to students who have not taken online courses, in the College of Professional Studies. Third hypothesis: the attributes of relative advantage, compatibility, complexity (simplicity), trialability and observability are positively related to faculty members who have taught online courses, in the College of Professional Studies. Fourth hypothesis: the attributes of relative advantage, compatibility, complexity (simplicity), trialability and observability are negatively related to faculty members who have not taught online courses in the College of Professional Studies. Variables Defined and Explained The rate of adoption is the dependent variable. The independent variables are: relative advantage, compatibility, complexity (simplicity), trialability, and observability. Relative advantage is the degree to which an innovation is perceived as better than the idea it takes the place of (Rogers, 2003). The sub-dimensions of this attribute include economic profitability, low initial cost, a decreased in discomfort, savings of time and effort, and immediacy of reward. Compatibility is the degree to which an innovation is perceived to be consistent with potential adopters’ values (Rogers, 2003). The sub-dimensions of this attribute include socio-cultural values and beliefs, past experiences, needs of potential adopters and name. Complexity is the degree to which an innovation is perceived as relatively difficult to understand and to use (Rogers, 2003). This attribute is reversed for this Innovation Diffusion & Online Education 15 hypotheses concerned with how graduate students perceive online learning. The last two hypotheses concerned with how faculty members perceive online teaching. Chapters Two and Three shared in the step of collecting data. In Chapter Two, secondary data was collected to guide this research project. On the other hand, in Chapter Three, primary data was collected for analysis that would support or not support the four hypotheses. The fourth step in the research process was to collect secondary and primary data. The secondary data came from text books, research articles, case studies, conceptual articles, and opinion papers. They were retrieved from Internet search engine and online databases. The three primary online databases are HPU Ebscohost, Wilson-Education, and Emerald Full-Text. As for the Internet search engine, Google Scholar was an appropriate source for the thesis. To maintain a high level of credibility, none of the articles came from commercial and issue-based sites. These sites usually have produced materials that support their positions. In addition, the materials lacked the rigor of academic standards. From the literature review, a research methodology to test the hypotheses was designed. The study was a quantitative research. The data was derived from answers by respondents in a scale rating survey. The researcher developed original questions for the survey. The survey included several questions from other studies that were relevant to the problem statement and hypotheses. Professors Glover and Ward of the Organizational Change program validated the survey questions. The administration of the survey was conducted in the beginning of June of 2006. There was a practical reason Innovation Diffusion & Online Education 16 to administer the surveys in that month. In June, students were not occupied with their final exams. The primary data came from student and faculty member responses to the survey. They were asked to answer 21 questions regarding the five attributes of online education. The data was collected in two ways. First, the researcher approached all the faculty members whose courses were taught on campus in the College of Professional Studies. After all the respondents who completed the surveys on campus, the researcher contacted the faculty members who were teaching online classes to distribute the survey link to their students. Chapter Four involved the actual analysis of the primary data collect through the survey collection tool. The statistical analysis utilized was descriptive statistics and frequency distribution. The fifth step in the research process was to analyze results of the data and to form conclusions. As for the analysis, the findings were subjected to a descriptive analysis. The objective was to ascertain the strength of the relationship between the independent and dependent variables. Chapter Five and the final chapter in this research project involved the summary of the findings. In addition, recommendations were required to complete this chapter. The final step in the research process was to offer recommendations and solutions to the problem statement and hypotheses. An experimental project would be ideal, but time and resources were important considerations to the research design. Results from an experiment would show direct causations between the variables. Every aspect of the Innovation Diffusion & Online Education 17 research design for this research paper was influenced by work of “Diffusion of Innovations.” The first assumption. The first assumption was that online education has become an integral part of higher education at HPU. The second assumption. The second assumption was that HPU would continue to increase the offering of online courses and degree programs. The third assumption. The third assumption was that the demand from students and faculty members at HPU for online education would continue to grow. Criteria for testing H/PS. Each independent variable was measured with Likert- type numerical values. First, the number “5” was designated as “strongly agree.” Second, the number “4” was designated as “agree.” Third, the number “3” was designated as “No opinion.” Fourth, the number “2” was designated as “disagree.” Fifth, the number “1” was designated as “strongly disagree.” The dependent variable is the rate of adoption, and it is defined by the relative speed with which an innovation is adopted by members of a social system (Roger, 2003). First, the number “1” was designated for people who have taken or taught an online course. Second, the number “2” was designated for people who have not taken or taught an online course. Faculty members and students received the same survey. Of courses, there were one to two questions that were slightly different between the two surveys. Limitations to the Research Biases in research. All researches contain biases. It is critical for a researcher to recognize and state them clearly (Roger, 2003; Leady & Ormrod, 2005). With this recognition, the credibility of the research project and the researcher would be enhanced. Innovation Diffusion & Online Education 20 Innovation diffusion researchers can overcome the individual-blame bias by applying at least one out of the three identified strategies (Rogers, 2003). One strategy is for diffusion researchers to seek other substitutes to individuals as a unit of measurement and analysis. Another strategy is to have an open mind regarding issues and not to take the positions of agencies at their face value. To go one step further, all stakeholders in the innovation diffusion process should be involved in defining the innovation problem. The agencies and their change agents should not have exclusive power in this process. A general bias that diffusion researchers may encounter is the results may not by representative of the actual population. This bias occurs when the sample is not randomly selected. The only time when random sampling is possible is when researchers have control of the population. To overcome this problem, the only way is to collect a large sample. Hopefully, the sample results would reflect the results of the population. Constraint in research. Time is the great constraint for researchers (Leady & Ormrod, 2005). Often, a researcher wants to conduct a research project that is comprehensive and significant for publishing purpose. The size of the research may take years. When time is not in the control of the researcher, he or she needs to reduce the scope of the research. The five attributes of innovation diffusion explain the adoption rate of innovations from 49 to 87 percent, but it does not explain everything (Rogers, 2003). In addition to main five, other variables such as (1) the type of innovation-decision, (2) the nature of communication channels diffusing the innovation at various stages in the innovation- decision process, (3) the nature of the social system in which the innovation is diffusing, and (4) the extent of change agents’ promotion efforts in diffusing the innovation, affect Innovation Diffusion & Online Education 21 an innovation’s adoption rate. The time frame it would take to explore these other variables may take years of research. Problems in research. Time is an important variable in the innovation diffusion research, but it is one of its greatest enemies (Rogers, 2003). The accuracy of the answers is highly dependent upon the ability of adopters to recall and the time frame. In addition, the educational level and memory of individuals affect the accuracy of the answers. Consequently, most innovation diffusion researchers prefer to survey respondents as soon as possible. Another problem in innovation diffusion research is the problem in determining causality (Rogers, 2003). The data collected from surveys cannot address the “why” in innovation diffusion research. Field experiment is a research methodology is appropriate in achieving this goal. Rogers (2003) stated that “field experiment is an experiment conducted under realistic conditions in which pre-intervention and post-intervention measurements are usually obtained by surveys” (p.128). An example of a field experiment is to use opinion leaders to assist in diffusing an innovation in one social system, and not to use opinion leaders to assist in diffusing an innovation in another social system. The accuracy of recollection is a major problem in innovation diffusion research, and field experiment is just one the ways to address it (Rogers, 2003). Another way would be to collect the data while the diffusion process is still in progress (Rogers, 2003). One option is to collect data at many points in the process. Adopters are asked to recall in a short period of time. Another option is to assess the perception at the time of the adoption. To supplement the memory of respondents, diffusion researchers could gather Innovation Diffusion & Online Education 22 archival records. The final way to increase the accuracy is to have quality survey questions through pre-testing and to train interviewers. Innovation Diffusion & Online Education 25 According to the author, communication is a process where people create and share information with one another to reach a mutual understanding (Rogers, 2003). Because innovation diffusion is a communication of new ideas, one of the two parties must not have any prior knowledge of the innovation. A communication channel is the way by which messages transfer from one individual to another (Rogers, 2003). The communication channel includes mass media, interpersonal communication, and interactive communication. Each communication channel serves a specific purpose in the diffusion process. Mass media is a communication channel that is more effective in informing potential adopters in a social system regarding the existence of an innovation (Rogers, 2003). Potential adopters are able to only receive general information about an innovation. In essence, the intent is to create awareness, and not to persuade. Mass media channel includes television, radio, magazines, and newspapers. On the other hand, interpersonal channel is more effective in convincing an individual to adopt an innovation, especially if the change agents and potential adopters are similar of education, socio-economic status, and other ways (Rogers, 2003). Interpersonal channels require the face-to-face interactions between two or more individuals. This is where potential adopters can obtain specific information about the innovation on it may help them solve their problems. Another important and emerging communication channel is interactive channels (Rogers, 2003). It involves using the speed and reach of the Internet to diffuse innovations. In my opinion, the Internet may not necessarily be a new communication channel. It seems to be a form of mass media and interpersonal channel. On the one Innovation Diffusion & Online Education 26 hand, it is a form of mass media when change agents mass e-mail about an innovation. On the other hand, it is a form of interpersonal channel when a change agent communicates with one or more individuals in real time with video and audio capabilities. The Internet will not entirely replace face-to-face interaction because many innovations require change agents to be physically at the location of potential adopters. In addition, the Internet has not become universally accessible (Rischard, 2002). This is especially true in impoverished countries. Besides the issue of access, many people have not been trained in using the Internet. There are many people who are not comfortable with using personal desktops and the Internet. The general agreement in communication theory is that communication is more effective if both parties are similar in education, socio-economic status, and other factors (Rogers, 2003). People tend to interact with others who are similar to them. This situation creates challenges in the diffusion process. Change agents are usually different to their potential adopters in terms of education, economic, culture, and language. Change agents tend to be better educated. This creates a barrier in the diffusion of the desired innovations. If a change agent and the people in the social system possess the same knowledge and expertise of the innovation, diffusion cannot occur. The solution is to have change agents share with potential adopters in similarities in education, cultural, and language factors, but not share in the knowledge of the innovation. Unfortunately, change agents need to have a different educational and cultural background in order to be expert in the innovation. Time is the third element in the diffusion process, and it has received numerous criticisms from diffusion researchers (Rogers, 2003). This element encompasses three Innovation Diffusion & Online Education 27 components: (1) the innovation-decision process of an individual from awareness to either adoption or rejection, (2) the innovativeness of an individuals or people in a social system, and (3) the adoption rate of an innovation in a social system. The innovation-decision component is the process, which individuals in a social system goes from gathering knowledge of an innovation, forming an attitude toward an innovation, deciding to either to adopt or reject, implementing and using the new idea, and confirming the decision (Rogers, 2003). If an innovation allows re-invention, it will occur at the implementation stage. During the confirmation stage of the innovation- decision process, adopters may choose to discontinue the innovation. Mass media is most effective at the knowledge gathering stage (Rogers, 2003). Here, potential adopters are seeking general information about the innovation in term of its purpose and functions. On the other hand, interpersonal channel is most important in the diffusion process (Rogers, 2003). Potential adopters are seeking more specific information on how the innovation can solve their particular problems. So, this is where change agents engage in face-to-face interactions to communicate the relative advantage of an innovation. The time it takes for an individual to go through the different stages of the innovation-decision process varies from person to person (Rogers, 2003). Some people only spend a short period of time in the knowledge and persuasion stages. Others may spend a length period of time. The innovativeness of potential adopters in a social system plays an important role in determining the time someone needs to go through all the stages. Innovation Diffusion & Online Education 30 status quos. Opinion leaders tend to be higher in educational and socio-economic echelon. Importantly, they have wide access to the interpersonal network. This unprecedented access is also attributable to their perceived extraordinary ability to conform to social’s norms. They make the ideal candidates for change agents to communicate an innovation to their peers. They do not have the difficulty in understanding the nature and functions of an innovation and in communicating it to their people in the social system. Their influence and respect could be diminished if they are too aggressive in supporting the positions of change agents. The effort of change agents affects on the adoption rate of an innovation (Rogers, 2003). They usually work for change agencies with specific social issues. They all have a university degree and possess in-depth knowledge of certain issues. Because of these attributes, it can be a barrier to effective communication with potential adopters. In addition to opinion leaders, change agents employ aides to assist them. Types of innovation decisions are another critical influence in the diffusion of innovations (Rogers, 2003). These three types are (1) optional innovation-decisions, (2) collective innovation-decisions, and (3) authority innovation-decisions. Potential adopters with the optional innovation-decisions type have the choice to either reject or adopt an innovation, without the demand of the social system. With the collective innovation-decisions type, individuals must surrender their decisions to either adopt or reject, to the will of the collective or social system. The diffusion of an innovation is known as authority innovation-decisions when potential adopters have little or no choice in the decision process. In this case, the authority innovation-decisions type is more important than the five main attributes of innovation diffusion. For the most part, Innovation Diffusion & Online Education 31 adopters must adopt an innovation even though they may perceive it to not have any relative advantage and compatibility with their past history, needs, and cultural values. The final component in a social system that affects the adoption or rejection of an innovation by potential adopters is consequences of the innovations (Rogers, 2003). Three categorizations are (1) desirable versus undesirable consequences, (2) direct versus indirect consequences, and (3) anticipated versus unanticipated consequences. Consequences that are desirable, direct, and anticipated have a positive on the adoption rate and its sustainability. Unfortunately, diffusion researchers and change agents are unable to predict the forms of consequences of an innovation. Change agents could influence the consequences by taking into account the cultural or compatibility factor. They can consider the factor of cultural due diligence by recognizing, respecting, and reconciling conflicting or opposing forces (Trompenaars & Hampden-Turner, 2002). First, people need to see the differences in cultures and things. Second, they need to understand that people have the right to be different in thinking and behavior. Third, they can reconcile opposing view points in two ways. On the one hand, they begin with their cultural orientation, then reconcile with the other cultural orientation. On the other hand, they could begin with other cultural orientation, then reconcile with their own cultural orientation. In a sense, the act of reconciliation is like the act of re-invention. As stated in Chapter One, researchers have applied the innovation diffusion model to the area of education. The following sections are devoted to examining the attributes of online education innovation. Innovation Diffusion & Online Education 32 Research related to the independent variables Rogers’ five main attributes of innovation diffusion affected the adoption rate of online education (Mwaura, 2004; Meyers, 2002; Jones, Lindner, Murphy, & Dooley; 2002; Dooley & Murphrey, 2000; Isman & Dabaj, 2005; Hyland, 2003). The attributes are relative advantage, compatibility, complexity, trialability, and observability (Rogers, 2003). Some studies focused on all of the five attributes, and others did not. Also, some studies did not explicitly state the variables as the innovation diffusion attributes; nevertheless, the attributes and its sub-dimensions were identifiable. Online education is becoming an important teaching technological tool for academic institutions and faculty members, but there are still some who are resisting the adoption this innovation (Mwaura, 2004). Eventually, students will not choose to attend universities that do not have an online component. This research report investigated how Rogers’ five attributes of innovation diffusion affected the adoption rate of online learning by faculty members. The researcher used a qualitative method to examine why many faculty members did not adopt online teaching tools that were readily accessible (Mwaura, 2004). This research study concentrated on this question, “What factors will influence the adoption or rejection of online teaching by faculty members?” Rogers’ diffusion of innovation provided the theoretical framework to answer this question. The researcher conducted interviews with 31 faculty members and five administrators at Ohio University and observed faculty members who attended various workshops and seminars to collect data (Mwaura, 2004). The faculty members who participated in this study were those have adopted online education, those planning to Innovation Diffusion & Online Education 35 the communication was much easier for them. Before the adoption of the online software, some professors printed out the materials and verbally communicate with the students. Now, students can read the instruction anywhere online. Finally, two professors even felt they can focus on the synthesis of concepts, rather than just repeating what students read from their textbooks. On the other hand, professors who rejected online education claimed that it increases their discomfort. One professor expressed this increased discomfort with how online education made him felt socially isolated. For the most part, faculty members found online education save time for them to do other important activities, such as research and service (Mwaura, 2004). The initial setup was time consuming. After the initial phase, they did not need to reproduce their work. Everything can be posted online for every semester. In addition, they were able to post assignments for students. Similarly, students were able to complete assignments quickly. They could choose complete assignments ahead of time and submit it. The professors who adopted online education were able to immediately see the benefits (Mwaura, 2004). Two important benefits that affected students were cited. Because students do not see their peers and the professors, they were forced to communicate with virtual discussions and e-mails. The other benefit was that students did not have to take any more notes. They could use the time to learn and to focus on their assignments. Compatibility is “the degree to which an innovation is perceived as being consistent with the existing values, past experiences, and needs of potential adopters (Rogers, 2003, p. 15). The results of the research indicated that compatibility was a Innovation Diffusion & Online Education 36 significant determinant of online education adoption rate by faculty members (Mwaura, 2004). For the faculty members who rejected online education, they felt this teaching tool was inconsistent with their socio-cultural values and beliefs (Mwaura, 2004). They rejected online education because it took away the job of real teaching. Also, they believed it was not appropriate to transfer complex ideas and concepts. On the other hand, faculty members who adopted online learning felt that this teaching tool was consistent with their existing socio-cultural values and beliefs (Mwaura, 2004). For those who used the software to complement their classroom instruction, they used the screens to visually aid their students to learn the materials. Past experience shaped the opinion of faculty members regarding the online technology (Mwaura, 2004). Professors, for the most part, were hesitant with new technology because they were comfortable with the traditional methods of teaching. This was especially true when they had negative experiences with past adoptions of online education technologies. New professors seemed to be the ones that were willing to embrace the online technology. They were eager to experiment with new teaching methods. The ability of the online technology to meet the needs of faculty members was an important sub-dimension (Mwaura, 2004). On the one hand, faculty members who adopted online education technologies explained that both professors and students were able to engage in learning without the restrictions of time and space. On the other hand, faculty members who rejected online learning technologies argued that they were not Innovation Diffusion & Online Education 37 measured by how well they teach. Instead, they tied promotion to their ability to produce quality research materials. Complexity was the last attribute that the researcher looked at in his study. According to Rogers (2003) it is “the degree to which an innovation is perceived as difficult to understand and use (p. 16). The degree of difficulty of online education technology was a critical factor in the decision either to adopt or reject (Mwaura, 2004). The difficulty was linked to the technical language that instructors used in training and to the pace of the training sessions. Many of them complained that they were lost in the training sessions. In addition, they were disappointed with the training sessions because it was designed to train them with the features of the software, and not with how to integrate the technology to their courses. The fear of trying something new also contributed to the perception that online education technologies were difficult to learn (Mwaura, 2004). One faculty member was very fearful with her first day of class using the online education technology. Another faculty member expressed her fear of this technology due to the fact she did not grow up with using it. Another factor associated with the complexity of online learning was the lack of academic training in educational theories and practices (Mwaura, 2004). In school, professors were trained in their respective disciplines, whatever it may be. They were not trained the mechanics and the theories of teaching. In conclusion, faculty members need to perceive that online education technology is consistent with their teaching styles, existing values, easy to understand and use, and have advantages over their current teaching methods, to adopt it. They need time to learn Innovation Diffusion & Online Education 40 Case “C” never received funding from the state for its online learning program, but its enrollment increased with the addition of new courses (Meyers, 2002). Faculty policies were supportive of compensation for developing and delivering online courses, online education courses taught in load, and an intellectual property policy of shared ownership. Other policies were not so supportive, including the existence of service areas and no state plan for online education. Like the other two cases, the mission statement did not mention the online education component. Case “D” received fund from the state every year of its online learning program, but did not added new courses in year 3 and had increased its new courses by 80% in year 4 (Meyers, 2002). Largest increases in enrollment were in the first and fourth years of online education offerings. Faculty policies were accommodating of faculty, including compensation for developing online courses, online courses taught in load, and a shared intellectual property rights. State policies were also supportive, including the elimination of service areas, an online education plan, and a specific mention of online education in the institution’s mission statement. Case E received funding from state in its final year of operation (Meyers, 2002). Similar to all other cases, it received compensation for new course development, workload allocation, and intellectual property ownership. It did not receive support for area services and state planning. Online education was not present in the institution’s mission statement. The research produced four conclusions (Meyers, 2002). First, state funding was not a factor in the growth of enrollment. All five institutions experienced healthy growth without the funding. Second, the growth of enrollment was on the upward trend even Innovation Diffusion & Online Education 41 though the growth was sporadic. Third, all five institutions had policies relating to compensation, workload, and intellectual property that were friendly to faculty members. Finally, the market was a powerful force in the adoption of online education by faculties. Both research studies concluded that funding for course development was an important factor in convincing faculty members to develop online courses. The specific finding from this case study confirmed Dr. Mwaura’s finding that low initial cost was not a factor in the diffusion process. Intellectual property was a component of economic profitability. Professors probably received some type of financial income from their online programs. This also corresponded with Dr. Mwaura’s finding. This finding, ironically, may contradict Dr. Mwaura’s findings that social prestige was not a factor in the diffusion process. According to Dr. Meyer’s findings, all five institutions allowed their faculty members to enjoy the benefit of owning their creation of online courses. No doubt financial reward was an important part of intellectual property. They may enjoy the pride of creating an online learning program that could be a model for their peers to emulate. Unfortunately, she did not explicitly state the benefits in detail. The option for the faculty members to have online courses either as part of their workload or as an addition is an element of the savings time and effort sub-dimension. It would not make sense for professors to have online courses in addition to their regular classes would defeat the purpose of saving time and effort. For the most part, a professor does not have to start assignment preparation over again. Conflicting views existed regarding the quality online education compared to traditional face-to-face teaching (Jones, Lindner, Murphy, & Dooley; 2002). On the one Innovation Diffusion & Online Education 42 hand, many professors believed that the quality of learning in an online education was not equivalent to the traditional face-to-face instruction. On the other hand, others believed that the quality was the same. The objective of this research project was to resolve this contradiction. Another view insisted that online courses required more time and effort from the professors. As mentioned by Jones, Lindner, Murphy, & Dooley (2002), Visser (2000) found that online courses can take up to twice as much time and effort to accomplish the task. However, the authors argued that further researches were needed. Another barrier to the adoption of online education by professors was the possible the incongruent values and beliefs (Jones, Lindner, Murphy, & Dooley; 2002). The ability of online technology to expand the educational opportunity for more people just do not sit well with some professors who believe a college education was for the selected few. Dooley & Murphy (2001) found that College of Agriculture faculty members at Texas A&M University did not have the skill in using online technology to effectively teach (Jones, Lindner, Murphy, & Dooley; 2002). This deficiency could be explained with the fact that the school did not provide sufficient training. Consequently, they felt the support infrastructure was not there. Lindner, Murphy & Dooley (2001) discovered that tenure status and academic rank had an effect on the adoption of online education (Jones, Lindner, Murphy, & Dooley; 2002). The professors without tenure had the highest adoption rate. This led to the conclusion that it was the expectation of their employment. The ones who were Innovation Diffusion & Online Education 45 The numerical results of competence, values, and information technology and support were calculated by summing all the responds of each respective factor (Jones, Lindner, Murphy, & Dooley; 2002). The total online education score was determined by summing responses to all of the 28 items used in part II of the survey. Table 1. Total Distance Education Score Online Education Scores Mean Standard Deviation Online Education Competence Score 32.0 8.3 Online Education Value Score 33.2 5.7 Online Education Information Technology & Support Score 23.4 5.7 Total Online learning Score 88.6 The results for objective indicated that majority did not oppose online education (Jones, Lindner, Murphy, & Dooley; 2002). Eighty-five percent of the faculty members were not philosophically opposed to online education. Fifteen percent of the faculty members were philosophically opposed to online education. Because the t is at 1.59, there is no significant relation between total online education score and the attitude of faculty members toward online education. Table 2 Total Distance Education Score Overall Online Education Score N M Standard Deviation T For 217 89.1 14.0 1.59 Opposed 35 85.1 14.5 Note: M= Summated Competency Score + Summated Value Score + Summated Information Technology and Support Score Innovation Diffusion & Online Education 46 The second objective of the research project was to ascertain whether the level of competence in the use of online technology affect the adoption rate of online education (Jones, Lindner, Murphy, & Dooley; 2002). According to the study, there was no correlation between the two factors. Table 3. Online Education Competence Score Online Education Competence Scores N M Standard Deviation T For 217 32.1 8.2 0.52 Opposed 35 31.3 9.2 Note: M= Summated 11 item-5 point Likert-type scale The third objective of this research was to determine whether the value of online education is consistent with the value of faculty members (Jones, Lindner, Murphy, & Dooley; 2002). The results indicated that a strong relationship between these two variables, with a 4.31 t-statistics. Table 4. Online Education Value Score Value N M Standard Deviation T For 217 33.8 5.6 4.31* Opposed 35 29.4 5.1 Note: M= Summated 9 item-5 point Likert-type scale; 1=strongly disagree, 2=disagree, 3=neutral, 4=agree, 5=strongly agree; *p<.05. The fourth objective was to determine the relationship between information technology and support and attitude toward online education (Jones, Lindner, Murphy, & Dooley; 2002). The researchers did not find a relationship between the two variables. The t-statistics was at – 1.05, which is below the 2.0 requirement. Innovation Diffusion & Online Education 47 Table 5. Online Education Information Technology & Support Score Information Technology and Support Score N M Standard Deviation T For 217 23.3 5.6 - 1.05 Opposed 35 24.4 6.3 Note: M= Summated 8 item-5 point Likert-type scale The findings of this research project concluded that faculty’s competence in online technology and information technology and support did not affect their adoption of online education. However, the value of online education did impact its adoption. The researchers recommended that change agents for the school need to effectively communicate how the value of online education matches the value of faculty members. Similar to the other research projects, this one relied on the survey method to collect its primary data. The reliability of the data was high when 80 percent of respondents returned their surveys. In term of quantitative analysis, the researchers went beyond descriptive statistics to include regression analysis. It was interesting for this research project to contradict the other two research findings that competence of electronic technology and technical support were not factors in influence the attitudes of faculty members. More literature reviews are needed to explore this contradiction. However, all three reports concurred that the compatibility attribute of innovation diffusion plays a crucial role in the adoption rate of online education. The following research project conducted by Dr. Dooley and Dr. Murphrey in the winter 2000 was more comprehensive than the above researches. They investigated how the perceptions of administrators, faculty members, and support staff affected the Innovation Diffusion & Online Education 50 Figure 3. Weaknesses Expressed by Respondents based on Group Affiliation Figure 4. Threats Expressed by Respondents based on Group Affiliation (n=42) Based upon Rogers' attributes (2003), it was evident that respondents perceived online education technologies to have a relative advantage in terms of reaching new audiences and enhancing teaching and learning; however, because there were inadequate incentives, respondents did not perceive it as compatible with their current situation. Respondents perceived technology usage to be exceedingly complex (e.g., the technology, scheduling, and policy issues) and the trialability of the technology to be limited due to the required time and effort to change courses into electronic format. Unless a department had its own support staff, proximity to equipment in the office or Innovation Diffusion & Online Education 51 building, or other rewards through tenure/promotion, development grants, etc., the observability was non-existent. The results of this research project confirmed that all five attributes of innovation diffusion have an affect on the adoption rate of online learning at Texas A&M University. In term of methodology, the researchers used a qualitative method. In addition, they were the only researchers who examined the perceptions of three stakeholders at the same time. Professor Annette Hyland of University of Otago in Dunedin, New Zealand conducted a research project that investigated the factors that influenced the adoption of online education by faculty members (Hyland, 2003). Unlike the four previous research projects, this one looked at how online education benefited students, from the perspective of their professors. The population of this research project was all the full-time faculty members in the department of Theology and Religious Studies. The researcher used a qualitative method to collect her primary data for the research (Hyland, 2003). She interviewed twelve faculty members and four part-time instructors on their attitude toward online education. A constructivist methodology was used to collect the primary data. After collecting all the data, she clustered together responses with similar views and issues into common themes and sub-themes. She invited the participants to analyze the data. The results of the research covered all of the attributes of innovation, but the researcher did not explicitly mention the attributes. To the faculty members, they would adopt online education if it offers job satisfaction and enjoyment (Hyland, 2003). Feeling comfortable with the electronic technology was an important factor in motivating faculty members to teach online courses (Hyland, 2003). According to the study, the researcher discovered Innovation Diffusion & Online Education 52 that the attitude of faculty members toward online education was swayed by their aptitude for computer technology. Table 1 shows how they perceive themselves affect their attitude and behavior. Table 1: Comparing Self-Perceptions of High- and Low-Tech Participant High Tech Self-Perception Low Tech Self-Perception Enjoy using computer/ Internet Find it stressful and frustrating Like playing with computer; experimenting Lack understanding about how it works Part of everyday life Use mainly at work Attend many IT courses Little IT training; mainly self-taught Use wide range of applications Restrict usage to word processing; email Purchase state of art equipment Raise ethical & moral issues about usage Confident about teaching with it Reluctant/ refuse to teach it The researcher found that faculty members with a positive attitude toward online education used their computers for complex activities than faculty members with a negative attitude toward online education (Hyland, 2003). The length of time in using a computer did not determine the attitude of computer. It was the degree of complexity in their work that mattered. Some of them even believed that computer training would not help them. To them, the computer and Internet were just too complex for them to understand. Interestingly, they even raised ethical and moral issues in using online technology to teach their courses. Table 2 below reveals how meaningful training can minimize the effect of complexity and increase the adoption of online education. Innovation Diffusion & Online Education 55 the possibility of using different technologies, such blackboard and online conferencing technology, to improve teaching effectiveness. They, also, recognized that many students cannot quit their jobs to go school. Finally, the technology had the ability to expand the opportunities for students from other areas and countries to get an education. The only fear that they may had was the outsourcing of their jobs to professors of other countries. It would so much cheaper to hire professors who was off-campus because they do did require office spaces and other benefits. The findings of this last research project agreed with the rest that adoption of online education is positively related to the attributes of relative advantage and compatibility, and negatively related to the attribute of complexity. It is the conclusion that surprised me. According to Dr. Hyland, the complexity of the education software affected how faculty members perceive the compatibility and relative advantage. This corresponded with what I stated earlier. This research project focused the adoption of online education from the perspective of students. Dr. Aytekin Isman and Mr. Fahme Dabaj presented a paper that explored how online education has diffused in north Cyprus (Isman & Dabaj, 2005). They used Rogers (2003) theory of innovation diffusion to study the adoption rate. The four elements of innovation diffusion, which include innovation, communication channels, time, and social system, was the focus of the analysis. For this research project, the researchers randomly sampled 100 undergraduates from Eastern Mediterranean University in Cyprus (Isman & Dabaj, 2005). 88 students were taking online courses on campus and 12 were in the two-year online education program. Because the focus of the thesis was on the attributes of an innovation, the other Innovation Diffusion & Online Education 56 three element of innovation were omitted. With online education program, Eastern Mediterranean University (EMU) was able to offer higher education to a wider audience (Isman & Dabaj, 2005). It would be very expensive for the EMU to fund new buildings to accommodate students. Consequently, many students had been rejected due to the limitation of space. Another relative advantage was that students were able to keep their jobs (Isman & Dabaj, 2005). Sixty-five of the one-hundred students maintained that it was impossible for them to not have a job. Their families depended on their income for family livelihood. The other thirty-five percent wanted to maintain their positions in the companies. Online education was compatible with most Turkish norms and social values (Isman & Dabaj, 2005). Most students who enrolled in online education programs wanted to increase their salary. Twenty-one of the participants received higher pay after completing their degrees via online education. In addition, they felt society in general was not against the idea of online education. To students, using the online technology was simple (Isman & Dabaj, 2005). They did not require any training in using the Internet and education software. Many believed that they learned as much from online courses compared to traditional face-to-face courses. The difficulty that they expressed was the nature of impersonal communication aspect. It was not possible to develop friendship with other students. Trialability was another important attribute that positively influence students to adopt online learning (Isman & Dabaj, 2005). If students liked online courses, they could continue to take them. If they did not like online courses, they could take the traditional on-campus format. Consequently, 85 percent of the participants answered that they liked online education as an alternative to the on-campus instructions (Isman & Dabaj, 2005). Innovation Diffusion & Online Education 57 According to this research, Rogers’ attributes of innovation influenced the adoption rate of online learning. Observability was the only attributes that was not mentioned in this research project. Like other researchers, they relied on a survey method to capture a snapshot of the perception at a particular moment in time. Curiously, the researchers did not go into detail like the other researchers mentioned in the literature review regarding their methodology. Therefore, it would be very difficult to determine the reliability and validity of the findings. Research related to the dependent variable The adoption rate of an innovation is the dependent variable. Rogers (2003) defined “rate of adoption is the relative speed with which an innovation is adopted by members of a social system” (p.221). It is commonly measured by the number of people who have adopted an innovation is specific time frame. There are two ways to measure the rate of adoption of innovation. One way is to measure the numbers of people who have adopted and rejected an innovation. The drawback with this method is that it is very difficult to conduct a correlation analysis in a single study. The other way is to measure the relative speed it takes an individual or individuals to adopt an innovation. With this method, researchers are able to perform correlation analysis in a single study. The drawback with this method is that the accuracy of the answers is questionable. This is especially true when participants are asked to recall adoption that occurred years ago. Research related to the relationships among the variables Universities around the world are integrating online education technology. Professors either use the technology as an add-on to their classes or use the technology to Innovation Diffusion & Online Education 60 the software to interact and learn. It can be argued that simplicity of the software helped with the adoption rate. It seemed like trialability was a factor. Students were given the opportunity to drop the online course to take a traditional face-to-face course if it does not work out. The article did not mention anything about the attribute of observability. So, I assume that it was not a factor the adoption of online learning by the students. Significance of proposed research to previous literature The findings from Rogers’ Innovation Diffusion book and six research articles indicated survey is the most common method used to collect primary data. Interviews and observations were also made in the primary data collection process, but it will not be part of this research. Initially, to measure the adoption rate, each participant was asked to recall the length of time it took to sign up or teach his or her first an online course. This type of measurement would be problematic because it would be very difficult for participants to accurately recall their memory. Archival documents of their enrollment would be required to verify their answers, and it was not possible due to institutional constraints. Instead, participants were asked to answer whether they have taken or taught online courses. The survey was designed to collect and measure of the responses on all five attributes of an innovation. It is despite the fact that in all of the research articles, not all five of the attributes are either positively or negatively related to the adoption rate of online education. Innovation Diffusion & Online Education 61 I changed my target population for the research project. Initially, I planned to survey only the students taking OC courses. Now, I surveyed the professors and students in the College of Professional Studies. Most of the existing research projects focused on the adoption of online learning by professors. I did find a few that focused from the perspective of students. However, I did not, from my literature review process, any research effort that has compared between college students and professors. The various methodologies mentioned in the articles influenced my own. They all used survey to collect primary data. One researcher spelled out the strategy to have a high return rate on the survey. I attempted to develop a similar plan to achieve this type of survey results. Innovation Diffusion & Online Education 62 Chapter 3 – Methodology Description of the research design All the research activities in this chapter were subordinated to the problem statement and four hypotheses. The activities went from deciding on the population; choosing a technique to sample it; minimizing the entrance of bias into the study; developing a valid and reliable method of collecting the primary data; and then actually collecting, recording, organizing, and analyzing it all. Description of how variables will be measured The responses of “strongly disagree” and “disagree” were categorized as negative. On the other hand, the responses of “disagree” and “strongly agree” were categorized as positive. For the independent variables, at least 50 percent of the sample respondents must either combined from “strongly disagree” and “disagree” or “agree” and “strongly agree” to determine whether it not support or support a hypothesis. In addition to the percentage distribution, the sample mean was used to establish the relationship. The combined ranks of 1 to 2 were considered to be negatively related. The combined ranks of 4 to 5 were considered to be positively related. It was necessary to use both measurements to determine the relationship. The analysis could be inaccurate because of the skewness. A sample of 3.5 seems to suggest a positive relationship between the independent and dependent variables. By including the frequency distribution, it may reveal that a significant number of responses were concentrated on rank 3 or no opinion and some responses in 4 or 5. By looking the frequency distribution and the sample mean for each sub-dimension and attribute, an accurate analysis and conclusion can be made. Innovation Diffusion & Online Education 65 sampled. The result from a survey would be questionable if the responds were less than 30 percent (Weiss, 2002). The result may suffer from a situation known as self-selection. It meant that the survey responses came from people who had an agenda or personal incentive to participate. A minimum of 75 responses from students 12 to 13 from faculty members were needed to avoid this bias. In addition, the minimum sample from each group provided the basis to determine the sampling error. Nature of the sample. It was important to acknowledge that the two samples were collected with a nonprobability sampling method. The method employed to collect the samples was the purposive sampling. In this method, the population was chosen for a particular purpose. Description of data collection methods and instruments Overview. The data collection method encompassed two principal steps. The first step in the data collection method was preparing for the research tools. After having all the necessary tools in place, it was possible to begin the data collection process. This step required the administration of a pilot test of the survey to a small sample of people before administering the actual survey. Research tools. Because the objective of this research project was to measure the perception of students and faculty members, it was critical to use research tools that can effectively reach them. The Internet, electronic mails, and the survey itself were the primary tools. Internet access was the first requirement in order to administer the surveys. It was not an issue because both the researcher and potential participants had Internet access. Innovation Diffusion & Online Education 66 Every individual that enrolled in the Summer II session was entitled to complimentary wireless and traditional Internet access. Besides, the prices of commercial basic Internet were affordable to students. The other critical research tool was the Internet survey. SurveyMonkey.com was the company of choice to provide the tool to administer the survey for two reasons. First, the price was affordable. The company charged only $20 per month, without any long- term agreement. Second, the interface design was quite simple to navigate. Potential participants just need only to click on the link to answer the survey questions. Once a participant completed all the questions, the results would be electronically mailed to the researcher for analysis. The survey contained three essential components: introduction, instructions, and confidentiality. The potential participants were informed the purpose of the survey. In addition, they received instructions in correctly completing the surveys. The participation in this survey was entirely voluntary on the part of the participants. They had the right to decline or discontinue taking the survey at anytime. The survey did not ask for any personal information from the potential participants. Surely, names and any information that would identify the identity were not included. For analysis purpose, they were requested to identify their program of study. The anonymity of the potential participants was important of the researcher. No one had access to the individual responses except for the researcher himself. The Dean and the Chairperson of each respective program would be likely interest in reading the results, but they only had access to the total responses. Innovation Diffusion & Online Education 67 The survey only contained 23 questions for potential participants to complete. 21 out of the 23 questions revolved around the dependent variable and the five independent variables. The first question pertained to the dependent variable of adoption rate. Questions 2 to 7 pertained to the six subdimensions of relative advantage. Next, questions 8 to 11 pertained to the four subdimensions of compatibility. Then, questions 12 to 14 pertained to the three subdimensions of complexity. Following, questions 15 to 17 pertained to the three subdimensions of trialability. Finally, questions 18 to 21 pertained to the subdimensions of observability. Question 22 seek to ascertain whether graduate students would continue or discontinue taking more online courses or faculty members would continue or discontinue teaching more online courses. The final question in the survey pertained to the program of study that each adopter was in. Research collection process. The data collection method followed a linear process, from pilot test to the actual collection process, to maximize the completion rate. In addition, the process was conducted in accordance with three principles. These three principles included opinion leadership, the attribute of complexity, and mass and interpersonal communication channels. Before elaborating on the data collection process, it was important to briefly discuss the principle of “opinion leadership.” Opinion leaders are people in a social system that has tremendous social influence on others (Rogers, 2003). They play a critical role in positively affecting the diffusion of an innovation. There are seven characteristics that distinguish between opinion leaders and followers (Rogers, 2003). The characteristics are that opinion leaders (1) have more exposure to mass media than their followers, (2) are more cosmopolite than their followers, (3) have greater contact with change agents than their followers, (4) have Innovation Diffusion & Online Education 70 responses and they were told that the session would take no more than five minutes. At the end, respondents were asked to sign a written consent form before completing their survey. After a few days, the researcher began the second stage of the data collection process. It involved the distribution of the survey link to the four program chairs. In turn, each program chair distributed the link to their faculty members. Twenty students and eight faculty members responded to the survey. Data sources. The source of data was the responses of the survey from the students and faculty members. The objective was to capture and measure their perception of online education in the context of Rogers’ five main attributes of innovation diffusion. Methods for data analysis Statistical methods to test PS/H. Two factors have determined the appropriate statistical methods to be used for testing the four hypotheses. The first factor was whether the variables were continuous or discrete. The appropriate statistical method would be descriptive statistics because both the dependent variable and independent variables are discrete (Newton & Rudestam, 1999). The primary data was input into the Microsoft Excel program and began a frequency analysis with tables. The results generated a percentage of respondents that answered strongly disagree, disagree, no opinion, agree, and strongly agree in a frequency chart. A histogram was created to represent a visualization of modes. In addition to conducting a frequency distribution, sample means for each attribute and its sub-dimensions was generated. It was accomplished with the descriptive statistics function in Microsoft. Besides the sample means, this function provided data on Innovation Diffusion & Online Education 71 the standard deviation to determine the confidence interval level. The purpose to ascertain the range of the population means (Barlow, 2005). Biases in the research project All social science researches contain personal bias in its content and research methodology. It is critical for a researcher to honestly and openly identify it to readers. Any acts of concealment may gravely damage the reputation of a researcher and invalid his or her research project (Leady & Ormrod, 2005). Because diffusion research has been extensively conducted in the social science community, researchers identified two biases (Rogers, 2003). The first bias is the pro-innovation bias that many innovation diffusion researchers and change agents have with their innovations (Rogers, 2003). They consciously assume that their innovations are beneficial to the targeted social systems because the innovations have benefited them. This pro-innovation bias has its origin in the ethno-centric thinking of the sub-conscious minds. Ethnocentrism is belief of cultural superiority of one culture over another (Adler, 2002 & Triandis, 2004). Naturally, one would use his or her own culture as the standard to judge other cultures. There is a tendency for people with ethnocentric thinking to change others to be like themselves. Any modification by adopters to the innovations is discouraged. To address this problem, the researcher raised the issue of discontinuance in innovation diffusion. A question in the survey asked whether the respondents will or will not continue to take or teach additional online courses in the future. Possibly, the responses with a high rate of discontinuance from students and/ or faculty members could force school administrators to reevaluate their push to offer online courses. What are Innovation Diffusion & Online Education 72 advantageous and compatible to the goals and needs of faculty members may not be the same to the students. The second bias is the individual-blame orientation that researchers and change agents hold with them (Rogers, 2003). When the adoption rate of an innovation does not reach critical mass, they often blame individuals in the social system. For the most part, the behavior of an individual is influenced by the dynamic interactions of the social system that he or she is in. By not taking into account the influence of systems, any social change will be only temporary. According to Anderson and Johnson (1997), a system is assemblage of interacting, interrelated, or interdependent parts that form a complex and cohesive whole. To overcome the individual-blame orientation, the research emphasized the students or faculty members as wholes. In addition to these two biases, the diffusion researcher faced another problem. It has to do with the degree of accuracy in the ability of respondents to accurately recollect their memory (Rogers, 2003). It would be advantageous to quantitatively measure the speed of adopters in adopting online education. Unfortunately, it was too difficult to accurately measure because access to records were not possible. Consequently, the only way for this research not to suffer from this problem was to ask open-ended questions. The sampling method of this research project probably would not produce results that were representative of the population, but it did not suffer from the bias of self- selection. The responses from both samples were above the minimum 30 percent requirement. People who participated in the survey did not only include the ones that have a personal agenda to either influence the direction of this research due to their preference for online education. Innovation Diffusion & Online Education 75 Presentation of findings Students Who Have Taken Online Courses Table 4-1 Relative Advantage (Economic Profitability) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 4 8% 2 Disagree 15 31% 3 No opinion 11 23% 4 Agree 12 25% 5 Strongly agree 6 13% Table 4-2 Relative Advantage (Saving of Time) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 5 10% 2 Disagree 19 40% 3 No opinion 5 10% 4 Agree 13 27% 5 Strongly agree 6 13% Table 4-3 Relative Advantage (Reduced Discomfort) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 5 10% 2 Disagree 13 27% 3 No opinion 12 25% 4 Agree 18 38% 5 Strongly agree 0 0% Innovation Diffusion & Online Education 76 Table 4-4 Relative Advantage (Immediacy of Reward) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 2 4% 2 Disagree 15 31% 3 No opinion 7 15% 4 Agree 24 50% 5 Strongly agree 0 0% Table 4-5 Relative Advantage (Saving of Effort) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 1 2% 2 Disagree 0 0% 3 No opinion 4 8% 4 Agree 28 58% 5 Strongly agree 15 31% Table 4-6 Relative Advantage (Low Initial Cost) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 6 13% 2 Disagree 15 31% 3 No opinion 16 33% 4 Agree 9 19% 5 Strongly agree 2 4% Table 4-7 Relative Advantage Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 23 8% 2 Disagree 77 27% 3 No opinion 55 19% 4 Agree 104 36% 5 Strongly agree 29 10% Innovation Diffusion & Online Education 77 Table 4-8 Compatibility (Need of Adopters) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 2 4% 2 Disagree 6 13% 3 No opinion 12 25% 4 Agree 25 52% 5 Strongly agree 3 6% Table 4-9 Compatibility (Cultural Values & Beliefs) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 1 2% 2 Disagree 7 15% 3 No opinion 25 52% 4 Agree 15 31% 5 Strongly agree 0 0% Table 4-10 Compatibility (Past Experience) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 2 4% 2 Disagree 26 54% 3 No opinion 11 23% 4 Agree 9 19% 5 Strongly agree 0 0% Table 4-11 Compatibility (Name) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 6 13% 3 No opinion 13 27% 4 Agree 26 54% 5 Strongly agree 3 6% Innovation Diffusion & Online Education 80 Table 4-20 Trialability Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 5 3% 2 Disagree 14 10% 3 No opinion 45 31% 4 Agree 62 43% 5 Strongly agree 18 13% Table 4-21 Observability (Observation-1) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 13 27% 3 No opinion 11 23% 4 Agree 19 40% 5 Strongly agree 5 10% Table 4-22 Observability (Observation-2) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 1 2% 2 Disagree 14 29% 3 No opinion 25 52% 4 Agree 7 15% 5 Strongly agree 1 2% Table 4-23 Observability (Observation-3) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 4 8% 2 Disagree 13 27% 3 No opinion 12 25% 4 Agree 18 38% 5 Strongly agree 1 2% Innovation Diffusion & Online Education 81 Table 4-24 Observability (Observation-Combined) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 5 3% 2 Disagree 40 28% 3 No opinion 48 33% 4 Agree 44 31% 5 Strongly agree 7 5% Table 4-25 Observation (Describing) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 2 4% 2 Disagree 7 15% 3 No opinion 9 19% 4 Agree 27 56% 5 Strongly agree 3 6% Table 4-26 Observability Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 7 4% 2 Disagree 47 24% 3 No opinion 57 30% 4 Agree 71 37% 5 Strongly agree 10 5% Innovation Diffusion & Online Education 82 Students Who Have Not Taken Online Courses Table 4-27 Relative Advantage (Economic Profitability) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 1 3% 2 Disagree 8 28% 3 No opinion 9 31% 4 Agree 7 24% 5 Strongly agree 4 14% Table 4-28 Relative Advantage (Saving of Time) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 3 10% 2 Disagree 6 21% 3 No opinion 15 52% 4 Agree 3 10% 5 Strongly agree 2 7% Table 4-29 Relative Advantage (Reduced Discomfort) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 3 10% 2 Disagree 9 31% 3 No opinion 8 28% 4 Agree 5 17% 5 Strongly agree 4 14% Table 4-30 Relative Advantage (Immediacy of Reward) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 1 3% 2 Disagree 12 41% 3 No opinion 9 31% 4 Agree 6 21% 5 Strongly agree 1 3% Innovation Diffusion & Online Education 85 Table 4-39 Simplicity (Use-1) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 9 31% 3 No opinion 13 45% 4 Agree 7 24% 5 Strongly agree 0 0% Table 4-40 Simplicity (Use-2) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 2 7% 2 Disagree 4 14% 3 No opinion 17 59% 4 Agree 6 21% 5 Strongly agree 0 0% Table 4-41 Simplicity (Use-Combined) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 2 3% 2 Disagree 13 22% 3 No opinion 30 52% 4 Agree 13 22% 5 Strongly agree 0 0% Table 4-42 Simplicity (Understanding) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 2 7% 2 Disagree 4 14% 3 No opinion 17 59% 4 Agree 6 21% 5 Strongly agree 0 0% Innovation Diffusion & Online Education 86 Table 4-43 Simplicity Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 4 5% 2 Disagree 17 20% 3 No opinion 47 54% 4 Agree 19 22% 5 Strongly agree 0 0% Table 4-44 Trialability (Installment Basis) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 3 10% 3 No opinion 18 62% 4 Agree 6 21% 5 Strongly agree 2 7% Table 4-45 Trialability (Ease of Trying) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 1 3% 3 No opinion 6 21% 4 Agree 15 52% 5 Strongly agree 7 24% Table 4-46 Trialability (Re-Invention) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 3 10% 3 No opinion 17 59% 4 Agree 7 24% 5 Strongly agree 2 7% Innovation Diffusion & Online Education 87 Table 4-47 Trialability Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 7 8% 3 No opinion 41 47% 4 Agree 28 32% 5 Strongly agree 11 13% Table 4-48 Observability (Observation-1) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 4 14% 3 No opinion 13 45% 4 Agree 12 41% 5 Strongly agree 0 0% Table 4-49 Observability (Observation-2) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 1 3% 2 Disagree 5 17% 3 No opinion 14 48% 4 Agree 9 31% 5 Strongly agree 0 0% Table 4-50 Observability (Observation-3) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 1 3% 3 No opinion 12 41% 4 Agree 13 45% 5 Strongly agree 3 10% Innovation Diffusion & Online Education 90 Table 4-58 Relative Advantage (Saving of Effort) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 1 10% 2 Disagree 2 20% 3 No opinion 0 0% 4 Agree 4 40% 5 Strongly agree 3 30% Table 4-59 Relative Advantage (Low Initial Cost) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 1 10% 2 Disagree 3 30% 3 No opinion 4 40% 4 Agree 2 20% 5 Strongly agree 0 0% Table 4-60 Relative Advantage Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 14 23% 2 Disagree 17 28% 3 No opinion 11 18% 4 Agree 11 18% 5 Strongly agree 7 12% Table 4-61 Compatibility (Need of Adopters) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 3 30% 3 No opinion 2 20% 4 Agree 2 20% 5 Strongly agree 3 30% Innovation Diffusion & Online Education 91 Table 4-62 Compatibility (Cultural Values) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 2 20% 3 No opinion 5 50% 4 Agree 2 20% 5 Strongly agree 1 10% Table 4-63 Compatibility (Past Experience) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 6 60% 3 No opinion 1 10% 4 Agree 2 20% 5 Strongly agree 1 10% Table 4-64 Compatibility (Name) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 0 0% 3 No opinion 0 0% 4 Agree 8 80% 5 Strongly agree 2 20% Table 4-65 Compatibility Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 11 28% 3 No opinion 8 20% 4 Agree 14 35% 5 Strongly agree 7 18% Innovation Diffusion & Online Education 92 Table 4-66 Simplicity (Use-1) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 4 40% 3 No opinion 0 0% 4 Agree 3 30% 5 Strongly agree 3 30% Table 4-67 Simplicity (Use-2) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 1 10% 2 Disagree 4 40% 3 No opinion 1 10% 4 Agree 3 30% 5 Strongly agree 1 10% Table 4-68 Simplicity (Use-Combined) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 1 5% 2 Disagree 8 40% 3 No opinion 1 5% 4 Agree 6 30% 5 Strongly agree 4 20% Table 4-69 Simplicity (Understanding) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 1 10% 2 Disagree 3 30% 3 No opinion 4 40% 4 Agree 2 20% 5 Strongly agree 0 0% Innovation Diffusion & Online Education 95 Table 4-78 Observability (Observation-Combined) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 3 10% 2 Disagree 4 13% 3 No opinion 7 23% 4 Agree 13 43% 5 Strongly agree 3 10% Table 4-79 Observability (Describing) Table 4-80 Observability Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 3 8% 2 Disagree 6 15% 3 No opinion 11 28% 4 Agree 17 43% 5 Strongly agree 3 8% Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 2 20% 3 No opinion 4 40% 4 Agree 4 40% 5 Strongly agree 0 0% Innovation Diffusion & Online Education 96 Faculty Who Have Not Taught Online Courses Table 4-81 Relative Advantage (Economic Profitability) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 0 0% 3 No opinion 1 50% 4 Agree 1 50% 5 Strongly agree 0 0% Table 4-82 Relative Advantage (Saving of Time) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 1 50% 3 No opinion 1 50% 4 Agree 0 0% 5 Strongly agree 0 0% Table 4-83 Relative Advantage (Reduced Discomfort) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 1 50% 3 No opinion 1 50% 4 Agree 0 0% 5 Strongly agree 0 0% Table 4-84 Relative Advantage (Immediacy of Reward) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 1 50% 2 Disagree 0 0% 3 No opinion 0 0% 4 Agree 1 50% 5 Strongly agree 0 0% Innovation Diffusion & Online Education 97 Table 4-85 Relative Advantage (Saving of Effort) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 0 0% 3 No opinion 1 50% 4 Agree 0 0% 5 Strongly agree 1 50% Table 4-86 Relative Advantage (Low Initial Cost) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 0 0% 3 No opinion 1 50% 4 Agree 0 0% 5 Strongly agree 1 50% Table 4-87 Relative Advantage Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 2 17% 2 Disagree 2 17% 3 No opinion 4 33% 4 Agree 3 25% 5 Strongly agree 1 8% Table 4-88 Compatibility (Need of Adopters) Rank Degree of agreement Frequency Relative Frequency 1 Strongly disagree 0 0% 2 Disagree 0 0% 3 No opinion 1 50% 4 Agree 1 50% 5 Strongly agree 0 0%