Journal of Digital Economy最新文献

In this study, a research agenda is proposed to understand the current development and future landscape of the digital economy. The shift from the industrial economy to a digital economy brings tremendous changes in terms of new production factors, new organizations, new models, and new contexts, calling for a new theoretical framework. Therefore, the IBCDE framework is proposed for digital economy research from five perspectives: digital infrastructure (I), to-B industry platforms (B), to-C two-sided platforms (C), data ecosystem (D), and economic contexts (E). By relating the current practice of the digital economy with extant literature, potential directions for future research are identified.

This paper starts with exploring the nature of industrialisation, combined with the current hot research topic in the field of technology - digitization, and it seeks to reveal the fusion process of industrialisation and digitization. The paper summarizes the authors' long-term research results on industrialisation and its industrial system into three main aspects: first, although the industrial system presents a variety of complexities, its essence can be defined as an input-output value creation system; second, industrialisation can be regarded as a process of the iterative evolution of industrial system design-construction, operation-delivery, and continuous improvement at the meso and micro levels; third, in the face of an increasingly unstable business environment, understanding the industrial systems and their ecosystems, as well as their interactive mechanisms, has become a major research topic that is urgently needed by current managers. On this basis, the paper discusses the fusion of industrialisation and digitalisation built upon two recent case observations, and suggests that industrial-digital fusion can be divided into three processes and one new transmuted outcome. From the process perspective, firstly, the fusion, entitled with the digitization of the industrial system, actually can significantly and effectively improve competitive attributes by adopting digital technologies in existing industrial systems, especially in its weak links. Secondly, the fusion, named as the industrialisation of digital technology, can thrust a new service model to create a new industry that is based on successful digital applications. And thirdly, with the further integration of the above two processes, the fusion can establish a reciprocal and iterative process between the above two processes and eventually achieve self-evolutions, effectively and healthily. From the transmutation outcome perspective, the fusion can be also recognised as a set of interactive mechanisms and principles within the metaverse and between the virtual-real worlds. At the end of the paper, some possible research directions are put forward.

This study explores the possibility of using a machine learning approach to analysing social media big data for tourism demand forecasting. We demonstrate how to extract the main topics discussed on Twitter and calculate the mean sentiment score for each topic as the proxy of the general attitudes towards those topics, which are then used for predicting tourist arrivals. We choose Sydney, Australia as the case for testing the performance and validity of our proposed forecasting framework. The study reveals key topics discussed in social media that can be used to predict tourist arrivals in Sydney. The study has both theoretical implications for tourist behavioural research and practical implications for destination marketing.

The authors report on the Haier Group, a multinational business that primarily produces and markets manufactured goods and whose multiple business ecosystems are based not on traditional management control, but rather on distributed organizational and information technology platforms. These platforms enable people to create business value through participating in structured, financially incentivized but essentially voluntary relationships, and by engaging their human creative freedom to serve each other and co-create business value. For example, Haier Group manufactures large home appliances in China by an ecosystem made up of over four thousand mostly small teams making things and trading with each other by way of self-organized, self-negotiated lateral relationships that are smart-contract enabled, with no vertical middle managers. The case will be of interest to scholars because of the unique philosophy and practice, and the success of the approach at promoting both human development on a wide scale, and dramatic marketplace and business success of the ecosystem. The authors are scholars of business ecosystems. They report that this case was not understandable to them through the logic of their previous study of business ecosystems, a logic of strategy and organization design that presumes a limited, knowable, and closed model of each person and a management philosophy of control. In contrast, Haier Group presumes an unlimited, unknowable, and open image of each person, and a management philosophy of augmented personal creativity and platform-enabled coevolution with others. The authors provide a new term adapted from biology, human ecosystem, to highlight human values on which business ecosystems can vary, with important consequences for human development as well as business performance as traditionally measured. They define the term broadly and encourage others to join them in co-developing it.

Emerging technologies have faced ethical challenges, and ethical governance has changed over time managing these technologies. The governance paradigm has gradually changed from scientific rationality to social rationality and ultimately to a higher ethical morality. The trend of seeking higher levels of ethics and morality provides a rich theoretical underpinning for the ethical governance of artificial intelligence (AI), which is a complex and comprehensive project that involves problem identification, path selection, and role configuration. Ethical problems in AI can also be identified in technology, value, innovation, and order systems. In the four major systems, the basic patterns of ethical problems can become uncontrolled risks, behavioral disorders, and ethical disorders. When considering the path selection, AI governance strategies such as ethical embedding, assessment, adaptation, and construction should be implemented within the technology life cycle at the stages of research and development, design and manufacturing, experimental promotion, and deployment and application, respectively. Looking at role configuration, multiple actors should assume different roles, including providing ethical factual information, expertise, and analysis, as well as expressing ethical emotions or providing ethical regulation tools under different governance strategies. This study provides a comprehensive discussion regarding the practical applicability of AI ethical governance using the case of autonomous vehicles.