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Article

Impact of Future Work Self-Salience on Proactive Behaviors: An Integrative and Comparative Study of Multiple Proactive Behaviors

by
Chen-Lu Yang
1,
Yuhui Li
1,* and
Kun Qiao
2
1
School of Labor and Human Resources, Renmin University of China, Beijing 100872, China
2
School of Economics and Management, Dalian University of Technology, Dalian 116024, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(20), 14912; https://doi.org/10.3390/su152014912
Submission received: 19 August 2023 / Revised: 25 September 2023 / Accepted: 11 October 2023 / Published: 16 October 2023
(This article belongs to the Special Issue Occupational and Organizational Psychology and Behavior Analysis for Sustainable Development)
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Abstract

:
The concept of Future Work-Salience (FWSS) plays a pivotal role in ensuring sustainable employability. Previous studies on FWSS have primarily focused on career-related outcomes, neglecting the broader domain of proactive behaviors. Furthermore, the existing literature lacks research that has comprehensively compared and analyzed multiple categories of proactive behavior within the same study. Drawing on the conservation of resources theory, the present study aims to examine the effects of FWSS on pro-organizational, prosocial, and pro-self-proactive behaviors via career planning, and the potential moderator of uncertainty avoidance. Data were collected using two-wave questionnaires from 191 Chinese employees and analyzed using PLS-SEM. The results showed that FWSS positively affected pro-organizational, prosocial, and pro-self-proactive behaviors via the mediator of career planning. Uncertainty avoidance weakened the positive effect of career planning on pro-self-proactive behavior, but did not significantly moderate the relationship between career planning and pro-organizational or prosocial–proactive behaviors. This study reveals the positive effects of FWSS on organizations, colleagues, and individuals, as well as the underlying mechanism and boundary conditions. By comparing the similarities and differences among multiple proactive behaviors, the theoretical applications and research scope of proactive behaviors were expanded. Finally, we have provided effective management suggestions for organizations on how to improve employees’ proactive behaviors.

1. Introduction

In an era defined by the imperative of sustainability, research endeavors are increasingly tasked with scrutinizing the multifaceted dimensions of this paramount global objective. One such pivotal element that has garnered increasing attention is the Future of Work-Salience (FWSS), a construct deeply interwoven with the fabric of sustainable employability [1]. The evolving landscape of work, characterized by dynamic shifts and evolving paradigms, necessitates a profound understanding of FWSS and its implications within the broader sustainability framework.
Rapid changes in employment patterns have made individual careers volatile and boundless. The obligation to take charge of one’s own career has progressively transitioned from employers to employees, and employees must actively manage their careers to ensure sustainable employability [2]. To determine the motivation for individuals’ proactive career management, “future work self” was proposed [1]. This construct is used to describe an individual’s imagined future self, which includes career aspirations and hopes. The future work self-salience (FWSS) is an essential attribution of this construct and relates to how easily individuals can envision and define their FWS [1,3]. The initial focus of research on FWSS was behaviors and outcomes related to ones’ career development, but insufficient attention was paid to current and more direct work behaviors. Research has found that FWSS positively influences on employees’ career behaviors and outcomes such as proactive career behaviors [1,4], employment status [5], career exploration [6], calling [7], and job search behaviors [8]. The research has suggested that individuals’ motivation and behavior may be influenced by the degree of future orientation in their self-system, indicating that FWSS may have more immediate and relevant outcomes related to current work [9]. Research has shown a positive correlation between FWSS and creativity [10], work performance [9], and employee well-being [11]. However, research on FWSS has received insufficient attention regarding current work behaviors and this field requires more empirical research.
Proactive behavior refers to behaviors that individuals initiate with the intention of improving a situation or themselves, without being prompted or instructed by external factors [12]. Such behavior not only promotes individual career development but also helps to establish positive interpersonal networks and is beneficial to organizational change and performance [13]. Proactive behaviors constitute a cornerstone in nurturing sustainable organizational ecosystems, aligning seamlessly with broader sustainability objectives. These actions bolster an organization’s long-term viability and competitiveness, underscoring their intrinsic connection with organizational sustainability. Therefore, improving employees’ proactive behavior is crucial. However, previous research has paid less attention to how individuals’ egoistic motivation, specifically their FWSS, enhances proactive behavior. This gap limits our understanding of the antecedents of proactive behavior. Investigating how FWSS influences employees’ proactive behavior can enhance our theoretical understanding of the effect of FWSS on current work, which may lead to mutual benefits for both employee career development and organizational outcomes. Moreover, the current study enriches our understanding of the reasons for proactive behavior from the perspective of egoistic motivation, which can also guide the improvement of employees’ proactivity in practice.
Proactive behavior can be classified into three categories based on its focus: pro-organizational proactive behavior, which targets the organization; prosocial–proactive behavior, which targets the work team or coworkers; and pro-self-proactive behavior, which targets oneself [12,14]. Most of the research has centered on either the general measurement of proactive behavior or a single form of proactive behavior, such as voice behavior. Few studies have simultaneously compared and discussed various categories of proactive behavior within the same investigation, leading to some degree of isolation in the existing literature [12,14]. This study aimed to concurrently measure and compare three categories of proactive behavior to enhance our understanding of both the commonalities and differences among these behaviors.
Proactive behavior is a behavioral process that involves three key stages of “anticipation–planning–action” rather than a single or isolated action [12]. However, only a few empirical studies have tested this process. According to the conservation of resources theory, FWSS serves as an “anticipation” and motivational resource, encouraging employees to engage in career “planning” in order to develop strategies for acquiring career resources. Pro-organizational, prosocial, and pro-self-proactive behaviors are all likely to lead to valuable career resources such as status, respect, interpersonal relationships, and performance improvement [15,16,17], which can be considered the “action” taken to obtain career resources. Hence, the primary objective of the present study is to explore how individuals’ FWSS affects pro-organizational, prosocial, and pro-self-proactive behaviors and to explore the role of career planning as a mediator.
Although proactive behavior can bring resources to individuals, there is also a risk of resource loss [14]. This poses a potential challenge for individuals in achieving their FWS as they may lose the necessary resources to reach their envisioned career goals. Individuals with a strong orientation to avoid uncertainty tend to act in a way that minimizes uncertainty and risk [18]. As different proactive behaviors have different risks of resource loss, individuals’ uncertainty avoidance may affect their choice of proactive behavior. Therefore, we examined how uncertainty avoidance moderates the association between career planning and different categories of proactive behavior, and compared the degree of these moderating effects.
The present study offers significant contributions to the existing literature in three main areas. First, this study adds to the field of research on FWSS by expanding its impact and mechanism on multiple proactive behaviors, thereby enriching the theory and enhancing the understanding of its positive effects on organizations, colleagues, and individuals. Second, by examining multiple proactive behaviors simultaneously, this study enhances the comprehension of the commonalities and differences among them, thereby supplementing the existing research on proactive behavior. Third, by examining the moderator of uncertainty avoidance and comparing the strength of different moderating effects, it provides insight into the factors that influence the extent to which FWSS promotes specific categories of proactive behavior. Figure 1 illustrates the research model used in this study.

2. Theory and Hypotheses

2.1. FWSS and Career Planning

FWS pertains to a representation of one’s future career identity that includes important aspirations and hope related to career [1], which is recognized as a constituent of the dynamic self-system [19], and functions as a motivational resource that can guide individual behaviors [1]. FWSS is an essential attribution of this construct, which concerns the ease and clarity with which individuals can visualize their career identity in the future [1].
Career planning encompasses the efforts and actions taken to shape and guide one’s future career, including setting specific career goals, exploring various career options, and formulating concrete plans to achieve these goals [20]. As suggested by the conservation of resources theory, individuals who possess an abundance of resources are less susceptible to resource depletion threats and are more inclined to invest in acquiring additional resources [21]. FWSS, functioning as a motivational resource, supports individuals in setting goals and generating strategies to achieve them [1]. Specifically, FWSS can aid in recognizing disparities between individuals’ current and future selves [1,22], motivating individuals to develop plans for achieving their ideal future selves. Second, FWSS is advantageous for the process of identity construction, where individuals redefine and clarify their values and aspirations to create a coherent vision of their future, which in turn leads to the development of related strategies [23,24]. Third, through mental simulation of future images, individuals recognize future needs and become aware of situational limitations in obtaining the desired future selves, such as the need for career development resources [25,26]. By engaging in this cognitive process, individuals can envision a range of possibilities for their future, which then enables them to formulate career planning to attain their desired outcomes [27]. Consequently, we proposed the hypothesis:
Hypothesis 1.
FWSS positively influences career planning.

2.2. The Mediating Role of Career Planning between FWSS and Proactive Behavior

The conservation of resources theory suggests that employees’ investments in certain activities depend on the resources they expect to gain from them [28]. Pro-organizational proactive behavior may be considered a way of acquiring career resources, which is beneficial for achieving FWS. First, pro-organizational proactive behavior aims to change the organizational environment, which may lead to better person–environment fit, helping individuals better utilize their resources and thus achieve higher career development [9]. In addition, pro-organizational proactive behavior is often regarded as loyalty and dedication to the organization, which can bring potential resources, such as job control, status, and respect, helping employees perform better and thus achieve higher performance evaluations [15]. Individuals with high performance are presented with greater opportunities for career development, such as promotion [29]. Furthermore, according to the leader-member exchange theory, if subordinates exhibit more pro-organizational proactive behavior, supervisors are more likely to consider them members of the inner circle, resulting in more support and protection from supervisors, opportunities to showcase abilities, challenging tasks, and performance improvement [30]. Employees who have a clear image of their career goals may expect to acquire career resource from engaging in pro-organizational proactive behavior. Namely, employees with a high level of FWSS may consider this behavior as a way of achieving their career goals when making career plans. Building on the previous discussion, we put forward the subsequent hypothesis:
Hypothesis 2.
FWSS positively influences pro-organizational proactive behavior via career planning.
Prosocial–proactive behavior may also be regarded as a way to obtain potential career resources for employees [31]. In terms of interpersonal resources, prosocial–proactive behavior is usually considered selfless. Consequently, prosocial–proactive behavior can lead to the development of good interpersonal relationships with colleagues, with the employee receiving expressions of gratitude and trust from their colleagues. Moreover, their colleagues would provide resources to them in return when needed, such as helping them obtain valuable information and enhancing their political status within the organization [32]. Furthermore, individuals who engage in active assistance to others receive emotional resources such as a sense of achievement and happiness, which can help them perform work tasks more effectively [33]. In terms of knowledge resources, individuals can deepen their existing knowledge and skills and obtain new insights through knowledge exchanges with colleagues when actively helping colleagues at work [34]. Interpersonal, emotional, and knowledge resources are critical to individuals’ career development [35]. Therefore, we speculate that employees with a salient FWS would expect to obtain important career resources through engaging in prosocial–proactive behavior. This suggests that employees with a high degree of FWSS may choose this behavior as a means of achieving their career goals when making career plans. Consequently, we proposed the hypothesis:
Hypothesis 3.
FWSS positively influences prosocial–proactive behavior via career planning.
Pro-self-proactive behavior may also be perceived as a means of obtaining career resources. First, pro-self-proactive behavior means that individuals take the initiative to negotiate their work, including task allocation, role expectations, and desired job changes. This can help them obtain beneficial resources through negotiations. Additionally, it can make the job better suited to their abilities and preferences, thereby promoting the efficient use of existing resources [36,37]. Second, pro-self-proactive behavior includes self-improvement and self-learning, which can help in obtaining the knowledge, skills, and other resources necessary for FWSS. Having more knowledge or skills increases the favorability of leaders and expands their networks, which is an effective way to obtain further career development resources [17]. Finally, engaging in pro-self-proactive behavior may involve actively seeking relevant information regarding one’s personal performance and the expectations of others. This allows individuals to promptly adjust their behavior and respond to environmental demands more effectively [36], ultimately leading to better performance and career development. Therefore, it is possible that individuals with a high level of FWSS may choose pro-self-proactive behavior to acquire the necessary resources to achieve their career goals when making plans. Consequently, we proposed the hypothesis:
Hypothesis 4.
FWSS positively influences pro-self-proactive behavior via career planning.

2.3. The Moderator of Uncertainty Avoidance

Uncertainty avoidance is a proposed dimension of cultural values that refers to the level of unease or perceived threat that individuals experience within a cultural group when encountering uncertain or ambiguous situations [38]. Many studies have demonstrated that cultural values vary across individuals, and the behavior of individuals can be significantly shaped by their personalized cultural values [39]. Employees who score high on the uncertainty avoidance tend to expect reduced ambiguity, have a strong need for predictability and structure, and generally seek security. However, individuals who display a lower degree of uncertainty avoidance are typically more open to new experiences, more comfortable with ambiguity and unpredictability, and more willing to take risks [40].
The conservation of resources theory posits that individuals work towards prevent resource depletion [28]. Proactive behavior is a way to acquire resources but may also carry the risk of resource depletion. First, transforming events that have not occurred into expected outcomes carries psychological risk, because the way events will unfold and how they will be changed before occurring is relatively uncertain [41]. Proactive behavior involves changing the status quo, which often implies facing situational obstacles and requires individuals to be willing to overcome barriers and accept unexpected consequences. For example, new ideas for improving work procedures may create uncertainty and risk, and an individual’s proposal or implementation of change may not necessarily be successful [42]. In addition, proactive behavior may lead to unexpected evaluations such as lower warmth perception and envy from coworkers [43,44]. Second, proactive behavior involves high action costs, including investing money, time, energy, or other valuable resources. If the results of the behaviors do not bring the expected benefits, individuals will face resource costs [45]. Finally, the failure of proactive behavior may have negative consequences for individuals, such as criticism for not following instructions [46], damage to their self-image [47], and negative performance evaluations [14]. Individuals with a high uncertainty avoidance perceive stronger risks in proactive behavior and are more inclined to act within organizational norms and procedures to reduce uncertainty and ensure that their resources are not depleted, thereby reducing their proactive behavior when planning their careers. Individuals who have a low inclination towards uncertainty avoidance are more prone to challenge existing norms and procedures to seek possible benefits [18], thereby increasing their proactive behaviors when planning careers. In other words, uncertainty avoidance negatively moderates the impact of career planning on proactive behavior.
Furthermore, there are other risks associated with pro-self-proactive behavior, besides those previously mentioned regarding all categories of proactive behavior. Pro-self-proactive behavior may be perceived as self-centered and lacking consideration for others, thus negatively affecting the evaluation of supervisors [48]. Furthermore, seeking direct feedback, engaging in career conversations with leaders, and negotiating work roles may also put individuals at risk [13]. This may signal a desire to leave the team or organization, and leaders can interpret it as low commitment [14]. For employees with a strong uncertainty avoidance, the perceived risk of pro-self-proactive behavior is higher because it may not only fail to provide resources for the FWS, but may also jeopardize current job security. Individuals who have a greater tendency towards uncertainty avoidance may opt for less pro-self-proactive behavior than pro-organizational or prosocial–proactive behaviors during career planning. In other words, the strongest negative moderating effect of uncertainty avoidance is on the association between career planning and pro-self-proactive behavior. Based on this, we proposed Hypothesis 5:
Hypothesis 5.
Uncertainty avoidance negatively moderates the association between (a) career planning and pro-organizational proactive behaviors, (b) career planning and prosocial-proactive behaviors, (c) career planning and pro-self-proactive behaviors, (d) and with the strongest negative moderating effect on the association between career planning and pro-self-proactive behaviors.

3. Methods

3.1. Participants and Data Collection

The data collection process of this present study involved a two-wave questionnaire with a one-month interval between them. The survey was conducted anonymously, and the participants were enrolled in a management training course at a university located in China. Participants completed the questionnaire in two waves. This study used a self-assessment method for data collection because it aimed to investigate the commonalities and differences among various forms of proactive behavior. Utilizing the same data source helped to minimize discrepancies arising from diverse evaluators. Additionally, individuals have a clearer understanding of their own behaviors and can better identify the differences between them [13].
At Time 1, participants assessed their FWSS and the demographic information. We also requested that participants provide their commonly used email addresses for the next survey. The survey at Time 1 was conducted using paper-based questionnaires. We administrated the questionnaires during breaks in the class. The survey was initiated by distributing 526 questionnaires to the participants at Time 1. After a waiting period of half an hour, the completed questionnaires were collected and scrutinized. In total, 497 questionnaires were successfully completed and returned, resulting in a response rate of 94.49%, which is considered to be relatively high for survey-based research.
At Time 2, participants assessed their career planning, uncertainty avoidance, and three categories of proactive behaviors. They were required to provide the same email address as at Time 1 to match the data from the two waves. The survey at Time 2 was conducted online, and the survey link was sent to all participants’ reserved email addresses. At Time 2, the researchers sent out 497 electronic questionnaires, out of which 226 valid responses were received, resulting in a response rate of 45.47%. We matched the data based on the consistency of the email addresses in the two waves. In total, 191 samples were successfully matched. We performed an independent samples t-test to evaluate the possibility of response bias between respondents and non-respondents at Time 2. The results demonstrated no statistically significant disparities in gender, age, education, or tenure between these two groups. These findings offer compelling evidence to indicate that response bias was not a predominant concern in our study.
Furthermore, we conducted a sample sufficiency test. Firstly, the sample size in our study aligns with the common guideline of being 5–10 times the number of items in the questionnaire [49]. Specifically, our questionnaire comprises 25 items, which suggests a recommended sample size ranging from 125 to 250. Our study boasts a sample size of 191, well within the established parameters. In addition, we utilized the G*Power 3.1 software for a more detailed assessment. By inputting effect size f2 (0.15 for a medium effect size), α err prob of 0.05, and Power (1-β err prob) of 0.95, the software computed a recommended total sample size of 89. It is noteworthy that our actual sample size of 191 significantly surpasses the computed requirement, further affirming the sufficiency of our sample.
In the valid matched sample, male participants comprised 41.58% of the sample, while females comprised 58.42%. The participants had an average age of 30.12 years and an average tenure of 6.94 years. Regarding the educational distribution, only 0.52% had a junior college degree or lower, while the majority (62.30%) held an undergraduate degree. Additionally, a significant proportion (37.18%) held a postgraduate degree or higher. The sample was distributed across 19 industries, with the financial industry having the highest proportion (15.79%).

3.2. Measurements

All scales in this study were seven-point Likert scales (1 = “strongly disagree” to 7 = “strongly agree”). To maintain the precision of the measurements, the scales utilized in the present study underwent translation from English to Chinese and back-translation.
FWSS. A five-item scale developed by Strauss et al. was utilized to measure FWSS [1], with items such as “This future is very easy for me to imagine”.
Career Planning. The four-item scale created by Bachman et al. was used to measure career planning [50], with items such as “I am thinking ahead to the next few years and plan what I need to do for my career”.
Uncertainty Avoidance. The scale developed by Yoo et al., comprising five items [51], was used to measure uncertainty avoidance. An example item from this scale is “It is important to have instructions spelled out in detail so that I always know what I am expected to do”.
Proactive Behavior. A scale integrated by Belschak and Den Hartog was utilized to measure the three categories of proactive behaviors [14]. Specifically, the pro-organizational proactive behavior scale includes three items such as “I take the initiative to suggest ideas for solutions for company problems”. Prosocial–proactive behavior scale comprises four items, for example, “I take the initiative to share knowledge with colleagues”. Pro-self-proactive behavior scale includes four items such as “I take the initiative to acquire new knowledge that will help my career”.

4. Results

4.1. Data Analysis Technique

The hypothesized relationships were assessed employing Partial Least Squares Structural Equation Modeling (PLS-SEM), a well-established analytical technique in prior management studies known for providing dependable estimates [52,53]. PLS-SEM is especially advantageous for investigating theoretical and causal relationships between constructs [54], particularly in studies characterized by smaller sample sizes [55]. With our sample comprising 191 responses, the application of Smart PLS-SEM was well-suited for our analysis. The procedure involved a two-stage analytical approach. Initially, we meticulously examined the measurement model, ensuring the measures demonstrated both validity and reliability. Following this, we delved into the structural model, evaluating the hypothesized relationships [56]. The significance of the path coefficients was established using a bootstrapping approach with 5000 resamples, in line with the method recommended by Hair and Alamer [54].

4.2. Measurement Model

This study rigorously examined the measurement model using various statistical techniques, including factor loading, construct reliability, and validity assessments. The results, detailed in Table 1, demonstrate that all item factor loadings comfortably exceed the recommended threshold of 0.70 [57]. Moreover, this study assessed the model’s reliability and validity using a combination of Cronbach’s alpha (CA), composite reliability (CR), and average variance extracted (AVE) values. Adhering to the guidelines set forth by [58], it is recommended that acceptable constructs should have values of >0.70 for Cronbach’s alpha. For composite reliability, the suggested threshold is >0.80. In terms of average variance extracted, a value greater than >0.50 is considered acceptable. As presented in Table 1, it is evident that all CA, CR, and AVE values surpass the specified benchmarks. Thus, this study has yielded highly favorable outcomes in the assessment of reliability and validity.
This study further assessed the discriminant validity among the constructs, using two distinctive techniques: the Fornell and Larcker method and the Heterotrait-Monotrait ratio (HTMT). The Fornell and Larcker (1981) is widely utilized in research for evaluating the discriminant validity of constructs. This approach considers the interaction between the square root of the Average Variance Extracted (AVE) and the correlations among the variables [54]. As Table 2 illustrates, the diagonal values consistently exhibit the square root of the AVE outperforming the correlation coefficients for all variable pairs [57]. Additionally, we used the HTMT method. As per [59], an approaching HTMT ratio of 0.90 indicates a potential deficit in discriminant validity among the variables. As depicted in Table 3, the highest observed HTMT value is 0.779, well under the 0.90 threshold. Consequently, both methodologies confirm the good discriminant validity observed in our study.

4.3. Structural Model

Following the validation of the measurement model, the subsequent phase involves assessing the structural model in our study. The structural model’s fitness was appraised by examining the value of the standardized root mean square residual (SRMR) [60]. As per reference [61], an adequate model should possess an SRMR value lower than 0.08. In this study, the SRMR yielded a value of 0.057, falling below the standard threshold of 0.08. This indicates that the model provides a good fit for testing the hypothesized relationships. Furthermore, the R2 values, which denote the extent to which the independent variables account for the variation in the dependent variable, were examined. In accordance with the guidance provided by reference [58], it is recommended that R2 values surpass the threshold of 0.10 to indicate a satisfactory fit of the model [58]. The findings revealed that FWSS explained 11.7% of the variation in career planning, 32.9% in pro-organizational proactive behaviors, 32.4% in prosocial–proactive behaviors, and 57.7% in pro-self-proactive behaviors. Consequently, the R2 values indicate a satisfactory model fit, demonstrating its effectiveness in predicting the dependent variable using the independent variables.
To scrutinize the research hypotheses and evaluate the proposed relationships, this study utilized Structural Equation Modeling (SEM). This analytical approach provides a comprehensive examination of the intricate interplay between variables, allowing for a nuanced understanding of their relationships. The outcomes of the path analysis, delineated in Table 4, furnish valuable insights into a multitude of direct, indirect, and moderating effects within the model. Career planning was positively influenced by FWSS (β = 0.342, t = 5.306, p < 0.001). The findings provided support for Hypothesis 1. Moreover, the results revealed that the indirect effect of FWSS on pro-organizational-proactive behavior through career planning was significant (β = 0.132, t = 3.471, p < 0.01). Consequently, the findings provided support for Hypothesis 2. Based on the results, there was a significant indirect effect of FWSS on prosocial–proactive behavior via career planning (β = 0.063, t = 1.953, p < 0.1). Thus, Hypothesis 3 was supported. The findings also demonstrated a significant indirect impact of FWSS on pro-self-proactive behavior through career planning (β = 0.176, t = 4.213, p < 0.001). The results indicated that the positive association between FWSS and pro-self-proactive behavior was mediated by career planning. Thus, Hypothesis 4 was supported. The results also demonstrated that there was no significant impact on pro-organizational-proactive behavior from the interaction between career planning and uncertainty avoidance (β = −0.011, t = 0.302, p > 0.1). Hypothesis 5a was not supported. The findings revealed that the association between career planning and prosocial–proactive behavior was not significantly moderated by uncertainty avoidance (β = 0.013, t = 0.263, p > 0.1). Hypothesis 5b was not supported. As shown in Table 4, the association between career planning and pro-self-proactive behavior was significantly weakened by uncertainty avoidance (β = −0.063, t = 2.033, p < 0.05). Thus, Hypothesis 5c received support based on these findings. According to the results, it appeared that uncertainty avoidance may have a diminishing effect on the positive association between career planning and pro-self-proactive behavior, but it did not seem to have a significant effect on the association between career planning and pro-organizational or prosocial–proactive behavior. This also implied that uncertainty avoidance had the strongest moderating effect on the association between career planning and pro-self-proactive behavior. Thus, Hypothesis 5d was supported.
Next, we performed a simple slope analysis to explore how uncertainty avoidance moderated the association between career planning and pro-self-proactive behavior. As shown in Figure 2, the association between career planning and pro-self-proactive behavior was stronger under a low level (−1 SD) of uncertainty avoidance, whereas it was weaker under a high level (+1 SD) of uncertainty avoidance.

5. Discussion

Using two-wave questionnaires from 191 Chinese employees, this study explored the effects of FWSS on proactive behaviors, which differ from previous research on FWSS on career-related behaviors. By concurrently measuring and comparing three categories of proactive behavior, this study enhanced our comprehension of the commonalities and differences among these behaviors and addressed the research gap. The results demonstrated that FWSS positively impacts pro-organizational, prosocial, and pro-self-proactive behavior through career planning. Additionally, the study revealed that uncertainty avoidance weakened the association between career planning and pro-self-proactive behavior, and weakened the indirect effect of FWSS on pro-self-proactive behavior through career planning. However, this study did not find evidence to suggest that uncertainty avoidance weakened the association between career planning and pro-organizational or prosocial–proactive behavior.

5.1. Theoretical Contributions

First, one potential implication of this study is that it extends the scope for research on FWSS. Existing study in this field has mainly concentrated on career-related behaviors and outcomes [1,4,5,7]. This study addressed Lin et al.’s suggestion to explore the association between FWSS and current workplace behaviors [9]. Lin et al. (2016) proposed that estimating the impact of FWSS on proactive behavior is difficult [9]. From one perspective, individuals may perceive proactive behaviors as a means of improving their standing and social connections, which, in turn, is beneficial to their career goals. In this context, FWSS may serve as an indicator of increased proactive behavior. From another perspective, employees characterized by a strong FWSS may prioritize investing their resources in activities that align with their future selves, which could lead to a decrease in their engagement in proactive behaviors. This study supports the first viewpoint of Lin et al. [9]. Employees characterized by a strong FWSS tend to exhibit more proactive behavior, which is considered an important means for acquiring career-related resources.
Second, the study contributes to research on proactive behavior. Previous studies have rarely considered and discussed the commonalities and differences between different categories of proactive behavior, leading to some degree of isolation within the literature [12,14]. Enhancing the understanding of common antecedents of diverse proactive behaviors can optimize the efficacy of research in this domain [12]. This study responded to the call and found that FWSS served as a common antecedent of pro-organizational, prosocial, and pro-self-proactive behaviors. FWSS also enriches “reason to do” motivation for different categories of proactive behavior [46]. The uncertainty avoidance only weakened the association between career planning and pro-self-proactive behavior. It indicates the differing levels of risk among different categories of proactive behavior, which means that pro-self-proactive behavior may pose a greater risk of resource loss, whereas pro-organizational and prosocial–proactive behaviors are more secure choices for obtaining career resources.
Third, this study enriches the existing literature by empirically examining the three stages of proactive behavior from a process perspective. Proactive behavior is considered a behavior process that involves three key stages of “anticipation–planning–action” [12]. However, empirical research on this behavioral process is limited. In this study, FWSS was utilized as a variable to capture the “anticipation” stage, career planning was used to represent the “planning” stage, and proactive behavior was used to represent the “action” stage. This current study revealed that FWSS would impact on proactive behavior through career planning, thus confirming the proactive behavior process.
Finally, the present study sheds light on the influence of cultural values. The majority of research on proactive behavior has historically originated from Western cultures [62], likely reflecting a cultural predisposition towards proactivity. In Western societies, individuals not only embrace taking initiative, but it is often an expected component of their professional roles [18]. In contrast, within China’s high uncertainty avoidance culture, there exists a prevalent expectation for individuals to conform to established norms [63], making the inclination towards proactive behavior an area ripe for exploration. Our study, conducted within the Chinese context, corroborates the impact of FWSS on individuals’ proactive behavior, emphasizing the universal relevance of FWSS as a powerful motivational factor. It reveals that individuals, regardless of cultural context, exhibit proactivity in their pursuit of realizing their envisioned future work selves. Notably, this propensity holds true even within cultures characterized by high uncertainty avoidance, where risk aversion is the prevailing norm.
To further probe into the nuanced role of FWSS amidst varying cultural values, we scrutinized the moderating effect of individual uncertainty avoidance on the relationship between FWSS and proactive behavior. This meticulous examination seeks to unveil potential disparities in how FWSS motivates proactive behavior across different cultural perspectives. The study revealed that uncertainty avoidance weakened the association between career planning and pro-self-proactive behavior. However, there was no moderating effect on association between FWSS and pro-organizational or prosocial–proactive behavior. This confirms that FWSS can be a powerful source of motivation [1], allowing individuals to overlook the potential risks of pro-organizational and prosocial–proactive behavior. If individuals engage in pro-organizational and prosocial–proactive behaviors but fail to achieve their intended outcomes, their actions may still be perceived as altruistic and, therefore, they are more likely to be forgiven. However, individuals who have a strong tendency towards uncertainty avoidance are inclined to be cautious about engaging in pro-self-proactive behavior in the pursuit of career resources. Although various fields propose boundaryless careers and encourage individuals to engage in proactive career management in uncertain external environments [2], individuals who score high in uncertainty avoidance may be concerned that engaging in pro-self-proactive behavior could negatively impact their commitment to their current organization and hinder their career development [14]. By delving into these intricacies, our study contributes not only to the broader literature on proactive behavior, but also illuminates the adaptability of motivational factors like FWSS across diverse cultural landscapes. It underscores the importance of considering cultural nuances in comprehending and harnessing individual motivation for proactive behavior, providing valuable insights for both academic research and practical applications in organizational contexts.

5.2. Practical Implications

First, one recommendation for organizations is to prioritize the enhancement of employees’ FWSS, as it can promote various forms of proactive behavior and positively impact organizations and teams. Organizations should prioritize assessing a candidate’s level of FWSS during recruitment. Candidates with a strong FWSS are inclined to display proactive behaviors, thereby contributing positively to the organization and team while fulfilling their career goals. In terms of training, organizations should facilitate employees in identifying their strengths, defining their career direction, and determining the person they aspire to be and the goals they aim to accomplish in their careers.
Second, organizations should consider employees’ proactive behaviors in their performance evaluations and promotion assessments. Proactive employees may be motivated to attain FWSS and acquire career resources. Therefore, organizations should provide proactive employees with expected resource rewards to motivate them to continue engaging in proactive behavior and set a good example for other employees. By establishing rules for performance evaluation and promotion assessments, organizations can specify the rewards that proactive behavior can bring and use institutional safeguards to encourage employees to participate in proactive behavior.
Finally, organizations should adopt a more tolerant and open attitude toward pro-self-proactive behavior. Individuals who score high in uncertainty avoidance often view pro-self-proactive behavior as riskier and fear that leaders will interpret their behavior as having low commitment, which would result in losing job security. Although organizations emphasize that employees should proactively manage their careers, there remains a lack of inclusiveness regarding boundaryless careers. Organizations should provide individuals with more tolerance and support, establish a sounder safeguard mechanism, support employee development of their skills through training, and establish an open platform to encourage them to express their career development aspirations. Only then can organizations truly support employees’ personal development and embrace boundaryless careers. In addition, organizations should provide support to employees who may be less comfortable with uncertainty. This can be achieved through training programs that teach employees how to manage uncertainty and take calculated risks.

5.3. Limitations and Future Directions

Although a two-wave data collection procedure was adopted in this study, career planning and proactive behavior were assessed simultaneously, making it challenging to establish a causal relationship and comprehensively understand the mediator of career planning. Future research can adopt a three-wave data collection approach to measure FWSS, career planning, and proactive behavior separately, which can provide a more accurate verification of the causal and mediating relationships among these variables. Additionally, longitudinal studies should be conducted to determine which categories of proactive behavior are effective in achieving FWSS. Moreover, future research can explore whether leaders or colleagues find that the motivation behind proactive behavior is aimed at obtaining resources for the achievement of the FWS rather than being altruistic, and whether it brings the expected resources to employees.
We acknowledge that the sample size in our study is relatively small. This is partly attributed to the potential attrition of participants in a multi-wave survey, which may lead to a smaller sample size than initially anticipated. In order to address potential limitations associated with our sample size, we employed rigorous statistical techniques such as Partial Least Squares Structural Equation Modeling (PLS-SEM) and bootstrapping. These techniques are widely recognized for their robustness, even when applied to smaller sample sizes. Additionally, the results of the G*Power analysis affirm that the sample size in our study exceeds the minimum threshold required for the chosen statistical methods. Nevertheless, it is imperative to acknowledge that the broader applicability of our findings may be influenced by the sample size. Therefore, for future research endeavors, we recommend replicating and validating our results with larger samples, potentially sourced from different countries or diverse types of populations. This approach would serve to bolster the external validity of our conclusions and provide a more comprehensive understanding of the phenomenon under investigation.
In addition to examining the mediator of career planning between FWSS and proactive behavior from a process perspective, future studies could also investigate other possible mediators from cognitive or emotional perspectives. For example, individuals with high FWSS may experience a greater sense of autonomy and control at work, which can increase their self-efficacy and provide them with the willpower and confidence to overcome obstacles and challenges that are critical factors for proactive behavior. Moreover, individuals driven by FWSS may try to bridge the gap between their desired career goals and their current work situation, resulting in a positive attitude and enthusiasm towards work, which can facilitate the generation of work passion. Work passion, in turn, is an emotional resource that can facilitate proactive behavior.
Future studies could also investigate potential moderators of the relationship between FWSS and proactive behavior, such as leadership styles. Transformational leadership, for instance, emphasizes vision setting and incentives by describing a compelling future for their subordinates and highlighting the organization’s goals and development direction. Furthermore, transformational leadership is characterized by individualized care, where leaders are more willing to help and support their subordinates. Therefore, FWSS may be more likely to lead to positive work outcomes under high levels of transformational leadership. Moreover, future research should consider other moderating factors at the organizational and team levels, such as promotion opportunities within the organization, the level of competition among colleagues, and relationships with leaders, which may affect individuals’ choices of proactive behavior.

Author Contributions

Conceptualization, C.-L.Y., Y.L. and K.Q.; methodology, C.-L.Y., Y.L. and K.Q.; software, C.-L.Y., Y.L. and K.Q.; validation, C.-L.Y., Y.L. and K.Q.; formal analysis, C.-L.Y., Y.L. and K.Q.; investigation, C.-L.Y., Y.L. and K.Q.; resources, C.-L.Y., Y.L. and K.Q.; data curation, C.-L.Y., Y.L. and K.Q.; writing—original draft preparation, C.-L.Y., Y.L. and K.Q.; writing—review and editing, C.-L.Y., Y.L. and K.Q.; visualization, C.-L.Y., Y.L. and K.Q.; supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China, grant number 21XNL009.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of the School of Labor and Human Resources, Renmin University of China (protocol code RUC-SLHR20230002).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 14912 g001
Figure 1. Research model.
Figure 1. Research model.
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Figure 2. Interaction effect between career planning and uncertainty avoidance on pro-self-proactive behavior.
Figure 2. Interaction effect between career planning and uncertainty avoidance on pro-self-proactive behavior.
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Table 1. Construct Reliability and Validity.
Table 1. Construct Reliability and Validity.
ConstructsItemsLoadingsCACRAVE
Future work self-salience 0.9240.9430.769
FWSS-10.794
FWSS-20.910
FWSS-30.923
FWSS-40.900
FWSS-50.852
Career planning 0.8670.9090.715
CP-10.872
CP-20.865
CP-30.841
CP-40.803
Pro-organizational proactive behavior 0.8500.9090.769
POB-10.886
POB-20.879
POB-30.866
Prosocial–proactive behavior 0.8870.9220.747
PSOB-10.855
PSOB-20.833
PSOB-30.909
PSOB-40.858
Pro-self-proactive behavior 0.9000.9300.770
PSEB-10.854
PSEB-20.852
PSEB-30.879
PSEB-40.923
Uncertainty avoidance 0.9020.9270.719
UA-10.819
UA-20.779
UA-30.862
UA-40.895
UA-50.880
Note. FWSS, future work self-salience; UA, uncertainty avoidance; CP, career planning; POB, pro-organizational proactive behavior; PSOB, prosocial–proactive behavior; PSEB, pro-self-proactive behavior; CA, Cronbach’s alpha; CR, Composite reliability; AVE, Average variance extracted.
Table 2. Discriminant Validity (Fornell-Larcker Criterion).
Table 2. Discriminant Validity (Fornell-Larcker Criterion).
CPFWSSPOBPSEBPSOBUA
CP0.846
FWSS0.3420.877
POB0.5140.2790.877
PSEB0.6890.3440.6840.877
PSOB0.3840.2660.6710.6430.864
UA0.4380.1760.4540.5760.5460.848
Note. FWSS, future work self-salience; UA, uncertainty avoidance; CP, career planning; POB, pro-organizational proactive behavior; PSOB, prosocial–proactive behavior; PSEB, pro-self-proactive behavior.
Table 3. Heterotrait-Monotrait Ratio (HTMT).
Table 3. Heterotrait-Monotrait Ratio (HTMT).
CPFWSSPOBPSEBPSOBUA
CP
FWSS0.381
POB0.5890.313
PSEB0.7740.3760.779
PSOB0.4350.2920.7770.720
UA0.4920.1890.5140.6360.603
Note. FWSS, future work self-salience; UA, uncertainty avoidance; CP, career planning; POB, pro-organizational proactive behavior; PSOB, prosocial–proactive behavior; PSEB, pro-self-proactive behavior.
Table 4. Structural model.
Table 4. Structural model.
HypothesesRelationshipsBeta Valuest-Valuep-Value
H1FWSS → CP0.3425.3060.000
H2FWSS → CP → POB0.1323.4710.001
H3FWSS → CP → PSOB0.0631.9530.051
H4FWSS → CP → PSEB0.1764.2130.000
H5aUA*CP → POB−0.0110.3020.763
H5bUA*CP → PSOB0.0130.2630.793
H5cUA*CP → PSEB−0.0632.0330.042
Note. FWSS, future work self-salience; UA, uncertainty avoidance; CP, career planning; POB, pro-organizational proactive behavior; PSOB, prosocial–proactive behavior; PSEB, pro-self-proactive behavior.
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Yang, C.-L.; Li, Y.; Qiao, K. Impact of Future Work Self-Salience on Proactive Behaviors: An Integrative and Comparative Study of Multiple Proactive Behaviors. Sustainability 2023, 15, 14912. https://doi.org/10.3390/su152014912

AMA Style

Yang C-L, Li Y, Qiao K. Impact of Future Work Self-Salience on Proactive Behaviors: An Integrative and Comparative Study of Multiple Proactive Behaviors. Sustainability. 2023; 15(20):14912. https://doi.org/10.3390/su152014912

Chicago/Turabian Style

Yang, Chen-Lu, Yuhui Li, and Kun Qiao. 2023. "Impact of Future Work Self-Salience on Proactive Behaviors: An Integrative and Comparative Study of Multiple Proactive Behaviors" Sustainability 15, no. 20: 14912. https://doi.org/10.3390/su152014912

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