Paper Info
Title
Data Analytics, Innovation, and Firm Productivity
Abstract
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 benefit; from using data analytics.
Year
DOI
Venue
2020
10.1287/mnsc.2018.3281
MANAGEMENT SCIENCE
Keywords
DocType
Volume
data analytics,novel innovation,recombination,productivity,big data,AI,automation,economics of IS
Journal
66
Issue
ISSN
Citations 
5
0025-1909
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Lynn Wu152.48
Lorin M. Hitt22426223.11
Bowen Lou300.68