Search results

Existing research on collaborative innovation mechanisms from the perspective of global operation is very limited. This paper aims to address the research gap by studying the factors influencing globally distributed teams’ innovation performance, especially how effective knowledge sharing between distributed teams promotes collaborative team innovation.

This research proposes a model to investigate how collaborative knowledge sharing affects global operations [team dispersion, task orientation, information and communication technology (ICT) usage] and innovation performance based on the data collected from 167 managers in 40 local Chinese IT and offshoring firms. Using the theory of Cognitive Diversity and Innovation Diffusion and Synergy, separate hierarchical regression analysis was used to test the proposed model.

The findings of this study demonstrate that effective collaborative knowledge sharing plays a crucial role in enhancing innovation performance in a global operation. Specifically, innovation capacity can be improved by task orientation, ICT usage and team dispersion.

This research study contributes to the development of global distributed operations and innovation among distributed teams in multinational corporations.

This paper aims to investigate investors' willingness to use robo-advisors from customers' perspectives and analyzes the factors that drive them to use robo-advisors, including perceived usefulness and emotional response.

The authors extend the Cognition-Affect-Conation (CAC) framework to the behavioral domain of robo-advisor users on financial technology platforms and conduct an empirical study based on 248 valid questionnaires.

The authors find two types of factors driving the willingness to use robo-advisors: perceived usefulness, trust and perceived risk as external driving forces and investor sentiment as an internal driving force. Trust has a significant positive effect on willingness to use, and arousal in emotional response plays a mediating role between perceived usefulness and willingness to use.

This research provides valuable insights for financial institutions to engage in robo-advisor innovation from customers' perspectives.

The rise of the mobile Internet has accumulated much text information in various online financial forums. Such information often contains the emotional attitudes of investors toward financial technology (fintech) platforms, so extracting the sentimental tendency information has great practical value for the development of fintech platforms. Based on the investor sentiment theory, the paper aims to analyze the relevant social media data and test the influence path of online news evaluation on the stock price fluctuation of fintech platforms.

Taking Oriental Fortune as the research object, this paper selects multiple variables such as stock bar popularity, snowball popularity, news popularity and news sentiment scores collected by UQER and combines the sentiment scores of single daily news into a daily sentiment score. Based on the period from November 1, 2019 to March 31, 2020, during the emergence of the coronavirus disease 2019 (COVID-19) pandemic as the background, the authors conduct the Granger causality test based on the vector autoregressive (VAR) model and analyze the relevant evaluation of Oriental Fortune through the empirical model.

The authors' results show that different online evaluations impact the rise and fall of stock prices differently, while news popularity has the most significant impact. Besides, news sentiment scores on share price fluctuation have a relatively substantial influence. These findings indicate that the authoritative news evaluation can strongly guide investors to make relevant investment behavior operations in the information dissemination process, significantly affecting stock prices.

The research findings of this paper have good inspiration and reference values for investors and financial regulators.

In the emerging economy, knowledge is now recognized as an important basis for competitive advantage and many firms are beginning to establish knowledge management systems (KMS). Within the last few years, although the topic of knowledge management (KM) has been studied, our understanding of how the design of the KMS affects its use and definition of KM is still rather limited. This paper presents a model of the enterprise’s knowledge trees, which is related to several knowledge management processes. The gray information and knowledge are, in their respective capacities, to provide the enterprise with great effectiveness. The gray dimension of enterprise knowledge is defined in line with the model of enterprise knowledge and fractal dimension. Also the key factors of KMS of shared knowledge are discussed in this paper. The results of the study will benefit not only the design of KMS, but also the business model transformation of competitive advantage.

Industry–university–research cooperation (IURC) is a crucial way to build an innovative country. How to improve the effectiveness of IURC has become an important issue to be solved urgently.

This paper studies the data of industry, university and research activities in various regions of China from 2016 to 2018 and analyzes the impact mechanism of innovation input and open innovation environment on the effectiveness of IURC based on innovation value chain theory.

This research finds that innovative talent input has an inverted U-shaped impact on the effectiveness of IURC. When there are more innovative funds invested, the marginal effect of IURC will decrease. When innovative talent input exceeds a certain value, the open innovation environment can alleviate the positive marginal effect of its decline.

This paper contributes to the literature and provides practical guidelines for improving the efficacy of IURC.

Excavating valuable outlier information of gray privacy products, the purpose of this study takes the online reviews of women’s underwear as an example, explores the outlier characteristics of online commentary data, and analyzes the online consumer behavior of consumers’ gray privacy products.

This research adopts the social network analysis method to analyze online reviews. Based on the online reviews collected from women’s underwear flagship store Victoria’s Secret at Tmall, this study performs word segmentation and word frequency analysis. Using the fuzzy query method, the research builds the corresponding co-word matrix and conducts co-occurrence analysis to summarize the factors affecting consumers’ purchase behavior of female underwear.

Establishing a formal framework of gray privacy products, this paper confirms the commonalities among consumers with respect to their perceptions of gray privacy products, shows that consumers have high privacy concerns about the disclosure or secondary use of personal private information when shopping gray privacy products, and demonstrates the big difference between online reviews of gray privacy products and their consumer descriptions.

The research lays a solid foundation for future research in gray privacy products. The factors identified in this study provide a practical reference for the continuous improvement of gray privacy products and services.

The paper focuses on the variables that help analyze peer-to-peer (P2P) lending platforms. It explores the characteristic factors of identifying problematic platforms, and designs a P2P platform risk early warning model.

With the help of web crawler software, this paper crawls the information of 1427 P2P platforms from the two largest third-party lending information platforms (i.e. P2Peye and WDZJ) in China. SPSS 22.0 was mainly used for basic descriptive statistical analysis, reliability and validity analysis, and regression analysis of the data. MPLUS 7.0 was used for confirmatory factor analysis and structural equation models analysis.

Based on the multi-dimensional information, this paper performs text mining to develop an investor sentiment index. This study shows that the characteristics of the platform (i.e. basic features, capital security, operations management, and social network) have a significant impact on identifying problematic platforms.

There are some limitations to this research. In the process of model construction, some external factors may be ignored, such as government policies. Future research will need to consider the impact of policy and other factors more comprehensively on P2P lending platform risk identification.

This study proposes an effective method for investors and regulators to identify the risk factors of P2P lending platforms. The research findings provide valuable insights for promoting government participation in platform management as well as a healthy development of the P2P lending industry.

The paper addresses the factors that influence platform risks to help analyze P2P lending platforms. Prior research has not explored how to identify problematic P2P lending platforms in-depth and is limited by only focusing on either soft information or hard information. It identifies the characteristic factors of identifying problematic platforms and designs a P2P platform risk early warning model.

The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant difference in length between the content and its title. In addition, relying solely on textual discrepancies between the title and content to distinguish between real and fake news has proven ineffective. The purpose of this paper is to develop a new approach called semantic enhancement network with content–title discrepancy (SEN–CTD), which enhances the accuracy of fake news detection.

The SEN–CTD framework is composed of two primary modules: the SEN and the content–title comparison network (CTCN). The SEN is designed to enrich the representation of news titles by integrating external information and position information to capture the context. Meanwhile, the CTCN focuses on assessing the consistency between the content of news articles and their corresponding titles examining both emotional tones and semantic attributes.

The SEN–CTD model performs well on the GossipCop, PolitiFact and RealNews data sets, achieving accuracies of 80.28%, 86.88% and 84.96%, respectively. These results highlight its effectiveness in accurately detecting fake news across different types of content.

The SEN is specifically designed to improve the representation of extremely short texts, enhancing the depth and accuracy of analyses for brief content. The CTCN is tailored to examine the consistency between news titles and their corresponding content, ensuring a thorough comparative evaluation of both emotional and semantic discrepancies.