Advances in the External Environment
In 1965, Intel co-founder Gordon Moore forecasted the exponential growth of computer processing power by observing that transistor density on integrated circuits doubles about every two years (Intel Corporation, 2005). In the time it takes a comprehensive literature review of technology in higher education to be published, new innovations are likely to emerge. Since the arrival of the Information Age, technology has been synonymous with computing power, electronic devices, and digital information. Historically, however, technological advances in the form of stone points predate modern humans.
By definition, information technology has existed throughout recorded history. Ever since information was captured and transmitted through clay, stone, papyri, paper, and digital medium, modern technology has progressed to make the information readily available and easily accessible. Some view the increase in technology as a disruptive change that will revolutionize education, while others view academic institutions as slower to respond to the methodical creep of the Digital Age.
Because technological change involves major shifts in the way information is gathered, stored, and transmitted, implications for higher education are both urgent and unclear. Barry Mills speaks to the immediacy of the external environment’s encroachment on the doorstep of his institution: “I am convinced that we cannot responsibly ignore the changing dynamics in the way that information is stored and delivered, because these changing dynamics will undoubtedly change our role as educators” (2011). The scope of this paper is to identify how the already documented advances in technology affect colleges, how those institutions respond to the affect, and how changes in the technological features of higher education are managed.
Innovation, Adaptation, and Diffusion
Individuals within organizations bring ideas from the outside into their work routines (or create their own new ideas from within), those ideas change their routines, and the good ideas are transmitted to others in the institution. These affects alone are not changes to the organizational level, but a response to the presence and impact of technology. Themes of innovation, adaptation, and diffusion are strategies often described in relation to technological change (Kezar, 2001; Renes & Strange, 2011). Innovation can be defined as a product, process or procedure within an organization that is new, intentional, not routine, aimed at producing benefits, and having public effects (Kezar, 2001, p. 14). Innovations in the technology of higher education can either be introduced from the external environment or created within the organization. These new, intentional changes might take the form of tangible products (e.g. computers, hard drives, projectors, smartphones), processes (e.g. electronic assessment), or procedures (e.g. electronic course registration). Adaptation refers to modifications and alterations in response to changes in an external environment (Kezar, 2001). Adaptation narrowly describes the types of changes that occur within an evolutionary change model, which is discussed in the next section. Finally, diffusion models are an important consideration for how individuals adapt to innovations, though they do not adequately describe organizational change.
Kezar (2001) describes diffusion as a series of phases that individuals traverse, moving from awareness to interest, evaluation, trial, and finally adoption. Renes and Strange (2011) reviewed studies on motivations to use technology in teaching and found that individuals at different levels are more or less likely to embrace innovative change. Drawing on the work of Rogers (1995) and Hagner and Schneebeck (2001), they identified a variety of labels used to described how members of a social system adopt innovations. Rogers identified “innovators” and “early adopters” as those who begin using new technology within a system, followed by “early majority” and “late majority” adopters who are subsequently introduced to the innovation and may require evidence to encourage their adoption (as cited in Renes & Strange, 2011). Furthermore, in a study of 240 faculty members, Hagner and Schneebeck (2001) identified four groups based on individual motivation to adopt new technologies: Entrepreneurs, risk adversives, reward seekers, and reluctants (as cited in Renes & Strange, 2011). The largest group, risk adversives, is characterized by a lack of technical expertise, a fear of new teaching environments, and a hesitation to engage in self-examination.
The diffusion process as a description of the way individuals adapt to innovation does not accurately represent how change occurs at the organizational level. However, it is important to understand the characteristics of individual members because their aggregate experience with the diffusion of technology has an impact on the overall change process.
Theoretical Model of Technological Change in Higher Education
“Writing about [information technology] is like taking a snapshot of a marathon that has no clear rules, no clear route, and new competitors being added at odd times” (Barratt, 2003). This simile helps to describe technological change in “third period” terms, viewing change as a process rather than an episode, and without clear beginning or end (Demers, 2007, p. 115). The process view of organizational change is also reinforced anecdotally by the president of Bowdoin College, Barry Mills, who writes: “Technology… will have the power, potentially, to incrementally, rather than disruptively, improve our educational model” (2011).
Specifically, the theoretical model of change that most adequately describes the process of adaptation to technological change in higher education is the behavioral learning approach, which is grounded in the natural evolution perspective. Unlike the population ecology framework, organizational evolution acknowledges that organizations are capable of responding to changes either internally or externally, and that they are not simply outlived by fitter organizational models (Demers, 2007). Organizations learn by adapting routines, which is an experimental (i.e. trial and error) process emphasizing perceived stability and change (Demers, 2007, p. 123). These changes occur as a result of a continuous process in which organizations develop relatively fixed rules that are designed to incorporate feedback into their evolving relationship with the environment.
Although individuals working in an organization have individual thoughts and emotions, at the systemic level, the organization is capable of “learning” and adapting as a whole. When learning “occurs simultaneously at various collective levels” within an organization, it is capable of learning and adapting in spite of individual differences in its constituent parts (Burke, 2011, p. 79). Learning organizations also demonstrate a capacity to change while promoting systemic thinking, and involve “widespread participation of employees… in decision making, dialogue, and information sharing” (Burke, 2011, p. 79). Organizations engaging in behavioral learning account for the flexibility and lack of restrictive direction necessary to adapt to turbulent and fast-paced technological changes entering from the external environment.
Facilitating Technological Change
Studies examining factors that influence the implementation of technology within higher education incorporate “third period” perspectives that utilize informal bottom-up and individualized behavioral learning approaches. Nicolle and Lou (2008) found “peer support along with institutional support and perceived improvement in student learning were key influences” (As cited in Renes & Strange, 2011, p. 207). Furthermore, the same study found that discussing technological innovations during informal lunch meetings was more productive than formal training by technology staff members. Nicolle and Lou (2008) concluded, “when faculty members can see a clear personal benefit for themselves and see an increase in learning potential for their students, they are more likely to begin using technology” (As cited in Renes & Strange, 2011, p. 207). These findings connect with several principles for change strategies identified by Kezar (2001). By articulating and maintaining core characteristics (emphasis on student learning), and connecting the change process to individual identity (clear personal benefit for faculty), successful adoption of innovative technologies can occur.
At the annual conference of the Professional and Organizational Development (POD) Network, an interactive workshop produced a list of “roadblocks, obstacles, and speed bumps” that stand in the way of technological change (Bruff, Harapnuik, & Julius, 2011). The list includes faculty mistrust of technology, faculty needing examples for effective use of technology, and lack of cultural openness to try new technology (Bruff et al., 2011). In this list, faculty acting as the “late majority” and risk adversives reiterate the role of the individual in adapting to technological change. Kezar’s change principle for creating a culture of risk and changing belief systems (p. 121) aligns with a key feature of the “third period” behavioral learning approach: Search rules.
The behavioral learning approach utilizes routine-based change, and search rules can be thought of as routines for minor problem solving. That is, the routines influence the range of innovative alternatives that are available for choice (Demers, 2007, p. 124). There is a tendency for agents to refine existing technology rather than explore new technology, thereby playing it safe and reducing the risk of choice. To counter this conservative tendency, Kezar’s principle for creating a culture of risk will allow the adoption and invention of new routines.
Leading by Thermostat: Unfreezing, Changing, and Refreezing
The nature of technological change in higher education does not allow for accurate long-term planning. Therefore, organizational leadership could seek to manage the change process by regulating the thermostat. Using Lewin’s three step model of freezing, changing, and refreezing, Burke describes the actions that could facilitate change along these overlapping stages (2011).
Unfreezing – To successfully unfreeze an organization’s technological patterns, structures, and processes, a leader can promote change by increasing awareness of external developments. By drawing attention to the latest websites, services, gadgets, programs, and upgrades, a leader can help members at the individual level begin to experiment with new routines. Edgar Schein (1987) elaborated on Lewin’s stages, and described the creation of psychological safety as it pertains to the unfreezing process. He said that individuals need “to have no fear of retribution or punishment for embracing the change” (As cited in Burke, 2011, p. 166). Creation of psychological safety to engage in change is similar to Kezar’s principle for creating a culture of risk that was previously mentioned. Mark Milliron, president of the consulting group Catalyze Learning International, commented on technological change in higher education at a technology forum: “The worst thing in the world you can do is have a leadership team come down and say, ‘Damn it, innovate.’ I think you catalyze conversations and get people moving” (Arbogast, 2008). Milliron speaks to an essential component of change embodied by “third period” thinking; innovation has to happen at every level, and cannot merely be handed down by bold leaders.
Milliron continues:
I think people have figured out that the trickle-down theory of technology does not work. They have invested a ton of money in the innovators and have expected that the innovators will go do rowdy, great things, and then that would trickle down into goodness for the rest of the institution. And what they ended up with is a lot of segregation (Arbogast, 2008).Changing – While Schein described changing as a cognitive restructuring, the change process of behavioral learning can be thought of as a systematic change in routine resulting from the integration of relatively fixed rules for continuously modifying the organization’s relationship with the external environment. So, while the change process is sufficiently described above, Schein’s description of Lewin’s change process can still provide useful guidelines for leaders. After unfreezing, the two processes necessary to promote change are the identification with a new vision, and scanning the environment for new relevant information (Burke, 2011). The leader could provide a new vision for adopting a certain process or piece of hardware. Additionally, the leader could align with a new routine or process and foster the diffusion of that idea to other individuals within the organization. If learning occurs at various simultaneous levels, then the organization itself can fundamentally change. Next, scanning the environment for new information would take the form of a search rule in the behavioral learning model. As in the case where faculty were more likely to adopt new technologies when they were introduced informally during lunch rather than at a training session hosted by IT staff, individuals engaging in a search for new routines will naturally go through a process of trial and error without being coaxed into a particular routine. This trial and error happens at the leadership level as well. Referring to online education, Richard Garrett, program director for the consulting group Eduventures, acknowledges that “everyone is experimenting; there is a lot of hype, a lot of possibility,” and concludes that the hype moves faster than the application of new technology, which, he says, does not have much velocity (Arbogast, 2008).
Refreezing – The integration of change consists of two parts, as identified by Schein (1987): “Helping the organizational member feel comfortable with the new behavior,” and “making sure that the new behavior fits well with others” (As cited in Burke, 2011, p. 167). These guidelines place a lot of emphasis on sustaining the change through reinforcing the new routines established at the individual level. The leader, in this case, can provide positive reinforcement to encourage the use of desired routines, and could relate new processes to core characteristics and personal identity to increase the chances of successful adoption of the new traits (Renes & Strange, 2011).
Conclusion
Technological change in higher education may seem both turbulent and incremental, depending on the frame of reference. At any given moment, the sheer pace of technological advances can seem quite chaotic and revolutionary. However, on a longer timeline, technological improvements are modeled as an exponential curve, and rarely make leaps (at least in terms of processing power) beyond what should be anticipated. On the other hand, although technological growth is steadily progressing, its characteristics are still largely unpredictable. As mentioned above, describing advances in technology is like taking a snapshot of a marathon with no clear course and no clear rules. In other words, while the pace is knowable, the direction, key players, and eventual outcomes are mysterious. Because of this nature, technological change in higher education can be viewed as grounded in the natural evolution perspective. Long-term forecasting is less beneficial, and institutions of higher education favor the flexibility to respond to the latest trends. However, these responses must be grounded at the individual level in order for the organization to “learn” and adapt to external changes. Because of this, the behavioral learning model is a helpful insight into the ways colleges change regarding technology.
[Photo courtesy of johnmuk. Licensed under CC BY-NC-SA 2.0.]
References
Arbogast, W., DeMillo, R. A., Garrett, R, & Milliron, M. D. (2008). IT on campuses: What the future holds. The Chronicle of Higher Education, 54(30) B6. Retrieved from http://chronicle.com/article/IT-on-the-Campuses-What-the/31631
Barratt, W. (2003). Information technology in student affairs. In Komives, S. R. & Woodard, D. B., Jr. (Eds.), Student Services: A Handbook for the Profession
(4th ed.) (379-396). San Francisco, CA: Jossey-Bass. Bruff, D., Harapnuik, D, & Julius, J. (2011). Revolution or evolution? Social technologies and change in higher education. The Chronicle of Higher Education. Retrieved from http://chronicle.com/blogs/profhacker/revolution-or-evolution-social-technologies-and-change-in-higher-education/29304
Burke, W. W. (2011). Organization Change: Theory and Practice (Foundations for Organizational Science series)
(3rd ed.). Thousand Oaks, CA: Sage Publications. Demers, C. (2007). Organizational Change Theories: A Synthesis
. Thousand Oaks, CA: Sage Publications. Intel Corporation. (2005). Moore’s Law. Retrieved from ftp://download.intel.com/museum/Moores_Law/Printed_Materials/Moores_Law_2pg.pdf
Kezar, A. J. (2001). Understanding and Facilitating Organizational Change in Higher Education in the 21st Century
. In ASHE-ERIC Higher Education Report: Vol. 28(4). San Francisco, CA: Jossey-Bass. Mills, B. (2011). The challenge of technology. Inside Higher Ed. Retrieved from http://www.insidehighered.com/views/2011/09/19/president_of_liberal_arts_college_considers_the_way_technology_should_and_should_not_change_academe
Renes, S. L., & Strange, A. T. (2011). Using technology to enhance higher education. Innovative Higher Education (36) 203-213.
