Effects of Idle Time on Well-Being: An Experimental Study
Abstract
Abstract: Idle time is a time during which employees are unable to complete work tasks for reasons beyond their control, which negatively impacts their performance and well-being. However, it has not yet been examined whether the objective event or the subjective appraisal of this situation causes these effects. Drawing on affective events theory, we conducted an experiment (N = 338) in which we manipulated objective idle time and measured the effects on well-being, mediated by subjective idle time. We found that objective idle time positively predicted subjective idle time, which in turn was negatively related to affect but not to task satisfaction. We found indirect effects of objective idle time on affect through subjective idle time. Boredom proneness was positively and age was negatively associated with subjective idle time. The results are consistent with affective events theory and suggest that idle time is appraised as such and negatively influences affect.
Zusammenfassung: Leerlaufzeiten sind Zeiten, in denen Beschäftigte aus Gründen, die außerhalb ihrer Kontrolle liegen, ihre Arbeitsaufgaben nicht erledigen können, was sich negativ auf ihre Leistung und ihr Wohlbefinden auswirkt. Ob das objektive Ereignis oder die subjektive Bewertung dieser Situation diese Auswirkungen verursacht, wurde bisher nicht untersucht. Auf Grundlage der Theorie der affektiven Ereignisse haben wir ein Experiment (N = 338) durchgeführt, bei dem wir objektive Leerlaufzeit manipuliert und die Auswirkungen auf das Wohlbefinden gemessen haben, mediiert durch subjektive Leerlaufzeit. Wir fanden, dass objektive Leerlaufzeit die subjektive Leerlaufzeit positiv vorhersagte, die negativ mit Affekt, aber nicht mit Aufgabenzufriedenheit assoziiert war. Wir fanden indirekte Effekte der objektiven Leerlaufzeit auf den Affekt durch subjektive Leerlaufzeit. Die Neigung zur Langeweile war positiv und Alter negativ mit der subjektiven Leerlaufzeit assoziiert. Die Ergebnisse stimmen mit der Theorie der affektiven Ereignisse überein und legen nahe, dass Leerlaufzeit als solche bewertet wird und den Affekt negativ beeinflusst.
Idle time is a time when employees cannot continue working for reasons beyond their control. It is common among employees and negatively impacts individual performance and well-being (Brodsky & Amabile, 2018; Lei et al., 2019). Idle time is an unexpected interruption that results in less rather than more work activity (Schubert et al., 2023; Schweisfurth & Greul, 2023) and does not constitute a work break because employees are expected to work during idle time. Examples include a train conductor who has to wait for the restaurant crew to arrive, an assembly-line worker who has to wait for a coworker to prepare a piece of work, or an office worker whose computer suddenly installs updates when they want to continue working on a document. Brodsky and Amabile (2018) showed that idle time negatively affected objective measures of job performance, and Lei et al. (2019) found that self-reported idle time was associated with lower self-reported job performance and job satisfaction. Zeschke and Zacher (2023) reported that self-reported idle time was associated with higher boredom, which, in turn, was associated with lower job satisfaction, higher turnover intentions, and higher counterproductive work behavior – but not with prosocial work behavior. Finally, Schweisfurth and Greul (2023) found that idle time benefits employee creativity when employees physically remain at their workplace.
Previous empirical studies of idle time have focused on either objective or subjective operationalizations of idle time as well as on either objective or subjective outcomes, but not both. Brodsky and Amabile (2018) and Schweisfurth and Greul (2023) used experimental manipulations of objective idle time and measured objective performance and creativity but neither subjective experiences of idle time nor subjective outcomes, such as well-being. Lei et al. (2019), Schubert et al. (2023), and Zeschke and Zacher (2023) investigated subjective measures of idle time and its consequences, but these studies did not include objective measures. Thus, there are no investigations on how objective idle time affects the subjective appraisal of this situation and how this appraisal affects well-being. Schubert et al. (2023) theoretically distinguished between objective and subjective idle time, arguing that an undifferentiated view of idle time may confound aspects of the work environment with individual experience. Not accounting for this difference may hinder the development of effective strategies for preventing idle time and its potential negative outcomes. Furthermore, we cannot adequately describe the impact of objective idle time in the workplace until we know the relationship between objective idle time and its subjective appraisal and how they affect subjective well-being outcomes. In addition, personal dispositions may affect the appraisal of idle time and how it affects individual well-being.
The affective events theory is a relevant theoretical framework separating the situational and individual components (AET; Weiss & Cropanzano, 1996). AET proposes that work events, broadly defined as incidents that happen to people at work, elicit employee appraisals and affective reactions. These, in turn, influence behaviors (e. g., helping) and work attitudes. In addition, according to AET, dispositions directly influence appraisals and affective reactions as well as the relationship between work events, appraisals, and affective reactions. In this study, we use this framework and conceptualize objective idle time as an event that causes subjective idle time as an appraisal, which in turn is negatively associated with subjective well-being (Cropanzano & Dasborough, 2015). Well-being refers to the “overall evaluation of the quality of a person’s life from her or his own perspective” (Diener et al., 2018, p. 1). While AET mainly focuses on job satisfaction as an aspect of well-being, well-being also encompasses positive and negative affect (a hedonic measure) and task satisfaction (a cognitive measure, similar to job satisfaction; Diener, 1984). The individual appraisal of objective idle time may explain its negative impact on well-being.
Based on AET, the present study experimentally tests the proposed distinction between objective idle time and subjective idle time and their impact on well-being. We manipulated objective idle time and measured its effect on subjective idle time and well-being. We also examined how dispositions (i. e., personality, coping styles, and boredom proneness) affect the experience of idle time (Ashton & Lee, 2009; Harris, 2000).
This article contributes to the literature on idle time in at least three ways: First, we apply AET to the context of idle time. We extend the theory by considering objective idle time as an event, subjective idle time as an appraisal, and affect and task satisfaction as outcomes, following an intraindividual approach to employee well-being (Cropanzano & Dasborough, 2015). Thereby, we can better depict everyday working life, where work events influence individual appraisals that subsequently affect well-being. Second, we examine whether objective idle time and subjective idle time are distinct, and whether they have unique influences on well-being (Weiss & Cropanzano, 1996). This separation allows us to explore the extent to which objective and subjective idle time are related, and whether a self-reported measure is sufficient in surveys where idle time can only be measured after individual appraisal. Third, we examine dispositions as moderators of the relationships between objective and subjective idle time (Weiss & Cropanzano, 1996) and between subjective idle time and well-being. We thus contribute to a better understanding of individual differences in employees’ experience of idle time. These findings are also useful for future research where subjective idle time may be skewed by individual response bias and relevant dispositions could be included as control variables in survey research.
Idle Time as an Affective Eventat Work
AET explains how work events affect well-being through the appraisals and affective reactions they elicit in individuals (Lazarus & Folkman, 1984; Weiss & Cropanzano, 1996). A positive event (e. g., receiving praise for one’s work) may lead to a positive appraisal (i. e., the “goodness” of an event) and, in turn, a positive affective reaction (e. g., joy) and, thus, greater job satisfaction. Similarly, negative events (e. g., customer complaints) may lead to a negative appraisal (i. e., the “badness” of an event) and, in turn, a negative affective reaction (e. g., anger) and thus lower job satisfaction (Weiss & Cropanzano, 1996). Whether the subjective appraisal of an event is positive or negative depends not only on the event itself but also on how people evaluate it relative to their expectations. Individuals respond positively to events when they perceive a match between the actual environment and workplace standards (i. e., employee values and workplace norms, such as personal beliefs or general job autonomy) and negatively when they perceive a mismatch (Edwards et al., 1998; Weiss & Cropanzano, 1996).
One such event may be idle time at work. Objective idle time as an event refers to specific properties and characteristics of an event, namely, the length of time during which in-role tasks cannot be performed. This, in turn, triggers subjective idle time as an individual appraisal of the event. Idle time defies the workplace standard of being busy during working hours and instead disrupts action regulation (Frese & Zapf, 1994; Schweisfurth & Greul, 2023). Therefore, idle time is a mismatch between the actual environment and workplace standards, resulting in employees appraising objective idle time as subjective idle time, that is, the “employees’ psychological experience that they, in the present moment or for a certain time period, are unable to work on their core job tasks” (Schubert et al., 2023, p. 3). This appraisal may mediate the negative effects of idle time on employee well-being (Peacock & Wong, 1989; Weiss & Cropanzano, 1996; Zeschke & Zacher, 2023).
The mismatch between employee perceptions (i. e., not being able to work) and workplace standards (i. e., engaging in one’s tasks; Weiss & Cropanzano, 1996) during idle time may be similar to that between the skills (i. e., action capabilities) and challenges (i. e., action opportunities) employees face (Nakamura & Csikszentmihalyi, 2009), which are most likely to negatively impact well-being (Puranik et al., 2020). Subjective idle time can also be described as the opposite of flow experience. Flow is an acute period of uninterrupted activity (Peifer et al., 2020) during which “time flies” (Christandl et al., 2018, p. 1). Flow involves a match between high skill and high challenge and is positively associated with employee well-being and performance (Baethge & Rigotti, 2013; Nakamura & Csikszentmihalyi, 2009). Because tasks cannot be completed during idle time (Brodsky & Amabile, 2018), idle time lacks the benefits of goal achievement and flow associated with higher well-being, namely, happiness, pleasure, relief, and optimism. Failure to achieve a goal is associated with lower well-being, namely, disappointment, unhappiness, and even anxiety (Basch & Fisher, 1998; Carver & Scheier, 1990; Frese & Zapf, 1994). According to AET and empirical research, we propose that objective idle time has detrimental effects on well-being, mediated by subjective idle time.
Hypothesis 1: Objective idle time negatively affects (a) affect and (b) task satisfaction.
Hypothesis 2: Higher subjective idle time mediates the effects of objective idle time on (a) affect and (b) task satisfaction.
Dispositions, also called personality, refer to a stable pattern of thoughts, feelings, and behaviors that are characteristic of an individual (Cervone & Pervin, 2019) and shape their experiences and behaviors. Because of their dispositions, some people are better suited to certain situations than others (Rauthmann & Sherman, 2020), and different employees have different appraisals of similar situations (Li et al., 2010). AET posits that individual dispositions could directly affect subjective idle time as an appraisal (Luhmann et al., 2021; Weiss & Cropanzano, 1996) and further affect the relationships between objective and subjective idle time and between subjective idle time and well-being (Colbert et al., 2004; Haehner et al., 2022). In the context of idle time and well-being, the personality facets of emotionality, extraversion, conscientiousness, and openness (Ashton & Lee, 2007, 2009) as well as the more specific dispositions of accommodation, assimilation, and boredom proneness (Hanfstingl et al., 2022; Tam et al., 2021) are of particular interest. We did not assume honesty-humility and agreeableness as moderators because they reflect how individuals typically relate to others rather than their perceptions, coping mechanisms related to time, affective responses, and overall well-being (Aghababaei & Arji, 2014). Because of word count restrictions of the journal, we only present our formal hypotheses on dispositions; the theoretical justifications for these hypotheses can be found in the electronic supplementary materials (ESM 1).
Hypothesis 3: Emotionality strengthens (a) the positive effect of objective on subjective idle time and the negative effects of subjective idle time on (b) affect and (c) task satisfaction.
Hypothesis 4: Extraversion strengthens (a) the positive effect of objective on subjective idle time and the negative effects of subjective idle time on (b) affect and (c) task satisfaction.
Hypothesis 5: Conscientiousness strengthens (a) the positive effects of objective on subjective idle time and the negative effect of subjective idle time on (b) affect and (c) task satisfaction.
Hypothesis 6: Openness to new experiences strengthens (a) the positive effect of objective on subjective idle time and the negative effects of subjective idle time on (b) affect and (c) task satisfaction.
Hypothesis 7: Assimilation strengthens (a) the positive effect of objective on subjective idle time and the negative effects of subjective idle time on (b) affect and (c) task satisfaction.
Hypothesis 8: Accommodation buffers (a) the positive effect of objective on subjective idle time and the negative effects of subjective idle time on (b) affect and (c) task satisfaction.
Hypothesis 9: Boredom proneness strengthens (a) the positive effect of objective on subjective idle time and the negative effects of idle time on (b) affect and (c) task satisfaction.
Method
Open Science
The design and hypotheses of this study were preregistered (see https://aspredicted.org/54q7a.pdf).
Study Design
We conducted an experiment to test our hypotheses. First, we explained to participants that we would be evaluating a new method of measuring personal characteristics with a new qualitative research approach in which participants are given sentence starters, and that these would be compared to their answers in a single-choice questionnaire. We told them their task in the study was to participate until the end of the survey and to complete everything as conscientiously and honestly as possible.
We first measured the HEXACO personality scales (Ashton & Lee, 2009) and then asked participants to answer a total of 24 predetermined open-ended questions in sentence form that were supposed to also measure the HEXACO dimensions. We asked participants to formulate statements about themselves they believed to be true. They had to include at least one of the keywords presented to them in each sentence and to avoid spelling mistakes. An example was “Please use the keywords ‘manipulate,’ ‘manipulative,’ ‘flatter,’ or ‘pretend!’” (to measure sincerity as a facet of honesty-humility). After the first eight sentences, we measured baseline affect and task satisfaction with the study. After another eight sentences, we presented participants with the information that artificial intelligence (cover story) would generate new sentence starters based on their responses in the first 16 sentences. This was our three-group experimental manipulation: The control group experienced a very short idle time of 5 seconds to ensure comparability with the experimental groups, whereas the two experimental groups experienced 5 minutes (medium idle-time condition) and 10 minutes (long idle-time condition) of idle time, respectively. These times exceed tolerable online wait times and can elicit changes in personal affect and well-being (Efrat-Treister et al., 2020; Nah, 2004). To avoid biasing the results, we instructed the participants to stay on the page in their browser that informed them that new sentence starters were being generated and to resume the study as soon as the “Continue” button appeared. This ensured that participants actually experienced idle time. Then, participants answered the final eight open-ended questions and reported again on their well-being. Finally, we debriefed participants about the actual purpose of the study. Mean survey completion time was M = 38.1 minutes (SD = 14.7); the differential results for the different experimental groups are reported in the ESM 3.
Participants
Our final sample consisted of N = 338 employees working at least 20 hours per week, 56.2 % of whom were female. Participants had a mean age of M = 45.7 years (SD = 12.0, range = 19 – 72 years) and were recruited by the online panel provider Bilendi. Prior to data collection, we conducted a power analysis using G*Power software (Faul et al., 2009). Expecting a small effect (f2 = 0.1), we calculated a sample size of N = 311 to achieve a power of .95 and planned for a final sample size of 320 to 350 participants. A total of 479 employees began the questionnaire, 141 of whom did not complete it. Before assignment to an experimental condition, 107 participants dropped out, and after the assignment, 34 participants dropped out. Of these, none were assigned to the control condition, six to the medium, and 28 to the long objective idle-time condition. This resulted in a final sample of N = 338. Because of a programming error, only N = 303 participants were presented with the boredom proneness items.
Materials
We recorded the responses on Likert scales ranging from 1 (strongly disagree) to 5 (strongly agree), unless otherwise noted.
Idle Time
For objective idle time, we used the three different experimental groups as predictors, namely, short (5 seconds), medium (5 minutes), and long (10 minutes) objective idle-time conditions.
To measure subjective idle time, we used three self-developed items based on the definition by Brodsky and Amabile (2018). The 3 items were “During the study, I experienced a phase where I was unable to continue,” “… there was a period when I was unable to make progress,” and “… I was unable to continue working because of reasons outside of my control.” Reliability was good with McDonald’s Omega (Hayes & Coutts, 2020) Ω = .88 (see Table S1 in the ESM 2).
Affect
We used a 16-item affective experience measure (Kessler & Staudinger, 2009) before and after the experimental manipulation. We used this scale because it consists of validated German items, is an established measure of well-being, and has the advantage of distinguishing whether idle time affects high or low arousal positive and negative affect. We measured high arousal positive affect (sample item: “At the moment, I feel enthusiastic”), low arousal positive affect (“… serene”), high arousal negative affect (“… annoyed”), and low arousal negative affect (“… without energy”) with 4 items each. Reliability was good for both pre- and post-manipulation affect, with values ranging from Ω = .88 for high arousal negative affect to Ω = .97 for low arousal positive and negative affect (see Table S1 in the ESM 2).
Task Satisfaction
We measured task satisfaction as an indicator of state well-being (Diener, 1984; Taber & Alliger, 1995) before and after the manipulation with 4 items adapted to the study context, such as “How enjoyable did you find the study?” (Weiss & Nowicki, 1981). Reliability was good, with Ω = .87 before and Ω = .89 after the manipulation (see Table S1 in the ESM 2).
Personality
We measured six facets of personality with the 60-item HEXACO (Ashton & Lee, 2009), with each dimension measured by 10 items. Sample items were: “I wouldn’t use flattery to get a raise or promotion at work, even if I thought it would succeed” (honesty-humility), “I sometimes can’t help worrying about little things“ (emotionality), “I prefer jobs that involve active social interaction to those that involve working alone” (extraversion), “I rarely hold a grudge, even against people who have badly wronged me” (agreeableness), “I often push myself very hard when trying to achieve a goal” (conscientiousness), and “I’m interested in learning about the history and politics of other countries” (openness to experience). Reliability was not ideal but comparable to the original scales (Ashton & Lee, 2009) (Ωemotionality = .73, Ωconscientiousness = .75, Ωextraversion = .76, Ωopenness = .75). Two HEXACO dimensions, namely, agreeableness (Ω = .67) and honesty-humility (Ω = .69) showed somewhat worse reliability (see Table S1 in the ESM 2).
Coping Styles
To measure accommodation and assimilation, we used 10 items for each subscale containing items such as “In general, when I have to do something that’s really important to me and it’s really difficult, I usually accept that I cannot achieve it” (accommodation) and “In general, when it turns out that I can’t do something that’s really important to me, I usually try to deal with the barriers that are getting in my way” (assimilation; Haratsis et al., 2015). Reliability was good (Ωaccommodation = .88 and Ωassimilation = .91; see Table S1 in the ESM 2).
Boredom Proneness
We measured boredom proneness with 8 items, such as “Many things I have to do are repetitive and monotonous” (Struk et al., 2017). Reliability was good with Ω = .90 (see Table S1 in the ESM 2).
Age
We used age (in years) as an objective demographic measure that is easy to measure and may affect how employees experience (idle) time (Wittmann & Lehnhoff, 2005).
Data Analysis
To analyze the data, we used hierarchical structural equations using lavaan 0.6 – 12 for R (Gana, 2018; Rosseel, 2012, 2021). We dummy-coded the experimental idle time groups as predictors using two separate dummy variables: “medium” and “long.” Participants in the medium and long objective idle time groups were coded as “1” in their respective variables and as “0” in the respective other variable. This coding allowed the experimental groups to be compared separately with the short idle time (control) group (coded as “0” in the two dummy variables) in a single model, preventing the accumulation of alpha errors (Ryffel, 2017). We used subjective idle time as the mediator and well-being as the outcome. Controlling for the respective baseline measures of well-being (i. e., affect and task satisfaction, respectively) allowed us to predict outcome changes. In additional models, we separately added the dispositions as moderators of the predictor-mediator and the mediator-outcome relationships. We used bootstrapped confidence intervals based on 1,000 Monte Carlo simulations (Harrison, 2010) because they are more reliable than p-values.
Results
Descriptive Statistics
Table S1 in the ESM 2 presents the means, standard deviations, internal scale consistency estimates, and correlations. Subjective idle time negatively correlated with age (r = –.17, p < .01), conscientiousness (r = –.19, p < .01), and extraversion (r = –.15, p < .01), and positively correlated with boredom proneness (r = .27, p < .01) and negative affect (rhigh arousal = .17, p < .01, rlow arousal = .22, p < .01). Surprisingly, subjective idle time was negatively related to task satisfaction both before (r = -.24, p < .01) and after the manipulation (r = -.21, p < .01).
Confirmatory Factor Analysis
Confirmatory factor analysis for the focal variables supported our assumption of a six-factor structure for subjective idle time, high and low arousal positive and negative affect, respectively, and task satisfaction (χ2(59, N = 338) = 504.965, p < .001, CFI = .962, RMSEA = . 069, SRMR = .044). The model fit the data better than a one-factor, three-factor, and four-factor solution. Confirmatory factor analysis further supported that objective and subjective idle time were distinct (see Table S2 in the ESM 3).
Manipulation Check
Subjective idle time differed significantly between the groups, which was supported by the results of a statistically significant multiple regression (R2 = .06, F(2, 335) = 12.05, p < .001). Medium (B = 0.61, p < .001) and long objective idle time (B = 0.76, p < .001) significantly predicted subjective idle time. There was no significant difference between the medium and long objective idle-time condition regarding subjective idle time (t(223.09) = 0.81, p = .419).
Hypothesis Tests
Using a mediated structural equation model, we first examined the effects of objective and subjective idle time on the outcomes, controlling for their respective baseline values (Model 1). Model fit was acceptable (χ2(25, N = 338) = 111.477, p < .001, CFI = .956, RMSEA = .101, SRMR = .076). The RMSEA was above the recommended cutoff of .06 (Hu & Bentler, 1999); however, Kenny et al. (2015) recommend not using the RMSEA when a model has few degrees of freedom to avoid falsely rejecting a model.
To test Hypothesis 1, whether objective idle time has negative effects on (a) affect and (b) task satisfaction, we examined the total effects (i. e., the overall impact of objective idle time on the outcomes), considering both the direct and indirect effects in Model 1. We found a positive total effect only for medium objective idle time on low arousal negative affect (B = 0.19, SE = 0.09, 95 % CI [0.03; 0.38]). Therefore, Hypothesis 1 was not supported for most of the assumed effects.
Hypothesis 2 states that the effects of objective idle time on (a) affect and (b) task satisfaction are mediated by higher subjective idle time. To this end, we analyzed the indirect effects of objective idle time on the outcomes through subjective idle time (Model 1). For both experimental objective idle-time conditions, we found an indirect negative effect on positive affect and an indirect positive effect on negative affect for both high and low arousal (see Table 1). Objective idle time had no indirect effect on task satisfaction. Overall, Hypothesis 2a was fully supported by the data, whereas Hypothesis 2b was not supported.
Hypotheses 3 to 9 state that dispositions moderate the positive effect of objective on subjective idle time and the negative effects of idle time on affect and job satisfaction. We calculated separate models in which these dispositions were specified as moderators of both these relationships. With CFIs and TLIs well below .95 and RMSEAs well above .06, none of the moderation models fit the data and were, therefore, inconclusive (Hu & Bentler, 1999). Accordingly, Hypotheses 3 to 9 were not supported. Because of the word-count restrictions imposed by the journal, the detailed results are shown in Tables S3 to S9 in the ESM 3.
Exploratory Results
Consistent with propositions of AET, we further examined whether dispositions moderated only the relationship between objective idle time as an event and subjective idle time as an appraisal (a-path) but not the relationship between subjective idle time and well-being outcomes (b-path; Weiss & Cropanzano, 1996). However, the fit indices of these supplemental models were also insufficient to be interpreted (see Tables S3 to S9 in the ESM 3).
We further examined how dispositions directly influenced subjective idle time as an appraisal, as suggested by AET (Weiss & Cropanzano, 1996). To test the direct effects of dispositions on subjective idle time, we used the baseline model (Model 1) and stepwise added boredom proneness in Model 2 as a predictor of subjective idle time. In Model 3, we added the HEXACO dimensions and the coping styles to examine their incremental validity to boredom proneness (Hunter et al., 2016; Sackett & Lievens, 2008), and in Model 4, we added age as an easily collected measure that influences time perception and, possibly, subjective idle time. Some research suggests that older individuals report that time passes more quickly (Wittmann & Lehnhoff, 2005), while other research suggests the opposite (Wearden, 2016). We found that boredom proneness was positively associated with subjective idle time (B = 0.39, SE = 0.09, 95 % CI [0.22; 0.56]; see Model 2 in Table 2 and Figure 1). The effect of boredom proneness on subjective idle time remained after controlling for all personality dimensions and coping styles (see Model 3 in Table 2) but not when age was added to the model (see Model 4 in Table 2). When we controlled for boredom proneness, none of the personality dimensions and coping styles were significantly associated with subjective idle time (see Table 2). We found that age was negatively associated with subjective idle time (B = –0.01, SE = 0.01, 95 % CI [–0.02; –0.00]) beyond all other dispositions (see Model 4 in Table 2).
Finally, we examined whether faster employees experienced idle time differently than slower employees (Table S12 in the ESM 3), whether idle time affected speed after the experimental manipulation (Table S13 in the ESM 3), whether the effect of objective idle time on task satisfaction was sequentially mediated by subjective idle time and affect (Table S14 in the ESM 3), and whether subjective idle time was the outcome of objective idle time, mediated by affect and task satisfaction (Table S15 in the ESM 3). The models either did not fit the data or did not return statistically significant results. Because of space constraints, the theoretical derivation and results are presented in the ESM 1.
Discussion
In this study, we used AET to examine how objective idle time as a work event influences subjective idle time as an individual appraisal and how this, in turn, is related to changes in well-being. We found that objective idle time caused subjective idle time, which was associated with a decrease in positive affect and an increase in negative affect, but no change in task satisfaction. The effects of objective idle time on affect were mediated by subjective idle time. Poor model fits did not allow us to interpret whether individual dispositions moderate these relationships. However, we found that boredom proneness showed a positive and age a negative direct relationship with subjective idle time.
Theoretical and Practical Implications
This study contributes to the growing literature on idle time (Brodsky & Amabile, 2018; Lei et al., 2019; Schubert et al., 2023) and AET (Cropanzano & Dasborough, 2015; Weiss & Cropanzano, 1996; Zeschke & Zacher, 2023). First, we tested the assumptions of AET and the distinction proposed by Schubert et al. (2023) by examining the relationship between objective idle time as an event and subjective idle time as an appraisal. We found that they are related but distinct constructs. We know how much idle time participants actually experienced and how they appraised it. Still, we can only partially say what predicts the subjective experience of idle time because the experimental manipulation of 5 or 10 minutes of objective idle time explained only little variance in the subjective appraisal of idle time. Furthermore, 5 and 10 minutes of idle time did not result in different appraisals. This has implications for idle-time research because reports of subjective idle time represent only part of the objective reality, that is, objective idle time (Schubert et al., 2023). Subjective idle time is likely to be biased by factors other than the experience of idle time itself, such as emotionality, motivation, or thought speed (Allman et al., 2014; Donaldson & Grant-Vallone, 2002).
The second theoretical contribution of this study is the appraisal of idle time and its mediating effect in the relationship between objective idle time and well-being. As little as 5 minutes of idle time worsened affect through the subjective experience of idle time, whereas task satisfaction was not affected in this study. This may be fundamentally different for actual job satisfaction over longer periods and for meaningful jobs to employees (Judge & Klinger, 2020). The negative effects of (subjective) idle time are likely to be even more pronounced in the field (Schubert et al., 2023; Zeschke & Zacher, 2023).
Third, based on the AET (Weiss & Cropanzano, 1996), we examined whether disposition directly influences subjective idle time as an appraisal and a moderating influence on the relationships between objective and subjective idle time and between subjective idle time and well-being. We could not conclude the moderating role of dispositions because of insufficient model fits. However, boredom proneness and age were directly related to subjective idle time above and beyond personality and coping styles. These findings support the direct path of dispositions in the AET but not the moderating path (Junça‐Silva et al., 2021; Weiss & Cropanzano, 1996). When accounting for the positive relationship of boredom proneness and the negative relationship of age with subjective idle time, the positive effect of objective idle time on subjective idle time and the negative effect of subjective idle time on affect were still present. This suggests that subjective idle time is important, but that boredom proneness and age may play an important role when examining the effects of idle time on individuals (Brissett & Snow, 1993; Tam et al., 2021) and should be treated as essential components in idle time research. Participants reported different subjective idle time, regardless of the amount of objective idle time. This suggests that they had different response tendencies (Furnham, 1986). Because surveys often cannot directly assess objective work conditions and events, future studies investigating idle time may control for the influences.
In addition to these theoretical implications, the study also makes some practical contributions. Experiencing idle time has a detrimental effect on affect and, therefore, employees and managers should be interested in avoiding idle time at work. Further, dispositions may influence how individuals report idle time. In our study, younger employees and employees prone to boredom reported higher levels of subjective idle time regardless of the objective amount of idle time. Employees may learn to cognitively reframe idle time as breaks and opportunities for recovery (Lyubykh et al., 2022; Nakamura & Csikszentmihalyi, 2009) rather than as idle or boring.
Limitations and Future Research
This study has some limitations that provide potential for future studies. First, subjective idle time appears to be difficult to elicit. Although participants experienced different amounts of objective idle time and the scale reliability of the subjective idle-time scale was good, they did not necessarily appraise this situation as such and reported only low levels of idle time. We did not test whether other influences, particularly from the job itself, such as identification with the job, supervisor support, or the reasons for idle time, may also play a role in how idle time is experienced (Haarhaus, 2016; Schubert et al., 2023). Waiting for feedback from a manager may affect employees differently than waiting for a computer update to finish. In addition, subjective idle time was positively related to high arousal negative affect before experiencing idle time, suggesting that dissatisfied individuals are more likely to report idle time. However, using subjective idle time as an appraisal measure, we found empirical support for the AET (Weiss & Cropanzano, 1996).
Measuring idle time in the workplace, where individuals are more likely to identify with their work goals than in an experiment, may be a promising approach (Miscenko & Day, 2016). In the field, idle time could lead to further negative and positive outcomes. Subjective idle time may represent the appraisal that one’s own skills are greater than the demands needed in a situation (Nakamura & Csikszentmihalyi, 2009). This could lead to negative affective reactions, such as boredom or mental satiation (i. e., being fed up with a situation; Mojzisch & Schulz-Hardt, 2007; Zeschke & Zacher, 2023), but also to positive affective reactions, such as recovery experiences (Peifer & Tan, 2021). However, when this situation becomes chronic, employees may feel underutilized, their work may feel like a waste of time, their needs may not be met, and job satisfaction may suffer (Cham et al., 2021; Schaffer, 1953). Promising approaches to investigate this include daily-diary studies, where researchers would ideally measure the amount of idle time objectively and subjectively and well-being outcomes (Ohly et al., 2010). A random intercept cross-lagged panel model could then examine the lagged effects in both directions (Mulder & Hamaker, 2020).
Second, in our study, we did not assess what the participants did once idle time occurred. It remains unclear what employees do when faced with idle time and how this affects the relationship between idle time and well-being. Individuals may use different strategies to buffer the negative effects of idle time, as Schubert et al. (2023) suggested. Participants were also unable to avoid idle time. Although we did not find that employees slowed down their work after experiencing idle time, suggesting that work stretching (i. e., working slower in the face of impending idle time) did not occur, this may be different in the field (Brodsky & Amabile, 2018; Schubert et al., 2023). Furthermore, there was only one task present that individuals were not allowed to work on, but in the field, when other tasks are present, employees may work on them but still think about the interrupted task (Schweisfurth & Greul, 2023; Syrek et al., 2017). Future studies could examine how proactive strategies can prevent idle time and what strategies individuals use to cope with idle time (Schubert et al., 2023), for example, in longitudinal studies.
Finally, we demonstrated the short-term negative effects of idle time at the individual level. Future research could consider additional features of AET (Weiss & Cropanzano, 1996), including work environment features and behavioral outcomes. Event system theory (Morgeson et al., 2015) expands on these assumptions, stating that events are influenced by and affect multiple levels within an organization. Within these frameworks, a focus on more than the individual level, namely, team, organizational, and environmental levels, is necessary to adequately describe the circumstances, antecedents, and consequences of idle time. Then, the long-term consequences of idle time could also be adequately assessed. For example, it may be interesting to examine how an employee who has not been able to work on a task for some time reacts when faced with a choice at the end of the workday: stay late to complete the task or adhere to their work hours, perhaps failing to meet their manager’s expectations. This suggests that idle time may also affect later performance and well-being (Christandl et al., 2018; Schweisfurth & Greul, 2023). Future studies should conduct workplace surveys over longer periods (e. g., diary studies) and consider different influences on and of idle time, such as managerial or team influences, as these are working conditions that affect work events.
Conclusion
Objective idle time is an event at work that unfolds its negative effects on affective well-being when subjectively appraised as such. Objective idle time and subjective idle time are highly related but not the same. Boredom proneness was positively associated with subjective idle time, whereas age was negatively associated with subjective idle time.
Electronic Supplementary Materials
The electronic supplementary material is available with the online version of the article at https://doi.org/10.1026/0932-4089/a000422
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