Influence of big data analytical capability on new product performance – the effects of collaboration capability and team collaboration in high-tech firm


Purpose
As the application of artificial intelligence (AI) becomes more prevalent, many high-tech firms have employed AI applications to deal with emerging societal, technological and environmental challenges. Big data analytical capability (BDAC) has become increasingly important in the AI application processes. Drawing upon the resource-based view and the theory of planned behavior, this study aims to investigate how BDAC and collaboration affect new product performance (NPP). Practically, a harmonic working team is particularly important for creating management synergies, this empirical analysis demonstrates the importance of BDAC and collaboration for NPP.


Design/methodology/approach
This paper focuses on the performance of firms that applied AI in their operations. This study collected data from firms in Greater China, including China and Taiwan, as Greater China is currently the leading manufacturer of semiconductor, electronic and electric products for AI applications in the manufacturing process. Confirmatory factor analysis and structural equation modeling is employed for statistical analysis.


Findings
The analytical results indicate that BDAC positively relates to collaboration capability (CC) in AI applications but not to team collaboration (TC). CC positively correlates with TC, and both CC and TC positively correlate with NPP. Further, the mediating effect was examined using the Sobel t-test, which reveals that CC is a significant mediator in the influence of BDAC on NPP.


Practical implications
The strategic implementation of BDAC and collaboration can allow an enterprise to improve its NPP when driven by the external environment to use AI, which further enhances NPP. These processes indicate that AI and BDAC are both crucial for the success of a company’s collaboration and for effective management to improve NPP in the face of global competition.


Originality/value
This study introduces the concept of BDAC to explain the relationship between CC and TC, as they pertain to NPP. This study presented a discussion of the theoretical and practical implications of the research findings and could provide a framework for managing BDAC.
