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We examine the relationship between data analytics capabilities and innovation using detailed firm-level data. To measure innovation, we first utilize a survey to capture two types of firm practices, process improvement and new technology development for 331 firms. We then use patent data to further analyze new technology development for a broader sample of more than 2,000 publicly traded firms. We find that data analytics capabilities are more likely to be present and are more valuable in firms that are oriented around process improvement and that create new technologies by combining a diverse set of existing technologies than they are in firms that are focused on generating entirely new technologies. These results are consistent with the theory that data analytics are complementary to certain types of innovation because they enable firms to expand the search space of existing knowledge to combine into new technologies, as well as the theoretical arguments that data analytics support incremental process improvements. Data analytics appears less effective for developing entirely new technologies or creating combinations involving a few areas of knowledge, innovative approaches where there is either limited data or limited value in integrating diverse knowledge. Overall, our results suggest that firms that have historically focused on specific types of innovation—process innovation and innovation by diverse recombination—may receive the most benefits from using data analytics. This paper was accepted by Chris Forman, information systems. [ABSTRACT FROM AUTHOR]

Data-analytics technology can accelerate the innovation process by enabling existing knowledge to be identified, accessed, combined, and deployed to address new problem domains. However, like prior advances in information technology, the ability of firms to exploit these opportunities depends on a variety of complementary human capital and organizational capabilities. We focus on whether analytics is more valuable in firms where innovation within a firm has decentralized groups of inventors or centralized ones. Our analysis draws on prior work measuring firm-analytics capability using detailed employee-level data and matches these data to metrics on intrafirm inventor networks that reveal whether a firm's innovation structure is centralized or decentralized. In a panel of 1,864 publicly traded firms from the years 1988–2013, we find that firms with a decentralized innovation structure have a greater demand for analytics skills and receive greater productivity benefits from their analytics capabilities, consistent with a complementarity between analytics and decentralized innovation. We also find that analytics helps decentralized structures to create new combinations and reuse of existing technologies, consistent with the ability of analytics to link knowledge across diverse domains and to integrate external knowledge into the firm. Furthermore, the effect primarily comes from the analytics capabilities of the noninventor employees as opposed to inventors themselves. These results show that the benefit of analytics on innovation depends on existing organizational structures. Similar to the IT–productivity paradox, these results can help explain a contemporary analytics–innovation paradox—the apparent slowdown in innovation despite the recent increase in analytics investments. This paper was accepted by Anandhi Bharadwaj, information systems. [ABSTRACT FROM AUTHOR]

This study examines how characteristics of an interfirm labor-flow network affect firm productivity. Using employee job histories to trace labor movement between organizations, we construct labor-flow networks for both information technology (IT) and non-IT labor and analyze how a firm's network structure for the two types of labor affects its performance. We find that hiring IT workers from a structurally diverse network of firms can substantially improve firm productivity, but that the same is not true for non-IT labor, where we find little benefit of network diversity. We hypothesize that these results reflect differences in the types of knowledge diffusion facilitated by different types of labor flows, with IT labor enabling the transfer of new and innovative firm practices, which benefits from diversity, while non-IT labor flows are more closely associated with implementation of complementary organizational practices, which may benefit from a critical mass of workers with a common knowledge base. Together, these results demonstrate the importance of incorporating a network perspective in understanding the full impact of spillover effects from organizational hiring activities. [ABSTRACT FROM AUTHOR]