IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i21p13764-d950772.html
   My bibliography  Save this article

Factors Affecting the Use of Social Networks and Its Effect on Anxiety and Depression among Parents and Their Children: Predictors Using ML, SEM and Extended TAM

Author

Listed:
  • Evon M. Abu-Taieh

    (Department of Computer Information Systems, Faculty of Information Technology and Systems, The University of Jordan, Aqaba 77110, Jordan)

  • Issam AlHadid

    (Department Information Technology, Faculty of Information Technology and Systems, The University of Jordan, Aqaba 77110, Jordan)

  • Ra’ed Masa’deh

    (Department of Management Information Systems, School of Business, The University of Jordan, Amman 77110, Jordan)

  • Rami S. Alkhawaldeh

    (Department of Computer Information Systems, Faculty of Information Technology and Systems, The University of Jordan, Aqaba 77110, Jordan)

  • Sufian Khwaldeh

    (Department Information Technology, Faculty of Information Technology and Systems, The University of Jordan, Aqaba 77110, Jordan
    Department Information Technology, Faculty of Information Technology and Systems, University of Fujairah, Fujairah P.O. Box 2202, United Arab Emirates)

  • Ala’aldin Alrowwad

    (Department of Business Management, School of Business, The University of Jordan, Aqaba 77110, Jordan)

Abstract

Previous research has found support for depression and anxiety associated with social networks. However, little research has explored parents’ depression and anxiety constructs as mediators that may account for children’s depression and anxiety. The purpose of this paper is to test the influence of different factors on children’s depression and anxiety, extending from parents’ anxiety and depression in Jordan. The authors recruited 857 parents to complete relevant web survey measures with constructs and items and a model based on different research models TAM and extended with trust, analyzed using SEM, CFA with SPSS and AMOS, and ML methods, using the triangulation method to validate the results and help predict future applications. The authors found support for the structural model whereby behavioral intention to use social media influences the parent’s anxiety and depression which correlate to their offspring’s anxiety and depression. Behavioral intention to use social media can be enticed by enjoyment, trust, ease of use, usefulness, and social influences. This study is unique in exploring rumination in the context of the relationship between parent–child anxiety and depression due to the use of social networks.

Suggested Citation

  • Evon M. Abu-Taieh & Issam AlHadid & Ra’ed Masa’deh & Rami S. Alkhawaldeh & Sufian Khwaldeh & Ala’aldin Alrowwad, 2022. "Factors Affecting the Use of Social Networks and Its Effect on Anxiety and Depression among Parents and Their Children: Predictors Using ML, SEM and Extended TAM," IJERPH, MDPI, vol. 19(21), pages 1-27, October.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:13764-:d:950772
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/21/13764/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/21/13764/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Weixin Yao & Longhai Li, 2014. "A New Regression Model: Modal Linear Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 656-671, September.
    2. Wilert Puriwat & Suchart Tripopsakul, 2021. "Explaining Social Media Adoption for a Business Purpose: An Application of the UTAUT Model," Sustainability, MDPI, vol. 13(4), pages 1-13, February.
    3. Alalwan, Ali Abdallah & Baabdullah, Abdullah M. & Rana, Nripendra P. & Tamilmani, Kuttimani & Dwivedi, Yogesh K., 2018. "Examining adoption of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness and trust," Technology in Society, Elsevier, vol. 55(C), pages 100-110.
    4. Shin-Il Lim & Sookyung Jeong, 2022. "The Relationship between Korean Parents’ Smartphone Addiction and That of Their Children: The Mediating Effects of Children’s Depression and Social Withdrawal," IJERPH, MDPI, vol. 19(9), pages 1-12, May.
    5. Min-Jung Kwak & Hyun Cho & Dai-Jin Kim, 2022. "The Role of Motivation Systems, Anxiety, and Low Self-Control in Smartphone Addiction among Smartphone-Based Social Networking Service (SNS) Users," IJERPH, MDPI, vol. 19(11), pages 1-16, June.
    6. Lifang Peng & Qinyu Liao & Xiaorong Wang & Xuanfang He, 2016. "Factors affecting female user information adoption: an empirical investigation on fashion shopping guide websites," Electronic Commerce Research, Springer, vol. 16(2), pages 145-169, June.
    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. Sameena Naaz & Sarah Ali Khan & Farheen Siddiqui & Shahab Saquib Sohail & Dag Øivind Madsen & Asad Ahmad, 2022. "OdorTAM: Technology Acceptance Model for Biometric Authentication System Using Human Body Odor," IJERPH, MDPI, vol. 19(24), pages 1-17, December.

    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. Ra’ed Masa’deh & Dmaithan A. AlMajali & Abdullah A. M. AlSokkar & Mohammad Alshinwan & Maha Shehadeh, 2023. "Antecedents of Intention to Use E-Auction: An Empirical Study," Sustainability, MDPI, vol. 15(6), pages 1-11, March.
    2. Dubey, Prince & Bajpai, Naval & Guha, Sanjay & Kulshreshtha, Kushagra, 2020. "Mapping gender and marital roles on customer delight by value perception for mobile technology in India," Technology in Society, Elsevier, vol. 62(C).
    3. Cheng-Feng Cheng & Chien-Che Huang & Ming-Chang Lin & Ta-Cheng Chen, 2023. "Exploring Effectiveness of Relationship Marketing on Artificial Intelligence Adopting Intention," SAGE Open, , vol. 13(4), pages 21582440231, December.
    4. Baozhuang Niu & Jingmai Wang & Carman K. M. Lee & Lei Chen, 2019. "“Product + logistics” bundling sale and co-delivery in cross-border e-commerce," Electronic Commerce Research, Springer, vol. 19(4), pages 915-941, December.
    5. Han-Jen Niu & Fei-Hsu Sun Hung & Po-Ching Lee & Yensen Ni & Yuhsin Chen, 2023. "Eco-Friendly Transactions: Exploring Mobile Payment Adoption as a Sustainable Consumer Choice in Taiwan and the Philippines," Sustainability, MDPI, vol. 15(24), pages 1-18, December.
    6. Yang, Jing & Tian, Guoliang & Lu, Fang & Lu, Xuewen, 2020. "Single-index modal regression via outer product gradients," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    7. Katarzyna Lukiewska, 2022. "Impact of Labor Productivity on the Export Performance of the Food Industry in EU Member States," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 74-83.
    8. Atandile Ngubelanga & Rodney Duffett, 2021. "Modeling Mobile Commerce Applications’ Antecedents of Customer Satisfaction among Millennials: An Extended TAM Perspective," Sustainability, MDPI, vol. 13(11), pages 1-29, May.
    9. Neng-Chieh Chang, 2020. "The Mode Treatment Effect," Papers 2007.11606, arXiv.org.
    10. Muhammad Umar Usman & Pawan Kumar, 2021. "Factors Influencing Consumer Intention to Shop Online in Nigeria: A Conceptual Study," Vision, , vol. 25(4), pages 407-414, December.
    11. Yen-Chi Chen, 2017. "Modal Regression using Kernel Density Estimation: a Review," Papers 1710.07004, arXiv.org, revised Dec 2017.
    12. Hu Yang & Ning Li & Jing Yang, 2020. "A robust and efficient estimation and variable selection method for partially linear models with large-dimensional covariates," Statistical Papers, Springer, vol. 61(5), pages 1911-1937, October.
    13. Long Yang & Haiyang Lu & Sangui Wang & Meng Li, 2021. "Mobile Internet Use and Multidimensional Poverty: Evidence from A Household Survey in Rural China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 158(3), pages 1065-1086, December.
    14. Jalan, Akanksha & Matkovskyy, Roman & Urquhart, Andrew & Yarovaya, Larisa, 2023. "The role of interpersonal trust in cryptocurrency adoption," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 83(C).
    15. Nisar Ahmed Dahri & Muhammad Saleem Vighio & Jairam Das Bather & Aijaz Ahmed Arain, 2021. "Factors Influencing the Acceptance of Mobile Collaborative Learning for the Continuous Professional Development of Teachers," Sustainability, MDPI, vol. 13(23), pages 1-23, November.
    16. Wang, Kangning & Li, Shaomin, 2021. "Robust distributed modal regression for massive data," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    17. Dading Jatimoy & Fatchur Rohman & Atim Djazuli, 2021. "The effect of perceived ease of use on continuance intention through perceived usefulness and trust: A study on Klikindomaret service users in Malang City," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 10(4), pages 430-437, June.
    18. Wang, Fang & Du, Zhao & Wang, Shan, 2023. "Information multidimensionality in online customer reviews," Journal of Business Research, Elsevier, vol. 159(C).
    19. Ullah, Aman & Wang, Tao & Yao, Weixin, 2023. "Semiparametric partially linear varying coefficient modal regression," Journal of Econometrics, Elsevier, vol. 235(2), pages 1001-1026.
    20. Francesco Dotto & Alessio Farcomeni & Luis Angel García-Escudero & Agustín Mayo-Iscar, 2017. "A fuzzy approach to robust regression clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(4), pages 691-710, December.

    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:gam:jijerp:v:19:y:2022:i:21:p:13764-:d:950772. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.