Time Poverty Erodes Intimate Relationship Well-Being via Negative Dyadic Coping and Its Spillover Effect on Job Performance

Time Poverty Erodes Intimate Relationship Well-Being via Negative Dyadic Coping and Its Spillover Effect on Job Performance | 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 Time Poverty Erodes Intimate Relationship Well-Being via Negative Dyadic Coping and Its Spillover Effect on Job Performance Nan Zhang, Xiaomin Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8395371/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 High-quality intimate relationships are highly valued. Prior research has underscored that limited shared time with partners can detrimentally impact relationship quality. However, time poverty—the pervasive subjective perception of not having enough time to accomplish desired or necessary tasks—may compromise relationship well-being. This impairment occurs not merely through diminished shared time but also by undermining dyadic interaction processes. Employing a three-wave dyadic design, we collected data from 163 full-time employed couples (326 participants, yielding 978 responses) across one-month intervals in China. Drawing on dyadic coping theory and utilizing the actor-partner interdependence model, we examined how time poverty influences relationship well-being and subsequently spills over to job performance. The findings reveal that time poverty negatively affects relationship well-being through negative dyadic coping for husbands but not for wives and these dynamics further influence both partners’ job performance. This research underscores the importance of not merely increasing shared time but also optimizing interpersonal interactions within that time, offering an interpersonal perspective on the broader implications of time poverty. time poverty dyadic stress relationship wellbeing job performance dyadic coping theory actor-partner interdependence model Figures Figure 1 Figure 2 Introduction Jane Austen famously asserted in Pride and Prejudice, "It is a truth universally acknowledged that a single man in possession of a good fortune must be in want of a wife." However, contrary to Austen’s observation, recent decades have seen a decline in marriage rates across various countries, despite economic advancements. For instance, marriage rates have decreased in the United States (U.S. Census Bureau, 2020), China (National Bureau of Statistics, 2022), and Canada (Statistics Canada, 2022), even as their gross domestic products have grown. This phenomenon prompts a critical question: what factors might explain this trend? With the acceleration of economic development and the evolution of social structures (Rosa, 2013), long working hours (Cha & Weeden, 2014) and fast-paced lifestyles have come to characterized modern life. Within this context, the concept of time poverty—defined as the pervasive feeling of having insufficient time to accomplish desired or necessary activities (Roxburgh, 2004; Szollos, 2009)—has emerged as a significant social phenomenon. This issue affects a range of countries, such as the United States (Whillans, 2019), China (Li et al., 2015), Canada (Zuzanek, 2004), and Germany (Merz & Rathjen, 2014). For individuals experiencing time poverty, there is often a consequential reduction in the energy, time, and psychological engagement they can devote to family life or close relationships (Zhang et al., 2024). Research indicates that time poverty impedes individuals’ abilities to nurture intimate relationships (De Sousa et al., 2018) and diminishes interpersonal communication (Li, 2023). Additionally, limited time spent with partners may undermine relationship interdependence (Garcia-Rada & Kim, 2021), leading to lower marital satisfaction and an elevated risk of divorce (Bodenmann, 2005). This effect stems from a lack of opportunities for self-disclosure, shared experiences, fostering a sense of “we-ness”, and engaging in dyadic coping (Bodenmann, 2000). Crucially, the adverse effects of time poverty on relationship well-being extend beyond merely reducing shared time; they also detrimentally impact interaction processes during the limited time that couples do spend together. Given that individuals pressed for time often struggle to increase the amount of shared time with their partners, it is crucial to understand how time poverty undermines the quality of interactions during the limited time they do spend together. Insights into these processes—essential for improving intimate relationship well-being—remain relatively scarce. This study aims to uncover the mechanisms linking time poverty to relationship well-being from the perspective of dyadic interactions. Specifically, we employ dyadic coping theory (Bodenmann, 1995, 2005) to construct our conceptual framework. This theory explains how shared dyadic stress affects close relationships through the interaction processes of both partners, encompassing dyadic coping behaviors. By adopting this interpersonal lens, our study advances existing research on time and intimate relationship well-being by shifting focus from merely the limited opportunities for shared time and experiences to the disruption of interaction patterns. Additionally, in considering the spillover effects between home and work domains (ten Brummelhuis & Bakker, 2012), we explore how time poverty extends from the home into the work domain, particularly its impact on job performance. These efforts not only reinforce the work-home resources model (ten Brummelhuis & Bakker, 2012) within the context of dual-earner couples but also provide a home-to-work explanation for the relationship between time poverty and job performance. Figure 1 illustrates the conceptual model. Conceptual Framework and Hypothesis Development Rooted in the systemic–transactional perspective, the theory of dyadic coping (Bodenmann, 1995, 2005) posits that individuals may experience stress unrelated to their partners, such as work-related stress. However, the ineffective or unsuccessful management of this stress can adversely affect their partners. This process unfolds as the stressed individual either expresses distress, seeks support, or as the partner perceives stress signals. Consequently, stress initially experienced individually is transmitted as dyadic stress, influencing both partners and initiating coping efforts from each. This phenomenon is referred to as the dyadic coping process (Bodenmann, 1995, 2005). In the context of the current study, existing research suggests that time poverty, often manifested as individual stress due to factors such as long working hours, can be transmitted as dyadic stress; this transmission occurs through the depletion of psychological and physical resources, and the redirection of time and attention away from family activities toward individual tasks (Zhang et al., 2024). Building on these insights, we propose that time poverty, as a form of dyadic stress, triggers negative dyadic coping behaviors. These behaviors, in turn, may detrimentally impact the intimate relationship well-being of the partner and extend their effects into the work domain. Mediating Effect of Negative Dyadic Coping Between Time Poverty and Relationship Well-being According to dyadic coping theory, dyadic stress triggers coping processes that involve both partners, a concept Bodenmann (1995, 2005) defines as "dyadic coping." This process encompasses three interactive components: the stress signals displayed by one partner, the perception of these stress signals by the other partner, and the subsequent coping reactions from the partner (Bodenmann, 2005). Furthermore, the communication of stress—whether through verbal or nonverbal signals—is critical in triggering these dyadic coping reactions (Bodenmann, 2005). Stress often compromises the quality of dyadic communication (Bodenmann, 1995), leading to negative communication patterns such as criticism, contempt, or withdrawal (Bodenmann, 2005). These maladaptive communication patterns, in turn, result in ineffective coping reactions. Negative dyadic coping refers to coping processes characterized by hostility (e.g., mocking or disparagement), ambivalence (e.g., grudging support), or superficiality (e.g., insincerity). In such cases, one partner responds to the other in a detrimental manner, often accompanied by negative communication behaviors. For instance, an individual may offer advice to their partner but simultaneously blame them for not managing stress effectively. These responses not only fail to alleviate stress but may exacerbate tension and conflict within the relationship. In our conceptual framework, we propose that time poverty acts as a form of dyadic stress, triggering negative dyadic coping through two primary mechanisms. First, time poverty compels individuals to prioritize unfinished tasks, thereby reducing the psychological engagement available for maintaining close relationships (Zhang et al., 2024). This reduction in engagement diminishes the attention and energy that could otherwise be devoted to partner communication, thereby compromising the quality of dyadic communication. As a result, such compromised communication further fosters negative coping patterns, where interactions may become strained or dysfunctional. Second, time poverty exhausts individuals both psychologically and physically (Teuchmann et al., 1999; Sharif et al., 2021), leading to increased negative emotional states such as sadness, drowsiness, or depression (Roxburgh, 2004), and heightened irritability (Höge, 2009). These emotional and physical depletions manifest in more negative interactions with partners. Consequently, communication often deteriorates, becoming characterized by increased criticism, contempt, or withdrawal. This shift in communication style directly influences dyadic coping, where reactions may become less constructive and more destructive. Meanwhile, Negative dyadic coping, resulting from time poverty, can significantly undermine partners’ relationship well-being. According to dyadic coping theory, while effective coping strategies can reduce stress and enhance the sense of "we-ness" between partners, negative dyadic coping has the opposite effect, diminishing relationship satisfaction (Bodenmann, 2005). This decline occurs because one partner perceives the support from the other as tainted by criticism, contempt, or withdrawal, all of which impair relationship well-being. Previous research supports this, finding that negative dyadic coping is associated with lower relationship satisfaction (Bodenmann, 2000). Therefore, individuals experiencing time poverty are more likely to resort to negative dyadic coping strategies, exacerbating the detrimental effects on relationship well-being. Additionally, relationship well-being is often interdependent within close relationships (Bodenmann, 2005; Ma et al., 2016); thus, an individual’s well-being may decline when their partner’s well-being is compromised. Based on these insights, we propose the following hypothesis: Hypothesis 1 : The relationship between individuals’ experience of time poverty and their partners’ relationship well-being is mediated by the individuals’ engagement in negative dyadic coping behaviors toward their partners. The Diffusion Effect to Job Performance The interplay between home dynamics and work outcomes has been extensively explored in the literature, exemplified by studies such as Carlson et al. (2018) and Wan et al. (2019). The work-home resources model (ten Brummelhuis & Bakker, 2012) elucidates the spillover of resources across work, individual, and home domains. Specifically, the model suggests that resources from the home domain, such as spousal support, can bolster personal resources like psychological or affective resources (e.g., optimism, fulfilment, empathy), which in turn enhance work outcomes such as productivity. In the context of the current study, a robust relationship with a partner facilitates greater support, leading to improved mental health outcomes (e.g., fewer depressive symptoms, Kim & McKenry, 2002). This support enhances personal resources, aiding in recovery from work stress (Zhang et al., 2023) and subsequently boosting job performance (Binnewies et al., 2009). Further supporting this, research indicates that positive relationship quality with family members is associated with home-based resource gains, which contribute to home-to-work enrichment (Wan et al., 2019). Conversely, relationship tensions have been found to impede job performance (Carlson et al., 2018). Building on the above arguments, we propose the following hypothesis: Hypothesis 2 : The effects of an individual’s time poverty on both their own and their partner’s relationship well-being can diffuse into the work domain. These diffusion effects manifest as the individual’s relationship well-being is positively associated with both their own and their partner’s job performance. Exploring Potential Gender Differences Traditional gender-role norms often designate men as the primary breadwinners, engaged predominantly in paid work, while women are expected to manage household responsibilities and provide unpaid care to family members (Cinamon & Rich, 2002). Empirical evidence suggests that these traditional beliefs persist, with women, even in modern societies, performing a greater share of household chores and childcare compared to men (Giurge et al., 2021). These divergent gender expectations could influence dyadic interaction processes differently for men and women. Firstly, men and women may exhibit different coping reactions in response to their own time poverty. Particularly when both partners experience time scarcity, women, adhering to societal expectations, often continue to assume a larger portion of family caregiving responsibilities and provide more support to family members. This disproportionate burden can hinder their ability to engage in constructive support with their partners, potentially leading to more prevalent negative dyadic coping behaviors among women experiencing time poverty. Second, gender differences may also manifest in how each partner copes with the other’s time poverty, influenced by entrenched gender-role beliefs (Cinamon & Rich, 2002). It is common for women to accept their husbands’ preoccupation with work-related tasks without surprise, given societal expectations. Conversely, men may hold expectations for their wives to fulfill family responsibilities and may experience dissatisfaction if these are not met, perceiving their wives’ engagement with work as neglectful. Indirect evidence supports this disparity; for instance, while husbands’ engagement in work-related activities through information and communication technologies after hours (WICT) does not typically lead to social undermining behaviors from their wives, the reverse scenario—wives using WICT—significantly predicts husbands’ social undermining (Ma et al., 2016). This suggests that husbands, rather than wives, are more likely to experience dissatisfaction with their partner’s time poverty and exhibit more negative dyadic coping behaviors. Based on these insights, we propose the following hypothesis: Hypothesis 3 : The influence of time poverty on negative dyadic coping varies between husbands and wives. Specifically, it is proposed that when experiencing time poverty, wives exhibit more negative dyadic coping compared to husbands. Conversely, when both partners experience time poverty, wives engage in less negative dyadic coping. Consequently, the indirect effects of time poverty on job performance also differ between husbands and wives. Methods Participants and Procedure The current study utilized a three-wave dyadic survey design, with assessments conducted at one-month intervals, targeting dual-earner couples in China. This multi-source, multi-wave approach is instrumental in mitigating common method variance (CMV), as suggested by Fuller et al. (2016) and Tehseen et al. (2017). In the first wave (Time 1), the study assessed the primary predictor—time poverty—along with control variables and demographic information. The mediator, negative dyadic coping, was measured in the second wave (Time 2). Finally, the outcomes, including intimate relationship well-being and job performance, were evaluated in the third wave (Time 3). Participants were recruited through a paid online research platform, Credamo (www.credamo.com). The recruitment advertisement specified eligibility criteria, which included: (a) cohabitation of partners, (b) both partners being employed full-time, and (c) willingness of both partners to participate in a three-wave survey. Participants were required to complete the surveys independently and to sign an online informed consent form prior to participation. Upon completion of the survey, participants were debriefed and received a compensation of 36 RMB (approximately $5 USD). The study's protocol was preregistered and is accessible at AsPredicted (https://aspredicted.org/fzpp-qbfk.pdf). Additionally, the collected data are available for review on the Open Science Framework (OSF) at OSF Data Repository (https://osf.io/b2d9w/?view_only=43c6ba06dbcd4b04b7e43eff2f3e4ce5). Initially, 218 dual-earner couples were recruited for the first wave of the study. By the second wave, 179 couples remained, and 163 couples completed the entire study, resulting in a final retention rate of 74.78%. The participants in the final sample had an average age of 32.81 years ( SD = 5.95) and reported working an average of 43.23 hours per week ( SD = 11.35). Regarding family composition, 66.3% of the participants had one child under the age of 18, 19.6% had no children, and 14.1% had two children. All participating couples were heterosexual and married, with an average relationship duration of 6.90 years ( SD = 5.22). Additionally, 33.0% of these families reported receiving assistance with household chores from relatives or housekeepers. Measures For scales not originally in Chinese, back-translation procedures were used to ensure validity when validated Chinese versions were unavailable. Time poverty. To assess time poverty, we employed a nine-item scale. Participants responded to items using a 7-point Likert scale, where 1 = does not apply at all and 7 = applies completely. High scores on this scale indicated greater levels of time poverty, with the exception of one reverse-coded item, “I have enough time for myself.” An example of a typical item is, “I never seem to have enough time to get everything done.” The scale demonstrated high reliability, with a Cronbach's alpha of .970. Negative dyadic coping. Measurement was conducted using four items from the Chinese version of the Dyadic Coping Inventory, revised by Xu et al. (2016) based on Bodenmann’s original work (2008). Each item was rated on a 7-point Likert scale, ranging from 1 (never) to 7 (very often). Higher scores on this scale signify greater engagement in negative coping behaviors. An example of these items is, “I blame my partner for not coping well enough with stress.” This measure demonstrated good reliability, with a Cronbach’s alpha of .764. Intimate relationship well-being. This variable was assessed using three items from the Quality of Marriage Index (QMI; Norton, 1983). Two of these items were rated on a 7-point scale, ranging from 1 (totally disagree) to 7 (totally agree), with example items like, “We have a good marriage.” The third item evaluated overall satisfaction with the romantic relationship on a 10-point scale. Higher scores on these scales indicated greater relationship satisfaction. The measure demonstrated good reliability, with a Cronbach’s alpha of .711. Job performance. Job performance was assessed using three items from the In-Role Job Performance Scale (Janssen et al., 2010), which was adapted for self-report purposes. Participants rated each item on a 7-point Likert scale from 1 (totally disagree) to 7 (totally agree). An example item from this scale is, “I fulfill all responsibilities required by my job.” The scale demonstrated good reliability, with a Cronbach’s alpha of .713. Control Variables. Several factors known to potentially influence intimate relationship well-being were controlled in our analysis, as indicated by prior research (Ma et al., 2014; Lu et al., 2009; Totenhagen et al., 2018). These included the number of children under 18 years and subjective socioeconomic status (SES). SES was measured using the MacArthur Scale of Subjective Social Status (Adler et al., 2000). Additionally, we controlled for shared time with the partner to isolate the specific effects of time poverty from those potentially confounded by limited shared time. Despite the inclusion of these control variables, the trends observed in our results remained consistent. Analysis Strategies . To address the dyadic structure of our data and the non-independence of observations within couples, we utilized the actor-partner interdependence model (APIM; Kenny et al., 2006). APIM is particularly suited for exploring both actor effects (e.g., how an individual’s time poverty affects their own intimate relationship well-being) and partner effects (e.g., how one partner’s time poverty affects the other partner’s intimate relationship well-being). We followed the procedures recommended by Kenny and Ledermann (2010) and Kenny (2013) to determine whether dyad members (e.g., wife and husband) are distinguishable within the model. The latent APIM analyses were conducted using Mplus version 7.4. Bootstrapping for indirect effects was performed with 5,000 bootstrap samples, and 95% confidence intervals (CI) were calculated. Effects were considered significant if the 95% CIs does not include zero. Results Preliminary Analyses To assess the measurement model, we conducted a multilevel confirmatory factor analysis (MCFA). This analysis incorporated our key variables—time poverty, negative dyadic coping, intimate relationship well-being, and job performance—with a four-factor model. Items were nested at two levels: the individual and the couple levels. The results from the MCFA indicated a good fit for the four-factor model (χ 2 [ df ] =584.694 [300], CFI = .920, TLI = .909, RMSEA = .054). This model demonstrated superior performance when compared to alternative configurations, including a three-factor model that combined intimate relationship well-being and job performance, a two-factor model that merged time poverty and negative dyadic coping, and a one-factor model. The comparisons and fit indices for these models are detailed in Table 1. To further assess the potential influence of CMV, we implemented the unmeasured latent method factor technique as described by Podsakoff et al. (2003). We constructed a two-level unmeasured latent CMV model to account for method effects not captured by the measured variables. Results from model comparisons, as detailed in Table 1, indicated minimal changes in the fit indices upon controlling for CMV. Specifically, the increases in the CFI and TLI were minor (∆CFI = .024; ∆TLI = .019), each less than the threshold of .100. Similarly, the decrease in the RMSEA was modest (∆RMSEA = .006), well below the .050 threshold. These findings suggest that CMV did not substantially bias the study’s results. Table 2 offers detailed descriptive statistics for both husbands and wives. Consistent with our hypotheses, we observed that an individual’s time poverty is positively related to their own negative dyadic coping ( r H = .418, p < .001; r W = .324, p < .001), and negatively related to their partner’s intimate relationship well-being ( r H→W = -0.300, p < .001; r W→H = -0.307, p < .001). Further, an individual’s negative dyadic coping is negatively related to the partner’s intimate relationship well-being ( r H→W = -.435, p < .001; r W→H = -.256, p < .001). Moreover, an individual’s intimate relationship well-being shows a strong positively relationship with both their own job performance ( r H = .664, p < .001; r W = .533, p < .001) and their partner’s job performance ( r H→W = .458, p < .001; r W→H = 547, p < .001). These findings provide initial support for our hypotheses. Distinguishability Testing In line with our third hypothesis, which posits potential difference between husbands and wives, we specified a distinguishable hypothesis model that allowed actor and partner effects to vary by gender. Following the methodologies recommended by Kenny and Ledermann (2010) and Kenny (2013), we first tested whether dyad members were indeed distinguishable in our dataset. Specifically, we compared the fit of the distinguishable hypothesis model with an indistinguishable effects model, in which actor and partner effects were constrained to be equal across genders. The results indicated that the distinguishable hypothesis model provided a significantly better fit than the indistinguishable model, Δχ 2 (Δ df ) = 37.38 (6), p < .001. These findings justify the use of distinguishable dyad models for subsequent analyses, providing initial support for the assertion that the effects differ between husbands and wives. Hypothesis Testing Hypothesis 1 posits that an individual’s negative dyadic coping toward their partner mediates the relationship between their time poverty and their partner’s intimate relationship well-being. We tested this using a latent APIM model, depicted in Figure 2a. The model demonstrated a satisfactory fit: χ 2 ( df ) = 945.869 (624), CFI = .934, TLI = .938, RMSEA = .056). The results indicated that husbands’ time poverty was significantly negatively related to their negative dyadic coping toward wives ( B = 0.136, SE = 0.055, p < .05). Conversely, the relationship between wives’ time poverty and their negative dyadic coping toward husbands was marginally significant ( B = 0.193, SE = 0.085, p = .068). Further, husbands’ negative dyadic coping toward their wives was significantly negatively associated with wives’ intimate relationship well-being ( B H→W = -0.399, SE = 0.112, p < .001). However, the corresponding relationship between wives’ negative dyadic coping toward husbands and husbands’ intimate relationship well-being did not reach statistical significance ( B W→H = 0.038, SE = 0.069, ns ). Further analyses were conducted to assess the indirect effects, as detailed in Table 3. The analysis revealed that the indirect effect of husbands’ time poverty on wives’ intimate relationship well-being, mediated by husbands’ negative dyadic coping, was significant (indirect effect = -0.054, SE = 0.026, p < .05). In comparison, the indirect effect of wives’ time poverty on husbands’ intimate relationship well-being, mediated by wives’ negative dyadic coping, was not significant (indirect effect = 0.007, SE = 0.014, ns ). Therefore, Hypothesis 1 was partially supported, suggesting that there may be some differences between husbands and wives in how time poverty and negative dyadic coping influence intimate relationship well-being. Hypothesis 2 posits that an individual’s intimate relationship well-being would have diffuse effects on both their own and their partner’s job performance, following the mediating effects established in Hypothesis 1. To test this hypothesis, a latent APIM model was constructed, as illustrated in Figure 2b. The model demonstrated a good fit (χ 2 [ df ] = 1258.835 [848], CFI = .924, TLI = .917, RMSEA = .055). The analysis revealed that husbands’ intimate relationship well-being was positively associated with both their own job performance ( B H→H = 0.598, SE = 0.103, p < .05) and their wives’ job performance ( B H→W = 0.218, SE = 0.084, p < .01). Likewise, wives’ intimate relationship well-being was positively associated with both their own job performance ( B W→W = 0.488, SE = 0.146, p < .001) and their husbands’ job performance ( B W→H = 0.482, SE = 0.153, p < .01). We further examined the indirect effects of time poverty on job performance through negative dyadic coping and intimate relationship well-being, as detailed in Table 3. For husbands, the analysis revealed significant indirect effect. Specifically, the pathway from husbands’ time poverty to their own job performance, mediated by husbands’ negative dyadic coping and wives’ intimate relationship well-being—“TP (H) — NDC (H) — IRW (W) — JP (H)”, was significant (indirect effect = -0.067, SE = 0.029, p < .05). Similarly, the indirect effect of husbands’ time poverty on wives’ job performance, proceeding through husbands’ negative dyadic coping and wives’ intimate relationship well-being—“TP (H) — NDC (H) — IRW (W) — JP (W)”, was significant (indirect effect = -0.031, SE = 0.015, p < .05). Conversely, the pathways for wives indicated non-significant results. The indirect effect of wives’ time poverty on their own job performance, mediated by their negative dyadic coping and husbands’ intimate relationship well-being—“TP (W) — NDC (W) — IRW (H) — JP (W)”, was not significant (indirect effect = -0.003, SE = 0.005, ns ). Additionally, the effect of wives’ time poverty on husbands’ job performance, through wives’ negative dyadic coping and husbands’ intimate relationship well-being—“TP (W) — NDC (W) — IRW (H) — JP (H)”, also did not reach statistical significance (indirect effect = 0.005, SE = 0.008, ns ). Therefore, Hypothesis 2 received partially support, as significant results were primarily observed in the pathways involving husbands. Hypothesis 3 posits gender differences in the effects detailed in Hypotheses 2. Given the disparate results observed for husbands and wives in these initial tests, we further examined these potential gender differences. Following recommendations by Kenny (2013) and Kenny & Ledermann (2010), we assessed whether the actor and partner effects in our full conceptual APIM model were equivalent for husbands and wives. This involved constructing three alternative models for comparison: (a) Equal actor-effect model, where the actor effects (i.e., the influence of an individual’s antecedents on their own outcomes) were constrained to be equal across genders; (b) Equal partner-effect model, where the partner effects (i.e., the influence of an individual’s antecedents on the partner’s outcomes) were constrained to be equal across genders; and (c) Equal effect model, where both actor and partner effects were constrained to be equal across genders. We compared the hypothesized APIM model against each of these alternative models. The results, detailed in Table 4, indicated that the hypothesized model provided a significantly better fit than the equal actor-effect model (∆χ²[∆ df ] = 33.734 [3], p < .001), the equal partner-effect model (∆χ²[∆ df ] = 70.243 [5], p < .001), and the equal effect model (∆χ²[∆ df ] = 229.038 [10], p < .001). These findings suggest that the actor and partner effects were not equal across genders, thereby providing empirical support for gender differences in the diffusion effects. Thus, Hypothesis 3 is supported. To further interpret the APIM results, we calculated the k parameter, which quantifies the ratio of the partner effect to the actor effect (Kenny & Ledermann, 2010). This parameter is crucial for capturing the underlying interaction pattern within the APIM. Specifically, when k ≈ 0 (actor effect ≠ 0, partner effect = 0), it indicates an actor-oriented pattern, where individuals’ outcomes are primarily influenced by their own antecedents. Whereas when k ≈ ∞ (actor effect = 0, partner effect ≠ 0), it denotes a partner-oriented pattern, implying that individuals’ outcomes are primarily influenced by their partner’s antecedents. When k ≈ 1, it signifies a couple-oriented pattern, where both actor and partner effects contribute equally to the outcomes. According to the current study’s finding (see Table 5), for the effect of time poverty on negative dyadic coping, husbands showed a k H value of 0.669, reflecting an intermediate pattern between actor-oriented and couple-oriented pattern, while wives demonstrated a purely actor-oriented pattern ( k W = 0). For the effect of negative dyadic coping on intimate relationship well-being, husbands displayed an actor-oriented pattern ( k H = 0), suggesting their intimate relationship well-being is mostly affected by their own coping behaviors. Conversely, wives exhibited a partner-oriented pattern ( k W = ∞), indicating their relationship well-being is heavily influenced by their husbands’ coping behaviors. For the effect of intimate relationship well-being on job performance, husbands approached a couple-oriented pattern ( k H = 0.806), whereas wives showed a pattern intermediate between actor-oriented and couple-oriented ( k W = 0.447). Discussion Family well-being is not only fundamental to the lives of individuals but also crucial to society at large, as families constitute the foundational units of societal structure (United Nations, 2012). Despite its significance, the detrimental impact of time poverty on family well-being, particularly among dual-earner couples, has not been thoroughly investigated in existing research. To our knowledge, this study is the first to explicitly examine the adverse effects of time poverty on intimate relationship well-being within dual-earner contexts and to explore its subsequent diffusion effects on job performance. Crucially, our findings suggest that the negative consequences of time poverty extend beyond the mere limitation of shared time. Rather, they arise from the disruption caused by time poverty to the dyadic interaction processes—specifically, the patterns of dyadic coping—within intimate relationships. Theoretical Implications Firstly, the current study significantly advances our understanding of time poverty by introducing an interactional perspective to examine its effects, a dimension previously underexplored in the literature. While existing studies have predominantly focused on the direct impacts of time poverty on individual outcomes—such as unhealthy lifestyles (Urakawa et al., 2020), poor physical (Gärling et al., 2016; Zuzanek, 2004), and mental health (Gärling et al., 2016; Roxburgh, 2004)—the interpersonal outcomes have largely been viewed through an intrapersonal lens. For instance, time poverty has been implicated in behaviors where individuals treat others as mere instruments for their own success (Jiang et al., 2024), and in contributing to poor work-home balance (Dugan et al., 2012; Höge, 2009; Zuzanek, 2004). Our study advances current understanding by examining the detrimental effects of time poverty not only on an individual’s intimate relationship well-being and job performance but also on their partner’s well-being. In addition, we investigate a mediation mechanism through maladaptive dyadic coping responses, highlighting the nuanced ways in which time constraints permeate and disrupt dyadic interactions. Consequently, our findings illustrate the broader cascading consequences of time poverty on both partners’ personal and professional lives, thereby expanding the scope of time poverty research to encompass the dynamics of dyadic interactions. Additionally, our study provides robust empirical support for the work-home resources model (ten Brummelhuis & Bakker, 2012). This model suggests that resources developed within the home domain, such as emotional support and shared responsibilities, can bolster personal resources, which in turn positively impact professional outcomes. Our findings substantiate the presence of a diffusion effect from the home to the work domain, highlighting how an individual’s job performance is influenced not only by their own well-being within the family context but also significantly by their partner’s well-being. This interdependence underscores the holistic nature of work and family life, suggesting that interventions aimed at enhancing job performance might benefit from strategies that bolster well-being across both domains. Secondly, our research significantly deepens the understanding of the factors that impair intimate relationship well-being. Previous research has highlighted the detrimental effects of scarcity—whether of time or money—on individual well-being (Bodenmann, 2005; Mullainathan & Shafir, 2013; Shah et al., 2012). While the link between financial stress or material poverty and diminished relationship well-being is well-established (Lucas et al., 2021; Totenhagen et al., 2018), the specific impact of time scarcity on relationship dynamics has been less thoroughly explored. Although some studies have addressed how limited shared time reduces interaction opportunities (e.g., Bodenmann, 2000, 2005), our study advances this line of inquiry by examining how time poverty specifically disrupts interaction patterns within couples. We reveal that time poverty not only limits these interactions but actively exacerbates negative dyadic coping processes, thereby amplifying its deleterious effects on relationship well-being. Thirdly, our study contributes significant insights into gender differences in how time poverty affects intimate relationship well-being through dyadic coping processes. Specifically, we found that husbands experiencing greater time poverty tend to engage in more negative dyadic coping, which in turn undermines their wives’ intimate relationship well-being. Additionally, considering the diffusion effects from home to work domain, the negative impact of time poverty appears more detrimental to women than to men. These findings offer further explanations for the observed gender differences in the beneficial effects of marriage. Previous research indicates that the benefits of marriage for men’s well-being tend to be greater than for women (e.g., Glenn & Weaver, 1988; Stronge et al., 2019; Taylor, 2011), often attributed to men receiving more social support from their romantic partners (Stronge et al., 2019). Our study extends this discussion by suggesting additional pathways through which gender disparities manifest: a) The detrimental impact of a partner’s time poverty on well-being is stronger for women than for men, and b) The harmful effects of a partner’s negative dyadic coping on well-being are more pronounced for women. These insights not only deepen our understanding of how time poverty influences intimate relationships but also highlight important gender-specific dynamics that may contribute to the unequal benefits of marriage reported in prior studies. Practical Implications Implications for Policymakers . Our findings underscore the detrimental effects of time poverty on intimate relationship well-being and its subsequent diffusion into job performance, potentially impacting broader social and economic development. This research offers insights into why individuals in fast-paced societies, such as China (National Bureau of Statistics, 2022), may be reluctant to marry, which has broader social implications. To counteract these challenges, policymakers should focus on strategies to alleviate time poverty. Specifically, enacting and enforcing regulations that limit excessive working hours and guarantee adequate leisure time during holidays can significantly enhance the quality of life. For instance, implementing more stringent work-hour caps and ensuring that such limits are adhered to can help individuals achieve a healthier work-life balance. Additionally, promoting policies that encourage flexible work arrangements could further facilitate this balance, allowing individuals to better manage their work and personal life commitments. Strengthening the implementation of these policies is crucial not only for improving individual and family well-being but also for enhancing overall societal health. Such measures could also serve to make the prospect of marriage and family life more appealing, thereby supporting demographic stability and long-term social sustainability. Implications for Organizations. Our study reveals that husbands’ time poverty has a ripple effect that not only diminishes their own job performance but also negatively impacts their wives’ performance. This is mediated through the mechanism of negative dyadic coping and diminished intimate relationship well-being. In response to these findings, organizations should consider implementing family-support programs that are tailored to meet the distinct needs of male and female employees. For male employees, it may be particularly beneficial to offer programs focused on developing positive dyadic coping strategies and enhancing relationship management skills (e.g., The Couples Coping Enhancement Training, Bodenmann, 2007). Training that encourages men to effectively support their partners could prove invaluable. Emphasizing the principle that “a happy wife makes for a happy life” could help to improve both personal well-being and organizational outcomes, reflecting the interconnectedness of personal satisfaction and professional productivity. Additionally, organizations might benefit from creating awareness programs that highlight the importance of supporting one’s partner in ways that contribute to mutual well-being. Such initiatives could encourage more empathetic interactions within relationships, ultimately fostering a more supportive and productive work environment. By addressing these dynamics, organizations not only enhance the well-being of their employees but also contribute to a more positive and effective workplace. This approach acknowledges the significant role that personal relationships play in overall job performance and underscores the potential benefits of proactive organizational support. Implications for Families . The findings from our study highlight the significant impact of time poverty on the quality of interactions between partners and, consequently, on intimate relationship well-being. These results reveal that for couples striving to maintain strong relationships despite time constraints, simply allocating more time together may not be enough. Instead, there is a critical need to focus on enhancing the quality of the time spent together. Particularly for husbands experiencing time poverty, it is essential to develop effective dyadic coping strategies. Training and resources that help couples engage in more positive and supportive interactions can be invaluable (Bodenmann, 2007). Programs or workshops that teach skills such as active listening, empathy, and constructive communication can help partners better manage the stresses associated with limited time. These skills are crucial for promoting healthier and more resilient relationship dynamics. Moreover, families should consider engaging in activities that maximize the quality of their interactions, even when time is limited. For example, planning meaningful activities that align with both partners’ interests can make the time spent together more satisfying and beneficial. Ultimately, fostering constructive interaction patterns can significantly counteract the adverse effects of time scarcity on intimate relationships. By focusing on improving how partners interact during the available time, couples can enhance their relationship quality and ensure a stronger, more supportive bond (Bodenmann, 2005). Limitations and Future Research The current study, while providing significant insights into the dynamics of intimate relationships under the strain of time poverty, has certain limitations that future research could address. Firstly, although data were collected from both partners in close relationships using self-reported surveys, consistent with methodologies employed in prior APIM studies (Coban-Tosyali & Bozo, 2023; Zhang et al., 2023), reliance on self-report may not fully capture the complexity of dyadic coping processes. To overcome these limitations, future research could incorporate multisource data to capture a more comprehensive view of dyadic interactions. Discrepancies often exist between one partner’s reported behavior and the other’s perception of that behavior. For example, husbands might report that they do not engage in negative dyadic coping or believe they are employing positive coping strategies. However, their partners might perceive these behaviors as negative if they feel that their emotional needs are being neglected. Additionally, qualitative interviews could explore partners’ perceptions in greater depth, offering richer insights into how actions are interpreted within the context of the relationship. Such multifaceted approaches could greatly enhance our understanding of how time poverty affects relationship dynamics and could lead to more targeted interventions to support couples facing these challenges. Secondly, while this study elucidated the mediating role of negative dyadic coping in the relationship between one partner’s time poverty and the other partner’s intimate relationship well-being, additional mediating mechanisms warrant exploration to broaden our understanding of these dynamics. Time poverty may also diminish perceived partner responsiveness—defined as the perception of a partner’s understanding, validation, and care—which is crucial for maintaining relationship quality. Prior research underscores the significance of perceived partner responsiveness in facilitating stress co-regulation and satisfying basic psychological needs, which are essential for relationship well-being (Tosyali & Harma, 2021; Coban-Tosyali & Bozo, 2023). Exploring how time poverty impacts perceived partner responsiveness could provide deeper insights into its effects on relationship dynamics. Future studies might consider integrating quantitative measures of perceived responsiveness and qualitative assessments to capture the depth and nuance of these perceptions. This approach could unveil additional pathways through which time poverty affects relationship quality, potentially leading to more effective interventions designed to mitigate its negative impacts. Thirdly, our study highlighted significant gender differences in the effects of time poverty on intimate relationship well-being, which were partially explained by variations in dyadic coping processes. To build on these findings, future research should delve deeper into the mechanisms underlying these gender disparities in partner interactions. One promising avenue for exploration involves the impact of traditional gender-role beliefs. Despite progress toward gender equality, these beliefs often persist, disproportionately burdening employed women with domestic responsibilities and comparatively relieving men from similar obligations, even in modern societies (Cinamon & Rich, 2002). Empirical studies that rigorously test the influence of these gender-role beliefs on partner interactions can provide critical insights into the persistence of gender inequalities within intimate relationships. The findings from such studies could be invaluable in informing interventions aimed at reducing these inequities. By understanding the root causes of gender disparities in relationship dynamics, policymakers and practitioners can develop targeted strategies to promote more equitable interactions within couples, ultimately enhancing relationship quality and well-being for both partners. Conclusion Family well-being is pivotal not only to individuals’ daily lives but also to societal stability. Utilizing the theory of dyadic coping, this study implemented a three-wave dyadic design to explore the impacts of time poverty on intimate relationship well-being and its subsequent diffusion into job performance. A key aspect of our investigation was the mediating role of negative dyadic coping. Our findings indicate significant gender differences in how time poverty affects relationships and work outcomes. Specifically, for husbands, time poverty was found to negatively impact their wives’ intimate relationship well-being and job performance, mediated through the husbands’ negative dyadic coping behaviors. In contrast, this pattern was not evident among wives, suggesting a unique gender-specific response to time poverty within marital dynamics. Declarations Author Contribution Nan Zhang finished the conception and design of the work and the acquisition, analysis, and interpretation of data; meanwhile, she wrote the draft of the work.Xiaomin Sun made substantial contributions to the conception and design of the work, and revised the work critically for important intellectual content. Data Availability The study's protocol was preregistered and is accessible at AsPredicted (https://aspredicted.org/fzpp-qbfk.pdf). 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Measurement model = time poverty, negative dyadic coping, intimate relationship well-being, and job performance. Three-factor model = time poverty, negative dyadic coping, and intimate relationship well-being / job performance. Two-factor model = time poverty / negative dyadic coping, and intimate relationship well-being / job performance. CMV = common method variance. * p ＜.05. ** p < .01. *** p ＜.001. Table 2 Descriptive Statistics and Correlations Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 1. Child(T1) — Measures from husband ( n = 163) 2. SES(T1) .081 — 3. STWP(T3) -.094 -.068 — 4. TP(T1) -.094 -.361 *** -.164 * — 5. NDC(T2) -.128 -.158 * -.075 .418 *** — 6. IRW(T3) .066 .163 * .124 -.369 *** -.539 *** — 7. JP(T3) .194 * .188 * .054 -.334 *** -.536 *** .664 *** — Measures from wife ( n = 163) 8. SES(T1) .022 .820 *** -.001 -.321 *** -.142 .093 .061 — 9. STWP(T3) -.073 .054 .646 *** -.118 -.061 .150 .040 .067 — 10. TP(T1) -.160 * -.316 *** -.230 ** .796 *** .407 *** -.307 *** -.288 *** -.295 *** -.313 *** — 11. NDC(T2) -.129 -.218 ** -.028 .301 *** .586 *** -.256 *** -.315 *** -.271 *** -.083 .324 *** — 12. IRW(T3) .040 .139 .074 -.300 *** -.435 *** .560 *** .547 *** .134 .057 -.315 *** -.298 *** — 13. JP(T3) .201 * .275 *** .020 -.341 *** -.355 *** .458 *** .641 *** .142 .006 -.293 *** -.341 *** .533 *** — M 32.807 5.870 26.423 3.607 2.023 -.0473 5.978 5.870 26.751 3.737 2.084 0.075 5.939 SD 5.951 1.293 16.213 1.642 0.832 0.831 0.757 1.322 14.627 1.704 0.930 0.688 0.659 Note. N = 163 dyads. SES = subjective social status. STWP = shared time with partner. TP = time poverty. NDC = negative dyadic coping. IRW = intimate relationship well-being. JP = job performance. * p ＜.05. ** p ＜.01. *** p ＜.001. Table 3 Tests of Indirect Effects Path Indirect Effect SE Hypothesis 1 TP (H) — NDC (H) — IRW (W) -0.054 * 0.026 TP (W) — NDC (W) — IRW (H) 0.007 0.014 Hypothesis 2 TP (H) — NDC (H) — IRW (W) — JP (H) -0.067 * 0.029 TP (H) — NDC (H) — IRW (W) — JP (W) -0.031 * 0.015 TP (W) — NDC (W) — IRW (H) — JP (W) -0.003 0.005 TP (W) — NDC (W) — IRW (H) — JP (H) 0.005 0.008 Note. N = 163 dyads. H = husband. W = wife. TP = time poverty. NDC = negative dyadic coping. IRW = intimate relationship well-being. JP = job performance. Table 4 Results of Model Comparisons Model χ 2 ( df ) CFI TLI RMSEA ∆ χ 2 (∆ df ) Hypothesized APIM model 1258.835 (848) .924 .917 .055 — Equal actor-effect model 1292.569 (851) .918 .91 .056 33.734 (3) *** Equal partner-effect model 1329.078 (853) .912 .904 .059 70.243 (5) *** Equal actor- and partner-effect model 1487.873 (858) .883 .874 .067 229.038 (10) *** Note. * p ＜.05. ** p ＜.01. *** p ＜.001. Table 5 Estimates of k Parameter Path k pattern TP — NDC Husband 0.669 an intermediate pattern between actor and couple pattern Wife 0 an actor-oriented pattern NDC — IRW Husband 0 an actor-oriented pattern Wife ∞ a partner-oriented pattern IRW — JP Husband 0.806 (close to 1) a couple-oriented pattern Wife 0.447 an intermediate pattern between actor and couple pattern Note. N = 163 dyads. TP = time poverty. NDC = negative dyadic coping. IRW = intimate relationship well-being. JP = job performance. Additional Declarations No competing interests reported. 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. 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00:08:37","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":142840,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8395371/v1/71fcc7c09be73337b07d16f6.html"},{"id":100343096,"identity":"c67cd2dd-61e7-46f7-b502-709403c0e6aa","added_by":"auto","created_at":"2026-01-16 00:08:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":81447,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;\u003cem\u003eFull Conceptual Model\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8395371/v1/40e06ca390a91ee850c0716d.png"},{"id":100343094,"identity":"fabdb3c9-d16d-4862-a653-7c233c899b65","added_by":"auto","created_at":"2026-01-16 00:08:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":214791,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSummary of Results\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8395371/v1/9999aafa048303f909f7059c.png"},{"id":100391309,"identity":"1691b603-64a6-47a2-ad29-e9c5753c1de0","added_by":"auto","created_at":"2026-01-16 11:24:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1161482,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8395371/v1/c9ecc0b4-41a2-473a-acc1-545693ea5bd9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Time Poverty Erodes Intimate Relationship Well-Being via Negative Dyadic Coping and Its Spillover Effect on Job Performance","fulltext":[{"header":"Introduction","content":"\u003cp\u003eJane Austen famously asserted in Pride and Prejudice, \u0026quot;It is a truth universally acknowledged that a single man in possession of a good fortune must be in want of a wife.\u0026quot; However, contrary to Austen\u0026rsquo;s observation, recent decades have seen a decline in marriage rates across various countries, despite economic advancements. For instance, marriage rates have decreased in the United States (U.S. Census Bureau, 2020), China (National Bureau of Statistics, 2022), and Canada (Statistics Canada, 2022), even as their gross domestic products have grown. This phenomenon prompts a critical question: what factors might explain this trend? \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith the acceleration of economic development and the evolution of social structures (Rosa, 2013), long working hours (Cha \u0026amp; Weeden, 2014) and fast-paced lifestyles have come to characterized modern life. Within this context, the concept of time poverty\u0026mdash;defined as the pervasive feeling of having insufficient time to accomplish desired or necessary activities (Roxburgh, 2004; Szollos, 2009)\u0026mdash;has emerged as a significant social phenomenon. This issue affects a range of countries, such as the United States (Whillans, 2019), China (Li et al., 2015), Canada (Zuzanek, 2004), and Germany (Merz \u0026amp; Rathjen, 2014). For individuals experiencing time poverty, there is often a consequential reduction in the energy, time, and psychological engagement they can devote to family life or close relationships (Zhang et al., 2024). Research indicates that time poverty impedes individuals\u0026rsquo; abilities to nurture intimate relationships (De Sousa et al., 2018) and diminishes interpersonal communication (Li, 2023). Additionally, limited time spent with partners may undermine relationship interdependence (Garcia-Rada \u0026amp; Kim, 2021), leading to lower marital satisfaction and an elevated risk of divorce (Bodenmann, 2005). This effect stems from a lack of opportunities for self-disclosure, shared experiences, fostering a sense of \u0026ldquo;we-ness\u0026rdquo;, and engaging in dyadic coping (Bodenmann, 2000). Crucially, the adverse effects of time poverty on relationship well-being extend beyond merely reducing shared time; they also detrimentally impact interaction processes during the limited time that couples do spend together.\u003c/p\u003e\n\u003cp\u003eGiven that individuals pressed for time often struggle to increase the amount of shared time with their partners, it is crucial to understand how time poverty undermines the quality of interactions during the limited time they do spend together. Insights into these processes\u0026mdash;essential for improving intimate relationship well-being\u0026mdash;remain relatively scarce.\u003c/p\u003e\n\u003cp\u003eThis study aims to uncover the mechanisms linking time poverty to relationship well-being from the perspective of dyadic interactions. Specifically, we employ dyadic coping theory (Bodenmann, 1995, 2005) to construct our conceptual framework. This theory explains how shared dyadic stress affects close relationships through the interaction processes of both partners, encompassing dyadic coping behaviors. By adopting this interpersonal lens, our study advances existing research on time and intimate relationship well-being by shifting focus from merely the limited opportunities for shared time and experiences to the disruption of interaction patterns.\u003c/p\u003e\n\u003cp\u003eAdditionally, in considering the spillover effects between home and work domains (ten Brummelhuis \u0026amp; Bakker, 2012), we explore how time poverty extends from the home into the work domain, particularly its impact on job performance. These efforts not only reinforce the work-home resources model (ten Brummelhuis \u0026amp; Bakker, 2012) within the context of dual-earner couples but also provide a home-to-work explanation for the relationship between time poverty and job performance. Figure 1 illustrates the conceptual model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConceptual Framework and Hypothesis Development\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRooted in the systemic\u0026ndash;transactional perspective, the theory of dyadic coping (Bodenmann, 1995, 2005) posits that individuals may experience stress unrelated to their partners, such as work-related stress. However, the ineffective or unsuccessful management of this stress can adversely affect their partners. This process unfolds as the stressed individual either expresses distress, seeks support, or as the partner perceives stress signals. Consequently, stress initially experienced individually is transmitted as dyadic stress, influencing both partners and initiating coping efforts from each. This phenomenon is referred to as the dyadic coping process (Bodenmann, 1995, 2005).\u003c/p\u003e\n\u003cp\u003eIn the context of the current study, existing research suggests that time poverty, often manifested as individual stress due to factors such as long working hours, can be transmitted as dyadic stress; this transmission occurs through the depletion of psychological and physical resources, and the redirection of time and attention away from family activities toward individual tasks (Zhang et al., 2024). Building on these insights, we propose that time poverty, as a form of dyadic stress, triggers negative dyadic coping behaviors. These behaviors, in turn, may detrimentally impact the intimate relationship well-being of the partner and extend their effects into the work domain.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMediating Effect of Negative Dyadic Coping Between Time Poverty and Relationship Well-being\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAccording to dyadic coping theory, dyadic stress triggers coping processes that involve both partners, a concept Bodenmann (1995, 2005) defines as \u0026quot;dyadic coping.\u0026quot; This process encompasses three interactive components: the stress signals displayed by one partner, the perception of these stress signals by the other partner, and the subsequent coping reactions from the partner (Bodenmann, 2005). Furthermore, the communication of stress\u0026mdash;whether through verbal or nonverbal signals\u0026mdash;is critical in triggering these dyadic coping reactions (Bodenmann, 2005). Stress often compromises the quality of dyadic communication (Bodenmann, 1995), leading to negative communication patterns such as criticism, contempt, or withdrawal (Bodenmann, 2005). These maladaptive communication patterns, in turn, result in ineffective coping reactions.\u003c/p\u003e\n\u003cp\u003eNegative dyadic coping refers to coping processes characterized by hostility (e.g., mocking or disparagement), ambivalence (e.g., grudging support), or superficiality (e.g., insincerity). In such cases, one partner responds to the other in a detrimental manner, often accompanied by negative communication behaviors. For instance, an individual may offer advice to their partner but simultaneously blame them for not managing stress effectively. These responses not only fail to alleviate stress but may exacerbate tension and conflict within the relationship.\u003c/p\u003e\n\u003cp\u003eIn our conceptual framework, we propose that time poverty acts as a form of dyadic stress, triggering negative dyadic coping through two primary mechanisms. First, time poverty compels individuals to prioritize unfinished tasks, thereby reducing the psychological engagement available for maintaining close relationships (Zhang et al., 2024). This reduction in engagement diminishes the attention and energy that could otherwise be devoted to partner communication, thereby compromising the quality of dyadic communication. As a result, such compromised communication further fosters negative coping patterns, where interactions may become strained or dysfunctional.\u003c/p\u003e\n\u003cp\u003eSecond, time poverty exhausts individuals both psychologically and physically (Teuchmann et al., 1999;\u0026nbsp;Sharif et al., 2021), leading to increased negative emotional states such as sadness, drowsiness, or depression (Roxburgh, 2004), and heightened irritability (H\u0026ouml;ge, 2009). These emotional and physical depletions manifest in more negative interactions with partners. Consequently, communication often deteriorates, becoming characterized by increased criticism, contempt, or withdrawal. This shift in communication style directly influences dyadic coping, where reactions may become less constructive and more destructive.\u003c/p\u003e\n\u003cp\u003eMeanwhile, Negative dyadic coping, resulting from time poverty, can significantly undermine partners\u0026rsquo; relationship well-being. According to dyadic coping theory, while effective coping strategies can reduce stress and enhance the sense of \u0026quot;we-ness\u0026quot; between partners, negative dyadic coping has the opposite effect, diminishing relationship satisfaction (Bodenmann, 2005). This decline occurs because one partner perceives the support from the other as tainted by criticism, contempt, or withdrawal, all of which impair relationship well-being. Previous research supports this, finding that negative dyadic coping is associated with lower relationship satisfaction (Bodenmann, 2000). Therefore, individuals experiencing time poverty are more likely to resort to negative dyadic coping strategies, exacerbating the detrimental effects on relationship well-being. Additionally, relationship well-being is often interdependent within close relationships (Bodenmann, 2005; Ma et al., 2016); thus, an individual\u0026rsquo;s well-being may decline when their partner\u0026rsquo;s well-being is compromised. Based on these insights, we propose the following hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 1\u003c/strong\u003e: The relationship between individuals\u0026rsquo; experience of time poverty and their partners\u0026rsquo; relationship well-being is mediated by the individuals\u0026rsquo; engagement in negative dyadic coping behaviors toward their partners.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe Diffusion Effect to Job Performance\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe interplay between home dynamics and work outcomes has been extensively explored in the literature, exemplified by studies such as Carlson et al. (2018) and Wan et al. (2019). The work-home resources model (ten Brummelhuis \u0026amp; Bakker, 2012) elucidates the spillover of resources across work, individual, and home domains. Specifically, the model suggests that resources from the home domain, such as spousal support, can bolster personal resources like psychological or affective resources (e.g., optimism, fulfilment, empathy), which in turn enhance work outcomes such as productivity.\u003c/p\u003e\n\u003cp\u003eIn the context of the current study, a robust relationship with a partner facilitates greater support, leading to improved mental health outcomes (e.g., fewer depressive symptoms, Kim \u0026amp; McKenry, 2002). This support enhances personal resources, aiding in recovery from work stress (Zhang et al., 2023) and subsequently boosting job performance (Binnewies et al., 2009). Further supporting this, research indicates that positive relationship quality with family members is associated with home-based resource gains, which contribute to home-to-work enrichment (Wan et al., 2019). Conversely, relationship tensions have been found to impede job performance (Carlson et al., 2018).\u003c/p\u003e\n\u003cp\u003eBuilding on the above arguments, we propose the following hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 2\u003c/strong\u003e: The effects of an individual\u0026rsquo;s time poverty on both their own and their partner\u0026rsquo;s relationship well-being can diffuse into the work domain. These diffusion effects manifest as the individual\u0026rsquo;s relationship well-being is positively associated with both their own and their partner\u0026rsquo;s job performance.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eExploring Potential Gender Differences\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTraditional gender-role norms often designate men as the primary breadwinners, engaged predominantly in paid work, while women are expected to manage household responsibilities and provide unpaid care to family members (Cinamon \u0026amp; Rich, 2002). Empirical evidence suggests that these traditional beliefs persist, with women, even in modern societies, performing a greater share of household chores and childcare compared to men (Giurge et al., 2021). These divergent gender expectations could influence dyadic interaction processes differently for men and women.\u003c/p\u003e\n\u003cp\u003eFirstly, men and women may exhibit different coping reactions in response to their own time poverty. Particularly when both partners experience time scarcity, women, adhering to societal expectations, often continue to assume a larger portion of family caregiving responsibilities and provide more support to family members. This disproportionate burden can hinder their ability to engage in constructive support with their partners, potentially leading to more prevalent negative dyadic coping behaviors among women experiencing time poverty.\u003c/p\u003e\n\u003cp\u003eSecond, gender differences may also manifest in how each partner copes with the other\u0026rsquo;s time poverty, influenced by entrenched gender-role beliefs (Cinamon \u0026amp; Rich, 2002). It is common for women to accept their husbands\u0026rsquo; preoccupation with work-related tasks without surprise, given societal expectations. Conversely, men may hold expectations for their wives to fulfill family responsibilities and may experience dissatisfaction if these are not met, perceiving their wives\u0026rsquo; engagement with work as neglectful. Indirect evidence supports this disparity; for instance, while husbands\u0026rsquo; engagement in work-related activities through information and communication technologies after hours (WICT) does not typically lead to social undermining behaviors from their wives, the reverse scenario\u0026mdash;wives using WICT\u0026mdash;significantly predicts husbands\u0026rsquo; social undermining (Ma et al., 2016). This suggests that husbands, rather than wives, are more likely to experience dissatisfaction with their partner\u0026rsquo;s time poverty and exhibit more negative dyadic coping behaviors. Based on these insights, we propose the following hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis 3\u003c/strong\u003e: The influence of time poverty on negative dyadic coping varies between husbands and wives. Specifically, it is proposed that when experiencing time poverty, wives exhibit more negative dyadic coping compared to husbands. Conversely, when both partners experience time poverty, wives engage in less negative dyadic coping. Consequently, the indirect effects of time poverty on job performance also differ between husbands and wives.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipants and Procedure\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study utilized a three-wave dyadic survey design, with assessments conducted at one-month intervals, targeting dual-earner couples in China. This multi-source, multi-wave approach is instrumental in mitigating common method variance (CMV), as suggested by Fuller et al. (2016) and Tehseen et al. (2017). In the first wave (Time 1), the study assessed the primary predictor\u0026mdash;time poverty\u0026mdash;along with control variables and demographic information. The mediator, negative dyadic coping, was measured in the second wave (Time 2). Finally, the outcomes, including intimate relationship well-being and job performance, were evaluated in the third wave (Time 3).\u003c/p\u003e\n\u003cp\u003eParticipants were recruited through a paid online research platform, Credamo (www.credamo.com). The recruitment advertisement specified eligibility criteria, which included: (a) cohabitation of partners, (b) both partners being employed full-time, and (c) willingness of both partners to participate in a three-wave survey. Participants were required to complete the surveys independently and to sign an online informed consent form prior to participation. Upon completion of the survey, participants were debriefed and received a compensation of 36 RMB (approximately $5 USD). The study\u0026apos;s protocol was preregistered and is accessible at AsPredicted (https://aspredicted.org/fzpp-qbfk.pdf). Additionally, the collected data are available for review on the Open Science Framework (OSF) at OSF Data Repository (https://osf.io/b2d9w/?view_only=43c6ba06dbcd4b04b7e43eff2f3e4ce5).\u003c/p\u003e\n\u003cp\u003eInitially, 218 dual-earner couples were recruited for the first wave of the study. By the second wave, 179 couples remained, and 163 couples completed the entire study, resulting in a final retention rate of 74.78%. The participants in the final sample had an average age of 32.81 years (\u003cem\u003eSD\u003c/em\u003e = 5.95) and reported working an average of 43.23 hours per week (\u003cem\u003eSD\u003c/em\u003e = 11.35). Regarding family composition, 66.3% of the participants had one child under the age of 18, 19.6% had no children, and 14.1% had two children. All participating couples were heterosexual and married, with an average relationship duration of 6.90 years (\u003cem\u003eSD\u003c/em\u003e = 5.22). Additionally, 33.0% of these families reported receiving assistance with household chores from relatives or housekeepers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMeasures\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor scales not originally in Chinese, back-translation procedures were used to ensure validity when validated Chinese versions were unavailable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTime poverty.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo assess time poverty, we employed a nine-item scale. Participants responded to items using a 7-point Likert scale, where 1 = does not apply at all and 7 = applies completely. High scores on this scale indicated greater levels of time poverty, with the exception of one reverse-coded item, \u0026ldquo;I have enough time for myself.\u0026rdquo; An example of a typical item is, \u0026ldquo;I never seem to have enough time to get everything done.\u0026rdquo; The scale demonstrated high reliability, with a Cronbach\u0026apos;s alpha of .970.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNegative dyadic coping.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMeasurement was conducted using four items from the Chinese version of the Dyadic Coping Inventory, revised by Xu et al. (2016) based on Bodenmann\u0026rsquo;s original work (2008). Each item was rated on a 7-point Likert scale, ranging from 1 (never) to 7 (very often). Higher scores on this scale signify greater engagement in negative coping behaviors. An example of these items is, \u0026ldquo;I blame my partner for not coping well enough with stress.\u0026rdquo; This measure demonstrated good reliability, with a Cronbach\u0026rsquo;s alpha of .764.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIntimate relationship well-being.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis variable was assessed using three items from the Quality of Marriage Index (QMI; Norton, 1983). Two of these items were rated on a 7-point scale, ranging from 1 (totally disagree) to 7 (totally agree), with example items like, \u0026ldquo;We have a good marriage.\u0026rdquo; The third item evaluated overall satisfaction with the romantic relationship on a 10-point scale. Higher scores on these scales indicated greater relationship satisfaction. The measure demonstrated good reliability, with a Cronbach\u0026rsquo;s alpha of .711.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eJob performance.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJob performance was assessed using three items from the In-Role Job Performance Scale (Janssen et al., 2010), which was adapted for self-report purposes. Participants rated each item on a 7-point Likert scale from 1 (totally disagree) to 7 (totally agree). An example item from this scale is, \u0026ldquo;I fulfill all responsibilities required by my job.\u0026rdquo; The scale demonstrated good reliability, with a Cronbach\u0026rsquo;s alpha of .713.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eControl Variables.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSeveral factors known to potentially influence intimate relationship well-being were controlled in our analysis, as indicated by prior research (Ma et al., 2014; Lu et al., 2009; Totenhagen et al., 2018). These included the number of children under 18 years and subjective socioeconomic status (SES). SES was measured using the MacArthur Scale of Subjective Social Status (Adler et al., 2000). Additionally, we controlled for shared time with the partner to isolate the specific effects of time poverty from those potentially confounded by limited shared time. Despite the inclusion of these control variables, the trends observed in our results remained consistent.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnalysis Strategies\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo address the dyadic structure of our data and the non-independence of observations within couples, we utilized the actor-partner interdependence model (APIM; Kenny et al., 2006). APIM is particularly suited for exploring both actor effects (e.g., how an individual\u0026rsquo;s time poverty affects their own intimate relationship well-being) and partner effects (e.g., how one partner\u0026rsquo;s time poverty affects the other partner\u0026rsquo;s intimate relationship well-being). We followed the procedures recommended by Kenny and Ledermann (2010) and Kenny (2013) to determine whether dyad members (e.g., wife and husband) are distinguishable within the model. The latent APIM analyses were conducted using Mplus version 7.4. Bootstrapping for indirect effects was performed with 5,000 bootstrap samples, and 95% confidence intervals (CI) were calculated. Effects were considered significant if the 95% CIs does not include zero.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePreliminary Analyses\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the measurement model, we conducted a multilevel confirmatory factor analysis (MCFA). This analysis incorporated our key variables\u0026mdash;time poverty, negative dyadic coping, intimate relationship well-being, and job performance\u0026mdash;with a four-factor model. Items were nested at two levels: the individual and the couple levels. The results from the MCFA indicated a good fit for the four-factor model\u0026nbsp;(\u0026chi;\u003csup\u003e2\u003c/sup\u003e[\u003cem\u003edf\u003c/em\u003e] =584.694 [300], CFI = .920, TLI = .909, RMSEA = .054).\u0026nbsp;This model demonstrated superior performance when compared to alternative configurations, including a three-factor model that combined intimate relationship well-being and job performance, a two-factor model that merged time poverty and negative dyadic coping, and a one-factor model. The comparisons and fit indices for these models are detailed in Table 1.\u003c/p\u003e\n\u003cp\u003eTo further assess the potential influence of CMV, we implemented the unmeasured latent method factor technique as described by Podsakoff et al. (2003). We constructed a two-level unmeasured latent CMV model to account for method effects not captured by the measured variables. Results from model comparisons, as detailed in Table 1, indicated minimal changes in the fit indices upon controlling for CMV. Specifically, the increases in the CFI and TLI were minor (∆CFI = .024; ∆TLI = .019), each less than the threshold of .100. Similarly, the decrease in the RMSEA was modest (∆RMSEA = .006), well below the .050 threshold. These findings suggest that CMV did not substantially bias the study\u0026rsquo;s results.\u003c/p\u003e\n\u003cp\u003eTable 2 offers detailed descriptive statistics for both husbands and wives. Consistent with our hypotheses, we observed that an individual\u0026rsquo;s time poverty is positively related to their own negative dyadic coping (\u003cem\u003er\u003c/em\u003e\u003csub\u003eH\u003c/sub\u003e = .418, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; \u003cem\u003er\u003c/em\u003e\u003csub\u003eW\u003c/sub\u003e = .324, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and negatively related to their partner\u0026rsquo;s intimate relationship well-being (\u003cem\u003er\u003c/em\u003e\u003csub\u003eH\u0026rarr;W\u003c/sub\u003e = -0.300, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; \u003cem\u003er\u003c/em\u003e\u003csub\u003eW\u0026rarr;H\u003c/sub\u003e = -0.307, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Further, an individual\u0026rsquo;s\u0026nbsp;negative dyadic coping is\u0026nbsp;negatively related to the partner\u0026rsquo;s intimate\u0026nbsp;relationship well-being\u0026nbsp;(\u003cem\u003er\u003c/em\u003e\u003csub\u003eH\u0026rarr;W\u003c/sub\u003e = -.435, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; \u003cem\u003er\u003c/em\u003e\u003csub\u003eW\u0026rarr;H\u003c/sub\u003e = -.256, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Moreover, an individual\u0026rsquo;s intimate relationship well-being shows a strong positively relationship with both their own job performance (\u003cem\u003er\u003c/em\u003e\u003csub\u003eH\u003c/sub\u003e = .664, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; \u003cem\u003er\u003c/em\u003e\u003csub\u003eW\u003c/sub\u003e = .533, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and their partner\u0026rsquo;s\u0026nbsp;job performance\u0026nbsp;(\u003cem\u003er\u003c/em\u003e\u003csub\u003eH\u0026rarr;W\u003c/sub\u003e = .458, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001; \u003cem\u003er\u003c/em\u003e\u003csub\u003eW\u0026rarr;H\u003c/sub\u003e = 547, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). These findings provide initial support for our hypotheses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDistinguishability\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eTesting\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn line with our third hypothesis, which posits potential difference between husbands and wives, we specified a distinguishable hypothesis model that allowed actor and partner effects to vary by gender. Following the methodologies recommended by Kenny and Ledermann (2010) and Kenny (2013), we first tested whether dyad members were indeed distinguishable in our dataset. Specifically, we compared the fit of the distinguishable hypothesis model with an indistinguishable effects model, in which actor and partner effects were constrained to be equal across genders. The results indicated that the distinguishable hypothesis model provided a significantly better fit than the indistinguishable model, \u0026Delta;\u0026chi;\u003csup\u003e2\u003c/sup\u003e(\u0026Delta;\u003cem\u003edf\u003c/em\u003e) = 37.38 (6), \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; .001. These findings justify the use of distinguishable dyad models for subsequent analyses, providing initial support for the assertion that the effects differ between husbands and wives.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHypothesis Testing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHypothesis 1 posits that an individual\u0026rsquo;s negative dyadic coping toward their partner mediates the relationship between their time poverty and their partner\u0026rsquo;s intimate relationship well-being. We tested this using a latent APIM model, depicted in Figure 2a. The model demonstrated a satisfactory fit:\u0026nbsp;\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(\u003cem\u003edf\u003c/em\u003e) = 945.869 (624), CFI = .934, TLI = .938, RMSEA = .056). The results indicated that husbands\u0026rsquo; time poverty was significantly negatively related to their negative dyadic coping toward wives (\u003cem\u003eB\u003c/em\u003e = 0.136, \u003cem\u003eSE\u003c/em\u003e = 0.055, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05). Conversely, the relationship between wives\u0026rsquo; time poverty and their negative dyadic coping toward husbands was marginally significant (\u003cem\u003eB\u003c/em\u003e = 0.193, \u003cem\u003eSE\u003c/em\u003e = 0.085, \u003cem\u003ep\u003c/em\u003e = .068). Further, husbands\u0026rsquo; negative dyadic coping toward their wives was significantly negatively associated with wives\u0026rsquo; intimate relationship well-being (\u003cem\u003eB\u003c/em\u003e\u003csub\u003eH\u0026rarr;W\u003c/sub\u003e = -0.399, \u003cem\u003eSE\u003c/em\u003e = 0.112, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). However, the corresponding relationship between wives\u0026rsquo; negative dyadic coping toward husbands and husbands\u0026rsquo; intimate relationship well-being did not reach statistical significance (\u003cem\u003eB\u003c/em\u003e\u003csub\u003eW\u0026rarr;H\u003c/sub\u003e = 0.038, \u003cem\u003eSE\u003c/em\u003e = 0.069, \u003cem\u003ens\u003c/em\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther analyses were conducted to assess the indirect effects, as detailed in Table 3. The analysis revealed that the indirect effect of husbands\u0026rsquo; time poverty on wives\u0026rsquo; intimate relationship well-being, mediated by husbands\u0026rsquo; negative dyadic coping, was significant (indirect effect = -0.054, \u003cem\u003eSE\u003c/em\u003e = 0.026, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05). In comparison, the indirect effect of wives\u0026rsquo; time poverty on husbands\u0026rsquo; intimate relationship well-being, mediated by wives\u0026rsquo; negative dyadic coping, was not significant (indirect effect = 0.007, \u003cem\u003eSE\u0026nbsp;\u003c/em\u003e= 0.014, \u003cem\u003ens\u003c/em\u003e). Therefore, Hypothesis 1 was partially supported, suggesting that there may be some differences between husbands and wives in how time poverty and negative dyadic coping influence intimate relationship well-being.\u003c/p\u003e\n\u003cp\u003eHypothesis 2 posits that an individual\u0026rsquo;s intimate relationship well-being would have diffuse effects on both their own and their partner\u0026rsquo;s job performance, following the mediating effects established in Hypothesis 1. To test this hypothesis, a latent APIM model was constructed, as illustrated in Figure 2b. The model demonstrated a good fit (\u0026chi;\u003csup\u003e2\u003c/sup\u003e[\u003cem\u003edf\u003c/em\u003e] = 1258.835 [848], CFI = .924, TLI = .917, RMSEA = .055).\u003c/p\u003e\n\u003cp\u003eThe analysis revealed that husbands\u0026rsquo; intimate relationship well-being was positively associated with both their own job performance (\u003cem\u003eB\u003c/em\u003e\u003csub\u003eH\u0026rarr;H\u003c/sub\u003e = 0.598, \u003cem\u003eSE\u003c/em\u003e = 0.103, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05) and their wives\u0026rsquo; job performance (\u003cem\u003eB\u003c/em\u003e\u003csub\u003eH\u0026rarr;W\u003c/sub\u003e = 0.218, \u003cem\u003eSE\u003c/em\u003e = 0.084, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01). Likewise, wives\u0026rsquo; intimate relationship well-being was positively associated with both their own job performance (\u003cem\u003eB\u003c/em\u003e\u003csub\u003eW\u0026rarr;W\u003c/sub\u003e = 0.488, \u003cem\u003eSE\u003c/em\u003e = 0.146, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and their husbands\u0026rsquo; job performance (\u003cem\u003eB\u003c/em\u003e\u003csub\u003eW\u0026rarr;H\u003c/sub\u003e = 0.482, \u003cem\u003eSE\u003c/em\u003e = 0.153, \u003cem\u003ep\u003c/em\u003e \u0026lt; .01).\u003c/p\u003e\n\u003cp\u003eWe further examined the indirect effects of time poverty on job performance through negative dyadic coping and intimate relationship well-being, as detailed in Table 3. For husbands, the analysis revealed significant indirect effect. Specifically, the pathway from husbands\u0026rsquo; time poverty to their own job performance, mediated by husbands\u0026rsquo; negative dyadic coping and wives\u0026rsquo; intimate relationship well-being\u0026mdash;\u0026ldquo;TP (H) \u0026mdash; NDC (H) \u0026mdash; IRW (W) \u0026mdash; JP (H)\u0026rdquo;, was significant (indirect effect = -0.067, \u003cem\u003eSE\u003c/em\u003e = 0.029, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05). Similarly, the indirect effect of husbands\u0026rsquo; time poverty on wives\u0026rsquo; job performance, proceeding through husbands\u0026rsquo; negative dyadic coping and wives\u0026rsquo; intimate relationship well-being\u0026mdash;\u0026ldquo;TP (H) \u0026mdash; NDC (H) \u0026mdash; IRW (W) \u0026mdash; JP (W)\u0026rdquo;, was significant (indirect effect = -0.031, \u003cem\u003eSE\u003c/em\u003e = 0.015, \u003cem\u003ep\u003c/em\u003e \u0026lt; .05).\u003c/p\u003e\n\u003cp\u003eConversely, the pathways for wives indicated non-significant results. The indirect effect of wives\u0026rsquo; time poverty on their own job performance, mediated by their negative dyadic coping and husbands\u0026rsquo; intimate relationship well-being\u0026mdash;\u0026ldquo;TP (W) \u0026mdash; NDC (W) \u0026mdash; IRW (H) \u0026mdash; JP (W)\u0026rdquo;, was not significant (indirect effect = -0.003, \u003cem\u003eSE\u003c/em\u003e = 0.005, \u003cem\u003ens\u003c/em\u003e). Additionally, the effect of wives\u0026rsquo; time poverty on husbands\u0026rsquo; job performance, through wives\u0026rsquo; negative dyadic coping and husbands\u0026rsquo; intimate relationship well-being\u0026mdash;\u0026ldquo;TP (W) \u0026mdash; NDC (W) \u0026mdash; IRW (H) \u0026mdash; JP (H)\u0026rdquo;, also did not reach statistical significance (indirect effect = 0.005, \u003cem\u003eSE\u003c/em\u003e = 0.008, \u003cem\u003ens\u003c/em\u003e). Therefore, Hypothesis 2 received partially support, as significant results were primarily observed in the pathways involving husbands.\u003c/p\u003e\n\u003cp\u003eHypothesis 3 posits gender differences in the effects detailed in Hypotheses 2. Given the disparate results observed for husbands and wives in these initial tests, we further examined these potential gender differences. Following recommendations by Kenny (2013) and Kenny \u0026amp; Ledermann (2010), we assessed whether the actor and partner effects in our full conceptual APIM model were equivalent for husbands and wives. This involved constructing three alternative models for comparison: (a) Equal actor-effect model, where the actor effects (i.e., the influence of an individual\u0026rsquo;s antecedents on their own outcomes) were constrained to be equal across genders; (b) Equal partner-effect model, where the partner effects (i.e., the influence of an individual\u0026rsquo;s antecedents on the partner\u0026rsquo;s outcomes) were constrained to be equal across genders; and (c) Equal effect model, where both actor and partner effects were constrained to be equal across genders. We compared the hypothesized APIM model against each of these alternative models. The results, detailed in Table 4, indicated that the hypothesized model provided a significantly better fit than the equal actor-effect model (∆\u0026chi;\u0026sup2;[∆\u003cem\u003edf\u003c/em\u003e] = 33.734 [3], \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), the equal partner-effect model (∆\u0026chi;\u0026sup2;[∆\u003cem\u003edf\u003c/em\u003e] = 70.243 [5], \u003cem\u003ep\u003c/em\u003e \u0026lt; .001), and the equal effect model (∆\u0026chi;\u0026sup2;[∆\u003cem\u003edf\u003c/em\u003e] = 229.038 [10], \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). These findings suggest that the actor and partner effects were not equal across genders, thereby providing empirical support for gender differences in the diffusion effects. Thus, Hypothesis 3 is supported.\u003c/p\u003e\n\u003cp\u003eTo further interpret the APIM results, we calculated the \u003cem\u003ek\u0026nbsp;\u003c/em\u003eparameter, which quantifies the ratio of the partner effect to the actor effect (Kenny \u0026amp; Ledermann, 2010). This parameter is crucial for capturing the underlying interaction pattern within the APIM. Specifically, when \u003cem\u003ek\u003c/em\u003e \u0026asymp; 0 (actor effect \u0026ne; 0, partner effect = 0), it indicates an actor-oriented pattern, where individuals\u0026rsquo; outcomes are primarily influenced by their own antecedents. Whereas when\u003cem\u003e\u0026nbsp;k\u003c/em\u003e \u0026asymp; \u0026infin; (actor effect = 0, partner effect \u0026ne; 0), it denotes a partner-oriented pattern, implying that individuals\u0026rsquo; outcomes are primarily influenced by their partner\u0026rsquo;s antecedents. When \u003cem\u003ek\u003c/em\u003e \u0026asymp; 1, it signifies a couple-oriented pattern, where both actor and partner effects contribute equally to the outcomes. According to the current study\u0026rsquo;s finding (see Table 5), for the effect of time poverty on negative dyadic coping, husbands showed a \u003cem\u003ek\u003c/em\u003e\u003csub\u003eH\u003c/sub\u003e value of 0.669, reflecting an intermediate pattern between actor-oriented and couple-oriented pattern, while wives demonstrated a purely actor-oriented pattern (\u003cem\u003ek\u003c/em\u003e\u003csub\u003eW\u003c/sub\u003e = 0).\u003c/p\u003e\n\u003cp\u003eFor the effect of negative dyadic coping on intimate relationship well-being, husbands displayed an actor-oriented pattern (\u003cem\u003ek\u003c/em\u003e\u003csub\u003eH\u003c/sub\u003e = 0), suggesting their intimate relationship well-being is mostly affected by their own coping behaviors. Conversely, wives exhibited a partner-oriented pattern (\u003cem\u003ek\u003c/em\u003e\u003csub\u003eW\u003c/sub\u003e = \u0026infin;), indicating their relationship well-being is heavily influenced by their husbands\u0026rsquo; coping behaviors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the effect of intimate relationship well-being on job performance, husbands approached a couple-oriented pattern (\u003cem\u003ek\u003c/em\u003e\u003csub\u003eH\u003c/sub\u003e = 0.806), whereas wives showed a pattern intermediate between actor-oriented and couple-oriented (\u003cem\u003ek\u003c/em\u003e\u003csub\u003eW\u003c/sub\u003e = 0.447).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFamily well-being is not only fundamental to the lives of individuals but also crucial to society at large, as families constitute the foundational units of societal structure (United Nations, 2012). Despite its significance, the detrimental impact of time poverty on family well-being, particularly among dual-earner couples, has not been thoroughly investigated in existing research. To our knowledge, this study is the first to explicitly examine the adverse effects of time poverty on intimate relationship well-being within dual-earner contexts and to explore its subsequent diffusion effects on job performance. Crucially, our findings suggest that the negative consequences of time poverty extend beyond the mere limitation of shared time. Rather, they arise from the disruption caused by time poverty to the dyadic interaction processes\u0026mdash;specifically, the patterns of dyadic coping\u0026mdash;within intimate relationships.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTheoretical Implications\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirstly, the current study significantly advances our understanding of time poverty by introducing an interactional perspective to examine its effects, a dimension previously underexplored in the literature. While existing studies have predominantly focused on the direct impacts of time poverty on individual outcomes\u0026mdash;such as unhealthy lifestyles (Urakawa et al., 2020), poor physical (G\u0026auml;rling et al., 2016; Zuzanek, 2004), and mental health (G\u0026auml;rling et al., 2016; Roxburgh, 2004)\u0026mdash;the interpersonal outcomes have largely been viewed through an intrapersonal lens. For instance, time poverty has been implicated in behaviors where individuals treat others as mere instruments for their own success (Jiang et al., 2024), and in contributing to poor work-home balance (Dugan et al., 2012; H\u0026ouml;ge, 2009; Zuzanek, 2004).\u003c/p\u003e\n\u003cp\u003eOur study advances current understanding by examining the detrimental effects of time poverty not only on an individual\u0026rsquo;s intimate relationship well-being and job performance but also on their partner\u0026rsquo;s well-being. In addition, we investigate a mediation mechanism through maladaptive dyadic coping responses, highlighting the nuanced ways in which time constraints permeate and disrupt dyadic interactions. Consequently, our findings illustrate the broader cascading consequences of time poverty on both partners\u0026rsquo; personal and professional lives, thereby expanding the scope of time poverty research to encompass the dynamics of dyadic interactions. Additionally, our study provides robust empirical support for the work-home resources model (ten Brummelhuis \u0026amp; Bakker, 2012). This model suggests that resources developed within the home domain, such as emotional support and shared responsibilities, can bolster personal resources, which in turn positively impact professional outcomes. Our findings substantiate the presence of a diffusion effect from the home to the work domain, highlighting how an individual\u0026rsquo;s job performance is influenced not only by their own well-being within the family context but also significantly by their partner\u0026rsquo;s well-being. This interdependence underscores the holistic nature of work and family life, suggesting that interventions aimed at enhancing job performance might benefit from strategies that bolster well-being across both domains.\u003c/p\u003e\n\u003cp\u003eSecondly, our research significantly deepens the understanding of the factors that impair intimate relationship well-being. Previous research has highlighted the detrimental effects of scarcity\u0026mdash;whether of time or money\u0026mdash;on individual well-being (Bodenmann, 2005; Mullainathan \u0026amp; Shafir, 2013; Shah et al., 2012). While the link between financial stress or material poverty and diminished relationship well-being is well-established (Lucas et al., 2021; Totenhagen et al., 2018), the specific impact of time scarcity on relationship dynamics has been less thoroughly explored. Although some studies have addressed how limited shared time reduces interaction opportunities (e.g., Bodenmann, 2000, 2005), our study advances this line of inquiry by examining how time poverty specifically disrupts interaction patterns within couples. We reveal that time poverty not only limits these interactions but actively exacerbates negative dyadic coping processes, thereby amplifying its deleterious effects on relationship well-being.\u003c/p\u003e\n\u003cp\u003eThirdly, our study contributes significant insights into gender differences in how time poverty affects intimate relationship well-being through dyadic coping processes. Specifically, we found that husbands experiencing greater time poverty tend to engage in more negative dyadic coping, which in turn undermines their wives\u0026rsquo; intimate relationship well-being. Additionally, considering the diffusion effects from home to work domain, the negative impact of time poverty appears more detrimental to women than to men.\u003c/p\u003e\n\u003cp\u003eThese findings offer further explanations for the observed gender differences in the beneficial effects of marriage. Previous research indicates that the benefits of marriage for men\u0026rsquo;s well-being tend to be greater than for women (e.g., Glenn \u0026amp; Weaver, 1988; Stronge et al., 2019; Taylor, 2011), often attributed to men receiving more social support from their romantic partners (Stronge et al., 2019). Our study extends this discussion by suggesting additional pathways through which gender disparities manifest: a) The detrimental impact of a partner\u0026rsquo;s time poverty on well-being is stronger for women than for men, and b) The harmful effects of a partner\u0026rsquo;s negative dyadic coping on well-being are more pronounced for women. These insights not only deepen our understanding of how time poverty influences intimate relationships but also highlight important gender-specific dynamics that may contribute to the unequal benefits of marriage reported in prior studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePractical Implications\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImplications for Policymakers\u003c/em\u003e. Our findings underscore the detrimental effects of time poverty on intimate relationship well-being and its subsequent diffusion into job performance, potentially impacting broader social and economic development. This research offers insights into why individuals in fast-paced societies, such as China (National Bureau of Statistics, 2022), may be reluctant to marry, which has broader social implications. To counteract these challenges, policymakers should focus on strategies to alleviate time poverty. Specifically, enacting and enforcing regulations that limit excessive working hours and guarantee adequate leisure time during holidays can significantly enhance the quality of life. For instance, implementing more stringent work-hour caps and ensuring that such limits are adhered to can help individuals achieve a healthier work-life balance. Additionally, promoting policies that encourage flexible work arrangements could further facilitate this balance, allowing individuals to better manage their work and personal life commitments.\u003c/p\u003e\n\u003cp\u003eStrengthening the implementation of these policies is crucial not only for improving individual and family well-being but also for enhancing overall societal health. Such measures could also serve to make the prospect of marriage and family life more appealing, thereby supporting demographic stability and long-term social sustainability.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImplications for\u003c/em\u003e\u003cem\u003e\u0026nbsp;Organizations.\u003c/em\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eOur study reveals that husbands\u0026rsquo; time poverty has a ripple effect that not only diminishes their own job performance but also negatively impacts their wives\u0026rsquo; performance. This is mediated through the mechanism of negative dyadic coping and diminished intimate relationship well-being. In response to these findings, organizations should consider implementing family-support programs that are tailored to meet the distinct needs of male and female employees. For male employees, it may be particularly beneficial to offer programs focused on developing positive dyadic coping strategies and enhancing relationship management skills (e.g., The Couples Coping Enhancement Training,\u0026nbsp;Bodenmann, 2007). Training that encourages men to effectively support their partners could prove invaluable. Emphasizing the principle that \u0026ldquo;a happy wife makes for a happy life\u0026rdquo; could help to improve both personal well-being and organizational outcomes, reflecting the interconnectedness of personal satisfaction and professional productivity.\u003c/p\u003e\n\u003cp\u003eAdditionally, organizations might benefit from creating awareness programs that highlight the importance of supporting one\u0026rsquo;s partner in ways that contribute to mutual well-being. Such initiatives could encourage more empathetic interactions within relationships, ultimately fostering a more supportive and productive work environment. By addressing these dynamics, organizations not only enhance the well-being of their employees but also contribute to a more positive and effective workplace. This approach acknowledges the significant role that personal relationships play in overall job performance and underscores the potential benefits of proactive organizational support.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eImplications for Families\u003c/em\u003e\u003cem\u003e.\u0026nbsp;\u003c/em\u003eThe findings from our study highlight the significant impact of time poverty on the quality of interactions between partners and, consequently, on intimate relationship well-being. These results reveal that for couples striving to maintain strong relationships despite time constraints, simply allocating more time together may not be enough. Instead, there is a critical need to focus on enhancing the quality of the time spent together.\u003c/p\u003e\n\u003cp\u003eParticularly for husbands experiencing time poverty, it is essential to develop effective dyadic coping strategies. Training and resources that help couples engage in more positive and supportive interactions can be invaluable (Bodenmann, 2007). Programs or workshops that teach skills such as active listening, empathy, and constructive communication can help partners better manage the stresses associated with limited time. These skills are crucial for promoting healthier and more resilient relationship dynamics. Moreover, families should consider engaging in activities that maximize the quality of their interactions, even when time is limited. For example, planning meaningful activities that align with both partners\u0026rsquo; interests can make the time spent together more satisfying and beneficial. Ultimately, fostering constructive interaction patterns can significantly counteract the adverse effects of time scarcity on intimate relationships. By focusing on improving how partners interact during the available time, couples can enhance their relationship quality and ensure a stronger, more supportive bond (Bodenmann, 2005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLimitations and Future Research\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study, while providing significant insights into the dynamics of intimate relationships under the strain of time poverty, has certain limitations that future research could address. Firstly, although data were collected from both partners in close relationships using self-reported surveys, consistent with methodologies employed in prior APIM studies (Coban-Tosyali \u0026amp; Bozo, 2023; Zhang et al., 2023), reliance on self-report may not fully capture the complexity of dyadic coping processes. To overcome these limitations, future research could incorporate multisource data to capture a more comprehensive view of dyadic interactions. Discrepancies often exist between one partner\u0026rsquo;s reported behavior and the other\u0026rsquo;s perception of that behavior. For example, husbands might report that they do not engage in negative dyadic coping or believe they are employing positive coping strategies. However, their partners might perceive these behaviors as negative if they feel that their emotional needs are being neglected. Additionally, qualitative interviews could explore partners\u0026rsquo; perceptions in greater depth, offering richer insights into how actions are interpreted within the context of the relationship. Such multifaceted approaches could greatly enhance our understanding of how time poverty affects relationship dynamics and could lead to more targeted interventions to support couples facing these challenges.\u003c/p\u003e\n\u003cp\u003eSecondly, while this study elucidated the mediating role of negative dyadic coping in the relationship between one partner\u0026rsquo;s time poverty and the other partner\u0026rsquo;s intimate relationship well-being, additional mediating mechanisms warrant exploration to broaden our understanding of these dynamics. Time poverty may also diminish perceived partner responsiveness\u0026mdash;defined as the perception of a partner\u0026rsquo;s understanding, validation, and care\u0026mdash;which is crucial for maintaining relationship quality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrior research underscores the significance of perceived partner responsiveness in facilitating stress co-regulation and satisfying basic psychological needs, which are essential for relationship well-being (Tosyali \u0026amp; Harma, 2021; Coban-Tosyali \u0026amp; Bozo, 2023). Exploring how time poverty impacts perceived partner responsiveness could provide deeper insights into its effects on relationship dynamics. Future studies might consider integrating quantitative measures of perceived responsiveness and qualitative assessments to capture the depth and nuance of these perceptions. This approach could unveil additional pathways through which time poverty affects relationship quality, potentially leading to more effective interventions designed to mitigate its negative impacts.\u003c/p\u003e\n\u003cp\u003eThirdly, our study highlighted significant gender differences in the effects of time poverty on intimate relationship well-being, which were partially explained by variations in dyadic coping processes. To build on these findings, future research should delve deeper into the mechanisms underlying these gender disparities in partner interactions.\u003c/p\u003e\n\u003cp\u003eOne promising avenue for exploration involves the impact of traditional gender-role beliefs. Despite progress toward gender equality, these beliefs often persist, disproportionately burdening employed women with domestic responsibilities and comparatively relieving men from similar obligations, even in modern societies (Cinamon \u0026amp; Rich, 2002). Empirical studies that rigorously test the influence of these gender-role beliefs on partner interactions can provide critical insights into the persistence of gender inequalities within intimate relationships. The findings from such studies could be invaluable in informing interventions aimed at reducing these inequities. By understanding the root causes of gender disparities in relationship dynamics, policymakers and practitioners can develop targeted strategies to promote more equitable interactions within couples, ultimately enhancing relationship quality and well-being for both partners.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFamily well-being is pivotal not only to individuals\u0026rsquo; daily lives but also to societal stability. Utilizing the theory of dyadic coping, this study implemented a three-wave dyadic design to explore the impacts of time poverty on intimate relationship well-being and its subsequent diffusion into job performance. A key aspect of our investigation was the mediating role of negative dyadic coping. Our findings indicate significant gender differences in how time poverty affects relationships and work outcomes. Specifically, for husbands, time poverty was found to negatively impact their wives\u0026rsquo; intimate relationship well-being and job performance, mediated through the husbands\u0026rsquo; negative dyadic coping behaviors. In contrast, this pattern was not evident among wives, suggesting a unique gender-specific response to time poverty within marital dynamics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eNan Zhang finished the conception and design of the work and the acquisition, analysis, and interpretation of data; meanwhile, she wrote the draft of the work.Xiaomin Sun made substantial contributions to the conception and design of the work, and revised the work critically for important intellectual content.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe study\u0026apos;s protocol was preregistered and is accessible at AsPredicted (https://aspredicted.org/fzpp-qbfk.pdf). 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Routledge. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003e\u003cem\u003eResults of Model Comparisons\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e(\u003cem\u003edf\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e∆\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e(∆\u003cem\u003e\u0026nbsp;df\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eMeasurement model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e584.694 (300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eThree-factor model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e585.844 (304)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e.911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e1.15 (4)\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eTwo-factor model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e654.760 (306)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e70.066 (6)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eOne-factor model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e723.266 (304)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e138.572 (12) \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eLatent CMV model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e462.718 (306)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e121.976 (36)\u003csup\u003e\u0026nbsp;***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eIndistinguishable model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e950.956 (638)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e.935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003eDistinguishable model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e913.576 (632)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e.947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e.052\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e37.38 (6)\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003eMeasurement model = time poverty, negative dyadic coping, intimate relationship well-being, and job performance. Three-factor model = time poverty, negative dyadic coping, and intimate relationship well-being / job performance. Two-factor model = time poverty / negative dyadic coping, and intimate relationship well-being / job performance. CMV = common method variance. \u003csup\u003e\u0026nbsp;*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e＜.05. \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep \u0026lt;\u0026nbsp;\u003c/em\u003e.01. \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e＜.001.\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003e\u003cem\u003eDescriptive Statistics and Correlations\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e1. Child(T1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\"\u003e\n \u003cp\u003eMeasures from husband (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 163)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2. SES(T1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e3. STWP(T3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e4. TP(T1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.361\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.164\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e5.\u0026nbsp;NDC(T2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.158\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.418\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e6. IRW(T3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.163\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.369\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.539\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e7.\u0026nbsp;JP(T3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.194\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.188\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.334\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.536\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.664\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"14\"\u003e\n \u003cp\u003eMeasures from wife (\u003cem\u003en\u0026nbsp;\u003c/em\u003e= 163)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e8. SES(T1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.820\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.321\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e9. STWP(T3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.646\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e10. TP(T1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.160\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.316\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.230\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.796\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.407\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.307\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.288\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.295\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.313\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e11.\u0026nbsp;NDC(T2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.218\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.301\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.586\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.256\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.315\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.271\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.324\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e12. IRW(T3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.300\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.435\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.560\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.547\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.315\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.298\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e13.\u0026nbsp;JP(T3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.201\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.275\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.341\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.355\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.458\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.641\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.293\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.341\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e.533\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e32.807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-.0473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.939\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cem\u003eSD\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.831\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e14.627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003eN\u003c/em\u003e = 163 dyads. SES = subjective social status. STWP = shared time with partner. TP = time poverty. NDC = negative dyadic coping. IRW = intimate relationship well-being. JP = job performance. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e＜.05. \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e＜.01. \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e＜.001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003e\u003cem\u003eTests of Indirect Effects\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003eIndirect Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cem\u003eSE\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eHypothesis 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eTP (H) \u0026mdash; NDC (H) \u0026mdash; IRW (W)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-0.054\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eTP (W) \u0026mdash; NDC (W) \u0026mdash; IRW (H)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eHypothesis 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eTP (H) \u0026mdash; NDC (H) \u0026mdash; IRW (W) \u0026mdash; JP (H)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-0.067\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eTP (H) \u0026mdash; NDC (H) \u0026mdash; IRW (W) \u0026mdash; JP (W)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-0.031\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eTP (W) \u0026mdash; NDC (W) \u0026mdash; IRW (H) \u0026mdash; JP (W)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eTP (W) \u0026mdash; NDC (W) \u0026mdash; IRW (H) \u0026mdash; JP (H)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003eN\u003c/em\u003e = 163 dyads. H = husband. W = wife. TP = time poverty. NDC = negative dyadic coping. IRW = intimate relationship well-being. JP = job performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003e\u003cem\u003eResults of Model Comparisons\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e(\u003cem\u003edf\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e∆\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e(∆\u003cem\u003e\u0026nbsp;df\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eHypothesized APIM model\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e1258.835 \u0026nbsp;(848)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eEqual actor-effect model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e1292.569 \u0026nbsp; \u0026nbsp; (851)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e33.734 (3) \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eEqual\u0026nbsp;partner-effect model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e1329.078 (853)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e70.243 (5) \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eEqual\u0026nbsp;actor- and partner-effect model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e1487.873 (858)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e229.038 \u0026nbsp; \u0026nbsp; (10) \u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u0026nbsp;\u003c/em\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e＜.05. \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e＜.01. \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e＜.001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u0026nbsp;\u003c/strong\u003e\u003cem\u003eEstimates of k Parameter\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u003cem\u003ek\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003epattern\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eTP\u0026nbsp;\u0026mdash; NDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eHusband\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003ean intermediate pattern between actor and couple pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eWife\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003ean actor-oriented pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eNDC \u0026mdash; IRW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eHusband\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003ean actor-oriented pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eWife\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e\u0026infin;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003ea partner-oriented pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eIRW \u0026mdash; JP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eHusband\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.806 (close to 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003ea couple-oriented pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eWife\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003ean intermediate pattern between actor and couple pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e \u003cem\u003eN\u003c/em\u003e = 163 dyads. TP = time poverty. NDC = negative dyadic coping. IRW = intimate relationship well-being. JP = job performance.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"time poverty, dyadic stress, relationship wellbeing, job performance, dyadic coping theory, actor-partner interdependence model","lastPublishedDoi":"10.21203/rs.3.rs-8395371/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8395371/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"High-quality intimate relationships are highly valued. Prior research has underscored that limited shared time with partners can detrimentally impact relationship quality. However, time poverty—the pervasive subjective perception of not having enough time to accomplish desired or necessary tasks—may compromise relationship well-being. This impairment occurs not merely through diminished shared time but also by undermining dyadic interaction processes. Employing a three-wave dyadic design, we collected data from 163 full-time employed couples (326 participants, yielding 978 responses) across one-month intervals in China. Drawing on dyadic coping theory and utilizing the actor-partner interdependence model, we examined how time poverty influences relationship well-being and subsequently spills over to job performance. The findings reveal that time poverty negatively affects relationship well-being through negative dyadic coping for husbands but not for wives and these dynamics further influence both partners’ job performance. This research underscores the importance of not merely increasing shared time but also optimizing interpersonal interactions within that time, offering an interpersonal perspective on the broader implications of time poverty.","manuscriptTitle":"Time Poverty Erodes Intimate Relationship Well-Being via Negative Dyadic Coping and Its Spillover Effect on Job Performance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 00:08:30","doi":"10.21203/rs.3.rs-8395371/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":"3d6f1c82-14fa-4580-bb48-c9bac5bba5cc","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"146342911654049755135684950290673218138","date":"2026-04-29T12:02:20+00:00","index":44,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-16T00:08:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 00:08:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8395371","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8395371","identity":"rs-8395371","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}