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This study aims to test the market anomaly of herding behavior driven by the response to supply chain disruptions in extreme market conditions such as those observed during COVID-19. The authors empirically test the response of the capital market participants for B2B firms, resulting in herding behavior.

Using the event study approach based on the market model, the authors test the impact of supply chain disruptions and resultant herding behavior across six sectors and among different B2B firms. The authors used cumulative average abnormal returns (CAAR) and cross-sectional absolute deviation (CSAD) to examine the significance of herding behavior across sectors.

The event study results show a significant effect of COVID-19 due to supply chain disruptions across specific sectors. Herding was detected across the automotive and pharmaceutical sectors. The authors also provide evidence of sector-specific disruption impact and herding behavior based on the black swan event and social learning theory.

The authors examine the impact of COVID-19 on herding in the stock market of an emerging economy due to extreme market conditions. This is one of the first studies analyzing lockdown-driven supply chain disruptions and subsequent sector-specific herding behavior. Investors and regulators should take sector-specific responses that are sophisticated during extreme market conditions, such as a pandemic, and update their responses as the situation unfolds.

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.

The purpose of this study is to understand the investment decision-making of real estate investors in housing, highlighting the interplay between rational and irrational factors. In this study, investment satisfaction was a mediator, while reinvestment intention was the dependent variable.

A quantitative, cross-sectional and descriptive research design was used, gathering data from a sample of 550 residential real estate investors using a multi-stage stratified sampling technique. The partial least squares structural equation modelling disjoint two-stage approach was used for data analysis. This methodological approach allowed for an in-depth examination of the relationship between rational factors such as location, profitability, financial viability, environmental considerations and legal aspects alongside irrational factors including various biases like overconfidence, availability, anchoring, representative and information cascade.

This study strongly supports the adaptive market hypothesis, showing that residential real estate investor behaviour is dynamic, combining rational and irrational elements influenced by evolutionary psychology. This challenges traditional views of investment decision-making. It also establishes that behavioural biases, key to adapting to market changes, are crucial in shaping residential property market efficiency. Essentially, the study uncovers an evolving real estate investment landscape driven by evolutionary behavioural patterns.

This research redefines rationality in behavioural finance by illustrating psychological biases as adaptive tools within the residential property market, urging a holistic integration of these insights into real estate investment theories.

The study reshapes property valuation models by blending economic and psychological perspectives, enhancing investor understanding and market efficiency. These interdisciplinary insights offer a blueprint for improved regulatory policies, investor education and targeted real estate marketing, fundamentally transforming the sector’s dynamics.

Unlike previous studies, the research uniquely integrates human cognitive behaviour theories from psychology and business studies, specifically in the context of residential property investment. This interdisciplinary approach offers a more nuanced understanding of investor behaviour.

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.

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.

The study examines the potential moderating effects of repeating purchase cues and product knowledge on the relationship between the varying consistency of the review set and causal attribution. This study also investigates how causal attribution correlates with the perceived misleadingness of the review set.

A scenario-based experiment was conducted with 170 participants to explore the relationship between the consistency of the review set and causal attribution and how repeating purchase cues and product knowledge moderates this relationship.

Findings suggest that inconsistent review sets lead to more product (vs reviewer) attribution than consistent review sets. The repeating purchase cues mitigate the negative relationship between the consistency of the review set and product attribution, whereas product knowledge mitigates the positive relationship between the consistency of the review set and reviewer attribution. Furthermore, the results indicate that high product attribution and low reviewer attribution are associated with low perceived misleadingness.

This study is novel because it examines the moderating effects of repeating purchase cues and product knowledge on the relationship between the consistency of the review set and causal attribution. It adds to the literature by shedding light on the causal attribution process underlying the formation of perceived misleadingness of online reviews. The findings of this study provide valuable insights for managers on how to enhance the positive effects of consistent review sets and mitigate the negative effects of inconsistent review sets.

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.

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 investigate the role of informational and relational mechanisms on equity crowdfunding investors' conformity behaviors by focusing on a relational culture of China.

The data of 108 financing projects and 7,688 investment records from a union of Chinese equity crowdfunding platforms are gathered. Lead investors' response to a campaign and follow-investors’ former links explain investors' conformity by social network analysis (SNA) and ordinary least squares (OLS) analysis.

The results show that informational and relational influences drive conformity in Chinese equity crowdfunding. Moreover, the informational influence weakens in a highly centralized structure of linked investors.

The results add new knowledge to follow-investors’ conformity behaviors in equity crowdfunding and enrich the literature on conformity theory by finding the contextual effect of information-influenced conformity and the adaption of conformity theory to cultural uniqueness. Besides, this preliminary work also suggests opportunities for future research.

The paper inspires new consideration on a strategical use of follow-investors’ conformity mentality to promote successfully financing and reminds platform managers to be alert to the interference of small groups formed based on informal relationships to the normal financing order.

This is the first study that discovers the non-informational influence and the limited influence of information on equity crowdfunding conformity through contextual concerns.

The non-fungible token (NFT) market had undergone dramatic growth and a sudden decline during 2021–2022. The market experienced a surge in prices in late 2021 and early 2022, with NFTs being sold at inflated prices. Despite this, by April 2022, the market underwent a correction, and the prices of NFTs returned to more reasonable levels. This can be a result of imitating the actions or judgments of a larger group, which is not systematically proven yet. Therefore, this study systematically investigates the applicability of herding behavior in the NFT market.

This research employs cross-sectional absolute deviation (CSAD) of returns and ordinary least squares (OLS) to test herding behavior with moving time windows of 10, 20 and 30 days based on the sales data collected from public interface of OpenSea between July 1, 2021 and June 30, 2022. Additionally, NFT-related keyword usage analysis is done for the detected herding periods.

As per the results of the data analyzed, herding behavior was evidenced using 10-, 20- and 30-day time windows from July 1, 2021 to June 30, 2022because of media movement. The findings revealed that this behavior was present and aligned with the overall behavior of the market.

This study introduces CSAD to examine herding behavior patterns within the NFT market. Complementing this method, keyword count-based analysis is employed to identify the underlying causes of herding behavior. Through this comprehensive approach, this study not only uncovers the roots of herding behavior but also offers an assessment of the time windows during which it occurs, considering the plausible socioeconomic contexts that influence these trends.