Web-Based Instruction (WBI) has experienced explosive growth, and
its use is becoming more attractive in higher education settings. Many
innovation models have been proposed to provide a theoretical framework
to facilitate the adoption of a new technology (Fullan, 2007; Rogers,
2003). An important point of view in the models is a person-centered
approach (Derntl & Motschnig-Pitrik, 2005). According to Emrick,
Peterson, and Agawala-Rogers (1977), two parallel dimensions exist
simultaneously in the change process: 1) a systemic dimension that
involves change in the environment of the user, and 2) a personal
dimension, including cognitive, behavioral, and affective components,
that involves the process of change within the individual. The
system-centered approach clarifies essential factors that lead to
technology integration changes processes (Hsu & Sharma, 2008).
However, a common limitation of this approach is that it fails to look
at the psychology of the innovation and, thus, the interventions are not
persuasive enough to bring about the desired change. Research is needed
to identify the personal-dimension variables that affect the adoption of
WBI by faculty members (Ertmer, 2005; Georgina & Olson, 2008).
The concerns that faculty members have when deciding whether to
integrate new technology are a critical condition that needs to be
considered along with other personal dimension variables for the
successful adoption of WBI in higher education settings(Adams, 2003;
Matthew, Parker, & Wilkinson, 1998; Sahin & Thompson; 2007). The
more concerns they have, the more likely they will be resistant to adopt
the WBI. For this reason, it is important to identify the factors that
can diminish faculty concern about adopting WBI. It is difficult and
costly to test the relevant variables in practice, but simulating the
impact of the model should allow educators and decision makers to assess
the effectiveness of factors that may support the implementation of WBI
in educational settings.
The purpose of this article is to propose a simulation model
designed to test the impacts of the factors that support faculty
adoption of WBI integration. In order to achieve this purpose, the
stages of concern of the faculty members were identified using the
Concern-Based Adoption Model (CBAM) (Hall, George, & Rutherford,
1977). The factors that support faculty WBI adoption regardless of the
stage of concern were then suggested. These identified factors were
based on a review of the literature on the diffusion of online
education. System dynamics was used to determine associations between
faculty concerns and these support factors. Finally, based on the
identified factors that occur during the different stages of concern, a
simulation model with examples of its use is presented.
Factors that decrease faculty concerns about adopting WBI
Faculty concern about integrating WBI
What concerns do faculty members have as they integrate new
technology into their courses? According to Fuller (1969), the process
of diffusion can be explained in terms of a psychological shift from the
properties of an innovation to the concerns of the users. He initially
proposed a model that described three phases of concern: a preteaching
phase, an early teaching phase, and a late teaching phase. These three
phases were later named "self," "task," and
"impact" concerns, respectively. Hall, George, &
Rutherford (1977) expanded upon these three stages of concern to end up
with a total of seven stages. According to these stages, adopters
advance from lower-level, self-oriented concerns (awareness,
informational, and personal) to intermediate-level, task-related
concerns (management), and finally to impact concerns (consequence,
collaboration, and refocusing). In the awareness stage (Stage 0), a
person has either little knowledge of or little involvement with the
innovation. Self concern refers to the questions we ask when we hear
about an innovation (Stage 1, informational) and think about how the
innovation may affect us (Stage 2, personal). Task concerns emerge as we
learn new skills such as time management and material usage (Stage 3,
management). Impact concerns describe our thoughts on how we can make an
innovation work better for our students (Stage 4, consequence), how to
make it work better by actively improving it with colleagues (Stage 5,
collaboration), and, ultimately, how to be successful with the
innovation and seek out positive changes to implement (Stage 6,
WBI, for the purpose of this paper, is the use of Web-based
computer devices such as desktop computers, laptops, handheld computers,
software, the Internet, and Learning Management Systems for
instructional purposes (Hew & Brush, 2007). It is defined as a type
of blended learning in which a significant portion of instructional
activities is delivered online but traditional classroom instruction is
not eliminated (Garnham & Kaleta, 2002). Most faculty members are
expected to have concerns when integrating WBI into their teaching.
However, these concerns will be different according to the stage of the
adoption process they find themselves in. Rogers (2003) presented the
steps in the innovation process as follows: knowledge, persuasion,
decision, implementation, and confirmation. In general, when people are
confronted with a new technology, they will gather information, test the
technology, and then consider whether the new technology is a sufficient
improvement to warrant the investment of their time and energy to learn
the skills required to use it (Rogers, 2003). Based on Roger's
innovation process, five different categories of faculty members are
assumed to appear in the WBI adoption process: 1) Faculty Unaware, who
have little interests in the adoption of WBI, 2) Faculty Aware, who are
aware of WBI and gather information about it, 3) Faculty Adopters, who
apply WBI to their teaching, 4) Faculty Implementers, who regularly use
WBI, and 5) Faculty Integrators, who are interested in extending the use
of WBI in their teaching practices.
Based on Hall's (1978) concern-based adoption model, faculty
members are expected to experience different concerns during the process
of innovation: awareness concerns for Faculty Unaware, information
concerns for Faculty Aware, personal concerns for Faculty Adopters,
management concerns for Faculty Implementers, and impact concerns for
Faculty Integrators. Hall's last three stages of concern
(consequence, collaboration, and refocusing) were combined into one
concern, called the impact concern, in this study because faculty
members experience all of these concerns after adopting the technology
into their classrooms.
Support factors that decrease faculty concerns about adopting WBI
How can we reduce these WBI-related concerns? Previous studies have
suggested various factors that may contribute to technology adoption
(Inan & Lowther, 2010; Ngai, Poon, & Chan, 2007; Sahin &
Thompson; 2007, Selim, 2007, Wang & Wang, 2009). These factors
include staff development opportunities, time, prompt technical,
incentives and positive attitudes towards the technology (Buckenmeyer,
2001), improved student learning, equipment availability (Hew &
Brush, 2007), ease of use, time needed to learn the skills required to
implement the new technology, compatibility with materials, training,
administrative support, personal comfort and colleague use (Olapiriyakul
& Scher, 2006), perceived value, available resources and
communication with other adopters (Keengwe, 2007), mission statements
and institutional culture, faculty development programs (Kahn &
Pred, 2000), personal conviction, motivation and experience, and
organizational support. According to Bradshaw (2002), different support
factors are needed based on the faculty member's stage of concern.
For instance, faculty members that are concerned with information need
to know more about the technology. In this study, therefore, the support
factors were categorized according to faculty stage of concern. The
following table summarizes the support factors and details the
strategies that may decrease each type of concern (Table 1).
Faculty members with information concerns should be given
entry-level information via available media, individually or in groups.
At this stage, faculty members need basic training and technology
support. Basic training includes downloading presentation software and
learning how to develop simple presentations, and use Internet
resources, e-mail, and simple course management software features, such
as creating a syllabus. Technology support is also important to reduce
this type of concern, which includes hardware and software support,
access to technical staff for Web-based course development and Web- page
development, and personal computers equipped with the required software
applications (Hew & Brush, 2007; Zhao, Pugh, Sheldon, & Byers,
Faculty members with personal concerns should be granted the
opportunity and encouragement to learn and talk about the technology and
how to best use the technology. At this stage, the faculty members are
in learning mode and need intermediate training and instructional
support. Intermediate training must have an instructional focus that
first guides faculty members to think about their teaching styles and
then helps them decide how to integrate the web-based technology into
their style of teaching. They must understand how technology can support
their educational objectives. The training should help faculty members
effectively use web-based technology in the classroom, incorporate
creativity in their presentations, use course-management software, and
troubleshoot. In addition to training, instructional support is needed;
faculty members work with an instructional designer to learn more about
the instructional use of technology and to design, develop, and evaluate
Faculty members with management concerns need practical help in
developing and implementing web-based activities. The strategies used to
reduce the management concerns include peer tutoring, administrative
support and incentives. Peer tutoring involves faculty mentors working
individually with those seeking help on their projects on an as-needed
basis. This encourages faculty members to "share expertise,
perspectives and strategies with each other" (Newcombe &
Kinslow, 2000). Faculty incentives include funding for technology
purchases, providing promotion/tenure as external motivation for faculty
members who integrate new technology methods, and time to a) develop and
maintain the web-based technology, b) learn more about the technology,
and c) attend training sessions and any other support activities. Lack
of administrative support is often cited by faculty members as a key
barrier to adopting new technology (Dooley & Murphrey, 2000).
Administrative support involves creating an institutional climate
conducive to technology use, and providing equipment.
Finally, faculty members who have impact concerns need to be
involved in envisioning and planning the use of the technology
(Bradshaw, 2002). The strategies used to reduce impact concerns should
focus on decreasing all three types: consequence, collaboration, and
refocusing. They consist of advanced training and administrative
support. Advanced training aims to provide information on analyzing
students' performance and on evaluating different strategies.
Administrative support should provide opportunities for faculty members
to work together each other so that they can share the knowledge and
skills that they have learned, and should improve the quality of
instructional and administrative support (Keengwe, 2007).
Modeling the WBI adoption process with system dynamics methodology
In this study, a system dynamics approach was adopted to
investigate the behaviors of faculty members as they adopt WBI. System
dynamics is a method used to enhance the learning of complex social
systems by helping interested persons to learn about the complexities
involved, understand the sources of resistance to policies, and design
more effective policies (Sterman, 2000)
An important step towards understanding a complex social system is
determining policy structure. A policy structure needs to be created to
represent the complexity of a certain system (Richardson, 1996). From
the perspective of policy structure, an aggregated policy-structure
diagram of the WBI integration model is presented in Figure 1; it
demonstrates the conceptual representation of the associations among
faculty groups and concern rates.
[FIGURE 1 OMITTED]
In the policy-structure diagram, the rectangular sectors represent
the structures by which the behaviors of faculty members are determined
by several variables. The circular sectors in the diagram represent the
structures by which rates of concerns are determined by a number of
associated variables. The solid lines represent the material or physical
flows that move between the sectors, faculty members. The lines
represent information flows transmitted among sectors and the effects of
the concern rates. For instance, the Impact Concern Rate is based on the
values of the two associated factors, "Advanced training" and
"Administrative support for impact." After the value of the
Impact Concern Rate is decided upon, the sector sends information, the
"Influence on Impact Concern Rate," to the "Behaviors of
Faculty Implementer" sector. The faculty members in the
"Faculty Implementer" group then decide their behaviors based
on the information they receive from the other sectors. These potential
behaviors include moving forward to become "Faculty
Integrators" or falling back to become "Faculty
Adopters." The core assumption is that the more "Advanced
training" and "Administrative support for impact" that
are provided, the lower the "Impact Concern Rate" will be. As
a result, more "Faculty Implementers" are moving forward to
become "Faculty Integrators" than are falling back to become
Another key concept involved in understanding a complex system is
feedback structure, i.e., the causal relationships among variables
(Richardson, 1996). There are two kinds of causal loops:
positive/reinforcing loops and negative/balancing loops. A change in the
value of a particular variable in a positive loop will result in a
behavioral change in the system that will eventually strengthen the
original change. On the other hand, a change in the value of a
particular variable in a negative loop will result in a behavioral
change in the system that will eventually counteract or weaken the
original change. A causal loop diagram for WBI integration is shown in
Figure 2. The arrows in the diagram represent the direction of
influence. For instance, an arrow that goes from A to B means that A
affects B. In addition, a positive or negative sign next to an arrow
implies that a positive or negative association exists between the two
connected variables. When two variables are positively related, they
behave in the same manner, meaning that as the value of one variable
increases or decreases, the value of the other variable increases or
decreases as well.
[FIGURE 2 OMITTED]
For example, when the value of "Faculty Entering"
increases, the value of "Faculty Unaware" also increases since
"Faculty Entering" has a positive effect on "Faculty
Unaware." Similarly, when the value of the "Faculty
Entering" decreases, the value of the "Faculty Unaware"
decreases as well. As we discussed previously, there are two types of
feedback loops used in the model: reinforcing positive loops, which are
referred to as (R) loops, and balancing negative loops, which are
referred to as (B) loops. Take the R1 loop, for example: when the value
of "Faculty Unaware" increases, the values of the
"Unaware Faculty Dropping" and the "Dropped Unaware
Faculty" variables also increase. An increase in the value of
"Dropped Unaware Faculty" results in increased values for both
"Returning Unaware Faculty" and "Faculty Entering,"
which will eventually lead to a further increase in the value of the
In line with the notions of complex social systems and
feedback-loop structures that have been proposed, this study considers
the adoption of WBI by faculty members as a complex social system that
involves various interactions among a number of relevant factors. A
system dynamics approach provides both individual faculty members and
decision makers/administrators with comprehensive insights into how the
indicated relevant factors affect the level of their concerns regarding
WBI and as a result influence the behaviors of the faculty members
during the WBI adoption process.
A simulation model that tests the impacts of support factors
Structure of the simulation model
A simulation model was constructed to investigate the impacts of
the factors that decrease faculty concerns about adopting WBI in
higher-education settings. Because simulation is flexible enough to
accommodate faculty dropout, constraints and uncertainty in the model,
and testing of potential impacts of the parameters in the model,
simulationbased methods are employed in this study rather than actual
data-collection methods such as surveys and interviews. The STELLA
modeling software was chosen to build the WBI adoption process model.
STELLA has several advantages over other simulation modeling software,
including an easier graphical interface, better classification of
variables in the system, easier description of the relationships between
variables, an automated process for running computations, and the
ability to visualize results with graphical output (Carr-Chellman, Choi,
& Hernadez-Serrano, 2001; Ruth & Lindhlom, 2002). STELLA allows
the researcher to view the simulation variables and stock-and-flow
structures in terms of the causal relationships among them (see Figure
[FIGURE 3 OMITTED]
In this model, all faculty members are classified into one of five
faculty groups based on their experience with WBI: "Faculty
Aware," "Faculty Unaware," "Faculty Adopter,"
"Faculty Implementer," and "Faculty Integrator." In
addition, four main faculty concern rates (Information Concern Rate,
Personal Concern Rate, Management Concern Rate, and Impact Concern Rate)
are included in the model based on their individual effects on the
behaviors of particular faculty groups. Support factors were
incorporated into the simulation model at different concern stages
during the adoption process. For instance, a Faculty Adopter may
experience a management concern as he or she moves toward being a
Faculty Implementer, his or her concern rate would be determined based
on the combinations of three support factors: the effects of the
administrative support for management, the effects of faculty
incentives, and the effects of peer tutoring.
User interface of the simulation model
A user-friendly interface was developed for the proposed model that
is easy for administrators and faculty members to operate. The interface
was designed to help users develop a general understanding of WBI
through customized simulations and may also be used to help
administrators select appropriate policies to facilitate the use of WBI.
The interface, as presented in Figure 4, includes two main parts: the
parameter adjustment and the simulation outcome.
The parameter adjustment, which is located at the bottom of Figure
4, includes four divisions that correspond to the different levels of
faculty concerns: "Information Concern Factors,"
"Personal Concern Factors," "Management Concern
Factors," and "Impact Concern Factors." Each concern rate
can be changed by adjusting the slide bars that represent the key
support factors affecting each concern rate. For example, for the
support factor, Basic Training, which falls under "Information
Concern Factors," a user can select a value between 0 and 30,
indicating the total number of hours of technical training per semester.
The ranges used were determined based on a review of the literature on
the diffusion of online learning and technology integration. Table 2
summarizes the support-factor ranges included in the simulation model.
The default value for "Basic Training" is 15 hours per faculty
member per semester. If an administrator intends to increase the number
of training hours to 20 hours per semester, then the slide bar should be
set to 20. The parameter adjustment also includes a brief description of
each variable; for example, the "Technology Support" factor,
which falls under "Information Support Factors," includes the
information that one technician should be provided for every 100 faculty
members. By referring to the brief descriptions provided, a user will
understand the meaning of the variable "Technology Support"
and make the proper adjustments for his or her simulation. For example,
by setting the value of "Technology Support" is set to 5, the
user assumes that for every 100 faculty members a total of five
technicians are assigned to provide technical support.
[FIGURE 4 OMITTED]
The simulation outcome, which is located at the top of Figure 4,
shows the results of the simulation. Users can select values for all the
variables based on their own assumptions, and click the "Click to
Run" button at the bottom to run the simulation. The model then
automatically performs the requested simulation and generates
corresponding graphs and numerical information when the simulation is
complete. The X-axis represents the time period for the simulation; for
example, the X-axis of the graph in Figure 4 which has an index of 0 to
72 indicates that the simulation time frame is 72 months. The Y-axis of
the graph represents the values of the five faculty groups at individual
points of time. On the top of each of the four sections of concern
factors, an information display bar is presented to show the assigned
value of the concern rate associated with a particular section in a
particular simulation. For instance, the "Information Concern
Rate" is 0.5, as shown on the information display bar, when the
value of the "Technology Support" is equal to 3 and the value
of the "Basic Training" is equal to 15.
Examples of the simulation run
The simulation environment for the base run was developed based on
a university with a total of 600 full-time faculty members. Since it was
difficult to determine the specific number of faculty members in each of
the five WBI faculty groups, researchers in this study generally assumed
that the initial number of faculty members in each of the five WBI
faculty groups was the same: 120. Figure 5A shows the result of the base
simulation run, in which we assumed that the university administrators
provided the proper support and managed to keep all of the concern rates
at 0.5. The X-axis represents the time in months, while the Y-axis
represents the number of faculty members in each of the faculty groups.
Figure 5A shows that the number of faculty members in the Faculty
Unaware (Graph 1) and Faculty Aware (Graph 2) groups decreased
noticeably over a time period of 18 months, while the number of faculty
members in the Faculty Adopter (Graph 3) and the Faculty Integrator
(Graph 5) groups increased considerably within the same period of time.
[FIGURE 5A OMITTED]
[FIGURE 5B OMITTED]
For the simulation, researchers created a couple of possible
scenarios. In the first scenario, it is assumed that the university
administrators intended to encourage the Faculty Aware individuals, the
faculty members who have been aware of WBI, to become Faculty Adopters,
that is faculty members who have started to use WBI, within a relatively
short period of time. As a result, the university administrators aimed
to reduce the concern rate of the individuals in the Faculty Aware group
(the personal concern) to 0 by providing the maximum level of
"Instructional Support" and "Intermediate Training."
Figure 5B shows the results of the first simulation run. The results
indicate that the number of Faculty Adopters (Graph 3) dramatically
increased over the first four months. This result implies that more
faculty members will adopt WBI when suggested supports such as
"Instructional Support" and "Intermediate Training"
[FIGURE 6A OMITTED]
[FIGURE 6B OMITTED]
In another scenario, it is assumed that the university
administrators were planning to encourage more Faculty Adopters, faculty
members who have been starting to apply WBI, to become Faculty
Implementers, that is, faculty members who regularly use WBI in their
teaching practice, within a relatively short period of time. As a
result, the university administrators aimed to reduce the concern rate
of the Faculty Adopters (management concern) to 0 by providing them with
the maximum levels of the relevant types of support:
"Administrative Support for Management," "Faculty
Incentives," and "Peer Tutoring." Figure 6B shows the
results of this simulation run. The result indicates that the numbers of
both Faculty Adopters (Graph 3) and Faculty Implementers (Graph 4)
increased within the same period of time. This implies that more
"Faculty Adopters" are persuaded to become "Faculty
Implementers" when the management concern rate is decreased to 0.
The results of the simulation also show that a decrease in the
management concern rate smoothes the progress of faculty members
adopting WBI. For instance, in Figure 6B, the total number of
"Faculty Integrators" (Graph 5) jumps to around 180 in the
eighteenth month. After the fourteenth month, the majority of the
faculty members choose to become "Faculty Adopters,"
"Faculty Implementers," or "Faculty Integrators."
The main purpose of this simulation model was to show
administrators and other decision makers how a change in the type of
support factors provided to faculty can affect the adoption of WBI. This
model will help administrators to better understand the dynamics of the
various related to the adoption of WBI by their faculty members. By
using this model, administrators can plan how many faculty members can
move from one stage to the next within a predetermined time period.
These results will provide them with the evidence they need to persuade
as many faculty members as possible to become faculty integrators and
will convince them to fund workshops and improve incentives to allow new
technology to be adopted. Although this simulation model was designed
mainly to help administrators of educational institutions make good
decisions that will result in increased use of WBI, it is also expected
to be useful for individual faculty members. By using this simulation
model, individual faculty members can get an idea of the kinds of
support factors available and how much these factors may decrease their
concerns. For example, a faculty member with information concerns could
identify the best combination of support factors to lower those concerns
and request that administrators provide those support factors such as
more technology support and basic training opportunities. The model will
help researchers and educational administrators to diagnose and evaluate
the potential impacts of factors that are predicted to lower faculty
concern and contribute to the successful adoption of WBI by the entire
The simulation model developed for this study suggests several
possibilities for future studies. First, a more accurate model that
represents the actual change process should be developed, since unknown
factors may still remain. More specifically, further efforts such as
surveys and interviews with faculty members are needed to clarify the
factors that affect each stage of concern. Second, future studies should
include relative importance of the support factors in the model. Because
the each support factor may have somewhat different significance, future
studies should include the relative importance that each factor may have
when it is used in the model. Third, interrelations between factors
should be thoroughly investigated. It is assumed that each support
factor such as faculty incentives and peer tutoring contributed
individually to decreases in faculty concerns. However, in practice,
these factors may be interrelated and thus, future studies should
identify these interrelationships. Therefore, future study should find
the interrelationships. Lastly, more factors should be incorporated into
the model. Each factor may include various sub- factors, and future
studies should include sub-factors that may affect the concerns of the
faculty members in order to build a better representative model of what
occurs in reality.
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Hae-Deok Song (1), Wei-Tsong Wang (2) and Chao-Yueh Liu (3)
(1) Department of Education, Chung-Ang University, Seoul, Korea //
(2) Department of Industrial and Information Management, National Cheng
Kung University, Tainan City, Taiwan // (3) Department of Public
Administration & Policy, State University of New York at Albany,
Albany, USA // email@example.com
Table 1. Faculty Concerns, Support Factors, and Strategies
Concerns Support Factors Strategies
Faculty Unaware Basic training Basic application software, use
(Information of the Internet resources, email
Concerns) and simple course-management
Technology Support for hardware and
support software, access to technical
staff, and other equipment
Faculty Aware Intermediate Effective use of web-based
(Personal training technology in the classroom, use
Concerns) of course management software,
Instructional Working with an instructional
support designer to learn more about the
instructional use of technology
Faculty Adopter Peer tutoring Working with faculty mentors on
(Management their projects on an as-needed
Faculty Funding for technology purchases
incentives (hardware and software),
Faculty Advanced Assessment training (how to
Implementer training analyze student performance and
(Impact evaluation strategies)
Administrative Institutional climate for
support technology, providing external
motivation (e.g., promotion or
releasing a course)
Table 2. Ranges for Support Factors
Concern Factor Range Unit
Information Technology 0-20 Technicians for every 100
Concern Rate Support faculty members
Basic Training 0-30 Hours of basic technical
Personal Instructional 0-20 Instructional specialists
Concern Rate Support for every 100 faculty
Intermediated 0-45 Hours of training in
Training instructional use of
Management Administrative 0-1 Level of satisfaction
Concern Rate Support with motivational support
Faculty 0-3500 Amount of reward per
Peer Tutoring 0-30 Hours working with
Impact Concern Administrative 0-1 Level of satisfaction
Rate Support with assessment,
Advanced 0-60 Hours of training for
Training refining current use of