Decision optimization in cooperation innovation: the impact of big data analytics capability and cooperative modes

Data element investment strategy: How can leaders and followers innovate in dynamic market environments

In the era of the digital economy, the data element investment strategy decisions and game mechanisms of leaders and followers are crucial issues to be studied. To explore the environment in which digital collaboration between enterprises benefits both parties, this study initially proposes a three-stage game model of leaders and followers based on the sequential game method. Subsequently, we analyze the investment strategy choices for leading and following enterprises across six scenarios within dynamic market environments. Finally, numerical simulations are employed to examine the effect of both strategies on the industry and society as a whole. The simulation shows that (1) The cooperation strategy is a more effective approach for enhancing data-driven innovation performance, but when it comes to mature markets, this strategy may conflict with the interests of followers. (2) Followers can benefit from the cooperation strategy by significantly boosting the growth rate of data elements, but it may cause enterprises to lose their original market scale. (3) Excessively high initial production costs can negatively affect the innovative performance of the industry and social wealth, whereas mature industries can achieve greater industry performance and social welfare through investment in data elements. Considering the environmental characteristics of the digital economy, the findings of this study elucidate the ramifications of innovation strategies on enterprises, industries, and society, providing positive insights for two types of enterprises with different strengths to make apt decisions regarding digital cooperation.

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

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.