Examining the Mediation Role of Parental Guilt in the Relationship Between Working Mothers' Smartphone Addiction and Their Children's Problematic Technology Use

doi:10.21203/rs.3.rs-3610998/v1

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Examining the Mediation Role of Parental Guilt in the Relationship Between Working Mothers' Smartphone Addiction and Their Children's Problematic Technology Use

https://doi.org/10.21203/rs.3.rs-3610998/v1

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This study aimed to examine the mediation of parental guilt in the relationship between smartphone addiction in working mothers and problematic technology use among their preschool children. A total of 260 working mothers, aged between 22 and 50, with preschool children, participated in the study. The study utilized the "Personal Information Form," "Smartphone Addiction Scale Short Form," "Parental Guilt Scale," and "Problematic Technology Use Scale for Children." SPSS bootstrapping analysis was applied to investigate the mediation of parental guilt in the relationship between mothers' smartphone addiction and their preschool children's problematic technology use. The analysis revealed that parental delinquency mediated the relationship between mothers' smartphone addiction and their children's problematic technology use. The study's findings were discussed in the context of existing literature, and recommendations were provided.

preschool period

problematic technology use

parental delinquency

smartphone addiction

working mother

Working mothers are expected to balance work, their spouses, children, and family, in addition to the effort they put in to reach the positions they aim for in their careers (Korkmaz, 2020). When the responsibilities of motherhood and working roles are combined, especially women with young children face difficulties (Gözübüyük, 2010). For women who try to balance their roles of work, wife, and mother, smartphones have become their helpers in fulfilling their responsibilities. However, digital conveniences are not limited to working life. Although needs are increasingly being met faster and easier through the applications developed, as Erbaş (2020) emphasizes, technological addictions have become widespread as a result of being able to access technology as easily as a smartphone.

Children are introduced to technology at a very young age due to their parents' use of smartphones in daily life. Today, especially with the diversification of technological devices, the rate of preschool children's access to these devices is rapidly increasing (Topçu, 2018). As a result of the gradual increase in technology use, problematic technology uses, such as technological addictions, have emerged, and technological addictions have started to be taken seriously in early childhood (Erbaş, 2020). In fact, children's predisposition to digital tools has become so high that in many current discussions, children are referred to as the "app generation," the "wireless generation," and the "e-child" (Selwyn & Odabaşı, 2022).

Mothers' high use of smartphones and permissive parental attitudes towards their children increase problematic technology use (Kol, 2021). It is expected that the behaviors exhibited by children who are exposed to their parents' attitudes and observe their habits will also follow this trend. Therefore, in studies examining children’s problematic use of technology, it is essential to investigate the technology habits and technology addictions of mothers, as they are the individuals with whom the child spends the most time from birth to primary school.

According to Aktaş (1994), due to the fact that mothers work and are busy for most of their days, they often experience feelings of guilt, believing they may be neglecting their children. Women who experience guilt tend to engage in negative behaviors as they perceive themselves as "I am a bad mother" (Elvin Nowak, 1999). Moreover, it is known that individuals experiencing guilt tend to adopt compensatory behaviors (Hoffman, 1996). Parents may demonstrate actions such as confessing, apologizing, offering delicious snacks, and offering more hugs to alleviate the guilt they feel (Aarntzen et al., 2019; Tangney, 1990). These compensatory behaviors, developed to alleviate guilt, often lead parents to adopt a permissive attitude. According to Baumrind (1966), parents with permissive attitudes may find it difficult to control their children's behavior because they allow them significant freedom, and children raised in this manner may have the freedom to eat, sleep, and use technology whenever they wish.

In the literature, studies conducted with parents regarding children's addictive behaviors are frequently encountered. For example, Yüksel et al. (2020) concluded that adolescents' internet addiction was affected by their parents' supervision of their internet use. In the study conducted by Büyükşahin Çevik and Çelikkaleli (2010), it was concluded that the internet addiction levels of adolescents who perceived parental attitudes as indifferent were significantly higher. Although there have been studies on technology use with a focus on parents, studies on working mothers have been found to be incomplete (Günüç & Atli, 2018; Işıkoğlu & Ergenekon, 2021; Oğuz & Kutluca, 2020). Especially considering the critical importance of the preschool period for a child's development, it was deemed necessary to examine the use of technology by working mothers and their children due to the increasing educational opportunities for women, changes in cultural structure, and economic concerns. In this context, it is believed that a mother's active use of a smartphone can influence the child. It is also believed that mothers, who serve as important role models for young children in technology use (Kurtoğlu Erden & Uslupehlivan, 2021), may exhibit permissive attitudes toward technology use as a way to alleviate guilt (Hoffman, 1996), which could be related to the child's problematic technology use. In this regard, it can be inferred that the child may develop problematic technology use as a result of the mother's increased guilt associated with smartphone use and her compensatory behaviors. This study aims to examine the relationship between working mothers' smartphone addiction and parental guilt with preschool children's problematic technology use.

This study, which aims to investigate the extent to which preschool children's levels of problematic technology use are related to working mothers' smartphone addiction and parental guilt, was conducted following the relational model. The relational model is employed to address doubts about the relationship between variables through a literature review and to provide justification for the identified relationship (Wood & Brink, 1998). This model is an attempt to determine the variable most highly correlated with the criterion variable (McMillan & Schumacher, 2006). In this study, the mediation of parental guilt in the relationship between working mothers' smartphone addiction and their children's problematic technology use is examined. The relationships among these variables, highlighting the mediating role, are illustrated in Fig. 1.

Participants

The research group comprises working mothers with preschool children. In this study, the convenience sampling method, preferred in cases where individuals available at a certain place and time voluntarily participate, was utilized (Martella et al., 2013). According to the Social Security Institution (2023), individuals who work for an employer, independently on their own behalf, or as public officials with insurance are categorized as employees. In light of this definition, we reached 260 working mothers, aged between 22 and 50, with preschool-age children, using the convenience sampling method. Demographic information about the participants in the research group is provided in Table 1.

Table 1

Frequency and Percentage Values of Demographic Information of the Participants
Participants’ characteristics	f	%
Mother’s Age
Under 35 years old	167	64.2
35 years or older	93	35.8
Mother’s Education Level
High school	32	12.3
Undergraduate	154	59.2
Graduate	74	28.5
Mother’s Weekly Working Time
Less than 20 hours	52	20
21–40 hours	109	41.9
41 hours or over	99	38.1
Child Age
Under 35 months	83	32.4
36–48 months	59	23
49–60 months	57	22.3
61 months or older	57	22.3
Child Gender
Girl	128	49.2
Boy	132	50.8

As shown in Table 1, 167 (64.2%) of the participating working mothers were under the age of 35 years old, while 93 (35.2%) were 35 years older. Regarding their educational background, 32 (12.3%) had completed their high school, 154 (59.2%) their undergraduate degrees, and 74 (28.5%) their graduate education. When analyzing the weekly working hours of the mothers in the study, it was observed that 52 (20%) of them worked less than 20 hours, 109 (41.9%) worked 21–40 hours, and 99 (38.1%) worked 41 hours or over. Turning to the ages of the participants' children, 83 (32.4%) were 35 months and under, 59 (23%) were between 36–48 months, 57 (22.3%) were between 49–60 months, and 57 (22.3%) were 61 months or older. Among the children, 128 (49.2%) were girls, and 132 (50.8%) were boys.

2.1. Data Collection and Data Collection Tools

The dataset was created by combining the personal information form with the measurement tools determined within the scope of the research. To conduct this study, ethical approval was obtained from a state university ethics committee following their decision. Within the dataset, participants were first provided with information about the research's purpose, the use of the data exclusively for this study, non-disclosure to third parties, guidelines for responding, and information about the researcher, all in the context of informed consent. After obtaining informed consent, the dataset included questions from the personal information form, instructions for the measurement tools, and the items for the scales. Scale forms were distributed through the Google Forms application on an online platform. Data were collected on a voluntary basis from individuals within the researcher's circle who met the research group's criteria. In cases where working mothers had more than one preschool-aged child, they were requested to complete the measurement tool for only one of their children. It took approximately 10 minutes to complete the measurement tools. Data collection took place between December 2022 and February 2023.

To collect data for the research, a 'Personal Information Form' was prepared to gather the participants' demographic information. The 'Smartphone Addiction Scale Short Form' was employed to measure the level of smartphone addiction among the study's participants, while the 'Parental Guilt Scale' was used to assess the degree of parental guilt experienced by working mothers. In their responses, working mothers self-evaluated using these measurement tools. To evaluate their children's problematic technology use, the 'Problematic Technology Use Scale for Children' was utilized. It's important to note that in this study, data were not directly collected from the mothers' children. Further details about the measurement tools are provided below.

2.1.1. Personal Information Form

The 'Personal Information Form' was developed by the researcher to gather demographic information voluntarily provided by the study participants. The form included variables such as age, education level, weekly working hours, and the age and gender of the child.

2.1.2. Problematic Technology Use Scale for Children

The 'Problematic Technology Use Scale for Children' was developed by Konca, Baltacı, and Akbulut (2022) for parents with children in the preschool developmental stage. The scale employs a 5-point Likert scale and consists of 26 items. During the scale's development phase, exploratory factor analysis involved 357 parents with preschool children, and for validity and reliability analyses, 402 volunteer parents with preschool children were reached. The exploratory factor analysis revealed a 4-factor structure, including continuity of use, resistance to control, impact on development, and deprivation-escape. Subsequently, confirmatory factor analysis confirmed that the goodness-of-fit index values were at an acceptable level. Cronbach's alpha coefficient for the total score of the scale was calculated as .93 (Konca, Baltacı, & Akbulut, 2022). In the reliability analysis conducted with the data from this study, Cronbach's alpha coefficient was found to be .93.

2.1.3. Smartphone Addiction Scale

The short form of the 'Smartphone Addiction Scale,' developed by Kwon et al. (2013) to assess smartphone addiction in adolescents, utilizes a 6-point Likert scale and comprises 10 items. In the adaptation study conducted by Noyan et al. (2015), 367 university students participated. The scale includes five factors: daily life functioning, deprivation, virtual environment-oriented relationships, overuse, and tolerance. Confirmatory factor analysis confirmed that the goodness-of-fit index values were at an acceptable level. The Cronbach's alpha coefficient for the total score of the scale items was found to be .86 (Noyan et al., 2015). In the reliability analysis conducted with the data from this study, the Cronbach's alpha coefficient was determined to be .90.

2.1.4. Parental Guilt Scale

The 'Parental Guilt Scale,' developed by Haslam et al. (2020) to assess the cognitive and emotional aspects of guilt experienced in relation to parenting, comprises 10 items rated on a 7-point Likert scale. In adaptation, reliability, and validity studies conducted by Polat et al. for the Turkish culture, 430 parents with children between the ages of 2–12 years participated. The scale does not have sub-dimensions. Confirmatory factor analysis confirmed that the goodness-of-fit index values were at an acceptable level. The Cronbach's Alpha reliability coefficient for the scale was calculated as .89 (Polat et al., 2022). In the reliability analysis conducted with the data from this study, Cronbach's alpha coefficient was found to be .90.

2.2. Data Analysis

A total of 313 individuals were reached as part of the research. However, 53 of the participants did not meet the criteria for inclusion in the targeted group due to either the mother not working or her child not being in the preschool period. Therefore, their data were excluded from the analysis. The final sample consisted of 260 working mothers who were reached and analyzed. Statistical analyses were conducted using the SPSS 23.0 package program and the SPSS PROCESS v.4.2 plug-in. A significance level of .05 was used as the reference.

Normality examinations were conducted within each subgroup. In normality analysis, skewness and kurtosis values should fall within the range of -1.96 to + 1.96, and various diagnostic tools such as frequency distribution graphs (histograms), normal Q-Q plots, and trend-free Q-Q plots should be evaluated (Can, 2022). It was observed that the variables in this study was convenient for univariate normality. Mahalanobis distance values were calculated to assess multivariate normality, and three data points with values less than .001 were identified as outliers and removed. Mahalanobis distance is a measure of the distance of a data point from the intersection point of the means of other data points (Tabachnick & Fidell, 2020). To unveil both direct and indirect effects, a regression-based mediation macro was employed following the recommendations of Preacher and Hayes (2018). Bootstrapping was applied to assess the significance of the mediation. When using the bootstrap technique, a 95% confidence interval was established using 5,000 samples. Model 4 was utilized in the analysis, with the model structured as follows: the independent variable, smartphone addiction (X) → mediating variable, parental delinquency (Mi) → dependent variable, children’s problematic use of technology (Y). Hayes (2018) argues that indirect effects will be considered significant if the lower and upper limits of the 95% confidence interval do not contain zero. In this study, the significance of the mediating role of parental delinquency in the relationship between smartphone addiction and children’s problematic use of technology was determined in accordance with this recommendation.

Pearson correlation analysis was conducted to determine the relationship between the child's problematic technology use, the mothers’ smartphone addiction, and parental delinquency variables. The results, which are presented in Table 2, reveal the strength and direction of these relationships.

Table 2

Pearson Correlation Analysis Between Problematic Technology Use, Smartphone Addiction and Parental Guilt
	Correlation
	1	2	3
1. Children’s Problematic Use of Technology	-
2. Mothers’ Smartphone Addiction	.25**	-
3. Parental Guilt	.30**	.18**	-
X̅	50.82	24.4	44.6
SD	19.14	10.68	13.75
**p<,01

As shown in Table 2, there is a significant positive relationship between the child's problematic technology use and the mothers’ smartphone addiction (r = .25; p < .01); the child's problematic technology use and parental delinquency (r = .30; p < .01); and the mothers’ smartphone addiction and parental delinquency (r = .18; p < .01). Since a significant positive relationship was observed between these variables, a mediation analysis was conducted. The model used to analyze the mediation of parental delinquency in the relationship between the problematic technology use of preschool children with working mothers and the mothers’ smartphone addiction is presented in Fig. 2.

Figure 2 illustrates the mediating role of parental delinquency in the relationship between mothers’ smartphone addiction and children's problematic technology use. When examining the direct effects of the model, it was found that mothers’ smartphone addiction predicted parental delinquency (B = .32, p < .001), mothers’ smartphone addiction predicted children’s problematic use of technology (B = .52, p < .001), and parental delinquency predicted children’s problematic use of technology (B = .37, p < .001). Before adding parental delinquency as a mediator variable to the model, it was observed that mothers’ smartphone addiction directly and significantly predicted children’s problematic use of technology (B = .52, p < .001). When the mediator variable was included in the model, the predictive power slightly decreased (B = .39, p < .001), but it remained significant. Consequently, these results indicate the presence of an indirect effect in the model, and parental delinquency plays a mediating role in the relationship between mothers’ smartphone addiction and children’s problematic use of technology. To test the significance of this mediation role, bootstrap results for indirect effects are presented in Table 3.

Table 3

Path Coefficients and Indirect Effect Results for Mediation Model
	Coefficient of Path	%95 Confidence Interval Level
	Coefficient of Path	LLCI	ULCI
Direct Effects
MSA→PG	.393	.162	.482
PG→CPUT	.368	.206	.539
Indirect Effects
MSA→PG→CPUT	.118	.050	.204
MSA: Mothers’ smartphone Addiction, PG: Parental Guilt, CPUT: Children’s Problematic Use of Technology, LLCI = Lower limit of confidence interval, ULCI = Upper limit of confidence interval

As shown in Table 3, the bootstrapping analysis applied to the model provides bootstrapping coefficients and 95% confidence intervals, indicating upper and lower values. Upon examining Table 3, it becomes evident that mothers’ smartphone addiction predicts problematic use in children through parental delinquency significantly (bootstrap coefficient = .118, 95% CI = .05, .20).

In this study, it was revealed that parental guilt played a mediating role in the relationship between working mothers' smartphone addiction and problematic technology use in their children. It is known that parents' and children's technology use habits are closely related to each other (Terras & Ramsay, 2016). Especially preschool children are more likely to use technological devices such as tablets and smartphones with their families than they are to use these devices alone (Marsh et al., 2015). In a study conducted by Lauricella et al. (2015), the correlation between parents' time spent with computers, smartphones, tablets and televisions and children's screen use time confirms that parents' and children's technology use habits are related to each other. Parents' smartphone use is not limited to the time they spend alone and has also become common during the care of their children (McDaniel, 2020). According to Bandura (1978), the foundation of learning consists of observation, modeling, and imitation. Consequently, children acquire a significant portion of the traits that guide their own behavior by observing adults (Aydın, 2016). Especially when considering that children spend the most time with their families during their early years, the role of parents as primary role models becomes evident. Preschool children's development of habits through positive or negative modeling of their parents in terms of technology use plays a crucial role in determining the extent of problematic technology use in children (Akkoyunlu & Tuğrul, 2002). Given that children emulate their parents' habits in the use of digital tools, Wong et al. (2020) identified a significant relationship between children's screen time and problematic technology use. Additionally, Park and Park (2021) reported that one out of every five preschool children with problematic smartphone use had parents who did not report problematic technology use for children not exposed to screens. In this context, it can be inferred that, according to mothers' perceptions, screen exposure is an indicator of problematic technology use in their children. As a result of this study, it was concluded that working mothers' smartphone addiction directly influences problematic technology use in children. Therefore, the observation and modeling of mothers' behaviors due to their smartphone addiction can determine whether children's technology use becomes problematic or not. According to the study's findings, it can be hypothesized that mothers who perceive themselves as heavy smartphone users are likely to have children who also use technology extensively. This is consistent with the findings of Lermi and Afat (2020), who suggest that a mother who cannot set limits on phone use as a parent may struggle to establish boundaries for her child. It is well-documented that technological addictions can develop in children whose parents fail to set limits on technology use. Therefore, it can be argued that a working mothers’ smartphone addiction may serve as an explanatory factor for children's problematic technology use.

The increasing participation of women in the workforce has ignited a growing debate regarding the effects of maternal employment on children (Heiland, Price, & Wilson, 2014). One of these effects pertains to the ways in which working mothers and their children utilize technology. For instance, as noted by Ersöz and Özmen (2020), the development of internet infrastructure in the business sphere has made it convenient for employees to conduct transactions using smartphones. However, smartphones are not limited to work-related activities, as social media applications have also become widely used. According to a report by Mail Online (2008), people have become so reliant on their cell phones that the mere thought of a low battery or disconnection can lead to increased stress levels. It is well-established that the stress experienced by mothers can have an impact on their parenting roles and, subsequently, on their children (Meighan, 2017). In addition to the stress caused by technology addiction, the work and family roles imposed on working mothers also create additional stress factors. For example, as noted by Korkmaz (2020), working mothers are expected to balance their work, spouse, child, and family roles while also striving to reach their career goals. The stress levels, particularly in working mothers, tend to escalate due to the conflict between their work, home, and parental responsibilities (Poduval J & Poduval M, 2009). It is inevitable for parents with elevated stress levels to experience feelings of guilt (Simon, 1995). According to Blatt et al. (1984), guilt is one of the fundamental elements of addiction. Studies conducted in this context have shown a relationship between addiction and guilt (Çam & Ayakdaş Dağlı, 2017; Kalyoncu et al., 2002). Additionally, guilt has also been associated with excessive smartphone use (Chatterjee & Kar, 2021). In addition to the guilt they experience due to their addiction, individuals also feel guilty for experiencing positive emotions resulting from the benefits gained through smartphone use (Lanette & Mazmanian, 2018). Moreover, the stress and guilt stemming from smartphone addiction play a crucial role in determining the relationship with their children. According to Kol (2021), children of mothers who use smartphones more intensively tend to learn technology at an early age. In this study, parental guilt was found to mediate the relationship between smartphone addiction among working mothers and problematic technology use in their children. As a result of this sense of guilt, individuals engage in compensatory behaviors to prevent the recurrence of similar situations (Özdemir & Düzgüner, 2020). In an attempt to alleviate guilt, mothers who feel a sense of inadequacy due to their guilt may provide their children with cell phones when they are not present and instruct them to call in case of need. However, these actions aimed at reducing guilt can inadvertently encourage unsupervised technology use by the child (Seagram & Daniluk, 2002). It can be inferred that mothers experience guilt as a result of the time they spend with their smartphones and exhibit addictive behaviors. Consequently, they may engage in guilt-compensating behaviors that center on a permissive attitude towards their child. Within a permissive attitude, children have the freedom to go to sleep, eat, and use technology whenever they please due to the lack of clear rules, inadequate supervision, and a high level of tolerance (Baumrind, 1966; Çetinkaya, 2020). In this context, it is conceivable that a guilt-ridden mother may adopt a permissive parental attitude and engage in behaviors aimed at compensating for her guilt.

As a result of this study, according to the assessments made by working mothers, it was determined that parental guilt had a partially mediating effect on the relationship between smartphone addiction and problematic technology use in their children. In this regard, the concept of digital parenting can be incorporated into psychoeducational programs designed to reduce problematic technology use among children. Furthermore, efforts can be made to ensure that families utilize technology consciously, both for themselves and their children. Specifically, professionals providing counseling to working mothers can address feelings of guilt and the accompanying sense of inadequacy, aiming to develop healthier mother-child relationships by reducing maternal guilt and associated compensatory behaviors. Additionally, experts in the field of addiction can work towards addressing and alleviating guilt as part of addiction treatment for their clients. To promote the appropriate use of technology by children, awareness-raising sessions can be organized for parents, emphasizing conscious technology use for both parents and their children.

This study investigates the mediating role of parental guilt in the relationship between problematic technology use in children and smartphone addiction in working mothers. Future research may explore additional mediating variables, such as parental attitudes, stress, marital satisfaction, and the parent-child relationship, alongside parental guilt in the relationship between problematic technology use in children and smartphone addiction in working mothers. Furthermore, research can extend to include non-working mothers and fathers as participants within the study group. The findings obtained in this study pertain specifically to problematic technology use among preschool children. In subsequent studies, problematic technology use during primary, secondary, and high school periods can be examined, with a particular focus on working mothers.

This research does not capture the changes experienced by mothers and their children over time. To address this limitation, longitudinal studies that track changes in the levels of problematic technology use in children, smartphone addiction, and parental guilt among working mothers can be planned. Additionally, the results of this study were generated based on the responses of working mothers to measurement tools within the framework of quantitative research, which established a relationship. To gain deeper insights, qualitative research methods, such as interviews and observations, can be employed to explore mothers' perceptions of problematic technology use, their beliefs about the impact of smartphone addiction on children's technology use, behaviors exhibited by children related to problematic technology use, and the influence of mothers' busy work schedules on their feelings of guilt.

As a result of this study, according to the assessments made by working mothers, it was determined that parental guilt had a partially mediating effect on the relationship between smartphone addiction and problematic technology use in their children. t can be thought that mothers feel guilty as a result of the time they spend with their smartphones and exhibit addictive behaviors, which may lead them to engage in guilt-compensating behaviors that promote a permissive attitude towards their children (Çetinkaya, 2020). Accordingly, the mother who feels guilty may adopt a permissive parental attitude and engage in behaviors that compensate for her guilt. Accordingly, a mother who feels guilty, adopting a permissive parental attitude and engaging in behaviors that compensate for her guilt, may partially mediate the development of problematic technology use in her child.

Aarntzen, L., Derks, B., van Steenbergen, E., Ryan, M., & van der Lippe, T. (2019). Work-family guilt as a straightjacket: An interview and diary study on consequences of mothers' work-family guilt. Journal of Vocational Behavior, 115, 103336. https://doi.org/10.1016/j.jvb.2019.103336
Akkoyunlu, B., & Tuğrul, B. (2002). Okuloncesi cocuklarin ev yasantisindaki teknolojik etkilesimlerinin bilgisayar okuryazarligi becerileri uzerindeki etkisi. Hacettepe Universitesi Egitim Fakültesi Dergisi, 23(23).
Aktaş, Y. (1994). Çalısan anne ve çocugu. Yasadıkca Egitim, 36, 7-11.
Aydın, A. (2016). Egitim psikolojisi. Pegem Akademi Yayincilik.
Bandura, A. (1978). On paradigms and recycled ideologies. Cognitive Therapy and Research, 2(1), 79-103. https://doi.org/10.1007/BF01172518
Baumrind, D. (1966). Effects of authoritative parental control on child behavior. Child development, 887-907. https://doi.org/10.2307/1126611
Blatt, S. J., Rounsaville, B., Eyre, S. L., & Wilber, C. (1984). The psychodynamics of opiate addiction. The Journal of nervous and mental disease, 172(6), 342-352.
Can, A. (2022). SPSS ile bilimsel arastirma sürecinde nicel veri analizi. Pegem Akademi Yayincilik.
Chatterjee, S., & Kar, S. K. (2021). Smartphone addiction and quality of sleep among Indian medical students. Psychiatry, 84(2), 182-191. https://doi.org/10.1080/00332747.2021.1907870
Çam, O. & Ayakdaş Dağlı, D. (2017). Alkol bagimlilarinda icsellestirilmiş damgalama, sucluluk ve utanc duyguları. Bagimlilik Dergisi, 18(4), 145-151.
Çetinkaya, B. (2020). Cocugun kisilik gelisiminde anne baba rolu. Pegem Akademi Yayincilik.
Çevik, G. B., & Çelikkaleli, Ö. (2010). Ergenlerin arkadas bagliligi ve internet bagimliliginin cinsiyet, ebeveyn tutumu ve annebaba egitim duzeylerine gore incelenmesi. Journal of the Cukurova University Institute of Social Sciences, 19(3).
Elvin‐Nowak, Y. (1999). The meaning of guilt: A phenomenological description of employed mothers' experiences of guilt. Scandinavian journal of psychology, 40(1), 73-83. https://doi.org/10.1111/1467-9450.00100
Erbaş, Y. H. (2020). Erken cocukluk egitiminde dijital teknoloji ve oğrenme. (Ed.,R. F. Ağmaz & F. Ergüleç). Pegem akademi.
Ersöz, B., & Özmen, M. (2020). Dijitallesme ve bilisim teknolojilerinin calisanlar uzerindeki etkileri. AJIT-e: Bilisim Teknolojileri Online Dergisi, 11(42), 170-179. DOI: 10.5824/ajite.2020.03.007.x
Gözübüyük, M. (2010). Anaokulunda cocugu olan calisan ve calismayan annelerden rol memnuniyetine gore depresyon ve kaygi belirtileri duzeylerinin karsilastirilmasi [Yayinlanmamis Yuksek Lisans Tezi]. Maltepe Üniversitesi.
Günüç, S., & Atli, S. (2018). 18-24 aylik bebeklerde teknolojinin etkisine yonelik ebeveyn gorusleri. Addicta: The Turkish Journal on Addictions, 5(2), 1-22. http://dx.doi.org/10.15805/addicta.2017.5.2.0047
Haslam, D., Filus, A., & Finch, J. (2020). The guilt about parenting scale (GAPS): development and initial validation of a self-report measure of parenting guilt, and the relationship between parenting guilt and work and family variables. Journal of Child and Family Studies, 29, 880-894. https://doi.org/10.1007/s10826-019-01565-8
Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: a regression-based approach. The Guilford Press.
Heiland, F., Price, J., & Wilson, R. (2014). Maternal employment and time investments in children. Review of Economics of the Household, 15(1), 53-67. https://doi.org/10.1007/s11150-014-9278-1
Hoffman, M. L. (1996). Empathy and moral development. The Annual Report Of Educational Psychology İn Japan, 35, 157-162. https://doi.org/10.5926/arepj1962.35.0_157
Işıkoğlu, N. & Ergenekon, E. (2021). Bebeklerin teknolojik araclari kullanmalariyla ilgili anne gorusleri. Ankara University Journal of Faculty of Educational Sciences (JFES), 54(1), 117-140. DOI: 10.30964/auebfd.767338
Kalyoncu, Ö. A., Mırsal, H., Özkan Pektaş, D., Gümüş, Ö., Tan, D., & Beyazyürek, M. (2002). Alkol bagimlilarinda sucluluk ve utanc duygulari. Journal of Dependence, 3(3), 160-164.
Kol, S. (2021). Erken cocuklukta teknoloji kullanimi. Pegem akademi.
Konca, A. S., Baltacı, Ö., & Akbulut, Ö. F. (2022). Problematic technology use scale for young children (ptus-yc): Validity and reliability study. International Journal of Assessment Tools in Education, 9(2), 267-289. DOI: 10.21449/ijate.888936
Korkmaz, E.V. (2020). Is hayatinda kadın calisanlara uygulanan destekler perspektifinde kadin calisanlarin psikolojik sahiplenme ve yilmazlik durumlari. Gazi Kitabevi
Kurtoğlu Erden, M, & Uslupehlivan, E. (2021) Dijital cagda degisen ebeveynlik rolleri: teknoloji kullaniminda anne babalar rehber mi engel mi olmali?. Sosyal Politika Calısmaları Dergisi, 21(51), 447-474. DOI: 10.21560/spcd.vi.671929
Kwon, M., Kim D.J., Cho H., & Yang S. (2013). The smartphone addiction scale: development and validation of a short version for adolescents. PloS One 2013; 8:e83558 https://doi.org/10.1371/journal.pone.0083558
Lanette, S., & Mazmanian, M. (2018). The smartphone" addiction" narrative is compelling, but largely unfounded. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-6). https://doi.org/10.1145/3170427.3188584
Lauricella, A. R., Wartella, E., & Rideout, V. J. (2015). Young children's screen time: The complex role of parent and child factors. Journal of Applied Developmental Psychology, 36, 11-17. https://doi.org/10.1016/j.appdev.2014.12.001
Lermi, U., & Afat, N. (2020). Ortaokul ogrencilerinde bilgisayar oyun bagimliliginin ebeveyn davranislari acisindan incelenmesi. İZU Egitim Dergisi, 2(3), 122-136. https://doi.org/10.46423/izujed.749654
Mail Online, (2008, 1 Şubat). Nomophobia. https://www.dailymail.co.uk/news/article-550610/Nomophobia-fear-mobile-phone-contact--plague-24-7-age.html
Marsh, J., Plowman, L., Yamada-Rice, D., Bishop, J., Lahmar, J., Scott, F., ... & Winter, P. (2015). Exploring Play and Creativity in Pre-schooler's use of apps: Final Project Report.
Martella, R. C., Nelson, R.C., Morgan, R. L. & Marchand Martella, N. E. (2013). Understanding and interpreting educational research. Guilford Press.
McMıllan, J.H., & Schumacher, S. (2006). Research in Education Evidence-Based Inquiry. Person Education
Meighan, M. (2017). Maternal role attainment becoming a mother. Nursing Theorists and Their Work-E-Book, 432-444.
Noyan, C. O., Enez Darçın, A., Nurmedov, S., Yılmaz, O., & Dilbaz, N. (2015). Akilli Telefon Bagimliligi Olceginin Kisa Formunun universite ogrencilerinde Turkce gecerlilik ve guvenilirlik calismasi. Anatolian Journal of Psychiatry/Anadolu Psikiyatri Dergisi, 16(Özel sayı.1): 73-81
Oğuz, B. N., & Kutluca, A. Y. (2020). Okul oncesi dönemde cocuklari olan ebeveynlerin teknoloji kullanimina yonelik goruslerinin incelenmesi. Ondokuz Mayis University Journal of Education Faculty, 39(2), 252-268.
Özdemir, N., & Düzgüner, S. (2020). Sucluluk, pismanlik ve gunahkarligin kapsami ve diger yakin kavramlarla iliskisi. Ondokuz Mayıs Üniversitesi İlahiyat Fakültesi Dergisi, (49), 497-529. https://doi.org/10.17120/omuifd.798703
Park, J. H., & Park, M. (2021). Smartphone use patterns and problematic smartphone use among preschool children. PLoS One, 16(3), e0244276. https://doi.org/10.1371/journal.pone.0244276
Poduval, J & Poduval, M. (2009), Poduval J, Poduval M. Working mothers: How much working, how much mothers, and where is the womanhood? Mens Sana Monographs, 7(1):63-79. doi: 10.4103/0973-1229.41799
Polat, M., Eryiğit, D., Aydoğdu, B. N., Ekşi, F., & Ekşi, H. (2022). Adapting the Guilt About Parenting Scale (GAPS) to Turkish culture: Validity and reliability study. Current Psychology, 1-9. https://doi.org/10.1007/s12144-022-03007-z
Preacher, K. J. ve Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879-891. https://doi.org/10.3758/BRM.40.3.879
Seagram, S., & Daniluk, J. C. (2002). “It goes with the territory” The meaning and experience of maternal guilt for mothers of preadolescent children. Women & Therapy, 25(1), 61-88. https://doi.org/10.1300/J015v25n01_04
Selwyn, N. & Odabaşı, H.F. (2022). Dijital yasamda çocuk. (Ed., H. F. Odabaşı). Pegem Akademi.
Simon, R. W. (1995). Gender, multiple roles, role meaning, and mental health. Journal of Health and Social behavior, 182-194. https://doi.org/10.2307/2137224
Social Security Institution (2023). Employee and Employer Insurance Scope. https://www.sgk.gov.tr/
Tabachnick, B. G. & Fidell, L. S. (2020). Cok degiskenli istatistiklerin kullanimi. (Çev. M. Baloğlu). Atlas Akademik Yayincilik.
Tangney, J. P. (1990). Assessing individual differences in proneness to shame and guilt: development of the Self-Conscious Affect and Attribution Inventory. Journal of personality and social psychology, 59(1), 102. https://doi.org/10.1037/0022-3514.59.1.102
Terras, M. M., & Ramsay, J. (2016). Family digital literacy practices and children’s mobile phone use. Frontiers in psychology, 7, 1957. https://doi.org/10.3389/fpsyg.2016.01957
Topçu, S. (2018). Cocuk ve sanal ortam. Kocatepe Tip Dergisi, 19(1), 27-33.
Wong, R. S., Tung, K. T., Rao, N., Leung, C., Hui, A. N., Tso, W. W., ... & Ip, P. (2020). Parent technology use, parent–child interaction, child screen time, and child psychosocial problems among disadvantaged families. The Journal of pediatrics, 226, 258-265. https://doi.org/10.1016/j.jpeds.2020.07.006
Wood, M. J., & Brink, P. J. (1998). Correlational designs. Advanced design in nursing research, 2.
Yüksel, R., Kocairi, C., Arslantaş, H., & Söylemez, B. (2020). Ergenlerde internet bagimliligini etkileyen faktorler. Balıkesir Saglık Bilimleri Dergisi, 9(1), 19-28.

No competing interests reported.

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Examining the Mediation Role of Parental Guilt in the Relationship Between Working Mothers' Smartphone Addiction and Their Children's Problematic Technology Use

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Abstract

Figures

1. Introduction

2. Method

2.1. Data Collection and Data Collection Tools

2.1.1. Personal Information Form

2.1.2. Problematic Technology Use Scale for Children

2.1.3. Smartphone Addiction Scale

2.1.4. Parental Guilt Scale

2.2. Data Analysis

3. Results

4. Discussion

5. Conclusion

References

Additional Declarations

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