What makes users adopt a new feature or tool?

How UX teams can provide key insights using technology adoption models

Hi, I’m Lawton Pybus. The ¼” Hole is a newsletter devoted to understanding the discipline of user research. Every month, I share resources to help you uplift your craft.

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“Will anyone actually use this?” It’s a common question from collaborators—and with good reason.

A startup’s product might have the best user experience (UX) in the world, but that’s cold comfort when they’re still searching for product-market fit. Even for established products, research from Pendo suggests that 80% of features added are rarely or never used.

UX Researchers don’t often ask users to predict their future behavior, since those answers can be unreliable. But for decades, academic researchers have been systematically predicting technology adoption, and you can use the best of what they’ve learned in your practice.

In this article, we’ll take a look at how the study of technology adoption has developed over time. And you’ll learn some tools that you can start implementing in your research practice today.

Diffusion of Innovations theory

In the early 1960s, Ohio State University sociologist Everett Rogers published a book titled Diffusion of Innovations.

In the book, he synthesized the findings of over 500 multidisciplinary studies covering a wide range of contexts, such as agriculture (e.g., hybrid seed and automated harvesters) and healthcare (e.g. hospital hygiene and cancer screenings). He then distilled major findings into three groups of five: the five categories of adopters, the five characteristics of an innovation that adopters consider, and the five stages of adoption.

(Credit: Hvassing)

Rogers found that over time, the groups adopting the innovation have meaningful differences: notably, that they are distinguished by their risk tolerance, financial liquidity and resilience, and their status and persuasiveness in the community. Members of each group share similarities in age, education, and conservatism.

Of the five groups of adopters Rogers identified, the earliest adopters are the innovators and early adopters, followed by the early and late majorities. The laggards are the last to adopt after finally being convinced to incorporate the innovation.

Rogers then found that each group considers five characteristics of the innovation when choosing whether to adopt it:

1. Compatibility: how well does the innovation work with existing tools, practices, and values?

2. Trialability: before you buy it or commit to it, can you try it out?

3. Relative advantage of the innovation over alternatives

4. Observability: can you see others using the innovation, and are the benefits noticeable?

5. Simplicity or complexity: Innovations that are easier to use spread more quickly. 

With a better understanding of an innovation and its potential users, Rogers’ framework can then be used to consider what he termed the five stages of acceptance: 

1. Awareness, or initial exposure to an innovation without the benefit of much information.

2. Persuasion, when a potential adopter becomes interested in and begins seeking information about the innovation.

3. Decision, the adopter makes an initial determination to adopt or reject the innovation.

4. Implementation, if adopted, where the innovation is used and evaluated.

5. Continuation, or an ongoing decision to continue using or ultimately reject the innovation. 

Diffusion of Innovations theory has been highly influential as a sophisticated and pioneering work in this domain. It’s valuable to understand users’ needs and contexts, especially for products seeking to create network effects—such as a social network or dating app—where the value increases with the number of users. An important note: as a sociological model it is best used to describe population-level trends rather than predict individual choices. 

A few suggested takeaways for UX practitioners:

• Select research participants, using behavioral and demographic screeners, who mirror the characteristics and traits of the adopter groups you are aiming to reach with your product or service. 

• Assess how your product measures up against competing solutions across the five characteristics that adopters consider through competitive analysis and benchmarking.

• Address the changing needs of potential adopters moving through the stages of adoption in your product and accompanying materials. You can find areas for improvement here using qualitative interviews.

Technology Acceptance Model

In the mid 1980s at MIT, Fred Davis wanted to understand how to get professionals to start using personal computing systems in the workplace as part of his dissertation. Combining key elements of Diffusion of Innovations theory and psychological models of behavior, he proposed and evaluated the Technology Acceptance Model (TAM) across a series of studies.

Davis and the other principal investigators in this line of research come from information systems, an academic sister to UX typically part of a university’s business department. Though its original context was institutional technology adoption—think the internal apps you use for operational and logistical tasks at work—it has since been used across many other areas, including consumer technology.

Technology Acceptance Model

Davis identified two primary factors with impressive explanatory power:

• Perceived usefulness: The belief that using the new technology would enhance performance on a job or task.

• Perceived ease of use: The extent to which using the new technology would be free from effort.

A consistent finding across thousands of replications and applications of the model since then is that although perceived ease of use is a meaningful influence, perceived usefulness plays a more important role. So UX practitioners should give the “effectiveness” dimension of usability extra scrutiny in any adoption-related projects.

One of TAM’s enduring strengths is its simplicity: you assess the product or feature on two key dimensions. You need not use the multi-item batteries from the original studies, as the UMUX-LITE is a short UX-specific instrument based on this research. 

Ease of use and usefulness plotted on four quadrants (author recreation of Keil and colleagues’ figure)

Once you measure your product’s perceived ease of use and usefulness, you may want to consider plotting the data along four quadrants with stakeholders. Successful products hope to achieve "Super Tool" status. Determine where your product currently stands and work with your team to develop a plan to move it closer to that goal.

In short, UX teams should:

• Measure perceived ease of use and usefulness (e.g., with UMUX-LITE) in usability studies to find opportunities for improving adoption rates along each dimension.

• Give added attention to perceived usefulness in adoption-related studies, highlighting its pivotal role in reports and study deliverables.

Other noteworthy developments

Although TAM remains simple and effective, you can make more accurate predictions with a more robust and theoretically sophisticated model.

That’s why Viswanath Venkatesh and colleagues extended the model with TAM 2 and 3, including other relevant elements like prior experience, independent choice, subjective norms, self-efficacy and anxieties related to the technology, and even objective usability. These models retain the essence of TAM in their final stages, but help researchers to better understand the drivers of perceived ease of use and usefulness.

Hedonic-Motivation System Adoption Model (Credit: Paul Benjamin Lowry)

For those in entertainment or leisure spaces, where perceived usefulness may seem less relevant to your work, researchers developed the Hedonic-Motivation System Adoption Model (HMSAM). This model targets contexts where practical utility doesn’t make sense as a primary driving motivator, such as games or gamified systems, shopping, learning/education, dating, music, or even social networking. Though perceived usefulness and ease of use remain important, this model introduces curiosity, joy and control as key considerations.

Venkatesh and colleagues developed the Unified Theory of Acceptance and Use of Technology (UTAUT) 1 and 2 as an attempt to integrate findings from across the domain. It incorporates many similar and overlapping elements from other models described here, and goes a step further with factors like gender, age, price, and habit. The latest version of UTAUT is simpler than TAM 3, and remarkably, predicts 70–74% of usage intentions and 35–52% of usage.

There are a few ways UX teams can apply these newer models:

• The latest versions of TAM point to concrete suggestions for improving perceived ease of use and usefulness, if next steps are unclear after initial measurement.

• HMSAM is better for entertainment and leisure products where usefulness is less relevant.

• UTAUT is the best predictive model if there’s adequate time and resources to measure all its elements.

TL;DR

Understanding technology adoption is crucial for product development, and UX Researchers can use validated models to help. This article took a look at five:

People’s own predictions about their future behavior are imperfect, but the best of these models can reliably predict 35–52% of later usage. Two key factors that UX teams can influence are ease of use and usefulness, common threads across these models.

By applying these models and tools, UX and product teams can better understand user behavior and make informed decisions about how to improve adoption rates.

Thanks to Hallie MacEldowney and Brian Utesch for reviewing a draft of this article.

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From the archive

Considering the UX of emerging technologies as a path towards greater adoption:

• Unlocking the potential of AI tools with UX research

• Crypto is still confusing for novice users

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ANOTHER THOUGHT…

What’s an insight?

What's our "value proposition" as UX Researchers? Many answers I've heard offer some variation of: "we provide insights to product teams."

But that raises another question. What exactly is an "insight?"

That's the title of a compelling literature review recently released by researchers Leilani Battle and Alvitta Ottley. They examined 125 scholarly articles on the topic. Here are some of the competing definitions they found:

• "data utterances," or any observation about data made by an individual

• "data facts," or computable descriptions and relationships that support a hypothesis

• "hypotheses and/or evidence," or a synthesized observation from multiple data points

• "knowledge links," or connecting units between data, findings, and knowledge

How do the authors adjudicate these differing ideas? They propose a new definition that encompasses them all: insights are collections of knowledge. It's an interesting read, and offers some compelling ideas for future research.

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Until next time, thank you for reading.

Cheers,
Lawton Pybus

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