Key Points:

• Organizations are investing more in technology, but ROI concerns persist due to slow staff adoption.

• The Unified Theory of Adoption and Use of Technology (UTAUT) helps explain up to 70% of differences in people's intention to use, and 50% of differences in actual use, of a new technology.

• Four key factors are known to influence adoption: usefulness, ease of use, social influence, and resources & support.

In an era of rapid digital transformation, organizations are investing heavily in technology as part of their organizational change efforts. In 2023, companies allocated an average of 5% of revenue to tech spending, up from 3% in 2018. However, this increased investment doesn't guarantee success. Alarmingly, 60% of organizational decision-makers express concern about the ROI on digital investments, primarily due to technology adoption challenges faced by staff.

As leaders navigating digital transformation efforts, understanding the factors that influence technology adoption is crucial. Let's explore the key elements that drive employee acceptance of new technologies and how you can leverage this knowledge to enhance your change initiatives and overcome technology adoption challenges.

The Science Behind Technology Adoption

While numerous frameworks exist to explain technology adoption, one stands out for its comprehensive approach and proven track record in addressing technology adoption challenges in organizational settings: the Unified Theory of Adoption and Use of Technology (UTAUT). This model integrates findings from various theories and has been shown to explain up to 70% of the differences in people's intentions to use new technology and 50% of the differences in actual use.

While it doesn't predict individual behavior with certainty, this theory provides valuable insights into the factors that influence adoption across a population of people. As such, it’s highly relevant for those involved in championing the adoption of new tech as part of organizational transformation efforts.

Four Key Factors Influencing Adoption

The UTAUT identifies four primary factors that shape employees' intentions and use of new technology. Insights into these areas can help change leaders identify and address technology adoption challenges.  

1. Usefulness (Performance Expectancy): How will this technology impact my job performance?

2. Ease of Use (Effort Expectancy): How much effort will it take to learn and use this technology?

3. Social Influence: What do others, who are important to me, think about this technology?

4. Resources and Support (Facilitating Conditions): How much support is there to help me use this technology effectively?

In addition, different versions of the model include various factors that could moderate the influence— increase or decrease — of these factors. However, most research undertaken with the model over the past twenty years, only use the core four.  

From Intention to Action: The Pathway to Adoption

Research on technology adoption is generally focused on predicting adoption, not measuring its use. Therefore, it tends to focus on attitudes about a particular technology or people’s intentions to use a new technology, rather than if they are using it regularly.

This is a bit different than ultimate interests of decision-makers in organizations undertaking technological change efforts — ROI. So, how can it be useful to leaders guiding their organizations through change?

First, research helps us to see that these different outcomes are like steps on a pathway to active and regular use of new technology — resulting in a good ROI.  It indicates that attitudes are the best predictor of intentions, and intentions are the best predictor of actual use. In other words, I think using this technology is a good/foolish idea (attitude), therefore I’m more/less likely to make a plan to use it in my work (intention), therefore, I’m more/less likely to use it in my work (use). You might think of them being stops along a pathway to active and regular use of a new technology. 

Second, some factors have been shown better predictors of different aspects of this process. For instance, ease of use and social influence have a moderate impact on intention to use a new technology but seem to be less important as people start using the technology.

However, perceptions about adequate resources and support, influence both intentions and actual use of technology. That suggests that things such as ensuring people feel they have the time, training and supportive infrastructure to succeed with the new technology is important throughout the life of an implementation.

Two take-aways.  As a decision-maker or change practitioner managing organizational change, it’s essential to remember that predictions are not guarantees, and intentions don't always translate into actions. Second, you can benefit from paying attention to the factors that are better gauges of early vs. late-stage adoption. For instance, you may focus early communication and awareness building on the benefits of the technology, and having leaders demonstrate their commitment to it. As time goes on, you may lessen the messages about potential benefits and ease of use, and focus more on support and ensuring that you remove obstacles that may be inhibiting people from full adoption.

Context and Type of Technology Make a Difference

The degree of influence these four factors have can vary depending on the type of technology, who’s using it, and the overall context in which they are doing so.

For example, the UTAUT model includes demographic factors like gender, age, and experience level. These have been shown to play a role in intentions and use, however their influence tends to vary based on the environment and the type of technology being implemented. For example, some studies indicate that women and older employees tend to be influenced more by ease of use and social influence; however, the more experience people have with technology, the less such factors matter. 

A meta-analysis that reviewed 700 published works on technology adoption, found that a group of 21 influence factors remained reasonably reliable predictors of adoption over a thirty-year period, however, the accuracy of the predictions often varied by technology type and industry. For instance, in healthcare and for e-commerce the factors tended to better predict attitudes towards technology (e.g., do I think using it is a good idea?), while for e-government, e-banking, and marketing services, they seem to be more effective at predicting intention to use, (e.g., do I plan to use it?) Still other reviews of UTAUT have indicated that different factors are more powerful for different types of technology — for instance, usefulness may be more influential for technologies that enable financial transactions and for those accessed on mobile devices.  

Bottom-line, where possible, it’s wise to look for research on technology adoption that is aligns well with your context (e.g., organizations vs. households, industry) and the technology you are implementing. This can help you better understand the factors that may be most predictive to your situation or better understand their limitations.

The AI Factor: Trust Becomes Even More Critical

Although these factors have been largely stable predictors of technology adoption for decades, one additional element seems to be particularly powerful related to the adoption of artificial intelligence: trust.

There is still much to be learned about AI and its use in organizations; however, trust is emerging as an important influencer of AI adoption in early studies. Some of the research on AI adoption has investigated trust from a functional standpoint, such as the reliability and accuracy of the AI being used. Given the anthropomorphic aspects of GPTs in particular, some other investigators have started to look more humanistic perceptions of trust in technology, such as does the technology have good intentions? Initial indications suggest trust influence attitudes towards AI and intentions to use AI.  

(See my accompanying article on AI Adoption for a deeper dive.)

Applying this Research to Your Change Leadership

As a leader or manager guiding digital transformation or technology adoption as part of an organizational change, you can leverage these insights to enhance your implementation strategies and overcome technology adoption challenges.

Consider how you can use these to:

1. Guide how you gather and analyze staff feedback: Researchers have developed validated scales, or groups of survey questions, that you can use to gauge people’s perceptions during a technology implementation effort. (See references for more details.)

   Use such surveys to help you identify strengths to amplify in your interactions and communications and weaknesses to address as you develop your change implementation strategies. Be sure to get feedback from a representative cross-section of end users, to avoid blind spots.

   Remember, correlation is not causation and small sample size can strongly influence results. Be careful not to interpret results as a guarantee of success or failure.

1. Help your team think critically: Assess your change initiative through the lens of these factors, keeping in mind potential differences based on your context and type of technology. What opportunities do you have to make the technology easier to use? What specific benefits will staff realize (not the just the organization)? Are you providing adequate resources for enable people to successfully use the new technology? What do you need leaders and managers saying and doing to reflect their commitment to the use of this new technology?

2. Develop shared, realistic expectations: A common blind spot in technology implementation efforts is to focus solely on the promise of the tech, not what it takes to enable many people to use it effectively to achieve results. Use these four factors to spur discussions with stakeholders at all levels to develop shared expectations about the effort required for successful adoption, and the role they can play in making it happen.

 Photo credit: Fauxels

References

Blut, M., Chong, A., Tsiga, Z., & Venkatesh, V. (2021). Meta-analysis of the unified theory of acceptance and use of technology (UTAUT): challenging its validity and charting a research agenda in the red ocean. Journal of the Association for Information Systems, forthcoming.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.  (*Includes scales (survey questions) in the Appendix.)

DiLorenzo , L. (2024a, March 25). From tech investment to impact: Strategies for allocating capital and articulating value. Deloitte Insights.

Kammeyer-Mueller, J. D., Rubenstein, A. L., & Barnes, T. S. (2024). The Role of Attitudes in Work Behavior. Annual Review of Organizational Psychology and Organizational Behavior, 11(1), 221-250.

Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 101925.

Marikyan, D., Papagiannidis, S., & Stewart, G. (2023). Technology acceptance research: Meta-analysis. Journal of Information Science, 01655515231191177.

Sweary, R. (n.d.). The State of Digital Adoption 2022-2023. WalkMe .

Turner, M., Kitchenham, B., Brereton, P., Charters, S., & Budgen, D. (2010). Does the technology acceptance model predict actual use? A systematic literature review. Information and software technology, 52(5), 463-479.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478. *Includes scales (survey questions) within the body of the paper.)

AI Statement: Consensus was used, in addition to Google Scholar and Google Search, to identify relevant research on this topic. Claude was used to develop initial drafts from an outline and notes provided by the author. Claude was used as an assistant to interpret statistical findings from sources.