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Understanding the adoption and usage of mobile payment services by using TAM3

Author

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  • Mohammed-Issa Riad Mousa Jaradat
  • Abedalellah Mohammed Al-Mashaqba

Abstract

This study aims at introducing a modified Technology Acceptance Model 3 (TAM3) to investigate key factors that affect individuals' intention to adopt and use mobile payment (M-payment) in Jordan. It also helps merchandisers avoid spending thousands or even millions of dollars that, on investments, may have minimal and modest effect on whether or not the individuals tend actually to adopt and to use. To reach such ends, a questionnaire was distributed to collect data from 475 undergraduate university students by using a proportional stratified random sample, and analysed by using a structural equation modelling (SEM). Results show that user's adoption and use of M-payment services can be anticipated from users' behavioural intentions, which are significantly affected by the following: perceived usefulness, perceived ease of use, subjective norm, image, output quality, self-efficacy, perceptions of external control, and playfulness. This study shows that there is a promising and bright future for M-payment services in Jordan as more than 62% of respondents said that they are willing to merchandise by using their mobile phones.

Suggested Citation

  • Mohammed-Issa Riad Mousa Jaradat & Abedalellah Mohammed Al-Mashaqba, 2014. "Understanding the adoption and usage of mobile payment services by using TAM3," International Journal of Business Information Systems, Inderscience Enterprises Ltd, vol. 16(3), pages 271-296.
    • Handle: RePEc:ids:ijbisy:v:16:y:2014:i:3:p:271-296
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    Citations

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    Cited by:

    1. Torsten J. Gerpott & Phil Meinert, 2016. "Correlates of using the billing system of a mobile network operator to pay for digital goods and services," Information Systems Frontiers, Springer, vol. 18(6), pages 1265-1283, December.
    2. Soyoung An & Thomas Eck & Huirang Yim, 2023. "Understanding Consumers’ Acceptance Intention to Use Mobile Food Delivery Applications through an Extended Technology Acceptance Model," Sustainability, MDPI, vol. 15(1), pages 1-14, January.
    3. Wang, Guoqiang & Tan, Garry Wei-Han & Yuan, Yunpeng & Ooi, Keng-Boon & Dwivedi, Yogesh K., 2022. "Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    4. Abbasi, Ghazanfar Ali & Sandran, Thiviya & Ganesan, Yuvaraj & Iranmanesh, Mohammad, 2022. "Go cashless! Determinants of continuance intention to use E-wallet apps: A hybrid approach using PLS-SEM and fsQCA," Technology in Society, Elsevier, vol. 68(C).
    5. Zura Kakushadze & Ronald P. Russo Jr, 2018. "Blockchain: Data Malls, Coin Economies and Keyless Payments," Papers 1802.07422, arXiv.org, revised Apr 2018.
    6. Senyo, PK & Osabutey, Ellis L.C., 2020. "Unearthing antecedents to financial inclusion through FinTech innovations," Technovation, Elsevier, vol. 98(C).
    7. Jorge Andrés-Sánchez & Laura González-Vila Puchades & Mario Arias-Oliva, 2023. "Factors influencing policyholders' acceptance of life settlements: a technology acceptance model," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 48(4), pages 941-967, October.
    8. de Luna, Iviane Ramos & Liébana-Cabanillas, Francisco & Sánchez-Fernández, Juan & Muñoz-Leiva, Francisco, 2019. "Mobile payment is not all the same: The adoption of mobile payment systems depending on the technology applied," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 931-944.
    9. Chayomchai Ampol, 2020. "The Online Technology Acceptance Model of Generation-Z People in Thailand during COVID-19 Crisis," Management & Marketing, Sciendo, vol. 15(s1), pages 496-512, October.
    10. Imdadullah Hidayat-ur-Rehman & Arshad Ahmad & Fahim Akhter & Mohd Ziaur Rehman, 2022. "Examining Consumers’ Adoption of Smart Wearable Payments," SAGE Open, , vol. 12(3), pages 21582440221, August.

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