1 Introduction

Social media are tools that include Web 2.0 technologies, social communication platforms, and social networks that allow users to create content and provide two-way interaction at the point of interest, thought, and information sharing (Eren & Aydın, 2014). In today’s world, where the use of social media is increasing day by day (Ağaoğlu Taşçı & Durmaz, 2021; Auxier & Anderson, 2021; Drouin et al., 2020; Keith & Steinberg, 2017), users who use social media platforms with Web 2.0 technology have become not only consumers but also producers (Bostancı, 2019; Çalapkulu & Sarı, 2022). People from many professions and age groups, such as academics, entrepreneurs, businesses, young people, and parents, use social media effectively for different expectations and purposes, such as commercial business networking, information search, marketing, education and socializing (Olanrewaju et al., 2020). Although the legal age for using social networks is 13 years in many countries, it has been noted that there are problems at the point of implementation. Some children between the ages of 9 and 12 use social networks by falsifying their age, and in countries where there is no age limit, younger children use social networks (Livingstone et al., 2011). The use of social media has become a tool in many areas such as evaluating leisure time, exchanging information, chatting, having fun, keeping in touch, following the agenda, looking at the pictures and videos of friends, sharing their own videos and pictures, relaxing, getting education, and giving education (Solmaz et al., 2013). The family, which constitutes the smallest building block of society, is very important because it contains the characteristics of the social structure (Konuk & Ilgın, 2019). Naturally, it is possible to say that parents also benefit from or are influenced by social media while raising their children. Some parents share their children’s photos, videos, and personal information on social media for various purposes. When examining the literature, it can be seen that these shares made by parents are expressed with the word “sharenting”, which is a combination of the words “share” and “parenting”, and it is also expressed as “sharing parenting” or “SMP” (Çoban & Doğan, 2022; Romero-Rodríguez et al., 2022).

Parents who effectively use social media platforms share not only their personal information and pictures but also their children’s personal information and pictures, so that their children leave their first digital traces from an early age (Milhomem, 2022). The number of parents who share information, pictures, and videos about their children on social media continues to increase worldwide, and although they state that they have the right to share information about their children, it should not be ignored that their children’s privacy is also violated (Amon et al., 2022; Brosch, 2016; Çoban & Doğan, 2022). They state that children generally have a negative attitude toward their parents’ sharing and that parents should ask their permission before sharing and listen to their answers (Sarkadi et al., 2020). Researchers should investigate what kind of information parents or relatives share with children on social media platforms, what are the reasons for such sharing, what problems children may face in their future lives, and what should be done to solve them. These studies only focus on the concept of sharenting. Looking at the literature, it can be seen that social media parenting (SMP) has been little researched and the scales are limited. Alemdar and Kahraman (2023) developed a scale related to mothers’ SMP (sharenting), Cansızlar and Şahin (2022) developed a scale related to SMP (sharenting). It is believed that the concept of sharenting does not fully explain the concept of SMP, and since the concept of SMP has a more complex structure that includes many structures and elements that interact with children and parents, it would be more appropriate to conduct research and define it accordingly. In this context, to contribute to research in this area, this study has expanded and defined the framework and definition of SMP, which also includes the concept of sharenting but has a broader meaning, and has attempted to develop an SMP scale. awareness of SMP in parental behavior can contribute positively to parent-child interaction and have positive consequences for parents in terms of being better parents, digital literacy for both parents and their children, and appropriate use of social media. The concept of SMP and the SMP scale are important for understanding parenting behaviors that have evolved in a different direction with social media. As a result of the increasing use of technology, digitalization and social media, it is thought that SMP should be expected of all parents as a new generation parenting behavior.

2 Conceptual framework

2.1 Sharenting

The term “sharenting” or “SMP” refers to the practice of parents who use social media to share photos, videos, and personal information of their children, usually minors, on social media (Çoban & Doğan, 2022; Romero-Rodríguez et al., 2022). Ayhan and ztürk (2021) posit that when these shares are excessive, they can be referred to as “sharenting”. A significant proportion of parents share videos of their children daily on YouTube and other social media platforms, which can result in some infringements of their children’s rights, including violations of their privacy (Karakoç & Ünlü, 2021). A review of the literature reveals a substantial body of research indicating that children are subjected to violations of their privacy and the loss of their rights (Silva, 2021; Silva et al., 2021; Yavuz, 2020). Yavuz (2020) posits that the data shared on social media is now in the public domain, that it can be processed illegally by third parties, that these images can be used in pedophile websites, and that it is challenging to regulate. Parents over the age of 30 years are more concerned about sharing information about their children than parents under the age of 30. Mothers tend to share more information about their children than fathers (Esgin & Eken, 2020). It has been observed that mothers of children aged between four and six who attend kindergarten frequently share posts about their children on social media. These mothers are pleased when their posts are liked and share them with the intention of creating memories (Aslan & Durmuş, 2020). The changing social structure over the years has also led to a shift in the roles of fathers. It is evident that there has been an increase in the number of fathers who not only provide economic opportunities but also assume responsibility for addressing the diverse needs and problems of their children, spend more time with their families, and share the time they spend with their children on social media (Erol & Eskici, 2022). It has been observed that parents tend to share their children’s special days, health conditions, and social activities with their families on social media. However, this practice has been linked to several adverse outcomes, including the creation of psychological problems, identity theft, and deterioration of the relationship between children and their parents. Furthermore, developmental issues have been reported in children exposed to this phenomenon (Ayhan & Öztürk, 2021). The sharing of personal information, including images, videos, comments, liked content, and location notifications on social media can be considered an important source of information (Bostancı, 2019). This information can be exploited by commercial companies as a commodity, which may pose a threat to the privacy and security of personal information. Although it is asserted that these posts violate rights and privacy, different studies have also been conducted in the field of literature. It has been posited that parents can utilize social media as a means of communication with other parents, facilitating social interaction and the satisfaction of certain affective needs, such as the appreciation of parenting practices (Kopuz et al., 2022). It has been demonstrated that mothers receive and provide support through social media more frequently than fathers and view social media as a valuable tool for parenting (Duggan et al., 2015). Hasanah (2021) posits that parents who share images of their children with disabilities create a documentary archive of their children’s childhood photos. Furthermore, followers of these parents on social media exhibit-positive behaviors toward children with disabilities and are inspired to be better parents. Upon examination of the literature, it is evident that the concept of “sharenting” is more commonly used than the concept of “SMP,” and it is also used as “sharing parenting” (Ayhan & Öztürk, 2021; Kopuz et al., 2022). However, it lacks an exact equivalent (Kopuz et al., 2022). Accordingly, it is proposed that the concept of “sharenting” be defined more accurately by linguists according to each country, while the concept of “SMP” be considered as a very comprehensive issue.

2.2 Parents’ uses of social media

Parents utilize social media not only to share their children but also for many other purposes, including the creation of profiles, social interaction, sharing of content, following other users, generation of income, and exchange of information (Çoban & Doğan, 2022). A study found that most individuals use social media, spending between one and three hours on social networks daily. Furthermore, social media is primarily used for communication with friends (Solmaz et al., 2013). Upon examination of the literature, it becomes evident that studies on social media use are predominantly focused on children, young people, older adults, educators, and various generations, including the Baby Boomer, X, Y, and Z generations. These generations are collectively referred to as the “social media generations” (Bell et al., 2013; Boer et al., 2020; Carpenter & Harvey, 2020; Güney, 2020; Tutgun Ünal & Deniz, 2020). The pervasive use of social media by parents underscores the necessity of engaging with this demographics as well.

2.3 Digital security

Although the legal minimum age for social media use is 13 years on most platforms, the age of use has dropped to 7–9 years old. This is because parents should learn the innovations related to social media use, follow technological developments, and teach them to their children (Okumuş & Parlar, 2018). The advent of digitalization has also transformed the manner in which children and parents communicate with one another. It has been observed that not only children but also parents require assistance in utilizing digital tools in a conscious manner, with regard to cyber security and digital literacy (Çeçen et al., 2023). The evolution of social media platforms has rendered them increasingly complex, necessitating not only the digital competence of adolescents and their parents (Daneels & Vanwynsberghe, 2017). It is of paramount importance for parents to ensure their children’s digital safety in digital environments. It is of particular importance that parents of children who utilize digital technologies also possess digital competence and knowledge regarding digital security (Gallego-Arrufat et al., 2019).

2.4 Learning parent

Furthermore, parents also access parenting information through digital technologies (Çeçen et al., 2023). Although the prevalence of smartphone use among parents is high, their use of smartphones for parenting-related purposes is relatively limited. Instead, they tend to utilize smartphones primarily for accessing information related to child rearing, illnesses, medications, education, development, communication and scientific research (Er & Durmuş, 2022).

While social media has made valuable contributions to the field of education, such as enhancing the quality of education, particularly for educators, and facilitating communication with students outside the classroom (Eryılmaz, 2023), it can also facilitate the acquisition of parenting-related knowledge and skills by parents due to its appeal to individuals across all age groups. Parents who are active on social media, primarily for the purpose of obtaining information and advice, are also more likely to compare their own children and experience feelings of inadequacy regarding their parenting abilities (Başoğlu, 2020).

2.5 Teaching parent (role)

A significant number of users can follow influencers on social media, with these influencers acting as opinion leaders for their followers. A study conducted on mothers who are social media influencers revealed that the most followed influencer mother posts on child development, which is her area of expertise. The second most followed mother posts on her own life and children, while the third most followed mother posts on pregnant pilates, pregnant yoga, and mother-baby yoga, which are her areas of expertise (Yapıcıoğlu Ayaz & Aytekin, 2021). It is evident that individuals are able to share their own areas of expertise, whereas non-experts are permitted to share their personal experiences and even bring their children to the forefront in these posts. Mothers frequently utilize social media accounts to identify solutions to the challenges they encounter in raising children, to provide support to other mothers, and to benefit from their own experiences and those of others. They may also become role models for users who follow them (Başoğlu, 2020). In this context, it is also asserted that mothers of autistic children blog about their daily lives with the intention of raising awareness about autism, educating the reader, parents, and teachers, demonstrating their own changes, and showcasing alternative approaches for parents of autistic children (Goldsmith, 2021).

2.6 Parental mediation

It is possible for social media posts to be created not only by parents but also by children. It is evident that parents should be the primary source of guidance for their children, imparting knowledge and skills that will equip them to navigate the digital landscape safely and responsibly. This includes raising awareness about the potential risks associated with online activities, ensuring their children’s safety, and teaching them to adhere to social norms (Livingstone & Byrne, 2018). Parents’ attitudes and behaviors are crucial in informing, supervising and controlling the issues that children may encounter when using social media and the internet (Dulkadir Yaman, 2019). The processes related to the regulation, supervision, control, and restriction of children’s Internet use by their parents are collectively referred to as “parental mediation” (Daneels & Vanwynsberghe, 2017; Dulkadir Yaman, 2019; Durak, 2019; Nagy et al., 2023). Parents of children between the ages of 0 and 8 tend to emphasize the potential risks associated with internet technologies, rather than the benefits they offer. Consequently, they often prefer to restrict their children’s internet usage, either by limiting the duration of their online activities or by completely prohibiting it (Özsoy & Atılgan, 2018). It has been posited that the risks associated with internet usage can be mitigated by parents engaging in activities with their children or installing software that monitors and filters the content accessed by their children on digital technologies (Turgut & Aslan, 2016). It has been posited that restrictive parental mediation may reduce online risks, but may also reduce online opportunities. Conversely, active mediation, which involves parents and children engaging in activities together, discussing the Internet, and encouraging their children, has been found to be more effective (Duerager & Livingstone, 2012).

2.7 SMP

The advent of new media tools and the internet has led to a transformation in the role of parenting. Whereas parents in the past were primarily concerned with their children’s activities outside the home, they are now becoming parents who are curious and concerned about their children’s use of social media, computers, and the internet (Dursun Çirci & Zeybekoğlu Akbaş, 2023). Considering these evolving parenting behaviors, it becomes evident that many definitions of parenting have emerged in the literature, with research conducted under a plethora of names, including digital parenting, helicopter parenting, online parenting, internet parenting, sharing parenting and parental mediation (Avcı & Güleç Şatır, 2020; Durak, 2019; Modecki et al., 2022; Spencer et al., 2020). In the contemporary era, some mothers disseminate information about themselves and their children via their social media accounts, thereby facilitating two-way communication. This entails sharing knowledge, experiences, and behaviors related to motherhood with their followers (Ergül & Yıldız, 2021). The reasons for sharing information about one’s children on social media are diverse, as are the outcomes of such sharing. It is expected that a well-functioning SMP will be aware of each and every post and will be able to foresee the positive and negative consequences before posting. A well-functioning social media platform (SMP) should be aware of each and every post and should be able to foresee the positive and negative consequences before posting. It is of paramount importance that children who utilize social media from an early age do so in a manner that is both appropriate and conscious, and that parents take the necessary precautions to prevent any potential harm (Okumuş & Parlar, 2018). Social media proficiency (SMP) can be defined as the ability to utilize social media in an effective and regular manner, directing, guiding or training parents in this context. SMP can also be defined as new parents learning all the developmental and psychological childcare skills necessary to raise a better child through social media. Alternatively, the data shared on social media accounts may be perceived as the sole responsibility of the mother and father in the virtual environment, rather than reflecting the reality of the situation. SMP can be perceived as a parent who supervises, sets limits, and monitors their children who use social media, or it can be defined as a parent who is addicted to social media and uses social media regularly every day. Upon examination of the literature, it is evident that the concept of SMP cannot be adequately explained by the phenomenon of sharenting alone. Therefore, it is necessary to express this concept in a more comprehensive manner. Social media parenting (SMP) can be defined as parents who use social media effectively, safely, and regularly, who are open to technological innovations, who pay attention to digital security, who learn and practice parenting from people they follow on social media, who provide education about parenting on social media, who are in an idol (role) position, who control and restrict their child’s use of social media, who inform their child, and who share information about their child on social media. As can be seen, the concept of SMP is thought to be multidimensional, and it may be more appropriate to investigate both its effect on the child and its effect on the parent in this context.

3 Method

3.1 Research design

As Karasar (2022) states, the person conducting the research can combine and interpret old data into a whole within the framework of their own observations by conducting a literature review and accessing old data, resources, and people on this subject. Quantitative research is characterized by its objectivity and impartiality (Williams, 2007). The research method that examines any situation as it is expressed as the descriptive survey method (Williams, 2007). In this study, a literature review was conducted initially, followed by the application of the descriptive survey method, which is a quantitative research method.

3.2 Participants

The research population comprises parents residing in Turkey. As Karasar (2022) notes, defining the population is relatively straightforward, but reaching it is a more challenging endeavor, often requiring significant effort and resources. As it was not possible to reach the population, a link to the scale prepared in Google Forms was sent to the parents of students in kindergartens, primary and secondary schools in a province in central Anatolia, and to the parents of trainees in women’s cultural centers through messages and social media. Furthermore, parents residing in various regions of Turkey were contacted via telephone, text message, and social media accounts belonging to individuals with diverse social media platforms. The research was conducted with 430 parents, selected at random from individuals residing in different provinces in Turkey, who returned the shared link on a completely voluntary basis. The results of the scale indicated that the most frequently used social media platforms were WhatsApp (94%), Instagram (85%), YouTube (63%), Facebook (57%), Twitter (35%), Snapchat (6%) and TikTok (5%). The demographic data of the parents are presented in Table 1.

Table 1 Demographic data of the parents
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3.3 Scale development process

To define SMP, a literature review was conducted initially, after which 20 different teachers from different branches in vocational high schools were interviewed. Their views on SMP were qualitatively consulted, and different types of parenting were investigated in the literature. In addition to the three parenting styles initially proposed by Baumrind (1966, 1971), namely democratic, authoritarian, and permissive, Maccoby and Martin (1983) introduced a fourth style, namely neglectful, to the definition of SMP. In delineating these boundaries, social media was incorporated into the parenting styles examined. The parents’ own behavior, interactions with their children, and attitudes toward their children were also evaluated. The ecological systems theory developed by Bronfenbrenner (1986) was examined in relation to the social, cultural, physical, and social development of the individual. The layers of Bronfenbrenner’s (1986) ecological systems theory are “microsystem, ecosystem, exosystem, macrosystem, chronosystem.” The child is at the center, whereas the family is located in the microsystem, which is closest to the individual. It is stated that the relationship between the child and parent is intertwined in much larger structures. The model depicted in Fig. 1 was developed by integrating parent and parent-child interaction research. Subsequently, an item pool was created in accordance with the model, and the opinions of seven academicians were sought. Of these, three were professors, two were associate professors, and two held doctorates in the Department of Computer Education and Instructional Technology. Academicians were consulted on the 60 items because of their research expertise in the fields of digital parenting, digital tools, digital security, and social media behaviors. In accordance with the expert opinions, new items were incorporated, some items were excluded, and some items were reorganized. Table 2 presents the selection of expert opinions.

Table 2 Examples of expert opinions on the item pool
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In accordance with the expert opinions, it was determined that parenting behaviors would vary depending on the age of the child. Therefore, parents with more than one child were asked to respond on the basis of a single child at the outset of the scale. Consequently, the phrase “my child” was written instead of “my children/children.” The scale was structured as 60 items through the implementation of minor corrections in terms of meaning. To ensure the face validity of the scale, the final version was presented to eight parents, four of whom literature were teachers. As a consequence of the feedback received, the negative statements in the scale were written in bold font, and it was established that there were no issues in understanding the scale items. At the outset of the scale, a section requesting the parents’ personal information was incorporated. Finally, the SMP Scale was prepared as 60 items in a five-point Likert-type scale with “Never = 1”, “Rarely = 2”, “Occasionally = 3”, “Frequently = 4”, “Always = 5”, requesting that parents rank their answers from negative to positive. Furthermore, the SMP Scale was implemented in six sections, namely “Sharenting, parental mediation, parental use of social media, digital safety, learning parenting, and teaching parenting (role model)”. To define SMP, a diagram (Fig. 1) was created with the support of the relevant literature.

Fig. 1
figure 1

Definition of the SMP

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4 Data analysis

Although there is no clear statement on the selection of sample size in social sciences (Keskin, 2020), a sample size of between 200 and 250 may be sufficient for most studies, provided that it is not less than five times the number of items (Gürbüz & Şahin, 2018). Before starting the analyses, the Kolmogorov-Smirnov and Skewness–Kurtosis tests were employed to assess the normality of the SMP. The construct validity of the SMP scale was then tested. The dataset comprised 430 observations, for which the Kaiser-Meyer-Olkin (KMO) value and Bartlett’s Sphericity Test values were analyzed. The KM0 value should be between 0 and 1 (Yaşlıoğlu, 2017). If the KMO value is higher than 0.60, the sample is sufficient for factor analysis. Furthermore, if the chi-square result is significant according to the Bartlett’s Sphericity Test value, it indicates that the data are appropriate (Büyüköztürk, 2021). In accordance with the aforementioned data, EFA was conducted to ascertain the existing structure, as it was not known with certainty which items the factors consisted of. Subsequently, CFA was conducted toin order to ascertain the existing structure, as it was not known with certainty which items the factors consisted of. Subsequently, CFA was conducted in order to assess the validity of the structure (Orçan, 2018).

Principal component analysis was employed to assess the structural validity of the SMP scale. Given that the scale comprises more than two factors, the Varimax technique, a vertical rotation technique, was used to discern more clearly which items were grouped under which factors (Gürbüz & Şahin, 2018). In the principal components analysis for factor analysis, items with factor loadings below 0.40 were removed one by one, and the analysis was continued by checking the factor loadings again when each item was removed (Gürbüz & Şahin, 2018). It was ensured that there were at least three items under each factor (DeCoster, 1998). While determining the number of factors, eigenvalues were also examined. Factors with eigenvalues greater than or equal to one were considered, and the factors up to the point where the eigenvalues in the scree test were connected to the horizontal were included in the scale (Karagöz & Kösterelioğlu, 2015). As it was observed that the factor items identified before the application of the scale were grouped under the same factors, no alterations were made to the factor names derived from the literature before the scale’s implementation.

The maximum likelihood technique was employed in the context of confirmatory factor analysis (CFA) to assess the extent to which the variables contributing to the factors identified through exploratory factor analysis (EFA) were adequately represented by the factors determined through the aforementioned technique (Aytaç & Öngen, 2012). In the context of the CFA analysis, the following statistical indices were employed to assess the model fit: Chi-Square (χ2), Root Mean Square Error of Approximation (RMSEA), Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Standardized Root Mean Square Residual (SRMR), Root Mean Square Residual (RMR), Comparative Fit Index (CFI), and deemed fit index (NFI). To achieve optimal fit values for the fit indices because of CFA, the following criteria must be met: 0 ≤ χ2/sd ≤ 2, 0.90 ≤ AGFI ≤ 1.00, 0.95 ≤ GFI ≤ 1.00, 0.97 ≤ CFI ≤ 1.00, 0.95 ≤ NFI ≤ 1.00, 0.97 ≤ NNFI ≤ 1.00, 0 ≤ RMSEA ≤ 0.05, 0 ≤ SRMR ≤ 0.05 (Erkorkmaz et al., 2013; Schermelleh-Engel & Moosbrugger, 2003). (Schermelleh-Engel & Moosbrugger, 2003). To have acceptable fit values for the fit indices because of CFA, the following criteria must be met: 2 < χ2/sd ≤ 3, 0.85 ≤ AGFI < 0.90, 0.90 ≤ GFI < 0.95, 0.95 ≤ CFI < 0.97, 0.90 ≤ NFI < 0.95, 0.95 ≤ NNFI < 0.97, 0.05 < RMSEA ≤ 0.08, 0.05 < SRMR ≤ 0.10 (Schermelleh-Engel & Moosbrugger, 2003).

To test the validity of the remaining 28 items in the scale, an independent sample t-test was applied and item discrimination was examined. The positive and high item-total correlations indicate a high internal consistency of the scale (Büyüköztürk, 2021). Once the upper and lower groups were identified by ranking the scores of each item in the scale from highest to lowest, the difference between the two groups was examined to determine whether this difference was significant. Given that the two variables were continuous and normally distributed, Pearson correlation was employed to examine item-total correlations (Büyüköztürk, 2021). Although there is no exact interpretation of the correlation coefficient in terms of magnitude, it is generally accepted that the absolute value of the correlation coefficient is between 0.00 and 1.00. A value of 0.00 indicates that there is no relationship, whereas a value approaching 1.00 indicates a high correlation.

To test the reliability of the scale, internal consistency coefficients and stability tests were conducted. Internal consistency refers to the degree to which items on a unidimensional or multidimensional scale measure the same construct in relation to each other (Şencan, 2005). To determine the internal consistency of the scale, the Cronbach’s alpha reliability coefficient, the correlation value between the two halves, the Sperman-Brown and Guttmann split-half reliability formulas were employed. Cronbach’s alpha coefficient, a reliability coefficient that can take values between 0 and 1, is desirable to achieve a value of 0.8 or above (Cevahir, 2020). However, it should be noted that a value of at least 0.70 is also acceptable. In scale development studies, it has been observed that the coefficient can be reduced to 0.60 (Gürbüz & Şahin, 2018). The consistency of the responses provided by the participants to the scale items serves as an indicator of the scale’s reliability. To assess the scale’s reliability, the internal consistency coefficient, Cronbach’s Alpha, was calculated, and a test-retest was performed to assess the scale’s stability (Cevahir, 2020). For the test-retest, the SMP scale was administered to the participants, and after 3 weeks, it was administered again to the same participants. Pearson correlation coefficients were calculated for the pre- and post-values of each item and factor. Pearson’s correlation coefficient (r) takes values between 0 and 1. A value of 0.00 indicates no relationship, whereas a value of 0.01–0.29 indicates a low level of relationship, 0.30–0.70 indicates a medium level of relationship, 0.71–0.99 indicates a high level of relationship, and 1.00 indicates a perfect relationship. A value of 1 indicates a negative linear relationship, whereas a value of 1 indicates a positive linear relationship (Ankara University Open Course Materials, 2023).

5 Results

5.1 Findings regarding the validity of the scale

The scale was administered to 430 parents, and after 10 parents were removed to ensure normality, the remaining 420 parents were analyzed using the SPSS 26 program. To assess the scale’s validity, construct validity, item-total correlations, and item discrimination were examined. In addition, internal consistency coefficients and stability tests were conducted to evaluate the scale’s reliability.

5.1.1 Construct validity

Findings related to EFA

Before starting the analyses, the Kolmogorov-Smirnov and Skewness–Kurtosis tests were employed to determine the normality of the SMP. The Skewness value for the 430 data points was between 1 and + 1, whereas the kurtosis value (1.149) was not between 1 and + 1. Furthermore, the Kolmogorov-Smirnov test (0.049, p < 0.05) was not between p > 0.05, indicating that the data did not exhibit a normal distribution (Büyüköztürk, 2021). Following the deletion of the 10 outlier data points, a second normality analysis was conducted. This revealed that the Kolmogorov-Smirnov (p) test yielded a p-value of 0.074 (p > 0.05), with Skewness values of 0.257 and 0.119 and kurtosis values of -0.401 and 0.238. These results indicate that the data exhibited a normal distribution. To test the construct validity of the SMP scale, the KMO value and Bartlett’s Sphericity Test value were examined to determine whether the sample size was sufficient. The KMO value was found to be 0.820, while the Bartlett’s Sphericity Test value was χ2 = 8149.206; sd = 1770, p = 0.000 (p < 0.05). This indicated that the sample was sufficient and suitable for factor analysis (Gürbüz & Şahin, 2018). As a consequence of the principal component analysis, the Varimax vertical rotation technique was employed, and items with item loadings below 0.40 were excluded from the scale. Two items were excluded from the scale because of their formation of a distinct factor that differed from the predicted outcome. Their impact on the explained variance was minimal, and it was not feasible to include a factor comprising two items (DeCoster, 1998). The original version of the SMP scale, comprising 60 items, was revised to include 28 items. Given that the SMP scale has not yet been fully defined and that it has an impact on both the child and the parent, the number of questions was kept as high as possible when creating the item pool. A greater number of items are included that are similar. During exploratory factor analysis (EFA), each item was reanalyzed after removal. A total of 32 items with factor loadings below 0.40 and overlapping items that spanned more than one factor were removed from the scale. As a consequence of the aforementioned analysis, the reverse items identified during the preparation of the scale items were removed, resulting in the current absence of reverse items in the remaining items. Upon conducting EFA on 28 items, it was observed that SMP was explained by the previously determined factors of “parental mediation, learning parenting, sharrenting, parental social media use, teaching parenting (role model), and digital safety”. The results of these processes indicated that the scale exhibited a six-factor structure, with a KMO value of 0.821 and a Bartlett’s Sphericity Test value of χ2 = 4149.339; sd = 378 (p = 0.000, p < 0.000). The scale items and factors collectively explained 57.635% of the total variance. Furthermore, the non-rotation values of the items in the scale ranged from 0.400 to 0.801. The initial SMP factor structure consisted of six factors, which were subsequently confirmed by EFA. Upon analysis of these factors, it was found that: The “Parental mediation (pm5, pm2, pm1, pm4, pm7)” factor comprises five items, the “Learning parenting (lp3, lp2, lp6, lp1, lp5)” factor comprises five items, the “Sharenting (sh5, sh8, sh2, sh1, sh9)” factor comprises five items, and the “Parents’ use of social media (sm4, sm1, sm3, sm9)” factor comprises five items. The factor “sm5, sm1, sm3, sm9” consists of five items, the factor “tp1, tp5, tp3, tp4” consists of four items, and the factor “dl6, dl5, dl1, dl11” consists of four items.

The number of factors on the scale is depicted in the scree scatter plot in Graphic 1. The slope scatter plot illustrates the explanatory power of the factors. This demonstrates that the factors contribute little to the explained variance after they begin to plateau or flatten (Gürbüz & Şahin, 2018). Upon accepting the factors with eigenvalues greater than 1 as significant (Yaşlıoğlu, 2017), it can be concluded that the factors after the first six points of the graph have a minimal impact on the variance. In the slope scatter plot, factors 1 and 2 on the y-axis exhibit a downward trajectory with higher and close ratios. Factors 3, 4, 5, and 6 also exhibit a downward trajectory with lower and close ratios. The eigenvalue of the 7th factor is below 1, while the remaining factors are horizontal with the 7th factor.

Graphic 1
figure a

Scree plot graph

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The item loadings of 28 items under six factors, the eigenvalues of the factors, and the variance explained are shown in Table 3.

Table 3 Factor and factor distribution loads of the SMP scale
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Table 3 reveals that the eigenvalue of the Pm factor is 5.223, contributing 11.588% to the overall variance. The factor loadings range from 0.553 to 0.882, and the factor consists of five items. The eigenvalue of the Lp factor is 3.776, representing a contribution of 10.425% to the overall variance. Its factor loadings range from 0.620 to 0.797 and consists of five items. The eigenvalue of factor Sh is 2.254, which contributes 9.725% to the overall variance. Its factor loadings range from 0.561 to 0.789 and consists of five items. The eigenvalue of the Sm factor is 1.783, representing a contribution of 9.718% to the overall variance. Its factor loadings range from 0.518 to 0.774 and consists of five items. The Tp factor has an eigenvalue of 1.671, a contribution to the overall variance of 8.497, factor loadings between 0.579 and 0.746, and consists of four items. The eigenvalue of the Dl factor is 1.431, representing a contribution to the overall variance of 7.682%. Its factor loadings are between 0.577 and 0.720, and it consists of four items. The items and factors in the scale collectively explain 57.635% of the total variance. Furthermore, the fact that the explained variance exceeds 50% of the total variance is also significant for the analysis of the factors (Yaşlıoğlu, 2017). In social sciences, although a variance between 40% and 60% is generally considered sufficient, a high variance indicates that the factor structure is also strong (Karagöz & Kösterelioğlu, 2008).

Findings on the CFA

The CFA was conducted to confirm the theoretical structure predicted by the six-factor structure that emerged because of the EFA (Li, 2016). The study group for the CFA analysis was identical to that used for the EFA analysis. The five-point Likert scale, sufficient sample size, and data meeting the normality assumption permitted the application of maximum likelihood, which is the most appropriate method for CFA analysis (Gürbüz & Şahin, 2018). The estimation values based on the results of the confirmatory factor analysis (CFA) are presented in Table 4.

Table 4 Standardized regression weights
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Upon examination of Table 4, it was observed that the standardized regression weights ranged from 0.327 to 0.871. Although 12 of these values were found to be significantly below 0.70, they were not removed from the scale to maintain content validity. Table 5 presents the goodness of fit values.

Table 5 CFA fit indicesvalues
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The Chi-square index was employed to assess the fit of the model with the data. The resulting value, χ2(sd = 329, N = 420) = 702.090, was found to be statistically significant (p < 0.001). Upon examination of Table 5, it can be seen that the χ2/sd value below 3 indicates that the model shows a good fit (Gürbüz & Şahin, 2018). Additionally, the RMSEA value, which indicates the compatibility of the model with the sample, is within the acceptable range. The AGFI value was within the acceptable range, the RMR value was above the acceptable value, the SRMR value was acceptable, the IFI value was within the acceptable value range, and the NFI value was within the acceptable value range. It was observed that the GFI value was very close to the acceptable value of 0.90, whereas the CFI, which tests the fit with other models, was not in the acceptable value range but was very close to acceptable values. Given that the GFI, CFI, and RMR values were all very close to the acceptable range, further covariation was not necessary. Upon examination of the overall model, it can be stated that the factors and structure that emerged following the EFA were validated. The factorial model of the scale and the factor-item relationship are illustrated in Fig. 2.

Fig. 2
figure 2

CFA diagram of the scale

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5.1.2 Item factor correlations

To test the level of serviceability of each item for general purpose, the item-total correlation was examined. The correlation values between each item and the factor to which it belongs were calculated and are shown in Table 6.

Table 6 Item- factor correlations
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When Table 6 was examined, the item correlation coefficients were found to be between 0.639 and 0.883 for the first factor Pm, between 0.667 and 0.835 for the second factor Lp, between 0.605 and 0.816 for the third factor Sh, between 0.623 and 0.798 for the fourth factor Sm, between 0.698 and 0.805 for the fifth factor Tp, and between 0.654 and 0.718 for the sixth factor Dl. It can be posited that each item exhibits a significant and positive correlation (p < 0.001) with the factors and that each item effectively fulfills the purpose of the factor and the scale.

5.1.3 Item discrimination

To calculate the discrimination of the scale items, the total scores of the items were ranked from highest to lowest. Out of 420 respondents, 113 respondents in the first 27% of the scale were identified as group 1, and 113 respondents in the last 27% of the scale were identified as group 2. The independent samples t-test calculated over the total scores in the two groups is given in Table 7.

Table 7 t-Test analysis result for the lower group and upper group mean scores
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When Table 7 is examined, it is evident that the t-test results of the items and factors vary between 2.981 and 16.040. The mean X̄ for the 28 items in the scale was found to be 94.184 for group 1 and 61.579 for group 2. The independent samples t-test yielded a value of 40.748, with a p-value of 0.000 (p < 0.05), indicating a statistically significant difference between the two groups. Although the item discrimination of the digital security factor and its items is lower than that of the other factors and items in the scale, the item discrimination of the other items and factors is high.

5.2 Findings regarding the reliability of the scale

A reliability analysis was conducted to examine the consistency of the SMP scale and the internal consistency of the scale items. In this context, an internal consistency analysis was conducted. The stability of the scale was analyzed using the test-retest method.

5.2.1 Internal consistency level

Cronbach’s alpha reliability coefficient, correlation value between the two halves, Sperman-Brown and Guttmann split-half reliability formula were calculated for all factors and the whole scale and are shown in Table 8.

Table 8 Reliability analysis results considering the whole of the scale and its factors
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When Table 8 is analyzed, it is evident that Cronbach’s Alpha values vary between 0.610 and 0.842, and the Cronbach’s Alpha value of the scale is 0.812. While the reliability coefficient of the scale should be above 0.70, it was not removed from the scale because the reliability coefficient of digital security was 0.610, which could be reduced to 0.60 in scale development studies (Gürbüz & Şahin, 2018).

5.2.2 Stability level

The test-retest method was used to examine the stability level of each item in the scale. After the scale was applied to 21 people, it was reapplied to the same people 3 weeks later, and the stability of the scale was tested. The results are shown in Table 9.

Table 9 Test-retest results of the items of the scale
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Table 9 shows that the Pearson correlation coefficients of the scale with the test-retest method are between 0.139 and 0.954 for the items, between 0.618 and 0.937 for the factors, and 0.928 for the total scale. It is evident that all Pearson correlation coefficients are positive and significant, and accordingly, the scale makes stable measurements.

6 Discussion

The objective of this study was to develop a scale for parents based on the Self-Monitoring of Personality (SMP) theory. Exploratory factor analysis (EFA) revealed that the scale exhibited a six-factor structure. To determine the validity of the construct formed following EFA, a CFA was conducted with the same study group. The results of the CFA demonstrated that the construct was confirmed by the data. The item-total correlations of the final 28-item scale were calculated, and the item discrimination levels were examined. The internal consistency of the scale was analyzed using Cronbach’s alpha, the Sperman-Brown formula, and the Guttmann split-half reliability formula. Following a 3-week interval, the scale was administered to 21 participants for the second time using the test-retest method. Although the term “sharenting” is used in the literature to define SMP, it should be noted that the concept of SMP is much broader in scope. A comprehensive literature review was conducted, and the scale items were created in six sections. The results of the analyses indicated that the items on the scale were grouped under previously identified factors. The six factors that comprise SMP can be expressed as follows:

6.1 Parental mediation

The term “parental mediation” is used to describe the behaviors of parents who monitor their children’s media use, look at the content they consume, supervise, control and restrict it (Daneels & Vanwynsberghe, 2017; Konok et al., 2020). It is postulated that parental mediation facilitates children’s comprehension of the information conveyed by the media, enabling them to develop a critical perspective, an understanding of their responsibilities, and contribute to their socialization (Katz et al., 2019).

6.2 Learning parenting

The advent of new media technologies has led to significant shifts in parenting and parent-child relationships (Ergül & Yıldız, 2021). In particular, it is evident that mothers can readily obtain a plethora of information pertinent to parenting and child rearing by following various social media accounts related to motherhood and child development.

6.3 Sharenting

The term “parental sharing” is used to describe the behavior of parents who share images, videos, personal information, and articles about their children on social media platforms (Brosch, 2016).

6.4 Parents’ use of social media

It is evident that the use of social media by parents varies considerably. Parents employ social media for several purposes, including the generation of commercial income, the pursuit of educational opportunities, the acquisition of information, the facilitation of communication, and the pursuit of socialization (Olanrewaju et al., 2020).

6.5 Teaching parenting

In the contemporary era, mothers have the opportunity to disseminate information and experiences related to child care, child nutrition, motherhood, experiences during pregnancy, and celebrations related to the newborn baby through social media. While mothers who are professionals in this field share their knowledge and experience on social media platforms, other mothers generally share every aspect of their children (Ergül & Yıldız, 2021).

6.6 Digital security

It can be posited that parents who use social media platforms should also be mindful of their digital security (Gallego-Arrufat et al., 2019).

In the contemporary era, it is more accurate to define SMP in terms of both children and parents. Social media parenting (SMP) can be defined as a person who informs, supervises, limits, and controls their children about the use of social media, follows their children on social media, receives support from social media for their child’s physical and psychological development, learns and practices parenting from social media, shares their own experiences about parenting with other parents and serves as a role model for them, uses social media effectively and regularly, and while doing all these, is open to technological innovations and pays attention to digital security. In conclusion, the results of the analyses indicate that the SMP scale for parents is a valid and reliable instrument.

7 Limitations and recommendations

A limitation of this study is that EFA and CFA were applied to the same study group and were limited to parents in Turkey. This study focused exclusively on the behaviors of parents, and the impact of these behaviors on children was not investigated. SMP represents a novel, multifaceted approach to parenting that is believed to influence both parents and children. Consequently, further investigation is required to establish the impact of parents’ SMP behaviors on children, their relationship with different variables and whether these behaviors differ according to variables such as age, economic status, educational status and gender. In particular, it is necessary for researchers to examine the SMP behaviors of parents from different cultures in different countries. The family is the smallest unit of society, comprising parents and children. It is posited that the implementation of desired and expected positive behavioral change in individuals is of paramount importance for the transformation of societies. As the utilization of social media becomes more prevalent, it is anticipated that the awareness of social media parenting (SMP) will become a crucial factor for children, parents and society in the future. It is of paramount importance that researchers, educators, policymakers, governments, countries, and all stakeholders related to this issue emphasize SMP awareness and intensify research. It is recommended that governments and policy makers should recognize the power of SMP, which is the new generation of parenting, and therefore develop policies to increase SMP awareness in order for societies to change and develop in the desired way. It is recommended that governments, policy makers, and relevant stakeholders start the process of raising awareness of SMP by preparing a public service announcement incorporating SMP, utilizing social media in all areas of life and sharing on different platforms, thereby using the power of social media. The Ministry of Family and Social Services can provide online or face-to-face training in SMP for parents. It is postulated that the acquisition of SMP awareness, which represents a novel approach to parenting, will result in many beneficial outcomes, including enhanced communication between parents and children, as well as between parents and others, and the utilization of digital platforms in more constructive ways. The concept of parenthood is experienced throughout the entirety of an individual’s life. Therefore, it is recommended that these training programs should be made available to new mothers and fathers before they have children. SMP is an acronym for “social media parent.” An individual who oversees and informs their children about social media, shares their children on social media, provides information about parenting, learns parenting from social media, pays attention to their digital security, and uses social media effectively and safely for themselves. It comprises several elements. Therefore, it is proposed that the training sessions should be divided into sections according to the components of the SMP. It is also necessary to investigate the effects of these training programs on children, parents, individuals and society as a whole. It is of the utmost importance that the concept of the SMP is fully understood by researchers, policymakers, countries, individuals, parents, and relevant stakeholders. It is essential to raise awareness and facilitate further exploration of the concept in all its aspects. The scale developed should be employed to quantify parents’ SMP behaviors. The factors obtained using the scale can be used separately or as a total score.