Adoption Factors and Processes

Brent Wilson, Lorraine Sherry, Jackie Dobrovolny, Mike Batty, and Martin Ryder

Information and Learning Technologies

University of Colorado at Denver

email: brent.wilson@cudenver.edu

Final reference: Wilson, B., Sherry, L., Dobrovolny, J., Batty, M., & Ryder, M. (2001). Adoption factors and processes. In H. H. Adelsberger, B. Collis, & J. M. Pawlowski (Eds.), Handbook on information technologies for education & training (pp. 293-307). New York: Springer-Verlag.

Abstract: As individuals and organisations complete the process of adopting new technologies to support learning, a number of factors come into play–including the technology’s design and usability; the fit with local culture and practices; the associated costs; and the expected benefits of adoption. Some factors are about the technology, others about the prospective user, still others about the local context of use. In addition to descriptions of factors and users, researchers have identified stages and repeating patterns that shape the adoption process. This chapter reviews these various factors and processes with an emphasis on school and university settings. We conclude with a reminder that adoption of technology depends on shared negotiation of values and priorities.

Introduction

For more than forty years, information technology (IT) has been part of the infrastructure supporting schools and universities. Essential functions such as central planning, budgeting, scheduling, grading, and maintaining student records have drawn on IT resources, beginning with mainframe computers and migrating to other platforms. Now routine business tasks are distributed throughout the workplace. Individual departments and faculty members regularly use tools like word processing, spreadsheets, publishing tools, email, and the Web. In these respects schools are similar to other businesses, drawing upon IT resources to perform the routine tasks required to stay in business.

Direct support for learning is a more specific use of IT, also with a history. The computer-based training systems of the past, once considered exotic, have their counterparts in the thousands of multimedia or hypertext programs available in different subject areas, accessed via CD-ROM or web. A variety of instructional formats are available, including simulation, tutorial, help systems, integrated learning systems (ILS), and teacher demonstration programs. In addition to instructional software, educators make classroom use of productivity tools and general-purpose programs. These programs are integrated into the curriculum through specially developed lessons and units. Students, working in a classroom or lab, are required to find information, create products, or solve problems using commonly available tools such as word processing, email, graphics tools, and Web browsers.

Technology integration into schools and universities certainly is not an anomaly–rather, schools have usually followed business and government in the adoption of new technologies. Many people assume the move toward technology is inexorable–we really have no choice if we want to survive in our present age. The pace of change is often said to be accelerating, with technology a big part of that rapid change.

How are we to understand the process of adopting technologies for learning? Why are some technologies adopted and some not? Why do some faculty or schools readily embrace new tools, while others are very slow to change? What factors are at play as people and organisations begin using new technologies? Our purpose in this chapter is to outline some key ideas underlying the diffusion and adoption of learning technologies. Because this area has been heavily studied for more than thirty years (cf. Burkman, 1987; Cuban, 1986; Farquhar & Surry, 1994; Holloway, 1996; Sherry, 1998a; Sherry, 1998b; Sherry, Billig, Tavalin, & Gibson, 2000), our review will be necessarily selective. We highlight key concepts and bring occasional new perspectives into the discussion.

Metaphors for Technology Adoption

Over the years, researchers have changed their views of technology adoption, just as they have changed their views of learning. Indeed, adoption is in many ways a learning process for individuals and organisations. Table 1 conveys three ways of viewing technology adoption, each relying on a fundamentally different metaphor of learning.

Table 1: Three views of technology adoption, based on behaviourism, cognitive learning theory, and cultural studies.

Technology adoption as…	Based on…	Outcome stressed…	Common research method…
Consumer behaviour	Behaviourism Market research Economic theory	Purchase and installation behaviours	National and regional demographic surveys
Information diffusion and rational choice	Information and organisational theories Cognitive psychology	Information leading to decision to adopt	User surveys within an organisation or department
Assimilation of cultural tools and practices	Anthropology Cultural studies Activity theory	Interactions and practices within a local community	Ethnographies or case studies

Seen as consumer behaviour, technology adoption can be measured in terms of units purchased or number of programs installed. This is consistent with behaviourist models: What users are thinking is secondary to their behaviour. General surveys at the state or regional level become useful benchmarks of adoption levels over time (e.g., Becker, 1994). These demographic data then become valuable information in the hands of policymakers and administrators seeking to allocate resources in fair and effective ways.

Adoption can also be seen as a process of information diffusion, culminating in a rational choice to use (or not use) the new technology. This perspective relies principally upon a view of learning as information acquisition (cf. Mayer, 1992, 1996). A prospective user engages in a process of inquiry concerning the technology (Hall & Hord, 1987; Rogers, 1995). After learning more about the pros and cons, the user (or group of users) commits to a testing, following by a full-scale adoption and implementation of the technology.

Finally, technology adoption can be seen as the assimilation of new cultural tools and practices. This view is consistent with theories that stress learners’ participation within communities of practice (Lave & Wenger, 1991). The focus is on socially constructed meanings and the sharing of those meanings through participation in purposive activities. The technology itself, in addition to its physical form and function, is also a social construction whose meaning is shared among community members. How the technology fits into existing social purposes and practices will largely determine its prospects for its appropriation and use by the community.

While acknowledging the utilitarian value of demographic surveys, we will focus on the information and social-practices views. To some extent, these latter two views complement each other well, emphasising in turn the mutual roles of individual and community in the adoption process.

The term ‘learning technologies’ is a surprisingly open concept. A technology is an artefact designed to address a specific problem or need in the world. While we usually refer to hardware and software tools when speaking of learning technologies, a learning technology is often more than that. Learning technologies may be resources intended for self-guided learners, designed interventions for instructional use, or new methods and models that solve specific instructional problems.

Is the Web a learning technology? Certainly, but it encompasses a whole array of tools, resources, and supporting infrastructure (Collis, 1996). More importantly, diffusion and adoption of the Web requires a change in mindset, a re-thinking of what is possible. The Web is an important carrier of social meanings and practices, as the third metaphor suggests.

Lowry (1996) defines three different relationships of technology to end user, each with different adoption concerns:

Market-type adoption. In this case, the technology is intended for mass distribution, like a textbook, software program, or hardware innovation. Examples would be Dreamweaver as a web-authoring tool or an upgraded PC platform that allows easier sharing of data among peripherals. The relationship between the developer and the end user is distant, and responsibility for successful adoption rests primarily with the adopting organisation.

Client-type adoption. In this case, a contractor or consultant develops a technology for a particular client. This custom-developed resource may draw on some generic technologies, but the designed solution addresses the specific concerns of the client. Resources of this kind are most commonly software programs, but a number of innovations and resources can be developed at this level. In these cases, designers and end users share responsibility for successful adoption of the resource.

Classroom-type adoption. Many times a teacher herself develops a technical solution or resource, with intended use limited to her own classroom or program. Here the designer and user roles are combined into one person, and adoption fades as an issue because the teacher is presumably aware of her own needs.

For reasons of scope, our discussion of technology adoption is limited to the first two categories–market-type and client-type technologies. However, for an interesting market analysis of distance-learning technologies in higher education, see Archer, Garrison, & Anderson (1999). This paper, based primarily on Christensen’s (1997) economic model explaining how well-run companies can go out of business, approaches distance-learning programs as "disruptive technologies" that fundamentally threaten the established delivery methods in universities and colleges (see also Daniel, 1996, 1997).

Facilitating Conditions

A key question pertinent to our discussion is, what conditions are favourable to technology adoption? What conditions within an organisation or group will tend to support successful technology adoption? Developing a list of contributing factors is a fairly practical form of theory development–not necessarily explaining underlying processes, but providing useful guidance to those responsible for technology adoption within a school or university.

Ely (1990, 1999) reported one such framework of facilitating factors. Based on field research in Chile, Peru, and Indonesia, Ely’s list includes attention to technology, human, and contextual variables (Ely, 1976). He and his students conducted a number of correlation studies to add empirical support to the framework, summarised in Table 2 below. The table presents each condition along with a short description and citations of supporting studies and articles.

Table 2: Eight conditions that facilitate the implementation of educational technology innovations (adapted from Ely, 1999).

Condition	Description	Linked to…
Dissatisfaction with the status quo	Feeling a need to change.	Leadership
Expertise	Access to the knowledge and skills required by the user.	Resources, rewards & incentives, leadership, and commitment
Resources	Things needed to make it work–funding, hardware, software, tech support, infrastructure, etc.	Commitment, leadership, and rewards & incentives
Time	Prioritised allocation of time to make it work.	Participation, commitment, leadership, and rewards & incentives
Rewards or incentives	Internal and external motivators preceding and following adoption.	Participation, resources, time, and dissatisfaction w/status quo
Participation	Shared decision-making; full communication; good representation of interests.	Time, expertise, rewards & incentives
Commitment	Firm and visible evidence of continuing endorsement and support.	Leadership, time, resources, and rewards & incentives
Leadership	Competent and supportive leaders of project and larger organisation.	Participation, commitment, time, resources, and rewards & incentives

Another project that studied conditions was the Peakview project (Wilson & Peterson, 1995; Wilson, Hamilton, Teslow, & Cyr, 1994). Colorado’s Peakview Elementary School opened its doors to students in 1993, using computers and software instead of textbooks. Wilson and his team of researchers found that teachers and students quickly embraced the technology and integrated it successfully into a progressive curriculum. Wilson’s research pointed to a number of conditions that contributed to the school’s success, including a supportive principal, a full-time tech co-ordinator, abundant technology, and extensive teacher training. This research, and many studies like it, can be made to fit Ely’s framework quite comfortably.

Features of the Technology

The leading researcher of the adoption of innovations is Everett Rogers (1995). While not specific to education (encompassing innovations in a number of domains, from agriculture to medicine to technology), his work continues to guide theory and practice in educational technology innovations. Construing the process of adoption primarily in information-diffusion terms, Rogers developed a list of six perceived features of the technology that largely determine its acceptance. Here the technology is the focus rather than the environment or external conditions. The acronym STORC helps make the list a memorable tool for practitioners:

S Simplicity (or conversely, complexity). Is the innovation easy to understand, maintain, and use? Can it be easily explained to others?

T Trialability. Can the innovation be tried out on a limited basis? Can the decision to adopt be reversed?

O Observability. Are the results of the innovation visible to others, so that they can see how it works and observe the consequences?

R Relative Advantage. Is the innovation seen as better than that which it replaces? Is the innovation more economical, more socially prestigious, more convenient, more satisfying?

C Compatibility. Is the innovation consistent with the values, past experiences, and needs of the potential adopters?

To this list, we add support:

S Support. Is there enough support to do this? Is there enough time, energy, money, and resources to ensure the project’s success? Is there also administrative and political support for the project?

These characteristics can be important benchmarks when a person considers whether to adopt or reject an innovation or technology. The more features present, the more likely the technology will be adopted. Like Ely’s framework, Rogers cites a number of research studies supporting these perceived features. Once a framework of contributing factors has been developed, it can be readily converted to a diagnostic tool to assess a situation, or into a prescriptive checklist to guide preparation for successful adoption.

A similar analysis of contributing conditions can help us understand why innovative projects often fail. Latham (1988), also cited in Dooley (1999), found a number of features common to failed innovations:

–Practitioners become disenchanted and disillusioned because the innovation is more difficult than expected, causing too much disruption and taking too much time.

–Innovation supporters leave or are not available.

–People lack training and lose enthusiasm.

–Funding runs out.

–There is inadequate supervision and support from management.

–The program lacks accountability.

–There is a "take-if-or-leave-it" attitude on behalf of program promoters.

Again, these negative conditions could be fit to Ely’s or Rogers’ frameworks. The negative phrasing can remind practitioners of dangers to avoid in their efforts to design effective interventions.

Users and Their Concerns

Everett Rogers (1995) is probably most famous for his typology of prospective users of an innovation–The term ‘early adopter’ has now entered mainstream business discourse. Noting that individuals respond very differently to innovations, Rogers conceived of a stable trait to account for these differences–with some people tending to be very change-oriented, and others being much slower to embrace change. The resulting scheme classifies people on a scale of receptivity to innovation:

• Innovators constitute a small minority of the population (2-3%). Innovators are venturesome and willing to take risks, and willing to invest the time and energy to learn and adapt to the demands of a new technology.

• Early Adopters (13-14% of the population) are often respected opinion leaders within an organisation. Their credibility and leadership are essential to successful adoption by the entire group.

• Members of the Early Majority (34% of the population) are more careful and deliberate. They are willing to adopt in due time, but unwilling to risk exposure in the process.

• Members of the Late Majority (another 34%) are sceptical of change and guarding of their interests. Peer pressure is often necessary to prompt these people to action.

• Laggards (an abominably value-laden label!) constitute about 16% of the population. Laggards consistently resist change out of fear, and comply only out of pressure or necessity.

Labels, for better or worse, are powerful markers of meaning. The idea that people fall on a receptivity continuum seems to have some empirical support, and can help us think about adoption in terms of meeting individuals’ needs. On the other hand, the same labels can be used as excuses for coercion or denial of resources–or to support a tacit assumption that a contemplated change is de facto desirable. Because of its heavy value-laden connotations, we would recommend against the use of ‘laggard’ for any purposes. Similarly, terms such as ‘techno-phobia’, ‘hand-holding,’–or even ‘resistance’ and ‘users’–carry connotative baggage that practitioners should be aware of. Change agents in particular should be careful that language doesn’t further aggravate some people’s sensitive feelings toward technology and change.

The Adoption Process

Conditions lists and typologies alone do not really explain technology adoption in a school setting. We need a deeper understanding of how change happens. What are the regular patterns or processes? Is there a predictable flow or cycle through which individuals and groups pass, as they move toward complete adoption and use of a new technology? In the section below, we explore efforts to articulate the process of adoption, either by progressive linear stages or by systemic cycles of change.

Stage Theories

Rogers (1995) is one of many researchers who represent the adoption process as a series of linear stages. His five-stage model is outlined below. Note the heavy role of information acquisition in the stages:

Stage 1: Knowledge. The person (or group) comes to know about the innovation and begins to learn about it, resulting in increased knowledge and skill.

Stage 2: Persuasion. The person forms an attitude or image (positive or negative) about the innovation through discussion and interaction with others.

Stage 3: Decision. The person resolves to seek additional information, leading to a decision to accept or reject the innovation.

Stage 4: Implementation. The person gains additional information needed to put the innovation into regular use.

Stage 5: Confirmation. The person looks for benefits of the innovation to justify its continued use. Use of the innovation is routinised and promoted to other people. Or conversely, the decision to use is reversed based on negative evidence.

A group of psychologists (Prochaska, DiClemente, & Norcross, 1992) developed a very similar 5-stage model to explain personal change, particularly with cessation of addictive behaviours. These researchers noted that individuals will very often move back and forth between stages as they eventually commit to change. Then, over a considerable period of time, individuals integrate the changed behaviours into their everyday routines. We believe the same pattern of varied movement is true in many cases of technology adoption.

Technology Integration: The ACOT Model

For much of the 1980s and early 1990s, Apple Inc. sponsored a continuing research program called the Apple Classroom of Tomorrow (ACOT). The ACOT program endowed a number of American schools with generous gifts of computer resources, then commissioned researchers to observe the effects of the technology on the teaching and learning process. The ACOT research sheds light on what happens when schools receive large numbers of computers directly placed in classrooms. Generalising across ACOT projects, Apple researchers (Dwyer, Ringstaff, & Sandholtz, 1991) observed five general phases of implementation, summarised below. These phases occurred in different schools dating back to 1986.

1. Entry phase. In this initial phase, teachers "struggled valiantly to establish order in radically transformed physical environments" (Dwyer, et al., 1991, p. 47). With the expected problems of beginning a school year, facing the added problems and benefits of computers was definitely a challenge for some teachers.

2. Adoption phase. Once teachers had recovered from the initial shock, the technology began to be integrated into the traditional classroom. Even though the arrangement was very different physically, traditional lecture and textbook teaching methods predominated. Student attitudes were high, and teachers reported individual student effects, but overall student achievement was basically unchanged.

3. Adaptation phase. At this phase, traditional teaching methods were still in place, but they were consistently supported with computer activities, particularly the use of word processing, database, some graphics programs, and computer-based instruction. Productivity and efficiency were the salient changes reported by teachers; for example, a computer-based math curriculum allowed 6th graders to finish in 60% of the time normally required.

4. Appropriation phase. This phase began in the second year of a project. "The change hinged on each teacher's personal mastery–or appropriation–of the technology" (p. 48). The teacher's increasing confidence in the technology, and time with the technology, resulted in more innovative instructional strategies. This phase was marked by "team teaching, interdisciplinary project-based instruction, and individually paced instruction" becoming more common at the sites.

5. Invention phase. This phase is less an actual phase than a mindset, implying a willingness to experiment and change. "Today, the staff of ACOT's classrooms are more disposed to view learning as an active, creative, and socially interactive process...Knowledge is now held more as something children must construct and less like something that can be transferred intact" (p. 50).

The use of computers thus can serve the role of change agent within the classroom environment, affording and stimulating reflection, redesign, and renewal of effective practices.

In the evaluation of several large-scale educational technology projects, Sherry and her colleagues (2000) found that teachers tended to go through five developmental stages. These were identified as learner, adopter, co-learner (with their students), reaffirmer or rejecter, and leader. Different strategies appeal to these teachers at different stages (Sherry et al., 2000, p. 45). One example of a successful strategy would be providing release time and new role assignments to allow teacher-leaders to serve as peer coaches and onsite trainers.

To conclude, theorists have posed a number of different stage models for technology adoption and implementation. These models typically begin with information-finding and attitude formation; then to commitment or decision to use the new technology; then to implementation and integration of new practices. Of particular interest to many policymakers is how a new technology gets integrated into everyday practices, allowing affordable, sustainable change to occur once initial investments have been made (Elmore, 1996). Also of interest is how a technology continues to evolve as users face new needs, challenges, and opportunities. This process, called "re-invention" by Rogers (1995), is particularly relevant to the constantly changing uses of technology in schools and universities.

Stage models such as the Rogers, ACOT, or Sherry models can provide a heuristic to practitioners by laying out a broad, roughly linear progression for change. Such models should not, however, be applied rigidly to force a linear or compelling move toward adoption. More important than the specific stages are the activities and changes underlying them–the individual and organisational learning that occurs over time. In the section below, we briefly explore ideas from activity theory and systems or complexity theory that relate to these underlying processes.

Activity Systems and Feedback Loops

Two interdisciplinary theories, both ascending in popularity over the past ten years, help explain how groups and individuals effect change. Activity theory is based on the work of Lev Vygotsky (Vygotsky, 1978) and his Soviet followers (Leont’ev, 1978). Vygotsky saw cognition as essentially a social or inter-subjective activity. Individuals work and learn within groups and communities that possess a relatively stable organisational structure. People interact purposefully with others, using tools and resources, abiding by certain rules of exchange and according to defined roles and expectations. Tools are essential to meaningful production, and they have both a physical and a cultural (or meaningful) existence. The most important tool is language, of course. Through language, people make sense of and explain the significance of their lives and activities.

Vygotsky’s beliefs in the social origins of cognition were influenced by his research with children’s learning interactions with their parents and teachers. Children begin to think by interacting with adults and peers. Only later do meaningful activities become internalised in the form of mental activities such as constructed thoughts, representations, and abstract ideas. Vygotsky also stressed the distributed nature of cognition; that is, thinking and intelligence are distributed among a group of interacting people, and among their tools and resources.

These ideas have implications for adoption of learning technologies. In place of strictly cognitive conceptions of rational decision-making, we take a closer look at group interactions and cultural practices. We note the major impact of tools within the total activity system. We acknowledge how various tools and technologies embody knowledge and expertise. Precepts of activity theory have also influenced psychologists promoting constructivism, situated cognition, and learning communities (e.g., Barab & Duffy, 2000; Lave & Wanger, 1991; Sherry, 1998b; Wilson, 2000). Because of the close connection between adoption and learning, we expect these concepts will continue to influence adoption thinking in the future.

Systems theory has a longstanding tradition within both hard sciences and soft sciences such as anthropology (Bateson, 1972; Harries-Jones, 1995). Complexity theory is a variation, based on the kind of complex, adaptive systems that are open, organic, and self-organised rather than closed and mechanical. Complex systems are commonly found in nature–e.g, schools of fish, ant colonies, flocks of birds, etc. Other systems with self-organising qualities include the human brain, democratic bodies, and online communities (Wilson & Ryder, 1996). Recent formulations of complexity theory have been applied in business, educational leadership, and other practical settings (Senge, 1990; Wheatley, 1992).

Bateson (1972) articulated a key concept of systems theory, distinguishing "first-order" from "second-order" change. First-order change is learning how to do something new. Second-order change is learning new ways to learn. The second kind of change reflects a deeper penetration of the system’s rules and structures. Deep change like this can be powerful, but rarely occurs. Elmore (1996) remarks that educational innovations that have helped teachers to do what they are already doing–but to do it better–are far more likely to be adopted than educational innovations that change the core of the teaching and learning process.

Systems principles help explain how first-order and second-order change happens. Based on research in a different of domains, we find that complex systems exhibit peculiar self-organising behaviours that have implications for technology adoption:

Maintenance loops. Feedback loops send information from the outside back into a body or system. Sometimes the information is used to maintain a balance or equilibrium, allowing a steady state to continue over time. These "maintenance loops" may be at work in some groups that successfully resist the introduction of a new technology. The technology may come into conflict with deeply established routines and beliefs. Rather than complete the exhausting task of redefining these established practices, the technology is rejected. An example of a failed technology innovation was the "Student Instructional Technology Corps", a class taught by Sherry and her colleagues in the summer of 1999. Incoming university freshmen acquired the knowledge and skills to serve as work-study technical support staff within their chosen academic departments. However, they met with insurmountable hurdles from the university computer centre and payroll system regarding access to computer labs and lack of work-study positions within their respective departments. The bureaucratic structures overpowered the energies pushing for innovation, and a stable state was maintained through rejection of the innovation.

Accelerating loops. Other times, conditions within a system are ripe for change. Introduction of a simple item of information may be sufficient to generate interest and precipitate change. In this case, the information exchange begins modestly, but rapidly snowballs into a cumulative force. Each cycle of exchange serves to accelerate the pace or scope of change. Accelerating feedback loops can be exciting and even scary, because they are in danger of over-reaching, inviting a counter loop in the form of a backlash or consolidating action to correct excesses. But if a technology meets a critical need or enables a highly valued outcome, the result can be rapid, snowball-like adoption. The Web has exhibited this kind of growth trajectory in its first several years.

Combining the outlook of activity theory with the processes relating to complexity theory, we seem to be poised for new understandings of technology adoption. If the promise of these theories is realised, approaches to technology adoption may move past descriptive lists of conditions, or even stage theories of linear progress–toward a deeper understanding of underlying processes and relationships.

Concluding Thought: Continuing the Value Conversation

Rogers’ Diffusion of Innovations (1995) includes a final chapter on the consequences of innovations. In this chapter he examines the value implications of different innovations. Because not all innovations should be adopted, technologies need to be critically evaluated from utilitarian and moral perspectives before they are integrated into peoples’ lives.

Along with the rise of technology in recent years, critics’ voices have become increasingly prominent. David Noble (1989, 1996), Neil Postman (1995), and Theodore Roszak (1986) are names associated with the resistance movement. Technology critics often take on a post-modern stance, questioning the modernist assumptions of unerring technological progress, grand explaining narratives, privileged methods of inquiry, and objective meanings (de Vaney, 1993, 1994; Hlynka & Belland, 1991; Hlynka & Yeaman, 1992). Post-modern theorists are similar to activity theorists in their analysis of culture and practices, but they differ somewhat by distancing themselves from an objective, truth-finding agenda (Giroux, 1983, 1985). Their larger concern is to raise questions about current practices, and stimulate more conversation about fundamental values and aims.

Critics can be doubly irritating to technology advocates: They not only oppose something we tend to see value in, but they have such different worldviews! A scientific worldview often clashes with a view shaped by critical traditions in the arts and humanities (Wilson, 1997). But it is important to listen carefully to critical voices–and to learn from them. Sherry (1998b) found that late adopters were quite articulate in voicing their concerns about the impact of the Internet on their core teaching strategies. They felt that the Internet may not support their vision of learning. In order to integrate learning technologies into schools and universities successfully, leaders must be sensitive to the huge impact differing worldviews can have on the adoption process. Because schooling institutions often pride themselves in democratic processes of shared governance, we must continue the "values" conversation and maintain a respectful conversation concerning new technologies. Precisely because learning technologies are here to stay, discussion of values and goals are essential parts of the process, thus assuring that technology remains in the service of the community–and not the reverse.

For those interested in sharing these issues with colleagues, we recommend two very accessible reports technology implementation obstacles and solutions. Leggett & Persichitte (1998) describe five important obstacles identified in over four decades of research–lack of time, expertise, access, resources, and support–with a list of possible solutions. Sherry et al. (2000) offer a cyclical model of the learning/adoption process, with effective strategies for each stage.

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Author Notes

The authors are all affiliated in some fashion as instructors and researchers at the University of Colorado at Denver. Please send inquiries to Brent Wilson (brent.wilson@cudenver.edu).