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JOURNAL OF MANAGERIAL ISSUES Vol. XXIII Number 4 Winter 2011

What’s Really Important? Examining the Relative Importance of Antecedents to Work-Family Conflict

Michael B. Hargis Assistant Professor of Management University of Central Arkansas

Lindsey M. Kotrba

Research & Development Director Denison Consulting

Ludmila Zhdanova

PhD Student Wayne State University

Boris B. Baltes

Professor of Psychology Wayne State University

Recent demographic changes in the workplace have led to substantial increases in the number of dual-income earning households, adults caring for aging parents and children, single parent families, and the average number of hours worked per week (Clark and Weismantle, 2003). These trends have fueled interest from researchers and practitioners who seek to understand the antecedents that influence how individuals experience and respond to the challenges of balancing work and family demands (Eby et al., 2005).

Prior independent studies and meta-analyses have linked work-family conflict (WFC) to important outcomes such as job and life satisfaction (Allen et al., 2000; Boles et al., 2001; Kossek and Ozecki, 1998), psychological distress (Frone, 2003), chronic health problems (Grzywacz, 2000), job performance and absenteeism (Allen et al., 2000), and turnover intentions and voluntary turnover (Boyar et al., 2003; Lee and Maurer, 1999). Recognizing the relationship between WFC and these important personal and work outcomes, researchers have attempted to identify the antecedents to WFC. For instance, three recent

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meta-analyses (Byron, 2005; Ford et al., 2007; Michel et al., 2011) demonstrate that certain factors (e.g., role involvement and family stressors) are consistently and uniquely related to various types of WFC. Although these meta-analyses, and the individual studies on which they are based, have advanced the literature and led to important workplace initiatives (Greenhaus and Parasuraman, 1999; Rothbard et al., 2005; Zedeck and Mosier, 1990), there remain two important areas for future contribution.

First, the statistics used in prior investigations have limited utility in accurately assessing the importance of a single antecedent (e.g., job stressors) among a number of other theoretically important antecedents (e.g., personality, family support, etc.). This is important to investigate because it is unlikely that individuals experience one WFC antecedent (e.g., job stressors) in isolation of other antecedents (e.g., family stressors, spousal support, etc.) so it is worthwhile to examine an antecedent’s importance relative to other antecedents simultaneously. For example, people simultaneously encounter different levels of stressors in the work and family domains, as well as appraise access to job and family support structures that could mitigate the experience of WFC. While it is logical to assume that the interplay among WFC antecedents is important, prior research has not fully examined the relative importance of antecedents to WFC across outcomes using appropriate statistics. In fact, Byron (2005) notes the need to understand the relative importance of antecedents and makes a call for future research to address this issue. Typically, researchers examining WFC antecedents have relied on correlation or regression coefficients to assess predictor importance (cf. Byron, 2005; Carlson, 1999; Ford et al., 2007). However, as noted by Darlington (1968) and Johnson (2000), these measures of importance are problematic and often produce misleading results.

Second, prior studies have only simultaneously examined a limited number of theoretically meaningful antecedents and criterion variables. Specifically, an increasing number of independent studies have started to link personality and other individual difference variables to WFC (Lenaghan et al., 2007; Bruck and Allen, 2003; Carlson, 1999); however, few studies have examined the relative importance of person-based antecedents and situation-based antecedents together across multiple facets of WFC (i.e., time-, strain-, and behavior-based work-interfering-with-family/family-interfering-with-work conflicts). There have been attempts to examine how certain variables (e.g., personality) relate to different types of WFC, but due to limited prior research neither the Byron (2005), Ford et al. (2007), nor the Michel et al. (2011) meta-analyses are able to examine the differential predictive validity of various traits across types of WFC.

Accordingly, the purpose of this study is to extend prior research by evaluating the importance of several theoretically meaningful antecedents to WFC simultaneously. This paper makes a unique contribution to the literature in two ways. First, this study uses statistical procedures (general dominance and relative weights analysis) that allow for a more direct assessment of an antecedents’ contribution to prediction when it is considered in combination with other theoretically meaningful antecedents. This is important because it allows researchers and practitioners to examine the unique pattern of relationships among work-, non-work, and person-based antecedents as well as identify which antecedents are most predictive across diverse criteria. Furthermore, examining multiple antecedents more accurately models the real world experience of employees. Second, this study expands the construct space by examining additional antecedents (e.g., personality) across a wide range of WFC criteria. Taken together, these extensions of prior research are

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important because they contribute to understanding of the antecedents, which could help individuals more effectively manage WFC and lead to more targeted, effective, and resource efficient (both time and financial) interventions to reduce the strain of WFC.

DEFINING WORK-FAMILY CONFLICT

Role conflict theory suggests that expectations from two domains (e.g., work or family)

can be incompatible due to different norms and obligations (Burke, 1986; Zedeck and Mosier, 1990); therefore, performance in one domain of life (e.g., work) reduces the amount of time, energy, and other resources available for other domains (e.g., family). Accordingly, WFC is defined as “a form of interrole conflict in which the role pressures from the work and family domains are mutually incompatible in some respect” (Greenhaus and Beutell, 1985: 77). Extant evidence suggests that these role pressures produce directional conflict between domains (Frone et al., 1992) and recent meta-analyses (Ford et al., 2007; Michel and Hargis, 2008) have focused on the degree to which work role obligations interfere with family role obligations (WIF) and the degree to which family role obligations interfere with work obligations (FIW).

Additionally, Greenhaus and Beutell (1985) introduce three distinct types of WFC: time-, strain-, and behavior-based. Time-based conflict results from the use of a limited commodity (time) and occurs when the time spent in one role (e.g., hours at work, out of town meetings, household duties) makes it difficult to comply with expectations from another role (Baltes and Heydens-Gahir, 2003). Strain-based conflict exists when elements of one role (e.g., role ambiguity or family conflict) cause stress and tension that affect performance in another role. For example, strain-based conflict may be experienced by employees who are exposed to difficult emotional or physical work demands when this strain impacts their behavior in the family domain. Finally, behavior-based conflict occurs when patterns of behavior in one role are incompatible with behavioral expectations in another role. For example, consider the impact of the demanding task-focused manager exhibiting the same pattern of behaviors in interactions with his/her family. This three factor typology is one of the common typologies in the WFC literature (Bruck and Allen, 2003).

This study combines these two conceptualizations of WFC to explore the relative importance of meaningful antecedents in predicting time-, strain-, and behavior-based WIF and FIW conflicts. It is important for research to focus on this expanded framework because prior research has demonstrated that antecedents (both situation and person- based) have a differential impact across types and forms of WFC (Bruck and Allen, 2003; Carlson, 1999).

ANTECEDENTS OF WIF AND FIW CONFLICT

WFC antecedents are typically categorized into three distinct domains: work, non-

work, and individual differences (Byron, 2005; Carlson, 1999; Michel et al., 2011). Two useful theoretical frameworks for linking these antecedents to WFC are role theory and resource drain theory (Barnett and Gareis, 2006; Edwards and Rothbard, 2000). According to role theory, each role (e.g., manager, friend, or parent) is accompanied by specific responsibilities, behavioral expectations, and norms (Kahn et al., 1964). To fulfill the

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demands imposed by multiple roles, individuals must rely on various resources, such as time and energy. The resource drain theory (Eckenrode and Gore, 1990; Small and Riley, 1990; Staines, 1980; Tenbrunsel et al., 1995) posits that these resources are finite and, therefore, using resources to fulfill obligations in one role (e.g., work) limits one’s capacity to fulfill role obligations in other roles (e.g., family).

Building off the three-domain framework, antecedents are selected from each domain that had theoretical links with the experience of work-family conflict through role and resource drain theory. Specifically, this study examines the importance of four work domain variables (job stressors, hours worked, supervisor support, and job involvement); five non- work domain variables (family stressors, number of children at home, age of youngest child, family involvement, and family social support); and two individual difference variables (affectivity and locus of control). In addition to their theoretical links to WFC, antecedents were chosen because of their consistent presence and empirical support in the work-family literature. All of the antecedents in this study have been included in previous quantitative reviews of WFC antecedents (Byron, 2005; Ford et al., 2007; Michel et al., 2011) and are frequently included in broader WFC models (e.g., Michel and Clark, 2009). However, it is important to note that this study will examine the antecedents simultaneously, which is important because it more accurately models real world experiences. The theoretical background for each antecedent is introduced below.

Role Stressors. As outlined above, role theory posits that individuals have multiple roles across the work and family domains (e.g., spouse, parent, or subordinate). Job and family stressors result from the pressures associated with these roles because there are specific expectations and appropriate patterns of behavior and these competing expectations can create tremendous demands and strain for individuals (Kahn et al., 1964). Job and family stressors, such as role conflict and role ambiguity, have been theorized to impede one’s ability to accomplish responsibilities associated with one’s role obligations (e.g., Greenhaus and Beutell, 1985). In reaction, the distribution of valued resources might become uneven with more resources allocated in the domain characterized by stressors. According to the resource drain theory (Edwards and Rothbard, 2000), this should decrease available resources to meet demands in another domain leading to strain and higher levels of WFC.

Indeed, the job and family stressors experienced in one life domain, such as role conflict and role ambiguity, are primary sources of stress and strain (Kopelman et al., 1983) and have been linked to decreased performance across domains (Frone et al., 1997). Further, role stressors and strain, have been consistently identified as antecedents of WFC (e.g., Boyar et al., 2003; Byron, 2005; Frone et al., 1992). Thus, based on prior research and theory, job and family stressors are expected to be important predictors of WIF and FIW respectively, particularly of strain-based conflict.

Social Support. Social support is commonly defined as the exchange of resources between individuals with the goal of facilitation or helping (Van Daalen et al., 2006). Based on role and resource drain theories, it is logical to assume that such support provides individuals with an additional resource for dealing with WIF/FIW conflict. More specifically, social support from both domains increases the resources, such as time and energy, available to the individual experiencing competing demands from multiple roles.

Not surprisingly, aspects of social support have been identified as important antecedents to WFC (e.g., Frone et al., 1997; Greenhaus and Beutell, 1985; Greenhaus and

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Parasuraman, 1999). For instance, research suggests that higher levels of work social support are related to lower levels of WIF conflict and that higher levels of family social support relate to lower levels of FIW conflict (e.g., Adams et al., 1996; Lapierre et al., 2008). Prior research also suggests that individuals with higher levels of supervisor support tend to be less affected by stressful environments (Young et al., 2007). Furthermore, family support has been negatively related to reported levels of stress and strain (Bernas and Major, 2000) and has been shown to buffer the negative relationship between care giving and work and family role strain (Lechner, 1993). Therefore, supervisor and family social support are expected to be negatively related to strain-based WIF and/or FIW conflict.

Job and Family Involvement. Job (family) involvement can be defined as the level of psychological involvement with one’s work or family role (Frone, 2003). Individuals high in job or family involvement have a psychological preoccupation with their role(s) in that domain. Thus, according to role and resource drain theories, job and family involvement should relate to WIF and FIW conflicts as preoccupation with one domain may increase the time and energy devoted to that domain and consequently make it difficult to attend to the competing domain (Edwards and Rothbard, 2000). Both job and family involvement have been reported to positively relate to WIF and FIW conflict respectively (Adams et al., 1996; Frone et al., 1992). Thus, prior research suggests that job and family involvement should be important predictors of WIF/FIW, particularly time-based conflicts, as high involvement in one role limits the time and energy resources (both psychological and physical) that are available to dedicate to the competing domain.

Hours Worked. Greenhaus and Beutell (1985) suggest that the number of hours an individual works influences WFC and subsequent empirical research has supported the positive relationship between time-based work demands and WFC (e.g., Frone et al., 1997; Gutek et al., 1991). Additionally, research by Ilies et al. (2007) indicates that an employee’s workload (both objective number of hours worked and subjective perceptions of workload) were related to reported WFC. Thus, prior theory (e.g., resource drain theory), independent studies (e.g. Gutek et al., 1991), and meta analyses (e.g., Byron, 2005; Michel et al., 2011) suggest that as the number of hours an individual spends on work-related activities increases, so does the likelihood that work obligations interfere with family obligations resulting in increased WFC. Consistent with prior research described above, the number of hours worked are expected to be a relatively strong predictor of WFC, particularly to time-based WIF conflict.

Number and Ages of Children. The number and ages of children are related to an individual’s ability to balance family responsibilities and work demands (Bedeian et al., 1988) because younger children tend to require more care and resources than older children do. Further, consistent with resource drain theory, parents with younger children have fewer time and energy resources (Grandey and Cropanzano, 1999) and tend to report more conflict between work and family (Burke et al., 1980). Meta-analytic results have shown the number of children and ages of children relate to WFC (Byron, 2005; Michel et al., 2011). Therefore, both the number and age of children are expected to be important predictors of time-based FIW conflict.

Personality. James and Mazerolle (2002) define personality as the dynamic mental structures and coordinated mental processes that shape how individuals interpret, and respond to, their environment. Thus, WFC researchers have theorized that personality traits often influence how individuals perceive their situation and, as a result, these

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perceptions should influence reported WFC (Eby et al., 2005; Michel and Clark, 2009). Indeed, researchers have started to consider the role of personality traits in WFC (Boyar and Mosley, 2007; Carlson, 1999; Lenaghan et al., 2007; Noor, 2003). Two traits that influence how individuals perceive their environment and have received attention within the work-family literature include locus of control and negative affectivity (Bruck and Allen, 2003; Stoeva et al., 2002).

Rotter (1966) introduced locus of control (LOC) to explain the generalized belief individuals form regarding the extent to which outcomes are caused by internal variables (e.g., hard work or ability) rather than external variables (e.g., luck or powerful others). Prior research suggests that internal LOC, or the belief that outcomes are a result of one’s control, is positively related to improved well-being and stress management (Lefcourt et al., 1981; Noor, 2003). Similarly, the belief that an individual actively controls their fate should also influence how individuals experience WFC because they will likely perceive that they have the ability to reduce the stress of WFC by seeking out resources (e.g., modified work schedules, day care, alternative forms of transportation, etc.) that would reduce role conflict and strain.

Negative affectivity, on the other hand, is frequently associated with psychological distress, anxiety, and general dissatisfaction (Watson et al., 1988). Researchers have linked negative affectivity to WFC based on the argument that individuals high on negative affectivity have a tendency to assess situations (e.g., home, work) in negative terms and are more likely to experience negative emotions towards conditions at work and at home (Eby et al., 2005). Indeed, prior research has linked negative affectivity to each of the three types of WFC (Carlson, 1999). Furthermore, Stoeva et al. (2002) find that negative affectivity acts as a mediator and a moderator of the relationship between job and family role stress and WFC. Therefore, based on the prior theoretical and empirical link with important elements of WFC, LOC and negative affectivity are expected to be important predictors of WFC.

In sum, role stressors, social support, involvement, hours worked, number and age of children, and personality are expected to be important predictors of different facets of WFC. Given previous theory and research, role stressors (i.e., job and family) and social support (i.e., supervisor and family) are anticipated to be particularly important predictors of strain-based conflict in their respective domains (i.e., WIF and FIW); job and family involvement, hours worked, and number and age of children are anticipated to be particularly important predictors of time-based conflict in respective domains (i.e., WIF and FIW); and personality is anticipated to be an important predictor of all types of WFC. However, given that individuals likely do not experience these antecedents in isolation, it is important to know the relative contribution each of these antecedents makes to the prediction of WFC when considered among other important antecedents. Due to statistical limitations, prior research has not been able to adequately examine the relative importance of each of these theoretically important antecedents when they are considered together.

METHOD

Participants and Procedure

Participants included 289 working adults (179 females; 110 males) living in a major Midwestern metropolitan area with a significant other, a child (or children), or both.

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Additionally, 136 (47%) Caucasian, 80 (28%) African-American, 29 (10%) Asian, 15 (5%) Arabic/Middle Eastern, ten (4%) Hispanic, four (1%) multi-racial, and 15 (5%) respondents of unknown ethnicity participated in this study. The average age was 37.67 years old (SD = 11.49). Respondents indicated that the average number of hours worked per week as 37.5 (SD = 15.1). The majority of the respondents reported being married (73%), living with their spouse or significant other (77%), and being a parent (81%). About 23% reported having at least one child younger than six years old in the household, about 16% reported at least one child between six and 12 years old, and about 23% reported having at least one child between 13 and 18 years old.

All data were collected in one session using an internet-based survey. Participants were recruited via snowball sampling (Heckathorn, 1997) where students recruited their contacts (i.e., friends, family) that fit the study criteria. Recruits were instructed to contact the study coordinator and were provided with an informed consent form and instructions directing them to the on-line measures. Participation was voluntary and there were no incentives for participation. To control for the influence of potential order effects and response fatigue, the survey was designed to randomly order the presentation of the questionnaires (Lavrakas, 2008; McFarland, 1981). Measures

The descriptive statistics (i.e., means and standard decisions), bivariate correlations, and reliability coefficients for the variables included in this study are presented in Table 1.

WIF and FIW Conflict. The six facets of WFC included in this study were measured with the 18-item scale developed by Carlson et al. (2000). This scale presents three items for each facet of WIF and FIW conflict. Sample items include: “My work keeps me from my family activities more than I would like” and “Due to stress at home, I am often preoccupied with family matters at work.” Responses were made using a five-point scale (1 = strongly disagree, 5 = strongly agree).

Job and Family Stressors. Job and family stressors were assessed using a 28-item scale developed by Frone et al. (1992). The job stressors scale (20-items) includes three subscales: work pressure (e.g., “Do you have too much work to do?”), lack of autonomy (e.g., “Do you feel that you are unable to influence your supervisor's decisions - even when they affect you?”), and role ambiguity (e.g., “Are you unsure about what people expect of you?”). The family stressors scale includes four marital stressor items (e.g., “How much tension is there between you and your husband/wife/partner?”) and four parental stressor items (e.g., “How often do your children do things that cause you problems or hassles?”). Responses were made using a six-point scale ranging from 1 (disagree strongly) to 6 (agree strongly).

Supervisor Support. Supervisor support was assessed using the five-item scale developed by Kossek (1990). A sample item from this scale is “I feel free to discuss child care issues with my supervisor.” Responses were made using a five-point scale (1 = strongly agree, 5 = strongly disagree). The items were reverse coded so that higher scores on this scale indicated higher levels of perceived supervisory support.

Job and Family Involvement. Job and family involvement were assessed using a 15-item scale developed by Frone et al. (1992). An example from the five-item job involvement scale is “Most of my interests center around my job.” An example item from the 10-item family involvement scale is “The most important things that happen to me involve my child(ren).”

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Responses were made using a six-point scale (1 = disagree strongly, 6 = agree strongly). Hours Worked Per Week. Participants were asked to indicate the number of hours

worked per week. Number of Children at Home and Age of Youngest Child. Respondents were asked to

indicate the number of children living at home and the age of the youngest child living at home.

Family Social Support. Family social support was assessed using a 12-item scale developed by Sargent and Terry (2000). Individuals were instructed to report the extent to which they were able to count on their spouse, family, and friends for various types of support. A sample item is “How much concern does he/she show for your feelings and problems?” Responses were made using a four-point scale ranging from 1 (not at all) to 4 (very much).

Locus of Control. Locus of control (LOC) was assessed with an 11-item version of Rotter’s (1966) I-E Scale recommended by Valecha and Ostrom (1974). Each external item (e.g., “Getting a good job depends mainly on being in the right place at the right time”) is paired with an internal item (e.g., “Becoming a success is a matter of hard work; luck has little to do with it”) and the measure utilizes a forced choice format. Low scores indicate an internal LOC.

Positive and Negative Affect. Affect was measured with the PANAS-X (Watson and Clark, 1994), which contains a list of 60 emotion-relevant adjectives; 23 of these items indicate negative emotions (e.g., afraid, hostile), 18 of these items indicate positive emotions (e.g., happy, confident), and the remaining 14 items measure other affective states (e.g., shyness) not relevant to this study. Participants rated the extent to which their average feelings matched the adjectives on a five-point scale ranging from 1 (very slightly/not at all) to 5 (extremely).

Data Analysis: Examining the Relative Importance of Antecedents to WFC

Johnson defines relative importance as “the contribution each variable makes to the prediction of a dependent variable considering both its unique contribution and its contribution when combined with other variables” (2000: 2). Traditionally, a number of statistics (e.g., squared correlations and beta weights) have been used to evaluate predictor importance. While each of these methods has strengths, they fail to consider the joint effects on the criterion shared by two or more correlated predictors (Johnson and LeBreton, 2004). Darlington (1968) notes that most measures of predictor importance are often misleading in the face of multicollinearity, which is often the case in the organizational sciences (Johnson and LeBreton, 2004). To overcome these limitations, two new statistical approaches have been introduced: dominance analysis (Budescu, 1993) and relative weights analysis (Johnson, 2000). Research in several areas of management practice has benefited from the use of these importance indices (Schleicher et al., 2006).

Dominance analysis provides a measure of the relative importance of multiple predictors by computing the mean squared semi-partial correlation across all possible subset regressions (Budescu, 1993). Statistically, for each variable (j), the general dominance weight is calculated (Cj) and this weight represents the average “usefulness” of a variable across all subset regressions (Azen and Budescu, 2003). Thus, dominance analysis furnishes an index of relative importance based on “a variable’s direct effect (i.e., when considered by

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itself), total effect (i.e., conditional on all other predictors), and partial effect (i.e., conditional on all subsets of predictors)” (Budescu, 1993: 544). Furthermore, general dominance analysis examines patterns of a predictor’s dominance, which can be used to determine the specific pattern of a variable’s relative importance across regression models. Full dominance occurs when a variable is consistently more important than any other variable across all subset regressions. Relative weights analysis (Johnson, 2000) provides a measure of the relative importance of multiple predictors by using a principal components approach to create a new set of uncorrelated predictors that are maximally correlated with the original set of predictors yet are orthogonal to one another. The criterion variables are then regressed on the transformed predictors yielding standardized regression coefficients. These coefficients are then squared and combined with the squared standardized regression coefficients to calculate importance estimates.

Both methods provide estimates of predictor importance based on the proportion of the model R2 that can be attributed to each independent predictor. Furthermore, all of these independent components add up to the model R2. Because these methods provide estimates of importance that sum to the model R2, it is possible to evaluate predictors using the metric of their proportionate contribution to the model R2. Additionally, it is possible to calculate rescaled relative importance estimates for both dominance and relative weights by dividing each raw estimate by the model R2. These rescaled importance estimates indicate the percentage of total explained variance contributed by each independent predictor, which is a helpful method to communicate results.

RESULTS

Generally, the bivariate correlations are consistent with prior research and are in the

expected direction (see Table 1). For example, job and family stressors were significantly and positively related to WIF and FIW. Furthermore, the age of the youngest child was significantly and positively related to dimensions of WIF and FIW. Locus of control and negative affectivity were also significant predictors of dimensions of WIF and FIW. Additionally, supervisor support was significantly related to dimensions of WIF and FIW. The results obtained via dominance and relative weights analysis were consistent across predictors and criterion (see Table 2) and are discussed in more detail below.

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0 0.

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Time-based WIF. Job stressors were the single most important predictor of time-based WIF accounting for approximately 46% of predicted variance and fully dominating the remaining antecedents. Age of youngest child and locus of control were the next most important predictors accounting for 21% and 18% of predicted variance, respectively. Together, these predictors accounted for 85% of predicted variance. The remaining predictors accounted for 5% or less of predicted variance.

Strain-based WIF. Negative affectivity accounted for approximately 43% of predicted variance and fully dominated the remaining antecedents. Job stressors were the next most important predictor accounting for approximately 26% of predicted criterion variance. Together, these predictors accounted for 69% of predicted variance in strain-based WIF conflict. The remaining antecedents accounted for 10% or less of the criterion variance.

Behavior-based WIF. Negative affectivity accounted for approximately 46% of predicted variance in behavior-based WIF and fully dominated the remaining antecedents. Family involvement and job stressors were the next most important predictors accounting for 16% and 13% of predicted criterion variance, respectively. Together, these three predictors accounted for 75% of predicted criterion variance. The remaining antecedents accounted for 5% or less of the predicted criterion variance.

Time-based FIW. Negative affectivity accounted for approximately 41% of the predicted variance in time-based FIW conflict and fully dominated the remaining antecedents. Job stressors were the next most important predictor accounting for approximately 30% of predicted criterion variance. Together, these two predictors accounted for 71% of predicted criterion variance. The remaining antecedents accounted for 9% or less of predicted criterion variance.

Strain-based FIW. Negative affectivity was the most important predictor accounting for approximately 47% of predicted variance in strain-based FIW. Job stressors were the second most important predictor accounting for approximately 20% of predicted criterion variance. Age of youngest child accounted for 14% of predicted variance. Taken together, these three antecedents account for 81% of predicted criterion variance. The remaining antecedents account for 7% or less of predicted criterion variance.

Behavior-based FIW. Again, negative affectivity was the most important predictor accounting for approximately 47% of predicted variance in behavior-based FIW. Job stressors were the second most important predictor accounting for approximately 28% of predicted criterion variance. Together, these two predictors account for approximately 75% of predicted variance in behavior-based FIW. Family stressors accounted for 10% of predicted variance and the remaining predictors account for 3% or less of predicted criterion variance.

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Table 2

Relative Importance Ratings of Predictors of WIF Conflict

Dependent Variable = Time WIF (R² = 0.11) Raw Importance Estimates Rescaled Estimates Variables rJY ßJ CJ RWJ CJ-RS RWJ-RS

Job Stress 0.23 0.31 0.06 0.06 0.46 0.45 Hours Per Week -0.02 -0.05 0.00 0.00 0.01 0.01 Supervisor Support 0.01 -0.07 0.00 0.00 0.02 0.02 Job Involvement 0.06 0.02 0.00 0.00 0.02 0.02 Family Stress 0.09 0.02 0.01 0.01 0.03 0.04 Number of Kids 0.09 0.01 0.00 0.00 0.03 0.03 Age of Youngest Child -0.17 0.26 0.02 0.02 0.21 0.21 Family Involvement 0.04 -0.01 0.00 0.00 0.01 0.01 Family Support 0.02 0.01 0.00 0.00 0.00 0.00 Locus of Control -0.12 -0.17 0.02 0.02 0.18 0.18 Negative Affect 0.00 -0.09 0.00 0.00 0.03 0.03 Positive Affect 0.00 -0.02 0.00 0.00 0.00 0.00 Totals 0.11 0.11 1.00 1.00

Dependent Variable = Strain WIF (R² = 0.15) Raw Importance Estimates Rescaled Estimates Variables rJY ßJ CJ RWJ CJ-RS RWJ-RS

Job Stress 0.25 0.19 0.04 0.04 0.26 0.26 Hours Per Week 0.02 0.04 0.00 0.00 0.01 0.01 Supervisor Support 0.03 -0.12 0.01 0.01 0.03 0.03 Job Involvement -0.02 -0.06 0.00 0.00 0.01 0.01 Family Stress 0.18 0.05 0.01 0.01 0.08 0.08 Number of Kids 0.07 0.04 0.00 0.00 0.02 0.02 Age of Youngest Child -0.16 0.08 0.01 0.01 0.10 0.10 Family Involvement -0.04 -0.04 0.00 0.00 0.01 0.01 Family Support 0.02 0.06 0.00 0.00 0.01 0.01 Locus of Control 0.08 -0.01 0.00 0.00 0.01 0.01 Negative Affect 0.32 0.25 0.07 0.07 0.43 0.43 Positive Affect -0.12 -0.01 0.01 0.01 0.03 0.03 Totals 0.15 0.15 1.00 1.00

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Table 2 (continued)

Dependent Variable = Behavior WIF (R² = 0.17) Raw Importance Estimates Rescaled Estimates Variables rJY ßJ CJ RWJ CJ-RS RWJ-RS

Job Stress 0.21 0.13 0.02 0.02 0.13 0.13 Hours Per Week -0.08 -0.09 0.01 0.01 0.04 0.04 Supervisor Support 0.12 -0.02 0.00 0.00 0.02 0.02 Job Involvement 0.02 -0.04 0.00 0.00 0.00 0.00 Family Stress 0.17 -0.00 0.01 0.01 0.04 0.04 Number of Kids 0.02 0.08 0.00 0.00 0.01 0.01 Age of Youngest Child -0.13 -0.03 0.01 0.01 0.05 0.05 Family Involvement -0.19 -0.17 0.03 0.03 0.16 0.16 Family Support -0.07 -0.04 0.00 0.00 0.01 0.02 Locus of Control 0.10 0.02 0.00 0.00 0.02 0.02 Negative Affect 0.33 0.31 0.08 0.08 0.46 0.46 Positive Affect 0.01 0.16 0.01 0.01 0.06 0.05 Totals 0.17 0.17 1.00 1.00

Dependent Variable = Time FIW (R² = 0.23) Raw Importance Estimates Rescaled Estimates Variables rJY ßJ CJ RWJ CJ-RS RWJ-RS

Job Stress 0.33 0.27 0.07 0.07 0.30 0.30 Hours Per Week 0.02 0.04 0.00 0.00 0.00 0.00 Supervisor Support 0.11 -0.06 0.00 0.00 0.02 0.02 Job Involvement -0.05 -0.13 0.01 0.01 0.03 0.03 Family Stress 0.19 0.06 0.01 0.01 0.06 0.06 Number of Kids -0.01 0.00 0.00 0.00 0.00 0.00 Age of Youngest Child -0.21 -0.11 0.02 0.02 0.09 0.09 Family Involvement -0.09 -0.04 0.00 0.00 0.01 0.01 Family Support 0.07 0.12 0.01 0.01 0.03 0.03 Locus of Control 0.07 -0.00 0.00 0.00 0.01 0.01 Negative Affect 0.37 0.33 0.10 0.10 0.41 0.41 Positive Affect -0.00 0.16 0.01 0.01 0.04 0.04 Totals 0.23 0.23 1.00 1.00

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Table 2 (continued)

Dependent Variable = Strain FIW (R² = 0.29) Raw Importance Estimates Rescaled Estimates Variables rJY ßJ CJ RWJ CJ-RS RWJ-RS

Job Stress 0.34 0.21 0.06 0.06 0.20 0.20 Hours Per Week -0.05 -0.03 0.00 0.00 0.00 0.00 Supervisor Support 0.20 0.01 0.01 0.01 0.04 0.04 Job Involvement -0.06 -0.11 0.01 0.01 0.02 0.02 Family Stress 0.25 0.07 0.02 0.02 0.07 0.07 Number of Kids 0.02 0.00 0.00 0.00 0.00 0.00 Age of Youngest Child -0.27 0.07 0.04 0.04 0.14 0.14 Family Involvement -0.14 -0.08 0.01 0.01 0.03 0.03 Family Support 0.02 0.08 0.00 0.00 0.01 0.01 Locus of Control 0.12 -0.01 0.00 0.00 0.01 0.01 Negative Affect 0.46 0.39 0.14 0.14 0.47 0.47 Positive Affect -0.09 0.10 0.00 0.00 0.01 0.01 Totals 0.29 0.29 1.00 1.00

Dependent Variable = Behavior FIW (R² = 0.13) Raw Importance Estimates Rescaled Estimates Variables rJY ßJ CJ RWJ CJ-RS RWJ-RS

Job Stress 0.24 0.18 0.04 0.04 0.28 0.28 Hours Per Week -0.06 -0.06 0.00 0.00 0.03 0.03 Supervisor Support 0.04 -0.08 0.00 0.00 0.02 0.02 Job Involvement 0.04 -0.03 0.00 0.00 0.00 0.00 Family Stress 0.17 0.07 0.01 0.01 0.10 0.10 Number of Kids 0.00 0.01 0.00 0.00 0.00 0.00 Age of Youngest Child -0.09 -0.07 0.01 0.01 0.03 0.03 Family Involvement 0.00 0.04 0.00 0.00 0.01 0.01 Family Support -0.07 -0.03 0.00 0.00 0.02 0.02 Locus of Control 0.05 -0.03 0.00 0.00 0.01 0.01 Negative Affect 0.29 0.27 0.07 0.07 0.47 0.47 Positive Affect -0.02 0.10 0.01 0.01 0.03 0.03 Totals 0.13 0.13 1.00 1.00

Note: rJY = correlations; ßJ = standardized regression coefficients; rescaled (RS) importance estimates were calculated by dividing the dominance (CJ) and relative weights (RWJ) by model R². Finally, while values for rJY and ßJ are presented, r

2 JY and ß

2 J are often used as estimates of relative importance because squared values

ignore the direction of the effect and such weights sum to the R2 when all predictors are uncorrelated.

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DISCUSSION

The purpose of this study was to address Byron’s (2005) call to further examine the relative importance of antecedents across different facets of WFC. The results of this study suggest that negative affectivity and job stressors are the first or second most important predictors across all six facets of WFC. In many instances, negative affectivity fully dominated the other 11 variables included in the study. The importance of the other predictors varies across criteria. This study makes a number of theoretical and practical contributions to the literature examining the person- and context-based factors leading to WFC.

Theoretical Implications

First, this study provides a direct examination of the relationship between antecedents from the work, non-work, and individual difference domains and different forms (work to family; family to work) and types (time-, strain-, and behavior-based) of work family conflict. Past research, using traditional measures of importance, has demonstrated a consistent link between the antecedents included in this study and various types of work- family conflict (Bruck and Allen, 2003; Byron, 2005; Ford et al., 2007; Michel et al., 2011). However, the results of the current study indicate that negative affectivity and job stressors are more important predictors than indicated in prior research. Further, when considered in combination with other theoretically grounded antecedents, some variables (e.g., hours worked per week, family support) contribute less to the prediction of WFC than previously reported. For instance, prior research suggests that family support was significantly related to facets of WFC (cf., Adams et al., 1996; Ford et al., 2007; Michel et al., 2011). In contrast, when considered simultaneously with a wide range of antecedents, family support accounts for between 0% and 5% of the predicted across facets of WFC. When considering hours worked, several prior studies suggest that hours worked per week was significantly related to WIF conflict (Baltes and Heydens-Gahir, 2003; Byron, 2005; Ford et al., 2007). In contrast, hours worked per week accounted for between 0% and 4% of predicted variance across the six facets of WFC included in this study. This makes sense within the context of a recent study conducted by Ilies et al. where they found that “subjective workload has an effect on work-to-family conflict that is largely independent of the effect of the amount of time spent at work” (2007: 1374). This suggests that subjective perceptions of workload are perhaps a more important predictor of work role demands than actual time spent at work. That is, when considering antecedent variables simultaneously, work hours becomes relatively less important than other work domain variables, such as perceptions of workload.

Negative affectivity was the most important predictor for strain- and behavior-based WIF and time-, strain-, and behavior-based FIW, accounting for 45-52% of the predicted criterion variance. This is much higher than the 2-30% indicated by prior research (Bruck and Allen, 2003; Carlson, 1999). Negative affectivity influences how people view and respond to certain elements of their life space and elevated levels of negative affectivity have been linked to increased levels of subjective stress and job strain in particular (Fogarty et al., 1999; Watson et al., 1999). These negative experiences such as feelings of nervousness, guilt, and anxiety that individuals high on negative affectivity are likely to

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experience generalize across various domains including work and family. Thus, it makes logical sense that negative affectivity would have a significant impact on how individuals experience conflict between work and family roles. In fact, given the magnitude of importance and the fact that negative affectivity was the most important predictor across five of the six facets of WFC, it appears that negative affectivity may be one of the central components of understanding how individuals respond to their work and family situations. A recent study conducted by Michel and Clark (2009) highlights the role of affectivity across outcomes such as satisfaction, enrichment, and work-family conflict.

Job stressors also emerged as one of the most important predictors for time-based WIF (accounting for 54% of predicted variance) and the second most important predictor for four of the remaining criteria. The importance of job stressors is not surprising in light of the resource drain theory (Edwards and Rothbard, 2000). Clearly, experiences of role conflict, role overload, and role ambiguity deplete resources (i.e., time) and put strain on fulfilling individual role obligations in other domains.

Taken together, it appears that negative affectivity and job stressors could be the driving forces behind the experience of WFC. Future research should continue to examine these two antecedents as well as the interplay between them. Theory and practice would benefit from a more complete understanding regarding how negative affectivity (a person- based variable) and job stressors (a work-based variable) work off of each other and impact how individuals interpret, and respond to, the competing demands of work and family life. Continued attention to these antecedents could help one better understand how to assist individuals in managing their WFC. Whether it is through coping strategies that are particularly helpful to individuals with high negative affectivity or programs designed to reduce job stressors, given the importance of these predictors, continued research would be of value.

It is also worth noting that many of the variables indicated as important antecedents of WFC in some prior research (e.g., supervisor support, hours worked, age of children) were not identified as important predictors of either WIF or FIW in the current investigation based on more appropriate statistical approaches and a more diverse set of antecedents. Given that techniques such as dominance analysis furnish an index of importance that is based on a predictor’s effect when considered by itself and its effect conditional on all other predictors and subsets of predictors, these statistical procedures provide a more direct assessment of an antecedent’s relative contribution to prediction of work-family conflict. Therefore, the current study contributes to the understanding of the importance of some of the most frequently investigated antecedents of work-family conflict and provides a more complete picture of which antecedents are meaningfully contributing to individual levels of WFC. For example, while there are numerous benefits associated with supportive supervision, when specifically attempting to alleviate WFC, the current evidence suggests that addressing role-based job stressors is likely to be more effective than expending effort and resources into developing supportive supervisors.

Another contribution of this study relates to current efforts to understand the interplay between work and family roles. While research has begun to explore how employees blur boundaries between work and family roles and how this integration relates to various work and family outcomes (Clark, 2000; Kreiner, 2006; Olson-Buchanan and Boswell, 2006; Rothbard et al., 2005), segmentation models remain the most dominant models of WFC. Segmentation models conceptualize work-based antecedents as affecting WIF and family-

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based antecedents as affecting FIW (Aryee et al., 1999; Frone et al., 1992; Carlson et al., 2000) with limited cross-domain impact.

The results from this study suggest work-related antecedents (i.e., job stressors) can be equally important to both WIF and FIW. Further, family involvement (a family-based antecedent) was identified as an important predictor of behavior-based WIF conflict, but was not identified as an important predictor of any facet of FIW conflict. Additionally, for some FIW criteria, supervisor support appeared to be an important predictor. These cross- domain effects could be due to the varying degrees of involvement individuals may have with their work and/or personal lives. Together, these results suggest that the work and family domains are potentially more intertwined than often theorized within the work- family conflict literature. This is consistent with the results of recent research suggesting that there is some cross-domain impact between family antecedents and voluntary turnover or intentions to quit (Boyar et al., 2003; Lee and Maurer, 1999). Future research should continue to explore these cross-domain linkages to better understand how individuals can achieve a desired balance. Additional individual level variables, such as role centrality, should be included in future studies. The notion that integrated relationships exist between the domains of work and family is not unique to the present study (e.g., Edwards and Rothbard, 2000). However, the WFC literature still largely advocates work-related antecedents to more strongly relate to WIF and family-related antecedents to more strongly relate to FIW.

Finally, the results of the current study further support Carlson’s (1999) call for researchers to consider the impact of dispositional factors in work-family research. Previous WFC research has largely focused on the importance of situation-based antecedents. The current study also finds situation-based antecedents (e.g., job stressors, family stressors) to be important. However, given that negative affectivity fully dominated all other predictors for five of the six criteria, with locus of control being an important predictor of the one remaining criteria, the role of personality should not be ignored in future research or when practitioners are designing interventions.

Practical Implications

The results of this study also have important practical implications. Understanding antecedent importance is critical because organizations have limited budgets and time for interventions. In order to provide direction for management practice and effective legislation, it is especially important to have a clear understanding of the antecedents to WFC. Given that the importance of the antecedents included in this study vary by criteria, it is essential for researchers and practitioners to recognize and understand how the antecedents relate to the various dimensions of WFC so that appropriate interventions can be developed for specific types of conflict.

As reviewed above, job stressors appear to be one of the most important predictors across all facets of work-family conflict. Thus, it seems that researchers and practitioners would benefit from a better understanding of the predictors of job stress. A recent paper discusses the role that coping strategies may play in influencing job stress (Baltes and Heydens-Gahir, 2003). Specifically, this study found coping behavior to relate to lower levels of job-stress, which subsequently related to lower levels of work-family conflict. Given that the current study suggests job stress to be one of the most important predictors of

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WFC, future research should continue to investigate the relationship between coping behaviors and job stress. For example, identifying the specific coping behaviors that relate to job stress could lead to the development of training programs geared toward teaching individuals how to deal with job stress and thus control their levels of work-family conflict. Such programs would likely be beneficial to both individuals and organizations.

Further, managers should be aware of the strong impact that negative affectivity and other personality variables have on work-family conflict. Working to reduce job stress, though likely effective, will not be enough to alleviate work-family conflict, particularly for individuals high in negative affectivity. Individuals who score highly in negative affectivity tend to be anxious, experience feelings of distress more easily, and are generally dissatisfied. More broadly, one’s personality (including negative and positive affectivity) influences how an individual reacts to, frames, and interprets their world. Thus, increasing a manager’s knowledge and understanding of various individual differences might improve the effectiveness of different organizational programs and interventions, such as employee assistance and wellness programs.

Limitations

As with all empirical research, there are limitations that should be acknowledged and addressed with future work. First, the cross-sectional nature of the data does not permit a test of causality. That said, the antecedents chosen for this study were strongly grounded by prior theory and research. However, incorporating data from multiple sources/methods would permit a stronger empirical test. Second, it is not practically feasible to measure every possible WFC antecedent in a single study. There are clearly other antecedents - particularly personality traits like Type A (Carlson, 1999) and perceptions of workload (Ilies et al., 2007) that have been examined in the literature. Thus, while the antecedents included in this study were present in the published reviews of the WFC literature and/or were grounded in the larger WFC literature, there are other antecedents from the work/non-work/individual differences categories that should be included in future investigations. Third, participants were not asked if they have experience caring for aging parents. Current evidence suggests that the number of adults caring for children and their aging parents is increasing (Clark and Weismantle, 2003). Finally, approximately 19% of the sample reported that they were not parents. Thus, for these individuals it is difficult to interpret their responses to questions related to childcare and/or children. That said, given the robust nature of the results regarding negative affectivity and job stressors, these results are likely to generalize to other populations. However, future research should target this population so that any differences that exist can be more accurately understood.

GENERAL CONCLUSION

In sum, this study extends prior research by examining the relative importance of

twelve antecedents to WFC across criteria and provides a foundation for future research and practice in a number of ways. First, the data indicate that person-based variables (e.g., negative affectivity) and situation-based variables (e.g., job stressors) can be important predictors across WFC outcomes. Prior research has consistently demonstrated the link between these variables and WFC; however, this study extends prior research by using

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statistics that allow for a direct examination of which antecedent is the most predictive among a battery of theoretically important and empirically supported antecedents. This factor is of vital importance as researchers continue to build theoretical models of the WFC construct space and practitioners develop interventions on limited budgets and restricted time lines. Second, the results expand knowledge regarding the cross-domain effects of various antecedents. This finding continues to highlight the importance of the new wave of research examining how individuals seek to integrate and balance the dual demands of work and personal lives.

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