AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (1.1 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Role of Technology Self-Efficacy and Digital Alliance in Digital Mental Health Tool Acceptance Among University Students in Singapore

Toh Hsiang Benny Tan1,2( )Sufang Lim2Chan Hua Nicholas Vun1
School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore
Joint NTU-WeBank Research Centre on Fintech, Singapore 637335, Singapore
Show Author Information

Abstract

Mental health challenges, accentuated by stress, are escalating in high-income countries, especially among adolescents and university students. Traditional mental health approaches face issues such as scalability and accessibility, making the emergence of digital tools crucial. However, adherence remains a challenge. This study examines the role of technology self-efficacy and digital alliance in influencing the acceptance of digital mental health tools among Singaporean university students. The results provide strong support for the role of digital alliance as a key factor impacting a student’s intention to use mental health tools, as well as technology self-efficacy and digital alliance as serial mediators of task-technology fit and intention to use, highlighting our ever-evolving relationship with technology.

References

[1]
F. D. Davis, A technology acceptance model for empirically testing new end-user information systems: Theory and results, PhD dissertation, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA, 1986.
[2]

F. D. Davis, Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Q., vol. 13, no. 3, pp. 319–340, 1989.

[3]

K. Mathieson, E. Peacock, and W. W. Chin, Extending the technology acceptance model, ACM SIGMIS Database DATABASE Adv. Inf. Syst., vol. 32, no. 3, pp. 86–112, 2001.

[4]

S. Taylor and P. A. Todd, Understanding information technology usage: A test of competing models, Inf. Syst. Res., vol. 6, no. 2, pp. 144–176, 1995.

[5]

K. M. Kuo, C. F. Liu, and C. C. Ma, An investigation of the effect of nurses’ technology readiness on the acceptance of mobile electronic medical record systems, BMC Med. Inform. Decis. Mak., vol. 13, p. 88, 2013.

[6]

S. A. Kamal, M. Shafiq, and P. Kakria, Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM), Technol. Soc., vol. 60, p. 101212, 2020.

[7]

M. El-Wajeeh, P. G. H. Galal-Edeen, and D. H. Mokhtar, Technology acceptance model for mobile health systems, IOSR J. Mob. Comput. Appl., vol. 1, no. 1, pp. 21–33, 2014.

[8]

J. Razmak and C. Bélanger, Using the technology acceptance model to predict patient attitude toward personal health records in regional communities, Inf. Technol. People, vol. 31, no. 2, pp. 306–326, 2018.

[9]

C. Nadal, C. Sas, and G. Doherty, Technology acceptance in mobile health: Scoping review of definitions, models, and measurement, J. Med. Internet Res., vol. 22, no. 7, p. e17256, 2020.

[10]

T. Shemesh and S. Barnoy, Assessment of the intention to use mobile health applications using a technology acceptance model in an Israeli adult population, Telemed. J. E Health, vol. 26, no. 9, pp. 1141–1149, 2020.

[11]

D. L. Goodhue and R. L. Thompson, Task-technology fit and individual performance, MIS Q., vol. 19, no. 2, pp. 213–236, 1995.

[12]

X. Wang, Y. D. Wong, T. Chen, and K. F. Yuen, Adoption of shopper-facing technologies under social distancing: A conceptualisation and an interplay between task-technology fit and technology trust, Comput Hum Behav, vol. 124, p. 106900, 2021.

[13]

D. Tao, Z. Chen, M. Qin, and M. Cheng, Modeling consumer acceptance and usage behaviors of m-health: An integrated model of self-determination theory, task-technology fit, and the technology acceptance model, Healthcare, vol. 11, no. 11, p. 1550, 2023.

[14]

I. Alyoussef, E-learning acceptance: The role of task-technology fit as sustainability in higher education, Sustainability, vol. 13, no. 11, p. 6450, 2021.

[15]

H. C. Lin, X. Han, T. Lyu, W. H. Ho, Y. Xu, T. C. Hsieh, L. Zhu, and L. Zhang, Task-technology fit analysis of social media use for marketing in the tourism and hospitality industry: A systematic literature review, Int. J. Contemp. Hosp. Manag., vol. 32, no. 8, pp. 2677–2715, 2020.

[16]

G. McLean, Examining the determinants and outcomes of mobile app engagement—A longitudinal perspective, Comput. Hum. Behav., vol. 84, pp. 392–403, 2018.

[17]

K. L. Hsiao, What drives smartwatch adoption intention? Comparing Apple and non-Apple watches, Libr. Hi Tech, vol. 35, no. 1, pp. 186–206, 2017.

[18]

A. Bandura, Self-efficacy: Toward a unifying theory of behavioral change, Psychol. Rev., vol. 84, no. 2, pp. 191–215, 1977.

[19]

D. R. Compeau and C. A. Higgins, Computer self-efficacy: Development of a measure and initial test, MIS Q., vol. 19, no. 2, pp. 189–211, 1995.

[20]

M. H. Hsu and C. M. Chiu, Internet self-efficacy and electronic service acceptance, Decis. Support. Syst., vol. 38, no. 3, pp. 369–381, 2004.

[21]
E. Neter and E. Brainin, eHealth literacy: Extending the digital divide to the realm of health information, J. Med. Internet Res., vol. 14, no. 1, p. e19, 2012.
[22]

A. Balapour, I. Reychav, R. Sabherwal, and J. Azuri, Mobile technology identity and self-efficacy: Implications for the adoption of clinically supported mobile health apps, Int. J. Inf. Manag., vol. 49, pp. 58–68, 2019.

[23]

I. Reychav, R. Beeri, A. Balapour, D. R. Raban, R. Sabherwal, and J. Azuri, How reliable are self-assessments using mobile technology in healthcare? The effects of technology identity and self-efficacy, Comput. Hum. Behav., vol. 91, pp. 52–61, 2019.

[24]

E. S. Bordin, The generalizability of the psychoanalytic concept of the working alliance, Psychother. Theory Res. Pract., vol. 16, no. 3, pp. 252–260, 1979.

[25]

S. D’Alfonso, R. Lederman, S. Bucci, and K. Berry, The digital therapeutic alliance and human-computer interaction, JMIR Ment. Health, vol. 7, no. 12, p. e21895, 2020.

[26]

M. Sucala, J. B. Schnur, M. J. Constantino, S. J. Miller, E. H. Brackman, and G. H. Montgomery, The therapeutic relationship in e-therapy for mental health: A systematic review, J. Med. Internet Res., vol. 14, no. 4, p. e110, 2012.

[27]

F. Tong, R. Lederman, S. D’Alfonso, K. Berry, and S. Bucci, Digital therapeutic alliance with fully automated mental health smartphone apps: A narrative review, Front. Psychiatry, vol. 13, p. 819623, 2022.

[28]

P. Henson, H. Wisniewski, C. Hollis, M. Keshavan, and J. Torous, Digital mental health apps and the therapeutic alliance: Initial review, BJPsych Open, vol. 5, no. 1, p. e15, 2019.

[29]
M. T. Dishaw, D. Strong, and D. Bandy, Extending the task-technology fit model with self-efficacy constructs, in Proc. 8th Americas Conf. Information Systems, Dallas, TX, USA, 2002, pp. 1021–1027.
[30]

T. Zhou, Y. Lu, and B. Wang, Integrating TTF and UTAUT to explain mobile banking user adoption, Computers in Human Behavior, vol. 26, no. 4, pp. 760–767, 2010.

[31]

M. T. Dishaw and D. M. Strong, Extending the technology acceptance model with task–technology fit constructs, Inf. Manag., vol. 36, no. 1, pp. 9–21, 1999.

[32]

M. J. Keith, J. S. Babb, P. B. Lowry, C. P. Furner, and A. Abdullat, The role of mobile-computing self-efficacy in consumer information disclosure, Inf. Syst. J., vol. 25, no. 6, pp. 637–667, 2015.

[33]

G. M. Marakas, M. Y. Yi, and R. D. Johnson, The multilevel and multifaceted character of computer self-efficacy: Toward clarification of the construct and an integrative framework for research, Inf. Syst. Res., vol. 9, no. 2, pp. 126–163, 1998.

[34]

J. G. Perle, L. C. Langsam, A. Randel, S. Lutchman, A. B. Levine, A. P. Odland, B. Nierenberg, and C. D. Marker, Attitudes toward psychological telehealth: Current and future clinical psychologists’ opinions of Internet-based interventions, J. Clin. Psychol., vol. 69, no. 1, pp. 100–113, 2013.

[35]

D. Compeau, C. A. Higgins, and S. Huff, Social cognitive theory and individual reactions to computing technology: A longitudinal study, MIS Q., vol. 23, no. 2, pp. 145–158, 1999.

[36]

H. Liang and Y. Xue, Avoidance of information technology threats: A theoretical perspective, MIS Q., vol. 33, no. 1, pp. 71–90, 2009.

[37]

V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, User acceptance of information technology: Toward a unified view, MIS Q., vol. 27, no. 3, pp. 425–478, 2003.

[38]

D. Gefen, E. Karahanna, and D. W. Straub, Trust and TAM in online shopping: An integrated model, MIS Q., vol. 27, no. 1, pp. 51–90, 2003.

[39]

V. Venkatesh and F. D. Davis, A theoretical extension of the technology acceptance model: Four longitudinal field studies, Manag. Sci., vol. 46, no. 2, pp. 186–204, 2000.

[40]

D. H. Shin and F. Biocca, Health experience model of personal informatics: The case of a quantified self, Comput. Hum. Behav., vol. 69, pp. 62–74, 2017.

[41]

P. A. Pavlou, Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model, Int. J. Electron. Commer., vol. 7, no. 3, pp. 101–134, 2003.

[42]

G. C. Moore and I. Benbasat, Development of an instrument to measure the perceptions of adopting an information technology innovation, Inf. Syst. Res., vol. 2, no. 3, pp. 192–222, 1991.

[43]

J. Tavares and T. Oliveira, Electronic health record patient portal adoption by health care consumers: An acceptance model and survey, J. Med. Internet Res., vol. 18, no. 3, p. e49, 2016.

[44]

C. K. Or, B. T. Karsh, D. J. Severtson, L. J. Burke, R. L. Brown, and P. F. Brennan, Factors affecting home care patients’ acceptance of a web-based interactive self-management technology, J. Am. Med. Inform. Assoc., vol. 18, no. 1, pp. 51–59, 2011.

[45]

C. M. Angst and R. Agarwal, Adoption of electronic health records in the presence of privacy concerns: The elaboration likelihood model and individual persuasion, MIS Q., vol. 33, no. 2, pp. 339–370, 2009.

[46]

H. F. Lin, The impact of website quality dimensions on customer satisfaction in the B2C E-commerce context, Total Qual. Manag. Bus. Excell., vol. 18, no. 4, pp. 363–378, 2007.

[47]

H. K. Y. Almathami, K. T. Win, and E. Vlahu-Gjorgievska, Barriers and facilitators that influence telemedicine-based, real-time, online consultation at patients’ homes: Systematic literature review, J. Med. Internet Res., vol. 22, no. 2, p. e16407, 2020.

[48]

V. Venkatesh and H. Bala, Technology acceptance model 3 and a research agenda on interventions, Decis. Sci., vol. 39, no. 2, pp. 273–315, 2008.

[49]

P. A. Pavlou and M. Fygenson, Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior, MIS Q., vol. 30, no. 1, pp. 115–143, 2006.

[50]

S. S. Al-gahtani, Empirical investigation of e-learning acceptance and assimilation: A structural equation model, Appl. Comput. Inform., vol. 12, pp. 27–50, 2016.

[51]

J. Kim and H. A. Park, Development of a health information technology acceptance model using consumers’ health behavior intention, J. Med. Internet Res., vol. 14, no. 5, p. e133, 2012.

[52]

N. Kock and P. Hadaya, Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods, Inf. Syst. J., vol. 28, no. 1, pp. 227–261, 2018.

[53]

W. W. Chin, Commentary: Issues and opinion on structural equation modeling, MIS Q., vol. 22, no. 1, pp. 7–16, 1998.

[54]
R. H. Hoyle, The structural equation modeling approach: Basic concepts and fundamental issues, in Structural Equation Modeling: Concepts, Issues, and Applications, R. H. Hoyle, ed. Thousand Oaks, CA, USA: Sage, 1995, pp. 1–15.
[55]

D. W. Straub, Validating instruments in MIS research, MIS Q., vol. 13, no. 2, pp. 147–169, 1989.

[56]

I. Klopping and E. McKinney, Extending the technology acceptance model and the task-technology fit model to consumer E-commerce, Information Technology, Learning, and Performance Journal, vol. 22, no. 1, pp. 35–48, 2004.

[57]

T. K. Yu and T. Y. Yu, Modelling the factors that affect individuals’ utilisation of online learning systems: An empirical study combining the task technology fit model with the theory of planned behaviour, Br. J. Educ. Technol., vol. 41, no. 6, pp. 1003–1017, 2010.

[58]

A. S. Ahadzadeh, S. P. Sharif, F. S. Ong, and K. W. Khong, Integrating health belief model and technology acceptance model: An investigation of health-related Internet use, J. Med. Internet Res., vol. 17, no. 2, p. e45, 2015.

[59]

S. B. Goldberg, S. A. Baldwin, K. M. Riordan, J. Torous, C. J. Dahl, R. J. Davidson, and M. J. Hirshberg, Alliance with an unguided smartphone app: Validation of the digital working alliance inventory, Assessment, vol. 29, no. 6, pp. 1331–1345, 2022.

[60]

D. Becker, Acceptance of mobile mental health treatment applications, Procedia Comput. Sci., vol. 98, pp. 220–227, 2016.

[61]

D. Gefen, D. Straub, and M. C. Boudreau, Structural equation modeling and regression: Guidelines for research practice, Commun. Assoc. Inf. Syst., vol. 4, no. 1, p. 7, 2000.

[62]

J. F. Hair, C. Ringle, and M. Sarstedt, PLS-SEM: Indeed a silver bullet, Journal of Marketing theory and Practice, vol. 19, no. 2, pp. 139–152, 2011.

[63]

J. C. Anderson and D. W. Gerbing, Structural equation modeling in practice: A review and recommended two-step approach, Psychol. Bull., vol. 103, no. 3, pp. 411–423, 1988.

[64]
J. F. Hair, G. T. M. Hult, C. M. Ringle, and M. Sarstedt, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Thousand Oaks, CA, USA: Sage Publications, 2021.
[65]
A. D. R. McQuarrie and C. L. Tsai, Regression and Time Series Model Selection. Singapore: World Scientific Publishing Co. Pte. Ltd., 1998.
International Journal of Crowd Science
Pages 101-109
Cite this article:
Tan THB, Lim S, Vun CHN. Role of Technology Self-Efficacy and Digital Alliance in Digital Mental Health Tool Acceptance Among University Students in Singapore. International Journal of Crowd Science, 2024, 8(3): 101-109. https://doi.org/10.26599/IJCS.2024.9100001

304

Views

37

Downloads

0

Crossref

0

Scopus

Altmetrics

Published: 19 August 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return