How to Forecast Adoption of New Information Technologies within the Enterprise

How to Forecast Adoption of New Information Technologies within the Enterprise

IT implementations fail at an astonishing rate. Some, such as CRM systems, are notorious. The impact of failure is brought into focus by considering the massive investments organizations plow into technology. Gartner forecasts enterprise software spending worldwide to reach $466 billion in 2020.

Forrester and other analysts cite non-adoption by users — avoidance, workarounds, even sabotage. Many organizations have little understanding of what drives technology adoption or how to design interventions to facilitate the adoption process.

In “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology” Fred D. Davis at the University of Michigan developed and tested a theoretical model aimed at predicting use of IT.

The model, known as TAM, positions intention to use as antecedent to actual use. It then identifies two predictors of usage intention:

In testing of the model, Perceived Usefulness proved especially significant with correlation of .85 with usage intention. Test results also suggested that Perceived Ease of Use may precede Perceived Usefulness. In other words, prospective users may consider a technology useful because they think it would be easy to use. And they may consider it useless because they think it would be difficult.

TAM has been often tested, has served as the foundation for much subsequent research, and continues in use today.

In 2000, Davis and Venkatesh published “A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies,” The model developed in this study is referred to as TAM2. It extended the original TAM by establishing a set of forces or determinants that drive perceptions of the usefulness of a new technology:

— Perceived Ease of Use: The degree to which a person believes that using the technology will be free of effort

— Subjective Norm: User perception or belief that people who are important to them think they should or should not use the technology

— Job Relevance: User perception or belief that the technology is applicable to their job

— Output Quality: User perception or belief that the technology performs their job tasks well

— Result Demonstrability: User perception or belief that the results of using the technology are tangible, observable, and communicable

— Image: User perception or belief that use of the technology will enhance their status within a social system

In testing of four unique information technologies within different organizations TAM2 explained up to 60% of the variance in Perceived Usefulness — the most powerful predictor of usage intention and actual use.

As TAM2 identified what causes prospective users to view a technology as useful or useless, Venkatesh sought to identify what causes them to view it as easy or difficult to use.

He accomplished this in a study: “Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model.” The model developed in the study further extended TAM by establishing determinants of Perceived Ease of Use.

Venkatesh’s research leveraged the “anchoring and adjustment” decision making heuristic most often associated with the work of Amos Tversky and Daniel Kahneman. He showed that people initially rely on general preexisting beliefs or “anchors” to assess whether an IT will be easy or difficult, but then adjust their assessment based upon actual usage experience.


— Computer Self-Efficacy: User perception or belief that they possess the ability to perform a specific task/job using a computer

— Perception of External Control: User perception or belief that organizational and technical resources exist to support users

— Computer Anxiety: User feeling of apprehension or fear when faced with the possibility of using a computer

— Computer Playfulness: User sense of cognitive spontaneity when using a computer


— Perceived Enjoyment: User perception that use of the technology is enjoyable in its own right, apart from any performance consequences resulting from its use

— Objective Usability: The actual level (rather than perception) of effort required to complete tasks using the technology

In testing, using these determinants, the extended TAM model explained the majority of the variance in Ease of Use. Testing also produced a surprising result: While actual experience with a technology influenced Perceived Ease of Use, preexisting general anchors retained a stronger influence upon perceptions of ease or difficulty even after the actual usage experience.

TAM, TAM2 and the Ease of Use research by Venkatesh are useful in predicting technology adoption, but stopped short of prescribing actions organizations could take to facilitate adoption.

To fill this gap, Venkatesh and Bala published “Technology Acceptance Model 3 and a Research Agenda on Interventions.” In what became known as TAM3, they identified sets of pre and post “implementation interventions.”

Pre-implementation interventions are activities aimed at accelerating adoption that take place during IT development and deployment:

— Design Characteristics: Elements of the technology such as modules and components, including user interfaces

— User Participation: Enlistment of user participation

— Management Support: Actions that support user perception or belief that management has committed to the successful implementation and use of the technology

— Incentive Alignment: Actions that support user perception or belief that the technology yields benefit

Post Implementation interventions support adoption after system deployment:

— Training: Provision of learning opportunities for how to operate or interact with the technology

— Organizational Support: Activities or functions performed by the organization to assist users in operating or interacting with the technology

— Peer Support: Activities or functions performed by coworkers to assist users in operating or interacting with the technology

The extensive body of research highlighted above offers tremendous insight and guidance for predicting technology adoption.

TAM and its extensions —

1. Identify and validate two predictors of intention to use a technology

2. Identify and validate sets of determinants or forces driving each of the predictors

3. Prescribe practical measures that can be employed to influence the determinants or forces so as to ultimately increase the likelihood of adoption

To apply this to actual use cases, first consider the characteristics of both the technology being deployed and the people using it. With this understanding as a platform, assess and ultimately rank — based on likely impact — the determinants of Perceived Usefulness and Perceived Ease of Use. It is then possible to prioritize and allocate resources to pre and post implementation interventions that would assert the desired influence against the highest ranked determinants.

How to Forecast Adoption of New Information Technologies within the Enterprise

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