Where Do I Find Myself on the Technological Adoption Curve?
Innovation Adoption Lifecycle
“The diffusion of innovations is the process by which a few members of a social system initially adopt an innovation, then over time more individuals adopt until all (or most) members adopt the new idea” (Valente 1996).
If I were to place myself along the spectrum of the Diffusions of Innovations Curve, I would place myself in the top 10% of adopters-putting me squarely in the “early adopters” category. I believe myself to fall within this category based on two factors:
My previous work experiences with technology and its implantation in varied environments and
my current hobbies and interests.
In my previous role, I served as a methods/procedures process engineer. I collected data to identify opportunities for process, communication, and training improvements. Additionally, I worked to improved measurable operating efficiencies and customer satisfaction scores by implementing improved troubleshooting processes and best practices. Many of these changes were guided by the principles of Lean and Six Sigma methodologies and measurement tools. We used technologies to help call center agents better serve customers. For instance, I created the first technical troubleshooting “decision tree/branched” user interface for the national knowledge management system, global customer relationship management system, and ATT.com. To my knowledge using programmed decision flows based on symbolic logic to assist internal and external clients troubleshoot cellular devices had never been done.
We used the technologies inherent in the existing systems in a new way. As Veletsianos (2016) points out, an emerging technology does not necessarily have to be new (p. 7). Rather if a technology is used in a new way in a new environment, a technology can fit the definition of an emerging technology. At the time, I was leading the charge for improved technological tools for employees and customers alike.
In my personal life I also embrace technology at an early stage. For instance, during COVID, I recognized that parents might be in need of ways to entertain their children once they were finished with school yet the parents were still working. I began designing and building high-end, custom PCs aimed at children. The PCs cases could include custom themes (one PC included an anime doll that would dance when the PC was powered on), lighting, and cooling solutions. Many of these cases made heavy use of high-density LED strips. These strips were controlled by Corsair’s iCUE software. Corsair sells lighting controllers to serve as interfaces between the iCUE software and the hardware that controlled the lighting. Rather than spending the money on expensive name brand Corsair controllers, I programmed Arduino Pro Micros to serve as the lighting controllers. The DIY lighting controllers were recognized by the Corsair software and worked seamlessly.
Sitting within the early adopter segment of the diffusion curve I do feel that I have a keen understanding of the impacts of technologies on learning design. During my time at AT&T, I had access to one of four prelaunch, first-generation iPhones as part of my roles as a senior instructional designer on the technical and new products design team. I was able, with the help of Apple developers, introduce AT&T specific training modules for the iPhone with the goal to familiarize employees with the new form factor and operating system. Buttressed with the new decision flows found within the knowledge management and customer relationship management software I built, employees had the training and the tools at their disposal to execute their job duties more efficiently and effectively.
My position on the diffusion curve might very well move closer to the innovator category as I learn about new and emergent technologies I have previously not encountered. By working with peers and mentors more versed in various technologies I hope that I would move towards being an innovator in how these emerging technologies can be implemented in learning environments.
First and foremost, I plan on utilizing the SMEs around me-primarily my instructors. The evening sessions put together by Dr. Salik have proven to be very useful. Another way I plan to build my knowledge is to pursue a course of self-study. Our community of practice group has already provided several resources where I can further my knowledge on generative AI prompting. My experience with AI text generators and image generators has been fairly limited. I hope “learning how to ask AI” the appropriate questions will help me in my professional development by learning how better to harness these technologies. I hope that as I work with these tools, I become more comfortable with using them rather than viewing them as technologies that may become replacements for me as a designer. I have already begun using AI generated images as part of my personal life and slowly becoming acquainted with how the engines work.
Understanding where I believe I fit on the diffusion curve helps me in a couple of ways. First, it helps me plan a roadmap to move closer to becoming and innovator, or at minimum and earlier adopter. Secondly, and more importantly, it helps better understand where others fall on the diffusion scale and why. With this knowledge, I can better design individualized learning experiences for those that may be at the back half of the scale and are reluctant to adopt technologies. Understanding this I can better empathize and slowly bring technology into their learning experiences at a more comfortable pace for them rather than them being forced into technologies fast pace evolution and implementation.
References
Valente, T. W. (1996). Social network thresholds in the diffusion of innovations. Social networks, 18(1), 69-89.
Veletsianos, G. (2016). Emergence and Innovation in Digital Learning: Foundations and Applications. Edmonton, AB: Athabasca University Press.
