The Status Dynamics of Role Blurring in the Time of COVID-19

Abstract

Has the coronavirus disease 2019 pandemic altered the status dynamics of role blurring? Although researchers typically investigate its conflictual aspects, the authors assess if the work-home interface might also be a source of status—and the relevance of schedule control in these processes. Analyzing data from nationally representative samples of workers in September 2019 and March 2020, the authors find that role blurring is associated with elevated status, but the onset of coronavirus disease 2019 weakens that effect. Likewise, schedule control enhances the status of role blurring, but its potency is also weakened during the pandemic. These findings align with the suggestion that role blurring signals a commitment to work and adherence to ideal worker norms. However, the pandemic changed that by intensifying role integration and possibly by reducing the degree of agency once associated with role blurring. The loss of choice around role blurring might have also diluted the distinctive status that it once carried.

Keywords

pandemic COVID-19 role blurring subjective social status schedule control work-family interface

For many, it has become normative for work to blend into other aspects of their lives. In 1974, when Lewis Coser characterized work as a “greedy institution,” he was drawing attention to the fact that work has the capacity to demand undivided time, attention, and effort. Coser articulated those insights well before the rapid transformations associated with communication technologies and their widespread proliferation. In recent decades, we have witnessed an accelerated shift in the ways employers use communication technologies to access and engage their employees and the effects this has on employees’ ability to perform work-related tasks remotely (Bittman, Brown, and Wajcman 2009). These transformations, and the flexibility in arrangements they afford, have altered norms and practices related to the boundaries between work and nonwork spheres (MacEachen, Polzer, and Clarke 2008; Olson-Buchanan and Boswell 2006).

With the onset of the coronavirus disease 2019 (COVID-19) pandemic, another major change is under way. In March 2020, as governments implemented virus mitigation strategies (e.g., social distancing), many workers were required to shift to remote work. Regional variations emerged: central Canada closed nonessential work on March 24, the Prairies started closing between March 23 and April 1, the west coast ordered certain types of businesses to close on March 26, Atlantic Canada started closing between March 18 and March 26, and northern Canada began closing on March 18. Collectively, these shifts abruptly reconfigured the boundaries between work and nonwork; more precisely, widespread “stay-at-home” requirements restricted the elements of nonwork to a narrower range of the home sphere. There is little doubt that the pandemic has reshaped the work-home interface, at least temporarily but potentially for the foreseeable future.¹ For social scientists who study role arrangements and their sometimes complicated coexistence, these rapid transformations present a unique opportunity to develop knowledge about the work-home interface, boundary dynamics, and their repercussions (Cho 2020; Rudolph et al. 2020).

Most research on the work-home interface tends to focus on the potential for inter-role conflict and its consequences for health and emotional well-being (Allen et al. 2000; Powell et al. 2019). A central theme in that narrative involves boundary-spanning demands that activate the integration of behaviors and thoughts associated with work and home roles, what some scholars have called role blurring (Glavin and Schieman 2012; Voydanoff 2005). According to Desrochers, Hilton, and Larwood (2005), role blurring is

a subjective, cognitive phenomenon involving perceived integration of work life and home life that is situated in a highly interdependent work-family context such as the simultaneous work and family demands that can be present when people bring their paid work into the home. (p. 449)

From this perspective, we delineate role blurring’s characteristic activities and normative expectations as involving the performance of work-related activities at home, attempting to multitask on work and family tasks at the same time while at home, sending or receiving communications about work-related matters outside of normal working hours, and the requirements for availability after work hours or on weekends to deal with work-related problems (Schieman and Glavin 2008, 2016; Thompson, Beauvais, and Lyness 1999; Voydanoff 2007).

Although the downsides of role blurring for instigating strains in the work-home interface are evident (Glavin, Schieman, and Reid 2011; Voydanoff 2005), little is known about the upsides. Could role blurring be a source of social status? The signaling of work devotion and “ideal worker” traits hints at this possibility (Blair-Loy 2009; Williams 2000). Understanding these upsides could provide insights into why some workers are willing to tolerate or accept the strains associated with role blurring. With these possibilities in mind, our first research question asks, What is the relationship between role blurring and social status? Then, drawing from perspectives that emphasize control over time as a job-related resource (Kelly and Moen 2007), we ask, Does schedule control moderate the relationship between role blurring and social status? Finally, we situate both of these questions in the broader context of the COVID-19 outbreak, asking, Did the onset of the pandemic alter the status dynamics of role blurring and the moderating potency of schedule control?² To address these aims, we analyze data from two waves of the Canadian Quality of Work and Economic Life Study (C-QWELS). In September 2019, we collected data from a nationally representative sample of workers to profile the quality of work and economic life, not anticipating a worldwide pandemic. Then, during a pivotal period of far-reaching shocks to work and the economy, we repeated the survey in March 2020 with another nationally representative sample.

Background

The Relationship between Role Blurring and Social Status

To assess the link between role blurring and social status, we focus on self-perceived relative standing in society, commonly referred to as subjective social status (SSS). According to Andersson (2018), “SSS holds great promise as a streamlined and effective measure of social status” (p. 622). Studies typically depict SSS as a ladder with 10 rungs that range from the lowest (1) to highest (10) level of social status (Adler et al. 2000; Cundiff et al. 2013). Scholars across different disciplines have typically analyzed SSS in their efforts to understand social patterns of health and well-being and whether SSS helps explain the health effects of objective socioeconomic status (SES) indicators such as education, occupation, and income (Adler et al. 2008; Demakakos et al. 2008; Ghaed and Gallo 2007; Scott et al. 2014; Singh-Manoux et al. 2005). It is important to study SSS, even during a worldwide pandemic, because research has linked it to health outcomes such as depression, self-rated health, obesity, and other biological risk factors (Adler et al., 2000, 2008; Demakakos et al. 2008; Goodman et al. 2001, 2003; Schnittker and McLeod 2005). Moreover, SSS is associated with these detrimental outcomes over and above more objective indicators of SES such as income, education, and occupation. Adler et al. (2008) articulated additional justification for the social scientific attention to SSS:

Objective SES measures have substantial “noise.” Because of sensitivities, income is often assessed in broad categories. Unless accompanied by measures of wealth, it may not capture people’s true economic resources. Occupation is complex; there is no consensually agreed-upon classification of jobs and characterizations of a given occupation may not reflect the conditions of a specific job. Educational attainment is relatively clear but limited since it does not take into account quality of the education. When making SSS ratings, individuals may factor in these other considerations. (p. 1036)

As described below, our analyses net out these conventional SES determinants of SSS to isolate the potential influence of role blurring. We then test competing hypotheses about the downsides and upsides of role blurring and what they predict about the link between role blurring and SSS.

The Downsides of Role Blurring Hypothesis

The main argument behind the prediction that role blurring should be associated with lower SSS emphasizes its demand elements. These dynamics represent the channeling of excessive workload that require extra commitments of time and energy, and these potentially undermine recovery efforts and optimal functioning in nonwork roles (Schieman and Glavin 2016; Voydanoff 2005). Occupational health psychologists have long emphasized the unfavorable implications of inadequate recovery from work for a range of outcomes, including self-esteem, role performance, and health (Sonnentag 2001, 2003; Sonnentag and Lischetzke 2018; Sonnentag et al. 2008; Zijlstra and Sonnentag 2006). Commonly expressed aspirations such as “balance” and “fit” echo the challenges individuals encounter in navigating the work-home interface (Allen, Cho, and Meier 2014; Moen 2015).

Border theory’s concern with the spatial, temporal, and psychological features of borders and the concept of permeability are central for understanding the potential downsides of role blurring (Clark 2000). A highly permeable border suggests that thoughts, expectations, and activities flow more easily across boundaries, and greater integration of work and home roles enables that process (Ashforth, Kreiner, and Fugate 2000; Voydanoff 2005). The proliferation of communication technologies has enhanced opportunities for role integration and facilitated the flow of boundary-spanning demands (Bittman et al. 2009; Chesley, Moen, and Shore 2003; Glavin and Schieman 2012; Voydanoff 2005). As Duxbury et al. (2007) described it, these represent “work-extending technologies” that, on one hand, might increase flexibility and the completion of remote work, but also elevates the risk for unending efforts that extend beyond the spatial, temporal, or psychological boundaries of work (Boswell and Olson-Buchanan 2007; Chesley 2014; Kelliher and Anderson 2010; Kossek and Lambert 2005; Matusik and Mickel 2011). Work-extending technologies take role integration and permeability to a new level by creating space for extrarole performance and the time commitments it entails (Chesley 2014; Duxbury, Lyons, and Higgins 2008; Matusik and Mickel 2011). The constant connectivity and availability that is emblematic of role blurring undercuts recovery (Sonnentag 2018). Collectively, these demand-laden dynamics might weaken one’s effectiveness in maintaining focused or undisrupted role performance that, in turn, diminishes one’s sense of social status. Some aspect of this diminished social status might be channeled through the process of reflected appraisals in which one’s role performance is perceived to be diminished (Asencio 2013). Less favorable reflected appraisals might diminish self-appraisals (e.g., lowered self-esteem) that undermine SSS (Jaret, Reitzes, and Shapkina 2005; Shrauger and Schoeneman 1979).

The Upsides of Role Blurring Hypothesis

Ideas about the signaling of work devotion provide a rationale for the alternative view of role blurring’s link with elevated social status. These ideas again call attention to the power of reflected appraisals (Carter and Fuller 2016), underscoring what role blurring might signal to others and the self: a commitment to work and the organization, and an alignment with ideal worker norms (Acker 1990; Blair-Loy 2009; Moen and Roehling 2005; Williams 2000). Blair-Loy’s (2003) ideas about the “work devotion schema” are apt, particularly cultural narratives about demanding work that compel all-embracing engagement; these contain powerful moral and affective motivations. A similar theme runs through Hochschild’s (1997) discoveries about the norm that “time spent on the job is an indicator of commitment” (p. 19). Role blurring activities represent “time spent on the job,” but it occurs outside of the usual spatial, temporal, and psychological parameters of the workplace.

Harkening back to Coser (1974), one can imagine “greedy” organizational imperatives behind role blurring activities and expectations. At the same time, work is a salient source of identity and values for many individuals, and this has implications for the agency behind dedicated effort and devoted service to the organization (Wharton and Blair-Loy 2002, 2006). Hochschild (1997) also emphasized this personal agency, noting that workers themselves can be “architects” of their own role enactments. This implies, for example, that individuals might choose to multitask on work and family tasks at the same time while at home, that individuals choose to send work-related e-mails after hours, and that individuals choose to be available after hours and on weekends in case a work-related problem arises. Some scholars have identified the status signals embedded in these actions. As Boswell and Olson-Buchanan (2007) observed, “staying connected after hours may be seen as a means to get ahead in the organization and profession more generally” (p. 595). Likewise, Williams, Blair-Loy, and Berdahl (2013) detected the status boost of work “beyond the clock”:

Work devotion both justifies and fuels very long work hours (Jacobs & Gerson, 2004). Whereas in the past leisure signaled elite status (“bankers’ hours”), today elite status is signaled by long hours of high-intensity work. Thus, work devotion becomes a “class act”—a way of signaling elite status (Williams, 2010, p. 6). Thus a “real professional” stays of his own volition until the job is done, in contrast to someone who just “punches the clock.” (p. 213)

Blair-Loy (2003) described elements of the work devotion schema that might represent aspects of status, such as an “adrenaline high” that comes with the trials and responsibilities of high-intensity work and the significant social relationships embedded in higher status roles. In turn, long hours and intense demands promote role blurring (Glavin and Schieman 2012; Schieman and Glavin 2008, 2016; Voydanoff 2005). In the effort to adhere to ideal worker norms, some might accept or even embrace the unwieldiness of job demands, thereby lending legitimacy to role blurring. This suggests a powerful ethic behind extrarole time and effort in exchange for the rewards of greater status (Blair-Loy 2003; Wharton, Chivers, and Blair-Loy 2008). To the extent that role blurring reflects these elements of work devotion and ideal worker norms, as well as a sense of agency, we would expect it to be associated with higher SSS, even after statistically netting out other aspects of SES and job qualities that typically also signify work devotion and effort.

The Status Dynamics of Role Blurring in the Time of COVID-19

In March 2020, as the response to the COVID-19 pandemic accelerated, governments implemented sweeping virus mitigation strategies. In Canada and countries around the world, many people were required to quickly adapt to remote work. With stay-at-home directives in effect, working at home seemed to become the “new normal.” Although this shock has undoubtedly transformed the work-home interface and the boundaries that define them (Cho 2020; Rudolph et al. 2020), we wonder, Did it alter the relationship between role blurring and SSS?

One plausible scenario is that social and economic responses to the COVID-19 pandemic intensified work-home role integration and the permeability that accompanies it, thereby escalating the demand-laden elements or expectations associated with role blurring activities. In the rapid deployment of remote work arrangements, the substance of boundary-spanning demands might have changed. Moreover, as vast swaths of workers began to work at home, role blurring’s sudden perceived pervasiveness in the population might have also diluted the distinctive status that it once carried. Before the pandemic, it is plausible that role blurring contained more elements of personal choice and agency. For example, before the pandemic, someone who responded to an e-mail at 9 p.m., beyond the temporal parameters of normal work hours, might have done so out of his or her own volition, in other words, deciding whether to log on and fire off an e-mail about a looming deadline. Such actions potentially signal to supervisors and coworkers that one is highly committed to one’s work role. In this regard, some actions associated with role blurring have positive reflected appraisals attached to them; that is, the perception of a status boost in the eyes of others signaled in the performance of work-related activities “after hours” reinforces and potentially enhances one’s own perception of SSS.

By contrast, during the pandemic, the same behavior—sending an e-mail at 9 p.m.—might be enacted less out of choice and more because of difficulties completing work-related tasks during the day because of nonwork responsibilities (e.g., childcare). The after-hours e-mail is the same behavior, but the context has modified the meaning others might give it, and this might alter the value one assigns to it. The same level of role blurring might be perceived differently when individuals have greater or lesser choice surrounding the activities. In one context, multitasking on work and family tasks might signal one’s capacity to “do it all.” In another context that diminishes agency, the same kind of multitasking activity might be performed to keep up. Role blurring therefore appears less special, to others and oneself, when an individual is required to do it, and this compulsory element might have increased during the implementation of work-from-home directives. Viewed through the lens of reflected appraisals, these processes might ultimately undermine the status points one assigns to the self.

Collectively, these ideas suggest that the onset of COVID-19 will have had a negative moderating effect; once again, however, the manifestation of this particular prediction depends on the observed direction of the main effect: it suggests either a stronger negative relationship between role blurring and SSS (the intensified downside hypothesis) or a weaker positive relationship between role blurring and SSS (the attenuated upside hypothesis). We test both possibilities and then turn our attention to schedule control as a resource and potential source of status in these processes.

The Resource and Status Elements of Schedule Control

Schedule control is central to any analyses of the work-home interface because it involves the extent to which workers are able to determine the hours they work, ranging from having no control at all to complete control over scheduling (Golden 2001; Kelly and Moen 2007; Schieman and Young 2010). The depiction of schedule control as an effective job-related resource is demonstrated in the organizational intervention research conducted by Kelly, Moen, and their colleagues (Kelly et al. 2014; Kelly, Moen, and Tranby 2011; Moen et al. 2016). The resource view predicts that schedule control should enhance work-home integration, thereby minimizing the disruptiveness of role blurring activities for nonwork roles (Allen et al. 2014; Glavin and Schieman 2012; Schieman, Milkie, and Glavin 2009). Although the direction of moderation foremost depends on the observed direction of the relationship between role blurring and SSS—that is, positive or negative—the resource view suggests that schedule control should either mitigate role blurring’s downsides or reinforce its upsides.

Part of the potency affixed to schedule control as a “resource” might also reflect its potentially independent association with social status (Keinan, Bellezza, and Paharia 2019). Although higher earnings tend to be the main job-specific reward commonly linked to higher SSS (Andersson 2018), the latitude and degree of choice provided by schedule control is another plausible source. For example, Voydanoff (2005) identified boundary-spanning resources such as control over the timing of work as an “asset” or structural advantage that facilitates further resource acquisition and enactment. Likewise, Perlow and Kelly (2014) contend that schedule control represents one element of an “accommodation model” in which employers provide it in order to enrich employees’ capacity to manage demands (Munsch, Ridgeway, and Williams 2014). When organizations provide workers with greater schedule control, it might reflect a strategy to ease competing work and family expectations (Rau and Hyland 2002) and, subsequently, boost workers’ productivity, morale, and well-being (Golden 2008; Kelly and Moen 2007).

Collectively, these arguments emphasize the resource- and status-based potency of schedule control, suggesting that it should either mitigate the downsides of role blurring or amplify the upsides of role blurring for SSS. However, the dynamics associated with schedule control must also be considered in the context of the pandemic. If the onset of COVID-19 has increased the demand-laden elements of role blurring, this might have also undermined schedule control’s protective efficacy as a resource, or even the status boost it might have provided before the pandemic. With these ideas in mind, we test the undermined resource hypothesis, which predicts a weakening in the protective or status-enhancing benefits of schedule control in the relationship between role blurring and SSS during the time of COVID-19.

Methods

Sample

To test our hypotheses, we analyze data from two nationally representative samples of Canadian workers. The first online survey (C-QWELS I) was fielded from September 19 to September 24, 2019 (n = 2,524); the second (C-QWELS II) was fielded from March 17 to March 23 (n = 2,530). All study participants are members of the Angus Reid Forum (ARF), a built and managed panel of Canadians who have agreed to participate in research. Panel participants are recruited through a variety of online and offline channels, extensively profiled, and measured to ensure accurate representation of the diversity across Canada’s adult population (http://angusreid.org).³ Sample selection for C-QWELS I and II started with creating a balanced sample matrix of the Canadian population. A randomized sample of ARF members was then selected to match this matrix to ensure a representative sample. The response rates were 42 percent and 43 percent, respectively. We weight all results according to the most current gender, age, education, and region census data to ensure broad representation of working Canadians.

Measures

SSS

SSS was measured using the MacArthur scale of SSS (Adler et al. 2000; Ostrove et al. 2000). Respondents were presented with a ladder and asked to rank themselves in terms of their standing in society; as a result, higher values indicate greater self-assessed social status. The scale ranges from the lowest value (1) to the highest value (10). This measure has a high degree of convergent and discriminant validity (Andersson 2018; Cundiff et al. 2013; Operario, Adler, and Williams 2004).

Role Blurring

We measure role blurring with five commonly used items (Glavin and Schieman 2012; Schieman and Glavin 2016; Voydanoff 2005). The first item asks, “How often do you do any paid or unpaid work at home that is part of your job?” Response choices are (1) “never,” (2) “a few times a year,” (3) “about once a month,” (4) “about once a week,” (5) “about once a week,” (6) “more than once a week,” (7) “every day.” The second item asks, “How often do coworkers, supervisors, managers, customers, or clients contact you about work-related matters outside of your normal work hours? Include any form of communication like email, text message, phone calls, etc.” Response choices are (1) “never,” (2) “occasionally,” (3) “fairly regularly but less than once a week,” (4) “once a week,” (5) “several times a week,” (6) “once a day,” and (7) “many times a day.” The third item asks, “How often do you try to ‘multitask’—that is, work on job tasks and home tasks at the same time while you are at home?” Response choices are coded (1) “never,” (2) “rarely,” (3) “sometimes,” (4) “often,” and (5) “very often.” The fourth and fifth items ask the level of agreement or disagreement with the following: “Employees where I work are often expected to take work home during nonwork hours and/or nonwork days” and “I need to be available after work hours or on weekends in case a problem at work comes up.” Response choices are (1) “strongly disagree,” (2) “somewhat disagree,” (3) “somewhat agree,” and (4) “strongly agree.” We standardized these items because of their different response choices and averaged them to create the role blurring index (C-QWELS I α = .83, C-QWELS II α = .80). Confirmatory factory analyses indicate that all five of these items load highly on one underlying construct that represents role blurring, and importantly, these factor loadings are highly similar for both the September 2019 and March 2020 surveys (see the Appendix). The eigenvalue for the first factor in the C-QWELS I is 3.04, with the second being considerably lower at .62; likewise, the eigenvalue for the first factor in the C-QWELS II is 2.85, with the second being considerably lower at .70. Overall, these items appear to have strong and consistent psychometric properties across the two study periods.

Schedule Control

Two items measure schedule control. The first is “How much control do you have in scheduling your work hours?” Response choices are (1) “none,” (2) “very little,” (3) “some,” (4) “a lot,” (5) “complete control.” The second asks the extent of agreement or disagreement with the following: “I have the schedule flexibility at work to manage my personal or family responsibilities.” Response choices are (1) “strongly disagree,” (2) “somewhat disagree,” (3) “somewhat agree,” and (4) “strongly agree.” We standardized the items because of different response choices and averaged them to create the schedule control index.

Education

Education is measured using the following question: “What is the highest level of education that you yourself completed?” We compare those with a university undergraduate degree or higher and those with less than a university undergraduate degree.

Occupation

We compare individuals in higher administrative, professional, and technical occupations with all others (e.g., sales, service, clerical, skilled labor or production).

Household Income

We compare individuals in the modal category, $50,000 to $99,999, with individuals in each of the following other categories: under $25,000, $25,000 to $49,999, $100,000 to $149,999, $150,000 to $199,999, and $200,000 or more.⁴

Financial Strain

We use three items to measure financial strain. The first two items ask the following: “How often in the past year did you have trouble paying the bills?” and “How often in the past year did you not have enough money to buy food, clothes or other things your household needed?” Response choices are (1) “never,” (2) “rarely,” (3) “sometimes,” (4) “often,” and (5) “very often.” A third item asks, “How do your finances usually work out by the end of the month? Would you say you have . . . not enough to make ends meet [coded 5], barely enough to get by [coded 4], just enough to make ends meet [coded 3], a little money left over [coded 2], or a lot of money left over [coded 1]?” We averaged these items to create the index (α = .85).

Job Pressure

We ask three questions that refer to the last three months: “How often did you feel overwhelmed by how much you had to do at work?” “How often did you have to work on too many tasks at the same time?” and “How often did the demands of your job exceed the time you have to do the work? Response choices are (1) “never,” (2) “rarely,” (3) “sometimes,” (4) “often,” and (5) “very often.” We averaged the items to create the index (α = .88).

Job Challenge

We ask level of agreement or disagreement with the following items: “My job requires that I keep learning new things,” “My job requires that I be creative,” “My job allows me to develop my skills and abilities,” and “I get to do a lot of different things on my job.” Response choices are (1) “strongly disagree,” (2) “somewhat disagree,” (3) “somewhat agree,” and (4) “strongly agree.” We averaged responses to create the job challenge index (α = .78).

Job Insecurity

We measure job insecurity using the following: “How likely is it that during the next one to two years you will lose your present job and have to look for a job with another employer or find a different line of work?” We compared those who reported “not at all likely” or “not too likely” with those who reported “somewhat likely” or “very likely.”

Work Hours

We asked the following: “How many hours do you usually work in a typical week at your job?” Response choices are coded in these groups: (1) fewer than 20 hours, (2) 20 to 29 hours, (3) 30 to 39 hours, (4) 40 to 49 hours, (5) 50 to 59 hours, and (6) 60 or more hours.

Employment Type

We compare workers in private for-profit businesses (reference) with those in three other categories: government, nonprofit organization, and self-employed or business owners.

Sociodemographic Variables

All analyses statistically adjust for gender, age, visible minority status, marital status, the number of children younger than 18 residing in the household, and region of residence. We control for these sociodemographic variables and occupation- and work-related variables listed above because of their potential influences on the focal measures being evaluated here. Given their potential links to SSS (Adler et al. 2000, 2008; Andersson 2018), it is important to net out the effects of these other factors in order to isolate the focal relationship between role blurring and SSS and any potential moderating effects of schedule control. Table 1 reports the weighted sample characteristics for both the C-QWELS I and II surveys, revealing an overall high degree of similarity between these two samples on focal variables and sociodemographic measures.

Table 1.

Weighted Descriptive Statistics for Study Variables.

	C-QWELS I (September 2019)		C-QWELS II (March 2020)
	Mean or Proportion	SD	Mean or Proportion	SD
Subjective social status	5.79	1.66	5.69	1.65
Role blurring	−.01	.78	−.01	.75
Schedule control	.01	.88	.01	.88
Bachelor’s degree or higher	.47		.43
Household income
<$25,000	.06		.08
$25,000–$49,999	.15		.14
$50,000–$99,999	.30		.30
$100,000–$149,999	.22		.22
$150,000–$200,000	.10		.10
>$200,000	.07		.06
Professional	.39		.39
Employment type
Government	.30		.26
Private for-profit	.47		.48
Nonprofit	.10		.10
Self-employed/business owner	.13		.16
Job pressure	3.20	1.09	3.16	1.06
Job challenge	3.01	.72	3.06	.71
High job insecurity	.209		.249
Work hours
<20	.08		.13
20–29	.10		.10
30–39	.33		.31
40–49	.35		.34
50–59	.08		.07
≥60	.06		.05
Gender
Female	.48		.48
Male	.51		.51
Nonbinary	.01		.01
Age	42.08	13.9	42.08	13.9
Visible minority	.14		.14
Marital status
Single, never married	.26		.26
Married	.59		.59
Living with partner	.05		.05
Separated	.03		.03
Divorced	.06		.06
Widowed	.01		.01
Number of children	.32	.47	.38	.49
Financial strain	2.29	1.05	2.29	1.03

Note: C-QWELS = Canadian Quality of Work and Economic Life Study.

Analytic Plan

In analyses that pool both C-QWELS I and II data, we use ordinary least squares regression techniques to test our hypotheses. In the base model, we regress SSS on role blurring and schedule control, with all control variables. To test if the association between role blurring and SSS differs between the September sample (before COVID-19) and the March sample (onset of COVID-19), we test an interaction between role blurring and a variable we call “COVID-19 onset” for short. We repeat this same step for the interaction between schedule control and COVID-19 onset. Then, we test a two-way interaction between role blurring and schedule control to evaluate if schedule control moderates the relationship between role blurring and SSS. Finally, we test a three-way term—role blurring × schedule control × COVID-19—to evaluate if the onset of COVID-19 changed the nature of any observed moderating effect of schedule control in the relationship between role blurring and SSS. Along with a range of sociodemographic variables (e.g., gender, age, marital status), all models control for education, income, and occupation and a set of work-related conditions such as job pressure, job challenge, insecurity, work hours, and financial strain. These statistical adjustments are important because SES and selected work-related conditions are also likely to be associated with levels of role blurring (Glavin and Schieman 2012) and SSS (Andersson 2018). By netting out these elements, we are able to more precisely isolate the relationship between role blurring and SSS and then test hypotheses about COVID-19 and schedule control as moderators.

Results

Model 1 of Table 2 shows that both role blurring (b = .177, p < .001) and schedule control (b = .156, p < .001) are independently associated with elevated levels of SSS. Model 1 adjusts for education, income, and occupation, each of which is also related positively to SSS. Among the work measures, job pressure and insecurity predict lower SSS, job challenge predicts elevated levels, and workers in government jobs have higher SSS than those in private for-profit jobs. Among the other control variables (not shown in the table), working fewer than 20 hours per week is associated with lower levels of SSS, relative to 40 to 49 hours, and those with more financial strain also report lower SSS.

Table 2.

Regression Estimates Predicting Subjective Social Status (n = 5,018).

	Model 1	Model 2	Model 3	Model 4	Model 5
COVID-19 onset	−.059 (.041)	−.061 (.041)	−.059 (.041)	−.054 (.041)	−.049 (.042)
Role blurring	.177*** (.035)	.268*** (.045)	.176*** (.035)	.177*** (.035)	.252*** (.045)
Schedule control	.156*** (.027)	.154*** (.028)	.230*** (.036)	.161*** (.027)	.221*** (.037)
Interaction terms
COVID-19 onset × role blurring		−.184** (.055)			−.152** (.056)
COVID-19 onset × schedule control			−.150** (.046)		−.125** (.047)
Role blurring × schedule control				.114*** (.030)	.141*** (.044)
COVID-19 onset × role blurring × schedule control					−.054 (.059)
SES and work-related controls
Bachelor’s degree or higher	.315*** (.047)	.317*** (.047)	.317*** (.047)	.322*** (.047)	.327*** (.047)
Household income (reference: $50,000–$99,999)
<$25,000	−.702*** (.113)	−.704*** (.112)	−.703*** (.112)	−.710*** (.113)	−.713*** (.112)
$25,000–$49,999	−.504*** (.070)	−.500*** (.070)	−.505*** (.070)	−.509*** (.070)	−.506*** (.070)
$100,000–$149,999	.352*** (.056)	.353*** (.056)	.351*** (.056)	.347*** (.056)	.347*** (.056)
$150,000–$200,000	.714*** (.069)	.715*** (.069)	.713*** (.069)	.708*** (.069)	.707*** (.069)
>$200,000	1.102*** (.097)	1.108*** (.097)	1.101*** (.097)	1.092*** (.097)	1.097*** (.096)
Professional	.327*** (.050)	.321*** (.050)	.321*** (.050)	.333*** (.050)	.322*** (.050)
Employment type (reference: for-profit)
Government	.197*** (.052)	.197*** (.052)	.195*** (.052)	.211*** (.052)	.210*** (.052)
Nonprofit	.010 (.073)	.010 (.073)	.012 (.073)	.015 (.073)	.018 (.073)
Self-employed/business owner	−.097 (.075)	−.092 (.075)	−.088 (.075)	−.128 (.076)	−.116 (.076)
Job pressure	−.062** (.023)	−.064** (.023)	−.062** (.023)	−.060** (.023)	−.061** (.023)
Job challenge	.072* (.035)	.073* (.035)	.070* (.035)	.075* (.035)	.075* (.035)
High job insecurity	−.156** (.054)	−.155** (.054)	−.157** (.054)	−.155** (.054)	−.154** (.054)

Note: Unstandardized regression coefficients with standard errors shown in parentheses. Models include all control variables (excluded for the sake of space but available upon request). COVID-19 = coronavirus disease 2019.

p < .05. **p < .01. ***p < .001.

Next, we assess if the onset of COVID-19 moderates the relationship between role blurring and SSS. The findings in model 2 suggest that it does: the coefficient for the interaction between role blurring and COVID-19 onset is negative and statistically significant (b = –.184, p < .01). This confirms that the relationship between role blurring and SSS differs statistically across the two time points—September 2019 and March 2020 (see Figure 1A)—indicating the weaker effect of role blurring during COVID-19 onset. We find similar patterns for schedule control: in model 3, its interaction with COVID-19 onset is negative and statistically significant (b = –.150, p < .01), indicating the weaker effect of schedule control during the onset of COVID-19 (see Figure 1B). Overall, across C-QWELS I and II, the coefficients for role blurring’s and schedule control’s associations with SSS decreased by about 63 percent and 57 percent, respectively.

Figure 1.

Subjective social status by levels of role blurring and schedule control before and during the coronavirus disease 2019 pandemic.

Next, we ask, Does schedule control moderate the relationship between role blurring and SSS, and if so, does that pattern generalize across C-QWELS I and II? In model 4, the two-way term (b = .114, p < .001) indicates that role blurring is associated more positively with SSS among those with greater schedule control. However, in model 5, the three-way term indicates that the role blurring–by–schedule control interaction is not statistically different across the two time points. Although we observe an overall weaker association between schedule control and SSS in March, the strength of schedule control’s moderating potency in the relationship between role blurring and SSS does not differ statistically across the time points. The left-hand side of Figure 2 shows that role blurring’s positive association with SSS is stronger among individuals who have greater schedule control before the pandemic; however, the right-hand side shows that schedule control’s moderating effect is diminished at the onset of COVID-19. Although appearing to differ, a test of the role blurring–by–schedule control interaction shows that it is not statistically different across the time points. These observations are evident even after adjusting for common determinants of SSS such as education, occupation, and income, along with other key work conditions that might also be influential.⁵

Figure 2.

The relationship between role blurring and subjective social status by levels of schedule control before and during the onset of coronavirus disease 2019.

Discussion

Sociological curiosity about role constellations and their potential internal tensions speaks to broader themes in the discipline related to social stratification, inequality, and status. The intersection of roles, especially those in the work-home interface, represents a particularly fertile domain in which to understand the ways that more macro-level social and economic dynamics play out in everyday life. Moreover, as a glaring representation of the power of macro-level changes, the onset of the COVID-19 pandemic and the rapid social and economic changes that followed have provided sociologists with an extraordinary opportunity to develop new knowledge about social role arrangements and integration, beyond the well-established orientations related to role strains and inter-role conflict.

In the present study, we identify discoveries about the ways a key element of the work-home interface, role blurring, enhances SSS. But these upsides are not equally experienced (1) across critical shock periods in the past year and (2) across workers with different levels of control over the timing of their work. First and foremost, we tested competing hypotheses about the association between role blurring and SSS. Our observations confirm the upsides of role blurring hypothesis: a positive relationship between role blurring and SSS. This pattern contradicts the demand-laden view of role blurring that is pervasive in the literature and instead more closely aligns with the suggestion that role blurring signals to the self and others a commitment to work and adherence to ideal worker norms (Acker 1990; Blair-Loy 2009; Hochschild 1997; Moen and Roehling 2005; Williams 2000). To some extent, we might interpret these findings as being consistent with the argument of Williams et al. (2013): the intensity of work that tends to accompany the work devotion schema reflects a “class act” that further reinforces the potent work ethic behind extrarole time and effort, and these extrarole performances are exchanged for the rewards of greater respect and standing in the workplace (Blair-Loy 2003; Boswell and Olson-Buchanan 2007). Through reflected appraisals, these perceptions circle back to shape self-conceptions, including social status.

And yet, despite the clear upsides for social status that seem to be linked to role blurring, the overall picture shifted considerably from September 2019. During mid-March 2020, as the COVID-19 pandemic was ramping up, the nature of role blurring’s association with status appears to have changed. We documented that the overall positive association between role blurring and SSS was weakened during the onset of COVID-19. Collectively, these patterns support the attenuated upsides hypothesis, suggesting that the qualitative nature of role blurring might have changed in ways that undermine what appeared to be its status-enhancing features, at least before the pandemic. One plausible explanation for these patterns is that the dramatic social and economic shifts increased work-home role integration and permeability, thereby intensifying the demand-laden features of role blurring and undermining choice and agency in these processes. In the hasty implementation of remote work arrangements, the “adrenaline high” that Blair-Loy (2003) described as linked to the challenges of work devotion might have come to feel more like an overdose. Moreover, with “stay-at-home” requirements in place, and remote work being primarily shifted into the home sphere, the seeming ubiquity of work-home integration might have tempered the distinctive status that role blurring once signaled. Suddenly the spatial, temporal, and psychological boundaries became more integrated—and the accompanying role permeability probably intensified—across a broader swath of workers. For a time at least, high integration and permeability in the work-home interface has seemingly become the new normal for many workers.

Discussions about the potency of schedule control are pervasive in the literature on the work-home interface (e.g., Kelly and Moen 2007; Schieman and Young 2010). Our study contributes to that discussion by finding support for the perspective that schedule control represents a job-related resource and source of social status; in our study, it specifically seems to amplify the status-based benefits of role blurring. We found that the positive relationship between role blurring and SSS was significantly stronger among individuals who possessed higher levels of schedule control. But we also demonstrated two important interrelated patterns: (1) like role blurring, the overall positive association between schedule control and SSS weakened during the COVID-19 onset, and (2) while the moderating effect of schedule control in the relationship between role blurring and SSS appears to have somewhat weakened during the early stages of the pandemic, we did not find clear evidence of a statistically significant three-way effect between role blurring and schedule control before and during the onset of COVID-19.

Before we conclude, several study limitations deserve mention. Although our surveys do not directly ask respondents if their work patterns have changed as a consequence of COVID-19 restrictions, our models repeat the same set of work- and job-related questions, and these are included in our models as statistical controls (e.g., work hours, job pressure, challenge). Moreover, we recognize that some workers continued to go to the office, while others continued to work from home. Our measure of role blurring takes these kinds of distinctions into account. In addition, if workers were not yet required to work remotely by the March data collection period, then our findings for the moderating role of the onset of COVID-19 may be on the conservative side. Additional waves of data collection will allow us to more explicitly trace changes in work location. We also acknowledge that many other aspects of work and economic life might have changed as a result of COVID-19. The C-QWELS II survey was purposely fielded early in the pandemic to capture a baseline level of experiences of workers as they stood at the edge of what has since become considerable social and economic transformations.

Some readers might also wonder about the measurement of role blurring. First, the average level of role blurring did not appear to change between September and March surveys, although it might have shifted to a greater degree in the months that followed. However, the average level of change in role blurring is not the focus of our paper; rather, we concentrate on the relationship between role blurring and SSS and (1) whether or not that relationship depends on schedule control and (2) if COVID-19 onset has altered these status dynamics. Although the construct validity of the role blurring measure holds up during the early onset of the COVID-19 pandemic, what does seem to change is the ways role blurring predicts levels of SSS. Moving forward, future inquiry can assess how these status dynamics shift as the restrictions associated with the pandemic persist and then eventually subside—and when they do, if the upsides of role blurring for SSS reemerge.

We also recognize limits to causal claims in this study. Like others, we argue that social role conditions predict levels of SSS, not the reverse. Although the alternative direction is plausible, it is difficult to imagine how one’s SSS would cause one’s client or customer or boss to send e-mails or call after work hours; likewise, it is less plausible that one’s self-placement on the social ladder predicts how much latitude one has in scheduling one’s own work hours. Moreover, it does not seem likely that a desire to claim higher status will inflate the objective level of schedule flexibility to manage personal or family responsibilities. As with other indicators of SES such as income, we believe that most of the influence in these processes accrues to SSS, not as a result of it. Nonetheless, longitudinal research that traces within-person changes over time is needed to better determine the underlying causal dynamics at play.

Finally, we wish to briefly revisit a comment we made earlier in this article related to the sensitivities in studying work-home issues during a pandemic. A recent piece by Rudolph et al. (2020) explicitly cites “work-family issues” as one of the most relevant topics for research related to the effects of the pandemic. Although we contribute to that effort, we recognize the severe economic and emotional toll of the pandemic for many Canadians, particularly those in vulnerable segments of the population. In this context, the study of role blurring and social status might seem like a trivial focus for research. However, as part of the larger C-QWELS project, we are also addressing a range of issues, including social isolation and loneliness, distress and anger, the sense of injustice about inequalities, feelings of powerlessness and alienation, job loss and insecurity, the loss of income, and so on. In the present article, our focus on role blurring and SSS evolved from our initial interest in work and social status using data from the September sample. We discovered those initial results, and then when the pandemic emerged, we collected the March data for a variety of other objectives, including to study the personal, role, and economic disruptions caused by COVID-19. In the effort to replicate the September patterns, we discovered these patterns with the March data and felt that the findings speak to core work-family issues and social-psychological processes. Questions about the conditions that shape SSS are important because of its influence on health and well-being, above and beyond objective indicators of SES. Collectively, these initial discoveries might be useful for framing patterns of change and role adjustments in the postpandemic period.

To conclude, our findings suggest that both role blurring and schedule control, at least during relatively “normal times,” appear to provide a status boost to many Canadian workers, even net of some of the most commonly observed determinants in the prior literature. However, the current times are anything but normal, and we have begun to see some early implications of the all-encompassing social and economic transformations that are currently under way. The documentation of robust patterns in such a short period of 6 months provides a compelling baseline for understanding how this collective societal shock and its aftermath have transformed the effects of role arrangements on SSS. Moving forward, we believe that the addition of qualitative data would further enable us to achieve a more nuanced understanding of the ways different workers have experienced role integration and permeability, how they have adjusted to these new arrangements, and ultimately how these relate to the ways that individuals perceive their own relative standing on the social ladder.

Footnotes

Appendix

Appendix.

Factor Loadings for Role Blurring Items in C-QWELS I and C-QWELS II.

	C-QWELS I	C-QWELS II
“How often do you do any paid or unpaid work at home that is part of your job?”	.83	.81
“How often do coworkers, supervisors, managers, customers, or clients contact you about work-related matters outside of your normal work hours?”	.82	.79
“How often do you try to “multitask”—that is, work on job tasks and home tasks at the same time while you are at home?”	.81	.81
“I need to be available after work hours or on weekends in case a problem at work comes up.”	.72	.68
“Employees where I work are often expected to take work home during nonwork hours and/or nonwork days.”	.70	.68

Note: All items are standardized because of different response choices.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Funding from the University of Toronto COVID-19 Action Initiative 2020 and Tri-Council Bridge funding supports this research (Scott Schieman, principal investigator).

ORCID iDs

Scott Schieman

Philip J. Badawy

Notes

Author Biographies

Scott Schieman is a professor of sociology and Canada Research Chair at the University of Toronto. He is the lead investigator of two longitudinal studies of Canadian workers: C-QWELS and the Canadian Work, Stress, and Health study. The former is tracking changes in employment, work, family, and well-being during the COVID-19 pandemic.

Philip J. Badawy is currently a PhD student in sociology at the University of Toronto. His research is focused on the intersection of work and family life and the implications of these dynamics for workers’ stress and health, with a specific focus on how these processes change over time.

References

Acker

Joan

. 1990. “Hierarchies, Jobs, Bodies: A Theory of Gendered Organizations.” Gender & Society 4(2):139–58.

Adler

Nancy E.

Epel

Elissa S.

Castellazzo

Grace

Ickovics

Jeanette R.

2000. “Relationship of Subjective and Objective Social Status with Psychological and Physiological Functioning: Preliminary Data in Healthy White Women.” Health Psychology 19(6):586–92.

Adler

Nancy E.

Singh-Manoux

Archana

Schwartz

Joseph

Stewart

Judith

Matthews

Karen

Marmot

Michael G.

2008. “Social Status and Health: A Comparison of British Civil Servants in Whitehall-II with European- and African-Americans in CARDIA.” Social Science & Medicine 66(5):1034–45.

Allen

Tammy D.

Herst

David E. L.

Bruck

Carly S.

Sutton

Martha

. 2000. “Consequences Associated with Work-to-Family Conflict: A Review and Agenda for Future Research.” Journal of Occupational Health Psychology 5(2):278–308.

Allen

Tammy D.

Cho

Eunae

Meier

Laurenz L.

2014. “Work-Family Boundary Dynamics.” Annual Review of Organizational Psychology and Organizational Behavior 1:99–121.

Andersson

Matthew A.

2018. “An Odd Ladder to Climb: Socioeconomic Differences Across Levels of Subjective Social Status.” Social Indicators Research 136(2):621–43.

Asencio

Emily K.

2013. “Self-Esteem, Reflected Appraisals, and Self-Views: Examining Criminal and Worker Identities.” Social Psychology Quarterly 76(4):291–313.

Ashforth

Blake E.

Kreiner

Glen E.

Fugate

Mel

. 2000. “All in a Day’s Work: Boundaries and Micro Role Transitions.” Academy of Management Review 25(3):472–491.

Bittman

Michael

Brown

Judith E.

Wajcman

Judy

. 2009. “The Mobile Phone, Perpetual Contact and Time Pressure.” Work, Employment and Society 23(4):673–91.

10.

Blair-Loy

Mary

. 2003. Competing Devotions: Career and Family among Women Financial Executives. Cambridge, MA: Harvard University Press.

11.

Blair-Loy

Mary

. 2009. “Work without End? Scheduling Flexibility and Work-to-Family Conflict among Stockbrokers.” Work and Occupations 36(4):279–317.

12.

Boswell

Wendy R.

Olson-Buchanan

Julie B.

2007. “The Use of Communication Technologies after Hours: The Role of Work Attitudes and Work-Life Conflict.” Journal of Management 33(4):592–610.

13.

Carter

Michael J.

Fuller

Celene

. 2016. “Symbols, Meaning, and Action: The Past, Present, and Future of Symbolic Interactionism.” Current Sociology Review 64(6):931–61.

14.

Chesley

Noelle

. 2014. “Information and Communication Technology Use, Work Intensification and Employee Strain and Distress.” Work, Employment and Society 28(4):589–610.

15.

Chesley

Noelle

Moen

Phyllis

Shore

Richard P.

2003. “The New Technology Climate.” Pp 220–41 in It’s about Time: Couples and Careers, edited by Moen

Phyllis

. Ithaca, NY: Cornell University Press.

16.

Cho

Eunae

. 2020. “Examining Boundaries to Understand the Impact of COVID-19 on Vocational Behaviors.” Journal of Vocational Behavior 119:103437.

17.

Clark

Sue C.

2000. “Work/Family Border Theory: A New Theory of Work/Family Balance.” Human Relations 53(6):747–70.

18.

Conger

Kate

. 2020. “Facebook Starts Planning for Permanent Remote Workers.” The New York Times, May 21. Retrieved May 30, 2020. https://www.nytimes.com/2020/05/21/technology/facebook-remote-work-coronavirus.html.

19.

Coser

Lewis

. 1974. Greedy Institutions. New York: Free Press.

20.

Cundiff

Jenny M.

Smith

Timothy W.

Uchino

Bert N.

Berg

Cynthia A.

2013. “Subjective Social Status: Construct Validity and Associations with Psychosocial Vulnerability and Self-Rated Health.” International Journal of Behavioral Medicine 20(1):148–58.

21.

Demakakos

Panayotes

Nazroo

James

Breeze

Elizabeth

Marmot

Michael

. 2008. “Socioeconomic Status and Health: The Role of Subjective Social Status.” Social Science & Medicine 67(2):330–40.

22.

Desrochers

Stephan

Hilton

Jeanne M.

Larwood

Laurie

. 2005. “Preliminary Validation of the Work-Family Integration-Blurring Scale.” Journal of Family Issues 26(4):442–66.

23.

Duxbury

Linda

Towers

Ian

Higgins

Christopher

Thomas

John A.

2007. “From 9 to 5 to 24/7: How Technology Has Redefined the Workday.” Pp. 305–32 in Information Resources Management: Global Challenges, edited by Law

Wai K.

Hershey, PA: Idea Group.

24.

Duxbury

Linda

Lyons

Sean

Higgins

Christopher

. 2008. “Too Much to Do, and Not Enough Time: An Examination of Role Overload.” Pp. 125–40 in Handbook of Work-Family Integration, edited by Korabik

Lero

D. S.

Whitehead

D. L.

London: Academic Press.

25.

Ghaed

Shiva G.

Gallo

Linda C.

2007. “Subjective Social Status, Objective Socioeconomic Status, and Cardiovascular Risk in Women.” Health Psychology 26(6):668–74.

26.

Glavin

Paul

Schieman

Scott

. 2012. “Work-Family Role Blurring and Work-Family Conflict: The Moderating Influence of Job Resources and Job Demands.” Work and Occupations 39(1):71–98.

27.

Glavin

Paul

Schieman

Scott

Reid

Sarah

. 2011. “Boundary-Spanning Work Demands and Their Consequences for Guilt and Psychological Distress.” Journal of Health and Social Behavior 52(1):43–57.

28.

Golden

Lonnie

. 2001. “Flexible Work Schedules: What Are We Trading Off to Get Them?” Monthly Labor Review 124(3):50–67.

29.

Golden

Lonnie

. 2008. “Limited Access: Disparities in Flexible Work Schedules and Work-at-Home.” Journal of Family and Economic Issues 29(1):86–109.

30.

Goodman

Elizabeth

Adler

Nancy E.

Daniels

Stephen R.

Morrison

John A.

Slap

Gail B.

Dolan

Lawrence M.

2003. “Impact of Objective and Subjective Social Status on Obesity in a Biracial Cohort of Adolescents.” Obesity Research 11(8):1018–26.

31.

Goodman

Elizabeth

Adler

Nancy E.

Kawachi

Ichiro

Lindsay

Frazier

Huang

Bin

Graham A.

Colditz

. 2001. “Adolescents’ Perceptions of Social Status: Development and Evaluation of a New Indicator.” Pediatrics 108(2):1–8.

32.

Hochschild

Arlie R.

1997. The Time Bind: When Work Becomes Home and Home Becomes Work. New York: Metropolitan.

33.

Jaret

Charles

Reitzes

Donald C.

Shapkina

Nadezda

. 2005. “Reflected Appraisals and Self Esteem.” Sociological Perspectives 48(3):403–19.

34.

Keinan

Anat

Bellezza

Silvia

Paharia

Neeru

. 2019. “The Symbolic Value of Time.” Current Opinion in Psychology 26:58–61.

35.

Kelliher

Clare

Anderson

Deirdre

. 2010. “Doing More with Less? Flexible Working Practices and the Intensification of Work.” Human Relations 63(1):83–106.

36.

Kelly

Erin L.

Moen

Phyllis

. 2007. “Rethinking the Clockwork of Work: Why Schedule Control May Pay Off at Work and at Home.” Advances in Developing Human Resources 9(4):487–506.

37.

Kelly

Erin L.

Moen

Phyllis

Oakes

J. Michael

Fan

Wen

Okechukwu

Cassandra

Davis

Kelly D.

Hammer

Leslie B.

, et al. 2014. “Changing Work and Work-Family Conflict: Evidence from the Work, Family, and Health Network.” American Sociological Review 79(3):485–516.

38.

Kelly

Erin L.

Moen

Phyllis

Tranby

Eric

. 2011. “Changing Workplaces to Reduce Work Family Conflict: Schedule Control in a White-Collar Organization.” American Sociological Review 76(2):265–90.

39.

Kossek

Ellen E.

Lambert

Susan J.

2005. “Future Frontiers: Enduring Challenges and Established Assumptions in the Work-Life Field.” Pp. 491–510 in Work and Life Integration: Organizational, Cultural, and Individual Perspectives, edited by Kossek

Ellen E.

Lambert

Susan J.

Hillsdale, NJ: Lawrence Erlbaum.

40.

MacEachen

Ellen

Polzer

Jessica

Clarke

Judy

. 2008. “‘You Are Free to Set Your Own Hours’: Governing Worker Productivity and Health through Flexibility and Resilience.” Social Science & Medicine 66(5):1019–33.

41.

Matsuik

Sharon F.

Mickel

Amy E.

2011. “Embracing or Embattled by Converged Mobile Devices? Users’ Experiences with a Contemporary Connectivity Technology.” Human Relations 64(8):1001–30.

42.

Moen

Phyllis

. 2015. “An Institutional/Organizational Turn: Getting to Work-Life Quality and Gender Equality.” Work and Occupations 42(2):174–82.

43.

Moen

Phyllis

Kelly

Erin L.

Fan

Wen

Lee

Shi-Rong

Almeida

David

Kossek

Ellen E.

Buxton

Orfeu M.

2016. “Does a Flexibility/Support Organizational Initiative Improve High-Tech Employees’ Well-Being? Evidence from the Work, Family, and Health Network.” American Sociological Review 81(1):134–64.

44.

Moen

Phyllis

Roehling

Patricia

. 2005. The Career Mystique: Cracks in the American Dream. Lanham, MD: Rowman & Littlefield.

45.

Munsch

Christin L.

Ridgeway

Cecilia L.

Williams

Joan C.

2014. “Pluralistic Ignorance and the Flexibility Bias: Understanding and Mitigating Flextime and Flexplace Bias at Work.” Work and Occupations 41(1):40–62.

46.

Olson-Buchanan

Julie B.

Boswell

Wendy R.

2006. “Blurring Boundaries: Correlates of Integration and Segmentation between Work and Nonwork.” Journal of Vocational Behavior 68(3):432–45.

47.

Operario

Don

Adler

Nancy E.

Williams

David R.

2004. “Subjective Social Status: Reliability and Predictive Utility for Global Health.” Psychology and Health 19(2):237–46.

48.

Ostrove

Joan M.

Adler

Nancy E.

Kuppermann

Miriam

Washington

A. Eugene

. 2000. “Objective and Subjective Assessments of Socioeconomic Status and Their Relationship to Self-Rated Health in an Ethnically Diverse Sample of Pregnant Women.” Health Psychology 19(6):613–18.

49.

Perlow

Leslie A.

Kelly

Erin L.

2014. “Toward a Model of Work Redesign for Better Work and Better Life.” Work and Occupations 41(1):111–34.

50.

Powell

Gary N.

Greenhaus

Jeffrey H.

Allen

Tammy D.

Johnson

Russell E.

2019. “Introduction to Special Topic Forum: Advancing and Expanding Work-Life Theory From Multiple Perspectives.” Academy of Management Review 44(1):54–71.

51.

Rau

Barbara L.

Hyland

Mary Anne M.

2002. “Role Conflict and Flexible Work Arrangements: The Effects on Applicant Attraction.” Personnel Psychology 55(1):111–36.

52.

Rudolph

Cort W.

Allan

Blake

Clark

Malissa

Hertel

Guido

Hirschi

Andreas

Kunze

Florian

Shockley

Kristen

Shoss

Mindy

Sonnentag

Sabine

Zacher

Hannes

. 2020. “Pandemics: Implications for Research and Practice in Industrial and Organizational Psychology.” Industrial and Organizational Psychology: Perspectives on Science and Practice. Retrieved July 13, 2020. https://www.siop.org/research-Publications/iop-Journal/IOP-Focal-Articles.

53.

Schieman

Scott

Glavin

Paul

. 2008. “Trouble at the Border? Gender, Flexibility at Work, and the Work-Home Interface.” Social Problems 55(4):590–611.

54.

Schieman

Scott

Glavin

Paul

. 2016. “The Pressure-Status Nexus and Blurred Work-Family Boundaries.” Work and Occupations 43(1):3–37.

55.

Schieman

Scott

Milkie

Melissa A.

Glavin

Paul

. 2009. “When Work Interferes with Life: Work-Nonwork Interference and the Influence of Work-Related Demands and Resources.” American Sociological Review 74(6):966–88.

56.

Schieman

Scott

Young

Marisa

. 2010. “Is There a Downside to Schedule Control for the Work-Family Interface?” Journal of Family Issues 31(10):1391–1414.

57.

Schnittker

Jason

McLeod

Jane D.

2005. “The Social Psychology of Health Disparities.” Annual Review of Sociology 31:75–103.

58.

Scott

Kate M.

Al-Hamzawi

Ali Obaid

Andrade

Laura H.

Borges

Guilherme

Caldas-de-Almeida

Jose Miguel

Fiestas

Fabian

Gureje

Oye

, et al. 2014. “Associations between Subjective Social Status and DSM-IV Mental Disorders: Results from the World Mental Health Surveys.” JAMA Psychiatry 71(12):1400–1408.

59.

Shrauger

Sidney J.

Schoeneman

Thomas J.

1979. “Symbolic Interactionist View of Self-Concept: Through the Looking Glass Darkly.” Psychological Bulletin 86(3):549–73.

60.

Singh-Manoux

Archana

Marmot

Michael

Adler

Nancy E.

2005. “Does Subjective Social Status Predict Health and Change in Health Status Better Than Objective Status?” Psychosomatic Medicine 67(6):855–61.

61.

Sonnentag

Sabine

. 2001. “Work, Recovery Activities, and Individual Well-Being: A Diary Study.” Journal of Occupational Health Psychology 6(3):196–210.

62.

Sonnentag

Sabine

. 2003. “Recovery, Work Engagement, and Proactive Behaviour: A New Look at the Interface between Work and Non-work.” Journal of Applied Psychology 88(3):518–528.

63.

Sonnentag

Sabine

. 2018. “The Recovery Paradox: Portraying the Complex Interplay between Job Stressors, Lack of Recovery, and Poor Well-Being.” Research in Organizational Behavior 38:169–185.

64.

Sonnentag

Sabine

Lischetzke

Tanja

. 2018. “Illegitimate Tasks Reach into After-Work Hours: A Multi-level Study.” Journal of Occupational Health Psychology 23:248–261.

65.

Sonnentag

Sabine

Mojza

Eva J.

Binnewies

Carmen

Scholl

Annika

. 2008. “Being Engaged at Work and Detached at Home: A Week-Level Study on Work Engagement, Psychological Detachment, and Affect.” Work & Stress 22(3):257–76.

66.

Thompson

Cynthia A.

Beauvais

Laura L.

Lyness

Karen S.

1999. “When Work-Family Benefits Are Not Enough: The Influence of Work-Family Culture on Benefit Utilization, Organizational Attachment, and Work-Family Conflict.” Journal of Vocational Behavior 54(3):392–415.

67.

Voydanoff

Patricia

. 2005. “Consequences of Boundary-Spanning Demands and Resources for Work-to-Family Conflict and Perceived Stress.” Journal of Occupational Health Psychology 10(4):491–503.

68.

Voydanoff

Patricia

. 2007. Work, Family, and Community: Exploring Interconnections. Hillsdale, NJ: Lawrence Erlbaum.

69.

Wharton

Amy

Blair-Loy

Mary

. 2002. “The ‘Overtime Culture’ in a Global Corporation: A Cross-National Study of Finance Professionals’ Interest in Working Part-Time.” Work and Occupations 29(1):32–63.

70.

Wharton

Amy

Blair-Loy

Mary

. 2006. “Long Work Hours and Family Life: A Cross National Study of Employees’ Concerns.” Journal of Family Issues 27(3):415–36.

71.

Wharton

Amy

Chivers

Sarah

Blair-Loy

Mary

. 2008. “Use of Formal and Informal Work Family Policies on the Digital Assembly Line.” Work and Occupations 35(3):327–50.

72.

Williams

Joan C.

2000. Unbending Gender: Why Family and Work Conflict and What to Do about It. New York: Oxford University Press.

73.

Williams

Joan C.

Blair-Loy

Mary

Berdahl

Jennifer L.

2013. “Cultural Schemas, Social Class, and the Flexibility Stigma.” Journal of Social Issues 69(2):209–34.

74.

Zijlstra

Fred R. H.

Sonnentag

Sabine

. 2006. “After Work Is Done: Psychological Perspectives on Recovery from Work.” European Journal of Work and Organizational Psychology 15(2):129–38.