A multilevel moderated mediational model of the daily relationships between hassles, exhaustion, ego-resiliency and resulting emotional inertia

Journal Pre-proofs Full Length Article A Multilevel Moderated Mediational Model of the Daily Relationships between Hassles, Exhaustion, Ego-resiliency and Resulting Emotional Inertia Guido Alessandri, Evelina De Longis, Nancy Eisenberg, Stevan E. Hobfoll PII: DOI: Reference:

S0092-6566(20)30002-7 https://doi.org/10.1016/j.jrp.2020.103913 YJRPE 103913

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Journal of Research in Personality

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24 January 2019 9 January 2020 10 January 2020

Please cite this article as: Alessandri, G., De Longis, E., Eisenberg, N., Hobfoll, S.E., A Multilevel Moderated Mediational Model of the Daily Relationships between Hassles, Exhaustion, Ego-resiliency and Resulting Emotional Inertia, Journal of Research in Personality (2020), doi: https://doi.org/10.1016/j.jrp.2020.103913

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1 RUNNING HEAD: A MULTILEVEL MODERATED MEDIATIONAL MODEL

Title A Multilevel Moderated Mediational Model of the Daily Relationships between Hassles, Exhaustion, Ego-resiliency and Resulting Emotional Inertia Authors Guido Alessandri & Evelina De Longis Sapienza, University of Rome Nancy Eisenberg Arizona State University Stevan E. Hobfoll Rush University Medical Center

Correspondence should be addressed to: Guido Alessandri, Department of Psychology, Sapienza University of Rome, Via dei Marsi 78, 00185 Rome (Italy). Email: [email protected]

2 Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Preparation of this manuscript was supported by two grants from Sapienza University of Rome (RM11715C809391B1, RG11816433CBD8D3) named “Progetti di Ateneo” (2017, 2018). to Guido Alessandri. Author contribution This study was conceptualized by Guido Alessandri. Data were collected and prepared by Guido Alessandri & Evelina De Longis. Data analysis were performed by Guido Alessandri and Evelina De Longis. The paper was written by Guido Alessandri, Nancy Eisenberg, Evelina De Longis. Stevan E. Hobfoll offered comments and revised the text. Acknowledgement The authors thanks prof. Peter Kuppens, University of Leuven for his comments helpful on an earlier draft of this manuscript.

3 RUNNING HEAD: A MULTILEVEL MODERATED MEDIATIONAL MODEL

Title A Multilevel Moderated Mediational Model of the Daily Relationships between Hassles, Exhaustion, Ego-resiliency and Resulting Emotional Inertia

4 Abstract Negative emotional inertia refers to the degree of which a current emotional state can be predicted by a previous emotional state and it represents a relevant marker of psychological maladjustment. The current study tested a theoretical model in which the dynamic impact of daily hassles on negative emotional inertia is mediated by exhaustion, and moderated by individuals’ level of ego-resiliency. Participants were 173 sophomore students (60% females) who completed two diaries per day (every morning and evening) for 18 days. In line with our predictions, the results suggest that ego-resiliency is a key personal resource that might be able to buffer the detrimental effects of daily stressors on individuals’ negative emotional inertia. In addition, our study introduced exhaustion as a potential antecedent of inertia of negative emotions. Overall, our results support the value of exhaustion as the mediator, and of ego resiliency as the moderator, of the longitudinal relation between daily hassles and emotional inertia.

Keywords: emotional inertia, ego-resiliency, exhaustion, daily hassles, moderated-mediation.

5 People differ in the way they respond and adapt to emotionally relevant events. Some individuals tend to experience few daily fluctuations in their emotions, whereas others are more reactive to environmental changes and show frequent emotional changes (Davidson, 1998; Frijda, 2009; Koval & Kuppens, 2012; Larsen 2000). Researchers have recently acknowledged that individual differences in the pattern of emotional ups and downs, also known as emotion dynamics, represent an important indicator of psychological adjustment and well-being (Davidson, 1998, 2015; Hollenstein, Lichtwarck-Aschoff & Potworowski, 2013; Houben, Van Den Noortgate, & Kuppens 2015; Trull, Lane, Koval, & Ebner-Priemer, 2015; Wichers, Wigman, & Myin-Germeys, 2015). Accordingly, within-individual daily emotional dynamics have recently been of interest because they could offer insight into people’s psychological functioning and emotional wellbeing (Koval & Kuppens, 2012; Koval, Sütterlin, & Kuppens, 2016). According to Kuppens, Stouten, and Mesquita (2009), individuals’ emotional experience reflects basic individual differences and tends to be context specific, thus showing great intraand inter- individual variability in terms of duration, intensity, and rate of affect repair. Temporal dependency, or emotional inertia (Kuppens, Allens, & Sheeber, 2010; Suls, Green, & Hillis, 1998), is an important component of the construct of emotion dynamics (Koval & Kuppens, 2012). It reflects the tendency of emotional states to be predictable over time and to be resistant to change, with lower levels indicating higher tendency to change and higher levels indicating greater resistance to change (Kuppens et al., 2010). Emotional inertia can be measured by using the autocorrelation of an emotion, which indicates the degree to which a current emotional state is correlated with a previous emotional state (Wang, Hamaker, & Bergeman, 2012). Higher autocorrelations suggest that any possible deviation from the mean of an emotion is likely to

6 persist for a long period of time. Conversely, low autocorrelations indicate low interdependency of measures of the same emotions at adjacent points, suggesting that emotional states are relatively unpredictable over time (Wang et al., 2012). Evidence indicates that high temporal dependency of negative emotional states is related to several markers of psychological maladjustment such as neuroticism (Suls et al., 1998), depression (Koval & Kuppens, 2012; Koval et al., 2016), low self-esteem (Kuppens et al., 2010), and rumination (Brose, Schmiedek, Koval, & Kuppens, 2015). It is important to note that whereas inertia of both negative and sometimes positive emotions have proved to be maladaptive, stronger evidence is available with regard to negative emotional inertia (Houben et al., 2015). Specifically, in the meta-analytic study conducted by Houben et al. (2015), inertia of positive emotions was found to be associated with lower well-being, but this relation was weaker than that for inertia of negative emotions. This result was also supported by the two studies conducted by Koval et al. (2016). This is not surprising because emotional flexibility might represent an adaptive mechanism both in the case of negative and positive emotions (Kashdan & Rottenberg, 2010; Hollenstein et al., 2013). Indeed, slow change in affective states may be a marker of a disconnection from environmental change and a lack of emotion regulation (Koval et al., 2016), and the unnecessary maintenance of negative emotional states might have negative effects for the individual (Kuppens et al., 2010). Thus, emotional inertia, especially for negative emotions, may provide key information on people’s emotional functioning (Koval & Kuppens, 2012; Kuppens et al., 2010). It is often assumed that emotional inertia results mostly from a dynamic interaction among various vulnerabilities or strengths (e.g., in terms of personality traits such as egoresiliency; Letziring, Block, & Funder, 2005) and environmental circumstances such as daily

7 hassles (see DeLongis, Coyne, Dakof, Folkman, & Lazarus, 1982). However, the role of these two different types of potential influences rarely has been simultaneously tested; nor have hypotheses regarding such an interaction been incorporated in an overarching theoretical model. In this study, using a relatively large sample of college students followed for 18 consecutive days, we tested a theoretical model (Figure 1) in which daily hassles indirectly predict emotional inertia of negative emotions through the mediation of exhaustion, and this relation is conditional on the individual’s level of ego-resiliency. We focused on negative emotional inertia because dynamic patterns of negative emotions have been found to be more predictive of psychological maladjustment than have the dynamics of positive emotions (Houben et al., 2015). In the following section, we briefly present each component included in our model and explain in detail the theoretical arguments and assumptions that corroborated their hypothesized role. Emotion Dynamics and Contextual Factors Daily hassles refer to ongoing strains that occur on a regular basis over the day (DeLongis et al., 1982). They represent randomly occurring challenges that disturb and/or interrupt everyday activities. These minor stressors refer to a number of situations, including arguments with significant others, dealing with deadlines, or experiencing minor health problems. Studies indicate that the exposure to this type of stressor may have long-term effects on well-being (Folkman, Lazarus, Gruen, & DeLongis, 1986), as well as on psychological and physiological health (Cooper, Kirkcaldy, & Brown, 1994; Gruen, Folkman, & Lazarus, 1988). Specifically, daily hassles have been found to be associated with negative affect (McCullough, Huebner, & Laughlin, 2000) and exhaustion (Alessandri, Perinelli, De Longis, Rosa, Theodorou, & Borgogni, 2016; Zohar, 1997) and have a stronger relation to health and somatic symptoms than major life events (DeLongis et al., 1982). The reason seems to be that major life events are

8 usually rare, whereas daily hassles can exert an immediate and cumulative effect on well-being (Lazarus, 1999; Zautra, 2003). Despite the well-known effects of stress on individual functioning, few studies have investigated its impact on emotion dynamics (Koval & Kuppens, 2012). Zautra, Berkhof, and Nicolson (2002), using an experience sampling methodology, found that higher levels of stress predicted change in mean levels and an increase in the variance of both negative and positive affect, as well as an increase in the inverse correlation between the two dimensions of affect. With regard to emotional inertia, Kuppens et al. (2010) found that depressed adolescents reported higher levels of inertia in both positive and negative emotions during emotionally taxing interactions. A study conducted by Koval and Kuppens (2012), however, found that a higher vulnerability to social stress was related to a drop in emotional inertia, indicating that both individual differences, and environmental factors may trigger changes in emotion dynamics. Koval and Kuppens (2012)’s results are not surprising because it is widely acknowledged that individuals’ emotional states, as well as their behaviour, typically result from what happens during their everyday life (Xanthopoulou & Meier, 2014). For example, individuals who experience greater daily exposure to challenges often refer “feeling of being overextended and depleted of one's emotional resources" (Janssen, Schaufeli, & Houkes, 1999, p. 75), a state known as exhaustion (Bakker & Costa, 2014). In line with Conservation of Resources Theory (COR; Hobfoll, 1989), we have conceptualized exhaustion as a net loss of physical, cognitive, or emotional resources, generated by prolonged exposure to stressful condition (e.g., daily hassles; Hobfoll & Shirom, 2001). Differently stated, exhaustion refers to a process of depletion of emotional resources (Hobfoll & Shirom, 2001). According to COR theory, when confronted with resource loss, individuals tend to protect the remaining resources by adopting a defensive posture

9 (Hobfoll, 2001), namely a strategy of temporarily conserving of resources for later action. This defensive approach allows individuals to minimize the loss of resources and to keep remaining resources readily available in case of a future loss. In some cases, this process results in successful adaptation and the gain of new resources, whereas at other times it leads to unsuccessful adaptation, which is related to negative functional and emotional outcomes (Hobfoll, 2001), such as emotional inertia. A similar point has been made by the strength of selfcontrol model (Baumeister, Vohs, & Tice, 2007; Muraven & Baumeister, 2000) stating that individuals tend to limit resource investment when they perceive depletion of their selfregulatory resources. This defensive strategy is more pronounced in those circumstances in which individuals anticipate the future need for self-regulation (Baumeister, Bratslavsky, Muraven, & Tice, 1998). According to the model, individuals possess a finite set of resources that they may invest in self-regulating behaviour, and these resources get progressively depleted by effortful attempts at self-regulation (Baumeister et al., 2007). A state of “exhaustion” emerges as the subjective feeling of ego depletion, experienced by those individuals who have exhausted their resources related to self-regulation. People experiencing such a state of depleted resources likely strive to avoid the cost of working to change their emotional state, given that elimination of the bad feelings requires the investment of further resources (Hobfoll & Shirom, 2001). In this regard, empirical studies have demonstrated that stress reduces the control exerted by the prefrontal cortex over the amygdala (PFC; Arnsten, 2009). This suggests the possibility that exhaustion from daily hassles (as shown by the pathway linking daily hassles to exhaustion in Figure 1) may lead to reduced PFC control and, thus, to greater emotional inertia (as shown by the pathway between exhaustion and emotional inertia in Figure 1). It is likely that people experiencing such a state of resources

10 depletion often do not even try to change their negative emotional state because in order to change it, they need to invest further resources that they feel they do not possess. Following this reasoning, it is probable that a possible consequence of the conservation of resources strategy is a chronic disconnection from environmental contingencies and a systematic reduction of the ability to adapt one’s own emotional states to changed environmental scenarios. This might result in emotional states that are slow to change (i.e., inertia) and therefore not appropriately attuned to events and situations. In this way, exhaustion might have consequences in terms of emotion regulation, for example, by predisposing individuals in an initially negative state to emotional inertia, or a continuous, relatively stable negative affect. The above reasoning led us to hypothesize that exhaustion mediates the relation between daily hassles and emotional inertia (as shown in Figure 1 by the two pathways linking daily hassles to exhaustion; and exhaustion to emotional inertia). Indeed, in the long run, daily hassles likely have the potential to predispose individuals to exhaustion and to significantly compromise their emotional dynamics. The higher the number and the severity of the hassles experienced, the more the emotional resources individuals must invest to cope with them. Given that individuals’ emotional resources are not infinite, exhaustion signals the point at which their resources start to run out. Having reached this point, individuals’ emotional states start to persist because people lack the emotional resources for actively tuning their emotional state to environmental contingencies. Another characteristic of our model is the assumption that individuals’ reactions to daily hassles vary on the basis of dispositional differences in their adaptability to emotional stressors (Mäkikangas & Kinnunen, 2003). Indeed, it is well known that people exposed to the same condition display different psychological and behavioural reactions (Kahn & Byosiere, 1992),

11 and that some individuals tend to show greater resistance to stress than others. In this regard, empirical studies point to ego-resiliency as an individual characteristic linked to the ability to respond flexibly to situational demands such as acute stress, conflicts or uncertainty, and conceptually, it is related to the constructs of competence, social intelligence, and coping (Block & Kremen, 1996). Theoretically, the construct of ego-resiliency refers to an individual characteristic “reflecting general resourcefulness, sturdiness of character, and flexibility of functioning in response to varying environmental circumstances” (Luthar, Cicchetti, & Becker, 2000, p. 546). It is often viewed as closely associated with self-regulation and reflects the ability of flexibly modulate the level of control in contexts and, hence, to deal well with stress and challenges (see Derryberry & Rothbart, 1997; Eisenberg, 2002). Individuals high in ego-resiliency usually show a greater ability to dynamically and appropriately self-regulate, as well as to quickly adapt to changing circumstances (Block & Kremen, 1996). Furthermore, people high in ego-resiliency tend to be more persistent when facing adversities (Funder & Block, 1989) and unexpected changes in their daily life (Alessandri, Zuffianò, Eisenberg, & Pastorelli, 2017). Interestingly, these individuals are usually able to successfully regulate negative emotions under stressful conditions (Waugh, Fredrickson, & Taylor, 2008). In line with Koval and Kuppens (2012), we hypothesized that the ability to optimally regulate emotions under stress helps individuals high in ego-resiliency to counteract the resource depletion effect of daily hassles (as shown in Figure 1 by the pathway linking ego-resiliency to the pathway between daily hassles and exhaustion). We expected a substantial positive relation between daily hassles and exhaustion to be observed for individuals low in ego-resiliency; in contrast, a nonsignificant relation between daily hassles and exhaustion was expected for

12 individuals high in ego-resiliency. The Present Study The present study was designed to add to current literature on emotional inertia by proposing an integrative model in which the impact of daily hassles on the inertia of negative emotions through exhaustion is moderated by between-individual differences in ego-resiliency. Whereas previous researchers have investigated the association between emotional inertia and daily hassles, to our knowledge none has investigated the relation between exhaustion and emotional inertia or between emotional inertia and ego-resiliency, or has simultaneously considered these variables as a part of a single model. The two-level structural equation model allowed us to investigate our hypotheses at the proper level of analysis, namely, the withinindividual level (or Level 1) and the between-individual level (or Level 2). Our main hypotheses were tested at level 2 of this model-- the level of individual differences. Our first hypothesis was represented by the first pathway connecting daily hassles to exhaustion. As stated above, daily hassles are common and frequent stressors that can disrupt daily life, activities, and goals. In COR theory parlance, they can be conceptualized as minor events that, taken together, accumulate to form significant loss of resources (Hobfoll & Shirom, 2001). These events can be stressful because they imply the loss of valuable resources, such as time and sense of role fulfilment (Hobfoll, 1991). As such, hassles represent a kind of stressor that may have an immediate effect on well-being that can be easily identified (Almeida, 2005; Pinquart, 2009). This may happen because daily hassles represent frequent disruptions of daily life, which often require immediate attention (Almeida, 2005; Zautra, 2003). As postulated by COR theory, loss events are particularly salient and, especially in the case of frequent daily hassles, being exposed to repeated episodic stress with few or short intervals between loss

13 events, may not allow individuals to regroup their resources (Hobfoll & Shirom, 2001). All in all, these conditions may result in the development of symptoms of exhaustion. Our second hypothesis is represented by the pathway from exhaustion to inertia of negative emotions. Given that emotional inertia has been found to be related with neuroticism and depression, which are both associated to exhaustion (Alarcon, Eschleman, & Bowling, 2009), it seems reasonable that exhaustion could be related to emotional inertia. Moreover, it is likely that individuals experiencing higher emotional inertia tend to protect their remaining resources and to focus on their state of resource depletion, disconnecting from ongoing events and emotion regulation (Hobfoll, 2001; Kuppens et al., 2012). Regulating emotions and changing one’s own emotional states requires the investment of psychophysiological resources that individuals experiencing emotional inertia seem to lack. Finally, our third hypothesis that ego-resiliency acts as a moderator of the relation between daily hassles and exhaustion is represented by the path from ego-resiliency to the middle of the path connecting daily hassles and exhaustion. The hypothesis is that ego-resiliency buffers the impact of daily hassles on exhaustion and, thus, the latter’s consequences for emotion regulation by acting as a personality resource (Hobfoll, 1998). The idea is that, even if the impact of hassles is similar for all individuals, those who are higher in ego-resiliency have better emotion regulation abilities and will therefore be better able to overcome them. On the other hand, those with lower levels of ego-resiliency might be more affected by hassles, and the effects may last for a longer period of time. Stated differently, we expected that the cumulative effect of daily hassles would be (1) low for resilient individuals, who have greater internal resources to successfully cope with stressors; (2) high for individuals low in ego-resiliency, who, in the long run, might find it even harder to cope with these unexpected demands, ending up with a weaker

14 pool of resources. Finally, whereas we were mainly interested in between-individual differences, the hypothesis that daily hassles lead to exhaustion can be probed also as a purely within-individual process (i.e., a Level 1 hypothesis). A final hypothesis, namely that during days with more reported daily hassles, individuals experience more exhaustion, is represented by the circle named “daily hassles” pointing to the other circle, named “exhaustion” represented at the bottom of Figure 1. In contrast, average levels of exhaustion and emotional inertia represent two between-individual differences, and as such, represent a Level 2 hypothesis. Exhaustion exists at both levels: at Level 1 it refers to daily levels of exhaustion, and at Level 2 it is represented by the average score of exhaustion. We tested our hypotheses with a sample of university students, followed for three weeks during their sophomore year. Psychological literature has documented the incidence of stress in the university setting and its implication for students’ achievement and health (Regehr, Glancy, & Pitts, 2013, for a review). Method Power Determination The number of individuals to be included in the study was determined on the basis of Lüdtke et al. (2008) recommendations for multilevel studies of considering (1) individuals per group, (2) the number of groups, (3) the intraclass correlation, and (4) the nature of the data. Then, we run a Monte Carlo simulation using the final “number of participants” and “available time points” from the final dataset, and parameter estimates obtained from our best fitting model (Bolger, Stadler, & Laurenceau, 2012). Results from the 1,000 simulated samples demonstrated an adequate average power to detect all within (i.e., 1.00) as well as between (i.e., .93) hypothesized paths. Full details on this simulation is presented in the online Appendix.

15 Participants At the beginning of the study (T0), participants were 180 sophomore students (60% females) enrolled in an introductory psychology class. Among them, 173 provided valid data. The average age of participants was 21.27 years (SD = 3.44). Students received partial course credit for participating. Procedure Along with other measures not relevant to the present study, measures of participants’ ego-resiliency, and basic sociodemographic characteristics were collected two weeks before the beginning of the study. Participants completed a daily diary for 18 days, both in the morning (before the beginning of the study day) and in the evening, after the conclusion of the study day. Students were told that only two days of absence from the study would be allowed and that participants not responding for more than two days (i.e., accumulating four absences: two morning and two evenings) would be excluded from the study. Participants were asked to fill momentary versions of the Positive and Negative Affect Schedule (PANAS; Watson, Clark, & Tellegen, 1988) twice per day (every morning and every evening). The evening version of the diary also included questions about hassles that occurred during the day, and two items assessing exhaustion. These instruments were online every day at a 24-hour interval, in the morning (from 8:30 am to 10:30 am), and in the evening (from 8:30 pm to 10:30 pm), for almost three consecutive weeks. To enhance study participation, students received an e-mail reminder at 8:30 am every morning, and at 8.30 pm every evening with a link to a website to complete the respective diary. This approach prevented participants from completing each questionnaire twice or outside the timeframe, an advantage of web-based diary studies over most paper-and-pencil diary studies.

16 Attrition Analysis Of the initial pool of 180 students, 7 students were excluded from the study due to a high number of absences (five or more), and their incomplete data were not considered in the study. Analyses were conducted using the 173 students who offered valid data. Among these, 114 (66%) responded to all questionnaires, and the average number of students participating each day was approximately 150 (i.e., 87% of the original sample). Although there was high daily participation, the lower proportion of individuals completing all diaries was likely due to the option given to each student to miss four data collections (two morning and two evening diaries). In any case, attrition was not systematic, and primarily due to the unavailability or unwillingness of individuals to take part on a specific day of the study. There was no relation, at any time point, between missing data and age, sex, exhaustion, resiliency, or daily hassles. MBox tests suggested that the participants included in the sample at any time point in time did not significantly differ from their missing counterparts (i.e., the participants who were not available at that specific time point). Measures Ego-resiliency At T0, participants were asked to indicate the degree to which they agreed with each of 10 statements on a scale ranging from 1 (does not apply at all) to 7 (applies very strongly) (ER89–R; Alessandri, Vecchio, Steca, Caprara, & Caprara, 2007). Sample items include “I like to do new and different things” and “I quickly get over and recover from being startled” (alpha assessed at T0 was 0.73). Exhaustion Exhaustion was assessed once per day (in the evening) with an adapted version of two

17 items (i.e., "I feel emotionally drained", and “I feel exhausted”) drawn from the Maslach Burnout Inventory-General Survey (MBI-GS; Maslach, Jackson, & Leiter, 1996). Each item was scored on a 5-point scale ranging from 1 (very little or not at all) to 5 (extremely). Alpha coefficients computed separately for each day varied from .82 to .97. Daily hassles Each evening participants completed a 10-item daily hassles checklist (e.g., "Had a disagreement or conflict with boyfriend/girlfriend", “Had a physical illness”, “Had a heavy academic load”) adapted from Gable, Reis, and Elliot (2000). This checklist contained events that occur frequently in the lives of university students (see also Nezlek & Plesko, 2001). Participants were instructed to check any negative event that occurred that day and rate how significant the hassle was on a 6-point scale (0 = It did not happen, 5 = It happened and it was extremely important). A composite index was created by averaging the nine indicators. An alpha coefficient is not an appropriate measure of reliability for this scale because it refers to a formative construct (i.e., a construct defined by an aggregation of relatively independent indicators). The average number of experienced daily hassles ranged from 1 to 6. Negative emotions Students’ negative affect was measured twice per day with 10 items drawn from the Positive and Negative Affect Schedule (PANAS; Watson, 1988). Participants were asked to indicate the extent to which they were experiencing each emotion at that time by using a 5-point scale that ranged from 1 (very slightly or not at all) to 5 (extremely). Negative emotions were the starting point to calculate students’ emotional inertia levels. Alpha coefficients computed separately for each day varied from .79 to .88. Estimating Inertia of Negative Emotions

18 We computed participants’ emotional inertia as the within-individual estimate of the negative affect autocorrelation obtained by using hierarchical linear modelling to take into account nested data structure and resulting dependencies (e.g., Bryk & Raudenbush, 1992). Previous studies have investigated emotional inertia at different timescales (Brose et al., 2015). In this study, we were particularly interested in the overall inertia characterizing a specific individual over the entire study period because, as stated by Brose et al. (2015, p. 529), investigating inertia “across changing contexts may further illuminate the kind of difficulties that individuals with high levels of inertia face.” Moreover, in accordance with our perspective, the estimated within-individual autoregressive coefficient represents a direct conceptualization of inertia as a trait-level, between-individual difference. Specifically, following Kuppens et al. (2010, p. 988), we estimated a longitudinal hierarchical regression in which at Level 1 of the model, we predicted individuals’ negative emotions mean level at time t by individuals’ negative emotions level at the previous assessment (t-1). At Level 2, the intercept and slope values of negative emotions were allowed to vary across participants. This model can be represented as follows: (1) Level 1: negative_emotions_tij = 𝜷0j + 𝜷1j (negative_emotions_t-1ij) + 𝜷2j (Timeij) + 𝜷3j (Time ij 2) + 𝜷4j (Moment) + rij , Note that i indexes the individual, j indexes the time of assessment, r represents the Level-1 residual, and u the Level-2 residual. The lagged predictor “negative_emotions_t-1ij” represents individual’ negative emotion level at t-1, and was person-mean centered to remove between-person effects from Level-1 parameter estimates (Enders & Tofighi, 2007, but see Koval et al., 2016). Moreover, we included the variable “Time”, namely the moment of

19 observation, numbered progressively from the first morning in day 1, or “T1”, to the last evening in day 36, namely “T36”, and “Time2” to account for the possible presence of trend and/or cyclic patterns (i.e., weekly) in mood that need to be modeled to reduce bias in estimating the autocorrelation coefficient. The variable “Moment” was introduced in the model as a covariate in order to take into account the possibility that morning reports are likely to be more similar than evening reports (how I feel in the morning on day 1 is more like how I feel in the morning on day 2, than in the night on day 2, suggesting time-of-day dependencies). All these coefficients were specified as “random slopes”, as follows: (2) 𝜷0j = 𝛾00 + u0j, (3) 𝜷1j = 𝛾10 + u1j, (4) 𝜷2j = 𝛾20 + u2j, (5) 𝜷3j = 𝛾30 + u3j, (6) 𝜷4j = 𝛾40 + u4j, As a result, the value of 𝜷0j, namely the Level-1 intercept, represents the individual i average level of emotions across all measurement points. 𝜷2j and 𝜷3j represent linear and quadratic individual variation in negative emotional change rate across occasions. Finally, 𝜷4j represents the effect of the moment of evaluation (morning vs evening). Of greatest interest for our purposes, 𝜷1j represents our more direct operationalization of emotional inertia (Koval et al., 2016). Indeed, the size of this coefficient reflects how strongly for person i negative emotions level at t are predicted (and thus are related) to his/her level of negative emotions at the previous

20 time point (i.e., t-1)1. The higher the value of this coefficient, the higher the level of emotional inertia for that individual. We computed the value of this coefficient for each participant using procedures described by Raudenbush and Bryk (2002) and used it as a direct, Level 2 measure of emotional inertia in all subsequent structural equation modeling analyses. Modeling Strategies Our theoretical model was tested as a multilevel path analytic model under the Muthén's general analytical framework (Muthén & Muthén, 1998-2017), which allows to take into account the dependency generated by the repeated measures. Our theoretical model was then specified as a doubly manifest multilevel model (see Marsh et al., 2009) in which at Level 2 (1) exhaustion was predicted by daily hassles, ego-resiliency, and the interaction between ego-resiliency and daily hassles, and (2) inertia of negative emotions was predicted by exhaustion. All variables were centered at the sample mean. At Level 1, exhaustion was specified as predicted by daily hassles. Both variables were centered at the individual (i.e., cluster) mean. Evaluation of the Level 2 Moderated Mediation Path Analytic Model Hypotheses were investigated within the framework of Multilevel SEM (ML-SEM; Preacher, Zyphur, & Zhang, 2010), using Mplus 8.30 (Muthén & Muthén, 1998-2017). ML-SEM disentangle between and within components of variances and do not include an autoregressive

1

We also tested the significance of the interaction between “moment” (i.e., morning or

evening”) and “negative_emotions_t-1ij. The significance of this interaction would mean that the effect of emotion at t-1 on emotion at t differs as a function of whether the emotion at t-1 happened at night or in the morning. This interaction term was not statistically significant, so it was dropped from the final model used to estimate emotional inertia.

21 covariance matrix, as this is often typical with daily experience studies. Model fit was assessed according to the following criteria: Yuan-Bentler (YB)𝜒2 likelihood ratio statistic, Comparative Fit Index (CFI), Root Mean Square Error of Approximation (RMSEA), and Standardized Root Mean Square Residual for level-1 (SRMR_L1) and for level-2 (SRMR_L2). The model fits the data well according to the following criteria: nonsignificant YB𝜒2, CFI>.90, RMSEA and SRMR<.08. Last, we used the Akaike Information Criteria (AIC; Burnham & Anderson, 2004). Finally, mediated effects were calculated using the procedures outlined by MacKinnon, Lockwood, Hoffman, West, and Sheets (2002). The values for the upper and lower confidence intervals (CI) for indirect effects were tested by using the Monte Carlo Method for Assessing Mediation CI method (Hayes & Scharkow, 2013) with 20,000 replications, by using the online calculator made available by Preacher and colleagues (Selig & Preacher, 2008). Results Descriptive Statistics and Correlations Means, standard deviations, and correlations among the variables of interest are presented in Table 1. At Level 1, daily hassles were significantly and, according to Cohen’s (1992) guidelines, moderately correlated with exhaustion. At Level 2, ego-resiliency was significantly, moderately and negatively correlated with exhaustion, and daily hassles showed a significant, moderate positive correlation with exhaustion and a significant, moderately low positive correlation with inertia of negative emotions. Exhaustion and inertia of negative emotions were significantly positively correlated. Finally, ego-resiliency was not significantly correlated with daily hassles or emotional inertia. Results from Multilevel Moderated Mediation Analysis The hypothesized model showed a good fit to the data as indicated by a nonsignificant

22 chi-square and by values of the alternative goodness of fit indices considered: 𝜒2(3) = 6.161, p = 0.10, CFI = 0.994, TLI = 0.979, RMSEA = 0.014, SRMRLevel-1 = .00, SRMRLevel-2 = 0.051. This model is represented in Figure 2. At Level 1, daily hassles significantly, positively predicted exhaustion. Of more interest, as hypothesized, at Level 2, exhaustion was significantly, positively predicted by daily hassles, and emotional inertia was significantly and positively predicted by exhaustion. Importantly, the direct relation between daily hassles and exhaustion was qualified by a significant “daily hassles X ego-resiliency” interaction. Thus, we moved forward and applied conventional procedures for computing simple slopes at one standard deviation above and below the mean of the scores on ego resiliency. Figure 3 displays the observed relationship between daily hassles and exhaustion as a function of ego-resiliency levels. Results showed that the relation (i.e., the slope) between daily hassles and exhaustion was significant and positive for individuals low (i.e. -1 SD) in ego-resiliency (simple slope B = .83, t = 5.16, p < .01), but nonsignificant for individuals high in ego-resiliency (simple slope B = .15, t = .29, p = .77). The conjoint significance prediction (1) of exhaustion by the product of “daily hassles X ego-resiliency,” and (2) of negative emotions inertia by exhaustion suggested that not only the direct path from daily hassles to exhaustion, but also the indirect relation between daily hassles and inertia of negative emotions might be moderated by individuals’ ego-resiliency levels. Indeed, according to our results, the indirect effect of daily hassles on inertia of negative emotions via exhaustion was significant and positive only for individuals with low levels of egoresiliency (.06, CI 95% = .02, .11), but not for individuals with high levels of ego-resiliency (.03, CI 95% =-.04, .12). Importantly, adding (1) the direct effects of ego-resiliency and daily hassles on inertia of

23 negative emotions, and (2) the direct effect of the interaction “daily hassles X ego-resiliency” predicting emotional inertia did not significantly improve the fit of the model (𝞓𝜒2(3) = 6.16, p = .10), offering support for a fully moderated mediational model. Ancillary Analyses Although not implied by our theorizing, we tested in a different model if ego resiliency at Level-2 moderated the relation between daily hassles and exhaustion at Level-1. This cross-level interaction was not significant (p = .227). Moreover, our conceptualization of negative emotional inertia assumed that a person’s negative affect in a given morning (t) is predicted by negative affect in the previous night (t-1). However, there may be concern that conceptually inertia estimated using this time lag may not reflect the kind of inertia triggered by the number of hassles experienced within a given day. For this reason, and to further test the robustness of our results, we computed emotional inertia across different time scales. In detail, we estimated two different models. In the first, we specified negative emotions in the evening as the dependent variable (predicted by negative emotions in the morning), and in the second it was specified as independent variable (predicting negative emotions in the morning). Results are presented in the online Appendix, and show that the prediction of inertia by exhaustion is statistically significant only for the slope computed specifying emotions in the evening as the independent variable. Finally, we also computed and compared the ICC of exhaustion and daily hassless (1) as nested within-person variables and (2) as nested within day variables. The ICC of the exhaustion (ICC = .019) and daily hassless (ICC = .03) nested within days suggested a no significant effect of nesting at this level. Instead, we found a significant effect of nesting “within-person” as attested by high ICC values (i.e., .61 for daily hassles, and .63 for exhaustion, see the online Appendix). Discussion

24 It is well understood that people’s emotional inertia reflects the dynamic adaption of their internal self-regulatory processes to the occurrence of external events (Larsen, 2000; Koval et al., 2016; Kuppens et al., 2010). Thus, fine-tuning emotional states in accordance with external contingencies likely has an important adaptive function and might gauge individuals’ fit with their environments (Frijda, 2009; Scherer, 2009). As previously discussed, investigators have usually found a close link between emotional inertia and various indicators of maladjustment (e.g., Fairbairn & Sayette, 2013; Kuppens et al., 2012; van de Leemput et al., 2014). These results support the need for a deeper understanding of the psychological and environmental factors associated with the state of the emotional slowing down called emotional inertia. Moving from the assumption that individuals need to invest psychological resources for self-regulation, we found support for our theoretical model in which the repeated experience of daily hassles contributes to inertia of negative emotions by depleting individuals’ resources for emotional regulation. Of importance, individuals’ ability to flexibly adapt to changing circumstances and to manage acute stress, conflicts, or uncertainty seemed to act as a gatekeeper, rendering the indirect relation between daily hassles and emotional inertia significant or not. These results have theoretical relevance, given that they expand current perspectives by shedding light on the role of minor life events (i.e., daily hassles) in determining resource depletion, and on the role of self-regulatory adaptability, as assessed by ego-resiliency, in buffering their nefarious impact. Further, they provide evidence regarding exhaustion, conceptualized as a net loss of resources (Hobfoll & Shirom, 2001), and its association with inertia of negative emotions. Major theories of stress, such as the COR theory (Hobfoll, 1989; 2001) and the egodepletion theory (Baumeister et al., 2007; Muraven & Baumeister, 2000; Muraven, Tice, & Baumeister, 1998), usually emphasize major life events and traumatic episodes as the key

25 circumstances in which individuals are likely to experience a momentous and rapid loss of resources. The role of daily hassles is usually considered to be minor, and their impact on individuals’ psychological functioning as somewhat limited (Hobfoll & Shirom, 2001). Our model moves from the assumption that minor life events (such as conflicts, negative feedback, fatigue, difficulties with studying) when repeated also may have a summative and deleterious impact on individuals’ self-regulatory processes, consuming resources and, in the long run, resulting in systemic stasis. Daily hassles may separately and immediately (e.g., limited to a single day) cause distress, or may progressively accumulate over a series of days resulting in greater stress (Almeida, 2005). The potential effect of daily hassles appeared to be mostly through its negative impact on individual’s emotional resources, which are in turn less available to be invested in efforts to adjust one’s emotional state to changes in environmental conditions. Of course, “trends” in daily hassles, such as repeated small conflicts or small financial hassles, may also reflect major stressors in people’s lives, such as an impending divorce or major conflict with one’s parents, or even poverty. In this way, daily hassles, may also represent larger major issues that produce micro events that stem from the larger stressor. It is likely that the minor nature of daily hassles prevents people from clearly acknowledging them as actual stressful events, making them less motivated to minimize their net loss of resources (Hobfoll, 1989). Alternatively, people may decide to behave as they usually do when faced with nonstressful situations (i.e., circumstances that do not threaten or deplete people’s resource reservoir), and thus continue to make resource investments without perceiving the need to preserve their internal reservoir (Hobfoll, 1989). In this way, the repeated experience of hassles might slowly trigger a downward spiral generated by the continuous need to invest more and more resources to cope with the stressors. Thus, it might not be so much the

26 demanding nature of the stressing events, but rather the reiterated occurrence of the hassles that, over time, drain individuals’ emotional resources. Finally, this lack of resources might breed further resource loss when individuals do not have time to recover, or when gains are not experienced. Emotional inertia represents the endpoint of this kind of stress cycle. Individuals with high levels of emotional inertia experience continuity of negative emotional states that could negatively impact their overall psychological functioning, for example, by directly contributing to the development of major psychological problems such as depressive symptomology (Kuppens et al., 2010). An interesting feature of our theoretical model is the role assigned to ego-resiliency which, in the long run, buffers the effect of daily hassles on exhaustion, and thus on (the lack of) emotion dynamics. Previous studies have supported the idea that individuals high in egoresiliency are well regulated, flexible in adapting to stress, and able to adapt quickly to external stressors; thus, they are expected to be able to cope effectively and flexibly with changes and difficulties (Alessandri et al., 2017). Our data further support this point by suggesting an enhanced ability by high ego-resiliency individuals to manage the negative impact of daily hassles on their emotional resources; the indirect association between daily hassles and emotional inertia disappeared for these individuals. The absence of a cross-level effect of individual-level ego-resiliency on the withinindividual relationship between daily hassles and exhaustion should be noted. If this interaction were significant, one would assume that individuals high in ego-resiliency would react to daily hassles in a different way from individuals low in ego resiliency. If there was that interaction, it would mean that the individual differences in ego resiliency predict variation on a daily basis in regard to their emotional exhaustion when experiencing daily hassles. In this case, the lack of a

27 cross-level interaction means that individual differences in ER probably do not affect daily variations in how a particular individual reacts to hassles. In sum, whereas the impact of daily hassles on any specific day can be considered independent from individual ego resiliency levels, it appears that individual differences in the level of ego-resiliency are associated with the average individual capacity to recover better (or to conserve more personal resources) after the repeated experience of daily hassles. This point is important to be considered and further examined in future empirical investigations and theoretical speculations. From an applied point of view, our results point to the need to equip people with the ability to appreciate the impairing potential that minor daily events and hassles have on their psychological functioning. Moreover, our data point to the need to identify individuals’ characteristics associated with a higher resistance to stress. Whereas ego-resiliency may prove to be a personality trait that buffers the experience of stress, mostly determined by brain maturation and early environmental experiences, we nonetheless believe that the self-regulatory abilities possessed by individual high in ego-resiliency can be better understood and improved. It is likely that other possible pathways connect daily hassles to emotional inertia, such as the use of dysfunctional emotion regulation strategies (such as suppression), the progressive reduction of emotional self-efficacy beliefs, or the erosion of one’s own self-esteem. These possibilities could be explored in future studies. Another point to be clarified, concerns the applicability of our results to the prediction of inertia of positive emotions. On the basis of previous studies (e.g., Gruber, Kogan, Quoidbach, & Mauss, 2013; Höhn et al., 2013; Koval, Butler, Hollenstein, Lanteigne & Kuppens, 2015), we have reason to believe that positive and negative emotion inertia rest on different psychological process. Yet, this is a point to be investigated in future studies.

28 Limitations Our results come with some limitations, mostly linked to the use of self-reports. Yet one might argue that individuals are the best reporters of what has occurred in their own life, and on their own emotional feelings. Of course, social desirability is always a source of concern when assessing personality traits such as ego-resiliency, and having an other-reported measure of this personality trait would have been less biased and could have highly increased our ability to estimate its impact within the theoretical model. Another limitation is that, despite the consistent time span covered, our study sampled only a limited portion of the daily experiences of a specific group of individuals. Thus, in the future, it would be desirable to test the generalizability of our findings across different populations and in different social and perhaps cultural contexts. The occurrence of certain daily hassles and events may, in fact, vary under various life conditions and across social contexts and cultures. Finally, we used a correlational design that prevents robust causal inference. Conclusion We found that minor daily events and hassles were significantly and negatively related with people’s emotional adjustment and functioning. Furthermore, our findings suggest that the potential impact of stressful minor daily events might be neutralized when individuals possess adequate regulatory flexibility, as assessed by the personality trait of ego-resiliency. Given the clinical relevance of emotional inertia, and its association with important aspects of psychological maladjustment, our results, despite the above reported limitations, have the potential to contribute to the literature by offering new theoretical and empirical insights.

29

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37 Highlights  Negative emotional inertia represents a relevant marker of psychological maladjustment.  Daily hassles may increase inertia by producing emotional exhaustion  Emotional exhaustion acts thus as mediators of the relation daily hassles – emotional inertia  Ego resiliency moderates the relationship of daily hassles with emotional exhaustion

38

Daily Hassles

Ego Resiliency

Exhaustion

Emotional Inertia

Level 2: Between Individual Level 1: Within Individual

Exhaustion

Emotional Inertia

Figure 1. Conceptual representation of the hypothesized Level 2 moderated mediational model.

39

Ego Resiliency

-.22 Exhaustion

-.19

Emotional Inertia

Exhaustion

-.34

Emotional Inertia

.60 Daily Hassles -.54

Ego Resiliency X Daily Hassles

Level 2: Between Individual Level 1: Within Individual

Figure 2. The Level-2 moderated mediational path analytic model. Note. All parameters estimates are significant at p < .01. Moreover, note that all parameters were standardized, except for those associated with ego-resiliency, daily hassles, and the interaction term: “Ego-resiliency X Daily Hassless” entered at Level-2. In these cases, we reported the unstandardized parameters in order to have more meaningful interpretations (see Aiken & West, 1991). At Level-2 egoresiliency and daily hassles were posited as correlated, although this parameter was estimated as nonsignificant (.01, p = .79). Likewise, the interaction term was left free to correlate with both egoresiliency and daily hassles. These paths were not represented in the Figure for sake of clarity.

40

Figure 3. Prediction of exhaustion by daily hassles as a function of ego-resiliency Note. Values for daily hassles are represented uncentered for sake of clarity (but the variable was entered in the model centered).

41 Table 1. Means, standard deviations, and correlations among variables at Level-1 and Level-2.

1. Ego-resiliency

Mean 4.86

SD .63

1 1

2 -

3 -

4 -

2. Daily Hassles

1.48

.45

0.03

1

0.34**

-

3. Emotional exahustion

2.14

.61

-0.27**

0.20**

1

-

4. Emotional inertia

0.00

.18

-0.10

0.28**

0.19*

1

Note. ** p < .01; ** p < .05. Means for daily hassles and exhaustion were aggregated within and across days. The Level-1 (or “daily level”) correlation between daily hassles and exhaustion is presented above the diagonal; Level-2 (or “individual level”) correlations are presented below the diagonal. All correlations are computed on centered variables.