Data Analytics Supports Decentralized Innovation

50 Pages Posted: 5 Apr 2019

See all articles by Lynn Wu

Lynn Wu

University of Pennsylvania - The Wharton School

Bowen Lou

University of Pennsylvania - The Wharton School

Lorin M. Hitt

University of Pennsylvania - Operations & Information Management Department

Date Written: March 11, 2019

Abstract

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 intra-firm 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 to 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 non-inventor 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.

Keywords: data analytics, AI, big data, automation, decentralization, organizational complements, innovation, recombination, novel innovation, productivity, economics of IS

JEL Classification: O3

Suggested Citation

Wu, Lynn and Lou, Bowen and Hitt, Lorin M., Data Analytics Supports Decentralized Innovation (March 11, 2019). Available at SSRN: https://ssrn.com/abstract=3351982 or http://dx.doi.org/10.2139/ssrn.3351982

Lynn Wu (Contact Author)

University of Pennsylvania - The Wharton School ( email )

3733 Spruce Street
Philadelphia, PA 19104-6374
United States

Bowen Lou

University of Pennsylvania - The Wharton School

3730 Walnut Street
Philadelphia, PA 19104
United States

Lorin M. Hitt

University of Pennsylvania - Operations & Information Management Department ( email )

571 Jon M. Huntsman Hall
Philadelphia, PA 19104
United States
215-898-7730 (Phone)
215-898-3664 (Fax)

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