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Drawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller’s local social network on her promotion effectiveness in social commerce.

The authors define a local social network as one formed by a focal seller, her directly connected users and all links among these users. Using data from a large social commerce website in China, the authors build econometric models to investigate how the density, grouping and centralization of local social networks affect the number of likes received by products posted by sellers.

Local social networks with low density, grouping and centralization are associated with more likes on sellers’ posted products. The negative effects of grouping and centralization are reduced when density is high.

The paper deepens the understanding of the determinants of social commerce success from a network structure perspective. In particular, it draws attention to the role of sellers’ local social networks, forming a foundation for future research on social commerce.

With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.

In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.

Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.

In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.

This study aims to examine individuals' tendency to strictly follow their own signal while ignoring predecessors' decisions when making decisions under varying degrees of uncertainty.

Using a controlled laboratory experiment, the authors separate the follow-own-signal behavior from other types of behavior such as Bayes consistent or herd-like (i.e. follow-the-majority) behavior.

As the authors systemically increase the degree of uncertainty in the information environment, participants are increasingly more likely to act only on their own signal. This suggests that financial decisions that are made under highly uncertain market conditions may be more signal revealing, and hence, may lead to better information aggregation than previously thought. The authors also find that as uncertainty increases, participants are more likely to switch in and out of this behavior, suggesting that behavior under highly uncertain conditions may also be more random and complex.

The authors are the first to examine how uncertainty affects the follow-own-signal behavior. The authors also offer potential testable empirical implications, such as an increase in contrarian investing, home bias, and own-company ownership under times of increased uncertainty or in more uncertain markets.

High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.

There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.

In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.

The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.

Crowdfunding (CF) has become an increasingly popular means of financing for entrepreneurs and has attracted significant attention from both researchers and practitioners in recent years. The purpose of this study is to investigate the core content and knowledge diffusion paths in the CF field. Specifically, we aim to identify the main topics and themes that have emerged in this field and to trace the evolution of CF knowledge over time.

This study employs co-word clustering and main path analysis (MPA) to examine the historical development of CF research based on 1,528 journal articles retrieved from the Web of Science Core Collection database.

The results of the analysis reveal that CF research focuses on seven themes: sustainability, entrepreneurial finance, entrepreneurship, fintech, social entrepreneurship, social capital, and microcredits. The analysis of the four main paths reveals that equity CF has been the dominant topic in the past years. Recently, CF research has tended to focus on topics such as fintech, the COVID-19 pandemic, competition, Brexit, and policy response.

To the authors' best knowledge, this is the first attempt to explore knowledge diffusion dynamics in the CF field. Overall, the study offers a structure for analyzing the paths through which knowledge is diffused, enabling scholars to effectively manage a large volume of research papers and gain a deeper understanding of the historical, current, and future trends in the development of CF.

This study proposes a framework based on salience theory and shows that focusing on one type of risk (idiosyncratic or systemic) can explain overpricing of securities ex ante, and resales at low prices during crisis periods.

The author consider an overlapping generations (OLG) model where each generation lives for two periods and there is no population growth. Agents (investors) start their lives with an endowment W > 0 and have mean-variance utility. They invest their endowment when young and consume when old. Each period, the young investors optimally choose their portfolio from different risky assets acquired from the old generation, all assumed to be in fixed supply.

The author show that investor salience bias can explain excess volatility of asset prices and the resulting fire-sales in periods of financial turmoil. A change in salience – from one component (idiosyncratic) to the other (systemic) – will generate excess volatility. Interestingly, higher risk aversion generally exacerbates the excess volatility of prices. Moreover, the model predicts that if a big systemic shock hits the financial system, due to salience bias the price of systemic assets falls sharply. This relates to the observed fire-sales of assets during the global financial crisis.

The proposed model and results suggest that there may be a scope for intervention in financial markets during turbulences. In terms of ex ante policies the study suggests that investors and regulator should use better risk assessment technologies.

This is the first study constructing a tractable model based on the argument that investor salience may exacerbate the excess volatility of prices during financial downturns. The author relate salience to two types of risk; idiosyncratic and systemic and assume that investors' risk perception is biased towards the type of risk that is currently salient based on prior beliefs or past data. The author show that the diversification fallacy of the precrisis period, where seemingly safe assets were overpriced, can be explained by agents overweighing idiosyncratic risk and ignoring systemic risk.

As a kind of knowledge-intensive team coordinated across physical distance, it is necessary to construct a meta-knowledge driven transactive memory system (TMS) for the knowledge management of distributed agile team (DAT). This study aims to explore the comprehensive antecedents of TMS establishment in DATs and considers how TMS establishment is affected by herding behavior under the artificial intelligence (AI)-related knowledge work environment that emerges with technology penetration.

The data derived from 177 students of 52 DATs in a well-known Chinese business school, which were divided into 26 traditional knowledge work groups and 26 AI-related task groups to conduct a random comparative experiment. The ordinary least squares method was used to analyze the conceptual model and ANOVA was used to examine the differences in herding behavior between the control groups (traditional knowledge work DATs) and treatment groups (DATs engaged in AI-related knowledge work).

The results showed that knowledge diversity, professional knowledge, self-efficacy and social system use had significantly positive effects on the establishment of TMS. Interestingly, the authors also find that herding behavior may promote the process of establishing TMS of the new team, and this effect will be more significant when AI tasks are involved in team knowledge work.

By exploring the comprehensive antecedents of the establishment of TMS, this study provided a theoretical basis for knowledge management of DATs, especially in AI knowledge work teams. From a practical perspective, when the DAT is involved in AI-related knowledge works, managers should appropriately guide the convergence of employees’ behaviors and use the herding effects to accelerate the establishment of TMS, which will improve team knowledge sharing and innovation.

This paper aims to provide a multidisciplinary framework that allows an integrated understanding of reasons of success or failure in equity crowdfunding (ECF), a Fintech digital innovation of the traditional entrepreneurial finance, defining a future research agenda.

A systematic literature review (SLR) has been conducted on 127 documents extracted from two multidisciplinary repositories (Elsevier’s Scopus and Clarivate Analytics Web of Science) for the period between 2015 and early 2022. After a systematized series of inclusion and exclusion criteria, in line with the objectives and conceptual boundaries, a final list of 32 peer-reviewed articles written in English was analyzed by the authors through a meta-synthesis and thematic analysis to identify the key themes and dominant concepts.

Results show that the body of literature is recent and fast growing. The proposed integrative framework of existing research indicates that the outcome of an ECF campaign is related to signals conveyed by entrepreneurs in the form of hard information (firm characteristics, financial information, business characteristics and project description) and soft information (intellectual capital, human capital, social capital and social media network), catalyzed by digital media that facilitate also personal interactions between entrepreneurs and investors. Similarly, external factors (investors and campaign characteristics, with the fundamental role of ECF platform managers in building trust between entrepreneurs and investors) allow for the alleviation of information asymmetries. The present study sheds light on which signal mechanisms are decisive in improving the outcome, taking into consideration various disciplines which follow different but complementary perspectives.

Entrepreneurs should adapt to the transition toward the digital era, exploiting alternative financial instruments and learning effective signaling strategies, within a large variety of skills requested. Platform managers can obtain more focused information on selected entrepreneurial projects more efficiently.

Although it is fast-growing, the field of research is very recent, still fragmented and limited to the perspective/discipline followed. This SLR is, to the best of the authors’ knowledge, the first multidisciplinary and integrative analysis of reasons that motivates success, or failure, of an equity-based crowdfunding campaign. The digital nature of ECF encourages future research to move toward more pioneering and unconventional theories and research methods. Hence, the authors add to the existing literature by proposing future patterns of research based on an integration of highly technological skills and behavioral/psychological approaches.

This study aims to provide an in-depth understanding of investors’ cognition and decision-making process with regard to internet financial products. The objective is to effectively guide users’ rational investments.

First, based on grounded theory, this study develops a tool for measuring users’ perceived value (PV) of internet financial products via in-depth interviews. Then, after comprehensively considering users’ environmental, individual and psychological characteristics, this study proposes a theoretical model of internet financial product investment decisions based on the PV of users. Finally, an empirical study is conducted on 693 valid sample data from e-commerce and online banking financial platforms.

The empirical results suggest that network externalities influence users’ financial behavior by herding (HE) (imitating others and discounting their own information) and PV. PV and HE are key factors in users’ investment decisions with regard to internet financial products. Moreover, users’ self-efficacy (SE) and platform type play moderate roles in the influence mechanism.

The research conclusions provide valuable references for designing financial products and establishing regulatory rules, which will help the internet financial industry to grow soundly and innovatively.

This study uncovers the mediating effect of HE and PV between network externalities and users’ investment intentions in the context of internet financial products. In addition, the moderating effect of users’ SE and platform types is revealed.

This paper aims to contribute to the scientific debate concerning the impact of equity crowdfunding on the performance of crowdfunded firms after campaigning. To this aim, the purpose of this paper is to investigate the relationship between the characteristics of the campaign and the subsequent firm innovativeness.

This study adopts a quantitative research approach to evaluate if the entrepreneurial choices affecting the characteristics of the equity crowdfunding campaigns have an impact on the post-campaign firm innovativeness.

The results of the models show that the campaign characteristics have a direct impact on the firm innovativeness, both in terms of offering and communication and the campaign performance.

This paper presents one of the first studies to investigate the relationship between the choice of campaign characteristics and the post-campaign firm innovativeness. As such, the study contributes to both the literature concerning start-up innovation and the literature about the impact of equity crowdfunding.