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Abstract

Drawing on the job demands-resources (JD-R) model and affective events theory (AET), the present study used the Job Resource Scale, Positive and Negative Affect Schedule, and State Job Performance Scale to examine the relationship between daily job resources and job performance, focusing on how emotions mediate this relationship from a within-person perspective. Fifty-eight full-time counselors from two universities in China were recruited by convenient sampling. We used an experience sampling method, collecting two surveys per day for 10 consecutive workdays (N = 580 full day-level data points). The sample consisted of 63.8% women and 36.2% men, and their average age was 32.26 years (SD = 3.49). Our multilevel path analysis results showed that: (1) day-level job resources are positively related to day-level job performance, day-level positive emotion, negatively related to day-level negative emotion; (2) day-level positive emotions are positively related to day-level job performance, while day-level negative emotions are not related to day-level job performance; (3) day-level positive emotions mediate the positive relationship between day-level job resources and job performance, whereas day-level negative emotions did not show a significant mediating effect. The current study provides a within-person perspective for testing the applicability of theoretical models and serves to inspire practitioners in the domain of university management.

Plain language summary

Emotions mediate the link between job resources and job performance: A daily experience-sampling method

The present study examined the relationship between daily job resources and job performance, focusing on how emotions mediate this relationship from a within-person perspective. The study found that both daily job resources and positive emotions can improve daily job performance. Day-level positive emotions mediate the positive relationship between day-level job resources and job performance, while negative emotions have no significant effect on daily job performance. The current study offers a within-person perspective for testing the applicability of the theoretical models and also inspires practitioners in the domain of university management. However, with the limitation of the experience sampling method, our study cannot totally avoid common method bias and the generalizability of the research conclusion still needs further verification.

Keywords

job resources positive emotions negative emotions job performance experience-sampling method

Introduction

With the development of economic globalization, competition in colleges and universities is becoming increasingly fierce (Carpini et al., 2017), and performance is the core of this competition. Whether employees can complete tasks efficiently and actively perform organizational citizenship behaviors (OCB) is crucial to organizational development. Therefore, the focus of the study is on how to improve the performance of employees (Tisu et al., 2022). Meanwhile, job performance serves as the primary dependent variable in almost all areas of management and organizational behavior (Carpini et al., 2017). It is an important indicator for measuring organizational structure and achievements, as well as reflecting the value of individual work.

Job performance mainly refers to the corresponding behavior and results to achieve the goal (Rich et al., 2010). It can be divided into task and contextual performance (Sørlie et al., 2022). Task performance refers to the activities required for the formal job role, and contextual performance refers to activities beyond the formal job’s responsibility range but considered to contribute to organizational effectiveness (Borman & Motowidlo, 1997; Dalal et al., 2020). This phenomenon exists in the workplace. Why are some employees of higher performance levels than others, and why do employees with high performance levels sometimes have poor performance? The first question reflects between-person differences in job performance, whereas the second question reveals the issue of within-person fluctuations (Kampf et al., 2021; Xanthopoulou et al., 2009). However, most studies on performance mainly focused on explaining between-person variance (Weiss & Merlo, 2020), and the within-person variance was usually treated as a measurement error (Dalal et al., 2014; Podsakoff et al., 2019). Therefore, when applying a cross-sectional or mean-level perspective to performance, it only explains the part of the between-person variance. Ignoring the within-person variance, will lose important information about the full spectrum of an employee’s performance and yield an incomplete picture (Dalal et al., 2020; Kampf et al., 2021; Weiss & Merlo, 2020).

Recently, scholars have realized that job performance fluctuates in a very short time frame (such as minutes, hours, days, and weeks). Researchers try to predict and exploit this variability (Dalal et al., 2020). For instance, Podsakoff et al. (2019) estimated that, on average, across 222 individual empirical studies in applied psychology, 50% and 45% of the variance in job performance and OCB in the measures included in applied experience sampling methods (ESM) studies is attributable to within-person. Meanwhile, McCormick et al. (2020) obtained similar results of 48.44% and 40.71% for performance and OCB in the field of management. Thus, within-person variance is meaningful and systematic. Transient states may be more important than stable traits for understanding organizational outcomes, such as absenteeism on a working day (Simbula, 2010). Therefore, it cannot be a measurement error.

Previous research indicates that job performance exhibits considerable fluctuations across times and situations (Demerouti et al., 2015; Sørlie et al., 2022). Individuals may experience good and terrible workdays based on their baseline characteristics (Ouweneel et al., 2012). What makes employees have different daily job performance? Some scholars have pointed out that affective states (mainly refer to emotions) and work situation factors (mainly refer to the job resources in this study) are important antecedents of momentary job performance (Dalal et al., 2020; Diener et al., 2020), and that has been shown to predict performance variability (Bakker & Bal, 2010; Kampf et al., 2021; Lavy, 2019; Nielsen et al., 2017; Zhang et al., 2022). Indeed, every working day, employees may use different job resources and develop different emotional experiences to influence work behavior. Job resources refer to the physical, social, and organizational aspects of work that facilitate the learning and development of individuals to achieve their work goals, such as job autonomy, team atmosphere, performance feedback, development opportunities, and leadership guidance (Bakker & Demerouti, 2007). Despite the positive correlation between the long line of research and job performance, there are still certain limitations. Most of the previous studies were cross-sectional design or longitudinal design. Finding evidence for a relationship between a person’s job resources and performance does not contribute knowledge concerning the effects of daily variations. Recently, Sørlie et al. (2022) research shows that daily job autonomy is significantly positively related to both task and contextual performance of daily work. However, only assessing job autonomy in job resources is not sufficient to explain the level of job performance. Bouckenooghe et al. (2022) found that leadership feedback at work can give employees much more control over work, compensate for the loss of resources due to focus on work, and reduce the impact of over-commitment on performance. Thus, it is incomprehensive to only focus on the effect of job autonomy among all the factors in job resources (Lu et al., 2017).

Moreover, emotions fluctuate with changes in people, and situational factors unfold over time (Diener et al., 2020). Research on the relationship between emotions and performance is common and has established powerful impacts (Kaplan et al., 2009; Zhang et al., 2022). Yet the results are inconsistent, and few studies have examined the combined effects of different emotions in short intervals. Some studies suggest positive emotions improve performance, and negative emotions reduce performance (Miner & Glomb, 2010; Shockley et al., 2012). However, research has verified that negative emotions may motivate employees to develop more contextually appropriate strategies to improve performance. For example, Forgas (2002) argued that positive emotions satisfy employees’ need to feel secure and that cognitive resources are less activated, leading to low levels of processing. Huang et al. (2020), through experimental research, found that emotions can influence moving target selection. Under the condition of positive emotion, the selected goal is faster. Still, there are more errors, while under the condition of negative emotion, the selected goal is slower, and there are fewer errors. Thus, negative emotions may lead to careful thinking and problem-solving for positive performance. Meanwhile, Shockley et al. (2012), according to a few studies that reported within-person correlations, speculated the direction of effects seems to be similar to the between-person correlations reported in the meta-analysis. Recently, two diary studies have shown that there may be some bias in this view. Zhang et al. (2022) found that on a daily basis, positive emotions are positively correlated with service performance, and negative emotions are negatively correlated with service performance. Yet, weekly, positive, and negative workplace moods were positively correlated with job performance (Kampf et al., 2021). Therefore, there are some differences in the results, which need further examination, and enriching the literature on daily emotions and job performance in short intervals.

To understand these fluctuations in job performance, we need further knowledge about the roles and mechanisms of job resources and affect reactions in short intervals to capture this dynamic. We bridge two major research streams, the motivational process of the job demands-resources (JD-R) model and the affective reactions of affective events theory (AET), and hopefully open new possibilities for application and theory development. Though theory-driven within-person effects of work situation factors and affective state on behavior: job characteristics → affect reactions → performance. Examine the role of work situation factors and affective experiences in explaining short-time frame within-person job performance variability. More specifically, we use ESM to examine whether daily job resources and emotions can be considered antecedents and influence daily job performance and the mediating role of daily emotions. Meanwhile, we also tested the applicability of the JD-R model and the AET within-person or dynamic constructs. Finally, we propose some suggestions for aiming to optimize the job performance of college counselors.

Theory and Research Hypothesis

Job Resources and Job Performance

The JD-R Model is a heuristic and parsimonious model specifying how health impairment and motivational processes may be produced due to work situations (Parker et al., 2023; Simbula, 2010). This model assumes that work characteristics can be distinguished: job demands and job resources (Bakker et al., 2003; Demerouti et al., 2001; Hakanen et al., 2006). Job demands refer to requiring continuous personal effort to cope with the challenges that arise in the workplace. It negatively impacts performance through a health impairment process (Tisu et al., 2022); Job resources refer to assisting individuals in accomplishing tasks, including physical, psychological, social, and organizational aspects, which enables individuals to achieve their work goals, reduce the stress caused by the job demands, and stimulate their growth, learning, and development, it initiates a motivational process that may lead to high performance through work engagement (Bakker & Demerouti, 2007; Demerouti et al., 2001; Xanthopoulou et al., 2008, 2009). The motivational potential of job resources plays a huge role in the work. On the one hand, they play an intrinsic motivational role; on the other hand, they play an extrinsic motivational role (Bakker & Bal, 2010; Bakker & Demerouti, 2007). Therefore, job resources are not only necessary for dealing with job demands and solving work problems, but they are also important in their own right (Hakanen et al., 2006).

Conversely, a lack of job resources may negatively influence work behaviors. A meta-analysis demonstrated that job resources positively affect employee motivation and organizational outcomes (Sánchez-Cardona et al., 2023). Specifically, the more resources employees perceive, the higher the sense of meaning in work they experience and the more positive their work behavior. In addition, the development of human resource management (HRM) practice has found dozens of job resources that can be used to improve performance (Tisu et al., 2022). Previous research demonstrates that some job resources also show direct associations with performance, such as feedback (Choi et al., 2018; Tagliabue et al., 2020) and autonomy (Park & Choi, 2020; Sørlie et al., 2022).

Despite their positive impact, some scholars argue that it fails to exert an all-encompassing positive link to performance and started questioning the universality of job resources (Tisu et al., 2022). For example, a meta-analysis showed a shared variance of 4% between job resources and performance, and the relationship between job resources and performance in a cross-sectional study is stronger than that in a longitudinal study (Nielsen et al., 2017). Furthermore, Ogbonnaya and Messersmith (2019) pointed out that some job resources (e.g., autonomy) may enhance stress and reduce performance. However, Sørlie et al. (2022) found that daily autonomy can promote task performance in a 30-day survey. Consistent with the above results, Lavy (2019) found daily supervisor support increased OCB the following day. Thus, it may be evident that job resources will only have a spiraling effect over a short period of time. Meanwhile, between-person studies cannot explain why individuals with high-performance levels also have an off day. During weekdays, employees can use their work resources to achieve work-related goals (Bakker & Bal, 2010); thus, we need to use a relatively short time to capture this dynamic relationship (Sørlie et al., 2022).

To measure daily job resources in college counselors’ work, in the present study, we included three resources (i.e., autonomy, team climate, and supervisory coaching) that were identified as the most important for them (Hakanen et al., 2006). Because daily job resource fluctuations occur in college counselors, they may serve different types and amounts of students, deal with different issues, and work with colleagues and supervisors. Thus, they have different levels of decision authority (Bakker et al., 2003), perception of different working atmospheres and cooperative relations with colleagues (Xanthopoulou et al., 2009), and receive different levels of resources and support to them (Tordera et al., 2008). As such, daily job resources will facilitate a direct increase in daily performance, and the following hypothesis is proposed:

H1: Day-level job resources are positively related to day-level job performance.

Job Resources and Emotions

How do we generate emotions? A very important factor is individual needs. If individuals’ needs are satisfied, they will generate positive emotions; otherwise, they will generate negative emotions. As previously stated, job resources have both intrinsic and extrinsic motivational potential and fulfill basic human needs, such as the needs for autonomy and relatedness (Bakker & Bal, 2010; Bakker & Demerouti, 2007; Simbula, 2010; Xanthopoulou et al., 2008). Conversely, lacking job resources may produce negative emotions due to not satisfying individual needs. Kaski and Kinnunen (2021) found that a lack of job resources may increase burnout. Meanwhile, emotions also have different social origins, meaning that pleasant or unpleasant social interactions will likely make people feel good or bad (Xanthopoulou et al., 2012). Counselors need frequent social interactions. For instance, different interactions with supervisors, colleagues, or students at work using different resources (i.e., team climate and supervisory coaching) may trigger positive or negative emotions. In a cross-sectional study, Schaufeli and van Rhenen (2006) showed that working in resourceful work conditions will produce positive emotions (i.e., enthusiasm and joy). Bono et al. (2007) found that employees who work with transformational leaders experience more positive emotions. Xanthopoulou et al. (2012) also showed that recognition from supervisors, positive behavior from colleagues, and having control are important antecedents of positive emotions at work. Similarly, the results in diary studies are consistent. Bakker and Bal (2010) described that previous diary studies have shown that job characteristics may vary daily and determine our daily mood or affect. Ketonen et al. (2018) also found that self-determined autonomous educational goals can significantly predict positive emotions in everyday academic situations through a 14-day study of 55 college students. Therefore, sufficient job resources are significant for the creation of a better work environment to satisfy the basic physical and mental needs of individuals and then produce the corresponding emotional experience. Hence, the following hypotheses were formulated:

H2: Day-level job resources are positively related to positive emotion.

H3: Day-level job resources are negatively related to negative emotion.

Emotion and Job Performance

The AET holds that in the entire work process of employees, affects play a role in accompanying, influencing, and shaping employees. It influences employees’ performance (Weiss & Cropanzano, 1996) and has established the powerful effects on individual effect on job performance (Zhang et al., 2022). It is pointed out that affective reactions mainly include mood and emotion. Moods are weak, calm, and lasting affect, with dispersion (no specific reason), often not realized by the perceiver (Kampf et al., 2021; Weiss & Cropanzano, 1996). For example, the counselor had a good mood recently, and it may be affected by work events, or may not be related to work events, or it is just because the weather is better. While emotions are focused on a specific target or cause generally realized by the perceiver of the emotion, it is relatively intense and very short-lived (Barsade & Gibson, 2007). Compared with mood, emotions are more related to specific work events. If positive feedback from leaders is received at work, happy emotions will be generated. Barsade and Gibson (2007) also indicate that affective feelings are present at any time we confront work issues that matter to us and our organizational performance, considered “affect umbrella term encompassing a broad range of feelings that individuals experience, including feeling states, such as moods and discrete emotions, and traits, such as trait positive and negative affectivity.” Thus, in the present study, affects mainly refer to emotions.

The AET believes that affect is one of the main drivers of behavioral outcomes. Job behaviors are categorized into affect-driven behaviors and judgment-driven behaviors. Affect-driven behaviors refer to emotions that directly influence work behavior. It expresses the direct relationship between emotions and performances, and emotions will change over time. The duration of affect-driven behavior is short and constantly changing (Weiss & Cropanzano, 1996). Namely, performance as a within-person dynamic outcome of emotions (Kampf et al., 2021). As mentioned, emotions and performance fluctuate in short intervals; thus, affect-driven behavior is another main theoretical view. Affective events will cause the corresponding emotional experience, and this emotional change will lead to various within-person performances. Indeed, some studies have shown that emotions can predict individual attitudes or behaviors. Kim and Lee (2022) used a three-wave longitudinal study (the interval was approximately 2 weeks) to investigate 116 college students and found that both positive and negative emotions experienced by individuals at within-person and between-person levels positively predicted job search behavior. Griep et al. (2022) showed positive active emotions and negative active emotions related positively to daily job crafting through a survey of 116 employees for five consecutive working days. In line with previous findings, during a 3-month weekly study, Kampf et al. (2021) found that positive and negative workplace moods can positively predict weekly level performance for 357 employees. However, in the service industry, Zhang et al. (2022) found that negative emotions negatively correlate with service performance by investigating 187 bank workers for 18 consecutive days. This finding is consistent with the relationship between trait affect and job performance (Kaplan et al., 2009). Furthermore, Lavy (2019) found an interesting result that negative emotions were associated with increased OCB on the following day, and, in addition, positive emotions were associated with decreased OCB on the following day. A pertinent question would be: what causes the disparity in outcomes? Perhaps variations in time intervals or types of performance, among other factors. So, what are the outcomes for the group of college counselors on the same day? In the present study, we hypothesize the following:

H4: Day-level positive emotions are positively related to the performance of the day.

H5: Day-level negative emotions are negatively related to the performance of the day.

The Mediating Role of Emotions

The AET points out that there is a multi-dimensional structure of emotions caused by work events. Work events are the direct cause of affective reactions. It is worth noting that the theory does not ignore the role of job characteristics (mainly refers to resources), believes that job characteristics induce emotional reactions through work events, and does not directly indicate that job characteristics will directly cause affective reactions. However, some scholars based on the motivational process of the JD-R model found that job resources can promote psychological states (i.e., affective reactions) and improve individual job performance (Bakker & de Vries, 2021; Schaufeli & Bakker, 2004). Therefore, we link two major theoretical views: the motivational process of job resources and the affect-driven behavior of affected reactions, the mediating role of examined emotion between job resources and performance in short-time frame intervals. Simply, job resources → emotions → performances. This view is consistent with the views of previous scholars. For instance, Beal et al. (2005) suggested that fluctuating work situation factors may determine employees’ affective states and, in turn, determine performance. Similarly, Bakker and Bal (2010) also believed that job characteristics develop critical psychological states that will drive individuals’ attitudes and behaviors. Stewart and Barling (1996) found that daily work stress impacts job performance of the day by inducing individual emotions. In addition, a lack of job resources will lead to a sense of loss of control over work. The loss of control will influence the psychological state of employees, resulting in negative emotions such as anxiety and irritability. Similarly, empirical studies show that mood plays a mediating role between commuting and job performance (Atis et al., 2022). Based on the above, we speculate that emotion plays a mediating role between job resources and job performance. Accordingly, the following hypothesis is proposed:

H6: Daily positive emotions mediate the relationship between day-level job resources and job performance.

H7: Daily negative emotions mediate the relationship between day-level job resources and job performance.

Methods

Experience Sampling Method

ESM allows the subjects to answer questions about the occurrence of events or, at random moments, to collect data (Duan & Chen, 2012). ESM is particularly effective for capturing short-term behaviors in the work environment. It requires subjects to record their job performance within a specific period. ESM research aims to examine personal experience and behavior in a specific environment and can better reflect individual performance. By repeatedly measuring the same individual, the changes in emotions and behaviors in the individual can be accurately captured, and more rigorous and credible model test results can be presented. Therefore, in the present study, ESM is one of the more appropriate methods to study job resources and emotional and job performance fluctuations in individuals.

Participants and Procedures

The University Committee on Human Research Protection approved this study. Informed consent was obtained from all participants of the present study. Our study ensured that their answers were anonymous and only used for research purposes. All methods were performed following standard guidelines and regulations. College counselors are subject to emotional labor, particularly during periods of epidemic prevention and control. This often entails a heavy workload, unbalanced daily job demands, and limited job resources, resulting in various emotional fluctuations. We employed convenience sampling to recruit full-time counselors from two universities in China. A total of 60 full-time counselors from these universities registered to participate. To improve the response rate and accuracy of the questionnaire, we implemented the following strategies: first, if the subjects did not submit the questionnaire on time, the researcher would remind them to fill it in through their mobile phones; second, each subject was provided with a gift; third, a specific number of days was stipulated for the subjects to respond, and data was deleted if less than 6 days.

Aligned with our interest in the within-person perspective of job performance, we tested our overall model in ESM. The study took place across 2 weeks and consisted of two phases. In the first phase, we sent participants an announcement which contained study details and an electric consent. Only those who gave consent were asked to complete a questionnaire to collect the counselors’ gender, age, education, and other demographic variables. In the second phase, which occurred 1 week following the first phase, as Reis and Wheeler (1991) believe that two consecutive weeks of records can predict an individual’s stable social life. Thus, we captured our core variables at two time points daily over 10 consecutive workdays. Specifically, we sent two daily survey links per day to 60 participants at two different time slots. The first period is to fill in the Job Resource Scale and the PANAS from 11:00 to 13:00; The second period is to fill in the Job Performance Scale from 17:00 to 18:00.

Among the 60 participants who signed up and completed the survey, two participants completed the survey in less than 6 days, and responses that were consistently the same option were considered as ineffective data. The remaining 58 participants provided 580 full day-level data points, yielding a response rate of 96.67%. Among the final sample, yielded a response rate of 96.67%. Among the final sample, there were 21 males (36.2%) and 37 females (63.8%); 7 undergraduates (12.1%), and 51 masters (87.9%); Respondents’ ages ranged from 24 to 37 years (M = 32.26, SD = 3.49); the average working years is 7.40 (SD = 3.77).

Measures

Job Resource

We measured daily job resources using the Day-level Job Resource scale (Xanthopoulou et al., 2009), it consists of three components: day-level job autonomy, day-level team climate, and day-level supervisory coaching, totaling 7 items. Day-level job autonomy (i.e., “Today during the shift, I could decide myself how to execute my job,” 2 items). Day-level team climate (i.e., “Today during the shift, there was a very good working atmosphere,” 2 items). Day-level supervisory coaching (i.e., “Today during the shift, my supervisor used his/her influence to help me solve my problems at work,” 3 items). The scale is scored on a 5-point scale (from 1 = do not agree at all to 5 = fully agree). Cronbach’s alphas range from .81 to .90 across the 10 days.

Positive and Negative Affect Schedule

We utilized the revised scale by Qiu et al. (2008), which was based on Watson et al.’s (1988) Positive and Negative Affect Schedule (PANAS), to assess daily emotions. The scale includes 18 items about emotions, of which positive emotions (i.e., enthusiastic, active, etc.) and negative emotions (i.e., upset, hostile, etc.) correspond to 9 items each. Assessment was conducted using a 5-point Likert scale (from 1 = very slightly or not at all to 5 = extremely). In this study, Cronbach’s alphas range from .93 to .97 across the 10 days of the positive emotion scale, and Cronbach’s alphas range from .87 to .93 across the 10 days of the negative emotion scale.

Job Performance

We were adopted the State Job Performance Scale (Xanthopoulou et al., 2008) to measure daily job performance. The scale consists of two subscales, each including two items: state in-role performance (“Today, I performed well,” and “Today, I fulfilled all the requirements for my job”) and state extra-role performance were measured with two items each (“Today, I voluntarily did more than was required of me,” and “Today, I helped my colleagues when they had too much work to do”). The scale is scored on a 5-point scale (from 1 = do not agree at all to 5 = fully agree). Cronbach’s alphas for this study ranged from .78 to .91 across 10 days.

Results

Primary Analysis

The mean, standard deviation, and correlation coefficient of each variable are shown in Table 1. The correlation coefficients between the observed variables were between −0.12 and 0.59, showing a moderate or lower correlation, indicating that emotions, job resources, and job performance are different constructs and have conceptual independence. Positive emotion was significantly positively correlated with job resources (r = .44, p < .01) and significantly positively correlated with job performance (r = .35, p < .01). Negative emotion was significantly negatively correlated with job resources (r = −.19, p < .01) and was significantly negatively correlated with job performance (r = −.12, p < .01). Meanwhile, job resources were significantly positively correlated with job performance (r = .59, p < .01). The above results provide preliminary support for testing the research hypothesis.

Table 1.

The Means, Standard Deviations, and Correlations.

Variables	Mean	SD	1	2	3
1 Positive emotions	2.79	1.01
2 Negative emotions	1.54	0.68	−.003
3 Job resources	3.68	0.84	.443**	−.190**
4 Job performance	3.77	0.87	.353**	−.121**	.592**

p < .01. ***p < .001 (two-tailed), same as below.

Variation of Variables Within-Person

This study is a repeated measurement of 58 subjects at multiple time points of multiple variables. We need to pay attention to the total variation of positive emotions, negative emotions, job resources, and job performance within 10 consecutive days and the interpretation of within-person variation to the total variation of each variable. We also need to calculate how much the between-person interpretation rate of the total variation is and whether it reaches a significant level. Finally, we have to verify whether each variable has dynamic characteristics. Therefore, we use HLM 7.0 software to establish a two-level model with positive emotions, negative emotions, job resources, and job performance as dependent variables with no predictors (the null model). The obtained within-person and between-person variance components are shown in Table 2. According to the classification criteria of Cohen (1988), when ICC is lower than .059, it means a low intraclass correlation coefficient. When ICC is greater than .138, it is considered to have a high intraclass correlation coefficient. This indicates that part of the variance in job performance (57.7%), positive emotions (49.8%), negative emotions (63.1%), and job resources (50.5%) could be attributed to within-person fluctuations (and error) and that the remaining part of the variance in job performance, positive emotions, negative emotions, and job resources (i.e., 42.3%, 50.2%, 36.9%, 49.5%, respectively) could be explained by differences between person. The above results show that the variation of each variable within individuals is not random but meaningful.

Table 2.

Variance Decomposition for Within-Person Variables.^a

Variables	Within-person variance (σ²)	Between-person variance (τ₀₀)	ICC
Job performance	8.499***	6.237***	.423
Positive emotions	42.613***	42.795***	.502
Negative emotions	23.013***	13.527***	.369
Job resources	18.718***	18.354***	.495

Note. ICC = τ_00/(τ₀₀₊σ²).

Represents the results of the Null-model analysis (uncentered).

Main Results

Examining the influence of job resources, positive emotions, and negative emotions on job performance on a daily basis, we have investigated the mediating roles of positive and negative emotions in the relationship between job resources and job performance and analyzed the direct effects using a hierarchical linear model. The mediating effect models are shown in Table 3.

Table 3.

Job Resources, Emotions, and Job Performance: Direct and Mediating Effects.

	Job performance		Positive emotions	Negative emotions
Variables	M1	M3	M2	N2
Intercept, γ₀₀	−0.829**	−0.824**	−0.032
Job resources, γ₁₀	0.296***	0.273***	0.402***
Positive emotions, γ₂₀		0.054***
Residual
Inter-individual	1.856	1.842	5.752
Intra-individual	2.149	2.131	5.992
R ²	4.620	4.542	35.910
Intercept, γ₀₀	−0.829**	−0.825**		−0.026
Job resources, γ₁₀	0.296***	0.294***		−0.121**
Negative emotions, γ₂₀		−0.009
Residual
Between-person	1.856	1.859		3.814
Within-person	2.149	2.156		4.638
R ²	4.620	4.648		21.511

Note. Group mean centered was conducted.

The results show that daily job resources positively impact the job performance of the day. It can be seen from M1 in Table 3 that the job resources of the day significantly positively predict job performance (γ₁₀ = 0.296, p < .001); hypothesis 1 is supported. From M2 in Table 3, daily job resources significantly and positively predict college counselors’ daily positive emotional level (γ₁₀ = 0.402, p < .001); hypothesis 2 is supported. From N2 in Table 3, it can be seen that daily job resources significantly negatively affect the daily negative emotions of college counselors (γ₁₀ = −0.121, p < .001); hypothesis 3 is supported. It can be seen from M3 in Table 3 that daily positive emotions have a significant positive impact on the job performance of the day (γ₂₀ = 0.054, p < .001); hypothesis 4 is supported. However, daily negative emotions do not significantly affect the job performance of the day (γ₂₀ = −0.009, p > .05); hypothesis 5 is not supported.

In Table 3, we have shown the mediation effect tests. From M1 in Table 3, daily job resources positively predict job performance (γ₁₀ = 0.296, p < .001) significantly. When the mediating variable positive emotion was added to equation M3, the day’s job resources positively predicted job performance (γ₁₀ = 0.273, p < .001) significantly. By comparing the coefficients, when positive emotions were added to the model, the daily job performance increased by 0.273 units for every 1 unit of job resources and decreased by 0.02 units compared to the previous day. It can be concluded that the daily positive emotions partially mediate between daily resources and daily job performance. The results of the multilevel mediation effect were conducted using Mplus 8.0 software by Fang et al. (2019), who used a Bayesian bootstrap approach with 50,000 simulated bias-corrected parameter estimates to calculate 95% confidence intervals (CIs) around indirect effects. The indirect effect of job resources on job performance via positive emotions was positive and significant (estimate = 0.013; 95% CI [0.002, 0.032]). Therefore, it was further verified that daily positive emotions played a mediating role between positive emotions and job performance on the day. In summary, hypothesis 6 is supported, indicating that daily positive emotions play a mediating role between work resources and job performance.

From M1 in Table 3, it can be seen that daily job resources significantly positively predict daily job performance (γ₁₀ = 0.296, p < .001). When the mediating variable (negative emotion) was added to equation M3, the daily job resources had a significant positive impact on the job performance of the day (γ₁₀ = 0.294, p < .001). When controlling for job resources, the daily negative emotions have no significant effect on the daily job performance (γ₂₀ = −0.009, p > .05). It can be concluded that daily negative emotions have no mediating effect between job resources and job performance on the day.

Discussion

In the present study, we bridge two major theoretical perspectives, the motivational process of the JD-R model and the affective reactions of the AET, using ESM to examine whether daily job resources induce individual emotions and affect job performance on the day. We found that sufficient daily job resources can promote individual positive emotions to improve daily job performance; however, the lack of daily job resources will cause individuals to have negative emotions, but will not further reduce job performance.

This study produced several important findings. First, we found that daily job resources can trigger various emotional states in individuals. The research results are consistent with previous (Bakker & Bal, 2010; Bono et al., 2007; Kaski & Kinnunen, 2021; Ketonen et al., 2018; Schaufeli & van Rhenen, 2006; Xanthopoulou et al., 2012). Employees all have three fundamental psychological needs: competence, autonomy, and relatedness. When these basic psychological needs are met, they facilitate individual development. These three psychological needs are innate, all individuals strive to fulfill these needs and tend toward environments that facilitate their satisfaction (Deci et al., 2017). Therefore, in both cross-sectional, longitudinal, and diary studies, job resources can elicit different emotions in employees.

Second, our study found that daily positive emotions can positively predict daily job performance. Consistent with the previous research results also remained consistent in longitudinal or diary studies (Griep et al., 2022; Kampf et al., 2021; Kim & Lee, 2022; Zhang et al., 2022). However, in Lavy’s (2019) study, positive emotions were associated with decreased OCB the following day. When individuals are happy and feeling good, they are not willing to engage in OCB the next day. This difference may be because OCB will need to pay for a certain number of resources, such as consuming their energy. Thus, individuals refuse to produce OCB to maintain the best state of pleasure.

However, this study found that daily negative emotions did not significantly predict daily work performance in a negative direction. It was inconsistent with previous research results were also inconsistent, and the effect is confusing in longitudinal or diary studies (Griep et al., 2022; Kampf et al., 2021; Kim & Lee, 2022; Lavy, 2019; Zhang et al., 2022). Specifically, negative emotions have a positive predictive effect (Griep et al., 2022; Kampf et al., 2021; Kim & Lee, 2022; Lavy, 2019) or have a negative predictive effect (Zhang et al., 2022). This difference may be due to different time intervals, occupational characteristics, and different types of performance. Thus, it indirectly may prove that the view of affect-driven behavior has certain limitations in a short time.

Additionally, another explanation exists. Emotions can also indirectly influence work behaviors through judgment-driven behaviors (Weiss & Cropanzano, 1996). For example, employee turnover is not solely a consequence of momentary emotional impulses; rather, it originates from the extended accumulation of negative emotional experiences, leading to reduced job satisfaction, diminished organizational commitment, and other associated factors, ultimately influencing decision-making processes (Weiss, 2002). Therefore, it is reasonable to suggest that transient negative emotions do not exert a substantial impact on work behavior. This implies that daily negative emotions have no significant effect on job performance, aligning with the perspective of judgment-driven behavior.

Finally, this study has confirmed the mediating role of only daily positive emotions, rather than daily negative emotions. The emotion-mediating model supported the mediating role of daily positive emotion in the relationship between daily resources and daily job performance. The results are similar to previous studies. For example, Zhou et al. (2022) found that family Supportive Supervisor Behavior can influence employees’ positive emotions and generate positive spillover effects. In short, employees who have access to sufficient daily job resources, such as the ability to flexibly manage their tasks, a positive team atmosphere, and leadership support can activate positive emotions, thereby enhancing their daily work performance.

However, the mediating effect of daily negative emotions between daily job resources and job performance is not significant. The results are inconsistent with previous research findings. For example, Atis et al. (2022) found that negative emotions mediate the relationship between commuting and job performance. Why does this phenomenon occur? One possible reason is the difference in research design. In cross-sectional designs (Atis et al., 2022), it is necessary to retrospectively assess overall negative emotions over a longer period, whereas, in our daily study, we capture daily emotional states. Another reason could be speculated that in the work environment, individuals facing insufficient job resources may experience negative emotions, but subsequent work behaviors may involve more complex psychological processes. For example, in cognitive appraisal, when individuals perceive the lack of job resources as a stressor, different cognitive appraisals of the stressor may lead to different outcomes. Specifically, if the stressor is perceived as a challenge appraisal, it may foster positive work outcomes; conversely, if perceived as a hindrance appraisal, the opposite may occur (Ma et al., 2021).

Theoretical Contribution

Our findings contribute to the literature in the following ways. First, this study validated and examined the dynamic changes in job performance within individuals over time in the context of Chinese universities. Dalal et al. (2020) pointed out that job performance has dynamic characteristics, and the changes in job performance should be discussed in more detail, such as daily (instantaneous) job performance. The results show that job performance showed more than half the level of change over time, and the within-person difference in job performance reached 57.7%, which was a significant level of explanation for job performance. This result is similar to the previous research conducted (i.e., Kampf et al., 2021; Lavy, 2019; McCormick et al., 2020; Podsakoff et al., 2019; Zhang et al., 2022), but within-person variation is different. Specifically, the with-person performance variation in the two meta-analyses accounted for 50% (Podsakoff et al., 2019) and 48.44% (McCormick et al., 2020), respectively. Yet daily OCB (Lavy, 2019), daily perceived service performance (Zhang et al., 2022), and weekly performance (Kampf et al., 2021) accounted for 25%, 30%, and 48%, respectively. The reason for the difference may be related to the type of performance and time interval. We proved that job performance has state characteristics, which fluctuate with changes in emotions and job resources.

Second, this study verifies that it is reasonable to integrate the theoretical views of the motivational process and the affective reactions in a short time frame variability. The major theoretical contribution, therefore, is a more in-depth understanding and study of work characteristics, affect, and behavior as dynamic constructs rather than disposal them by studying them as static constructs that vary only between-person. Meanwhile, the validity of the application of the JD-R model was verified. The model believes that job resources can activate the individual’s motivation process (i.e., work engagement), which has a positive impact. Our study found that the availability of job resources promotes key psychological states (positive emotions), thereby improving job performance. Our findings are consistent with the model’s theoretical perspective, which also works when measured in smaller units, further confirming the breadth and flexibility of the JD-R model.

However, our results may reveal that the affect-driven behavior in AET has some limitations in a short time frame. It holds that positive or negative work events experienced by employees will trigger corresponding emotional experiences of employees, and emotional experiences further affect individual attitudes and behaviors (Weiss & Cropanzano, 1996). There are two paths for emotional experience to affect behavior. The first one is that emotions directly affect behavior, that is, affect-driven behavior. If an individual has a positive emotional experience, he/she will decide to use a more serious work attitude and action to reward the organization. Conversely, if the individual has a negative emotional experience, it will have a negative impact. This study found that daily positive emotions can positively predict daily job performance. Consistent with the above theoretical point of view. However, the relationship between negative emotions and work behavior is not completely consistent with the affect-driven behavior point of view. Thus, it may prove that the view of affect-driven behavior has certain limitations in a short time. By subdividing different emotional states and clarifying the logical relationship between emotion and job performance, our study promotes the understanding of AET and provides a reference for future research on negative emotions.

Practical Implications

First, this study found that daily job resources have positive effects on daily job performance. Therefore, improving employees’ workplace resources may be an important HRM and occupational health policy goal. Nielsen et al. (2017) found that the individual, the group, the leader, and the organizational resources levels were related to employee well-being and performance. It is necessary to provide different levels of job resource models according to the different career stages of counselors to grasp the regular pattern of their internal and external needs, then to motivate employees to improve job performance. Nowadays, the competition among colleges and universities is becoming more and more fierce. Building a high-quality and stable talented team is the magic weapon for success and strong competitive capital. Change in quantity affects quality, and long-term performance goals can only be achieved by improving daily job performance.

At the same time, this study shows that daily positive emotions mediate the relationship between job resources and job performance on the day. This means that improving individual positive emotions is very important to individual performance. Therefore, creating a better job resource and environment is necessary to encourage individuals to produce positive emotions. Organizations can enhance employees’ positive emotional experience in many ways, such as reducing uncivilized behavior at work, providing full respect and recognition, creating cooperative workplace culture, and providing physically comfortable and safe workplaces (Shockley et al., 2012). Meanwhile, some training programs can also achieve good results. For instance, emotional experience or appropriate psychological training could cultivate positive emotions, stimulating individual performance.

Thus, organizations can target building resources at any of four levels to provide adequate job resources. This is a good way to improve employees’ competency and creativity, develop a healthy organization, and achieve a virtuous circle.

Second, this study may better explain why employees with high job performance levels are less productive on certain days. Individuals must use job resources to accomplish work-related goals or tasks during each workday. According to the JD-R model, these resources will help cope with the educational process’s emotional demands and affect the counselor’s job performance. These resources may be lacking on some special days. For example, colleagues and leaders are too busy to provide the appropriate support, which may affect job performance on the day. Therefore, studying performance changes in a short period of time can improve our understanding of the mechanism of differences between actual performance levels and performance ratings (Dalal et al., 2020).

Additionally, this study found that negative emotions in the short term have no significant prediction of job performance. If individuals are in a negative state for a long time, it may have a greater impact on their work attitudes and behaviors. Success lies in previous preparations, and there will be a failure without previous preparations. Managers can intervene in advance to improve individual emotional regulations, such as by providing stress management and mindfulness training programs.

Limitations and Future Directions

Firstly, this study used ESM to better avoid common method bias and social approval, but the subjects themselves fill out all scales; therefore, it cannot be completely avoided. Future research can collect data from other aspects, such as daily job performance that colleagues or leaders can evaluate, or objective performance indicators (Kampf et al., 2021). However, objective performance indicators may not accurately measure the performance of employees in a short time frame, such as it is difficult to objectively measure OCB in daily work. Meta-analysis results show objective performance ratings provided weaker relationships between resources and performance than self-rated (Nielsen et al., 2017). Thus, it can be measured from a combination of self-report, others’ evaluation, and objective indicators. In the present study, we only consider individual-level performance. Future research can consider short-time frame performance variability at other levels (i.e., team or organizational performance).

Secondly, our initial design was to closely link job resources with emotions to ensure that emotions are induced by job resources (autonomy, team climate, and supervisory coaching). We measured resources and emotions until 1 pm, leaving time for further emotions to occur, so more frequent measurements can be taken in future research to ensure that emotions are induced due to work characteristics. Therefore, future studies may consider using portable physiological monitors to measure employees’ emotional states more accurately and frequently (Gabriel et al., 2019). In addition, emotions are contagious, and future research can examine how and when employee or leadership emotions are transferred to colleagues and ultimately affect performance (Wan et al., 2022).

Thirdly, this study only focuses on the dynamic changes within-person. It does not conduct in-depth and detailed research and exploration of the between-person effects, such as the impact of leadership emotions on job performance (Jo et al., 2023) or the impact of between-person stable traits on within-person changes (Kampf et al., 2021) as shown that daily negative emotions have no significant impact on the job performance of the day, whether the internal mechanism is affected by the differences in certain traits between individuals in the process. For example, the higher the individual’s neuroticism, the more likely it is to have negative emotional fluctuations at work. Future research still needs further exploration.

Finally, the subject of this study was Chinese college counselors with higher education. Whether this conclusion can be extended to other groups needs further verification. In addition, performance can be used as an antecedent variable. For instance, in Lavy’s (2019) study, daily OCB was found to significantly positively predict negative emotions for the next day. Similarly, Zhang et al. (2022) agree with the above view. They believed that the perceived daily service performance level might also affect the emotional state of the next day. In future research, we can consider job performance as an antecedent variable affecting individual emotions, attitudes, etc., or consider cyclic effects. Meanwhile, it is difficult to make causal inferences from this study. More rigorous experimental methods can further verify future research.

Conclusion

Drawing on the job demands-resources model and affective events theory, we developed a model indicating how job resources manifested their effect on job performance through positive or negative emotions on the day level. We found that sketching a preliminary but crucial picture of the day-level frame job resources only affects job performance through positive emotions, rather than negative emotions. The findings help to shape educators into “happy-productive workers,” leading to more efficient and better-performing work and helping build healthy organizations in the domain of university management.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the “Thirteenth Five-Year Plan” Social Science Project, Education Department of Jilin Province, China (JJKH200013SK).

ORCID iDs

Kexin Wang

Yangyang Fan

Data Availability Statement

The datasets used and analyzed during the current study are available from the first author upon reasonable request.

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Relationship Between Daily Job Resources and Job Performance: The Mediating Role of Emotions From Within-Perspective

Abstract

Plain language summary

Keywords

Introduction

Theory and Research Hypothesis

Job Resources and Job Performance

Job Resources and Emotions

Emotion and Job Performance

The Mediating Role of Emotions

Methods

Experience Sampling Method

Participants and Procedures

Measures

Job Resource

Positive and Negative Affect Schedule

Job Performance

Results

Primary Analysis

Variation of Variables Within-Person

Main Results

Discussion

Theoretical Contribution

Practical Implications

Limitations and Future Directions

Conclusion

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iDs

Data Availability Statement

References