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Impact of Web-Based Instruction (WBI) in Schools

Info: 8109 words (32 pages) Dissertation
Published: 29th Oct 2021

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Tagged: EducationMediaTechnology

Nowadays the use of Web-Based Instruction (WBI) has significant impacts on every aspect of our lives. In the context of education industry more and more school and education institutions have come to realize the potential impact of using the WBI in the classroom as part of the learning environment. Despite the many challenges yet to be overcome, the advantages of WBI have been widely recognized. Some of these major advantages include flexibility and broader accessibility (Lee, Cheung, & Chen, 2005), improved students’ performance (Alavi, 1994), reflective evaluation of the learning experience (Hiltz, 1995), and higher computer self-efficacy (Piccoli, Ahmad, & Ives, 2001). Academic institutions also benefit in terms of cost reductions and increasing revenues (Saadé and Bahli, 2005). The success of Web usage for learning is primarily due to its potential to integrate various types of media such as audio, video, graphics, animation and text and delivered in various forms.

Statement of the problem

Schools are witnessing a profound increase in the use of multimedia presentations, video teleconferencing, and, more currently, Web-Based Instruction (WBI). WBI presents great potential for instructional improvement by providing ready access to information and allowing more interaction between teachers and learners (Hill, 1997). In order to meet the diverse needs of their teachers when integrating WBI into their subjects, most schools have adopted a few major brands of commercial course management software. Nowadays we have heard that information technologies are going to change school education especially in the way teachers teach and the way our students will learn. But most of us have seen little evidence to support the claim. In fact, teachers utilization of innovative technologies has remained low (Surry and Land, 2000).

The integration of technology such as WBI into the classroom has remained low and educational technology use has been minimal, infrequent, and limited as an add-on rather than as indispensable to teaching and learning (Becker, 1991). Surry and Ely (2002) diagnosed, as a reason for this lack of utilization, which instructional designers had focused on developing. They added that there is no guarantee for diffusion of instructional technologies itself. While the diffusion and implementation of innovation is important. Rogers (1995) and Stockdill and Morehouse (1992) described, it is a complex process that is influenced by many factors. Technological superiority is only one of a number of factors that influence a person’s decision about whether or not to adopt an innovation. A more complex interaction of social, economic, organizational, and individual factors influence which technologies are adopted and how much they are used after they have been adopted.

As one of the major areas of diffusion of innovation study, instructional technologies have focused on the identification of the significant factors contributing to educational technology implementation. Most studies of this issue have been simply investigating factors or have confined the research scope to only examine either the psychological perspective of factors (Marcinkiewicz, 1994; McKinney, Sexton, & Meyerson, 1999; Olech, 1997), or the external or environmental perspective of factors (Daugherty and Funke, 1998; Groves & Zemel, 2000), disregarding other relevant variables.

Daugherty and Funke’s (1998) study focused only on the teacher’s perceived supports or incentives as factors influencing the use of Web-Based instruction. They surveyed school teachers and students involved in Web-Based instruction on the advantages, disadvantages, and general effectiveness of using the Internet as a teaching and learning tool. Teachers reported the lack of technical support, lack of software or adequate equipment, lack of teachers or administrative support, the amount of preparation time, and student resistance are barriers to use Web-Based instruction.

According to Hamilton and Thompson (1992) in reality it is assumed that a person will be influenced by psychological and also environmental factors at the same time for a decision to adopt or utilize an innovation and Ely (1999) identified eight environmental conditions. His approach recognizes that the characteristics of adopters and the innovation are not the only factors influencing its diffusion. His research suggests that the environment such as supports and incentives in which the innovation is to be introduced can play an equally important role in determining a change effort’s success.

In the this study, the three categories of variables known to relate to the level of innovation use are identified based on the diffusion and innovation models. First, in the area of personal characteristics, previous experience and self-efficacy are selected as key variables. Second, complexity and relative advantage in this study are selected for the area of perceived attributes of innovation. Last, for the area of perception of influence and support from the environment, supports, and time are selected. To go beyond the single-equation approach using multiple regressions and address the associated limitations, structural equation modeling (SEM) will be used. Using this technique, indirect effects among variables are identified in the model that is specified from the literature and theories by the researcher. These indirect effects, when added to the direct effects in the model, allow the determination of total causal effects.

Research Objective

Identifying the direct, indirect and total effects of the identified predictor variables (self-efficacy, relative advantage, complexity, computer experience, supports and time) on criterion variable (level of WBI use).

Research Questions

What are the direct, indirect and total effects of the identified predictor variables (self-efficacy, relative advantage, complexity, computer experience, supports and time) on criterion variable (level of WBI use)?

Purpose of the Study

The purpose of the study is to build a model to predict the level of diffusion and utilization of Web-Based Instruction in school. To test the model six independent variables (self-efficacy, relative advantage, complexity, computer experience, supports and time) from the three perspectives affecting the diffusion and utilization of WBI will be used. The selection of the variables is substantiated by empirical evidence from previous relevant innovation studies (Rogers, 1995; Ely, 1999).

The result of this study would also be helpful to instructional designers. When it comes to successful educational program design, the consideration of the target audience’s characteristics is essential to the analysis phase in most instructional design models. Because the predictor variables are susceptible to interventions such as training or staff development, the identification of the potential factors that are highly related to the integration of a new technology.

Operational Definition

Predictor Variables

Six independent variables which are selected from the three perspectives affecting the diffusion and utilization of WBI. The variables are computer experience, self-efficacy, complexity, relative advantage, supports and time.

Diffusion of Innovations

The adoption and utilization of Web as a teaching tool.

Level of Use

Degree of integration of WBI that has been attained by teachers in order to attain existing instructional goals.

Web-Based Instruction

A hypermedia-based instructional program which utilizes the attributes and resources of the World Wide Web to create a meaningful learning environment such as Blackboard and WebCT.

Chapter II: Literature Review

The objective of the study is to identify factors affecting the likelihood of diffusion in educational setting is usually perceived from one of three major perspectives. The first of these is concerned with the characteristics of the adopter, such as computer experience and self-efficacy. The second perspective is focuses on the characteristics of the innovation itself. The third perspective focused on the characteristics of the environment in which the innovation is to be introduced. This approach highlights the importance of factors outside the innovation which can set the stage for its success or failure. The review will be focus on diffusion of innovation, relation to factors affecting the diffusion and implementation of Web-Based Instruction in an educational setting, informational technology diffusion models, model constructs and Web-Based Instruction (WBI).

Diffusion of Innovation

Sanders and Morrison (2001) have identified three reasons why the study of diffusion theory is beneficial to the field of instructional technology. The first reason is most instructional technologists lack the knowledge of why their products are or are not adopted. They believe a study of diffusion theory could rectify this situation. Second, the field of instructional technology is often associated with the concept of innovations and they suggested that if instructional technologists understand the diffusion and diffusion of innovation theory. They will be more prepared to work effectively with potential adopters. The third reason is the studies of the diffusion theory could result in developing a systematic model of diffusion and diffusion for the instructional technology field.

Everett Rogers is the most widely cited author in the area of general diffusion theory. Rogers’ (1995) theories form the basis of most studies related to diffusion. Rogers’ theories seem to be common elements of most diffusion theories. They are diffusion process, adopter categories, innovation attributes, and rate of diffusion. So the instructional technologists not only need to create well-designed products but need to ensure the diffusion of these products. The main concern of the diffusion of innovation research is how innovations are adopted and why innovations are adopted at different rates.

The diffusion process outlined by Rogers (1995) has five steps knowledge, persuasion, decision, implementation, and confirmation. According to this theory, potential adopters of an innovation have to learn about an innovation and are persuaded to try it out before making a decision to adopt or reject the innovation. The adopters decide to either continue using the innovation or stop using it. This theory is very important because it shows that diffusion is not a momentary irrational act, but an ongoing process that can be studied, facilitated and supported.

Factors Affecting Diffusion of Innovation

The experts in diffusion of innovation find that there is no single or a certain group of factors identified to explain the lack of use of Web-Based Instruction in school education. In this section, I will explore the factors have been examined and identified from many studies. The experts in educational technology have done numerous studies to find out the factors affecting the diffusion of Web-Based Instruction in school.

Morris (2001) have found that the lack of technical support, lack of adequate equipment, amount of time required, student resistance or lack of computer skills, network problems and identified lack of teachers or administrative support are the barriers that teachers confronted when incorporating Web-Based instruction. From a survey of 557 teachers, Anderson, Varnhagen and Campbell (1998) also found that although most teachers believe that learning and communications technologies are essential to improving the quality of school education, many barriers were identified to realizing that capacity. They identified nine factors as major or minor barriers. The greatest barrier identified was lack of funding. The second greatest barrier was lack of time to learn technologies. The others are classroom infrastructure, adequate computer hardware or connectivity, institutional incentives, knowledge about applying technology in teaching, access to software tools, lack of training and support, and information about available technology.

Pitman, Gosper and Rich (1999) examined teacher’s use of instructional technology in a school classroom. In this study, they limited instructional technology to internet-related technologies including e-mail and the World Wide Web. The study identified significant relationships between teaching style, perceived effectiveness of technology, perceived access to technology and perceived administrative support and the use of technology. Beggs (2000) have conducted the survey of 348 teachers. In this survey teachers at a school were asked about their self-perceived use of technology, factors influencing their use of technology, and barriers to the use of technology in the classroom. The factors are improved student learning, advantage over traditional teaching, equipment availability, increased student interest, ease of use, compatibility with discipline, time needed to learn, materials in discipline, compatibility with materials, training, administrative support, personal comfort and colleague use. Rogers (2000) have conducted the study to examine barriers to technology diffusion through a structured interview conducted on the telephone or in-person. The barriers that he identified are need technical support staff, need release time and time for training, funds, and lack of sharing best practices across system.

Through this through review, it seems that the factors emerge into three categories as like personal characteristics which include factors such as years of teaching, previous experience, teaching style, self-efficacy, and anxiety, innovation characteristics such as relative advantage, complexity, and compatibility, and environmental and social factors such as support and time. In the case of a factor of support, the factors like accessibility or availability, technical and administrative, workshop, and incentive may be grouped into a single factor as support.

Refer to importance of considering both the person and the social environment as joint determinants of behavior, Surry and Farquhar (1997) described adopter based theories as opposite to developer-based theories. Developer-based theories are to increase diffusion by maximizing the efficiency, effectiveness and elegance of an innovation. They assume that the best way to bring about educational change is to create a system or product that is significantly superior to existing products or systems.

In summary, this section focused on the studies conducted to find out the factors affecting the diffusion of instructional technology. Since these studies have not looked at the interactional effects of determinants on an adopter’s behavior so more attention seems to be needed on the interrelationships among identified variables.

Innovation Diffusion Models

In contrast to the studies that focus on single factors or a list of factors, a few models have been developed and empirically studied to identify the interactional effects of variables on innovation usage. These models focused on the identification of the determinants of usage, such as attitudes, social influences, and facilitation conditions (Davis, Richard & Paul, 1989; Mathieson, 1991).

Theory of Reasoned Action

The Theory of Reasoned Action (TRA) was first proposed by Azjen and Fishbein (1975). The theory specified a causal relationship between individual behavioral intention and actual behavior. The components of TRA are behavioral intention, attitude, and subjective norm. TRA suggests behavioral intention depends on a person’s attitude toward behavior and subjective norm. Behavioral intention measures a person’s relative strength of intention to perform a behavior. Attitude is comprised of beliefs about the consequences of performing the behavior multiplied by his or her valuation of those consequences. Subjective norm is seen as a combination of perceived expectations from referent individuals or groups along with intentions to comply with these expectations. (Azjen and Fishbein, 1975).

TRA became the basis for developing the following two models, Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM). In fact, to account for conditions where individuals do not have complete control over their behavior, TPB extended TRA.

Theory of Planned Behavior

Azjen and Madden (1986) modified TRA and generated a model named the Theory of Planned Behavior (TPB). The only difference between the TRA and TPB is the inclusion of perceived behavioral control. Perceived behavioral control reflects a person’s ability to actually perform a behavior. It is influenced by the effects facilitating conditions and self-efficacy. Hoffman and Novak (1994) included ease of access, ease of use, price, knowledge, past experience, and skill to represent the perceived behavioral control in their study of hypermedia using TPB. Each of the determinants of intention, like attitude, subjective norm and perceived behavioral control, is determined by underlying belief structures. These are referred to as attitudinal beliefs, normative beliefs, and control beliefs which are related to attitude, subjective norm and perceived behavioral control respectively.

Technology Acceptance Model

Technology Acceptance Model (TAM) was developed by Davis (1986) and introduced by Davis, et al. (1989). This model is an adaptation of the Theory of Reasoned Action (TRA). TAM contends two distinct constructs like perceived usefulness and perceived ease of use. Davis (1989) defined perceived usefulness as “the degree to which an individual believes that using particular system would enhance his or her job performance” and ease of use as “the degree to which an individual believes that using a particular system would be free of physical and mental effort”.

This model is more specific and simple because it only provides two factors which are important determinants of innovation usage (Mathieson, 1991). These factors are specific, easy to understand, and can be manipulated through system design and implementation. In addition, they should also be generalizable across settings. Although it is a special case of the TRA, TAM excludes the influence of social and personal control factors on behavior, which is also identified as important factors in the previous research (Groves & Zemel, 2000; Knutel, 1998).

Components of the Study Model Constructs

The six predictor variables believed to be important in influencing the diffusion of innovation which has derived from the Rogers’ model and other relevant constructs from other models and other reviewed studies. Followed is the explanation of each of the six predictor variables and the criterion variable in more detail.

Personal Characteristics

Computer Experience

Computer experience is defined as the extent to which adopters perceive previous computer experience and performance with internet connection as good. Also, it includes amounts of time using computer with internet connection in this study. The more positive experiences one has, the more confident one is in a similar innovation (Stone & Henry, 2003). In other words, positive past experience with computers will increase one’s confidence while negative experience will reduce it. This view is supported by Ertmer, Evenbeck, Cennamo and Lehman (1994), who found that although positive computer experience increased computer confidence, the actual amount of experience was not correlated with the confidence beliefs of students. This suggests that it is the quality, not the quantity, of experience is a critical factor in determining self-efficacy beliefs, which is one of the most important and popular variables in the diffusion and utilization of innovations studies.

There have been numerous studies involving the experience and attitude-behavior relationship (Anderson, Varnhagen, & Campbell, 1998; Christoph, Schoenfeld, & Tansky, 1998; Daugherty & Funke, 1998; Ellsworth, 1998; Groves & Zemel, 2000; Hill, Stone & Henry, 2003; Kao, Wedman, &Placier, 1995). Bandura (1977) suggests that experience is likely to reduce anxieties and induce individuals to change their behavior. The information gained by performance accomplishments provides the most influential source of efficacy information (Bandura, Adams, & Beyer, 1977 and Zimmerman, 2000). Hill, Smith, & Mann (1987) provide evidence that experience with computer technology lead to a higher likelihood of technology adoption through changes in perceived self-efficacy.


Self-efficacy, a key element in Bandura’s social learning theory (1977), refers to one’s belief in one’s capability to use Internet in this study. Self-efficacy has been found to influence the decision to use computers (Hill, Smith and Mann, 1987). Bandura (1997) defined perceived self-efficacy as personal judgments of one’s capabilities to organize and execute subjects of action to attain designated goals, and he sought to assess its level, generality and strength across activities and contexts.

Zhang and Espinoza (1998) found that comfort or anxiety about computers perceived by students predicted their confidence levels about computers and the confidence level is a significant predictor in deciding their desirability of learning technology skills. In addition, from the findings in his qualitative study Zollinhofer (1998) supported that teachers who have low self-efficacy are susceptible to cyber anxiety which can increase resistance to learning new technologies.

According to Bandura’s (1977) self-efficacy theory, judgments of self-efficacy are based on several kinds of information including performance accomplishments, vicarious experiences, verbal persuasion, and emotional arousal. Venkatesh and Davis (1994) theorize that perceptions about a new system’s usefulness and a new system’s ease of use influences and are anchored on an individual’s general computer self-efficacy. From this evidence, it can be hypothesized that self-efficacy influences perceived relative advantage and ease of use of innovation, and also influences utilization of an innovation through those two intervening variables.

Perceived attributes of innovation

Rogers (1995), Wolfe (1994), and Farguhar and Surry (1994) identified perceived by potential adopters, relative advantage, compatibility, complexity, trialability, and observability as five main attributes of an innovation as important factors in determining the rate of diffusion. According to Rogers’ theory, potential adopters of an innovation have to learn about an innovation and are persuaded to try it out before making a decision to adopt or reject the innovation. This five attributes are frequently cited as playing a key role in the perceptions of adopters in regard to the implementation of instructional innovations. For this study, although perceived attributes compatibility, observability and trialability could contribute to some extent in diffusion process but only relative advantage and complexity which distinguished by Vinson (1996) and Moskal, Martin, and Foshee (1997) are included. This is because they have the strongest influence from Rogers’ five attributes.

Relative Advantage

Relative advantage is defined as the degree to which an innovation of WBI as an instructional technology in this study is perceived as being better than the technology it supersedes and other solutions being considered (Rogers, 1995). The degree of relative advantage is often expressed as economic profitability, social prestige, or other benefits. The degree of use is expected to be increased by the teachers’s perceived relative advantage of WBI.

Rogers generalized from previous research that “the relative advantage of an innovation, as perceived by members of a social system, is positively related to its rate of diffusion”. In their study, Venkatesh and Davis (1994) tested the effect of self-efficacy on the perceived ease of use construct using two different information technologies, E-mail and Gopher. They found that the perceptions about a new system’s ease of use are anchored on a person’s general computer self-efficacy.


Complexity is defined as the degree to which the WBI as an instructional technology is perceived as difficult to understand and use (Rogers, 1995). It is similar to the ease of use construct used by Davis, Bagozzi, & Warshaw (1989). They define it as the degree to which an individual believes that using a particular system would be free of physical and mental effort. In their study they find a positive correlation between perceived ease of use and behavioral intentions. They found ease of use to be a strong determinant of use. It is expected that the more complex WBI appears to teachers, the less they will use it.

An innovation which is perceived as being difficult to use will meet with greater resistance to its use and diffusion than those which are considered as easy to learn. Hence, another generalization drawn by Rogers was that “the complexity of an innovation, as perceived by members of a social system, is negatively related to its rate of diffusion”. Then, who perceives an innovation as being more or less difficult? The findings (Ghaith & Yaghi, 1997; Guskey, 1988) indicate that more efficacious teachers considered an innovation as less difficult to implement.

Perception of influence and support from the environment

Groves and Zemel (2000) from their study has been identified that environment as a category of influencing factors on diffusion and utilization of innovation. Ely (1999) proposed eight environmental condition dissatisfaction with the status quo, existence of knowledge and skills, availability of resources, availability of time, existence of rewards or incentives for participation, expectation and encouragement of participation, commitment by stakeholders involved, and evidence of leadership. A few studies have been conducted to determine the best predictors among the eight conditions using stepwise multiple regression analysis. Ravitz (1999) found out availability of resources, availability of time, existence of rewards or incentives, commitment, and leadership are the most important determinants related to the implementation of innovation. In another pure survey study, Daugherty and Flunke (1998) reported the barriers confronted by teachers when incorporating Web-Based instruction are lack of technical support, lack of software or adequate equipment; amount of time required and lack of teachers or administrative support. From reviewing the related studies, supports and time were selected as key variables for this study.


Groves & Zemel (2000) found out that the supports like training available on how to use, information or materials available, and administrative support were rated as very important factors influencing use of instructional technologies in teaching. Morris (2001) found out that lack of technical support, lack of adequate equipment or software, and lack of teachers or administrative support are the barriers teachers confronted when incorporating distance education.

Farquhar and Surry (1994) proposed organizational factors with the adopter’s individual factors as influential factors which affect the diffusion and utilization of the instructional product. They asserted that inappropriate environmental support can often be an important hindrance factor of successful innovation diffusion. The teachers training and other resources to use and learn the WBI technology can be effective and productive by lessen teachers perceived level of complexity to use or learn WBI as an instructional technology.


Seminoff and Wepner (1997) discovered that of the 77 respondents in their study on instructional-based projects, 64% indicated that release time for preparation of technology-based projects was not being provided. In the survey study about factors influencing the use of technology and perceived barriers to use of technology, Groves & Zemel (2000) found that teacher’s perceived time needed to learn as an important factor in influencing use of technology.

Plater (1995) indicates that managing teacher’s time is the single most important asset of the school. In the past teachers had only a few time-related issues, including meeting classes, keeping office hours, and attending teachers meetings. Plater goes on to say that schools must recognize teacher’s time as valuable resource and begin to think about departmental needs and prepare individual teachers to meet these needs.

While teachers training should be part of the overall preparation for WBI, teachers training can only be effective and productive if there is adequate preparation time to incorporate what has been learned in training. In the present study time is defined as perceived available time needed to learn and use WBI as an instructional technology. The more available time teachers perceive, the less complex they perceive to learn and use WBI as an instructional technology.

Level of Use

Level of using Web-Based Instruction is a dependent variable for this study. Moersch (1995) proposed a conceptual framework that measures levels of technology use. In this framework, seven distinguished implementation levels teachers can demonstrate. According to Moersch (1995), as a teacher progresses from one level to the next, a series of changes to the instructional curriculum is observed. The instructional focus shifts from being teacher-centered to being learner-centered. Computer technology is used as a tool that supports and extends students’ understanding of the pertinent concepts, processes and themes involved when using databases, telecommunications, multimedia, spreadsheets, and graphing applications. Traditional verbal activities are gradually replaced by authentic hands-on inquiry related to a problem issue or theme. Heavy reliance on textbook and sequential instructional materials is replaced by use of extensive and diversified resources determined by the problem areas under discussion. Traditional evaluation practices are supplanted by multiple assessment strategies that utilize portfolios, open-ended questions, self-analysis, and peer review.

To measure the level of innovation use, in addition to above levels of innovation use which are used to measure the degree to which an adopter integrates the innovation into practice, a number of studies (Cartas, 1998; Lin & Jeffres, 1998; Jaber, 1997; Wallace, 1998) in the studies of diffusion and utilization of instructional technologies have used three different categories of questions to measure the usage level the frequency of technology use, the amount of hours in using a technology and the number of programs or functions used.

Since it seemed that levels of use studies (Moersch, 1995; Reiber & Welliver, 1989) dealt with questions pertaining to the specific aspects of computer technologies to measure the levels of technology, the present study created the questions including the three categories of questions pertaining to WBI use.

Web-Based Instruction (WBI)

WBI is defined as an innovative approach for delivering instruction to a remote audience using the World Wide Web as the instructional delivery system (Khan, 1997). Web-Based learning environments use the resources of the Web to create a context in which learning is supported and fostered.

Web-Based Instruction is growing faster than any other instructional technology (Crossman, 1997). More and more school teachers are using WBI as an integral part of instructional activities. School cannot work in isolation and must respond to societal change (Innovation in Distance Education (IDE), 1997). WBI offers medium for school education to accommodate the information age and a networked world. From the survey research of school teachers, Morris (2001) identified a few benefits. First, students gain knowledge on how to use numerous technology-based applications such as e-mail, PowerPoint, and HTML. In fact, those are considered essential skills for today's workforce. Secondly, students tend to become independent learners, are more motivated to explore related topics on their own, and develop critical thinking skills. This can be interpreted in terms of promoting interaction for learning among teachers and learners.


A number of studies have been performed to identify factors affecting the likelihood of diffusion of instructional technology in educational setting. Most of the studies have based their theoretical foundation on Roger's diffusion model. However, they have mostly reported the influencing factors based on the regression-based approach, not focusing on the interactional relationship among the factors.

Recently, there have been a few models developed and empirically studied to find out the interactional effects of variable on innovation usage. Among those models, the three models (Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB), and Technology Acceptance Model (TAM)) seem to be of importance and related to the present study. Based on the results of these models and other studies, the identified six factors seemed to be usually perceived from one of three major perspectives: adopter, innovation, and condition. This chapter was dedicated to a discussion of research related to diffusion predictors in terms of those six factors are Computer Experience, Self-efficacy, Complexity, Relative Advantage, Supports and Time. The criterion variable is level of WBI use.


The focus of the present study is WBI use by school teachers. Specifically, this study examines six factors in terms of their direct and indirect relationships to Web-Based Instruction (WBI) use among the teachers of school. To accomplish the goals of this study the model will be tested with data collected from a sample of teachers. The survey method, which had been the most commonly used method of data gathering in diffusion research studies, will be used. This chapter includes the sections describing participants, study variables and hypotheses, measurement instrument, and research procedures for conducting the current study.

To test whether the study model is consistent with the data, structural equation modeling (SEM) approach is used and the following hypotheses refer to each relationship among the six variables in the model. The hypotheses are described from the perspective of the variables' relationships to each other. In other words, they describe whether a variable is positively related to another variable or the effect of a variable is mediated by another variable or other variables.

Populations and samples

Population for this study is secondary school teachers. The sample will be selected using a random sampling method. For this study 250 secondary school teachers will be selected as a respondent. Muller (1996) recommended a sample of at the very least 120 or preferably 240 respondents as a minimum sample required for SEM analysis.

Analysis of the Data

This study is designed to build a model that would predict the level of diffusion and utilization with regard to technology use by school teachers. The data will analyzed using the scores obtained from the questionnaires. Descriptive statistics, such as frequency distributions, means, standard deviations, and percentages will be used to describe data using a SPSS program. Inferential statistics in the form of hypotheses testing will be used to test the hypothesized study model using a technique called Structural Equation Modeling (SEM). SEM can be used to test theories of causal relationships among variables (Gall, Borg, & Gall, 1996).

The standard SEM analysis steps are used from Tate (1998). The step was Model Specification Based on theory, experience, and the literature, the researcher specified the hypothesized model consisting of a network of direct causal links among the variables. The second was Model Identification The identification of a model refers to the question of whether there is sufficient information to allow estimation of all of the model parameters. T-rule was used for model identification. The third step was Confirmatory Factor Analysis for all latent variables with multiple indicators was conducted. Then, SEM for the Full Study Model was evaluated, including an assessment of the fit of the model to the data. If the model is not acceptable, the researcher may consider one or more revisions of the model based on theoretical credibility. If a theoretically credible model with acceptable fit is obtained, the associated estimated direct, indirect, and total causal effects will be described.

Limitations of the study

Limitations that may potentially influence the generalization from this study. Since the participants are limited to users of WBI at only secondary school setting, the generalizability of this study should be limited to similar types of innovations occurring within similar settings. This opens the door for further research to identify to what extent the differences persist across institutions of different missions and size.


Alavi, M. (1994). Computer-mediated collaborative learning: An empirical evaluation. MIS Quarterly, 18(2), 159-174.

Anderson, T., Varnhagen, S. & Campbell, K. (1998). Faculty adoption of teaching and learning technologies: Contrasting earlier adopters and mainstream faculty. The Canadian Journal of Higher Education. 28(2, 3), 71-98.

Azjen, I. & Madden, T. J. (1986) Prediction of goal-directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of Experimental Social Psychology. 22, 453-474.

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

Beggs, T. A. (2000). Influences and barriers to the adoption of instructional technology. Proceedings of the Mid-south Instructional Technology Conference. (ERIC Document Reproduction Service No. ED 446 764).

Cartas, R. A. (1998). The relationship between technology use by classroom teachers and factors which promote innovation adoption. Unpublished doctoral dissertation, University of Southern California, CA.

Christoph, R., Schoenfeld, G., & Tansky, J. (1998). Overcoming barriers to training utilizing technology: The influence of self-efficacy factors on multimedia-based training receptiveness. Human Resource Development Quarterly, spring.

Crossman, D. M. (1997). The evolution of the World Wide Web as an emerging instructional technology tool. In B. H. Khan (Ed.), Web-Based Instruction. Englewood Cliffs, NJ: Educational Technology Publications, Inc.

Daugherty, M. & Funke, B. (1998). University faculty and student perceptions of Web-Based instruction. Journal of Distance Education, 13(1), 21-39.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-339.

Davis, F. D., Richard, P. B., & Paul, R. W. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982-1003.

Ellsworth, J. B. (1998). Factors affecting participant reactions to new training devices (United States Army, Instructional Technology, Military Training). Unpublished doctoral dissertation, Syracuse University.

Ely, D. P. (1999). Conditions that facilitate the implementation of educational technology innovations. Educational Technology, 32(6). 23-27.

Farquhar, J. & Surry, D. (1997). Diffusion theory and instructional technology. Journal of Instructional Science and Technology, 2(1). Available at: http://www.usq.edu.au/electpub/ejist/docs/old/vol2no1/contents.htm.

Gall, M. D., Borg, W. R., & Call, J. P. (1996). Educational Research: An introduction (6th ed.). New York: Longman.

Green, K.C. & Gilbert, S.W. (1995). Academic productivity and technology. Academe, 81(1), 19-25.

Groves, M. & Zemel, P. (2000). Instructional technology adoption in higher education: an action research case study. International Journal of Instructional Media, 27(1), 57-65.

Hall, G. E. & Hord, S. M. (1987). Change in Schools: Facilitating the process. Albany, NY: State University of New York Press.

Hamilton, J., & Thomson, A. (1992). The adoption and diffusion of an electronic network of education. Proceedings of Selected Research and Development Presentation at the Convention of the Association for Educational Communications and Technology. (ERIC Document Reproduction Service No. ED 347 991).

Hill, T., Smith, N., & Mann, M. (1987). Role of efficacy expectations in predicting the decision to use advanced technologies: The case of computers. Journal of Applied Psychology, 72(2), 307-313.

Hill, J. R. (1997). Web-Based learning environments: Guidelines for development and implementation. Journal of Public Services & Outreach, 2(3), 36-43.

Hiltz, S. R. (1995). Teaching in a virtual classroom. International Journal of Educational Telecommunications, 1(2), 185.

Hoffman, D.L. & Novak, T.P, (1994), "Commercializing the Information Super Highway: Are We In for a Smooth Ride?" The Owen Manager, 15(2), 2-7.

Jaber, W. E. (1997). A survey of factors which influence teachers' use of computer-based technology. Unpublished doctoral dissertation, Virginia Polytechnic Institute and State University. Blacksburg, Virginia.

Kao, h., Wedman, J., & Placier, P. (1995). Lessons relearned: Another faulty implementation of an educational innovation. International Journal of Instructional Media, 22(3). 201-213.

Khan, B. H. (1997). Web-Based instruction (WBI): What is it and why is it? In B. H. Khan (Ed.), Web-Based Instruction. Englewood Cliffs, NJ: Educational Technology Publications, Inc.

Knutel, P. G. (1998). Adoption of an innovation: The process through which faculty decide whether to use instructional technology. Unpublished doctoral dissertation, University of Michigan.

Lin, C. A., & Jeffres, L. W. (1998). Factors influencing the adoption of multimedia Cable Technology. Journalism & Mass Communication Quarterly, 75(2), 341-352.

Marcinkiewicz, H. R. (1994). Computers and teachers: Factors influencing computer use in the classroom. Journal of Research on Computing in Education, 25(2), 220-237.

Marcinkiewicz, H. R. & Regstad, N. G. (1996). Using subjective norms to predict teachers' computer use. Journal of Computing in Teacher Education, 13(1), 27-33.

Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191.

McKinney, M., Sexton, T., & Meyerson, M. (1999). Validating the Efficacy-Based Change Model. Teaching and Teacher Education, 15, 471-485.

Moersch, C. (1995). Levels of Technology Implementation (LoTi): A Framework for Measuring Classroom Technology Use, Learning and Leading with Technology, 23(3), 40-42.

Moskal, Martin, & Foshee, (1997). Educational Technology and Distance Education in Central Florida: An Assessment of Capabilities. The American journal of distance education. 11(1), 6-23.

Piccoli, G., Ahmad, R., & Ives, B. (2001). Web-Based virtual learning environments: A research framework and a preliminary assessment of effectiveness in basic IT skills training. MIS Quarterly, 25(4), 401.

Pitman, A. J., Gosper, M. and Rich, D. C. (1999) Internet based teaching in geography at Macquarie University: An analysis of student use, Australian Journal of Educational Technology 1999, 15(2), 167-187.

Plater, W. M. (1995). Future work: Faculty time in the 21st century. Change, May/June, 23-33.

Ravitz, J. (1999). Conditions that facilitate teachers' Internet use in schools with high Internet connectivity. Syracuse, NY: Unpublished doctoral dissertation, Syracuse University.

Rieber, D. C. & Welliver, P. W. (1989). Infusing educational technology into mainstream educational computing. International Journal of Instructional Media, 16(1), 21-31.

Rogers, E. M. (1995). Diffusion of innovations. (4th ed.). New York: The Free Press.

Rogers, P. L. (2000). Barriers to adoption emerging technologies in education. Journal of Educational Computing Research, 22(4), 455-472.

Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Information & Management, 42(2), 261-386.

Seels, B. & Richey, R.C. (1994). Defining a field: A case study of the development of 1994 definition of instructional technology. In D.P. Ely (Ed.), Educational media and technology yearbook: 1994. Englewood, CO: Libraries unlimited.

Seminoff, N. E. & Wepner, S. B. (1997). What should we know about technology-based projects for tenure and promotion? Journal of Research on Computing in Education, 30(1), 67-82.

Surry, D., & Land, S. (2000). Strategies for Motivating Higher Education Faculty to Use Technology. Innovations in Education and Training International, 37(2), 145-153.

Surry, D. W. & Ely, D. P. (2002). Adoption, diffusion, implementation, and institutionalization of instructional design and technology. In R. A. Reiser & J. V. Dempsey (Eds.), Trends and issues in instructional design and technology. Upper Saddle River, NJ: Merrill Prentice Hall.

Stockdill, S. H. & Morehouse, D. L. (1992). Critical factors in the successful adoption of technology: A checklist based on TDC findings. Educational Technology, 1. 57-58.

Stone, R. W. & Henry, J. W. (2003). The Roles of Computer Self-Efficacy and Outcome Expectancy in Influencing the Computer End-User's Organizational Commitment. Journal of End User Computing 15(1), 38-53.

Tate, R. (1998). An introduction to modeling outcomes in the behavioral and social sciences. Minnesota: Burgess International Group, Inc.

Venkatesh, V. & Davis, F. (1994). Computer self-efficacy and the evolution of perceived ease of use with training. University of Minnesota, working paper series MISRC-WP-93-07.

Vinson, C. (1996). Factors related to adoption of innovation: An evaluation of the San Jose network communication project. Unpublished doctoral dissertation, University of Southern California. CA.

Wallace, P. R. (1998). Diffusion of Internet Adoption: A study of the relationship between innovativeness, the attitude of teachers toward using the internet, and internet use. Unpublished doctoral dissertation, University of Tennessee. Knoxville, TN.

Wolfe, R. (1994). Organizational innovation: Review, critique and suggested research directions. Journal of Management Studies, 31(3), 405-431.

Zimmermann, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational Psychology, 25, 82-91.

Zhang, Y. & Espinoza, S. (1998). Relationships among computer self-efficacy, attitudes toward computers, and desirability of learning computing skills. Journal of Research on Computing in Education, 30(4), 420-431.

Zollinhofer, M. H. (1998). Boon or bane: The role of computing technologies on the professional practice of higher education faculty. Unpublished doctoral dissertation, University of Virginia.

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