Personal Characteristics and Hedonic Shopping Orientation on Apparel Adult Shoppers’ Repatronage Behavioral Intention

A new direction of consumer centric marketing is now advocated to be included in the marketing module, yet there is a lack of shopping repatronage research based on a solid theoretical background pertinent to the issue. Based on the Reasoned Action Approach by Fishbein and Ajzen (2010), this study endeavors to examine the relationships between the personal characteristics, hedonic shopping orientation and repatronage behavioral intention. The structural equation modeling was used to analyze the causal relationships for the self-administrated data gathered from 569 apparel adult shoppers aged 30 to 60 years old. The refined hypothesized model was relatively good fitted. The personal characteristics of need for activity, impulsiveness, shopping confidence and susceptibility to influence were found significantly related to the repatronage behavioral intention. These relationships were fully mediated by the intervening variable of hedonic shopping orientation. The hypothesized model of hedonic mediating structural model explained 56% of the repatronage behavioral intention. The study advanced the understanding of the importance of hedonic shopping orientation as well as the personal characteristics in consumer-centric marketing

Personal characteristics and hedonic shopping orientation on apparel adult shoppers' repatronage behavioral intention

A new direction of consumer centric marketing is now advocated to be included in the marketing module, yet there is a lack of shopping repatronage research based on a solid theoretical background pertinent to the issue. Based on the Reasoned Action Approach by Fishbein and Ajzen (2010), this study endeavors to examine the relationships between the personal characteristics, hedonic shopping orientation and repatronage behavioral intention. The structural equation modeling was used to analyze the causal relationships for the self-administrated data gathered from 569 apparel adult shoppers aged 30 to 60 years old. The refined hypothesized model was relatively good fitted. The personal characteristics of need for activity, impulsiveness, shopping confidence and susceptibility to influence were found significantly related to the repatronage behavioral intention. These relationships were fully mediated by the intervening variable of hedonic shopping orientation. The hypothesized model of hedonic mediating structural model explained 56% of the repatronage behavioral intention. The study advanced the understanding of the importance of hedonic shopping orientation as well as the personal characteristics in consumer-centric marketing

Target-Side Augmentation for Document-Level Machine Translation

Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data, increasing the risk of learning spurious patterns. To address this challenge, we propose a target-side augmentation method, introducing a data augmentation (DA) model to generate many potential translations for each source document. Learning on these wider range translations, an MT model can learn a smoothed distribution, thereby reducing the risk of data sparsity. We demonstrate that the DA model, which estimates the posterior distribution, largely improves the MT performance, outperforming the previous best system by 2.30 s-BLEU on News and achieving new state-of-the-art on News and Europarl benchmarks. Our code is available at https://github.com/baoguangsheng/target-side-augmentation.Comment: Accepted by ACL2023 main conferenc

Token-Level Fitting Issues of Seq2seq Models

Sequence-to-sequence (seq2seq) models have been widely used for natural language processing, computer vision, and other deep learning tasks. We find that seq2seq models trained with early-stopping suffer from issues at the token level. In particular, while some tokens in the vocabulary demonstrate overfitting, others underfit when training is stopped. Experiments show that the phenomena are pervasive in different models, even in fine-tuned large pretrained-models. We identify three major factors that influence token-level fitting, which include token frequency, parts-of-speech, and prediction discrepancy. Further, we find that external factors such as language, model size, domain, data scale, and pretraining can also influence the fitting of tokens.Comment: Accepted by ACL 2023 Workshop on RepL4NLP, 9 page