Synergistic Rewards for Proactive Behaviors: A Study on the Differentiated Incentive Mechanism for a New Generation of Knowledge Employees Using Mixed fsQCA and NCA Analysis

Abstract

In practice, the new generation of knowledge-based employees often exhibits a “lying flat” attitude. This reflects the failure of organizational incentive mechanisms. In order to improve the incentive system and encourage employees to be proactive, the study explores and compares the synergistic effects of different rewards tools on various forms of proactive behavior in the new generation of knowledge employees. After conducting fsQCA and NCA analyses on paired data from 93 leaders and 210 employees based on the ERG theory, the findings indicate that no single reward tool is a necessary condition for triggering high proactive behavior. Instead, different reward tools need to work in synergy to produce effective motivation. Three patterns drive employees to exhibit high individual task proactivity. They are the “Dual-Drive Salary Security and Moderate Labor Dominant” pattern, the “Moderate Labor Dominant” pattern, and the “Salary Security Dominant” pattern. Two patterns drive employees to demonstrate high team member proactivity, namely the “Employee Care Dominant High-Investment” pattern and the “Pay Fairness Dominant High-Investment” pattern. Additionally, good work experience (i.e., colleague relationships) in the workplace has a significant impact on both types of proactive behavior. The research conclusions will provide insights and references for enterprise managers to design more targeted compensation incentive policies and unleash the vitality of the new generation of knowledgeable employees.

1. Introduction

A complex and ever-changing external environment is currently influencing organizational management. Additionally, there is a transformation occurring within organizations, shifting towards more autonomous and flexible internal management models. Uncertainty, complexity, and dynamism are all on the rise [1]. In order to achieve sustainable and thriving development, an organization must focus on the future, anticipate and assess dynamic changes in the environment, and make proactive and timely adjustments. This is essential for enhancing organizational resilience and improving core competitiveness [2]. The achievement of this goal fundamentally depends on employees, especially frontline workers. In this context, relying solely on employees to passively execute tasks can no longer meet the demands of development. It is urgently necessary to cultivate “change leaders” with a forward-looking vision who proactively anticipate problems, seize opportunities, and spontaneously exhibit proactive behaviors oriented toward change and the future (proactive behavior). This is essential to enhance organizational resilience and adaptability, ensuring the effectiveness of the enterprise in an environment characterized by frequent changes in competition and technology [3]. This reflects the most fundamental and important role of employees in the enterprise, especially the critical role of the new generation of knowledge-based employees as the driving force behind current technological innovation and high-quality development [4].

Today, the post-90s generation has become the main workforce in the workplace. According to statistics, there are over 180 million post-90s youth in China, with about 36.32% holding a college degree or higher [5]. Various surveys also show that post-90s employees account for over 40% of high-tech fields such as 5G, aerospace, artificial intelligence, chips, and intelligent manufacturing, as well as in industries like education, media, internet, and finance [6]. This indicates that in the era of the integration of the knowledge economy and digital economy, the new generation of knowledge employees, equipped with professional knowledge and skills, has become the most competitive human capital for enterprises and the backbone of high-level innovation and creativity. However, two reports from Chinese recruitment agencies, Zhaopin and Liepin (Beijing, China), show that over 70% of post-95s enjoy “slacking off”, and more than 80% of post-90s have a “lying flat” mentality [7,8]. These reports also point out most of them are content with the status quo, satisfied with meeting the minimum job requirements, and lack team spirit, rarely contributing their wisdom proactively to the team. This overall lack of individual proactivity and foresight not only fails to utilize the value and talent of young knowledge employees fully but also hinders enterprises from identifying and effectively responding to increasingly complex internal and external environmental changes.

Many classic management motivation theories indicate that employee behavior is driven by motivation [9]. Latent “absenteeism” reflects a lack of motivation due to a broken psychological contract, where new-generation knowledge workers shrink their work boundaries as an adaptive response to ineffective organizational incentives. “Salary” incentives are key in creating value exchanges between employees and organizations, shaping human capital, and ensuring employees are motivated to contribute to organizational goals [6]. Therefore, the essence of poor organizational incentive effectiveness can be attributed to the systemic failure of the compensation incentive mechanism.

As post-90s and post-95s knowledge workers gradually enter the workforce, there is a growing demand for management approaches to evolve or even transform to address their diverse needs and preferences better [6]. Consequently, compensation incentives, as the primary tool for organizational management, have evolved from focusing on extrinsic monetary rewards to encompassing intrinsic non-monetary rewards and the synergy between intrinsic and extrinsic rewards—i.e., total rewards [7]. This comprehensive and flexible incentive approach best meets the unique needs and preferences of the new generation of knowledge employees, making it the most powerful incentive tool in the current organizational environment [7]. The current “lying flat” trend indicates that compensation incentives are falling short of expectations, highlighting the need to rethink how the system should be restructured. Motivating the new generation of knowledge workers to embrace future-oriented, change-driven behaviors and unlock their potential while also enhancing organizational resilience has become a critical challenge for managers [8,10].

So, why is the effect of salary incentives not good? Through survey reports and a literature review, this study identifies two possible reasons: First, the uniqueness, heterogeneity, and synergistic interactions between different reward tools are not considered, overlooking the possibility that different reward tools can compensate, substitute, or balance to meet the diverse needs and preferences of the new generation of knowledge employees [11]. Motivation is a typical multi-dimensional construct with a complex structure [12]. Existing research often treats total rewards as a holistic construct, placing equal importance on its internal reward tools [13]. In reality, total rewards is an incentive system where various forms of rewards and incentives are interrelated and influence each other [14]. At the same time, different rewards satisfy different needs, and for each individual, there is a priority ranking and complementary substitution effect among these needs [7,11]. Moreover, different rewards drive employee attitudes and behaviors through different mechanisms, and their combined effects may result in mutual cancelation or synergistic enhancement [15,16]. Therefore, indiscriminately applying all reward tools leads to resource overlap, waste, and increased costs. But what are the minimum expected needs of individuals? Which type(s) or specific needs are employees concerned about and need to be simultaneously addressed? Which needs can be substituted for one another? These key questions have yet to be answered, and the lack of research in these areas makes it difficult for enterprises to build precise incentive combination strategies.

Second, the heterogeneity of different forms of proactive behaviors is overlooked. The effectiveness of incentives varies depending on the outcome variables. Based on the level of focus on change, proactive behaviors can be divided into two types: individual task proactivity (ITP) and team-member proactivity (TMP). The former changes the individual’s work methods, while the latter influences the team’s operational model as a team member [17]. Comparatively, the latter is not only a risky behavior but also involves more complex issues, such as team system design and changes in the work roles of other team members [18]. The two types of proactive behavior show significant differences in dimensions such as goal orientation, risk taking, and resource dependency. In practice, it has been observed that the new generation of knowledge-based employees may excel in individual task execution but exhibit “selective silence” and “social loafing” in team collaboration scenarios. This suggests that proactive behavior has multidimensional characteristics, and incentive tools designed to stimulate ITP may not necessarily be effective in driving proactive behaviors that contribute to team efforts [19].

However, to date, theoretical research and practical applications have rarely classified or traced the sources of proactive behaviors. In particular, the driving mechanisms behind TMP are still in the early stages of research, and there is a lack of exploration into the differentiated compensation incentives for different types of proactive behavior [20]. The lack of theoretical guidance has resulted in a lack of practical direction. Continuously applying the same reward policies in practice will inevitably affect the effectiveness of incentives.

Therefore, this study suggests that a systematic approach, beginning with the needs of the new generation of knowledge employees, can enhance organizational incentive effectiveness. It explores how the synergistic interactions of various reward tools within total rewards impact ITP and TMP. Alderfer’s ERG theory is a representative content-based motivation theory, categorizing employee needs into Existence, Relatedness, and Growth, which provide the basic elements of motivation [18], perfectly encompassing the diverse needs satisfied by various reward tools in total rewards. Currently, the research method that best explains the complex causal relationships between multiple condition variables and outcomes is the Qualitative Comparative Analysis (QCA), which focuses on the internal interactions of multiple elements through a configuration perspective. Therefore, this study, based on the ERG theory, uses a configuration approach to answer how different reward tools within total rewards synergistically combine to drive high levels of ITP and TMP. Finally, based on the research conclusions, this study summarizes several possible types of incentives that can stimulate ITP and TMP among the new generation of knowledge employees, providing insights and references for enterprise managers to design more targeted compensation incentive policies and unleash the vitality of the new generation of knowledge employees, in order to cope with the unprecedented uncertainty caused by multiple crises [21].

2. Research Design

2.1. Theoretical Foundation and Model Construction

2.1.1. Outcome Variables

Proactive behavior was initially defined as “taking initiative to improve the current situation or create new situations; it involves challenging the status quo rather than passively adapting to current conditions” [22]. Later, scholars expanded the definition beyond the individual, recognizing that employees’ work behaviors not only represent themselves but also depend on the social embeddedness of their work roles, such as being part of a work or organizational entity, and their behaviors may affect different organizational levels. This led to the proposal that the self, others (colleagues), and the organization are different targets of employees’ proactive behaviors [17]. Griffin (2007) and other scholars made significant contributions in this area by distinguishing proactive behaviors into individual task proactivity (ITP) and team-member proactivity (TMP) based on the focus of change [15,17]. ITP refers to self-initiated, future-oriented behaviors that change one’s own work methods or roles, such as a nurse improving their intravenous injection skills. When individuals become part of a team and their work depends on the team’s performance, they begin to transcend their individual work roles and take proactive actions to influence the team’s operational mode, demonstrating TMP, such as a nurse suggesting adjustments to the team’s shift schedule. It should be noted that YMP differs from team proactivity, which represents collective thinking and capability [7]. TMP remains an individual-level variable, representing the individual efforts of team members to engage in proactive behaviors independently of other team members rather than team efforts.

2.1.2. Selection of Antecedent Conditions

Total rewards are defined as “everything of value in the employment relationship”, a new reward concept proposed by the WorldatWork Association that includes various tangible and intangible returns. It is an organizational total-reward design system focused on motivating and retaining employees, with greater attention to employee growth and psychological states [23]. This system has evolved with the changing needs of Western employees, from a five-dimensional model in 2006 (compensation, benefits, work–life balance, performance and recognition, development, and career opportunities) to a strategic model in 2021 that includes compensation, well-being, benefits, development, and recognition. Due to cross-cultural factors, Chinese employees’ needs differ from those in the West. In recent years, some domestic scholars have begun to explore the structural dimensions of total rewards in the Chinese organizational context and their impact on employee attitudes and behaviors, further advancing the localization of total-rewards research [23,24]. In addition to the above dimensions, they found that work experience, income linked to skills, and good relationships with leaders and colleagues have also become focal points for Chinese workplace employees [5]. This study uses a scale developed by Jin Weize and Yang Junqing (2022) based on grounded theory [25]. The scale includes seven dimensions: salary security (SS), pay fairness (PF), moderate labor (ML), work experience (WE), employee care (EC), career development (CD), and personal value (PV). It comprehensively covers different reward forms within the Chinese organizational context to meet the needs of the new generation of knowledge employees. In theory, the compensation design system is an incentive system at the organizational level, but in practice, this design is difficult to quantify and measure. At the same time, each person reacts differently to the compensation design system, which stems from individual backgrounds, cognition, personality, and other factors. Therefore, using employee perceptions as an agent variable for the organization’s objective practices is a common method in academic research, such as in studies measuring high-performance work systems (HPWS) and other strategic human resource management research [26]. This study also uses a total compensation perception scale to measure the various specific incentive elements within the organization.

2.1.3. Model Construction

According to management incentive theory, individual behavior is driven by intrinsic motivation, with needs being the fundamental force behind actions. Effective motivation relies on understanding and meeting these needs to inspire the desired behavior [9]. Although there are many theories of needs, the ERG theory developed by Clayton Alderfer of Yale University is widely accepted and recognized among content-based motivation theories. This theory builds upon and revises Maslow’s hierarchy of needs, categorizing human needs into three core types: Existence, Relatedness, and Growth. These correspond to three philosophical categories: the external world, interpersonal relationships, and the self. Existence needs refer to the physiological and material needs essential for survival; Relatedness needs are the emotional and subjective needs for maintaining important interpersonal relationships; Growth needs are the desires to develop personal potential and abilities [27].

Based on the ERG theory framework, first, in the workplace, the reward incentive elements that can meet the existing needs are mainly reflected in “salary”, benefits, and work intensity. Therefore, the reward tools within total rewards that satisfy existing needs primarily include salary security (SS), pay fairness (PF), and moderate labor (ML). SS not only includes basic salary and fundamental welfare systems (such as social insurance and housing fund) but also involves the timely disbursement of “salary” and the stability of welfare standards. Secured salary meets employees’ basic survival and safety needs, reducing anxiety and insecurity caused by economic instability. This not only enhances their psychological ownership and sense of security but also effectively reduces their expectations of risk, thereby boosting their sense of self-efficacy. This positive psychological state helps employees better cope with anticipated risks and has a positive impact on various proactive behaviors [28]. PF emphasizes merit-based and fair performance distribution, expecting a reciprocal relationship between effort and reward. This output-based individual reward is more effective in motivating goal-oriented behaviors, as it makes employees perceive the cost of taking proactive actions as lower, making them more likely to engage in proactive behaviors such as voice [29]. In addition, the fairness of pay distribution procedures and outcomes can enhance the standardization, objectivity, and accuracy of performance evaluations, thereby increasing the legitimacy and effectiveness of the system. It also sends a signal of organizational trust and recognition, which in turn strengthens employees’ organizational commitment and fosters psychological safety and positive emotional experiences. These are all important triggers for both types of proactive behavior [17]. ML, from the perspective of managing work intensity and health protection, essentially represents an extension of existing needs. It considers the intensity of work and the balance between work and life. Empirical research indicates that increasing work intensity and excessive labor can lead to physical and mental exhaustion, as well as the issue of “overwork”. Work–life balance, as an individual’s perception of the coordination of multiple roles, can not only enhance job satisfaction, organizational commitment, and work performance but also can generate work–family enrichment effects, bringing positive resources and emotions and enhancing employee proactivity [30]. However, when the workload is excessive, employees perceive high work stress, negatively affecting extra-role behaviors and altruistic behaviors like TMP [18].

Second, the reward incentive elements that can meet the relatedness needs in the workplace are mainly reflected in the exchange relationships between employees, leaders, and colleagues. Therefore, the reward tools within total rewards that satisfy relatedness needs are work experience (WE) and employee care (EC). We focus on cooperation and interaction with colleagues. When employees have high-quality interpersonal relationships with colleagues, they tend to share knowledge and goals, respect each other, and communicate proactively, increasing meaning-making through cooperative interactions. This not only enhances ITP but also promotes team change. The resulting supportive atmosphere has been proven to promote group reflection, indirectly influencing employee proactive behaviors and enhancing individual contributions to team performance [31]. EC reflects leaders’ trust, respect, and humanistic care for employees. By implementing participatory management practices (such as joint decision making and incorporating feedback), authority is decentralized, hierarchical barriers are weakened, and employees’ perception of organizational hierarchy differences is reduced. This, in turn, enhances the sense of organizational fairness and improves psychological safety. This egalitarian leader–member relationship encourages employees to reciprocate by putting in extra effort, making them more willing to take on the risks of change and thereby engaging in transformative behaviors that benefit the organization [32]. At the same time, when leaders express care and support for employees, it can significantly enhance employees’ social and emotional capital. This, in turn, helps internalize organizational goals as personal missions and, through team identity internalization, deeply aligns individual values with collective goals [33]. They are more likely to take additional proactive actions to contribute to team goals [18].

Third, the reward incentive elements that can meet the Growth needs correspond to career development (CD) and personal value (PV) within total rewards. CD, emphasizing skill training and internal promotion, can positively impact innovative, proactive behaviors through enhanced career adaptability and directly influence proactive career behaviors [34]. In the context of digital transformation, organizations need to help employees adapt to technological iterations through continuous skill updates. Providing more training opportunities can accumulate constructive resources for employees, creating a positive cycle of “skill enhancement—role expansion—proactive behavior” [35]. PV can be defined as the process in which an organization, through providing opportunities and recognition, helps employees construct meaning by aligning their abilities, showcasing their talents, and creating social value. Personal value enhances employees’ understanding of work meaning and the “positive self”, making future work self-clarity clearer. Employees become more passionate, responsible, and mission-driven, further exhibiting proactive behaviors with prosocial motivations [36]. The new generation of knowledge-based employees views PV as their core work value. Relying on the accumulation of successful experiences, they strengthen their sense of self-efficacy, aligning their strengths and potential with organizational goals. This enhances value recognition, which motivates them to proactively seek challenging tasks and ignite behavioral proactivity by increasing their task engagement [37].

In addition to categorizing workplace needs into the above three types, the ERG theory also proposes three principles: (1) the principle of concurrent needs, meaning that multiple, multi-dimensional needs can coexist simultaneously, and the satisfaction of lower-level needs is not a prerequisite for the emergence of higher-level needs. In other words, the satisfaction of lower-level needs is not a necessary condition for higher-level needs to take the leading role. Any level of need can become the dominant one at any given time. (2) The principle of need regression, meaning that when higher-level needs are not met, the desire to satisfy lower-level needs increases, creating a compensatory effect. (3) The principle of need strengthening, meaning that when a certain need is basically satisfied, it may not weaken, but rather it could strengthen [38]. These three principles provide a theoretical explanation and underlying logic for the functional synergy and substitution of different compensation tools across various dimensions of total compensation. They confirm that the synergy of incentives essentially stems from the non-linear and compensatory characteristics of needs. Based on these three principles, the Existence, Relatedness, and Growth needs satisfied by different reward tools can coexist and emerge without hierarchy, and there are complementary substitutions among needs, creating synergistic interactions. Existing research has touched on the synergistic relationships between different reward tools, such as attempting to combine intrinsic and extrinsic motivation through internal motivation and internalized external motivation to impact individuals’ psychology, attitudes, and behaviors [39]. However, the conclusions on such synergistic interactions are inconsistent, and intrinsic and extrinsic motivation have not been further subdivided. Therefore, further detailed research is needed. The best scientific method to address the above issues is Qualitative Comparative Analysis (QCA). It offers the dual advantages of being both theory-driven and configuration-oriented. QCA not only supports exploratory deductive induction based on the research questions but also effectively addresses the complexity of multiple concurrent causal factors, where compensation combinations form differentiated incentive paths through non-linear interactions [40]. In summary, based on the ERG theory and using a configuration approach, this study constructs the following research model (Figure 1).

3. Materials and Methods

3.1. Sample and Procedure

Questionnaires and surveys are commonly used in social science research to gather data and analyze trends [41,42]. To ensure the universality and representativeness of the study, this research conducted a questionnaire survey on post-90s knowledge employees engaged in technology, research and development, and management in over 90 enterprises across more than 10 provinces, including Beijing, Shaanxi, Shanxi, and Guangzhou, spanning 17 industries such as finance, information transmission, software and technology services, real estate, construction, and leasing and business services. These enterprises have implemented total-rewards incentive strategies to varying degrees. However, the data were collected at the same point in time. To reduce common method bias, leader–employee paired data were used for the survey. To reduce common method bias and social desirability bias, this study uses paired data from leaders and employees. This is because self-assessments of behavior and performance outcomes can be biased due to self-enhancement motivations, making self-ratings less valid than objective ratings. However, employees are more familiar with their work and roles, as well as their perceptions of organizational incentive policies. Therefore, the antecedent survey on compensation and reward tools uses self-assessment [43]. Employees self-rated their perceptions of the seven reward tools within total rewards. They reported basic characteristics, while their real direct leaders evaluated employees’ individual task proactivity and team-member proactivity and filled in enterprise and leader basic information.

The research team, along with friends, used a snowball sampling method to connect with companies broadly. After receiving support, one contact person was identified within each company to handle tasks such as matching employee and leader IDs, preparing sealed envelopes and small gifts, distributing and collecting questionnaires on site, and mailing the questionnaires.

Given the sensitivity of the survey content, which involves employees’ perceptions of enterprise reward policies, to alleviate respondents’ concerns and reduce social desirability bias, the survey was conducted anonymously. The introductory section of the questionnaire highlighted the importance of confidentiality, assuring participants that the study would be used exclusively for academic purposes. It also guaranteed that other employees, as well as the leaders and employees involved in peer evaluations, would not have access to the content of the completed questionnaires. The company contact person immediately collected the completed questionnaires in envelopes, sealed them, and wrote only the ID number on the envelope. To ensure the quality of the survey, the questionnaires were matched against the list provided by the human resources department. Once all participants had completed the questionnaires, they were immediately mailed to the research team. A total of 297 questionnaires were distributed. Finally, 93 leaders and 210 employees’ valid paired questionnaires were obtained, with an effective response rate of 70.7%.

The data-screening process is as follows: first, incomplete questionnaires and those with excessive single-option responses are removed; second, questionnaires that cannot be matched or have obvious duplications are excluded; finally, questionnaires that show a lack of logical reasoning or coherence are discarded. For example, if there are significant differences or contradictions between similar measurement items, it is considered a lack of logical consistency, and such samples are excluded from the analysis.

Among the surveyed enterprises, 49.46% were state-owned enterprises, 31.18% were large enterprises with over 500 employees, 59.14% were in the growth and maturity stages, and 34.41% were in the transition stage. Among the surveyed employees, 41.43% were male, 44.29% were post-95s, and the average age was 27.34 years. All had a college degree or higher, with 70.1% holding a bachelor’s degree or higher. The average tenure was 3.87 years, and the average time working with the leader was 2.91 years.

3.2. Variable Measurement and Reliability and Validity Testing

The two outcome variables, individual task proactivity and team-member proactivity, were measured using Griffin et al.’s (2007) scales, each with three items [15]. Example items include “This employee tries to propose improved methods for completing important work tasks” and “This employee suggests methods to improve teamwork efficiency” [17]. The antecedent condition variables were measured using the total rewards scale developed by Jin Weize and Yang Junqing (2022) based on the Chinese organizational context [25]. Specifically, it covers the following seven dimensions: SS with four items, example item: “My current fixed “salary” provides basic living security”; PF with five items, example item: “My work rewards match my work efforts”; ML with six items, example item: “The company ensures rest days and holidays”; WE with three items, example item: “Colleagues have harmonious relationships and a good atmosphere”; EC with five items, example item: “The company proactively understands my needs”; CD with six items, example item: “In this company, I can clearly see my future development direction”; PV with four items, example item: “I clearly understand the value of my work”. All scales used a 6-point Likert scale (1 = strongly disagree, 6 = strongly agree). The Cronbach’s α coefficients of all scales were greater than 0.7, the composite reliability (CR) was greater than 0.7, and the average variance extracted (AVE) was greater than 0.5, indicating good reliability and validity [41,44].

Specific results are shown in

Table 1. Scale reliability and validity analysis results (N = 210).

Variables			Cronbach’s α	CR	AVE
Condition Variables	Existence	SS	0.747	0.807	0.515
		PF	0.896	0.841	0.516
		ML	0.931	0.919	0.655
	Relatedness	WE	0.897	0.873	0.697
		EC	0.910	0.828	0.503
	Growth	CD	0.892	0.847	0.513
		PV	0.845	0.866	0.527
Outcome Variables		ITP	0.864	0.866	0.683
		TMP	0.825	0.771	0.529

.

3.3. Selection Method

Fuzzy Set Qualitative Comparative Analysis (fsQCA) is a data analysis method based on set theory, holism, and Boolean operations. It aims to explain the causal complexity of outcome variables arising from the complex interactions and joint effects of different antecedent variables (non-binary or multi-valued variables) from a configurational perspective [22]. Compared to interaction analysis and latent profile analysis in traditional regression analysis, fsQCA has characteristics such as equivalence, causal asymmetry, and the ability to distinguish between core and peripheral conditions. In recent years, it has been increasingly applied in management research. However, before conducting a sufficiency analysis of “enabling” outcomes, it is necessary first to clarify the causal test for the necessity of “non-enabling” conditions. While QCA focuses on sufficiency analysis and can also perform necessity tests, it only allows for qualitative identification. In contrast, the Necessary Condition Analysis (NCA) method not only identifies whether specific conditions are necessary for an outcome but also quantitatively analyzes the effect size of necessary conditions (also known as bottleneck levels) [45]. Therefore, this study chooses to combine fsQCA and NCA methods to make the results more robust, responding to scholars’ calls for more granular complex causal analysis by considering the integration of both approaches [22].

4. Results and Discussion

4.1. Variable Calibration

Since fsQCA analyzes set relationships, it is necessary to calibrate the antecedent conditions and outcome variables to transform them into membership scores between 0 and 1 before conducting necessity and sufficiency analyses [46]. As mentioned in the data collection process, to reduce common method bias and social desirability bias, this study uses paired leader–employee data. Although the measurement of the conditioning and outcome variables comes from different subjects, the same calibration method is applied in this study. No differentiated calibration thresholds were set; instead, the study follows the approach outlined by Jacobs and Cambré (2020) [45]. Setting the full membership, crossover point, and full non-membership at 6, 4, and 1, respectively. Both leader and employee evaluations exhibit right-skewed distributions due to factors like overconfidence, desire to please, and protective tendencies, especially in a collectivist culture. As both types of evaluations show systematic right-skew bias, using the same calibration threshold is deemed practical and consistent with the data characteristics [47]. Third, previous studies using Likert scale questionnaires typically use the maximum value, midpoint/mean, and minimum value as the three anchor points for full membership, crossover point, and full non-membership. For example, a 5-point scale is calibrated with values of 5, 3, and 1 [48]. The data’s bias and distribution characteristics were also taken into account, and the calibration standards were adjusted accordingly for bias. For instance, the research of Jacobs and Cambré indicates a 5-point scale is calibrated with values of 5, 3.5, and 1. Considering the potential social desirability bias associated with the reward theme of this study and based on the descriptive statistical analysis of the sample, this paper adopts a biased calibration method from existing research. When the calibrated membership score of a case is exactly 0.5, we follow mainstream practices by adding a constant of 0.001 to the value of 0.5 for calculation. The specific descriptive statistics are shown in

Table 2. Descriptive statistics of variables.

Set	Mean	Standard Deviation	Maximum Value	Minimum Value
SS	4.436	0.821	6.000	1.333
PF	3.988	0.913	5.800	1.000
ML	4.437	0.998	6.000	1.000
WE	4.762	0.911	6.000	1.333
EC	4.136	0.913	6.000	1.600
CD	4.225	0.820	6.000	1.833
PV	4.415	0.723	6.000	1.750
ITP	4.394	0.793	6.000	1.000
TMP	4.221	0.807	6.000	1.000

.

4.2. Necessity Analysis of Single Conditions

Necessity analysis addresses whether a particular antecedent condition is a necessary condition for the outcome to occur. If necessary, it should be explicitly identified in the fuzzy set truth table analysis; otherwise, it may be included in the “logical remainder” and simplified out in the solution. First, necessity testing is conducted using fsQCA 3.0 software, with the results shown in

Table 3. The results of the necessity test for individual conditions using the QCA method.

Condition Variables	Outcome Variables
	High ITP		High TMP
	Consistency	Coverage	Consistency	Coverage
SS	0.856	0.833	0.891	0.812
~SS	0.509	0.889	0.508	0.831
PF	0.764	0.848	0.814	0.845
~PF	0.596	0.855	0.586	0.785
ML	0.835	0.812	0.857	0.780
~ML	0.488	0.853	0.489	0.799
WE	0.887	0.776	0.893	0.749
~WE	0.409	0.896	0.421	0.862
EC	0.662	0.913	0.707	0.912
~EC	0.715	0.817	0.709	0.758
CD	0.808	0.855	0.847	0.838
~CD	0.562	0.860	0.567	0.811
PV	0.849	0.834	0.888	0.816
~PV	0.524	0.900	0.528	0.849

. As can be seen from Table 3, the necessity consistency of each single condition variable is less than 0.9. Therefore, the QCA method indicates that there are no necessary reward conditions for generating high ITP or high TMP.

Further, R programming was used to perform NCA (Necessary Condition Analysis). Since the variables in this study are continuous and have more than five levels, ceiling regression (CR) was chosen to generate the effect size of the upper bound function for the analysis. According to the results in

Table 4. NCA method Necessary Condition Analysis results—CR regression.

Conditions	Accuracy		Ceiling Zone		Range (ITP/TMP)	Effect Size (d)		p Value
	ITP	TMP	ITP	TMP		ITP	TMP	ITP	TMP
SS	99.00%	99.00%	0.031	0.036	0.690	0.045	0.052	0.224	0.304
PF	97.10%	99.00%	0.049	0.038	0.720	0.067	0.052	0.015	0.180
ML	97.60%	99.50%	0.049	0.034	0.750	0.065	0.045	0.076	0.389
WE	98.60%	99.00%	0.067	0.072	0.740	0.090	0.098	0.118	0.197
EC	99.00%	97.60%	0.021	0.048	0.740	0.029	0.065	0.236	0.026
CD	98.60%	99.00%	0.044	0.033	0.710	0.063	0.046	0.017	0.181
PV	100%	100%	0.011	0.056	0.710	0.016	0.079	0.803	0.286

Note: The p-values were calculated using Monte Carlo simulation permutation tests with 10,000 resampling iterations.

, the accuracy of both outcome variables is greater than 95%. While both PF and CD are significant at the 0.05 level, the effect size is too small. The WE variable has an effect size close to 0.1 for both outcome variables, but the p-value is not significant. In summary, all antecedent conditions fail to meet the necessary judgment criteria for effect size (d) greater than 0.1 and significant p-values (p < 0.05) [49]. Therefore, the results from both methods are consistent.

The effect size analysis of necessary conditions in NCA can quantitatively calculate the minimum level of antecedent conditions required for an outcome to reach a certain threshold (%). According to the bottleneck level analysis results in

Table 5. NCA method bottleneck level analysis results—ITP.

ITP	SS	PF	ML	WE	EC	CD	PV
0	NN	NN	NN	NN	NN	NN	NN
10	NN	NN	NN	NN	NN	NN	NN
20	NN	NN	NN	NN	NN	NN	NN
30	NN	NN	NN	NN	NN	NN	NN
40	NN	NN	NN	NN	NN	NN	NN
50	NN	NN	NN	NN	NN	NN	0.4
60	NN	NN	4.8	2.3	1.7	NN	1.5
70	0.1	7.6	10.3	12.4	4.4	7.6	2.6
80	10.1	16.8	15.9	22.5	7.0	15.6	3.7
90	20.1	26.0	21.4	32.6	9.7	23.6	4.8
100	30.1	35.2	26.9	42.8	12.4	31.6	5.9

and

Table 6. NCA method bottleneck level analysis results—TMP.

TMP	SS	PF	ML	WE	EC	CD	PV
0	NN	NN	NN	NN	NN	NN	NN
10	NN	NN	NN	NN	NN	NN	NN
20	NN	NN	NN	NN	NN	NN	NN
30	NN	NN	NN	NN	NN	NN	NN
40	NN	NN	NN	NN	NN	NN	NN
50	NN	NN	NN	NN	2.0	NN	2.0
60	NN	1.7	NN	2.9	6.4	NN	7.5
70	NN	7.3	NN	13.6	10.8	NN	13.0
80	10.5	12.9	7.8	24.4	15.2	9.1	18.5
90	23.9	18.6	21.5	35.2	19.5	21.5	24.0
100	37.3	24.2	35.2	45.9	23.9	33.9	29.5

, there are no necessary conditions for low levels of ITP and TMP (0–40%). When higher levels of ITP or TMP are required (≥50%), a 0.4% level of PV or a 2% level of EC and PV is necessary. To further increase the level of proactivity (≥70%), all antecedent conditions need to be elevated to corresponding levels. This further suggests that individual reward factors have limited influence on the different forms of proactive behaviors of the new generation of knowledge employees. They do not constitute fundamental factors, and only by fully leveraging their synergies can high proactivity behaviors be achieved. The conclusion aligns with the first principle of the ERG theory, which asserts that various needs can coexist simultaneously.

4.3. Construction of Truth Tables and Configuration Generation

In this study, the case frequency threshold is set to 4, retaining approximately 80% of the cases. Referring to the standard QCA threshold settings, the original consistency threshold (raw consistency) is set to 0.8, and the PRI consistency threshold is set to 0.75. Since no single condition is necessary, this study allows for the presence or absence of each antecedent condition during counterfactual analysis. By comparing intermediate and simplified solutions, the core and peripheral conditions of each solution are identified. The configuration results are shown in

Table 7. Configuration analysis results.

Conditions		ITP						TMP
		H1a	H1b	H1c	H1d	H2	H3	S1	S2a	S2b
Existence	SS	⬤	⬤	⬤	⬤		⬤	•		•
	PF			⊗	⊗	•	⊗		⬤	⬤
	ML	⬤	⬤	⬤	⬤	⬤	⊗	•	•	•
Relatedness	WE	•	•	•		•	•	•	•	•
	EC		⊗	⊗	⊗	⊗	⊗	⬤	⊗	⊗
Growth	CD	•		•	⊗	•	⊗	•	•
	PV	•	•		•	•	⊗	•	•	•
Raw Coverage		0.678	0.538	0.424	0.397	0.497	0.372	0.648	0.526	0.531
Unique Coverage		0.164	0.003	0.005	0.011	0.007	0.028	0.177	0.006	0.011
Consistency		0.929	0.949	0.972	0.963	0.972	0.973	0.958	0.961	0.961
Overall Consistency of the Solution		0.916						0.942
Overall Coverage of the Solution		0.754						0.713

Note: ⬤ = “Core condition exists” means that the condition plays a central, dominant role or is at a high level in that configuration. • = “Peripheral condition exists” means that the condition plays a peripheral role in that configuration. ⊗ = “Peripheral condition is missing” means that the condition is absent or at a low level in that configuration. A blank space means that the presence or absence of the antecedent condition is acceptable, indicating that the condition is irrelevant to that configuration.

.

The configurations for high ITP consist of 6 solutions (H1a, H1b, H1c, H1d, H2, H3), which are summarized into three categories (H1, H2, H3) according to the core solution consistency principle. The total consistency is 0.916, and the total coverage is 0.754. The configurations for high TMP consist of 3 solutions (S1, S2a, S2b), which are summarized into two categories (S1, S2) according to the core solution consistency principle. The total consistency is 0.942, and the total coverage is 0.713. The consistency of each configuration is greater than 0.9, and the raw coverage is also high, indicating that the analysis results have good validity and strong explanatory power.

4.4. Configuration Result Analysis

4.4.1. Analysis of Configuration Paths Leading to High Individual Task Proactivity

Configuration H1, due to differing peripheral conditions, is divided into four paths (H1a to H1b), and the sum of their unique coverage is 0.183, much higher than that of the H2 and H3 configurations. The uniqueness of this configuration lies in the simultaneous presence of two core antecedent conditions—SS and ML—which are present together. In contrast, in H2 and H3 configurations, these conditions exist separately. In H1a, regardless of whether PF and EC are present, the three peripheral conditions—WE, CD, and PV—can help generate high ITP. H1b shows that the importance of CD is relatively diminished. H1c and H1d indicate whether CD is present or not, along with WE or PV, which can help generate high ITP. It is evident that, apart from the core conditions, the other five reward tools are either missing or absent. Drawing from the logic of previous QCA studies in naming configurations, this paper names H1 as the “SS and ML Dual-Drive Dominant” configuration.

Configuration H2 demonstrates a multi-element synergy mechanism, where high ML, as a core condition, can synergize with high PF, WE, CD, and PV in the absence of high EC. Notably, the dimensions that satisfy the personal growth needs (CD and PV) exhibit co-occurrence characteristics, so this configuration is named the “ML Dominant” configuration.

Configuration H3, on the other hand, exhibits a minimal condition combination feature. When the core condition of high SS and the peripheral condition of high WE form a binary incentive structure, even if other factors are at non-high levels, it can still effectively trigger high ITP. This finding complements the traditional incentive theory and initiatives, and therefore, it is named the “SS Dominant” configuration.

Further comparative analysis of the configuration results reveals that the synergy optimization of compensation, welfare systems, and moderate labor is essential to stimulate the ITP of the new generation of knowledge-based employees. Suppose an organization can only meet the basic compensation security needs, and other incentive measures are limited. In that case, compensation should be achieved through the optimization of work experience (such as a more streamlined decision-making process, a harmonious work atmosphere, etc.). Suppose the organization’s compensation is significantly lower than the competitive level. In that case, both institutional compensation (such as a fair compensation system and career development support) and emotional compensation (such as value recognition and achievement feedback) need to be fully coordinated, with a primary focus on balancing the workload. Only then can equivalent results be achieved.

These findings align well with the realities of the workplace. Chinese Zhaopin’s “2021 Employee Incentive Mechanism Research Report” and Liepin Big Data Research Institute’s “2021 Insight Report on the Current Situation of Contemporary Young Professionals” show that “salary” and compensation are still the primary factors for the new generation of employees when choosing a job. The report on the current workplace situation of the new generation of employees shows they are not “working for passion alone” but are also focused on material rewards and wealth increase. Work–life imbalance and overwork are the biggest sources of pressure for young professionals, with over 70% indicating they often work overtime and over 60% considering work–life balance their top goal. The phrase “as long as the pay is right, nearly 80% of post-90s workers say they can accept the 996-work schedule” suggests there is a certain degree of substitution between salary security and workload, further highlighting their emphasis on practical benefits. Simultaneously, 44.7% of new employees report being troubled by colleague relationships and feeling anxious about workplace social dynamics. While concerns like career development, job interest alignment, and work significance also matter, they rank lower than the first three factors. Moreover, the new generation of knowledge-based employees in today’s digital work environment may place more emphasis on autonomy and achievement feedback rather than traditional emotional attachment. Their psychological contract tends to be more transactional than relational, which could potentially provide a theoretical basis for the phenomenon of young people “rejecting empty promises” in the workplace, which aligns with the lack of employee care in driving individual task proactivity. Deloitte Global 2024 Gen Z and Millennial Survey—connected with more than 22,800 respondents in 44 countries—reveals that financial insecurity and the cost of living remain their top concerns this year. They believe that personal job satisfaction and happiness are only achieved through work that aligns with their values and needs. At the same time, they place great importance on work–life balance, which is even a primary consideration when choosing an employer, followed by compensation and opportunities for learning and development. These conclusions are consistent with the findings above and further expand their international applicability.

The findings above can be theoretically analyzed using the three principles of the ERG theory. First, each of the three configurations contains at least two types of needs (configurations H1 and H2 include all three types of needs). This validates the ERG theory’s departure from the rigid hierarchy of needs, emphasizing that the three types of needs can coexist. Secondly, the three core needs—Existence, Relatedness, and Growth—correspond to the philosophical categories of the external world, interpersonal relationships, and the self, respectively. The study shows that knowledge workers place the highest value on Growth needs, followed by Relatedness needs, while Existence needs are relatively less important, especially for post-90s knowledge workers [50]. When an organization fails to address higher-level needs like growth and relatedness, employees may focus on strengthening their lower-level needs, such as salary security and reasonable workload, leading to proactive behavior. The need reinforcement principle in ERG theory suggests that even when basic Existence needs are met, employees may still seek further satisfaction in these areas [18]. Based on these three principles, we can conclude that the three types of needs fulfilled by different reward tools can coexist, with complementary and substitutive relationships between the needs, forming a synergistic interaction.

Additionally, the conclusion that salary security and reasonable workload are core incentive conditions for promoting proactive behavior in personal tasks can also be supported by other theories. For example, for the new generation of knowledge employees, the results above align with theories suggesting that external compensation, such as “salary” and bonuses, does not diminish individual motivation from a control perspective but rather facilitates the internalization of external motivation into self-driven behavior by providing positive feedback and reinforcement, thereby producing positive outcomes. Furthermore, although research on work–life balance is limited, studies on the “double-edged sword” effect of work pressure are more abundant. This paper’s results more strongly support the negative effects of work pressure when challenge-related and hindrance-related stressors are not differentiated [30]. Additionally, the role of workplace friendships, organizational atmosphere, work meaning perception, and career development in driving positive employee behaviors has been further corroborated [36]. This also aligns with the basic assumption of Maslow’s Hierarchy of Needs theory, which states that individuals can only allocate more resources and address higher-level needs to engage in more proactive behaviors once their existing needs (basic needs and essential resources) are satisfied [51]. These theories also provide theoretical support for the complex demand structure, balanced satisfaction of needs, and the multidimensional synergy and complementarity of incentives for the new generation of knowledge-based employees.

4.4.2. Analysis of Configuration Paths Leading to High Team-Member Proactivity

Compared to the configurations for high individual task proactivity, the conditions leading to high TMP are more complex and require all three needs—Existence, Relatedness, and Growth—to be met. This suggests that organizations need to invest more resources and energy into stimulating TMP. Therefore, all configurations for high TMP are named High-Investment Configurations. Among them, configuration S1 is based on EC as the core reward practice, with SS, ML, WE, CD, and PV as peripheral reward practices. Therefore, S1 is named “EC Dominant High-Investment Configuration”. This configuration has the highest raw and unique coverage, reaching 64.8% and 17.7%, respectively. Configuration S2, with slightly different peripheral conditions, is divided into two paths: S2a and S2b. These two paths show that S2 is based on PF as the core condition. In the absence of EC, the remaining conditions—ML, WE, PV, CD (or SS)—work together to help generate high TMP. Thus, S2 is named “PF Dominant High-Investment Configuration”.

In summary, the organization’s implementation of humanistic care and fair and reasonable compensation has differentiated mechanisms of action on two different forms of proactive behavior. EC and PF have little impact on ITP, but they are core reward conditions affecting TMP. Additionally, the three need to work together in a complementary and synergistic manner to generate high TMP, and CD and SS (or PF) show a certain degree of substitution. At the same time, these results align with current theories on TMP. El Baroudi et al. (2019) found that TMP differs from ITP in that it is more likely to face resistance from other team members, making the social cost of implementing such initiatives high [17]. Therefore, individual characteristics or situational factors that influence ITP may not necessarily be effective for TMP. El Baroudi et al. also proposed that mixed rewards (including monetary rewards), output-based rewards (similar to PF), leader–member relationships (similar to EC), team cohesion (similar to WE), employee capabilities, and positive emotions are more likely to encourage team-based behaviors and improve team efficiency, which stimulates TMP [19], which echoes the results from the above configurations [36].

The previous two reports on the Chinese workplace context also indicate that over 60% of employees believe their supervisors do not encourage the expression of differing opinions, and nearly half of employees feel disappointed with their leaders due to their limited abilities and knowledge. However, employees indicate that trust and support from leadership are more likely to motivate them to take on additional tasks within the team. Additionally, the new generation of young professionals values a fair work environment, a friendly atmosphere, and the realization of self-worth and work meaning. Only when these conditions are met are they likely to contribute to the team. “Asia Pacific Workforce Hopes and Fears Survey 2023” from PwC—a survey of 19,500 Asia Pacific employees—showed that 52% of employees are willing to offer innovative suggestions to improve team performance, but require the company to invest more resources in building and developing “hexagonal teams”. These global survey results provide practical support for the above conclusions.

4.5. Robustness Test

This study uses two methods to test the robustness of the original configurations: adjusting the consistency threshold and reducing the number of cases. Following previous research [52], one method increases the PRI consistency threshold from 0.7 to 0.8. In this case, the configurations for high ITP remain unchanged, with the total consistency and coverage remaining the same. In contrast, the configurations for high TMP decrease to two without substantial change. Additionally, when approximately half of the sample from the original dataset is included in the analysis, the configurations for high ITP focus on three configurations, and the configurations for high TMP focus on one configuration, all of which are subsets of the original configurations. Therefore, the analysis results are robust.

5. Conclusions and Implications

5.1. Conclusions

This study, based on the ERG theory, explores the driving paths of different reward tools in motivating the individual task proactivity and team-member proactivity of the new generation of knowledge employees. Through fsQCA and NCA Qualitative Comparative Analysis of paired data from 210 new-generation knowledge employees, the following conclusions were drawn:

(1)

There is no single reward tool that acts as a necessary condition for generating high proactive behavior; different reward tools need to work in synergy to create effective motivation.

(2)

Three patterns drive employees to demonstrate high individual task proactivity, namely: the “Dual-Drive Salary Security and Moderate Labor Dominant” pattern, the “Moderate Labor Dominant” structure, and the “Salary Security Dominant” structure. Two patterns drive employees to demonstrate high team-member proactivity, namely the “Employee Care Dominant High-Investment” pattern and the “Pay Fairness Dominant High-Investment” pattern.

(3)

There are significant differences in the motivational factors for individual task proactivity and team-member proactivity. Salary security and moderate labor are key to motivating individual task proactivity, while motivating team-member proactivity requires not only the synergy of multiple reward tools but also the indispensable core practices of employee care and pay fairness.

(4)

Work experience, based on good colleague relationships, plays an important role in promoting both forms of proactivity.

(5)

A substitution effect exists both within and between the demand elements of total rewards that drive proactive behavior. The study further reveals that career development and personal value exhibit a substitution effect when driving high individual task proactivity, proving that both external growth paths (such as job promotions) and intrinsic growth paths (such as task significance) can yield equivalent outcomes. It also confirms that within the same need (e.g., Growth needs), compensation and incentive tools are interchangeable. The substitution effect between compensation security and career development in driving high team-member proactivity aligns with the dynamic adjustment theory of the needs hierarchy.

5.2. Theoretical Contributions

This study focuses on post-90s new-generation knowledge employees, analyzing the root causes of passive workplace behaviors such as “lying flat” and “slacking off”, which are often a result of ineffective compensation and incentive policies. In the context of the knowledge and digital economy, where AI and digital technologies have replaced simple labor, competition among companies now focuses on the recruitment and retention of knowledge employees. The post-90s generation has become the backbone of the workforce, and a lack of individual task proactivity or team-member proactivity can have significant negative impacts on organizations and teams. It is detrimental to employees’ ability to cope with the complex internal and external environments, and it also hinders the improvement of organizational resilience. However, the existing research on the motivation of the new generation of employees has largely remained at the conceptual description and inductive stage, with a notable lack of quantitative research on motivation for this group. The findings add valuable insights to the existing theoretical research on motivating new-generation knowledge workers.

This study expands on the research of proactive behavioral consequences within total rewards and, based on the complementary configuration effects of different compensation incentive tools, breaks through the limitations of existing explanations that view total rewards as an integrated incentive or a simple two-dimensional opposing framework. It provides new insights and methodological tools for the theory of total compensation. Research in the fields of management and applied psychology regarding compensation theory often focuses narrowly on the motivational effects of individual compensation elements (such as performance-based pay, benefits, etc.). This linear, independent research paradigm is clearly misaligned with the mixed use of both monetary and non-monetary rewards in the overall compensation framework commonly employed in organizational practice. Most existing studies focus on either individual compensation tools or the binary division of monetary versus non-monetary rewards, failing to adequately reveal the synergistic mechanisms between different compensation elements and neglecting employees’ heterogeneous preferences for differentiated compensation combinations. This study, using ERG (Existence, Relatedness, Growth) theory and complex systems configuration analysis, examines how seven different compensation reward tools interact to form effective motivational paths. This approach goes beyond traditional regression methods, offering a more nuanced understanding of compensation’s role in motivation. It expands on the traditional distinction between intrinsic and extrinsic rewards, providing a more practical and comprehensive approach to organizational incentives. From a more comprehensive and systematic perspective, this study provides an innovative solution to the long-standing issue of the gap between theoretical research and practical application in compensation management.

Additionally, this study fills the gap in the research on the classification of proactive behaviors with a different focus on change and, from the dual “mixed antecedents” perspective of total compensation and its constituent elements, addresses the theoretical gap in the relationship between compensation incentives and different types of proactive behaviors. There are two significant theoretical limitations in current research on proactive behaviors: First, existing studies either focus on the generality of proactive behavior measurement or are limited to specific forms of proactive behavior, neglecting the multidimensional nature of proactive behaviors. Second, while there is rich research on the antecedents of proactive behavior, most follow a single-factor explanatory paradigm, focusing on individual traits or situational stimuli. This isolated testing approach breaks the holistic characteristic of proactive behavior as a complex adaptive system.

Proactive behavior is essentially a dynamic game between expected rewards and perceived risks, and its risk decision-making characteristics require that antecedent variables must interact through multiple factors to form an effective “incentive combination” that drives behavior. Total compensation, as a new concept integrating both monetary and non-monetary rewards, inherently aligns with the requirements of mixed antecedents for proactive behavior and should be an effective framework for explaining different proactive behaviors. However, existing research on the association between total compensation, its combination of elements, and proactive behavior remains insufficient, especially in the exploration of differentiated incentive antecedents for different types of proactive behaviors. This study, based on constructing a mixed antecedent model of synergistic interaction, attempts to clarify the core theoretical issue of “when and how different compensation combinations will trigger different proactive behaviors”. It not only responds to the academic call for classification and mixed antecedent research on proactive behaviors but also reveals the intrinsic mechanisms between compensation combinations and different proactive behaviors, effectively bridging the theoretical gap in the relationship between compensation incentives and proactive behavior research.

Finally, this study reaffirms and expands upon the theoretical research on the differentiated, synergistic, and balanced satisfaction of employee needs from the perspective of the ERG theory. The results indicate that reward elements such as material rewards, development opportunities, and work experience exhibit multidimensional dynamic synergy effects during the motivation process. These elements not only demonstrate a functional complementarity in specific contexts but also display substitutional compensation characteristics under resource constraints. This non-linear interaction mechanism breaks through the traditional incentive theory’s isolated understanding of the effectiveness of individual elements.

For the first time, this study quantitatively verifies the non-linear synergy trajectories between different dimensions of needs within the total compensation framework using the ERG theory. It fully reveals the configurational mapping relationship between compensation element combinations and motivational effects. This expands the cognitive process of employees’ needs being met through “synergy, balance, and substitution” and offers a fresh theoretical perspective for incentive theory.

5.3. Managerial Implications

This study offers significant managerial insights for organizations in managing and motivating new-generation knowledge employees. Proactive behavior, in response to complex and ever-changing internal and external environments, provides a solution from the micro-employee behavior perspective to enhance organizational resilience.

Policymakers within companies should break away from a single material incentive model and establish and implement a total compensation incentive system that synergizes both economic and non-economic rewards. In other words, the overall compensation design system should be elevated to the strategic level of the company, and a proactive behavior classification incentive strategy should be implemented based on the company’s development stage and resource reserves. Employee motivation should be placed on equal footing with the pursuit of company profits, aiming for a win–win outcome by encouraging proactive employee behavior.

Building on the above incentive strategy and framework, the HR department of the companies should identify the types of missing proactivity in employees through techniques such as behavioral profiling, leadership observation, performance evaluation, 360-degree feedback assessments, and one-on-one conversations. This is helpful for line managers to categorize and trace the root causes of passive behaviors like “lying flat” and “slacking off”. By observing, surveying, and engaging in discussions with employees, line managers can better understand the insufficient proactivity and determine whether the lack of proactivity is related to individual goals or team goals. This helps identify whether the issue lies in individual task proactivity or team-member proactivity.

Second, based on the management’s strategic approach to categorizing employee incentives, the HR department can develop targeted incentive tools to encourage different types of proactive behavior. Specifically, when employees are reluctant to change their work habits, human resources policy should prioritize ensuring basic “salary” and welfare while adjusting workloads to promote work–life balance, especially under resource constraints. Once these core compensation practices are met, further support can be provided by enhancing career skill training and career planning, as well as increasing work challenges and meaning to address growth needs. For team-oriented proactivity issues, the human resources policy should reflect on whether employees’ reasonable concerns and demands are being addressed promptly and whether performance assessments and bonus distributions are fair. In addition to implementing employee care and pay fairness, the human resources policy can provide rewards such as guaranteed salaries, flexible work hours, clear promotion channels, and meaningful work to foster team-oriented behavior. Line managers can build trust and cooperation by clarifying team goals, holding open discussions, organizing team-building activities, and encouraging proactive behaviors.