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Using a random sample consisting of hundreds of companies worldwide, we are testing the impact on company performance of investing in big data projects targeted on three major business domains (namely, customer interface, company supply chain and competitors). The performance test relies on a so-called trans-logarithmic production function, allowing for a more direct test of the complementarity between big data capital and big data labour investments; further, we have used a Heckman correction to adjust for the fact that companies investing in big data are generally more productive than their peers. We confirm and extend early results of a productivity impact from big data. We find that for the average of our sample, more productive firms are also faster adopters of big data than their industry peers (this explains 2.5% of productivity difference). Big data investments in labour and IT architecture are complements, with a total productivity growth effect of about 5.9%. Big data projects targeting customers and competitive intelligence domains bring slightly more performance than big data projects aimed at supply chain improvements.

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This paper looks at how collaborative technologies are being adapted inside the enterprise, based on global fi eld research involving more than 2,800 executives, and enriched by an online board discussion contrasting motivations of early versus late adopters of ' enterprise 2.0 ' . It is observed that ' enterprise 2.0 ' diffusion is relatively rapid and that one key rationale of adoption is to leverage new dimensions of collaborations that can form the basis for a new source of competitive advantage. While there is evidence of large pay-offs from the use of ' enterprise 2.0 ' , the key challenge lies not in the promise of the technology, but more into superb execution -the paper closes with a set of key themes for successful implementation.

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