Antecedents and outcomes of work-family conflict: A mega-meta path analysis.

This study examines the mediating role of work-to-family conflict and family-to-work conflict between the Big Five personality traits and mental health thereby enhancing theoretical development based upon empirical evidence. Integrating Conservation of Resources theory with the self-medication hypothesis, we conducted a mega-meta analytic path analysis examining the relationships among employees' Big Five traits, work-to-family conflict and family-to-work conflict, anxiety and depression, and substance use. We produced a ten-by-ten synthetic correlation matrix from existing meta-analytic bivariate relationships to test our sequential mediation model. Results from our path analysis model showed that agreeableness and conscientiousness predicted substance use via mediated paths through both work-to-family conflict and family-to-work conflict and sequentially through depression as well as through family-to-work conflict followed by anxiety. Extroversion and openness-to-experience had relatively weaker influences on substance use through work-to-family conflict, anxiety, and depression. Neuroticism was the strongest driver of the two forms of conflict, the two mental health conditions, and substance use. From this model it can be inferred that work-to-family conflict and family-to-work conflict may be generative mechanisms by which the impact of personality is transmitted to mental health outcomes and then to substance use when analyzed via a Conservation of Resources theory lens.

Introduction

The ever-growing body of research around work-to-family conflict began with the theoretical work [1] and has been followed by immense interest in its measurement, correlates, antecedents, and outcomes. Work-to-family conflict is a form of inter-role conflict where “participation in the work [family] role is made more difficult by virtue of participation in the family [work] role” [1] [p. 77]. Because employees face pressure from work and family simultaneously, demands in one role interfere with meeting the requirements in the other role thus resulting in conflict [2, 3]. The definition implies a bi-directional feature of employees’ work and family lives, such that in some cases there is work-to-family conflict and in other cases it is family-to-work conflict [4]. This conflict is likely to have serious consequences for one’s roles in their family and at work.

With the development of valid instruments [2, 5–7] research on the antecedents, correlates, and outcomes of work-to-family conflict (WFC) and family-to-work conflict (FWC) has grown in number and type. The important directionally different flow of conflict between these two major domains of life [8] results in WFC and FWC being only moderately positively correlated at .41 [9]. It is notable that work-family conflict takes unique bi-directional forms of WFC and FWC. The former is defined as when the work demands interfere with performing family responsibilities, while the latter is defined as when family demands interfere with the performance of work duties [4]. Examples of the former include work demands like forced overtime and dealing with rude customers. Examples of the latter include family demands like caring for sick children and marital strife. These two directions of conflict have enough unique variance to act as separate and stand-alone constructs because they only overlap by about 16% [9]. A large body of literature [2, 5, 9] has documented that WFC and FWC are theoretically distinct, are predicted by different factor, and result in different consequences [2, 5, 9]. Because these two forms of conflict often relate differently to other variables [10, 11] such that they are not interchangeable.

The purpose of the current study is to link some antecedents of WFC and FWC with some outcomes of the two in a mediation model not previously examined. Although some primary studies have assessed the mediating role of WFC and/or FWC in some of these relationships, most focus on antecedents or on outcomes but not usually both. By our count there are at least 54 different antecedents and outcomes of WFC and/or FWC in published primary studies, many of which have been analyzed numerous times. This accumulation of the study of some of these relationships has led to meta-analyses of the relationships between WFC/FWC and variables such as personality traits [9, 12–16], workplace policies [15, 17, 18], issues of home life [10, 18], demographics [19, 20], various forms of stress and strain [14, 21], and psychological disorders and issues [22]. However, not every relationship examined in a WFC/FWC-based primary study has been meta-analyzed.

We assembled available meta-analytic effect sizes as a synthetic input correlation matrix to a path model that tests the mediating role of WFC and FWC in the relationship between personality and mental health tied together under one major theoretical framework. A synthetic matrix of all previously analyzed associations with WFC and FWC would require a 56-by-56 matrix consisting of 1540 unique off-diagonal meta-analytic effect sizes. The overwhelming majority of the cells of such a matrix have not been meta-analyzed. We integrate some of the loosely connected segments of the nomological network of WFC/FWC via our secondary use of meta-analytic data [SUMAD] [23] as we seek to connect some elements of the left hand side [i.e. antecedents] with other elements of the right hand side [i.e. outcomes] of the nomological network surrounding WFC/FWC. The advantage of our use of SUMAD in a path model is that it allows us to simultaneously examine numerous complex relationships based upon precise meta-analytic estimates of population parameters [23].

We make several contributions to the study of WFC using the theoretical framework of conservation of resources [COR] theory [24] supplemented by the self-medication hypothesis [Khantzian, 1987]. First, our model allows us to integrate the sometimes-disconnected fields of management, personality, clinical psychology, and public health based upon existing meta-analyses. Second, by applying COR theory [24], our study brings insight into WFC/FWC as a generative mechanism by which personality affects mental health. Third, by integrating COR theory [24] with the self-medication hypothesis [25] we empirically examine theoretical arguments in a nuanced, in-depth explanation of the mechanism underlying these relationships. Our study addresses this issue both theoretically and empirically by examining the sequential [serial] mediating effects of both WFC and FWC as well as two mental health disorders on the personality-substance use relationship. By providing scholars with a more comprehensive understanding of the proposed sequential mediating effects of WFC and FWC, we expand extant research and connect related but not often well-integrated areas of research. To be clear, there are likely a host of other mediators of the personality-to-mental-health relationship but the focus of this study is on the role of WFC/FWC.

Theoretical framework

Conservation of Resources [COR] theory [24, 26], argues that individuals have a natural tendency to protect their valuable resources because their personal outcomes will be impaired if resource losses occur. There are four resources in COR theory: object resources, conditions, personal characteristics, and energy [24]. Personality traits are examples of personal characteristics that can aid stress resistance [24, 27] and serve as key resources [24, 27–29] in personal struggles. Specifically, self-discipline which is embodied in the personality trait of conscientiousness is a resource [27]. For example, a person high in conscientiousness may retreat and dig more diligently into their work duties as a response to distress and conflict at work. Compared with resources that are easily depleted such as energy and time, personality traits are examples of key resources that are theorized as relatively stable resources that “provide an explanation for why some people are better than others in coping with stressful circumstances” [p.550] [30]. Thus, certain personality traits can help one to cope effectively with challenging situations [31] such as incompatible work and family demands.

Personality plays a role in the allocation of one’s self to one’s problems and as such is likely to be relied upon as an antidote of sorts by persons experiencing conflict in the workplace. Because one’s resources are finite, individuals involved in multiple role requirements tend to experience substantial resource drain [1, 32, 33]. It is also well-known that personality predicts substance use [34, 35] and the Big Five personality traits in particular [36] have a well-validated impact on substance use [37-39]. Unfortunately, the current literature presents a complex picture of these relationships. For example, neuroticism is consistently documented to be positively associated with substance use, but the other Big Five traits have been much less consistent in the prediction of substance use [38, 40–44]. These inconsistent findings suggest the need for stronger theoretical links between the Big Five traits and substance use. In line with COR theory, we consider the Big Five traits as critical characteristics that impact employees’ WFC/FWC, which will further affect anxiety, depression, and substance use.

Nomological network

Left side of the nomological network

The Big Five personality traits have been shown to influence both directions of conflict [12, 45, 46] and some traits play a preventive role in WFC [47]. Conscientious individuals tend to be determined, diligent, organized, and achievement-oriented [36]. The orderliness, self-discipline, and effectiveness of conscientiousness help employees effectively manage their time and simultaneous demands that arise between work and family domains, thereby triggering less WFC [46, 48–50]. Agreeable individuals are cooperative, trusting, and soft-hearted [36] and are more likely to establish interpersonal bonds with colleagues and family members. These social connections help in handling work or family struggles and thus protect employees from possible conflict in either domain [12, 51]. Openness-to-experience entails being creative, flexible, and open to new perspectives [52] and is likely to assist in problem solving or coping strategies for those who are facing simultaneous demands in both work and family roles, resulting in lower WFC. Extroverted individuals are talkative, sociable, and highly active [53]. Individuals with high extroversion normally perceive events positively [54] and are inclined to proactively look for specific solutions to challenges such as WFC [55, 56]. Individuals with high agreeableness, conscientiousness, extroversion, and openness-to-experience deal better with the two-way demands of the work and family domains [46, 55]. These four global traits are consistently negatively related to WFC.

However, neuroticism has an opposite effect on WFC and FWC compared to the other four traits. Neuroticism consists of the tendency to have low self-esteem, irrational perfectionist beliefs, and inferior emotional adjustments such as pessimismand being temperamental [54, 57]. Individuals with high neuroticism are vulnerable to experiencing emotional turmoil and encounter negative life experiences [58]. Moreover, neurotic employees are more likely to feel worried or focus on negativity [59], leaving them with less of the calm and resilience that are necessary to accomplish their mutual work and family demands 59. Because this Big Five trait plays a role in maladaptive thoughts and behavior [60], neuroticism is positively related to WFC/FWC.

Right side of the nomological network

Work-family conflict has been found to affect anxiety and depression [47, 61–65]. Such conflict results in “a negative state of being” (p. 355) [32], often manifesting itself in these two psychological disorders [14, 63, 66, 67]. Anxiety in this study refers to clinically diagnosed generalized anxiety disorder (GAD), often characterized by excessive fear or worry that interferes with one’s daily activities [68]. Depression in this study refers to clinically diagnosed major depressive disorder (MDD), a common medical illness with the symptoms of profound sadness and loss of interest that negatively affect how one feels, thinks, and acts [68]. There is a well-documented body of research on the relationship between WFC/FWC and both anxiety and depression [14, 47, 63, 64, 69–72] supporting the notion that that both WFC and FWC are positively related to anxiety and depression.

Conflict at work or home also positively relates to employees’ substance use [66, 70, 73–76]. The United State loses more than $400 billion annually in low work productivity, absenteeism, increased health care expenses, law enforcement, and criminal justice costs [77] because of the use of psychoactive substances such as alcohol and illicit drugs [e.g., steroids, cannabis, stimulants, opioids, sedatives, and hallucinogens]. There were 19.7 million American adults engaged in substance use in 2017 [78]. The prevalence of substance use in the United States is both detrimental and costly to organizations [79, 80] and therefore the importance of understanding factors that predict substance use cannot be understated. In this study, substance use refers to an excessive use of alcohol and/or a sub-clinical level of use of illicit drugs and/or abuse of prescription medication, none of which could be considered severe enough to rise to the level of a substance use disorder but all of which are likely to cause problems with one’s family life or work or both. We suggest that the self-medication hypothesis [25, 81], which proposes that individuals sometimes attempt to alleviate the anxious or depressive symptomatology through the use of illicit substances [82, 83], can help integrate these three outcomes of WFC/FWC still under the heading of COR. In sum, when individuals are at a resource loss state [depression and anxiety] they look to other resources (substances) to fill that void as a considerable number of studies have generally supported a positive relationship between anxiety, depression, and substance use [84]. For purposes of simplicity we hereafter refer to anxiety, depression, and substance use simply as mental health outcomes except when referring to specific hypotheses or model linkages or to properly differentiate them from each other or from other aspects of mental health.

Simple mediation

In our overall test of the model, we focus on WFC and FWC as simple mediators of the relationships between personality with anxiety and depression as well as a sequential mediator in the relationship between personality, WFC/FWC, anxiety/depression, followed by substance use. Consistent with COR theory, the interference between work and family reflects resource depletion, as the time, strain, and behavioral interferences from one role drain the resources needed to complete duties in the other role [2, 24, 26, 32]. Negative strain can take the form of anxiety and depression [64, 69, 75]. In particular, the personality traits of agreeableness, conscientiousness, extroversion, and openness-to-experience help employees effectively utilize their other resources [24, 27, 30] to manage the interference of WFC/FWC [12, 45, 59]. The reduced conflict further decreases the occurrence of psychological disorders such as anxiety and depression [62, 63]. In contrast, employees with high neuroticism experience higher conflict, due to the fact that they are low in critical personal resources such as self-esteem and calmness that are needed to handle stressful role demands in work and family domains. Consequently, the higher level of WFC/FWC impairs employees’ well-being [24] by yielding higher levels of anxiety and depression. Thus, work-to-family and family-to-work conflict are generative mechanisms by which the influence of personality is linked to anxiety and depression. These arguments lead to our first set of hypotheses:

Hypotheses 1a-d: Work-to-family conflict mediates the negative relationship between the Big Five traits of (a) agreeableness, (b) conscientiousness, (c) extroversion, and (d) openness-to-experience and anxiety.

Hypothesis 1e: Work-to-family conflict mediates the positive relationship between the Big Five trait of (e) neuroticism and anxiety.

Hypotheses 2a-d: Family-to-work conflict mediates the negative relationship between the Big Five traits of (a) agreeableness, (b) conscientiousness, (c) extroversion, and (d) openness-to-experience and anxiety.

Hypothesis 2e: Family-to-work conflict mediates the positive relationship between the Big Five trait of (e) neuroticism and anxiety.

Hypotheses 3a-d: Work-to-family conflict mediates the negative relationship between the Big Five traits of (a) agreeableness, (b) conscientiousness, (c) extroversion, (d) openness-to-experience and depression.

Hypothesis 3e: Work-to-family conflict mediates the positive relationship between the Big Five trait of (e) neuroticism and depression.

Hypotheses 4a-d: Family-to-work conflict mediates the negative relationship between the Big Five traits of (a) agreeableness, (b) conscientiousness, (c) extroversion, and (d) openness-to-experience and depression.

Hypothesis 4e: Family-to-work conflict mediates the positive relationship between the Big Five trait of (e) neuroticism and depression.

The self-medication hypothesis [25, 81] suggests that persons experiencing high anxiety or depression tend to engage in substance use, which serves a self-medicating role for reducing psychological disorders [84-86]. Building on COR theory [24, 26] and the self-medication hypothesis [25], we contend that the psychological disorders of anxiety and depression mediate the relationship between WFC/FWC and substance use. That said, the inter-role conflict between work and family domains reflects resource depletion, with the resource drain in one domain leaving fewer resources needed to fulfill the requirements in the other domain [32]. The resource loss may generate strains in terms of higher levels of anxiety and depression to employees [14, 65, 70, 75]. In turn, anxious or depressive employees are more likely to self-medicate with substances to reduce the symptoms of anxiety and depression [84]. At least two studies have previously suggested the sequential linkage from WFC to psychological disorders to substance use. For example, in a conceptual model regarding WFC and health outcomes, it was proposed that negative emotions (prevalent to both anxiety and depression) mediated the relationship between two directions of WFC and unhealthy behaviors such as substance use [47]. Work-to-family conflict was found to be positively associated with overall emotional distress, which further resulted in increased weekly alcohol consumption [76]. We suggest that anxiety and depression act as generative mechanisms by which the impact of work-to-family and family-to-work conflict are transmitted to substance use. Therefore, we next propose that:

Hypothesis 5a-b: (a) Anxiety and (b) depression simultaneously mediate the positive relationship between work-to-family conflict and substance use.

Hypothesis 6a-b: (a) Anxiety and (b) depression simultaneously mediate the positive relationship between family-to-work conflict and substance use.

Sequential mediation

Substance use can be partially explained by the sequential mediating effects of personality, WFC, FWC, and the psychological disorders of anxiety and depression. Guided by COR theory [24, 26, 27, 87], we expect that individuals with high levels of the personality traits of agreeableness, conscientiousness, extroversion, and openness-to-experience will be able to effectively address the simultaneous pressures from work and family domains that are mutually incompatible [88], leading to a lower level of WFC and FWC [46]. In turn, employees with lower WFC and FWC are less likely to suffer anxiety or depression [63], which reduces the occurrence of substance use [81]. Conservation of resources theory [24, 26] and the self-medication hypothesis [25] help explain the indirect effect between neuroticism and substance use. Neuroticism consists of characteristics such as insecurity, tension, and emotional instability that lead employees to be internally worried and made vulnerable by the stress of incompatible work and family demands [89]. The inefficiency of resource usage for coping with work and family challenges leads to a high level of WFC, which further increases the levels of anxiety or depression, as the stress of WFC gives rise to negative mental health consequences [75]. The sequential psychological resource loss, represented by anxiety or depression, may ultimately increase substance use [84, 90] as employees tend to adopt illicit substance use to alleviate their psychological disorders [81]. In this sequence of effects, WFC/FWC, anxiety/depression sequentially mediate the relationship between personality and substance use. Lastly, we propose the following hypotheses:

Hypotheses 7a-d: Work-to-family conflict followed by anxiety sequentially mediate the negative relationship between the Big Five traits of (a) agreeableness, (b) conscientiousness, (c) extroversion, (d) openness-to-experience and substance use.

Hypothesis 7e: Work-to-family conflict followed by anxiety sequentially mediate the positive relationship between the Big Five trait of (e) neuroticism and substance use.

Hypotheses 8a-d: Family-to-work conflict followed by anxiety sequentially mediate the negative relationship between the Big Five traits of (a) agreeableness, (b) conscientiousness, (c) extroversion, (d) openness-to-experience and substance use.

Hypothesis 8e: Family-to-work conflict followed by anxiety sequentially mediate the positive relationship between the Big Five trait of (e) neuroticism and substance use.

Hypotheses 9a-d: Work-to-family conflict followed by depression sequentially mediate the negative relationship between the Big Five traits of (a) agreeableness, (b) conscientiousness, (c) extroversion, (d) openness-to-experience and substance use.

Hypothesis 9e: Work-to-family conflict followed by depression sequentially mediate the positive relationship between the Big Five trait of (e) neuroticism and substance use.

Hypotheses 10a-d: Family-to-work conflict followed by depression sequentially mediate the negative relationship between the Big Five traits of (a) agreeableness, (b) conscientiousness, (c) extroversion, (d) openness-to-experience and substance use.

Hypothesis 10e: Family-to-work conflict followed by depression sequentially mediate the positive relationship between the Big Five trait of (e) neuroticism and substance use.

Methods

Analytic technique

Typically, primary meta-analyses are on a few bivariate effects size at a time [91] although the development of meta-analytic structural equation modeling (MASEM) [92, 93] has greatly aided in the understanding of how a cascade of variables relate to each other. Despite many primary studies on WFC/FWC using many different theoretical frameworks to explain the same relationship and certainly to explain different relationships even within the same primary study, we focus our analysis only on those model relationships that can be unified under COR theory as supplemented by the self-medication hypothesis and, of course, on relationships which are actually available as published meta-analyses. We refer to our approach as a mega-meta path analysis to differentiate it from a second-order or meta-meta-analysis that synthesizes two or more existing meta-analyses of the same bivariate relationship [94, 95]. Ours is one of the first to rely exclusively on other researchers’ meta-analytic correlations rather than some primary study correlations of our own or of others combined with some meta-analytic correlations of our own or of others that are assembled into one complete input matrix suitable for analysis. To our knowledge, only one study [96] has assembled a synthetic correlation matrix solely from prior published meta-analyses to compare the predictive validity of different matrices on the same model.

Other researchers have assembled partial input correlation matrices from existing meta-analyses to supplement their own primary study effects or their own meta-analyses of other variable relationships. Several studies [17, 97–99] have conducted meta-analyses for some cells in a correlation matrix and then supplemented missing cells not meta-analyzed in their own study with previously published meta-analytic correlations from other studies to test a path model. Others [15] filled their missing cells in their own primary single-sample study input matrix with meta-analytically derived correlations from other studies to test their model. The major advantage of our mega-meta analytic path analysis is that it allowed us to test a theoretical model not previously tested in any primary study [100]. Minor advantages include the fact that the bivariate components of the input matrix are: (1) based upon very large collective sample sizes, (2) based upon a large number of primary published studies, and (3) are sample size weighted and corrected for unreliability.

This required the construction of a 10-by-10 synthetic correlation matrix [96] with 45 unique off-diagonal meta-analytically derived correlations to be used as input to a structural equation modeling test. In some cells of the input matrix, more than one meta-analysis had been conducted on the same bivariate relationship which both facilitated and complicated the secondary use of meta-analytic data (SUMAD) [23]. The taxonomy of SUMAD [23] suggests that our use is a variation of the Type 4 variety which is a full-information MASEM (FIMASEM) [95]. The Type 4 variety addresses effect size heterogeneity or the variability in true score correlations in a population [101] by using bootstrapped samples built off of the cell correlation values and the standard deviation of those meta-analytic correlations to produce 80% credibility intervals for the paths and fit indices. However, it is implied [23] that both Type 3 which is a traditional MASEM [93] and Type 4 assume that only one meta-analytic correlation is available for each cell of the input correlation matrix. In most cases we have more than one meta-analysis per cell. However, in a simulation study [102] the FIMASEM technique [95] "…showed that bootstrap credibility intervals (CVs) work reasonably well, whereas test statistics and goodness of fit indices do not" thereby making the comparison of alternative models difficult. The focus of our study is on model fit as we seek to theoretically integrate rarely connected fields of research using prior validated bivariate relationships. Our study addresses effect size heterogeneity by capitalizing on the aforementioned complication of more than one meta-analytic value being available for most of the synthetic input matrix cells and therefore using MASEM on multiple permutations of our input matrices. The model tests are not nested within other models but rather the different meta-analytic input matrices allow us to test the same model with a variety of heterogenous meta-analytic correlations. The availability of different input matrices serves as a variation of the FIMASEM technique [95] such that we can compare input matrices that are known to be different rather than different models using the same singular input matrix for which the technique was designed [95] and which requires the standard deviations of the correlation cell values to produce bootstrapped results.

Literature search

To collect meta-analytically derived correlations, we searched three online databases (PsychInfo, ABI-Inform, and Web of Science) for meta-analyses of each of the necessary 45 bivariate relationships. We used the search term strings of "meta-analy* OR quantit* rev*" combined with variations of keywords like "work-family conflict," "work-family conflict," "WFC," "family-work conflict," "family work conflict," "FWC," "Big Five," "Five Factor model," "FFM," "conscientiou*," agreeabl*," "neuroti*," "emotional stab*," "openness-to-experience," "openness," "extraver*," "extrover*," "introver*," "anxiety," "anxious*," "depres*," "substance use," and "substance abuse." The search effort uncovered 5859 unique possible meta-analyses. After the removal of 1039 duplicates from the three databases there were 5253 articles that remained eligible for screening.

Article inclusion criteria

To be included in our study a meta-analysis had to meet four criteria: (1) it must have been written in English, (2) it had to be published in a peer reviewed scholarly journal, (3) it had to be a meta-analysis and not a primary study, and (4) it had to have reported an effect size for at least one of the 45 unique relationships necessary for our correlation matrix input. Three meta-analyses used the same unpublished dissertation [105] for their meta-analytic intercorrelations between the Big Five traits [109-111]. After coding them and comparing them to the dissertation [105] it was determined that all three correctly reported those correlations. In order to avoid duplication, the dissertation [105] was added to the coded studies list and the three published articles were removed. After all screening was completed, 12 meta-analyses were coded to fill 45 cells with 75 coded effect sizes. We used a PRISMA flow chart [112] to guide our collection of studies to code (Fig 1).

Fig 1

PRISMA flowchart for study selection.

The steps below were used to identify and select studies for coding.

Meta-analytic coding

The meta-analytic data extracted for analysis included: (1) type of corrected effect size (e.g. ρ, r, d, odds ratio [OR]), (2) corrected bivariate effect size, (3) total sample size reported [i.e. n] in the meta-analysis, and (4) total number of independent samples reported (i.e. k) in the meta-analysis. Eleven of the 45 cells were cross-coded by two of the authors. After a number of coding trials completed side-by-side, interrater agreement was perfect (See Table 1 for the coding results). The remaining 34 cells were then evenly split between the authors.

Table 1

Effect size coding from published meta-analyses.

Meta-analytic relationship	Study	d	OR	r	k	n
WFC and FWC	Shockley and Singla (2011) [9]	--	--	.41	97	41429
WFC and agreeableness	Allen et al. (2012) [12]	--	--	-.17	12	4514
	Michel et al. (2011) [13]	--	--	-.18	13	5309
WFC and conscientiousness	Allen et al. (2012) [12]	--	--	-.16	21	6427
	Michel et al. (2011) [13]	--	--	-.22	20	6924
WFC and extroversion	Allen et al. (2012) [12]	--	--	-.09	14	5112
	Michel et al. (2011) [13]	--	--	-.11	17	8094
WFC and neuroticism	Allen et al. (2012) [12]	--	--	.31	27	9085
	Michel et al. (2011) [13]	--	--	.36	29	11775
WFC and openness-to-experience	Allen et al. (2012) [12]	--	--	-.02	9	4026
	Michel et al. (2011) [13]	--	--	-.05	11	4810
WFC and depression	Amstad et al. (2011) [14]	--	--	.23	14	9869
WFC and anxiety	Amstad et al. (2011) [14]	--	--	.14	3	4804
WFC and substance use	Amstad et al. (2011) [14]	--	--	.08	3	4900
FWC and agreeableness	Allen et al. (2012) [12]	--	--	-.19	9	3901
FWC and conscientiousness	Allen et al. (2012) [12]	--	--	-.20	14	4494
FWC and extroversion	Allen et al. (2012) [12]	--	--	-.07	13	4849
FWC and neuroticism	Allen et al. (2012) [12]	--	--	.27	20	6566
FWC and openness-to-experience	Allen et al. (2012) [12]	--	--	-.05	9	4026
FWC and depression	Amstad et al. (2011) [14]	--	--	.22	10	6712
FWC and anxiety	Amstad et al. (2011) [14]	--	--	.19	3	4804
FWC and substance use	Amstad et al. (2011) [14]	--	--	.10	2	4686
Agreeableness and conscientiousness	Lipnevich et al. (2017) [103]	--	--	.32	28	10113
	Steel, Schmidt, & Shultz (2008) [104]	--	--	.18	27	9558
	Ones (1993) [105]	--	--	.27	344	162975
Agreeableness and extroversion	Lipnevich et al. (2017) [103]	--	--	.20	27	9821
	Steel et al. (2008) [104]	--	--	.19	28	9912
	Ones (1993) [105]	--	--	.17	234	135529
Agreeableness and neuroticism	Lipnevich et al. (2017) [103]	--	--	-.11	28	10114
	Steel et al. (2008) [104]	--	--	-.23	34	12036
	Ones (1993) [105]	--	--	-.25	561	415679
Agreeableness and openness-to-experience	Lipnevich et al. (2017) [103]	--	--	.21	27	9819
	Steel et al. (2008) [104]	--	--	.10	28	9912
	Ones (1993) [105]	--	--	.11	236	144205
Agreeableness and depression	Kotov et al. (2010) [38]	--	--	-.06	25	21082
Agreeableness and anxiety	Kotov et al. (2010) [38]	.18	--	.05a	3	6642
Agreeableness and substance use	Hakulinen, et al. (2015)	--	1.09	.03a	8	24454
	Kotov et al. (2010) [38]	-.60	--	-.27	25	23811
Conscientiousness and extroversion	Lipnevich et al. (2017) [103]	--	--	.21	27	9818
	Steel et al. (2008) [104]	--	--	.28	31	10974
	Ones (1993) [105]	--	--	.00	632	683001
Conscientiousness and neuroticism	Lipnevich et al. (2017) [103]	--	--	-.17	28	10111
	Steel et al. (2008) [104]	--	--	-.33	37	13098
	Ones (1993) [105]	--	--	-.26	587	490296
Conscientiousness and openness	Lipnevich et al. (2017) [103]	--	--	.16	27	9816
	Steel et al. (2008) [104]	--	--	.01	28	9912
	Ones (1993) [105]	--	--	-.06	338	356680
Conscientiousness and depression	Kotov et al. (2010) [38]	--	--	-.36	25	20747
Conscientiousness and anxiety	Kotov et al. (2010) [38]	--	--	-.29	3	6642
Conscientiousness and substance use	Hakulinen, et al. (2015)	--	0.99	.00a	8	24454
	Kotov et al. (2010) [38]	-1.1	--	-.44a	25	23811
Extroversion and neuroticism	Lipnevich et al. (2017) [103]	--	--	-.16	27	9819
	Steel et al. (2008) [104]	--	--	-.33	57	20178
	Ones (1993) [105]	--	--	-.19	710	440440
Extroversion and openness	Lipnevich et al. (2017) [103]	--	--	.14	27	9819
	Steel et al. (2008) [104]	--	--	.24	28	9912
	Ones (1993) [105]	--	--	.17	418	252004
Extroversion and depression	Kotov et al. (2010) [38]	-.62	--	-.25a	55	56823
Extroversion and anxiety	Kotov et al. (2010) [38]	-1.02	--	-.18a	10	29065
Extroversion and substance use	Hakulinen, et al. (2015) [41]	--	.91	-.04a	8	24454
	Kotov et al. (2010) [38]	-.36	--	-.16	49	12290
Neuroticism and openness-to-experience	Lipnevich et al. (2017) [103]	--	--	-.07	27	9818
	Steel et al. (2008) [104]	--	--	-.09	33	11682
	Ones (1993) [105]	--	--	-.16	423	254937
Neuroticism and depression	Kotov et al. (2010) [38]	1.33	--	.47a	63	75229
Neuroticism and anxiety	Kotov et al. (2010) [38]	1.96	--	.34a	14	46244
Neuroticism and substance use	Kotov et al. (2010) [38]	.97	--	.36a	58	68075
Openness-to-experience and depression	Kotov et al. (2010) [38]	-.21	--	-.08a	26	24886
Openness-to-experience and anxiety	Kotov et al. (2010) [38]	-.40	--	-.09a	4	10609
Openness-to-experience and substance use	Hakulinen, et al. (2015)	--	.90	-.04a	8	24454
	Kotov et al. (2010) [38]	-.16	--	-.07a	26	7709
Depression and anxiety	Jacobson & Newman (2017) [106]	--	2.55	.34a	38	38902
Depression and substance use	Lai, Cleary, Sitharthan, & Hunt (2015) [107]	--	2.42	.32a	34	504319
	Conner, Pinquart, & Duberstein (2008) [108]	--	--	.08	7	--
Anxiety and substance use	Lai et al. (2015) [107]	--	2.11	.27	31	504319

Either converted from odds ratios or d-statistics.

Effect sizes reported in meta-analyses as Cohen’s d-statistic or as OR required transformation to correlations. Transformation from d to r is commonplace but from OR to r is not as straightforward. When the case/treatment and control groups are dissimilar in size, as they were in the meta-analyses coded here, it is recommended [113] that the values be transformed as an approximation of the tetrachoric correlation [114] which is preferred over a Pearson transformation [115] which requires equal marginal probabilities. Alas, none of the studies that computed meta-analytic effect sizes as odds ratios reported their outcomes as continuous variables, or had similar size samples in the cells of the contingency table, or had equal marginal probabilities so the OR values were transformed into an approximation of the tetrachoric correlation using the formula [114] below:

Creation of correlation matrices for input

As can be seen in the online supplementary materials, the 45 unique off-diagonal cells of the matrix had between 1 and 3 meta-analyses each that were coded. Thus, we had an impossibly large 120,932,352 different possible permutations of the input matrix. We ran three different matrices comprised of meta-analytic correlations that were not just a randomly assembled set of correlations serving as one of the over 120 million possible combinations. Our matrices were purposeful and were (a) the most recent meta-analyses in the cells under the assumption that these would be the most comprehensive, (b) the meta-analyses that had the largest individual number of subjects or largest n in each cell, and (c) the meta-analyses that had the largest number of samples used or largest k per cell. The other potentially viable but randomly assembled candidates of possible input matrices were not examined here. Because the model input effect sizes for the relationship between anxiety and substance use and between depression and substance use were meta-analyzed on longitudinal data some inference of causality can be inferred for these two pairs of variables. Personality is omnipresent, conflict is not usually episodic, and mental disorders usually take time to develop so they all tend to co-occur and temporal precedence is difficult to establish between them. However, our model and data imply that anxiety and depression temporally precede substance use thus lending some causality to an otherwise correlational model.

Data analysis

The hypothesized model in Fig 2 was tested using each of the three aforementioned correlation matrices as input to Mplus 8.2 SEM software [116]. The maximum likelihood estimator function was used on the summary data. Unstandardized paths were not computed because the use of correlations without standard deviations or means for model variables served as the data input. Following well-known recommendations [93], the harmonic mean was used to determine the sample size of each of the three input matrices. Bootstrapping was used to estimate confidence intervals around path coefficients. The disturbance terms for work-to-family conflict and family-to-work conflict were allowed to correlate. Separately, the disturbance terms for anxiety and depression were also allowed to correlate. We then evaluated the indirect effects by constructing bias-corrected 95% confidence intervals [117, 118].

Fig 2

Hypothesized model.

The model of some antecedents and outcomes of work-family and family-work conflict.

Results

The input matrix comprised of the most recently published meta-analytic correlations using the harmonic mean of 8942, shown in Table 2, yielded better fit [χ2 = 744.69***, df = 7, SRMR = .04, CFI = .93, RMSEA = .109 [90% C.I. = .102; .115] than the models run on the two other input matrices using different respective harmonic means [model with meta-analytic largest n: χ2 = 2769.49***, df = 7, SRMR = .06, CFI = .84, RMSEA = .183 [90% C.I. = .177; .189] and the model with meta-analytic largest k: χ2 = 2506.86***, df = 7, SRMR = .07, CFI = .85, RMSEA = .177 [90% C.I. = .171; .183] and was the focus of further analysis. The SRMR indicates excellent fit [119], the CFI indicates good fit [120], and the RMSEA shows poor fit [121] for the model using the most recent meta-analytic correlations as input. Overall, that model ran can be said to show adequate fit to the data and better fit than the models run on the other two input matrices. The fit indices and path coefficients detailed below are likely to be quite robust in that the average number of studies used in the meta-analyses in each cell was 20.56 thus exceeding the minimum recommendation of 10 per cell [91].

Table 2

Input matrix comprised of correlationsa and standard deviations from most recent meta-analyses.

Variables	1.	2	3.	4.	5.	6.	7.	8.	9.	10.
1. Work-to-family conflict	--	.1732	.0548	.1049	.1732	.1049	.0837	.0288	.0288	.0460
2. Family-to-work conflict	0.41	--	.0707	.0894	.0316	.1049	.0707	.0371	.1436	.0516
3. Agreeableness	-0.17	-0.19	--	.1900	.2000	.2700	.1900	.2448	.3347	.0153
4. Conscientiousness	-0.16	-0.20	0.32	--	.1200	.2500	.2300	.1618	.1267	.0128
5. Extroversion	-0.09	-0.07	0.20	0.21	-	.2500	.1400	.2659	.3123	.0178
6. Neuroticism	0.31	0.27	-0.11	-0.17	-0.16	--	.1700	.2418	.2079	.0153
7. Openness	-0.02	-0.05	0.21	0.16	0.22	-0.07	--	.2631	.2508	.0127
8. Depression	0.23	0.22	-0.06	-0.36	-0.25	0.47	-0.08	--	.1453	.0479
9. Anxiety	0.14	0.19	0.05	-0.29	-0.18	0.34	-0.09	0.34	--	.0180
10. Substance use	0.08	0.10	0.03	0.00	-0.04	0.36	-0.04	0.32	0.27	--

Correlations are on bottom left side of matrix below the diagonal and standard deviations are on top right side above the diagonal. Harmonic mean = 8942. All non-zero correlations are significant at p < .001.

In post hoc tests using the FIMSEM technique [95] on our best fitting input matrix, which required the correlation matrix as well as the standard deviation matrix, we ran 500 bootstrapped samples. We found that the bootstrapped path coefficients were close approximations of our model test but that the fit indices were not reproduced well [102] nor were they meaningful. The interested reader can use the correlations and standard deviations in Table 2 to reproduce these FIMASEM results.

Simple mediation results

Hypotheses 1 through 6 predicted simple mediation. Results for these tests can be found in Table 3. Hypotheses 1a through 1e all involved WFC as a mediator between each of the Big Five traits and anxiety. Because the path between WFC and anxiety was .00, the indirect paths from these traits through WFC to anxiety were also .00. Therefore, none of these five hypotheses were supported.

Table 3

Results for simple mediation.

			95% CI
Hypothesis	Path	Effect	Lower	Upper
1a	Agreeableness->WFC->Anxiety	.00	-.003	.002
1b	Conscientiousness->WFC->Anxiety	.00	-.002	.002
1c	Extroversion->WFC->Anxiety	.00	.000	.000
1d	Openness->WFC->Anxiety	.00	-.001	.001
1e	Neuroticism->WFC->Anxiety	.00	-.006	.006
2a	Agreeableness->FWC->Anxiety	-.01***	-.016	-.009
2b	Conscientiousness-> FWC ->Anxiety	-.12***	-.015	-.008
2c	Extroversion-> FWC ->Anxiety	.00	.000	.004
2d	Openness-> FWC ->Anxiety	.00	-.001	.003
2e	Neuroticism-> FWC ->Anxiety	.02***	.017	.028
3a	Agreeableness->WFC->Depression	-.01***	-.009	-.004
3b	Conscientiousness->WFC-> Depression	-.004***	-.006	-.002
3c	Extroversion->WFC-> Depression	.00	-.002	.000
3d	Openness->WFC-> Depression	.002**	.001	.003
3e	Neuroticism->WFC-> Depression	.02***	.009	.020
4a	Agreeableness->FWC-> Depression	-.01***	-.009	-.004
4b	Conscientiousness-> FWC -> Depression	-.01***	-.009	-.004
4c	Extroversion-> FWC -> Depression	.00	.000	.002
4d	Openness-> FWC -> Depression	.00	-.001	.002
4e	Neuroticism-> FWC -> Depression	.01***	.008	.017
5a	WFC->Anxiety->Substance use	.00	-.004	.004
5b	WFC->Depression->Substance use	.04***	.008	.019
6a	FWC->Anxiety->Substance use	.02***	.013	.021
6b	FWC->Depression->Substance use	.01***	.008	.018

Note: WFC = work-to-family conflict; FWC = family-to-work conflict; CI = confidence interval.

***p < .001.

Hypotheses 2a through 2e all involved FWC as a mediator between the Big Five traits and anxiety. Hypothesis 2a, that FWC mediates the linkage from agreeableness to anxiety and was supported. Hypothesis 2b, that FWC mediates the linkage from conscientiousness to anxiety also was supported. Hypotheses 2c and 2d, about extroversion and openness respectively, were not supported. Finally, Hypothesis 2e, that FWC mediates the linkage from neuroticism to anxiety was supported.

Hypotheses 3a through 3e all involved WFC as a mediator between each of the Big Five traits and depression. Hypothesis 3a, that WFC mediates the linkage from agreeableness to depression was supported as was Hypothesis 3b about the linkage from conscientiousness to WFC to depression. and. Hypotheses 3c about extroversion was not supported nor was Hypothesis 3d about the linkage from openness to WFC to depression because the direct of the effect was opposite that predicted. Finally, Hypothesis 3e about the linkage from neuroticism to WFC to depression was supported.

Hypotheses 4a through 4e all involved FWC as a mediator between the Big Five traits and depression. Hypothesis 4a, which predicted a mediating effect of FWC for the agreeableness to depression relationship, was supported as was Hypothesis 4b, that predicted a mediating effect of FWC for the conscientiousness to depression relationship. Hypotheses 4c [extroversion] and Hypothesis 4d [openness] were not supported. Finally, Hypothesis 4e, which predicted a mediating effect for FWC for the neuroticism to depression relationship, was supported.

Hypotheses 5a and 5b were about the linkage from WFC through anxiety and depression, respectively, to substance use. Because the path between WFC and anxiety was .00, Hypothesis 5a was not supported. However, the indirect path through depression was significant offering support for Hypothesis 5b. Hypotheses 6a and 6b were about the linkages from FWC through both anxiety and depression to substance use. The indirect effects through anxiety and depression on the relationship between FWC and substance use were both significant providing support for Hypotheses 6a and 6b.

Sequential mediation results

The results for our tests of sequential mediation for Hypotheses 7a-10e can be found in Table 4. Hypotheses 7a-e predicted sequential mediation between each of the Big Five personality traits one at a time and substance use through WFC and anxiety. None of these indirect effects were significant as the path between WFC and anxiety was .00, so Hypotheses 7a-e were not supported. Hypotheses 8a-e predicted sequential mediation between each of the Big Five personality traits one at a time and substance use through FWC and anxiety. Hypothesis 8a (agreeableness), 8b (conscientiousness), and 8e (neuroticism) were all supported. However, Hypotheses 8c (extroversion) and 8d (openness) were not supported. Hypotheses 9a-e predicted sequential mediation between each of the Big Five personality traits one at a time and substance use through WFC and depression. The hypotheses for agreeableness (9a), conscientiousness (9b), and neuroticism (9e) were all supported while Hypotheses 9c (extroversion) was not. Although significant results were found for Hypothesis 9d (openness), the indirect effect was not in the predicted direction and the confidence interval contained zero, therefore Hypothesis 9e also was not supported. Finally, Hypotheses 10a-e predicted sequential mediation between each of the Big Five personality traits one at a time and substance use through FWC and depression. These results followed the same pattern as the sequential mediation for anxiety and FWC in that Hypothesis 10a (agreeableness), 10b (conscientiousness), and 10e (neuroticism) were all supported while Hypotheses 10c (extroversion) and 10d (openness) were not. See Fig 3 for the statistical results of the mega-meta path analysis model test.

Table 4

Results for sequential mediation.

			95% CIa
Hypothesis	Path	Effect	Lower	Upper
7a	Agreeableness->WFCb->Anxiety->Substance Use	.000	.000	.000
7b	Conscientiousness->WFC->Anxiety->Substance Use	.000	.000	.000
7c	Extroversion->WFC->Anxiety->Substance Use	.000	.000	.000
7d	Openness->WFC->Anxiety->Substance Use	.000	.000	.000
7e	Neuroticism->WFC->Anxiety->Substance Use	.000	-.001	.001
8a	Agreeableness->FWCc->Anxiety->Substance Use	-.002***	-.003	-.002
8b	Conscientiousness-> FWC ->Anxiety->Substance Use	-.002***	-.003	-.001
8c	Extroversion-> FWC ->Anxiety->Substance Use	.000	.000	.001
8d	Openness-> FWC ->Anxiety->Substance Use	.000	.000	.001
8e	Neuroticism-> FWC ->Anxiety->Substance Use	.004***	.003	.005
9a	Agreeableness->WFC->Depression->Substance Use	-.002***	-.002	-.001
9b	Conscientiousness->WFC-> Depression->Substance Use	-.001***	-.002	-.001
9c	Extroversion->WFC-> Depression->Substance Use	.000	.000	.000
9d	Openness->WFC-> Depression->Substance Use	.001**	.000	.001
9e	Neuroticism->WFC-> Depression->Substance Use	.004***	.002	.005
10a	Agreeableness->FWC-> Depression->Substance Use	-.002***	-.002	-.001
10b	Conscientiousness-> FWC -> Depression->Substance Use	-.002***	-.002	-.001
10c	Extroversion-> FWC -> Depression->Substance Use	.000	.000	.001
10d	Openness-> FWC -> Depression->Substance Use	.000	.000	.000
10e	Neuroticism-> FWC -> Depression->Substance Use	.003***	.002	.004

a CI = confidence interval.

b WFC = work-to-family conflict.

c FWC = family-to-work conflict.

***p < .001.

Fig 3

Model of statistical results.

Path coefficients resulting from mega-meta path analysis. Note. Values in parentheses indicate direct paths from each trait to work-to-family conflict, depression, anxiety, and family-to-work conflict in that order. Paths > .020 are statistically significant. For clarity, the intercorrelations between the Big Five traits and the correlations between the disturbance terms for the endogenous variables are omitted.

Discussion

The goal of our study was to test part of the nomological network of WFC and FWC via a simultaneous investigation of some of their antecedents and outcomes that integrated the loosely connected fields of management, personality, clinical psychology, and public health research. Specifically, we examined the antecedent role of the Big Five personality traits in the prediction of the maladaptive behavior of substance use through the sequential mediation of WFC, FWC, clinical anxiety, and clinical depression. Consistent with COR theory [24], we hypothesized and found meaningful sequential mediation effects among the personality traits of agreeableness, conscientiousness, and neuroticism, with both WFC and FWC, and depression in the prediction of substance use. As expected, the results suggest that agreeableness and conscientiousness are key resources that one can utilize to reduce the incompatible role conflict between work and family domains [49, 51], resulting in lower depression and further mitigating substance use. On the other hand, neuroticism greatly hinders one’s use of resources that then increase the propensity for WFC/FWC as well as contributes to depression and substance use. Due to dysfunctional emotional adjustments, sensitivity, and vulnerability to negative experiences [54, 59, 89], neurotic employees have fewer resources with which to manage incompatible work and family demands [46], leading to higher WFC, which in turn increases depression and substance use.

Regarding the mediating role of anxiety, family-to-work conflict but not work-to-family conflict, was a significant mediator linking agreeableness, conscientiousness, and neuroticism with anxiety and substance use. This is probably because of the different contexts of work-to-family conflict and family-to-work conflict. As work-to-family conflict is the interference from work into the family domain (e.g., having to reply to emails or work on projects during family time) that may require additional contextual resources, such as family member support [122] and supervisor support [16], to lower the levels of anxiety and substance use. As for neuroticism, the findings demonstrate that its indirect effect on substance use is channeled through work-to-family conflict, family-to-work conflict, anxiety, or depression to impact substance use. Moreover, our findings indicate that extroversion and openness-to-experience have weak or nonsignificant effects on substance use through WFC and anxiety or through depression. Taken together, this study suggests that personality, as a stable key resource [29], can facilitate or hinder WFC/FWC and mental health. Positive, or adaptive, traits like agreeableness and conscientiousness provide strength to overcome conflict in life and work. Negative, or maladaptive, traits like neuroticism detract from one’s ability to weather the storm of conflict and play a direct role in increasing conflict and mental health disorders. For some employees, adaptive traits can serve as a reliable buttress to the struggles of life at work and at home and the sometimes-inevitable blurring of the boundaries between the two.

Study strengths, limitations and future directions

This study has several strengths. First, we used large sample sizes based upon meta-analyses of hundreds of different primary published studies thus helping ameliorate second order sampling error. The overall number of participants in the meta-analyses of the relationships used to create our best fitting input correlation matrix was 1,213,933. However, it is likely that some participants were counted more than once as some correlations in each input matrix came from the same study and presumably were calculated from the same primary study participants. Second, most of the meta-analytically derived correlations were corrected for attenuation by the original meta-analysts thus more closely approximating population parameters. Third, the meta-analyses were collected from many top journals covering general psychology, general management, organizational behavior, human resource management, clinical psychology, and epidemiology. Finally, using COR theory [24], in conjunction with the self-medication hypothesis, we were able to theoretically integrate the left-hand side (i.e. antecedents) with the right-hand side (i.e. outcomes) of the WFC/FWC network that are usually explored separately. Integrating these seemingly disparate meta-analytic correlations from different fields of study serves as an effective comprehensive explanation for the modeled relationships. These strengths lend credibility to the findings reported here.

Our study is not without limitations. The socio-demographic variables such as type of work and organizational tenure related to work were not, and largely could not be, included in the study. The body of literature around the relationship between them and WFC/FWC is growing but meta-analyses do not exist for most work-related socio-demographic variables with WFC/FWC and definitely do not exist for the relationship between them and the two disorders and substance use. Like with meta-analyses in general there are many judgment calls [123] with which to contend when using a mega-meta path analytic framework. For example, we are limited by the way other scholars coded their variables in their meta-analyses. Given that some variables may have been dichotomized or truncated in other ways, future primary study researchers could include more nuanced measures of anxiety and depression, thus providing more accurate meta-analytic insights. Additionally, the use of discrete categories to describe mental disorders likely attenuated some model relationships. If only the severest of cases can be categorized as clinically anxious or depressed, then the relationship between these disorders and the other variables in the model were likely weaker here than they could be if anxiety and depression were measured on continuous scales. Thus, our model may actually underreport the strength of these relationships. We were also not able to test some seemingly logical model relationships because published meta-analyses could not be found between all such variables. For example, although we focused on the clinical forms of depression and anxiety we could not include substance use disorder in our model because meta-analyses do not exist between that construct and all nine other variables in our model. Instead we focused on sub-clinical substance use because that is what was available.

The mega-meta path analytic technique can be limited by the availability of existing meta-analyses. Another potential weakness is that of effect size heterogeneity [101] in that there were quite a range of meta-analytic values reported for the same relationship in different meta-analyses. For example, one meta-analysis [41] reported an odds ratio between agreeableness and substance use that was transformed into a correlation of .03. For that same relationship, another meta-analysis [38] reported a d-statistic that was transformed into a correlation of -.27. The heterogeneity of these effect sizes suggests one other related limitation: the transformation of odds ratios is only an approximation of a tetrachoric correlation and not a direct Pearson correlation of continuous measures. Additionally, we only tested three of the over 120 million possible permutations of the input correlation matrix that were possible. It is highly likely that more than one of the other possible matrices would fit our model even better than those which we tested.

Conclusion and practical implications

In conclusion, we designed this research to connect some of the antecedents and outcomes of the nomological network surrounding work-to-family and family-to-work conflict. These two forms of conflict are mediators of the relationship between personality and mental health. We found that the three personality traits of agreeableness, conscientiousness, and neuroticism worked through the mediating mechanisms of work-to-family conflict and family-to-work conflict with depression as well as through family-to-work conflict and anxiety to play mediating roles in their relationship with substance use. Thus, this research supports the notion that the work-family interface may play a critical role in understanding why employees experience adverse mental health outcomes and engage in substance use.

We suggest that organizations take kinder note of employees facing difficulties at work or at home and that they appropriate funding for employee assistance programs to address mental health and substance use problems. Many of the types of issues giving rise to $400 billion in annual losses to businesses in the U.S. [77] are the result of mental health issues and/or substance use and abuse. The loss to businesses is not only financial but interpersonal as the spill-over effect of idiosyncratic problems of one worker may unintentionally affect another worker as the cascade of despair infects the organization. Given the rise in interest in personality testing by managers in the workplace we recommend that those employees with an identifiably aberrant combination of traits (e.g. extremely low agreeableness and extremely high neuroticism) be given an opportunity to receive wise counsel from supervisors regarding interpersonal interactions with coworkers and customers. However, the relative unmalleability of personality traits makes such trait levels particularly difficult to ameliorate but the encouragement of self-awareness can likely be beneficial. To facilitate such self-awareness, we recommend training in emotional intelligence which helps employees build skills devoted to helping them be aware of and manage the emotions of themselves and others. The goal of a business organization should be not only to earn a profit but to develop its people to their fullest human capacity.

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Reviewer #1: I appreciate the opportunity to review this article. The authors conducted a mega-meta analytic path analysis examining the relationships among Big Five traits, work-to-family conflict and family-to-work conflict, anxiety and depression, and substance use.

In my opinion the objective is relevant and the selection of variables and the theoretical models are also well founded.

The methodology is complex and allows responding to the objectives set.

However, there are some doubts related to the definition of some variables and the methodology used.

- I think that the constructs “work-to-family conflict and family-to work conflict” should be defined more extensively in the introduction.

- In the discussion some traits are considered as adaptive and one trait as maladaptive. However, the Big Five Traits Model is a normal personality model. According to the model, no trait is maladaptive in itself, but it will depend on the context or environment in which the person is and the influence of other variables. Is neuroticism considered a maladaptive trait in the context of the interpretation of the results?

- I understand that only clinical cases of anxiety and depression with a clinical diagnosis have been considered. Is that correct? Could any diagnostic category be considered of both types of disorders?

- When describing the Article Inclusion Criteria, it is not clear what the reasons were for excluding the 115 articles from the last step of Figure 1.

- Although I am not an expert in mega-meta

analytic path analysis, I think that as described by the authors, the working method can be followed without difficulty.

- I think that within the limitations it should be included that the influence of sociodemographic variables and variables related to work (type of work, years of dedication, occupational risks, temporality) have not been considered.

- Practical implications should be included. According to the results, what strategies could be implemented to reduce work-to-family conflict and family-to-work conflict? How to prevent them? Could it be predicted through the personality profile?

In summary, I think it is an interesting study that uses a complex methodology; however, there are some aspects that should be clarified.

Reviewer #2: Thank you very much for this opportunity to read this interesting study. Although, the paper is highly interesting and worth publishing, I have some remarks and questions.

Introduction

I would have wanted stronger theoretical and conceptual background in the introduction.

For example, to clarify what is the difference of WFC and FWC? And why they need to be considered separately?

Why anxiety, depression and substance use were selected as outcomes?

Could the relationship go other way around? I mean could it be that anxiety, depression and/or substance use affect WFC and/or FWC?

Results

In table 3, 2e Neuroticism-> FWC-> Anxiety, should the effect be positive, instead of -.02***? The 95% confidence intervals are positive

Is 3a in the same table correct effect?

Now there are quite many analyses. Did you use e.g. Bonferroni correction for the p-values?

I was wondering, have you tested e.g. to combine WFC and FWC to a common conflict measure and run the analyses with it? As e.g. Byron 2005 (your reference number 10) shows that coping styles and skills have quite similar relations with both WFC and FWC, and I think personality traits might behave similarly. What comes to your reference number 11, it considers positive side of work-family interface, not conflict, what you concentrate on, so I don’t think it is the best one for your purposes.

I would ask the same with anxiety and depression, such as have you tried to combine these to general mood or mental health measure and run the analyses with it?

Do these combinations affect the effect sizes? Or to the model fit indexes?

RMSEA shows poor fit, is that a reason to be concerned?

**********

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18 Jan 2022

Response to Reviewers of PLOS-D-21-39844

Reviewer #1 Comments:

1. I appreciate the opportunity to review this article. The authors conducted a mega-meta analytic path analysis examining the relationships among Big Five traits, work-to-family conflict and family-to-work conflict, anxiety and depression, and substance use. In my opinion the objective is relevant and the selection of variables and the theoretical models are also well founded. The methodology is complex and allows responding to the objectives set. However, there are some doubts related to the definition of some variables and the methodology used.

Authors' Response: We thank the reviewer for these comments and address them one-by-one below in a numbered fashion.

2. I think that the constructs “work-to-family conflict and family-to work conflict” should be defined more extensively in the introduction.

Authors' Response: Thank you for your suggestion. We elaborate as suggested on the definitions of work-to-family conflict and family-to-work conflict on newly inserted lines 46-60 into the second paragraph of our paper:

"With the development of valid instruments [2, 5, 6, 7] research on the antecedents, correlates, and outcomes of work-to-family conflict (WFC) and family-to-work conflict (FWC) has grown in number and type. The important directionally different flow of conflict between these two major domains of life [8] results in WFC and FWC being only moderately positively correlated at .41 [9]. It is notable that work-family conflict takes unique bi-directional forms of WFC and FWC. The former is defined as when the work demands interfere with performing family responsibilities, while the latter is defined as when family demands interfere with the performance of work duties [4]. Examples of the former include work demands like forced overtime and dealing with rude customers. Examples of the latter include family demands like caring for sick children and marital strife. These two directions of conflict have enough unique variance to act as separate and stand-alone constructs because they only overlap by about 16% [9]. A large body of literature [2, 5, 9] has documented that WFC and FWC are theoretically distinct, are predicted by different factors, and result in different consequences [2, 5, 9]. Because these two forms of conflict often relate differently to other variables [10, 11] they are not interchangeable."

3. In the discussion some traits are considered as adaptive and one trait as maladaptive. However, the Big Five Traits Model is a normal personality model. According to the model, no trait is maladaptive in itself, but it will depend on the context or environment in which the person is and the influence of other variables. Is neuroticism considered a maladaptive trait in the context of the interpretation of the results?

Authors' Response: We appreciate this request for clarification and hope that our response is not pedantic in any way. Our understanding of the Big Five follows. The traits in the model are construed as a spectrum which ranges from very low to very high levels of each trait. Some of the traits are known by their polar opposite endpoint name. For example, agreeableness could be referred to (but rarely is) as "disagreeableness" if the need arose. Such a need might be in the explanation of its positive relationship with Anti-Social Personality Disorder (ASPD). Alternatively, ASPD is negatively correlated with trait agreeableness.

The negative relationship between agreeableness scores and ASPD would likely make agreeableness adaptive in some cases. Low scores on agreeableness, or alternatively construed as high scores on disagreeableness, would make it somewhat maladaptive. It is the same trait, it just matters from which direction it is measured. We are quite certain that the reviewer already knows this but wanted to make sure that our understanding of the Big Five traits matches that of the reviewer.

Now we directly address the reviewer's issue. For many years, the trait of Emotional Stability was known as Neuroticism which is likely a holdover of Eysenck's two factor model of personality. When the trait is measured such that high scores on it indicate high levels of neuroticism and therefore low levels of emotional stability then in some contexts it can be maladaptive in its behavioral, cognitive, and affective outcomes. In the studies coded in the meta-analyses gathered for use in this paper the meta-analysts tended to measure that Big Five trait as neuroticism which is negatively related to most favorable outcomes and positively related to most maladaptive outcomes like WFC, FWC, depression, anxiety, and substance use. We found that in the context of the work-family interface, neuroticism is strongly related to all five. We consider this to be evidence of its maladaptive nature.

Methodologically, we would only need to reverse the sign of the model relationships and we would have results of an opposite but equal magnitude. In that case all Big Five Traits in our model would be negatively related (adaptive) to the various hypothesized outcomes in our model. We are happy to go back and reverse the signs to accomplish this but theoretically it weakens our argument about Conservation of Resources Theory which helps the reader understand the importance of neuroticism in the prediction of maladaptive outcomes like depression, anxiety, and substance use. We hope this helps explain our thinking and we thank the reviewer for this comment.

4. I understand that only clinical cases of anxiety and depression with a clinical diagnosis have been considered. Is that correct? Could any diagnostic category be considered of both types of disorders?

Authors' Response: Yes, only clinically diagnosed cases were considered because that was all that was available in the published meta-analyses used as part of the synthetic input correlation matrix for this study. In those meta-analyses, different meta-analytically derived values for different directional influences of WFC and FWC on depression and anxiety were available. Meta-analytic effect sizes do exist for depression and anxiety in the antecedent prediction of WFC and FWC. To our knowledge none considered sub-clinical levels of depression and anxiety. Our study can only be thought of as truly cross-sectional we opted for the meta-analytic values of WFC and FWC as truly and timely precursors of depression and anxiety to lend a modicum of causality to the model. However, these are not consistent with the field of research surrounding WFC/FWC which developed along the lines of clear and distinct antecedents of conflict and clearly theoretical outcomes of conflict. We noted our lack of the ability to infer causality as a limitation in the original submitted version of our study which we still include and can be found on the newly numbered lines 391-396 and make sure to limit any notion of causality.

5. When describing the Article Inclusion Criteria, it is not clear what the reasons were for excluding the 115 articles from the last step of Figure 1.

Authors' Response: Thanks for the opportunity to explain this. We excluded those studies because they were meta-analyses on relationships other than the 45 necessary for input into our synthetic correlation matrix. We have changed the verbiage in two of the boxes in the flow chart to be more transparent. Instead of one box stating "Records excluded (n = 4087)" we rewrote it as "Non-meta-analyses excluded (n = 4087)". These records were excluded because they merely referenced a meta-analysis and were not meta-analyses in and of themselves. To directly address the reviewer's point, we also rewrote "Full text articles excluded (n = 115)" as "Irrelevant meta-analyses excluded (n = 115)". In our search we uncovered many meta-analyses that only referenced WFC and/or FWC and the Big Five or the psychological outcomes in our model. These 115 studies did not meta-analyze any of our focal relationships. We also found a typographical error in the text on line 345 where we deleted "606" and typed in "1039" to match the flow chart.

6. Although I am not an expert in mega-meta analytic path analysis, I think that as described by the authors, the working method can be followed without difficulty.

Authors' Response: Thanks very much! It is indeed a complicated method so we tried very hard to make it understandable.

7. I think that within the limitations it should be included that the influence of sociodemographic variables and variables related to work (type of work, years of dedication, occupational risks, temporality) have not been considered.

Authors' Response: We thank the reviewer for this suggestion. We added the following to lines 543-548:

" The socio-demographic variables such as type of work and organizational tenure related to work were not, and largely could not be, included in the study. The body of literature around the relationship between them and WFC/FWC is growing but meta-analyses do not exist for most work-related socio-demographic variables with WFC/FWC and definitely do not exist for the relationship between them and the two disorders and substance use."

8. Practical implications should be included. According to the results, what strategies could be implemented to reduce work-to-family conflict and family-to-work conflict? How to prevent them? Could it be predicted through the personality profile?

Authors' Response: We thank the reviewer for this opportunity to add to our paper. On lines 586 -601 we have added the following:

"We suggest that organizations take kinder note of employees facing difficulties at work or at home and that they appropriate funding for employee assistance programs to address mental health and substance use problems. Many of the types of issues giving rise to $400 billion in annual losses to businesses in the U.S. [77] are the result of mental health issues and/or substance use and abuse. The loss to businesses is not only financial but interpersonal as the spill-over effect of idiosyncratic problems of one worker may unintentionally affect another worker as the cascade of despair infects the organization. Given the rise in interest in personality testing by managers in the workplace we recommend that those employees with an identifiably aberrant combination of traits (e.g. extremely low agreeableness and extremely high neuroticism) be given an opportunity to receive wise counsel from supervisors regarding interpersonal interactions with coworkers and customers. However, the relative unmalleability of personality traits makes such trait levels particularly difficult to ameliorate but the encouragement of self-awareness can likely be beneficial. To facilitate such self-awareness, we recommend training in emotional intelligence which helps employees build skills devoted to helping them be aware of and manage the emotions of themselves and others. The goal of a business organization should be not only to earn a profit but to develop its people to their fullest human capacity."

We hope that we did not get too flowery in our language but we feel passionate about this set of implications and hope that they meet the reviewer's expectations.

9. In summary, I think it is an interesting study that uses a complex methodology; however, there are some aspects that should be clarified.

Authors' Response: We thank the reviewer for the insightful comments and suggestions and hope that we have adequately addressed them.

Reviewer #2 Comments:

1. Thank you very much for this opportunity to read this interesting study. Although, the paper is highly interesting and worth publishing, I have some remarks and questions.

Authors' Response: We thank the reviewer for these suggestions, comments, and concerns and address them point-by-point below.

2. Introduction. I would have wanted stronger theoretical and conceptual background in the introduction. For example, to clarify what is the difference of WFC and FWC? And why they need to be considered separately?

Authors' Response: Thank you. Per your suggestion, we clarified the differences of WFC and FWC in the introduction and also explained why the two dimensions of work-family conflict need to be considered separately. The revised sentences can be found on the newly inserted lines 46-60 in the second paragraph of our paper:

"With the development of valid instruments [2, 5, 6, 7] research on the antecedents, correlates, and outcomes of work-to-family conflict (WFC) and family-to-work conflict (FWC) has grown in number and type. The important directionally different flow of conflict between these two major domains of life [8] results in WFC and FWC being only moderately positively correlated at .41 [9]. It is notable that work-family conflict takes unique bi-directional forms of WFC and FWC. The former is defined as when the work demands interfere with performing family responsibilities, while the latter is defined as when family demands interfere with the performance of work duties [4]. Examples of the former include work demands like forced overtime and dealing with rude customers. Examples of the latter include family demands like caring for sick children and marital strife. These two directions of conflict have enough unique variance to act as separate and stand-alone constructs because they only overlap by about 16% [9]. A large body of literature [2, 5, 9] has documented that WFC and FWC are theoretically distinct, are predicted by different factors, and result in different consequences [2, 5, 9]. Because these two forms of conflict often relate differently to other variables [10, 11] they are not interchangeable."

Thus, following the existing literature, we consider the two variables separately in the study.

3. Why anxiety, depression and substance use were selected as outcomes? Could the relationship go other way around? I mean could it be that anxiety, depression and/or substance use affect WFC and/or FWC?

Authors' response: We heavily considered running alternative models such as that. The fit of such a model would be exactly the same as our hypothesized model and the paths would likely only differ by the smallest of margins. It is true that mental disorders and substance use intuitively cause conflict at home and at work but the meta-analytic effect sizes gathered for use in this paper were computed on primary studies that largely had the intent of conflict being antecedent to the disorders. Thus, in the area of research around FWC and WFC depression, anxiety, and substance use have most often been studied as outcomes of FWC and WFC. Our paper is designed to strategically combine extant research from the growing field of WFC and FWC. We, in essence, combine the right half (i.e. antecedents ) and the left half (i.e. outcomes) of hundreds of studies of WFC and FWC based in our paper upon a unifying theory. Also, because our model can truly only be thought of as cross-sectional, because the nature of the meta-analytic results gathered for inclusion in our input matrix were calculated without any inference of directionality by the original meta-analysts. As we developed our model we scoured the databases for all primary studies of the antecedents and outcomes of WFC and FWC. We found a total of 56 different variables that neatly fell into either category. Alas, only the most oft-studied antecedent and outcome relationships had been meta-analyzed so we could not create a synthetic 56x56 matrix without relying on single primary studies. Additionally, some of the left-hand meta-analyses just did not combine well with some of the right-hand meta-analyses under any one or two particular theories. In essence, the field is a hodge-podge of loosely connected studies that share a central focus of WFC and FWC and largely based upon cross-sectional primary studies combined into non-directional effect sizes in meta-analyses.

4. Results. In table 3, 2e Neuroticism-> FWC-> Anxiety, should the effect be positive, instead of -.02***? The 95% confidence intervals are positive. Is 3a in the same table correct effect?

Authors' Response: Oops. We apologize for this error in our results table. Thanks for the very keen eye for detail!

5. Now there are quite many analyses. Did you use e.g. Bonferroni correction for the p-values?

Authors' Response: We did not need to adjust for the many tests because we used structural equation modeling to test the model for fit and all model relationships for their relationships in one fell swoop. Consider, for example, a regression model with 2, or 5, or even 50 predictors. Each regression coefficient is already corrected for the propensity for Type I error in the model test. This might be a little off-note but there is some growing acknowledgement that when using meta-analytically derived effects that are corrected for measurement error as part of the meta-analysis one can refer to the path model test as a structural equation model test because such tests also correct for measurement error. Regardless of if we consider ours a structural equation model test or a path model test control of Type I error is an inherent part of the underlying algorithm.

6. I was wondering, have you tested e.g. to combine WFC and FWC to a common conflict measure and run the analyses with it? As e.g. Byron 2005 (your reference number 10) shows that coping styles and skills have quite similar relations with both WFC and FWC, and I think personality traits might behave similarly. What comes to your reference number 11, it considers positive side of work-family interface, not conflict, what you concentrate on, so I don’t think it is the best one for your purposes.

Authors' Response: We did not combine WFC and FWC because the two variables are theoretically and empirically different, as shown in previous studies (Amstad et al., 2011; Mesmer-Magnus & Viswesvaran, 2005; Shockley & Singa, 2011; Whitely & England, 1977). Consistent with existing research on the theoretical difference, we tested WFC and FWC separately in the model. Our findings indicate that WFC and FWC have different mediating effects linking Big Five personality and mental health outcomes. It should also be noted that we cannot combine two meta-analytically derived correlations. Recall that we did not do the meta-analyses ourselves and thus could not recode the primary studies for any that combined both forms of conflict into one construct. We would also need nine more correlations of the combined form of WFC/FWC with each antecedent and outcome in the model. These statistics just do not exist.

We cited reference number 11 (Byron, 2005) to show that WFC and FWC are different from other work-family constructs such as work-family enrichment. There are positive sides to the work-family interface such as work-family enrichment which is a field still growing and likely to grow more in the future and thus be meta-analyzed and ripe for inclusion in a mega-meta. We would, of course, be happy to remove this citation per your request.

7. I would ask the same with anxiety and depression, such as have you tried to combine these to general mood or mental health measure and run the analyses with it? Do these combinations affect the effect sizes? Or to the model fit indexes?

Authors' Response: Similar to our response above, these statistics just do not exist in the meta-analytic literature surrounding WFC and FWC. As above, if they existed, we would need a meta-analytic effect size between the combination of depression and anxiety (two disorders that often are comorbid) and every other variable in the model. Thus, if only one or two such meta-analyses existed we would have a very, very simple model as most cells in the 10x10 synthetic input matrix would be empty.

8. RMSEA shows poor fit, is that a reason to be concerned?

Authors' Response: It is our understanding that the RMSEA is sensitive to model complexity. Our serial mediation model is likely more complex than most. We are pleased that, as we noted in our paper, the SRMR is excellent and the CFI indicates good fit. We hope the excellent SRMR sort of offsets the poor RMSEA, but as with all supplemental fit indices that is a judgment call. If our model had been a simple model with five exogenous predictor variables (the Big Five traits) in the prediction of five endogenous outcome variables (WFC, FWC, depression, anxiety, and substance use) our RMSEA would likely have been better. However, such a simple model belies the extant literature on WFC and to some extent does not give fodder for our use of Conservation of Resources Theory. If all fit indices were poor we would have found some evidence that researchers of the antecedents of WFC and other researchers of the outcomes of WFC might be examining incompatible segments of the nomological network surrounding WFC. We are happy to add more text about the poor RMSEA if the reviewer insists but we hope that the reader will recognize the excellent and good fit of the other indices.

Reviewer 3 Comments:

1. The authors should pay attention to small errors in the text.

Authors' Response: We apologize for the errors and have made the following corrections:

Line 47: Replaced the brackets with parentheses around the acronym

Line 48: Replaced the brackets with parentheses around the acronym

Line 83: Replaced "[Oh, 2020] with "[23]"

Line 85: Corrected the spelling of "supplemented"

Line 146: Replaced "...pessimism, and temperamentalism" with "...pessimism and being temperamental"

Line 179: Replaced the brackets around "[substances]" with parentheses

Line 345: Replaced "606" with the correct difference value of "1039"

Line 336: This line appeared twice due to adding text previous to it. We have now renumbered the lines from line 340 onward

Lines 392-394: Replaced brackets around "a", "b", and "c" with parentheses

Lines 491-503: Replaced brackets around trait names (e.g. agreeableness) and hypothesis numbers with parentheses

Lines 564-565: Replaced brackets with parentheses where appropriate

2. Please provide bibliographic references in all parts of the introduction. You cannot affirm something if it is not based on literature.

Authors' Response: In the five paragraphs comprising the introduction section we have 23 sentences. Twelve of the sentences have bibliographic citations. In that section there are 25 citations. The sentences without citations are summary sentences built upon previous sentences with the referenced sources such as line 44-45: "This conflict is likely to have serious consequences for one's roles their family and work." Most of the other sentences without citations are explaining the purpose of the study and how we assembled our input correlation matrix. We are not sure where we might need additional references but would be happy to do so with further guidance.

3. Please summarize or improve the introduction. It is very long and there are parts that are not understood. It must be more specific and fit the objectives and hypotheses of the study.

Authors' Response: Our introduction section is only five paragraphs long with three of them devoted to the purpose of the study, how "data" were collected/assembled, and the contributions of the study. We respectfully disagree that it should be any shorter than it already is without skipping vital information for the reader. We hoped to show the reader why we wanted to do the research, how it was going to be conducted, and how we hoped to make a contribution to the literature. We are happy to remove some small bits if they can be directly specified.

4. Regarding the structure of the Introduction section, I would suggest presenting objectives and hypotheses after adequately stating the background and justification rather than in reverse or mixed order for clarity reasons.

Authors' Response: We think that perhaps the reviewer is suggesting changes to the Theoretical Framework section rather than the Introduction section. Our model is rather complicated by its nature as a serial mediation model. We hoped to "walk the reader through" the model by proceeding left to right, that is, from antecedents to mediators to outcomes. Given the very large number of hypotheses we very much prefer to discuss one part of the model and then give its hypotheses then followed by a discussion of the next part of the model and its hypotheses, etc. this sort of piecemeal procedure from left to right also serves our stated purpose of joining the left hand side of the nomological network (see the heading in line 126) with the right hand side (see line 153). We are willing to put all hypotheses at the end of the Theoretical Framework section but they will take up almost three entire pages of text. We think that might be a bit overwhelming to the reader so we hope to stay with the current arrangement.

5. Please discuss the potential theoretical and clinical implications of your work deeply.

Author's Response: We thank the reviewer for this suggestion but are hesitant to discuss clinical implications. We have lines 517-551 where we discuss the findings within the Theory of Conservation of Resources. For example lines 529-533 reads as follows: "Due to dysfunctional emotional adjustments, sensitivity, and vulnerability to negative experiences [54, 59, 89], neurotic employees have fewer resources with which to manage incompatible work and family demands [46], leading to higher WFC, which in turn increases depression and substance use." However, we have added a section on practical implications to lines 612-627. Our paper's central focus is work-related. The main constructs are WFC and FWC. The personality trait antecedents are measured using instruments designed to measure personality in sub-clinical populations. Admittedly our study does use variables like clinical depression, clinical anxiety, and sub-clinical substance use but it is not a clinical paper, per se. As we state a few times in the paper we used variables that existed, that were found in previous meta-analyses on variables related to WFC/FWC, and could not use variables for which the necessary statistics did not exist. We hope the reviewer understands and we respectfully suggest that such additions to the paper are not in its best interest.

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

24 Jan 2022

Antecedents and Outcomes of Work-Family Conflict:  A Mega-Meta Path Analysis

PONE-D-21-39844R1

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28 Jan 2022

PONE-D-21-39844R1

Antecedents and Outcomes of Work-Family Conflict: A Mega-Meta Path Analysis

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