Pathways Analysis of Different Types Platform Enterprises’ Digital Innovation: from the Perspective of Digital Capability and Ambidextrous Learning
Yuan Cheng et al.
What the paper says
Digital innovation is crucial for platform enterprises to gain a competitive advantage in their digital transformation. In order to identify the necessary factors and differentiated paths for platform enterprises to achieve high-level digital innovation, this study employs organisational learning theory and digital capability theory，utilises Necessary Condition Analysis (NCA) and Fuzzy-Set Qualitative Comparative Analysis (FsQCA) to examine the impact of platform type, digital capability, ambidextrous learning approaches, and their matching mechanisms on digital innovation among 139 enterprises. The NCA results showed that neither a single learning style nor digital capability alone constituted a necessary condition for achieving high-level digital innovation. The FsQCA results identified five paths to achieving high-level digital innovation. Specifically, for producer-led platforms, their digital infrastructure and platform capability are generally strong. If executives also have strong digital leadership capabilities, they can collaborate with low-level exploratory learning. If the digital leadership capabilities of executives are weak, it can be combined with low-level exploratory learning and high-level exploitative learning. For buyer-led platforms, the digital platform capability of enterprises and the digital leadership capabilities of executives are generally strong, but the digital infrastructure is weak and needs to be combined with high-level exploratory learning and high-level exploitative learning. For bidirectional platforms, high-level digital innovation requires high level of executive digital leadership capabilities and the adoption of both high-level exploratory learning and high-level exploitative learning. When different types of platform enterprises pursue high-level digital innovation, they must base themselves on their digital capabilities and choose appropriate ambidextrous learning methods.
1 citation
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.16 × 0.4 = 0.06 |
| M · momentum | 0.53 × 0.15 = 0.08 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.