IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0247582.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0247582
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0247582&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0247582?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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).
    2. Gil-Alana, Luis Alberiko & Abakah, Emmanuel Joel Aikins & Rojo, María Fátima Romero, 2020. "Cryptocurrencies and stock market indices. Are they related?," Research in International Business and Finance, Elsevier, vol. 51(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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. 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).
    5. 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.
    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. 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).
    8. 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).
    9. 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.
    10. 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.
    11. 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).
    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. 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.
    15. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bildirici, Melike E. & Sonustun, Bahri, 2021. "Chaotic behavior in gold, silver, copper and bitcoin prices," Resources Policy, Elsevier, vol. 74(C).
    2. Duan, Kun & Zhao, Yanqi & Urquhart, Andrew & Huang, Yingying, 2023. "Do clean and dirty cryptocurrencies connect with financial assets differently? The role of economic policy uncertainty," Energy Economics, Elsevier, vol. 127(PA).
    3. Tiwari, Aviral Kumar & Aikins Abakah, Emmanuel Joel & Adekoya, Oluwasegun B. & Hammoudeh, Shawkat, 2023. "What do we know about the price spillover between green bonds and Islamic stocks and stock market indices?," Global Finance Journal, Elsevier, vol. 55(C).
    4. Chen, Bin-xia & Sun, Yan-lin, 2024. "Risk characteristics and connectedness in cryptocurrency markets: New evidence from a non-linear framework," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    5. Ahmed, Walid M.A., 2021. "Stock market reactions to upside and downside volatility of Bitcoin: A quantile analysis," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    6. Moreno, David & Antoli, Marcos & Quintana, David, 2022. "Benefits of investing in cryptocurrencies when liquidity is a factor," Research in International Business and Finance, Elsevier, vol. 63(C).
    7. Corbet, Shaen & Larkin, Charles & Lucey, Brian, 2020. "The contagion effects of the COVID-19 pandemic: Evidence from gold and cryptocurrencies," Finance Research Letters, Elsevier, vol. 35(C).
    8. Wang, Xuetong & Fang, Fang & Ma, Shiqun & Xiang, Lijin & Xiao, Zumian, 2024. "Dynamic volatility spillover among cryptocurrencies and energy markets: An empirical analysis based on a multilevel complex network," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    9. Abakah, Emmanuel Joel Aikins & Gil-Alana, Luis Alberiko & Madigu, Godfrey & Romero-Rojo, Fatima, 2020. "Volatility persistence in cryptocurrency markets under structural breaks," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 680-691.
    10. Paeng, Seongcheol & Senteney, Dave & Yang, Taewon, 2024. "Spillover effects, lead and lag relationships, and stable coins time series," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 45-60.
    11. Elgammal, Mohammed M. & Ahmed, Walid M.A. & Alshami, Abdullah, 2021. "Price and volatility spillovers between global equity, gold, and energy markets prior to and during the COVID-19 pandemic," Resources Policy, Elsevier, vol. 74(C).
    12. Aikins Abakah, Emmanuel Joel & Gil-Alana, Luis A. & Tripathy, Trilochan, 2022. "Stochastic structure of metal prices: Evidence from fractional integration non-linearities and breaks," Resources Policy, Elsevier, vol. 78(C).
    13. Goodell, John W. & Goutte, Stephane, 2021. "Diversifying equity with cryptocurrencies during COVID-19," International Review of Financial Analysis, Elsevier, vol. 76(C).
    14. Noman, Abu Hanifa Md & Karim, Muhammad Mahmudul & Hassan, Mohammad Kabir & Khan, Muhammad Asif & Pervin, Sajeda, 2023. "COVID-19 pandemic and the dynamics of major investable assets: What gives shelter to investors?," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 14-30.
    15. Piñeiro-Chousa, Juan & López-Cabarcos, M. Ángeles & Sevic, Aleksandar & González-López, Isaac, 2022. "A preliminary assessment of the performance of DeFi cryptocurrencies in relation to other financial assets, volatility, and user-generated content," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    16. Ivanovski, Kris & Hailemariam, Abebe, 2023. "Forecasting the stock-cryptocurrency relationship: Evidence from a dynamic GAS model," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 97-111.
    17. Achraf Ghorbel & Wajdi Frikha & Yasmine Snene Manzli, 2022. "Testing for asymmetric non-linear short- and long-run relationships between crypto-currencies and stock markets," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 12(3), pages 387-425, September.
    18. Juan Antonio Galán-Gutiérrez & Rodrigo Martín-García, 2022. "Fundamentals vs. Financialization during Extreme Events: From Backwardation to Contango, a Copper Market Analysis during the COVID-19 Pandemic," Mathematics, MDPI, vol. 10(4), pages 1-23, February.
    19. Pritpal Singh BHULLAR & Dyal BHATNAGAR, 2020. "Bitcoins as a determinant of stock market movements: A comparison of Indian and Chinese Stock Markets," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(3(624), A), pages 193-202, Autumn.
    20. Borgards, Oliver & Czudaj, Robert L., 2021. "Features of overreactions in the cryptocurrency market," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 31-48.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0247582. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.