Unveiling the effects of big data analytics capability on innovation capability through absorptive capacity: why more and better insights matter

Abstract

Purpose

Despite the increase in studies focused on analyzing the potential of big data analytics capability (BDAC) as a driver of product and process innovation, it is still necessary to understand how the use of insights generated by BDAC in innovation may be maximized through articulation with individuals' intellect and other processes involving the assimilation and transformation of knowledge. This study thus aims to analyze the impact of BDAC's deployment on innovation capability (IC – process and product innovation capabilities), taking absorptive capacity (AC) as mediating variable in this relationship.

Design/methodology/approach

Structural equations were used to test the research model with survey data from 112 firms located in an emerging country that is one of the digital transformation leaders in the region.

Findings

The results show that 37% of process IC variance is explained by the indirect relationship via the variable mediator (AC), while in the case of product IC this percentage is 34%.

Originality/value

These results allow us to ascertain the extent to which individuals continue to be relevant to generating product and process innovation in the digital age at a time when the literature anticipates a total loss of prominence due to the arrival of new digital technologies. However, in the case of the relationship between BDAC and ICs, the existence of a partial mediation of AC indicates that individuals continue to play a role that, albeit not being the most prominent, remains relevant in ensuring that a company maximizes the assimilation and transformation of the insights generated by BDAC in new products and processes.

Citation

Lozada, N., Arias-Pérez, J. and Henao-García, E.A. (2023), "Unveiling the effects of big data analytics capability on innovation capability through absorptive capacity: why more and better insights matter", Journal of Enterprise Information Management, Vol. 36 No. 2, pp. 680-701. https://doi.org/10.1108/JEIM-02-2021-0092