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With the widespread penetration of digital technologies, disruptive innovation is not developed by a single firm but is increasingly achieved by an ecosystem. However, limited research has examined the mechanisms involved in achieving disruptive innovation in the context of digitalization and ecosystems. To address this gap, we explore the impact of three dimensions of specialized complementary assets (SCAs) within the innovation ecosystem, human capital SCA (HCSCA), production SCA (PSCA) and marketing SCA (MSCA), on disruptive innovation in core firms through the mediation of digital capability, comprising digital operation capability (DOC) and digital resource collaborative capability (DRCC). Furthermore, innovation ecosystem embeddedness is examined as a moderator between digital capability and disruptive innovation.

Survey data were collected from 234 core firms in China’s high-tech industry. Hierarchical regression, AMOS, and PROCESS tools were used to examine the data.

The results reveal the following: (1) HCSCA and PSCA positively affect disruptive innovation, while MSCA is negatively correlated with disruptive innovation. (2) Digital capability mediates the relationship between HCSCA and disruptive innovation, as well as PSCA and disruptive innovation. However, it suppresses the negative impact of MSCA on disruptive innovation. (3) Innovation ecosystem embeddedness strengthens the influence of DOC on disruptive innovation, but weakens the influence of DRCC on disruptive innovation.

The findings advance the knowledge of disruptive innovation, SCAs within the innovation ecosystem, digital capability and innovation ecosystem embeddedness. They also provide practical insights into the effective implementation of disruptive innovation.

The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

In this research, we explore the adverse impact of foreign ownership on operational security, a critical operational implication of the liability of foreignness (LOF).

The empirical analysis is based on a multi-country dataset from the World Bank Enterprises Survey, which contains detailed firm-level information from over 8,902 firms in 82 emerging market countries. We perform a series of robustness checks to further confirm our findings.

We find that a high ratio of foreign ownership is associated with an increased likelihood of security breaches and higher security costs. Our results also indicate that high levels of host countries’ institutional quality and firms’ local embeddedness can mitigate such vulnerability in operational security.

This study is one of the first to uncover the critical operational implication of the LOF, indicating that a high ratio of foreign ownership exposes firms to operational security challenges.