Exploiting organizations' innovation performance via big data analytics: an absorptive knowledge perspective

Abstract

Purpose

The advantages of applying big data analytics for organizations to boost innovation performance are enormous. By collecting and analysing substantial amounts of data, firms can discern what works for their customer needs and update existing products while innovating new ones. Notwithstanding the evidence about the effects of big data analytics, the link between big data analytics and innovation performance is still underestimated. Especially in today's fast-changing and complicated environments, companies cannot simply take big data analytics as one innovative technical tool without fully understanding how to deploy it effectively.

Design/methodology/approach

This study tries to investigate this relationship by building on the knowledge absorptive capacity perspective. The authors conceptualized effective use of big data analytics tools as one general absorptive capacity rather than a simple technical element or skill. Specifically, effectively utilize big data analytics tools can provide values and insights for new product innovation performance in a turbulent environment. Using online survey data from 108 managers, the authors assessed their hypotheses by applying the structural equation modelling method.

Findings

The authors found that big data analytics capacity, which can be conceptualized as one absorptive capacity, can positively influence product innovation performance. The authors also found that environmental turbulence has strong moderation effects on these two main relationships.

Originality/value

These results establish big data analytics can be regarded as one absorptive capacity, which can positively boost an organization's innovation performance.

Citation

Tseng, H.-T., Jia, S.(J)., Nisar, T.M. and Hajli, N. (2024), "Exploiting organizations' innovation performance via big data analytics: an absorptive knowledge perspective", Information Technology & People, Vol. 37 No. 6, pp. 2279-2301. https://doi.org/10.1108/ITP-03-2022-0237