Feeling Fishy When Buying from Third Parties through E-commerce Apps: An Analysis of Users' Experiences with the Apps' Interfaces
Resumo
E-Commerce moves trillions of dollars worldwide every year, connecting buyers and sellers. However, users, increasingly attentive, view the process of paying for and receiving products and services over the Internet with suspicion, whether due to reports of negative experiences from other users or even their own. Cases of fraud are damaging to a company's image. In this article we analyze a large volume of user reviews of different marketplace-type e-commerce applications (apps) with the aim of mapping the user experience behind the interface for the transaction mediation service between buyers and sellers offered by such apps. Our results show that the impression of insecurity occurs mainly due to two elements: 1) Interface complexity - the interfaces of these applications tend to have many usability elements that may end up hindering the clarity of the process; and, 2) Communication of responsibility - when users often do not know that they are dealing with a third party or they do know, but are unaware of the limitations of the platform's mediation service. The results of the analysis carried out here demonstrate that the loss in incremental profit, when the user uninstalls the application, tends to be much greater than the losses caused directly by fraud, highlighting the need to not only provide security, but also to communicate it.
Palavras-chave:
User Opinion Mining, User Reviews, Marketplace, Transaction Mediation
Referências
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Abdulaziz Alshayban, Iftekhar Ahmed, and Sam Malek. 2020. Accessibility issues in Android apps: state of affairs, sentiments, and ways forward. In Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering (Seoul, South Korea) (ICSE ’20). Association for Computing Machinery, New York, NY, USA, 1323–1334. DOI: 10.1145/3377811.3380392
Courtni Byun, Piper Vasicek, and Kevin Seppi. 2023. Dispensing with Humans in Human-Computer Interaction Research. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (Hamburg, Germany) (CHI EA ’23). Association for Computing Machinery, New York, NY, USA, Article 413, 26 pages. DOI: 10.1145/3544549.3582749
Jiuhai Chen, Lichang Chen, Heng Huang, and Tianyi Zhou. 2023. When do you need Chain-of-Thought Prompting for ChatGPT? arXiv:2304.03262 [cs.AI] [link]
Adelina Ciurumelea, Andreas Schaufelbühl, Sebastiano Panichella, and Harald C. Gall. 2017. Analyzing reviews and code of mobile apps for better release planning. In 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER). 91–102. DOI: 10.1109/SANER.2017.7884612
Jacob Cohen. 1960. A coefficient of agreement for nominal scales. Educational and psychological measurement 20, 1 (1960), 37–46.
Paulo Sérgio Henrique Dos Santos, Alberto Dumont Alves Oliveira, Thais Bonjorni Nobre De Jesus, Wajdi Aljedaani, and Marcelo Medeiros Eler. 2024. Evolution may come with a price: analyzing user reviews to understand the impact of updates on mobile apps accessibility. In Proceedings of the XXII Brazilian Symposium on Human Factors in Computing Systems (Maceió, Brazil) (IHC ’23). Association for Computing Machinery, New York, NY, USA, Article 52, 11 pages. DOI: 10.1145/3638067.3638081
Marcelo Medeiros Eler, Leandro Orlandin, and Alberto Dumont Alves Oliveira. 2019. Do Android app users care about accessibility? an analysis of user reviews on the Google play store. In Proceedings of the 18th Brazilian Symposium on Human Factors in Computing Systems (Vitória, Espírito Santo, Brazil) (IHC ’19). Association for Computing Machinery, New York, NY, USA, Article 23, 11 pages. DOI: 10.1145/3357155.3358477
Joseph L Fleiss, Bruce Levin, Myunghee Cho Paik, et al. 1981. The measurement of interrater agreement. Statistical methods for rates and proportions 2, 212-236 (1981), 22–23.
Ridong Han, Tao Peng, Chaohao Yang, Benyou Wang, Lu Liu, and Xiang Wan. 2023. Is Information Extraction Solved by ChatGPT? An Analysis of Performance, Evaluation Criteria, Robustness and Errors. arXiv:2305.14450 [cs.CL] [link]
Claudia Iacob and Rachel Harrison. 2013. Retrieving and analyzing mobile apps feature requests from online reviews. In 2013 10th Working Conference on Mining Software Repositories (MSR). 41–44. DOI: 10.1109/MSR.2013.6624001
Hourieh Khalajzadeh, Mojtaba Shahin, Humphrey O Obie, Pragya Agrawal, and John Grundy. 2022. Supporting developers in addressing human-centric issues in mobile apps. IEEE Transactions on Software Engineering 49, 4 (2022), 2149–2168.
Urvashi Kishnani, Naheem Noah, Sanchari Das, and Rinku Dewri. 2023. Assessing Security, Privacy, User Interaction, and Accessibility Features in Popular E-Payment Applications. In Proceedings of the 2023 European Symposium on Usable Security (Copenhagen, Denmark) (EuroUSEC ’23). Association for Computing Machinery, New York, NY, USA, 143–157. DOI: 10.1145/3617072.3617102
Byung-Hak Leem and Seong-Won Eum. 2021. Using text mining to measure mobile banking service quality. Industrial Management & Data Systems 121, 5 (2021), 993–1007.
M. H. Maqbool, Umar Farooq, Adib Mosharrof, A. B. Siddique, and Hassan Foroosh. 2023. MobileRec: A Large Scale Dataset for Mobile Apps Recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (Taipei, Taiwan) (SIGIR ’23). Association for Computing Machinery, New York, NY, USA, 3007–3016. DOI: 10.1145/3539618.3591906
Saba Naseem, Toqeer Mahmood, Muhammad Asif, Junaid Rashid, Muhammad Umair, and Mohsin Shah. 2021. Survey on Sentiment Analysis of User Reviews. In 2021 International Conference on Innovative Computing (ICIC). 1–6. DOI: 10.1109/ICIC53490.2021.9693029
Tier Nolan. 2013. Re: Alt chains and atomic transfers. [link] Last accessed 20 July 2023.
Yun Kyung Oh and Jung-Min Kim. 2022. What improves customer satisfaction in mobile banking apps? An application of text mining analysis. Asia Marketing Journal 23, 4 (2022), 3.
Dany Pratmanto, Rousyati Rousyati, Fanny Fatma Wati, Andrian Eko Widodo, Suleman Suleman, and Ragil Wijianto. 2020. App Review Sentiment Analysis Shopee Application In Google Play Store Using Naive Bayes Algorithm. Journal of Physics: Conference Series 1641, 1 (nov 2020), 012043. DOI: 10.1088/17426596/1641/1/012043
Vinicius Facco Rodrigues, Lucas Micol Policarpo, Diórgenes Eugênio da Silveira, Rodrigo da Rosa Righi, Cristiano André da Costa, Jorge Luis Victória Barbosa, Rodolfo Stoffel Antunes, Rodrigo Scorsatto, and Tanuj Arcot. 2022. Fraud detection and prevention in e-commerce: A systematic literature review. Electronic Commerce Research and Applications 56 (2022), 101207.
Mara Taynar Santiago and Anna Beatriz Marques. 2022. Are user reviews useful for identifying accessibility issues that autistic users face? an exploratory study. In Proceedings of the 21st Brazilian Symposium on Human Factors in Computing Systems. 1–11.
Vincent F Taylor and Ivan Martinovic. 2017. To update or not to update: Insights from a two-year study of android app evolution. In Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security. 45–57.
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al . 2022. Emergent abilities of large language models. arXiv preprint arXiv:2206.07682 (2022).
Xiang Wei, Xingyu Cui, Ning Cheng, Xiaobin Wang, Xin Zhang, Shen Huang, Pengjun Xie, Jinan Xu, Yufeng Chen, Meishan Zhang, et al. 2023. Zero-shot information extraction via chatting with chatgpt. arXiv preprint arXiv:2302.10205 (2023).
Ge Zhang, Zhao Li, Jiaming Huang, Jia Wu, Chuan Zhou, Jian Yang, and Jianliang Gao. 2022. efraudcom: An e-commerce fraud detection system via competitive graph neural networks. ACM Transactions on Information Systems (TOIS) 40, 3 (2022), 1–29.
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, et al . 2023. A survey of large language models. arXiv preprint arXiv:2303.18223 (2023).
Publicado
07/11/2024
Como Citar
CLEMENTINO, Tiago Lucas Pereira; MOURA, José Antão Beltrão.
Feeling Fishy When Buying from Third Parties through E-commerce Apps: An Analysis of Users' Experiences with the Apps' Interfaces. In: SIMPÓSIO BRASILEIRO SOBRE FATORES HUMANOS EM SISTEMAS COMPUTACIONAIS (IHC), 23. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 648-659.
