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Standing up for or against: A text-mining study on the recommendation of mobile payment apps

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  • Verkijika, Silas Formunyuy
  • Neneh, Brownhilder Ngek

Abstract

Mobile payment systems offer enormous potential as alternative payment solutions. However, the diffusion of mobile payments over the years has been less than optimal despite the numerous studies that have explored the reasons for its adoption. Consequently, there is an increased interest in exploring alternative actions for promoting its diffusion, especially user recommendation of the technology. This is because positive recommendations can enormously influence the decisions of potential consumers to use the technology while negative recommendations can increase resistance to it. The few extant studies in this domain have followed the traditional survey approach with hypothetic-deductive reasoning, thus limiting an understanding of factors outside their conceptual models that could influence recommendations. To address this shortcoming, this study uses a qualitative text-mining approach that explores themes from user reviews of mobile payment applications (apps). Using 5955 reviews from 16 mobile payment apps hosted on the Google Play store, this study applied the latent Dirichlet allocation (LDA) text-mining method to extract themes from the reviews that help to explain why users provide positive or negative recommendations about mobile payment systems. A total of 13 themes (i.e. ease of use, usefulness, convenience, security, reliability, satisfaction, transaction speed, time-saving, customer support, output quality, perceived cost, usability and trust) were generated from the LDA model which provides both theoretical and practical insights for advancing mobile payments diffusion and research.

Suggested Citation

  • Verkijika, Silas Formunyuy & Neneh, Brownhilder Ngek, 2021. "Standing up for or against: A text-mining study on the recommendation of mobile payment apps," Journal of Retailing and Consumer Services, Elsevier, vol. 63(C).
  • Handle: RePEc:eee:joreco:v:63:y:2021:i:c:s096969892100309x
    DOI: 10.1016/j.jretconser.2021.102743
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    Cited by:

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    5. Neena Sinha & Nidhi Singh, 2023. "Moderating and mediating effect of perceived experience on merchant's behavioral intention to use mobile payments services," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 28(3), pages 448-465, September.
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    9. Laddawan Kaewkitipong & Charlie Chen & Jiangxue Han & Peter Ractham, 2022. "Human–Computer Interaction (HCI) and Trust Factors for the Continuance Intention of Mobile Payment Services," Sustainability, MDPI, vol. 14(21), pages 1-15, November.
    10. Li, Xinwei & Xu, Mao & Zeng, Wenjuan & Tse, Ying Kei & Chan, Hing Kai, 2023. "Exploring customer concerns on service quality under the COVID-19 crisis: A social media analytics study from the retail industry," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
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    15. Kumar, Anand & Bala, Pradip Kumar & Chakraborty, Shibashish & Behera, Rajat Kumar, 2024. "Exploring antecedents impacting user satisfaction with voice assistant app: A text mining-based analysis on Alexa services," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
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    17. Chakraborty, Debarun & Siddiqui, Aaliyah & Siddiqui, Mujahid & Rana, Nripendra P. & Dash, Ganesh, 2022. "Mobile payment apps filling value gaps: Integrating consumption values with initial trust and customer involvement," Journal of Retailing and Consumer Services, Elsevier, vol. 66(C).
    18. Hamzah, Muhammad Iskandar & Ramli, Faten Aisyah Ahmad & Shaw, Norman, 2023. "The moderating influence of brand image on consumers' adoption of QR-code e-wallets," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).
    19. Oleksandr Melnychenko, 2021. "Energy Losses Due to Imperfect Payment Infrastructure and Payment Instruments," Energies, MDPI, vol. 14(24), pages 1-20, December.
    20. Zhong, Junying & Chen, Tiao, 2023. "Antecedents of mobile payment loyalty: An extended perspective of perceived value and information system success model," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).

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