Why Do Consumers Buy Impulsively in Livestreaming Commerce? A Deep Learning-Based Dual-Stage SEM-ANN Analysis

The power of livestreaming commerce to rake in billions of revenues within hours has thrust this nascent commercial model into the global spotlight; that said, despite the prevalence of impulsive buying in livestreaming commerce, the existing knowledge regarding the phenomenon remains relatively scarce. This research seeks to unravel the critical determinants that influence consumers’ impulsive buying in livestreaming. Grounded in the Stimulus-Organism-Response paradigm, a framework is proposed to elucidate the underlying mechanism on how parasocial interaction, social contagion, vicarious experience, scarcity persuasion, and price perception translate into impulsive buying urge and behaviour in livestreaming commerce via the cognitive-affective processing system. A self-administered online questionnaire survey was conducted with 295 respondents. The data collected was validated empirically through a multi-analytical hybrid structural equation modelling-artificial neural network (SEM-ANN) technique. The results reveal that parasocial interaction, vicarious experience, scarcity persuasion, and price perception can drive cognitive and affective reactions, which in turn, induce impulsive buying urge, subject to the boundary condition of impulsive buying tendency. In sum, the findings have drawn some insightful theoretical and practical implications that can facilitate the advancement of livestreaming commerce in the modern business arena

Influence Of Short Video Live Broadcast On Network User Behavior And Sales—From Meta-Analysis, Experimental Analysis To Empirical Analysis

Background: There are more than 1 billion users of short video media in China, and enterprises have long recognized that the influence of short video media has strong marketing achievements, such as short video community and information sharing platform participating in sales. Many enterprises have established active short video media so that they can directly interact with their customers. Among them, the content of webcasting can also stimulate and influence users' participation (like, comment or share) and users' stickiness (collection and attention). Research purposes and methods: In order to explore the underlying logical relationship between "media-content-goods-users-sales", firstly, it is necessary to find out the influence mechanism of short video live broadcast on network users' participation and sales. Therefore, in the first experiment, we conducted a meta-analysis on a large number of researches on webcasting media and short video media, and analyzed their differences along the content differences, background (industry, presentation, life cycle, demand and platform) and characteristics of webcasting media. Secondly, it is necessary for us to find out the differences in the influence of the level of webcasting and the behavior of network users (participation behavior and sticky behavior) on the sales level. Therefore, in Experiment 2, based on the data of 802 live events in Tik Tok, the internal relationship between the degree of webcasting and the degree of commodity sales was analyzed. Thirdly, it is necessary for us to understand the mechanism of the influence of webcasting marketing on consumer decision-making. Therefore, in Experiment 3, the psychological mechanism of webcasting affecting purchase intention was verified by structural equation model, and the adoption intention of webcasting users was determined and verified by three factors-webcasting, participation behavior and sticky behavior. Research results: This paper makes a comprehensive and in-depth analysis and verification through three experiments. The result of Experiment 1 supports some existing viewpoints, for example, webcasting media can promote the sales of new products more effectively, but it highlights some new insights. The webcasting media mainly promotes the sales of goods and hardly affects the stickiness of users. The average citation of webcasting media stickiness is 0.137, and the sales citation is 0.353. In addition, the research results also put forward a better way to adjust the content of webcasting media to meet the communication target. Experiment 2 found that it has a higher degree of webcasting (β1=.715, P < .01; β2=-.090, p<0.01) more effectively produces the degree of commodity sales, and further verifies the potential moderating effects of two kinds of user behaviors: user participation behavior and user stickiness behavior. Experiment 3 verified that webcasting (b = 0.247, t=3.317, p< 0.05) directly and significantly affected the degree of online sales, while the behavior of online users (participation of live users and live fans) adjusted the degree of online sales to a certain extent, which indirectly affected the degree of online goods sales. Research conclusion: With the rapid development of short video media today, enterprises can realize the online sales of goods more efficiently through a powerful short video live media based on media content and media users, and further realize the balanced operation between network users and fan users in a healthy and orderly manner. Keywords: Webcast, Meta-analysis, User behavior, Social media sales, Field experiment DOI: 10.7176/NMMC/104-13 Publication date: August 31st 202

e-Business challenges and directions: important themes from the first ICE-B workshop

A three-day asynchronous, interactive workshop was held at ICE-B’10 in Piraeus, Greece in July of 2010. This event captured conference themes for e-Business challenges and directions across four subject areas: a) e-Business applications and models, b) enterprise engineering, c) mobility, d) business collaboration and e-Services, and e) technology platforms. Quality Function Deployment (QFD) methods were used to gather, organize and evaluate themes and their ratings. This paper summarizes the most important themes rated by participants: a) Since technology is becoming more economic and social in nature, more agile and context-based application develop methods are needed. b) Enterprise engineering approaches are needed to support the design of systems that can evolve with changing stakeholder needs. c) The digital native groundswell requires changes to business models, operations, and systems to support Prosumers. d) Intelligence and interoperability are needed to address Prosumer activity and their highly customized product purchases. e) Technology platforms must rapidly and correctly adapt, provide widespread offerings and scale appropriately, in the context of changing situational contexts

NAIS: Neural Attentive Item Similarity Model for Recommendation

Item-to-item collaborative filtering (aka. item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency in real-time personalization. It builds a user's profile as her historically interacted items, recommending new items that are similar to the user's profile. As such, the key to an item-based CF method is in the estimation of item similarities. Early approaches use statistical measures such as cosine similarity and Pearson coefficient to estimate item similarities, which are less accurate since they lack tailored optimization for the recommendation task. In recent years, several works attempt to learn item similarities from data, by expressing the similarity as an underlying model and estimating model parameters by optimizing a recommendation-aware objective function. While extensive efforts have been made to use shallow linear models for learning item similarities, there has been relatively less work exploring nonlinear neural network models for item-based CF. In this work, we propose a neural network model named Neural Attentive Item Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an attention network, which is capable of distinguishing which historical items in a user profile are more important for a prediction. Compared to the state-of-the-art item-based CF method Factored Item Similarity Model (FISM), our NAIS has stronger representation power with only a few additional parameters brought by the attention network. Extensive experiments on two public benchmarks demonstrate the effectiveness of NAIS. This work is the first attempt that designs neural network models for item-based CF, opening up new research possibilities for future developments of neural recommender systems

Addressing the new generation of spam (Spam 2.0) through Web usage models

New Internet collaborative media introduce new ways of communicating that are not immune to abuse. A fake eye-catching profile in social networking websites, a promotional review, a response to a thread in online forums with unsolicited content or a manipulated Wiki page, are examples of new the generation of spam on the web, referred to as Web 2.0 Spam or Spam 2.0. Spam 2.0 is defined as the propagation of unsolicited, anonymous, mass content to infiltrate legitimate Web 2.0 applications.The current literature does not address Spam 2.0 in depth and the outcome of efforts to date are inadequate. The aim of this research is to formalise a definition for Spam 2.0 and provide Spam 2.0 filtering solutions. Early-detection, extendibility, robustness and adaptability are key factors in the design of the proposed method.This dissertation provides a comprehensive survey of the state-of-the-art web spam and Spam 2.0 filtering methods to highlight the unresolved issues and open problems, while at the same time effectively capturing the knowledge in the domain of spam filtering.This dissertation proposes three solutions in the area of Spam 2.0 filtering including: (1) characterising and profiling Spam 2.0, (2) Early-Detection based Spam 2.0 Filtering (EDSF) approach, and (3) On-the-Fly Spam 2.0 Filtering (OFSF) approach. All the proposed solutions are tested against real-world datasets and their performance is compared with that of existing Spam 2.0 filtering methods.This work has coined the term ‘Spam 2.0’, provided insight into the nature of Spam 2.0, and proposed filtering mechanisms to address this new and rapidly evolving problem

Automated Deployment of an End-to-End Pipeline on Amazon Web Services for Real-Time Visual Inspection using Fast Streaming High-Definition Images

This thesis investigates various degrees of freedom and deployment challenges of building an end-to-end intelligent visual inspection system for use in automotive manufacturing. Current methods of fault detection in automotive assembly are highly manual and labor intensive, and thus prone to errors. An automated process can potentially be fast enough to operate within the real-time constraints of the assembly line and can reduce errors. In automotive manufacturing, components of the end-to-end pipeline include capturing a large set of high definition images from a camera setup at the assembly location, transferring and storing the images as needed, executing object detection within a given time frame before the next car arrives in the assembly line, and notifying a human operator when a fault is detected. As inference of object detection models are typically very computing- and memory-intensive, meeting the time, memory and resource constraints requires careful consideration of the choice of object detection model and model parameters, along with adequate hardware and environmental support. Some automotive manufacturing plants lack floor space to set up the entire pipeline on an edge platform. Thus, we have developed a template for Amazon Web Services (AWS) in Python using the BOTO3 libraries that can deploy the entire end-to-end scalable infrastructure in any region in AWS. In this thesis, we design, develop, and experimentally evaluate the performance of system components, including the throughput and latency to upload high definition images to an AWS cloud server, the time required by AWS components in the pipeline, and the tradeoffs of inference time, memory and accuracy for twenty-four popular object detection models on four hardware platforms