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Incentives


I have been re-reading Steve Weber's great book:

Weber, Steven. 2004. The Success of Open Source. Harvard University Press.

…and I wanted to share this wonderful passage:

…theorizing about collective action is not a matter of trying to decide whether behavior can be labeled as 'rational.' It is a matter of understanding first and foremost under what conditions individuals find that the benefits of participation exceed the costs. This means, of course, understanding a great deal about how people assess costs and benefits.

Weber nicely captures somethings that I have been thinking about for a long time: the notion of 'rational' can sometimes be a cop-out, a too-convenient shortcut that sidesteps the messy (but necessary) work of understanding the dispositions, attitudes, and social conditions that influence decision-making in specific contexts.

My dissertation research is all about motivation – why do people participate in online collective action? What are they getting out of it? As I've written about before, just because people aren't working for money doesn't mean they're working for free.

Anyway, when you study motivation, you think a lot about how to measure it. Recently I've been seeing some studies that measure motivation with survey questions. In these studies, the researchers come up with a list of potential motivations and ask participants to respond, usually in the form of Likert-style agreement statements. There are several excellent examples of interesting papers that use variations of this method, for example:

Oreg, Shaul, and Oded Nov. 2008. “Exploring motivations for contributing to open source initiatives: The roles of contribution context and personal values.” Comput. Hum. Behav. 24:2055-2073.

Kuznetsov, Stacey. 2006. “Motivations of contributors to Wikipedia.” SIGCAS Comput. Soc. 36:1.

Using surveys to assess motivation has many benefits. Principal among them, I'd say, is that surveys allow us to collect a large amount of data quickly. And studies such as the ones I cite above certainly provide valuable insights. However, surveys are limited in their ability to tell us about motivation. From my point of view there are at least two big challenges to measuring motivations (or anything, for that matter) with surveys:

  1. For many people – even most – motivations are soft or implicit attitudes. When you ask someone to respond to a direct question about something they are not used to thinking directly about, they may be forced to take a position on an issue they do not feel strongly about (their attitude is 'soft') or, more problematically, they may act on an attitude that they are unaware of or cannot express (their attitude is implicit). In either case, surveys that tick off motivations and ask participants to agree / disagree can sometimes come up with noisy data at best, and downright wrong data at worst.
  2. When it comes to motivation, social desirability is a big problem. When you ask people why they contribute to Wikipedia there are norms and expectations operating that mean fewer people will probably say they contribute to gain fame, and more people will say they contribute to do something good for the world. In the grand scheme of surveys, asking about motivation for online participation may be less susceptible to social desirability than other controversial issues – say, gay marriage. But still, it means survey data can only take us so far.

As I have been reading about the recent dust-up around LinkedIn's plan to crowdsource it's site translation, I've been thinking about what other methods could supplement and compliment survey data. Here's how LinkedIn asked its members about what they would get out of the translation project:

Hmmm. Interesting and useful, but only to a point. How else should we be investigating this?

One great alternative is interviews, which help to mitigate both of the issues above. When you get people talking about their lives, you often find through rigorous analysis that there are consistent themes and patterns in the narratives that participants may not even be aware of. While social desirability is still an issue, part of the theory of qualitative work is that when you develop rapport with a participant, when you're non-judgmental and open, you find that they will tell you all sorts of things. Here's a great example of interview-based research on incentives of online participation:

Ames, Morgan, and Mor Naaman. 2007. “Why we tag: motivations for annotation in mobile and online media.” Pp. 971-980 in Proceedings of the SIGCHI conference on Human factors in computing systems. San Jose, California, USA: ACM.

In social psychology the gold standard in motivation research is a truly behavioral measure. This is why experiments are so great. If we manipulate one factor and then look at some measure of contributions or their characteristics as evidence of motivations, we get a view of motivation that's largely free of the problems I outlined above. This type of research isn't always practical, but it isn't all that uncommon in industry either. To find out what motivates users, companies often try out a new incentive program with a subset of users, then compare the results to the old way. I'm not sure if LinkedIn had the luxury to do this, though, and it can get tedious especially in cases where there are many incentives and motivations at work, as is the case when we talk about online participation.

Sticking with surveys, there is some really interesting innovation in methodology aimed at combating the social desirability problem. It's called the List Experiment. (For an overview of the method, check out this paper.) The basic idea is this: we distribute a survey that has 3 statements about issues that people may or may not agree with. The question is not which statements you agree with, but how many. For a random subset of our sample, we also add a 4th statement about the controversial issue that we expect has a social desirability problem. The difference in the number of statements agreed with between the people who got 3 items and the people who got 4 is the percentage of people who agree with that 4th statement. And because we never asked anyone to say which ones they agree with, we mitigated the problem of social pressure.

This is not yet a widely used method, but in studies about hot-button issues like racism, list experiment results have turned up very different results than the traditional direct-question method. In the paper linked above, for example, the author investigated attitudes about immgration and found a difference of more than 20 percentage points between the traditional method and the list experiment. Far more people supported cutting off immigration to the US than we thought.

Anyway, this has now turned into a very long post, and I have no blockbuster conclusion. In summary, I think assessing motivation with Likert-style questions is interesting, valuable, and important. However, it's subject to some important limitations – just like any method is. The best solution is a mixed-methods approach. Interviews, surveys, experiments. I'm sure I'll be thinking about this issue for a long time, and I think there is an opportunity here for some real methodological creativity.

The NYTimes has a front-page story today (with cameo from the great Robert Cialdini!) about power utilities using social psychology to encourage customers to save energy by comparing them to their neighbors:

Utilities Turn Their Customers Green, With Envy

The power company used smiley or frowny faces to let customers know how their power consumption compared to their neighbors. They found that giving customers positive feedback in the form of smileys encouraged them to do even better on average (2%). Frowny faces, on the other hand, made customers mad and write angry letters, so the utility quit giving out negative feedback.

The inefficacy of frownys is an interesting thing. On the face of it, we might expect competitive feedback like that to encourage people to improve, to catch up with their peers. Or, maybe it would invoke a fairness norm, or simply a power consumption norm, and encourage people to catch up with the average.

I'd guess that the problem isn't lower-than-average feedback, though… it's smileys. Granted, the NYTimes doesn't give us much on the specifics of the program, so it's hard to tell. But smileys are such an ambiguous vehicle… how does one interpret their significance? I have a feeling a frown from the power company doesn't convey the intended message, it just makes people feel scolded by the power company. And what right does the power company have to scold anyone? That's just not going to work.

This same problem dooms a recent CHI paper, which comes from the good tradition of research on MovieLens, but ends up being so ambiguous that the findings are impossible to interpret:

Al Mamunur, Rashid, Kimberly Ling, Regina D. Tassone, P. Resnick, R.E. Kraut., and John Riedl. 2006. "Motivating Participation by Displaying the Value of Contribution." in ACM Conference on Human-Computer Interaction. Montreal, Canada.

Smiley, stars, etc… they're all efforts to encapsulate feedback in a way that people can quickly understand. But this is an example of how they can be counter-productive. Instead of giving blanket positive or negative feedback, the feedback should always give people something to hang their hat on. If you can find any category of power use that a given customer is doing better than average on – tell them that. Then point out the other categories where they can improve. Of course, that's a risk too. Your average consumer isn't going to spend much time looking at their feedback on the power bill.