IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i17p6078-d401952.html
   My bibliography  Save this article

Social Network Communications in Chilean Older Adults

Author

Listed:
  • Francisco Javier Rondán-Cataluña

    (Department Business Management and Marketing, University of Seville, 41004 Sevilla, Spain)

  • Patricio E. Ramírez-Correa

    (School of Engineering, Universidad Católica del Norte, Coquimbo 1780000, Chile)

  • Jorge Arenas-Gaitán

    (Department Business Management and Marketing, University of Seville, 41004 Sevilla, Spain)

  • Muriel Ramírez-Santana

    (Department of Public Health, Faculty of Medicine, Universidad Católica del Norte, Coquimbo 1780000, Chile)

  • Elizabeth E. Grandón

    (Department of Information Systems, Universidad del Bío-Bío, Concepción 4030000, Chile)

  • Jorge Alfaro-Pérez

    (School of Engineering, Universidad Católica del Norte, Coquimbo 1780000, Chile)

Abstract

The growth of older adults in new regions poses challenges for public health. We know that these seniors live increasingly alone, and this impairs their health and general wellbeing. Studies suggest that social networking sites (SNS) can reduce isolation, improve social participation, and increase autonomy. However, there is a lack of knowledge about the characteristics of older adult users of SNS in these new territories. Without this information, it is not possible to improve the adoption of SNS in this population. Based on decision trees, this study analyzes how the elderly users of various SNS in Chile are like. For this purpose, a segmentation of the different groups of elderly users of social networks was constructed, and the most discriminating variables concerning the use of these applications were classified. The results highlight the existence of considerable differences between the various social networks analyzed in their use and characterization. Educational level is the most discriminating variable, and gender influences the types of SNS use. In general, it is observed that the higher the educational level, the more the different social networking sites are used.

Suggested Citation

  • Francisco Javier Rondán-Cataluña & Patricio E. Ramírez-Correa & Jorge Arenas-Gaitán & Muriel Ramírez-Santana & Elizabeth E. Grandón & Jorge Alfaro-Pérez, 2020. "Social Network Communications in Chilean Older Adults," IJERPH, MDPI, vol. 17(17), pages 1-17, August.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:17:p:6078-:d:401952
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/17/6078/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/17/6078/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. G. V. Kass, 1980. "An Exploratory Technique for Investigating Large Quantities of Categorical Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(2), pages 119-127, June.
    2. Milanović Marina & Stamenković Milan, 2016. "CHAID Decision Tree: Methodological Frame and Application," Economic Themes, Sciendo, vol. 54(4), pages 563-586, December.
    Full references (including those not matched with items on IDEAS)

    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. Arnold Tóth & Tímea Juhász & Botond Kálmán, 2020. "Determinants Of Financial Habits," Economy & Business Journal, International Scientific Publications, Bulgaria, vol. 14(1), pages 237-256.
    2. Gholamreza Shiran & Reza Imaninasab & Razieh Khayamim, 2021. "Crash Severity Analysis of Highways Based on Multinomial Logistic Regression Model, Decision Tree Techniques, and Artificial Neural Network: A Modeling Comparison," Sustainability, MDPI, vol. 13(10), pages 1-23, May.
    3. Strobl, Carolin & Boulesteix, Anne-Laure & Augustin, Thomas, 2007. "Unbiased split selection for classification trees based on the Gini Index," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 483-501, September.
    4. I. Albarrán & P. Alonso-González & J. M. Marin, 2017. "Some criticism to a general model in Solvency II: an explanation from a clustering point of view," Empirical Economics, Springer, vol. 52(4), pages 1289-1308, June.
    5. Yousaf Muhammad & Dey Sandeep Kumar, 2022. "Best proxy to determine firm performance using financial ratios: A CHAID approach," Review of Economic Perspectives, Sciendo, vol. 22(3), pages 219-239, September.
    6. Archana R. Panhalkar & Dharmpal D. Doye, 2020. "An approach of improving decision tree classifier using condensed informative data," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 47(4), pages 431-445, December.
    7. Bas Donkers & Richard Paap & Jedid‐Jah Jonker & Philip Hans Franses, 2006. "Deriving target selection rules from endogenously selected samples," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 549-562, July.
    8. Lea Piscitelli & Annalisa De Boni & Rocco Roma & Giovanni Ottomano Palmisano, 2023. "Carbon Farming: How to Support Farmers in Choosing the Best Management Strategies for Low-Impact Food Production," Land, MDPI, vol. 13(1), pages 1-16, December.
    9. H Seol & H Lee & S Kim & Y Park, 2008. "The impact of information technology on organizational efficiency in public services: a DEA-based DT approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(2), pages 231-238, February.
    10. Vanhoucke, Mario & Maenhout, Broos, 2009. "On the characterization and generation of nurse scheduling problem instances," European Journal of Operational Research, Elsevier, vol. 196(2), pages 457-467, July.
    11. Todor Krastevich, 2013. "Using Predictive Modeling to Improve Direct Marketing Performance," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 3, pages 25-55.
    12. Wei, Xiupeng & Kusiak, Andrew & Li, Mingyang & Tang, Fan & Zeng, Yaohui, 2015. "Multi-objective optimization of the HVAC (heating, ventilation, and air conditioning) system performance," Energy, Elsevier, vol. 83(C), pages 294-306.
    13. Adrien Ehrhardt & Christophe Biernacki & Vincent Vandewalle & Philippe Heinrich, 2019. "Feature quantization for parsimonious and interpretable predictive models," Papers 1903.08920, arXiv.org.
    14. Onur Doğan & Hakan Aşan & Ejder Ayç, 2015. "Use Of Data Mining Techniques In Advance Decision Making Processes In A Local Firm," European Journal of Business and Economics, Central Bohemia University, vol. 10(2), pages 6821:10-682, January.
    15. Jae-Dong Kim & Tae-Hyeong Kim & Sung Won Han, 2023. "Demand Forecasting of Spare Parts Using Artificial Intelligence: A Case Study of K-X Tanks," Mathematics, MDPI, vol. 11(3), pages 1-10, January.
    16. Agapito, Dora & Mendes, Julio & Valle, Patricia, 2011. "The Sea as a Connection between Residents and Tourists in Coastal Destinations: A Case in Algarve," Spatial and Organizational Dynamics Discussion Papers 2011-13, CIEO-Research Centre for Spatial and Organizational Dynamics, University of Algarve.
    17. Timmins, Christopher & Vissing, Ashley, 2022. "Environmental justice and Coasian bargaining: The role of race, ethnicity, and income in lease negotiations for shale gas," Journal of Environmental Economics and Management, Elsevier, vol. 114(C).
    18. Rike Stotten & Michaela Maurer & Hannes Herrmann & Markus Schermer, 2019. "Different Forms of Accommodation in Agritourism: The Role of Decoupled Farmer-Based Accommodation in the Ötztal Valley (Austria)," Sustainability, MDPI, vol. 11(10), pages 1-27, May.
    19. A. Israëls & J. Driel, 1983. "Use of the chi-square statistic for selecting explanatory variables in multiway tables," Quality & Quantity: International Journal of Methodology, Springer, vol. 17(2), pages 103-116, April.
    20. Nocella, Giuseppe & Stefani, Gianluca & Romano, Donato, 2011. "Preferences, trust and willingness to pay for food information: An analysis of the Italian Market," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114606, European Association of Agricultural Economists.

    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:17:y:2020:i:17:p:6078-:d:401952. 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.