skip to main content
10.1145/3631991.3632006acmotherconferencesArticle/Chapter ViewAbstractPublication PageswsseConference Proceedingsconference-collections
research-article

What drives the continuous adoption of mobile stock trading applications among Gen-Z traders? An investigation of the TAM, social influence, trust, and perceived security

Published: 26 December 2023 Publication History

Abstract

This work investigates the factors influencing the continuous adoption of mobile stock trading applications among Gen-Z traders in Malaysia. A research model was proposed based on the Technology Acceptance Model, social influence, trust, and perceived security. We employed the Partial Least Squares Structural Equation Modelling to assess the model based on 220 valid responses from Malaysian Gen-Z traders. The results endorsed perceived usefulness, perceived ease of use, social influence, and perceived security as significant drivers of the continuous adoption of mobile stock trading applications. Trust does not impact Malaysian Gen-Z traders' intention to continuously use mobile stock trading applications. We discuss how these findings implicate service providers to leverage functional elements, social influence, and perceived security to promote the technology's long-term adoption among Gen-Z traders.

References

[1]
L. L. Chong, H. B. Ong, S. H. Tan, Acceptability of mobile stock trading application: A study of young investors in Malaysia, Technology in Society 64 (2021) 101497–101497.
[2]
G. Scott, Mobile Trading (January 2022). URL https://www.investopedia.com/terms/m/mobile-trading.asp
[3]
D. Curry, Stock Trading & Investing App Revenue and Usage Statistics (2022) (September 2022). URL https://www.businessofapps.com/data/stock-trading-app-market/
[4]
S. Rega, How Robinhood and Covid opened the floodgates for 13 million amateur stock traders (October 2022). URL https://www.investopedia.com/terms/m/mobile-trading.asp
[5]
C. Schwab, Charles Schwab Survey: Generation Investor (2020). URL https://content.schwab.com/web/retail/public/about-schwab/charles_schwab_gen_investor_survey_findings_042117NM.pdf
[6]
V. Venkatesh, M. G. Morris, G. B. Davis, F. D. Davis, User acceptance of information technology: Toward a unified view, MIS quarterly (2003) 425–478.
[7]
S. Majumdar, V. Pujari, Exploring usage of mobile banking apps in the UAE: a categorical regression analysis, Journal of Financial Services Marketing 27 (3) (2022) 177–189.
[8]
F. D. Davis, Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS quarterly (1989) 319–340.
[9]
I. A. Jumaan, N. H. Hashim, B. M. Al-Ghazali, The role of cognitive absorption in predicting mobile internet users' continuance intention: An extension of the expectation-confirmation model, Technology in Society 63 (2020) 101355–101355.
[10]
P. O. H. Putra, S. Nugroho, A. N. Hidayanto, Factors Affecting User Retention of Mobile Mutual Fund Investment Applications: Evidence from Indonesia, Human Behavior and Emerging Technologies (2022).
[11]
F. D. Schoorman, R. C. Mayer, J. H. Davis (2007).
[12]
S. M. Tan, T. W. Liew, Multi-chatbot or single-chatbot? The effects of m-commerce chatbot interface on source credibility, social presence, trust, and purchase intention, Human Behavior and Emerging Technologies (2022).
[13]
S. M. Tan, T. W. Liew, Designing embodied virtual agents as product specialists in a multi-product category E-commerce: The roles of source credibility and social presence, International Journal of Human-Computer Interaction 36 (12) (2020) 1136–1149.
[14]
T. W. Liew, S. M. Tan, Exploring the effects of specialist versus generalist embodied virtual agents in a multi-product category online store, Telematics and Informatics 35 (1) (2018) 122–135.
[15]
D. H. Mcknight, V. Choudhury, C. Kacmar (2002).
[16]
L. Gao, K. A. Waechter, X. Bai, Understanding consumers' continuance intention towards mobile purchase: A theoretical framework and empirical study-A case of China, Computers in Human Behavior 53 (2015) 249–262.
[17]
C. Morosan, Toward an integrated model of adoption of mobile phones for purchasing ancillary services in air travel, International journal of contemporary hospitality management (2014).
[18]
L. Y. Chen, Antecedents of customer satisfaction and purchase intention with mobile shopping system use, International Journal of Services and Operations Management 15 (3) (2013) 259–274.
[19]
S. San-Martin, B. López-Catalán, How can a mobile vendor get satisfied customers, Industrial Management & Data Systems (2013).
[20]
S. Mamonov, R. Benbunan-Fich, An empirical investigation of privacy breach perceptions among smartphone application users, Computers in Human Behavior 49 (2015) 427–436.
[21]
J. P. Onnela, F. Reed-Tsochas, Spontaneous emergence of social influence in online systems, Proceedings of the National Academy of Sciences 107 (43) (2010) 18375–18380.
[22]
N. Koenig-Lewis, M. Marquet, A. Palmer, A. L. Zhao, Enjoyment and social influence: predicting mobile payment adoption, The Service Industries Journal 35 (10) (2015) 537–554.
[23]
S. Alwi, M. N. M. Salleh, S. A. Razak, N. Naim, Consumer acceptance and adoption towards payment-type fintech services from Malaysian perspective, International Journal of Advanced Science and Technology 28 (15) (2019) 216–231.
[24]
X. Lu, H. Lu, Understanding chinese millennials' adoption intention towards third-party mobile payment, Information Resources Management Journal (IRMJ) 33 (2) (2020) 40–63.
[25]
M. Merhi, K. Hone, A. Tarhini, A cross-cultural study of the intention to use mobile banking between Lebanese and British consumers: Extending UTAUT2 with security, privacy and trust, Technology in Society 59 (2019) 101151–101151.
[26]
A. P. Oghuma, C. F. Libaque-Saenz, S. F. Wong, Y. Chang, An expectation-confirmation model of continuance intention to use mobile instant messaging, Telematics and Informatics 33 (1) (2016) 34–47.
[27]
C. M. Cheung, M. K. Lee, Understanding consumer trust in Internet shopping: A multidisciplinary approach, Journal of the American society for Information Science and Technology 57 (4) (2006) 479–492.
[28]
P. A. Pavlou, H. Liang, Y. Xue, Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective, MIS quarterly (2007) 105–136.
[29]
S. U. Ady, M. Winedar, I. Farida, D. O. S. Susena, F. M. Putri, Trading Robots: Effective but Limited in Replacing Human Traders for Short-Term Investors, International Conference on Advance Research in Social and Economic Science (2023) 248–254.
[30]
M. Government, M. A, Types of Investments (2021). URL https://www.malaysia.gov.my/portal/content/31012
[31]
MCMC (2021). [link]. URL https://www.mcmc.gov.my/skmmgovmy/media/General/pdf2/FULL-REPORT-HPUS-2021.pdf
[32]
C. P. Ng, L. T. Cheong, P. K. Tee, L. Kim-Yew, S. T. Hai, W. C. Hoo, A. H. H. Ng, Will Perceived Behavioural Control Influence Malaysian Generation Y and Z to Join in Stock Market, NeuroQuantology 20 (6) (2022) 60–60.
[33]
F. Faul, E. Erdfelder, A. Buchner, A. G. Lang, Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses, Behavior research methods 41 (4) (2009) 1149–1160.
[34]
Y. M. Tai, Y. C. Ku, Will stock investors use mobile stock trading? A benefit-risk assessment based on a modified UTAUT model, Journal of Electronic Commerce Research 14 (1) (2013) 67–67.
[35]
P. S. Nair, A. Shiva, N. Yadav, P. Tandon, Determinants of mobile apps adoption by retail investors for online trading in emerging financial markets, Benchmarking: An International Journal 30 (5) (2023) 1623–1648.
[36]
R. Reith, M. Fischer, B. Lis, Explaining the intention to use social trading platforms: an empirical investigation, Journal of Business Economics 90 (2020) 427–460.
[37]
J. F. Hair, G. T. Hult, C. M. Ringle, M. Sarstedt, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), SAGE Publications, 2022.
[38]
M. K. Cain, Z. Zhang, K. H. Yuan, Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation, Behavior Research Methods 49 (5) (2016) 1716–1735.
[39]
J. Hair, C. L. Hollingsworth, A. B. Randolph, A. Y. Chong, An updated and expanded assessment of PLS-SEM in information systems research, Industrial Management & Data Systems 117 (3) (2017) 442–458.
[40]
J. Hair, J. Risher, M. Sarstedt, C. Ringle, When to use and how to report the results of PLS-SEM, European Business Review 31 (1) (2019) 2–24.
[41]
N. Kock, Common method bias in PLS-SEM: A full collinearity assessment approach, International Journal of eCollaboration 11 (4) (2015) 1–10.
[42]
G. Franke, M. Sarstedt, Heuristics versus statistics in discriminant validity testing: a comparison of four procedures, Internet Research 29 (3) (2019) 430–447.
[43]
J. Henseler, G. Hubona, P. Ray (2015).
[44]
J. Cohen, Statistical power analysis for the behavioral sciences, Lawrence Erlbaum, Mahwah, NJ, 1988.
[45]
G. Shmueli, M. Sarstedt, J. F. Hair, J. H. Cheah, H. Ting, S. Vaithilingam, C. M. Ringle, Predictive model assessment in PLS-SEM: guidelines for using PLSpredict, European Journal of Marketing (2019).
[46]
M. C. Lee, Predicting and explaining the adoption of online trading: An empirical study in Taiwan, Decision support systems 47 (2009) 133–142.
[47]
F. P. Afif, P. W. Handayani, A. A. Pinem, Determinant factors of new investor intention for using online trading system, 2018 Third International Conference on Informatics and Computing (ICIC) (2018) 1–6.
[48]
A. Singh, M. Malhotra, Factors influencing the adoption of online trading: A study of individual investors, IOSR Journal of Business and Management 18 (10) (2016) 21–26.
[49]
T. Ramayah, K. Rouibah, M. Gopi, G. J. Rangel, A decomposed theory of reasoned action to explain intention to use Internet stock trading among Malaysian investors, Computers in Human Behavior 25 (6) (2009) 1222–1230.
[50]
N. V. Khuong, N. T. T. Phuong, N. T. Liem, C. T. M. Thuy, T. H. Son, Factors Affecting the Intention to Use Financial Technology among Vietnamese Youth: Research in the Time of COVID-19 and Beyond, Economies 10 (3) (2022) 57–57.

Index Terms

  1. What drives the continuous adoption of mobile stock trading applications among Gen-Z traders? An investigation of the TAM, social influence, trust, and perceived security

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    WSSE '23: Proceedings of the 2023 5th World Symposium on Software Engineering
    September 2023
    352 pages
    ISBN:9798400708053
    DOI:10.1145/3631991
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 26 December 2023

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. Gen-Z
    2. TAM
    3. mobile stock trading applications
    4. perceived security
    5. social influence

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Conference

    WSSE 2023

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 54
      Total Downloads
    • Downloads (Last 12 months)52
    • Downloads (Last 6 weeks)5
    Reflects downloads up to 26 Jan 2025

    Other Metrics

    Citations

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media