IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v10y2018i11p3928-d179035.html
   My bibliography  Save this article

Exploring Users’ Self-Disclosure Intention on Social Networking Applying Novel Soft Computing Theories

Author

Listed:
  • Yang-Chieh Chin

    (Department of Commerce Technology and Management, Chihlee University of Technology, New Taipei City 22050, Taiwan)

  • Wen-Zhong Su

    (Department of Business Administration, Chihlee University of Technology, New Taipei City 22050, Taiwan)

  • Shih-Chih Chen

    (National Kaohsiung University of Science & Technology, Kaohsiung 82444, Taiwan)

  • Jianing Hou

    (Business School, University of Hubei, Wuhan 430062, China)

  • Yu-Chuan Huang

    (Department of Accounting Information, Southern Taiwan University of Science and Technology, Tainan City 71005, Taiwan)

Abstract

In recent years, users have increasingly focused on the privacy of social networking sites (SNS); users have reduced their self-disclosure intention. To attract users, SNS rely on active platforms that collect accurate user information, even though that information is supposed to be private. SNS marketers must understand the key elements for sustainable operation. This study aims to understand the influence of motivation (extrinsic and intrinsic) and self-disclosure on SNS through soft computing theories. First, based on a survey of 1108 users of SNS, this study used a dominance-based rough set approach to determine decision rules for self-disclosure intention on SNS. In addition, based on 11 social networking industry experts’ perspectives, this study validated the influence between the motivation attributes by using Decision-Making Trial and Evaluation Laboratory (DEMATEL). In this paper, the decision rules of users’ self-disclosure preference are presented, and the influences between motivation attributes are graphically depicted as a flow network graph. These findings can assist in addressing real-world decision problems, and can aid SNS marketers in anticipating, evaluating, and acting in accord with the self-disclosure motivations of SNS users. In this paper, practical and research implications are offered.

Suggested Citation

  • Yang-Chieh Chin & Wen-Zhong Su & Shih-Chih Chen & Jianing Hou & Yu-Chuan Huang, 2018. "Exploring Users’ Self-Disclosure Intention on Social Networking Applying Novel Soft Computing Theories," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:3928-:d:179035
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/10/11/3928/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/10/11/3928/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jose Ramon Saura & Ana Reyes-Menendez & Cesar Alvarez-Alonso, 2018. "Do Online Comments Affect Environmental Management? Identifying Factors Related to Environmental Management and Sustainability of Hotels," Sustainability, MDPI, vol. 10(9), pages 1-20, August.
    2. Truong, Yann & McColl, Rod, 2011. "Intrinsic motivations, self-esteem, and luxury goods consumption," Journal of Retailing and Consumer Services, Elsevier, vol. 18(6), pages 555-561.
    3. Kao-Yi Shen & Min-Ren Yan & Gwo-Hshiung Tzeng, 2017. "Exploring R&D Influences on Financial Performance for Business Sustainability Considering Dual Profitability Objectives," Sustainability, MDPI, vol. 9(11), pages 1-21, October.
    4. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    5. Kao-Yi Shen & Gwo-Hshiung Tzeng, 2018. "Advances in Multiple Criteria Decision Making for Sustainability: Modeling and Applications," Sustainability, MDPI, vol. 10(5), pages 1-7, May.
    6. Jie Zhao & Jianfei Wang & Suping Fang & Peiquan Jin, 2018. "Towards Sustainable Development of Online Communities in the Big Data Era: A Study of the Causes and Possible Consequence of Voting on User Reviews," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
    7. Umit Can & Bilal Alatas, 2017. "Big Social Network Data and Sustainable Economic Development," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    8. Blaszczynski, Jerzy & Greco, Salvatore & Slowinski, Roman, 2007. "Multi-criteria classification - A new scheme for application of dominance-based decision rules," European Journal of Operational Research, Elsevier, vol. 181(3), pages 1030-1044, September.
    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. Hung-Yue Suen & Kuo-En Hung & Fan-Hsun Tseng, 2020. "Employer Ratings through Crowdsourcing on Social Media: An Examination of U.S. Fortune 500 Companies," Sustainability, MDPI, vol. 12(16), pages 1-15, August.

    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. Xiaowei Chen & Cong Zhai, 2023. "Bagging or boosting? Empirical evidence from financial statement fraud detection," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(5), pages 5093-5142, December.
    2. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    3. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    4. Rosenbaum, Mark S. & Ramirez, Germán Contreras & Campbell, Jeffrey & Klaus, Philipp, 2021. "The product is me: Hyper-personalized consumer goods as unconventional luxury," Journal of Business Research, Elsevier, vol. 129(C), pages 446-454.
    5. Suhadianto & Aliffia Ananta & Fattah Hanurawan & Nur Eva & Hetti Rahmawati, 2024. "Academic Self-Concept as Mediator Between Mindfulness and Academic Procrastination of University University Students," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(7), pages 2752-2768, July.
    6. Zahra Fozouni Ardekani & Seyed Mohammad Javad Sobhani & Marcelo Werneck Barbosa & Ehsan Amiri-Ardekani & Samaneh Dehghani & Najmeh Sasani & Hans De Steur, 2024. "Determinants of household food waste behavior during the COVID-19 pandemic in Iran: an integrated model," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(10), pages 26205-26235, October.
    7. Jin, Ting & Prentice, Catherine & Shao, Wei, 2021. "Identifying antecedent conditions for luxury brand purchase," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
    8. Khan, Nawab Ali & Azhar, Mohd & Rahman, Mohd Nayyer & Akhtar, Mohd Junaid, 2022. "Scale development and validation for usage of social networking sites during COVID-19," Technology in Society, Elsevier, vol. 70(C).
    9. Adriana Csikosova & Maria Janoskova & Katarina Culkova, 2020. "Application of Discriminant Analysis for Avoiding the Risk of Quarry Operation Failure," JRFM, MDPI, vol. 13(10), pages 1-14, September.
    10. Vasile-Daniel Păvăloaia & Elena-Mădălina Teodor & Doina Fotache & Magdalena Danileţ, 2019. "Opinion Mining on Social Media Data: Sentiment Analysis of User Preferences," Sustainability, MDPI, vol. 11(16), pages 1-21, August.
    11. Fernandez, Eduardo & Navarro, Jorge & Bernal, Sergio, 2010. "Handling multicriteria preferences in cluster analysis," European Journal of Operational Research, Elsevier, vol. 202(3), pages 819-827, May.
    12. Yulian Zhang & Shigeyuki Hamori, 2020. "Forecasting Crude Oil Market Crashes Using Machine Learning Technologies," Energies, MDPI, vol. 13(10), pages 1-14, May.
    13. Pawel Lezanski & Maria Pilacinska, 2018. "The dominance-based rough set approach to cylindrical plunge grinding process diagnosis," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 989-1004, June.
    14. Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
    15. Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
    16. Omer Gokcekus & Yui Suzuki, 2014. "Is there a Corruption-effect on Conspicuous Consumption?," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 8(3), pages 215-235, August.
    17. Azam, Nouman & Zhang, Yan & Yao, JingTao, 2017. "Evaluation functions and decision conditions of three-way decisions with game-theoretic rough sets," European Journal of Operational Research, Elsevier, vol. 261(2), pages 704-714.
    18. Yanfang Zhang & Mushang Lee, 2019. "A Hybrid Model for Addressing the Relationship between Financial Performance and Sustainable Development," Sustainability, MDPI, vol. 11(10), pages 1-15, May.
    19. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    20. Barboza, Flavio & Altman, Edward, 2024. "Predicting financial distress in Latin American companies: A comparative analysis of logistic regression and random forest models," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).

    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:jsusta:v:10:y:2018:i:11:p:3928-:d:179035. 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.