How business model design drives innovation performance: The roles of product innovation capabilities and technological turbulence

Chenglin Jin, Along Liu, Hefu Liu, Jibao Gu and Meng Shao

Technological Forecasting and Social Change, 2022, vol. 178, issue C

Abstract: While prior studies indicate that business model design (BMD) can affect innovation performance, empirical evidence on how and when BMD drives innovation performance is still limited. By integrating dynamic capability theory into business model research, we empirically investigate the influence of BMD on innovation performance. Based on the matched survey data and the objective performance data of 282 Chinese high-tech firms, we employed bootstrap tests to estimate the mediating effects of product innovation capability (PIC) and used hierarchical regression analysis to estimate the moderating effects of technological turbulence. The results show that novelty-centered BMD positively relates to innovation performance and that this link is partially mediated by radical PIC and incremental PIC. In contrast, efficiency-centered BMD cannot directly impact innovation performance, but can indirectly affect it through incremental PIC. We further find that the indirect effect of novelty-centered BMD on innovation performance via incremental PIC is stronger than that of efficiency-centered BMD. In addition, technological turbulence strengthens the positive effect of novelty-centered BMD on PIC while weakening the positive impact of efficiency-centered BMD on PIC. Our study thus provides high-tech firm managers with an in-depth understanding of how and when BMD drives innovation performance.

Keywords: Dynamic capability theory; Business model design; Product innovation capability; Technological turbulence (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:178:y:2022:i:c:s0040162522001238

DOI: 10.1016/j.techfore.2022.121591

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