Development and validation of the Social Media Moral Inconsistency Scale (SMMIS): Assessing moral disengagement in digital contexts

Development and validation of the Social Media Moral Inconsistency Scale (SMMIS): Assessing moral disengagement in digital contexts | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and validation of the Social Media Moral Inconsistency Scale (SMMIS): Assessing moral disengagement in digital contexts Nesrullah OKAN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9403077/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background The rapid expansion of digital communication environments has transformed how individuals regulate and express moral behaviour. While people tend to endorse moral values such as fairness, respect, and responsibility in offline settings, social media platforms may facilitate behaviours that deviate from these standards. Features such as anonymity, reduced accountability, and shifting social norms contribute to this discrepancy. Drawing on the theory of moral disengagement, this study conceptualises moral inconsistency as the divergence between endorsed values and actual behaviours in social media contexts. Methods This study aimed to develop and validate the Social Media Moral Inconsistency Scale (SMMIS). An initial pool of 21 items was generated based on theoretical frameworks and relevant literature. Following expert evaluation, 18 items were retained for empirical testing. Exploratory factor analysis (EFA) was conducted with 261 participants to identify the underlying structure. Confirmatory factor analysis (CFA) was then performed on an independent sample of 155 participants to validate the model. Internal consistency and criterion-related validity were also assessed. Results EFA results supported a unidimensional structure comprising 12 items. CFA confirmed the adequacy of this structure, demonstrating acceptable model fit indices (χ²/df = 2.916, RMSEA = .077, SRMR = .033, IFI = .922, CFI = .921, GFI = .909). The scale showed high internal consistency (Cronbach’s α = .896). Criterion-related validity was supported by a significant negative correlation with the Moral Integrity Scale (r = − .540, n = 55), indicating that higher moral inconsistency is associated with lower moral integrity. Conclusions The findings indicate that the SMMIS is a reliable and valid instrument for assessing moral disengagement in social media contexts. Unlike existing measures that focus primarily on cognitive mechanisms, this scale captures the behavioural manifestation of discrepancies between moral values and online behaviour. The SMMIS provides a context-specific tool for future research on digital ethics and online behaviour. digital morality moral inconsistency moral disengagement cognitive dissonance digital behaviour Figures Figure 1 Figure 2 Introduction The accelerated proliferation of social media has precipitated a profound metamorphosis in the manner by which individuals communicate, construct identities, and navigate moral decision-making in digitally mediated environments. Recent global estimates indicate that social media involves more than five billion user identities, thus highlighting its central role in everyday social interaction and behavioural regulation (Kemp, 2025 ). Within these environments, individuals not only exchange information but also continuously negotiate social norms, manage impressions, and make morally consequential choices under conditions that differ substantially from offline contexts (Valkenburg et al., 2021 ; Kross et al., 2021 ). A mounting body of research suggests that digital environments may potentially augment the probability of individuals exhibiting behaviours that are incongruent with their professed moral standards (Best et al., 2014 ; Choukas-Bradley & Nesi, 2020 ). The structural characteristics of online platforms, such as reduced social cues, perceived anonymity, invisibility, and asynchronous communication, have the capacity to weaken self-regulatory processes and facilitate ethically problematic behaviours (Suler, 2004 ; Joinson, 2007 ; Lapidot-Lefler & Barak, 2012 ). From a broader ethical perspective, the increasing complexity of the "infosphere" has further blurred traditional boundaries of responsibility, making moral regulation in digital contexts more context-dependent and situationally flexible (Floridi, 2016 ). In the context of social media, such dynamics may manifest in the form of behaviours including cyberbullying, the propagation of misleading information, exaggerated or idealised self-presentation, and impulsive or aggressive communication patterns. These behaviours are not merely isolated incidents, but may reflect deeper inconsistencies between individuals' internalised moral values and their enacted behaviours (Hinduja & Patchin, 2013 ; Przybylski & Weinstein, 2013 ; Marengo et al., 2020 ). From a psychological standpoint, such discrepancies are significant because they may signal disruptions in moral self-regulation rather than simple behavioural lapses. The present study approaches this phenomenon through the framework of moral disengagement. According to Bandura ( 1999 , 2016 ), moral disengagement refers to a set of cognitive mechanisms that enable individuals to justify or neutralise actions that violate their moral standards, thereby maintaining a positive self-concept despite engaging in ethically questionable behaviour. In the context of digital environments, these mechanisms may become particularly salient due to reduced accountability, diffusion of responsibility, and the normalization of harmful conduct (Runions & Bak, 2015 ). Cognitive dissonance theory (Festinger, 1957 ) elucidates the psychological discomfort arising from inconsistencies between beliefs and behaviours. In contrast, moral disengagement provides a more direct explanation of how such inconsistencies are cognitively resolved and behaviourally sustained. These processes assume particular significance during late adolescence and emerging adulthood, periods distinguished by ongoing identity formation, moral reasoning, and self-regulation (Arnett, 2000 ; Narvaez, 2010 ; Rest, 1986 ). During these stages, social media platforms have the potential to amplify moral vulnerability by prioritising visibility, peer approval, and immediate feedback over reflective ethical judgement (Moreno & Uhls, 2019 ; Jackson & Luchner, 2018 ). Consequently, individuals may be more prone to engaging in behaviours that are incongruent with their moral values, while simultaneously employing mechanisms of disengagement to justify or minimise these inconsistencies. Notwithstanding the burgeoning interest in digital ethics, online behaviour, and moral psychology, the extant literature remains deficient in terms of measurement tools that directly capture the discrepancy between individuals' moral standards and their social media behaviours. Existing instruments primarily assess related constructs such as general moral disengagement or online disinhibition (Udris, 2014 ; Runions & Bak, 2015 ). While these tools provide valuable insights into cognitive mechanisms, they do not specifically focus on the behavioural manifestation of moral disengagement in the form of value–behaviour inconsistency within social media contexts. In order to address this gap, the present study introduces the Social Media Moral Inconsistency Scale (SMMIS). In this study, moral inconsistency is conceptualised not as an independent theoretical construct, but rather as a context-specific behavioural manifestation of moral disengagement in social media environments. The primary objective of the present study is to develop a psychometrically sound instrument capable of assessing the extent to which individuals' social media behaviours diverge from their internalised moral standards. In order to achieve this aim, the study pursues two interrelated objectives. Firstly, the item structure of the SMMIS is developed on the basis of relevant theoretical frameworks and expert evaluation. Secondly, the psychometric properties of the scale are systematically examined through exploratory factor analysis, confirmatory factor analysis, internal consistency analysis, and criterion-related validity. It is anticipated that the resulting scale will contribute to extant literature on digital behaviour, moral self-regulation, and online ethics, while concurrently providing practitioners with a context-sensitive instrument for identifying and addressing moral difficulties in social media environments. Literature Review Moral Behavior and Moral Disengagement in Digital Environments Moral behaviour is generally defined as adherence to social norms, ethical principles, and internalised values that guide individual actions across contexts (Turiel, 2006 ). In offline environments, moral conduct is typically regulated through direct interpersonal interaction, immediate feedback, and clearly defined accountability structures. Conversely, digital environments engender psychological and situational factors that have the potential to diminish the congruence between moral standards and behaviour (Christen et al., 2020 ; Valkenburg et al., 2021 ). One of the most widely cited explanations for this shift is the online disinhibition effect (Suler, 2004 ), which suggests that anonymity, invisibility, and reduced social cues lower behavioural inhibition and increase the likelihood of norm-violating actions. Empirical research has demonstrated an association between such conditions and cyberbullying, online aggression, and impulsive communication patterns (Hinduja & Patchin, 2013 ; Lapidot-Lefler & Barak, 2012 ; Runions & Bak, 2015 ). Furthermore, digital environments enable the cultivation of a curated self-presentation and the management of impressions, through which individuals can construct identities that deviate from their offline personas (Goffman, 1959 ; Jackson & Luchner, 2018 ; Isbulan et al., 2024 ). From a theoretical perspective, these patterns can be more precisely understood through the framework of moral disengagement. Bandura ( 1999 , 2016 ) proposed that individuals employ cognitive mechanisms, including moral justification, diffusion of responsibility and dehumanisation, to neutralise self-sanctions and maintain a positive self-image despite engaging in ethically questionable behaviour (Bandura, 1999 , p. 123). In the context of digitally mediated environments, these mechanisms may be amplified due to reduced accountability, the normalisation of harmful conduct, and the structural features of online interaction (Runions & Bak, 2015 ; Boursier et al., 2020 ). Recent research in the field of developmental studies has indicated that adolescents and young adults are particularly susceptible to these processes. During these stages, individuals are actively constructing moral reasoning, identity, and self-regulatory capacities (Rest, 1986 ; Narvaez, 2010 ). Digital environments, which frequently prioritise immediacy, visibility, and social approval, have the potential to interfere with reflective ethical judgement and increase vulnerability to disengagement processes (Moreno & Uhls, 2019 ; Choukas-Bradley & Nesi,2020). Social Media and Moral Decision-Making Social media platforms have been demonstrated to exert a significant influence on the formation of moral judgement and ethical decision-making processes. In contrast to face-to-face interactions, where moral evaluation is supported by emotional feedback and social consequences, social media environments operate through indirect, delayed, and algorithmically mediated forms of reinforcement (Floridi, 2016 ; Kubin et al., 2021 ). Algorithmic curation exerts a pivotal influence on the moral decision-making processes that occur within social media environments, by prioritising content that is emotionally engaging and attention-driven. Empirical research has demonstrated that emotionally charged information disseminates more expeditiously and extensively within online networks, thereby reinforcing impulsive reactions and diminishing deliberative moral evaluation (Vosoughi et al., 2018 ; Kross et al., 2021 ). In a similar vein, the formation of echo chambers has been demonstrated to limit exposure to diverse viewpoints, intensify group polarization, and facilitate moral disengagement by normalising ethically questionable behaviour (Sunstein, 2017 ). Furthermore, normative social influence within digital communities has been demonstrated to cause individuals to align their expressed attitudes with perceived group norms, even when these norms contradict their personal values (Dickie et al., 2017 ; Schneider et al., 2022 ). As demonstrated in Fig. 1 , these mechanisms function in an interactive manner: emotionally amplified content enhances engagement and reactivity, echo chambers reinforces homogeneous moral perspectives, and normative pressures promote adherence to group-based moral standards. Collectively, these processes contribute to the emergence of context-specific moral disengagement and increase the likelihood of inconsistencies between individuals' internalised values and their online behaviours. Furthermore, normative social influence exerts a substantial impact on the shaping of online moral expression. It has been demonstrated that individuals may align their publicly expressed attitudes with perceived group norms, even when these norms conflict with their personal values. This alignment reflects the interaction between social reinforcement mechanisms and moral disengagement processes, whereby individuals adapt their behaviour to maintain social belonging while minimizing internal moral conflict. Taken together, these findings suggest that moral decision-making in social media environments is not solely an individual process, but is shaped by the interaction between cognitive mechanisms, platform structures, and social dynamics. Implications for Counselling and Psychological Assessment From an applied psychology perspective, moral difficulties emerging in digital contexts have important implications for counselling and psychological assessment. Individuals may encounter a discord between their internalised moral standards and their online behaviour, frequently accompanied by feelings of guilt, discomfort, or ambivalence. These experiences can be interpreted within the framework of cognitive dissonance (Festinger, 1957 ), which describes the psychological discomfort arising from inconsistencies between beliefs and actions. In digital contexts, such dissonance may be compounded by repeated exposure to morally ambiguous situations and by the normalisation of ethically questionable behaviours (Pavarini et al., 2021 ; White & Hanley, 2024 ). This phenomenon can potentially lead to adverse psychological consequences, including emotional distress, identity confusion, and compromised moral self-regulation (Narvaez, 2010 ; Kross et al., 2021 ). From a developmental standpoint, identity formation processes (Marcia, 1966 ) may be further complicated by the pressures of maintaining idealised or socially desirable online identities (Jackson & Luchner, 2018 ). For counsellors and mental health professionals, these patterns underscore the significance of evaluating not only observable behaviours but also the underlying discrepancies between values and actions. It is submitted that a more profound understanding of these discrepancies may facilitate the implementation of more effective interventions. Such interventions are proposed to be aimed at strengthening ethical awareness, self-regulation, and psychological well-being in digital environments. Existing Measures and Limitations Despite the considerable expansion of research in the fields of online behaviour and digital ethics, extant measurement tools continue to exhibit limitations in their capacity to directly evaluate the discrepancies between moral values and social media behaviour. Instruments such as the Online Disinhibition Scale (Udris, 2014 ) focus on reduced behavioural inhibition, while measures of cyberbullying primarily assess overt harmful actions (Hinduja & Patchin, 2013 ). In a similar vein, moral disengagement scales evaluate justificatory cognitive mechanisms as opposed to the behavioural manifestation of value–behaviour inconsistency (Bandura, 2016 ; Runions & Bak, 2015 ). While these instruments provide important insights into related constructs, they do not specifically capture the extent to which individuals' online behaviours diverge from their internalised moral standards within social media contexts. This limitation restricts both empirical investigation and applied assessment of moral self-regulation in digital environments (Parry et al., 2021 ). The Need for the SMMIS In response to this gap, the present study introduces the Social Media Moral Inconsistency Scale (SMMIS). In this theoretical framework, moral inconsistency is conceptualised as a context-specific behavioural manifestation of moral disengagement in social media environments. The SMMIS has been developed for the purpose of evaluating the extent to which individuals' online behaviours deviate from their endorsed moral values. The development of this scale is of particular relevance to the fields of counselling, education and mental health practice, where professionals require theoretically grounded and empirically validated tools to assess digital moral functioning. The operationalisation of value–behaviour discrepancies may facilitate the identification of individuals experiencing moral conflict, support targeted interventions, and contribute to the promotion of ethical self-regulation in digital contexts. Accordingly, the SMMIS aims to complement existing measures by providing a more context-sensitive assessment tool, thereby advancing research on moral psychology, digital behaviour, and online ethics. Methods Research Design The present study employed a quantitative, cross-sectional design in order to develop and validate the Social Media Moral Inconsistency Scale (SMMIS). The scale was conceptualised as a context-specific operationalisation of moral disengagement in social media environments, reflected in discrepancies between individuals' internalised moral standards and their enacted online behaviours. The scale development process was conducted in accordance with established psychometric principles, incorporating sequential stages of item generation, content validation, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and reliability and validity assessment (Clark & Watson, 1995 ; DeVellis, 2017 ; Kline, 2016 ). This multi-stage approach was designed to ensure conceptual clarity, structural validity, and measurement reliability. Participants Participants were recruited through convenience sampling across three independent data collection phases corresponding to EFA, CFA, and criterion validity analyses. The participants were all university students who were actively engaged in the use of social media. The EFA sample consisted of 261 participants (169 female, 92 male; M_age = 23.40), the CFA sample included 155 participants (97 female, 58 male), and the criterion validity sample comprised 55 participants (33 female, 22 male). Participants were drawn from multiple regions of Turkey, including Elâzığ, Diyarbakır, Konya, Bilecik, Istanbul, and Bursa. The demographic characteristics for each phase are outlined in Table 1 . Table 1 Demographic Characteristics of the Study Group Analysis Type Sample Size (n) Female (n) Male (n) Exploratory Factor Analysis (EFA) 261 169 92 Confirmatory Factor Analysis (CFA) 155 97 58 Criterion Validity 55 33 22 The age range of 18–27 years corresponds to the developmental stages of late adolescence and emerging adulthood, which are characterised by ongoing moral reasoning, identity formation, and self-regulation (Arnett, 2000 ; Narvaez, 2010 ). The inclusion criteria stipulated that participants must have been currently enrolled in a university programme and to have a regular engagement with social media platforms. Participation was voluntary, anonymous, and informed consent was obtained from all subjects. Instrument Development Item Generation The development of the SMMIS commenced with a theoretically driven item generation process grounded in moral disengagement theory (Bandura, 1999 ) and research on digital behaviour and online disinhibition (Suler, 2004 ). Moreover, the conceptual boundaries of the construct were informed by classical frameworks of moral development (Kohlberg, 1981 ; Turiel, 2006 ). An initial pool of 21 items was generated to capture behavioural manifestations of value–behaviour discrepancies in social media contexts. The items were designed to reflect situations in which individuals act inconsistently with their endorsed moral standards in digitally mediated interactions. Content Validation The content validity of the scale was evaluated by a panel of seven experts in psychology, measurement, and digital behaviour. The items were then subjected to a rigorous evaluation by experts, who appraised them based on three criteria: relevance, clarity, and the representativeness of the construct. The Content Validity Ratio (CVR) was calculated using Lawshe's (1975) method. In light of the expert feedback received, the item pool was reduced to 18 items. This action was taken to ensure adequate content representation while improving clarity and precision. A pilot study (n = 30) was conducted to evaluate the comprehensibility of items and the clarity of responses. Minor revisions were made based on participant feedback. Data Collection Procedure The data were collected through an online survey administered via Google Forms. Participants were recruited via university-based communication channels, including email groups and student networks. Participation was voluntary and anonymous, and no incentives were provided. Prior to participation, informed consent was obtained digitally. Measures Social Media Moral Inconsistency Scale (SMMIS) The SMMIS is a self-report instrument developed in this study to assess behavioural manifestations of moral disengagement in social media contexts. Following exploratory factor analysis, the final scale consisted of 12 items with a unidimensional structure. The items are evaluated using a five-point Likert scale, ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). Scores on this scale are positively correlated with levels of moral inconsistency, which is indicative of a greater degree of disengagement from internal moral standards in digital environments. Moral Integrity Scale (MIS) The criterion validity of the scale was assessed using the Moral Integrity Scale (MIS; Schlenker, 2008 ), which had been adapted into Turkish by Okan and Ekşi (2020). The MIS has been developed as a measure of the extent to which individuals act in accordance with their moral values. In addition, it provides an external criterion for evaluating the SMMIS that is theoretically relevant. Data Analysis The data were analysed using SPSS 25 and AMOS 24. Exploratory Factor Analysis (EFA) was performed using Principal Axis Factoring with Promax rotation to identify the underlying factor structure. The adequacy of the samples was assessed using the Kaiser–Meyer–Olkin (KMO) measure and Bartlett's test of sphericity. Confirmatory Factor Analysis (CFA) was conducted to evaluate the fit of the factor structure obtained from EFA. The model fit was assessed using multiple indices, including the chi-squared divided by degrees of freedom (χ²/df), the Comparative Fit Index (CFI), the Incremental Fit Index (IFI), the Goodness-of-Fit Index (GFI), the Root Mean Square Error of Approximation (RMSEA), and the Standardised Root Mean Square Residual (SRMR). The internal consistency of the scale was evaluated using Cronbach's alpha. The criterion validity of the SMMIS was examined through Pearson correlation analysis between the SMMIS and the Moral Integrity Scale. Findings Preliminary Analyses Prior to the extraction of factors, the dataset was subjected to rigorous evaluation to ascertain its suitability for factor analysis. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was .881, indicating a high level of shared variance among items and confirming that the sample was appropriate for factor analytic procedures. Bartlett's Test of Sphericity was statistically significant (χ² = 835.008, df = 66, p < .001), demonstrating that the correlation matrix significantly deviates from an identity matrix. This finding suggests that the observed variables are sufficiently intercorrelated to justify the application of factor analysis. When considered as a whole, these indices offer compelling evidence that the data satisfy the underlying assumptions necessary for reliable and interpretable factor extraction. Exploratory Factor Analysis and Factor Structure Exploratory Factor Analysis (EFA) was conducted using Principal Axis Factoring with Promax rotation to examine the latent structure of the Social Media Moral Inconsistency Scale (SMMIS). The selection of Principal Axis Factoring was based on its suitability for identifying latent constructs in psychological data that may deviate from multivariate normality, while Promax rotation was preferred to allow for potential correlations among underlying dimensions. Prior to the extraction of factors, the adequacy of the dataset was rigorously assessed. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was found to be .881, indicating a very good level of adequacy, while Bartlett's Test of Sphericity was statistically significant (χ²(66) = 835.008, p < .001). These results confirm that the correlation matrix was factorable and that the data met the necessary assumptions for factor analysis. The EFA yielded a clear and interpretable unidimensional structure, accounting for 47.07% of the total variance (see Table 2 ). Within the domain of psychological scale development, particularly within the social sciences, this level of explained variance is regarded as acceptable in light of the complexity and multidimensional nature of human behaviour. The emergence of a single dominant factor suggests that the items coherently reflect a unified latent construct. This finding provides empirical support for the conceptualisation of moral inconsistency as a structurally integrated and context-specific psychological phenomenon within social media environments. Table 2 Exploratory Factor Analysis Results for the SMMIS Measure Value KMO .881 Bartlett’s χ² (df = 66) 835.008 p-value < .001 Total Variance Explained (%) 47.07 Item Statement Factor Loading m15 I may ignore my moral values to get likes and approval on social media. .764 m12 I try to make myself look different and better than I am on social media. .728 m14 Lying on social media is not a problem for having fun or getting attention. .716 m11 I may compromise my own values for the sake of being popular on social media. .714 m19 I usually exaggerate my real life in my posts. .707 m20 I may share inaccurate information in order to gain the favour of others. .682 m9 I make posts on social media that I am not sincere in order not to be criticised by others. .678 m22 I may mislead others on social media for my own interests. .670 m16 Although I am not honest in my social media posts, I consider this situation normal. .668 m18 I display attitudes and values on social media that I do not apply in real life. .659 m13 I may do things I would not normally do in order to attract attention on social media. .624 m10 I do not mind spreading false information in order to gain profit on social media. .606 Note : All items loaded on a single factor representing Social Media Moral Inconsistency. Items with factor loadings below .50 were excluded during the analysis. Item retention was guided by statistical thresholds and theoretical considerations. In line with established psychometric recommendations, items with factor loadings below 0.50 were excluded in order to ensure a robust and interpretable factor structure. Following this process, the final scale comprised 12 items. As shown in Table 2 , all the items that were kept had substantial factor loadings, ranging from 0.606 to 0.764. This indicates that they are strongly associated with the underlying latent construct. The relatively high and homogeneous distribution of factor loadings suggests that the items function as stable and reliable indicators of the same construct, thereby reinforcing the scale's internal coherence at a structural level. Taken together, these findings provide strong preliminary evidence for the construct validity of the SMMIS. The convergence of all items on a single factor indicates that moral inconsistency in social media contexts can be operationalised as a unidimensional construct. This lends support to the theoretical positioning of moral inconsistency as a behavioural manifestation of moral disengagement, reflecting a consistent pattern of discrepancies between values and behaviour within digitally mediated environments. Confirmatory Factor Analysis (CFA) Confirmatory Factor Analysis (CFA) was conducted to evaluate the adequacy of the one-factor model identified through the exploratory analysis. The analysis was conducted utilising AMOS 24 within a structural equation modelling framework. Figure 2 presents the standardized path diagram of the proposed model. The model specifies a single latent construct, termed Social Media Moral Inconsistency, to which all 12 observed variables are assigned. All items demonstrated meaningful and statistically substantial standardized loadings, ranging between approximately .56 and .70, indicating that each item contributes adequately to the representation of the latent construct. The magnitude of these loadings suggests a stable measurement structure and supports the assumption that the items function as reliable indicators of a common underlying dimension. Model fit was evaluated using multiple goodness-of-fit indices to provide a comprehensive assessment of the model’s adequacy (see Table 3 ). Table 3 Goodness-of-Fit Indices Fit Index Good Fit Acceptable Fit Obtained Value χ²/df ≤ 2 ≤ 3 2.916 RMSEA ≤ .05 ≤ .08 .077 SRMR ≤ .05 ≤ .10 .033 IFI ≥ .95 ≥ .90 .922 CFI ≥ .95 ≥ .90 .921 GFI ≥ .90 ≥ .85 .909 The goodness-of-fit indices demonstrate that the proposed model exhibits an acceptable and theoretically coherent level of fit. Despite the fact that the χ²/df and RMSEA values do not meet the more stringent criteria for "good fit," they fall within widely accepted thresholds, thereby suggesting that the model adequately represents the observed data. Conversely, the SRMR value (.033) signifies a robust fit, indicative of a minimal residual error. In a similar vein, the comparative fit indices – CFI (.921) and IFI (.922) – exceed the recommended minimum threshold of .90, thereby providing further support for the adequacy of the model. The GFI value (.909) further indicates an acceptable level of global model fit. When considered as a whole, these indices indicate that the model attains an acceptable equilibrium between parsimony and explanatory power. It is important to note that the results of the CFA replicate and confirm the unidimensional structure identified in the EFA, thereby providing robust evidence for the structural validity of the SMMIS. Reliability and Criterion-Related Validity The concurrent examination of the reliability and criterion-related validity of the Social Media Moral Inconsistency Scale (SMMIS) was conducted. The scale's internal consistency was found to be high, with a Cronbach's alpha coefficient of .896 indicating strong inter-item homogeneity. Additionally, the McDonald's omega coefficient (ω = .902) supports the robustness of the scale further, offering a more precise estimate of composite reliability, especially when the assumption of tau-equivalence is not fully met. Furthermore, the item–total correlation coefficients ranged from .52 to .71, indicating that all items meaningfully contributed to the overall construct and demonstrated satisfactory discrimination (See Table 4 ). Table 4 Reliability and Criterion-Related Validity Results for the SMMIS Measure Value Cronbach’s α .896 McDonald’s ω .902 Item–Total Correlation Range .52 – .71 Correlation with MIS (r) −.540*** Note. ***p < .001. MIS = Moral Integrity Scale. Criterion-related validity was assessed by examining the relationship between the SMMIS and the Moral Integrity Scale (MIS) in an independent sample of 55 participants. The analysis revealed a statistically significant and moderately strong negative correlation (r = − .540, p < .001), indicating that higher levels of moral inconsistency in social media contexts are associated with lower levels of moral integrity. The magnitude and direction of this relationship are theoretically consistent with the conceptualisation of moral inconsistency as a behavioural manifestation of moral disengagement. Taken together, these findings provide strong evidence for the internal consistency and criterion-related validity of the SMMIS. Summary of Psychometric Findings The findings of the present study provide robust and converging evidence for the psychometric adequacy of the Social Media Moral Inconsistency Scale (SMMIS). The results of the study can be summarised as follows. Initially, the scale exhibited a discernible and theoretically consistent unidimensional structure, signifying that moral inconsistency in social media contexts can be conceptualised as a singular latent construct. Secondly, the factor solution accounted for a substantial proportion of the total variance, thereby supporting the explanatory power of the scale within the domain of social and behavioural research. Thirdly, the internal consistency of the scale was found to be high, as evidenced by Cronbach's alpha and McDonald's omega coefficients. These findings indicate strong inter-item homogeneity and measurement reliability. Fourthly, confirmatory factor analysis yielded acceptable goodness-of-fit indices, thereby providing empirical support for the structural validity of the proposed model. The scale demonstrated theoretically consistent criterion-related validity, as reflected in its significant negative association with moral integrity. Taken together, these findings indicate that the SMMIS is a psychometrically sound, reliable, and valid instrument for assessing value–behaviour discrepancies in social media environments. Discussion The present study sought to develop and validate the Social Media Moral Inconsistency Scale (SMMIS) as a context-sensitive instrument capturing discrepancies between individuals' internalised moral standards and their behaviour in social media environments. The findings provide consistent and converging evidence that the scale demonstrates a stable unidimensional structure, acceptable model fit, high internal consistency, and theoretically meaningful criterion-related validity. A pivotal finding of the study is the demonstration that moral inconsistency in digital environments can be conceptualised as a coherent and measurable psychological construct, rather than a collection of isolated or situational behaviours. The emergence of a unidimensional structure is consistent with prior research suggesting that online moral behaviour is systematically shaped by underlying cognitive and regulatory processes rather than episodic reactions (Bandura, 1999 ; Suler, 2004 ; Runions & Bak, 2015 ; Christen et al., 2020 ; Valkenburg et al., 2021 ). From a theoretical standpoint, the findings provide substantial support for the proposition that moral inconsistency may be conceptualised as a behavioural manifestation of moral disengagement. The observed negative correlation between moral integrity and online behaviour provides empirical evidence in support of the argument that individuals who exhibit a greater discrepancy between their values and online behaviour are more likely to disengage from internal moral standards. This finding aligns with Bandura's (1999, 2016) framework and is consistent with research indicating that moral disengagement predicts ethically problematic online behaviours, including cyberbullying and online aggression (Hinduja & Patchin, 2013 ; Runions & Bak, 2015 ; Boursier et al., 2020 ). Significantly, the present findings extend this framework by situating moral disengagement within the specific affordances of digital environments. The presence of features such as anonymity, reduced accountability, and invisibility has been demonstrated to facilitate behavioural disinhibition and weaken moral self-regulation (Joinson, 2007 ; Lapidot-Lefler & Barak, 2012 ; Best et al., 2014 ). Furthermore, the presence of algorithmically mediated environments has been demonstrated to have the capacity to amplify emotionally charged content and reinforce normative pressures, thereby increasing the likelihood of morally inconsistent behaviour (Vosoughi et al., 2018 ; Sunstein, 2017 ; Kross et al., 2021 ; Schneider et al., 2022 ). The results of the study can also be interpreted through the lens of cognitive dissonance theory (Festinger, 1957 ). As demonstrated in previous studies, individuals tend to experience psychological discomfort when their behaviour deviates from their personal values. This discomfort often prompts individuals to engage in efforts to restore internal consistency, as theorised by Festinger ( 1957 ). However, in digital contexts, this tension may be mitigated through mechanisms of disengagement, thereby enabling individuals to sustain a coherent self-concept despite behavioural inconsistencies (Bandura, 1999 ; Suler, 2004 ; Gámez-Guadix et al., 2012 ). From a broader theoretical perspective, the findings reinforce the view that moral behaviour is context-dependent and dynamically shaped by environmental affordances (Turiel, 2002 ; Floridi, 2016 ; Moreno & Uhls, 2019 ). It is evident that social media environments differ from offline contexts in terms of immediacy, visibility, and social reinforcement processes, which have the capacity to alter the conditions under which moral decisions are made (Gillespie, 2018 ; Kubin et al., 2021 ). Theoretical Contributions The present study makes several important contributions to the existing literature on the subject. Firstly, it introduces a psychometrically validated instrument that directly captures value–behaviour discrepancies in social media contexts. In contrast to extant measures that predominantly evaluate moral disengagement mechanisms or particular online behaviours, the SMMIS operationalises the behavioural expression of moral disengagement, thereby furnishing a more direct and context-sensitive evaluation of moral functioning in digital environments. Secondly, the study contributes to the conceptual clarification of moral inconsistency as a distinct but related construct. Moral disengagement is defined as the cognitive mechanisms that justify unethical behaviour. Conversely, moral inconsistency is reflected in the observable divergence between internalised values and enacted behaviours. In this sense, the SMMIS captures the behavioural manifestation of these underlying mechanisms rather than the mechanisms themselves. By distinguishing between these levels, the study advances a more nuanced understanding of how moral processes unfold in digitally mediated contexts. Thirdly, the findings contribute to the integration of moral psychology and digital behaviour research by situating moral disengagement within the structural and social affordances of social media environments. This integration underscores the manner in which platform characteristics—such as anonymity, visibility, and algorithmic reinforcement—interact with cognitive mechanisms to shape moral behaviour (Bandura, 1999 ; Suler, 2004 ; Valkenburg et al., 2021 ). Fourthly, the study lends support to the expanding corpus of literature positing that moral functioning is not stable across contexts but is instead dynamically shaped by technological environments. The study provides empirical support for the view that moral regulation is context-dependent and influenced by digital affordances (Floridi, 2016 ; Gillespie, 2018 ; Parry et al., 2021 ) by demonstrating that value–behaviour discrepancies can be systematically measured in social media contexts. Practical Implications From an applied perspective, the SMMIS offers a valuable tool for assessing moral functioning in digital contexts. It has been demonstrated by previous research that digital literacy and ethical awareness are significant factors in the prevention of harmful online behaviours (Hinduja & Patchin, 2013 ; Best et al., 2014 ). The SMMIS may support such efforts by enabling practitioners to identify discrepancies between moral values and behaviour. Within the context of counselling and educational settings, the scale has been demonstrated to facilitate interventions designed to enhance self-regulation and ethical decision-making processes (White & Hanley, 2024 ). Furthermore, it may be employed to evaluate interventions targeting moral disengagement and online behaviour. At a societal level, the scale provides a framework for examining issues such as misinformation, cyberbullying, and online polarization, where moral inconsistency plays a critical role (Sunstein, 2017 ; Vosoughi et al., 2018 ; Choukas-Bradley & Nesi, 2020 ). Limitations and Future Directions Despite the contributions made by this study, it is important to acknowledge the limitations of the research. The utilisation of a university student sample serves to constrain the extent to which the findings can be generalised, and it is therefore recommended that future research examine the scale across diverse populations and cultural contexts. The utilisation of self-report measures engenders the potential for the occurrence of social desirability bias. It is recommended that future research endeavours consider the integration of behavioural or multi-method approaches, with a view to enhancing the rigour and relevance of the findings. The cross-sectional design of the study also imposes limitations on the extent to which causal interpretations can be made. It is imperative that longitudinal studies are conducted in order to examine the development of moral inconsistency over time and its relationship to psychological outcomes. In light of the rapidly evolving nature of digital environments, future research should explore platform-specific dynamics and periodically reassess the scale's validity. Conclusion In conclusion, the present study provides robust empirical evidence that the Social Media Moral Inconsistency Scale (SMMIS) is a reliable and valid instrument for assessing value–behaviour discrepancies in social media contexts. The study operationalises moral inconsistency as a measurable construct, thereby advancing the assessment of moral functioning in digitally mediated environments. Of particular significance is the integration of moral disengagement theory with research on digital behaviour, which offers a theoretically grounded framework for understanding how contextual affordances of social media shape ethical self-regulation. By adopting this approach, the SMMIS transcends mere descriptive accounts of online behaviour, thereby facilitating the systematic investigation of underlying moral processes. The scale provides a methodological tool and a conceptual lens for future research, contributing to a more nuanced understanding of moral functioning, self-regulation, and ethical behaviour in contemporary digital contexts. Declarations Compliance with Ethical Standards Conflict of Interest The author(s) declare that there is no conflict of interest. Informed Consent Informed consent was obtained from all individual participants included in the study. Ethical Approval This study was approved by the Ethics Committee of Fırat University (Approval No: 33842, Date: 24.04.2025). All procedures performed were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments. Consent to Publish The participants provided informed consent for the publication of anonymized data. Funding: TÜBITAK Author Contribution N.O. conceived the study, designed the methodology, conducted the analyses, and wrote the manuscript. The author reviewed and approved the final version of the manuscript. References Arnett JJ. Emerging adulthood: A theory of development from the late teens through the twenties. Am Psychol. 2000;55(5):469–80. https://doi.org/10.1037/0003-066X.55.5.469 . Bandura A. Moral disengagement in the perpetration of inhumanities. Personality Social Psychol Rev. 1999;3(3):193–209. https://doi.org/10.1207/s15327957pspr0303_3 . Bandura A. Moral disengagement: How people do harm and live with themselves. Worth; 2016. Best P, Manktelow R, Taylor B. Online communication, social media and adolescent wellbeing: A systematic narrative review. Child Youth Serv Rev. 2014;41:27–36. https://doi.org/10.1016/j.childyouth.2014.03.001 . Boursier V, Gioia F, Griffiths MD. Do selfie-expectancies and social appearance anxiety predict adolescents’ problematic social media use? Comput Hum Behav. 2020;110:106395. https://doi.org/10.1016/j.chb.2020.106395 . Choukas-Bradley S, Nesi J. (2020). Applying developmental theory to adolescent peer influence processes in the social media context. In N. Van Zalk & C. P. Monks, editors, Online peer engagement in adolescence: Positive and negative aspects of online social interaction (pp. 140–162). Routledge. https://doi.org/10.4324/9780429468360-9 Christen M, Gordijn B, Loi M, editors. The ethics of cybersecurity. Springer; 2020. Clark LA, Watson D. Constructing validity: Basic issues in objective scale development. Psychol Assess. 1995;7(3):309–19. https://doi.org/10.1037/1040-3590.7.3.309 . DeVellis RF. Scale development: Theory and applications. 4th ed. SAGE; 2017. Dickie R, Rasmussen S, Cain R, Williams L, MacKay W. The effects of perceived social norms on handwashing behaviour in students. Psychol Health Med. 2017;23(2):154–9. https://doi.org/10.1080/13548506.2017.1338736 . Festinger L. A theory of cognitive dissonance. Stanford University Press; 1957. Floridi L. The onlife manifesto: Being human in a hyperconnected era. Springer; 2016. https://doi.org/10.1007/978-3-319-04093-6 . Gámez-Guadix M, Villa-George FI, Calvete E. Measurement and analysis of the cognitive-behavioral model of generalized problematic internet use among Mexican adolescents. J Adolesc. 2012;35(6):1581–91. https://doi.org/10.1016/j.adolescence.2012.06.005 . Gillespie T. Custodians of the internet: Platforms, content moderation, and the hidden decisions that shape social media. Yale University Press; 2018. Goffman E. The presentation of self in everyday life. Doubleday; 1959. Hinduja S, Patchin JW. Social influences on cyberbullying behaviors among middle and high school students. J Youth Adolesc. 2013;42:711–22. https://doi.org/10.1007/s10964-012-9902-4 . Isbulan O, Cam E, Griffiths MD. The mediating effect of social network identity management on the relationship between personality traits and social media addiction among pre-service teachers. BMC Psychol. 2024;12:146. https://doi.org/10.1186/s40359-024-01653-5 . Jackson CA, Luchner AF. Self-presentation mediates the relationship between self-criticism and emotional response to Instagram feedback. Pers Indiv Differ. 2018;133:1–6. https://doi.org/10.1016/j.paid.2017.04.052 . Joinson AN. (2007). Disinhibition and the internet. In J. Gackenbach, editor, Psychology and the internet: Intrapersonal, interpersonal, and transpersonal implications (2nd ed., pp. 75–92). Academic Press. https://doi.org/10.1016/B978-012369425-6/50023-0 Kemp S. (2025). Digital 2025: Global overview report. DataReportal . https://datareportal.com/reports/digital-2025-global-overview-report Kline RB. Principles and practice of structural equation modeling. 4th ed. Guilford Press; 2016. Kohlberg L. The philosophy of moral development: Moral stages and the idea of justice. Harper & Row; 1981. Kross E, Verduyn P, Sheppes G, Costello CK, Jonides J, Ybarra O. Social media and well-being: Pitfalls, progress, and next steps. Trends Cogn Sci. 2021;25(1):55–66. https://doi.org/10.1016/j.tics.2020.10.005 . Kubin E, Puryear C, Schein C, Gray K. (2021). Personal experiences bridge moral and political divides better than facts. Proceedings of the National Academy of Sciences, 118 (6), e2008389118. https://doi.org/10.1073/pnas.2008389118 Lapidot-Lefler N, Barak A. Effects of anonymity, invisibility, and lack of eye-contact on toxic online disinhibition. Comput Hum Behav. 2012;28(2):434–43. https://doi.org/10.1016/j.chb.2011.10.014 . Lawshe CH. A quantitative approach to content validity. Pers Psychol. 1975;28(4):563–75. https://doi.org/10.1111/j.1744-6570.1975.tb01393.x . Marcia JE. Development and validation of ego-identity status. J Personal Soc Psychol. 1966;3(5):551–8. https://doi.org/10.1037/h0023281 . Marengo D, Sindermann C, Elhai JD, Montag C. One social media company to rule them all: Associations between use of Facebook-owned social media platforms, sociodemographic characteristics, and the Big Five personality traits. Front Psychol. 2020;11:936. https://doi.org/10.3389/fpsyg.2020.00936 . Moreno MA, Uhls YT. Applying an affordances approach and a developmental lens to approach adolescent social media use. Digit Health. 2019;5:1–4. https://doi.org/10.1177/2055207619826678 . Narvaez D. Moral complexity: The fatal attraction of truthiness and the importance of mature moral functioning. Perspect Psychol Sci. 2010;5(2):163–81. https://doi.org/10.1177/1745691610362351 . Okan N, Eksi H. The adaptation of the Moral Integrity Scale into Turkish and analysis of the psychometric features. Spiritual Psychol Couns. 2020;5:327–40. https://doi.org/10.37898/spc.2020.5.3.124 . Parry DA, Davidson BI, Sewall CJR, Fisher JT, Mieczkowski H, Quintana DS. A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use. Nat Hum Behav. 2021;5(11):1535–47. https://doi.org/10.1038/s41562-021-01117-5 . Pavarini G, Hamdi L, Lorimer J, Singh I. Young people's moral attitudes and motivations towards direct-to-consumer genetic testing for inherited risk of Alzheimer disease. Eur J Med Genet. 2021;64(10):104180. https://doi.org/10.1016/j.ejmg.2021.104180 . Przybylski AK, Weinstein N. Can you connect with me now? How the presence of mobile communication technology influences face-to-face conversation quality. J Social Personal Relationships. 2013;30(3):237–46. https://doi.org/10.1177/0265407512453827 . Rest JR. Moral development: Advances in research and theory. Praeger; 1986. Runions KC, Bak M. Online moral disengagement, cyberbullying, and cyber-aggression. Cyberpsychology Behav Social Netw. 2015;18(7):400–5. https://doi.org/10.1089/cyber.2014.0670 . Schlenker BR. Integrity and character: Implications of principled and expedient ethical ideologies. J Soc Clin Psychol. 2008;27(10):1078–125. Schneider S, Beege M, Nebel S, Schnaubert L, Rey GD. The cognitive-affective-social theory of learning in digital environments (CASTLE). Educational Psychol Rev. 2022;34(1):1–38. https://doi.org/10.1007/s10648-021-09626-5 . Suler J. The online disinhibition effect. CyberPsychology Behav. 2004;7(3):321–6. https://doi.org/10.1089/1094931041291295 . Sunstein CR. Republic: Divided democracy in the age of social media. Princeton University Press; 2017. https://doi.org/10.1515/9781400884711 . Turiel E. The culture of morality: Social development, context, and conflict. Cambridge University Press; 2002. https://doi.org/10.1017/CBO9780511613500 . Turiel E. The development of morality. In: Eisenberg N, Damon W, Lerner RM, editors. Handbook of child psychology: Social, emotional, and personality development. 6th ed. Wiley; 2006. pp. 789–857. Udris R. Cyberbullying among high school students in Japan: Development and validation of the online disinhibition scale. Comput Hum Behav. 2014;41:253–61. https://doi.org/10.1016/j.chb.2014.09.036 . Valkenburg PM, Beyens I, Pouwels JL, van Driel II, Keijsers L. Social media use and adolescents’ self-esteem: Heading for a person-specific media effects paradigm. J Communication. 2021;71(1):56–78. https://doi.org/10.1093/joc/jqaa039 . Vosoughi S, Roy D, Aral S. The spread of true and false news online. Science. 2018;359(6380):1146–51. https://doi.org/10.1126/science.aap9559 . White E, Hanley T. Current ethical dilemmas experienced by therapists who use social media: A systematic review. Counselling Psychother Res. 2024;24:396–418. https://doi.org/10.1002/capr.12678 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 28 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 19 Apr, 2026 Editor invited by journal 18 Apr, 2026 Editor assigned by journal 16 Apr, 2026 Submission checks completed at journal 16 Apr, 2026 First submitted to journal 13 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9403077","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":630378466,"identity":"bc8466bc-1121-401f-90a7-ebcbd6ea7f63","order_by":0,"name":"Nesrullah OKAN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYNCCAwwM/CA6oYAULZINIC0GpGgxOABiEKPFnL392YcPZ+zyjM+vTvzwwIBBnl/sAH4tlj1njGfOuJFcbHbj7WYJoMMMZ85OwK/F4EYOMzPPB+bEbTfObgBpSTC4TUjL/eePgVrqEzfPOLv5B3FabjAYM/PcOJy4gb93G5G2nMkxZpxx5njijBu82ywSDCSI8Mvx448ZPhyrTuzvP7v55o8KG3l+aQJaEEACrFKCWOUgwH+AFNWjYBSMglEwkgAAwydJg0cZvJcAAAAASUVORK5CYII=","orcid":"","institution":"Fırat University","correspondingAuthor":true,"prefix":"","firstName":"Nesrullah","middleName":"","lastName":"OKAN","suffix":""}],"badges":[],"createdAt":"2026-04-13 11:09:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9403077/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9403077/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108071206,"identity":"6c9a91ce-e902-487d-941a-00fe61ab2908","added_by":"auto","created_at":"2026-04-29 06:07:13","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":272806,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMechanisms through which algorithmic curation shapes moral decision-making in social media environments.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9403077/v1/24d1c86f265b635feb881eb4.jpg"},{"id":108181839,"identity":"c114453d-87f6-4557-87da-839df68e9c51","added_by":"auto","created_at":"2026-04-30 08:58:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":79722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConfirmatory Factor Analysis Path Diagram of the SMMIS\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9403077/v1/e75ad758c64de5ddf8b8c9bb.png"},{"id":108183811,"identity":"d9c0d073-727d-4d41-a86d-731ebaad9514","added_by":"auto","created_at":"2026-04-30 09:02:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":695030,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9403077/v1/be8f95bb-c6bc-4fb5-a033-cfaca7fe8548.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of the Social Media Moral Inconsistency Scale (SMMIS): Assessing moral disengagement in digital contexts","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe accelerated proliferation of social media has precipitated a profound metamorphosis in the manner by which individuals communicate, construct identities, and navigate moral decision-making in digitally mediated environments. Recent global estimates indicate that social media involves more than five billion user identities, thus highlighting its central role in everyday social interaction and behavioural regulation (Kemp, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Within these environments, individuals not only exchange information but also continuously negotiate social norms, manage impressions, and make morally consequential choices under conditions that differ substantially from offline contexts (Valkenburg et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kross et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A mounting body of research suggests that digital environments may potentially augment the probability of individuals exhibiting behaviours that are incongruent with their professed moral standards (Best et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Choukas-Bradley \u0026amp; Nesi, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The structural characteristics of online platforms, such as reduced social cues, perceived anonymity, invisibility, and asynchronous communication, have the capacity to weaken self-regulatory processes and facilitate ethically problematic behaviours (Suler, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Joinson, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lapidot-Lefler \u0026amp; Barak, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). From a broader ethical perspective, the increasing complexity of the \"infosphere\" has further blurred traditional boundaries of responsibility, making moral regulation in digital contexts more context-dependent and situationally flexible (Floridi, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the context of social media, such dynamics may manifest in the form of behaviours including cyberbullying, the propagation of misleading information, exaggerated or idealised self-presentation, and impulsive or aggressive communication patterns. These behaviours are not merely isolated incidents, but may reflect deeper inconsistencies between individuals' internalised moral values and their enacted behaviours (Hinduja \u0026amp; Patchin, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Przybylski \u0026amp; Weinstein, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Marengo et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). From a psychological standpoint, such discrepancies are significant because they may signal disruptions in moral self-regulation rather than simple behavioural lapses. The present study approaches this phenomenon through the framework of moral disengagement. According to Bandura (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), moral disengagement refers to a set of cognitive mechanisms that enable individuals to justify or neutralise actions that violate their moral standards, thereby maintaining a positive self-concept despite engaging in ethically questionable behaviour. In the context of digital environments, these mechanisms may become particularly salient due to reduced accountability, diffusion of responsibility, and the normalization of harmful conduct (Runions \u0026amp; Bak, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Cognitive dissonance theory (Festinger, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1957\u003c/span\u003e) elucidates the psychological discomfort arising from inconsistencies between beliefs and behaviours. In contrast, moral disengagement provides a more direct explanation of how such inconsistencies are cognitively resolved and behaviourally sustained.\u003c/p\u003e \u003cp\u003eThese processes assume particular significance during late adolescence and emerging adulthood, periods distinguished by ongoing identity formation, moral reasoning, and self-regulation (Arnett, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Narvaez, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rest, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). During these stages, social media platforms have the potential to amplify moral vulnerability by prioritising visibility, peer approval, and immediate feedback over reflective ethical judgement (Moreno \u0026amp; Uhls, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jackson \u0026amp; Luchner, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Consequently, individuals may be more prone to engaging in behaviours that are incongruent with their moral values, while simultaneously employing mechanisms of disengagement to justify or minimise these inconsistencies. Notwithstanding the burgeoning interest in digital ethics, online behaviour, and moral psychology, the extant literature remains deficient in terms of measurement tools that directly capture the discrepancy between individuals' moral standards and their social media behaviours. Existing instruments primarily assess related constructs such as general moral disengagement or online disinhibition (Udris, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Runions \u0026amp; Bak, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). While these tools provide valuable insights into cognitive mechanisms, they do not specifically focus on the behavioural manifestation of moral disengagement in the form of value\u0026ndash;behaviour inconsistency within social media contexts.\u003c/p\u003e \u003cp\u003eIn order to address this gap, the present study introduces the Social Media Moral Inconsistency Scale (SMMIS). In this study, moral inconsistency is conceptualised not as an independent theoretical construct, but rather as a context-specific behavioural manifestation of moral disengagement in social media environments. The primary objective of the present study is to develop a psychometrically sound instrument capable of assessing the extent to which individuals' social media behaviours diverge from their internalised moral standards. In order to achieve this aim, the study pursues two interrelated objectives. Firstly, the item structure of the SMMIS is developed on the basis of relevant theoretical frameworks and expert evaluation. Secondly, the psychometric properties of the scale are systematically examined through exploratory factor analysis, confirmatory factor analysis, internal consistency analysis, and criterion-related validity. It is anticipated that the resulting scale will contribute to extant literature on digital behaviour, moral self-regulation, and online ethics, while concurrently providing practitioners with a context-sensitive instrument for identifying and addressing moral difficulties in social media environments.\u003c/p\u003e"},{"header":"Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMoral Behavior and Moral Disengagement in Digital Environments\u003c/h2\u003e \u003cp\u003eMoral behaviour is generally defined as adherence to social norms, ethical principles, and internalised values that guide individual actions across contexts (Turiel, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In offline environments, moral conduct is typically regulated through direct interpersonal interaction, immediate feedback, and clearly defined accountability structures. Conversely, digital environments engender psychological and situational factors that have the potential to diminish the congruence between moral standards and behaviour (Christen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Valkenburg et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). One of the most widely cited explanations for this shift is the online disinhibition effect (Suler, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), which suggests that anonymity, invisibility, and reduced social cues lower behavioural inhibition and increase the likelihood of norm-violating actions. Empirical research has demonstrated an association between such conditions and cyberbullying, online aggression, and impulsive communication patterns (Hinduja \u0026amp; Patchin, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lapidot-Lefler \u0026amp; Barak, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Runions \u0026amp; Bak, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, digital environments enable the cultivation of a curated self-presentation and the management of impressions, through which individuals can construct identities that deviate from their offline personas (Goffman, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1959\u003c/span\u003e; Jackson \u0026amp; Luchner, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Isbulan et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a theoretical perspective, these patterns can be more precisely understood through the framework of moral disengagement. Bandura (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) proposed that individuals employ cognitive mechanisms, including moral justification, diffusion of responsibility and dehumanisation, to neutralise self-sanctions and maintain a positive self-image despite engaging in ethically questionable behaviour (Bandura, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, p. 123). In the context of digitally mediated environments, these mechanisms may be amplified due to reduced accountability, the normalisation of harmful conduct, and the structural features of online interaction (Runions \u0026amp; Bak, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Boursier et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Recent research in the field of developmental studies has indicated that adolescents and young adults are particularly susceptible to these processes. During these stages, individuals are actively constructing moral reasoning, identity, and self-regulatory capacities (Rest, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Narvaez, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Digital environments, which frequently prioritise immediacy, visibility, and social approval, have the potential to interfere with reflective ethical judgement and increase vulnerability to disengagement processes (Moreno \u0026amp; Uhls, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Choukas-Bradley \u0026amp; Nesi,2020).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSocial Media and Moral Decision-Making\u003c/h3\u003e\n\u003cp\u003eSocial media platforms have been demonstrated to exert a significant influence on the formation of moral judgement and ethical decision-making processes. In contrast to face-to-face interactions, where moral evaluation is supported by emotional feedback and social consequences, social media environments operate through indirect, delayed, and algorithmically mediated forms of reinforcement (Floridi, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kubin et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Algorithmic curation exerts a pivotal influence on the moral decision-making processes that occur within social media environments, by prioritising content that is emotionally engaging and attention-driven. Empirical research has demonstrated that emotionally charged information disseminates more expeditiously and extensively within online networks, thereby reinforcing impulsive reactions and diminishing deliberative moral evaluation (Vosoughi et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kross et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In a similar vein, the formation of echo chambers has been demonstrated to limit exposure to diverse viewpoints, intensify group polarization, and facilitate moral disengagement by normalising ethically questionable behaviour (Sunstein, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, normative social influence within digital communities has been demonstrated to cause individuals to align their expressed attitudes with perceived group norms, even when these norms contradict their personal values (Dickie et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Schneider et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, these mechanisms function in an interactive manner: emotionally amplified content enhances engagement and reactivity, echo chambers reinforces homogeneous moral perspectives, and normative pressures promote adherence to group-based moral standards. Collectively, these processes contribute to the emergence of context-specific moral disengagement and increase the likelihood of inconsistencies between individuals' internalised values and their online behaviours.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, normative social influence exerts a substantial impact on the shaping of online moral expression. It has been demonstrated that individuals may align their publicly expressed attitudes with perceived group norms, even when these norms conflict with their personal values. This alignment reflects the interaction between social reinforcement mechanisms and moral disengagement processes, whereby individuals adapt their behaviour to maintain social belonging while minimizing internal moral conflict. Taken together, these findings suggest that moral decision-making in social media environments is not solely an individual process, but is shaped by the interaction between cognitive mechanisms, platform structures, and social dynamics.\u003c/p\u003e\n\u003ch3\u003eImplications for Counselling and Psychological Assessment\u003c/h3\u003e\n\u003cp\u003eFrom an applied psychology perspective, moral difficulties emerging in digital contexts have important implications for counselling and psychological assessment. Individuals may encounter a discord between their internalised moral standards and their online behaviour, frequently accompanied by feelings of guilt, discomfort, or ambivalence. These experiences can be interpreted within the framework of cognitive dissonance (Festinger, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1957\u003c/span\u003e), which describes the psychological discomfort arising from inconsistencies between beliefs and actions. In digital contexts, such dissonance may be compounded by repeated exposure to morally ambiguous situations and by the normalisation of ethically questionable behaviours (Pavarini et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; White \u0026amp; Hanley, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This phenomenon can potentially lead to adverse psychological consequences, including emotional distress, identity confusion, and compromised moral self-regulation (Narvaez, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kross et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). From a developmental standpoint, identity formation processes (Marcia, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1966\u003c/span\u003e) may be further complicated by the pressures of maintaining idealised or socially desirable online identities (Jackson \u0026amp; Luchner, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For counsellors and mental health professionals, these patterns underscore the significance of evaluating not only observable behaviours but also the underlying discrepancies between values and actions. It is submitted that a more profound understanding of these discrepancies may facilitate the implementation of more effective interventions. Such interventions are proposed to be aimed at strengthening ethical awareness, self-regulation, and psychological well-being in digital environments.\u003c/p\u003e\n\u003ch3\u003eExisting Measures and Limitations\u003c/h3\u003e\n\u003cp\u003eDespite the considerable expansion of research in the fields of online behaviour and digital ethics, extant measurement tools continue to exhibit limitations in their capacity to directly evaluate the discrepancies between moral values and social media behaviour. Instruments such as the Online Disinhibition Scale (Udris, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) focus on reduced behavioural inhibition, while measures of cyberbullying primarily assess overt harmful actions (Hinduja \u0026amp; Patchin, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In a similar vein, moral disengagement scales evaluate justificatory cognitive mechanisms as opposed to the behavioural manifestation of value\u0026ndash;behaviour inconsistency (Bandura, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Runions \u0026amp; Bak, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). While these instruments provide important insights into related constructs, they do not specifically capture the extent to which individuals' online behaviours diverge from their internalised moral standards within social media contexts. This limitation restricts both empirical investigation and applied assessment of moral self-regulation in digital environments (Parry et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eThe Need for the SMMIS\u003c/h3\u003e\n\u003cp\u003eIn response to this gap, the present study introduces the Social Media Moral Inconsistency Scale (SMMIS). In this theoretical framework, moral inconsistency is conceptualised as a context-specific behavioural manifestation of moral disengagement in social media environments. The SMMIS has been developed for the purpose of evaluating the extent to which individuals' online behaviours deviate from their endorsed moral values. The development of this scale is of particular relevance to the fields of counselling, education and mental health practice, where professionals require theoretically grounded and empirically validated tools to assess digital moral functioning. The operationalisation of value\u0026ndash;behaviour discrepancies may facilitate the identification of individuals experiencing moral conflict, support targeted interventions, and contribute to the promotion of ethical self-regulation in digital contexts. Accordingly, the SMMIS aims to complement existing measures by providing a more context-sensitive assessment tool, thereby advancing research on moral psychology, digital behaviour, and online ethics.\u003c/p\u003e"},{"header":"Methods","content":" \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eResearch Design\u003c/h2\u003e \u003cp\u003eThe present study employed a quantitative, cross-sectional design in order to develop and validate the Social Media Moral Inconsistency Scale (SMMIS). The scale was conceptualised as a context-specific operationalisation of moral disengagement in social media environments, reflected in discrepancies between individuals' internalised moral standards and their enacted online behaviours. The scale development process was conducted in accordance with established psychometric principles, incorporating sequential stages of item generation, content validation, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and reliability and validity assessment (Clark \u0026amp; Watson, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; DeVellis, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kline, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This multi-stage approach was designed to ensure conceptual clarity, structural validity, and measurement reliability.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eParticipants were recruited through convenience sampling across three independent data collection phases corresponding to EFA, CFA, and criterion validity analyses. The participants were all university students who were actively engaged in the use of social media. The EFA sample consisted of 261 participants (169 female, 92 male; M_age\u0026thinsp;=\u0026thinsp;23.40), the CFA sample included 155 participants (97 female, 58 male), and the criterion validity sample comprised 55 participants (33 female, 22 male). Participants were drawn from multiple regions of Turkey, including El\u0026acirc;zığ, Diyarbakır, Konya, Bilecik, Istanbul, and Bursa. The demographic characteristics for each phase are outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Characteristics of the Study Group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalysis Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSample Size (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMale (n)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExploratory Factor Analysis (EFA)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConfirmatory Factor Analysis (CFA)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCriterion Validity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe age range of 18\u0026ndash;27 years corresponds to the developmental stages of late adolescence and emerging adulthood, which are characterised by ongoing moral reasoning, identity formation, and self-regulation (Arnett, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Narvaez, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The inclusion criteria stipulated that participants must have been currently enrolled in a university programme and to have a regular engagement with social media platforms. Participation was voluntary, anonymous, and informed consent was obtained from all subjects.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eInstrument Development\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eItem Generation\u003c/h2\u003e \u003cp\u003eThe development of the SMMIS commenced with a theoretically driven item generation process grounded in moral disengagement theory (Bandura, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) and research on digital behaviour and online disinhibition (Suler, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Moreover, the conceptual boundaries of the construct were informed by classical frameworks of moral development (Kohlberg, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1981\u003c/span\u003e; Turiel, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). An initial pool of 21 items was generated to capture behavioural manifestations of value\u0026ndash;behaviour discrepancies in social media contexts. The items were designed to reflect situations in which individuals act inconsistently with their endorsed moral standards in digitally mediated interactions.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eContent Validation\u003c/h2\u003e \u003cp\u003eThe content validity of the scale was evaluated by a panel of seven experts in psychology, measurement, and digital behaviour. The items were then subjected to a rigorous evaluation by experts, who appraised them based on three criteria: relevance, clarity, and the representativeness of the construct. The Content Validity Ratio (CVR) was calculated using Lawshe's (1975) method. In light of the expert feedback received, the item pool was reduced to 18 items. This action was taken to ensure adequate content representation while improving clarity and precision. A pilot study (n\u0026thinsp;=\u0026thinsp;30) was conducted to evaluate the comprehensibility of items and the clarity of responses. Minor revisions were made based on participant feedback.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eData Collection Procedure\u003c/h2\u003e \u003cp\u003eThe data were collected through an online survey administered via Google Forms. Participants were recruited via university-based communication channels, including email groups and student networks. Participation was voluntary and anonymous, and no incentives were provided. Prior to participation, informed consent was obtained digitally.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eSocial Media Moral Inconsistency Scale (SMMIS)\u003c/h2\u003e \u003cp\u003eThe SMMIS is a self-report instrument developed in this study to assess behavioural manifestations of moral disengagement in social media contexts. Following exploratory factor analysis, the final scale consisted of 12 items with a unidimensional structure. The items are evaluated using a five-point Likert scale, ranging from 1 (Strongly Disagree) to 5 (Strongly Agree). Scores on this scale are positively correlated with levels of moral inconsistency, which is indicative of a greater degree of disengagement from internal moral standards in digital environments.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMoral Integrity Scale (MIS)\u003c/h2\u003e \u003cp\u003eThe criterion validity of the scale was assessed using the Moral Integrity Scale (MIS; Schlenker, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), which had been adapted into Turkish by Okan and Ekşi (2020). The MIS has been developed as a measure of the extent to which individuals act in accordance with their moral values. In addition, it provides an external criterion for evaluating the SMMIS that is theoretically relevant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eThe data were analysed using SPSS 25 and AMOS 24. Exploratory Factor Analysis (EFA) was performed using Principal Axis Factoring with Promax rotation to identify the underlying factor structure. The adequacy of the samples was assessed using the Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) measure and Bartlett's test of sphericity. Confirmatory Factor Analysis (CFA) was conducted to evaluate the fit of the factor structure obtained from EFA. The model fit was assessed using multiple indices, including the chi-squared divided by degrees of freedom (χ\u0026sup2;/df), the Comparative Fit Index (CFI), the Incremental Fit Index (IFI), the Goodness-of-Fit Index (GFI), the Root Mean Square Error of Approximation (RMSEA), and the Standardised Root Mean Square Residual (SRMR). The internal consistency of the scale was evaluated using Cronbach's alpha. The criterion validity of the SMMIS was examined through Pearson correlation analysis between the SMMIS and the Moral Integrity Scale.\u003c/p\u003e \u003c/div\u003e "},{"header":"Findings","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003ePreliminary Analyses\u003c/h2\u003e \u003cp\u003ePrior to the extraction of factors, the dataset was subjected to rigorous evaluation to ascertain its suitability for factor analysis. The Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) measure of sampling adequacy was .881, indicating a high level of shared variance among items and confirming that the sample was appropriate for factor analytic procedures. Bartlett's Test of Sphericity was statistically significant (χ\u0026sup2; = 835.008, df\u0026thinsp;=\u0026thinsp;66, p \u0026lt; .001), demonstrating that the correlation matrix significantly deviates from an identity matrix. This finding suggests that the observed variables are sufficiently intercorrelated to justify the application of factor analysis. When considered as a whole, these indices offer compelling evidence that the data satisfy the underlying assumptions necessary for reliable and interpretable factor extraction.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eExploratory Factor Analysis and Factor Structure\u003c/h2\u003e \u003cp\u003eExploratory Factor Analysis (EFA) was conducted using Principal Axis Factoring with Promax rotation to examine the latent structure of the Social Media Moral Inconsistency Scale (SMMIS). The selection of Principal Axis Factoring was based on its suitability for identifying latent constructs in psychological data that may deviate from multivariate normality, while Promax rotation was preferred to allow for potential correlations among underlying dimensions. Prior to the extraction of factors, the adequacy of the dataset was rigorously assessed. The Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) measure of sampling adequacy was found to be .881, indicating a very good level of adequacy, while Bartlett's Test of Sphericity was statistically significant (χ\u0026sup2;(66)\u0026thinsp;=\u0026thinsp;835.008, p \u0026lt; .001). These results confirm that the correlation matrix was factorable and that the data met the necessary assumptions for factor analysis.\u003c/p\u003e \u003cp\u003eThe EFA yielded a clear and interpretable unidimensional structure, accounting for 47.07% of the total variance (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Within the domain of psychological scale development, particularly within the social sciences, this level of explained variance is regarded as acceptable in light of the complexity and multidimensional nature of human behaviour. The emergence of a single dominant factor suggests that the items coherently reflect a unified latent construct. This finding provides empirical support for the conceptualisation of moral inconsistency as a structurally integrated and context-specific psychological phenomenon within social media environments.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExploratory Factor Analysis Results for the SMMIS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKMO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBartlett\u0026rsquo;s χ\u0026sup2; (df\u0026thinsp;=\u0026thinsp;66)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e835.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ep-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Variance Explained (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor Loading\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003em15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI may ignore my moral values to get likes and approval on social media.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.764\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003em12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI try to make myself look different and better than I am on social media.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.728\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003em14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLying on social media is not a problem for having fun or getting attention.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003em11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI may compromise my own values for the sake of being popular on social media.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.714\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003em19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI usually exaggerate my real life in my posts.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.707\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003em20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI may share inaccurate information in order to gain the favour of others.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003em9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI make posts on social media that I am not sincere in order not to be criticised by others.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003em22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI may mislead others on social media for my own interests.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003em16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlthough I am not honest in my social media posts, I consider this situation normal.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.668\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003em18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI display attitudes and values on social media that I do not apply in real life.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003em13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI may do things I would not normally do in order to attract attention on social media.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003em10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI do not mind spreading false information in order to gain profit on social media.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eNote\u003c/b\u003e: All items loaded on a single factor representing Social Media Moral Inconsistency. Items with factor loadings below .50 were excluded during the analysis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eItem retention was guided by statistical thresholds and theoretical considerations. In line with established psychometric recommendations, items with factor loadings below 0.50 were excluded in order to ensure a robust and interpretable factor structure. Following this process, the final scale comprised 12 items. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, all the items that were kept had substantial factor loadings, ranging from 0.606 to 0.764. This indicates that they are strongly associated with the underlying latent construct. The relatively high and homogeneous distribution of factor loadings suggests that the items function as stable and reliable indicators of the same construct, thereby reinforcing the scale's internal coherence at a structural level.\u003c/p\u003e \u003cp\u003eTaken together, these findings provide strong preliminary evidence for the construct validity of the SMMIS. The convergence of all items on a single factor indicates that moral inconsistency in social media contexts can be operationalised as a unidimensional construct. This lends support to the theoretical positioning of moral inconsistency as a behavioural manifestation of moral disengagement, reflecting a consistent pattern of discrepancies between values and behaviour within digitally mediated environments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eConfirmatory Factor Analysis (CFA)\u003c/h2\u003e \u003cp\u003eConfirmatory Factor Analysis (CFA) was conducted to evaluate the adequacy of the one-factor model identified through the exploratory analysis. The analysis was conducted utilising AMOS 24 within a structural equation modelling framework. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the standardized path diagram of the proposed model. The model specifies a single latent construct, termed Social Media Moral Inconsistency, to which all 12 observed variables are assigned. All items demonstrated meaningful and statistically substantial standardized loadings, ranging between approximately .56 and .70, indicating that each item contributes adequately to the representation of the latent construct. The magnitude of these loadings suggests a stable measurement structure and supports the assumption that the items function as reliable indicators of a common underlying dimension.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eModel fit was evaluated using multiple goodness-of-fit indices to provide a comprehensive assessment of the model\u0026rsquo;s adequacy (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGoodness-of-Fit Indices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFit Index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood Fit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcceptable Fit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObtained Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eχ\u0026sup2;/df\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRMSEA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSRMR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIFI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCFI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGFI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe goodness-of-fit indices demonstrate that the proposed model exhibits an acceptable and theoretically coherent level of fit. Despite the fact that the χ\u0026sup2;/df and RMSEA values do not meet the more stringent criteria for \"good fit,\" they fall within widely accepted thresholds, thereby suggesting that the model adequately represents the observed data. Conversely, the SRMR value (.033) signifies a robust fit, indicative of a minimal residual error. In a similar vein, the comparative fit indices \u0026ndash; CFI (.921) and IFI (.922) \u0026ndash; exceed the recommended minimum threshold of .90, thereby providing further support for the adequacy of the model. The GFI value (.909) further indicates an acceptable level of global model fit. When considered as a whole, these indices indicate that the model attains an acceptable equilibrium between parsimony and explanatory power. It is important to note that the results of the CFA replicate and confirm the unidimensional structure identified in the EFA, thereby providing robust evidence for the structural validity of the SMMIS.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eReliability and Criterion-Related Validity\u003c/h2\u003e \u003cp\u003eThe concurrent examination of the reliability and criterion-related validity of the Social Media Moral Inconsistency Scale (SMMIS) was conducted. The scale's internal consistency was found to be high, with a Cronbach's alpha coefficient of .896 indicating strong inter-item homogeneity. Additionally, the McDonald's omega coefficient (ω\u0026thinsp;=\u0026thinsp;.902) supports the robustness of the scale further, offering a more precise estimate of composite reliability, especially when the assumption of tau-equivalence is not fully met. Furthermore, the item\u0026ndash;total correlation coefficients ranged from .52 to .71, indicating that all items meaningfully contributed to the overall construct and demonstrated satisfactory discrimination (See Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability and Criterion-Related Validity Results for the SMMIS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCronbach\u0026rsquo;s α\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.896\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMcDonald\u0026rsquo;s ω\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.902\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eItem\u0026ndash;Total Correlation Range\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.52 \u0026ndash; .71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCorrelation with MIS (r)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;.540***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cb\u003eNote.\u003c/b\u003e ***p \u0026lt; .001. MIS\u0026thinsp;=\u0026thinsp;Moral Integrity Scale.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCriterion-related validity was assessed by examining the relationship between the SMMIS and the Moral Integrity Scale (MIS) in an independent sample of 55 participants. The analysis revealed a statistically significant and moderately strong negative correlation (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.540, p \u0026lt; .001), indicating that higher levels of moral inconsistency in social media contexts are associated with lower levels of moral integrity. The magnitude and direction of this relationship are theoretically consistent with the conceptualisation of moral inconsistency as a behavioural manifestation of moral disengagement. Taken together, these findings provide strong evidence for the internal consistency and criterion-related validity of the SMMIS.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eSummary of Psychometric Findings\u003c/h2\u003e \u003cp\u003eThe findings of the present study provide robust and converging evidence for the psychometric adequacy of the Social Media Moral Inconsistency Scale (SMMIS). The results of the study can be summarised as follows. Initially, the scale exhibited a discernible and theoretically consistent unidimensional structure, signifying that moral inconsistency in social media contexts can be conceptualised as a singular latent construct. Secondly, the factor solution accounted for a substantial proportion of the total variance, thereby supporting the explanatory power of the scale within the domain of social and behavioural research. Thirdly, the internal consistency of the scale was found to be high, as evidenced by Cronbach's alpha and McDonald's omega coefficients. These findings indicate strong inter-item homogeneity and measurement reliability. Fourthly, confirmatory factor analysis yielded acceptable goodness-of-fit indices, thereby providing empirical support for the structural validity of the proposed model. The scale demonstrated theoretically consistent criterion-related validity, as reflected in its significant negative association with moral integrity. Taken together, these findings indicate that the SMMIS is a psychometrically sound, reliable, and valid instrument for assessing value\u0026ndash;behaviour discrepancies in social media environments.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study sought to develop and validate the Social Media Moral Inconsistency Scale (SMMIS) as a context-sensitive instrument capturing discrepancies between individuals' internalised moral standards and their behaviour in social media environments. The findings provide consistent and converging evidence that the scale demonstrates a stable unidimensional structure, acceptable model fit, high internal consistency, and theoretically meaningful criterion-related validity. A pivotal finding of the study is the demonstration that moral inconsistency in digital environments can be conceptualised as a coherent and measurable psychological construct, rather than a collection of isolated or situational behaviours. The emergence of a unidimensional structure is consistent with prior research suggesting that online moral behaviour is systematically shaped by underlying cognitive and regulatory processes rather than episodic reactions (Bandura, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Suler, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Runions \u0026amp; Bak, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Christen et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Valkenburg et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a theoretical standpoint, the findings provide substantial support for the proposition that moral inconsistency may be conceptualised as a behavioural manifestation of moral disengagement. The observed negative correlation between moral integrity and online behaviour provides empirical evidence in support of the argument that individuals who exhibit a greater discrepancy between their values and online behaviour are more likely to disengage from internal moral standards. This finding aligns with Bandura's (1999, 2016) framework and is consistent with research indicating that moral disengagement predicts ethically problematic online behaviours, including cyberbullying and online aggression (Hinduja \u0026amp; Patchin, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Runions \u0026amp; Bak, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Boursier et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Significantly, the present findings extend this framework by situating moral disengagement within the specific affordances of digital environments. The presence of features such as anonymity, reduced accountability, and invisibility has been demonstrated to facilitate behavioural disinhibition and weaken moral self-regulation (Joinson, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lapidot-Lefler \u0026amp; Barak, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Best et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Furthermore, the presence of algorithmically mediated environments has been demonstrated to have the capacity to amplify emotionally charged content and reinforce normative pressures, thereby increasing the likelihood of morally inconsistent behaviour (Vosoughi et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sunstein, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kross et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Schneider et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results of the study can also be interpreted through the lens of cognitive dissonance theory (Festinger, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1957\u003c/span\u003e). As demonstrated in previous studies, individuals tend to experience psychological discomfort when their behaviour deviates from their personal values. This discomfort often prompts individuals to engage in efforts to restore internal consistency, as theorised by Festinger (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1957\u003c/span\u003e). However, in digital contexts, this tension may be mitigated through mechanisms of disengagement, thereby enabling individuals to sustain a coherent self-concept despite behavioural inconsistencies (Bandura, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Suler, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; G\u0026aacute;mez-Guadix et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). From a broader theoretical perspective, the findings reinforce the view that moral behaviour is context-dependent and dynamically shaped by environmental affordances (Turiel, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Floridi, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Moreno \u0026amp; Uhls, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is evident that social media environments differ from offline contexts in terms of immediacy, visibility, and social reinforcement processes, which have the capacity to alter the conditions under which moral decisions are made (Gillespie, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kubin et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eTheoretical Contributions\u003c/h2\u003e \u003cp\u003eThe present study makes several important contributions to the existing literature on the subject. Firstly, it introduces a psychometrically validated instrument that directly captures value\u0026ndash;behaviour discrepancies in social media contexts. In contrast to extant measures that predominantly evaluate moral disengagement mechanisms or particular online behaviours, the SMMIS operationalises the behavioural expression of moral disengagement, thereby furnishing a more direct and context-sensitive evaluation of moral functioning in digital environments. Secondly, the study contributes to the conceptual clarification of moral inconsistency as a distinct but related construct. Moral disengagement is defined as the cognitive mechanisms that justify unethical behaviour. Conversely, moral inconsistency is reflected in the observable divergence between internalised values and enacted behaviours. In this sense, the SMMIS captures the behavioural manifestation of these underlying mechanisms rather than the mechanisms themselves. By distinguishing between these levels, the study advances a more nuanced understanding of how moral processes unfold in digitally mediated contexts.\u003c/p\u003e \u003cp\u003eThirdly, the findings contribute to the integration of moral psychology and digital behaviour research by situating moral disengagement within the structural and social affordances of social media environments. This integration underscores the manner in which platform characteristics\u0026mdash;such as anonymity, visibility, and algorithmic reinforcement\u0026mdash;interact with cognitive mechanisms to shape moral behaviour (Bandura, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Suler, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Valkenburg et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Fourthly, the study lends support to the expanding corpus of literature positing that moral functioning is not stable across contexts but is instead dynamically shaped by technological environments. The study provides empirical support for the view that moral regulation is context-dependent and influenced by digital affordances (Floridi, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gillespie, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Parry et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) by demonstrating that value\u0026ndash;behaviour discrepancies can be systematically measured in social media contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003ePractical Implications\u003c/h2\u003e \u003cp\u003eFrom an applied perspective, the SMMIS offers a valuable tool for assessing moral functioning in digital contexts. It has been demonstrated by previous research that digital literacy and ethical awareness are significant factors in the prevention of harmful online behaviours (Hinduja \u0026amp; Patchin, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Best et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The SMMIS may support such efforts by enabling practitioners to identify discrepancies between moral values and behaviour. Within the context of counselling and educational settings, the scale has been demonstrated to facilitate interventions designed to enhance self-regulation and ethical decision-making processes (White \u0026amp; Hanley, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, it may be employed to evaluate interventions targeting moral disengagement and online behaviour. At a societal level, the scale provides a framework for examining issues such as misinformation, cyberbullying, and online polarization, where moral inconsistency plays a critical role (Sunstein, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vosoughi et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Choukas-Bradley \u0026amp; Nesi, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e \u003cp\u003eDespite the contributions made by this study, it is important to acknowledge the limitations of the research. The utilisation of a university student sample serves to constrain the extent to which the findings can be generalised, and it is therefore recommended that future research examine the scale across diverse populations and cultural contexts. The utilisation of self-report measures engenders the potential for the occurrence of social desirability bias. It is recommended that future research endeavours consider the integration of behavioural or multi-method approaches, with a view to enhancing the rigour and relevance of the findings. The cross-sectional design of the study also imposes limitations on the extent to which causal interpretations can be made. It is imperative that longitudinal studies are conducted in order to examine the development of moral inconsistency over time and its relationship to psychological outcomes. In light of the rapidly evolving nature of digital environments, future research should explore platform-specific dynamics and periodically reassess the scale's validity.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, the present study provides robust empirical evidence that the Social Media Moral Inconsistency Scale (SMMIS) is a reliable and valid instrument for assessing value\u0026ndash;behaviour discrepancies in social media contexts. The study operationalises moral inconsistency as a measurable construct, thereby advancing the assessment of moral functioning in digitally mediated environments. Of particular significance is the integration of moral disengagement theory with research on digital behaviour, which offers a theoretically grounded framework for understanding how contextual affordances of social media shape ethical self-regulation. By adopting this approach, the SMMIS transcends mere descriptive accounts of online behaviour, thereby facilitating the systematic investigation of underlying moral processes. The scale provides a methodological tool and a conceptual lens for future research, contributing to a more nuanced understanding of moral functioning, self-regulation, and ethical behaviour in contemporary digital contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompliance with Ethical Standards\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConflict of Interest\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe author(s) declare that there is no conflict of interest.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInformed Consent\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInformed consent was obtained from all individual participants included in the study.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical Approval\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThis study was approved by the Ethics Committee of Fırat University (Approval No: 33842, Date: 24.04.2025). All procedures performed were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to Publish\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe participants provided informed consent for the publication of anonymized data.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e T\u0026Uuml;BITAK\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eN.O. conceived the study, designed the methodology, conducted the analyses, and wrote the manuscript. The author reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArnett JJ. Emerging adulthood: A theory of development from the late teens through the twenties. Am Psychol. 2000;55(5):469\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/0003-066X.55.5.469\u003c/span\u003e\u003cspan address=\"10.1037/0003-066X.55.5.469\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBandura A. Moral disengagement in the perpetration of inhumanities. Personality Social Psychol Rev. 1999;3(3):193\u0026ndash;209. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1207/s15327957pspr0303_3\u003c/span\u003e\u003cspan address=\"10.1207/s15327957pspr0303_3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBandura A. Moral disengagement: How people do harm and live with themselves. Worth; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBest P, Manktelow R, Taylor B. Online communication, social media and adolescent wellbeing: A systematic narrative review. Child Youth Serv Rev. 2014;41:27\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.childyouth.2014.03.001\u003c/span\u003e\u003cspan address=\"10.1016/j.childyouth.2014.03.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoursier V, Gioia F, Griffiths MD. Do selfie-expectancies and social appearance anxiety predict adolescents\u0026rsquo; problematic social media use? Comput Hum Behav. 2020;110:106395. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chb.2020.106395\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2020.106395\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoukas-Bradley S, Nesi J. (2020). Applying developmental theory to adolescent peer influence processes in the social media context. In N. Van Zalk \u0026amp; C. P. Monks, editors, \u003cem\u003eOnline peer engagement in adolescence: Positive and negative aspects of online social interaction\u003c/em\u003e (pp. 140\u0026ndash;162). Routledge. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4324/9780429468360-9\u003c/span\u003e\u003cspan address=\"10.4324/9780429468360-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristen M, Gordijn B, Loi M, editors. The ethics of cybersecurity. Springer; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClark LA, Watson D. Constructing validity: Basic issues in objective scale development. Psychol Assess. 1995;7(3):309\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/1040-3590.7.3.309\u003c/span\u003e\u003cspan address=\"10.1037/1040-3590.7.3.309\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeVellis RF. Scale development: Theory and applications. 4th ed. SAGE; 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDickie R, Rasmussen S, Cain R, Williams L, MacKay W. The effects of perceived social norms on handwashing behaviour in students. Psychol Health Med. 2017;23(2):154\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13548506.2017.1338736\u003c/span\u003e\u003cspan address=\"10.1080/13548506.2017.1338736\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFestinger L. A theory of cognitive dissonance. Stanford University Press; 1957.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFloridi L. The onlife manifesto: Being human in a hyperconnected era. Springer; 2016. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-04093-6\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-04093-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026aacute;mez-Guadix M, Villa-George FI, Calvete E. Measurement and analysis of the cognitive-behavioral model of generalized problematic internet use among Mexican adolescents. J Adolesc. 2012;35(6):1581\u0026ndash;91. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.adolescence.2012.06.005\u003c/span\u003e\u003cspan address=\"10.1016/j.adolescence.2012.06.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGillespie T. Custodians of the internet: Platforms, content moderation, and the hidden decisions that shape social media. Yale University Press; 2018.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoffman E. The presentation of self in everyday life. Doubleday; 1959.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHinduja S, Patchin JW. Social influences on cyberbullying behaviors among middle and high school students. J Youth Adolesc. 2013;42:711\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10964-012-9902-4\u003c/span\u003e\u003cspan address=\"10.1007/s10964-012-9902-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsbulan O, Cam E, Griffiths MD. The mediating effect of social network identity management on the relationship between personality traits and social media addiction among pre-service teachers. BMC Psychol. 2024;12:146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40359-024-01653-5\u003c/span\u003e\u003cspan address=\"10.1186/s40359-024-01653-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJackson CA, Luchner AF. Self-presentation mediates the relationship between self-criticism and emotional response to Instagram feedback. Pers Indiv Differ. 2018;133:1\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.paid.2017.04.052\u003c/span\u003e\u003cspan address=\"10.1016/j.paid.2017.04.052\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoinson AN. (2007). Disinhibition and the internet. In J. Gackenbach, editor, \u003cem\u003ePsychology and the internet: Intrapersonal, interpersonal, and transpersonal implications\u003c/em\u003e (2nd ed., pp. 75\u0026ndash;92). Academic Press. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-012369425-6/50023-0\u003c/span\u003e\u003cspan address=\"10.1016/B978-012369425-6/50023-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKemp S. (2025). Digital 2025: Global overview report. \u003cem\u003eDataReportal\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://datareportal.com/reports/digital-2025-global-overview-report\u003c/span\u003e\u003cspan address=\"https://datareportal.com/reports/digital-2025-global-overview-report\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKline RB. Principles and practice of structural equation modeling. 4th ed. Guilford Press; 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKohlberg L. The philosophy of moral development: Moral stages and the idea of justice. Harper \u0026amp; Row; 1981.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKross E, Verduyn P, Sheppes G, Costello CK, Jonides J, Ybarra O. Social media and well-being: Pitfalls, progress, and next steps. Trends Cogn Sci. 2021;25(1):55\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.tics.2020.10.005\u003c/span\u003e\u003cspan address=\"10.1016/j.tics.2020.10.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKubin E, Puryear C, Schein C, Gray K. (2021). Personal experiences bridge moral and political divides better than facts. \u003cem\u003eProceedings of the National Academy of Sciences, 118\u003c/em\u003e(6), e2008389118. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.2008389118\u003c/span\u003e\u003cspan address=\"10.1073/pnas.2008389118\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLapidot-Lefler N, Barak A. Effects of anonymity, invisibility, and lack of eye-contact on toxic online disinhibition. Comput Hum Behav. 2012;28(2):434\u0026ndash;43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chb.2011.10.014\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2011.10.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawshe CH. A quantitative approach to content validity. Pers Psychol. 1975;28(4):563\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1744-6570.1975.tb01393.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1744-6570.1975.tb01393.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarcia JE. Development and validation of ego-identity status. J Personal Soc Psychol. 1966;3(5):551\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1037/h0023281\u003c/span\u003e\u003cspan address=\"10.1037/h0023281\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarengo D, Sindermann C, Elhai JD, Montag C. One social media company to rule them all: Associations between use of Facebook-owned social media platforms, sociodemographic characteristics, and the Big Five personality traits. Front Psychol. 2020;11:936. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpsyg.2020.00936\u003c/span\u003e\u003cspan address=\"10.3389/fpsyg.2020.00936\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreno MA, Uhls YT. Applying an affordances approach and a developmental lens to approach adolescent social media use. Digit Health. 2019;5:1\u0026ndash;4. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/2055207619826678\u003c/span\u003e\u003cspan address=\"10.1177/2055207619826678\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNarvaez D. Moral complexity: The fatal attraction of truthiness and the importance of mature moral functioning. Perspect Psychol Sci. 2010;5(2):163\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/1745691610362351\u003c/span\u003e\u003cspan address=\"10.1177/1745691610362351\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkan N, Eksi H. The adaptation of the Moral Integrity Scale into Turkish and analysis of the psychometric features. Spiritual Psychol Couns. 2020;5:327\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.37898/spc.2020.5.3.124\u003c/span\u003e\u003cspan address=\"10.37898/spc.2020.5.3.124\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParry DA, Davidson BI, Sewall CJR, Fisher JT, Mieczkowski H, Quintana DS. A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use. Nat Hum Behav. 2021;5(11):1535\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41562-021-01117-5\u003c/span\u003e\u003cspan address=\"10.1038/s41562-021-01117-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePavarini G, Hamdi L, Lorimer J, Singh I. Young people's moral attitudes and motivations towards direct-to-consumer genetic testing for inherited risk of Alzheimer disease. Eur J Med Genet. 2021;64(10):104180. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejmg.2021.104180\u003c/span\u003e\u003cspan address=\"10.1016/j.ejmg.2021.104180\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrzybylski AK, Weinstein N. Can you connect with me now? How the presence of mobile communication technology influences face-to-face conversation quality. J Social Personal Relationships. 2013;30(3):237\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/0265407512453827\u003c/span\u003e\u003cspan address=\"10.1177/0265407512453827\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRest JR. Moral development: Advances in research and theory. Praeger; 1986.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRunions KC, Bak M. Online moral disengagement, cyberbullying, and cyber-aggression. Cyberpsychology Behav Social Netw. 2015;18(7):400\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/cyber.2014.0670\u003c/span\u003e\u003cspan address=\"10.1089/cyber.2014.0670\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchlenker BR. Integrity and character: Implications of principled and expedient ethical ideologies. J Soc Clin Psychol. 2008;27(10):1078\u0026ndash;125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchneider S, Beege M, Nebel S, Schnaubert L, Rey GD. The cognitive-affective-social theory of learning in digital environments (CASTLE). Educational Psychol Rev. 2022;34(1):1\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10648-021-09626-5\u003c/span\u003e\u003cspan address=\"10.1007/s10648-021-09626-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuler J. The online disinhibition effect. CyberPsychology Behav. 2004;7(3):321\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/1094931041291295\u003c/span\u003e\u003cspan address=\"10.1089/1094931041291295\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSunstein CR. Republic: Divided democracy in the age of social media. Princeton University Press; 2017. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/9781400884711\u003c/span\u003e\u003cspan address=\"10.1515/9781400884711\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTuriel E. The culture of morality: Social development, context, and conflict. Cambridge University Press; 2002. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/CBO9780511613500\u003c/span\u003e\u003cspan address=\"10.1017/CBO9780511613500\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTuriel E. The development of morality. In: Eisenberg N, Damon W, Lerner RM, editors. Handbook of child psychology: Social, emotional, and personality development. 6th ed. Wiley; 2006. pp. 789\u0026ndash;857.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUdris R. Cyberbullying among high school students in Japan: Development and validation of the online disinhibition scale. Comput Hum Behav. 2014;41:253\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chb.2014.09.036\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2014.09.036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValkenburg PM, Beyens I, Pouwels JL, van Driel II, Keijsers L. Social media use and adolescents\u0026rsquo; self-esteem: Heading for a person-specific media effects paradigm. J Communication. 2021;71(1):56\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/joc/jqaa039\u003c/span\u003e\u003cspan address=\"10.1093/joc/jqaa039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVosoughi S, Roy D, Aral S. The spread of true and false news online. Science. 2018;359(6380):1146\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.aap9559\u003c/span\u003e\u003cspan address=\"10.1126/science.aap9559\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite E, Hanley T. Current ethical dilemmas experienced by therapists who use social media: A systematic review. Counselling Psychother Res. 2024;24:396\u0026ndash;418. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/capr.12678\u003c/span\u003e\u003cspan address=\"10.1002/capr.12678\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"digital morality, moral inconsistency, moral disengagement, cognitive dissonance, digital behaviour","lastPublishedDoi":"10.21203/rs.3.rs-9403077/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9403077/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe rapid expansion of digital communication environments has transformed how individuals regulate and express moral behaviour. While people tend to endorse moral values such as fairness, respect, and responsibility in offline settings, social media platforms may facilitate behaviours that deviate from these standards. Features such as anonymity, reduced accountability, and shifting social norms contribute to this discrepancy. Drawing on the theory of moral disengagement, this study conceptualises \u003cem\u003emoral inconsistency\u003c/em\u003e as the divergence between endorsed values and actual behaviours in social media contexts.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study aimed to develop and validate the Social Media Moral Inconsistency Scale (SMMIS). An initial pool of 21 items was generated based on theoretical frameworks and relevant literature. Following expert evaluation, 18 items were retained for empirical testing. Exploratory factor analysis (EFA) was conducted with 261 participants to identify the underlying structure. Confirmatory factor analysis (CFA) was then performed on an independent sample of 155 participants to validate the model. Internal consistency and criterion-related validity were also assessed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEFA results supported a unidimensional structure comprising 12 items. CFA confirmed the adequacy of this structure, demonstrating acceptable model fit indices (χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.916, RMSEA = .077, SRMR = .033, IFI = .922, CFI = .921, GFI = .909). The scale showed high internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.896). Criterion-related validity was supported by a significant negative correlation with the Moral Integrity Scale (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.540, n\u0026thinsp;=\u0026thinsp;55), indicating that higher moral inconsistency is associated with lower moral integrity.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe findings indicate that the SMMIS is a reliable and valid instrument for assessing moral disengagement in social media contexts. Unlike existing measures that focus primarily on cognitive mechanisms, this scale captures the behavioural manifestation of discrepancies between moral values and online behaviour. The SMMIS provides a context-specific tool for future research on digital ethics and online behaviour.\u003c/p\u003e","manuscriptTitle":"Development and validation of the Social Media Moral Inconsistency Scale (SMMIS): Assessing moral disengagement in digital contexts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 06:07:09","doi":"10.21203/rs.3.rs-9403077/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"281195890943097402737069718968807184043","date":"2026-04-28T07:09:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T14:40:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244806670109638332461645138322523186532","date":"2026-04-20T14:04:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T00:31:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-18T11:23:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-16T04:25:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-16T04:25:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-04-13T11:02:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2d3cfc58-b3b2-4e16-a81c-25c9939e320a","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T06:07:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 06:07:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9403077","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9403077","identity":"rs-9403077","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}