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Models and Theories in Human-Computer Interaction/Diffusion Innovation and TAM

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TAM and Engineering

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The technology acceptance model was a little more relate-able to me, especially since I work on engineering software. In our industry, engineers are reluctant to change because they get comfortable working in a system and may even have existing projects that have already been created and managed in another system. For us, it can be difficult for new designs to be accepted even though they may be more usable. I believe that many of our users already have a negative attitude towards using new software because they may feel that it will make their work more difficult and they will have to learn a new system.

The current systems are quite difficult to use and many users complain about it, but at the same time, they have taken the time to learn it and can use it. A new interface means they need to learn how to use it again and they may remember the experiences they had when trying to learn the current system. Davis does not mention how previous experiences could affect the attitude and perceived ease of use of the new technology which are both important factors as I think that perceived usefulness exists with the users, especially if a new, useful feature has been added.

I can understand the reluctance our engineers have to using new technologies, but if we can truly provide a useful product, demo the ease of use, and provide successful marketing we can try to gain more acceptance from users.

The Diffusion Cell Phone Technology

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In the late 1990s during the widespread adoption of cell phone technology cell phone customers typically had both a cell phone and a LAN line. Since this time cell phone technology has become so widespread that fewer customers are using LAN lines.

The rate in which consumers have adopted mobile phone usage in lieu of LAN lines is an example of Diffusion of Innovation, the adoption of a system based on sociological factors over time.

The adoption of cell phone technology can be compared to Roger’s hybrid seed study where due to societal and socioeconomic influences new farming technology became commonplace. Former farming methods can be compared to former telephone usage that no longer provided the same affordances. Eventually when enough people adopted the new technology and it became mainstream.

Could it be that late adaptors of cell phone technology may have had a perceived idea that cell phones were luxury items and therefore were not affordable? Eventually those who adopted cell phone technology did so while still maintaining a physical LAN line in there home. Was the lack of trust in cell phone technology the same as the lack of trust that farmer’s had for the hybrid seed program? Cell phone quality and reliability were factors that influenced the decisions of many to keep their LAN line active. There eventually came a point in time where the telephone was no longer needed and that the cell phone satisfied all communication needs. It was reliable and affordable.

“Having had a cell phone and a LAN line since 2000, in 2007 my cell phone replaced my LAN line entirely. It took 7 years for me to fully adopt cell phone technology as my primary source of communications. My mother on the other hand, an even later adopter, has had a cell phone since around 2000. She recently within the past year decided to discontinue the LAN line in her home. The LAN line was no longer economically feasible. Why pay for something that was never used?”

Whether it be maintaining a physical LAN line or hand harvesting the seed from a crop of corn, discontinuing a familiar practice and adopting a new system takes time.


Small Business and TAM

The Technology Acceptance Model is best used to measure the rate in which companies adopt new technology that impacts work performance. Usefulness and ease of use as the primary factors used to evaluate new technologies in the workplace. The TAM model differs greatly from the Diffusion model, which relies heavily on the evaluation of societal factors over time.

In the case for a local, family owned heating and air conditioning business that has been in business for 20 years the decision to upgrade their computer systems and office management software to Windows 8 was not easy. The upgrade was at least 10 years overdue. The cost associated with such a change and the efficacy rate was a major factor. The perceived usefulness and ease of use was skewed by the cost-benefit paradigm. For small companies the decision to adopt new technology can be a painstaking one.

Technology Acceptance Model – Valid as Theory?

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Overview of the Model

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The Technology Acceptance Model, or TAM, has the premise that there are attributes of a system that will affect two key areas, the perceived ease of use and the perceived usefulness of the system. Depending on whether these values are a high value for perceived usefulness or a low learning curve for ease of use, affects the perception of the system, and ultimately whether the system will be adopted.

Criticisms of the Model

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The major criticisms of the model is that it’s primarily used for predicting trivial items, when it manages to predict anything at all. Another criticism is that the model fails to explain why the prediction is valid.

Example of the model – Tablet adoption

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A good example of a system adoption that illustrates the precepts of TAM is the adoption of tablet technology before and after the introduction of the iPad. Prior to the iPad, tablets were perceived as having a higher difficulty learning curve, and low perceived usefulness when compared to traditional notebook computers. The iPad was introduced, with many skeptics reflecting on the continued perceived usefulness being low. However, the iPad was introduced with the ideal of lowering the learning curve when related to tablets.

This lowering of the learning curve actually resulted in tablets being adopted at a higher rate, leading to an increased perceived usefulness, to the point that many users now believe they can replace their notebook computers with tablets for basic tasks.

Summary

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The model of the iPad adoption when compared to the basic TAM setup does illustrate the constraints of TAM, in that there is not a more in depth explanation for tablet adoption than they were made easier to use. Additionally, the model does not account of adjustments to either of the two main constraints caused by the adjustments to the other main constraint, in the case of the iPad example ease of use brought perceived usefulness up eventually. Overall, TAM still can serve as a basic model for system adoption, and provide guidance for further study for system adoption and for predicting future adoption.

The Diffusion of Innovation for the Product Development Process (Amara Poolswasdi)

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As it applies to the product design process, the Diffusion of Innovation theory holds as a strong point of reference for designers, product managers, and decision makers alike. The theory explains how ideas or products gain momentum and spreads through a specific population. Because people in a social system adopt technology at different speeds and for different reasons, the theory is able to segment the consumers who adopt the new technology into one of five categories: innovators, early adopters, early majority, late majority, and laggards.

In the product design cycle, everyone who contributes to the creation of an application or product uses the Theory of Diffusion as a model to construct what is generally referred to as a minimum viable product (MVP). This MVP is not only built on a strong core of limited features, but is also meant to be designed and developed in phases around these categories of consumers. To design for all of the categories of customers would bloat the feature set but also require more throwaway work that would not be particularly useful to the innovators and early adopters categories.

Because the theory was also constructed with product adoption in mind, it also lends very useful context to the product development process.

Diffusion of Innovations

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The organization that I work for typically relies on Diffusion of Innovations when software is implemented. The process of knowledge, persuasion, decision, implementation, and confirmation occurs in a textbook fashion. However, this process typically happens after the software has already been developed. The knowledge is spread to users that a change is coming. From here, we sometimes start to see the early adopters come out and get excited and ask to be involved in a higher level with the project. This is during the persuasion stage.

Next, is probably the most difficult stage in our organization. While some decisions are forced (e.g. all users must start using this software by x date) most users have the freedom to decide if they want to adopt the innovative project. I am always so excited for those that make the decision to accept the innovation. As the article states, this time is very important. There can be decisions based on their own personality, like whether they are familiar with the innovation, if their position at the organization if seen adopting the software, and if they are personally inclined to take on the change. At my organization, socio economic status is typically not a factor.

After this, the users usually seek out training or resources to help them better understand the project. This is when it gets fun for me, as I can start to see how different users like the innovation, start to use the project in their own manner, and find their own ways the innovation is useful to them. Afterwards, we follow through with the last stage of the adoption process. During the confirmation stage, users shake out - those that continue to use the new software or those that choose find a different way to get the business requirement completed.

https://bb.its.iastate.edu/bbcswebdav/pid-2108171-dt-content-rid-23889786_1/courses/12015-HCI__-587_-ALL/everett%20review.pdf

Diffusion of Innovation

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Diffusion is the process by which innovation is communicated through certain channels over time among members of a social system. This diffusion process has been found to follow a bell curve with innovators adopting immediately, all the way through the laggards who wait until the very end to adopt. While many factors contribute to adoption behavior, it's not always the reasons you expect that contribute to adoption. For instance, how this idea was born, was through a study about hybrid corn adoption among farmers. According to Everett, there was a 20% profit increase available from switching over to hybrid corn. Farmers, don't make a whole lot of money, so one would think that switching over would have been quick. Everett later states that 13 years were needed to complete the corn diffusion study.

Today's online marketing has followed suit with the adoption. In the very early stages of the Internet, online marketing was near non-existent, and today, it seems as though you cannot escape. Pop-up advertisements were common in the 90s when broadband started becoming available to the masses, and with these advertisements came revenue. However, at this time, an Internet user could use typical search engines and video websites without advertisements. Companies were still gaining knowledge and trying to understand the economic advantage of advertising online in addition to other sources of media such as the newspaper or television. As the Internet evolved into what it is today, there are numerous video streaming services and search engines that all have, not just adds, but targeted advertisements to make money. Nearly everyone who is providing a service online has made it an almost standard practice to advertise in one way or another suggesting online marketing is in, or near the laggard portion of the bell curve.

Diffusion of innovation theory is very useful when applied to online applications. It took years for companies to value the relative advantage, compatibility, and complexity of online marketing, and not it seems as though we cannot escape it.

Diffusion vs. TAM: Diffusion A More Representative Model

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TAM theory states that there are mainly 2 things that influence user adoption of a new technology: the users' perceived usefulness and the perceived ease of use of the new technology both coming together to influence the users' attitude about the technology which result in the user making a decision of whether or not to adopt said technology. Diffusion theory on the other hand has a much larger breadth. Diffusion theory states that there are other factors, namely social factors that influence user decision for adoption. A simple look at the technologies that are prevalent today seem to me to quite markedly show that TAM is an incomplete model. Take the global tech monster that is Apple. Could Apple products be labeled as "useful" and "easy to use"? Yes, of course - this is one of their main claims to fame and selling points. The complete explosion and adoption of Apple products, however, also has a social component. Their advertising is different from most other large tech companies. They have used a variety of new and innovative marketing strategies that many believe created a "hype" around the company - a sort of "cool factor". It cannot go without saying that some of Apple's success has been due to this marketing and advertising and how they have created a brand. People now trust the brand, love the brand, talk about the brand, and thus adopt the brand. This is entirely a social process, and one that the TAM theory would not pick up on or recognize. To me, it would be a huge misrepresentation to say that a company like Apple's success comes from only them making useful and easy to use products. The social aspect of their brand also plays a role - one that Diffusion theory acknowledges and addresses.


Diffusion of Innovation, what’s trending?

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One can find that the theory of Diffusion of innovation has a very logical and almost intuitive nature about it. It highlights the social aspect that influences a culture to adopt an innovation, which in the age of viral videos, social media, and viral marketing, should make a lot of sense. The theory of Diffusion of innovation can be used to create a study and test the adoption of a technology. The theory also sets out a framework that helps understand the history or progress of a technology adoption and extract learning points. Through a small study or scaled-down technology launch the theory of Diffusion of innovation can be applied to extrapolate what how the technology might be adopted on a larger stage. A case study titled “ Introduction of shared electronic records: Multi-site case study using diffusion of innovation theory [1]” describes how a research team studied the introduction of centrally stored electronic patient records in England’s healthcare system. Greenhalgh et. al. identified early adopters of this technology and used Diffusion of innovation theories to illustrate influences and complexities that would ultimately slow down wide spread adoption. Their findings ultimately explained why the first year of using these electronic patient records yielded mixed results and attributed negative influences such as: adopters concerns of workload, negative past experiences leading to strong resistive attitudes, and technological problems such found in the implementation process.

1. Greenhalgh, T., Stramer, K., Bratan, T., Byrne, E., Mohammad, Y., & Russell, J. (n.d.). Introduction of shared electronic records: Multi-site case study using diffusion of innovation theory. BMJ, A1786-A1786.


Diffusion of Innovations

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The sociological underpinnings of the diffusion of innovation theory are easily relatable to the idea of “word of mouth” as it pertains to the expansion of some technology. And in this sense it feels almost as if it were common sense – it is a logical and evident approach to how ideas can spread. Innovators and Early Adopters, often those persons who are more stable (financially, in social standing, or in some other regard), are the first to jump on the bandwagon when an innovation is presented as they are mostly likely not as largely affected by the potential failure because of their relative stability. Following suit, should these two groups spread positive word-of-mouth and the innovation is not deemed a “failure”, the early majority are persuaded to take part, and eventually the late majority and laggards slowly overcome skepticism or are driven by some other force to take on the innovation. There are numerous modern examples that show this theory in action.

One great example of the diffusion of innovation at play was the introduction of Xbox Live – a paid subscription on Xbox consoles that allow users to connect via the internet to one another as well as to other online applications and capabilities – towards the end of 2002. By the end of 2002, Microsoft touted the total number of subscriptions were double that of their expectations – over 250,000 in the matter of roughly 60 days (Microsoft). Innovators took to the brand new service to try the online functionality and experience for themselves if it was all that Microsoft had hyped it up to be. Chris Morris, a writer for CNN Money commented that “Microsoft may well be setting the standard for online console gaming”, thus spreading a positive message to onlookers thinking about trying or skeptical to the service (Morris). Ten years later in early 2013, Microsoft has touted the number has risen past 46 million worldwide subscribers, and that number has inevitably increased in the two years since then (Agnello). And as more and more features in the Xbox consoles are locked behind the Xbox Live subscription paywall, the late majority and the laggards have been slowly pushed to accept the service over this time.

References:

Diffusion of Innovations and the Adoption of Smartphones (Tamara Sutton)

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The Diffusion of Technology theory seeks to explain why innovations are adopted by the population majority. There are 3 components of variable classification, the characteristics of innovations, characteristics of innovators and environmental context. E. M Rogers also defined 5 qualities that is believed to influence the rate of adoption: Relative advantage, compatibility, complexity, trialability, and observability. Relative advantage and complexity are quite similar to the theories of the Technology Acceptance Model (TAM).

The rate of adoption of smartphone users could be described easily by the Diffusion of Technology theory. When smartphones were released, current cellular phone users had a product that, in most part, met their needs. The smartphone product was introducing new features that had not been available. Users may not have had an immediate need to switch and were left wondering if they would actually use the new features and if it was worth the cost. However, they were able to observe the usage and satisfaction among early adopters, which may influence their decision to switch.

The advertisements created by smartphone companies (i.e apple, samsung, etc) display a desirable image of the advantages of smartphones over their predecessors. There was/is a social pressure at times to switch to a smartphone, as many users refer to this as “upgrading”. I often remember hearing comments from peers, such as, “Wow, you still have a flip phone, why haven’t you upgraded?”. And even now, I will hear people make comments like “You need to get out of the dark ages.”, to standard cellular phone users.

Based on a Gallup poll in December of 2013 of 1031 national individuals, 65% had a smart phone and 42% had a standard cellular phone. This is would indicate that we are still in the “late majority” of adopters in Everett Rogers' 'Category of Adopters' bell curve.

References: Americans' Tech Tastes Change With Times. (n.d.). Retrieved June 11, 2015, from http://www.gallup.com/poll/166745/americans-tech-tastes-change-times.aspx

Nickerson, R., Austreich, M., & Eng, J. (2014). Mobile Technology and Smartphone Apps: A Diffusion of Innovations Analysis. Retrieved June 11, 2015, from http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1022&context=amcis2014

Rogers, E. M. 2003. Diffusion of Innovations, 5th ed., New York, NY: Free Press.


Diffusion of Innovation and Obsolescence

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The diffusion of innovations model has unique challenges in varying sectors. In technology innovation is so fast paced that many often wonder is it worth the time/effort/expense to adopt the next innovation or simply to wait for the next one to leapfrog it. Similarly, in technology innovation is met with reluctance due to the incompatibilities new innovative products can be challenged with. Take, for example, the video media format wars beginning in the 1970’s and continuing until today. Most are familiar with the battle of Betamax, and VHS in the 70’s and 80’s and later HDDVD and Blu-Ray in the 2000’s, but many are unaware Video 2000, V-cord, GTM, CED, VHDD, CHDVD, LaserDisc, and others also competed during this time frame. Most were, briefly, the most innovative until the next made improvements in functionality or speed, but since they were incompatible with one another and required users to purchase dedicated hardware the wars led to users sitting on the sidelines to wait for the game to run its course. Today, for physical media, Blu-ray is the accepted standard, yet despite over 15 years since early prototypes and 10 years since its official release Blu-ray commands just 1% of many video stores’ shelf space. This is because the next innovation in this sector, streaming video, was introduced before the conversion to Blu-ray was adopted by the late majority. In this sector this trend can be traced back to Betamax with perhaps only VHS and DVD formats ever being fully adopted by the late majority. In this arena the diffusion of innovation model is lacking. Additions to the model have added processes such as re-invention which captures changes implemented by adopters during the diffusion process, but the model still does not well represent innovations made obsolete mid cycle relative to their replacements.

Diffusion of Innovation Retrospective for the Video Industry (Daphne Mintz)

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In studying models and theories in HCI, I find it fairly straightforward to apply concepts from Fitts, GOMS, and TAM, including TAM derivatives, directly to the video-industry related applications for which I am a product manager. My user base includes stakeholders for both business-to-consumer and business-to-business applications. All of the users involved use a screen, a pointer device, and either a tactile or virtual keyboard. They all have buttons to press to complete their tasks. It would be very easy for me to use these models to guide improvements in user performance by finding ways to reduce keystrokes and button presses. When I first watched a class video on the Diffusion of Innovation, I thought I was being introduced to another alternative for evaluating design and usability. What I soon discovered is that the scope Diffusion Modeling goes well beyond the usability lab and the concept of “let’s get the job done as simply as possible.” I liked that Diffusion Modeling addresses marketing concerns and that the research goes beyond initial implementation to market penetration over a period of years. I also found the breakdown of users on a timeline based on user type to be incredibly insightful.

As I ventured into more depth by reading Rogers (2004) and Wenjert (2014), I became distracted by the breadth and depth of this methodology. Much of Wenjert’s framework addresses politics and social demands. Roger’s examples address HIV/AIDs education and prevention. The examples my mind landed on as I tried to map the concepts to familiar topics had nothing to do with software (my industry). Instead, I found myself contemplating the strategies the Carter Center employed to eradicate guinea worm in West Africa and wondering if anyone is using this technique to empower women in third world countries. The model seemed predisposed to grand problems and I work in the land of “what do you want to watch on TV tonight?”

In discussing the Diffusion Model with a friend, she reminded me that I have been an active participant in the diffusion of video innovation from the days of radio frequency analog signals, through the digital takeover, on to today’s over-the-top streaming functionality.

I returned to Wenjert’s framework to see if I could embrace Diffusion Modeling as an applicable methodology for my industry. I found that some of the components of Wenjert’s framework translated readily to software design and development:

• Benefits versus Cost is on every software executive’s mind. If you don’t pass that gate, your project is dead.

• Familiarity with the Innovation can be addressed in a product roadmap.

• Status Characteristics can be addressed in timelines showing the rollout of low-volume/high-cost transitioning to high-volume/low-cost plans. Though I did not have the Diffusion Model as a guide when I began my career in video and in usability, I can see where the framework would have been applicable to the innovations we were working on to replace radio frequency analog signals with compressed video in a digital format.

• Position in Social Networks – The end users may not have understood the shift in technology, or the fact that they were early adopters experiencing a hybrid technology (set-tops supported both analog and digital signals for several years). What the users knew was that they had a lot more channels to watch with the advent of digital technology. They were not the decision makers regarding the technology they used beyond the decision to have cable or not have cable.

• Societal Entity of Innovators – The drive for set-tops that supported this technology was not for consumers. As the FCC, a very powerful political entity acting as a consumer advocate, constantly devised requirements for set-top functionality to be supported upstream at the cable head-end and not in the consumer’s household, our executives were keen on keeping set-tops relevant, as set-tops were our bread and butter.

• Status Characteristics – The consumers helped my company and the cable companies without realizing it as early adopters, and then majority adopters, tossed out their analog TVs and replaced them with digital units. Eventually, viewers with analog TVs (aka, laggards) were forced to install devices to convert analog to digital. We were able to retire analog altogether and stop producing hybrid set-tops.

Now I work in over-the-top video, bringing premium video content directly to consumers via consumer-selected/purchased devices such as AppleTV, Roku, xBox, and PlayStation. Netflix set the standard for early adopters of streaming video subscription service; and now, Amazon, Vudu, and Hulu are household words as the majority users cut the cable cord. Once again, set-top manufacturers are trying to stay relevant, but they are not adapting to the concept that they need to compete with consumer devices by abandoning the wholesale, cable operator market and entering the retail/consumer market. Instead, they are trying to keep their customer base, the cable operators, relevant. If I were describing them in a Diffusion Model, I would list them as laggards.

References

Rogers, E. M. (2004). A Prospective and Retrospective Look at the Diffusion Model. Journal of Health Communication, Vol. 9, pages 13-19, Taylor & Francis, Inc.

Wenjert, B. (2014). Integrating models of diffusion of innovations: a conceptual framework.. Annual Review of Sociology (2002): 297+. Business Insights: Essentials. Web. 10 June 2014.

http://www.cartercenter.org/health/guinea_worm/index.html

http://redef.com/original/the-state-and-future-of-netflix-v-hbo-in-2015


Leveraging TAM knowledge in the Learning Experience

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The TAM model of innovation adoption indicates that the top two areas of perception that most influence adoption are whether the innovation will help the user be better at his or her goal, usually a job, and how easy the innovation is to use. Perceptions about these two areas, usefulness and ease of use, were "significantly correlated with self-reported indicants of system use" (Davis, p. 333).

This was tremendous insight into how users decide whether to try a new innovation. At my company, we have found that users sometimes ask for features which are already in the product-- features which have been in the product for up to two releases. Despite marketing slicks and What's New topics in the help system, this two year lag in awareness, let alone adoption, remains consistent. Even Microsoft runs into this issue. "When Microsoft asked their users what they wanted added to Office, they found that 90% of the requested features were already there" (Traynor).

This is not an awareness issue. The information about new features is out there. What is not there is how the new features will help the user be better at his or her job, and how easy they are to use. Although I am not using TAM directly for an innovation, leveraging the psychometrics about TAM interpretation in my What's New may help make it so that new features do not fall into the vast wasteland of Never Used innovations.


Citations

Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. 'MIS Quarterly, 12(3)', 319-40.

Traynor, D. (2014, January 12). Why New Features Usually Flop | Inside Intercom. Retrieved June 15, 2015.

Diffusion of Innovations

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Diffusion of Innovations shows high comprehensiveness regarding to evaluating existing products. The characteristics of it assess almost all aspects of a product or system. Especially it evaluates complexity, trialability, and observability.

Among the five characteristics, complexity is the most important one as it is closely related to trialability and observability. If a commercial product is complicate, the trialability of it will be low. Unlike a game, which could have high complexity and high trialability, because users love to solve the puzzles in game. But that is not the purpose of a commercial product. A success product should help users solve problems instead of create ones. The high complexity of a product also makes users hard to see the results come out of their actions. Then the lower observability decreases the trialability in the end.

The only doubt I have is about the compatibility. I’m not sure how compatible a product is could reveals how good or bad it is. There are a lot of products show high level of compatibility whereas the adopt rate is low. The early version of Windows CE (Windows mobile system) is very similar to Windows desktop system. It even has similar start menu as desktop does. User could easily understand where to start based on their experiences on PC. But that doesn’t help Windows CE dominate the market or make it a better system. As we are evaluating technology and innovation here. Users always want to get new experience on the products. The compatibility of iOS is low when they first launched in 2007, it doesn’t looks like any of the systems in the market. But innovators and early adopters are not afraid to try new things, and the low compatibility doesn’t lead to the low adoption rate.

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