The Impact of Internet Use on the Social Networks of the Elderly in China—The Mediating Effect of Social Participation
Abstract
:1. Introduction
2. Literature Review
2.1. The Concept and Measurement of Social Network
2.2. Internet Use and the Social Network of the Elderly
2.3. Internet Use, Social Participation, and the Social Network of the Elderly
3. Materials and Methods
3.1. Data
3.2. Variables and Operationalization
3.2.1. Dependent Variable
3.2.2. Independent Variable
3.2.3. Mediating Variable
3.2.4. Control Variable
3.3. Model Construction
3.3.1. The Basic Model
3.3.2. Propensity Score Matching Model
3.3.3. Mediating Effect Model
3.4. Endogenous Problems
4. Results
4.1. OLS Regression Analysis
4.2. Propensity Score Matching
4.3. Endogeneity Test
4.4. Mediation Effect Analysis
4.5. Different Group Analysis
5. Discussion
5.1. Implications
5.2. Limitations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Zunzunegui, M.-V.; Alvarado, B.E.; Del Ser, T.; Otero, A. Social Networks, Social Integration, and Social Engagement Determine Cognitive Decline in Community-Dwelling Spanish Older Adults. J. Gerontol. B Psychol. Sci. Soc. Sci. 2003, 58, S93–S100. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- He, Z.P. Socioeconomic Status and Social Support Network of the Rural Elderly and Their Physical and Mental Health. Soc. Sci. China 2002, 3, 135–148, 207. [Google Scholar]
- Zhang, J.A.; Zhang, W.H. Social Network Types and Elderly Subjective Well-being. J. Soc. Develop. 2019, 6, 79–96, 233–244. [Google Scholar]
- Wei, P. A Preliminary Discussion about the Social Network of Supporting the Elderly in Poor Rural. Popul. Develop. 2010, 16, 76–82, 51. [Google Scholar]
- Qian, X.H.; Shen, S.G. The Elderly Security in Informal System Arrangement: An Analysis of Social Network. Reform 2011, 9, 137–142. [Google Scholar]
- Lu, X.L.; Zhu, J.W. Active Aging and Health Promotion from the Perspective of Social Capital. Chin. J. Gerontol. 2018, 38, 1000–1003. [Google Scholar]
- Du, P.; Wang, B. How Does Internet Use Affect Life Satisfaction of the Chinese Elderly? Popul. Res. 2020, 44, 3–17. [Google Scholar]
- Jin, Y.A.; Zhao, M.H. Internet Use and the Elderly’s Active Aging in China—A Study Based on 2016 China Longitudinal Aging Social Survey. Popul. J. 2019, 41, 44–55. [Google Scholar]
- Wright, K. Computer-Mediated Social Support, Older Adults, and Coping. J. Commun. 2000, 50, 100–118. [Google Scholar] [CrossRef]
- Heo, J.; Chun, S.; Lee, S.; Lee, K.H.; Kim, J. Internet Use and Well-Being in Older Adults. Cyberpsychol. Behav. Soc. Netw. 2015, 18, 268–272. [Google Scholar] [CrossRef]
- Khosravi, P.; Rezvani, A.; Wiewiora, A. The Impact of Technology on Older Adults’ Social Isolation. Comput. Hum. Behav. 2016, 63, 594–603. [Google Scholar] [CrossRef]
- Kraut, R.; Patterson, M.; Lundmark, V.; Kiesler, S.; Mukophadhyay, T.; Scherlis, W. Internet Paradox: A Social Technology That Reduces Social Involvement and Psychological Well-Being? Am. Psychol. 1998, 53, 1017–1031. [Google Scholar] [CrossRef] [PubMed]
- Nie, N.; Hillygus, D.S. The impact of Internet use on society: Time diary findings. IT Soc. 2002, 1, 1–20. [Google Scholar]
- Hage, E.; Wortmann, H.; van Offenbeek, M.; Boonstra, A. The Dual Impact of Online Communication on Older Adults’ Social Connectivity. Inf. Technol. People 2016, 29, 31–50. [Google Scholar] [CrossRef]
- Radcliffe-Brown, A.R. On Social Structure. J. R. Anthropol. Inst. Great Br. Irel. 1940, 70, 1–12. [Google Scholar] [CrossRef]
- Coleman, J.S. Foundations of Social Theory; Belknap Press of Harvard University Press: Cambridge, MA, USA, 2008; pp. 282–284. [Google Scholar]
- He, Z.H. A review of research on social support networks abroad. Soc. Sci. Abroad 2001, 1, 76–82. [Google Scholar]
- Wang, W.D. Measuring the Social Network Capital in the Chinese Sociocultural Context. Chin. J. Sociol. 2009, 29, 146–158, 227. [Google Scholar]
- Marsden, P.V. Core Discussion Networks of Americans. Am. Sociol. Rev. 1987, 52, 122–131. [Google Scholar] [CrossRef]
- McPherson, M.; Smith-Lovin, L.; Brashears, M.E. Social Isolation in America: Changes in Core Discussion Networks over Two Decades. Am. Sociol. Rev. 2006, 71, 353–375. [Google Scholar] [CrossRef] [Green Version]
- Burt, R.S. Network Items and the General Social Survey. Soc. Netw. 1984, 6, 293–339. [Google Scholar] [CrossRef] [Green Version]
- Lubben, J.; Blozik, E.; Gillmann, G.; Iliffe, S.; von Renteln Kruse, W.; Beck, J.C.; Stuck, A.E. Performance of an Abbreviated Version of the Lubben Social Network Scale Among Three European Community-Dwelling Older Adult Populations. Gerontologist 2006, 46, 503–513. [Google Scholar] [CrossRef] [Green Version]
- Bian, Y.J.; Li, Y. Social network capital of urban families in China. Tsinghua Sociol. Rev. 2000, 2, 1–18. [Google Scholar]
- Liu, Y.; Ji, X.H. Study of Social Network Size and Structure of the Aged—Discussion on the Dilemma of Support for the Aged in One-Child Family. J. Dalian Univ. Tech. (Soc. Sci.) 2013, 34, 71–76. [Google Scholar]
- Zhang, D. Impacts of Intergenerational Support and Social Network on the Life Quality of the Elderly. J. China Univ. Labor Relat. 2021, 35, 15–25. [Google Scholar]
- Ahn, D.; Shin, D.-H. Is the Social Use of Media for Seeking Connectedness or for Avoiding Social Isolation? Mechanisms Underlying Media Use and Subjective Well-Being. Comput. Hum. Behav. 2013, 29, 2453–2462. [Google Scholar] [CrossRef]
- Pénard, T.; Poussing, N. Internet Use and Social Capital: The Strength of Virtual Ties. J. Econ. Issues 2010, 44, 569–595. [Google Scholar] [CrossRef] [Green Version]
- Yu, R.P.; Mccammon, R.J.; Ellison, N.B.; Langa, K.M. The Relationships That Matter: Social Network Site Use and Social Wellbeing among Older Adults in the United States of America. Ageing Soc. 2016, 36, 1826–1852. [Google Scholar] [CrossRef] [Green Version]
- Jin, Y.A.; Liu, W.L.; Zhao, M.; Wang, D.H.; Hu, W.B. Short Video APP Use and the Life of Mid-age and Older Adults: An Exploratory Study Based on a Social Survey. Popul. Res. 2021, 45, 31–45. [Google Scholar]
- Antonucci, T.C.; Ajrouch, K.J.; Birditt, K.S. The Convoy Model: Explaining Social Relations from a Multidisciplinary Perspective. Gerontologist 2014, 54, 82–92. [Google Scholar] [CrossRef] [Green Version]
- Diggs, J. Activity Theory of Aging. In Encyclopedia of Aging and Public Health; Loue, S.J., Sajatovic, M., Eds.; Springer Science & Business Media: New York, NY, USA, 2008; pp. 79–81. [Google Scholar]
- Wu, Y.J. Internet Participation of the Elderly in the Active Aging. Acad. Exch. 2021, 326, 141–155. [Google Scholar]
- Feng, T.Y.; Liu, Y. The Impact of Internet Use on the Subjective Class Identity of the Elderly in the Digital Age. J. Xi’an Jiaotong Univ. (Soc. Sci.) 2022, 42, 122–131. [Google Scholar]
- Han, Y.C. The Internet Use, Communication Perimeter and Generational Difference in General Trust. J. Shenzhen Univ. (Humanit. Soc. Sci. Ed.) 2021, 38, 114–124. [Google Scholar]
- Hou, L.M.; Qin, G.Q. An Operationalization of Chinese Classification Using EGP: Based on Chinese General Social Survey (CGSS) Data. Sociol. Rev. China 2019, 7, 16–26. [Google Scholar]
- Wen, Z.L.; Ye, B.J. Analyses of Mediating Effects: The Development of Methods and Models. Acta Psychol. Sinica 2014, 5, 731–745. [Google Scholar] [CrossRef]
- Lu, M.Y.; Peng, X.Z.; Lu, M.H. The Effect of Internet Use on Employment of the Elderly. Econ. Perspect. 2020, 10, 77–91. [Google Scholar]
- Hu, A.N. Propensity Score Matching and Causal Inference: A methodological review. Sociol. Stud. 2012, 27, 221–242, 246. [Google Scholar]
- Guo, Z.H.; Zhu, B.Y. Does Mobile Internet Use Affect the Loneliness of Older Chinese Adults? An Instrumental Variable Quantile Analysis. Int. J. Environ. Res. Public. Health 2022, 19, 5575. [Google Scholar] [CrossRef]
- Carstensen, L.L.; Fung, H.H.; Charles, S.T. Socioemotional Selectivity Theory and the Regulation of Emotion in the Second Half of Life. Motiv. Emot. 2003, 27, 103–123. [Google Scholar] [CrossRef]
- Sipowicz, K.; Podlecka, M.; Mokros, Ł.; Pietras, T. Lonely in the City—Sociodemographic Status and Somatic Morbidities as Predictors of Loneliness and Depression among Seniors–Preliminary Results. Int. J. Environ. Res. Public. Health 2021, 18, 7213. [Google Scholar] [CrossRef]
- Zhang, W.H.; Ruan, D.Q.; Pan, Y.K. Social network of rural residents in Tianjin. Sociol. Stud. 1999, 2, 110–120. [Google Scholar]
- Zhang, Y.Q. Comparative Study on the Support of Social Network for Senior Citizen: A case study in Xiamen. Sociol. Stud. 2001, 4, 11–21. [Google Scholar] [CrossRef]
- Walder, A.G.; Li, B.; Treiman, D.J. Politics and Life Chances in a State Socialist Regime: Dual Career Paths into the Urban Chinese Elite, 1949 to 1996. Am. Sociol. Rev. 2000, 65, 191–209. [Google Scholar] [CrossRef]
Variable | Mean/% | SD | |
---|---|---|---|
Dependent variable | |||
Social network size | 1.728 | 0.968 | |
Social network heterogeneity | 2.680 | 2.649 | |
Social network upper reachability | 2.982 | 1.996 | |
Independent variable | |||
Internet use | 0.241 | 0.428 | |
Mediating variable | |||
Social participation | 1.004 | 1.976 | |
Control variable | |||
Gender (%) | Men | 48.79 | |
Women | 51.21 | ||
Age | 69.29 | 7.432 | |
Marital status (%) | Have spouse | 73.59 | |
No spouse | 26.41 | ||
Education (%) | Illiteracy | 24.06 | |
Literacy | 75.94 | ||
Religious relief (%) | Irreligion | 88.41 | |
Profess a religion | 11.59 | ||
Political status (%) | Communist | 15.26 | |
Noncommunist | 84.74 | ||
Individual annual income (ln) | 8.183 | 3.488 | |
Self-rated health | 3.012 | 1.111 | |
Non-agricultural work (%) | Engaged | 7.70 | |
Not engaged | 92.30 | ||
Living style (%) | Living alone | 20.85 | |
Living with others | 79.15 | ||
Number of children | 2.430 | 1.537 | |
Type of hukou (%) | City | 40.94 | |
Rural | 59.06 | ||
Family size | 2.505 | 1.649 | |
Annual household income (ln) | 9.770 | 2.379 | |
Region (%) | Western | 21.50 | |
Central | 32.36 | ||
Eastern | 46.15 |
Social Network Size | Social Network Heterogeneity | Social Network Upper Reachability | |
---|---|---|---|
Internet use | 0.277 *** | 1.206 *** | 0.619 *** |
(0.072) | (0.200) | (0.150) | |
Gender (reference: women) | 0.005 | 0.161 | 0.059 |
(0.057) | (0.158) | (0.118) | |
Age | −0.012 *** | −0.009 | −0.002 |
(0.004) | (0.012) | (0.009) | |
Marital status (reference: no spouse) | 0.101 | −0.021 | −0.004 |
(0.083) | (0.230) | (0.172) | |
Education (reference: illiteracy) | 0.054 | 0.261 | 0.258 * |
(0.071) | (0.196) | (0.146) | |
Religious belief (reference: irreligion) | −0.021 | −0.246 | −0.207 |
(0.090) | (0.248) | (0.186) | |
Political status (reference: noncommunist) | 0.240 *** | 0.425 * | 0.534 *** |
(0.080) | (0.220) | (0.165) | |
Individual annual income | 0.014 | 0.004 | 0.032 |
(0.010) | (0.027) | (0.020) | |
Self-rated health | 0.084 *** | 0.092 | 0.064 |
(0.025) | (0.070) | (0.053) | |
Non-agricultural work (reference: not engaged) | 0.515 *** | 0.439 | 0.321 |
(0.101) | (0.280) | (0.210) | |
Living style (reference: living alone) | 0.141 | 0.042 | −0.084 |
(0.090) | (0.248) | (0.186) | |
Number of children | 0.027 | −0.058 | 0.010 |
(0.020) | (0.056) | (0.042) | |
Type of hukou (reference: rural) | −0.014 | 0.506 *** | 0.228 |
(0.068) | (0.187) | (0.140) | |
Family size | 0.022 | −0.010 | 0.007 |
(0.018) | (0.050) | (0.038) | |
Annual household income | 0.028 * | 0.069 * | 0.052 * |
(0.015) | (0.040) | (0.030) | |
Region (Western) | |||
Central | 0.155 ** | 0.280 | 0.141 |
(0.074) | (0.203) | (0.152) | |
Eastern | −0.006 | −0.257 | 0.190 |
(0.079) | (0.217) | (0.163) | |
N | 1363 | 1363 | 1363 |
R2 | 0.122 | 0.110 | 0.113 |
Variable | Matching Situation | Treatment Group | Control Group | Deviation (%) | Deviation Reduction (%) | T | p |
---|---|---|---|---|---|---|---|
Gender | Unmatching | 0.574 | 0.478 | 19.4 | 82.4 | 2.87 | 0.004 |
Matching | 0.566 | 0.583 | −3.4 | −0.41 | 0.685 | ||
Age | Unmatching | 66.965 | 69.916 | −42.2 | 98.6 | −6.01 | 0.000 |
Matching | 67.168 | 67.128 | 0.6 | 0.07 | 0.941 | ||
Marital status | Unmatching | 0.858 | 0.714 | 35.6 | 96.3 | 4.96 | 0.000 |
Matching | 0.853 | 0.848 | 1.3 | 0.18 | 0.859 | ||
Education | Unmatching | 0.972 | 0.700 | 79.0 | 98.4 | 9.89 | 0.000 |
Matching | 0.971 | 0.967 | 1.3 | 0.31 | 0.760 | ||
Religious belief | Unmatching | 0.080 | 0.104 | −8.5 | 49.1 | −1.23 | 0.220 |
Matching | 0.082 | 0.070 | 4.3 | 0.56 | 0.577 | ||
Political status | Unmatching | 0.311 | 0.101 | 53.8 | 73.6 | 8.99 | 0.000 |
Matching | 0.290 | 0.346 | −14.2 | −1.41 | 0.159 | ||
Individual annual income | Unmatching | 10.129 | 7.636 | 85.5 | 93.1 | 11.30 | 0.000 |
Matching | 10.075 | 9.904 | 5.9 | 1.01 | 0.311 | ||
Self-rated Health | Unmatching | 3.346 | 2.931 | 39.1 | 98.5 | 5.67 | 0.000 |
Matching | 3.326 | 3.320 | 0.6 | 0.08 | 0.940 | ||
Non-agricultural work | Unmatching | 0.152 | 0.058 | 31.2 | 87.4 | 5.23 | 0.000 |
Matching | 0.136 | 0.148 | −3.9 | −0.40 | 0.687 | ||
Living style | Unmatching | 0.145 | 0.227 | −21.1 | 72.5 | −2.99 | 0.003 |
Matching | 0.151 | 0.128 | 5.8 | 0.76 | 0.446 | ||
Number of children | Unmatching | 1.595 | 2.65 | −78.1 | 95.9 | −10.71 | 0.000 |
Matching | 1.613 | 1.656 | −3.2 | −0.50 | 0.619 | ||
Type of hukou | Unmatching | 0.889 | 0.491 | 95.4 | 97.1 | 12.77 | 0.000 |
Matching | 0.885 | 0.874 | 2.8 | 0.42 | 0.673 | ||
Family size | Unmatching | 2.426 | 2.455 | −1.8 | −345.0 | −0.27 | 0.788 |
Matching | 2.423 | 2.554 | −8.0 | −1.04 | 0.299 | ||
Annual household income | Unmatching | 11.094 | 9.385 | 90.2 | 99.0 | 11.50 | 0.000 |
Matching | 11.038 | 11.056 | −0.9 | −0.18 | 0.856 | ||
Region | Unmatching | 1.367 | 1.882 | −70.6 | 90.8 | −10.10 | 0.000 |
Matching | 1.370 | 1.322 | 6.5 | 0.89 | 0.375 |
Variables | Matching Method | ATT | Bootstrap SE | T |
---|---|---|---|---|
Social network size | K nearest neighbor matching in caliper | 0.281 | 0.125 | 2.60 ** |
Radius matching | 0.271 | 0.102 | 2.79 *** | |
Kernel matching | 0.271 | 0.110 | 2.79 *** | |
Social network heterogeneity | K nearest neighbor matching in caliper | 1.000 | 0.326 | 3.48 *** |
Radius matching | 0.921 | 0.278 | 3.51 *** | |
Kernel matching | 0.928 | 0.279 | 3.53 *** | |
Social network upper reachability | K nearest neighbor matching in caliper | 0.536 | 0.241 | 2.67 *** |
Radius matching | 0.482 | 0.204 | 2.53 ** | |
Kernel matching | 0.487 | 0.196 | 2.55 ** |
Variable | One-Stage Internet Use | Two-Stage Social Network Size | Two-Stage Social Network Heterogeneity | Two-Stage Social Network Upper Reachability |
---|---|---|---|---|
Internet use | 0.620 *** (0.189) | 1.689 *** (0.522) | 1.295 *** (0.392) | |
Instrumental variable | 0.319 *** (0.023) | |||
Control variable | YES | YES | YES | YES |
One-stage F value | 198.759 | |||
Endogenous test p-value | 0.000 |
Action Path | Effect | Coefficient | Bootstrap SE | LLCI | ULCI |
---|---|---|---|---|---|
Internet use-Social network size | Direct effect | 0.237 | 0.080 | 0.082 | 0.394 |
Indirect effect | 0.026 | 0.014 | 0.003 | 0.059 | |
Internet use-Social network heterogeneity | Direct effect | 0.960 | 0.213 | 0.554 | 1.377 |
Indirect effect | 0.206 | 0.065 | 0.102 | 0.357 | |
Internet use-Social networks upper reachability | Direct effect | 0.530 | 0.146 | 0.257 | 0.820 |
Indirect effect | 0.087 | 0.029 | 0.040 | 0.156 |
Variable | Gender | Age | Type of Hukou | |||
---|---|---|---|---|---|---|
Male | Female | 60–69 | 70 and over | City | Rural | |
Social network size | 0.221 ** (0.108) | 0.344 *** (0.062) | 0.293 *** (0.095) | 0.252 ** (0.117) | 0.306 *** (0.088) | 0.214 (0.162) |
Social network heterogeneity | 1.625 *** (0.278) | 0.760 *** (0.293) | 1.442 *** (0.258) | 0.664 ** (0.331) | 1.196 *** (0.246) | 2.138 *** (0.424) |
Social network upper reachability | 0.831 *** (0.206) | 0.495 ** (0.222) | 0.911 *** (0.187) | 0.110 (0.259) | 0.684 *** (0.171) | 0.721 * (0.370) |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhang, Q.; Li, Z. The Impact of Internet Use on the Social Networks of the Elderly in China—The Mediating Effect of Social Participation. Int. J. Environ. Res. Public Health 2022, 19, 9576. https://doi.org/10.3390/ijerph19159576
Zhang Q, Li Z. The Impact of Internet Use on the Social Networks of the Elderly in China—The Mediating Effect of Social Participation. International Journal of Environmental Research and Public Health. 2022; 19(15):9576. https://doi.org/10.3390/ijerph19159576
Chicago/Turabian StyleZhang, Qunlin, and Zhibin Li. 2022. "The Impact of Internet Use on the Social Networks of the Elderly in China—The Mediating Effect of Social Participation" International Journal of Environmental Research and Public Health 19, no. 15: 9576. https://doi.org/10.3390/ijerph19159576