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Article

Influence of Core Self-Evaluations on Work Engagement: The Mediating Role of Informal Field-Based Learning and the Moderating Role of Work Design

by
Yu Ma
1,
Zhichao Qian
2,* and
Lifeng Zhong
1
1
Business School, Renmin University of China, Beijing 100872, China
2
School of Labor Relations and Human Resources, China University of Labor Relations, Beijing 100048, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(9), 5319; https://doi.org/10.3390/su14095319
Submission received: 22 March 2022 / Revised: 22 April 2022 / Accepted: 26 April 2022 / Published: 28 April 2022
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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Abstract

:
This study aims to examine the effect of employees’ CSE on their work engagement, along with its underlying mechanisms and boundary conditions. Based on the job demands–resources (JD-R) model and conservation of resources (COR) theory, we propose and test a moderated mediation model that examines IFBL as the mediator and work design as the moderator in the relationship between CSE and work engagement. We use time-lagged data from 231 employees to analyze the data. The results indicate that: (1) CSE is positively related to work engagement of employees; (2) IFBL mediates the relationship between CSE and work engagement; (3) problem solving reinforces the effect of IFBL on work engagement, which consequently enhances the mediated relationship between CSE and work engagement via IFBL. We further discuss the limitations and future research directions of this study.

1. Introduction

In recent years, work engagement has become a popular research and practice topic [1]. Work engagement, which is defined as “a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption” (p. 74) [2], positively predicts employee well-being [3], organizational commitment, performance [4], and organizational citizenship behavior [5]. Employees with high work engagement are fully immersed in their jobs, radiating energy throughout the organization [2]. This is crucial to the mental health of the employee and organizational sustainability. Therefore, improving and maintaining work engagement has been a primary concern in many organizations. Moreover, exploring the potential antecedents of work engagement has expectedly received tremendous attention from both academia and practitioners. Understanding the factors that can enhance employee engagement has significant implications for increasingly developed organizations.
Previous research indicates that individual personality factors can influence work engagement [6,7,8]. Core self-evaluation (CSE) is a high-order construct that encompasses broad evaluative traits including self-esteem, generalized self-efficacy, emotional stability, and locus of control [9]. CSE is considered an important antecedent of work engagement [10,11,12]. However, several issues remain unresolved in the literature on the relationship between CSE and work engagement. First, beyond confirming its direct effects, the underlying mechanisms through which CSE influences work engagement have not been explored sufficiently [10,13]. Second, although individual factors are considered important, the work environment still needs to be addressed as a major consideration. The interaction between individuals and their work environment should be considered in the process of enhancing work engagement. While the effect of individual differences and job characteristics on work engagement has been explored sufficiently, studies have recently begun exploring the effect of the interaction between personality factors and job characteristics on work engagement [14]. Therefore, there is a need to explore individual factors in conjunction with job characteristics that collectively evaluate work engagement.
As previously mentioned, it is important to identify the mechanism that links CSE and work engagement, along with its boundary conditions [10,13]. To address the research questions, we investigate the mediating processes between CSE and work engagement through informal field-based learning (IFBL), based on COR theory. Informal field-based learning is defined as “engaging in intentional self-directed behaviors aimed at learning new, work-oriented, and organizationally valued content outside of a formal learning program” (p. 16) [15]. It is further considered an important underlying mechanism in the relationship between personality traits and employee outcomes. Previous studies indicate that IFBL is influenced by personality traits, such as promotion focus and future work self, and is related to outcomes, such as performance and creativity [15,16,17]. This study asserts that CSE is linked with a positive outlook and a greater feeling of control over work, providing more personal resources for employees to engage in IFBL to obtain greater resources, which help them put more effort into their work.
Furthermore, the effects of IFBL on work engagement may depend on different moderating conditions. We propose problem solving, which refers to “the degree to which a job requires unique ideas or solutions, and reflects the more active cognitive processing requirements of a job” [18], as a boundary condition for the mediating effects of IFBL on the relationship between CSE and work engagement. Based on the JD-R model, we chose problem solving as a boundary condition, because it offers a promising work design perspective on why employees with job resources are more likely to devote themselves to their work. Jobs with high levels of problem solving enable employees to work in challenging and innovative contexts [19], which drives them to use the knowledge and skills obtained from IFBL to improve their work. Drawing on the IFBL and work design literature, we expect a positive relationship between IFBL and work engagement to be stronger under the context of high problem solving. This study conceptualizes and tests a moderated mediation model that examines the effect of problem solving on the indirect relationships (via IFBL) between CSE and work engagement.
The purpose of this study is to identify the internal process that predicts work engagement based on the JD-R model and COR theory and to identify the role of personal resources and job resources in this process. We posit an internal process in which the CSE affects IFBL, which in turn affects work engagement. In addition, problem solving is posited to strengthen the path from IFBL to work engagement, and the indirect path that leads to work engagement. Thus, we tested a moderated mediation model to test an integrated model. This study contributes to the literature in three ways: First, recent studies have begun to investigate CSE as a personal resource that stimulates work engagement [10]. We add to these recent studies and adopt COR theory to link CSE with work engagement, providing a theoretical perspective to understand how CSE influences employee work engagement. Second, this study examines the relationship between CSE and IFBL, expanding the scope of the antecedents of IFBL in a manner that recalls the study by Wolfson et al. [15]. Moreover, by examining IFBL as a mediating variable in the relationship between CSE and work engagement, this study enables researchers to better understand the relationship between work engagement and its antecedents, and responds to calls for research on the mechanism of the influencing traits of work engagement [14]. Third, in light of COR theory, this study sheds new light on the underlying process as well as the boundary conditions for the CSE–engagement relationship. Considering problem solving as a moderator, we extend previous studies by proposing that the mediated relationships between CSE and work engagement through IFBL depend on the different levels of problem solving at work. The results of the moderated mediation test show that personality traits and work design have a synthesized effect on work engagement. Additionally, research on CSE predicts that work engagement is often cross-sectional, which cannot address the issue of causality [14,20]. This study further tests this relationship using time-lagged data to provide more reliable results regarding the antecedents of work engagement and its mechanism. Figure 1 illustrates the research model.

2. Theory and Research Hypothesis

2.1. Core Self-Evaluations and Work Engagement

CSE are higher-order traits that represent people’s fundamental evaluations of themselves and their values and abilities [9]. CSE encompass four traits, namely self-esteem, generalized self-efficacy, locus of control, and emotional stability [21]. Self-esteem is the overall value that one places on oneself. General efficacy is defined as the assessment of one’s ability to handle different situations. Locus of control, which includes internal and external locus of control, refers to an individual’s beliefs about the causes of events in life. Neuroticism is the tendency to have a negative pattern of perception and to focus on negative aspects of oneself [21]. CSE have been examined as predictors of employee job and career satisfaction [22] and job performance [23].
Drawing on COR theory [24], people strive to obtain, retain, protect, and foster their resources. Individuals with more resources can prevent resource depletion and gain more resources by investing resources [25]. Personality is likely to affect the amount of psychological resources and attitude under different conditions and events. Individuals with certain personalities may have more energy, which may enhance their performance at work. COR theory provides a framework for understanding why CSE can promote work engagement. Work engagement is influenced by both contextual and individual factors [26]. In light of COR theory, the resources people possess influence their capacities to achieve goals, and individuals with more personal resources (i.e., CSE) are intrinsically motivated to pursue their goals and actively engage in work [27].
We expected CSE to be positively related to work engagement. First, CSE may be particularly beneficial for work engagement, because it supplies the resources necessary to motivate individuals to engage in their work. Individuals with high CSE have a more positive outlook on their jobs, which may help them find positive job characteristics [28]. Compared with individuals with low CSE, those with high CSE perceive work as more enriching [29], and they are more likely to feel enthusiastic and fully concentrate on their work. Second, individuals with high CSE think of themselves positively, are fully confident in their abilities, and are confident in controlling their lives. People with high CSE have high self-efficacy and tend to have the ability to manage demanding workplace conditions. Compared with other employees, those with high self-efficacy are allegedly more likely to experience flow [30] and have higher levels of engagement [31]. Third, individuals with high CSE engage in more effective coping efforts [32], which generates greater work engagement for individuals with high CSE compared with those with low CSE [33]. Accordingly, we propose the following hypothesis:
Hypothesis 1 (H1).
CSE are positively related to work engagement.

2.2. Mediating Role of Informal Field-Based Learning

IFBL is defined as “engaging in intentional self-directed behaviors aimed at learning new, work-oriented, and organizationally valued content outside of a formal learning program” (p. 16) [15]. IFBL encompasses three dimensions: (1) Experimentation and new experiences refer to seeking, experiencing, and performing new tasks differently. (2) Feedback and reflection refer to seeking feedback and advice from colleagues or professionals. (3) Vicarious learning behaviors refers to intentionally observing other people work and learning from them through talking. Employees engage in IFBL while completing work tasks or interacting with other people in their workplaces outside formally designated and structured learning contexts. IFBL can be characterized as informal, intentional, and self-directed. In other words, IFBL does not occur either unconsciously or accidentally. During IFBL, employees are conscious of and active in acquiring knowledge and skills to attain specific goals. Furthermore, they set learning goals and are responsible for the entire learning process rather than a trainer or instructor sharing responsibility for their learning. Within informal learning literature, engagement in informal learning has been linked to job performance, manager effectiveness, job satisfaction, organizational commitment, creativity, and work engagement [17,34,35]. More specifically, engaging in IFBL behaviors helps to develop employee skills and promotes important individual outcomes [15].
IFBL is considered a learning behavior that occurs in particular contexts. Therefore, both the context of the job and the individual predispositions and characteristics of the learner are important. Previous research indicates that individual predispositions and characteristics have a salient effect on informal learning, such as learning goal orientations [36], the Big Five dimensions of personality [37], self-efficacy [37], learner motivation [38], and individual regulatory foci [15]. These positive individual characteristics can provide individuals with more personal resources. According to COR theory, individuals with more personal resources have greater control over situations and can manage difficulties more effectively [39]. They use existing resources to generate other resources and create resource caravans, resulting in positive outcomes [39]. CSE is an individual disposition that influences how individuals achieve goals and manage difficulties. It is connected to people’s self-appraisals under work conditions [9]. Researchers emphasize the key role of CSE in promoting proactive behavior. Therefore, we propose that employee CSE has a positive relationship with IFBL.
First, employees with high CSE actively pursue positive outcomes; they perceive learning as valuable and act on it [40]. Previous research indicates that individuals with high CSE have a high learning goal orientation [41]. Moreover, there is evidence of a link between learning goal orientation and informal learning [42]. Additionally, employees with high CSE are more goal-directed and committed to pursuing their goals. Further, they tend to set goals to attain higher goal attainment [23]; therefore, it is possible that they are more likely to engage in IFBL on the job to achieve their goals. Employees with a positive CSE focus more on the positive characteristics of the task, which consequently evokes motivation and enthusiasm [43]. These individuals are more likely to perceive challenging tasks as opportunities to acquire skills and improve themselves [44]. They tend to seek greater challenges and try challenging tasks [45], which are important activities during IFBL. Researchers indicate that these individuals are more likely to engage in feedback-seeking behaviors and perform tasks that require the acquisition of new skills [38]. In conclusion, individuals with high CSE feel confident to control their lives, and they are more likely to engage in IFBL because they have a greater feeling of control over learning outcomes. They believe that they can successfully acquire knowledge and skills and achieve goals by taking a series of proactive learning actions. Overall, individuals with positive CSE greatly value learning and development and possess greater emotional resources. Accordingly, they are expected to engage in more IFBL.
Moreover, we expect IFBL to enhance work engagement. While CSE may directly influence work engagement, it can also have an indirect effect on work engagement through IFBL. Work engagement, which reflects employees’ active and energetic state at work, refers to “a positive, fulfilling, work-related state of mind that is characterized by vigor, dedication, and absorption” (p. 74) [2]. Research shows that work engagement is mainly influenced by individual and organizational factors [1]. This study asserts that IFBL has a direct positive impact on work engagement. According to COR theory, individuals strive to maintain, protect, and build valuable resources. Individuals with more resources are not easily affected by the loss of resources and are more capable of acquiring new resources [26]. IFBL can help employees obtain rich resources and avoid losing resources. Simultaneously, abundant resources help employees generate a positive state. First, employees acquire valuable knowledge and skills and accumulate work experience by engaging in informal learning, which helps them perform better at work and attain goals [46]. Therefore, IFBL provides important knowledge resources for employees to achieve their work goals, which gives them psychological identification with work and makes them more willing to put in more effort. Second, when employees engage in IFBL, they need to experience, practice, interact with others, and so on. When employees are involved in the workplace to acquire knowledge and skills, they are more likely to be absorbed and deeply engrossed in their work, thereby improving their work engagement. In conclusion, employees can enrich themselves, reduce negative emotions such as anxiety, and generate positive psychological resources through IFBL. Research shows that informal learning can help employees overcome negative emotions, generate positive emotions, and sustain engagement and productivity [47]. Richter et al. found a positive relationship between informal learning activities and work engagement [48]. Through a meta-analysis, Cerasoli et al. found that participating in informal learning activities enhances employees’ work engagement [34]. Therefore, we propose that employees with high CSE may engage in IFBL more frequently to acquire knowledge and skills, which further enhances their work engagement. In conjunction, we expect CSE to be indirectly related to work engagement through IFBL.
Hypothesis 2a (H2a).
CSE are positively related to IFBL.
Hypothesis 2b (H2b).
IFBL mediates the relationship between CSE and work engagement.

2.3. Moderating Role of Problem Solving

Work design refers to “the content and organization of work tasks, activities, relationships, and responsibilities” (p. 662) [49]. Integrating previous work characteristics, Morgeson and Humphrey further proposed knowledge characteristics, which reflect that a job can be designed to increase the requirements of knowledge, skills, and abilities [18]. Specifically, problem solving is a knowledge characteristic associated with work engagement [1]. Problem solving is defined as “the degree to which a job requires unique ideas or solutions, and reflects the more active cognitive processing requirements of a job” (p. 1323) [18]. Problem solving encompasses formulating unique or innovative ideas or solutions, managing non-routine problems, avoiding mistakes, and recovering from errors [50,51]. A job with high problem solving tends to be related to tasks that require creativity and information processing [52]. While problem solving may motivate and satisfy employees [18], it usually requires enhanced mental demands and considerable cognitive resources, thereby enhancing cognitive ability requirements. Individuals in jobs with high decision-making tend to benefit from innovative learning environments [53].
Based on the JD-R model, both job resources and personal resources have an important influence on work engagement. The JD-R model indicates that job resources have been related to strengthening work engagement due to a motivational process [54]. According to COR theory, in addition to job resources, personal resources can also affect work engagement [5]. Employees who possess personal resources are energetic, are strongly involved in their work, and are fully concentrated on their work [55]. In the present study, we used the perspectives of both job resources and personal resources in the JD-R model and COR theory; we tested the interaction of job characteristics with IFBL. This study asserts that problem solving can reinforce the relationship between employees’ IFBL and work engagement, which consequently enhances the indirect effect of CSE on work engagement via IFBL. Job resources are instrumental resources that enable employees to concentrate on work tasks. Therefore, in resource-rich environments, tasks can be successfully completed, hence automatically achieving higher goals. Empirical evidence shows that both job and personal resources drive work engagement [56]. We specifically focus on problem solving as a resource because it is a motivational job characteristic that motivates employees by generating meaningfulness, responsibility, and knowledge of the results. Employees with job resources that help achieve work goals are more likely to invest energy and resources in their work roles [57,58]. Jobs with high levels of problem solving enable employees to work in challenging and innovative contexts, which enables them to feel competent at work [59]. In these jobs, employees are expected to be more motivated and satisfied [58]. When employees engage in IFBL, they have more opportunities to generate new ideas and solutions. Additionally, an increase in problem solving allows employees to make use of these learning outcomes to improve their work. If employees who engage in IFBL have a job with creative problem-solving requirements, they will put in considerable effort in solving problems and engendering ideas for work improvement. Moreover, a job with high problem solving requires innovative solutions and advanced cognitive ability, which enhances employees’ sense of value at work. In this situation, employees who engage in informal learning activities are more likely to give valuable advice for improving performance and are motivated to put in effort in creative processes that result in work involvement and absorption. Therefore, engaging in problem-solving activities, coping with uncertain problems, and learning novel skills to perform the job may reinforce the value of IFBL.
Hypothesis 3 (H3).
Problem solving moderates the effect of IFBL on work engagement such that the relationship is stronger when problem solving is higher rather than lower.
We propose a moderated mediation model in which the indirect effect of CSE on work engagement through IFBL is moderated by problem solving. According to the JD-R model and COR theory, both job resources and personal resources can predict work engagement [5], and these resources do not exist in isolation [60]. Problem solving is one of the typical job resources that are motivating and satisfying for employees. Under the condition that problem solving is high, individuals are more likely to have opportunities to make use of knowledge and skills obtained from IFBL to complete and improve their work, strengthening the mediating role of IFBL in accounting for the association between CSE and work engagement. In situations where employees have a job with high problem-solving requirements, they are confronted with tasks that involve diagnosing and solving non-routine problems that are new and unknown [61]. High-CSE individuals have greater emotional resources to cope with these challenging situations and adopt a series of informal learning behaviors to improve themselves [62]. Such employees try their best to apply their knowledge and skills to improve their jobs, which could drive their work engagement positively. We thus anticipate that as problem-solving tasks performed by high-CSE employees increase, they are more likely to conduct IFBL, and as a result, their levels of work engagement will be higher. In contrast, jobs with low problem solving involve normal and routine tasks, which reduces the chances for employees to utilize their abilities. On the one hand, when problem solving is low, employees with high CSE are less likely to perceive their work as important and challenging; they are less willing to increase their work engagement via engaging in IFBL. On the other hand, high-CSE workers who experience low levels of problem solving already feel that they cannot fulfill their talents and potential, and perceive IFBL as less important for them to be fully engaged in their work. Therefore, for jobs with high problem solving, the indirect effect of CSE on work engagement via IFBL is higher. We propose the following hypotheses:
Hypothesis 4 (H4).
Problem solving moderates the indirect relationship between CSE and work engagement via IFBL such that the relationship is stronger when problem solving is higher rather than lower.

3. Method

3.1. Sample and Procedure

Two hundred and eighty native respondents recruited from a professional survey platform recognized by many authoritative international journals [63,64], Credamo, evaluated the content adequacy of the generated scale items. The respondents are employees from multiple companies located in diverse provinces (e.g., Guangdong, Jiangsu, and Zhejiang, given their internet protocol (IP) addresses recorded by the platform) of China.
In this platform, similar to Amazon Mechanical Turk, each participant was assigned an anonymous unique respondent ID when s/he registered the account [63]. We ensured validity and legitimacy through three procedures. First, we added a detection item to identify problematic responses [64]. Second, we use the quality control service of this platform to improve the quality and security of data. Each ID can only be completed once and intelligent man-machine verification is conducted before completing the survey. Third, at the beginning of the questionnaire, we briefly informed participants that the survey was about organizational management and informal learning, all the participation process was voluntary and anonymous, and their responses would only be used for academic purposes. Only those providing online informed consent continued in the study.
To reduce common method bias, we conducted a two-wave questionnaire survey and provided participants with construct definitions of the four items in the first wave. For the first-wave data collection (24 August 2021, Time 1, T1), demographic information, CSE, IFBL, and problem solving were evaluated at Time 1. A total of 280 participants completed the first-wave survey and were paid RMB ¥10.00, which follows the suggestion in previous studies [63]. After two weeks (8 September 2021, Time 2, T2), we conducted the second survey to assess work engagement. A total of 231 completed our second-wave survey (response rate: 82.5%) and were paid another RMB ¥10.00. Each participant’s ID was used to match the survey at two different time points. Among the 231 respondents, the average age of the respondents was 30.990 years (SD = 6.625), and 68.4% of them were female. The average organizational tenure was 5160 years (SD = 3.912).

3.2. Measures

All English-based measures were translated into Chinese according to the “translation/back-translation” procedures. Unless otherwise indicated, the questionnaire items were evaluated on a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree).
Core self-evaluations: CSE was assessed using a 12-item scale developed in [21]. Sample items included “I complete tasks successfully” and “Overall, I am satisfied with myself” (α = 0.900).
Informal field-based learning: We evaluated IFBL using a 9-item scale developed in [15]. Sample items include “Actively seeking feedback from others” and “Intentionally observing someone do his or her job” (α = 0.798).
Work engagement: We evaluated work engagement using a 9-item scale developed by Schaufeli et al. [65]. Work engagement was measured on a 7-point Likert scale (1 = Strongly disagree, 7 = Strongly agree). Sample items included “When I get up in the morning, I feel like going to work” (α = 0.924).
Problem solving: We evaluated problem solving using a 4-item scale developed in [18]. Sample items include “The job requires unique ideas or solutions to problems” (α = 0.703).
Control variables: Following the existing research, we controlled for age, gender (0 = “female” and 1 = “male”), and organizational tenure, which have been shown to predict informal learning and work engagement [1,34,66].

4. Results

4.1. Correlations among Study Variables

Table 1 shows the means, standard deviations, and inter-correlations associated with our variables. The results show that CSE was positively correlated with IFBL (r = 0.692, p < 0.01) and work engagement (r = 0.705, p < 0.01). IFBL was further shown to be positively correlated with work engagement (r = 0.677, p < 0.01). Problem solving was positively correlated with IFBL (r = 0.655, p < 0.01) and work engagement (r = 0.597, p < 0.01).

4.2. Confirmatory Factor Analyses

Using MPLUS 8.3, we conducted confirmatory factor analyses (CFA) to examine the discriminant validity of all the variables, namely, CSE, IFBL, work engagement, and problem solving. Table 2 clearly shows that the chi-square of each of the other models shows a significant increase compared with that of the four-factor model (M1). Since the four-factor model (M1: χ2 = 666.106, df = 344, CFI = 0.910, TLI = 0.901, RMSEA = 0.064, SRMR = 0.053) is better than the other fit indices, we conclude that the four variables are empirically distinct from each other, representing four distinct constructs.

4.3. Common Method Bias

We used Harman’s single-factor test of common method variance to assess the common method bias in this study [67]. All the items in our study were loaded into exploratory factor analysis, and the results show that the first factor accounted for 38.91% of the covariance between the measures, which was less than 40%. Moreover, the results show that the goodness-of-fit statistics of the one-factor model were significantly poorer than those of other models (Table 2). We added a CMV factor to the measurement model. However, the model fit indicators of the five-factor model were not significantly better than those of the four-factor model (ΔCFI = +0.022, ΔTLI = +0.019, ΔRMSEA = −0.007, ΔSRMR = +0.070). We conducted a multicollinearity test based on variance inflation factors (VIFs). Our estimations showed that all the VIFs of the latent variables ranged from 1.207 to 2.431, which is below the acceptable level of 10. Therefore, common method variance was not a pervasive issue in this study.

4.4. Hypothesis Testing

The results of Model 2 (see Table 3) show that CSE was positively related to work engagement (β = 0.692, p < 0.001). Hence, Hypothesis 1 is supported. The results of Model 7 in Table 3 show that CSE is positively related to IFBL (β = 0.692, p < 0.001). As shown in Model 3, when controlling for CSE, IFBL is positively related to work engagement (β = 0.358, p < 0.001). Additionally, to test the indirect effect, we used bias-corrected bootstrapping techniques (5000 replications). Consistent with our expectations, the results show that CSE had an indirect effect on work engagement via IFBL (indirect effect = 0.248, SE = 0.044, 95% CI [0.162, 0.334]). Moreover, the 95% confidence interval excluded zero (see Table 4). Considering that CSE was still related to work engagement (β = 0.444, p < 0.001), we conclude that IFBL partially mediated the relationship between CSE and work engagement. Hence, Hypothesis 2 is supported.
The results of Model 5 in Table 3 show that the interaction term between IFBL and problem solving is significant (β = 0.150, p < 0.05) (work engagement as the dependent variable). According to the recommendations of Preacher et al. [68], we conducted simple slope analyses. As shown in Figure 2, the slope test showed that the effect of IFBL on work engagement was stronger when the values of problem solving were higher. The results of the interaction term, slope test, and Figure 2 strongly support Hypothesis 3.
Hypothesis 4 suggests that problem solving would moderate the strength of the mediated relationship between CSE and work engagement via IFBL such that the mediated relationship would be positive and stronger when problem solving is high than when it is low. Table 5 shows the results of the moderated mediation analysis. Following the procedures suggested by Preacher et al. [69], we examined the conditional indirect effects of CSE on work engagement via IFBL at two values of problem solving: one standard deviation above the mean score of problem solving (high-level condition), and one standard deviation below the mean score of problem solving (low-level condition). As shown in Table 5, the conditional indirect effect was significant at the higher level of problem solving (95% CI = [0.215, 0.680]) and at the lower level of problem solving (95% CI = [0.082, 0.394]). The between-conditions difference was significant (95% CI = [0.005, 0.345]). Hence, Hypothesis 4 is supported.

5. Discussion

This study aims to examine the effect of employees’ CSE on their work engagement, along with its underlying mechanisms and boundary conditions. Based on the JD-R model and COR theory, we examined the mediation effect of IFBL on the relation between CSE and work engagement and the moderating role of problem solving as a job resource in the mediation process. We hypothesized a mediation effect of IFBL between CSE and work engagement and a moderation effect of problem solving on the relationship between IFBL and work engagement. Comprehensively, we expected that the hypothesized mediation path would be moderated by problem solving.
As expected, the results of the survey conducted on 231 individuals support our contention that CSE is significantly associated with work engagement, and that IFBL mediates this relationship. Specifically, the relationship between CSE and work engagement via IFBL is statistically significant if problem solving is high and vice versa. All the hypotheses are supported.
The results show a significant, positive correlation between CSE and work engagement. This implies that employees with high CSE have a more positive outlook on their jobs. Thus, they will put more effort into their work and be more involved in it. Previous research has also found a significant, positive correlation between CSE and work engagement [10,11,12]. This study agrees with the predictions from the COR theory; high-CSE individuals have more personal resources, so they are intrinsically motivated to pursue their goals and actively engage in work, enabling them to strive and focus on their tasks.
Moreover, the results demonstrate that IFBL plays a mediating role in CSE and work engagement. As a valuable personal resource, CSE has an important positive impact on employees’ informal learning. Many studies have shown that IFBL can be affected by personality traits, such as promotion focus and future work self [15,16,17]. Therefore, employees with positive CSE greatly value learning and development, possess greater emotional resources, and are more likely to engage in IFBL. Furthermore, studies have revealed that informal learning activities enhance employees’ work engagement [34]. When employees obtain valuable knowledge and skills and accumulate rich resources by engaging in IFBL, they devote themselves to work in a more active and fuller state of mind and can realize their work value. We also found support for the partial indirect path from CSE to work engagement via IFBL. The results reveal that IFBL is the factor that links the relation between CSE and work engagement.
In addition, we also found that problem solving moderated the relationship between IFBL and work engagement. In particular, the mediating relationship between CSE and work engagement through IFBL was stronger when problem solving was high. According to the JD-R model, job resources and personal resources are the main predictors of work engagement [70]. Under conditions of high problem solving, employees who engage in IFBL have more resources for work improvement and invest effort in their work and vice versa. Additionally, a job design with high problem solving creates a more innovative environment for these high-CSE employees, which further enables and inspires them to engage in IFBL and become engrossed in their work. In contrast, under conditions of low problem solving, the role of IFBL in driving employees’ work engagement is limited by job resources, which further weakens the indirect effect of CSE on work engagement via IFBL. Our findings contribute to the literature and management practices.

5.1. Theoretical Implication

This study contributes to the literature in the following several respects.
First, previous studies investigated the antecedents of work engagement, such as conscientiousness [1], proactive personality [7], the Big Five personality traits, and trait emotional intelligence [71]. CSE is a broad personality trait that is a significant predictor of job satisfaction and work-related well-being. However, the research on its relationship with work engagement is scant [11]. Recent studies have begun to investigate CSE as a personal resource that stimulates work engagement [10]. We add to these recent studies and adopt COR theory to link CSE with work engagement, providing a theoretical perspective to understand how CSE influences employees’ work engagement.
Second, this study examines the relationship between CSE and IFBL and finds that CSE is positively related to IFBL. This highlights the important role of CSE in a learning context, furthers our understanding of IFBL, and provides useful insights into how to motivate proactive informal learning in workplaces. This study further expands the scope of the antecedents of IFBL, recalling the study by Wolfson et al. [15]. Moreover, by examining IFBL as a mediating variable in the relationship between CSE and work engagement, we respond to a recent call in the organizational literature to investigate the underlying mechanisms that link CSE to employee work engagement [10,13]. Although the literature on work engagement notes that informal learning can affect work engagement [34,48], it is unclear whether IFBL has a similar effect. This study enables researchers to better understand the relationship between work engagement and its antecedents and responds to calls for research on the mechanism of the personality traits influencing work engagement [14].
Third, in light of COR theory, this study sheds new light on the underlying process as well as the boundary conditions of the CSE–engagement relationship. Considering problem solving as a moderator, we extend previous studies by proposing that the mediated relationships between CSE and work engagement through IFBL depend on the different levels of problem solving at work. The results of the moderated mediation test show that personality traits and work design have a synthesized effect on work engagement. This study also contributes to the literature by providing evidence of the collective contribution of work design and informal learning behaviors to positive work-related outcomes (e.g., work engagement), hence responding to a recent call in the IFBL literature to expand moderators between IFBL and individual outcomes. Additionally, research on CSE predicts that work engagement is often cross-sectional, which cannot address the issue of causality [20]. This study tests this relationship using time-lagged data to provide more reliable results on the antecedents of work engagement and its mechanism.

5.2. Practical Implications

Our study also has important practical implications.
First, according to our findings, employees’ CSE promote IFBL and work engagement. Organizations and managers can use the CSE scale to assess employees’ level of CSE to determine whether individuals are positive and self-confident. Organizations can also utilize experiential exercises to ascertain individuals’ reactions to different situations at the workplace and provide assistance programs to help employees with different levels of CSE learn and improve at work.
Second, organizations might adopt IFBL while undertaking practices at work to help employees improve their job skills and careers. Moreover, regarding workplace practices, enterprises can implement appropriate management measures to provide opportunities for employees’ IFBL and support their learning activities. For example, organizations should create practices and learning opportunities for employees, assisted with appropriate resources and support. Moreover, enterprises may encourage employees to set their own goals and plans and actively participate in IFBL, which can help employees do their best at work and generate a state of energetic engagement.
Third, organizations can improve employee engagement by designing jobs and motivational job characteristics that particularly consider problem solving in job design. Enterprises might encourage employees to formulate innovative and novel solutions and ideas at work and moderately increase the challenge of work. Through IFBL, organizations can promote an organizational environment and culture in which employees devote themselves to work engagement.

5.3. Limitations and Future Directions

First, although the data were collected in two waves, we could not easily determine the causal relationship between the variables. Owing to time and resource constraints, CSE, IFBL, and problem solving in this study were collected simultaneously. Further, all the variables were based on self-report. Although the statistical control method was adopted in this study to control for common method bias within an acceptable range, it was still impossible to avoid possible common method bias [67]. Future research should consider employing experience-sampling methods and multiple sources to collect data and conduct repeated verification of the conclusions of this study to enhance the accuracy, reliability, and validity of the conclusions.
Second, while we discuss the relationship between CSE and work engagement, future studies should explore other antecedents of work engagement such as self-efficacy and psychological capital [72,73]. Additionally, we find that IFBL is an intervening process mechanism that can link CSE and work engagement. Hentrich et al. noted that the impact of CSE on work engagement might be generated by employees’ emotions and behaviors [13]. Therefore, this should be further explored to consider career adaptability, career-related behaviors, etc. [14].
Third, we show that CSE promotes work engagement by identifying the moderating mechanism of problem solving in the relationship between IFBL and work engagement. Future studies should examine other boundary conditions of the IFBL–work engagement relationship, especially other motivating job characteristics such as skill variety, job complexity, and job autonomy.

6. Conclusions

To conclude, the results support our contention that CSE was positively related to work engagement and that IFBL mediates this relation. Specifically, the relationship between CSE and work engagement via IFBL is statistically significant if problem solving is high and vice versa. The study helps to clarify a mechanism and a boundary condition of the link between CSE and work engagement. From a practical perspective, organizations can take advantage of the CSE scale to assess employees’ level of CSE, create practices and learning opportunities for employees, and improve employee engagement by designing jobs.

Author Contributions

Conceptualization, Y.M. and L.Z.; methodology, Z.Q.; software, Y.M.; formal analysis, Y.M. and Z.Q.; investigation, Y.M; writing—original draft preparation, Y.M. and Z.Q.; writing—review and editing, Y.M., Z.Q. and L.Z.; supervision, L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 71672190.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to thank all the anonymous reviewers and the editors for their constructive comments and suggestions for this paper.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 14 05319 g001 550
Figure 1. Research Model.
Figure 1. Research Model.
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Figure 2. Moderating effect of problem solving on the IFBL–work engagement relationship.
Figure 2. Moderating effect of problem solving on the IFBL–work engagement relationship.
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Table
Table 1. Means, Standard Deviations, and Correlations.
Table 1. Means, Standard Deviations, and Correlations.
MeanSD1234567
1. Gender0.3200.466
2. Age30.9906.6250.005
3. Tenure5.1603.9120.1090.752 **
4. CSE4.0230.5950.0020.0290.132 *(0.900)
5. IFBL4.1560.470−0.0120.0590.1120.692 **(0.798)
6. Work Engagement4.8930.928−0.0200.1040.196 **0.705 **0.677 **(0.924)
7. Problem Solving3.9040.6260.0230.0700.1020.488 **0.655 **0.597 **(0.703)
Note. n = 231. Internal consistency coefficients are shown in parentheses on the diagonal. For gender, 1 = male, and 0 = female. Age was coded in years. Tenure is coded in years. Abbreviations: CSE = core self-evaluations, IFBL = informal field-based learning. * p < 0.05. ** p < 0.01.
Table
Table 2. Results of Confirmatory Factor Analyses (n = 231).
Table 2. Results of Confirmatory Factor Analyses (n = 231).
Modelχ2dfCFITLIRMSEASRMR
M1: CSE, PS, IFBL, WE666.1063440.9100.9010.0640.053
M2: CSE+PS, IFBL, WE809.6283470.8700.8580.0760.062
M3: CSE+PS, IFBL+WE882.4353490.8500.8380.0810.069
M4: CSE+PS+IFBL+WE1057.8183500.8010.7850.0940.070
Note. n = 231. All alternative models were compared with the hypothesized model (M1). Abbreviations: CSE = core self-evaluations, PS = problem solving, IFBL = informal field-based learning, WE = work engagement.
Table
Table 3. Summary of regression analysis.
Table 3. Summary of regression analysis.
Variable Work EngagementIFBL
Model 1Model 2Model 3Model 4Model 5Model 6Model 7
Gender−0.05−0.033−0.028−0.039−0.042−0.029−0.012
Age−0.1090.006−0.013−0.074−0.087−0.0640.051
Tenure0.2840.1040.1100.174 *0.184 *0.164−0.016
CSE 0.692 ***0.444 *** 0.692 ***
IFBL 0.358 ***0.486 ***0.577 ***
Problem Solving 0.267 ***0.280 ***
IFBL * Problem Solving 0.150 *
R20.0450.5100.5760.5160.5280.0150.480
ΔR20.0450.4640.5310.4700.0120.0150.465
F3.589 *58.690 ***61.124 ***47.901 ***41.753 ***1.14952.178 ***
Note. n = 231. * p < 0.05. *** p < 0.001.
Table
Table 4. The indirect effect of CSE on work engagement through IFBL.
Table 4. The indirect effect of CSE on work engagement through IFBL.
EffectBoot SEBoot LLCIBoot ULCI
CSE→IFBL→WE0.2480.0440.1620.334
Note. n = 231; number of bootstrap samples = 5000; level of confidence = 95 percent; LLCI = lower confidence interval; ULCI = upper confidence interval; SE = standard error; CSE = core self-evaluations, IFBL = informal field-based learning, WE = work engagement.
Table
Table 5. Moderated mediation results across levels of problem solving.
Table 5. Moderated mediation results across levels of problem solving.
Problem SolvingEffectBoot SEBoot LLCIBoot ULCI
High (+1 SD)0.4310.1200.2150.680
Low (−1 SD)0.2350.0810.0820.394
Difference0.1960.0890.0050.345
Note. n = 231; number of bootstrap samples = 5000; level of confidence = 95 percent; LLCI = lower confidence interval; ULCI = upper confidence interval; SE = standard error; SD = standard deviation.
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Ma, Y.; Qian, Z.; Zhong, L. Influence of Core Self-Evaluations on Work Engagement: The Mediating Role of Informal Field-Based Learning and the Moderating Role of Work Design. Sustainability 2022, 14, 5319. https://doi.org/10.3390/su14095319

AMA Style

Ma Y, Qian Z, Zhong L. Influence of Core Self-Evaluations on Work Engagement: The Mediating Role of Informal Field-Based Learning and the Moderating Role of Work Design. Sustainability. 2022; 14(9):5319. https://doi.org/10.3390/su14095319

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Ma, Yu, Zhichao Qian, and Lifeng Zhong. 2022. "Influence of Core Self-Evaluations on Work Engagement: The Mediating Role of Informal Field-Based Learning and the Moderating Role of Work Design" Sustainability 14, no. 9: 5319. https://doi.org/10.3390/su14095319

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