After Venkatesh and Davis released the TAM 2 model, Venkatesh further expanded the variables that went into our calculation of Perceived Usefulness in TAM 3:

Venkatesh divided the determinants of Perceived Ease of Use into two categories: Anchor determinants and Adjustment determinants. Anchor determinants where the user’s baseline and came from their general beliefs about computer and usage. In Bayesian terms, this would be the user’s “prior.” It would create the foundational attitude towards the technology in question.

Anchor determinants included:

Computer self efficacy – How proficient the user is with the current technology paradigm (i.e. how comfortable they are with computers)

Perception of External Control – How much organizational support there is for the system to be accepted?

Computer Anxiety – Is there apprehension or fear involved with using a computer?

Computer Playfulness – Spontaneity in computer interactions.

Then, Venkatesh added the Adjustment determinants. These factors come from direct experience with the technology in question and are used to “adjust” the user’s attitude towards the technology. Again, this is a very Bayesian cognitive process.

Adjusting Determinants included:

Perceived Enjoyment – Is using the system enjoyable?

Objective Usability – Is the effort actually required what it was perceived to be (resulting in either positive or negative reinforcement)

Over time some of the Anchor factors will diminish in importance (Playfulness, Anxiety) and adjustments will become stronger.

So now, in TAM 3, we have essentially the same process of acceptance, but with much more granularity in the definition of the determinants that go into the Perceptions of Ease of Use. However, with the division of determinants into the categories of “Anchor” and “Adjustment” Venkatesh starts to hit at the iterative nature of this process of acceptance. We create a baseline belief or attitude and then this becomes updated, either though external forces (the Subjective Norm) or our own internal experiences (the Adjusting Determinants). While the model indicates a linear decision path it’s now appearing likely that it’s a more recursive path.

As an interesting aside, Brown, Massey, Motoya-Weiss and Burkman (2002) said that they found variance in the importance of Perceived Ease of Use. Remember, in the original TAM model, Davis indicated that while Perceived Ease of Use does have impact over the original attitude towards a technology, he found that Perceived Usefulness is a more powerful indicator of usage intention. Brown et al found that this varies depending on whether the acceptance of a technology is mandatory or voluntary. If acceptance is mandatory, they found that Perceived Ease of Use may actually have a more important impact on system acceptance.

After TAM 3, there was a further attempt to round up the competing theories that all contributed to the evolution of TAM. This was the hopefully named Unified Theorty of Acceptance and Use of Technology (or UTAUT) – proposed in 2003 by Venkatesh, Morris, Davis and Davis:

So, what began as an attempt to simplify our understanding of the acceptance of technology became a rather unwieldy beast. Bagozzi, who worked with Davis on the original TAM model, finally had step back in and comment:

The exposition of UTAUT is a well-meaning and thoughtful presentation. But in the end we are left with a model with 41 independent variables for predicting intentions and at least eight independent variables for predicting behavior. Even here, arguments can be made that important independent variables have been left out, because few of the included predictors are fundamental, generic or universal and future research is likely to uncover new predictors not subsumable under the existing predictors. The IS field risks being overwhelmed, confused and misled but the growing piecemeal evidence behind decision making and action in regard to technology adoption/acceptance/rejection.

Bu the biggest criticism of Venkatesh’s models from Bagozzi (2007)  pointed out the same fundamental flaw that I mentioned – the assumption that intention leads to usage. Here are Bagozzi’s main concerns with the evolution of TAM:

Is Behavior the End Goal? – Bagozzi says:

The models…fail to consider that many actions are taken not so much as ends in and of themselves but rather as means to more fundamental ends or goals.

TAM ends at behavior. Actually, it ends at intent, as it assumes that intent always leads to behavior, but we’ll come back here in a moment. Bagozzi’s point is that behavior is dependent on a broader context, and there could be an end-goal that will impact acceptance that is completely ignored in the model. For example, lets say that the specific technology to be accepted is a tool to analyze conversion data in online campaigns. The end goal is to improve ROI from all online campaigns. But, to reach this end goal, there are a number of contributory goals, including, but not limited to:

• More efficient budget allocations
• Improving Landing Page performance
• Better tracking of performance data
• Improve click throughs on online ads
• Improve the online conversion path

The tool in question is a subset of the third item in the list. Because the decision to accept this tool is contingent on meeting a number of broader goals, it obviously becomes a critical factor in that acceptance. But Bagozzi’s point is the only place this is accounted for, presumably, is in the anticipated belief upstream in the model. Once again, TAM falls victim to its own quest for parsimony. Bagozzi argues a better approach is to understand that this is a process, and as such will include goal striving. And that is a recursive process:

In goal striving, intention formation is succeeded by planning (e.g, when, where and how to act instrumentally), overcoming obstacles, resisting temptations, monitoring progress to goal achievement, readjusting actions, maintaining effort and willpower, and reassessing and even changing goals and means. These processes fill the gaps between intention and behavior and between behavior and goal attainment and are crucial for the successful adoption and use of technology.

Finally! An acknowledgement that it’s not a straight line from intention to behavior!

The Gap between Attitude and Intention – First of all, Bagozzi disagreed with the elimination of Attitude as a preliminary step to Intention. Further, he says that even with Attitude back in place, there may be very compelling reasons why a person may agree that the technology is acceptable, but still choice not to accept it. To fill in the gap, he proposes borrowing from the Belief-Desire model. Here, even if the Belief is in place, there also needs to be Desire before the intention to act is formed. So, to recap, a User may have all the right determinants in place to decide that the technology in question is perceived to be useful (PU) and that it will be sufficiently easy to learn to use (PEU) but still not have any desire to accept the technology. Perhaps the decision to accept was made by her boss, who is an asshole and she’s resisting on principle.

In Bagozzi’s paper, he goes on at some length to outline the limitations in any of the proposed TAM models. In addition to the above points, he also suggests that things like the group, cultural and social aspects of technology acceptance are not adequately dealt with in the model, as well as emotions, self-regulation and other mediators that are common in our pursuit of goals. In short, he says that technology acceptance is too reliant on context to lend itself to general models and the decision path itself is much more complex than the models would indicate. He advocates a new foundation, based on his work of goal setting and striving:

This lays out the decision making core process, not specific to technology acceptance, but applicable to the striving towards any goal. But, in this model, there is the opportunity to “plug in” factors specific to the acceptance of a technology within a specific context. For example, inputs into Goal Desire could include any number of things: first of all, the goal itself (taking into account the entire goal hierarchy that leads to the focal goal – the acceptance of a specific technology), anticipated and anticipatory emotions, relative advantage, job fit, attitudes toward success, failure, outcome expectancies, and, of course, Perceived Usefulness and Perceived Ease of Use. The exact balance will be contingent on circumstance.

The arrow leading up to Action Desire represents mediating factors such as group norms, subjective norms, social identity, effort expectancy and attitudes towards an act.

A key addition to Bagozzi’s proposed model  is self regulation. Somewhere between desire and intention, humans have the ability to reflect on their desires and decide if they are comfortable intending to act on that desire. Specifically, in the case of technology adoption, we have to decide if the behaviors we would undertake would sit well with our moral and belief framework. Let’s say, for instance, that the technology adoption being considered would increase efficiency dramatically, allowing the company to decrease head count. You may be aware of the human cost of the decision to adopt and this may cause you to regulate your desire (increased efficiency) against it.

Below is an expanded version of Bagozzi’s model with all the inputs shown.

What is interesting in Bagozzi’s model is the chain of decision making which separates Goals desire from Behavioral desire. This is a further exploration of the transition from Attitude to Action, which was so deterministic in Venkatesh’s models. Bagozzi sets it in what, to me, is a more palatable framework. We have goals, which likely include broad goals and sub goals in some type of hierarchy. Our desire to reach these goals then have to be translated into intentions, where the actual execution required is begun to be planned out. This helps us understand the required behaviors, which then leads to behavioral desires. These desires then get translated into intentions. But throughout this chain, there are a number of mediating factors, both internal and external, that can cause reflection, resetting, outright abandonment or modification of both desires and intentions. There is no single arrow pointing to the right. There is, instead, an iterative process that allows for looping back to any one of the previous stages.

This post has become much longer and much more technical than I originally intended, so I think we’ll wrap this up and start fresh next time with a recap of the various models of Technology Acceptance, and my attempt to build a model that allows for iterative reflection and adjustment.