The Effects Of Consumer Perceived Value On Purchase Intention In E-Commerce Platform: A Time-Limited Promotion Perspective

In order to stimulate consumption, most e-commercial giants of China conduct sales promotions centered on “price war”, thus impeding the healthy development of e-commercial enterprises to some degree. Under this condition, it’s every e-commercial enterprise’s top priority to understand the perceived dimension of the central value of the customers’ purchase intention and thus reposition their marketing direction. Therefore, centered on the customers’ purchase intention, this paper structures a four-dimension (i.e., price, functional, emotional and social) model of the perceived value; and, in the meantime, considering the time-limit characteristic of the promotion, as a moderator variable, time pressure is used in the model to conduct the empirical analysis. According to the study, among the dimensions of the perceived value, only the price value and emotional value have a significantly positive influence on the customers’ purchase intention, and the influence by emotional value is bigger than the other. In different time pressure, the influence on purchase intention by perceived value makes significant difference. When there is difference in product category, the time pressure adjustment makes big difference too

To Study Effects of Using Human Presenter in Product Image: Applying an Eye-tracker VS Facial Expression Translation

Eye tracking is the process of measuring either the point of gaze or the motion of an eye relative to the head. An eye tracker is a device for measuring eye positions and eye movement. Eye trackers are used in research on the visual system, in psychology, in psycholinguistics, marketing, as an input device for human-computer interaction, and in product design. Previous study applies an eye-tracker to investigate effects of using human presenter in product images and conclude that eye-tracker data can be used for eye-gaze data collection and analyzed for further statistical conclusion [8]. The result indicates that product image with positive emotion female presenter gets the highest fixation duration, however, not significantly higher than fixation duration of other types of product images. However, Eye tracking by professional eye-tracker is not an affordable research method for most researches. Facial expression translation is a new function comes from “Youdao translate officer” which can be downloaded from apple APP store for free; It can indicate human facial expression in eight dimensions (i.e., happiness, angry, fear, contempt, disgust, calm, surprise, sad) with values. We are proposed to use this free technical to investigate effects of using human present in product images and compare the results with studies applies eye-tracker previously. A fresh accepted research method could be discovered by this study, and give an optional research mothed in relative field

Smart Manufacturing Capability Maturity Model: Connotation, Feature And Trend

In March 2015, the Chinese government unveiled InternetPlus, an action plan expected to push forward the Chinese economy. The plan aims to integrate mobile Internet, cloud computing, big data, and the Internet of Things (IoT) with traditional industries to promote economic restructuring, improve people’s livelihoods, and even transform government and enterprises functions. However for the enterprises, how to evaluate the capability is still an unsolved issue. In this study, considering capability maturity theory and model existed, we summarized the concepts of smart manufacturing and relative research field, combined with the development trend of smart manufacturing and characteristics of the enterprise\u27s competition, a smart manufacturing capability maturity initial model with five levels and seven dimensions was defined. With this model, the connotation of smart manufacturing capability was unveiled and the model also provides reference for enterprises to assess and improve smart manufacturing capability

Research Of E-Commerce Enterprises Capability Maturity Theory And Initial Model Construction

With the constant development and evolution of “Internet+” strategic thinking, the electronic commerce enterprises have obtained the unprecedented growth, but also faced with great survival pressure and challenges. This research is based on the review and combing the historical development of capability maturity and in the light of the characteristics of e-commerce enterprises building a capability maturity model which contains five levels: the initial level, the repeatable level, the standard level, the managed level and the optimal level and five dimensions: strategy, organization, process, personnel and technical support. The capability maturity initial model of e-commerce enterprises establishes basic demand are obtained earnings, controlling risk and optimizing resources and with different stages of target the capabilities the electronic commerce enterprises should owned, at last this model generalizes a clear direction and standard for the e-commerce enterprises management

Incentives, Positive Emotions and SWOM Intention: Moderating Roles of Allocation Type and Emotion Regulation

With the increasing popularity of social networking sites (SNS), companies are adopting monetary incentives to generate eWOM on SNS (SWOM). Drawing on emotion and equity theories, this study explores effects of perceived magnitude of monetary incentives and emotions on consumer SWOM intention. In addition, this study investigates the moderating effects of allocation types of rewards (positive inequity and negative inequity) and emotion regulation (reappraisal and suppression emotion regulation) on these relationships. An online situational experiment was conducted and yielded valid responses from 193 WeChat users in China. With the preliminary data, we tested the direct, mediation, and moderation effects using SmartPLS 3.0. The empirical results show that (1) perceived monetary incentives have a positive effect on SWOM intention; (2) positive emotions of senders mediates the relationship between incentives and SWOM intention; (3) negative-inequity incentives negatively moderates the relationship between incentives and positive emotion, while the moderating effect of positive-inequity incentives is insignificant on the relationship between incentives and positive emotion; (4) reappraisal emotion regulation strengthens the effect of positive emotion on SWOM intention, whereas the moderating effect of suppression emotion regulation between positive emotion and SWOM intention is not statistically significant

E-Service Quality Model of B2C Online Shopping Platform Based on User’S Perspective

In recent year, B2C has gradually become the main driving force of the network shopping market development, many B2C online shopping platform weakens the differences in product and price, which intensify the competition on e-service quality among B2C online shopping platform. Therefore, this paper discusses the composition of electronic service quality model of B2C online shopping platform from the user’s perspective, combining with the analysis of literature and the trading process. And the e-service quality model, which has carried on the empirical test, determined the evaluation index of e-service quality evaluation for B2C e-commerce platform. The results of this study show that e-service quality evaluation model of B2C online shopping platform is composed of seven dimensions and twenty-nine measurement items

Adversarial Directed Graph Embedding

Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector. However, these methods learn the source and target vectors separately. For the node with very low indegree or outdegree, the corresponding target vector or source vector cannot be effectively learned. In this paper, we propose a novel Directed Graph embedding framework based on Generative Adversarial Network, called DGGAN. The main idea is to use adversarial mechanisms to deploy a discriminator and two generators that jointly learn each node's source and target vectors. For a given node, the two generators are trained to generate its fake target and source neighbor nodes from the same underlying distribution, and the discriminator aims to distinguish whether a neighbor node is real or fake. The two generators are formulated into a unified framework and could mutually reinforce each other to learn more robust source and target vectors. Extensive experiments show that DGGAN consistently and significantly outperforms existing state-of-the-art methods across multiple graph mining tasks on directed graphs.Comment: 8 pages, 5 figure

KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoning

Generative commonsense reasoning which aims to empower machines to generate sentences with the capacity of reasoning over a set of concepts is a critical bottleneck for text generation. Even the state-of-the-art pre-trained language generation models struggle at this task and often produce implausible and anomalous sentences. One reason is that they rarely consider incorporating the knowledge graph which can provide rich relational information among the commonsense concepts. To promote the ability of commonsense reasoning for text generation, we propose a novel knowledge graph augmented pre-trained language generation model KG-BART, which encompasses the complex relations of concepts through the knowledge graph and produces more logical and natural sentences as output. Moreover, KG-BART can leverage the graph attention to aggregate the rich concept semantics that enhances the model generalization on unseen concept sets. Experiments on benchmark CommonGen dataset verify the effectiveness of our proposed approach by comparing with several strong pre-trained language generation models, particularly KG-BART outperforms BART by 5.80, 4.60, in terms of BLEU-3, 4. Moreover, we also show that the generated context by our model can work as background scenarios to benefit downstream commonsense QA tasks.Comment: 10 pages, 7 figures, Appear in AAAI 202