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Digital infrastructure construction and corporate innovation efficiency: evidence from Broadband China Strategy
Adopting the Broadband China Strategy as a quasi-natural experiment, we construct a multi-period Difference-in-Differences (DID) model to examine the impact of digital infrastructure construction on corporate innovation efficiency with panel data from Chinese listed companies between 2010 to 2022. Our findings indicate that the development of digital infrastructure significantly boosts corporate innovation efficiency. Mechanistic analysis reveals that financing constraints negatively moderates this innovation impact, while human capital positively moderates it. The effects of the Broadband China Strategy are particularly pronounced in non-state-owned enterprises, non-high-tech enterprises, and firms located in the non-eastern region of China. Our research provides important insights for enterprises seeking to enhance their innovation efficiency, while also offering strong empirical evidence on the role of digital infrastructure in fostering corporate innovation. Our study contributes to the literature on digital economy and innovation, with practical implications for policymakers and firms aiming to leverage digital infrastructure for sustained competitive advantage.
Liability of origin imprints: how do the origin imprints influence corporate innovation? Evidence from China
In transforming emerging economies, many state-owned enterprises (SOEs) underwent privatization, transferring property rights from the state to private entities. This transition not only facilitated the establishment of entrepreneurial family firms but also encouraged the emergence of privatized family firms as property rights were transferred to individuals and families. Consequently, the roots of property rights in these settings can be traced back to either direct establishment or privatization. In this study, we examine how these origin imprints influence corporate innovation. By analyzing a dataset of A-share Chinese listed non-financial family firms spanning from 2005 to 2021, we find that pre-privatization organizational imprints which primarily focus on societal well-being, tend to persist within these privatized family firms, resulting in a lower degree of corporate innovation compared to their entrepreneurial counterparts. Moreover, additional subsample analysis indicates that the adverse impact of privatized family firms on corporate innovation is intensified by strong political connections while mitigated by a well-developed institutional environment in the region. Our results are robust to various econometric methods, alternative explanations, and approaches to address endogeneity concerns such as the two-stage least squares (2SLS), Generalized Method of Moments (GMM), and propensity score matching (PSM) techniques. Overall, this study highlights a source of heterogeneity within the family firms and reveals how organizational imprints inherited from a pre-privatization economic regime can diminish the positive effects usually associated with family ownership.
The risk effects of corporate digitalization: exacerbate or mitigate?
This study elaborates on the risk effects of corporate digital transformation (CDT). Using the ratio of added value of digital assets to total intangible assets as a measure of CDT, this study overall reveals an inverse relationship between CDT and revenue volatility, even after employing a range of technical techniques to address potential endogeneity. Heterogeneity analysis highlights that the firms with small size, high capital intensity, and high agency costs benefit more from CDT. It also reveals that advancing information infrastructure, intellectual property protection, and digital taxation enhances the effectiveness of CDT. Mechanism analysis uncovers that CDT not only enhances financial advantages such as bolstering core business and mitigating non-business risks but also fosters non-financial advantages like improving corporate governance and ESG performance. Further inquiries into the side effects of CDT and the dynamics of revenue volatility indicate that CDT might compromise cash flow availability. Excessive digital investments exacerbate operating risks. Importantly, the reduction in operating risk associated with CDT does not sacrifice the potential for enhanced company performance; rather, it appears to augment the value of real options.
The logarithmic memristor-based Bayesian machine
The demand for explainable and energy-efficient artificial intelligence (AI) systems for edge computing has led to growing interest in electronic systems dedicated to Bayesian inference. Traditional designs of such systems often rely on stochastic computing, which offers high energy efficiency but suffers from latency issues and struggles with low-probability values. Here, we introduce the logarithmic memristor-based Bayesian machine, an innovative design that leverages the unique properties of memristors and logarithmic computing as an alternative to stochastic computing. We present a prototype machine fabricated in a hybrid CMOS/hafnium-oxide memristor process. We validate the versatility and robustness of our system through experimental validation and extensive simulations in two distinct applications: gesture recognition and sleep stage classification. The logarithmic approach simplifies the computational model by converting multiplications into additions and enhances the handling of low-probability events, which are crucial in time-dependent tasks. Our results demonstrate that the logarithmic Bayesian machine achieves superior performance in terms of accuracy and energy efficiency compared to its stochastic counterpart, particularly in scenarios involving complex probabilistic models. This approach enables the development of energy-efficient and reliable AI systems for edge devices.
Brain inspired iontronic fluidic memristive and memcapacitive device for self-powered electronics
Ionic fluidic devices are gaining interest due to their role in enabling self-powered neuromorphic computing systems. In this study, we present an approach that integrates an iontronic fluidic memristive (IFM) device with low input impedance and a triboelectric nanogenerator (TENG) based on ferrofluid (FF), which has high input impedance. By incorporating contact separation electromagnetic (EMG) signals with low input impedance into our FF TENG device, we enhance the FF TENG’s performance by increasing energy harvesting, thereby enabling the autonomous powering of IFM devices for self-powered computing. Further, replicating neuronal activities using artificial iontronic fluidic systems is key to advancing neuromorphic computing. These fluidic devices, composed of soft-matter materials, dynamically adjust their conductance by altering the solution interface. We developed voltage-controlled memristor and memcapacitor memory in polydimethylsiloxane (PDMS) structures, utilising a fluidic interface of FF and polyacrylic acid partial sodium salt (PAA Na+). The confined ion interactions in this system induce hysteresis in ion transport across various frequencies, resulting in significant ion memory effects. Our IFM successfully replicates diverse electric pulse patterns, making it highly suitable for neuromorphic computing. Furthermore, our system demonstrates synapse-like learning functions, storing and retrieving short-term (STM) and long-term memory (LTM). The fluidic memristor exhibits dynamic synapse-like features, making it a promising candidate for the hardware implementation of neural networks. FF TENG/EMG device adaptability and seamless integration with biological systems enable the development of advanced neuromorphic devices using iontronic fluidic materials, further enhanced by intricate chemical designs for self-powered electronics.
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