Connected – case studies
As I mentioned in an earlier post about the book Connected, I wanted to share a few of the case studies from the book that I found the most compelling. I can’t believe I wrote that post in October 2009! I have had this post in draft and finally pushed it through to the finish line, which I think speaks to the staying power of these case studies and their theoretical underpinnings.
After introducing the basic concepts, the authors begin to explore case studies on a variety of topics, including Emotions (Chapter 2), Love and Sex (Chapter 3), Health (Chapter 4), Money, Politics (Chapter 6), Evolution, and Technology. Although the entire book was well done, I found Chapters 2, 3, 4, and 6 the most interesting, so I’ll focus on those below. You can read about the others on the Connected website.
Emotional contagion. Very simply, our emotions are affected by those around us. We are aware of our feelings, which in turn affect our physical and neurophysiological state. This results in change of brain structure, and ultimately in perceivable external behaviors. It may be that these reactions are a survival mechanism from pre-language communication.
Unfortunately, both positive and negative emotions are contagious. Suicide, for example, is common in groups of young people under 24 that are known to each other. In general, unhappy people cluster together (p.51), and they are more peripheral in the network. Happiness is also contagious, and the good news is that happiness is more contagious than unhappiness! Futhermore, happy people make each other happier “a person is about 15 percent more likely to be happy if directly connected person is happy”. Being happy doesn’t result in more friends or make you more central (p.53) – “being located in the middle of the network leads to happiness rather than the other way around”. And happiness has it’s benefits, since “positive mood is associated with a range of team-performance-enhancing changes, including great altruistic behavior, increased creativity, and more efficient decision-making” (p.49).
Marriage. In the early 1800s, the British government created the Registrar’s General Office to track births and deaths. William Farr was a physician working in the office, and he used his role to establish a national statistics program. He first studied the mortality rates of difference occupations. Later (based on an analysis of 25 million French adults), he showed the health benefits of marriage – which appeared based on his research to extend life. More recent research shows that bad marriage accelerates poor health associated with aging.
However, there is the ongoing question of which came first, health or marriage. There are three possible reasons, not just for marriage but for the analysis of all ties:
- Homogamy – fit marry fit, unfit also marries unfit (die earlier)
- Confounding – another factor confounds ability to discern what’s going on – environmental factors, for example
- True causal effect
We do know that mortality rates are 40% higher in the six months after death of a spouse, but we also know it’s also different for men and women:
The benefits of marriage are different for men and women. Men gain “social support and connection”, but after marrying, women are better off financially. One theory on the different responses to the death of a spouse is that women keep money after death of a spouse, but men lose the social ties when their spouse passes away. There interesting variations in the benefits with homosexual and interracial couples, for example, black couples or men married to black women do not experience the widowhood effect. These are further elaborated in the book.
The authors also explain that you are most likely to meet your future spouse through a common friend. The tendency toward homogamy (like to marry like) is a driver as well, as people appear to have a “preference for relative attractiveness” that is stronger than a “preference for relative income”.
Obesity. In 1948, the Framingham Heart Study began collecting data from about two-thirds of all adult residents. The research program tracks people’s medical history, as well as their family and social connections. That information enabled the authors of Connected to reconstruct the participants’ social networks. Through an analysis of that information, they learned that there was “substantial clustering of obese and nonobese individuals and that the clustering was not due to chance” (p.108). Further analysis showed that if a “mutual friend becomes obese, it nearly triples a person’s risk of becoming obese” (p.109). This was not because they spent time together or because they shared some other non-related factor – it really was causal (one person could actually cause weight gain in others). Findings also show “variation by nature of the friendship tie” – that is, a mutual friend triples a person’s risk. In effect, obesity is a multicentric epidemic – it is contagious. The question now remains how the transmission occurs, whether it’s imitation or about social norms? Based on the available research, it seems like norms are at play. In the case of smoking, normative behavior is even stronger – people are “quitting together, in droves”. In the case of smoking, social norms isolate people to periphery of network, which prevents mixing and reinforces group behavior.
In the case of drinking, friends of the same sex affect each other more than spouses or members of the opposite sex. In the case of education, it “appears to amplify a person’s ability to influence others” (p.118). I also thought the chapter on politics was very interesting. The authors ask why do “millions of people vote in spite of these [poor] odds and payoffs?” (p.179). What becomes apparent is that “a single decision to vote in fact increases the likelihood that others will vote … [which indicates] that social connections may be the key to solving the voting puzzle” (p.181).
There were so many other great sections of the book … too many to mention here! If you have a look at the Connected website you can learn more about all the many areas that were discussed.
In closing, I would re-iterate my recommendation for this book. It is a super interesting topic, easy to read, with lots of different cases studies which demonstrate the wide applicability of social network theory to a variety of social issues. My only critique is that the authors are maybe a little naïve about how this information can be used for good.
The phrase “the strength of weak ties” was coined by Mark Granovetter, who argued that “weaker connections frequently act as bridges from one group to another and therefore play a critical role … [in binding] groups together into the larger society” (p.157). The authors go on to say that
[p]eople who have many weak ties will be frequently sought out for advice of offered opportunities in exchange for their information or access. In other words, people who act as bridges between groups can become central to the overall network and so are more likely to be rewarded financially and otherwise. (p.159)
In fact a recent study about IBM sales people demonstrated that individuals that were more central in their networks were more successful, this has also proven true for traders exchanging a flurry of instant messages during critical moments in trading. But since by now you’ve read my two posts on the topic, that shouldn’t surprise you!
Since reading the book nearly two years ago, I have been thinking about it’s applicability for my work at SAP. I currently work in a knowledge management organization, and I think the company could benefit from understanding more about how information travels both at SAP and throughout our ecosystem. That understanding would then permit us to enable the spread of that knowledge more effectively. For example, today the company actively cascades the corporate strategy through a variety of workshops and trainings. Could networks be used to transmit the key ideas? How could an understanding of sales people’s networks and social norms improve sales enablement? In the future, I hope to have the opportunity to explore these ideas through my work at SAP.