The appeal of green advertisements on consumers' consumption intention based on low-resource machine translation

被引:0
|
作者
Xue Yu
机构
[1] China University of Mining and Technology,School of Economics and Management
[2] Anhui University of Finance and Economics,School of Art
来源
关键词
Low-resource language; Machine translation; The appeal of green advertisements; Consumption intention;
D O I
暂无
中图分类号
学科分类号
摘要
To study the impact of the appeal of green advertisements on consumers' consumption intention, this paper first studies low-resource language machine translation based on Internet of Things (IoT) edge computing. Some related theories, such as low-resource language machine translation (MT) and the appeal of green advertisements, are introduced. Secondly, the questionnaire survey is taken as the research method. Additionally, citizens of a city in China are selected as a research sample to study the relationship between the egoistic and altruistic appeal of green advertisements and impression management mechanisms, the mediating effect of green product attitudes, and consumers' purchase intentions, and draw relevant conclusions. The research results manifest that the low-resource language machine translation based on IoT edge computing has excellent performance, effectively improving the work efficiency and accuracy of low-resource language machine translation. For well-known brands, consumers are mainly young people aged between 26 and 35, and most of these young people have a bachelor’s degree. After low-resource language MT interprets the appearance and experience of clothing products, there will be gaps in the actual situation. However, most consumers will still consider continuing to buy such products. In the impression management mechanism, the direct effect value and the mediation effect value of altruistic green advertising are 4.8642 and 4.563, respectively; the values of egoistic green advertising are 5.3652 and 5.89621, respectively. Product attitudes under the altruistic appeal of green advertisements are better than the egoistic appeal. In addition, product attitude will play a partial intermediary role between the altruistic and egoistic appeal of green advertisements and purchase intention. On account of low-resource language MT, this research analyzes the influence of green advertising appeal on consumers' willingness to consume, enriches the relevant theories of consumers' green purchasing willingness and advertising demand, and provides a theoretical basis for subsequent research on the internal impact. It also provides the relevant mechanism of green advertising demand on purchase intention, constructive advertising and marketing suggestions for enterprises, and promotes the green and healthy development of enterprises.
引用
收藏
页码:5086 / 5108
页数:22
相关论文
共 50 条
  • [31] Neural Machine Translation of Low-Resource and Similar Languages with Backtranslation
    Przystupa, Michael
    Abdul-Mageed, Muhammad
    FOURTH CONFERENCE ON MACHINE TRANSLATION (WMT 2019), VOL 3: SHARED TASK PAPERS, DAY 2, 2019, : 224 - 235
  • [32] Extremely low-resource neural machine translation for Asian languages
    Rubino, Raphael
    Marie, Benjamin
    Dabre, Raj
    Fujita, Atushi
    Utiyama, Masao
    Sumita, Eiichiro
    MACHINE TRANSLATION, 2020, 34 (04) : 347 - 382
  • [33] Introduction to the Special Issue on Machine Translation for Low-Resource Languages
    Liu, Chao-Hong
    Karakanta, Alina
    Tong, Audrey N.
    Aulov, Oleg
    Soboroff, Ian M.
    Washington, Jonathan
    Zhao, Xiaobing
    MACHINE TRANSLATION, 2020, 34 (04) : 247 - 249
  • [34] Revisiting Low-Resource Neural Machine Translation: A Case Study
    Sennrich, Rico
    Zhang, Biao
    57TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2019), 2019, : 211 - 221
  • [35] Morpheme-Based Neural Machine Translation Models for Low-Resource Fusion Languages
    Gezmu, Andargachew Mekonnen
    Nuenberger, Andreas
    ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING, 2023, 22 (09)
  • [36] Rethinking the Exploitation of Monolingual Data for Low-Resource Neural Machine Translation
    Pang, Jianhui
    Yang, Baosong
    Wong, Derek Fai
    Wan, Yu
    Liu, Dayiheng
    Chao, Lidia Sam
    Xie, Jun
    COMPUTATIONAL LINGUISTICS, 2023, 50 (01) : 25 - 47
  • [37] A Diverse Data Augmentation Strategy for Low-Resource Neural Machine Translation
    Li, Yu
    Li, Xiao
    Yang, Yating
    Dong, Rui
    INFORMATION, 2020, 11 (05)
  • [38] Semantic Perception-Oriented Low-Resource Neural Machine Translation
    Wu, Nier
    Hou, Hongxu
    Li, Haoran
    Chang, Xin
    Jia, Xiaoning
    MACHINE TRANSLATION, CCMT 2021, 2021, 1464 : 51 - 62
  • [39] Can Cognate Prediction Be Modelled as a Low-Resource Machine Translation Task?
    Fourrier, Clementine
    Bawden, Rachel
    Sagot, Benoit
    FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, ACL-IJCNLP 2021, 2021, : 847 - 861
  • [40] Revisiting Back-Translation for Low-Resource Machine Translation Between Chinese and Vietnamese
    Li, Hongzheng
    Sha, Jiu
    Shi, Can
    IEEE ACCESS, 2020, 8 (08) : 119931 - 119939