Investigating Factors Affecting Electronic Word-Of-Mouth In The Open Market Context: A Mixed Methods Approach

Electronic Word-of-Mouth (eWOM) has been identified as one of key factors affecting online sales. There has been, however, lack of understanding about the factors leading to eWOM in the open market context. As many Internet vendors have adopted the open market business, it is essential to understand the factors leading to eWOM for the success of open market business. This study investigates factors affecting eWOM in the open market context based on a sequential combination of qualitative and quantitative research methods. The exploratory findings in the qualitative study become the basis for the quantitative study, survey research. The findings from the mixed methods explain the significance of three new factors (information sharing desire, self-presentation desire, and open market reward) and two other factors (open market satisfaction and open market loyalty) affecting eWOM directly and indirectly. This study contributes to research by adding to the broader literature on eWOM. The findings also can inform open market providers on how to promote and manage eWOM for their online business success

FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural Networks

Personalized federated learning is aimed at allowing numerous clients to train personalized models while participating in collaborative training in a communication-efficient manner without exchanging private data. However, many personalized federated learning algorithms assume that clients have the same neural network architecture, and those for heterogeneous models remain understudied. In this study, we propose a novel personalized federated learning method called federated classifier averaging (FedClassAvg). Deep neural networks for supervised learning tasks consist of feature extractor and classifier layers. FedClassAvg aggregates classifier weights as an agreement on decision boundaries on feature spaces so that clients with not independently and identically distributed (non-iid) data can learn about scarce labels. In addition, local feature representation learning is applied to stabilize the decision boundaries and improve the local feature extraction capabilities for clients. While the existing methods require the collection of auxiliary data or model weights to generate a counterpart, FedClassAvg only requires clients to communicate with a couple of fully connected layers, which is highly communication-efficient. Moreover, FedClassAvg does not require extra optimization problems such as knowledge transfer, which requires intensive computation overhead. We evaluated FedClassAvg through extensive experiments and demonstrated it outperforms the current state-of-the-art algorithms on heterogeneous personalized federated learning tasks.Comment: Accepted to ICPP 2022. Code: https://github.com/hukla/fedclassav

The Effects of Visual Complexity in a Fashion Store Environment on Consumer Emotions and Approach Behavior

This study investigated the consumers\u27 affective response to the visual complexity of a fashion retail environment both self-report and psychophysiological measures. We developed two types of virtual fashion stores with different levels of visual complexity, which were manipulated using decorative patterns and type of layout (grid vs. free form). The results showed that the fashion store\u27s visual complexity was related to increases in arousal, but visual complexity has no main effect on pleasure. However, the moderating role of fashion involvement suggests that consumers with high fashion involvement exhibited more pleasure in a visually complex store than in a simple store. Additional analysis confirmed the moderated mediation effect of pleasure and mediation effect of arousal on relationships between stores\u27 visual complexity and store attractiveness. The research findings obtained through the psychophysiological measures enrich existing literature on visual complexity and provide theoretical and managerial implications