8 research outputs found

    The Research on the Impact of Service Failure Severity on Customer Service Failure Attribution in the Network Shopping

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    In recent years, the rapid development of electronic commerce in China has made online shopping one of the most important shopping ways. However, there are more and more service failures on online shopping, and complaints about them are increasing, which will hinder the development of e-commerce in China. After service failure occurring, customers are going to decide the parties who are responsible for the failure typically based on specific failure situation and personal experience, namely service failure attribution. A lot of research has discussed the effect of service failure attribution on service recovery, customer satisfaction, trust and loyalty, as well as consumer intent of sequent behavior. And yet, the research on service failure attribution process is relatively less. Based on the literatures, this paper examines the effect of failure severity on service failure attribution of locus, controllability, and the moderating role of customer relationship and social responsibility image. The results of this study suggest that: Severity of failure has a significantly positive effect on service failure attribution of locus, controllability; Customer relationship significantly moderates the influences of failure severity on service attribution of locus, controllability; Social responsibility image significantly moderates the influences of failure severity on service attribution of locus, controllability

    The Gender Differences in the Effect of Two-sidedness E-WOM Presentation Order on Product Attitude

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    In the Internet environment, electronic word-of mouth plays an important role in affecting consumers’ attitude toward product and service. However, there exists a widespread situation that consumers may receive two-sidedness e-WOM (An e-WOM has both positive and negative message about the same object), and past research has shown that gender differences and situational involvement affect consumers’ perception of e-WOM. This article demonstrates how presentation order of two-sidedness electronic word-of-mouth and gender influence in consumers’ perception and attitude toward product under different situations. Our study found that two-sidedness e-WOM presentation order and gender influenced product attitude: under low-involvement situation, males (females) exhibited primacy (recency)effects when receiving a two-sidedness e-WOM which containing both positive and negative messages about product. Under high-involvement situation, all respondents appeared to process two-sidedness e-WOM systematically and consider more relevant information in their evaluations. The result revealed that females continued to exhibit recency effects, but the primacy effects with males disappeared, they will exhibit obvious recency effects

    Purchasing Motivations Toward Counterfeit Luxury Goods on E-marketplaces

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    This research is designed to study consumers’ purchasing attitudes to counterfeit luxury goods on electronic marketplaces (e-marketplaces). And two research hypotheses are proposed in this research. Based on data analysis of 243 samples, this study explores the dimensions of consumer attitudes (morality and law, accessibility, burden-bearing, function effectiveness, economical efficiency) and motivations (conspicuous psychology, rebel psychology, social identity, self-enjoying and cost performance) to luxury counterfeit goods on e-marketplaces. It is found that the major reasons for consumers to choose e-business channels to buy luxury counterfeits are convenience, information acquisition, product and service. In particular, the findings indicate that online consumers’ attitudes toward luxury counterfeit products significantly impact purchasing motivation; online consumers’ attitudes and motivations positively impact purchasing intention

    Curriculum-based Asymmetric Multi-task Reinforcement Learning

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    We introduce CAMRL, the first curriculum-based asymmetric multi-task learning (AMTL) algorithm for dealing with multiple reinforcement learning (RL) tasks altogether. To mitigate the negative influence of customizing the one-off training order in curriculum-based AMTL, CAMRL switches its training mode between parallel single-task RL and asymmetric multi-task RL (MTRL), according to an indicator regarding the training time, the overall performance, and the performance gap among tasks. To leverage the multi-sourced prior knowledge flexibly and to reduce negative transfer in AMTL, we customize a composite loss with multiple differentiable ranking functions and optimize the loss through alternating optimization and the Frank-Wolfe algorithm. The uncertainty-based automatic adjustment of hyper-parameters is also applied to eliminate the need of laborious hyper-parameter analysis during optimization. By optimizing the composite loss, CAMRL predicts the next training task and continuously revisits the transfer matrix and network weights. We have conducted experiments on a wide range of benchmarks in multi-task RL, covering Gym-minigrid, Meta-world, Atari video games, vision-based PyBullet tasks, and RLBench, to show the improvements of CAMRL over the corresponding single-task RL algorithm and state-of-the-art MTRL algorithms. The code is available at: https://github.com/huanghanchi/CAMRLComment: Accepted by TPAMI (IEEE Transactions on Pattern Analysis and Machine Intelligence

    Master-slave Deep Architecture for Top-K Multi-armed Bandits with Non-linear Bandit Feedback and Diversity Constraints

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    We propose a novel master-slave architecture to solve the top-KK combinatorial multi-armed bandits problem with non-linear bandit feedback and diversity constraints, which, to the best of our knowledge, is the first combinatorial bandits setting considering diversity constraints under bandit feedback. Specifically, to efficiently explore the combinatorial and constrained action space, we introduce six slave models with distinguished merits to generate diversified samples well balancing rewards and constraints as well as efficiency. Moreover, we propose teacher learning based optimization and the policy co-training technique to boost the performance of the multiple slave models. The master model then collects the elite samples provided by the slave models and selects the best sample estimated by a neural contextual UCB-based network to make a decision with a trade-off between exploration and exploitation. Thanks to the elaborate design of slave models, the co-training mechanism among slave models, and the novel interactions between the master and slave models, our approach significantly surpasses existing state-of-the-art algorithms in both synthetic and real datasets for recommendation tasks. The code is available at: \url{https://github.com/huanghanchi/Master-slave-Algorithm-for-Top-K-Bandits}.Comment: IEEE Transactions on Neural Networks and Learning System
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