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The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis

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  • Ghazanfar Ali Abbasi
  • Lee Yin Tiew
  • Jinquan Tang
  • Yen-Nee Goh
  • Ramayah Thurasamy

Abstract

In recent years, the growth of cryptocurrency has undergone an enormous increase in cryptocurrency markets all around the world. Sadly, only insignificant heed has been paid to the unveiling of determinants of cryptocurrency adoption globally, particularly in emerging markets like Malaysia. The purpose of the study is to examine whether the application of deep learning-based dual-stage Partial Least Square-Structural Equation Modelling (PLS-SEM) & Artificial Neural Network (ANN) analysis enable better in-depth research results as compared to single-step PLS-SEM approach and to excavate factors which can predict behavioural intention to adopt cryptocurrency. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model were extended with the inclusion of trust and personnel innovativeness. The model was further validated by introducing a new path model compared to the original UTAUT2 model and the moderating role of personal innovativeness between performance expectancy and price value, with a sample of 314 respondents. Contrary to previous technology adoption studies that used PLS-SEM & ANN as single-stage analysis, this study further enhanced the analysis by applying a deep learning-based dual-stage PLS-SEM and ANN method. The application of deep learning-based dual-stage PLS-SEM & ANN analysis is a novel methodological approach, detecting both linear and non-linear associations among constructs. At the same time, it is regarded as a superior statistical approach as compared to traditional hybrid shallow SEM & ANN single-stage analysis. Also, sensitivity analysis provides normalised importance using multi-layer perceptron with the feed-forward-back-propagation algorithm. Furthermore, the deep learning-based dual-stage PLS-SEM & ANN revealed that trust proved to be the strongest predictor in driving user intention. The introduction of this new methodology and the theoretical contribution opens the vistas of the extant body of knowledge in technology-adoption related literature. This study also provides theoretical, practical and methodological contributions.

Suggested Citation

  • Ghazanfar Ali Abbasi & Lee Yin Tiew & Jinquan Tang & Yen-Nee Goh & Ramayah Thurasamy, 2021. "The adoption of cryptocurrency as a disruptive force: Deep learning-based dual stage structural equation modelling and artificial neural network analysis," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-26, March.
    • Handle: RePEc:plo:pone00:0247582
    DOI: 10.1371/journal.pone.0247582
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    References listed on IDEAS

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    1. Omane-Adjepong, Maurice & Alagidede, Imhotep Paul, 2020. "High- and low-level chaos in the time and frequency market returns of leading cryptocurrencies and emerging assets," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
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    1. Mohamed Bouteraa & Brahim Chekima & Nelson Lajuni & Ayesha Anwar, 2023. "Understanding Consumers’ Barriers to Using FinTech Services in the United Arab Emirates: Mixed-Methods Research Approach," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
    2. Bin-Nashwan, Saeed Awadh & Sadallah, Mouad & Bouteraa, Mohamed, 2023. "Use of ChatGPT in academia: Academic integrity hangs in the balance," Technology in Society, Elsevier, vol. 75(C).
    3. Chenlu Dang & Fan Wang & Zimo Yang & Hongxia Zhang & Yufeng Qian, 2022. "RETRACTED ARTICLE: Evaluating and forecasting the risks of small to medium-sized enterprises in the supply chain finance market using blockchain technology and deep learning model," Operations Management Research, Springer, vol. 15(3), pages 662-675, December.
    4. Basco, Rodrigo & Hair, Joseph F. & Ringle, Christian M. & Sarstedt, Marko, 2022. "Advancing family business research through modeling nonlinear relationships: Comparing PLS-SEM and multiple regression," Journal of Family Business Strategy, Elsevier, vol. 13(3).
    5. Mohammad El Hajj & Imad Farran, 2024. "The Cryptocurrencies in Emerging Markets: Enhancing Financial Inclusion and Economic Empowerment," JRFM, MDPI, vol. 17(10), pages 1-27, October.
    6. Larbi-Siaw, Otu & Xuhua, Hu & Owusu, Ebenezer & Owusu-Agyeman, Abigail & Fulgence, Brou Ettien & Frimpong, Samuel Akwasi, 2022. "Eco-innovation, sustainable business performance and market turbulence moderation in emerging economies," Technology in Society, Elsevier, vol. 68(C).
    7. Chen, Xia & Miraz, Mahadi Hasan & Gazi, Md. Abu Issa & Rahaman, Md. Atikur & Habib, Md. Mamun & Hossain, Abu Ishaque, 2022. "Factors affecting cryptocurrency adoption in digital business transactions: The mediating role of customer satisfaction," Technology in Society, Elsevier, vol. 70(C).
    8. Zhengtang Fu & Peiwu Dong & Siyao Li & Yanbing Ju, 2021. "An intelligent cross-border transaction system based on consortium blockchain: A case study in Shenzhen, China," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-22, June.
    9. Albahri, A.S. & Alnoor, Alhamzah & Zaidan, A.A. & Albahri, O.S. & Hameed, Hamsa & Zaidan, B.B. & Peh, S.S. & Zain, A.B. & Siraj, S.B. & Alamoodi, A.H. & Yass, A.A., 2021. "Based on the multi-assessment model: Towards a new context of combining the artificial neural network and structural equation modelling: A review," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    10. Ashaari, Mohamed Azlan & Singh, Karpal Singh Dara & Abbasi, Ghazanfar Ali & Amran, Azlan & Liebana-Cabanillas, Francisco J., 2021. "Big data analytics capability for improved performance of higher education institutions in the Era of IR 4.0: A multi-analytical SEM & ANN perspective," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    11. Sudersan Behera & Sarat Chandra Nayak & A. V. S. Pavan Kumar, 2024. "Evaluating the Performance of Metaheuristic Based Artificial Neural Networks for Cryptocurrency Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1219-1258, August.
    12. Messner, Wolfgang, 2024. "Exploring multilevel data with deep learning and XAI: The effect of personal-care advertising spending on subjective happiness," International Business Review, Elsevier, vol. 33(1).
    13. Bommer, William H. & Milevoj, Emil & Rana, Shailesh, 2023. "The intention to use cryptocurrency: A meta-analysis of what we know," Emerging Markets Review, Elsevier, vol. 55(C).
    14. Călin Florin Băban & Marius Băban, 2022. "An Orchestration Perspective on Open Innovation between Industry–University: Investigating Its Impact on Collaboration Performance," Mathematics, MDPI, vol. 10(15), pages 1-23, July.
    15. Munish Gupta & Sanjay Taneja & Vikas Sharma & Amandeep Singh & Ramona Rupeika-Apoga & Kshitiz Jangir, 2023. "Does Previous Experience with the Unified Payments Interface (UPI) Affect the Usage of Central Bank Digital Currency (CBDC)?," JRFM, MDPI, vol. 16(6), pages 1-23, May.
    16. Hwang Kim, 2024. "An empirical analysis of navigation behaviors across stock and cryptocurrency trading platforms: implications for targeting and segmentation strategies," Electronic Commerce Research, Springer, vol. 24(3), pages 2113-2141, September.
    17. Mark P. Doblas & Jishanis Mae G. Becaro & Jayendira P. Sankar & Vinodh K. Natarajan & Yoganandham G. & Arumugasamy G., 2024. "Testing Integrative Models of the Change Behavior in the Intention to Adopt Cryptocurrency," SAGE Open, , vol. 14(2), pages 21582440241, May.
    18. Kanellos Toudas & Démétrios Pafos & Paraskevi Boufounou & Athanasios Raptis, 2024. "Cryptocurrency, Gold, and Stock Exchange Market Performance Correlation: Empirical Evidence," FinTech, MDPI, vol. 3(2), pages 1-13, June.
    19. repec:hal:journl:hal-04861233 is not listed on IDEAS
    20. Ghazanfar Ali Abbasi & Noor Fareen Abdul Rahim & Hongyan Wu & Mohammad Iranmanesh & Benjamin Ng Chee Keong, 2022. "Determinants of SME’s Social Media Marketing Adoption: Competitive Industry as a Moderator," SAGE Open, , vol. 12(1), pages 21582440211, January.

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