Michael Wu, Ph.D. is Lithium's Principal Scientist of Analytics, digging into the complex dynamics of social interaction and group behavior in online communities and social networks.
Michael was voted a 2010 Influential Leader by CRM Magazine for his work on predictive social analytics and its application to Social CRM.He's a regular blogger on the Lithosphere's Building Community blog and previously wrote in the Analytic Science blog. You can follow him on Twitter at mich8elwu.
Welcome back! Apologies for taking a little bit longer to write this post. I have been a little busy recently – and I was in Troy, NY last week, giving a series of lectures about Social CRM at RPI. The psychology of motivation is a broad topic, and I will have to be fairly brutal in my summarization and triaging to cut it down to a reasonable length.
Last time I briefly introduced Fogg’s Behavior Model (FBM), and used it to analyze why and how game mechanics/dynamics are able to drive actions. FBM asserts that human behavior is a result of the precise temporal convergence of three factors:
Motivation: the person wants desperately to perform the behavior (i.e. he is highly motivated)
Ability: the person can easily carry out the behavior (i.e. he considers the behavior very simple)
Trigger: the person is triggered to do the behavior (i.e. he is cued, reminded, asked, called to action, etc.)
Game mechanics and game dynamics are able to positively influence human behavior because they are designed to drive the players above the activation threshold (i.e. the upper right of the ability-motivation axis), and then trigger them into specific actions. In other words, successful gamification is all about making these three factors occur at the same time. As I mentioned last time, the temporal convergence is the key.
Today, I will talk about the first factor in FBM: the science of motivation. This topic is not new. In fact there has been a lot of interest and research in the field of psychology around motivation. Subsequently there are many models which describe what can motivate people and why. Since it would be impossible to cover all of them without turning this into a book, I will talk about three psychological models of motivation and behavior that I believe are useful in the gamification setting.
From Maslow’s Needs to Pink’s Drive
One of the earliest and best known theories of motivation comes from the renowned psychologist, Abraham H. Maslow. The now famous Hierarchy of Needs was published in 1943. I’m sure most of you have seen the pyramid depicting the five levels of needs, in one form or another.
Physiological: air, food, water, sex, sleep, excretion, etc.
Safety: health, personal well being, financial and employment stability, security against accidents, etc.
Belonging: love, intimacy, friendship, family, social cohesion, etc.
Esteem: self-esteem, confidence, achievement, respects, etc.
Maslow believes these needs are what motivate people to do the things they do. In essence, human behaviors are driven by their desire to satisfy physical and psychological needs. It is easy to understand the lower four levels of needs, and Maslow refers to them as deficiency-needs. But what is self-actualization? If you read Maslow’s work carefully, he referred to this highest level as being-needs or meta-needs, and it is actually a combination of many meta-motivators, which I’ve summarize in a word cloud (figure 1).
If you think Maslow is a little old school, you might appreciate Daniel Pink’s more recent book, Drive: The Surprising Truth About What Motivates Us, published in 2009. Pink hypothesizes that in the modern society where the lower levels of the Maslow’s hierarchy are more or less satisfied, people become more and more motivated by other intrinsic motivators. These intrinsic motivators are precisely the meta-motivators that Maslow is referring to in the self-actualization level, and Pink specifically focuses on three of these:
If you hadn’t noticed, many of these needs and motivators are very similar to game mechanics and dynamics. Zynga, for example, realizes that majority of the population have the gaming personality of a socializer and need a sense of belonging. They created FarmVille to address the socializer’s need for social cohesion/acceptance. Status, achievements, ranks and reputation are some of the most commonly used game mechanics, but they are really nothing more than “esteem in disguise”. The progression dynamics and levels are simply Dan Pink’s mastery. See the parallel? If not, I hope figure 2 will make it more obvious.
Skinnerian Conditioning and Learning
B. F. Skinner’s Radical Behaviorism is a very different school of psychology. It is actually a full behavior model, like that of B. J. Fogg, and it claims that human behavior is a result of the cumulative effects of environmental reinforcements and learning.
However, much of Skinner’s research on reinforcement and operant conditioning (not classical conditioning) can be applied to understand motivation. Skinner’s theory disregards innate needs and uses only external conditions/reinforcement to manipulate and shape people’s behavior. In essence, the conditioned reinforcers (which are usually some kind of points in most gamification settings) are learned, and they become the motivator.
Many game dynamics have been developed using the principles from Skinner’s work, because a point system is often core to many game dynamics, including progression dynamics and levels. Points by themselves are not inherently rewarding – in fact, points can be a detraction if used inappropriately. Proper use of points depends on the reward schedule (or reinforcement schedule in psychology terminology), that is, when, how many, and at what rate the points are given (or taken away).
Skinner characterized the effects of many different types of reward schedules on the response rate of the subject (the player) and what actions each type of schedule helps invoke. For example, fixed-interval schedule is great for driving increase activity near deadlines. This is the basis of the countdown and appointment dynamic. Both fixed-interval and fixed-ratio schedules are great for learning new behaviors, but the variable-interval schedule is far more efficient for reinforcing established behaviors. The variable-ratio schedule is best for maintaining a behavior, so it is responsible for many forms of game addiction, including gambling. This schedule emphasizes the importance of surprise in gamification, and it is the foundation for the lottery mechanic and other anticipatory motivators.
Flow: The Fine Line between Certainty and Uncertainty
I wrote about flow in an earlier post. It is a mental state characterized by another renowned psychologist in 1975, Mihaly Csikszentmihalyi. Flow is an optimal state of intrinsic motivation, where people become totally immersed in what they are doing. People experiencing flow often forget about physical feelings, passage of time, and their ego fades away.
Despite the fact that flow is an extremely desirable mental state, it is not easy to get into the state of flow. Part of the reason is because there is an inherent discordance in what people want. In a 2006 TED talk by Anthony Robbins, a popular motivation author, he talked about the six emotional needs of humans. The first is the need for certainty, but paradoxically the second is the need for uncertainty, which is in direct conflict with the first need. It may seem that people are not perfectly consistent, but there is actually a very fine line between certainty and uncertainty, and it is precisely Csikszentmihalyi’s state of flow.
For the most part, people love to be in the control (overlearning) state, because it gives them a sense of security and safety. But people also hate boredom. However, as we acquire skills over time, we inadvertently move into the relaxation/boredom state if we don’t pick a more challenging task. So as humans, we are also motivated by some challenges, surprises, and varieties, to avoid boredom.
In real life, this often pushes us into the arousal state, because it is usually very hard to find tasks with the right level of challenge that match people’s skills exactly. They are either far too easy (boring) or too hard (frustrating). So the apparent paradox of human motivation is really our attempt to find that fine line between certain and uncertainty.
So let me summarize what have we learned today:
Abraham Maslow (and recently Dan Pink) tells us a lot about what people need, and these innate needs are what motivate people. Maslow's need theory is basically the carrot and the stick theory of motivation.
B. F. Skinner on the other hand believes that under a proper reinforcement schedule, we can ignore people’s innate needs and just give them points instead, and people will learn and be motivated simply by accumulating points. Surprising isn’t it? But it is all true!
However, blindly giving people points (or whatever they need) is not going to work over the long term, because people get tired and bored rather quickly. Successful gamification need to adapt with people’s skill and find that fine line between certainty and uncertainty (i.e. Csikszentmihalyi’s Flow), a state of optimal intrinsic motivation.
Alright, we covered a lot of psychology today. Yet I barely scraped the surface of the psychological research available on motivation. There are many other psychological models of motivation, and what I’ve covered is by no means near complete. I just hope this has given you a quick introduction to the science of motivation. Next time, we’ll examine the second factor of the FBM: Ability. In the meantime, I welcome any comments, critiques, kudos and discussion.
Dr. Michael Wu was the Chief Scientist at Lithium Technologies from 2008 until 2018, where he applied data-driven methodologies to investigate and understand the social web. Michael developed many predictive social analytics with actionable insights. His R&D work won him the recognition as a 2010 Influential Leader by CRM Magazine. His insights are made accessible through “The Science of Social,” and “The Science of Social 2”—two easy-reading e-books for business audience. Prior to industry, Michael received his Ph.D. from UC Berkeley’s Biophysics program, where he also received his triple major undergraduate degree in Applied Math, Physics, and Molecular & Cell Biology.
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