The impact of intrinsic motivation and social interaction on parents’ engagement with mobile health apps in Taiwan

Abstract

Objective

Although mobile health applications (mHealth apps) are widely used, few studies have explored their adoption from parents’ perspectives for early disease detection in toddlers.

Method

This study employs the Uses and Gratifications (U&G) theory to identify key adoption factors. Structural equation modeling (SEM) was conducted using AMOS SEM 26 and SPSS 22 for statistical analysis.

Results

A survey of 308 parents using mHealth apps revealed that intrinsic motivation does not moderate the relationship between information-seeking and satisfaction, whereas social interaction positively moderates this relationship. Parents with strong social interaction tendencies exhibit higher satisfaction in information-seeking.

Conclusion

This study contributes to the literature on mHealth adoption, offering insights for developers and policymakers to enhance early detection initiatives and improve parental engagement with mHealth apps.

Keywords

information seeking intrinsic motivation social interaction mHealth

Background

With the rapid growth of mobile technologies, mobile health (mHealth) applications have become an essential tool for parents seeking timely, reliable health information about their children’s well-being.¹ Parents frequently use mHealth apps to obtain information on child development, disease prevention, and health care decision-making, motivated by convenience, immediacy, and accessibility to expert-verified content.² As demonstrated in recent studies, these digital tools can enhance parents’ knowledge and health literacy, support decision-making, and facilitate effective communication with healthcare providers.³

The quality and functionality of mHealth apps targeted at parents, however, vary considerably. A systematic review of publicly available mHealth apps for parents of medically complex infants found that while most apps offered accurate information and functional usability, they performed poorly in engagement, highlighting a critical gap between parental needs and app design.² Engagement closely linked to how well an app meets users’ psychological and social needs has thus become a vital factor in understanding parental satisfaction and continued app use.

The Uses and Gratifications (U&G) theory provides a robust framework for explaining why and how individuals adopt and engage with media technologies to fulfill specific needs for information, interaction, and satisfaction.^4,5 In the context of mHealth, parents actively select and use digital resources to meet needs such as information-seeking, reassurance, and social connection. Recent research has demonstrated that parents’ satisfaction with mHealth apps is driven by their information-seeking behaviors and the social interaction opportunities these platforms afford, while intrinsic motivation plays a less central role.⁶ This suggests that the relational and participatory aspects of app use such as connecting with other parents or interacting with health professionals enhance perceived usefulness and sustained engagement.

Building on prior empirical findings, this study applies the U&G theory to explore the psychological and social factors influencing Taiwanese parents’ engagement with mHealth apps. This research seeks to understand the mechanisms underlying parental adoption and satisfaction with mHealth technologies. Specifically, it examines how information-seeking, intrinsic motivation, and social interaction contribute to parental satisfaction and engagement, thereby offering evidence to guide future mHealth app development and public health promotion strategies.

mHealth apps have transformed healthcare by providing remote diagnostic tools, enhancing self-management, and increasing access to health information.⁷ Prior studies have examined mHealth apps in the context of chronic disease management.^8,9 Studies have explored parents’ applications on mHealth. Parents commonly seek information to support their children’s health and well-being.¹⁰ Parents commonly use the internet to search for information about their child’s health-related symptoms and guide parental health-related decisions.¹¹ Parents combine all available information resources, including online sources, to enhance their knowledge or verify the validity of information received about their child’s health.¹² Even though health literacy could affect parents’ technology adoption, the study showed that parents’ desire for use of technologies for provider communication was overall high and did not differ by health literacy.¹³ The study also suggested that the incorporation of eHealth literacy courses into parenting programs is crucial to promote the eHealth literacy of parents.¹⁴ Moreover, trustworthiness and accessibility emerged as critical criteria for health resources.¹⁵

Despite the proliferation of mHealth apps, few studies have examined how parental motivations, particularly intrinsic motivation and social interaction, influence engagement with mHealth apps.⁶ This study seeks to bridge this gap by exploring factors influencing parental engagement in Taiwan using the U&G theory.⁴

U&G theory posits that individuals adopt media technologies to fulfill specific needs, including entertainment, information-seeking, and social interaction.⁵ Guided by this framework, we hypothesize that Figure 1:

1. H1: Information-seeking has a positive relationship with intrinsic motivation.

2. H2: Intrinsic motivation positively correlates with satisfaction in using mHealth apps.

3. H3: Information-seeking positively affects satisfaction in using mHealth apps.

4. H4: Intrinsic motivation mediates the effect of information-seeking on satisfaction.

5. H5: Social interaction moderates the relationship between information-seeking and satisfaction.

Figure 1.

Research model.

Method

Participants were recruited through the inclusion criteria required respondents to be parents or caregivers of children aged 0–3 who had experience using mHealth apps. Data collection occurred in May 2022, yielding 308 valid responses. Convenience sampling was adopted by questionnaires system, which invalid submissions were filtered out based on the predefined criteria of the online questionnaire system. The restriction to filter out invalid sample: respondents should be involved in babies’ care and visit the selected Apps for experience response.

Statistical analysis

Descriptive statistics were calculated using SPSS 22. Structural equation modeling (SEM) was conducted using AMOS 26 to test hypothesized relationships. Confirmatory factor analysis ensured construct validity, and hierarchical regression was used to analyze moderation effects.

A structured questionnaire assessed four key constructs: information-seeking, intrinsic motivation, social interaction, and satisfaction. Items were adapted from prior validated studies.^16,17 Responses were measured on a five-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree).

Results

Table 1 shows demographic information. Notably, there is a slightly higher representation of females (54.9%) compared with males (45.1%). The age distribution falls within the 31-40 age group, indicating that this range constitutes most of the population. Most respondents hold a bachelor’s degree, highlighting it as the predominant level of education among the respondents. Furthermore, most participants identified themselves as employees, making it the most prevalent career attribution in the sample.

Table 1.

Demographic profile of respondents.

Demographic	Variables	Frequency	Percent
Gender	Male	139	45.1
Gender	Female	169	54.9
Age	20-30	79	25.6
	31-40	132	42.9
	41-50	78	25.3
	51-60	13	4.2
	Others	6	1.9
Education	Middle School	4	1.3
	Junior College	50	16.2
	Bachelor’s Degree	215	69.8
	Master’s Degree or above	39	12.7
Career	Employee	199	64.6
	Manager	34	11.0
	COE	5	1.6
	Freelance	24	7.8
	Full-time parents	27	8.8
	Others	19	6.2

Note. n = 308.

Figure 2 and Tables 2–4 illustrate estimated values for each construct, which is crucial in our research methodology. We initiated the confirmatory factor analysis to assess the suitability of measuring the constructs and items analyzed in our model. Figure 2 shows that they exhibit factor loadings on their respective constructs at >0.5 threshold.¹⁸ The robustness of this factor loading underscores the suitability of the selected elements to measure the intended constructs. The confidence intervals developed for the paired correlations among constructs did not include the value of 1, consistent with the principles of discriminant validity,¹⁹ confirming the soundness of our research model (Table 2).

Figure 2.

Relations between constructs and factors.

Table 2.

Squared multiple correlations.

Parameter	Estimate	Lower	Upper	P
InfoSeek <—> So.Int.	.781	.671	.874	.001
InfoSeek <—> Satisfied	.796	.702	.886	.001
InfoSeek <—> Motiva.	.799	.651	.917	.001
So.Int. <—> Satisfied	.731	.632	.816	.001
So.Int. <—> Motiva.	.813	.706	.908	.001
Satisfied <—> Motiva.	.789	.687	.888	.001

(※Adopt Bias-corrected percentile method).

Table 3.

Constructs properties.

Constructs	Cronbach’s alpha	Composite reliability	Average variance extracted
Info seeking	.732	.735	.481
Social Interaction	.795	.797	.568
Motivation	.664	.673	.41
Satisfaction	.833	.836	.63

Table 4.

Assessment of normality.

Variable	Min	Max	skew	c.r.	Kurtosis	c.r.
Mot.1	2.000	5.000	−.316	−2.266	−.103	−.368
Mot.2	1.000	5.000	−.384	−2.752	.714	2.559
Mot.3	1.000	5.000	−.506	−3.624	.560	2.004
Inform3	1.000	5.000	−.749	−5.366	1.201	4.304
Sat1	2.000	5.000	−.299	−2.146	.420	1.504
Sat2	2.000	5.000	−.346	−2.482	.875	3.134
Sat3	2.000	5.000	−.248	−1.774	.515	1.844
ineraction3	1.000	5.000	−.497	−3.564	.212	.758
ineraction2	1.000	5.000	−.351	−2.513	.227	.813
ineraction1	1.000	5.000	−.402	−2.880	.423	1.515
Inform2	1.000	5.000	−.974	−6.978	2.626	9.406
Inform1	1.000	5.000	−.907	−6.498	2.681	9.605
Multivariate					63.936	30.607

Table 3 reveals that almost all Cronbach’s alpha values for the constructs were greater than the 0.7 threshold, indicating good internal consistency.^20,21 When a construct is >0.6, it aligns with acceptance criteria,²² which is deemed as acceptable when the values are>0.5. Experts asserted that a value <0.5 is unacceptable.²³

Additionally, the overall composite reliability values meet the 0.6 standard.²⁴ The average variance extracted (AVE) values are close to the reference value of 0.5 (Table 3), indicating that AVE values <0.5 but with composite reliability >0.6, still suffice for convergent validity.²⁵ This evidence indicates that all the constructs within our research model possess acceptable convergent validity.

Table 4 shows the normality assessment and reveals that the absolute values of the skewness indicators for all variables are <3 and the kurtosis values are <10; the pattern indicates a normal distribution. Furthermore, the multivariate value, 30.67, aligns with the criterion for the multivariate normal distribution, satisfying the condition (<the summary result of 12 × 12 + 2 = 146), as interpreted by scholars.²⁶ These findings affirm that our study variables exhibit characteristics consistent with a normal distribution.

Table 5 displays the correlation matrix, affirming that constructs exhibit significant correlations at 0.01. This robust pattern of significant correlations further substantiates the acceptable convergent validity of constructs within our research model. Cumulative evidence from various analyses strengthens the reliability and validity of our research framework, confirming the validity of our findings.

Table 5.

Means, SD, Pearson correlations, and discriminant validity.

Constructs	1	2	3	4
1. Inform Seek	.693
2. Social Interaction	.599**	.753
3. Motivation	.559**	.596**	.640
4. Satisfaction	.623**	.596**	.607**	.793

Note. n = 308; the square roots of AVE are located along the diagonal; **p < .01.

Table 6 presents the outcomes of direct effects, specifically Models 6-a and 6-b. In Model 6-a, information seeking demonstrated a notable positive impact on intrinsic satisfaction (β = 0.559***, p = 0.000), thus corroborating the support for H1. When transitioning to Model 6-b, information seeking (β = 0.413***, p = 0.000) and intrinsic motivation (β = 0.376***, p = 0.000) positively influenced overall satisfaction, providing substantial support for H2 and H3. These results underscore the crucial roles of information seeking and intrinsic motivation in shaping parents’ satisfaction with M-health apps.

Table 6.

Direct effects coefficients.

Variables	Model 6-a			Model 6-b
Variables	Standardized coefficients	t	p-values	Standardized coefficients	t	p-values
IS	.559	11.783	.000	.413	8.348	.000
IM				.376	7.598	.000

Note. Dependent variable: satisfaction, IS: information seeking, IM: intrinsic motivation.

To gauge the moderation effect of intrinsic motivation and social interaction on the relationship between information seeking and satisfaction, we conducted a comprehensive hierarchical regression test (Tables 7, 8). Table 7 shows that intrinsic motivation does not significantly moderate (β = 0.085, p = 0.056) the impact of information seeking on satisfaction. Consequently, H4 is unsupported as a highlight of these findings. Conversely, Table 8 reveals that social interaction positively moderates (β = 0.168***, p = 0.000) the impact of information seeking on satisfaction, substantiating support for H5. These results underscore the nuanced interplay between social interaction and information seeking in influencing parent satisfaction levels using M-health apps.

Table 7.

The mediation effects of intrinsic motivation.

Variables	Model 1		Model 2		Model 3
Variables	β	P-value	β	P-value	β	P-value
IS	.623	.000***	.413	.000***	.449	.000***
IM			.376	.000***	.371	.000***
IS*SI					.085	.056

Note. Dependent variable: satisfaction, IS: information seeking, IM: intrinsic motivation.

**p<.01, ***p<.001.

Table 8.

The mediation effects of social interaction.

Variables	Model 1		Model 2		Model 3
Variables	β	P-value	β	P-value	β	P-value
IS	.623	.000***	.415	.000***	.476	.000***
SI			.347	.000***	.335	.000***
IS*SI					.168	.000***

Note. Dependent variable: satisfaction, IS: information seeking; SI: social interaction.

*p<.05, **p<.01, ***p<.001.

Figure 3 visually represents the interaction diagram, elucidating that when social interaction is high, the impact of information seeking on satisfaction is significant, whereas, in low social interaction, the impact is nonsignificant. This graphical representation provides a clear intuitive understanding of the nuanced dynamics at play, underscoring the conditional nature of the relationship between information seeking and satisfaction across varying levels of social interaction.

• H1: Information-seeking positively influences intrinsic motivation (β = 0.559, p < 0.001).

• H2: Intrinsic motivation significantly predicts satisfaction (β = 0.376, p < 0.001).

• H3: Information-seeking directly affects satisfaction (β = 0.413, p < 0.001).

• H4: No significant mediation effect of intrinsic motivation.

• H5: Social interaction positively moderates the relationship between information-seeking and satisfaction (β = 0.168, p < 0.001).

Figure 3.

Interaction plot.

Discussion

This study highlights the crucial role of social interaction in enhancing parental satisfaction with mobile health (mHealth) applications. The findings indicate that while information-seeking behavior and intrinsic motivation are both significant predictors of satisfaction, only social interaction exerts a moderating effect on this relationship. In contrast, intrinsic motivation does not mediate the association between information-seeking and satisfaction. These results suggest that parents’ engagement with mHealth apps is shaped more by the social and relational gratifications they experience such as peer support, reassurance, and connection with other caregivers or professionals than by internal enjoyment or curiosity alone.

This result aligns with the standpoint of previous study,² which found that mHealth apps for parents of medically complex infants performed well in terms of accuracy and functionality but scored low in user engagement. Their systematic review emphasized that most apps fail to maintain sustained interaction once basic informational needs are met. Likewise, the other study³ reported that parents who used a child healthcare information app demonstrated higher health literacy and confidence in making medical decisions. These findings collectively support the notion that parental engagement is driven by the perceived usefulness and interactive qualities of mHealth tools, especially when these tools enable parents to exchange information and emotional support.

The observed importance of information-seeking corroborates prior research showing that parents frequently turn to online resources to locate, compare, and validate health information regarding their children.^2,10,11 Within the Uses and Gratifications (U&G) theoretical framework,^4,5 such behavior represents a cognitive gratification—where users actively adopt digital technologies to satisfy their informational and problem-solving needs. In this context, mHealth apps serve not merely as repositories of medical facts but as interactive environments that address multiple parental needs, including reassurance, social belonging, and perceived competence.

The moderating role of social interaction underscores that user satisfaction increases when mHealth apps facilitate community participation and dialogue. Parents are more likely to feel confident and emotionally supported when they can interact with others experiencing similar concerns. This observation corresponds with a specific research, which found that emotional connection and relational bonding significantly enhance sustained engagement with mHealth apps.⁶ Furthermore, it resonates with local research in Taiwan,¹⁴ which demonstrated that higher eHealth literacy among parents leads to more effective and confident online health information-seeking behaviors. Together, these findings suggest that mHealth apps that foster trust, encourage feedback, and provide peer reassurance can substantially improve satisfaction and engagement.

The social component of mHealth also carries important essential cultural implications. In many Asian contexts, parents may hesitate to openly discuss their children’s health conditions due to stigma or social expectations.²⁷ Digital health platforms provide a discreet and accessible channel for these parents to share experiences and seek guidance without fear of judgment. By enabling supportive social exchanges, mHealth apps can alleviate isolation and foster a sense of belonging. This finding explains why social interaction, rather than intrinsic motivation, emerged as a significant factor in this study.

From a practical perspective, the results offer several actionable implications:

1. For Developers: Incorporate interactive and socially oriented features such as discussion boards, experience-sharing spaces, or direct messaging to strengthen engagement. These tools can transform apps from static information portals into dynamic, community-driven platforms.

2. For Healthcare Organizations: Collaborate with mHealth platforms to provide verified information and facilitate professional consultation. Studies highlighted that maintaining consistent communication channels between users and healthcare providers promotes adherence, trust, and satisfaction.^8,9

3. For Policymakers: Establish guidelines ensuring content accuracy, privacy protection, and equitable access. Research emphasized that policy support is crucial to sustain app quality and reach vulnerable or underserved parent groups.^7,15

Limitations and future research

This study’s findings should be interpreted in light of certain limitations. First, data collection relied on an online self-administered survey, which may introduce sampling bias by over-representing tech-savvy parents. Future studies could adopt mixed methods, combining quantitative surveys with in-depth interviews to capture richer user insights. Second, the research focused on parents of infants and toddlers in Taiwan; thus, cultural or demographic differences may limit generalizability. Comparative studies across different populations could clarify whether the observed relationships hold in other cultural settings.

Third, satisfaction and engagement were assessed through self-reported measures. Future research should integrate objective behavioral data (e.g., app usage logs) to verify reported engagement levels and identify behavioral patterns. Finally, although this study applied U&G theory, additional constructs such as trust, privacy concerns, and habit formation could be incorporated into future models to enhance explanatory depth.

Conclusion

This study identifies critical factors influencing parents’ engagement with mHealth apps, and emphasizes the moderating role of social interaction. Findings suggest that policymakers and developers should consider social elements to improve parental satisfaction. Moreover, our research results indicate that using a specific mHealth app to monitor infants for potential retinoblastoma risks can facilitate early disease detection and inform suitable treatment actions to prevent disease progression. The mHealth app provides a more friendly channel for Asian parents who are not good at expressing themselves regarding concerns of their children’s disease status, aiming to detect and treat the disease early. Future research should also extend this framework to diverse healthcare technologies.

Supplemental material

Supplemental Material - The impact of intrinsic motivation and social interaction on parents’ engagement with mobile health apps in Taiwan

Supplemental Material for The impact of intrinsic motivation and social interaction on parents’ engagement with mobile health apps in Taiwan by Chia-San Chang, Ying-Chun Li and Tsuang Kuo in Health Informatics Journal.

Footnotes

Acknowledgements

The author wishes to thank the participants in the study.

ORCID iDs

Chia-San Chang

Ying-Chun Li

Tsuang Kuo

Ethical considerations

This study was approved by the ethical Institutional Review Board [Kaohsiung Veterans General Hospital, Kaohsiung V. G.H Institutional Review Board] (Approval No. [KSVGH21-CT7-08]). Data collection adhered to ethical guidelines ensuring confidentiality and voluntary participation.

Consent to participate

All participants provided written informed consent before participation.

Author contributions

Chia-San Chang: Conceptualization, Methodology, Investigation, Formal analysis. Writing, Original Draft, and Editing, Project administration.

Dr. Ying-Chun Li: Visualization, Investigation, Supervision, and Review.

Dr. Tsuang Kuo: Conceptualization, Investigation, Supervision, and Review.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Declaration of generative AI in scientific writing

During the preparation of this manuscript, the authors used Grammarly to assist in correcting the grammar. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

Supplemental material

Supplemental material for this article is available online.

References

Bernhardt

Felter

. Online pediatric information seeking among mothers of young children: results from a qualitative study using focus groups. Journal of medical Internet research 2004; 6(1): e36. https://doi.org/10.2196/jmir.6.1.e7

McKechnie

Swanson

Jantraporn

, et al. A systematic review and evaluation of mobile health apps designed for parents who are preparing or caring for medically complex infants. Mhealth 2025; 11: 38. https://doi.org/10.21037/mhealth-24-84

Sakamoto

Ishikawa

Suzuki

. Evaluation of parents’ use of a child health care information app and their health literacy: Cross-sectional study. JMIR Pediatrics and Parenting 2024; 7(1): e48478. https://doi.org/10.2196/48478

Blumler

Katz

(eds). The Uses of Mass Communications: Current Perspectives on Gratifications Research. Sage Annual Reviews of Communication Research 1974; III.

Katz

Blumler

Gurevitch

. Uses and gratifications research. The public opinion quarterly 1973; 37(4): 509–523. https://doi.org/10.1086/268109

Zhang

, et al. Patients’ emotional bonding with MHealth apps: An attachment perspective on patients’ use of MHealth applications. International Journal of Information Management 2020; 51: 102054. https://doi.org/10.1016/j.ijinfomgt.2019.102054

Martinez-Perez

de la Torre-Diez

Lopez-Coronado

. Mobile health applications for the most prevalent conditions by the World Health Organization: review and analysis. J Med Internet Res 2013; 15(6): e120. https://doi.org/10.2196/jmir.2600

Cafazzo

Casselman

Hamming

, et al. Design of an mHealth app for the self-management of adolescent type 1 diabetes: a pilot study. J Med Internet Res 2012; 14(3): e70. https://doi.org/10.2196/jmir.2058

Anstey Watkins

Goudge

Gomez-Olive

, et al. mHealth text and voice communication for monitoring people with chronic diseases in low-resource settings: a realist review. BMJ Glob Health 2018; 3(2): e000543. https://doi.org/10.1136/bmjgh-2017-000543

10.

Hendry

Straker

Bourne

, et al. Parental practices and perspectives on health and digital technology use information seeking for children aged 0–36 months. Health Promotion Journal of Australia 2024; 35(4): 1174–1183. https://doi.org/10.1002/hpja.849

11.

Kubb

Foran

. Online health information seeking by parents for their children: systematic review and agenda for further research. Journal of Medical Internet Research 2020; 22(8): e19985. https://doi.org/10.2196/19985

12.

Baumann

Jaks

Robin

, et al.

Parents’ health information seeking behaviour–does the child’s health status play a role?

BMC family practice 2020; 21(1): 266. https://doi.org/10.1186/s12875-020-01342-3

13.

Meyers

Glick

Mendelsohn

, et al. Parents’ use of technologies for health management: a health literacy perspective. Academic pediatrics 2020; 20(1): 23–30. https://doi.org/10.1016/j.acap.2019.01.008

14.

Chang

Chiu

Chen

, et al. Relationship Between Parental and Adolescent eHealth Literacy and Online Health Information Seeking in Taiwan. Cyberpsychol Behav Soc Netw 2015; 18(10): 618–624. https://doi.org/10.1089/cyber.2015.0110

15.

Zysset

Schwärzler

Dratva

. Seeking Health in a Digital World: Exploring Immigrant Parents’ Quest for Child Health Information—A Scoping Review. International journal of environmental research and public health 2023; 20(19): 6804. https://doi.org/10.3390/ijerph20196804

16.

Lin

H-F

Chen

C-H

. Combining the technology acceptance model and uses and gratifications theory to examine the usage behavior of an augmented reality tour-sharing application. Symmetry 2017; 9(7): 113. https://doi.org/10.3390/sym9070113

17.

Pelletier

Rocchi

Vallerand

, et al. Validation of the revised sport motivation scale (SMS-II). Psychology of sport and exercise 2013; 14(3): 329–341. https://doi.org/10.1016/j.psychsport.2012.12.002

18.

Falk

Miller

. A primer for soft modeling. University of Akron Press, 1992.

19.

Torkzadeh

Koufteros

Pflughoeft

. Confirmatory analysis of computer self-efficacy. Structural Equation Modeling 2003; 10(2): 263–275. https://doi.org/10.1207/s15328007sem1002_6

20.

Chin

. The partial least squares approach to structural equation modeling. Modern methods for business research 1998; 295(2): 295–336.

21.

Straub

. Validating instruments in MIS research. MIS quarterly 1989; 13: 147–169. https://doi.org/10.2307/248922

22.

Gliem

(eds). Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales. Midwest Research-to-Practice Conference in Adult, Continuing, and Community Education. The Ohio State University, Columbus, OH, October 8-10, 2003.

23.

George

Mallery

. SPSS for Windows step by step: A simple guide and reference. 11.0 update. 4th ed. Allyn and Bacon, 2003, p. 2003.

24.

Fornell

Larcker

. Structural equation models with unobservable variables and measurement error: Algebra and statistics. Sage publications Sage CA, 1981.

25.

Huang

C-C

Wang

Y-M

T-W

, et al. An empirical analysis of the antecedents and performance consequences of using the moodle platform. International Journal of Information and Education Technology 2013; 3(2): 217–221. https://doi.org/10.7763/ijiet.2013.v3.267

26.

Kuo

Y-K

Kuo

T-H

Wang

J-H

, et al. The Antecedents of University Students’ E-Learning Outcome under the COVID-19 Pandemic: Multiple Mediation Structural Path Comparison. Sustainability 2022; 14(24): 16794. https://doi.org/10.3390/su142416794

27.

Giannotti

Bonatti

Tanaka

, et al. Parenting stress and social style in mothers and fathers of children with autism spectrum disorder: A cross-cultural investigation in Italy and Japan. Brain sciences 2021; 11(11): 1419. https://doi.org/10.3390/brainsci11111419

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