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. 2026 Sep 7;18(5):e70215. doi: 10.1111/aphw.70215

Work–family conflict to unsafe pre‐work commuting: A road through sleep and affect

Fiyinfunjah Dosumu 1, Jessica A Gass 2, Charles Calderwood 3,✉
PMCID: PMC13550809  PMID: 42706680

Abstract

The potential for connections between work and commuting experiences (i.e. commuting spillover) is the subject of increasing attention in research and practice. Most commuting spillover work has focused on same‐day associations, with little attention to the potential for work‐related experiences at home to yield across‐day spillover that impairs next‐morning commuting safety. We map the process through which work–family conflict at home influences next‐morning pre‐work commute hard braking, emphasising the interceding roles of sleep and affect in across‐day commuting spillover associations. In a 2‐week daily survey study incorporating objective measures of unsafe commuting (N = 50), we observed nightly work–family conflict to influence next‐morning pre‐work commute hard braking through next‐morning positive affect extending from nightly sleep quality. We discuss theoretical implications of our findings for the commuting spillover literature and provide practical recommendations surrounding the implications of our findings for worker safety while commuting.

Keywords: affect, commuting, sleep, work recovery, worker safety, work–family conflict

INTRODUCTION

Commuting to work by car is one of the first and most commonly occurring work‐related experiences that many employees encounter each workday (Burd et al., 2021). Given the prevalence of this activity, understanding influences on commuting safety behaviours is crucial. For example, a 2017 survey by the US National Safety Council reveals that of the adult drivers that were involved in a car crash in the past 3 years, 26% reported that these crashes occurred during their commute to or from work (National Safety Council, 2017). Additionally, in a systematic review and meta‐analysis of crash injuries in the United States, Adeyemi et al. (2021) found that the rush hour commuting period, characterised by periods of higher traffic volume, was related to a 41% increase in the risk of fatal crash injury. Thus, commuting safety behaviours are critical for occupational safety and society at large.

Though the earliest foundations of organisational research on commuting centred on the workday performance implications of pre‐work commuting (Novaco et al., 1979), subsequent studies on commuting safety have focused disproportionately on the postwork commute (see Calderwood & Mitropoulos, 2021, for a review). Little attention has been given to how work‐related experiences in the home environment may spillover to influence subsequent next‐day commuting safety behaviours (Jachimowicz et al., 2021; McAlpine & Piszczek, 2023). There is thus a need to broaden the timescale (i.e. moving from within‐day comparisons to across‐day comparisons) and scope (i.e. incorporating work‐relevant experiences in the home environment) of commuting spillover research and applications.

The goal of this project is to enrich understanding of whether and how daily work–family conflict, a common and impactful work‐relevant experience in the home environment (French et al., 2022), relates to next‐morning pre‐work commuting safety. Synthesising the Effort—Recovery Model (ERM; Meijman & Mulder, 1998) with research on the cognitive and driving safety implications of negative and positive affect (NA and PA, respectively; Biss & Hasher, 2011; Eherenfreund‐Hager et al., 2017; Kanfer & Ackerman, 1996), we develop an across‐day spillover model in which nightly work–family conflict influences next‐morning NA and PA by reducing sleep quality, yielding greater next‐morning unsafe commuting (see Figure 1). Importantly, we propose that affective states indicative of insufficient recovery (i.e. higher next‐morning state NA) and affective states indicative of sufficient recovery (i.e. higher next‐morning state PA; Sonnentag et al., 2008) can increase pre‐work unsafe commuting. These predictions draw novel connections of the widely supported basic research findings on the task performance implications of state affect (e.g. Biss & Hasher, 2011; Brose et al., 2012; Compton, 2000; Kanfer & Ackerman, 1996; Sarason et al., 1996) to the applied context of commuting spillover. Furthermore, if supported, these predictions would suggest the need to attend to both intended and unintended consequences that efforts to intervene to improve next‐morning affective states (e.g. reducing state NA and increasing state PA) could have for unsafe commuting. From a practical perspective, this novel synthesis suggests that efforts to diminish work–family conflict and improve postwork recovery could have potentially countervailing and partially counterproductive influences on next‐morning pre‐work commuting safety.

FIGURE 1.

FIGURE 1

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A conceptual model of how nightly work–family conflict influences next‐morning pre‐work commute hard braking.

We aim to make two primary contributions to the literatures on commuting, work recovery, and occupational safety through our effort to understand the process linking daily work–family conflict to next‐morning pre‐work unsafe commuting. First, we aim to broaden the conversation surrounding work‐related influences on unsafe commuting beyond within‐day associations (e.g. how experiences at work today influence postwork unsafe commuting on the same day; Burch & Barnes‐Farrell, 2020) to encompass the influences of work‐relevant experiences at home that are key to understanding across‐day commuting spillover relations. The influence of variables at the work/family interface on unsafe commuting has been identified as a critical research need within the commuting literature (Calderwood & Mitropoulos, 2021), but the nascent work on this topic has focused predominately on understanding the commute as a work /home transition (Jachimowicz et al., 2021; McAlpine & Piszczek, 2023), rather than the contributions of work/family experiences to subsequent unsafe commuting. We enrich the commuting spillover literature by casting work recovery and its implications for sleep and next‐day affect (Sonnentag et al., 2008) as vital to understanding how a work‐relevant experience at home (work–family conflict) can bring about affective reactions that influence pre‐work unsafe commuting the next‐day. We focus on work–family conflict specifically because it is a frequently occurring work‐relevant experience at home (French et al., 2022) theorised to influence both the sleep (Crain et al., 2018) and affective (Judge et al., 2006) processes we develop as critical to across‐day commuting spillover within our conceptual model. This contribution of our work broadens the conceptual scope of the commuting spillover construct to encompass across‐day associations, integrates key aspects of work recovery theory into the commuting spillover literature, and yields actionable targets for future practically focused intervention efforts aimed at improving pre‐work commuting safety.

Second, we coalesce empirical findings on the relevance of affective states to commuting safety that are scattered across various literatures (e.g. organisational, transportation, and ergonomics) within our synthesis of commuting spillover and work recovery theorising (Calderwood & Mitropoulos, 2021; Meijman & Mulder, 1998). While NA and PA are typically positioned as respectively being deleterious and beneficial to organisationally valued criteria such as subjective well‐being (e.g. Wirtz et al., 2009) and job performance (e.g. Kaplan et al., 2009), the implications of these affective states for psychomotor and attentionally dependent tasks are more complex (Biss & Hasher, 2011; Brose et al., 2012; Compton, 2000; Eherenfreund‐Hager et al., 2017; Kanfer & Ackerman, 1996; Sarason et al., 1996). These potentially complex contributions of state affect are not well accounted for within the existing commuting spillover literature, which has disproportionately emphasised the relevance of daily work stressor exposure to postwork commuting safety or the relevance of stressful morning commuting to employee states and contributors to job performance upon arriving at work (Calderwood & Mitropoulos, 2021). Our work harnesses complexities in how state affect is recognised to influence acute task performance to suggest that there can be commuting safety costs to both insufficient (i.e. higher negative affect that drives off‐task attention and distraction while commuting) and sufficient (i.e. higher positive affect that yields greater distraction and attention to irrelevant information in the commuting environment) work recovery from day‐to‐day. This contribution of our work applies basic and applied knowledge on affective states generated in the cognitive psychology and transportation literatures to enrich conceptual and theoretical understanding of contributors to commuting spillover and the implications of daily work recovery for this spillover.

THEORETICAL BACKGROUND

We draw on a synthesis of the ERM (Meijman & Mulder, 1998) with theoretical and empirical perspectives to the cognitive implications of affect for task performance (Biss & Hasher, 2011; Kanfer & Ackerman, 1996) to develop our predictions. The ERM focuses on how daily work‐relevant demands yield strain reactions that are reversible through the process of recovery (Meijman & Mulder, 1998), with sleep long‐recognised as a particularly critical nightly restorative process that drives recovery (Crain et al., 2018). Although the original formulation of the ERM focused largely on the role of attentional and energetic resources in the recovery process, subsequent extensions of this model have emphasised the relevance of recovery to the restoration of affective personal resources (Sonnentag et al., 2022). The theorising we develop to justify our hypotheses specifically builds on research focused on the potential of work recovery to be relevant to affective spillover (i.e. the carryover of affect across life domains; Sonnentag & Binnewies, 2013). This research base has demonstrated that insufficient and sufficient nightly recovery is relevant to next‐morning states of activated NA and PA, respectively (Sonnentag et al., 2008). As we develop further below, higher levels of these specific affective states have shown a consistent potential to undermine both task and driving performance within the cognitive and transportation literatures (e.g. Brose et al., 2012; Compton, 2000; Hu et al., 2013; Kanfer & Ackerman, 1996; Sarason et al., 1996), which motivated our decision to focus on next‐morning affective mechanisms specifically when developing our conceptual model.

HYPOTHESIS DEVELOPMENT

The ERM (Meijman & Mulder, 1998) posits that exposure to demands engenders strain that can be reversed through the process of recovery. Empirical applications of this model have supported these contentions, while further developing understanding of how recovery unfolds over acute and longer term timescales (see Sonnentag et al., 2022, for a review). High‐quality sleep is critical to nightly recovery (Åkerstedt et al., 2009), as it allows employees to progress naturally through the phases of the sleep cycle (Barnes, 2012). Nightly work–family conflict, reflecting interference between the demands of work and nonwork roles (Greenhaus & Beutell, 1985), can be particularly detrimental to nightly sleep quality as it prolongs work‐related activation and interferes with unwinding (Crain et al., 2018). We accordingly expect that:

Hypothesis 1

Nightly work–family conflict negatively covaries with sleep quality that night.

Exploring the intersection of recovery, sleep, and affect, Sonnentag et al. (2008) extended the ERM to encompass implications of work recovery for next‐day affect, arguing that insufficient recovery yields greater next‐morning NA and that sufficient recovery yields higher next‐morning PA through alterations in nightly sleep. These affective implications of recovery and sleep were supported by these authors in an initial empirical evaluation, findings which have been bolstered in numerous subsequent studies across a range of different time intervals (e.g. day‐to‐day and week‐to‐week; Fritz et al., 2010; Virtanen et al., 2021). Based on these theoretical and empirical extensions of the ERM to the context of across‐day affective spillover, we anticipate that:

Hypothesis 2

Nightly sleep quality (a) negatively covaries with next‐morning negative affect and (b) positively covaries with next‐morning positive affect.

When considering the implications of this across‐day spillover of affect for pre‐work unsafe commuting, we apply theory and insights from basic research on affect and cognitive interference (e.g. Biss & Hasher, 2011; Kanfer & Ackerman, 1996; Sarason et al., 1996) to the commuting spillover context. Higher state NA has been theorised and empirically demonstrated to interfere with numerous cognitive and attentional processes relevant to unsafe driving (e.g. impaired working memory, a decreased ability to shift attentional focus, greater intrusive thoughts that undermine attention; Brose et al., 2012; Kanfer & Ackerman, 1996; Compton, 2000; Sarason et al., 1996). State NA has also been found to associate with unsafe driving behaviour through the influence of mind wandering, a common contributor to driver distraction (Albert et al., 2022).

Higher state PA also entails cognitive and attentional consequences relevant to unsafe driving. Extensions of the neuropsychological theory of PA and cognition (Ashby et al., 1999) demonstrate that this type of affect yields greater distractibility and impulsivity (Dreisbach & Goschke, 2004), which contributes to Biss and Hasher's (2011) suggestion that those experiencing higher state PA are ‘delighted and distracted’ (p. 1474). Additionally, based on empirical extensions of broaden‐and‐build theory (Fredrickson, 2001), PA can overly broaden the scope of attention to irrelevant information in the environment (Biss & Hasher, 2011; Eherenfreund‐Hager et al., 2017). The cognitive interference implications of both negatively and positively valenced state affect have been replicated in the context of unsafe driving, with consistent observations that state NA leads to unsafe driving (e.g. Albert et al., 2022; Bowen et al., 2020; Hu et al., 2013; Trick et al., 2004) and that state PA relates to more distracted driving (e.g. Eherenfreund‐Hager et al., 2017; Hu et al., 2013; Rhodes & Pivik, 2011). However, there have been limited efforts to extend this reasoning to the context of unsafe commuting.

Given that state NA and PA are particularly relevant to indicators of unsafe driving that result from greater levels of distraction, as an initial examination of the role of these affective variables in unsafe commuting, we focused specifically on the relevance of these affective states to the likelihood of pre‐work commute hard braking. Hard braking is widely recognised within the transportation literature to reflect more distracted driving (e.g. Haque & Washington, 2015; Harbluk et al., 2007). It is suggested that this link is due to distraction causing delayed initial braking and reaction times to stimuli (Ali & Haque, 2023). Given the aforementioned centrality of distraction to the implications of NA and PA for driving safety, hard braking is thus a particularly relevant criteria for considering the implications of these affective states on pre‐work unsafe commuting. Applying theory and research on the cognitive and behavioural implications of state NA and PA to the pre‐work commuting spillover context, we expect that:

Hypothesis 3

Next‐morning negative affect positively covaries with the likelihood of hard braking during the pre‐work commute.

Hypothesis 4

Next‐morning positive affect positively covaries with the likelihood of hard braking during the pre‐work commute.

Combining our mechanistic‐focused arguments, we expect that nightly work–family conflict undermines high‐quality sleep, manifesting in greater next‐morning state NA and lower next‐morning state PA. This affective spillover extending from the overnight recovery process is expected to have countervailing implications for next‐morning pre‐work commuting safety, in light of our expectations that (1) the increasing state NA resulting from insufficient recovery enhances the likelihood of hard braking but that (2) the diminishment of state PA resulting from insufficient recovery reduces the likelihood of hard braking. Summarising these anticipated processes, we expect that:

Hypothesis 5

Nightly work–family conflict is positively indirectly associated with the likelihood of next‐morning pre‐work commute hard braking through next‐morning negative affect extending from nightly sleep quality.

Hypothesis 6

Nightly work–family conflict is negatively indirectly associated with the likelihood of next‐morning pre‐work commute hard braking through next‐morning positive affect extending from nightly sleep quality.

METHOD

Procedure and sample

Participants were employees at a large university in the Southeastern United States located in an urban commuting environment. The study was conducted under the oversight of the IRB of this university. To be eligible for the study, participants were required to be at least 18 years old, work every weekday (Monday through Friday), work at least 32 h per week, and commute to work via car for at least 15 min each way. Seventy participants completed an online opt‐in survey including eligibility questions and demographics. Fifty‐five of these participants attended a roughly 15‐min study training session held at the end of their work week (Friday afternoon). During this study training session, after first obtaining informed consent, participants were given detailed instructions for a subsequent daily survey recording protocol, received study equipment (a smartphone preloaded with an application to detect unsafe driving [described subsequently] and a corresponding charger), and were provided with hands‐on training in how to use the smartphone application used to objectively monitor unsafe driving.

A daily survey period began on the Monday following this session, during which participants completed both a start‐of‐day survey (within 30 min before their pre‐work commute) and an end‐of‐workday survey (within 30 min before their postwork commute) for two consecutive work weeks (10 days). The start‐of‐day survey contained measures of nightly work–family conflict, nightly sleep quality, state NA, state PA, and nightly sleep quantity (the latter of which was evaluated as a potential statistical control), while the end‐of‐workday survey contained measures of daily workday experiences and affective states. Because we focused on pre‐work commuting experiences, we only included data from the start‐of‐day survey in the current study. 1 During the 2‐week daily survey period, driving behaviour was tracked objectively using the Greenroad app (Greenroad, 2023). The app uses an accelerometer‐type sensor (Musicant et al., 2010) to detect and record unsafe driving events and has successfully been used in past organisational research to objectively track unsafe commuting (Calderwood & Ackerman, 2019).

Out of 55 participants who completed the opt‐in survey and attended the study training session, we excluded five participants who completed daily surveys but did not have driving performance data available. Each of these five participants encountered technical issues that prevented accurate driving performance data recording (e.g. errors in the app running on the provided smartphone; potential user errors in starting and exiting out of the app; cell phone battery life issues). The final sample for statistical analysis consisted of 50 participants (N = 50) who completed 447 out of 500 possible start‐of‐day surveys (response rate = 89.4%). Participants were predominantly female (76.2%), roughly half of the participants were White (50.0%), and almost all participants worked an average of 40 h per week (SD = 0.93). Participants reported different job titles within the university, most commonly working as administrative assistants (18%), academic advisors (14%), or accountants (14%). The average morning pre‐work commute for participants across the 10‐day study was 32 min, though with a fairly high standard deviation (SD = 45 min) consistent with data being collected in an urban setting that may entail more variable commuting conditions (see Kluger, 1998). 2

Measures

Nightly work–family conflict

Nightly work–family conflict was measured in the start‐of‐day survey with six items from the Work–Family Conflict Scale (Carlson et al., 2000), using a 6‐point Likert‐type scale (1, Strongly disagree; 6, Strongly agree). These items were drawn from the time‐ and strain‐based conflict scales of this measure. 3 A sample item is ‘Last night, work kept me from my family activities more than I would like’ (average α across the 10 study days = .92; range = .88–.96).

Nightly sleep quality

Sleep quality was measured in the start‐of‐day survey using one item adapted from the Pittsburgh Sleep Quality Index (Buysse et al., 1989), which was ‘How would you rate your sleep quality last night?’ Participants responded to the item using a 4‐point Likert‐type scale (1, Very bad; 4, Very good).

Next‐morning NA and PA

State NA and PA were measured in the start‐of‐day survey using 10 items each (20 items total) from the Positive and Negative Affect Schedule (Watson & Clark, 1994), with participants instructed to respond to these items on a 6‐point Likert‐type scale (1, Very untrue of me; 6, Very true of me). A sample item for NA is ‘I feel irritable’ (average α across the 10 study days = .91; range = .89–.94). A sample item for PA is ‘I feel excited’ (average α across the 10 study days = .95; range = .93–.97).

Pre‐work commute hard braking

Pre‐work commute hard braking was assessed using the Greenroad app (Greenroad, 2023), a cellphone‐based application that monitors unsafe driving behaviours using an accelerometer‐based technology. We used the application to numerically quantify instances of hard braking during the participant's commute to work each morning. Participants were provided with a cell phone preinstalled with the Greenroad app, along with an in‐car cell phone holder installed unobtrusively on the dashboard of their car. We enabled a blinded profile mode which disabled all auditory and visual notifications to reduce participant reactivity. The Greenroad app has been shown to reliably and validly track unsafe driving behaviours in a wide range of validation studies (e.g. Musicant et al., 2010). Occurrences of hard braking reflect detectable instances of lateral deceleration estimated via kinematic data.

Consistent with past organisational research on commuting safety (e.g. Calderwood & Ackerman, 2019) and broader research on driving safety (e.g. Hickman et al., 2018), we coded whether an instance of each hard braking event did not (a code of 0) or did (a code of 1) occur during a given pre‐work commuting trip. Similar to the rationales offered for this decision point in other commuting and driver safety research, we observed a relative infrequency of instances of hard braking (detected during only 13.2% of pre‐work commuting trips), which would present difficulties in modelling this criterion as a continuous variable (due to the skewing of observations towards trips in which no instances of this unsafe driving behaviour occurred) or as a count variable (due to significant zero inflation caused by the preponderance of trips in which no hard braking behaviours occurred; Coxe et al., 2009). Accordingly, we elected to instead focus on the prediction of the presence or absence of hard braking on a given pre‐work commuting trip when conducting our analyses.

Nightly sleep quantity

We measured sleep quantity by asking participants to self‐report the number of hours that they slept the previous night in the start‐of‐day survey. Sleep quantity was evaluated as a potential statistical control variable.

Pre‐work commute duration

We estimated pre‐work commuting duration objectively using trip start and end time stamps available within the Greenroad app. Pre‐work commute duration was evaluated as a potential statistical control variable.

Analytic approach

We analyzed our data using a multilevel path analysis with a binary criterion variable (i.e. the presence or absence of at least one instance of hard braking during the pre‐work commute) in MPlus Version 8.3 (Muthén & Muthén, 1998–2017). We included nightly work–family conflict as a predictor of sleep quality, which in turn was modelled to predict next‐morning NA and next‐morning PA. These affective states were then modelled to predict the presence or absence of next‐morning pre‐work commute hard braking. In other words, the predictors and mediators were included in the model as continuous variables, while the pre‐work commute hard braking variable was binary and thus evaluated in a manner akin to a multilevel logistic regression with a corresponding odds ratio indexing the decrease or increase in likelihood of an instance of hard braking per one unit change in state NA or PA relative to a participant's baseline average across the 10‐day study. All pathways were modelled as fixed effects in light of our exclusive focus on the within‐person process connecting previous night's work–family conflict to next‐morning pre‐work commute hard braking. We allowed NA and PA to intercorrelate, as we expected these affective states to covary based on past empirical work (e.g. Watson et al., 1988).

We entered day of the week and day of study participation as statistical control variables for pathways indexing all hypothesised within‐person relations, based on best practice recommendations for experience sampling designs (Gabriel et al., 2019; Ohly et al., 2010) and empirical evidence (e.g. Calderwood & Ackerman, 2011; Cranford et al., 2006) suggesting that affective states and related covariates can change across the days of the week and the duration of a participant's involvement in an experience sampling study. Nightly sleep quantity was also entered as a statistical control variable for pathways indexing all hypothesised within‐person relations. We elected to focus on sleep quality rather than sleep quantity within our model based on empirical evidence that sleep quality has particular relevance to states of activated affect (e.g. greater alertness and increased feelings of energy; Buysse et al., 2007; Pilcher et al., 1997). However, we still elected to consider sleep quantity as a potential statistical control when estimating our model to account for potential shared variance (Barnes, 2012) in sleep quality and sleep quantity indicators that may be relevant to pre‐work unsafe commuting. Finally, we entered pre‐work commuting duration as a statistical control variable to account for the likelihood that longer duration trips present a greater opportunity for instances of hard braking to occur (Calderwood & Ackerman, 2019). 4 Following best practice recommendations for statistical control variables (e.g. Spector & Brannick, 2011), we estimated our model with and without statistical control variables to evaluate potential differences in the pattern of results across these comparison models. Results from the nonstatistical control version of the model are included within the OSF repository corresponding to the manuscript for interested readers.

To provide a full test of our proposed mediation hypotheses (i.e. Hypotheses 5 and 6), we used model constraints in MPlus to specify the total effect, the direct effect, the total indirect effect, and each specific indirect effect for the hypothesised serial mediation pathways. We evaluated the statistical significance of each hypothesised specific indirect effect using 95% confidence intervals (CIs) computed using the Monte Carlo method with 20,000 repetitions (Preacher & Selig, 2012). Level 1 predictors were person‐mean centred (Ohly et al., 2010), while no Level 2 predictors were included in the model. Missing data bias was accounted for via the use of full information maximum likelihood robust estimation (Enders, 2001).

RESULTS

Table 1 presents descriptive statistics, reliability estimates, and intercorrelations; Table 2 presents full multilevel path model results; and Figure 2 presents a visual representation of the alignment of these model results with the predictions encompassed within our conceptual model. We first computed an unconditional model to estimate the proportion of variance in the predictors and mediators at the within‐ and between‐person levels. 5 All variables exhibited within‐person variation (range = 43.5% for next‐morning NA to 83.6% for nightly sleep quality), justifying the use of multilevel modelling. We then computed a series of MCFA analyses to evaluate the fit of the hypothesised measurement model for self‐report measures of nightly work–family conflict, next‐morning NA, and next‐morning PA against a series of theoretically plausible alternative models. 6 Results of these analyses suggested an acceptable fit of the hypothesised measurement model (χ 2 [17] = 46.16, p < .05; CFI = .96; RMSEA = .06; SRMRwithin = .04) that was superior to the fit of a single‐factor model (χ 2 [20] = 338.34, p < .05; Δ χ 2 = 292.18, p < .01; CFI = .52; RMSEA = .19; SRMRwithin = .18), a model in which state PA and state NA loaded on a common factor (χ 2 [19] = 264.70, p < .05; Δ χ 2 = 218.54, p < .01 CFI = .63; RMSEA = .17; SRMRwithin = .15), and a model in which nightly work–family conflict and state NA loaded on a common factor (χ 2 [19] = 161.34, p < .05; Δ χ 2 = 115.18, p < .01 CFI = .79; RMSEA = .13; SRMRwithin = .11). We thus proceeded to test our hypotheses in our planned measurement model.

TABLE 1.

Descriptive statistics, internal consistency estimates, and correlations for study variables.

M SD 1 2 3 4 5 6 7 8 9
  • 1

    Nightly sleep quantity

 6.80 1.15 .50** .01 .13** .05 −.04 .00 .04 .04
  • 2

    Nightly sleep quality

 2.98 0.67 .33* −.10* .29** −.06 −.02 −.01 .05 .05
  • 3

    Nightly work–family conflict

 1.56 0.98 .06 −.30** (.92) −.19** .17** −.01 −.06 .06 .01
  • 4

    Next‐morning positive affect

 3.79 1.12 .25** .48** −.12* (.95) −.29** .06 −.17** .01 −.01
  • 5

    Next‐morning negative affect

 1.67 0.83 .13** −.31** .14** −.32** (.91) −.03 −.06 .01 .00
  • 6

    Next‐morning pre‐work commute hard braking

 0.13 0.34 −.02 .05 −.01 .07 −.06 ‐ −.01 .03 −.03
  • 7

    Next‐morning pre‐work commute duration

 31:58 44:38 −.06 −.06 −.03 .01 −.02 .08 ‐ −.02 .02
  • 8

    Day of week

 3.00 1.42 ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ .49**
  • 9

    Day of participation

 5.50 2.88 ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐
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Note: N = 50. We note that differential rates of missing data across variables measured in the start‐of‐day survey and the objectively measured unsafe driving criteria resulted in judgements of statistical significance for bivariate correlations being based on Level 1 sample sizes ranging from 414 to 448. Internal consistency estimates are provided in parentheses on the main diagonal. Within‐person correlations are above the main diagonal, while between‐person correlations are below the main diagonal.

*

p < .05, and

**

p < .01.

TABLE 2.

Empirical test of the hypothesised model.

Sleep quality State negative affect (NA) State positive affect (PA) Pre‐work commute hard braking
Predictor γ SE z γ SE z γ SE z γ SE z
Intercept (threshold) .00 .00 .98 .00 .00 .54 .00 .00 −.90 (2.28) (.25) (9.25)**
Statistical controls
Day of week −.00 .03 −.07 −.01 .02 −.48 .02 .03 .80 .16 .14 1.13
Day of participation .01 .01 .39 −.01 .01 −.40 −.02 .02 −1.11 −.06 .06 −.91
Sleep quantity .32 .05 6.87** .06 .06 1.26 −.01 .06 −.24 −.16 .16 −.99
Pre‐work commute duration −.00 .00 −6.69** −.00 −.00 −1.90 .00 .00 .28 .00 .00 1.33
Level 1 predictors
Work–family conflict −.09 .04 −2.36* .12 .04 2.71** −.17 .07 −2.64** −.01 .25 −.05
Sleep quality −.07 .08 −.82 .35 .10 3.55** −.13 .27 −.47
State NA −.14 .39 −.36
State PA .29 .17 1.75
Residual variance
Level 1 .24 .03 8.59** .25 .05 4.93** .49 .07 7.12**
Level 2 1.03 .45 2.27*
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Note: N = 50. There were 413 day‐level time points available for statistical analysis (n = 413). Reported coefficients are unstandardised. Level 1 predictors and mediators were person‐mean centred to statistically control for interindividual influences on intraindividual relationships. Pre‐work commute hard braking was modelled as a binary variable (0 = did not occur during a given pre‐work commuting trip; 1 = did occur during a given pre‐work commuting trip). State PA and State NA were allowed to correlate when estimating the model.

FIGURE 2.

FIGURE 2

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Multilevel path modelling results for empirical test of conceptual model, with bolded solid lines representing direct effects and dashed lines representing indirect effects.

In support of Hypothesis 1, nightly work–family conflict negatively related to sleep quality that night (γ = −.09, SE = .04, z = −2.36, p = .018). Hypothesis 2a was not supported, as we had no evidence to link nightly sleep quality with next‐morning NA (γ = −.07, SE = .09, z = −.82, ns, p = .410). However, Hypothesis 2b was supported, as nightly sleep quality positively covaried with next‐morning PA (γ = .35, SE = .10, z = 3.55, p < .001). Hypotheses 3 and 4 were not supported as we had no evidence to suggest that next‐morning NA (γ = −.14, SE = .39, z = −.36, ns, p = .723) or PA (γ = .29, SE = .17, z = 1.75, p = .081) related to pre‐work commute hard braking.

However, although the second‐stage pathway in the serial mediation chain for state PA (i.e. next‐morning state PA to next‐morning pre‐work commute hard braking) just missed the cutoff for statistical significance, the specific indirect effect of previous night's work–family conflict on next‐morning pre‐work commute hard braking through next‐morning PA extending from nightly sleep quality was statistically significant (γ = −.009, 95% CI = [−.07288, −.003194]). Because the signs of the direct and indirect effect pathway coefficients were in the same direction (i.e. both negative) in this analysis, we concluded that this was unlikely to be the result of suppression. Instead, despite the individual state PA—hard braking pathway not being supported, we conclude that the transmission of the indirect effect from previous night's work–family conflict to next‐morning pre‐work commute hard braking through next‐morning PA extending from nightly sleep quality was supported. This pattern of results yields support for Hypothesis 6. In contrast, we found no evidence to support an indirect effect of nightly work–family conflict on pre‐work commute hard braking through next‐morning NA extending from nightly sleep quality (γ = .000; 95% CI = [−.006161, .0314]). This pattern of results provides no support for Hypothesis 5. We note that, consistent with one serial mediation pathway being nonstatistically significant and the other pathway being rather modest in size, the total effect (γ = −.08; 95% CI = [−.528, .369]) and the total indirect effect (γ = −.07; 95% CI = [−.200, .068]) were both not statistically significant. Interestingly, the direct effect of previous night's work–family conflict on next‐morning pre‐work commute hard braking was also not statistically significant (γ = −.01, SE = .25, z = −.05, ns, p = .957), suggesting the potential that next‐morning PA extending from nightly sleep quality fully mediates the relation between previous night's work–family conflict and next‐morning pre‐work commute hard braking. 7

DISCUSSION

We sought to evaluate and establish the process through which nightly work–family conflict can extend across days to influence next‐morning pre‐work unsafe commuting by altering sleep and affect. We observed nightly work–family conflict to influence next‐morning pre‐work commute hard braking through PA extending from nightly sleep quality. Thus, although we did not support the safety‐impairing across‐day spillover of work–family conflict through next‐morning state NA extending from nightly sleep quality that we had anticipated, we did find that lower levels of work–family conflict covary with a decrement to next‐morning pre‐work commuting safety through enhancements to next‐morning state PA extending from nightly sleep quality.

Theoretical implications

Our expectation was that nightly work–family conflict would trigger processes that could undermine next‐morning pre‐work commuting safety through multiple affective pathways, with higher work–family conflict increasing the likelihood of hard braking through next‐morning state NA and lower work–family conflict enhancing the likelihood of hard braking through next‐morning PA. In both cases, we theorised that alterations in nightly sleep quality would be partially responsible for relations linking work–family conflict to next‐morning state affect, consistent with the ERM (Meijman & Mulder, 1998) and empirical extensions focusing on the role of sufficient and insufficient nightly recovery in next‐morning affective activation (Sonnentag et al., 2008). In finding support for the next‐morning state PA pathway but not the next‐morning state NA pathway within our conceptual model, we found little support for the problematic spillover of higher nightly work–family conflict to our next‐morning pre‐work commuting safety criteria of hard braking but did obtain evidence that lower nightly work–family conflict may come with the cost of across‐day spillover to next‐morning pre‐work commuting safety (i.e. a greater likelihood of hard braking) through next‐morning PA resulting from nightly sleep quality. This demonstrates a rare instance in which a process relevant to the spillover of PA across domains (i.e. from the home to the commuting environment) can yield a potential safety cost for employees, which stands in contrast to the larger literature on the well‐being (e.g. Sonnentag & Binnewies, 2013) and work–family (e.g. Ilies et al., 2009) benefits that positive affective spillover processes can entail.

In evaluating what we expected to be complementary serial mediation pathways (i.e. we hypothesised both next‐morning NA and next‐morning PA to positively covary with a greater likelihood of hard braking within these pathways), we instead did not find support for a total effect, a total indirect effect combining the two hypothesised mediated pathways, or a direct effect of previous night's work–family conflict on next‐morning pre‐work commute hard braking. Further, the direct covariation we expected between next‐morning PA and next‐morning pre‐work commute hard braking just missed the cutoff for statistical significance. The lack of an observed total effect and total indirect effect follows naturally from the lack of support for the hypothesised next‐morning NA serial mediation pathway within the test of the overall conceptual model, while the absence of an observed direct effect is suggestive of the potential for full mediation of the next‐morning PA serial mediation pathway. We continue to infer support for the latter specific indirect effect despite the presence of a nonstatistically significant pathway (i.e. next‐morning PA to next‐morning pre‐work commute hard braking) based on the generally accepted rationale outlined by Hayes and Rockwood (2017) that ‘tests of significance for the individual paths a and b are not required to determine whether M mediates the effect of X on Y, contrary to the causal steps logic that requires that both a and b are statistically significant’ (p. 43). Thus, our results may suggest that the combined mechanisms of next‐morning PA and sleep quality may be central to explaining the implications of previous night's work–family conflict for next‐morning pre‐work commute hard braking.

Work‐related experiences at home have to date received little attention in studies of commuting spillover. Our findings suggest that there is substantial nuance in unraveling across‐day commuting spillover associations, which is consistent with observations in both the work recovery (e.g. Sonnentag et al., 2022) and affect (e.g. Aitken et al., 2025) literatures that additional theoretical connective tissue is needed to move from explaining same‐day to across‐day relations. Our demonstration that lower, rather than higher, levels of work–family conflict can undermine next‐morning commuting safety through alterations to next‐morning state PA extending from nightly sleep quality expands the scope of commuting spillover research and applications to illustrate important complexities when unraveling across‐day commuting spillover associations. More specifically, because safety behaviours while driving incorporate many different attentional, psychomotor, multitasking and motivational processes that are influenced by a range of different emotional states (Matthews, 2002), our findings suggest the critical value of incorporating cognitive perspectives to state affect into studies of across‐day commuting spillover. We view this as a key step to developing theoretical understanding of whether, when, and how work‐related experiences in the home environment will have salutary or harmful implications for next‐day commuting safety criteria.

Building on this observation, we note that the theorising we developed to explain the process through which work–family conflict influences next‐day pre‐work unsafe commuting enhances understanding of the intersection of work recovery, affect, and commuting safety. Consistent with predictions from our synthesis of the ERM (Meijman & Mulder, 1998) with basic and transportation safety research on the implications of PA (e.g. Biss & Hasher, 2011; Hu et al., 2013; Rhodes & Pivik, 2011), we found that lower levels of work–family conflict engendered more frequent hard braking during the next‐morning pre‐work commute through higher levels of next‐morning PA extending from a higher quality night's sleep. State PA has most typically been positioned as a favourable contributor to (e.g. Van Hooff & De Pater, 2017) and outcome of (e.g. Sonnentag et al., 2008) sufficient work recovery. Our findings temper this research stream by showing that a PA boost directly resulting from better nightly recovery could have costs for next‐morning commuting safety. We expect this contribution of our work to be a catalyst for more nuanced considerations of the cognitive and behavioural implications of PA in research and applications on commuting safety.

Interestingly, our findings generally failed to support a role for across‐day NA spillover in next‐morning pre‐work commuting safety when considering the specific criteria of hard braking. While state NA has been consistently implicated in unsafe driving and commuting (e.g. Albert et al., 2022; Bowen et al., 2020; Calderwood & Ackerman, 2019; Hu et al., 2013), the simultaneous inclusion and modelling of both state NA and PA in these empirical studies has been relatively uncommon. Given the long‐standing recognition that state NA and PA tend to intercorrelate (Emmons & Diener, 1985), our approach of simultaneously including both types of affective states may have yielded greater insight into the unique aspects of across‐day commuting spillover driven by state PA extending from nightly sleep quality that do not overlap with the contributions of state NA. Furthermore, it is also possible that other commuting‐related experiences that we did not measure in the current study may have particular relevance to how state NA is involved in the across‐day commuting spillover process. For example, we did not give direct consideration in the current project to commute impedance, which reflects the combination of physical and subjective stressors that hinder progress during the commute (Novaco et al., 1979). This impedance is viewed as particularly relevant to NA states that arise and intensify while commuting (Novaco et al., 1990; Novaco et al., 1991). Most of the empirical work on the commute impedance construct has focused on its role in commute‐to‐work spillover (i.e. the influences of commuting experiences on at‐work criteria; Calderwood & Mitropoulos, 2021), which informed our decision not to directly consider the role of commute impedance within the conceptual model we developed here. However, giving greater consideration to whether and how commute impedance is relevant to the across‐day spillover process may help to better develop the contributions of NA states to across‐day commuting spillover.

Practical implications

Our findings surrounding the combined implications of recovery (i.e. sleep quality) and positive affect spillover in explaining how previous night's work–family conflict can yield unsafe next‐morning pre‐work commuting support the idea that work recovery has implications for safety behaviours, even when considering across‐day associations. We observed that there can be driving safety costs to lower work–family conflict through this recovery and positive affective spillover focused process. Obviously, efforts to improve employees' experiences and behaviours relevant to work–family conflict, sleep, and work recovery have myriad and potentially wide‐ranging benefits for employees (i.e. occupational health improvements) and organisations (i.e. potential job performance enhancement), so we certainly do not seek to discourage such efforts. Instead, we would advocate that employees take steps to dampen positive affect to a more neutral level (Diener et al., 2022) prior to the commute or diminish distraction while driving (Hill et al., 2020) to disrupt the mediation process connecting previous night's work–family conflict to hard braking. This step may help to ensure that both occupational health and commuting safety are maximised. Engaging in activities prior to the commute that are conducive to a relaxing state (e.g. brief mindfulness interventions; Kiburz et al., 2017) may also yield a pairing of positive affect and state relaxation that has been suggested to be useful to reducing the harmful implications of state PA on driving safety (Eherenfreund‐Hager et al., 2017).

The lack of support for direct covariation of next‐morning NA or PA with next‐morning pre‐work commute hard braking could suggest that relationships between these affective states and commuting safety behaviours exhibit nonlinear or other more complex associations than the linear relationships that were our focus in this manuscript. Based on the emotion‐performance model (Cai & Lin, 2011), it is possible that moderate levels of emotional arousal balance attention during driving performance episodes. We were underpowered in the current study to provide detailed evaluations of complex, nonlinear associations of state affect with unsafe commuting. However, future research should consider exploring potential nonlinear relationships linking state PA and NA to unsafe commuting behaviours. Practically, this research could contribute to understanding of how positive and negative emotions can be balanced while commuting, rather than focusing on eliminating or fully dampening these emotions entirely.

Limitations and future directions

One limitation of our study is a potential lack of generalisability, as participants were sampled from a single university community and primarily worked in office jobs. Furthermore, it is possible that participants who tend to drive more unsafely were less likely to participate in the study because they did not want their driving behaviours tracked objectively during the study period. In addition, by recruiting participants from only one urban geographic region, we were not able to compare the hypothesised process model across urban, suburban, and rural commuting environments. This also limited our ability to explore how other local environmental factors, such as traffic volume and weather, may inform across‐day spillover of work–family conflict to next‐day pre‐work commuting. As another potential limitation that may be relevant to generalisability, our participants were predominantly female. Although the relevance of gender in commuting spillover processes specifically has not been established to our knowledge, prior work has found that men tend to engage in riskier driving behaviours than women (e.g. Granié et al., 2020), although evidence has also shown that women may be more likely to engage in distracted driving behaviours (e.g. Bone & Mowen, 2006; Irwin et al., 2011) that could be relevant to the hard braking criterion variable that we focused on in this manuscript. We thus encourage research to better understand interrelationships of previous day work‐relevant home experiences and next‐day commuting experiences across a broader range of populations, occupational contexts and types of commuting environments.

While our study design involved repeated measures, some of our variables were measured in a manner that was functionally cross sectional in nature. More specifically, work–family conflict, affect, and sleep quality were all measured within the start‐of‐day survey. This is particularly relevant for our mediation model as a lack of temporal separation across the stages of a mediated model can lead to biased estimates (Maxwell & Cole, 2007). We did establish some degree of temporal precedence through the use of different time frames of reference in the different measures included in the start‐of‐day survey (e.g. work–family conflict the previous evening, sleep quality last night, state PA and NA experienced at the current moment). Nonetheless, these measures were still evaluated at the same time point, and so there is still potential for bias in the estimates introduced through our approach. We thus encourage future work on both within‐ and across‐day commuting spillover that allows for more complex temporal modelling of indirect effects over time.

An additional limitation of our study is that, while our study design had the advantage of objectively tracking unsafe driving behaviours that may be susceptible to self‐response distortions (Lajunen & Summala, 2003), we were unable to evaluate why instances of hard braking occurred. In other words, we were unable to determine whether it was sometimes necessary or beneficial to brake quickly in specific situations for participants (e.g. an animal suddenly ran out onto the road; a driver in front of the participant's vehicle unexpectedly slammed on their brakes). The inclusion of more comprehensive objective assessments of employee behaviours in the commuting environment (e.g. naturalistic driving data that encompasses both video‐based and kinematic sources of information; Dingus et al., 2006) may yield the potential to better understand the environmental circumstances surrounding unsafe commuting behaviours. Efforts of this nature could be critical to better understanding theoretically and practically meaningful cycles of relationships between work, home, and commuting variables that emerge within the day, across days, and at more enduring levels of analysis.

ETHICS STATEMENT

The empirical study reported in this manuscript was conducted under the oversight of the Virginia Commonwealth University IRB (Protocol Title: ‘Driving Behaviors Study’; IRB # HM20005932).

CONFLICTS OF INTEREST

The authors declare no conflicts of interest.

Dosumu, F. , Gass, J. A. , & Calderwood, C. (2026). Work–family conflict to unsafe pre‐work commuting: A road through sleep and affect. Applied Psychology: Health and Well‐Being, 18(5), e70215. 10.1111/aphw.70215

Funding information Material in this publication was supported by a grant from the National Institute for Occupational Safety and Health (NIOSH) under the Centers for Disease Control and Prevention (CDC) (Grant R01OH011772) (PI: Calderwood). The content is solely the responsibility of the authors and does not necessarily represent the official views of NIOSH or the CDC.

Data included in this manuscript have been previously presented at the 2024 Society for Industrial and Organizational Psychology Annual Conference in Chicago, Illinois, U.S.A.

ENDNOTES

1

This manuscript is drawn from a larger data collection effort that focuses on understanding both prework and postwork commuting safety.

2

Fully deidentified data and all syntax and output used to conduct and interpret statistical analyses are available at the following Open Science Framework (OSF) repository link: https://osf.io/xhejm/?view_only=99646c44ed0640a89f0454dd02656704.

3

We did not include items from the behaviour‐based conflict subscale due to concerns that we would not see sufficient within‐person variability in this form of work–family conflict at the daily level of analysis.

4

We note that, due to the presence of missing data on a small subset (n = 34) of day‐level objectively measured pre‐work commuting duration values, the effective Level 1 sample size for the statistical control version of the model was 413 (rather than 447, which would be the Level 1 sample size if based solely on self‐reported predictor variables). Accordingly, the statistical control version of the model provides a somewhat more conservative test of our hypotheses than the nonstatistical control version of the model.

5

We note that because the next‐morning pre‐work commute hard braking variable is binary, the residual within‐person variance component for this variable is estimated as a fixed quantity (equal to π2/3), which precludes the decomposition of this criterion variable into within‐ and between‐person sources of variance.

6

We faced some challenges in achieving model convergence and reliable estimates for some of our planned MCFA models at a Level 2 sample size of 50 when including all individual item‐to‐factor loading pathways, as this Level 2 sample size approaches the lower boundary at which parameter estimates and standard error estimates stabilize in an MCFA analysis (Hox et al., 2010; Maas & Hox, 2005). These issues were further exacerbated by potential nonnormality in the distribution of scores on the items and factor scales, particularly for the nightly work–family conflict and next‐morning NA measures. Accordingly, to reduce the number of parameter estimates in order to maximize our statistical power and to facilitate model convergence in estimating these models, we created 3–4 item parcels for each scale that were specified to load on each hypothesized latent factor and fixed factor variances to 1 for all MCFA analyses.

7

Because of the frequently observed intercorrelation of next‐morning PA and NA in the literature (Watson & Clark, 1994) that we had accounted for in our model testing approach, we also gave more direct consideration to the potential that next‐morning NA could be serving as a suppressor variable that is resulting in a statistically significant specific indirect effect for the next‐morning PA pathway in the model. The pattern of results that we observed involving the next‐morning PA specific indirect effect pathway was robust to the removal of next‐morning NA from the model (γ = −.009, 95% CI = [−.07311, −.003303]). We include the results of this alternative model in which next‐morning NA is removed from the model in the OSF repository corresponding to this manuscript for interested readers.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are openly available on an OSF repository at https://osf.io/xhejm/?view_only=99646c44ed0640a89f0454dd02656704.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data that support the findings of this study are openly available on an OSF repository at https://osf.io/xhejm/?view_only=99646c44ed0640a89f0454dd02656704.

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