A new model for evaluating the influence of social networks,social learning,and supportive policies on the desire of women for fertility

Abstract

BACKGROUND:

Nowadays, social networks become so famous and attract a lot of users. In recent eras, the increase of online social networks and the digitization of communication types have meant that online social networks have become a significant part of social network examination.

OBJECTIVE:

In this paper, we investigate the social networks to study the desire of women for fertility. The study has delivered new visions into the elements of reproductive behavior and has discussed the development of increasingly refined and realistic theories of fertility desire.

METHODS:

A questionnaire is intended for evaluating the elements of the model. Questionnaires were reviewed by experts with significant experiences in this domain. From 384 users of Telegram as an important social network in Iran, data are collected. For statistical examination, the SPSS 22 and SMART- PLS 3.2 software are also utilized.

RESULTS:

Results confirmed the validity of the model for assessing of the desire of women for fertility. The outcomes have indicated that the social network has a negative effect on the desire of women for fertility. Besides, the results have shown that the role of social networks on social learning is significant and positive. Furthermore, the role of social learning and supportive policies on the desire of women for fertility is positive and significant.

CONCLUSIONS:

According to findings, managers have enough precision in training women and daughters through social networks and social learning to enhance the desire for fertility. Finally, it is significant to note that since data are self-reported, they could be affected by rationalization and may not correlate with fertility behavior. In future studies, by gathering a comprehensive sample, other important elements can be considered that cause the desire of women for fertility.

Keywords

Human social network social learning supportive policies desire women fertility

Pei-lin Chen was born in 1978 in Xinxiang, Henan Province, China. He received his Ph.D. from Beijing Normal University, and then did postdoctoral research at Beihang University. Currently, He works at the China University of Labor Relations in Beijing. His research interests include decision analysis and application, project management and system simulation, psychometric measurement and evaluation, etc.

Tahere Nazari Saman received her master degree in Executive Business Administration (Executive Management) and her bachelor degree in Industrial Engineering) Systems Analysis). She has been working in a petrochemical company in Iran.

1 Introduction

Online communities are gaining popularity because of the number of members increasing. The social network gained high attraction all around the world by people’s influential communications [1]. It has been described as relations among people and prepares an appealing platform to exchange social resources and enabling the connections among its users [2]. People can share pictures and opinions with their associates and friends via some famous social networks such as Facebook 1 , Instagram 2 , Twitter 3 and Telegram 4 , Also, some e-commerce platform such as Wikipedia 5 , Slashdot 6 , and on-line sharing networks such as UBER 7 and Blablacar 8 for cars or Airbnb 9 for accommodation provides facilities to share knowledge and expertise [3]. Despite the variety of on-line communities, all of them have many common features such as sharing, posting, and messaging. Daily, many people are participating social networks and communicating with other people. Even they did not know each other previously. In addition, it is prevalent facility that prepares a stage for users to build their own profiles and share desired contexts with others [4].

On the other hand, the most important concern of many people is deciding on the number of children. Through quantitative-qualitative trade, fertility selection is closely linked to human fund, which is the key to the success of modern societies [5]. The fertility rate has decreased in most developed countries and is now below the replacement rate [6, 7]. The analysis of fertility time and rate varies widely in many developing countries. To explain these changes, demographers have expanded and involved some theories that incorporate social interactions that connect individuals [8, 9]. In addition, they believed that modern facilities of current decades, such as social networks might cause this phenomenon. Studies illustrated that albeit fertility is a personal choice, it is profoundly influenced by social norms [10, 11]. Social networks vary broadly in size and conflictual qualities (e.g., critical, demanding) [12]. Humans as social activists, are rooted in social interactions with others. The basic premise of social network theory is that fundamental decisions made in a person’s life cycle, such as the decision to become a parent or the desire of women for fertility, are not only determined by their characteristics, but also by the behaviors of other people interacting with them [13]. As a result, social researchers emphasize that the explanation of fertility should be based on behavioral models that are rooted in social networks with a specific structure and operate concerning their social environment. Also, social learning is based on the premise that new behaviors are often acquired through the observation of models. Social learning helps a person to evaluate the performance of a new action without endangering any risk of negative consequences, such as failure or social disapproval. In terms of the desire of women for fertility, this means that when other couples have children on the social network, previous unknown pleasures and challenges of having children become apparent and act as potential behavioral models [14]. Therefore, in this paper, we study social learning, supportive policies and social networks to analyze the desire of women for fertility. The research provides new visions into the factors of reproductive behavior and contributes to the development of theories of fertility variation. In particular, we examine whether social networking content is important for the desire of women for fertility. The goals of this paper are as follows:

Providing a special framework to define significant factors affecting women’s desire for fertility;

Highlighting the role of social networks in the desire of women for fertility;

Investigating the impact of social learning on the desire of women for fertility;

Examining the role of supportive policies in the desire of women for fertility.

The paper proceeds as follows. The subsequent section presents related literature, the conceptual framework and hypotheses of research. Section 3 presents the empirical study (data collection and sample, measures, analysis method and results). Section 4 presents a discussion. The conclusion is presented in Section 5. Section 6 provides the implications of the study. Finally, limitations and future directions are discussed in Section 7.

2 Related literature and background

In this section, we have a look at related work, fertility and social networks, social learning, and supportive policies, as well as offering the proposed conceptual framework and hypotheses.

2.1 Related work

Buyukkececi, Leopold [15] examined the influence of network partners on individual fertility decisions employing data from the Social Statistical Data (SSD) sets of statistics Netherlands. The results showed that colleagues’ and siblings’ fertility have direct consequences for an individual’s fertility. Furthermore, colleague effects are concentrated in female-female interactions, and women are more strongly influenced by their siblings, regardless of siblings’ gender.

Islam [16] examined the effects of social networks on contraceptive adoption. Data were gathered from 430 couples. The results have shown that social networks of both men and women had a positive influence on existing contraceptive use. Also, the results showed that men’s social network and women’s social network were also related to selecting a modern contraceptive method.

Also, Kebede [17] compared the relative role of female training on fertility desire at the individual, community, and country levels. The results revealed that at the individual level, female training has a more substantial effect compared to household wealth. At the community level, the relative impact of female training is even more striking. This study validated the findings of previous studies that have looked at the relationship and causal link between actual fertility and women’s level of training attainment.

Yoon [18] studied the impact of three supportive environment sources for families, government, spouses, parents, or in-laws on the goals and fertility of women with regard to second children. Binary logistic regression has analyzed the factors. The consequences indicate that supportive environments for the family have more influences on fertility behavior than fertility goals. Women who are aware of childcare leave that is reserved for fathers used to have a second child more than women who are unaware of this. Spousal support for home care and child care and intensive child care support for primary parenting or grooming parents increases the likelihood of second birth.

Also, De Silva and Tenreyro [19] discussed that population-control policies likely played a central role in the global decline in fertility rates in recent decades and can clarify some patterns of that fertility decline that are not well accounted for by other socioeconomic factors.

Bernardi, Keim [20] highlighted the profits of using social networking perspectives on family research and fertility. The results indicated that all social mechanisms impact the beliefs and norms that individuals have about childbearing, their perception of childbearing, and the context of the opportunities and constraints in which childbearing is made.

Moreover, Cheng [21] examined the contraceptive knowledge impact on fertility when Taiwan’s family planning was under influence. Data were obtained through interviews. The dataset contains data on women’s reproductive history, a number of children desired, knowledge, and utilization of contraceptive methods that are a unique source for investigating the relationship between fertility and contraceptive knowledge. The outcomes indicated that social networks and mass media have an essential role in disseminating contraceptive knowledge. Besides, the conclusion has illustrated that women turn their knowledge into behavior, which is contraceptive knowledge decreases fertility.

Finally, Kohler, Behrman [8] examined the relationship between social networks and fertility decisions. The information from the longitudinal household survey and semi-structured set interviews and focus groups that were gained in four rural sub-locations. The results indicated that social learning is mostly related to high activity of market; however, in areas with only moderate market activity, social influence is an important tool for influencing social networks on the utilization of contraceptives.

2.2 Women and fertility desires

In many cultures, women’s character is identified by child bearing [22]. The propensity of bearing affects social standing, individual recognition, and partnership stability [23, 24]. Many factors, like features and culture of woman her husband, are considered on a woman’s choice for a number of children [25, 26]. According to Finocchario-Kessler, Sweat [27] fertility desire and fertility intention have great impact on modern societies. In recent past 10 years, in many parts of the industrialized world, there has been a significant trend of women delaying child bearing and delaying their reproductive years. [28, 29]. Former research has illustrated that waiting for college degrees is the main reason for delays in pregnancy and child birth [30, 31]. In addition, the fertility rate means the number of children each woman can give [32]. Accordingly, the fertility rate in Iran from 1978 to 2011 is collapsed, and it reached from 6.5 children to 1.8 children 10 .

2.3 Social networks

With the recent advances in technology, there is an emerging presence of social media and social networking systems [33, 34]. Internet-based communication has become a vital part of people’s lives especially the younger [35, 36]. Sites of social networking are the hottest online communication platform that allows users to build a public or semi-public profile, build and see social activities of other users [37, 38]. A social network is a suitable theoretical structure in the social sciences to investigate the relationships between individuals, groups, organizations, or even whole societies [1, 39]. Online social networks are gaining fame and are the most prevalent web sites on Web [40]. Unlike the web that is basically formed near content, online social networks are managed near users [41]. Social networks are highly dynamic objects and they grow and change fast through the addition of new connections [42]. They have an important role as a mediator for the dissemination of information, ideas and influence among their users [43, 44]. Social network includes the sub-indicators of communication, social protection and social influence.

Therefore, the proposed model based on the discussed theoretical considerations must test the following hypotheses:

Social network influences the desire of women for fertility (Social network ⟶ The desire of women for fertility).

Social network influences social learning (Social network ⟶ Social learning).

2.4 Social learning

When confronted with new tasks, individuals can attain at adaptive solutions either through individual or via social learning [45]. Social learning has been employed to refer to all types of processes of learning. So, its meaning has become unclear. Social learning refers to learning in one social environment via witnessing and mimicry of the other [46]. Also, social learning is the main factor for the transfer of real information about the vital features of the environment [47]. Theories of situated learning and knowledge construction support social learning, so, have to effect on nature, the procedure, and the learning results [48]. Moreover, social learning defines the platform in which persons can learn by witnessing others’ behavior [49]. A social learning perspective for construct testing [50]. Social learning includes the sub-indicators of learning through social networks, learning through the media, and learning through the family.

So, the proposed model based on the discussed theoretical considerations must test the following hypothesis:

Social learning influences the desire of women for fertility (Social learning ⟶ The desire of women for fertility).

2.5 Supportive policies

Public policies form the context in which individuals’ reproductive decisions happen. They control the employment process, define the level of services for health, and explain the rights and parents’ duties [51, 52]. Furthermore, the realization of individuals’ child bearing strategies often needs supporting policies. E.g., without changes in employment situations, the onus of the commitment to child bearing within a couple will usually rest solely on women. Fertility decisions during the reproductive age are affected not only by the preferences and values but also by the factors that are outside the control of women [53]. E.g., the timing of child birth, the length of time they withdraw from employment for child care, the timing of return to employment, and choice to work part- or full-time are all formed the decision of women. These selections may be partly due to the availability of reasonable day care, the number of income women earn while in employment, and the preferences between a full-time housewife and a mix of occupations and responsibilities. These choices also depend on the institutional and cultural settings to which women are confronted, and which are shaped by public rules [54]. Supportive policies include the sub-indicators of setting employment conditions, social services, and support, providing educational services and providing health services.

Therefore, the proposed model based on the discussed theoretical considerations must test the following hypothesis:

Supportive policies influence the desire of women for fertility (Supportive policies ⟶ The desire of women for fertility).

2.6 Research framework

The trend of people participating and forming shared groups is essential in the social structure, and how such groups take shape is a fundamental task [55]. Users spending extra time on popular social networks, as well as storing and sharing a wealth of personal information [56]. The main aim of this study is to study the influence of social networks on the desire of women for fertility. Standing on the former researches, a new framework was designed to perform this study. Nine sub-indicator during three variables are talked. These variables are social networks, social learning, and supportive policies. In the social network, factors are communication, social protection and social influence. The variable of social learning includes learning through social networks, learning through the media and learning through the family. Finally, setting employment conditions, social services and support, providing educational services, and providing health services were identified in supportive policies variable. Figure 1 illustrates a framework including those variables, as this study develops.

Fig.1

The Conceptual model of research.

3 Empirical study

The research model of this study contains social network and supportive policies as two main independent elements, and social learning, the desire of women for fertility as two dependent elements (Fig. 2). The rest of this section describes the data collection and sample, measures, analysis method, and obtained results, respectively.

Fig.2

Structural model.

3.1 Data collection and sample

The users of Telegram as important social networks in Iran are included in the statistical population of the study. According to Morgan’s table, for the statistical society with an infinite number, 384 questionnaires were distributed among users (Appendix A). The answers were collected for the final analysis. After reducing the incomplete questionnaires, an ultimate sample of 350 answers was employed to test the model. We pointed to the proposed range of 20 : 1 per construct in the research model concerning issue of suitable sample size [57]. Since there are just four constructs in the study model, a sample size of more than 80 was appropriate. The respondents represented these characteristics: 19.3% were less than 20 years old, 48.5% were 20–30 years old, 21.2% were 30–40 years old, and10% were 40 years and older.

3.2 Measures

In this study, a questionnaire has been utilized for gathering the needed info to determine the relationship between research variables. The survey is a pre-formulated question group that answerers select their response among a range of specific options. The five-point Likert scale [58] is also used, which is from 1 (strongly disagree) to 5 (strongly agree). Before analyzing by PLS-SEM, the gathered information was inserted into SPSS 23 for statistical analyses, such as frequency, standard deviation, percentage, and correlations. For testing the hypotheses, SMART PLS 3.2 as a SEM tool was employed [59, 60].

3.3 Analysis of method

According to the report of Hair, Ringle [61], choosing a two-step approach for SEM is suitable: first, measurement model evaluation; second, structural model assessment [62]. The PLS-PM is also utilized for this research [63]. PLS is an iterative method of solving the blocks of the measurement model, and it can estimate the path factors in the constructional model. So, PLS-PM can describe the remaining variance of assertion variables and hidden ones in the model [64]. In PLS, Average Variance Extracted (AVE) and compound validity can be inspected to check the multivariate analysis assumptions [65].

3.4 Results

On the first analysis level, the model is examined by estimating reliability and validity (Tables 1 and 2). As an internal consistency degree, compound validity has been progressed, and Joreskog (1974) performs the same task as the Cronbach’s alpha. Abnormally (1978) proposes 0.7 as the ‘average’ reliability measure applicable in the early levels of study and 0.8 as a more accurate value for fundamental research. Another reliability measure is AVE [66]. The measures the variance of a structure from its indices [67]. It attends to be stuffier than compound validity. AVE degrees must be more than 0.50 [68]. So, the ratio models and outer loadings were considered acceptable.

Table 1
Measurement model

Variable AVE Composite reliability Cronbach’s alpha

Social network 0.788 0.949 0.931

Social learning 0.519 0.842 0.769

Supportive policies 0.617 0.887 0.833

The desire of women for fertility 0.529 0.835 0.756

Variable	AVE	Composite reliability	Cronbach’s alpha
Social network	0.788	0.949	0.931
Social learning	0.519	0.842	0.769
Supportive policies	0.617	0.887	0.833
The desire of women for fertility	0.529	0.835	0.756

Table 2

Correlation between constructs and AVE

Variable	Social network	Social learning	Supportive policies	The desire of women for fertility
Social network	0.888
Social learning	0.552	0.721
Supportive policies	0.697	0.583	0.786
The desire of women for fertility	0.646	0.659	0.682	0.727

To direct the validity of the differentiation, we compare whether the AVE is higher than the square correlation between the manufactures in the model [69]. To make the calculation process fast, a reverse procedure is fulfilled [70]. To specify construct discriminant validity, we compute the square root of the AVE, and it should be more than each of the construct factors. These amounts are illustrated in Table 2, where diagonal elements signify the square root of the AVE [68].

The PLS extends some scales between reflector structures and their indices, standard regression factors between structures, and multiple determination coefficients (R2) for all interior manufacturers. In PLS, the communication between a structure and its indicators can be shaped as constructive or intellectual [71]. R² should be more than 0.1 for endogenous variables [72]. In this study, the social learning and the final dependent construct (The desire of women for fertility) have an R² value of 0.633 and 0.612, which is able to satisfy. Other structures in the sample are also at admitted stages.

In PLS, no universal standard is considered and the evaluation of the overall model is not possible. Tenenhaus, Amato [73] have suggested a universal scale of goodness-of-fit (GoF) that defines a solution for this gap and is able to be utilized to accredit the PLS model to solve this issue. The GoF scale is a geometric way of mean communality and the average R². The mean of the joints is calculated as the weight of the various joints with the number of explicit variables or the index of each structure as the weight [74]. The average R² and mean communality index value [75]: $GOF = \sqrt{\bar{Communality * R^{2}}}$ (1) $GOF = \sqrt{0.613 * 0.622} = 0.617$

The GoF index is bounded between 0 and 1.There is not any inference-based sill for judging the actuarial importance of their amounts [64]. If the amount of GoF is equal to or higher than 0.36, it is acceptable. The GoF value is 0.617, which is considered as acceptable.

The constructional model indicates the communications between the variables assumed in the model of study. As the basic purpose of PLS is to predict, the goodness of a theoretical model is determined by the robustness of each of the structural and hybrid prediction paths (R²) [76]. From the primary set of ways, one is 0.01, and the remaining three are considerable at the 0.001 stages, as illustrated in Tables 3–5.

Table 3

Path coefficients and T-value for social network

Questionnaires	Path coefficients	T-value	Significance level
Social networks are valuable social learning tools.	0.98	631.01	0.001
Social networks provide opportunities to support the social structures of knowledge.	0.91	119.67	0.001
The level of integrity of people in social networks affects the positive attitude of the child.	0.90	125.58	0.001
Satellite networks have a negative attitude toward having many children.	0.85	55.70	0.001
Social networks showed that living costs increase with childbearing.	0.76	27.23	0.001
Social network ⟶ The desire of women for fertility	–0.32	3.31	0.001
Social network ⟶ Social learning	0.79	52.29	0.001

Table 4

Path coefficients and T-value for social learning

Questionnaires	Path coefficients	T-value	Significance level
Learning through social networks has a positive impact on the desire of women for fertility.	0.63	13.22	0.001
Learning through the family has a positive effect on the desire of women for fertility.	0.69	19.85	0.001
Learning through the media has a positive impact on the desire of women for fertility.	0.66	17.11	0.001
A friend’s pregnancy has a positive impact on the individual’s risk of becoming a parent.	0.83	46.89	0.001
Other important people (Family, close friends) influence the intentions and fertility behaviors of people.	0.76	29.51	0.001
Social learning ⟶ The desire of women for fertility	0.19	3.03	0.01

Table 5

Path coefficients and T-value for supportive policies

Questionnaires	Path coefficients	T-value	Significance level
Encouraging others will change my desire for fertility.	0.81	39.78	0.001
Providing appropriate job opportunities will change my attitude toward fertility.	0.51	10.87	0.001
Providing social support and social services will increase my desire for fertility.	0.87	67.38	0.001
Providing educational services has an impact on my attitude toward fertility.	0.88	99.14	0.001
Providing health services has an impact on my attitude toward fertility.	0.78	37.00	0.001
Supportive policies ⟶ The desire of women for fertility	0.90	7.70	0.001

3.4.1 Social network

According to the three defined sub-indicators for the social network, questions SN1–SN5 were measured its relevance on the desire of women for fertility. The relationship between social networks with the desire of women for fertility has significant T-value (3.31) and β-value (–0.32). The results have indicated that the hypothesis was confirmed at a substantial level of 99.9%. Moreover, the relationship between social networks with social learning has positive and considerable T-value (52.29) and β-value (0.79). The results have shown that the hypothesis was confirmed at a significant level of 99.9%. The path coefficients and T-values for social network are provided in Table 3.

3.4.2 Social learning

Questions SL1–SL5 tries to evaluate the influence of the social learning on the desire of women for fertility. The relationship between social learning with the desire of women for fertility is significant (T-value is 3.01, β-value is 0.19 and a significant level of 95%). The obtained results have indicated that the hypothesis was confirmed at a considerable level of 99%. The path coefficients and T-values for social learning questions are provided in Table 4.

3.4.3 Supportive policies

According to three sub-indicators of supportive policies, questions SP1–SP5 were designed to measure its relevance to the desire of women for fertility. The relationship between supportive policies with the desire of women for fertility found significant T-value (7.70) and β-value (0.90). The obtained consequences indicate that the hypothesis was confirmed at a considerable level of 99.9%.The path coefficients and T-values for supportive policy questions are given in Table 5.

4 Discussion

While there is presently a wide range of agreement that influences of social interaction are essential for understanding fertility change, there is little evidence regarding their magnitude. Having a better understanding of the dynamic nature of social effects and their relative contributions to social innovation could estimate the future impact of policies on behavioral change [77]. First, as far as we know, this research is the first experimental research that inspects the desire of women for fertility on the background of social networking. Second, the findings in the domain of social networks and the desire of women for fertility are very important. A model of research is examined social network influence on the desire of women for fertility. By a questionnaire administered to 384 users of Telegram, the model was evaluated. The hypothesized model was tested using PLS-SEM.As indicated in Table 6, the path factor and the sample t-test results mentioned that social network has a negative and significant influence on the desire of women for fertility (path coefficient = –0.32, T-value = 3.31). Social networks, in addition to positive functions, have some negative functions, because, in contrast to real social relationships, they reinforce the spirit of pleasure in people. So virtual social networking ads are about making a better life and highlighting child-friendly costs. As a result, these networks have a negative impact on women’s thoughts and inclination to fertility. The results of this study are consistent with the results of Islam [16], Bernardi, Keim [20] and, Kohler, Behrman [8]. The influence between the social network and social learning is positive and significant (path coefficient = 0.79, T-value = 52.29). The results of this part are consistent with the results Cheng [21] and Haythornthwaite and De Laat [78]. In addition, the social learning influence on the desire of women for fertility is positive and significant (path coefficient = 0.19, T-value = 3.03). These results are consistent with the results Kebede [17] and Bernardi, Keim [79]. Moreover, supportive policies influence the desire of women for fertility is significant and positive (path coefficient = 0.90, T-value = 7.70). They are consistent with the results Yoon [18] and De Silva and Tenreyro [19].

Table 6
Summer of the results

Path Path coefficient T statistic Sign

Social network ⟶ The desire of women for fertility –0.32 3.31 *

Social network ⟶ Social learning 0.79 52.29 *

Social learning ⟶ The desire of women for fertility 0.19 3.03

Supportive policies ⟶ The desire of women for fertility 0.90 7.70 *

Path	Path coefficient	T statistic	Sign
Social network ⟶ The desire of women for fertility	–0.32	3.31	***
Social network ⟶ Social learning	0.79	52.29	***
Social learning ⟶ The desire of women for fertility	0.19	3.03	**
Supportive policies ⟶ The desire of women for fertility	0.90	7.70	***

5 Conclusions

The timing of fertility has central concern of researchers and policy makers in recent decades. In addition, a social network is a beneficial platform in modern sciences. Also, this study explored the influence of social networks, social learning and supportive policies on the desire of women for fertility. The study will provide a comprehensive framework and suggest a model of critical factors for the desire for fertility. The statistical outcomes revealed that the three assumed hypotheses were supported. Findings showed a negative and significant relationship between social networks and the desire of women for fertility. Social network indicators included communication, social protection, and social influence. The findings also showed that social network is viewed as the main factor in social learning. Moreover, the results indicated that the influence of the social learning variable (learning through social networks, learning through the media and learning through the family) on the desire of women for fertility is significant and positive. Finally, the results showed a positive and meaningful relationship between supportive policies and the desire of women for fertility. Supportive policy indicators included setting employment conditions, social services, and support, providing educational services and providing health services. The findings of this paper confirmed that having social support could affect the decision of couples about their first childbirth. Informal types of social supports were more common compared to the formal ones.

6 Implications

The implication section divided into two parts; theoretical and practical implications.

By considering some vital topics, the current examination shows a substantial theoretical role for the related scholars. In fact, the present article represents a good direction by investigative the influence of social networks, social learning, and supportive policies on the desire of women for fertility, yet not been well assessed. So, the present paper meaningfully contributes to the knowledge and literature by emphasizing more on the social networks as more innovative technologies are calling for more understanding, testing other vital elements, and using improved statistical analysis techniques.

Individuals learn, transmit and, negotiate social norms in social connections. Research on fertility has paid attention to the position of social contexts, including family, peer groups and healthcare providers on fertility aims. Development interventions increasingly recognize that the most influential sources of information are the close ties of social networks, and targeting specific network partners as ‘opinion leaders’ has been a successful strategy in disseminating contraceptive behavior [80, 81]. The results further suggested that, where the main interest is in reducing fertility, a much more extensive range of more culturally acceptable approaches must be directed.

According to the results of the research, through proper management of the active forces of the society, including women, it is possible to prevent the reduction of fertility rate. It is done by paying attention to family-centered policies and social networks. In fact, improving women’s employment laws to better care for children, providing affordable childcare services for working or studying mothers, paying attention to maternity leave, and rules that help combine maternal and social roles can be effective. In this way, women can engage in activities outside their home, including education, employment, and social activities while caring for their children. In this regard, encouraging the government to involve men in housework, having children, supporting childcare and formal childcare in the workplace of working women, despite the time and cost, can play an essential role in increasing the desire of working women for fertility.

7 Limitations and future directions

The study has several limitations. First, cross-sectional data is used, which allowed us to test the associations between variables but not to show causality. Further, in future studies, by gathering a comprehensive sample, other important elements can be considered that cause the desire of women for fertility. Finally, it is significant to note that since data are self-reported, they could be affected by rationalization and may not correlate with fertility behavior.

The findings validate the results of previous studies, which indicate a considerable communication between social networks, social learning, and supportive policies and the desire of women for fertility. Therefore, some suggestions are provided as follow:

To increase family planning acceptance and to reduce fertility risks, policymakers need to target women based on parity and family sex composition.

The desire for sons is embedded within the culture of many East Asian societies, and this preference can alter social norms and people’s attitudes.

Managers have enough precision in training women and daughters to enhance their desire for fertility.

Collecting data related to the CMV test and examining its relationship with fertility

Finally, some tools, such as virtual meetings and exchanging experiences on fertility can be applied.

Footnotes

Appendix A

Table A1

Morgan Table

N	S	N	S	N	S	N	S	N	S
10	10	100	80	280	162	800	260	2800	338
15	14	110	86	290	165	850	265	3000	341
20	19	120	92	300	169	900	269	3500	246
25	24	130	97	320	175	950	274	4000	351
30	28	140	103	340	181	1000	278	4500	351
35	32	150	108	360	186	1100	285	5000	357
40	36	160	113	380	191	1200	291	6000	361
45	40	170	118	400	196	1300	297	7000	364
50	44	180	123	420	201	1400	302	8000	367
55	48	190	127	440	205	1500	306	9000	368
60	52	200	132	460	210	1600	310	10000	373
65	56	210	136	480	214	1700	313	15000	375
70	59	220	140	500	217	1800	317	20000	377
75	63	230	144	550	225	1900	320	30000	379
80	66	240	148	600	234	2000	322	40000	380
85	70	250	152	650	242	2200	327	50000	381
90	73	260	155	700	248	2400	331	75000	382
95	76	270	159	750	256	2600	335	100000	384

References

Sharif

, Mahmazi

, Navimipour

, Aghdam

. A review on search and discovery mechanisms in social networks. International Journal of Information Engineering and Electronic Business. 2013;5(6):64.

Ashida

, Schafer

. Social Networks, Social Relationships, and Their Effects on the Aging Mind and Brain. The Wiley Handbook on the Aging Mind and Brain. 2018:17–36.

Urena

, Kou

, Dong

, Chiclana

, Herrera-Viedma

. A review on trust propagation and opinion dynamics in social networks and group decision making frameworks. Information Sciences. 2019;478:461–75.

Fan

, Yeung

. Similarity between community structures of different online social networks and its impact on underlying community detection. Communications in Nonlinear Science and Numerical Simulation. 2015;20(3):1015–25.

Doepke

, Tertilt

. Women’s Empowerment, the Gender Gap in Desired Fertility, and Fertility Outcomes in Developing Countries. 2018.

Collins

, Page

. The heritability of fertility makes world population stabilization unlikely in the foreseeable future. Evolution and Human Behavior. 2019;40(1):105–11.

Olson

, Clark

, Reynolds

. Can a conditional cash transfer reduce teen fertility? The case of Brazil’s Bolsa Familia. Journal of health economics. 2019;63:128–44.

Kohler

H-P

, Behrman

, Watkins

. The density of social networks and fertility decisions: Evidence from South Nyanza District, Kenya. Demography. 2001;38(1):43–58.

Robertson

, Sharkey

. Seminal fluid and fertility in women. Fertility and sterility. 2016;106(3):511–9.

10.

Firouzbakht

, Tirgar

, Hajian-Tilaki

, Ebadi

, Bakouei

, Nikpour

, et al. Social capital and fertility behaviors among female workers in healthcare settings: study protocol of a sequential explanatory mixed methods study. Reproductive health. 2018;15(1):67.

11.

Schlenker

, McCormick

, Smith

, Pospisil

, Schoolcraft

, Katz-Jaffe

. Efficiency of elective oocyte vitrification for reproductive aging women seeking fertility preservation to delay motherhood. Fertility and Sterility. 2018;110(4):e187.

12.

Birditt

, Manalel

, Sommers

, Luong

, Fingerman

. Better off alone: Daily solitude is associated with lower negative affect in more conflictual social networks. The Gerontologist. 2019;59(6):1152–61.

13.

Smelser

, Baltes

. International encyclopedia of the social & behavioral sciences: Elsevier Amsterdam; 2001.

14.

Friedman

, Hechter

, Kanazawa

. A theory of the value of children. Demography. 1994;31(3):375–401.

15.

Buyukkececi

, Leopold

, van Gaalen

, Engelhardt

. Family, Firms, and Fertility: A Study of Social Interaction Effects. Demography. 2020:1–24.

16.

Islam

. Social networks and their effects on the choice of contraceptive use and method in Bangladesh. International Journal of Human Rights in Healthcare. 2019.

17.

Kebede

. The Relative Importance of Female Education on Fertility Desires in Sub-Saharan Africa: A Multi-Level Analysis. 2019.

18.

Yoon

S-Y

. Fertility attitudes and behavior: the effects of gender equity on fertility in South Korea: University of Illinois at Urbana-Champaign; 2017.

19.

De Silva

, Tenreyro

. Population control policies and fertility convergence. Journal of Economic Perspectives. 2017;31(4):205–28.

20.

Bernardi

, Keim

, Klärner

. Social networks, social influence, and fertility in Germany: Challenges and benefits of applying a parallel mixed methods design. Mixed methods social network research: Design and applications. 2014:121–52.

21.

Cheng

K-W

. The effect of contraceptive knowledge on fertility: The roles of mass media and social networks. Journal of family and economic issues. 2011;32(2):257–67.

22.

Zafeiris

, Kaklamani

. Completed fertility during the twentieth century: An example from six settlements in northern greece. Journal of biosocial science. 2019;51(1):118–37.

23.

Laher

, Todd

, Stibich

, Phofa

, Behane

, Mohapi

, et al. A qualitative assessment of decisions affecting contraceptive utilization and fertility intentions among HIV-positive women in Soweto, South Africa. AIDS and Behavior. 2009;13(1):47–54.

24.

Wagner

, Huinink

, Liefbroer

. Running out of time? Understanding the consequences of the biological clock for the dynamics of fertility intentions and union formation. Demographic Research. 2019;40:1–26.

25.

Salari

. The impact of intergenerational cultural transmission on fertility decisions. Economic Analysis and Policy. 2018;58:88–99.

26.

Prior

, Lew

, Hammarberg

, Johnson

. Fertility facts, figures and future plans: an online survey of university students. Human Fertility. 2018:1–8.

27.

Finocchario-Kessler

, Sweat

, Dariotis

, Trent

, Kerrigan

, Keller

, et al. Understanding high fertility desires and intentions among a sample of urban women living with HIV in the United States. AIDS and Behavior. 2010;14(5):1106–14.

28.

Panozzoa

, Battegayb

, Friedlc

, Vernazzaa

. High risk behaviour and fertility desires among heterosexual HIV-positive patients with a serodiscordant partner–two challenging issues. women. 2003;46:60.

29.

Moran

, Butler

, Moore

, MacHugh

, Creevey

. Differential gene expression in the endometrium reveals cytoskeletal and immunological genes in lactating dairy cows genetically divergent for fertility traits. Reproduction, Fertility and Development. 2017;29(2):274–82.

30.

Virtala

, Vilska

, Huttunen

, Kunttu

. Childbearing, the desire to have children, and awareness about the impact of age on female fertility among Finnish university students. The European Journal of Contraception & Reproductive Health Care. 2011;16(2):108–15.

31.

Carrenho-Sala

, Sala

, Fosado

, Pereira

, Garcia

, Lopez

, et al. 107 factors that influence fertility in an ivf embryo transfer program in dairy heifers. Reproduction, Fertility and Development. 2016;28(2):183–4.

32.

Hammarberg

, Zosel

, Comoy

, Robertson

, Holden

, Deeks

, et al. Fertility-related knowledge and information-seeking behaviour among people of reproductive age: a qualitative study. Human Fertility. 2017;20(2):88–95.

33.

Konstas

, Stathopoulos

, Jose

, editors. On social networks and collaborative recommendation. Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval; 2009: ACM.

34.

Sharif

, Khanekharab

. Identity confusion and materialism mediate the relationship between excessive social network site usage and online compulsive buying. Cyberpsychology, Behavior, and Social Networking. 2017;20(8):494–500.

35.

Subrahmanyam

, Greenfield

. Online communication and adolescent relationships. The future of children. 2008:119–46.

36.

Sironi

. Fertility histories and chronic conditions later in life in Europe. European Journal of Ageing. 2019:1–14.

37.

Subrahmanyam

, Reich

, Waechter

, Espinoza

. Online and offline social networks: Use of social networking sites by emerging adults. Journal of applied developmental psychology. 2008;29(6):420–33.

38.

Barbovschi

, Machá

, čková Olafsson

. Underage use of social network sites: it’s about friends. Cyberpsychology, Behavior, and Social Networking. 2015;18(6):328–32.

39.

Naseri

, Rajabion

, Zaravar

, Khordehbinan

, Moradi

. A new model for evaluating the impact of ergonomic architectural design of gym, social network and digital knowledge on the desire of students for sport activities. Human Systems Management. 2019;38(4):347–55.

40.

Fang

Y-H

, Li

C-Y

. Electronic word-of-mouth on social networking sites: Cue validity and cue utilization perspectives. Human Systems Management. 2016;35(1):35–50.

41.

Mislove

, Marcon

, Gummadi

, Druschel

, Bhattacharjee

, editors. Measurement and analysis of online social networks. Proceedings of the 7th ACM SIGCOMM conference on Internet measurement; 2007: ACM.

42.

Souri

, Nourozi

, Rahmani

, Navimipour

. A model checking approach for user relationship management in the social network. Kybernetes. 2019.

43.

Kempe

, Kleinberg

, Tardos

, editors. Maximizing the spread of influence through a social network. Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining; 2003: ACM.

44.

Faria

. Therapeutic Navigations and Social Networking: Mozambican Women’s Quests for Fertility. Medical anthropology. 2018:1–15.

45.

Mesoudi

. An experimental comparison of human social learning strategies: payoff-biased social learning is adaptive but underused. Evolution and Human Behavior. 2011;32(5):334–42.

46.

Pahl-Wostl

, Craps

, Dewulf

, Mostert

, Tabara

, Taillieu

. Social learning and water resources management. Ecology and society. 2007;12(2).

47.

Pongrácz

, Miklósi

, Timár-Geng

, Csányi

. Verbal attention getting as a key factor in social learning between dog (Canis familiaris) and human. Journal of comparative psychology. 2004;118(4):375.

48.

Boticki

, Baksa

, Seow

, Looi

C-K

. Usage of a mobile social learning platform with virtual badges in a primary school. Computers & Education. 2015;86:120–36.

49.

Çelen

, Kariv

, Schotter

. An experimental test of advice and social learning. Management Science. 2010;56(10):1687–701.

50.

Zuhaira

, Ye-Zhuang

, Azeez

. Corporate commitment, identification, ethical leadership and social responsibility. Human Systems Management. 2018;37(3):345–52.

51.

Hugo

. Declining fertility and policy intervention in Europe: Some lessons for Australia? Journal of the Australian Population Association. 2000;17(2):175.

52.

Bhatia

, Salignac

. Gendered patterning of relationships: Insights from reproductive health networks in Bangladesh. International Journal of Sociology and Social Policy. 2018;38(5-6):360–77.

53.

Sleebos

. Low fertility rates in OECD countries. 2003.

54.

Seminar

ÖIfF

, Bagavos

, Martin

. Low Fertility, Families and Public Policies: Synthesis Report: Austrian Institute for Family Studies; 2001.

55.

Backstrom

, Huttenlocher

, Kleinberg

, Lan

, editors. Group formation in large social networks: membership, growth, and evolution. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining; 2006: ACM.

56.

Stringhini

, Kruegel

, Vigna

, editors. Detecting spammers on social networks. Proceedings of the 26th annual computer security applications conference; 2010: ACM.

57.

Wang

, Yeh

RK-J

, Chen

, Tsydypov

. What drives electronic word-of-mouth on social networking sites? Perspectives of social capital and self-determination. Telematics and Informatics. 2016;33(4):1034–47.

58.

Likert

. A technique for the measurement of attitudes. Archives of psychology. 1932.

59.

Amin

, Azhar

, Amin

, Akter

, editors. Applying the technology acceptance model in examining Bangladeshi consumers’ behavioral intention to use mobile wallet: PLS-SEM approach. Computer and Information Technology (ICCIT), 2015 18th International Conference on; 2015: IEEE.

60.

Ringle

, Wende

, Will

. SmartPLS release: 2.0 (beta). SmartPLS, Hamburg, Germany URL http://www.smartpls.de. 2005.

61.

Hair

, Ringle

, Sarstedt

. PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice. 2011;19(2):139–52.

62.

Khoshmaram

, Zarafshani

, Alibaygi

, Mirakzadeh

. Exploring the Role of Social Capital in Agricultural Entrepreneurial Opportunity Recognition: Application of Smart PLS. International Journal of Agricultural Management and Development. 2017;7(4):395–406.

63.

Popp

, Wilson

. Investigating the role of identification for social networking Facebook brand pages. Computers in Human Behavior. 2018;84:141–52.

64.

Vinzi

, Trinchera

, Amato

. PLS path modeling: from foundations to recent developments and open issues for model assessment and improvement. Handbook of partial least squares: Springer; 2010. p. 47–82.

65.

Munir

, Shafi

, Khan

, Ahmed

. Elucidating the relationship of social media usage and e-WOM with brand related purchase decision involvement: an integrated meta-framework approach. WALIA Journal. 2018;34(1):59–64.

66.

Fornell

, Larcker

. Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of marketing research. 1981:382–8.

67.

Chin

. PLS-Graph user’s guide. CT Bauer College of Business, University of Houston, USA. 2001:15.

68.

Barroso

, Carrión

, Roldán

. Applying maximum likelihood and PLS on different sample sizes: studies on SERVQUAL model and employee behavior model. Handbook of partial least squares: Springer; 2010. p. 427–47.

69.

Götz

, Liehr-Gobbers

, Krafft

. Evaluation of structural equation models using the partial least squares (PLS) approach. Handbook of partial least squares: Springer; 2010. p. 691–711.

70.

Fornell

, Bookstein

. Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing research. 1982:440–52.

71.

Chin

, Newsted

. Structural equation modeling analysis with small samples using partial least squares. Statistical strategies for small sample research. 1999;1(1):307–41.

72.

Falk

, Miller

. Infrared spectrum of carbon dioxide in aqueous solution. Vibrational spectroscopy. 1992;4(1):105–8.

73.

Tenenhaus

, Amato

, Esposito Vinzi

, editors. A global goodness-of-fit index for PLS structural equation modelling. Proceedings of the XLII SIS scientific meeting; 2004.

74.

Raposo

MLB

, Alves

HMB

, Duarte

PAO

, editors . A study of higher education choice factors: The case of University of Beira Interior. Proceedings of the 6th International Congress on Public and Non Profit Marketing: University of Minho, Braga, Portugal, 14–15th june 2007; 2007.

75.

Ringle

, Sarstedt

, Straub

. A critical look at the use of PLS-SEM in MIS Quarterly. 2012.

76.

Duarte

PAO

, Raposo

MLB

. A PLS model to study brand preference: An application to the mobile phone market. Handbook of partial least squares: Springer; 2010. p. 449–85.

77.

Bernardi

, Klärner

. Social networks and fertility. Demographic research. 2014;30:641–70.

78.

Haythornthwaite

, De Laat

, editors. Social networks and learning networks: Using social network perspectives to understand social learning. Proceedings of the 7th international conference on networked learning; 2010: Lancaster University Aalborg, Denmark.

79.

Bernardi

, Keim

, Von der Lippe

. Social influences on fertility: A comparative mixed methods study in Eastern and Western Germany. Journal of mixed methods research. 2007;1(1):23–47.

80.

Chiregi

, Navimipour

. A new method for trust and reputation evaluation in the cloud environments using the recommendations of opinion leaders’ entities and removing the effect of troll entities. Computers in Human Behavior. 2016;60:280–92.

81.

Aghdam

, Navimipour

. Opinion leaders selection in the social networks based on trust relationships propagation. Karbala International Journal of Modern Science. 2016;2(2):88–97.

A new model for evaluating the influence of social networks,social learning,and supportive policies on the desire of women for fertility

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1 Introduction

2 Related literature and background

2.1 Related work

2.2 Women and fertility desires

2.3 Social networks

2.4 Social learning

2.5 Supportive policies

2.6 Research framework

3.2 Measures

3.3 Analysis of method

3.4 Results

Table 1 Measurement model Variable AVE Composite reliability Cronbach’s alpha Social network 0.788 0.949 0.931 Social learning 0.519 0.842 0.769 Supportive policies 0.617 0.887 0.833 The desire of women for fertility 0.529 0.835 0.756

3.4.2 Social learning

3.4.3 Supportive policies

4 Discussion

6 Implications

7 Limitations and future directions

Footnotes

Appendix A

References

Table 1
Measurement model

Variable AVE Composite reliability Cronbach’s alpha

Social network 0.788 0.949 0.931

Social learning 0.519 0.842 0.769

Supportive policies 0.617 0.887 0.833

The desire of women for fertility 0.529 0.835 0.756