The Digital Dynamics in Romantic Relationships Scale (DDRRS): Development and Validation in Türkiye

The Digital Dynamics in Romantic Relationships Scale (DDRRS): Development and Validation in Türkiye | 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 The Digital Dynamics in Romantic Relationships Scale (DDRRS): Development and Validation in Türkiye Zeynep Tekkuş Set This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8793559/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Digital technologies are fundamentally woven into the fabric of modern romantic relationships, yet existing measurement tools are often problem-focused, unidimensional, and developed in Western cultures. This creates a critical need for a holistic instrument that assesses the complex role of technology within relationships in diverse cultural contexts. This study aimed to develop and validate the Digital Dynamics in Romantic Relationships Scale, a multidimensional instrument grounded in the Turkish context. Data were collected from two independent samples (total N = 735). Exploratory Factor Analysis (n = 364) yielded a 53-item, eight-factor structure capturing both constructive (Communication Quality, Commitment, Online-Offline Integration) and destructive (Harassment/Abuse, Digital Surveillance/Control, Jealousy, Conflict, Ghosting) dynamics. Confirmatory Factor Analysis (n = 371) supported this model with good fit indices (χ²(1286) = 2702.89, CFI = .91, TLI = .90, SRMR = .07, RMSEA = .05). The subscales showed strong internal consistency (α = .78–.97) and robust validity. The Digital Dynamics in Romantic Relationships Scale thus offers a psychometrically sound instrument for researchers and clinicians to systematically evaluate the specific digital processes influencing modern partnerships. Psychology Romantic relationships Digital dynamics Scale development Psychometrics Türkiye Figures Figure 1 Introduction Digital technologies are no longer a separate domain but have become fundamentally woven into the fabric of how modern romantic relationships are initiated, maintained, and terminated (Goldberg et al., 2022 ; Sánchez et al., 2017 ). The once-clear distinction between "online" and "offline" interactions has become increasingly blurred; today, nearly all partner relationships, regardless of their origin, possess a significant digital component. This reality reframes the core research question from studying "online relationships" as a niche category to understanding the role of digital dynamics within all romantic relationships. This shift is particularly relevant in Türkiye, where internet use among individuals aged 16–74 is 88.8% (TurkStat, 2024) and digital platforms are central arenas for relational life (Sánchez et al., 2015 ). Indeed, with research indicating that a significant portion of modern relationships now begin online (Harris & Aboujaoude, 2016 ; Şimşek Özkan & Siyez, 2023 ), it is clear that digital interactions are integral to every phase of a partnership (Fox & Warber, 2013 ; Lukacs & Quan-Haase, 2015 ). The impact of this integration on romantic relationships is inherently dualistic, presenting a complex interplay of constructive opportunities and destructive risks (Billedo et al., 2020 ; Marcum et al., 2018 ; Şimşek Özkan & Siyez, 2023 ). On the one hand, digital platforms can foster constructive dynamics. They afford persistent contact that enhances intimacy and commitment (Billedo et al., 2020 ; Sánchez et al., 2015 ; Schade et al., 2013 ), and the reduced-cue environment can even accelerate self-disclosure and closeness (Wang & Chang, 2010 ; Whitty, 2013 ). Partners can also use social media to publicly affirm their bond, reinforcing their commitment (Goldberg et al., 2022 ; Quiroz & Mickelson, 2021 ). On the other hand, these same technological affordances can fuel destructive dynamics. The expectation of constant connectivity can lead to "technoference," where technology intrudes upon couples' interactions and heightens conflict (Kwok & Wescott, 2020 ). Platform features like visibility and searchability have given rise to digital surveillance, which can trigger jealousy and controlling behaviors (Fox & Tokunaga, 2015 ; Halpern et al., 2017 ; Marcum et al., 2018 ). Moreover, cyberspace has become a venue for new forms of partner abuse and aggression that often co-occur with offline violence (Borrajo et al., 2015 ; Leisring & Giumetti, 2014 ; Wolford-Clevenger et al., 2016 ). Even relationship dissolution has been altered, with distressing practices like "ghosting"—the abrupt cessation of all digital contact—emerging as a common breakup strategy (Biolcati et al., 2022 ; Herrera-López et al., 2024 ). The Duality of Digital Dynamics in Relationships The theoretical foundation for understanding technology's role in relationships rests on the recognized duality that digital environments can simultaneously foster connection and create conflict (Döring, 2002 ). This dual potential requires a framework that accounts for both constructive and destructive dynamics. The constructive potential is best understood by moving past early "cues-filtered-out" perspectives, which argued that the lack of nonverbal cues made computer-mediated communication inherently impersonal (Cornwell & Lundgren, 2001 ; Donn & Sherman, 2002 ; Whitty, 2013 ). A more robust explanation comes from Social Information Processing Theory, which posits that users strategically adapt to these channels, compensating for fewer cues through more elaborate and intentional textual exchanges (Anderson, 2005 ). Building on this, Walther’s ( 1996 ) hyperpersonal model argues that this adaptation can lead to interactions that are even more intimate than face-to-face encounters (Pauley & Emmers-Sommer, 2007 ). While these theories were developed in the context of purely online relationships, their core principles—selective self-presentation, idealization of the partner, and the optimization of messages afforded by asynchronicity (Whitty, 2013 )—are now crucial for explaining how partners in any relationship use digital channels to manage impressions and build closeness. This provides the theoretical justification for measuring constructive dimensions like Communication Quality and Commitment in a digital context. Conversely, the destructive potential of these dynamics is powerfully explained by concepts such as Suler’s ( 2004 ) Online Disinhibition Effect. Suler argued that factors like perceived anonymity and asynchronicity lower inhibitions, leading to either benign disinhibition (e.g., deep self-disclosure) or toxic disinhibition (e.g., aggression, rudeness, and threats). This concept of toxic disinhibition provides a direct theoretical grounding for measuring dimensions such as Harassment/Abuse (Eichenberg et al., 2017 ). Other theories explain additional destructive dynamics. For instance, the drive to reduce ambiguity about a partner’s activities, as described by Uncertainty Reduction Theory, offers a clear motive for the behaviors captured in the Digital Surveillance/Control dimension (Tokunaga, 2011 ). Finally, the concept of technoference—which frames technology as an external stressor that intrudes upon dyadic exchanges even during physical co-presence—provides a clear rationale for measuring technology-induced Conflict (González-Rivera & Hernández-Gato, 2019 ). Together, these theories establish a solid foundation for a multidimensional scale that assesses both the positive and negative facets of digital life within romantic relationships. Measuring Digital Dynamics in Relationships While existing instruments have made valuable contributions, they suffer from three critical limitations that create a clear and urgent need for a new measurement tool. First, the most pervasive limitation is a narrow and problem-focused scope. The majority of existing scales concentrate on negative, often singular, phenomena. For example, even comprehensive tools like the Cyber Dating Violence Questionnaire (Borrajo et al., 2015 ) and the Intimate Partner Cyber Abuse Inventory (Fissel et al., 2022 ) focus exclusively on aggression and abuse. This trend is amplified in scales that isolate a single behavior, such as the Romantic Ghosting Scale (Herrera-López et al., 2024 ), the Interpersonal Electronic Surveillance Scale (Tokunaga, 2011 ), or the Social Media Infidelity-Related Behaviors Scale (McDaniel et al., 2017 ). This predominantly pathology-centric approach fails to provide a holistic view, ignoring the constructive dynamics that simultaneously occur within the same relationship. Second, general relationship quality scales are insufficient. Instruments that measure global concepts like relationship satisfaction are valuable for assessing the overall outcome of a partnership. However, they cannot identify the specific digital processes and behaviors that contribute to that outcome. They answer "how satisfied" a couple is, but not "why" their digital interactions might be enhancing or undermining that satisfaction. Thus, they cannot offer the diagnostic granularity needed to understand the mechanisms of digital dynamics, a gap our scale is designed to fill. Third, many existing scales suffer from cultural and technological irrelevance. Many are platform-specific (e.g., focusing only on Facebook; González-Rivera & Hernández-Gato, 2019 ) or technologically outdated (e.g., focusing on 1990s Usenet; Parks & Floyd, 1996 ). More importantly, they have been overwhelmingly developed and tested in Western cultures (e.g., Abbasi, 2019 ; de Lenne et al., 2019 ; Fox & Tokunaga, 2015 ). The assumption that these tools are universally applicable is flawed, as the meaning of online behaviors varies across cultures (Anderson, 2005 ; Furman & Buhrmester, 2009 ; Whitty, 2013 ). This is powerfully illustrated in adaptation studies conducted in Türkiye. For example, Nacar et al. ( 2021 ) found that the factor structure of the Cyber Aggression in Relationships Scale was not confirmed, and items about monitoring a partner’s account had to be removed because they could be interpreted as trust in the Turkish context. Similarly, Şimşek Özkan and Siyez’s ( 2023 ) adaptation of the Cyberdating Q_A yielded unacceptably low reliability for key subscales (.48 and .59). These issues are not unique to Türkiye, with similar cultural specificity noted in studies from Taiwan (Wang & Chang, 2010 ), Chile (Halpern et al., 2017 ), and India (Nayar & Koul, 2021 ). These limitations collectively underscore the need for a multidimensional, culturally-grounded instrument. The Current Study The limitations detailed above—the narrow scope of existing scales, the insufficiency of general quality measures, and the critical lack of culturally-grounded instruments—highlight a clear gap in the literature. The field requires a tool that moves beyond the flawed "online relationship" concept to assess the specific digital processes within modern partnerships. To address this gap, the primary objective of this study was to develop and validate the DDRRS, an instrument empirically grounded in the Turkish cultural context. The term "Dynamics" was deliberately chosen to signify a focus on the interactive processes, behaviors, and communication patterns that shape a relationship in the digital sphere, rather than just its overall outcome. In line with the dualistic theoretical framework, the scale was designed to holistically assess both constructive and destructive dynamics. Each dimension was selected based on its established relevance in the literature. The constructive dynamics include “Commitment”, the digitally expressed intention to maintain the relationship (Abbasi, 2018 ; Parks & Floyd, 1996 ); “Communication Quality”, the perceived depth and effectiveness of digitally mediated exchanges (Anderson & Emmers-Sommer, 2006 ; Nayar & Koul, 2021 ); and “Online-Offline Integration”, the seamless and healthy transition between digital and face-to-face interactions (Goldberg et al., 2022 ; Whitty, 2013 ). The destructive dynamics consist of “Conflict”, referring to disagreements arising from technology use itself (González-Rivera & Hernández-Gato, 2019 ; Kwok & Wescott, 2020 ); “Digital Surveillance/Control”, which involves strategies to monitor a partner’s online activities (Borrajo et al., 2015 ; Tokunaga, 2011 ); “Jealousy”, negative emotional reactions to a partner's social media activities (Fox & Warber, 2013 ; Sánchez et al., 2017 ); “Harassment/Abuse”, the use of technology to harm or control a partner (Fissel et al., 2022 ; Wolford-Clevenger et al., 2016 ); and “Ghosting”, the unilateral cessation of digital communication to end a relationship (Freedman et al., 2024 ; Herrera-López et al., 2024 ). The inclusion of a termination strategy like ghosting is vital for a truly holistic scale, as it reflects a key, digitally-enabled behavior within the complete relationship lifecycle, from initiation to dissolution. To establish the scale's criterion validity and demonstrate its utility, its relationship with a general measure of romantic relationship quality was examined. Grounded in extensive literature showing that dynamics such as commitment and high-quality communication are consistently linked to positive relational outcomes (e.g., Abbasi, 2018 ; Anderson & Emmers-Sommer, 2006 ), while conflict, jealousy, and abuse are linked to negative outcomes (e.g., Borrajo et al., 2015 ; González-Rivera & Hernández-Gato, 2019 ), it was explicitly hypothesized that the constructive dynamics measured by the DDRRS (Commitment, Communication Quality, Online-Offline Integration) would be positively correlated with relationship quality, while the destructive dynamics (Conflict, Surveillance, Jealousy, Harassment, Ghosting) would be negatively correlated. This approach positions the DDRRS not as a replacement for satisfaction scales, but as a complementary diagnostic tool that helps explain the specific digital mechanisms influencing those broader outcomes. The ultimate aim is to equip researchers and mental health professionals in Türkiye with an up-to-date, psychometrically sound instrument to comprehensively assess and understand romantic relationships in the digital age. Method Recruitment and Participants To develop and validate the scale, a cross-validation design was employed, with data collected from two independent samples. Participants were recruited using a convenience sampling method through online social media platforms (Instagram, Facebook, Twitter/X) and university student groups. The inclusion criteria for the study were (a) being 18 years of age or older, (b) being currently in a romantic relationship, and (c) using digital platforms for communication within that relationship. An initial total of 776 participants was recruited (390 for Sample 1; 386 for Sample 2). During data screening, participants who demonstrated careless responding patterns (e.g., invariant responses across all items) were excluded (26 from Sample 1 and 15 from Sample 2). Consequently, the final analytic sample consisted of two independent groups. Sample 1 (n = 364) was used for the Exploratory Factor Analysis (EFA), and Sample 2 (n = 371) was used for the Confirmatory Factor Analysis (CFA). The sample sizes exceeded recommended benchmarks for scale development, which suggest a sample of 300 is "good" (Comrey & Lee, 2013 ) and a minimum of five participants per item (Bryman & Cramer, 2002 ). Table 1 Demographic characteristics of study participants Characteristic Sample 1 ( n = 364) Sample 2 ( n = 371) Total ( N = 735) n % n % n % Gender Female 200 54.9 206 55.5 406 55.2 Male 164 45.1 165 44.5 329 44.8 Age 18 – 25 314 86.3 316 85.2 630 85.7 26–35 27 7.4 30 8.1 57 7.8 36–45 23 6.3 25 6.7 48 6.5 Education High school or below 11 3.0 16 4.3 27 3.7 University 332 91.2 334 90.0 666 90.6 Graduate 21 5.8 21 5.7 42 5.7 Relationship Status Dating (unmarried) 334 91.8 338 91.1 672 91.4 Married 30 8.2 33 8.9 63 8.6 Relationship Duration 0–1 years 156 42.9 160 43.1 316 43.0 1–3 years 137 37.6 135 36.4 272 37.0 3–5 years 37 10.2 38 10.2 75 10.2 5–10 years 17 4.7 20 5.4 37 5.0 > 10 years 17 4.7 18 4.9 35 4.8 Perceived Role of Online Platforms Negative 34 9.3 33 8.9 67 9.1 Neutral 123 33.8 124 33.4 247 33.6 Positive 207 56.9 214 57.7 421 57.3 The demographic characteristics of the samples are presented in Table 1 . The combined sample consisted predominantly of women (55.2%) and emerging adults aged 18–25 years (85.7%). The majority of participants were university graduates (90.6%) and were in an unmarried/dating relationship (91.4%). Relationship duration varied, with 43.0% reporting relationships of 0–1 year and 37.0% reporting 1–3 years. Regarding perceptions of technology's role in their romantic relationship, 57.3% reported a positive role, 33.6% a neutral role, and 9.1% a negative role. The demographic profiles were highly comparable across the two samples, confirming their suitability for cross-validation. Procedure This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of [blinded] (Date: [blinded], Approval No.: [blinded]). Data were collected between May 2025 and June 2025. Participation was voluntary, and no incentives were offered. Data were collected via an online survey platform. After reviewing an online information sheet detailing the study's purpose, participants provided electronic informed consent. Subsequently, they completed the newly developed DDRRS, the Perceived Romantic Relationship Quality Scale (PRRQS), and a demographic information form. Measures Development of the Digital Dynamics in Romantic Relationships Scale (DDRRS) The objective of this phase was to develop a comprehensive scale to assess the multidimensional digital dynamics that characterize modern romantic relationships. Based on an extensive literature review, an initial item pool of 84 items spanning eight dimensions was created. The scale development process drew upon prior research on romantic relationships in digital context (Billedo et al., 2020 ; Borrajo et al., 2015 ; Fissel et al., 2022 ; Fox & Tokunaga, 2015 ; González-Rivera & Hernández-Gato, 2019 ; Herrera-López et al., 2024 ; Leisring & Giumetti, 2014 ; Lukacs & Quan-Haase, 2015 ; Montero-Fernández et al., 2022 ; Nacar et al., 2021 ; Parks & Floyd, 1996 ; Sánchez et al., 2015 ; Tokunaga, 2011 ; Wang et al., 2022 ; Watkins et al., 2018 ; Wolford-Clevenger et al., 2016 ; Wright, 2015 ). The eight dimensions were identified as Commitment, Communication Quality, Online–Offline Integration, Conflict, Digital Surveillance/Control, Ghosting, Jealousy, and Harassment/Abuse. To ensure content validity, expert evaluations were obtained from five academics holding doctoral degrees in psychology or psychological counseling with research experience in romantic relationships in digital context. Experts were asked to evaluate each item as “necessary,” “useful but not necessary,” or “unnecessary.” They were also asked to review wording clarity, resolve ambiguities, and provide dimensional feedback. Based on this expert feedback, 13 items were removed from the initial pool of 84 items, resulting in a 71-item provisional form. Prior to the main study, the 71-item draft scale was piloted with 68 university students (37 female, 31 male; M age = 21.00, SD = 3.70) who currently in a romantic relationship and actively used digital platforms to communicate with their partner. Following the pilot, individual interviews were conducted to obtain feedback regarding clarity, interpretability, and response difficulty. The majority of participants rated the items as “comprehensible,” and several items were revised for improved clarity, producing the final 71-item version. The final scale encompasses eight dimensions, including “Commitment” (7 items; assesses dedication to the current relationship), “Communication Quality” (15 items; assesses the depth, responsiveness, and effectiveness of digitally mediated exchanges), “Online–Offline Integration” (6 items; assesses the seamless and healthy integration between digital and face-to-face interactions), “Conflict” (8 items; assesses technology-related disagreements and tension), “Digital Surveillance/Control” (12 items; assesses monitoring and controlling behaviors in digital environments), “Ghosting” (7 items; assesses abrupt, unexplained withdrawal from digital communication), “Jealousy” (6 items; assesses jealousy triggered by partners’ digital activities), and “Harassment/Abuse” (10 items; assesses digitally mediated coercive, degrading, or threatening behaviors). Responses were given on a 5-point Likert-type scale from 1 ( Strongly Disagree ) to 5 ( Strongly Agree ). Higher scores on each dimension indicate more frequent experience of that specific dynamic. Several items were reverse-coded to reduce response bias. Perceived Romantic Relationship Quality Scale Romantic relationship quality was assessed using the 6-item PRRQS developed by Fletcher et al. ( 2000 ) and adapted to Turkish by Sağkal and Özdemir ( 2018 ). Sample items include "How satisfied are you with your relationship?" and "How much do you love your partner in your relationship?" Participants rated each item on a 7-point Likert-type scale from 1 ( Not at All ) to 7 ( Very Much ). Higher scores indicate higher perceived romantic relationship quality. The Turkish adaptation study reported strong validity and reliability values (Cronbach's α = .86, composite reliability = .87, test-retest reliability = .81) (Sağkal & Özdemir, 2018 ). In the current study, Cronbach's α was .92, indicating excellent internal consistency. Data Analysis Data analyses were performed using IBM SPSS Statistics 26.0 and AMOS 22.0. As a preliminary step, Harman's single-factor test was conducted to check for common method variance. In both samples, the first unrotated factor accounted for less than 50% of the variance (26.7% in Sample 1 and 27.1% in Sample 2), suggesting that common method bias was not a significant threat to the validity of the findings (Podsakoff et al., 2003 ). To establish the scale's factor structure, a two-stage approach was used. First, an EFA was conducted on the data from Sample 1 to identify the underlying dimensional structure of the initial item pool. Second, a CFA was performed on the data from Sample 2 to test and confirm the model derived from the EFA. Following the factor analyses, the internal consistency of the DDRRS subscales was assessed using Cronbach’s alpha coefficients. Finally, to establish criterion validity, Pearson correlation analyses were conducted to examine the relationships between the DDRRS subscales and scores on the PRRQS. Results Exploratory Factor Analysis EFA was conducted with data from Sample 1 ( n = 364) to determine the factor structure of the DDRRS, which initially contained 71 items. In the initial screening phase, items that failed to load on any factor, had primary loadings below .40, or showed cross-loadings were removed iteratively, re-running the analysis after each deletion. During this process, a total of 18 items (items 5–11, 16–19, 22, 23, 25, 34–36, and 45) were removed from the scale. The EFA results conducted with the remaining 53 items are presented in Table 2 . Prior to analysis, the suitability of the data for factor analysis was examined using the Kaiser-Meyer-Olkin (KMO) sampling adequacy test and Bartlett's test of sphericity. The KMO value was found to be .93, and this value was determined to be at an "excellent" level (Tabachnick & Fidell, 2013 ). Bartlett's test of sphericity was significant ( χ² (1378) = 15324.60, p < .001). These findings supported that the data were suitable for factor analysis. Table 2 EFA results of DDRRS Item Factor Loading Item Factor Loading Factor 1: Harassment/Abuse ( α = .97) Factor 4: Communication Quality ( α = .85) Item 62 .75 Item 12 .59 Item 63 .89 Item 13 .71 Item 64 .88 Item 14 .77 Item 65 .87 Item 15 .76 Item 66 .90 Item 20 .75 Item 67 .89 Item 21 .75 Item 68 .88 Factor 5: Conflict ( α = .85) Item 69 .80 Item 29 .73 Item 70 .70 Item 30 .84 Item 71 .76 Item 31 .82 Factor 2: Digital Surveillance/Control ( α = .90) Item 32 .61 Item 37 .71 Item 33 .58 Item 38 .51 Factor 6: Jealousy ( α = .82) Item 39 .66 Item 56 .59 Item 40 .60 Item 57 .72 Item 41 .68 Item 58 .76 Item 42 .71 Item 59 .58 Item 43 .75 Item 60 .67 Item 44 .66 Item 61 .63 Item 46 .69 Factor 7: Online-Offline Integration ( α = .82) Item 47 .61 Item 24 .56 Item 48 .59 Item 26 .80 Factor 3: Ghosting ( α = .94) Item 27 .82 Item 49 .64 Item 28 .77 Item 50 .68 Factor 8: Commitment ( α = .78) Item 51 .81 Item 1 .71 Item 52 .79 Item 2 .68 Item 53 .77 Item 3 .70 Item 54 .74 Item 4 .67 Item 55 .74 As a result of the EFA conducted using principal components extraction with varimax rotation, 8 factors with eigenvalues greater than 1 were obtained. These factors together explain 67.79% of the total variance. The variance ratios explained by the factors are 16.11%, 11.28%, 9.85%, 7.77%, 7.03%, 6.07%, 5.01%, and 4.66%, respectively. The scree plot (Fig. 1 ) also provided supportive information in determining the number of factors; inspection showed a marked inflection after the eighth factor, after which the curve leveled off. When the factor loadings after rotation were examined, it was observed that the items loaded on theoretically expected factors. All rotated loadings exceeded .40 and each item loaded cleanly on a single factor. The first factor contains 10 items representing the Harassment/Abuse dimension (factor loadings between .70–.90), the second factor contains 10 items representing the Digital Surveillance/Control dimension (factor loadings between .51–.75), the third factor contains 7 items representing the Ghosting dimension (factor loadings between .64–.81), the fourth factor contains 6 items representing the Communication Quality dimension (factor loadings between .59–.77), the fifth factor contains 5 items representing the Conflict dimension (factor loadings between .58–.84), the sixth factor contains 6 items representing the Jealousy dimension (factor loadings between .58–.76), the seventh factor contains 4 items representing the Online–Offline Integration dimension (factor loadings between .56–.82), and the eighth factor contains 4 items representing the Commitment dimension (factor loadings between .67–.71). Internal consistency for the eight dimensions of the DDRRS identified via EFA was assessed using Cronbach's α . All subscales demonstrated high reliability, with α coefficients ranging from .78 to .97, exceeding the conventional .70 criterion (Hair et al., 2010 ). Confirmatory Factor Analysis Based on the EFA results from Sample 1, the DDRRS was found to have a 53-item, eight-factor structure. To confirm this structure, CFA was conducted using data from Sample 2 ( n = 371). Model fit was evaluated using χ², CFI, TLI, SRMR, and RMSEA, applying commonly cited cutoffs (Schumacker & Lomax, 2004 ; Kline, 2011 ). According to these standards, CFI and TLI values of .90 or greater are considered acceptable (and .95 or greater excellent); SRMR and RMSEA values of .08 or less are acceptable (and .05 or less excellent). Initial model fit was inadequate, with results of χ² (1297) = 3351.47, p < .001, CFI = .86, TLI = .86, SRMR = .10, RMSEA = .07 (90% CI [.063, .068]). Inspection of modification indices indicated localized areas of strain; therefore, theoretically justifiable residual covariances were added iteratively between item pairs with closely overlapping content within the same latent factor (Ghosting: items 49–50; Jealousy: 60–61; Communication Quality: 20–21; Harassment/Abuse: 63–64; Digital Surveillance/Control: 39–40). The re-specified model demonstrated acceptable fit with results of χ² (1286) = 2702.89, p < .001, CFI = .91, TLI = .90, SRMR = .07, RMSEA = .05 (90% CI [.047, .052]). All standardized factor loadings were significant (range = .53–.94). Convergent and Discriminant Validity Composite reliability (CR) and average variance extracted (AVE) were calculated for each latent construct using the CFA results from Sample 2. For convergent validity, AVE values were evaluated. Fornell and Larcker ( 1981 ) recommend that these values should be higher than .50. However, if AVE is below .50 but CR exceeds .60, convergent validity may still be considered adequate because AVE is a conservative index (Fornell & Larcker, 1981 ). As shown in Table 3 , since the AVE values of all dimensions except the Commitment dimension were above .50, convergent validity was achieved for these dimensions. Since the AVE value of the Commitment dimension was slightly below the critical threshold (.46) but the CR value was above .60 (.77), it was concluded that convergent validity was also achieved for this dimension. For discriminant validity, the square roots of AVE values presented diagonally in Table 3 were examined. According to the Fornell–Larcker criterion, each square root of AVE should exceed the corresponding inter-construct correlations (Hair et al., 2010 ). The results show that the square roots of AVE values are larger than the correlations between factors. Thus, discriminant validity was supported. Additionally, it was observed that the CR values calculated for all dimensions ranged between .77 and .97 and were above the recommended threshold value of .70 (Fornell & Larcker, 1981 ). These high CR values further support the internal consistency and reliability of the dimensions. Table 3 Convergent and discriminant validity results Variable CR AVE Correlations (1) (2) (3) (4) (5) (6) (7) (8) (1) Harassment/Abuse .97 .77 (.88) (2) Digital Surveillance/Control .92 .51 .52 ** (.71) (3) Ghosting .93 .67 .56 ** .52 ** (.82) (4) Communication Quality .86 .51 − .19 ** .13 * − .15 ** (.71) (5) Conflict .87 .58 .38 ** .43 ** .52 ** .01 (.76) (6) Jealousy .90 .60 .38 ** .48 ** .48 ** .12 * .62 ** (.77) (7) Online-Offline Integration .82 .54 − .19 ** .03 − .28 ** .46 ** − .05 − .06 (.73) (8) Commitment .77 .46 − .30 ** − .13 * − .41 ** .38 ** − .18 ** − .10 .47 ** (.68) Note. Values in parentheses are the square roots of AVE values. CR = Composite Reliability. AVE = Average Variance Extracted. * p < .05, ** p < .01. Criterion Validity To provide evidence of criterion-related validity for the newly developed DDRRS, Pearson product–moment correlations were calculated between its subscales and scores on the PRRQS using data from Sample 2. The correlation coefficients are presented in Table 4 . Table 4 Criterion validity results Variable M SD (1) (2) (3) (4) (5) (6) (7) (8) (9) (1) PRRQ 5.36 1.43 - (2) Harassment/Abuse 1.49 0.87 − .27 ** - (3) Digital Surveillance/Control 2.10 0.85 − .08 * .52 ** - (4) Ghosting 1.79 0.95 − .42 ** .56 ** .52 ** - (5) Communication Quality 3.62 0.95 .42 ** − .19 ** .13 * − .15 ** - (6) Conflict 2.36 1.02 − .22 ** .38 ** .43 ** .52 ** .01 - (7) Jealousy 2.47 1.05 − .14 ** .38 ** .48 ** .48 ** .12 * .62 ** - (8) Online-Offline Integration 3.74 1.06 .51 ** − .19 ** .03 − .28 ** .46 ** − .05 − .06 - (9) Commitment 3.92 0.88 .60 ** − .30 ** − .13 * − .41 ** .38 ** − .18 ** − .10 .47 ** - Note. PRRQ = Perceived Romantic Relationship Quality. * p < .05, ** p < .01. As expected, the DDRRS subscales representing constructive relational dynamics showed moderate to strong positive associations with overall romantic relationship quality. These included Communication Quality ( r = .42, p < .01), Online–Offline Integration ( r = .51, p < .01), and Commitment ( r = .60, p < .01). Conversely, the subscales representing destructive dynamics demonstrated small to moderate negative correlations with romantic relationship quality. These were Harassment/Abuse ( r = − .27, p < .01), Digital Surveillance/Control ( r = − .08, p < .05), Ghosting ( r = − .42, p < .01), Conflict ( r = − .22, p < .01), and Jealousy ( r = − .14, p < .01). Discussion The primary objective of this study was to develop the DDRRS, a psychometrically sound and comprehensive instrument designed to assess the multidimensional digital dynamics of romantic relationships in the Turkish cultural context. The research findings demonstrated that the resulting 53-item, eight-factor scale (Harassment/Abuse, Digital Surveillance/Control, Ghosting, Communication Quality, Conflict, Jealousy, Online–Offline Integration, and Commitment) exhibited a valid and reliable structure based on both exploratory and confirmatory factor analyses. Consistent with theoretical frameworks in the literature, this structure offers a holistic perspective by concurrently assessing constructive and destructive facets of digital relationships. Criterion validity analyses showed that the constructive dimensions of the DDRRS were positively correlated with perceived romantic relationship quality, while the destructive dimensions were negatively correlated, confirming theoretical coherence and supporting the instrument's validity. Unlike existing scales in the literature, which often focus on a single negative dynamic (e.g., harassment/abuse, digital surveillance/control), the DDRRS includes both constructive dimensions such as Communication Quality and Commitment and destructive dimensions such as Harassment/Abuse and Conflict, thereby filling a significant gap in this area. This supports the dual nature of digital environments, which can pave the way for both benign and toxic behaviors, as suggested by Suler ( 2004 ) in the Online Disinhibition Effect framework. Furthermore, the fact that the scale was developed with data directly obtained from participants within Turkish culture is significant for addressing the cultural adaptation problems encountered in the studies of Nacar et al. ( 2021 ) and Şimşek Özkan and Siyez ( 2023 ) and for providing a measurement tool sensitive to local dynamics. The findings of this study have important implications for both theory and practice. Theoretically, the DDRRS offers a holistic and multidimensional framework for understanding the digital domains of relationships. By transcending narrow approaches focused solely on pathological aspects and incorporating constructive dynamics such as commitment and communication quality, this model allows researchers to examine modern relationships from a more balanced perspective. From a practical perspective, the DDRRS is a valuable assessment tool for mental health professionals, counselors, and therapists. Clinicians working with couples can use this scale to differentiate sources of relational strain more clearly; for example, it can guide assessment of whether the underlying dynamic of a couple’s conflict reflects digital surveillance or jealousy. This can allow for more targeted and effective intervention planning. Furthermore, the scale can be used to enrich the content and evaluate the effectiveness of psychoeducational programs for young people in schools and universities on topics such as healthy digital relationship habits, cyber dating violence, and digital privacy. Despite this study’s significant contributions, it has several limitations. First, the sample largely consisted of young, university-educated, unmarried adults aged 18–25, which limits the generalizability of the findings to older age groups, individuals with different educational levels, or married couples. Future studies examining the psychometric properties of the DDRRS across groups differing in age, marital status, and socioeconomic status (including formal tests of measurement invariance) would broaden the instrument's utility. Second, this study employed a cross-sectional design, and data were collected via self-report surveys, precluding causal inferences. Future longitudinal studies could examine changes in digital relationship dynamics over time and how these dynamics influence outcomes such as relationship satisfaction or breakup. Furthermore, because self-reporting is susceptible to biases such as social desirability, future mixed-method or dyadic designs (e.g., partner-paired data, in-depth interviews) could provide a richer and more multifaceted understanding. Finally, cross-cultural studies comparing relationship dynamics in Türkiye with those in other cultures using the DDRRS will be illuminating for distinguishing universal versus culture-specific technology-related relational processes. In conclusion, this study developed the DDRRS, a multidimensional and current measurement tool grounded in Turkish culture. Psychometric analyses demonstrated that the 53-item, eight-factor scale validly and reliably measures both constructive and destructive aspects of digital romantic relationships. The DDRRS addresses a significant gap in the literature by overcoming the limitations of existing tools, such as their one-dimensional focus or exclusive development within Western cultural contexts. It is anticipated that this scale can be used as a resource for researchers and mental health professionals in Türkiye to understand and evaluate romantic relationships in the digital age and to develop effective interventions for related problems. References Abbasi, I. S. (2018). Social media and committed relationships: What factors make our romantic relationship vulnerable? Social Science Computer Review, 37 (3), 425-434 . https://doi.org/10.1177/0894439318770609 Abbasi, I. S. (2019). Social media addiction in romantic relationships: Does user's age influence vulnerability to social media infidelity? Personality and Individual Differences , 139 , 277-280. https://doi.org/10.1016/j.paid.2018.10.038 Anderson, T. L. (2005). 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Cyber aggression within adolescents’ romantic relationships: Linkages to parental and partner attachment. Journal of Youth and Adolescence, 44 (1), 37-47. https://doi.org/10.1007/s10964-014-0147-2 Additional Declarations The authors declare no competing interests. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted 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-8793559","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":586158748,"identity":"ff520a84-7c82-4cee-8fd6-c5aac1c2efdd","order_by":0,"name":"Zeynep Tekkuş Set","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0002-0856-7813","institution":"Department of Psychology, Faculty of Arts and Sciences, Istanbul Arel University, Istanbul, Turkey","correspondingAuthor":true,"prefix":"","firstName":"Zeynep","middleName":"Tekkuş","lastName":"Set","suffix":""}],"badges":[],"createdAt":"2026-02-05 07:28:39","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8793559/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8793559/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102036217,"identity":"6a97d320-3685-4669-9c6f-4bdcdbe68a82","added_by":"auto","created_at":"2026-02-06 12:07:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":34969,"visible":true,"origin":"","legend":"\u003cp\u003eScree plot graph of DDRRS EFA\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8793559/v1/8036388956ad59e4fe3f453b.png"},{"id":102295533,"identity":"78d55633-7afa-45a1-9833-a7072d6d945b","added_by":"auto","created_at":"2026-02-10 10:12:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1080672,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8793559/v1/910f7ae4-e2e2-4c12-a778-73b0c1e38fab.pdf"},{"id":102036216,"identity":"69230379-5b81-4d56-afd3-ce797b220a04","added_by":"auto","created_at":"2026-02-06 12:07:14","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":24925,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8793559/v1/d64c4815df9af960c4a614ee.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eThe Digital Dynamics in Romantic Relationships Scale (DDRRS): Development and Validation in Türkiye\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDigital technologies are no longer a separate domain but have become fundamentally woven into the fabric of how modern romantic relationships are initiated, maintained, and terminated (Goldberg et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; S\u0026aacute;nchez et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The once-clear distinction between \"online\" and \"offline\" interactions has become increasingly blurred; today, nearly all partner relationships, regardless of their origin, possess a significant digital component. This reality reframes the core research question from studying \"online relationships\" as a niche category to understanding the role of digital dynamics within all romantic relationships. This shift is particularly relevant in T\u0026uuml;rkiye, where internet use among individuals aged 16\u0026ndash;74 is 88.8% (TurkStat, 2024) and digital platforms are central arenas for relational life (S\u0026aacute;nchez et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Indeed, with research indicating that a significant portion of modern relationships now begin online (Harris \u0026amp; Aboujaoude, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Şimşek \u0026Ouml;zkan \u0026amp; Siyez, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), it is clear that digital interactions are integral to every phase of a partnership (Fox \u0026amp; Warber, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lukacs \u0026amp; Quan-Haase, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe impact of this integration on romantic relationships is inherently dualistic, presenting a complex interplay of constructive opportunities and destructive risks (Billedo et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Marcum et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Şimşek \u0026Ouml;zkan \u0026amp; Siyez, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). On the one hand, digital platforms can foster constructive dynamics. They afford persistent contact that enhances intimacy and commitment (Billedo et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; S\u0026aacute;nchez et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Schade et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and the reduced-cue environment can even accelerate self-disclosure and closeness (Wang \u0026amp; Chang, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Whitty, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Partners can also use social media to publicly affirm their bond, reinforcing their commitment (Goldberg et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Quiroz \u0026amp; Mickelson, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the other hand, these same technological affordances can fuel destructive dynamics. The expectation of constant connectivity can lead to \"technoference,\" where technology intrudes upon couples' interactions and heightens conflict (Kwok \u0026amp; Wescott, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Platform features like visibility and searchability have given rise to digital surveillance, which can trigger jealousy and controlling behaviors (Fox \u0026amp; Tokunaga, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Halpern et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Marcum et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, cyberspace has become a venue for new forms of partner abuse and aggression that often co-occur with offline violence (Borrajo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Leisring \u0026amp; Giumetti, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Wolford-Clevenger et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Even relationship dissolution has been altered, with distressing practices like \"ghosting\"\u0026mdash;the abrupt cessation of all digital contact\u0026mdash;emerging as a common breakup strategy (Biolcati et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Herrera-L\u0026oacute;pez et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eThe Duality of Digital Dynamics in Relationships\u003c/h3\u003e\n\u003cp\u003eThe theoretical foundation for understanding technology's role in relationships rests on the recognized duality that digital environments can simultaneously foster connection and create conflict (D\u0026ouml;ring, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This dual potential requires a framework that accounts for both constructive and destructive dynamics.\u003c/p\u003e \u003cp\u003eThe constructive potential is best understood by moving past early \"cues-filtered-out\" perspectives, which argued that the lack of nonverbal cues made computer-mediated communication inherently impersonal (Cornwell \u0026amp; Lundgren, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Donn \u0026amp; Sherman, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Whitty, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A more robust explanation comes from Social Information Processing Theory, which posits that users strategically adapt to these channels, compensating for fewer cues through more elaborate and intentional textual exchanges (Anderson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Building on this, Walther\u0026rsquo;s (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) hyperpersonal model argues that this adaptation can lead to interactions that are even more intimate than face-to-face encounters (Pauley \u0026amp; Emmers-Sommer, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). While these theories were developed in the context of purely online relationships, their core principles\u0026mdash;selective self-presentation, idealization of the partner, and the optimization of messages afforded by asynchronicity (Whitty, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u0026mdash;are now crucial for explaining how partners in any relationship use digital channels to manage impressions and build closeness. This provides the theoretical justification for measuring constructive dimensions like Communication Quality and Commitment in a digital context.\u003c/p\u003e \u003cp\u003eConversely, the destructive potential of these dynamics is powerfully explained by concepts such as Suler\u0026rsquo;s (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) Online Disinhibition Effect. Suler argued that factors like perceived anonymity and asynchronicity lower inhibitions, leading to either benign disinhibition (e.g., deep self-disclosure) or toxic disinhibition (e.g., aggression, rudeness, and threats). This concept of toxic disinhibition provides a direct theoretical grounding for measuring dimensions such as Harassment/Abuse (Eichenberg et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Other theories explain additional destructive dynamics. For instance, the drive to reduce ambiguity about a partner\u0026rsquo;s activities, as described by Uncertainty Reduction Theory, offers a clear motive for the behaviors captured in the Digital Surveillance/Control dimension (Tokunaga, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Finally, the concept of technoference\u0026mdash;which frames technology as an external stressor that intrudes upon dyadic exchanges even during physical co-presence\u0026mdash;provides a clear rationale for measuring technology-induced Conflict (Gonz\u0026aacute;lez-Rivera \u0026amp; Hern\u0026aacute;ndez-Gato, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Together, these theories establish a solid foundation for a multidimensional scale that assesses both the positive and negative facets of digital life within romantic relationships.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMeasuring Digital Dynamics in Relationships\u003c/h2\u003e \u003cp\u003eWhile existing instruments have made valuable contributions, they suffer from three critical limitations that create a clear and urgent need for a new measurement tool.\u003c/p\u003e \u003cp\u003eFirst, the most pervasive limitation is a narrow and problem-focused scope. The majority of existing scales concentrate on negative, often singular, phenomena. For example, even comprehensive tools like the Cyber Dating Violence Questionnaire (Borrajo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and the Intimate Partner Cyber Abuse Inventory (Fissel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) focus exclusively on aggression and abuse. This trend is amplified in scales that isolate a single behavior, such as the Romantic Ghosting Scale (Herrera-L\u0026oacute;pez et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the Interpersonal Electronic Surveillance Scale (Tokunaga, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), or the Social Media Infidelity-Related Behaviors Scale (McDaniel et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This predominantly pathology-centric approach fails to provide a holistic view, ignoring the constructive dynamics that simultaneously occur within the same relationship.\u003c/p\u003e \u003cp\u003eSecond, general relationship quality scales are insufficient. Instruments that measure global concepts like relationship satisfaction are valuable for assessing the overall outcome of a partnership. However, they cannot identify the specific digital processes and behaviors that contribute to that outcome. They answer \"how satisfied\" a couple is, but not \"why\" their digital interactions might be enhancing or undermining that satisfaction. Thus, they cannot offer the diagnostic granularity needed to understand the mechanisms of digital dynamics, a gap our scale is designed to fill.\u003c/p\u003e \u003cp\u003eThird, many existing scales suffer from cultural and technological irrelevance. Many are platform-specific (e.g., focusing only on Facebook; Gonz\u0026aacute;lez-Rivera \u0026amp; Hern\u0026aacute;ndez-Gato, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) or technologically outdated (e.g., focusing on 1990s Usenet; Parks \u0026amp; Floyd, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). More importantly, they have been overwhelmingly developed and tested in Western cultures (e.g., Abbasi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; de Lenne et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fox \u0026amp; Tokunaga, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The assumption that these tools are universally applicable is flawed, as the meaning of online behaviors varies across cultures (Anderson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Furman \u0026amp; Buhrmester, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Whitty, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This is powerfully illustrated in adaptation studies conducted in T\u0026uuml;rkiye. For example, Nacar et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that the factor structure of the Cyber Aggression in Relationships Scale was not confirmed, and items about monitoring a partner\u0026rsquo;s account had to be removed because they could be interpreted as trust in the Turkish context. Similarly, Şimşek \u0026Ouml;zkan and Siyez\u0026rsquo;s (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) adaptation of the Cyberdating Q_A yielded unacceptably low reliability for key subscales (.48 and .59). These issues are not unique to T\u0026uuml;rkiye, with similar cultural specificity noted in studies from Taiwan (Wang \u0026amp; Chang, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), Chile (Halpern et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and India (Nayar \u0026amp; Koul, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These limitations collectively underscore the need for a multidimensional, culturally-grounded instrument.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe Current Study\u003c/h3\u003e\n\u003cp\u003eThe limitations detailed above\u0026mdash;the narrow scope of existing scales, the insufficiency of general quality measures, and the critical lack of culturally-grounded instruments\u0026mdash;highlight a clear gap in the literature. The field requires a tool that moves beyond the flawed \"online relationship\" concept to assess the specific digital processes within modern partnerships. To address this gap, the primary objective of this study was to develop and validate the DDRRS, an instrument empirically grounded in the Turkish cultural context. The term \"Dynamics\" was deliberately chosen to signify a focus on the interactive processes, behaviors, and communication patterns that shape a relationship in the digital sphere, rather than just its overall outcome.\u003c/p\u003e \u003cp\u003eIn line with the dualistic theoretical framework, the scale was designed to holistically assess both constructive and destructive dynamics. Each dimension was selected based on its established relevance in the literature. The constructive dynamics include \u0026ldquo;Commitment\u0026rdquo;, the digitally expressed intention to maintain the relationship (Abbasi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Parks \u0026amp; Floyd, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1996\u003c/span\u003e); \u0026ldquo;Communication Quality\u0026rdquo;, the perceived depth and effectiveness of digitally mediated exchanges (Anderson \u0026amp; Emmers-Sommer, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Nayar \u0026amp; Koul, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); and \u0026ldquo;Online-Offline Integration\u0026rdquo;, the seamless and healthy transition between digital and face-to-face interactions (Goldberg et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Whitty, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The destructive dynamics consist of \u0026ldquo;Conflict\u0026rdquo;, referring to disagreements arising from technology use itself (Gonz\u0026aacute;lez-Rivera \u0026amp; Hern\u0026aacute;ndez-Gato, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kwok \u0026amp; Wescott, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); \u0026ldquo;Digital Surveillance/Control\u0026rdquo;, which involves strategies to monitor a partner\u0026rsquo;s online activities (Borrajo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tokunaga, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e); \u0026ldquo;Jealousy\u0026rdquo;, negative emotional reactions to a partner's social media activities (Fox \u0026amp; Warber, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; S\u0026aacute;nchez et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2017\u003c/span\u003e); \u0026ldquo;Harassment/Abuse\u0026rdquo;, the use of technology to harm or control a partner (Fissel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wolford-Clevenger et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); and \u0026ldquo;Ghosting\u0026rdquo;, the unilateral cessation of digital communication to end a relationship (Freedman et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Herrera-L\u0026oacute;pez et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The inclusion of a termination strategy like ghosting is vital for a truly holistic scale, as it reflects a key, digitally-enabled behavior within the complete relationship lifecycle, from initiation to dissolution.\u003c/p\u003e \u003cp\u003eTo establish the scale's criterion validity and demonstrate its utility, its relationship with a general measure of romantic relationship quality was examined. Grounded in extensive literature showing that dynamics such as commitment and high-quality communication are consistently linked to positive relational outcomes (e.g., Abbasi, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Anderson \u0026amp; Emmers-Sommer, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), while conflict, jealousy, and abuse are linked to negative outcomes (e.g., Borrajo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gonz\u0026aacute;lez-Rivera \u0026amp; Hern\u0026aacute;ndez-Gato, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), it was explicitly hypothesized that the constructive dynamics measured by the DDRRS (Commitment, Communication Quality, Online-Offline Integration) would be positively correlated with relationship quality, while the destructive dynamics (Conflict, Surveillance, Jealousy, Harassment, Ghosting) would be negatively correlated. This approach positions the DDRRS not as a replacement for satisfaction scales, but as a complementary diagnostic tool that helps explain the specific digital mechanisms influencing those broader outcomes. The ultimate aim is to equip researchers and mental health professionals in T\u0026uuml;rkiye with an up-to-date, psychometrically sound instrument to comprehensively assess and understand romantic relationships in the digital age.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eRecruitment and Participants\u003c/h2\u003e \u003cp\u003eTo develop and validate the scale, a cross-validation design was employed, with data collected from two independent samples. Participants were recruited using a convenience sampling method through online social media platforms (Instagram, Facebook, Twitter/X) and university student groups. The inclusion criteria for the study were (a) being 18 years of age or older, (b) being currently in a romantic relationship, and (c) using digital platforms for communication within that relationship.\u003c/p\u003e \u003cp\u003e An initial total of 776 participants was recruited (390 for Sample 1; 386 for Sample 2). During data screening, participants who demonstrated careless responding patterns (e.g., invariant responses across all items) were excluded (26 from Sample 1 and 15 from Sample 2). Consequently, the final analytic sample consisted of two independent groups. Sample 1 (n\u0026thinsp;=\u0026thinsp;364) was used for the Exploratory Factor Analysis (EFA), and Sample 2 (n\u0026thinsp;=\u0026thinsp;371) was used for the Confirmatory Factor Analysis (CFA). The sample sizes exceeded recommended benchmarks for scale development, which suggest a sample of 300 is \"good\" (Comrey \u0026amp; Lee, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and a minimum of five participants per item (Bryman \u0026amp; Cramer, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2002\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 study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSample 1 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;364)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eSample 2 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;371)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eTotal (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;735)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003cb\u003e\u0026ndash;\u003c/b\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e85.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e90.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eRelationship Status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDating (unmarried)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e91.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e91.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eRelationship Duration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;1 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e43.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;3 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e37.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003ePerceived Role of Online Platforms\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeutral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e57.3\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 demographic characteristics of the samples are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The combined sample consisted predominantly of women (55.2%) and emerging adults aged 18\u0026ndash;25 years (85.7%). The majority of participants were university graduates (90.6%) and were in an unmarried/dating relationship (91.4%). Relationship duration varied, with 43.0% reporting relationships of 0\u0026ndash;1 year and 37.0% reporting 1\u0026ndash;3 years. Regarding perceptions of technology's role in their romantic relationship, 57.3% reported a positive role, 33.6% a neutral role, and 9.1% a negative role. The demographic profiles were highly comparable across the two samples, confirming their suitability for cross-validation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003e This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of [blinded] (Date: [blinded], Approval No.: [blinded]). Data were collected between May 2025 and June 2025. Participation was voluntary, and no incentives were offered. Data were collected via an online survey platform. After reviewing an online information sheet detailing the study's purpose, participants provided electronic informed consent. Subsequently, they completed the newly developed DDRRS, the Perceived Romantic Relationship Quality Scale (PRRQS), and a demographic information form.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eDevelopment of the Digital Dynamics in Romantic Relationships Scale (DDRRS)\u003c/h2\u003e \u003cp\u003eThe objective of this phase was to develop a comprehensive scale to assess the multidimensional digital dynamics that characterize modern romantic relationships. Based on an extensive literature review, an initial item pool of 84 items spanning eight dimensions was created. The scale development process drew upon prior research on romantic relationships in digital context (Billedo et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Borrajo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fissel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fox \u0026amp; Tokunaga, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Gonz\u0026aacute;lez-Rivera \u0026amp; Hern\u0026aacute;ndez-Gato, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Herrera-L\u0026oacute;pez et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Leisring \u0026amp; Giumetti, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lukacs \u0026amp; Quan-Haase, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Montero-Fern\u0026aacute;ndez et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nacar et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Parks \u0026amp; Floyd, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; S\u0026aacute;nchez et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tokunaga, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Watkins et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wolford-Clevenger et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wright, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The eight dimensions were identified as Commitment, Communication Quality, Online\u0026ndash;Offline Integration, Conflict, Digital Surveillance/Control, Ghosting, Jealousy, and Harassment/Abuse.\u003c/p\u003e \u003cp\u003eTo ensure content validity, expert evaluations were obtained from five academics holding doctoral degrees in psychology or psychological counseling with research experience in romantic relationships in digital context. Experts were asked to evaluate each item as \u0026ldquo;necessary,\u0026rdquo; \u0026ldquo;useful but not necessary,\u0026rdquo; or \u0026ldquo;unnecessary.\u0026rdquo; They were also asked to review wording clarity, resolve ambiguities, and provide dimensional feedback. Based on this expert feedback, 13 items were removed from the initial pool of 84 items, resulting in a 71-item provisional form.\u003c/p\u003e \u003cp\u003ePrior to the main study, the 71-item draft scale was piloted with 68 university students (37 female, 31 male; \u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eage\u003c/em\u003e\u003c/sub\u003e = 21.00, \u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.70) who currently in a romantic relationship and actively used digital platforms to communicate with their partner. Following the pilot, individual interviews were conducted to obtain feedback regarding clarity, interpretability, and response difficulty. The majority of participants rated the items as \u0026ldquo;comprehensible,\u0026rdquo; and several items were revised for improved clarity, producing the final 71-item version.\u003c/p\u003e \u003cp\u003eThe final scale encompasses eight dimensions, including \u0026ldquo;Commitment\u0026rdquo; (7 items; assesses dedication to the current relationship), \u0026ldquo;Communication Quality\u0026rdquo; (15 items; assesses the depth, responsiveness, and effectiveness of digitally mediated exchanges), \u0026ldquo;Online\u0026ndash;Offline Integration\u0026rdquo; (6 items; assesses the seamless and healthy integration between digital and face-to-face interactions), \u0026ldquo;Conflict\u0026rdquo; (8 items; assesses technology-related disagreements and tension), \u0026ldquo;Digital Surveillance/Control\u0026rdquo; (12 items; assesses monitoring and controlling behaviors in digital environments), \u0026ldquo;Ghosting\u0026rdquo; (7 items; assesses abrupt, unexplained withdrawal from digital communication), \u0026ldquo;Jealousy\u0026rdquo; (6 items; assesses jealousy triggered by partners\u0026rsquo; digital activities), and \u0026ldquo;Harassment/Abuse\u0026rdquo; (10 items; assesses digitally mediated coercive, degrading, or threatening behaviors).\u003c/p\u003e \u003cp\u003eResponses were given on a 5-point Likert-type scale from 1 (\u003cem\u003eStrongly Disagree\u003c/em\u003e) to 5 (\u003cem\u003eStrongly Agree\u003c/em\u003e). Higher scores on each dimension indicate more frequent experience of that specific dynamic. Several items were reverse-coded to reduce response bias.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003ePerceived Romantic Relationship Quality Scale\u003c/h3\u003e\n\u003cp\u003eRomantic relationship quality was assessed using the 6-item PRRQS developed by Fletcher et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and adapted to Turkish by Sağkal and \u0026Ouml;zdemir (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Sample items include \"How satisfied are you with your relationship?\" and \"How much do you love your partner in your relationship?\" Participants rated each item on a 7-point Likert-type scale from 1 (\u003cem\u003eNot at All\u003c/em\u003e) to 7 (\u003cem\u003eVery Much\u003c/em\u003e). Higher scores indicate higher perceived romantic relationship quality. The Turkish adaptation study reported strong validity and reliability values (Cronbach's \u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.86, composite reliability = .87, test-retest reliability = .81) (Sağkal \u0026amp; \u0026Ouml;zdemir, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the current study, Cronbach's \u003cem\u003eα\u003c/em\u003e was .92, indicating excellent internal consistency.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eData analyses were performed using IBM SPSS Statistics 26.0 and AMOS 22.0. As a preliminary step, Harman's single-factor test was conducted to check for common method variance. In both samples, the first unrotated factor accounted for less than 50% of the variance (26.7% in Sample 1 and 27.1% in Sample 2), suggesting that common method bias was not a significant threat to the validity of the findings (Podsakoff et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo establish the scale's factor structure, a two-stage approach was used. First, an EFA was conducted on the data from Sample 1 to identify the underlying dimensional structure of the initial item pool. Second, a CFA was performed on the data from Sample 2 to test and confirm the model derived from the EFA. Following the factor analyses, the internal consistency of the DDRRS subscales was assessed using Cronbach\u0026rsquo;s alpha coefficients. Finally, to establish criterion validity, Pearson correlation analyses were conducted to examine the relationships between the DDRRS subscales and scores on the PRRQS.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eExploratory Factor Analysis\u003c/h2\u003e \u003cp\u003eEFA was conducted with data from Sample 1 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;364) to determine the factor structure of the DDRRS, which initially contained 71 items. In the initial screening phase, items that failed to load on any factor, had primary loadings below .40, or showed cross-loadings were removed iteratively, re-running the analysis after each deletion. During this process, a total of 18 items (items 5\u0026ndash;11, 16\u0026ndash;19, 22, 23, 25, 34\u0026ndash;36, and 45) were removed from the scale. The EFA results conducted with the remaining 53 items are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003ePrior to analysis, the suitability of the data for factor analysis was examined using the Kaiser-Meyer-Olkin (KMO) sampling adequacy test and Bartlett's test of sphericity. The KMO value was found to be .93, and this value was determined to be at an \"excellent\" level (Tabachnick \u0026amp; Fidell, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Bartlett's test of sphericity was significant (\u003cem\u003eχ\u0026sup2;\u003c/em\u003e(1378)\u0026thinsp;=\u0026thinsp;15324.60, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). These findings supported that the data were suitable for factor analysis.\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\u003eEFA results of DDRRS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFactor Loading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFactor Loading\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eFactor 1: Harassment/Abuse (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eFactor 4: Communication Quality (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eFactor 5: Conflict (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eFactor 2: Digital Surveillance/Control (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eFactor 6: Jealousy (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eFactor 7: Online-Offline Integration (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eFactor 3: Ghosting (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eFactor 8: Commitment (\u003cem\u003eα\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eItem 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem 55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e As a result of the EFA conducted using principal components extraction with varimax rotation, 8 factors with eigenvalues greater than 1 were obtained. These factors together explain 67.79% of the total variance. The variance ratios explained by the factors are 16.11%, 11.28%, 9.85%, 7.77%, 7.03%, 6.07%, 5.01%, and 4.66%, respectively. The scree plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) also provided supportive information in determining the number of factors; inspection showed a marked inflection after the eighth factor, after which the curve leveled off.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen the factor loadings after rotation were examined, it was observed that the items loaded on theoretically expected factors. All rotated loadings exceeded .40 and each item loaded cleanly on a single factor. The first factor contains 10 items representing the Harassment/Abuse dimension (factor loadings between .70\u0026ndash;.90), the second factor contains 10 items representing the Digital Surveillance/Control dimension (factor loadings between .51\u0026ndash;.75), the third factor contains 7 items representing the Ghosting dimension (factor loadings between .64\u0026ndash;.81), the fourth factor contains 6 items representing the Communication Quality dimension (factor loadings between .59\u0026ndash;.77), the fifth factor contains 5 items representing the Conflict dimension (factor loadings between .58\u0026ndash;.84), the sixth factor contains 6 items representing the Jealousy dimension (factor loadings between .58\u0026ndash;.76), the seventh factor contains 4 items representing the Online\u0026ndash;Offline Integration dimension (factor loadings between .56\u0026ndash;.82), and the eighth factor contains 4 items representing the Commitment dimension (factor loadings between .67\u0026ndash;.71).\u003c/p\u003e \u003cp\u003eInternal consistency for the eight dimensions of the DDRRS identified via EFA was assessed using Cronbach's \u003cem\u003eα\u003c/em\u003e. All subscales demonstrated high reliability, with \u003cem\u003eα\u003c/em\u003e coefficients ranging from .78 to .97, exceeding the conventional .70 criterion (Hair et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eConfirmatory Factor Analysis\u003c/h2\u003e \u003cp\u003eBased on the EFA results from Sample 1, the DDRRS was found to have a 53-item, eight-factor structure. To confirm this structure, CFA was conducted using data from Sample 2 (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;371). Model fit was evaluated using χ\u0026sup2;, CFI, TLI, SRMR, and RMSEA, applying commonly cited cutoffs (Schumacker \u0026amp; Lomax, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Kline, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). According to these standards, CFI and TLI values of .90 or greater are considered acceptable (and .95 or greater excellent); SRMR and RMSEA values of .08 or less are acceptable (and .05 or less excellent).\u003c/p\u003e \u003cp\u003eInitial model fit was inadequate, with results of \u003cem\u003eχ\u0026sup2;\u003c/em\u003e(1297)\u0026thinsp;=\u0026thinsp;3351.47, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, CFI = .86, TLI = .86, SRMR = .10, RMSEA = .07 (90% CI [.063, .068]). Inspection of modification indices indicated localized areas of strain; therefore, theoretically justifiable residual covariances were added iteratively between item pairs with closely overlapping content within the same latent factor (Ghosting: items 49\u0026ndash;50; Jealousy: 60\u0026ndash;61; Communication Quality: 20\u0026ndash;21; Harassment/Abuse: 63\u0026ndash;64; Digital Surveillance/Control: 39\u0026ndash;40). The re-specified model demonstrated acceptable fit with results of \u003cem\u003eχ\u0026sup2;\u003c/em\u003e(1286)\u0026thinsp;=\u0026thinsp;2702.89, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001, CFI = .91, TLI = .90, SRMR = .07, RMSEA = .05 (90% CI [.047, .052]). All standardized factor loadings were significant (range = .53\u0026ndash;.94).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eConvergent and Discriminant Validity\u003c/h2\u003e \u003cp\u003eComposite reliability (CR) and average variance extracted (AVE) were calculated for each latent construct using the CFA results from Sample 2. For convergent validity, AVE values were evaluated. Fornell and Larcker (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1981\u003c/span\u003e) recommend that these values should be higher than .50. However, if AVE is below .50 but CR exceeds .60, convergent validity may still be considered adequate because AVE is a conservative index (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, since the AVE values of all dimensions except the Commitment dimension were above .50, convergent validity was achieved for these dimensions. Since the AVE value of the Commitment dimension was slightly below the critical threshold (.46) but the CR value was above .60 (.77), it was concluded that convergent validity was also achieved for this dimension.\u003c/p\u003e \u003cp\u003eFor discriminant validity, the square roots of AVE values presented diagonally in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e were examined. According to the Fornell\u0026ndash;Larcker criterion, each square root of AVE should exceed the corresponding inter-construct correlations (Hair et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The results show that the square roots of AVE values are larger than the correlations between factors. Thus, discriminant validity was supported. Additionally, it was observed that the CR values calculated for all dimensions ranged between .77 and .97 and were above the recommended threshold value of .70 (Fornell \u0026amp; Larcker, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1981\u003c/span\u003e). These high CR values further support the internal consistency and reliability of the dimensions.\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\u003eConvergent and discriminant validity results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c11\" namest=\"c4\"\u003e \u003cp\u003eCorrelations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(1) Harassment/Abuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(2) Digital Surveillance/Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.52\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(3) Ghosting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.56\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.52\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(4) Communication Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.19\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.13\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.15\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(5) Conflict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.38\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.43\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.52\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(6) Jealousy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.38\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.48\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.48\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.12\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.62\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(7) Online-Offline Integration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.19\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.28\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.46\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(8) Commitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.30\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.13\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.41\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.38\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.18\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.47\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cem\u003eNote.\u003c/em\u003e Values in parentheses are the square roots of AVE values. CR\u0026thinsp;=\u0026thinsp;Composite Reliability. AVE\u0026thinsp;=\u0026thinsp;Average Variance Extracted. * \u003cem\u003ep\u003c/em\u003e \u0026lt; .05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; .01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCriterion Validity\u003c/h2\u003e \u003cp\u003eTo provide evidence of criterion-related validity for the newly developed DDRRS, Pearson product\u0026ndash;moment correlations were calculated between its subscales and scores on the PRRQS using data from Sample 2. The correlation coefficients are presented in Table\u0026nbsp;\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\u003eCriterion validity results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(4)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e(5)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e(6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e(7)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e(8)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e(9)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(1) PRRQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(2) Harassment/Abuse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.27\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(3) Digital Surveillance/Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.08\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.52\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(4) Ghosting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.42\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.56\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.52\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(5) Communication Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.42\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.19\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.13\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.15\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(6) Conflict\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.22\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.38\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.43\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.52\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(7) Jealousy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.14\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.38\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.48\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.48\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.12\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.62\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(8) Online-Offline Integration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.51\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.19\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.28\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.46\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(9) Commitment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.60\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.30\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.13\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.41\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.38\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.18\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e.47\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cem\u003eNote.\u003c/em\u003e PRRQ\u0026thinsp;=\u0026thinsp;Perceived Romantic Relationship Quality. * \u003cem\u003ep\u003c/em\u003e \u0026lt; .05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; .01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs expected, the DDRRS subscales representing constructive relational dynamics showed moderate to strong positive associations with overall romantic relationship quality. These included Communication Quality (\u003cem\u003er\u003c/em\u003e = .42, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01), Online\u0026ndash;Offline Integration (\u003cem\u003er\u003c/em\u003e = .51, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01), and Commitment (\u003cem\u003er\u003c/em\u003e = .60, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01). Conversely, the subscales representing destructive dynamics demonstrated small to moderate negative correlations with romantic relationship quality. These were Harassment/Abuse (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.27, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01), Digital Surveillance/Control (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.08, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05), Ghosting (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.42, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01), Conflict (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.22, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01), and Jealousy (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.14, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe primary objective of this study was to develop the DDRRS, a psychometrically sound and comprehensive instrument designed to assess the multidimensional digital dynamics of romantic relationships in the Turkish cultural context. The research findings demonstrated that the resulting 53-item, eight-factor scale (Harassment/Abuse, Digital Surveillance/Control, Ghosting, Communication Quality, Conflict, Jealousy, Online\u0026ndash;Offline Integration, and Commitment) exhibited a valid and reliable structure based on both exploratory and confirmatory factor analyses. Consistent with theoretical frameworks in the literature, this structure offers a holistic perspective by concurrently assessing constructive and destructive facets of digital relationships. Criterion validity analyses showed that the constructive dimensions of the DDRRS were positively correlated with perceived romantic relationship quality, while the destructive dimensions were negatively correlated, confirming theoretical coherence and supporting the instrument's validity.\u003c/p\u003e \u003cp\u003eUnlike existing scales in the literature, which often focus on a single negative dynamic (e.g., harassment/abuse, digital surveillance/control), the DDRRS includes both constructive dimensions such as Communication Quality and Commitment and destructive dimensions such as Harassment/Abuse and Conflict, thereby filling a significant gap in this area. This supports the dual nature of digital environments, which can pave the way for both benign and toxic behaviors, as suggested by Suler (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) in the Online Disinhibition Effect framework. Furthermore, the fact that the scale was developed with data directly obtained from participants within Turkish culture is significant for addressing the cultural adaptation problems encountered in the studies of Nacar et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Şimşek \u0026Ouml;zkan and Siyez (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and for providing a measurement tool sensitive to local dynamics.\u003c/p\u003e \u003cp\u003eThe findings of this study have important implications for both theory and practice. Theoretically, the DDRRS offers a holistic and multidimensional framework for understanding the digital domains of relationships. By transcending narrow approaches focused solely on pathological aspects and incorporating constructive dynamics such as commitment and communication quality, this model allows researchers to examine modern relationships from a more balanced perspective. From a practical perspective, the DDRRS is a valuable assessment tool for mental health professionals, counselors, and therapists. Clinicians working with couples can use this scale to differentiate sources of relational strain more clearly; for example, it can guide assessment of whether the underlying dynamic of a couple\u0026rsquo;s conflict reflects digital surveillance or jealousy. This can allow for more targeted and effective intervention planning. Furthermore, the scale can be used to enrich the content and evaluate the effectiveness of psychoeducational programs for young people in schools and universities on topics such as healthy digital relationship habits, cyber dating violence, and digital privacy.\u003c/p\u003e \u003cp\u003eDespite this study\u0026rsquo;s significant contributions, it has several limitations. First, the sample largely consisted of young, university-educated, unmarried adults aged 18\u0026ndash;25, which limits the generalizability of the findings to older age groups, individuals with different educational levels, or married couples. Future studies examining the psychometric properties of the DDRRS across groups differing in age, marital status, and socioeconomic status (including formal tests of measurement invariance) would broaden the instrument's utility. Second, this study employed a cross-sectional design, and data were collected via self-report surveys, precluding causal inferences. Future longitudinal studies could examine changes in digital relationship dynamics over time and how these dynamics influence outcomes such as relationship satisfaction or breakup. Furthermore, because self-reporting is susceptible to biases such as social desirability, future mixed-method or dyadic designs (e.g., partner-paired data, in-depth interviews) could provide a richer and more multifaceted understanding. Finally, cross-cultural studies comparing relationship dynamics in T\u0026uuml;rkiye with those in other cultures using the DDRRS will be illuminating for distinguishing universal versus culture-specific technology-related relational processes.\u003c/p\u003e \u003cp\u003eIn conclusion, this study developed the DDRRS, a multidimensional and current measurement tool grounded in Turkish culture. Psychometric analyses demonstrated that the 53-item, eight-factor scale validly and reliably measures both constructive and destructive aspects of digital romantic relationships. The DDRRS addresses a significant gap in the literature by overcoming the limitations of existing tools, such as their one-dimensional focus or exclusive development within Western cultural contexts. It is anticipated that this scale can be used as a resource for researchers and mental health professionals in T\u0026uuml;rkiye to understand and evaluate romantic relationships in the digital age and to develop effective interventions for related problems.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbasi, I. S. (2018). Social media and committed relationships: What factors make our romantic relationship vulnerable? \u003cem\u003eSocial Science Computer Review,\u003c/em\u003e \u003cem\u003e37\u003c/em\u003e(3), 425-434\u003cem\u003e.\u003c/em\u003e https://doi.org/10.1177/0894439318770609\u003c/li\u003e\n\u003cli\u003eAbbasi, I. S. (2019). 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Oxford University Press. https://doi.org/10.1093/acprof:oso/9780199639540.003.0004\u003c/li\u003e\n\u003cli\u003eWolford-Clevenger, C., Zapor, H., Brasfield, H., Febres, J., Elmquist, J., Brem, M., ... \u0026amp; Stuart, G. L. (2016). An examination of the Partner Cyber Abuse Questionnaire in a college student sample. \u003cem\u003ePsychology of Violence, 6\u003c/em\u003e(1), 156-162. https://doi.org/10.1037/a0039442 \u003c/li\u003e\n\u003cli\u003eWright, M. F. (2015). Cyber aggression within adolescents\u0026rsquo; romantic relationships: Linkages to parental and partner attachment. \u003cem\u003eJournal of Youth and Adolescence, 44\u003c/em\u003e(1), 37-47. https://doi.org/10.1007/s10964-014-0147-2\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Istanbul Arel University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Romantic relationships, Digital dynamics, Scale development, Psychometrics, Türkiye","lastPublishedDoi":"10.21203/rs.3.rs-8793559/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8793559/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDigital technologies are fundamentally woven into the fabric of modern romantic relationships, yet existing measurement tools are often problem-focused, unidimensional, and developed in Western cultures. This creates a critical need for a holistic instrument that assesses the complex role of technology within relationships in diverse cultural contexts. This study aimed to develop and validate the Digital Dynamics in Romantic Relationships Scale, a multidimensional instrument grounded in the Turkish context. Data were collected from two independent samples (total N\u0026thinsp;=\u0026thinsp;735). Exploratory Factor Analysis (n\u0026thinsp;=\u0026thinsp;364) yielded a 53-item, eight-factor structure capturing both constructive (Communication Quality, Commitment, Online-Offline Integration) and destructive (Harassment/Abuse, Digital Surveillance/Control, Jealousy, Conflict, Ghosting) dynamics. Confirmatory Factor Analysis (n\u0026thinsp;=\u0026thinsp;371) supported this model with good fit indices (χ\u0026sup2;(1286)\u0026thinsp;=\u0026thinsp;2702.89, CFI = .91, TLI = .90, SRMR = .07, RMSEA = .05). The subscales showed strong internal consistency (α\u0026thinsp;=\u0026thinsp;.78\u0026ndash;.97) and robust validity. The Digital Dynamics in Romantic Relationships Scale thus offers a psychometrically sound instrument for researchers and clinicians to systematically evaluate the specific digital processes influencing modern partnerships.\u003c/p\u003e","manuscriptTitle":"The Digital Dynamics in Romantic Relationships Scale (DDRRS): Development and Validation in Türkiye","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-06 12:07:09","doi":"10.21203/rs.3.rs-8793559/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e73c1f8-8f8c-46d8-9715-7a9b3a325f12","owner":[],"postedDate":"February 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62364800,"name":"Psychology"}],"tags":[],"updatedAt":"2026-02-06T12:07:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-06 12:07:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8793559","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8793559","identity":"rs-8793559","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}