Project description:As the pace of enterprise digital transformation accelerates, intellectual capital (IC) has become a core driving force of gaining market competitive advantages and enhancing value creation capabilities. The paper aims to investigate the impact of IC and its components on financial performance of Chinese ecological protection and environmental governance companies during 2018-2021. In addition, the moderating effect of digital transformation between them is examined. IC is measured by the modified value added intellectual coefficient (MVAIC) model, and the measurement of digital transformation is based on text mining. The results suggest that IC can improve firm financial performance, especially during COVID-19. Physical capital, human capital (HC), and relational capital (RC) positively affect financial performance, while structural and innovation capitals have no significant impact. In addition, digital transformation strengthens the positive relationship between IC and its two elements (HC and RC) and financial performance. Heterogeneous analysis finds that the relationship between RC and innovation capital and financial performance is positive before COVID-19, and it is not significant during COVID-19. For highly leveraged companies, structural capital negatively affects financial performance, and RC has a positive impact. These impacts are not significant for low leveraged companies. This paper provides some new insights for managers who seek new ways to improve firm performance in the process of digital transformation.
Project description:Digital transformation, as a significant shift in optimizing enterprise resource allocation and enhancing information connectivity, offers the opportunity to stimulate the endogenous dynamics of corporate green governance. Employing a sample of 3,002 listed companies in China, a fixed-effects model, and the entropy power method to formulate a green governance index system, this study examines how digital transformation affects corporate green governance concerning carbon peaking and carbon neutrality objectives. According to these findings, the implementation of the digital transformation improves corporate green governance, each unit increase in digital transformation correlates with a 1.91% enhancement in green governance. Moreover, an examination of the mechanisms shows that green governance can be promoted by addressing information asymmetry and enhancing operational efficiency. Additionally, the association between corporate green governance and digital transformation is moderated favorably by strategic aggressiveness. Furthermore, our results indicate that digital transformation contributes significantly to the advancement of green governance within enterprises located in areas with high digital financing and strong technology integration capacities. Digitalization has a stronger effect on promoting green governance for enterprises in pilot regions than in non-pilot regions in terms of carbon emission trading. This study not only assists enterprises in elucidating the developmental trajectory of digital transformation amid carbon peaking and carbon neutrality goals but also provides a reference for decision-making on how digital technology can empower corporate green governance and promote sustainable economic growth.
Project description:Current discussions of the ethical aspects of big data are shaped by concerns regarding the social consequences of both the widespread adoption of machine learning and the ways in which biases in data can be replicated and perpetuated. We instead focus here on the ethical issues arising from the use of big data in international neuroscience collaborations. Neuroscience innovation relies upon neuroinformatics, large-scale data collection and analysis enabled by novel and emergent technologies. Each step of this work involves aspects of ethics, ranging from concerns for adherence to informed consent or animal protection principles and issues of data re-use at the stage of data collection, to data protection and privacy during data processing and analysis, and issues of attribution and intellectual property at the data-sharing and publication stages. Significant dilemmas and challenges with far-reaching implications are also inherent, including reconciling the ethical imperative for openness and validation with data protection compliance and considering future innovation trajectories or the potential for misuse of research results. Furthermore, these issues are subject to local interpretations within different ethical cultures applying diverse legal systems emphasising different aspects. Neuroscience big data require a concerted approach to research across boundaries, wherein ethical aspects are integrated within a transparent, dialogical data governance process. We address this by developing the concept of "responsible data governance," applying the principles of Responsible Research and Innovation (RRI) to the challenges presented by the governance of neuroscience big data in the Human Brain Project (HBP).
Project description:Digital transformation plays a crucial role in improving the quality development of companies in this era of digital economy with ever-changing technologies. This paper empirically investigates the impact of corporate digital transformation on total factor productivity and the mechanism of action, using A-share listed companies in Shanghai and Shenzhen from 2011-2021 as the research sample, and found that the digital transformation of companies significantly improves total factor productivity, with the plausibility of the findings being verified by a series of robustness tests. Based on the heterogeneity study, it is found that such effect is stronger for private companies, non-high-tech companies, and companies with a high degree of industry competition. The mechanism test indicates that digital transformation facilitates total factor productivity through four ways: strengthening company technological innovation, reducing operational costs, increasing resource allocation efficiency, and improving human capital structure. The findings of this paper support a better understanding of the micro effects of digital transformation and provide empirical evidence for policy formulation and adjustment.
Project description:Corporate financialization poses serious challenges to the development of the real economy. In the context of promoting the deep integration of the digital economy and the real economy, it is crucial to explore whether digital transformation can inhibit corporate financialization. Using data from Chinese listed companies from 2009 to 2021, we construct a fixed effects model and find that digital transformation significantly reduces the level of corporate financialization, a conclusion that still holds after a series of robustness tests such as propensity score matching and adding control variables. Channel analysis shows that that digital transformation inhibits corporate financialization by enhancing the information mobility and operational capability of corporations. In addition, this effect is more pronounced at higher levels of industry competition as well as marketization. Finally, we also find structural differences in the impact of digital transformation on corporate financialization. Our study explores the determinants of corporate financialization in terms of a firm's mode of operation and type of strategy, and the findings provide a theoretical basis for the active development of digital technologies in emerging markets that are undergoing economic transitions, as well as for guarding against the shift of the economy from the real to the virtual.
Project description:Based on empirical analysis of Chinese listed companies from 2010 to 2018, we demonstrate that enterprise digital transformation has a significant impact on improving capacity utilization. Digital transformation is a significant driving force behind enterprise-specific production and innovation. Furthermore, enterprise innovation and enterprise-specialized production play a mediating role in the impact of enterprise digital transformation on capacity utilization. Based on these baseline findings, heterogenous analysis reveals that the impact of digital transformation on capacity utilization is significant for firms with larger capital scales or poor governance and manufacturing abilities. However, it is less important for enterprises with small- and medium-sized capital scales or with more standardized governance, as well as non-manufacturing (service) enterprises.
Project description:This paper investigates the impact of the digital economy on urban environmental pollution by analyzing panel data from 283 prefecture-level cities in China from 2011 to 2019 and using the digital technology comprehensive pilot zone of China as a natural experiment. The results demonstrate that digital technology has a significant effect in reducing pollutant emissions and empowering urban environmental governance. The findings are proven to be robust based on various tests, including parallel trend, PSM-DID, and placebo tests. Our analysis further shows that digital technology is particularly effective in controlling pollution in old industrial areas, high digital areas, and low energy efficiency areas. We also find that the national digital technology integrated pilot zone can mitigate environmental pollution in prefecture-level cities by increasing public environmental awareness and encouraging green technology innovation. Moreover, our research indicates that digital technology-enabled urban pollution control can contribute to the formation of a new urbanization pattern in China. These findings provide valuable insights for promoting the digital economy and achieving the goal of carbon reduction in China.
Project description:The Ontario Brain Institute (OBI) has begun to catalyze scientific discovery in the field of neuroscience through its large-scale informatics platform, known as Brain-CODE. The platform supports the capture, storage, federation, sharing, and analysis of different data types across several brain disorders. Underlying the platform is a robust and scalable data governance structure which allows for the flexibility to advance scientific understanding, while protecting the privacy of research participants. Recognizing the value of an open science approach to enabling discovery, the governance structure was designed not only to support collaborative research programs, but also to support open science by making all data open and accessible in the future. OBI's rigorous approach to data sharing maintains the accessibility of research data for big discoveries without compromising privacy and security. Taking a Privacy by Design approach to both data sharing and development of the platform has allowed OBI to establish some best practices related to large-scale data sharing within Canada. The aim of this report is to highlight these best practices and develop a key open resource which may be referenced during the development of similar open science initiatives.
Project description:BACKGROUND:The opioid epidemic has enabled rapid and unsurpassed use of big data on people with opioid use disorder to design initiatives to battle the public health crisis, generally without adequate input from impacted communities. Efforts informed by big data are saving lives, yielding significant benefits. Uses of big data may also undermine public trust in government and cause other unintended harms. OBJECTIVES:We aimed to identify concerns and recommendations regarding how to use big data on opioid use in ethical ways. METHODS:We conducted focus groups and interviews in 2019 with 39 big data stakeholders (gatekeepers, researchers, patient advocates) who had interest in or knowledge of the Public Health Data Warehouse maintained by the Massachusetts Department of Public Health. RESULTS:Concerns regarding big data on opioid use are rooted in potential privacy infringements due to linkage of previously distinct data systems, increased profiling and surveillance capabilities, limitless lifespan, and lack of explicit informed consent. Also problematic is the inability of affected groups to control how big data are used, the potential of big data to increase stigmatization and discrimination of those affected despite data anonymization, and uses that ignore or perpetuate biases. Participants support big data processes that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions. Recommendations for ethical big data governance offer ways to narrow the big data divide (e.g., prioritize health equity, set off-limits topics/methods, recognize blind spots), enact shared data governance (e.g., establish community advisory boards), cultivate public trust and earn social license for big data uses (e.g., institute safeguards and other stewardship responsibilities, engage the public, communicate the greater good), and refocus ethical approaches. CONCLUSIONS:Using big data to address the opioid epidemic poses ethical concerns which, if unaddressed, may undermine its benefits. Findings can inform guidelines on how to conduct ethical big data governance and in ways that protect and respect patients and society, ensure justice, and foster patient and public trust in public institutions.