Journal of Digital Economy最新文献

Analyzing and assessing the quality risks is essential to leverage the value of data assets. In this paper, a framework for proactively assessing the quality risks of data assets based on an improved FMEA is proposed. First, quality risk metrics are identified from a lifecycle perspective through literature research and experts' discussions. Then, Triangular Fuzzy Numbers are adopted to express uncertain and complex information about the expert's assessment. Subsequently, a new risk factor ‘C' is introduced to describe the difficulty of risk controlling and a DEA approach is applied to calculate the weights of risk factors. Finally, a practical case is provided to demonstrate the proposed FMEA framework, and several recommendations are provided to control data asset quality risks.

Artificial intelligence (hereafter AI) is widely considered as a driving force in the current digital economy, with many firms having already invested in AI. Since AI is unconstrainted by humans' cognitive limitations and inflexibility, and thus a key assumption in popular press is that AI is crucial for firms' success in digital economy. However, surprisingly, many managers indicate they are yet to benefit from their AI investments. To address this issue, the main purpose of this paper is to summarize the extant literature on AI in business and management fields to identify how AI can create competitive advantages and underpin the key barriers that prevent AI from realizing its full potentials. Our results suggest AI can increase revenue by improving employee productivity, increasing consumer evaluation, setting competitive price and creating unique resources. AI can also reduce cost by improving efficiency and reducing risks. However, our results also indicate that AI adoption, task nature and AI management are the key barriers preventing AI from realizing its full potentials. This is because AI lacks interpersonal skills. Thus, we encourage future research to focus on improving AI's interpersonal skills.

The purpose of this paper is to explore the relationship between the development of entrepreneurial ecosystems and economic growth at the urban level from the knowledge-based view. This paper also scrutinizes the moderating roles of industrial diversities and digital technology service. Based on the data of 32 cities in China from 2008 to 2018, the findings show that entrepreneurial ecosystems' development promotes municipal economic growth significantly via knowledge creation and knowledge flow. Moreover, industrial diversity and digital technology service are found to positively moderate the relationship between entrepreneurial ecosystems’ development and the urban economic growth. This study extends the literature on entrepreneurial ecosystems and regional economic development at the urban level from the perspective of knowledge-based view. The findings also provide policymakers and stakeholders a different mentality when forming strategies and policies on entrepreneurship.

The patent system is instrumental in contributing to firms' innovation and nations’ economic growth. However, the system has been plagued by a series of persistent problems that prevent it from playing its full role. For example, the fundamental issue of who should be awarded the patent has not yet been resolved; the massive backlog of patent applications in patent offices worldwide has become a major headache for policymakers and innovating firms. In the paper, we propose and discuss a framework that digital technologies could offer promising solutions to these long-standing issues, thereby significantly improving the efficiency of the patent system. Meanwhile, we also present and discuss a few challenges faced by the patent system due to the cumulative nature and interconnectedness of digital technologies. Therefore, the digital era opens up new possibilities for the patent system but also brings about new challenges. This paper hopes to shed light on the discussion on the reform of the patent system in the digital era and point out a few possibly fruitful research directions in this area.

As an emerging information technology, blockchain has aroused extensive discussions around the world and been suggested as a solution to address current issues in supply chain finance (SCF). The Chinese government also attaches great importance to this technology, and many Chinese state-owned enterprises have invested in establishing their own blockchain research and development centres. However, there is a lack of studies on identifying challenges when deploying this technology; theoretical framework and conceptual exposition are also scarcely seen. Therefore, the aim of this study is to investigate the challenges and obstacles in the adoption of blockchain technology in SCF. An exploratory case study of a Chinese state-owned enterprise was conducted to build up an initial conceptual framework. Semi-structured interview was applied to collect data from the case firm's employees, top management, and technical specialists. The results of the analysis indicate that in the adoption of blockchain technology, there are technological, operational, and other challenges. From a technological perspective, framework identification, cross-chain interoperability, and data governance are major barriers; whereas, from an operational perspective, the new business process and transformation in the entire supply chain are identified as challenges. Besides, other obstacles such as the elimination of jobs and regulatory issues are also not neglectable. This study contributes to research on blockchain and supply chains by shedding light on the challenges of blockchain adoption through an exploratory case study of a Chinese state-owned enterprise. A conceptual framework was generated as a basis for future research, and the findings also provide insights for companies that may or are planning to adopt blockchain technology.

Purpose

While blockchain is considered to have many unprecedented characteristics, and its application is recognized as another new opportunity for the development of e-commerce, there is limited evidence on the factors affecting the adoption of blockchain in the commercial e-commerce sector. This study aims to identify determinants influencing consumers' intention to adopt blockchain technology in e-commerce.

Design

/methodology/approachDrawing on the classic technology acceptance model (TAM), a conceptual framework is developed and empirically assessed to present the relationships between the core characteristics of blockchain and consumers' adoption intention. Survey data were collected from 228 users of the blockchain e-commerce system in China. The structural equation modeling (SEM) approach is used to test the hypotheses.

Findings

The results indicate that cost saving and traceability have a positive effect on perceived usefulness while insignificant associations are found between data privacy security and perceived usefulness, and perceived ease of use and consumers' adoption intention.

Research limitations/implications

The research only examined Chinese users, which may affect the generalizability of the findings. Future research is encouraged to conduct comparative studies beyond this region, e.g., emerging markets versus developed economies. It would also be useful to explore mediating and moderating effects of other new technologies that complement the application and adoption of blockchain.

Practical implications

The research results also bring managerial implications with the ways of attracting customers via blockchain technology, including improving system ability to reduce cost and enhance traceability.

Originality/value -

This paper is one of the early empirical endeavors that examines determinant factors affecting individual users towards the adoption of blockchain technology in e-commerce that is absent in the extant research. This study further contributes to the development of the knowledge bank of blockchain via the conceptual framework of its adoption under the e-commerce context, in particular considering its technical features.

Analyzing data of your customers and providing them with the best product or service is no longer sufficient within the digital economy to make your customers satisfied or even enthusiastic about your company in the long run. These days the approach of customer needs analysis seems to be extended towards big data and customer analytics. But is collecting data really helpful, especially for SMEs with limited resources? Research shows that pure data collection does not provide any additional strategic value. In fact, most companies have no clue what to do with the collected big data and how to gain strategic value out of it. In this empirical paper, drawing on the ecosystem theory, we argue that customers should not any longer be seen as pure raw material of data, but as active knowledge partners. This requires a complete mind shift in how SMEs deal with their customers. In this paper, we contribute to the existing literature by providing an interaction framework to show how companies can create a well-functioning knowledge partnership based on the customer's motivational foundations to benefit from different contributions and strategic values customers are willing to make.

In China's labor market, enterprises are allowed for some flexibility in deciding whether to provide “social insurance and housing fund” to laborers. This paper uses micro-data from two leading Internet recruitment platforms and finds that in a labor market with double-side information asymmetry, “social insurance and housing fund” serves as not only a cost but also a signal. Providing workers with “social insurance and housing fund”, enterprises send a signal of stable operation to the labor market while identifying high-quality workers for enterprises. We further construct an instrument variable (IV) of local average social security payment rate, and show that the signaling effect remains significant after accounting for the endogeneity issue using IV regressions. In addition, “housing fund” has a stronger signaling effect than “social insurance”. Heterogeneity analysis indicates that the strength of the two signaling effects is affected by the scale of the enterprises and the level of local payment rates. A theoretical framework capturing two micro-mechanisms — signaling and screening — is developed to fit our empirical findings. This paper provides explicit policy implications. It is suggested to strengthen the information disclosure and the propagation of social security payment, and further reduce the financial burden of enterprises.

The digital economy is pervasive, all-encompassing, and a pan-industrial revolution. This paper pioneers constructing a digital economy concern index by extracting the web search volumes of keywords through crawler technology and analyzes the dynamic causal relationship with the Chinese stock markets via time-varying Granger tests. The results reveal that digital economy attention has a significant predictive effect on stock prices in a time-varying pattern and that the causal spillover varies across industry segments, with higher success rates and longer duration of causal detection under recursive algorithms. Moreover, the causal impact of digital economy attention on stock prices is generally limited in sluggish market states, mainly reflected during the COVID-19 pandemic and again after the epidemic had passed for some time with significant causality. This paper provides new evidence and analytical perspectives on the performance of the digital economy in financial markets, informing the digital transformation of various industries and investment decisions of investors.

The benefits to data analytics and machine learning have been distributed unevenly across firms around the world. Research on IT productivity points to intangible capital as a key driver of value creation from innovation in computing. We argue that a crucial component of intangible capital is organization-wide technological architecture, which is idiosyncratic and difficult to measure. We use a novel survey instrument to quantify large corporations’ data architecture capabilities by their closeness to “best practices” of frontier digital companies. Using the prevalence of third-party maintenance as a proxy for legacy servers before 2016 and an instrument for data architecture coherence, we find that improving data architecture coherence increases machine learning capabilities. Legacy servers reduce data architecture coherence particularly at corporations with complex software systems, consistent with the hypothesis that costs of digital transformation are greater when workers need to develop more complicated co-invention processes to interact with technical systems.