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<journal-id journal-id-type="publisher-id">Front. Comput. Sci.</journal-id>
<journal-title>Frontiers in Computer Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Comput. Sci.</abbrev-journal-title>
<issn pub-type="epub">2624-9898</issn>
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<article-id pub-id-type="doi">10.3389/fcomp.2024.1387681</article-id>
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<subject>Computer Science</subject>
<subj-group>
<subject>Original Research</subject>
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</article-categories>
<title-group>
<article-title>Willingness to pay for digital wellbeing features on social network sites: a study with Arab and European samples</article-title>
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<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Alshakhsi</surname> <given-names>Sameha</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<name><surname>Babiker</surname> <given-names>Areej</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<name><surname>Sindermann</surname> <given-names>Cornelia</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author">
<name><surname>Al-Thani</surname> <given-names>Dena</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<name><surname>Montag</surname> <given-names>Christian</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
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<name><surname>Ali</surname> <given-names>Raian</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<aff id="aff1"><sup>1</sup><institution>College of Science and Engineering, Hamad Bin Khalifa University</institution>, <addr-line>Doha</addr-line>, <country>Qatar</country></aff>
<aff id="aff2"><sup>2</sup><institution>Computational Digital Psychology, Interchange Forum for Reflecting on Intelligent Systems, University of Stuttgart</institution>, <addr-line>Stuttgart, Baden-W&#x00FC;rttemberg</addr-line>, <country>Germany</country></aff>
<aff id="aff3"><sup>3</sup><institution>Department of Molecular Psychology, Institute of Psychology and Education, Ulm University</institution>, <addr-line>Ulm, Baden-Wurttemberg</addr-line>, <country>Germany</country></aff>
<author-notes>
<fn fn-type="edited-by" id="fn0005">
<p>Edited by: Hyunji Kim, Utrecht University, Netherlands</p>
</fn>
<fn fn-type="edited-by" id="fn0006">
<p>Reviewed by: Sanaul Haque, LUT University, Finland</p>
<p>Barbara Caci, University of Palermo, Italy</p>
</fn>
<corresp id="c001">&#x002A;Correspondence: Sameha Alshakhsi, <email>sameha.a.alshakhsi@gmail.com</email></corresp>
<corresp id="c002">Raian Ali, <email>raali2@hbku.edu.qa</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>03</day>
<month>06</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>6</volume>
<elocation-id>1387681</elocation-id>
<history>
<date date-type="received">
<day>18</day>
<month>02</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>03</day>
<month>05</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2024 Alshakhsi, Babiker, Sindermann, Al-Thani, Montag and Ali.</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Alshakhsi, Babiker, Sindermann, Al-Thani, Montag and Ali</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>In recent years, concerns over the potential negative impacts of social network sites (SNS) on users&#x2019; digital wellbeing are on the rise. These concerns have sparked a growing demand for SNS to introduce changes to their business model and offer features that prioritize users&#x2019; wellbeing, even if it means introducing fees to users. Still, it is questionable whether such a new model is welcomed by users and commercially valid. In this paper, we investigate (i) people&#x2019;s willingness to pay (WTP) for digital wellbeing services designed to foster more autonomy, control, and personal growth in users and (ii) the influence of sociodemographic variables, personality, and social networks use disorder (SNUD) on WTP. Data were collected through an online survey with participants from two distinct cultural contexts, the European and Arabic. The samples comprised 262 participants from Europe (Males: 57.63%) and 251 from Arab countries (Males: 60.56%). The participants ranged in age from 18 to 66 years (<italic>M</italic><sub>Europe</sub> =&#x2009;29.16, SD&#x2009;=&#x2009;8.42; <italic>M</italic><sub>Arab</sub> =&#x2009;31.24, SD&#x2009;=&#x2009;8.23). The results revealed that a notable proportion of participants were willing to pay for digital wellbeing services (Europe: 24%; Arab: 30%). Females in the European sample demonstrated a higher WTP for &#x201C;Mental Health Issues Minimization&#x201D; compared to males. In the Arab sample, males showed a higher WTP for &#x201C;Safeguarding Data Privacy&#x201D; than females. Multiple regression analyses revealed that SNUD and the need for cognition emerged as significant and positive predictors of WTP in both the European and Arab samples. Differences in the relations of personality traits and sociodemographic variables on WTP in each sample were noted. These insights contribute to our understanding of the factors shaping individuals&#x2019; preferences and valuation related to digital wellbeing services on SNS and highlight the importance of considering sociodemographic variables and personal factors as well as cultural contexts when planning and introducing them.</p>
</abstract>
<kwd-group>
<kwd>digital wellbeing</kwd>
<kwd>willingness to pay</kwd>
<kwd>data business model</kwd>
<kwd>social network sites</kwd>
<kwd>social networks use disorder</kwd>
<kwd>social media disorder</kwd>
</kwd-group>
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<fig-count count="0"/>
<table-count count="9"/>
<equation-count count="0"/>
<ref-count count="115"/>
<page-count count="16"/>
<word-count count="13768"/>
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<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Human-Media Interaction</meta-value>
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</front>
<body>
<sec sec-type="intro" id="sec1">
<label>1</label>
<title>Introduction</title>
<p>In the era of digital connectivity, social network sites (SNS) have emerged as a vital medium for communication, interaction, entertainment, and information consumption (<xref ref-type="bibr" rid="ref42">Grover et al., 2022</xref>). SNS are web-based platforms where users create profiles, interact and share information (<xref ref-type="bibr" rid="ref15">Boyd and Ellison, 2007</xref>). These platforms enable users to engage with friends worldwide with a few clicks, access a vast amount of information from various sources, such as news, and even influence public opinion (<xref ref-type="bibr" rid="ref37">Fuentes and Peterson, 2021</xref>). Despite the immense benefits of SNS, they pose potential risks to users&#x2019; digital wellbeing (DWB). For instance, the prolonged use of these platforms can potentially trigger addictive behaviors (<xref ref-type="bibr" rid="ref6">Ali et al., 2015</xref>). DWB is a state in which an individual experiences healthy and safe usage of the Internet (<xref ref-type="bibr" rid="ref68">Nansen et al., 2012</xref>), while also achieving a balance between the benefits derived from and the potential drawbacks associated with its usage (<xref ref-type="bibr" rid="ref104">Vanden Abeele, 2020</xref>).</p>
<p>Research suggests that the data-driven business model of SNS could contribute to their negative impacts on users&#x2019; DWB (<xref ref-type="bibr" rid="ref89">Sindermann et al., 2020</xref>). In this model, users indirectly pay for SNS services by sharing their data, which is then utilized by the service providers for various applications, including marketing (<xref ref-type="bibr" rid="ref61">Matz et al., 2017</xref>). As more personal data are collected, the revenue for these service providers increases (<xref ref-type="bibr" rid="ref31">Dhawan et al., 2022</xref>). This data holds value as it allows for microtargeting through advanced algorithms that understand users&#x2019; profiles, preferences, and behaviors, leading to personalized advertisements (<xref ref-type="bibr" rid="ref110">Winter et al., 2021</xref>). The more high-quality data gathered, the better the algorithms refine the understanding of user preferences and produce more personalized advertisement content. The targeting of content including advertisements, in turn, maximizes user&#x2019;s interaction and increases the duration of user engagement with the platform to reveal further user preferences, and present more time to display advertising, which again increases revenue (<xref ref-type="bibr" rid="ref31">Dhawan et al., 2022</xref>). For example, <xref ref-type="bibr" rid="ref61">Matz et al. (2017)</xref> demonstrated that personalized advertising based on the Facebook &#x201C;Like&#x201D; feature can significantly increase advertising clicks and purchases. Consequently, SNS developers are motivated to prioritize features that promote more data collection (<xref ref-type="bibr" rid="ref32">Eikelboom, 2017</xref>). Such features, including infinite scrolling, rewards, and personalization, are designed to be persuasive, interactive and engaging, making users spend more time on the sites (<xref ref-type="bibr" rid="ref3">Abreu and Campos, 2022</xref>), and potentially develop problematic behaviors (<xref ref-type="bibr" rid="ref6">Ali et al., 2015</xref>; <xref ref-type="bibr" rid="ref24">Cemiloglu et al., 2021</xref>; <xref ref-type="bibr" rid="ref90">Sindermann et al., 2022</xref>).</p>
<p>Recent research has advocated an alternative to the data business model that mitigates its negative effects and fosters DWB (<xref ref-type="bibr" rid="ref65">Montag and Hegelich, 2020</xref>; <xref ref-type="bibr" rid="ref89">Sindermann et al., 2020</xref>). <xref ref-type="bibr" rid="ref89">Sindermann et al. (2020)</xref> proposed a monetary payment model, where users would pay a reasonable monetary fee for enhanced DWB services, including the assurance of content quality, privacy protection, and a design that refrains from personalizing content or extending usage time. Their study showed only 21.43% of a German sample was willing to pay (WTP) for such an improved design. This relatively low percentage was justified by the unawareness of people about the impact of the data business model, or the perceived benefits offered by SNS offsetting its negative impacts. The authors also recommended exploring a more integrative model to offer more benefits and value to the users for them to pay. Additional studies have explored the monetary models in different contexts, such as on Chinese platforms (WeChat and QQ) (<xref ref-type="bibr" rid="ref91">Sindermann et al., 2022</xref>), and among Facebook users (<xref ref-type="bibr" rid="ref94">Sunstein, 2020</xref>), focusing on WTP for the existing social media services versus willingness to get paid to discontinue using such services to assess their value to the users. Other studies focused on the WTP for specific existing services (<xref ref-type="bibr" rid="ref81">Schreiner and Hess, 2015</xref>; <xref ref-type="bibr" rid="ref18">Brynjolfsson et al., 2019</xref>).</p>
<p>Therefore, further investigation is still necessary to evaluate the potential of this alternative model in addressing the concerns raised in relation to the data business model and understand whether people are interested in and willing to pay to support this alternative model when they are used to monetarily free platforms (<xref ref-type="bibr" rid="ref31">Dhawan et al., 2022</xref>). Understanding people&#x2019;s interest in adopting this alternative model and the services it offers to enhance their DWB is important, as studies have consistently shown that user interest and acceptance play a crucial role for services to be used and yield benefits (<xref ref-type="bibr" rid="ref28">Davis, 1993</xref>). WTP can also be used as a proxy measure of valuation and to estimate the likelihood of using these services, accordingly.</p>
<p>Furthermore, previous studies have primarily focused on WEIRD samples (Western, Education, Industrialized, Rich, and Democratic) (<xref ref-type="bibr" rid="ref45">Henrich et al., 2010</xref>). These samples may not accurately represent the interests and perspectives of SNS users from other cultural backgrounds, including Arab countries. Our study aims to bridge this gap by exploring WTP and preferences for digital wellbeing services separately in each of the two culturally distinct groups: Europeans and Arabs. This exploratory approach aims to enhance understanding of whether digital wellbeing services can be broadly applicable in different cultures or if their effectiveness varies based on cultural context.</p>
<sec id="sec2">
<label>1.1</label>
<title>Background and motivation</title>
<p>While design elements of SNS may be advantageous in certain situations, such as newsfeeds tailored to users&#x2019; digital footprints, which can enhance user experience by filtering out irrelevant content, the benefits to their users remain debatable (<xref ref-type="bibr" rid="ref5">Aguirre et al., 2015</xref>). This personalization of content can also contribute to unhealthy usage of SNS, as constant access to personalized content challenges users&#x2019; self-control, regardless of their individual characteristics (<xref ref-type="bibr" rid="ref43">Gui et al., 2017</xref>). This can potentially result in problematic behaviors, including excessive use of SNS and stimulation of obsessive and compulsive behaviors (<xref ref-type="bibr" rid="ref7">Alrobai et al., 2014</xref>). Such problematic usage of SNS has been associated with negative consequences for users&#x2019; wellbeing, including depression (<xref ref-type="bibr" rid="ref10">Andreassen et al., 2016</xref>; <xref ref-type="bibr" rid="ref112">Yoon et al., 2019</xref>), and reduced productivity (<xref ref-type="bibr" rid="ref22">Cao and Yu, 2019</xref>).</p>
<p>Furthermore, algorithmic personalization based on data collected from users and their social circle is assumed by some experts to contribute to an information environment similar to a &#x201C;filter bubble.&#x201D; This term refers to a situation in which individuals are primarily exposed to content that aligns with their existing beliefs and perspectives, consequently limiting their exposure to different viewpoints (<xref ref-type="bibr" rid="ref76">Pariser, 2011</xref>). Similarly, the phenomenon of &#x201C;echo chambers,&#x201D; in which individuals are mostly exposed to information that aligns with their attitudes, is debated in the context of SNS (<xref ref-type="bibr" rid="ref88">Sindermann et al., 2022</xref>). By filtering content based on predicted user preferences, personalized algorithms can negatively affect individuals&#x2019; understanding of different perspectives. Some scholars argue that it could promote radicalization and undermine democratic principles (<xref ref-type="bibr" rid="ref97">Thompson, 2011</xref>; <xref ref-type="bibr" rid="ref16">Bozdag and van den Hoven, 2015</xref>). For example, when individuals rely solely on SNS as their primary source of political news, they risk being subjected to content that reinforces only their own extreme views. The isolation from diverse perspectives can create an environment that nurtures radical ideologies, thereby threatening democratic dialogue and societal harmony. Moreover, research demonstrated that SNS platforms not only facilitate but also accelerate the dissemination of misinformation including extremism (<xref ref-type="bibr" rid="ref106">Vosoughi et al., 2018</xref>; <xref ref-type="bibr" rid="ref78">Piazza, 2022</xref>).</p>
<p>The spread of misinformation on SNS is, therefore, another significant concern that impacts users&#x2019; digital wellbeing. SNS platforms, driven by their business model that prioritizes user engagement and content sharing, can enable the rapid spread of misinformation, often surpassing the spread of true information (<xref ref-type="bibr" rid="ref106">Vosoughi et al., 2018</xref>). SNS have been identified as the primary source of fake news, contributing to 88% of its spread (<xref ref-type="bibr" rid="ref13">Beauvais, 2022</xref>). Consequently, in what is now referred to as &#x201C;an era of fake news,&#x201D; users are invariably exposed to misinformation as they interact with and share content on SNS. Research found a positive association between extensive use of SNS and an increased likelihood of encountering and sharing of misinformation (<xref ref-type="bibr" rid="ref66">Morosoli et al., 2022</xref>) and other types of harmful content such as extremism (<xref ref-type="bibr" rid="ref26">Costello et al., 2016</xref>). Prolonged and repeated exposure to misinformation can lead individuals to accept and act upon false information (<xref ref-type="bibr" rid="ref77">Pennycook et al., 2018</xref>; <xref ref-type="bibr" rid="ref23">Carrieri et al., 2019</xref>). Furthermore, the spread of misinformation can negatively impact users&#x2019; trust in SNS sources and hinder their capacity to distinguish between true and misinformation, thereby affecting their digital wellbeing (<xref ref-type="bibr" rid="ref103">Van Duyn and Collier, 2019</xref>).</p>
<p>Data privacy, the control individuals have over their personal data and how it is used, is another concern related to the usage of SNS and its business model, with users becoming increasingly concerned about the potential data misuse (<xref ref-type="bibr" rid="ref55">Krasnova et al., 2009</xref>). This rising concern is closely tied to data security issues, which refer to data protection from unauthorized access and breaches (<xref ref-type="bibr" rid="ref85">Sharma and Bollavarapu, 2015</xref>). Breaches and misuse of personal data can impact users&#x2019; digital wellbeing, leading to stress, anxiety, and a sense of powerlessness over the control of personal information (<xref ref-type="bibr" rid="ref34">Elhai and Hall, 2015</xref>; <xref ref-type="bibr" rid="ref33">Elhai et al., 2017</xref>). Instances of data security breaches have been prevalent in recent years, with 3,800 data breaches reported globally in the first half of 2019 alone, compromising 4.1 billion data records (<xref ref-type="bibr" rid="ref109">Winder, 2019</xref>). Adding to these security concerns, misuse of data also emerged, most notably in the Facebook-Cambridge Analytica (CA) scandal of 2018 (<xref ref-type="bibr" rid="ref30">Deley and Dubois, 2020</xref>). In this case, data from about 87 million Facebook users were used by CA without obtaining explicit consent to create personalized ads, with the aim of influencing users voting behavior in the 2016 US presidential election (<xref ref-type="bibr" rid="ref107">Ward, 2018</xref>). The CA scandal not only marked a significant violation of privacy but also showed the potential for misuse of personal data in ad campaigns.</p>
<p>Given the aforementioned issues, there are growing concerns about the design of SNS centered on the current business model, which prioritizes the collection of user data, heightened user engagement, and the personalization of content and advertisements, while the quality of content and user wellbeing are not a priority and often compromised. Hence, it is important to seek a viable solution that addresses these concerns while still allowing SNS to be commercially viable.</p>
</sec>
<sec id="sec3">
<label>1.2</label>
<title>Overview of the current study</title>
<p>In this study, we further investigate the alternative monetary model for SNS, focusing specifically on user interest in this model and the DWB services it could provide. We build upon the existing literature by assessing user demand for a broader range of digital wellbeing services. DWB denotes a balanced and healthy relationship with technology, achieved by utilizing its benefits while managing its potential drawbacks (<xref ref-type="bibr" rid="ref104">Vanden Abeele, 2020</xref>). Research on DWB often focuses on two dimensions: hedonic dimension, which involves enhancing pleasant experience during SNS use without adverse effects on mental health or leading to problematic and potentially addictive behaviors, and the eudaimonic dimension, which is about seeking to enrich one&#x2019;s digital activities with meaning and to promote a good life (<xref ref-type="bibr" rid="ref35">Extremera et al., 2011</xref>; <xref ref-type="bibr" rid="ref92">Steinert and Dennis, 2022</xref>). Drawing upon the aspects from the DWB framework (<xref ref-type="bibr" rid="ref19">B&#x00FC;chi, 2021</xref>) and the digital competences framework (<xref ref-type="bibr" rid="ref59">Martzoukou et al., 2020</xref>), we acknowledge that DWB is a multi-faceted concept, intertwining elements such as privacy, safety, awareness, controlled digital usage and digital flourishing (satisfaction and fulfillment). The DWB services considered in this paper prioritize safety and responsible use of SNS, advancing user privacy and data security, and enabling users to monitor and control their usage, which fosters awareness and controlled usage of SNS. Additionally, our services include a proactive mitigation to the spread of misinformation and extremism, fostering a safer and more comfortable environment. We also incorporated services that aim to minimize potential adverse impacts of SNS on mental health, such as stress and fear. In promoting digital flourishing, our services provide opportunities for users to nurture their digital virtue and personal growth. By integrating these diverse aspects of DWB into our design, we provide a comprehensive understanding of users&#x2019; demands, paving the way towards an enhanced digital wellbeing environment.</p>
<p>In exploring who is interested in such an alternative model that prioritizes users&#x2019; digital wellbeing, the differences in interest based on sociodemographic and personality characteristics are important (<xref ref-type="bibr" rid="ref89">Sindermann et al., 2020</xref>, <xref ref-type="bibr" rid="ref91">2022</xref>). In the study conducted by <xref ref-type="bibr" rid="ref89">Sindermann et al. (2020)</xref>, gender, age, and the Big Five personality traits &#x2014;extraversion, agreeableness, conscientiousness, neuroticism, and openness&#x2014;were assessed in examining who supports the alternative monetary model going along with DWB services (<xref ref-type="bibr" rid="ref79">Rammstedt and John, 2007</xref>). Their findings revealed that only the agreeableness trait was positively correlated with WTP. However, their sample was limited to Germany, and their study included lesser DWB services that investigated in the current study. Furthermore, differences related to sociodemographic factors and personality traits have been found to influence SNS use behaviors and motivations. For example, studies have indicated that females are more likely to use SNS excessively and are more prone to social networks use disorder (SNUD) (<xref ref-type="bibr" rid="ref93">Su et al., 2020</xref>; <xref ref-type="bibr" rid="ref99">Twenge and Martin, 2020</xref>), while those high in neuroticism may use SNS obsessively to cope with stress (<xref ref-type="bibr" rid="ref72">Orchard et al., 2014</xref>; <xref ref-type="bibr" rid="ref49">Huang, 2022b</xref>). Similarly, individuals with a high need for cognition (NFC), those who enjoy thinking and information processing tasks (<xref ref-type="bibr" rid="ref21">Cacioppo et al., 1996</xref>), actively seek information, potentially leading to information overload and cognitive fatigue due to the vast, easily accessible data on SNS (<xref ref-type="bibr" rid="ref56">Lee and Ma, 2012</xref>; <xref ref-type="bibr" rid="ref74">Ouardi et al., 2016</xref>; <xref ref-type="bibr" rid="ref67">Nam and Hwang, 2021</xref>). Given that these personal differences influence the usage behavior of SNS, it is intriguing to investigate whether these differences are reflected in the interests towards DWB services and WTP for them. Furthermore, research suggests that personality differences can influence strategic decision-making (<xref ref-type="bibr" rid="ref44">Haley and Stumpf, 1989</xref>), and are related to different reactions to design elements (<xref ref-type="bibr" rid="ref90">Sindermann et al., 2022</xref>). Therefore, it is valuable to investigate the influence of personality traits and cognitive dispositions on the DWB services as design elements on SNS.</p>
<p>In addition, a potential correlation between excessive use or social networks use disorder and users&#x2019; interest in DWB services can be postulated. Users of SNS might perceive these platforms as &#x201C;Wasting Time Goods,&#x201D; (<xref ref-type="bibr" rid="ref94">Sunstein, 2020</xref>), which implies that despite extensive time spent on these platforms, users may regard them as valueless and means of wasting times. Consequently, excessive use of SNS may not be associated with WTP. This assumption was supported by a previous study which found WTP for social media did not significantly correlate with excessive or social networks use disorder within the context of WeChat and QQ (<xref ref-type="bibr" rid="ref91">Sindermann et al., 2022</xref>). This raises the question whether the same applies to other SNS and when incorporating more DWB features. Our study aims to address this gap, determining to what extent SNUD will impact the WTP for additional DWB features on SNS.</p>
<p>In summary, we aim to investigate people&#x2019;s interest in a novel different business model on SNS that prioritizes users&#x2019; wellbeing in exchange for monetary payment. This study will contribute to our understanding of the feasibility of such model and identify the individuals who are more likely to adopt these services, i.e., to pay for them. Therefore, this paper aims to answer the following research questions for each of the two samples: Arab and European (predominantly Germans):</p>
<disp-quote>
<p>RQ1: What is the extent to which people are willing to pay for digital wellbeing features on social network sites?</p>
<p>RQ2: Are sociodemographic variables including age, gender, education, and financial status associated with willingness to pay for digital wellbeing features on social network sites?</p>
<p>RQ3: Do social networks use disorder, need for cognition, and personality traits predict the willingness to pay for digital wellbeing features on social network sites?</p>
</disp-quote>
</sec>
</sec>
<sec sec-type="methods" id="sec4">
<label>2</label>
<title>Methodology</title>
<p>The survey study was designed on SurveyMonkey<xref ref-type="fn" rid="fn0001"><sup>1</sup></xref> and Qualtrics<xref ref-type="fn" rid="fn0002"><sup>2</sup></xref>, web-based survey platforms. The survey was created in English for the European sample and subsequently translated into Arabic for the Arab sample. The authors, who were proficient in both languages, performed the translation following the suggested back-translation procedure (<xref ref-type="bibr" rid="ref17">Brislin, 1970</xref>). The survey included two main parts:</p>
<p>Initially, respondents were asked if they were familiar with the data business model, and the concept was defined to ensure a shared understanding. By providing this information, we aimed to reduce potential bias that could arise if some respondents were knowledgeable about the model while others were not. The first part of the survey examined participants&#x2019; WTP for digital wellbeing services on SNS. The second part of the survey assessed participants&#x2019; personal characteristics focusing on SNUD, personality traits, and NFC. The questionnaire also asked participants to provide sociodemographic information including gender, age, education level, financial status, employment status and country of origin.</p>
<sec id="sec5">
<label>2.1</label>
<title>Participants and procedure</title>
<p>A total of 513 participants were recruited for this study through the Prolific<xref ref-type="fn" rid="fn0003"><sup>3</sup></xref> and Cint<xref ref-type="fn" rid="fn0004"><sup>4</sup></xref> platforms, consisting of 262 participants from Europe and 251 from Arab countries. The inclusion criteria for participants required them to be 18&#x2009;years or older, users of SNS, and fluent English speakers for European participants or Arabic speakers for Arab participants. Participants were invited to take part in the study and were given information about the study. Those who met the inclusion criteria and agreed to participate in the study were given a link to the anonymous questionnaire. Participants were informed that they were free to stop at any time. Four attention checks were included in the survey to ensure data quality. Participants who failed two or more attention checks were excluded from the analysis. Eligible participants were compensated for their participation after completion. The study was approved by the Institutional Review Board (IRB) of Qatar Biomedical Research Institute (QBRI) at Hamad Bin Khalifa University.</p>
<p>The European dataset included participants from Germany, the Netherlands, Finland, Sweden, Norway, Switzerland, and Denmark. The Arab dataset included participants from Egypt, Jordan, Morocco, Tunisia, Lebanon, Syria, Palestine, Algeria, Oman, Bahrain, Saudi Arabia, Sudan, Iraq, Yemen, UAE, Kuwait, and Somalia. The countries in each of the two group share cultural similarities as outlined by the World Values Survey, a large-scale cross-national research study that explores global cultural, societal, and political attitudes and values (<xref ref-type="bibr" rid="ref50">Inglehart et al., 2022</xref>). The data used in this study are part of a large survey aimed at exploring WTP on social media.</p>
</sec>
<sec id="sec6">
<label>2.2</label>
<title>Measures</title>
<sec id="sec7">
<label>2.2.1</label>
<title>Willingness to pay for digital wellbeing</title>
<p>Willingness to pay for digital wellbeing services for SNS was measured by 10 items. All items were rated on a five-point Likert scale (1&#x2009;=&#x2009;very unlikely to 5&#x2009;=&#x2009;very likely). Five of the 10 items were developed by <xref ref-type="bibr" rid="ref104">Vanden Abeele (2020)</xref>. Due to the exploratory nature of our study, the scope was broadened. By scanning the literature and looking at the digital wellbeing concepts, another five items were developed in our current study. The 10 items are provided in the <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S1</xref>. The total score was calculated for the 10 items, and its reliability was assessed using Cronbach&#x2019;s alpha, <italic>&#x03B1;</italic> =&#x2009;0.93 for the European sample, and <italic>&#x03B1;</italic> =&#x2009;0.94 for the Arab sample, indicating very good reliability.</p>
</sec>
<sec id="sec8">
<label>2.2.2</label>
<title>Social networks use disorder</title>
<p>Social networks use disorder was measured using the Social Media Disorder (SMD) scale (<xref ref-type="bibr" rid="ref100">Van Den Eijnden et al., 2016</xref>). The SMD scale consists of nine items corresponding to nine diagnostic criteria: Persistence, Tolerance, Withdrawal, Preoccupation, Escape, Problems, Deception, Displacement, and Conflict. Each item was answered using a five-point scale (1&#x2009;=&#x2009;never to 5&#x2009;=&#x2009;always). The reliability of the scale was assessed using Cronbach&#x2019;s alpha, <italic>&#x03B1;</italic> =&#x2009;0.90 for the European sample and <italic>&#x03B1;</italic> =&#x2009;0.85 for the Arab sample, indicating a good reliability.</p>
</sec>
<sec id="sec9">
<label>2.2.3</label>
<title>Big five inventory &#x2013; 10</title>
<p>Big five inventory (BFI)-10 measures the Big Five personality traits using 10 items on a 5-point Likert scale (1&#x2009;=&#x2009;strongly disagree to 5&#x2009;=&#x2009;strongly agree) (<xref ref-type="bibr" rid="ref79">Rammstedt and John, 2007</xref>). The Big Five comprises five dimensions of extraversion, agreeableness, neuroticism, openness, and conscientiousness. The score of each dimension was calculated based on two out of the 10 questions.</p>
</sec>
<sec id="sec10">
<label>2.2.4</label>
<title>Need for cognition</title>
<p>The need for cognition (NFC) was measured using six-item scale developed by <xref ref-type="bibr" rid="ref29">de Holanda Coelho et al. (2020)</xref>. This scale is a shortened version scale of the original 18-item scale developed by <xref ref-type="bibr" rid="ref20">Cacioppo and Petty (1982)</xref>. The scale items were measured on a 5-point scale (1&#x2009;=&#x2009;extremely uncharacteristic of me, 5&#x2009;=&#x2009;extremely characteristic of me). The scale was reliable in the two samples, with Cronbach&#x2019;s alpha values of 0.86 and 0.81 for the present European and Arab samples respectively, indicating a good reliability.</p>
</sec>
</sec>
<sec id="sec11">
<label>2.3</label>
<title>Data analysis</title>
<p>A combination of descriptive statistics, Exploratory Factor Analysis (EFA), t-tests, correlations, and regression analyses was performed on each sample using JASP version 0.17.1 (<xref ref-type="bibr" rid="ref52">JASP Team, 2022</xref>). Descriptive statistics were used to provide an overview of the data, and EFA was performed to determine whether the 10 willingness to pay (WTP) items could be categorized into meaningful factors and used as dependent variables in the regression analyses. We applied parallel analysis, which is recommended as an accurate method to estimate the number of factors to extract (<xref ref-type="bibr" rid="ref46">Henson and Roberts, 2006</xref>). Principle Axis Factor estimator and oblique rotations (Oblimin) were employed in the EFA analysis, which allowed for correlations among the factors because there was no <italic>a priori</italic> reason to assume that the factors would be orthogonal. The factors derived from EFA were scored by averaging the respective individual item scores. <italic>T</italic>-test and correlations were subsequently used to examine the association between the total score of WTP and WTP factors, on one hand, and sociodemographic variables, on the other. Multiple regression analyses were conducted separately for each of the European and Arab samples to examine the relations of social networks use disorder and psychological factors with the WTP total score. Furthermore, a series of multiple linear regression analyses were performed to investigate the relations of the variables of interest with WTP for &#x201C;Monitoring SN Usage&#x201D; and &#x201C;Usage not Prolonged.&#x201D; Given the existing body of literature that focuses extensively on the overuse of internet, including SNS, and associations with digital addiction, it is important to further investigate the demands of our designed services that mitigate these concerns. For instance, a review study on digital wellbeing, have identified screen time and addiction as the most frequently explored themes (<xref ref-type="bibr" rid="ref108">Widjaja and Baek, 2023</xref>).</p>
</sec>
</sec>
<sec sec-type="results" id="sec12">
<label>3</label>
<title>Results</title>
<sec id="sec13">
<label>3.1</label>
<title>Participants</title>
<p>In total, the study comprises 513 participants from European and Arab countries. The sociodemographic breakdown revealed that 303 participants were male (59.06%). The average age was 30.18&#x2009;years (SD&#x2009;=&#x2009;8.39). In the per-sample description, the Arab participants showed descriptively slightly higher WTP (<italic>M</italic><sub>Arab</sub> =&#x2009;29.55, SD&#x2009;=&#x2009;10.80) than the European participants (<italic>M</italic><sub>European</sub> =&#x2009;27.76, SD&#x2009;=&#x2009;10.26). Detailed descriptive statistics for each sample are shown in <xref ref-type="table" rid="tab1">Tables 1</xref>, <xref ref-type="table" rid="tab2">2</xref>.</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Descriptive statistics of sociodemographic variables.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top">Variables</th>
<th/>
<th align="center" valign="top">Whole sample<break/><italic>N</italic> (513)</th>
<th align="center" valign="top">European sample<break/><italic>N</italic> (262)</th>
<th align="center" valign="top">Arab sample<break/><italic>N</italic> (251)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Gender</td>
<td align="left" valign="middle">Male (%)<break/>Female (%)</td>
<td align="center" valign="middle">303 (59.06)<break/>210 (40.94)</td>
<td align="center" valign="middle">151 (57.63)<break/>111 (42.37)</td>
<td align="center" valign="middle">152 (60.56)<break/>99 (39.44)</td>
</tr>
<tr>
<td align="left" valign="top">Age</td>
<td align="left" valign="middle">M (SD)<break/>Range</td>
<td align="center" valign="middle">30.18 (8.39)<break/>18&#x2013;66</td>
<td align="center" valign="middle">29.16 (8.42)<break/>18&#x2013;66</td>
<td align="center" valign="middle">31.24 (8.23)<break/>18&#x2013;59</td>
</tr>
<tr>
<td align="left" valign="top">Employment (%)</td>
<td align="left" valign="middle">Student<break/>Employed/Self-employed<break/>Not employed</td>
<td align="center" valign="middle">133 (25.93)<break/>340 (66.28)<break/>40 (7.80)</td>
<td align="center" valign="middle">76 (29.01)<break/>170 (64. 89)<break/>16 (6.11)</td>
<td align="center" valign="middle">57 (22.71)<break/>170 (67.73)<break/>24 (9.56)</td>
</tr>
<tr>
<td align="left" valign="top">Education (%)</td>
<td align="left" valign="middle">Basic Education<break/>Higher Education</td>
<td align="center" valign="middle">194 (37.82)<break/>319 (62.18)</td>
<td align="center" valign="middle">135 (51.53)<break/>127 (48.47)</td>
<td align="center" valign="middle">59 (23.51)<break/>192 (76.49)</td>
</tr>
<tr>
<td align="left" valign="top">Financial Status</td>
<td align="left" valign="middle">Not at all well of<break/>Not so well off<break/>Average<break/>Quite well off<break/>Very well off</td>
<td align="center" valign="middle">26 (5.07)<break/>102 (19.88)<break/>260 (50.68)<break/>107 (20.86)<break/>18 (3.51)</td>
<td align="center" valign="middle">16 (6.11)<break/>76 (29.01)<break/>102 (38.93)<break/>61 (23.28)<break/>7 (2.67)</td>
<td align="center" valign="middle">10 (3.98)<break/>26 (10.36)<break/>158 (62.95)<break/>46 (18.33)<break/>11 (4.38)</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Descriptive statistics of willingness to pay for digital wellbeing services.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="middle">Variables</th>
<th align="center" valign="middle">Whole sample<break/><italic>M</italic> (SD)</th>
<th align="center" valign="middle">European<break/>sample<break/><italic>M</italic> (SD)</th>
<th align="center" valign="middle">Arab<break/>sample<break/><italic>M</italic> (SD)</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Total score of Willingness to Pay (WTP)</td>
<td align="center" valign="middle">28.63 (10.56)</td>
<td align="center" valign="middle">27.76 (10.26)</td>
<td align="center" valign="middle">29.55 (10.80)</td>
</tr>
<tr>
<td align="left" valign="middle">Data not used for marketing.</td>
<td align="center" valign="middle">2.63 (1.31)</td>
<td align="center" valign="middle">2.62 (1.27)</td>
<td align="center" valign="middle">2.65 (1.35)</td>
</tr>
<tr>
<td align="left" valign="middle">Data protection.</td>
<td align="center" valign="middle">2.98 (1.38)</td>
<td align="center" valign="middle">2.90 (1.32)</td>
<td align="center" valign="middle">3.05 (1.44)</td>
</tr>
<tr>
<td align="left" valign="middle">Usage not prolonged.</td>
<td align="center" valign="middle">2.71 (1.34)</td>
<td align="center" valign="middle">2.71 (1.36)</td>
<td align="center" valign="middle">2.71 (1.31)</td>
</tr>
<tr>
<td align="left" valign="middle">Fake news reduction.</td>
<td align="center" valign="middle">3.09 (1.43)</td>
<td align="center" valign="middle">3.06 (1.43)</td>
<td align="center" valign="middle">3.12 (1.43)</td>
</tr>
<tr>
<td align="left" valign="middle">Extremism reduction.</td>
<td align="center" valign="middle">3.06 (1.39)</td>
<td align="center" valign="middle">3.02 (1.40)</td>
<td align="center" valign="middle">3.11 (1.37)</td>
</tr>
<tr>
<td align="left" valign="middle">Usage monitoring features.</td>
<td align="center" valign="middle">2.56 (1.33)</td>
<td align="center" valign="middle">2.41 (1.33)</td>
<td align="center" valign="middle">2.71 (1.32)</td>
</tr>
<tr>
<td align="left" valign="middle">External Apps access allowed.</td>
<td align="center" valign="middle">2.52 (1.27)</td>
<td align="center" valign="middle">2.45 (1.26)</td>
<td align="center" valign="middle">2.60 (1.28)</td>
</tr>
<tr>
<td align="left" valign="middle">Mental health issues minimization.</td>
<td align="center" valign="middle">2.97 (1.37)</td>
<td align="center" valign="middle">2.97 (1.37)</td>
<td align="center" valign="middle">2.96 (1.38)</td>
</tr>
<tr>
<td align="left" valign="middle">Digital virtue ensuring.</td>
<td align="center" valign="middle">2.85 (1.30)</td>
<td align="center" valign="middle">2.61 (1.25)</td>
<td align="center" valign="middle">3.11 (1.29)</td>
</tr>
<tr>
<td align="left" valign="middle">Personal development.</td>
<td align="center" valign="middle">3.27 (1.33)</td>
<td align="center" valign="middle">3.02 (1.32)</td>
<td align="center" valign="middle">3.53 (1.30)</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>More details on the 10 items are provided in <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S1</xref>.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec14">
<label>3.2</label>
<title>Exploratory factor analysis</title>
<p>To explore whether the 10 items of WTP could be categorized into a smaller number of variables, Exploratory Factor Analysis (EFA) was conducted using the entire dataset as well as each of the two samples separately. To evaluate the adequacy and suitability of the data for factor analysis, the Kaiser&#x2013;Meyer&#x2013;Olkin (KMO) measure of sampling adequacy and Bartlett&#x2019;s test of sphericity were calculated. The overall KMO measures for the entire dataset, the European sample, and the Arab sample were 0.87, 0.84, and 0.88, respectively. Bartlett&#x2019;s test of sphericity for the three datasets was statistically significant (<italic>p</italic> &#x003C;&#x2009;0.001). These results indicate that the data are appropriate for the EFA (<xref ref-type="bibr" rid="ref12">Bartlett, 1954</xref>; <xref ref-type="bibr" rid="ref53">Kaiser, 1974</xref>). For the entire dataset and the European and the Arab samples, the parallel analysis yielded four factors, accounting for 76, 73, and 80% of the variance, respectively. Furthermore, the commonly applied criteria of visual inspection of the scree plot and interpretability indicated that the four factors should be retained.</p>
<p>The items, &#x201C;Usage not prolonged&#x201D; and &#x201C;Mental health issues minimization,&#x201D; were removed because they had low factor loadings (&#x003C;0.4) and/or cross-loadings close to their main loadings on another factor (difference&#x2009;&#x003C;&#x2009;0.20) in both the combined and the Arab samples (<xref ref-type="bibr" rid="ref60">Matsunaga, 2010</xref>). Further, within the European sample, &#x201C;Usage not prolonged&#x201D; had factor loading lower than 0.4. The factor loadings for all other items were above 0.40. The four factors were identified as Factor 1: Safeguarding Data Privacy (20% of the total variance using the entire dataset), Factor 2: Reducing Harmful Content (20%), Factor 3: Monitoring Social Networking Usage (17%), and Factor 4: Fostering Ethical &#x0026; Personal Growth (19%). In the European context, the variance explained by each factor was as follows: Factor 1: Safeguarding Data Privacy (19%), Factor 2: Reducing Harmful Content (22%), Factor 3: Monitoring Social Networking Usage (16%), and Factor 4: Fostering Ethical &#x0026; Personal Growth (16%). Simultaneously, within the Arabic sample, the proportion of variance explained by each factor was: Factor 1: Safeguarding Data Privacy (22%), Factor 2: Reducing Harmful Content (14%), Factor 3: Monitoring Social Networking Usage (22%), and Factor 4: Fostering Ethical &#x0026; Personal Growth (23%). The factor loadings of the rotated solution are shown in <xref ref-type="table" rid="tab3">Table 3</xref>.</p>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>EFA results of the eight items of willingness to pay.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top" colspan="4">European sample</th>
<th align="center" valign="top" colspan="4">Arab sample</th>
</tr>
<tr>
<th/>
<th align="center" valign="top">1</th>
<th align="center" valign="top">2</th>
<th align="center" valign="top">3</th>
<th align="center" valign="top">4</th>
<th align="center" valign="top">1</th>
<th align="center" valign="top">2</th>
<th align="center" valign="top">3</th>
<th align="center" valign="top">4</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Factor 1: Safeguarding Data Privacy</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">1 Data not used for marketing</td>
<td align="center" valign="middle">0.87</td>
<td/>
<td/>
<td/>
<td align="center" valign="middle">0.83</td>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">2 Data protection</td>
<td align="center" valign="middle">0.86</td>
<td/>
<td/>
<td/>
<td align="center" valign="middle">0.88</td>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Factor 2: Reducing Harmful Content</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">1 Fake news reduction</td>
<td/>
<td align="center" valign="middle">0.91</td>
<td/>
<td/>
<td/>
<td align="center" valign="middle">0.67</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">2 Extremism reduction</td>
<td/>
<td align="center" valign="middle">0.93</td>
<td/>
<td/>
<td/>
<td align="center" valign="middle">0.50</td>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Factor 3: Monitoring SN Usage</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">1 Usage monitoring features</td>
<td/>
<td/>
<td align="center" valign="middle">0.75</td>
<td/>
<td/>
<td/>
<td align="center" valign="middle">0.84</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">2 External Apps access allowed</td>
<td/>
<td/>
<td align="center" valign="middle">0.80</td>
<td/>
<td/>
<td/>
<td align="center" valign="middle">0.83</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Factor 4: Fostering Ethical &#x0026; Personal Growth</td>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
<td/>
</tr>
<tr>
<td align="left" valign="middle">1 Digital virtue ensuring</td>
<td/>
<td/>
<td/>
<td align="center" valign="middle">0.78</td>
<td/>
<td/>
<td/>
<td align="center" valign="middle">1.02</td>
</tr>
<tr>
<td align="left" valign="middle">2 Personal development</td>
<td/>
<td/>
<td/>
<td align="center" valign="middle">0.80</td>
<td/>
<td/>
<td/>
<td align="center" valign="middle">0.63</td>
</tr>
<tr>
<td align="left" valign="middle">Cronbach&#x2019;s alpha, <italic>&#x03B1;</italic> =</td>
<td align="center" valign="middle">0.87</td>
<td align="center" valign="middle">0.93</td>
<td align="center" valign="middle">0.77</td>
<td align="center" valign="middle">0.80</td>
<td align="center" valign="middle">0.87</td>
<td align="center" valign="middle">0.85</td>
<td align="center" valign="middle">0.88</td>
<td align="center" valign="middle">0.89</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>The 10 DWB items represent proposed services to be included in an alternative model to the data business model. Consequently, our study aims to examine all 10 services. However, during the exploratory factor analysis (EFA), &#x201C;Usage not Prolonged&#x201D; and &#x201C;Mental Health Issues Minimization&#x201D; items were removed due to low loading and cross-loadings into factors in the Arab sample, indicating a potential cultural difference in the perception and expectation of SNS use and its effects on wellbeing. Therefore, we investigated &#x201C;Usage not Prolonged&#x201D; and &#x201C;Mental Health Issues Minimization&#x201D; items separately as two individual variables.</p>
</sec>
<sec id="sec15">
<label>3.3</label>
<title>The extent to which people are willing to pay for digital wellbeing</title>
<p>As shown in <xref ref-type="table" rid="tab4">Table 4</xref>, participants were categorized based on their aggregated willingness to pay (WTP) scores for the 10 proposed digital wellbeing services on SNS. Participants with scores ranging from 1.00 to 2.50 were categorized as unwilling to pay, those with scores from 2.51 to 3.50 were considered neutral, and participants with scores from 3.51 to 5.00 were grouped as willing to pay. An alternative method to derive percentage values from this measure has been provided by <xref ref-type="bibr" rid="ref9001">Kannen et al. (2024)</xref>, which would result in different numbers.</p>
<table-wrap position="float" id="tab4">
<label>Table 4</label>
<caption>
<p>Proportion of participants who were/were not willing to pay on average for digital wellbeing services.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top">European sample</th>
<th align="center" valign="top">Arab sample</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Not willing to pay</td>
<td align="center" valign="middle">39%</td>
<td align="center" valign="middle">34%</td>
</tr>
<tr>
<td align="left" valign="middle">Neutral</td>
<td align="center" valign="middle">37%</td>
<td align="center" valign="middle">36%</td>
</tr>
<tr>
<td align="left" valign="middle">Willing to pay</td>
<td align="center" valign="middle">24%</td>
<td align="center" valign="middle">30%</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>The proportion of participants was calculated based on the average scores of the 10 items. Not willing to pay on average (scores 1.00&#x2013;2.50), neutral (scores 2.51&#x2013;3.50), and willing to pay (scores 3.51&#x2013;5.00). Please note that different numbers would be observed when following an alternative rule to calculate percentages as reported in <xref ref-type="bibr" rid="ref9001">Kannen et al. (2024)</xref>.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec16">
<label>3.4</label>
<title>Association between willingness to pay for digital wellbeing and sociodemographic variables</title>
<p>An independent-samples <italic>t</italic>-test was used to examine gender differences in willingness to pay (WTP) for digital wellbeing services. The assumption of normality was not violated, as assessed by skews and kurtosis and by Q-Q plot. The homogeneity of variance was assessed by Levene&#x2019;s test, which showed no significant differences in all cases. <xref ref-type="table" rid="tab5">Table 5</xref> shows the results of the <italic>t</italic>-test, specifically examining gender differences for each of the European and Arab samples.</p>
<table-wrap position="float" id="tab5">
<label>Table 5</label>
<caption>
<p>Willingness to pay for digital wellbeing with gender differences.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top" colspan="3">European<break/>(<italic>N</italic> =&#x2009;262)</th>
<th align="center" valign="top" colspan="3">Arab<break/>(<italic>N</italic> =&#x2009;251)</th>
</tr>
<tr>
<th/>
<th align="center" valign="top">Male<break/>(151)</th>
<th align="center" valign="top">Female<break/>(111)</th>
<th align="center" valign="top">Gender differences</th>
<th align="center" valign="top">Male<break/>(152)</th>
<th align="center" valign="top">Female<break/>(99)</th>
<th align="center" valign="top">Gender differences</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Total willingness to pay</td>
<td align="center" valign="middle">27.24<break/>(10.14)</td>
<td align="center" valign="middle">28.47<break/>(10.43)</td>
<td align="center" valign="middle"><italic>t</italic>(260)&#x2009;=&#x2009;&#x2212;0.96, <italic>p</italic> =&#x2009;0.339, <italic>d</italic> =&#x2009;&#x2212;0.12</td>
<td align="center" valign="middle">30.09<break/>(10.76)</td>
<td align="center" valign="middle">28.72<break/>(10.85)</td>
<td align="center" valign="middle"><italic>t</italic>(249)&#x2009;=&#x2009;0.98, <italic>p</italic> =&#x2009;0.327, <italic>d</italic> =&#x2009;0.13</td>
</tr>
<tr>
<td align="left" valign="middle">Safeguarding data privacy</td>
<td align="center" valign="middle">2.73<break/>(1.24)</td>
<td align="center" valign="middle">2.80<break/>(1.20)</td>
<td align="center" valign="middle"><italic>t</italic>(260)&#x2009;=&#x2009;&#x2212;0.45, <italic>p</italic> =&#x2009;0.652, <italic>d</italic> =&#x2009;&#x2212;0.06</td>
<td align="center" valign="middle">3.00<break/>(1.28)</td>
<td align="center" valign="middle">2.63<break/>(1.33)</td>
<td align="center" valign="bottom"><italic>t</italic>(249)&#x2009;=&#x2009;2.21, <italic>p</italic> =&#x2009;0.028, <italic>d</italic> =&#x2009;0.29</td>
</tr>
<tr>
<td align="left" valign="middle">Reducing harmful content</td>
<td align="center" valign="middle">2.95<break/>(1.36)</td>
<td align="center" valign="middle">3.16 (1.37)</td>
<td align="center" valign="middle"><italic>t</italic>(260)&#x2009;=&#x2009;&#x2212;1.26, <italic>p</italic> =&#x2009;0.209, <italic>d</italic> =&#x2009;&#x2212;0.16</td>
<td align="center" valign="middle">3.20<break/>(1.30)</td>
<td align="center" valign="middle">2.99<break/>(1.31)</td>
<td align="center" valign="bottom"><italic>t</italic>(249)&#x2009;=&#x2009;1.26, <italic>p</italic> =&#x2009;0.208, <italic>d</italic> =&#x2009;0.16</td>
</tr>
<tr>
<td align="left" valign="middle">Monitoring SN usage</td>
<td align="center" valign="middle">2.42<break/>(1.19)</td>
<td align="center" valign="middle">2.44 (1.14)</td>
<td align="center" valign="middle"><italic>t</italic>(260)&#x2009;=&#x2009;&#x2212;0.11, <italic>p</italic> =&#x2009;0.911, <italic>d</italic> =&#x2009;&#x2212;0.01</td>
<td align="center" valign="middle">2.68<break/>(1.22)</td>
<td align="center" valign="middle">2.62<break/>(1.24)</td>
<td align="center" valign="bottom"><italic>t</italic>(249)&#x2009;=&#x2009;0.36, <italic>p</italic> =&#x2009;0.723, <italic>d</italic> =&#x2009;0.05</td>
</tr>
<tr>
<td align="left" valign="middle">Fostering ethical and personal growth</td>
<td align="center" valign="middle">2.79<break/>(1.20)</td>
<td align="center" valign="middle">2.85 (1.15)</td>
<td align="center" valign="middle"><italic>t</italic>(260)&#x2009;=&#x2009;&#x2212;0.45, <italic>p</italic> =&#x2009;0.651, <italic>d</italic> =&#x2009;&#x2212;0.06</td>
<td align="center" valign="middle">3.33<break/>(1.23)</td>
<td align="center" valign="middle">3.30<break/>(1.24)</td>
<td align="center" valign="bottom"><italic>t</italic>(249)&#x2009;=&#x2009;0.20, <italic>p</italic> =&#x2009;0.846, <italic>d</italic> =&#x2009;0.03</td>
</tr>
<tr>
<td align="left" valign="middle">Usage not prolonged</td>
<td align="center" valign="middle">2.65<break/>(1.37)</td>
<td align="center" valign="middle">2.80<break/>(1.35)</td>
<td align="center" valign="middle"><italic>t</italic>(260)&#x2009;=&#x2009;&#x2212;0.90, <italic>p</italic> =&#x2009;0.369, <italic>d</italic> =&#x2009;&#x2212;0.11</td>
<td align="center" valign="middle">2.74<break/>(1.34)</td>
<td align="center" valign="middle">2.67<break/>(1.29)</td>
<td align="center" valign="bottom"><italic>t</italic>(249)&#x2009;=&#x2009;0.45, <italic>p</italic> =&#x2009;0.652, <italic>d</italic> =&#x2009;0.06</td>
</tr>
<tr>
<td align="left" valign="middle">Mental health issues minimization</td>
<td align="center" valign="middle">2.83<break/>(1.36)</td>
<td align="center" valign="middle">3.17 (1.36)</td>
<td align="center" valign="middle"><italic>t</italic>(260)&#x2009;=&#x2009;&#x2212;2.02, <italic>p</italic> =&#x2009;0.044, <italic>d</italic> =&#x2009;&#x2212;0.25</td>
<td align="center" valign="middle">2.94<break/>(1.36)</td>
<td align="center" valign="middle">2.99 (1.43)</td>
<td align="center" valign="bottom"><italic>t</italic>(249)&#x2009;=&#x2009;&#x2212;0.28, <italic>p</italic> =&#x2009;0.784, <italic>d</italic> =&#x2009;&#x2212;0.04</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>The results indicated that, in the European sample, females (<italic>M</italic> =&#x2009;3.17, SD&#x2009;=&#x2009;1.36) demonstrated a significantly higher WTP for the item &#x201C;Mental Health Issues Minimization&#x201D; than males (<italic>M</italic> =&#x2009;2.83, SD&#x2009;=&#x2009;1.36). In the Arab sample, a gender difference was found significant in the WTP factor for &#x201C;Safeguarding data privacy,&#x201D; where the mean value for the male group (<italic>M</italic> =&#x2009;3.00, SD&#x2009;=&#x2009;1.28) was higher than that of the female group (<italic>M</italic> =&#x2009;2.63, SD&#x2009;=&#x2009;1.33). For the other items, no significant gender differences were observed.</p>
<p>Pearson&#x2019;s correlation was performed to analyze the associations between willingness to pay (WTP) and the demographic factors of age, and education level (operated as Point-biserial correlation). Due to its ordinal nature, Spearman&#x2019;s correlation was used for financial status. The results, presented in <xref ref-type="table" rid="tab6">Table 6</xref>, revealed intriguing patterns. In the European context, a significant negative correlation was observed between age and the WTP item &#x201C;Mental Health Issues Minimization&#x201D; (<italic>r</italic> =&#x2009;&#x2212;0.13, <italic>p</italic> =&#x2009;0.041). Additionally, a significant positive correlation was observed between education level and the WTP item &#x201C;Usage not Prolonged&#x201D; (<italic>r</italic> =&#x2009;0.20, <italic>p</italic> =&#x2009;0.001). On the other hand, in the Arab sample, no significant association were found between WTP and the three factors of age, education, and financial status.</p>
<table-wrap position="float" id="tab6">
<label>Table 6</label>
<caption>
<p>Pearson&#x2019;s correlations of WTP variables, age, education level, and financial status.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top">WTP total score</th>
<th align="center" valign="top">Safeguarding data privacy</th>
<th align="center" valign="top">Reducing harmful content</th>
<th align="center" valign="top">Monitoring SN usage</th>
<th align="center" valign="top">Fostering ethical and personal growth</th>
<th align="center" valign="top">Usage not prolonged</th>
<th align="center" valign="top">Mental health issues minimization</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle" colspan="8">European sample</td>
</tr>
<tr>
<td align="left" valign="middle">Age</td>
<td align="center" valign="middle">&#x2212;0.01</td>
<td align="center" valign="middle">0.01</td>
<td align="center" valign="middle">&#x2212;0.02</td>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">&#x2212;0.02</td>
<td align="center" valign="middle">0.06</td>
<td align="center" valign="middle">&#x2212;0.13&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Education (Basic) <sup>(1)</sup></td>
<td align="center" valign="middle">0.07</td>
<td align="center" valign="middle">0.06</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.05</td>
<td align="center" valign="middle">0.07</td>
<td align="center" valign="middle">0.20&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.03</td>
</tr>
<tr>
<td align="left" valign="middle">Financial status <sup>(2)</sup></td>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">0.04</td>
<td align="center" valign="middle">&#x2212;0.01</td>
<td align="center" valign="middle">0.06</td>
<td align="center" valign="middle">0.01</td>
<td align="center" valign="middle">0.06</td>
<td align="center" valign="middle">&#x2212;0.03</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="8">Arab sample</td>
</tr>
<tr>
<td align="left" valign="middle">Age</td>
<td align="center" valign="middle">&#x2212;0.02</td>
<td align="center" valign="middle">0.01</td>
<td align="center" valign="middle">&#x2212;0.04</td>
<td align="center" valign="middle">0.01</td>
<td align="center" valign="middle">0.04</td>
<td align="center" valign="middle">&#x2212;0.03</td>
<td align="center" valign="middle">&#x2212;0.11</td>
</tr>
<tr>
<td align="left" valign="middle">Education (Basic) <sup>(1)</sup></td>
<td align="center" valign="middle">0.05</td>
<td align="center" valign="middle">0.08</td>
<td align="center" valign="middle">0.03</td>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">0.08</td>
<td align="center" valign="middle">0.02</td>
</tr>
<tr>
<td align="left" valign="middle">Financial status <sup>(2)</sup></td>
<td align="center" valign="middle">0.10</td>
<td align="center" valign="middle">0.06</td>
<td align="center" valign="middle">0.11</td>
<td align="center" valign="middle">0.09</td>
<td align="center" valign="middle">0.10</td>
<td align="center" valign="middle">0.09</td>
<td align="center" valign="middle">0.10</td>
</tr>
<tr>
<td colspan="8"/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>&#x002A;<italic>p</italic> &#x003C;&#x2009;0.05, &#x002A;&#x002A;<italic>p</italic> &#x003C;&#x2009;0.01, &#x002A;&#x002A;&#x002A;<italic>p</italic> &#x003C;&#x2009;0.001. <sup>(1)</sup>Equivalent to Point-biserial correlation; <sup>(2)</sup>Spearman&#x2019;s correlation.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec17">
<label>3.5</label>
<title>Do social networks use disorder and psychological factors predict willingness to pay for digital wellbeing?</title>
<p>To gain a better understanding of the factors potentially influencing willingness to pay (WTP) for digital wellbeing services, we conducted multiple regression analyses for each sample. The purpose of the analysis was to examine the extent to which the independent variables, including gender, age, education level, social networks use disorder, personality traits and need for cognition, explain the variation in the dependent variable, the total WTP score. Furthermore, a series of multiple linear regression analyses were performed for each sample to investigate the impact of the variables of interest on WTP for &#x201C;Monitoring SN Usage&#x201D; and &#x201C;Usage not Prolonged.&#x201D;</p>
<p>The multiple linear regression assumptions were checked and verified. The data did not exhibit outliers deviated from the model as the Standardized Residuals did not exceed &#x2212;3.29 and +3.29. Residuals were independent as assessed by a Durbin-Watson statistic values between 1 and 3. There was no evidence of multicollinearity, as assessed by VIF values lower than 5 (for all predictors, values were less than 2) and by tolerance values greater than 0.1. Pearson&#x2019;s correlation was also performed and confirmed no multicollinearity among the variables, as shown in <xref ref-type="table" rid="tab7">Table 7</xref>. The assumption of normality was met, as assessed by skewness and kurtosis values between &#x2212;2 and 2. Additionally, a Q-Q plot for residuals indicated that most data points were on or close to the line, supporting normality of the data.</p>
<table-wrap position="float" id="tab7">
<label>Table 7</label>
<caption>
<p>Pearson&#x2019;s correlation between WTP for digital wellbeing services, social networks use disorder, need for cognition, and personality traits.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top">WTP total score</th>
<th align="center" valign="top">Safeguarding data privacy</th>
<th align="center" valign="top">Reducing harmful content</th>
<th align="center" valign="top">Monitoring SN usage</th>
<th align="center" valign="top">Fostering ethical and personal growth</th>
<th align="center" valign="top">Usage not prolonged</th>
<th align="center" valign="top">Mental health issues minimization</th>
<th align="center" valign="top">SNUD</th>
<th align="center" valign="top">Need for cognition</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle" colspan="10">European sample</td>
</tr>
<tr>
<td align="left" valign="middle">SNUD <sup>(1)</sup></td>
<td align="center" valign="middle">0.25&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.13&#x002A;</td>
<td align="center" valign="middle">0.18&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.28&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.15&#x002A;</td>
<td align="center" valign="middle">0.29&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.25&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">&#x2014;</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Need for cognition</td>
<td align="center" valign="middle">0.21&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.16&#x002A;</td>
<td align="center" valign="middle">0.18&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.15&#x002A;</td>
<td align="center" valign="middle">0.19&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.25&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.11</td>
<td align="center" valign="middle">&#x2212;0.13&#x002A;</td>
<td align="center" valign="middle">&#x2014;</td>
</tr>
<tr>
<td align="left" valign="middle">Extraversion</td>
<td align="center" valign="middle">0.06</td>
<td align="center" valign="middle">0.05</td>
<td align="center" valign="middle">0.09</td>
<td align="center" valign="middle">0.03</td>
<td align="center" valign="middle">0.04</td>
<td align="center" valign="middle">0.05</td>
<td align="center" valign="middle">0.03</td>
<td align="center" valign="middle">0.03</td>
<td align="center" valign="middle">0.14&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Agreeableness</td>
<td align="center" valign="middle">0.16&#x002A;</td>
<td align="center" valign="middle">0.16&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.11</td>
<td align="center" valign="middle">0.09</td>
<td align="center" valign="middle">0.156&#x002A;</td>
<td align="center" valign="middle">0.12</td>
<td align="center" valign="middle">0.13&#x002A;</td>
<td align="center" valign="middle">0.09</td>
<td align="center" valign="middle">&#x2212;0.02</td>
</tr>
<tr>
<td align="left" valign="middle">Conscientiousness</td>
<td align="center" valign="middle">0.13&#x002A;</td>
<td align="center" valign="middle">0.13&#x002A;</td>
<td align="center" valign="middle">0.12</td>
<td align="center" valign="middle">0.13&#x002A;</td>
<td align="center" valign="middle">0.13&#x002A;</td>
<td align="center" valign="middle">0.07</td>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">&#x2212;0.13&#x002A;</td>
<td align="center" valign="middle">0.37&#x002A;&#x002A;&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Neuroticism</td>
<td align="center" valign="middle">0.07</td>
<td align="center" valign="middle">0.04</td>
<td align="center" valign="middle">0.03</td>
<td align="center" valign="middle">0.08</td>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">0.10</td>
<td align="center" valign="middle">0.14&#x002A;</td>
<td align="center" valign="middle">0.23&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.27&#x002A;&#x002A;&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Openness</td>
<td align="center" valign="middle">0.05</td>
<td align="center" valign="middle">0.01</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.09</td>
<td align="center" valign="middle">0.07</td>
<td align="center" valign="middle">0.07</td>
<td align="center" valign="middle">0.04</td>
<td align="center" valign="middle">0.034</td>
<td align="center" valign="middle">0.15&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle" colspan="10">Arab sample</td>
</tr>
<tr>
<td align="left" valign="middle">SNUD <sup>(1)</sup></td>
<td align="center" valign="middle">0.32&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.21&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.25&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.34&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.27&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.25&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.29&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">&#x2014;</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Need for cognition</td>
<td align="center" valign="middle">0.26&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.26&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.23&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.21&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.20&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.18&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.22&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.09</td>
<td align="center" valign="middle">&#x2014;</td>
</tr>
<tr>
<td align="left" valign="middle">Extraversion</td>
<td align="center" valign="middle">0.04</td>
<td align="center" valign="middle">0.08</td>
<td align="center" valign="middle">0.06</td>
<td align="center" valign="middle">&#x2212;0.05</td>
<td align="center" valign="middle">0.03</td>
<td align="center" valign="middle">0.05</td>
<td align="center" valign="middle">0.03</td>
<td align="center" valign="middle">0.06</td>
<td align="center" valign="middle">0.00</td>
</tr>
<tr>
<td align="left" valign="middle">Agreeableness</td>
<td align="center" valign="middle">0.11</td>
<td align="center" valign="middle">0.09</td>
<td align="center" valign="middle">0.09</td>
<td align="center" valign="middle">0.09</td>
<td align="center" valign="middle">0.12</td>
<td align="center" valign="middle">0.10</td>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">0.01</td>
<td align="center" valign="middle">0.01</td>
</tr>
<tr>
<td align="left" valign="middle">Conscientiousness</td>
<td align="center" valign="middle">0.13&#x002A;</td>
<td align="center" valign="middle">0.10</td>
<td align="center" valign="middle">0.13&#x002A;</td>
<td align="center" valign="middle">0.12</td>
<td align="center" valign="middle">0.14&#x002A;</td>
<td align="center" valign="middle">0.04</td>
<td align="center" valign="middle">0.05</td>
<td align="center" valign="middle">&#x2212;0.21&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">0.22&#x002A;&#x002A;&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Neuroticism</td>
<td align="center" valign="middle">&#x2212;0.07</td>
<td align="center" valign="middle">&#x2212;0.07</td>
<td align="center" valign="middle">&#x2212;0.05</td>
<td align="center" valign="middle">&#x2212;0.05</td>
<td align="center" valign="middle">&#x2212;0.09</td>
<td align="center" valign="middle">&#x2212;0.03</td>
<td align="center" valign="middle">&#x2212;0.02</td>
<td align="center" valign="middle">0.30&#x002A;&#x002A;&#x002A;</td>
<td align="center" valign="middle">&#x2212;0.23&#x002A;&#x002A;&#x002A;</td>
</tr>
<tr>
<td align="left" valign="middle">Openness</td>
<td align="center" valign="middle">0.05</td>
<td align="center" valign="middle">0.05</td>
<td align="center" valign="middle">0.03</td>
<td align="center" valign="middle">0.03</td>
<td align="center" valign="middle">0.06</td>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">0.05</td>
<td align="center" valign="middle">0.01</td>
<td align="center" valign="middle">0.19&#x002A;&#x002A;</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>&#x002A;<italic>p</italic> &#x003C;&#x2009;0.05, &#x002A;&#x002A;<italic>p</italic> &#x003C;&#x2009;0.01, &#x002A;&#x002A;&#x002A;<italic>p</italic> &#x003C;&#x2009;0.001. <sup>(1)</sup> SNUD: Social networks use disorder.</p>
</table-wrap-foot>
</table-wrap>
<p>The initial multiple regression models demonstrated a statistically significant prediction of the total WTP score in each sample [European: <italic>F</italic>(10, 251)&#x2009;=&#x2009;4.79<italic>, p</italic> &#x003C;&#x2009;0.001<italic>, adj. R<sup>2</sup></italic> =&#x2009;0.13; Arab: <italic>F</italic>(10, 240)&#x2009;=&#x2009;6.54<italic>, p</italic> &#x003C;&#x2009;0.001<italic>, adj. R<sup>2</sup> =</italic> 0.18]. As depicted in <xref ref-type="table" rid="tab8">Table 8</xref>, the results revealed that social networks use disorder (<italic>&#xA7B5;<sub>European</sub> =</italic> 0.25, <italic>p</italic> &#x003C;&#x2009;0.001; <italic>&#xA7B5;<sub>Arab</sub></italic> =&#x2009;0.38, <italic>p</italic> &#x003C;&#x2009;0.001), and need for cognition (<italic>&#xA7B5;<sub>European</sub></italic> =&#x2009;0.24, <italic>p</italic> &#x003C;&#x2009;0.001; <italic>&#xA7B5;<sub>Arab</sub></italic> =&#x2009;0.25, <italic>p</italic> &#x003C;&#x2009;0.001) were significant and positive predictors of the total WTP score in each sample. However, differences were observed in the other predictors between the samples. In the European sample, agreeableness was a significant positive predictor (<italic>&#x03B2;</italic> =&#x2009;0.15, <italic>p&#x2009;=&#x2009;0</italic>.014), and neuroticism was marginally significant (<italic>&#x03B2;</italic> =&#x2009;0.13, <italic>p</italic> =&#x2009;0.068). Conversely, in the Arab sample, conscientiousness was marginally significant (<italic>&#x03B2;</italic> =&#x2009;0.11, <italic>p</italic> =&#x2009;0.089).</p>
<table-wrap position="float" id="tab8">
<label>Table 8</label>
<caption>
<p>Multiple regression analysis for predicting total willingness to pay for digital wellbeing services.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top" colspan="4">European sample</th>
<th align="center" valign="top" colspan="4">Arab sample</th>
</tr>
<tr>
<th align="left" valign="top">WTP total score</th>
<th align="center" valign="top"><italic>R</italic><sup>2</sup></th>
<th align="center" valign="top">Adjusted <italic>R</italic><sup>2</sup></th>
<th align="center" valign="top"><italic>F</italic></th>
<th align="center" valign="top"><italic>p</italic></th>
<th align="center" valign="top"><italic>R</italic><sup>2</sup></th>
<th align="center" valign="top">Adjusted <italic>R</italic><sup>2</sup></th>
<th align="center" valign="top"><italic>F</italic></th>
<th align="center" valign="top"><italic>p</italic></th>
</tr>
</thead>
<tbody>
<tr>
<td/>
<td align="center" valign="middle">0.16</td>
<td align="center" valign="middle">0.13</td>
<td align="center" valign="middle">4.79</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">0.21</td>
<td align="center" valign="middle">0.18</td>
<td align="center" valign="middle">6.54</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td align="left" valign="middle">Predictors</td>
<td align="center" valign="middle"><italic>&#x03B2;</italic></td>
<td align="center" valign="middle"><italic>t</italic></td>
<td align="center" valign="middle"><italic>p</italic></td>
<td/>
<td align="center" valign="middle"><italic>&#x03B2;</italic></td>
<td align="center" valign="middle"><italic>t</italic></td>
<td align="center" valign="middle"><italic>p</italic></td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Gender (Male)</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.04</td>
<td align="center" valign="middle">0.965</td>
<td/>
<td align="center" valign="middle">&#x2212;0.02</td>
<td align="center" valign="middle">&#x2212;0.25</td>
<td align="center" valign="middle">0.803</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Education (Basic)</td>
<td align="center" valign="middle">0.05</td>
<td align="center" valign="middle">0.85</td>
<td align="center" valign="middle">0.399</td>
<td/>
<td align="center" valign="middle">0.03</td>
<td align="center" valign="middle">0.42</td>
<td align="center" valign="middle">0.676</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Age</td>
<td align="center" valign="middle">&#x2212;0.02</td>
<td align="center" valign="middle">&#x2212;0.33</td>
<td align="center" valign="middle">0.745</td>
<td/>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">&#x2212;0.02</td>
<td align="center" valign="middle">0.984</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">SNUD <sup>(1)</sup></td>
<td align="center" valign="middle">0.25</td>
<td align="center" valign="middle">4.13</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td/>
<td align="center" valign="middle">0.38</td>
<td align="center" valign="middle">6.27</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Need for cognition</td>
<td align="center" valign="middle">0.24</td>
<td align="center" valign="middle">3.66</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td/>
<td align="center" valign="middle">0.25</td>
<td align="center" valign="middle">3.77</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Extraversion</td>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">0.24</td>
<td align="center" valign="middle">0.810</td>
<td/>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.07</td>
<td align="center" valign="middle">0.942</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Agreeableness</td>
<td align="center" valign="middle">0.15</td>
<td align="center" valign="middle">2.46</td>
<td align="center" valign="middle">0.014</td>
<td/>
<td align="center" valign="middle">0.07</td>
<td align="center" valign="middle">1.07</td>
<td align="center" valign="middle">0.285</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Conscientiousness</td>
<td align="center" valign="middle">0.08</td>
<td align="center" valign="middle">1.30</td>
<td align="center" valign="middle">0.194</td>
<td/>
<td align="center" valign="middle">0.11</td>
<td align="center" valign="middle">1.71</td>
<td align="center" valign="middle">0.089</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Neuroticism</td>
<td align="center" valign="middle">0.13</td>
<td align="center" valign="middle">1.83</td>
<td align="center" valign="middle">0.068</td>
<td/>
<td align="center" valign="middle">&#x2212;0.07</td>
<td align="center" valign="middle">&#x2212;1.05</td>
<td align="center" valign="middle">0.296</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Openness</td>
<td align="center" valign="middle">&#x2212;0.04</td>
<td align="center" valign="middle">&#x2212;0.63</td>
<td align="center" valign="middle">0.530</td>
<td/>
<td align="center" valign="middle">0.01</td>
<td align="center" valign="middle">0.20</td>
<td align="center" valign="middle">0.841</td>
<td/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p><sup>(1)</sup> SNUD, social networks use disorder.</p>
</table-wrap-foot>
</table-wrap>
<p>To further explore the impact of the variables of interest on the factors of WTP that are related to monitoring or preventing prolonged usage of SNS, a series of multiple linear regressions was performed, as shown in <xref ref-type="table" rid="tab9">Table 9</xref>. The initial set of multiple regression models revealed a statistically significant prediction of WTP for the &#x201C;Monitoring SN Usage&#x201D; in each of the European and Arab samples [European: <italic>F</italic>(10,250) <italic>=</italic> 4.59, <italic>p</italic> &#x003C;&#x2009;0.001, <italic>adj. R<sup>2</sup></italic> =&#x2009;0.12; Arab: <italic>F</italic>(10, 240) <italic>=</italic> 6.22<italic>, p</italic> &#x003C;&#x2009;0.001<italic>, adj. R<sup>2</sup></italic> =&#x2009;0.17]. Two significant predictors were identified: social networks use disorder (<italic>&#xA7B5;<sub>European</sub></italic> =&#x2009;0.31, <italic>p</italic> &#x003C;&#x2009;0.001; <italic>&#xA7B5;<sub>Arab</sub></italic> = <italic>0.41</italic>, <italic>p</italic> &#x003C;&#x2009;0.001) and need for cognition (<italic>&#xA7B5;<sub>European</sub></italic> =&#x2009;0.16, <italic>p</italic> =&#x2009;0.017; <italic>&#xA7B5;<sub>Arab</sub></italic> = <italic>0.20</italic>, <italic>p</italic> =&#x2009;0.002), demonstrating a positive relationship with WTP for the &#x201C;Monitoring SN Usage.&#x201D; Conscientiousness was marginally significant in each sample (<italic>&#x03B2;<sub>European</sub></italic> =&#x2009;0.12, <italic>p</italic> =&#x2009;0.063; <italic>&#x03B2;<sub>Arab</sub></italic> =&#x2009;0.12, <italic>p</italic> =&#x2009;0.079).</p>
<table-wrap position="float" id="tab9">
<label>Table 9</label>
<caption>
<p>Multiple regression analysis for predicting usage-related factors of WTP for digital wellbeing services.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top" colspan="4">European sample</th>
<th align="center" valign="top" colspan="4">Arab sample</th>
</tr>
<tr>
<th/>
<th align="center" valign="top"><italic>R</italic><sup>2</sup></th>
<th align="center" valign="top">Adjusted <italic>R</italic><sup>2</sup></th>
<th align="center" valign="top"><italic>F</italic></th>
<th align="center" valign="top"><italic>p</italic></th>
<th align="center" valign="top"><italic>R</italic><sup>2</sup></th>
<th align="center" valign="top">Adjusted <italic>R</italic><sup>2</sup></th>
<th align="center" valign="top"><italic>F</italic></th>
<th align="center" valign="top"><italic>p</italic></th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Monitoring SN usage&#x002A;</td>
<td align="center" valign="middle">0.16</td>
<td align="center" valign="middle">0.12</td>
<td align="center" valign="middle">4.59</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">0.21</td>
<td align="center" valign="middle">0.17</td>
<td align="center" valign="middle">6.22</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td/>
<td align="center" valign="middle"><italic>&#x03B2;</italic></td>
<td align="center" valign="middle"><italic>t</italic></td>
<td align="center" valign="middle"><italic>p</italic></td>
<td/>
<td align="center" valign="middle"><italic>&#x03B2;</italic></td>
<td align="center" valign="middle"><italic>t</italic></td>
<td align="center" valign="middle"><italic>p</italic></td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Gender (Male)</td>
<td align="center" valign="middle">&#x2212;0.06</td>
<td align="center" valign="middle">&#x2212;0.88</td>
<td align="center" valign="middle">0.381</td>
<td/>
<td align="center" valign="middle">0.01</td>
<td align="center" valign="middle">0.20</td>
<td align="center" valign="middle">0.840</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Education (Basic)</td>
<td align="center" valign="middle">0.05</td>
<td align="center" valign="middle">0.77</td>
<td align="center" valign="middle">0.442</td>
<td/>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">&#x2212;0.06</td>
<td align="center" valign="middle">0.950</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Age</td>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">0.33</td>
<td align="center" valign="middle">0.740</td>
<td/>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">0.32</td>
<td align="center" valign="middle">0.748</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">SNUD</td>
<td align="center" valign="middle">0.31</td>
<td align="center" valign="middle">5.13</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td/>
<td align="center" valign="middle">0.41</td>
<td align="center" valign="middle">6.65</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Need for cognition</td>
<td align="center" valign="middle">0.16</td>
<td align="center" valign="middle">2.41</td>
<td align="center" valign="middle">0.017</td>
<td/>
<td align="center" valign="middle">0.20</td>
<td align="center" valign="middle">3.07</td>
<td align="center" valign="middle">0.002</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Extraversion</td>
<td align="center" valign="middle">&#x2212;0.03</td>
<td align="center" valign="middle">&#x2212;0.40</td>
<td align="center" valign="middle">0.686</td>
<td/>
<td align="center" valign="middle">&#x2212;0.08</td>
<td align="center" valign="middle">&#x2212;1.37</td>
<td align="center" valign="middle">0.173</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Agreeableness</td>
<td align="center" valign="middle">0.08</td>
<td align="center" valign="middle">1.35</td>
<td align="center" valign="middle">0.179</td>
<td/>
<td align="center" valign="middle">0.05</td>
<td align="center" valign="middle">0.73</td>
<td align="center" valign="middle">0.467</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Conscientiousness</td>
<td align="center" valign="middle">0.12</td>
<td align="center" valign="middle">1.87</td>
<td align="center" valign="middle">0.063</td>
<td/>
<td align="center" valign="middle">0.12</td>
<td align="center" valign="middle">1.77</td>
<td align="center" valign="middle">0.079</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Neuroticism</td>
<td align="center" valign="middle">0.10</td>
<td align="center" valign="middle">1.33</td>
<td align="center" valign="middle">0.185</td>
<td/>
<td align="center" valign="middle">&#x2212;0.08</td>
<td align="center" valign="middle">&#x2212;1.27</td>
<td align="center" valign="middle">0.206</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Openness</td>
<td align="center" valign="middle">0.05</td>
<td align="center" valign="middle">0.76</td>
<td align="center" valign="middle">0.448</td>
<td/>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">0.31</td>
<td align="center" valign="middle">0.758</td>
<td/>
</tr>
<tr>
<td/>
<td align="left" valign="middle"><italic>R</italic><sup>2</sup></td>
<td align="left" valign="middle">Adjusted <italic>R</italic><sup>2</sup></td>
<td align="left" valign="middle"><italic>F</italic></td>
<td align="left" valign="middle"><italic>p</italic></td>
<td align="left" valign="middle"><italic>R</italic><sup>2</sup></td>
<td align="left" valign="middle">Adjusted <italic>R</italic><sup>2</sup></td>
<td align="left" valign="middle"><italic>F</italic></td>
<td align="left" valign="middle"><italic>p</italic></td>
</tr>
<tr>
<td align="left" valign="bottom">Usage not prolonged</td>
<td align="center" valign="middle">0.23</td>
<td align="center" valign="middle">0.20</td>
<td align="center" valign="middle">7.46</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td align="center" valign="middle">0.12</td>
<td align="center" valign="middle">0.09</td>
<td align="center" valign="middle">3.32</td>
<td align="center" valign="middle">&#x003C;0.001</td>
</tr>
<tr>
<td/>
<td align="center" valign="middle"><italic>&#x03B2;</italic></td>
<td align="center" valign="middle"><italic>t</italic></td>
<td align="center" valign="middle"><italic>p</italic></td>
<td/>
<td align="center" valign="middle"><italic>&#x03B2;</italic></td>
<td align="center" valign="middle"><italic>t</italic></td>
<td align="center" valign="middle"><italic>p</italic></td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Gender (Male)</td>
<td align="center" valign="middle">0.01</td>
<td align="center" valign="middle">0.07</td>
<td align="center" valign="middle">0.942</td>
<td/>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">0.984</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Education (Basic)</td>
<td align="center" valign="middle">0.19</td>
<td align="center" valign="middle">3.05</td>
<td align="center" valign="middle">0.003</td>
<td/>
<td align="center" valign="middle">0.08</td>
<td align="center" valign="middle">1.22</td>
<td align="center" valign="middle">0.223</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Age</td>
<td align="center" valign="middle">0.03</td>
<td align="center" valign="middle">0.55</td>
<td align="center" valign="middle">0.586</td>
<td/>
<td align="center" valign="middle">&#x2212;0.02</td>
<td align="center" valign="middle">&#x2212;0.28</td>
<td align="center" valign="middle">0.778</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">SNUD</td>
<td align="center" valign="middle">0.30</td>
<td align="center" valign="middle">5.17</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td/>
<td align="center" valign="middle">0.28</td>
<td align="center" valign="middle">4.31</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Need for cognition</td>
<td align="center" valign="middle">0.29</td>
<td align="center" valign="middle">4.53</td>
<td align="center" valign="middle">&#x003C;0.001</td>
<td/>
<td align="center" valign="middle">0.19</td>
<td align="center" valign="middle">2.75</td>
<td align="center" valign="middle">0.006</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Extraversion</td>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">0.26</td>
<td align="center" valign="middle">0.794</td>
<td/>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">0.37</td>
<td align="center" valign="middle">0.709</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Agreeableness</td>
<td align="center" valign="middle">0.11</td>
<td align="center" valign="middle">1.90</td>
<td align="center" valign="middle">0.058</td>
<td/>
<td align="center" valign="middle">0.09</td>
<td align="center" valign="middle">1.31</td>
<td align="center" valign="middle">0.192</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Conscientiousness</td>
<td align="center" valign="middle">&#x2212;0.02</td>
<td align="center" valign="middle">&#x2212;0.24</td>
<td align="center" valign="middle">0.809</td>
<td/>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">0.32</td>
<td align="center" valign="middle">0.753</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Neuroticism</td>
<td align="center" valign="middle">0.18</td>
<td align="center" valign="middle">2.66</td>
<td align="center" valign="middle">0.008</td>
<td/>
<td align="center" valign="middle">&#x2212;0.03</td>
<td align="center" valign="middle">&#x2212;0.46</td>
<td align="center" valign="middle">0.648</td>
<td/>
</tr>
<tr>
<td align="left" valign="middle">Openness</td>
<td align="center" valign="middle">&#x2212;0.01</td>
<td align="center" valign="middle">&#x2212;0.22</td>
<td align="center" valign="middle">0.829</td>
<td/>
<td align="center" valign="middle">&#x2212;0.01</td>
<td align="center" valign="middle">&#x2212;0.21</td>
<td align="center" valign="middle">0.837</td>
<td/>
</tr>
</tbody>
</table>
</table-wrap>
<p>Additional multiple regression models indicated a statistically significant prediction of the WTP for &#x201C;Usage not Prolonged&#x201D; in each of the European and Arab samples [European: <italic>F</italic>(10, 251)&#x2009;=&#x2009;7.46, <italic>p</italic> &#x003C;&#x2009;0.001, <italic>adj. R<sup>2</sup></italic> =&#x2009;0.20; Arab: <italic>F</italic>(10, 240)&#x2009;=&#x2009;3.32, <italic>p</italic> &#x003C;&#x2009;0.001, <italic>adj. R<sup>2</sup></italic> =&#x2009;0.09]. Social networks use disorder (<italic>&#x03B2;<sub>European</sub></italic> =&#x2009;0.30, <italic>p</italic> &#x003C;&#x2009;0.001; <italic>&#x03B2;<sub>Arab</sub></italic> =&#x2009;0.28, <italic>p</italic> &#x003C;&#x2009;0.001) and need for cognition (<italic>&#x03B2;<sub>European</sub></italic> =&#x2009;0.29, <italic>p</italic> &#x003C;&#x2009;0.001; <italic>&#x03B2;<sub>Arab</sub></italic> =&#x2009;0.19, <italic>p</italic> =&#x2009;0.006) were significant and positive predictors of WTP in each of the two samples. In the European sample, neuroticism (<italic>&#x03B2;<sub>European</sub></italic> =&#x2009;0.18, <italic>p</italic> =&#x2009;0.008) was a significant positive predictor, while agreeableness (<italic>&#x03B2;<sub>European</sub></italic> =&#x2009;0.11, <italic>p</italic> =&#x2009;0.058) was marginally significant. Notably, in the European sample, having a higher level of education (<italic>p</italic> =&#x2009;0.003) significantly predicted the WTP for &#x201C;Usage not Prolonged.&#x201D;</p>
</sec>
</sec>
<sec sec-type="discussion" id="sec18">
<label>4</label>
<title>Discussion</title>
<p>The aim of this study was to explore the interests and willingness to pay (WTP) of social network sites (SNS) users for services that enhance their digital wellbeing (DWB) and mitigate the negative consequences associated with their use. Specifically, the study focused on services related to data protection, combating fake news and extremisms, controlling excessive usage, minimizing mental health issues, and fostering personal growth. To ensure the robustness of our findings, we assessed our objectives in each sample: European and Arab participants.</p>
<sec id="sec19">
<label>4.1</label>
<title>Willingness to pay for digital wellbeing services</title>
<p>Our study offers important theoretical contributions to the research on user demand and interest in DWB within the context of SNS platforms. In line with our first research question, we discerned that a noticeable number of people are willing to pay for social network sites in exchange for offering enhanced digital wellbeing services. Specifically, our findings indicated a noticeable proportion of participants from both Europe (24%) and Arab (30%) samples expressing their willingness to pay (in total) for such services. Although these proportions were not high, they exceeded the willingness to pay observed in a comparative study conducted by <xref ref-type="bibr" rid="ref89">Sindermann et al. (2020)</xref>. While the increase in WTP may initially suggest an emerging trend, it is crucial to approach this interpretation with caution due to possible differences in measurements, sample characteristics, and other variables between our study and Sindermann et al.&#x2019;s. Nevertheless, it is an observation worth noting and potentially indicative of shifting attitudes towards the value of enhanced DWB services.</p>
<p>It is also noteworthy that the proportion of those not willing to pay decreased to approximately one-third, compared to Sindermann et al.&#x2019;s study, in which almost half of the participants were not willing to pay. A potential explanation for this shift could be the increasing recognition of these platforms as integral components of modern life, acknowledging not only for their benefits in various domains such as education, business, and entertainment but also for their drawbacks (<xref ref-type="bibr" rid="ref40">Greenhow et al., 2009</xref>; <xref ref-type="bibr" rid="ref38">Fusi and Feeney, 2018</xref>). Notably, during crises such as the COVID-19 pandemic, it served as an invaluable resource for effectively disseminating information and preserving social connections (<xref ref-type="bibr" rid="ref39">Goel and Gupta, 2020</xref>). However, parallel to this growing appreciation of social network sites&#x2019; benefits, awareness of its potential risks has also increased (<xref ref-type="bibr" rid="ref75">Pagoto et al., 2019</xref>). Concerns around privacy, misinformation, and effects on its excessive use on mental health, such as anxiety and depression have increased (<xref ref-type="bibr" rid="ref87">Shu et al., 2019</xref>; <xref ref-type="bibr" rid="ref51">Jain et al., 2021</xref>; <xref ref-type="bibr" rid="ref48">Huang, 2022a</xref>).</p>
<p>The growing awareness of the potential risks associated with excessive use of SNS arises from both academic studies establishing a link between problematic use of SNS and mental health issues (<xref ref-type="bibr" rid="ref10">Andreassen et al., 2016</xref>; <xref ref-type="bibr" rid="ref112">Yoon et al., 2019</xref>), and from campaigns aimed at raising awareness about the addictive nature of SNS and their potential impact on mental health (<xref ref-type="bibr" rid="ref82">Sedek, 2021</xref>). Furthermore, initiatives by public health organizations and SNS themselves, such as &#x2018;digital wellbeing&#x2019; features that encourage users to monitor and manage their time spent on these platforms, have increased the discourse around mindful SNS use (<xref ref-type="bibr" rid="ref95">Svensson, 2019</xref>). This heightened awareness may trigger the increased interest in DWB services, aimed at enabling a more mindful, efficient, and healthier interaction with these platforms. It also suggests users&#x2019; demands for proactive approaches to safeguard their wellbeing while continuing to leverage the benefits of social networking.</p>
</sec>
<sec id="sec20">
<label>4.2</label>
<title>Sociodemographic variables and willingness to pay for digital wellbeing</title>
<p>The second aim of our study was to explore sociodemographic differences in the willingness to pay for DWB services, specifically examining gender, age, education level and financial status in European and Arab samples. Findings from the European sample revealed gender differences in WTP, with females expressing a higher WTP for services related to &#x201C;Mental Health Issues Minimization&#x201D; compared to the European males. This finding suggests an increased concern amongst European females regarding the potential mental health implications associated with SNS use. One plausible explanation for this finding could be linked to findings in the literature that indicate females are more likely to be susceptible to negative consequences associated with SNS use (<xref ref-type="bibr" rid="ref14">Booker et al., 2018</xref>), as they tend to use SNS more frequently and excessively (<xref ref-type="bibr" rid="ref99">Twenge and Martin, 2020</xref>). Furthermore, previous studies suggest that females are more proactive in seeking mental health services (<xref ref-type="bibr" rid="ref83">Seidler et al., 2016</xref>). As a result, females may exhibit an increased demand for proactive services aimed to mitigate potential mental health risks associated with the use of social networking, at least in Europe.</p>
<p>However, in the Arab sample, a gender difference emerged in the WTP for services related to &#x201C;Safeguarding data privacy,&#x201D; with male participants demonstrating a higher WTP. This suggests a higher sensitivity or awareness about data privacy issues among Arab males. One potential explanation for this observation could be that women are more likely to trust others and less likely to perceive themselves as vulnerable, consequently reducing their perceived need for data protection services (<xref ref-type="bibr" rid="ref98">Thompson, 2018</xref>). Nonetheless, the literature on privacy concerns between genders is inconsistent; while some studies reported higher privacy concerns among females (<xref ref-type="bibr" rid="ref101">Van der Schyff et al., 2020</xref>), others found no gender differences (<xref ref-type="bibr" rid="ref71">Oomen and Leenes, 2008</xref>). Privacy is a sensitive issue among Arabs, irrespective of gender (<xref ref-type="bibr" rid="ref2">Abokhodair et al., 2017</xref>). However, the conventional gender norms within Arab societies expect men to take more responsibility for ensuring protection, which is reflected in the digital context (<xref ref-type="bibr" rid="ref2">Abokhodair et al., 2017</xref>). In this context, within our sample, men demonstrated a higher interest in privacy protection services. It is also noteworthy that in our data, males reported a higher financial status, which might have influenced their greater WTP for privacy protection services. Despite these preliminary interpretations, the factors underlying this gender difference in WTP for data privacy services within the Arab context call for further investigation.</p>
<p>In examining associations of age, education level and financial status with the WTP for DWB services, it was observed that majority of our correlation findings did not reach statistical significance, indicating generally weak or non-existent associations with these sociodemographic variables. However, a few noteworthy relationships emerged. Specifically, there was a significant and inverse relationship between age and the WTP for &#x201C;Mental Health Issues Minimization&#x201D; within the European sample. This finding suggests that younger Europeans may prioritize the importance of mitigating the mental health implications associated with the use of SNS. Our findings align with those from previous studies, indicating that younger individuals are more likely to express concerns about potential mental health effects from social networking use (<xref ref-type="bibr" rid="ref69">O&#x2019;Reilly et al., 2018</xref>). Previous studies also showed significant association between SNS use and depression, anxiety, and psychological stress among adolescent and young adults (<xref ref-type="bibr" rid="ref62">McCrae et al., 2017</xref>).</p>
<p>Correlation analyses also indicated an association between higher levels of education and higher willingness to pay for &#x201C;Usage not Prolonged&#x201D; among the European participants. Prior research suggested that those with higher levels of education and higher income tend to use SNS more (<xref ref-type="bibr" rid="ref47">Hruska and Maresova, 2020</xref>). The present study also revealed a significant positive association between education level and financial status. Given these observations, it can be suggested that individuals with higher levels of education are potentially more aware of the adverse effects of prolonged social networking use and have greater financial means. Consequently, these individuals may demonstrate a higher interest in investing in services that support them in effectively managing their use of SNS. Moreover, previous studies demonstrated that individuals with higher education levels utilize SNS more for information-seeking or work-related purposes (<xref ref-type="bibr" rid="ref102">Van Deursen et al., 2015</xref>). While these findings suggest a possibility that those with higher education can better manage their use of social networks for essential purposes, our study suggests that they might also show a higher interest in services that provide supportive environments to prevent potential risk of prolonged use. However, given that many of our correlation results were not statistically significant, these interpretations should be considered exploratory and require further research to substantiate these potential connections.</p>
<p>For the European sample, the lack of gender and age difference in attitudes towards data privacy may reflect a shared understanding and awareness of the importance of personal data security. This could be a result of widespread emphasis on data protection in European legislation, such as the General Data Protection Regulation (GDPR) (<xref ref-type="bibr" rid="ref105">Voigt and Von dem Bussche, 2017</xref>). On the other hand, for the Arab sample, the lack of significant gender and age differences in the willingness to pay for &#x201C;Mental Health Issues Minimization&#x201D; might could be related to the growing recognition of the importance of mental wellbeing across the Arab population in recent years (<xref ref-type="bibr" rid="ref113">Zeinoun et al., 2020</xref>).</p>
</sec>
<sec id="sec21">
<label>4.3</label>
<title>Psychological factors and willingness to pay for digital wellbeing</title>
<p>The results of multiple regression analysis showed that social networks use disorder (SNUD) measured by the Social Media Disorder scale significantly and positively predicted the total WTP for DWB services in each of the European and Arab samples. It suggests that individuals exhibiting higher levels of SNUD are more likely to show higher WTP for DWB services. This result could be explained by the understanding that individuals experiencing SNUD are likely to recognize their problematic behavior and, as a result, demonstrate a higher interest in digital wellbeing services. A previous study investigating 41 digital wellbeing tools available on Google Play found that many users perceived their excessive usage as problematic and used such tools to mainly mitigate their usage habits of the smartphone which were mainly triggered by apps such as social media (<xref ref-type="bibr" rid="ref54">Ko et al., 2015</xref>). However, there is still no evidence of the effectiveness of available digital wellbeing tools, which mainly provide self-monitoring and self-regulation features. The findings of the current study might suggest a demand for a broader set of features in digital wellbeing services, going beyond self-monitoring and regulation.</p>
<p>Furthermore, users with higher SNUD are often associated with excessive use of SNS, which makes them more exposed to other risks on SNS, such as susceptibility to fake news and privacy-related issues. For instance, a study conducted across six countries found that heavy social media users were generally more willing to engage with and disseminate misinformation online (<xref ref-type="bibr" rid="ref66">Morosoli et al., 2022</xref>). Additionally, spending more time on SNS makes users feel overwhelmed with the amount of shared information and feeling out of control, and therefore, less motivated to check its accuracy (<xref ref-type="bibr" rid="ref111">Xu et al., 2022</xref>). In recent years, the spread of misinformation on social networking has become a major concern. It spreads quickly, potentially leading to negative effects on different aspects such as political, economic, and wellbeing related issues (<xref ref-type="bibr" rid="ref106">Vosoughi et al., 2018</xref>). As a result, those who spend excessive time on SNS, potentially with a higher level of SNUD, may be aware of the potential risks of social networking and demand all potential services that could help ensure healthier and more balanced usage of SNS.</p>
<p>The regression analyses also revealed that NFC significantly and positively predicted the total WTP for DWB services, WTP for &#x201C;Monitoring Usage,&#x201D; and WTP for &#x201C;Usage not Prolonged,&#x201D; in each of the European and Arab samples. These findings suggest that individuals with high NFC, a tendency for intellectual information and problem-solving, are more likely to show an interest and WTP for DWB services. This could be explained through Social Cognitive Theory (SCT), positing that individuals with higher NFC are motivated to seek and process information, observe behavior, and engage in cognitive activities to make informed decisions (<xref ref-type="bibr" rid="ref11">Bandura, 1986</xref>). These individuals are more likely to be motivated by the pursuit of positive outcomes (<xref ref-type="bibr" rid="ref70">Oiknine et al., 2021</xref>). When considering DWB services, these individuals are likely to think critically into understanding the implications of using such services and appreciate the value and benefits of these services; they could recognize how such services could enhance their online experiences, such as personal growth opportunities, safeguard mental health, and ensuring accuracy of online information. Another possible explanation could be their awareness of the potential risks associated with SNS use. Research has shown that those high in NFC are keen on seeking information online (<xref ref-type="bibr" rid="ref27">Curseu, 2011</xref>) and that extensive time spent on SNS consuming news can lead to cognitive fatigue (<xref ref-type="bibr" rid="ref57">Lee et al., 2016</xref>; <xref ref-type="bibr" rid="ref114">Zheng and Ling, 2021</xref>). In the European and Arab samples, the consistent associations between SNUD, NFC, and total WTP for DWB services suggest the potential for these trends to generalize to different cultural contexts.</p>
<p>Regarding personality traits, the regression model for the European sample revealed that agreeableness significantly positively correlated with the WTP for DWB services. These findings are consistent with previous research (<xref ref-type="bibr" rid="ref89">Sindermann et al., 2020</xref>) and suggest that individuals with higher agreeableness are more interested in DWB services. Agreeable individuals are socially oriented and tend to be cooperative, friendly, trusting, and striving for harmony, a characteristic that extends to their use of SNS platforms (<xref ref-type="bibr" rid="ref84">Seidman, 2013</xref>). For example, previous studies revealed that highly agreeable people tend to have higher privacy concerns and be protective of their own and other&#x2019;s private information in social media (<xref ref-type="bibr" rid="ref73">Osatuyi, 2015</xref>).</p>
<p>Similarly, in the European context, the findings showed that high-neuroticism individuals indicated a willingness to pay for &#x201C;Usage not Prolonged&#x201D; services, potentially due to their sensitivity to the adverse impacts of excessive social networking use (<xref ref-type="bibr" rid="ref1">Abbasi and Drouin, 2019</xref>). However, these individuals might be less inclined towards proactive &#x201C;Monitoring Usage&#x201D; services due to potential anxiety triggers (<xref ref-type="bibr" rid="ref96">Tehrani and Yamini, 2020</xref>). Moreover, in each of the European and Arab samples, conscientious individuals, characterized by their self-discipline and independence (<xref ref-type="bibr" rid="ref25">Costa and McCrae, 1995</xref>), showed a preference for services enabling the monitoring and control of social networking use, consistent with previous studies that associated conscientiousness with more frequent engagement with self-tracking apps (<xref ref-type="bibr" rid="ref58">Maltseva and Lutz, 2018</xref>).</p>
</sec>
<sec id="sec22">
<label>4.4</label>
<title>Study implications and limitations</title>
<p>The findings of this study have important implications for promoting DWB on SNS. By assessing people&#x2019;s interests and preferences for DWB services, as well as the factors influencing these interests in European and Arab contexts, researchers, policymakers, and social networking sites designers are better equipped to develop effective, user-centric, and culturally sensitive digital wellbeing interventions. Personality has been found to play role on accepting technologies and the way they are used (<xref ref-type="bibr" rid="ref9">Alves et al., 2020</xref>). Our findings highlighted different impacts of sociodemographic and personality traits on peoples&#x2019; preferences in digital wellbeing services in the European and Arab contexts, suggesting cultural factors may play a role in such different preferences; while also some generalizable results were found. Research may further explore these findings to advance our understanding of the culture factors impacting these preferences. Future research may also evaluate the effectiveness of interventions designed based on digital wellbeing features introduced in this study.</p>
<p>SNS designers can offer digital wellbeing services tailored to cultural contexts, taking into consideration relevant factors associated with user interest in such services. This approach aligns with the literature emphasizing the importance of cultural factors in effective Human-Computer Interaction (HCI) designs (<xref ref-type="bibr" rid="ref4">Adnan et al., 2020</xref>). In line with our findings, services providing enhanced data privacy could be emphasized to Arab users to alleviate privacy concerns, particularly among males. Conversely, European female users may benefit from services designed to mitigate mental health issues associated with SNS use. Nonetheless, the interpretation of these results warrants caution, as the WTP for DWB services might be significantly influenced by the level of trust users place in SNS platforms (<xref ref-type="bibr" rid="ref81">Schreiner and Hess, 2015</xref>). Trust issues could underpin a reluctance to pay, despite recognized need or interest. Therefore, while our findings provide an insightful understanding of users&#x2019; preferences for DWB services, the impact of trust in SNS platforms on these preferences and willingness to pay deserves more in-depth exploration. Furthermore, data protection legislation was reported as a low priority among Arab countries (<xref ref-type="bibr" rid="ref36">Fatafta and Samaro, 2021</xref>). Our findings could be used by policymakers in Arab countries to prioritize developing and enforcing data protection legislation to address these concerns.</p>
<p>The current study is subject to certain limitations, and it is important to consider them when interpreting the results. The present study is cross-sectional, and our findings do not necessarily infer causal relationships among variables. The data were collected through self-reporting methods, which may lack accurate reporting and are subject to response biases such as social desirability. Some measures were taken to mitigate the influence of self-reporting bias on the findings. Data was collected anonymously to ensure confidentiality and reduce the likelihood of social desirability bias. Attention checks were embedded in the survey to ensure data quality and assess participants&#x2019; attentiveness throughout the survey. To further address limitations associated with self-report measures, future studies could benefit from integrating the Implicit Association Test (IAT) (<xref ref-type="bibr" rid="ref41">Greenwald et al., 2009</xref>). The IAT measures automatic association between concepts in one&#x2019;s mind through cognitive tasks, uncovering subconscious biases and attitudes that may influence decision-making processes in ways that aren&#x2019;t captured by direct questioning. Such deeper insights into users&#x2019; attitudes and beliefs, which individuals may be unaware of, might not be fully captured through self-reported data. Integrating such tools alongside traditional self-report methods could offer a more robust understanding of digital wellbeing perceptions.</p>
<p>It is also important to note that while WTP was utilized to assess user&#x2019;s valuation of the DWB services, it may not be the optimal measurement to assess actual payment behavior. Research has shown that there is a significant gap between people&#x2019;s intention regarding a specific behavior and the actual behavior (<xref ref-type="bibr" rid="ref86">Sheeran, 2002</xref>), meaning that individuals&#x2019; stated willingness to pay does not always translate into real financial transactions. As such, the implications of this research should be treated with caution. Furthermore, future studies could also benefit from incorporating theoretical frameworks such as Protection Motivation Theory (PMT) to more precisely investigate how perceptions of risk and the efficacy of coping strategies influence individuals&#x2019; WTP for DWB services (<xref ref-type="bibr" rid="ref80">Rogers, 1983</xref>). By applying PMT, researchers can examine how the perceived severity and vulnerability to digital threats (risk appraisal), alongside the perceived benefits and self-efficacy regarding protective actions (coping appraisal), affect economic decisions related to healthy usage of technologies.</p>
<p>Moreover, our study focused on only two cultures, the European and Arab countries, which may limit the generalizability of the findings to other cultures. SNS use behavior may differ among countries within these cultures. For example, a prior study showed social media use and behavior differences between Egypt and UEA (<xref ref-type="bibr" rid="ref63">Mohamed, 2010</xref>). It is essential to conduct cross-cultural and geographical studies to gain a more comprehensive understanding of the factors impacting the interest in and demands for digital wellbeing features. Future research could also explore the cultural factors underlying the impact of demographic and personality traits on users&#x2019; preferences in digital wellbeing services. To gain a deeper understanding, future studies could examine whether people&#x2019;s interest in digital wellbeing services is specific to particular SNS, allowing for more targeted interventions and strategies to promote digital wellbeing in the context of various platforms. By addressing these limitations, future research can build upon our findings and contribute to the advancement of knowledge in the field of digital wellbeing.</p>
</sec>
</sec>
<sec sec-type="conclusions" id="sec23">
<label>5</label>
<title>Conclusion</title>
<p>This study sheds light on the importance of designing digital wellbeing services aiming to mitigate issues associated with the use of SNS. Research showed that SNS can be designed to foster digital wellbeing, e.g., by adding functions that help users to mitigate the problematic fear of missing out (<xref ref-type="bibr" rid="ref8">Alutaybi et al., 2020</xref>). Users&#x2019; demand for such services can be expressed through their WTP (a reasonable amount of fee) to be provided (<xref ref-type="bibr" rid="ref64">Mohammed et al., 2023</xref>). This study investigates the extent that people are interested in and willing to pay for services that ensure healthier and balanced usage of SNS revealing that while WTP was slightly higher than previous findings, it remains moderate. Our findings also revealed that individuals with high social networks use disorder and need for cognition perceived the importance of digital wellbeing features in the European and Arab samples. However, the influence of gender, age, education level, and personality traits on the willingness to pay varied for the different digital wellbeing services in the European and Arab samples.</p>
<p>These insights can guide researchers, designers, and policymakers in developing effective, user-centric, and culturally sensitive digital wellbeing services. For example, SNS designers can prioritize services that promote the prevention of excessive use, and develop methods beyond self-control, more specifically tailored for users high in neuroticism. By conducting further research, researchers can explore other factors influencing people&#x2019;s interest in digital wellbeing services and advance our understanding of the important aspects of digital wellbeing across different individual characteristics and cultures.</p>
</sec>
<sec sec-type="data-availability" id="sec24">
<title>Data availability statement</title>
<p>The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found at: <ext-link xlink:href="https://osf.io/r3kw2" ext-link-type="uri">https://osf.io/r3kw2</ext-link>.</p>
</sec>
<sec sec-type="ethics-statement" id="sec25">
<title>Ethics statement</title>
<p>The studies involving humans were approved by Institutional Review Board (IRB) of Qatar Biomedical Research Institute (QBRI) at Hamad Bin Khalifa University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.</p>
</sec>
<sec sec-type="author-contributions" id="sec26">
<title>Author contributions</title>
<p>SA: Conceptualization, Formal analysis, Investigation, Methodology, Visualization, Writing &#x2013; original draft, Writing &#x2013; review &#x0026; editing. AB: Validation, Writing &#x2013; review &#x0026; editing. CS: Methodology, Writing &#x2013; review &#x0026; editing. DA-T: Writing &#x2013; review &#x0026; editing. CM: Methodology, Writing &#x2013; review &#x0026; editing. RA: Conceptualization, Investigation, Methodology, Supervision, Writing &#x2013; review &#x0026; editing, Validation.</p>
</sec>
</body>
<back>
<sec sec-type="funding-information" id="sec27">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This publication was supported by NPRP 14 Cluster grant # NPRP 14C-0916&#x2013;210015 from the Qatar National Research Fund (a member of Qatar Foundation). The findings herein reflect the work and are solely the responsibility of the authors.</p>
</sec>
<ack>
<p>We acknowledge the support of the Ministerium f&#x00FC;r Wissenschaft, Forschung und Kunst Baden-W&#x00FC;rttemberg (MWK, Ministry of Science, Research and the Arts Baden-W&#x00FC;rttemberg under Az. 33-7533-9-19/54/5) in &#x201C;K&#x00FC;nstliche Intelligenz &#x0026; Gesellschaft: Reflecting Intelligent Systems for Diversity, Demography and Democracy (IRIS3D)&#x201D; and the support by the &#x201C;Interchange Forum for Reflecting on Intelligent Systems&#x201D; (IRIS) at the University of Stuttgart. Further, we acknowledge the support by the Stuttgart Center for Simulation Science (SimTech).</p>
</ack>
<sec sec-type="COI-statement" id="sec28">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="sec29">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="sec30">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fcomp.2024.1387681/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fcomp.2024.1387681/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Table_1.DOCX" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
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<fn-group>
<fn id="fn0001">
<p><sup>1</sup>
<ext-link xlink:href="https://www.surveymonkey.com" ext-link-type="uri">https://www.surveymonkey.com</ext-link>
</p>
</fn>
<fn id="fn0002">
<p><sup>2</sup>
<ext-link xlink:href="https://www.qualtrics.com" ext-link-type="uri">https://www.qualtrics.com</ext-link>
</p>
</fn>
<fn id="fn0003">
<p><sup>3</sup>
<ext-link xlink:href="http://www.prolific.co" ext-link-type="uri">www.prolific.co</ext-link>
</p>
</fn>
<fn id="fn0004">
<p><sup>4</sup>
<ext-link xlink:href="http://www.cint.com" ext-link-type="uri">www.cint.com</ext-link>
</p>
</fn>
</fn-group>
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