Category Archives: Cognition

The Psychology of Usefulness: The Acceptance of Technology – Part One

oldpeopletech7_317161In the last post, I talked about what it takes to break a habit built around an online tool, website or application. In today’s post, I want to talk about what happens when we decide to replace that functional aid, whatever it might be.

So, as I said last time, the biggest factor contributing to the breakdown of habit is the resetting of our expectation of what is an acceptable outcome. If our current tools no longer meet this expectation, then we start shopping for a new alternative. In marketing terms, this would be the triggering of need.

Now, this breakdown of expectation can play out in one of two ways. First, if we’re not aware of an alternative solution, we may just feel an accumulation of frustration and dissatisfaction with our current tools. This build up of frustration can create a foundation for further “usefulness foraging” but generally isn’t enough by itself to trigger action. This lends support to my hypothesis that we’re borrowing the evolved Marginal Value algorithm to help us judge the usefulness of our current tools. To put it in biological terms we’re more familiar with, “A bird in the hand is worth two in the bush.” You don’t leave a food patch unless: A) you are reasonably sure there’s another, more promising, patch that can be reached with acceptable effort or B) you have completely exhausted the food available in the patch you’re in. I believe the same is true for usefulness. We don’t throw out what we have until we either know there’s an acceptable alternative that promises a worthwhile increase in usefulness or our current tool is completely useless. Until then, we put up with the frustration.

The Technology Acceptance Model

Let’s say that we have decided that it’s worth the effort to find an alternative. What are the mechanisms we use to find the best alternative? Fred Davis and Richard Bagozzi tackled that question in 1989 and came up with the first version of their Technology Acceptance Model. It took the Theory of Reasoned Action, developed by Martin Fishbein and Icek Ajzen, put forward a decade earlier (1975, 1980) and tried to apply it to the adoption of a new technology. They also relied on the work Everett Rogers did in the diffusion of technology.

First of all, like all models, the TAM had to make some assumptions to simplify real world decisions down to a theoretical model. And, in doing so, it has required a number of revisions to try to bring it closer to what technology adoption decisions look like in the real world.

Let’s start with the foundation of the Theory of Reasoned Action. In it’s simplest form, the TRA says that voluntary behavior is predicted by an individual’s attitude towards that behavior and how they think others would think of them if they performed that behavior.

TRA

So, let’s take the theory for a test drive – if you believe that exercising will increase your health and you also believe that others in your social circle will applaud you for exercising, you’ll exercise. With this example, I think you begin to see where the original TRA may run into problems. Even with the best of intentions, we may not actually make it to the gym. Fishbein and Ajzen’s goal was to create an elegant, parsimonious model that would reliably predict both behaviors and intentions, creating a distinction between the two. Were they successful?

In a meta-analysis of TRA, Sheppard et al (1988) found that attitude was a fairly accurate predictor of intention. If you believe going to the gym is a good thing, you will probably intend to go to the gym. The model didn’t do quite as good a job in predicting behavior. Even if you did intend to go to the gym, would you actually go?

The successful progression from intention to behavior seemed to be reliant on several real world factors, including the time between intention and action (the longer the time interval, the more the degree of erosion of intention) and also lack of control. For example, in the gym example, what if your gym suddenly increased it’s membership fees, or a sudden snowstorm made it difficult to drive there.

Also, if you were choosing from a set of clear alternatives and had to choose one, TRA did a pretty good job of predicting behaviors. But if alternatives were undetermined, or there were other variables to consider, then the predictive accuracy of TRA dropped significantly.

Let me offer an example of how TRA might not work very well in a real world setting. In my book, The BuyerSphere Project, I spent a lot of time looking at the decision process in B2B buying scenarios. If we used the TRA model, we could say that if a buyer had to choose between 4 different software programs for their company, we could use their attitudes towards each of the respective programs as well as the aggregated (and weighted  – because not every opinion should carry the same weight) attitudes towards these programs of the buyer’s co-workers, peers and bosses to determine their intention. And once we have their intention, that should lead to behavior.

But in this scenario, let’s look at some of the simplifying assumptions we’ve had to make to try to cram a real world scenario into the Fishbein Azjen model:

  • We assume a purchase will have to be made from one of the four alternatives. In a real world situation, the company may well decide to stick with what they have
  • We assume the four choices will remain static and we won’t get a new candidate out of left field
  • We assume that attitudes towards each of the alternatives will remain static through the behavioral interval and won’t change. This almost never happens in B2B buying scenarios
  • We assume the buyer – or rational agent – will be in full control of their behaviors and the ultimate decision. Again, this is rarely the case in B2B buying decisions.
  • We assume that there won’t be some mitigating factor that arises in between intention and behavior – for example a spending freeze or a change in requirements.

As you can see, in trying to create a parsimonious model, Fishbein and Azjen ran into a common trap – they had to simplify to the point where it failed to work consistently in the real world.

But, in this review by Alice Darnell, she pointed out Sheppard’s main criticism of the TRA model:

Sheppard et al. (1988) also addressed the model’s main limitation, which is that it fails to account for behavioural outcomes which are only partly under the individual’s volitional control.

I’ve added bolding to the word volitional on purpose. I’ve highlighted many external factors that may lie beyond the volitional control of the individual, but I think the biggest limitation of the TRA lies in its name: Theory of Reasoned Action. It assumes that reason drives our intentions and behaviors. It doesn’t account for emotion.

Applying Reasoned Action to Technology Acceptance

Now, let’s see how Rogers and Bagozzi took Fishbein and Azien’s foundational work and applied it to the acceptance of new technologies.

In their first model (1989) they took attitudes and subjective norms (the attitudes of others) and adapted them for a more applied activity, the use of a new technological tool. They came up with two attitude drivers: Perceived Usefulness and Perceived Ease of Use. If you think back to Charnov’s Marginal Value Theorem, this is exactly the same risk/reward mechanism at work here.  In foraging, it would be yield of food over perceived required effort. In Technology Acceptance, Perceived Usefulness is the reward and Perceived Ease of Use is the risk to be calculated. In the mental calculation, Rogers and Bagozzi assume the user would do a quick mental calculation, using their own knowledge and the knowledge of others to come up with a Usefulness/Ease value that would create their attitude towards using.  This then becomes their Behavioral Intention to Use – which should lead to Actual System Use.

tam

The TAM model was clean and parsimonious. There was just one problem. It didn’t do a very good job of predicting usage in real world situations. There seemed to be much more at work here in actual decisions to accept technologies. In the next post, we’ll look at how the TAM model was modified to bring it closer to real behaviors.

The Psychology of Usefulness: How Online Habits are Broken

google-searchLast post, I talked about how Google became a habit – Google being the most extreme case of online loyalty based on functionality I could think of. But here’s the thing with functionally based loyalty – it’s very fickle. In the last post I explained how Charnov’s Marginal Value Theorem dictates how long animals spend foraging in a patch before moving on to the next one. I suspect the same principles apply to our judging of usefulness. We only stay loyal to functionality as long as we believe there are no more functional alternatives available to us for an acceptable investment of effort. If that functionality has become automated in the form of a habit, we may stick with it a little longer, simply because it takes our rational brain awhile to figure out there may be better options, but sooner or later it will blow the whistle and we’ll start exploring our options. Charnov’s internal algorithm will tell us it’s time to move on to the next functional “patch.”

Habits break down when there’s a shift if one of the three prerequisites: frequency, stability or acceptable outcomes.

If we stop doing something on a frequent basis, the habit will slowly decay. But because habits tend to be stored at the limbic level (in the basal ganglia), they prove to be remarkably durable. There’s a reason we say old habits die hard. Even after a long hiatus we find that habits can easily kick back in. Reduction of frequency is probably the least effective way to break a habit.

A more common cause of habitual disruption is a change in stability. Suddenly, if something significant changes in our task environment, our  “habit scripts” start running into obstacles. Think about the last time you did a significant upgrade to a program or application you use all the time. If menu options or paths to common functions change, you find yourself constantly getting frustrated because things aren’t where you expect them to be. Your habit scripts aren’t working for you anymore and you are being forced to think. That feeling of frustration is how the brain protects habits and shows how powerful our neural energy saving mode is. But, even if the task environment becomes unstable for a time, chances are the instability is temporary. The brain will soon reset its habits and we’ll be back plugging subconsciously away at our tasks. Instability does break a habit, but it just rebuilds a new one to take its place.

A more permanent form of habit disruption comes when outcomes are no longer acceptable. The brain hates these types of disruptions, because it knows that finding an alternative could require a significant investment of effort. It basically puts us back at square one. The amount of investment required is dependent on a number of things, including the scope of change required (is it just one aspect of a multi-step task or the entire procedure?), current awareness of acceptable alternatives (is a better solution near at hand or do we have to find it?), the learning curve involved (how different is the alternative from what we’re used to using), are there other adoption requirements (do we have to make an investment of resources – including time and/or money?) and how much down time will be involved in order to adopt the alternative. All these questions are the complexities that can be factors in the Marginal Value Theorem.

Now, let’s look at how each of these potential habit breakers applies to Google. First of all, frequency probably won’t be a factor because we will search more, not less, in the future.

Stability may be a more likely cause. The fact is, the act of online searching hasn’t really changed that much in the last 20 years. We still type in a query and get a list of results. If you look at Google circa 1998, it looks a little clunky and amateurish next to today’s results page, but given that 16 years have come and gone, the biggest surprise is that the search interface hasn’t changed more than it has.

Google now and then

A big reason for this is to maintain stability in the interface, so habits aren’t disrupted. The search page relies on ease of information foraging, so it’s probably the most tested piece of online real estate in history. Every pixel of what you see on Google, and, to a lesser extent, it’s competitors, has been exhaustively tested.

That has been true in the past but because of the third factor, acceptability of outcomes, it’s not likely to remain true in the future. We are now in the age of the app. Searching used to be a discrete function that was just one step of many required to complete a task. We were content to go to a search engine, retrieve information and then use that information elsewhere with other tools or applications. In our minds, we had separate chunks of online functionality that we would assemble as required to meet our end goal.

Let me give you an example. Let’s imagine we’re going to London for a vacation. In order to complete the end goal – booking flights, hotels and whatever else is required – we know we will probably have to go to many different travel sites, look up different types of information and undertake a number of actions. We expect that this will be the best path to take to our end goal. Each chunk of this “master task” may in turn be broken down into separate sub tasks. Along the way, we’ll be relying on those tools that we’re aware of and a number of stored procedures that have proven successful in the past. At the sub-task level, it’s entirely possible that some of those actions have been encoded as habits. For an example of how these tasks and stored procedures would play out in a typical search, see my previous post, A Cognitive Walkthrough of Searching.

But we have to remember that the only reason the brain is willing to go to all this work is that it believes it’s the most efficient route available to it. If there were a better alternative that would produce an acceptable outcome, the brain would take it. Our expectation of what an acceptable outcome would be would be altered, and our Marginal Value algorithm would be reset.

Up to now, functionality and information didn’t intersect too often online. There were places we went to get information, and there were places we went to do things. But from this point forward, expect those two aspects of online to overlap more and more often. Apps will retrieve information and integrate it with usefulness. The travel aggregator sites like Kayak and Expedia are an early example of this. They retrieve pricing information from vendors, user content from review sites and even some destination related information from travel sites. This ups the game in terms of what we expect from online functionality when we book a trip. Our expectation has been reset because Kayak offers a more efficient way to book travel than using search engines and independent vendor sites. That’s why we don’t immediately go to Google when we’re planning a trip.

Let’s fast-forward a few years to see how our expectations could be reset in the future. I suspect we’re not too far away from having an app where our travel preferences have been preset. This proposed app would know how we like to travel and the things we like to do when we’re on vacation. It would know the types of restaurants we like, the attractions we visit, the activities we typically do, the types of accommodation we tend to book, etc.  It would also know the sources we tend to use when qualifying our options (i.e. TripAdvisor). If we had such an app, we would simply put in the bare details of our proposed trip: departure and return dates, proposed destinations and an approximate itinerary. It would then go and assemble suggestions based on our preferences, all in one location. Booking would require a simple click, because our payment and personal information would be stored in the app. There would be no discrete steps, no hopping back and forth between sites, no cutting and pasting of information, no filling out forms with the same information multiple times. After confirmation, the entire trip and all required information would be made available on your mobile device.  And even after the initial booking, the app would continue to comb the internet for new suggestions, reviews or events that you might be interested in attending.

This “mega-app” would take the best of Kayak, TripAdvisor, Yelp, TripIt and many other sites and combine it all in one place. If you love travel as much as I do, you couldn’t wait to get your hands on such an app. And the minute you did, your brain would have reset it’s idea of what an acceptable outcome would be. There would be a cascade of broken habits and discarded procedures.

This integration of functionality and information foraging is where the web will go next. Over the next 10 years, usefulness will become the new benchmark for online loyalty. As this happens, our expectation set points will be changed over and over again. And this, more than anything, will be what impacts user loyalty in the future. This changing of expectations is the single biggest threat that Google faces.

In the next post I’ll look at what happens when our expectations get reset and we have to look at adopting a new technology.

Our Brain on Books

Brain-on-BooksHere’s another neuroscanning study out of Emory University showing the power of a story.

Lead researcher Gregory Burns and his team wanted to “understand how stories get into your brain, and what they do to it.” Their findings seem to indicate that stories, in this case a historical fiction novel about Pompeii, caused a number of changes in the participants brain, at least in the short term. Over time, some of these changes decayed, but more research is required to determine how long lasting the changes are.

One would expect reading to alter related parts of the brain and this was true in the Emory study. The left temporal cortex, a section of the brain that handles language reception and interpretation showed signs of heightened connectivity for a period of time after reading the novel. This is almost like the residual effects of exercise on a muscle, which responds favorably to usage.

What was interesting, however, was that the team also saw increased connectivity in the areas of the brain that control representations of sensation for the body. This relates to Antonio Damasio’s “Embodied Semantics” theory where the reading of metaphors, especially those relating specifically to tactile images, activate the same parts of the brain that control the corresponding physical activity. The Emory study (and Damasio’s work) seems to show that if you read a novel that depicts physical activity, such as running through the streets of Pompeii as Vesuvius erupts, your brain is firing the same neurons as it would if you were actually doing it!

There are a number of interesting aspects to consider here, but what struck me is the multi-prong impact a story has on us. Let’s run through them:

Narratives have been shown to be tremendously influential frameworks for us to learn and update our sense of the world, including our own belief networks. Books have been a tremendously effect agent for meme transference and propagation. The structure of a story allows us to grasp concepts quickly, but also reinforces those concepts because it engages our brain in a way that a simple recital of facts could not. We relate to protagonists and see the world through their eyes. All our socially tuned, empathetic abilities kick into action when we read a story, helping to embed new information more fully. Reading a story helps shape our world view.

Reading exercises the language centers of our brain, heightening the neural connectivity and improving the effectiveness. Neurologists call this “shadow activity” – a concept similar to muscle memory.

Reading about physical activity fires the same neurons that we would use to do the actual activity. So, if you read an action thriller, even through you’re lying flat on a sofa, your brain thinks you’re the one racing a motorcycle through the streets of Istanbul and battling your arch nemesis on the rooftops of Rome. While it might not do much to improve muscle tone, it does begin to create neural pathways. It’s the same concept of visualization used by Olympic athletes.

For Future Consideration

As we learn more about the underlying neural activity of story reading, I wonder how we can use this to benefit ourselves? The biggest question I have is if a story in written form has this capacity to impact us at all the aforementioned levels, what would  more sense-engaged media like television or video games do? If reading about a physical activity tricks the brain into firing the corresponding sensory controlling neurons, what would happen if we are simulating that activity on an action controlled gaming system like Microsoft’s X Box? My guess would be that the sensory motor connections would obviously be much more active (because we’re physically active). Unfortunately, research in the area of embodied semantics is still at an early stage, so many of the questions have yet to be answered.

However, if our stories are conveyed through a more engaging sensory experience, with full visuals and sound, do we lose some opportunity for abstract analysis? The parts of our brain we use to read depend on relatively slow processing loops. I believe much of the power of reading lies in the requirements it places on our imagination to fill in the sensory blanks. When we read about a scene in Pompeii we have to create the visuals, the soundtrack and the tactile responses. In all this required rendering, does it more fully engage our sense-making capabilities, giving us more time to interpret and absorb?

Psychological Priming and the Path to Purchase

First published March 27, 2013 in Mediapost’s Search Insider

In marketing, I suspect we pay too much attention to the destination, and not enough to the journey. We don’t take into account the cumulative effect of the dozens of subconscious cues we encounter on the path to our ultimate purchase. We certainly don’t understand the subtle changes of direction that can result from these cues.

Search is a perfect example of this.

As search marketers, we believe that our goal is to drive a prospect to a landing page. Some of us worry about the conversion rates once a prospect gets to the landing page. But almost none of us think about the frame of mind of prospects once they reach the landing page.

“Frame” is the appropriate metaphor here, because the entire interaction will play out inside this frame. It will impact all the subsequent “downstream” behaviors. The power of priming should not be taken likely.

Here’s just one example of how priming can wield significant unconscious power over our thoughts and actions. Participants primed by exposure to a stereotypical representation of a “professor” did better on a knowledge test than those primed with a representation of a “supermodel.”

A simple exposure to a word can do the trick. It can frame an entire consumer decision path. So, if many of those paths start with a search engine, consider the influence that a simple search listing may have.

We could be primed by the position of a listing (higher listings = higher quality alternatives).  We could be primed (either negatively or positively) by an organization that dominates the listing real estate. We could be primed by words in the listing. We could be primed by an image. A lot can happen on that seemingly innocuous results page.

Of course, the results page is just one potential “priming” platform. Priming could happen on the landing page, a third-party site or the website itself. Every single touch point, whether we’re consciously interacting with it or not, has the potential to frame, or even sidetrack, our decision process.

If the path to purchase is littered with all these potential landmines (or, to take a more positive approach, “opportunities to persuade”), how do we use this knowledge to become better marketers? This does not fall into the typical purview of the average search marketer.

Personally, I’m a big fan of the qualitative approach (I know — big surprise) in helping to lay down the most persuasive path possible. Actually talking to customers, observing them as they navigate typical online paths in a usability testing session, and creating some robust scenarios to use in your own walk-throughs will yield far better results than quantitative number-crunching. Excel is not a particularly good at being empathetic.

Jakob Nielsen has said that online, branding is all about experience, not exposure. As search marketers, it’s our responsibility to ensure that we’re creating the most positive experience possible, as our prospects make their way to the final purchase.

The devil, as always, is in the details — whether we’re paying conscious attention to them or not.

Weighing Positive and Negative Impacts on Users

First published January 31, 2013 in Mediapost’s Search Insider

We humans hate loss. In fact, we seem to value losing something about twice as high as gaining something. For example, imagine I gave you a coffee cup and then offered to buy it back from you. That’s scenario 1. In scenario 2, I ask you to buy the same coffee cup from me. The price you assign to the coffee cup in the first scenario will be, on the average, about twice as much as in the second. And yes, there’s research to back this up.

When it comes to winning and losing, it’s been proven that “loss looms larger than gains.” It’s just one of the weird glitches in our logical circuitry.  We tend to be hardwired to look at glasses as half empty.

Recently, I was reviewing an academic study done in 2008, with this scintillating title: “Procedural Priming and Consumer Judgment: Effects on the Impact of Positively and Negatively Valenced Information” by Shen and Wyer. If you can get beyond the rather dry title, you find a treasure trove of tidbits to consider when crafting your online user experience.

For example, when we evaluate a product for potential purchase, we may run across both positive and negative information. The order we run into this information can have a dramatic impact on what we do downstream from that interaction. To use psychological terms, it “primes” our mental framework.  And, because we tend to focus on negatives, less favorable information has a greater impact on our decision than positive information.

But it’s not just that we pay more attention to bad news than good news. It’s that bad news can hijack the entire consideration process. According to Shen and Wyer, if we run into negative information, it can change our information-seeking strategies, leading us down further negatively biased channels to confirm the initial information we saw. Bad news tends to lead to more bad news.

Also, we can get “bad news” hangovers. If we compare negatives in one decision process, that negative mental framework can carry over to an entirely different decision that has nothing to do with the first, giving us a heightened awareness of negative information in the new situation.

Here’s another interesting finding. If we’re rushed for time, this preoccupation with the negatives will dramatically affect the decision we make. But, if we have all the time in the world, the impact is relatively insignificant. Given time, we seem to cancel out our inherently negative biases.

All this news is not bad for marketers, however. It seems that simply getting users to state their preference for one feature over another, even though they’re not actively considering purchase at that time, leads to a much greater likelihood of purchase in the future. It seems that if you can get users to compare alternatives — and, more importantly, to commit to saying they prefer one alternative over another — they clear the mental hurdle of deciding “will I buy?” and instead start considering  “what will I buy?”

Finally, there is also a recency effect, especially if prospects had ample time to consider all their alternatives. Shen and Wyer found that the last information considered seemed to have the greatest effect on the buyer.  So, if information was both positive and negative, it was good to get the least favorable information in front of the prospect early, and then move to the most favorable information. Again, this is true only if the user had plenty of time to weigh the options. If they were rushed, the opposite was true.

All in all, these are all intriguing concepts to consider when crafting an ideal online user experience. They also underscore the importance of first impressions, especially negative ones.

A Look at the Future through Google Glasses?

First published June 7, 2012 in Mediapost’s Search Insider

“A wealth of information creates a poverty of attention.” — Herbert Simon

Last week, I explored the dark recesses of the hyper-secret Google X project.  Two X Projects in particular seem poised to change our world in very fundamental ways: Google’s Project Glass and the “Web of Things.”

Let’s start with Project Glass. In a video entitled “One Day…,” the future seen through the rose-colored hue of Google Glasses seems utopian, to say the least. In the video, we step into the starring role, strolling through our lives while our connected Google Glasses feed us a steady stream of information and communication — a real-time connection between our physical world and the virtual one.

In theory, this seems amazing. Who wouldn’t want to have the world’s sum total of information available instantly, just a flick of the eye away?

Couple this with the “Web of Things,” another project said to be in the Google X portfolio.  In the Web of Things, everything is connected digitally. Wearable technology, smart appliances, instantly findable objects — our world becomes a completely inventoried, categorized and communicative environment.

Information architecture expert Peter Morville explored this in his book “Ambient Findability.”  But he cautions that perhaps things may not be as rosy as you might think after drinking the Google X Kool-Aid. This excerpt is from a post he wrote on Ambient Findability:  “As information becomes increasingly disembodied and pervasive, we run the risk of losing our sense of wonder at the richness of human communication.”

And this brings us back to the Herbert Simon quote — knowing and thinking are not the same thing. Our brains were not built on the assumption that all the information we need is instantly accessible. And, if that does become the case through advances in technology, it’s not at all clear what the impact on our ability to think might be. Nicholas Carr, for one, believes that the Internet may have the long-term effect of actually making us less intelligent. And there’s empirical evidence he might be right.

In his book “Thinking, Fast and Slow,”Noble laureate Daniel Kahneman says that while we have the ability to make intuitive decisions in milliseconds (Malcolm Gladwell explored this in “Blink”), humans also have a nasty habit of using these “fast” mental shortcuts too often, relying on gut calls that are often wrong (or, at the very least, biased) when we should be using the more effortful “slow” and rational capabilities that tend to live in the frontal part of our brain. We rely on beliefs, instincts and habits, at the expense of thinking. Call it informational instant gratification.

Kahneman recounts a seminal study in psychology, where four-year-old children were given a choice: they could have one Oreo immediately, or wait 15 minutes (in a room with the offered Oreo in front of them, with no other distractions) and have two Oreos. About half of the children managed to wait the 15 minutes. But it was the follow-up study, where the researchers followed what happened to the children 10 to 15 years later, that yielded the fascinating finding:

“A large gap had opened between those who had resisted temptation and those who had not. The resisters had higher measures of executive control in cognitive tasks, and especially the ability to reallocate their attention effectively. As young adults, they were less likely to take drugs. A significant difference in intellectual aptitude emerged: the children who had shown more self-control as four year olds had substantially higher scores on tests of intelligence.”

If this is true for Oreos, might it also be true for information? If we become a society that expects to have all things at our fingertips, will we lose the “executive control” required to actually think about things? Wouldn’t it be ironic if Google, in fulfilling its mission to “organize the world’s information” inadvertently transgressed against its other mission, “don’t be evil,” by making us all attention-deficit, intellectual-diminished, morally bankrupt dough heads?

As We May Remember

First published January 12, 2012 in Mediapost’s Search Insider

In his famous Atlantic Monthly essay “As We May Think,” published in July 1945, Vannevar Bush forecast a mechanized extension to our memory that he called a “memex”:

Consider a future device for individual use, which is a sort of mechanized private file and library. It needs a name, and to coin one at random, “memex” will do. A memex is a device in which an individual stores all his books, records, and communications, and which is mechanized so that it may be consulted with exceeding speed and flexibility. It is an enlarged intimate supplement to his memory.

Last week, I asked you to ponder what our memories might become now that Google puts vast heaps of information just one click away. And ponder you did:

I have to ask, WHY do you state, “This throws a massive technological wrench into the machinery of our own memories,” inferring something negative??? Might this be a totally LIBERATING situation? – Rick Short, Indium Corporation

Perhaps, much like using dictionaries in grade school helped us to learn and remember new information, Google is doing the same? Each time we “google” and learn something new aren’t we actually adding to our knowledge base in some way? – Lester Bryant III

Finally, I ran across this. Our old friend Daniel Wegner (transactive memory) and colleagues Betsy Sparrow and Jenny Liu from Columbia University actually did research on this very topic this past year. It appears from the study that our brains are already adapting to having Internet search as a memory crutch. Participants were less likely to remember information they looked up online when they knew they could access it again at any time. Also, if they looked up information that they knew they could remember, they were less likely to remember where they found it. But if the information was determined to be difficult to remember, the participants were more likely to remember where they found it, so they could navigate there again.

The beautiful thing about our capacity to remember things is that it’s highly elastic. It’s not restricted to one type of information. It will naturally adapt to new challenges and requirements. As many rightly commented on last week’s column, the advent of Google may introduce an entirely new application of memory — one that unleashes our capabilities rather than restricts them. Let me give you an example.

If I had written last week’s column in 1987, before the age of Internet Search, I would have been very hesitant to use the references I did: the Transactive Memory Hypothesis of Daniel Wegner, and the scene from “Annie Hall.”  That’s because I couldn’t remember them that well. I knew (or thought I knew) what the general gist was, but I had to search them out to reacquaint myself with the specific details of each. I used Google in both cases, but I was already pretty sure that Wikipedia would have a good overview of transactive memory and that Youtube would have the clip in question. Sure enough, both those destinations topped the results that Google brought back. So, my search for transactive memory utilized my own transactive memorizations. The same was true, by the way, for my reference to Vannevar Bush at the opening of this column.

By knowing what type of information I was likely to find, and where I was likely to find it, I could check the references to ensure they were relevant and summarize what I quickly researched in order to make my point. All I had to do was remember high-level summations of concepts, rather than the level of detail required to use them in a meaningful manner.

One of my favorite concepts is the idea of consilience – literally, the “jumping together” of knowledge. I believe one of the greatest gifts of the digitization of information is the driving of consilience. We can now “graze” across multiple disciplines without having to dive too deep in any one, and pull together something useful — and occasionally amazing. Deep dives are now possible “on demand.” Might our memories adapt to become consilience orchestrators, able to quickly sift through the sum of our experience and gather together relevant scraps of memory to form the framework of new thoughts and approaches?

I hope so, because I find this potential quite amazing.

Risk, Reward and the Buying Matrix

First published December 23, 2010 in Mediapost’s Search Insider

Last week, I explored how two parts of our brain, the nucleus accumbens and the anterior insula, are key in driving our buying behaviors. I compared them to the gas pedal and brake of our buying “engine.” The balance between the two is key to understanding how we are driven towards our ultimate decisions. The nucleus accumbens drives our anticipation of an emotional reward, and the anterior insula creates anxiety around areas of risk.

As it turns out, you can plot the two as the axes of a matrix on which, theoretically, you could plot any purchase. The four quadrants would be, starting in the lower left and going clockwise: low risk/low reward,  low risk/high reward, high risk/high reward and, finally, high risk/low reward. Let’s take a deeper dive in each quadrant to see what kind of purchases fall into each.

Low Risk/Low Reward

This is the stuff of everyday life. If you’re a “to-do” list kind of person, these types of purchases would probably be on that list. Think of household supplies like toilet paper and laundry detergent, or the milk, dry goods, etc. that make up a large percentage of your grocery list. This is the world of consumer packaged goods. The only real exceptions are those products that represent personal indulgences, like a steak or your favorite premium ice cream.

There is a huge piece of the B2B market that falls into this category as well: office  and industrial supplies, parts and other often-purchased items.

There is no gas pedal and no brake on these purchases. While the low prices remove any real risk, these are also not the types of shopping trips you look forward to all day. You simply have to get them done. This means the personal engagement with the actual act of purchasing will be minimal. Here, we are creatures of habit. We go to the same places to buy the same things because we really don’t want to invest any more time than is necessary to get the job done. If you compete in this space, you have one strategy and one strategy only: provide the fastest and easiest path to purchase.

Low Risk/High Reward

Here, we have our little indulgences; the day-to-day treats that make life worth living. The entire premium consumer product industry lives squarely in this quadrant: premium desserts, pre-made meals, beauty care products, wines, craft beers and, moving into slightly greater degrees of risk, clothes, accessories, shoes, costume jewelry and electronic gadgets.  This is also where you’d find CDs, DVDs and books. It’s in this quadrant where Amazon rules.

These purchases are all gas and little brake.  If you ever make a purchase on impulse, it’s almost guaranteed to fall into this part of the behavioral matrix.  When women plan shopping trips, it’s to indulge their reward center with these types of purchases. But men are also vulnerable to the siren call of the indulgent purchase: gadgets, tools, sporting goods, electronic games — and, for the metro-men amongst us, clothes and accessories. By the way, manicures, pedicures and spa visits all qualify, along with movies, concerts and dining out.

This quadrant is particularly timely this time of year, because when you buy a gift for someone, you hope you’ve hit this quadrant. The tough part is knowing your recipients well enough to figure out what will kick their nucleus accumbens into high gear.

While the degree of risk doesn’t merit a lot of intensive research, here the buying can be as much fun as the owning, which generally means a higher degree of engagement on the part of the buyer. Shopping environments that enhance the reward part of the equation will be attractive. Buyers are susceptible to suggestion, especially if it comes through our social connections. And brand affinities are powerful here.

In my next column, I’ll provide some examples of the other two quadrants to see what kind of purchases fall into each. Then, we’ll see how each of these buying scenarios might map on the online consumer landscape.

The Insula and The Accumbens: Driving Online Behavior

First published December 16, 2010 in Mediapost’s Search Insider

One of the more controversial applications of new neurological scanning technologies has been a quest by marketers for the mythical “buy button” in our brains. So far, no magical nook or cranny in our cranium has given marketers the ability to foist whatever crap they want on it, but a couple of parts of the brain have emerged as leading contenders for influencing buying behavior.

The Nucleus Accumbens: The Gas Pedal

The nucleus accumbens has been identified as the reward center of the brain. Although this is an oversimplification, it definitely plays a central role in our reward circuit. Neuroscanning studies show that the nucleus accumbens “lights up” when people think about things that have a reward attached: investments with big returns, buying a sports car or participating in favorite activities. Dopamine is released and the brain benefits from a natural high. Emotions are the drivers of human behavior — they move us to action (the name comes from the Latin movere, meaning “to move”). The reward circuit of the brain uses emotions to drive us towards rewards, an evolutionary pathway that improves our odds for passing along our genes.

In consumer behaviors, there are certain purchase decisions that fire the nucleus accumbens. Anything that promises some sort of emotional reward can trigger our reward circuits. We start envisioning what possession would be like: the taste of a meal, the thrill of a new car, the joy of a new home, the indulgence of a new pair of shoes. There is strong positive emotional engagement in these types of purchases.

The Anterior Insula: The Brake

But if our brain was only driven by reward, we would never say no. There needs to be some governing factor on the nucleus accumbens. Again, neuroscanning has identified a small section of the brain called the anterior insula as one of the structures serving this role.

If the nucleus accumbens could be called the reward center, the anterior insula could be called the Angst Center of our brains. The insula is a key part of our emotional braking system.  Through the release of noradrenaline and other neurochemicals, it creates the gnawing anxiety that causes us to slow down and tread carefully. In extreme cases, it can even evoke disgust. If the nucleus accumbens drives impulse purchasing, it’s the anterior insula that triggers buyer’s remorse.

The Balance Between the Two 

Again, at the risk of oversimplification, these two counteracting forces drive much of our consumer behavior. You can look at any purchase as the net result of the balance between them; a balancing of risk and reward, or in the academic jargon, prevention and promotion. High-reward and low-risk purchases will have a significantly different consumer behavior pattern than low-reward and high-risk purchases. Think about the difference between buying life insurance and a new pair of shoes. And because they have significantly different behavior profiles, the online interactions that result from these purchases will look quite different as well. In the next column, I’ll look at the four different purchase profiles (High Risk/High Reward, High Risk/Low Reward, Low Risk/High Reward and Low Risk, Low Reward) and look at how the online maps might look in each scenario.

Is the Internet Making Us Stupid – or a New Kind of Smart?

First published September 9, 2010 inn Mediapost’s Search Insider

As I mentioned a few weeks back, I’m reading Nicholas Carr’s book “The Shallows.” His basic premise is that our current environment, with its deluge of available information typically broken into bite-sized pieces served up online, is “dumbing down” our brains.  We no longer read, we scan. We forego the intellectual heavy lifting of prolonged reading for the more immediate gratification of information foraging. We’re becoming a society of attention-deficit dolts.

It’s a grim picture, and Carr does a good job of backing up his premise. I’ve written about many of these issues in the past. And I don’t dispute the trends that Carr chronicles (at length). But is Carr correct is saying that online is dulling our intellectual capabilities, or is it just creating a different type of intelligence?

While I’m at it, I suspect this new type of intelligence is much more aligned with our native abilities than the “book smarts” that have ruled the day for the last five centuries. I’m an avid reader (ironically, I’ve been reading Carr’s book on an iPad) and I’m the first to say that I would be devastated if reading goes the way of the dodo.  But are we projecting our view of what’s “right” on a future where the environment (and rules) have changed?

A Timeline of Intellect

If you expand your perspective of human intellectualism to the entire history of man, you find that the past 500 years have been an anomaly. Prior to the invention of the printing press (and the subsequent blossoming of intellectualism) our brains were there for one purpose: to keep us alive. The brain accomplished this critical objective through one of three ways:

Responding to Danger in Our Environments

Reading is an artificial human activity. We have to train our brains to do it. But scanning our surroundings to notice things that don’t fit is as natural to us as sleeping and eating. We have sophisticated, multi-layered mechanisms to help us recognize anomalies in our environment (which often signal potential danger).  I believe we have “exapted” these same mechanisms and use them every day to digest information presented online.

This idea goes back to something I have said repeatedly: Technology doesn’t change behavior, it enables behavior to change. Change comes from us pursuing the most efficient route for our brains. When technology opens up an option that wasn’t previously available, and the brain finds this a more natural path to take, it will take it. It may seem that the brain is changing, but in actuality it’s returning to its evolutionary “baseline.”

If the brain has the option of scanning, using highly efficient inherent mechanisms that have been created through evolution over thousands of generations, or reading, using jury-rigged, inefficient neural pathways that we’ve been forced to build from scratch through our lives, the brain will take the easiest path. The fact was, we couldn’t scan a book. But we can scan a Web site.

Making The Right Choices

Another highly honed ability of the brain is to make advantageous choices. We can consider alternatives using a combination of gut instincts (more than you know) and rational deliberation (less than you think) and more often than not, make the right choice. This ability goes in lock step with the previous one, scanning our environment.

Reading a book offers no choices. It’s a linear experience, forced to go in one direction. It’s an experience dictated by the writer, not the reader. But browsing a Web site is an experience littered with choices.  Every link is a new choice, made by the visitor. This is why we (at my company) have continually found that a linear presentation of information (for example, a Flash movie) is a far less successful user experience than a Web site where the user can choose from logical and intuitive navigation options.

Carr is right when he says this is distracting, taking away from the focused intellectual effort that typifies reading. But I counter with the view that scanning and making choices is more naturally human than focused reading.

Establishing Beneficial Social Networks

Finally, humans are herders. We naturally create intricate social networks and hierarchies, because it’s the best way of ensuring that our DNA gets passed along from generation to generation. When it comes to gene propagation, there is definitely safety in numbers.

Reading is a solitary pursuit. Frankly, that’s one of the things avid readers treasure most about a good book, the “me” time that it brings with it. That’s all well and good, but bonding and communication are key drivers of human behavior. Unlike a book, online experiences offer you the option of solitary entertainment or engaged social connection. Again, it’s a closer fit with our human nature.

From a personal perspective, I tend to agree with most of Carr’s arguments. They are a closer fit with what I value in terms of intellectual “worth.” But I wonder if we fall into a trap of narrowed perspective when we pass judgment on what’s right and what’s not based on what we’ve known, rather than on what’s likely to be.

At the end of the day, humans will always be human.