The current issue of McKinsey Quarterly features an interesting article on firms crowd-sourcing strategy formulation. This is another way that technology may shake up the strategy field (See also Mike’s discussion of the MBA bubble). The article describes examples in a variety of companies. Some, like Wikimedia and Redhat aren’t much of a surprise given their open innovation focus. However, we should probably take notice when more traditional companies (like 3M, HCL Technologies, and Rite-Solutions) use social media in this way. For example, Rite-Solutions, a software provider for the US Navy, defense contractors and fire departments, created an internal market for strategic initiatives:
Would-be entrepreneurs at Rite-Solutions can launch “IPOs” by preparing an Expect-Us (rather than a prospectus)—a document that outlines the value creation potential of the new idea … Each new stock debuts at $10, and every employee gets $10,000 in play money to invest in the virtual idea market and thereby establish a personal intellectual portfolio Read the rest of this entry »
In an earlier post, I noted Target’s costly decision to end its on-line outsourcing arrangement with Amazon’s cloud service and take all its work in-house. The short-term costs were considerable, both in direct outlays and in performance degradation, and the long-term benefits were hard to pin down. Vague paranoia rather than careful analysis seemed to have driven the decision. I pointed out that firms often seemed unwilling to “sleep with the enemy,” i.e. purchase critical inputs from a direct rival, but the case for such reluctance was weak.
A few months ago, an apparent counterexample popped up. Swatch, the Swiss wristwatch giant, decided unilaterally to cease supplying mechanical watch assemblies to a host of competing domestic brands that are completely dependent on Swatch for these key components. These competitors (including Constant, LVMH, and Chanel) sued, fruitlessly, to force Swatch to continue to sell to them. The Swiss Federal Administrative Court backed up a deal Swatch cut with the Swiss competition authorities that allows Swatch to begin reducing its shipments to rivals. The competition authority will report later this year on how much grace time Swatch’s customers must be given to find new sources of supply, and these customers may appeal to the highest Swiss court. For now, Swatch’s customers are scrambling for alternative sources of supply in order to stay in business. The stakes are especially high because overall business is booming, with lots of demand in Asia.
Twitter is emerging as a popular source of data for scientists — see various twitter-related arXiv articles here. For example, here’s a piece validating the Dunbar number by looking at social interactions among 1.7 million people on Twitter (now published in PLoS ONE). At orgtheory.net I posted about a recently published Science piece attempting to measure aggregate mood by analyzing millions of tweets.
Here’s a set of papers studying twitter and health-related issues. One paper suggests that monitoring the Twittersphere makes “bio-surveillance” possible – OMG U got flu? Analysis of shared health messages for bio-surveillance.
Here’s the abstract:
Background: Micro-blogging services such as Twitter offer the potential to crowdsource epidemics in real-time. However, Twitter posts (‘tweets’) are often ambiguous and reactive to media trends. In order to ground user messages in epidemic response we focused on tracking reports of self-protective behaviour such as avoiding public gatherings or increased sanitation as the basis for further risk analysis. Results: We created guidelines for tagging self protective behaviour based on Jones and Salath\’e (2009)’s behaviour response survey. Applying the guidelines to a corpus of 5283 Twitter messages related to influenza like illness showed a high level of inter-annotator agreement (kappa 0.86). We employed supervised learning using unigrams, bigrams and regular expressions as features with two supervised classifiers (SVM and Naive Bayes) to classify tweets into 4 self-reported protective behaviour categories plus a self-reported diagnosis. In addition to classification performance we report moderately strong Spearman’s Rho correlation by comparing classifier output against WHO/NREVSS laboratory data for A(H1N1) in the USA during the 2009-2010 influenza season. Conclusions: The study adds to evidence supporting a high degree of correlation between pre-diagnostic social media signals and diagnostic influenza case data, pointing the way towards low cost sensor networks. We believe that the signals we have modelled may be applicable to a wide range of diseases.