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Despite the burgeoning popularity of virtual gifting in live streaming, research lacks an in-depth understanding of the drivers behind this behavior. Using para-social relationship (PSR), this study aims to capture viewers’ lively social feelings toward the streamer as the key factor leading to the purchase behavior of virtual gifts. It also aims to establish a theoretical link between PSR and viewers’ holistic experience in live streaming as captured by cognitive absorption and aims to investigates the role of technological features (i.e. viewer–streamer and viewer–viewer interactivity, streamer-level and viewer-level deep profiling and design aesthetics) in shaping viewers’ experience.

Based on 433 survey responses, this study employs a combination of structural equation modeling and neural networks to offer valuable insights into the relationships between the technological environment, viewer experience and viewer behavior.

Our results highlight the salience of PSR in promoting the purchase of virtual gifts through cognitive absorption and the importance of the technological environment in eliciting the viewer experience. This study sheds light on the development of PSR in a technological environment and its relationship with cognitive absorption.

By applying PSR to conceptualize viewers’ perceived connection with the streamer, this study extends the research on purchase behavior in the non-shopping context by providing an enlightened understanding of virtual gift purchase behavior in live streaming. Moreover, by theoretically linking PSR with cognitive absorption, virtual gift purchase and technological features of live streaming, it enriches the theory of PSR and bridges the gap between the design practice of supporting the IT infrastructure of live streaming and research.

Charitable crowdfunding features are embedded in social media platforms to encourage pro-social behaviors. Although such new practice allows practitioners to leverage the power of a highly connected crowd, accomplishing the fundraising goal is still a challenge. This study seeks to understand what drives the donation intention in charitable crowdfunding features on social media platforms by examining the roles of social experience, empathy and personal impulsiveness.

A survey questionnaire was distributed to social media users in China to collect data. A total of 206 valid responses were analyzed using structural equation modeling to test the proposed hypotheses.

The results showed that empathy mediates interaction with the fundraiser and perceived proximity with the donatee on a user's donation intention. We also found that social influence on social media platforms positively influences empathy and donation intention. In addition, personal impulsiveness was found to moderate the relationship between empathy and donation intention.

This study contributes to existing literature and practices. It identifies three dimensions of social experience and examines their effects on donation intention, providing insights into the charitable crowdfunding features on social media. Moreover, this study extends the understanding to empathy by delineating its mediating role in the relationship between social experience and donation intention and examining how personal impulsiveness moderates the effect of empathy on donation intention. Furthermore, this study provides valuable insights for practitioners to craft strategies to stimulate pro-social behaviors and increase donations.

The purpose of this paper is to investigate what factors can affect people’s continuous watching and consumption intentions in live streaming.

This research conducted a mixed-methods study. The semi-structured interview was deployed to develop a research model and a live streaming typology. A survey was then used for quantitative assessment of the research model. Survey data were analyzed using partial least squares-structural equation modeling.

The results suggest that sex and humor appeals, social status display and interactivity play considerable roles in the viewer’s behavioral intentions in live streaming and their effects vary across different live streaming types.

This research is conducted in the Chinese context. Future research can test the research model in other cultural contexts. This study can also be extended by incorporating the roles of viewer gender and price sensitivity in the future.

This study provides managerial insights into how live streaming platforms and streamers can improve their popularity and profitability.

The paper introduces a novel form of social media and a new business model. It illustrates what will affect people’s behavioral intentions in such a new context.

The purpose of this paper is to investigate the effects of trust in service and structural assurance on the continuance intention of FinTech services, and the roles of technical factors (i.e. situational normality and system quality) and social factors (i.e. herding and subjective norm) in developing trust in service and structural assurance. YuEbao is selected as the subject as it is a representative example of FinTech services in China.

A survey questionnaire was deployed and a ten-point sliding scale with two-decimal points was applied to improve the accuracy of the questionnaire. Partial least squares structural equation modeling was used to analyze the data.

Trust in service and structural assurance can encourage continuance intention of FinTech service. System quality, situational normality and subjective norm can boost the development of trust in service. Both herding and subjective norm can affect structural assurance significantly.

The study highlights the important roles played by technical factors (i.e. situational normality and system quality) and social factors (i.e. herding and subjective norm) in developing the two levels of trust (i.e. trust in service and structural assurance). It also validates the influences of trust in service and structural assurance on encouraging customers’ continuance intention in the novel context of FinTech.

The findings of this study can be used by practitioners to encourage customers to continue using their FinTech services. To encourage continuance, service providers can improve the quality of their system, design the system to be aligned with customers’ using habits and show customers that their close friends are also using the service.

This study adds to the existing body of trust literature by investigating the direct effects of trust in service and structure assurance on continuance intention and how these two levels of trust are developed from technical and social aspects. It generates interesting insights into customers’ continuance behavior of FinTech services.

Swift guanxi and trust can influence consumers’ decision making in social commerce. What factors will influence the formation of swift guanxi and trust between buyers and sellers in social commerce has not been clearly investigated. The purpose of this paper is to identify antecedents and impacts of swift guanxi and trust in social commerce.

An online questionnaire was used to collect the data, and Partial Least Squares Structural Equation Modeling was employed for data analysis. Social support and presence are introduced as the antecedents for swift guanxi and trust, leading to the repurchase intention (RI) and social sharing intention of customers.

The results indicate that social support and presence can influence swift guanxi and trust. Social support and presence are positively associated with swift guanxi and trust which further lead to RI and social sharing intention.

The findings can be used to guide sellers in social commerce platforms to improve their services and make good use of platform features to improve customers’ perception of presence. To attract new customers and retain old customers, sellers should also build swift guanxi and trust through the recommendation and experience sharing of previous buyers on social media.

This study combines social support theory and presence theory to investigate the factors that influence customers’ purchase decision and social sharing intention in the context of social commerce in China. The integration of social support theory and presence theory explains both the social and technical factors that influence swift guanxi and trust in social commerce.

The purpose of this paper is to identify antecedents of e-finance continuance intention with Alibaba’s Yu’E Bao as an example.

An online questionnaire was used to collect the data (n=293), and partial least squares structural equation modeling was employed for data analysis. Four e-finance features (perceived reputation, website quality, e-finance familiarity and situational normality) are introduced with trust acting as a moderator between the users’ satisfaction and continuance intention to use an e-finance platform.

The results find that website quality, familiarity and situational normality can influence perceived ease of use (PEOU) and perceived usefulness (PU). PEOU and PU, together with reputation, are positively associated with confirmation which further leads to satisfaction. The positive effects that satisfaction and trust have on e-finance continuance intention are confirmed, and trust is found to be a significant moderator on the relationship between satisfaction and continuance intention.

The findings can be used to guide e-finance providers to improve their platform design and services to retain users.

This study combines the theory of trust, Technology Acceptance Model and Expectations Confirmation Theory to investigate the factors that influence the continuance intention in the context of e-finance in China.

Retaining customers is very important for the survival of e-commerce sellers. The purpose of this paper is to investigate the roles of computer-mediated communication (CMC) tools, interactivity, trust and perceived effectiveness of e-commerce institutional mechanisms (PEEIM) in influencing customer’s repurchase intention in the Chinese online e-commerce marketplace.

The research model is empirically tested using survey data analyzed with partial least squares structural equation modeling.

This study confirms the positive relation between customer satisfaction and trust in the seller, which further contributes to repurchase intention. Results also support the positive influences of effective use of an instant messenger and feedback system on customer perceived interactivity, which helps enhance trust in the seller. PEEIM demonstrates interesting results regarding its moderating effects.

In the future, researchers can extend the study to other e-commerce platforms and take trust transfer effects and product categories into consideration.

This study highlights the importance to manage trust, PEEIM, interactivity and CMC tools in e-commerce platforms, assisting practitioners to develop appropriate business strategies and processes to retain customers.

This study extends previous investigations by integrating trust and PEEIM with interactivity and testing the model in the context of the Chinese online marketplace.

Human–robot collaboration (HRC) is an emerging research field for the construction industry along with construction robot adoption, but its implementation remains limited in construction sites. This paper aims to identify critical risk factors and their interactions of HRC implementation during engineering project construction.

Literature research, expert interviews, a questionnaire survey and a social network analysis (SNA) method were used. First, literature research and expert interviews were employed to identify risk factors of HRC implementation and preliminarily understand factor interactions. Second, a questionnaire survey was conducted to determine the degree of interactions between risk factors. Third, based on the data collected from the questionnaire survey, SNA metrics were used to find critical risk factors and critical interactions.

The critical risk factors consist of robot technology reliability, robot-perceived level, conflict between designers and users of construction robots, organisational culture, organisational strength, project cost requirements, changeability of project construction, project quality requirements and project safety requirements. The interactions between risk factors are strong and complex. Robot technology risk factors were relatively fundamental risk factors, and project risk factors had a direct influence on the risk of HRC implementation. The implementation cost of HRC was not identified as a critical risk factor. Individual risk factors could be mitigated by improving technical and organisational factors.

This paper contributes to the body of knowledge in the field of both HRC behaviours and its risk management in construction project management. Identifying the critical risk factors and their interactions of HRC implementation in the construction industry and introducing social network theory to the research on critical risk factors are the innovations of this paper. The findings and proposed suggestions could help construction professionals to better understand the HRC risk factors and to manage the risk of HRC implementation more effectively.

The purpose of this paper is to investigate if online reviews (e.g. valence and volume), online promotional strategies (e.g. free delivery and discounts) and sentiments from user reviews can help predict product sales.

The authors designed a big data architecture and deployed Node.js agents for scraping the Amazon.com pages using asynchronous input/output calls. The completed web crawling and scraping data sets were then preprocessed for sentimental and neural network analysis. The neural network was employed to examine which variables in the study are important predictors of product sales.

This study found that although online reviews, online promotional strategies and online sentiments can all predict product sales, some variables are more important predictors than others. The authors found that the interplay effects of these variables become more important variables than the individual variables themselves. For example, online volume interactions with sentiments and discounts are more important than the individual predictors of discounts, sentiments or online volume.

This study designed big data architecture, in combination with sentimental and neural network analysis that can facilitate future business research for predicting product sales in an online environment. This study also employed a predictive analytic approach (e.g. neural network) to examine the variables, and this approach is useful for future data analysis in a big data environment where prediction can have more practical implications than significance testing. This study also examined the interplay between online reviews, sentiments and promotional strategies, which up to now have mostly been examined individually in previous studies.