基于计划行为理论预测生育意愿：一项三水平元分析

doi:10.3724/SP.J.1042.2025.1926

摘要/Abstract

摘要：

生育意愿是预测生育行为的关键因素, 诸多研究探讨了计划行为理论(Theory of Planned Behavior, TPB)在预测生育意愿中的作用, 但其结论尚不一致。为检验TPB理论在解释生育意愿中的适用性, 本研究采用了随机效应模型对纳入的33项研究(包括128个效应值、共47923个被试)进行三水平元分析。结果表明：TPB变量均与生育意愿显著相关, 其中态度与生育意愿的关系最强(r⁺= 0.41), 其次是主观规范(r⁺= 0.30)和感知行为控制(r⁺= 0.23)。TPB与生育意愿的相关强度受研究对象的性别、生育经历和社会经济背景的调节, 但不受个体−集体主义文化背景、时间框架的调节。本研究澄清了TPB变量与生育意愿之间的关系, 为开发生育意愿提升方案与建设生育友好型社会提供了一定的学理支撑。

关键词: 元分析, 生育意愿, 计划行为理论, 态度, 主观规范, 感知行为控制

Abstract:

Background:

Persistently low fertility intentions among the population of young people of childbearing age represent a critical social challenge demanding urgent solutions in most nations. The Theory of Planned Behavior (TPB), which emphasizes the role of socio-psychological factors and behavioral intentions in fertility decision-making and conceptualizes fertility intentions as a multidimensional construct, provides a key explanatory framework for understanding fertility behavior. While numerous studies have investigated the utility of TPB in predicting fertility intentions, its applicability in this specific domain remains debated. Findings regarding the relationships between TPB variables (attitudes, subjective norms, and perceived behavioral control) and fertility intentions are inconsistent and even contradictory. A systematic evaluation of the relationship between TPB and fertility intentions is currently lacking, making a meta-analysis to quantify the strength of this relationship and explore potential moderating factors particularly necessary.

Objective:

This study aimed to: (1) systematically review the application of TPB in fertility intention research; (2) employ a three-level meta-analysis to examine the strength of the associations between attitudes, subjective norms, perceived behavioral control and fertility intentions; and (3) investigate the moderating effects of gender, childbearing history(parenthood status), cultural background (individualism-collectivism), economic level (GDP per capita), and temporal framing.

Methods:

We conducted a scoping literature review following the following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement, we conducted literature search, screening, coding, quality assessment, and publication bias testing. Effect size estimation employed a three-level random-effects model implemented using the metafor package in R4.3.2, with model parameters estimated via Restricted Maximum Likelihood (REML) estimation. The sample-size-weighted average correlation coefficient (r⁺) was calculated. All correlation coefficients were converted to Fisher’s Z scores for main effect and moderation effect analyses.

Results:

A total of 33 studies met the inclusion criteria, comprising N = 43,427 participants and 128 independent effect sizes. Main effect analysis revealed significant positive correlations between all core TPB variables and fertility intentions. The strongest correlation was observed for attitude (r⁺ = 0.41), followed by subjective norm (r⁺ = 0.30) and perceived behavioral control (r⁺ = 0.23). Moderation analysis revealed that the strength of the associations between TPB variables and fertility intentions was significantly moderated by participant gender, childbearing history (parenthood status), and socioeconomic background (GDP per capita), but not by individualism-collectivism cultural background or temporal framing.

Conclusions:

(1) TPB variables (attitudes, subjective norms, perceived behavioral control) significantly predict individual fertility intentions: stronger positive expectations of fertility benefits, greater perceived social pressure, and more perceived resources for having children are associated with higher fertility intentions.

(2) Among TPB variables, attitude is the strongest predictor of fertility intentions, followed by subjective norms and perceived behavioral control.

(3) Gender moderates the attitude-intention relationship: The positive predictive effect of fertility attitude on intention is significantly stronger for women compared to men.

(4) Parenthood status moderates the effects of attitude and perceived behavioral control: Compared to parents, attitudes and perceived behavioral control demonstrate stronger predictive power for fertility intentions among individuals without children.

(5) GDP per capita significantly moderates the overall predictive efficacy of TPB variables on fertility intentions.

(6) Individualism-collectivism cultural background and time frame did not exhibit significant moderating effects on the predictive utility of TPB variables.

This study is the first to systematically synthesize the relationship between TPB and fertility intentions using meta-analysis. It clarifies the strength of associations between core TPB variables and fertility intentions and validates the robustness of the TPB model in fertility research. The investigation of moderating variables provides an important theoretical foundation for designing interventions to enhance fertility intentions and fostering a fertility-supportive society.

Key words: meta-analysis, fertility intentions, theory of planned behaviour, attitude, subjective norm, perceived behavioral control

中图分类号:

B849
C91

梁英, 赵何钧, 赵宝旭, 岳云帆, 何宁. (2025). 基于计划行为理论预测生育意愿：一项三水平元分析. 心理科学进展 , 33(11), 1926-1941.

LIANG Ying, ZHAO Hejun, ZHAO Baoxu, YUE Yunfan, HE Ning. (2025). Predicting fertility intentions with the Theory of Planned Behaviour: A three-level meta-analysis. Advances in Psychological Science, 33(11), 1926-1941.

图/表 10

参考文献 121

	(*为纳入元分析的文献)
[1]	陈思静, 沈家辉, 姜侨桀, 杨莎莎. (2024). 对他人生育态度和行为的低估抑制了Z世代未生育个体的生育意愿. 心理学报, 56(6), 759-779. doi: 10.3724/SP.J.1041.2024.00759
[2]	杜苏旻. (2023). 社会支持视角下育龄人口再生育意愿的影响研究——基于计划行为理论框架* [硕士学位论文]. 杭州电子科技大学.
[3]	罗丝秋. (2023). 我国未育青年的生育意愿不确定性及影响因素研究* [硕士学位论文]. 四川大学.
[4]	马芮. (2023). 基于计划行为理论的枝江市农村居民生育意愿影响因素研究. 中文科技期刊数据库(全文版)医药卫生*, (5), 38-41.
[5]	孟现鑫, 陈怡静, 王馨怡, 袁加锦, 俞德霖. (2024). 学校联结与抑郁的关系:一项三水平元分析. 心理科学进展, 32(2), 246-269. doi: 10.3724/SP.J.1042.2024.00246
[6]	王钧林, 王法强. (2023). 成人、齐家与化俗:家礼教化功能探析. 中州学刊, (10), 119-125.
[7]	王小倩. (2023). 政策工具对公众生育或再生育意愿的影响:基于计划行为理论的视角* [硕士学位论文]. 西北师范大学.
[8]	吴佳桧, 傅海伦. (2024). 成就目标定向与学业成绩关系的元分析: 自我效能感、学习投入的中介作用. 心理科学进展, 32(7), 1104-1125. doi: 10.3724/SP.J.1042.2024.01104
[9]	俞婷. (2020). 基于计划行为理论的流动女性生育意愿研究* [硕士学位论文]. 武汉大学.
[10]	袁悦, 吴志明, 谢秋实. (2024). 时间压力对个体工作结果的作用效果: 基于元分析的证据. 心理科学进展, 32(3), 465-485. doi: 10.3724/SP.J.1042.2024.00465
[11]	张淼. (2020). “全面二孩”政策下职业女性生育意愿影响因素研究——以上海市A企业为例* [硕士学位论文]. 上海交通大学.
[12]	张亚利, 李森, 俞国良. (2019). 自尊与社交焦虑的关系: 基于中国学生群体的元分析. 心理科学进展, 27(6), 1005-1018. doi: 10.3724/SP.J.1042.2019.01005
[13]	中华人民共和国国家统计局. (2024). 中华人民共和国2023年国民经济和社会发展统计公报. 2024-02-29取自https://www.stats.gov.cn/sj/zxfb/202402/t20240228_1947915.html.
[14]	周国红, 何雨璐, 杨均中. (2021). "生育主力"缘何有名无实?——基于743份城市青年生育意愿的问卷调查分析. 浙江社会科学*, (5), 77-86+157-158.
[15]	Agar, A. D. (2018). Mandatory motherhood and compulsory careers: An application of theory of planned behaviour to young women’s mothering and career intentions* [Unpublished doctoral dissertation]. University of Saskatchewan.
[16]	Ahinkorah, B. O., Seidu, A. A., Budu, E., Agbaglo, E., Adu, C., Dickson, K. S.,... Schack, T. (2021). Which factors predict fertility intentions of married men and women? Results from the 2012 Niger Demographic and Health Survey. PLoS One, 16(6), e0252281.
[17]	Ajzen, I. (1991). The Theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
[18]	Ajzen, I. (2002). Perceived behavioral control, self-efficacy, locus of control, and the theory of planned behavior¹. Journal of Applied Social Psychology, 32(4), 665-683.
[19]	Ajzen, I., & Klobas, J. (2013). Fertility intentions: An approach based on the theory of planned behavior. Demographic Research*, 29(8), 203-232.
[20]	Alizadeh, T., Moodi, M., Salmani, F., & Moudi, A. (2023). Factors affecting childbearing intention in women employees in Birjand: Application of the theory of planned behavior. Journal of Education and Health Promotion*, 12(1), 446.
[21]	Armitage, C. J., & Conner, M. (2001). Efficacy of the theory of planned behaviour: A meta-analytic review. British Journal of Social Psychology, 40(4), 471-499.
[22]	Assink, M., & Wibbelink, C. J. (2016). Fitting three-level meta-analytic models in R: A step-by-step tutorial. The Quantitative Methods for Psychology, 12(3), 154-174.
[23]	Azmoude, E., Behnam, H., Barati-Far, S., & Kabirian, M. (2017). The relationship of socio-demographic factors, fertility behavior and child’s perceived value with fertility. International Journal of Community Based Nursing and Midwifery, 5(2), 123-133. pmid: 28409166
[24]	Banaei, M., Kariman, N., Nia, H.S., & Mokhtarian-Gilani, T. (2023). The role of the Coronavirus pandemic on childbearing intentions in Iranian women based on path analysis: A cross-sectional study. International Journal of Reproductive BioMedicine*, 21(1), 43-52. doi: 10.18502/ijrm.v21i1.12665 pmid: 36875500
[25]	Barber, J. S. (2000). Intergenerational influences on the entry into parenthood: Mothers' preferences for family and nonfamily behavior. Social Forces, 79(1), 319-348.
[26]	Barber, J. S. (2001). Ideational influences on the transition to parenthood: Attitudes toward childbearing and competing alternatives. Social Psychology Quarterly, 64(2), 101-127.
[27]	Becker, G. S., & Barro, R. J. (1988). A reformulation of the economic theory of fertility. The Quarterly Journal of Economics, 103(1), 1-25.
[28]	Bein, C., Gauthier, A. H., & Mynarska, M. (2021). Religiosity and fertility intentions: Can the gender regime explain cross-country differences? European Journal of Population, 37(2), 443-472.
[29]	Bernardi, L., Keim, S., & Von der Lippe, H. (2007). Social influences on fertility: A comparative mixed methods study in Eastern and Western Germany. Journal of Mixed Methods Research, 1(1), 23-47.
[30]	Bhattacharjee, N. V., Schumacher, A. E., Aali, A., Abate, Y. H., Abbasgholizadeh, R., Abbasian, M.,... Bahri, R. A. (2024). Global fertility in 204 countries and territories, 1950-2021, with forecasts to 2100: A comprehensive demographic analysis for the Global Burden of Disease Study 2021. The Lancet, 403(10440), 2057-2099.
[31]	Billari, F. C., Philipov, D., & Testa, M. R. (2009). Attitudes, norms and perceived behavioural control: Explaining fertility intentions in Bulgaria/Attitudes, normes et contrôle perçu du comportement: Une explication des intentions de fécondité en Bulgarie. European Journal of Population/Revue Européenne de Démographie*, 25(4), 439-465.
[32]	Buber-Ennser, I. (2011). Are you ready for a child? A methodological triangulation on fertility intentions in Austria (No. 3/2011)*. Vienna Institute of Demography Working Papers (Vol. 1, pp. 1-28).
[33]	Bühler, C., & Philipov, D. (2005). Social capital related to fertility: Theoretical foundations and empirical evidence from Bulgaria. Vienna Yearbook of Population Research, 3, 53-81.
[34]	Bulatao, R. A. (1981). Values and disvalues of children in successive childbearing decisions. Demography, 18(1), 1-25. pmid: 7202782
[35]	Card, N. A. (2012). Applied meta-analysis for social science research. The Guilford Press.
[36]	Chae, M., Cho, I., & Kim, J. I. (2016). Influencing factors on fertility intention of women university students: Based on the theory of planned behavior. Advanced Science and Technology Letters, 132*, 37-42
[37]	Chao, R. K. (2000). The parenting of immigrant Chinese and European American mothers: Relations between parenting styles, socialization goals, and parental practices. Journal of Applied Developmental Psychology, 21(2), 233-248.
[38]	Chappell, A. (2024). Factors that influence plans to have children: An application of the theory of planned behavior to fertility decision-making across the reproductive years* [Unpublished doctoral dissertation]. West Virginia University.
[39]	Chen, M., Lo, C. K. M., Chen, Q., Chan, K. L., & Ip, P. (2024). Fertility intention in Hong Kong: Declining trend and associated factors. Applied Research in Quality of Life*, 19(3), 1309-1335.
[40]	Cheng, H., Luo, W., Si, S., Xin, X., Peng, Z., Zhou, H.,... Yu, Y. (2022). Global trends in total fertility rate and its relation to national wealth, life expectancy and female education. BMC Public Health, 22, 1346. doi: 10.1186/s12889-022-13656-1 pmid: 35836246
[41]	Cheng, Y. A. (2020). Ultra-low fertility in East Asia: Confucianism and its discontents. Vienna Yearbook of Population Research, 18(1), 83-120.
[42]	Chesnais, J. (1996). Fertility, family, and social policy in contemporary Western Europe. Population and Development Review, 22(4), 729-739.
[43]	Cheung, M. W. L. (2014). Modeling dependent effect sizes with three-level meta-analyses: A structural equation modeling approach. Psychological Methods, 19(2), 211-229. doi: 10.1037/a0032968 pmid: 23834422
[44]	Cheung, M. W. L. (2019). A guide to conducting a meta-analysis with non-independent effect sizes. Neuropsychology Review, 29(4), 387-396. doi: 10.1007/s11065-019-09415-6 pmid: 31446547
[45]	Ciliberto, F., Miller, A. R., Nielsen, H. S., & Simonsen, M. (2016). Playing the fertility game at work: An equilibrium model of peer effects. International Economic Review, 57(3), 827-856. pmid: 27605729
[46]	Ciritel, A. A., De Rose, A., & Arezzo, M. F. (2019). Childbearing intentions in a low fertility context: The case of Romania. Genus*, 75, 4.
[47]	Cislaghi, B., & Shakya, H. (2018). Social norms and adolescents' sexual health: An introduction for practitioners working in low and mid-income African countries. African journal of reproductive health, 22(1), 38-46. doi: 10.29063/ajrh2018/v22i1.4 pmid: 29777641
[48]	Cohen, J. (1992). Quantitative methods in psychology: A power primer. Psychological Bulletin, 112, 1155-1159.
[49]	Comolli, C. L. (2023). Social climate, uncertainty and fertility intentions: From the great recession to the Covid-19 crisis. European Journal of Population, 39, 35.
[50]	Cook, R. D., & Weisberg, S. (1982). Criticism and influence analysis in regression. Sociological Methodology, 13, 313-361.
[51]	Cooke, R., Dahdah, M., Norman, P., & French, D. P. (2016). How well does the theory of planned behaviour predict alcohol consumption? A systematic review and meta-analysis. Health Psychology Review, 10(2), 148-167. doi: 10.1080/17437199.2014.947547 pmid: 25089611
[52]	Davis, L.S., & Williamson, C.R. (2019). Does individualism promote gender equality? World Development, 123, 104627.
[53]	Dodell-Feder, D., & Tamir, D.I. (2018). Fiction reading has a small positive impact on social cognition: A meta-analysis. Journal of Experimental Psychology: General, 147(11), 1713-1727.
[54]	Dommermuth, L., Klobas, J., & Lappegård, T. (2011). Now or later? The theory of planned behavior and timing of fertility intentions. Advances in Life Course Research, 16(1), 42-53.
[55]	Dommermuth, L., Klobas, J., & Lappegård, T. (2015). Realization of fertility intentions by different time frames. Advances in Life Course Research, 24, 34-46. doi: 10.1016/j.alcr.2015.02.001 pmid: 26047988
[56]	Duval, S., & Tweedie, R. (2000). Trim and fill: Asimple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics, 56(2), 455-463. doi: 10.1111/j.0006-341x.2000.00455.x pmid: 10877304
[57]	Eagly, A. H., & Wood, W. (2012). Social role theory. In Paul A. M. Van Lange, Arie W. Kruglanski, & E. Tory Higgins (Eds.), Handbook of Theories of Social Psychology (Vol. 2, pp. 458-476). SAGE Publications Ltd.
[58]	Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. BMJ, 315(7109), 629-634. doi: 10.1136/bmj.315.7109.629 pmid: 9310563
[59]	Elder Jr., G. H. (1998). The life course as developmental theory. Child Development, 69(1), 1-12. pmid: 9499552
[60]	Erfani, A. (2017). Low fertility intention in Tehran, Iran: The role of attitudes, norms and perceived behavioural control. Journal of Biosocial Science*, 49(3), 292-308. doi: 10.1017/S0021932016000109 pmid: 27086667
[61]	Erfani, A., Jahanbakhsh, R., & Kalantari, A. (2020). Individualism and fertility intentions. Journal of Population Association of Iran, 15(29), 239-264.
[62]	Esping-Andersen, G., & Billari, F.C. (2015). Re-theorizing family demographics. Population and Development Review, 41(1), 1-31.
[63]	Fahlén, S., & Oláh, L.S. (2018). Economic uncertainty and first-birth intentions in Europe. Demographic Research, 39, 795-834.
[64]	Gao, S., Yu, D., Assink, M., Chan, K. L., Zhang, L., & Meng, X. (2024). The association between child maltreatment and pathological narcissism: A three-level meta-analytic review. Trauma, Violence, & Abuse, 25(1), 275-290.
[65]	Ghasemi, J., Safizadeh, M., Khajeh, Z., & Nakhaee, N. (2023). Immediate Antecedents of Intentions for Having Children in Southeast Iranian Women. Korean Journal of Family Medicine*, 44(5), 289-294. doi: 10.4082/kjfm.23.0048 pmid: 37599004
[66]	Guo, Y., Du, Y., Lerkiatbundit, S., Liu, J., Bai, J., Yang, Y.,... Chongsuvivatwong, V. (2022). Factors affecting fertility intention among people living with HIV in Kunming, China. HIV/AIDS-Research and Palliative Care*, 14, 265-273.
[67]	Hagger, M. S., Chatzisarantis, N. L., & Biddle, S. J. (2002). A meta-analytic review of the theories of reasoned action and planned behavior in physical activity: Predictive validity and the contribution of additional variables. Journal of Sport and Exercise Psychology, 24(1), 3-32.
[68]	Han, K. M., & Jung, H. S. (2010). The factors affecting the fertility intention in general-hospital nurses based on the theory of planned behavior. Korean Journal of Occupational Health Nursing*, 19(1), 41-49.
[69]	Harrer, M., Cuijpers, P., Furukawa, T., & Ebert, D. (2021). Doing meta-analysis with R: A hands-on guide. Chapman and Hall/CRC.
[70]	Higgins, J. P., Thompson, S. G., Deeks, J. J., & Altman, D. G. (2003). Measuring inconsistency in meta-analyses. BMJ, 327(7414), 557-560.
[71]	Hofstede, G., Hofstede, G. J., & Minkov, M. (2010). Cultures and organizations: Software of the mind: 3rd edition. Library of Congress Cataloging-in-Publication Data.
[72]	Ibrahim, F. M., & Arulogun, O. S. (2020). Posterity and population growth: Fertility intention among a cohort of Nigerian adolescents. Journal of Population Research*, 37(1), 25-52.
[73]	Jorgensen, S. R., & Adams, R. P. (1988). Predicting Mexican-American family planning intentions: An application and test of a social psychological model. Journal of Marriage and Family*, 50(1), 107-119.
[74]	Kahan, D. M., & Braman, D. (2006). Cultural cognition and public policy. Yale Law & Policy Review, 24, 149.
[75]	Kepes, S., & Thomas, M. A. (2018). Assessing the robustness of meta-analytic results in information systems: Publication bias and outliers. European Journal of Information Systems, 27(1), 90-123.
[76]	Khorram, R., Hasani, M., Karimy, M., Mohammadi, A., & Ranjbaran, M. (2017). Factors related to women's fertility intent: A study based on the theory of rational action. Journal of Holistic Nursing and Midwifery*, 27(3), 57-66.
[77]	Kim, J. Y., & Kim, Y. J. (2022). Factors influencing Fertility intentions of unmarried men and women-Application of theory of planned behavior. Journal of Industrial Convergence*, 20(5), 93-100.
[78]	Krisprimada, N. H., Kusumaningrum, T., & Nastiti, A. A. (2019). Analisis faktor niat pengambilan keputusan dalam menentukan jarak kehamilan pada ibu primipara di Puskesmas Mulyorejo Surabaya. NURSCOPE: Jurnal Penelitian dan Pemikiran Ilmiah Keperawatan*, 5(1), 23-31.
[79]	Kuhnt, A. K., Minkus, L., & Buhr, P. (2021). Uncertainty in fertility intentions from a life course perspective: Which life course markers matter? Journal of Family Research, 33(1), 184-207.
[80]	Li, X., Fan, Y., Assanangkornchai, S., & McNeil, E. B. (2019). Application of the Theory of Planned Behavior to couples' fertility decision-making in Inner Mongolia, China. PloS One, 14(8), e0221526.
[81]	Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gøtzsche, P. C., Ioannidis, J. P.,... Moher, D. (2009). The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration. BMJ, 339, b2700
[82]	Liberman, N., & Trope, Y. (1998). The role of feasibility and desirability considerations in near and distant future decisions: A test of temporal construal theory. Journal of Personality and Social Psychology, 75(1), 5-18.
[83]	Light, R. J., & Pillemer, D. B. (1984). Summing up: The science of reviewing research. Harvard University Press.
[84]	Lyngstad, T. H., & Prskawetz, A. (2010). Do siblings’ fertility decisions influence each other? Demography, 47(4), 923-934. doi: 10.1007/BF03213733 pmid: 21308564
[85]	Manski, C. F., & Mayshar, J. (2003). Private incentives and social interactions: Fertility puzzles in Israel. Journal of the European Economic Association, 1(1), 181-211.
[86]	Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50(4), 370-396
[87]	Matera, C., Dommermuth, L., Bacci, S., Bertaccini, B., Minello, A., & Vignoli, D. (2023). Perceived economic uncertainty and fertility intentions in couples: A dyadic extension of the Theory of Planned Behaviour. Journal of Family and Economic Issues*, 44(4), 790-806.
[88]	McDonald, P. (2000). Gender equity in theories of fertility transition. Population and Development Review, 26(3), 427-439.
[89]	McDonald, P. (2006). Low fertility and the state: The efficacy of policy. Population and Development Review, 32(3), 485-510.
[90]	McEachan, R. R. C., Conner, M., Taylor, N. J., & Lawton, R. J. (2011). Prospective prediction of health-related behaviours with the theory of planned behaviour: A meta-analysis. Health Psychology Review, 5(2), 97-144.
[91]	Mencarini, L., Vignoli, D., & Gottard, A. (2015). Fertility intentions and outcomes: Implementing the theory of planned behavior with graphical models. Advances in Life Course Research, 23, 14-28. doi: 10.1016/j.alcr.2014.12.004 pmid: 26047838
[92]	Meng, T., Zhang, Y., Lv, J., Zhu, C., Lan, L., & Zhang, T. (2023). Effect of the COVID-19 pandemic on women’s fertility intentions and its policy implications for China and the rest of the world: A perspective essay. Biodemography and Social Biology, 68(2-3), 87-100.
[93]	Merz, E. M. (2012). Fertility intentions depend on intergenerational relations: A life course perspective. Family Science, 3(3-4), 237-245.
[94]	Michalos, A. C. (2014). Encyclopedia of quality of life and well-being research. Dordrecht: Springer Netherlands.
[95]	Miller, W. B. (2011). Differences between fertility desires and intentions: Implications for theory, research and policy. Vienna Yearbook of Population Research, 9, 75-98.
[96]	Mills, M., Rindfuss, R. R., McDonald, P., Te Velde, E., & ESHRE Reproduction and Society Task Force. (2011). Why do people postpone parenthood? Reasons and social policy incentives. Human Reproduction Update, 17(6), 848-860. doi: 10.1093/humupd/dmr026 pmid: 21652599
[97]	Mooi-Reci, I., Trinh, T. A., Vera-Toscano, E., & Wooden, M. (2023). The impact of lockdowns during the COVID-19 pandemic on fertility intentions. Economics & Human Biology, 48, 101214.
[98]	Mynarska, M. (2021). Application of the theory of planned behaviour for explaining childbearing intentions-assessment of operationalization and measurement. Studia Psychologica: Theoria et Praxis.*, (Special), 21-36.
[99]	Panda, D., Singhal, D., Jena, S., & Tripathy, S. (2024). Assessment of Indian consumers’ green purchase intention: An integration of theory of planned behaviour and meta-analysis. Environment, Development and Sustainability, 13, 1-22.
[100]	Peterson, R. A., & Brown, S. P. (2005). On the use of beta coefficients in meta-analysis. Journal of Applied Psychology, 90(1), 175-181. doi: 10.1037/0021-9010.90.1.175 pmid: 15641898
[101]	Philipov, D., Spéder, Z., & Billari, F. C. (2006). Soon, later, or ever? The impact of anomie and social capital on fertility intentions in Bulgaria (2002) and Hungary (2001). Population Studies, 60(3), 289-308.
[102]	Raybould, A., & Sear, R. (2021). Children of the (gender) revolution: A theoretical and empirical synthesis of how gendered division of labour influences fertility. Population Studies, 75(2), 169-190.
[103]	Rosenthal, R. (1995). Writing meta-analytic reviews. Psychological Bulletin, 118(2), 183-192.
[104]	Rossier, C., & Bernardi, L. (2009). Social Interaction Effects on Fertility: Intentions and Behaviors: Les effets des interactions sociales sur la fécondité: Intentions et comportements. European Journal of Population/Revue Européenne de Démographie, 25, 467-485.
[105]	Scalco, A., Noventa, S., Sartori, R., & Ceschi, A. (2017). Predicting organic food consumption: A meta-analytic structural equation model based on the theory of planned behavior. Appetite, 112, 235-248. doi: S0195-6663(17)30193-9 pmid: 28188865
[106]	Schoen, R., Astone, N. M., Kim, Y. J., Nathanson, C. A., & Fields, J. M. (1999). Do fertility intentions affect fertility behavior? Journal of Marriage and Family, 61(3), 790-799.
[107]	Shen, Y., & Jiang, L. (2020). Reproductive choices of highly educated employed women with two children under the universal two-child policy. Journal of Family Issues, 41(5), 611-635.
[108]	Song, Y., Qin, Z., Kang, T., & Jin, K. (2019). Robot helps when robot fits: Examining the role of baby robots in fertility promotion. Healthcare*, 7(4), 147.
[109]	Spéder, Z., & Bálint, L. (2024). Realization of short-term fertility intentions in a comparative perspective: Which macro-level conditions matter? Population Research and Policy Review, 43, 72.
[110]	Starfelt Sutton, L. C., & White, K. M. (2016). Predicting sun-protective intentions and behaviours using the theory of planned behaviour: A systematic review and meta-analysis. Psychology & Health, 31(11), 1272-1292.
[111]	Takac, C., Hinz, O., & Spann, M. (2011). The social embeddedness of decision making: Opportunities and challenges. Electronic Markets, 21, 185-195.
[112]	Trope, Y., & Liberman, N. (2003). Temporal construal. Psychological Review, 110(3), 403-421. doi: 10.1037/0033-295x.110.3.403 pmid: 12885109
[113]	Van Wijk, D. C., de Valk, H. A., & Liefbroer, A. C. (2021). Temporary employment and family formation: An income or insecurity effect? European Sociological Review, 37(4), 641-658.
[114]	Viechtbauer, W. (2010). Conducting Meta-Analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1-48.
[115]	Viechtbauer, W., & Cheung, M. W. L. (2010). Outlier and influence diagnostics for meta-analysis. Research Synthesis Methods, 1(2), 112-125. doi: 10.1002/jrsm.11 pmid: 26061377
[116]	Wagner, M., Huinink, J., & Liefbroer, A.C. (2019). Running out of time? Understanding the consequences of the biological clock for the dynamics of fertility intentions and union formation. Demographic Research, 40, 1-26.
[117]	Williamson, L. E. A., & Lawson, K. L. (2015). Young women’s intentions to delay childbearing: A test of the theory of planned behaviour. Journal of Reproductive and Infant Psychology*, 33(2), 205-213.
[118]	Xie, J., & Hong, X. (2022). Research on factors affecting Chinese college students' marriage intention: Applying the theory of planned behaviour. Frontiers in Psychology, 13, 868275.
[119]	Xu, J., Li, L., Ma, X. Q., Zhang, M., Qiao, J., Redding, S. R.,... Ouyang, Y. Q. (2023). Fertility intentions, parenting attitudes, and fear of childbirth among college students in China: a cross-sectional study. Journal of Pediatric and Adolescent Gynecology, 36(1), 65-71.
[120]	Yao, Y. (2024). Research on influencing factors of college teachers' second child fertility intentions——Taking Jinan as an example. PLoS ONE*, 19(5), e0299838
[121]	Yu, X., & Liang, J. (2022). Social norms and fertility intentions: Evidence from China. Frontiers in Psychology, 13, 947134.

变量关系	项目 (k)	样本量 (n)	加权平均相关 r⁺	95% CI		异质性 Q	水平2方差		水平3方差
变量关系	项目 (k)	样本量 (n)	加权平均相关 r⁺	下限	上限	异质性 Q	σ²	I²	σ²	I²
ATT→FI	30	19775	0.41^***	0.27	0.53	1262.38^***	0.005^***	3.70%	0.13^***	96.29%
SN→FI	41	33640	0.30^***	0.22	0.38	975.28^***	0.004^***	7.92%	0.05^***	92.07%
PBC→FI	35	25882	0.23^***	0.09	0.36	1787.42^***	0.01^***	8.54%	0.11^***	91.45%

变量关系	项目 (k)	样本量 (n)	加权平均相关 r⁺	95% CI		异质性 Q	水平2方差		水平3方差
变量关系	项目 (k)	样本量 (n)	加权平均相关 r⁺	下限	上限	异质性 Q	σ²	I²	σ²	I²
ATT→FI	30	19775	0.41^***	0.27	0.53	1262.38^***	0.005^***	3.70%	0.13^***	96.29%
SN→FI	41	33640	0.30^***	0.22	0.38	975.28^***	0.004^***	7.92%	0.05^***	92.07%
PBC→FI	35	25882	0.23^***	0.09	0.36	1787.42^***	0.01^***	8.54%	0.11^***	91.45%

变量关系	调节变量	类别	k	Intercept/mean z [95% CI]	β [95% CI]	F	p	水平2 方差	水平3 方差
ATT→FI	女性比		21	0.39[0.20, 0.59]	0.07[0.016, 0.130]	7.10	0.015^*	0.001^***	0.150^***
	人均GDP		30	−0.62[−1.59, 0.34]	0.112[0.01, 0.21]	5.15	0.031^*	0.008^***	0.088^***
	生育经历					9.61	0.008^**	0.002^***	0.051^***
		未育	7	0.58[0.42, 0.75]
		已育	9	0.42[0.25, 0.60]	−0.16[−0.27, −0.05]
	时间框架					0.65	0.429	0.005^***	0.076^***
		短期	12	0.45[0.21, 0.69]
		长期	12	0.57[0.38, 0.76]	0.12[−0.19, 0.43]
	文化					1.62	0.214	0.006^***	0.112^***
		低IDV	19	0.41[0.25, 0.57]
		高IDV	11	0.52[0.32, 0.72]	0.11[−0.07, 0.29]
SN→FI	女性比		34	0.29[0.19, 0.40]	0.02[−0.04, 0.09]	0.53	0.473	0.002^***	0.052^***
	人均GDP		41	−0.30[−0.94, 0.34]	0.07[−0.002, 0.13]	3.82	0.058	0.005^***	0.039^***
	生育经历					0.54	0.470	0.006^***	0.021^***
		未育	11	0.32[0.21, 0.43]
		已育	10	0.28[0.15, 0.40]	−0.04[−0.17, 0.08]
	时间框架					1.16	0.289	0.004^***	0.026^***
		短期	23	0.32[0.22, 0.43]
		长期	13	0.40[0.29, 0.51]	0.08[−0.07, 0.23]
	文化					0.26	0.612	0.005^***	0.044^***
		低IDV	26	0.30[0.21, 0.40]
		高IDV	15	0.34[0.20, 0.47]	0.03[−0.10, 0.17]
PBC→FI	女性比		28	0.22[0.03, 0.40]	0.066[−0.11, 0.24]	0.61	0.441	0.010^***	0.099^***
	人均GDP		35	−0.89[−1.93, 0.14]	0.12[0.01, 0.23]	4.99	0.032^*	0.011^***	0.091^***
	生育经历					5.02	0.04^*	0.007^***	0.073^***
		未育	9	0.23[0.04, 0.42]
		已育	9	0.08[−0.13, 0.28]	−0.15[−0.30, −0.01]
	时间框架					2.08	0.160	0.010^***	0.097^***
		短期	17	0.15[−0.08, 0.39]
		长期	12	0.37[0.16, 0.58]	0.22[−0.09, 0.53]
	文化					0.56	0.459	0.012^***	0.107^***
		低IDV	22	0.21[0.06, 0.36]
		高IDV	13	0.29[0.08, 0.49]	0.08[−0.13, 0.28]

变量关系	调节变量	类别	k	Intercept/mean z [95% CI]	β [95% CI]	F	p	水平2 方差	水平3 方差
ATT→FI	女性比		21	0.39[0.20, 0.59]	0.07[0.016, 0.130]	7.10	0.015^*	0.001^***	0.150^***
	人均GDP		30	−0.62[−1.59, 0.34]	0.112[0.01, 0.21]	5.15	0.031^*	0.008^***	0.088^***
	生育经历					9.61	0.008^**	0.002^***	0.051^***
		未育	7	0.58[0.42, 0.75]
		已育	9	0.42[0.25, 0.60]	−0.16[−0.27, −0.05]
	时间框架					0.65	0.429	0.005^***	0.076^***
		短期	12	0.45[0.21, 0.69]
		长期	12	0.57[0.38, 0.76]	0.12[−0.19, 0.43]
	文化					1.62	0.214	0.006^***	0.112^***
		低IDV	19	0.41[0.25, 0.57]
		高IDV	11	0.52[0.32, 0.72]	0.11[−0.07, 0.29]
SN→FI	女性比		34	0.29[0.19, 0.40]	0.02[−0.04, 0.09]	0.53	0.473	0.002^***	0.052^***
	人均GDP		41	−0.30[−0.94, 0.34]	0.07[−0.002, 0.13]	3.82	0.058	0.005^***	0.039^***
	生育经历					0.54	0.470	0.006^***	0.021^***
		未育	11	0.32[0.21, 0.43]
		已育	10	0.28[0.15, 0.40]	−0.04[−0.17, 0.08]
	时间框架					1.16	0.289	0.004^***	0.026^***
		短期	23	0.32[0.22, 0.43]
		长期	13	0.40[0.29, 0.51]	0.08[−0.07, 0.23]
	文化					0.26	0.612	0.005^***	0.044^***
		低IDV	26	0.30[0.21, 0.40]
		高IDV	15	0.34[0.20, 0.47]	0.03[−0.10, 0.17]
PBC→FI	女性比		28	0.22[0.03, 0.40]	0.066[−0.11, 0.24]	0.61	0.441	0.010^***	0.099^***
	人均GDP		35	−0.89[−1.93, 0.14]	0.12[0.01, 0.23]	4.99	0.032^*	0.011^***	0.091^***
	生育经历					5.02	0.04^*	0.007^***	0.073^***
		未育	9	0.23[0.04, 0.42]
		已育	9	0.08[−0.13, 0.28]	−0.15[−0.30, −0.01]
	时间框架					2.08	0.160	0.010^***	0.097^***
		短期	17	0.15[−0.08, 0.39]
		长期	12	0.37[0.16, 0.58]	0.22[−0.09, 0.53]
	文化					0.56	0.459	0.012^***	0.107^***
		低IDV	22	0.21[0.06, 0.36]
		高IDV	13	0.29[0.08, 0.49]	0.08[−0.13, 0.28]

作者	国家/地区	抽样方法	样本量	年龄	AT-I	SN-I	PBC-I	女性比	人口学特征	时间框架	个体主义指数	人均GDP (2021)
王小倩(2023)	中国	网络抽样	998	20~49		0.37	0.1	51.30%	成年人	长期	20	10, 409
罗丝秋(2023)	中国	网络抽样	500	18~34	0.319	0.22	0.136	60%	未育		20	10, 409
杜苏旻(2023)	中国	网络抽样	319	20~49		0.159		52%	已育	长期	20	10, 409
俞婷(2020)	中国	方便抽样	356	18~45	0.155	0.498	0.083	1	成年人	短期	20	10, 409
张淼(2020)	中国	随机抽样	596	18~50		0.237	0.061	1	成年人		20	10, 409
Williamson (2015)	加拿大	实验随机	69	21	0.57	0.4	0.68	1	未育	长期	80	43, 538
Banaei (2023)	伊朗	网络抽样	400	33.41	−0.303	0.244	0.579	1	成年人		41	2, 746
Song (2019)	中国	网络抽样	180	>20	0.283			43.90%	成年人		20	10, 409
Yao (2024)	中国	便利抽样	194	>21	0.18	0.37	0.02	62.90%	已育	长期	20	10, 409
Ibrahim (2020)	尼日利亚	随机抽样	600	14.57	0.204	0.132	−0.035	46%	青少年		20	2, 075
Matera (2023)	意大利	随机组间设计	331	20~40	0.787	0.475	0.691	1	成年人	短期	76	31, 923
Matera (2023)	意大利	随机组间设计	331	20~53	0.75	0.605	0.528	0	成年人	短期	76	31, 923
Dommermuth (2023)	挪威	随机抽样	549	18~40		0.085	0.076	47%	已育	短期	69	68, 340
Dommermuth (2023)	挪威	随机抽样	758	18~40		0.12	0.07	48%	未育	短期	69	68, 340
Agar (2018)	加拿大	网络随机	349	18~29	0.71	0.38	0.46	1	未育	长期	80	43, 538
Erfani (2017)	伊朗	随机抽样	2267	女15~35 男< 36	0.462	0.373	−0.274	未报告	未育	长期	41	2, 746
Erfani (2017)	伊朗	随机抽样	900	女15~35 男< 36	0.393	0.422	−0.480	未报告	已育	长期	41	2, 746
Chae (2016)	韩国	便利抽样	548		0.59	0.45	0.36	1	未育	长期	18	31, 721
Ghasemi (2023)	伊朗	便利抽样	998	34.8		0.452		1	成年人	短期	41	2, 746
Ajzen (2013)	俄罗斯	随机抽样	667	25~34	0.344	0.285	0.158	未报告	已育	短期	39	10, 108
	意大利		544		0.295	0.138	0.109	未报告	已育	短期	76	31, 923
	德国		217		0.334	0.442	0.128	未报告	已育	短期	67	46, 749
	法国		214		0.364	0.148	0.344	未报告	已育	短期	71	39, 180
	匈牙利		544		0.422	0.207	0.07	未报告	已育	短期	80	16, 132
Chappell (2024)	美国	随机抽样	289	24~40	0.7	0.58	0.34	49%	未育	长期	91	64, 317
Alizadeh (2023)	伊朗	随机抽样	405	37.82	−0.276	−0.426	−0.487	1	已婚		41	2, 746
Guo (2022)	中国		487	20~50	0.086	0.128	0.097	50.30%	成年人	短期	20	10, 409
Ciritel (2019)	罗马尼亚	随机抽样	1683	18~45		0.102	0.188	35.77%	未育	短期	30	13, 047
Ciritel (2019)	罗马尼亚	随机抽样	1521	18~45		0.055	0.036	51.68%	已育	短期	30	13, 047
Billari (2009)	保加利亚	随机抽样	1479	18~34		0.344	−0.039	1	成年人	短期	30	10, 148
			2081			0.207	0.07	0	成年人	短期	30	10, 148
			1433			0.256		1	成年人	短期	30	10, 148
			1293			0.237		0	成年人	短期	30	10, 148
Buber-Ennser (2011)	奥地利	随机抽样	2023	18~34				51.80%	未育	短期
			975			0.334		0	未育	短期	55	48, 789
			1048			0.354		1	未育	短期	55	48, 789
Mynarska (2021)	波兰	随机抽样	2075	18~79	0.37			0	成年人	短期	60	15, 817
			2762			0.43		0	成年人	短期	60	15, 817
			2081				−0.07	0	成年人	短期	60	15, 817
			2640		0.43			1	成年人	短期	60	15, 817
			3484			0.45		1	成年人	短期	60	15, 817
			2609				−0.04	1	成年人	短期	60	15, 817
周国红(2021)	中国	随机抽样	743	18~44			0.333	54.40%	成年人	长期	20	10, 409
马芮(2023)	中国	网络随机	312	18~50			0.251	61.86%	成年人		20	10, 409
Jorgensen (1988)	墨西哥	随机抽样	708	17~45	0.55	0.27		1	成年人	短期	30	8, 896
Chen (2024)	中国香港	两阶段分层抽样	1714	15~44	0.257				未育	长期	25	46, 109
Chen (2024)	中国香港	两阶段分层抽样	817	15~44	0.052				已育	长期	25	46, 109
Krisprimada (2019)	印度尼西亚	总体抽样	104	17~42	0.587	−0.050	0.668	1	成年人	长期	14	3, 896
Han (2010)	韩国	随机抽样	674	20~39	0.588	0.432	0.394	1	成年人	长期	18	31, 721
Kim (2022)	韩国	随机抽样	168	20~49	0.764	0.523	0.698	70.80%	成年人		18	31, 721
Khorram (2017)	伊朗	随机抽样	483	24.7	0.45	0.41		1	成年人		41	2, 746