People occasionally ask me why busy, highly-skilled, highly-compensated programmers freely donate their time to open-source projects. In the past, I’ve nattered about the satisfaction of sharing with the community, the pleasure of programming as a hobby even if you do it for your day job, the “just make it work” attitude that often prevails among techies, altruism, idealism, the musings of people like Linus Torvalds, or research like the Lakhni and Wolf MIT/BCG study of developer motivation. (Speaking for myself, I code to solve problems, and I am naturally inclined to share what I do with others, and derive pleasure from having it be useful to others. The times I’ve written code for a living, I’ve always been lucky to have employers who were willing to let me open-source anything which wasn’t company-specific.)
But a chapter in Dan Ariely’s book Predictably Irrational got me thinking about a simpler way to explain it: Programmers contribute to free software projects for reasons that are similar to the reasons why lawyers do pro bono work.
The book posits that exchanges follow either social norms or market norms. If it’s a market exchange, we think in terms of money. If it’s a social exchange, we think in terms of human benefits. It’s the difference between a gift and a payment. Mentioning money (“a gift worth $10”) immediate transforms something into a market exchange. The book cites the example of lawyers being asked to do pro bono work — offered $30/hour to help needy clients, they refused, but asked to do it for free, there were plenty of takers. The $30/hour was viewed through the mental lens of a market exchange, mentally compared to their usual fees and deemed not worthwhile. Doing it for free, on the other hand, was viewed as a social exchange, evaluated on an entirely separate basis than the dollar value.
Contributing to free software follows the norms of the social exchange. The normative difference is also interesting in light of Richard Stallman’s assertion of the non-equivalence of “free software” and “open source”, and some of the philosophical debates that simmer in the background of the open-source movement; Stallman’s “free software” philosophy is intricately tied into the social community of software development.
The book also notes that issues occur when one tries to mix social norms and market norms. For instance, if you ask a friend to help you move, but he’s volunteering his time alongside paid commercial movers, that’s generally going to be seen as socially unacceptable. Commercial open-source projects conflate these two things all the time — which may go far to explaining why few commercialy-started projects gain much of a committer base beyond the core organizations and developers who care and are paid to do so (either directly, or indirectly via an end-user organization that makes heavy use of that software).
(Edit: I just discovered that Ariely has actually done an interview on open source, in quite some depth.)
If you deal with pricing, or for that matter, marketing or sales in general, and you’re going to read one related book this year, read Predictably Irrational: The Hidden Forces That Shape Our Decisions, by Dan Ariely. (I mentioned an article by him in a previous post on the impact of transparent pricing for CDNs, and I’ve finally had time to read his book.)
The book deals with behavioral economics, which can be summed up as the science of the way we perceive value and make economic decisions. It’s an entertaining read, describing a variety of experiments, their outcomes, and the broader conclusions that can be drawn. The book does an excellent job of demonstrating that we do not make such decisions in a fully rational manner, even when we think we are — but because there’s a predictable pattern to this irrationality, you can market and sell accordingly.
Two thoughts, among many others that I’m mulling over as a result:
Ariely asserts that people don’t know what new things ought to cost — they have no basis for comparison. Thus, establishing a basis for comparison creates the sense of value, and can be used to manipulate people’s mental pricing baselines and influence their decisions. For instance, given a thing, an inferior version but cheaper version of that thing, and some other less-similar thing, people will generally choose the thing. That’s relevant when you think about the way people compare CDN services, especially first-time enterprise buyers.
Ariely also shows that given a useful but brand-new thing, people might not know whether it’s a good value and thus may choose not to buy it — but establish a comparison in the form of a bigger but much more expensive form of that thing, and people will see the original as a good value and buy it. This is hugely relevant in the emerging cloud computing market, where people aren’t yet certain what the billing units should be and what they should cost.
Relating this to my usual topics of interest: Amazon has essentially established a transparent baseline in both the cloud computing and CDN markets, with clearly-articulated, readily-available pricing, and as a result, they have implicit control of the conversation around pricing. Broadly, any vendor who puts a public stake in the ground on prices is going to exert influence over a customer’s perception of not only their value, but every other comparable vendor.
The New Scientist has an interesting article commenting that the long tail may be less potent than previously postulated — and that peer pressure creates a winner-take-all situation.
I was jotting this blog post about Gartner clients and the target audience for the Magic Quadrant, and that article got me thinking about the social context for market research and vendor recommendations.
Gartner’s client base is primarily mid-sized business to large enterprise — our typical client is probably $100 million or more in revenue, but we also serve a lot of technology companies who are smaller than that. Beyond that subscription base, though, we also talk to people at conferences; those attendees usually represent a much more diverse set of organizations. But it’s the subscription base that we mostly talk to. (I carry an unusually high inquiry load — I’ll talk to something on the order of 700 clients this year.)
Normally, I’m interested in the comprehensive range of a vendor’s business (at least insofar as it’s relevant to my coverage). When I do an MQ, though, my subscriber base is the lens through which I evaluate companies. While I’m interested in the ways vendors service small businesses at other times, when it’s in the context of an MQ, I care only about a vendor’s relevance to our clients — i.e., the IT buyers who subscribe to Gartner services and who are reading the MQ to figure out what vendors they want to short-list.
Sometimes, when vendors think about our client base, they mistakenly assume that it’s Fortune 1000 and the largest of enterprises. While we serve those companies, we have more than 10,000 client organizations — so obviously, we serve a lot more than giant entities. The customers I talk to day after day may have a single cabinet in colocation — or fifty data centers of their own. (Sometimes both.) They might have one or two servers in managed hosting, or dozens of websites deployed via multi-dozen-server contracts. They might deliver less than a TB of content per month via a CDN, or they might be one of the largest media companies on the planet, with staggering video volumes.
These clients span an enormous range of wants and needs, but they have one significant common denominator: They are the kinds of companies that subscribe to a research and advisory firm, which means they make enough tech purchases to justify the cost of a research contract, and they have a culture which values (or at least bureaucratically mandates) seeking a neutral outside opinion.
That ideal of objectivity, however, often masks something more fundamental that ties back to the article that I mentioned: namely, the fact that many clients have an insatiable hunger to know “What are companies like mine doing?“. They are not necessarily seeking best practice, but common practice. Sometimes they seek the assurance that their non-ideal situation is not dissimilar to that of their peers at similar companies. (Although the opening line of Tolstoy’s Anna Karenina — “Happy families are all alike, but every unhappy family is unhappy in its own way” — quite possibly applies to IT departments, too.)
This is also reflected in the fact that customers often have a deep desire to talk to other customers of the same vendor, on an informal and social basis. That hunger is sometimes satisfied by online forums, but the larger the company, the more reluctant they are to discuss their business in public, although they may still share freely in a one-on-one or directly personal context.
IBM was the ultimate winner-take-all company (to use the New Scientist phrase) — the company that everyone was buying from, thus guaranteeing that you were unlikely to get fired buying IBM. Arguably, it and its brethren still are at the fat forefront of the outsourced IT infrastructure market share curve, while the bazillion hosting companies out there are spread out over the long tail. Even within the narrower confines of pure hosting, which is a highly fragmented market, and despite massive amounts of online information, peer influence has concentrated market share in the hands of relatively few vendors.
To quote the article: Which leads to a curious puzzle: why, when we have so much information at our fingertips, are we so concerned with what our peers like? Don’t we trust our own judgement? Watts thinks it is partly a cognitive problem. Far from liberating us, the proliferation of choice that modern technology has brought is overwhelming us — making us even more reliant on outside cues to determine what we like.
So I can sum up: A Magic Quadrant is an outside cue, offering expert opinion that factors in aggregated peer opinion.
Science magazine recently published a report by Whitson and Galinsky entitled Lacking Control Increases Illusory Pattern Perception.
The gist of the article is that people who lack control are more likely to perceive patterns that are not there. Of particular relevance is this: In one of the six experiments detailed in the article, people were told about the financial performance of two companies, with the same number of positive and negative statements for both, but only given half as much information about the second company. They were then asked to make an investment decision about the companies. If told that the stock market was volatile, they had a heightened awareness of the negatives of the company that they were provided less information about.
The entire article is worth reading. It certainly has implications for analysts and analyst and investor relations folks, in this current environment of high market volatility and generalized economic uncertainty.