The Impact of Impulse Buying and Network Platforms on Consumer Purchasing Behaviour: A Case Study of a Technical Product

This paper investigates the effects of impulse buying and network platforms on the online purchasing behaviour for experience goods using a 2 (low impulse buying tendency vs. high impulse buying tendency) × 2 (mobile platform vs. computer platform) between-subjects factorial experiment.A technical product is a typical type of experience good. To increase the external validity of the conclusions, this study takes software as a technical product and employs an online scenario-simulated experiment to collect data. The results indicate that low impulse consumers tend to spend less money and conduct more stringent budget control when purchasing a technical product. However, no significant difference existed between high and low impulse consumers regarding the purchasing decision. Furthermore, consumers who shop on a mobile platform tend to spend more money, conduct looser budget control and make more careful purchasing decisions. In addition, a remarkable effect also exists between impulse buying and network platforms regarding the average viewing time of the single page and the commodity details page

User Needs Mining Based on Topic Analysis of Online Reviews

The purpose of this paper is to aggregate the topic information of online review text and clarify the user needs. We conducted the study on online reviews of women’s clothing store of Taobao.com with semantic analysis and text mining. Online reviews were collected by means of web crawler. Using Chinese word segmentation tool and data analysis tool, the word frequency statistics was realized. The statistical software was used for the clustering analysis and multidimensional scaling analysis of high frequency keywords. The results show that the content of online reviews mainly includes four topics: basic features of products, additional features of products, user experience and product display. It reveals the potential user needs of women’s clothing store of Taobao.com, which cannot only help consumers to make rational decisions, but also provide guidance to merchants and manufacturers

Impacts of Psychological Distance-based Sales Promotion on Online Purchasing Behaviors under Different Involvement

Via consumer surveys after the “Double 11” promotion, we studied consumers’ consumption behavior and its influencing factors (temporal distance, social distance, product types and purchase decision involvement) based on the CLT and involvement theory with logistic regression modeling. The results show that the effect of temporal distance on purchasing decisions is increasing in high-involvement products and decreasing in low-involvement products, while social distance has a negative impact on purchasing decisions in both high and low-involvement products. Consumers’ purchase decision involvement is reinforced by temporal distance, while is no relevant to social distance. Specifically, when consumers are temporally distant from knowing the promotion issues, their purchase decision involvement tends to be higher and cost more in online promotion. Results provide practical marketing implications and help to enrich marketing theory

Observation of forbidden phonons and dark excitons by resonance Raman scattering in few-layer WS2_2

The optical properties of the two-dimensional (2D) crystals are dominated by tightly bound electron-hole pairs (excitons) and lattice vibration modes (phonons). The exciton-phonon interaction is fundamentally important to understand the optical properties of 2D materials and thus help develop emerging 2D crystal based optoelectronic devices. Here, we presented the excitonic resonant Raman scattering (RRS) spectra of few-layer WS$_2$ excited by 11 lasers lines covered all of A, B and C exciton transition energies at different sample temperatures from 4 to 300 K. As a result, we are not only able to probe the forbidden phonon modes unobserved in ordinary Raman scattering, but also can determine the bright and dark state fine structures of 1s A exciton. In particular, we also observed the quantum interference between low-energy discrete phonon and exciton continuum under resonant excitation. Our works pave a way to understand the exciton-phonon coupling and many-body effects in 2D materials.Comment: 14 pages, 11 figure

Mobile SNS Addiction and User Continuance: An Empirical Investigation of WeChat

Mobile social networking sites (SNS) have become one of the most popular means of online social interactions. However, that excessive and uncontrolled SNS use may have negative consequences. Recent statistics show that use of mobile SNS can be addictive for some individuals, which can lead to some significant behavioural or psychological problems. This study has extended the dual-process continuance model by examining the impact of users’ levels of addiction as important factors that influence their continuance intention to use WeChat, as one of the most popular mobile SNS in China. Structural equation modelling was used to validate the proposed models and assumptions. Based on survey data from 495 valid responses of WeChat users, we found that the level of mobile SNS addiction augments users’ perceptions of perceived usefulness, perceived enjoyment, trusting beliefs and habit, whereas it has no impact on users’ satisfaction and loyalty. It can serve as a platform for future research on technology addiction, and for better understanding of addiction-driven system overuse behaviours and their effects on users. The results suggest that the increase in usage time and frequency of use does not necessarily mean higher satisfaction and loyalty to the mobile SNS, perhaps certain addictive symptoms occurring

Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing

The current neuron reconstruction pipeline for electron microscopy (EM) data usually includes automatic image segmentation followed by extensive human expert proofreading. In this work, we aim to reduce human workload by predicting connectivity between over-segmented neuron pieces, taking both microscopy image and 3D morphology features into account, similar to human proofreading workflow. To this end, we first construct a dataset, named FlyTracing, that contains millions of pairwise connections of segments expanding the whole fly brain, which is three orders of magnitude larger than existing datasets for neuron segment connection. To learn sophisticated biological imaging features from the connectivity annotations, we propose a novel connectivity-aware contrastive learning method to generate dense volumetric EM image embedding. The learned embeddings can be easily incorporated with any point or voxel-based morphological representations for automatic neuron tracing. Extensive comparisons of different combination schemes of image and morphological representation in identifying split errors across the whole fly brain demonstrate the superiority of the proposed approach, especially for the locations that contain severe imaging artifacts, such as section missing and misalignment. The dataset and code are available at https://github.com/Levishery/Flywire-Neuron-Tracing.Comment: 9 pages, 6 figures, AAAI 2024 accepte