The Influences of Live Streaming Affordance in Cross-Border E-Commerce Platforms

Despite the promise of cross-border e-commerce, attracting consumers is still a worldwide challenge. Many cross-border e-commerce platforms have responded to the challenges by embracing innovative tools like live streaming. However, there has been limited understandings of the unique nature of live streaming and its empirical influence. Taking an affordance view of live streaming, this study defines affordance of live streaming as the capacities provided by live streaming and examines how affordance of live streaming affect consumer behavior in the cross-border e-commerce context based on information transparency perspective. Results show that although live streaming does not directly affect consumers’ cross-border purchase intention, it can increase consumers’ purchase intention through increasing perceived information transparency. In addition, affordance of live streaming can further moderate the relationship between different types of information transparency and consumers’ cross-border purchase intention. The findings provide a much-needed contribution to academia and business.

The Influences of Live Streaming Affordance in Cross-border E-commerce Platform

Despite the promise of cross-border e-commerce, attracting consumers is still a worldwide challenge. Many cross-border e-commerce platforms have responded to the challenges by embracing innovative tools like live streaming. However, there has been limited understandings of the unique nature of live streaming and its empirical influence. Taking an affordance view of live streaming, this study defines affordance of live streaming as the capacities provided by live streaming and examines how affordance of live streaming affect consumer behavior in the cross-border e-commerce context based on information transparency perspective. Results show that although live streaming does not directly affect consumers’ cross-border purchase intention, it can increase consumers’ purchase intention through increasing perceived information transparency. In addition, affordance of live streaming can further moderate the relationship between different types of information transparency and consumers’ cross-border purchase intention. The findings provide a much-needed contribution to academia and business.

Deep Neural Network–based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions

Internet-enabled smartphones and ultra-wide displays are transforming a variety of visual apps spanning from on-demand movies and 360° videos to video-conferencing and live streaming. However, robustly delivering visual content under fluctuating networking conditions on devices of diverse capabilities remains an open problem. In recent years, advances in the field of deep learning on tasks such as super-resolution and image enhancement have led to unprecedented performance in generating high-quality images from low-quality ones, a process we refer to as neural enhancement. In this article, we survey state-of-the-art content delivery systems that employ neural enhancement as a key component in achieving both fast response time and high visual quality. We first present the components and architecture of existing content delivery systems, highlighting their challenges and motivating the use of neural enhancement models as a countermeasure. We then cover the deployment challenges of these models and analyze existing systems and their design decisions in efficiently overcoming these technical challenges. Additionally, we underline the key trends and common approaches across systems that target diverse use-cases. Finally, we present promising future directions based on the latest insights from deep learning research to further boost the quality of experience of content delivery systems.

Face Recognition Attendance System

Face recognition systems are used in practically every industry in this digital age. One of the most widely utilized biometrics is face recognition. It can be used for security, authentication, and identity, among other things. Despite its low accuracy relative to iris and fingerprint identification, it is extensively utilized because it is a contactless and non-invasive technique. Face recognition systems can also be used to track attendance in schools, colleges, and companies. Because the existing manual attendance system is time consuming and difficult to maintain, this system intends to create a class attendance system that employs the concept of face recognition. There’s also the possibility of proxy attendance. As a result, the demand for this system grows. Database development, face detection, face recognition, and attendance updating are the four steps of this system. The photos of the kids in class are used to generate the database. Faces are discovered and recognized from the classroom's live streaming footage. At the end of the session, the attendance will be mailed to the appropriate faculty. Keywords: Smart Attendance System, NFC, RFID, OpenCV, NumPy

I give discounts, I share information, I interact with viewers: a predictive analysis on factors enhancing college students' purchase intention in a live-streaming shopping environment

Purpose Grounding the research in the stimulus-organism-resource (S-O-R) framework, this study aims to address the research gap of explaining and predicting the relationship between price discounts, interactivity and professionalism on college students’ purchasing intention in live-streaming shopping. It also attempts to understand if trust plays the role of mediator in the effect of these relationships. Design/methodology/approach This study collected data using a questionnaire protocol adapted and refined from the original scales in existing studies. The partial least squares structural equation modeling was used to analyze data collected from 258 college students in China. Other than assessing the path model’s explanatory power, this study examined the model’s predictive power toward predicting new cases using PLS predict. Findings Results indicated that all three predictors have a positive significant relationship with trust, while only price discounts demonstrate a significant relationship with purchase intention. Simultaneously, the mediation results provide support to the S-O-R framework demonstrating that external factors (professionalism, interactivity and price discounts) can arouse organism (trust), which in return, generate a behavioral outcome (purchase intention). Originality/value This study is the first few studies that focus on college students’ behavioral responses in an online shopping environment. At the same time, this is the first study supplement the explanatory perspective with a predictive focus, which is of particular importance in making sound recommendations on managerial decision-making.

Audience size, moderator activity, gender, and content diversity: Exploring user participation and financial commitment on Twitch.tv

User participation has long been recognized as a cornerstone of thriving online communities. Social live-streaming service (SLSS) communities are built on a subscription-based model and rely on viewers’ participation and financial support. Using the collective effort model and heuristics of social influence, this study examines the influence of streamer and viewer behaviors on viewers’ participation and financial commitment on the SLSS, Twitch.tv. Findings from behavioral data collected over 7 weeks show larger audiences diminish individual participation and financial commitment while moderation may encourage more. Female streamers benefit from increased moderation, earning two to three times more in financial commitment compared to men, who streamed more frequently and for longer durations but attracted much smaller audiences. Viewers’ participation and financial commitment did not differ across streams with more content diversity. Our results demonstrate how group factors influence individual participation and financial commitment in newer subscription-based media.

Twitch Users' Motivations and Practices During Community Mental Health Discussions

Live streaming is a form of media that allows streamers to directly interact with their audience. Previous research has explored mental health, Twitch.tv and live streaming platforms, and users' social motivations behind watching live streams separately. However, few have explored how these all intertwine in conversations involving intimate, self-disclosing topics, such as mental health. Live streams are unique in that they are largely masspersonal in nature; streamers broadcast themselves to mostly unknown viewers, but may choose to interact with them in a personal way. This study aims to understand users' motivations, preferences, and habits behind participating in mental health discussions on live streams. We interviewed 25 Twitch viewers about the streamers they watch, how they interact in mental health discussions, and how they believe streamers should discuss mental health on live streams. Our findings are contextualized in the dynamics in which these discussions occur. Overall, we found that the innate design of the Twitch platform promotes a user-hierarchy in the ecosystem of streamers and their communities, which may affect how mental health is discussed.

Joint Optimization Decision of Online Retailers’ Pricing and Live-Streaming Effort in the Postepidemic Era

The isolation requirements of the coronavirus epidemic and the intuitive display advantages of live-streaming have led to an increasing number of retailers shifting to social live-streaming platforms and e-commerce live-streaming platforms to promote and sell their products in real time. However, the provision of live-streaming services will also incur high live-streaming effort costs. In this paper, we develop two decision models for retailers to sell goods through a single online shop and both online shop and live-streaming room; we also present the optimal decisions of pricing and live-streaming efforts. Furthermore, we identify the profitability conditions for retailers to determine when to provide live-streaming services. In addition, we examine the impact of the provision of live-streaming services on the optimal price and live-streaming effort. We obtain three findings. First, there is a unique optimal decision on the price and live-streaming effort under certain conditions. Second, when the effect coefficient of the live-streaming room reaches a certain threshold, there are enough customers who enter the live-streaming room to watch and buy and it is profitable for retailers to provide live-streaming service. Finally, the optimal price and live-streaming effort increase with the increase in average return loss, the effect coefficient of live-streaming effort, and the extra return rate and decrease with the increase in the proportion of customers who choose to buy in the online shop and the price discount coefficient in the live-streaming room.

HetInf: Social Influence Prediction With Heterogeneous Graph Neural Network

With the continuous enrichment of social network applications, such as TikTok, Weibo, Twitter, and others, social media have become an indispensable part of our lives. Web users can participate in their favorite events or pay attention to people they like. The “heterogeneous” influence between events and users can be effectively modeled, and users’ potential future behaviors can be predicted, so as to facilitate applications such as recommendations and online advertising. For example, a user’s favorite live streaming host (user) recommends certain products (event), can we predict whether the user will buy these products in the future? The majority of studies are based on a homogeneous graph neural network to model the influence between users. However, these studies ignore the impact of events on users in reality. For instance, when users purchase commodities through live streaming channels, in addition to the factors of the host, the commodity is also a key factor that influences the behavior of users. This study designs an influence prediction model based on a heterogeneous neural network HetInf. Specifically, we first constructed the heterogeneous social influence network according to the relationship between event nodes and user nodes, then sampled the user heterogeneous subgraph for each user, extracted the relevant node features, and finally predicted the probability of user behavior through the heterogeneous neural network model. We conducted comprehensive experiments on two large social network datasets. Furthermore, the experimental results show that HetInf is significantly superior to the previous homogeneous neural network methods.