Motherhood in the Making: Key Determinants of Parenthood Motivation in Young Adult Women

Abstract

Background: Demographic changes are often prone to purely social perspectives, while individual differences are overlooked. This research examines the role of attachment and emotional intelligence in childbearing motivation. Methods: In total, 234 female students participated in an online survey, including sociodemographic data, adult attachment, emotional competencies, and parenting-related variables. Results: Statistical analyses revealed that the students express positive and negative childbearing motivation almost equally and moderately. They were both linked to religiosity and emotional management. We further explore the differences in relationship status and address the role of religious beliefs. The demographic measures identified as most important include healthcare availability for both mother and child, subsidizing housing loans or rent, and encouraging fathers to become more actively involved in the care of infants. Conclusion: These emerging trends deserve further investigation and social incentives.

1. Introduction

Social and economic changes have led to different family patterns. Contemporary families have fewer members, women give birth to their first child later, and they combine motherhood with full-time work [1] In economically developed countries, parenthood is considered a personal decision. The ideal of a family with two children has been maintained in Europe for decades [2]. However, the number of children in each family is influenced by numerous factors, such as social norms and the social structure of society [3], as well as governmental support and religious beliefs. According to data from the National Bureau of Statistics [4], in Croatia, women give birth to their first child at the age of 30, and in 2011, the average household consisted of 2.80 members. The delay in starting a family appears to be partially rooted in financial insecurity [5]) since employed individuals are more inclined to have children, as are those who are legally married (compared to non-official long-term relationships), according to some authors [6]. Practicing religion is associated with acceptance of family values [7]) and lower perceived costs of having children [8]. Also, individuals in a romantic relationship tend to report higher positive motivation for parenthood. Similarly, legal marriage is also related to a more positive motivation, just like employment, and being a sexual minority often leads to a lower desire for parenthood [9].

1.1. Motivation for Parenting

Motivation for parenting is based on an individual’s assessment of the possible consequences of having or not having children [10]. Rabin [11] defined it as parents’ expectations of their children and the needs their children will meet. He also suggested four parenting motivation categories: altruistic, fatalistic, narcissistic, and instrumental. In one domestic study, altruistic motivation was the most prominent among Croatian students, especially females [12]. His theory, however, lacked the strength to elaborate on the reasons that deter individuals from having children. Hoffman and Hoffman [13] further listed nine values of a having a child, which Kohlmann [14] revised into three aspects: economic security, ensuring one’s social position, and emotional love.

Miller [10] developed the TDIB (Traits–Desires–Intentions–Behavior) theoretical framework based on three assumptions. Firstly, our biological tendencies determine our reaction to children and nurturing. Secondly, parenting preferences are shaped by individual experiences in childhood and early adulthood, creating two general motivational forces (positive and negative attitudes towards the birth and upbringing of children). The third assumption is that these motivational forces influence behavior, meaning parenting [10]. Research [15] confirms the main assumptions of the model, but in a different cultural context [16], this theory was not supported.

Another theoretical framework is the Theory of Planned Behavior [17]. According to this theory, behavior can best be predicted by measuring intentions that consist of attitudes, subjective norms, and perceived behavioral control. Thus, more positive attitudes and societal norms towards childbirth, accompanied by a higher level of perceived control over childbirth, will lead to higher motivation for parenting. According to research, intentions against having children are a stronger predictor [18], partially because it is more difficult to conceive than to use contraception, while others [19] find that positive intentions predict having children better than the use of contraception. The most significant determinants of positive intentions are a stable partner relationship, stable employment, and social pressure from family and friends. Some authors [20] critiqued this notion, stating that the intention grows gradually and thus cannot be measured only once. Different personal constructs, such as attachment styles, emotional competencies, and religiousness, might predict the desire for offspring. We further describe their components and their relation to parental motivation.

1.2. Attachment Styles

Attachment develops through three stages, from non-discriminatory social interactions to separation anxiety and reciprocal relationships. Ainsworth [21] developed a theory about three main attachment styles: anxious–avoidant, secure, and anxious–resistant. However, stable, early attachment patterns can be changed through experiences in romantic and friendly relationships [22]. Attachment styles (ASs) are vital for establishing and maintaining long-term romantic relationships in adulthood. Insecure attachment leads to less satisfaction in romantic relationships, which is the main predictor of relationship quality [23]. Despite dissatisfaction in the relationship, insecurely attached people try to maintain their relationship and are more inclined to use negative partner retention strategies [24]. Avoidance is negatively related to motivation for parenthood and the desired number of children [25], while securely attached individuals show a greater interest in infants [26].

1.3. Emotional Intelligence

As defined by Salovey and Mayer [27], emotional intelligence (EI) represents the ability to recognize, express, and monitor emotions and the ability to use them to facilitate cognitive processes. Their model includes four primary abilities, ordered according to the complexity of psychological processes: the ability to evaluate and express emotions, the ability to perceive and generate feelings that facilitate thinking, the ability to understand knowledge about emotions, and the ability to regulate emotions for emotional and intellectual development [28]. More complex abilities of EI develop with age and experience [29]. EI is a significant predictor of individual adjustment in different aspects of life. Specifically, emotional regulation predicts satisfaction in marriage [30] and romantic relationships [31]. Emotionally intelligent mothers consider themselves more successful in their maternal role [32]. Future parents with higher emotional intelligence show fewer symptoms of depression and anxiety during pregnancy [33]. There is evidence on how EI individuals are more committed to their partners [34,35]. However, the relationship between EI and parenting motives remains unclear due to insufficient research. Our proposed research model, based on every construct described in the introduction section, is presented in Figure 1.

1.4. Research Aims and Hypothesis

It is still unknown how individuals approach decisions related to reproductive behavior and starting a family, since factors contributing to developing positive and negative motivations for parenthood, as well as general socio-demographic data, are not precisely defined. Having such sparse insight into what predicts motivation, especially for females in early adulthood, we set out to determine the incremental value of personal factors in childbearing motivation contexts among female students in Croatia, a Western upper middle-income country.

Specifically, we aim to examine the levels of positive and negative motivation for parenthood in our sample (1), and to test the relations between the dimensions of attachment, emotional intelligence abilities, religiosity, and motivation for parenting and to enquire into possible differences regarding sexual orientation and relationship status (2). Next, we aim to examine the intentions related to having children, such as the desired number, the ideal age for starting a family, contraception use, and attitudes toward the women who choose a children-free lifestyle (3). And finally, our participants had to rate different demographic measures based on their importance in the decision to have a child (4). Based on the limited theoretical background, we assume that positive motivation for parenting will be linked to lower anxiety and avoidance, more prominent EI domains, and religiousness. On the contrary, negative motivation is expected for more anxious and avoidant ASs, low-EI cases, and non-religious tendencies. Regarding their intentions, we could hypothesize that participants will reflect current trends such as starting a family at the age of 30, having two children, and using contraception to prevent unwanted pregnancies.

2. Materials and Method

2.1. Participants

Data were collected from a total of 234 female students (two male students were excluded from the analysis, as seen in Figure 2). We chose a female-only sample because women make most birth-related decisions in societies with a more equal approach to parenting. They predominantly study social sciences (66.7%), followed by humanities (12.4%), and biomedicine and healthcare (7.7%). The age range was from 20 to 31 years, with an average age of 23.29 (SD = 2.34). Most of the participants (90.6%) were heterosexual, and the rest consisted of the following sexualities: bisexuals (8.1%) and homosexuals (1.3%). In total, 47.4% of the participants were currently single, 51.7% in a relationship, while 0.9% were married. Around 34% of participants are residents of the capital (Zagreb). They are estimated to have mostly average (39.3%) or slightly lower than average (23.9%) living standards. Around 45% are currently unemployed. Around 32% of them are not very religious (answers 1–3), while almost 28% are very religious (answers 8–10), and the rest fall in the middle.

2.2. Instruments

Socio-demographic data, including gender (male–female), age, sexual orientation (homosexual, bisexual, or heterosexual), romantic status (single, in a relationship, married), year and field of study, residence (by number of inhabitants), place of birth (rural-urban), current work status (working full-time, part-time, occasionally, or unemployed), and living standard (at, above, or below the average, according to the average salary in Croatia), were used to determine the more general characteristics of our sample. Religiosity was assessed using a one-item measure (On a scale of 1 to 10, how would you rate yourself in terms of religiosity?) where the number 1 is assigned to those who are not religious at all, and 10 to those who are very religious, as seen in the European Social Survey, C15 [36]. Contraception usage was estimated based on a single question (How often do you use contraception to prevent pregnancy?) on a Likert 1–5 scale ranging from 1 (never) to 5 (during every instance of intercourse).

The Revised Adult Attachment Scale (RAAS) consists of 18 items evenly distributed in three subscales representing attachment dimensions: closeness, dependence, and anxiety [37]. Participants rate their answers on a Likert-type scale from 1 to 5. We used the version that refers to behavior in all close relationships. The subscale results represent the average score. An alternative way of scoring allows the assessment of four attachment styles. Reported reliability [38] ranges between 0.77 and 0.85, while we found a similar range (0.73–0.89).

The Emotional Skills and Competence Questionnaire (ESCQ-45) measures emotional competence based on the model of Mayer and Salovey [28]. It contains 45 items divided into three subscales [39]: the ability to perceive and understand emotions (15 items), the ability to express and name emotions (14 items), and the ability to manage emotions (16 items). Participants rate their usual thoughts and feelings on a Likert scale from 1 to 5, and the total scores can be easily obtained by summing the scores. We encountered reliability coefficients between 0.77 and 0.93.

The Childbearing Motivation Scale (CMS) consists of 47 items measuring positive and negative motivation levels for having children [40]. They were identified through the literature review and the focus group analysis. The subscale of positive motivation (PM) includes 26 items (e.g., giving meaning to your life), which are grouped according to the factor model which contained four factors of positive motivation for having children: socioeconomic aspects, continuity, personal fulfillment and couple relationship. The negative motivation (NM) subscale includes 21 items (e.g., Feeling that I do not have the necessary qualities to become a mother) grouped according to the factor model containing five factors: the burden of upbringing and immaturity, social and environmental concerns, marital stress, financial problems and economic constraints, and physical suffering and concern about body appearance. The items are rated on a Likert scale (1–5), and the total scores represent the average value. Reliability coefficients range between 0.76 and 0.95. The scale was translated using a back-translation procedure [41].

2.3. Procedure

The research was conducted online after we received ethical approval and permission from the different authors listed in the Instruments section. Following a snowball method, we approached participants through social media and platforms such as Reddit. The instruction clearly stated the research aims and emphasized the scientific use of the collected data and data protection. After the formal content, we introduced two exclusion criteria questions about gender and student status. On average, participants responded within 20 min.

2.4. Ethical Considerations

The study was conducted following the principles of the Declaration of Helsinki after receiving approval from the Department for Psychology Council on the 29 June (Klasa: 640-16/23-2/000; Ur. broj: 380-58/506-23-0007). After clarifying the aims of the following study, all participants provided explicit informed consent.

3. Results

3.1. Descriptive Statistics

All analyses were conducted using IMB’s SPSS version.22 (Armonk, NY, USA).

Table 1. Descriptive data with normality test (N = 234).

	Min	Max	M	SD	Sk	Kk	KS
Positive motivation	1	5	2.791	0.856	−0.251	−0.452	0.069
Socioeconomic aspects	1	4.50	2.000	0.859	0.570	−0.575	0.001
Personal fulfillment	1	5	3.284	0.972	−0.626	−0.207	0.001
Continuity	1	5	2.940	1.065	−0.095	−0.813	0.001
Couple relationship	1	5	3.162	1.072	−0.355	−0.555	0.001
Negative motivation	1	5	2.892	0.982	−0.002	−0.960	0.037
Childrearing burden and immaturity	1	5	3.022	1.141	−0.209	−1.095	0.001
Social and ecological worry	1	5	3.180	1.116	−0.260	−0.930	0.001
Marital stress	1	5	2.312	1.121	0.529	−0.783	0.001
Economic constraints	1	5	2.944	1.258	0.060	−1.206	0.001
Body-image concerns	1	5	2.954	1.131	0.114	−0.818	0.002
Attachment
Closeness	1.33	5	3.455	0.709	−0.163	−0.386	0.001
Dependence	1.20	4.80	3.214	0.718	−0.252	−0.391	0.001
Anxiety	1.00	5	2.838	1.036	−0.006	−0.953	0.001
Emotional intelligence	110	213	165.782	20.745	0.023	−0.288	0.200
Perception and understanding	26	75	56.653	9.664	−0.283	0.101	0.049
Expression and naming	24	67	49.931	9.193	−0.301	−0.446	0.017
Emotional management	42	79	59.196	7.307	0.171	−0.097	0.001
Religiosity	1	10	5.25	2.879	−0.067	−1.220	0.001

KS = Kolmogorov–Smirnov normality test, Sk = skewness, Kk = kurtosis.

presents the descriptive data collected in the research. The mean values obtained on the scales of positive and negative motivation for parenting were in close proximity (for positive motivation, M = 2.79, SD = 0.86, and for negative motivation, M = 2.89, SD = 0.98). Participants’ religiosity was moderate (M = 5.25 and SD = 2.88). Based on available norms for the student population, available in Appendix A—

Table A1. Student norms for ESCQ-45 total score.

Result	Z-Score	Result	Z-Score	Result	Z-Score	Result	Z-Score	Result	Z-Score
117	−2.20	137	−1.11	157	−0.02	177	1.07	197	2.16
118	−2.14	138	−1.05	158	0.04	178	1.13	198	2.22
119	−2.09	139	−1.00	159	0.09	179	1.18	199	2.27
120	−2.03	140	−0.94	160	0.15	180	1.24	200	2.33
121	−1.98	141	−0.89	161	0.20	181	1.29	201	2.38
122	−1.92	142	−0.83	162	0.26	182	1.35	202	2.44
123	−1.87	143	−0.78	163	0.31	183	1.40	203	2.49
124	−1.81	144	−0.72	164	0.37	184	1.46	204	2.54
125	−1.76	145	−0.67	165	0.42	185	1.51	205	2.60
126	−1.71	146	−0.62	166 *	0.47 *	186	1.56	206	2.65
127	−1.65	147	−0.56	167	0.53	187	1.62	207	2.71
128	−1.60	148	−0.51	168	0.58	188	1.67	208	2.76
129	−1.54	149	−0.45	169	0.64	189	1.73	209	2.82
130	−1.49	150	−0.40	170	0.69	190	1.78	210	2.87
131	−1.43	151	−0.34	171	0.75	191	1.84	211	2.93
132	−1.38	152	−0.29	172	0.80	192	1.89	212	2.98
133	−1.32	153	−0.23	173	0.86	193	1.95	213	3.04
134	−1.27	154	−0.18	174	0.91	194	2.00	214	3.09
135	−1.22	155	−0.13	175	0.96	195	2.05	215	3.14
136	−1.16	156	−0.07	176	1.02	196	2.11	216	3.20

* Marks the average result in our sample.

, participants’ emotional competencies were grouped at around a 0.47 z value, which corresponds to an average score for this population. Data show deviations in the distribution in all variables except positive motivation for parenting. Nevertheless, skewness and kurtosis values range from −1 to 1 and, considering the sample size, this justifies the use of parametric tests.

3.2. Correlation Analysis Results

Using the SPSS bivariate correlation (two-tailed) option, Pearson’s correlation coefficients were calculated and are presented in

Table 2. Person correlation matrix (positive motivation, attachment, EI, and religiosity); N = 234.

	2	3	4	5	6	7	8	9	10	11	12	13
(1) Closeness	0.414 **	−0.502 **	0.438 **	0.154 *	0.518 **	0.387 **	0.022	−0.105	0.064	0.065	0.068	0.014
(2) Dependence		−0.477 **	0.205 **	0.024	0.234 **	0.256 **	0.097	0.016	0.103	0.084	0.164 *	0.047
(3) Anxiety			−0.400 **	−0.165 *	−0.426 **	−0.382 **	−0.038	0.017	−0.016	−0.073	−0.088	−0.070
(4) Total EI score				0.773 **	0.841 **	0.758 **	0.164 *	0.009	0.215 **	0.180 **	0.176 **	0.060
(5) PUE					0.428 **	0.334 **	0.138 *	0.039	0.165 *	0.164 *	0.110	0.070
(6) ENE						0.564 **	0.060	−0.068	0.119	0.082	0.083	−0.024
(7) ME							0.206 **	0.059	0.243 **	0.191 **	0.249 **	0.107
(8) PM								0.831 **	0.910 **	0.904 **	0.863 **	0.499 **
(9) SEA									0.612 **	0.657 **	0.625 **	0.387 **
(10) C										0.783 **	0.765 **	0.478 **
(11) PF											0.733 **	0.454 **
(12) CR												0.426 **
(13) Religiosity

* p > 0.05, ** p > 0.01. Note: PUE = the ability to perceive and understand emotions; ENE = the ability to express and name emotions; ME = the ability to manage emotions; PM = positive motivation; SEA = socioeconomic aspect; C = continuity; PF = personal fulfillment; CR = couple relationship.

and

Table 3. Person correlation matrix (negative motivation, attachment, EI, and religiosity); N = 234.

	8	9	10	11	12	13	14
(1) Closeness	−0.235 **	−0.246 **	−0.217 **	−0.184 **	−0.131 *	−0.211 **	0.014
(2) Dependence	−0.230 **	−0.236 **	−0.219 **	−0.129 *	−0.201 **	−0.165 *	0.047
(3) Anxiety	0.284 **	0.296 **	0.286 **	0.136 *	0.235 **	0.229 **	−0.070
(4) Total EI score	−0.333 **	−0.377 **	−0.276 **	−0.179 **	−0.274 **	−0.257 **	0.060
(5) PUE	−0.169 **	−0.229 **	−0.104	−0.058	−0.140 *	−0.144 *	0.070
(6) ENE	−0.271 **	−0.301 **	−0.262 **	−0.159 *	−0.208 **	−0.180 **	−0.024
(7) ME	−0.380 **	−0.388 **	−0.317 **	−0.233 **	−0.331 **	−0.313 **	0.107
(8) NM		0.912 **	0.871 **	0.830 **	0.825 **	0.775 **	−0.374 **
(9) BUI			0.765 **	0.683 **	0.659 **	0.640 **	−0.314 **
(10) SEC				0.649 **	0.665 **	0.591 **	−0.357 **
(11) MS					0.588 **	0.626 **	−0.307 **
(12) FPEC						0.552 **	−0.275 **
(13) PSCABA							−0.355 **
(14) Religiosity

* p > 0.05; ** p > 0.01. Note: PUE = the ability to perceive and understand emotions; ENE = the ability to express and name emotions; ME = the ability to manage emotions; NM = negative motivation; BUI = the burden of upbringing and immaturity; SEC = social and environmental concerns; MS = marital stress; FPEC = financial problems and economic constraints; PSCABA = physical suffering and concern about body appearance.

. Positive motivation for parenting was closely and moderately related to religiosity (r = 0.49, p < 0.01), and weak correlations are detected between PM and the ability to perceive and understand emotions (r = 0.14, p < 0.01). On the subscale level, personal fulfillment correlates to the management of emotions (r = 0.24, p < 0.05), along with continuity (r = 0.19, p < 0.05) and relationship with a partner (r = 0.25, p < 0.05). Negative motivation yielded a more significant bivariate correlation with all three attachment dimensions and EI, and the strongest linear relationship was observed for managing emotions (r = −0.38, p < 0.05) and religiosity (r = −0.37, p < 0.05).

3.3. Forecasting Positive and Negative Parental Motivation Levels in Female Students

The next step was to conduct two separate linear regression analyses in SPSS (enter method). The criteria or dependent variables were positive and negative motivation levels for having children. Given the absence of correlations between positive motivation for parenting and adult attachment dimensions, they were omitted from this regression analysis for positive motivation, leaving only emotional abilities and religiosity as predictors or dependent variables. For negative motivation, the regression model included the adult attachment dimension, along with emotional abilities and religiosity. The results are presented in

Table 4. Linear regression analysis (criterion—positive motivation for having children); N = 234.

Predictors	Positive Motivation
	(β)
The ability to perceive and understand emotions	0.073
The ability to express and name emotions	−0.049
The ability to manage emotions	0.159 *
Religiosity	0.475 *
R	0.526
R2	0.277
R2 adjusted	0.264
F	21.907 *

* p < 0.05; β—standardized regression coefficient; R—multiple correlation coefficient; R²—coefficient of multiple determination; R² adjusted —the estimated coefficient of multiple determination; F—overall significance of linear regression model.

and

Table 5. Linear regression analysis (criterion—negative motivation for having children); N = 234.

Predictors	Negative Motivation
	(β)
Attachment—closeness	−0.020
Attachment—dependence	−0.088
Attachment—anxiety	0.085
The ability to perceive and understand emotions	−0.016
The ability to express and name emotions	−0.073
The ability to manage emotions	−0.235 *
Religiosity	−0.339 *
R	0.536
R2	0.287
R2 adjusted	0.265
F	13.024 *

* p < 0.05; β—standardized regression coefficient; R—multiple correlation coefficient; R²—coefficient of multiple determination; R² adjusted—the estimated coefficient of multiple determination; F—overall significance of linear regression model.

, respectively.

The regression model for the positive motivation criterion was statistically significant, as it explained 27.7% of the variance (R² = 0.277, F = 21.907, p < 0.05; see Table 4). The most significant predictor variable was religiosity (β = 0.475, p < 0.05), and management of emotions was the only EI ability dimension with a significant effect size (β = 0.159, p < 0.05).

The model for negative motivation was also statistically significant as it explained 28.7% of the variance (R² = 0.287, F = 13.024, p < 0.05; see Table 5). Adult attachment dimensions were not significant in predicting negative motivation for having a child. Again, emotional management (β = −0.235, p < 0,05) and religiosity (β = −0.339, p < 0.05) were the only significant predictors (β = −0.339, p < 0.05). Differences based on relationship status will be address in the discussion section.

3.4. Predicting Parental Motivation Levels Based on Relationship Status

We then proceeded with four separate stepwise analyses for both positive and negative childbearing motivation based on relationship status, specifically with two broader brackets: females who are in a relationship or married (52.6% or 123 students) and females who are currently single (47.4 or 111 students). We conducted four hierarchical regressions, with a forward selection method and three predetermined sets of predictors: socio-demographic variables, attachment, and emotional competence dimensions. In that way, adding the predictors, we are given information on the incremental value of selected variables beyond the demographic data.

Another advantage of a stepwise method regards its usage as an exploratory analysis whenever there is no existing theoretical foundation or there are no sufficient theoretical models to draw from. On another note, it is related to inflated Type I errors [42] and therefore should be interpreted with caution. The independent variables included self-assessed religiosity, contraception usage, age, sexual orientation, attachment, and EI dimensions. The results are shown in

Table 6. Stepwise analysis on positive and negative motivation for having children: subsample of single female students (n = 111).

Model Summary: Positive Motivation							Model Summary—Negative Motivation
	R	R2	R2 adj	SE	F Change (df)	p		R	R2	R2 adj	SE	F Change (df)	p
Religiosity	0.464	0.215	0.208	0.7923	29.888 (1.109)	0.000	Religiosity	0.383	0.147	0.139	0.8954	18.790 (1.109)	0.000
Religiosity, Sexual Orientation	0.557	0.310	0.297	0.7463	14.864 (1.108)	0.000	Religiosity, Contraception	0.459	0.211	0.196	0.8652	8.757 (1.108)	0.004
Religiosity, Sexual Orientation, Managing Emotions	0.589	0.347	0.328	0.7297	5.977 (1.107)	0.016	Religiosity, Contraception, Sexual Orientation	0.493	0.243	0.222	0.8513	4.551 (1.107)	0.035
							Religiosity, Contraception, Sexual Orientation, Anxiety	0.557	0.310	0.284	0.8166	10.289 (1.106)	0.002
							Religiosity, Contraception, Sexual Orientation, Anxiety, Managing Emotions	0.636	0.405	0.377	0.7620	16.721 (1.105)	0.000

R—multiple correlation coefficient; R²—coefficient of multiple determination; R² adj—the estimated coefficient of multiple determination; SE = std. error of the estimate; F—overall significance of the regression model; df—degrees of freedom

and

Table 7. Stepwise analysis on positive and negative motivation for having children: subsample of female students in relationship or married (n = 123).

Model Summary: Positive Motivation							Model Summary—Negative Motivation
	R	R2	R2 adj	SE	F Change (df)	p		R	R2	R2 adj	SE	F Change (df)	p
Religiosity	0.538	0.289	0.283	0.7016	49.188 (1.121)	0.000	Religiosity	0.376	0.141	0.134	0.9314	19.885 (1.121)	0.000
							Religiosity, Dependence	0.466	0.217	0.204	0.8927	11.704 (1.120)	0.001
							Religiosity, Dependence, Managing Emotions	0.498	0.248	0.229	0.8787	4.855 (1.119)	0.029

R—multiple correlation coefficient; R²—coefficient of multiple determination; R² adj—the estimated coefficient of multiple determination; SE = std. error of the estimate; F—overall significance of the regression model; df—degrees of freedom

, along with

Table A2. Regression coefficients (dependent variable: negative motivation) for single females (n = 111).

		B	Std. Error	β	t	Sig.	Tolerance	VIF
1	(Constant)	3.627	0.180		20.122	0.000
	Religiosity	−0.125	0.029	−0.383	−4.335	0.000	1.000	1.000
2	(Constant)	2.999	0.274		10.932	0.000
	Religiosity	−0.096	0.030	−0.294	−3.236	0.002	0.888	1.126
	Contraception	0.142	0.048	0.268	2.959	0.004	0.888	1.126
3	(Constant)	2.245	0.445		5.045	0.000
	Religiosity	−0.077	0.030	−0.236	−2.530	0.013	0.813	1.230
	Contraception	0.145	0.047	0.274	3.065	0.003	0.887	1.127
	Sexual Orientation	0.594	0.278	0.188	2.133	0.035	0.911	1.098
4	(Constant)	4.595	0.863		5.321	0.000
	Religiosity	−0.081	0.027	−0.249	−2.981	0.004	0.812	1.231
	Contraception	0.126	0.043	0.238	2.970	0.004	0.880	1.136
	Sexual Orientation	0.268	0.257	0.085	1.045	0.298	0.859	1.164
	Anxiety	0.165	0.083	0.159	1.993	0.049	0.886	1.128
5	(Constant)	4.595	0.863		5.321	0.000
	Religiosity	−0.081	0.027	−0.249	−2.981	0.004	0.812	1.231
	Contraception	0.126	0.043	0.238	2.970	0.004	0.880	1.136
	Sexual Orientation	0.268	0.257	0.085	1.045	0.298	0.859	1.164
	Anxiety	0.165	0.083	0.159	1.993	0.049	0.886	1.128
	Managing Emotions	−0.042	0.010	−0.335	−4.089	0.000	0.844	1.185

,

Table A3. Regression coefficients (dependent variable: positive motivation) for single females (n = 111).

		B	Std. Error	β	t	Sig.	Tolerance	VIF
1	(Constant)	2.023	0.159		12.682	0.000
	Religiosity	0.140	0.026	0.464	5.467	0.000	1.000	1.000
2	(Constant)	3.198	0.340		9.409	0.000
	Religiosity	0.111	0.025	0.368	4.396	0.000	0.912	1.097
	Sexual Orientation	−0.940	0.244	−0.323	−3.855	0.000	0.912	1.097
3	(Constant)	1.740	0.683		2.549	0.012
	Religiosity	0.110	0.025	0.366	4.468	0.000	0.912	1.097
	Sexual Orientation	−0.803	0.245	−0.276	−3.280	0.001	0.864	1.157
	Managing Emotions	0.023	0.009	0.197	2.445	0.016	0.941	1.062

,

Table A4. Regression coefficients (dependent variable: positive motivation) for female students in a relationship or married (n = 123).

		B	Std. Error	Β	T	Sig.	Tolerance	VIF
1	(Constant)	1.992	0.130		15.299	0.000
	Religiosity	0.159	0.023	0.538	7.013	0.000	1.000	1.000

and

Table A5. Regression coefficients (dependent variable: negative motivation) for female students in a relationship or married (n = 123).

		B	Std. Error	Β	t	Sig.	Tolerance	VIF
1	(Constant)	3.526	0.173		20.395	0.000
	Religiosity	−0.134	0.030	−0.376	−4.459	0.000	1.000	1.000
2	(Constant)	4.827	0.415		11.637	0.000
	Religiosity	−0.129	0.029	−0.360	−4.446	0.000	0.997	1.003
	Dependence	−0.399	0.117	−0.277	−3.421	0.001	0.997	1.003
3	(Constant)	6.172	0.735		8.403	0.000
	Religiosity	−0.119	0.029	−0.333	−4.129	0.000	0.974	1.027
	Dependence	−0.321	0.120	−0.223	−2.673	0.009	0.910	1.099
	Managing Emotions	−0.027	0.012	−0.186	−2.203	0.029	0.889	1.124

in the Appendix A section, displaying regression coefficients for each dependent variable and subsample. It should be noted that VIF values do not exceed 1.3, suggesting low collinearity (Table A2, Table A3, Table A4 and Table A5).

3.5. Planned Behavior, Parental Intentions, and the Role of Demographic Incentives

Most of our participants believe that the ideal age for a woman to become a mother for the first time is between 25 and 29 years (62.9%). Only a minority believes there is no such thing as an ideal age (9,8%). Almost 50% of participants plan to have two children, and almost 30% want three or more, while only 13% express no desire to have a child. Half of students use contraception during every instance of intercourse (50.9%), as opposed to the 26.5% who never use contraceptives. They give sufficient support for females who choose not to have children, excluding infertility reasons (M = 4.38, SD = 1.06). When inspected, these variables show low or moderate but significant correlations (

Table 8. Parental motivation, intention, and attitude correlation tables.

	Positive Motivation	Negative Motivation	Religiosity
The desired number of children	0.465 **	−0.463 **	0.412 **
Contraception usage during intercourse	−0.140 *	0.216 **	−0.192 **
Attitudes toward women who choose not to have children	−0.481 **	0.338 **	−0.467 **

** Significance level 95%; * significance level 99%.

).

Lastly, we asked our participants to rate demographic policies and measures that would encourage them to have more children than planned, and the results are presented in Figure 3.

4. Discussion

4.1. Addressing the Descriptive Parameters and Indicators

This research examined the role of adult attachment and emotional intelligence in predicting positive and negative motives for having offspring. According to our data, female students express moderate levels of positive and negative motivation to an equal extent. The most pronounced motives are personal fulfillment, such as the meaning of life (positive motivation), and social and environmental concerns, including environmental devastation and social dangers (negative motivation). The least prominent motives were socioeconomic aspects (positive motivation) and marital stress (negative motivation), corresponding to the fact that only 0.9% of female students are married.

This is aligned with a quote [43], stating that students were still exploring their sense of purpose and meaning in life, making it their priority. Over and above that, students are particularly interested in and dedicated to their mental health and well-being, which are strongly related to finding meaning. On a more environmental note, most older and female students support slower economic development, accompanied by support for stronger environmental protection or a holistic ethos [44].

Even though our participants are aware of the financial burden of a child, they remain unheeding in terms of recognizing the impact on marital stress. Similarly, others [45] found predominantly positive attitudes, with socioeconomic factors weighing on the decisions. Socioeconomic concerns are of vital influence on the verge of a global recession, but even more so when considering that 44.4% of our sample is unemployed and 39.7% of students estimated their standard of living to be below average. This liable threat is most likely acknowledged due to their personal experience, whereas they failed to anticipate marital stress stemming from the transition into parenthood.

Almost every major study has shown how transitioning from a couple into a family of three is challenging in many different ways, including financial costs. For instance, one meta-analysis [46] claims that marital satisfaction decreased during the first and second year postpartum for both parents. Mothers’ childcare stress was negatively correlated with marital satisfaction in a Chinese sample, whereas mothers’ depression could be decreased by partners’ cognitive empathy but exacerbated with affective empathy [47]. The difference can be attributed to the ability to recognize and understand another’s mental state, according to the theory of mind [47], a construct very similar to that of EI abilities.

Some speculate how positive motivation stems from having enough time to prepare for or plan for a child, in contrast to unexpected pregnancies [48]. While few findings advocate a more pronounced negative motivation in women [15], our results indicate an average or neutral motivation based on self-reports from female students with an average age of 21, corresponding to the middle of their college education. Having no dyadic information, we could not estimate how their motivation transposes to their (prospective) partners; since both partners influence the intent to have a child, but women are more influential in the decision made on the number of children [49].

4.2. Addressing the Correlation Analyses

Positive and negative motivation for parenting manifested strong correlations with self-reported religiosity, similar to findings from another study [6]. According to Hayford and Morgan [50], higher fertility among Catholics—a dominant religion in Croatia—was associated with the prohibition of contraceptives. The stronger effect of religiosity on fertility intentions was found in countries with a more traditional regime. In one longitudinal European survey [51] on short-term fertility intentions, practicing Christians generally intended to have more children than other groups, but such differences were minimal in terms of fulfilling such intentions, spotlighting the disadvantages of the Theory of Planned Behavior in predicting real-life outcomes.

Unexpected findings mostly refer to the nonsignificant relationship between adult attachment dimensions and positive motives for having a child (Table 2), while the correlations between adult attachment dimensions and negative motivation are in the expected direction (Table 3). Attachment dimensions may work to develop certain behaviors that predict parenting motivation, and participants do not have to be aware of their own attachment style as much as their child-rearing motives, or the objects of their adult attachment differ, which could affect our results. Several findings suggested adult attachment styles affect parental behavior, stating that avoidant attachment is negatively correlated with positive mothering behaviors [52] or how secure attached childless participants share a higher interest in infants—a precondition for quality parental caregiving [26], while attachment anxiety negatively influences parenting satisfaction [53].

Like Međedović et al. [25], whose study involved both genders, with an average age of 42, the anxiety dimension was negatively related to reproductive motivation as secure romantic bonding is evolutionarily adaptive. Two EI dimensions, the ability to express and name emotions and the ability to manage emotions, were significantly related to negative but not positive motivation for parenting (see Table 2 and Table 3). Although our initial hypothesis was partially rejected, it was based on a small amount of available research that indirectly assesses the relationship between emotional intelligence and parenting [32]. One plausible explanation relies on the notion that EI is vital when dealing with challenges, and the scale of negative motivation emphasizes possible difficulties when raising a child [35]. Two EI dimensions, the ability to express and name emotions and the ability to manage emotions, were significantly related to negative but not positive motivation for parenting. This conclusion refutes the idea that self-report scales are often highly correlated due to shared method variance. Roberts [54] questions self-assessment measures due to various biases and the problem of insight. Respondents do not have to be in contact with their emotions, so giving realistic assessments without overlapping personality traits is exigent. In other words, compared to attitudes about parenting, assessing our emotional abilities could be more challenging. Although our initial hypothesis was partially rejected, only a few available studies indirectly assessed the relationship between emotional intelligence and parenting [32]. Based on these results only, the management of emotions could indicate an individual’s (im)maturity in terms of commitment to a parental role.

4.3. Predictive Value of Socio-Demographic and Personal Factors

Emotion regulation and religiosity are statistically significant predictors of both positive and negative parenting motivation (see Table 4 and Table 5). Given that the abilities to regulate and manage emotions are more complex abilities of emotional intelligence, we can assume that the most complex abilities are crucial when assessing our capabilities for being a parent since EI women are considered more successful in the role as mothers [32]. Even without the experience, they could feel more prepared to face the challenges of parenthood or life in general.

Religiosity predicts positive and negative parenting motivations, which aligns with other consistent findings [6]. Religious individuals report greater benefits and fewer costs associated with childbearing [8]. They are socialized to accept family values, namely the importance of having children [55]. In addition, women who practice religion, especially Catholics, give birth to more children than non-religious women [56].

In search of more precise findings, we then proceeded to inspect a broader regression model, including socio-demographic variables such as sexual orientation and age, along with contraception usage, and previously described constructs of adult attachment and EI. In summary, previous research suggested pronounced positive motives for older and more religious heterosexual individuals expressing secure attachment styles and prominent emotional competencies. In contrast, negative motives are highlighted in younger females, sexual minorities, less religious individuals, anxious-or-preoccupied attachment styles, and women with lower emotional abilities. The difference between expected and observed outcomes is discussed in the next section.

4.4. Predictiong Parental Motivation Based on Relationship Status

Upon conducting a more detailed analysis of different subsamples based on relationship status, it was found that positive motivation among individuals in a relationship or among those who are married could be predicted only by religiosity. Religion is a viable part of cultural identity. Thus, the self-assessment of our own religious beliefs could serve as a proxy measure of having more traditional gender and family attitudes. In contrast, negative motivation had more than one predictor, including religiosity, dependency, and emotional regulation (Table 7 and Table A5). Both dependency and emotional management had negative but significant beta values and lower predictive values than religiosity. As expected, religiosity transpired as a relatively stable predictor across the three-step regression analysis, gradually decreasing slightly. This suggests the dual impact of religiosity in forming young adults’ tendencies and intentions to start a family, meaning higher levels of religious commitment can enhance motivations related to the perceived value of parenthood, while simultaneously influencing concerns or hesitations about child-rearing responsibilities, costs, etc.

These findings suggest that the dimension of attachment, particularly the comfort level in relying on others, plays a significant role in shaping fertility intentions, similarly to the ability to manage emotions. The importance of self-control and awareness of one’s emotional abilities, sometimes referred to as meta-emotional intelligence [57], could serve as an indication of emotional maturation being necessary to raise offspring. Not feeling comfortable relying on others for help and poor emotional management could also indicate impaired well-being or relational issues.

For single females, positive motivation was primarily associated with religiosity, sexual orientation, and emotional management (Table 6 and Table A3). Among all three, religiosity was the strongest predictor, but declined slightly when other predictors were introduced. This would imply that married heterosexual women, who are more religious and perceive themselves as more adept at managing emotions, should express positive motivation toward having children. In contrast, negative motivation could be predicted by factors including religiosity, contraception use, sexual orientation, anxiety, and emotional management (Table 6 and Table A2). Alternatively phrased, less religious women who use contraception regularly exhibit more anxiety in close relationships and who are estimated to have lower emotional management skills are prone to express negative motivation for parenting.

In accordance with expectations derived from prior research, sexual minorities exhibited lower parental intentions; however, these findings may be attributable to a variety of underlying factors, such as the expected cost and health toll of artificial insemination, a lack of support from a romantic partner, and internalized shame. Contraception was a relatively stable predictor of negative motivation, but another insightful indicator is the anxiety dimension. Attachment anxiety is often linked to an individual’s desire to have children through its influence on emotional needs, relationship dynamics, and coping mechanisms. Having children can be seen as a way to strengthen bonds with our partners, but for single individuals, anxiety could potentially aggravate ambivalence due to concerns about their ability to provide a stable emotional context for a child’s upbringing or even worries about not receiving enough support from their partner.

Consequently, the hypothesized model was more effective in predicting negative motivation in single females, while positive motivation—though influenced by fewer variables—had one key predictor that accounted for nearly 30% of the variance in the dependent variable, which is the level of religiosity.

4.5. Addressing Parental Intentions in Early Adulthood and the Role of Demographic Incentives

The majority of our sample intends to have two children, a prominent trend for decades [2], almost 30% want three or more, while only 13% express no desire to have a child, and only half of them use contraception during every instance of intercourse. A combination of low levels of positive motivation and a higher level of negative motivation predicts contraception usage, compliant with previous research [18] and Miller’s theory [10]. Regarding their intentions, this particular finding suggests female students mostly have a strong desire for children, confirming the conclusion made by Guzzo and Hayford [58] on how shifts in fertility goals are merely an indication of the growing difficulty in achieving such goals, not a motivation alone. In fact, research conducted by Nitsche and Hayford [59] demonstrates how the desire alone is not enough, since less educated women will more likely become mothers and have more children than educated women, due to different timings of the (first) marriage.

The desired number of children was contingent on religiosity, just like religiosity was previously related to having more children [60], or changing the desired number of children based on social encouragement—namely, from grandparents [61]. Female students estimate that the ideal age to become a mother is between 25 and 29 years, similar to other reports [62] and corresponding to the Croatian average [63]. Bellieni [64] propounds how the advantages of delayed parenthood are promoted without information on its risks and that cultures differ based on the social pressures, creating a division between those who wait and those who do not, but ultimately states that these should be decided by romantic partners alone.

One of the more optimistic findings, regarding overall support for the decision to not have a child, contrasts with the conclusion made by Maftei [65], where negative attitudes still prevailed among older citizens with children—perhaps due to mechanisms of cognitive dissonance. Correlation analysis reveals that negative attitudes are expressed by more religious females and those who discern the positive sides of parenthood.

Most popular demographic measures refer to ensuring the conditions for having children, that is, ensuring the healthcare of mother and child and subsidizing housing loans or subletting, which tells us that female students are focused on existential problems. The Republic of Croatia needs many primary care physicians, specifically 257 family physicians, 110 gynecologists, and 90 pediatricians [66]. Furthermore, female students also chose the measure of encouraging fathers to be more actively involved in the care of infants, and such results may be related to more traditional roles in Croatian society and the desire of Croatian female students to change this situation. Research shows that very few fathers take care of children while the mother works, i.e., 90% of fathers in the European Union do not take parental leave [67]. A small number of female students chose demographic measures related to specific problems related to the financial costs of a child, such as free extracurricular activities and exemption from paying the communal contribution, which may be a consequence of the fact that the female students are not yet familiar with such costs. A smaller number of female students believe that a single-child allowance would encourage them to have children or more children. A possible explanation for these answers is that female students are focused on long-term and higher costs and acquiring the primary conditions for having children.

4.6. Limitations and Future Research

There are several limitations, dilemmas, and recommendations for future surveys that we would like to express. First of all, religiosity was measured with a single-item scale. Single-item measures are generally not recommended because their reliability cannot be estimated [68]. Spector [69] states that they do tap into complex constructs and demonstrate higher correlations with other variables. Additionally, single-item measures are parsimonious, less ambiguous, not tiresome, more accessible to administer, and suitable for vulnerable or clinical populations [70]. Although our sample was not clinical, due to the length of our questionnaire, we opted to shorten only the religiosity measures, assuming it would hold its face and construct validity. This decision unquestionably limits our conclusions. There are other ways to assess religiosity, such as measuring religious commitment or attendance and the importance of religion in daily life well as a religious affiliation. In respect of instruments, other available options with fewer items and a different factor structure, such as the Motivation to have a child scale [71], could provide more relevant data.

Secondly, Morgan and King [72] speculate that childbearing incentives compete with other interests, making a parenting decision a complex product of biological predispositions, social coercion, and a rational decision-making process. Consequently, it is necessary to further explore the stages of this thought process and more complex social factors, such as family values and societal pressure. Thirdly, the analysis based on sexual orientation, which we excluded from the final manuscript due to the unequal distribution of categories, is our next target.

Whether it is suitable to measure childbearing motivation at such a young age remains unresolved. For females in our sample with an average age of 23, who are already invested in the educational process, and for whom half are not even in a romantic relationship, it could be too soon to delve into such intense life decisions. Average values range from 2.18 to 3.47 for positive motivation and 2.15 to 3.41 for negative motivation. Such a small range may indicate that the participants do not yet have a firmly defined attitude or intention regarding parenthood. Our decision not to separate different types of adult attachment (friendship vs. romantic partners) and include those currently not in a romantic relationship perhaps influenced such unexpected outcomes. Based on this assumption, other suggestions are to include variables related to the quality/satisfaction of the romantic relationship or to shift towards dyadic measures, influenced by the evidence of how parenthood is, in most cases, a consensual decision. Likewise, the recommendations would be to investigate couples with children and inspect the motives for expanding families. Finally, the studied constructs would benefit from a longitudinal research design, respecting the statement made by Morgan and Bachrach [20] about single time-point surveys.

5. Conclusions

Our students express positive and negative childbearing motivation at around the same levels, meaning there are no extreme levels since values do not exceed 3.5, nor do they go below 2. Parental intentions are linked to religiosity and emotional management abilities. Positive motives are not related to adult attachment dimensions, unlike negative motives which have only low correlations. Both motivation scales can be predicted by religiosity and emotional management. The hypothesized model, including age, contraception usage, sexual orientation, attachment, EI, and religiosity, was more successful in predicting the motivation of single females. Age is a significant predictor for positive motivation, and contraception predicts negative motivation among single girls exclusively. Most female students want to have two children and believe that the ideal age for having their first child is between 25 and 29. Half of them use contraception during every intercourse, and about 25% never use it. The vast majority support other women’s decisions not to have children. Participant report how having adequate healthcare for both mother and child, housing, and paternal participation in the child-rearing practice is essential in their decision-making process.