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Research Article

Navigating the double-edged sword: how does big data affect firm innovation under different investment combinations?

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Published online: 02 Feb 2024
 

ABSTRACT

Although big data has been claimed to revolutionise the way firms innovate, there is still a lack of knowledge about how big data affects firm innovation under different investment combinations. Drawing on the resource-based view, organisation learning theory, and organisation change theory, we introduce big data into a Kline-Rosenberg chain-linked model to propose that firms harness information value from big data to improve innovation output while organisation-wide adjustments increase innovation risk. Based on the data collected from Chinese listed firms, our empirical results reveal that combining big data and R&D investment can improve innovation output and reduce innovation risk. In contrast, combining big data and IT investment significantly increases innovation risk. The finding that overinvesting in IT enhances firm innovation risk contributes novel insights to the literature by contradicting the belief that investment is beneficial to innovation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors acknowledge financial support from the National Natural Science Foundation of China [grant number 72173014].

Notes on contributors

Cheng Ma

Cheng Ma is currently a Ph.D. student on School of Economics and Management, Dalian University of Technology. Currently, her main research interests include digital technology and firm innovation. E-mail: [email protected]

Shuming Ren

Shuming Ren is currently a full professor on School of Economics and Management, Dalian University of Technology. Currently, her main research interests include digital platform, fintech and innovation. Shuming Ren’s research has been published in various refereed journals such as Business Strategy and the Environment, and Technological Forecasting and Social Change. E-mail: [email protected]

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