Employee Motivation and Job Performance of Non-Academic Staff in Chinese Universities

Abstract

This study investigates the relationship between monetary and non-monetary motivations and financial and non-financial job performance among non-academic staff in Chinese universities. Using data from 356 respondents, analyzed via Structural Equation Modeling (SEM-PLS) with Smart-PLS4, this study finds that both incentive types significantly affect performance. Monetary incentives such as salaries and bonuses primarily enhance financial performance; on the contrary, non-monetary incentives such as training, career advancement, and supportive work environments have a stronger impact on nonfinancial performance, including job satisfaction and service quality. The findings underscore the importance of implementing balanced motivation strategies that integrate both financial rewards and developmental support. From a policy perspective, this study recommends customized incentive systems to improve administrative effectiveness and contribute to the strategic development of universities. These insights offer practical guidance for strengthening human resource practices and maximizing the performance of non-academic personnel in the context of higher education in China.

1. Introduction

Motivation fuels individuals’ determination and sustains their behavior toward achieving specific goals [1]. Cui’s work emphasized that employees with high levels of positive motivation are crucial for an organization’s sustained success [2]. As a result, organizations endeavor to keep their workforce engaged and motivated, thereby enhancing work efficiency and supporting overall development [3]. Job performance is defined as the quality and abilities a person demonstrates at work—a key metric for evaluating employee capabilities [4]. In today’s rapidly changing and competitive environment, the ability of employees to consistently deliver excellent performance is critical to the survival and growth of organizations [5].

Non-academic staff at Chinese universities are facing high workloads and monotonous tasks, leading to burnout; this burnout can adversely affect the teaching quality and institutional development [6]. With the growing competition in education, it is crucial for universities to focus on the performance of non-academic staff. Hence, this study aims to investigate how monetary and non-monetary incentives impact the financial and non-financial performance of non-academic university staff, addressing four specific questions related to these relationships.

This paper is structured into several sections: an introduction outlining the research questions and objectives, a literature review, a Methodology Section with the hypotheses and questionnaire design, data collection and analysis using Smart-PLS4 software, and a conclusion that addresses the research limitations and future research directions.

2. The Literature Review

Maslow’s hierarchy of needs [7] remains a foundational theory in understanding employee motivation, organizing human requirements into five progressive levels (physiological, safety, social, esteem, and self-actualization) which helps to explain the motivational priorities of non-academic university staff [8,9,10]. Herzberg’s two-factor theory [11] further distinguishes between hygiene factors (e.g., income and job security) and motivators (e.g., recognition and personal growth), emphasizing that while adequate compensation prevents dissatisfaction, it does not guarantee sustained motivation [12,13]. In addition, McClelland’s Achievement Motivation Theory [14] identifies three dominant human needs—namely achievement, affiliation, and power—as critical drivers of motivation once basic needs are met [15,16].

Additionally, self-determination theory [17,18] asserts that motivation is highest when individuals experience autonomy, competence, and relatedness. This theory is particularly relevant in the university setting, where non-academic staff often seek meaningful engagement, self-directed work, and positive workplace relationships. Next, expectancy theory suggests that an individual’s behavioral motivation comes from the expectation of results and the evaluation of the value of the results model [19].

Expectancy theory provides a useful theoretical framework for the management of non-academic staff in Chinese universities. China’s university system has its own unique management culture, educational background, and organizational structure, which greatly affect non-academic staff’s cognition of expectations, instrumentality, and reward value. Other related theories such as Psychological Empowerment Theory [20] highlight the motivational role of four psychological dimensions: meaning, competence, self-determination, and impact.

In Chinese universities, where bureaucratic structures often dominate, fostering psychological empowerment may offer a sustainable path to enhancing employee engagement and institutional performance [21,22]. Non-academic university staff—as all personnel engaged in non-teaching and non-research roles—encompasses departments such as Academic Affairs, Student Affairs, Finance, Logistics, Security, and various administrative and Party-related units [23]. These staff members play crucial roles in institutional operations, bridging communication between academic stakeholders and university leadership. This study adopts Qiu’s definition of monetary motivation [24], referring to quantifiable financial rewards including basic salary, bonuses, performance-related pay, and welfare benefits, governed by performance evaluation mechanisms. Compensation can be distinguished between direct compensation (e.g., salary and incentives) and indirect compensation (e.g., employee benefits) [25]; on this note, salary remains a dominant extrinsic motivator [26]. Wang and Fu argued that monetary rewards are essential in securing loyalty, commitment, and work ethic among employees [27].

Past studies on staff motivation in Chinese higher education tended to focus on the academic staff. For example, Zhao et al. [28] explored intrinsic motivation among Chinese lecturers through value-based leadership and generational differences, and similarly, Wang et al. [29] focused on instructors’ intrinsic and extrinsic motivational drivers. However, studies on non-academic personnel appear to be limited; hence, addressing this gap, the present study examines motivational mechanisms and influencing factors for non-academic staff, extending public sector motivation theory and broadening the understanding of incentive structures within Chinese universities.

In contrast, non-monetary motivation encompasses intrinsic and social rewards that are not financially quantifiable. Zhang and Shen define these as factors such as recognition, career growth, autonomy, meaningful work, and positive interpersonal relationships [30,31]. Contemporary studies [21,32,33,34] have increasingly emphasized the role of non-financial incentives in promoting performance, job satisfaction, and innovation. The impact of training, promotion, supportive leadership, and a positive work climate has been shown to significantly affect employee engagement. Drawing on McClelland’s framework, Zhang, Yan and Yao argued that motivation directly drives innovation and performance, resulting in measurable economic benefits [35,36,37]. This study, therefore, defines financial performance as quantifiable outputs such as efficiency, cost savings, and revenue contributions driven by non-academic staff.

Wan highlighted that incentives influence not only financial outcomes but also non-financial performance indicators such as employee satisfaction, service quality, and institutional reputation, factors that, while not directly quantifiable, are essential for long-term organizational success [38]. Ran et al. [39] similarly found that satisfying intrinsic needs leads to improved satisfaction and social impact, underscoring the importance of incorporating non-monetary drivers into performance evaluation. Consequently, non-financial performance in this study refers to intangible contributions such as morale, service excellence, innovation, and social influence within the university ecosystem.

2.1. Research Framework

Researches [7,11,14] serve as the underpinning theories for the framework of this study. Figure 1 presents the link between the motivation and job performance of non-academic staff; the hypotheses’ development is discussed subsequently. By sorting out the above theories on the relationship between motivation and job performance, and combining the motivational factors mentioned in previous studies, the variables employed in monetary motivations are basic salary, performance bonus, salary increase, and benefits. Variables included in non-monetary motivations are workplace environment, training, promotion, and organizational management. The financial performance mainly includes generating income, fund management, and increasing income, while non-financial performance mainly includes satisfaction, service quality, and social influence. Figure 1 shows the framework of this study on the relationship between monetary and non-monetary motivations and the operational and service job performance of university non-academic staff. The framework enables analysis of how different motivational drivers, monetary and non-monetary, impact various dimensions of non-academic staff performance, offering robust theoretical guidance for empirical validation.

2.2. Description of Variables

Basic salary remains the cornerstone of monetary motivation for non-academic staff, and is regarded as a fundamental component of income security [40]. It satisfies low-level needs, symbolizes the institution’s recognition of staff contributions, and provides extrinsic satisfaction that directly influences work performance [41,42]. Recent findings confirm that salary security is critical in maintaining job morale, especially under increasing administrative workloads [28].

In recent years, performance-based pay has gained prominence in Chinese universities as a dynamic monetary incentive, aligning compensation with individual contributions [22,43]. This system, which embodies fairness and efficiency, motivates staff by linking compensation with outcomes [44]. Bonuses, meanwhile, provide flexibility in rewarding specific tasks or short-term achievements, and have been shown to significantly enhance innovation and work initiative when structured effectively [45,46].

Additionally, the long-term welfare system plays a crucial role in signaling institutional care, with recent studies noting that comprehensive health, housing, and family support policies foster organizational loyalty [47]. Moreover, an improved work environment, including space, scheduling, and interpersonal dynamics, is shown to enhance job satisfaction and performance by reducing stress and distractions [48,49].

Training and development opportunities are another vital motivational tool. Universities that invest in professional growth demonstrate their recognition of staff potential, leading to improved morale and retention [50]. Job promotion, often valued more than salary increments, offers long-term career security and satisfaction, with recent findings linking promotion prospects to reduced turnover and higher commitment [51,52].

Modern research also emphasizes the significance of organizational management in motivation. Effective leadership, tailored to the diverse needs of non-academic staff, fosters engagement and reduces attrition [28,53]. Furthermore, the capacity for income generation and efficient fund management reflects non-academic staff’s contributions to institutional sustainability and resource optimization [54,55,56].

Lastly, job satisfaction—including satisfaction with the work itself, institutional support, and public recognition—has been validated as a key mediator between motivation and performance outcomes [57]. To address this, the recent literature stresses the strategic importance of non-academic staff in maintaining university operations and enhancing institutional reputation [58,59].

2.3. Research Hypotheses

This study explores the relationship between employee motivation and job performance among non-academic staff in Chinese universities. The motivations are operationalized into two categories, monetary motivations and non-monetary motivations, while job performance is assessed through two key dimensions: financial performance and non-financial performance. The development of the research hypotheses is grounded in established motivation and performance theories.

2.3.1. Hypothesis 1: Monetary Motivations and Financial Performance

The Expectancy Theory [19] posits that monetary rewards such as base salaries, performance bonuses, and outcome-based pay enhance employees’ motivation to engage in goal-directed behavior. These incentives are particularly effective in roles that emphasize efficiency, compliance, and cost management as they align individual interests with institutional performance objectives.

Liu [60] observed that the implementation of performance-based incentives in university administrative departments led to notable improvements in work efficiency and a reduction in transaction processing times. Likewise, Sun [55] found that monetary reward systems increased staff engagement in procurement reforms and resource management, contributing to enhanced financial oversight and institutional efficiency.

H1: 

There is a positive relationship between monetary motivations and financial performance among non-academic staff in Chinese universities.

2.3.2. Hypothesis 2: Non-Monetary Motivations and Financial Performance

Self-determination theory [17] posits that employees exhibit higher levels of task engagement and performance when their psychological needs for competence, autonomy, and relatedness are fulfilled. Within administrative settings, non-monetary motivators such as professional recognition, participatory decision-making, and opportunities for skill development can enhance intrinsic motivation. These factors contribute to improved resource utilization, administrative efficiency, and process optimization, even in the absence of direct financial incentives.

Liu [61] found that feedback, recognition, and autonomy in decision-making significantly increased employee commitment and led to improvements in resource allocation and administrative efficiency. Their study demonstrated that non-monetary motivators independently fostered efficiency-driven behaviors among support staff, reinforcing the importance of intrinsic motivational strategies in institutional settings.

H2: 

There is a positive relationship between non-monetary motivations and financial performance among non-academic staff in Chinese universities.

2.3.3. Hypothesis 3: Monetary Motivations and Non-Financial Performance

Reinforcement Theory [62] posits that behaviors reinforced by positive outcomes are more likely to be repeated. In the context of service-oriented roles, performance-based financial incentives can act as reinforcers for desired behaviors such as responsiveness, accuracy, and service quality. When monetary rewards are tied to specific service outcomes, they can motivate employees to maintain high standards and prioritize client satisfaction.

Wang [45] demonstrated that monetary incentives linked to performance goals significantly improved employee engagement in customer service settings, resulting in faster and more accurate service delivery. Similarly [63] found that in higher education institutions, bonuses tied to service quality metrics led to measurable improvements in student-facing administrative functions, suggesting that financial rewards can effectively drive service-oriented behaviors among non-academic staff.

H3: 

There is a positive relationship between monetary motivations and non-financial performance among non-academic staff in Chinese universities.

2.3.4. Hypothesis 4: Non-Monetary Motivations and Non-Financial Performance

Maslow’s hierarchy of needs [7]—particularly the higher-order needs of esteem and self-actualization—along with Organizational Support Theory [64] highlight the importance of intrinsic rewards in shaping sustained workplace engagement. Recognition, growth opportunities, and supportive organizational culture are key non-monetary drivers that encourage employees to voluntarily participate in roles that enhance institutional reputation, stakeholder satisfaction, and community engagement.

Zhang [57] reported that both academic and administrative staff exhibited increased engagement when institutions offered career development pathways and publicly recognized staff contributions. Likewise, Li [65] found that institutional loyalty and commitment to service excellence were significantly higher in environments characterized by team cohesion and strong alignment with the university’s mission and values. These findings suggest that non-monetary motivators play a critical role in reinforcing service-oriented behaviors among non-academic personnel.

H4: 

There is a positive relationship between non-monetary motivations and non-financial performance among non-academic staff in Chinese universities.

3. Methodology

3.1. Research Design

The questionnaire of this study consists of two parts, as shown in

Table 1. Questionnaire sections.

Part	Variable	Project Total
Part A	Monetary motivations	20
	Non-monetary motivations	20
Part B	Financial performance	15
	Non-financial performance	15

Note: Monetary motivation variable consists of basic salary, performance bonus, salary, and monetary benefits; non-monetary motivations consist of workplace, training, promotion, and organization; financial performance variable consists of institutional resource, use of resources, financial sustainability; non-financial performance consists of employee engagement, service efficiency, and institutional reputation.

. The first part includes 6 statistical questions about the background information of the non-academic personnel surveyed, including the gender, age, marital status, educational level, work content, and working age; the second part is a scale about the correlation between motivational factors and work performance, which mainly includes four parts. The first part includes the valid questionnaires of [66,67,68,69], which consist of four items for measuring monetary motivations, each with 5 measurement items; the second part includes the valid questionnaires of [60,70,71], including 20 items for measuring non-monetary motivations; the third part includes the valid questionnaires of [72,73,74], including 15 measures for evaluating financial performance; the fourth part includes the valid questionnaires of [47,75,76,77], including 15 measures for evaluating non-financial performance. The questionnaire was designed for the specific context of this study and used a 5-point scale to set a five-point rating—1 (Strongly Disagree); 2 (Disagree); 3 (Neutral); 4 (Agree); 5 (Strongly Agree)—allowing respondents to choose based on their feelings or opinions, thereby quantifying subjective experience to measure the importance of motivational factors, actual satisfaction, and overall motivational performance.

3.2. Data Collection

Employing a stratified sampling method, this study surveyed non-academic staff from selected private Chinese universities to explore how motivation affects work performance. The online questionnaire, distributed between February and May 2024, yielded 531 responses. To ensure validity, responses were screened for completeness, internal consistency, and alignment with the target population—specifically, non-academic administrative staff as defined by [23]. A total of 175 responses were excluded due to missing data and respondent ineligibility (academic staff), resulting in 356 valid responses. This represents a 67% valid response rate, exceeding the minimum required sample size of 68 determined via G*Power 3.1.9.7 (effect size = 0.25, α = 0.05, power = 0.80), ensuring adequate statistical power for the SEM-PLS analysis.

To address concerns of common method variance (CMV) inherent in single-source data, Harman’s one-factor test was conducted to assess the potential for CMV; the variance is only 27.42%, which is less than 50% of the total variance, indicating that there is no common method bias. This aligns with the empirical findings by Fuller et al. (2016), who demonstrated through simulation that typical levels of CMV in survey research (10–20%) rarely result in biased relationships unless the CMV exceeds 60–70%.

3.3. Measurement Model Assessment

This section presents the assessment of measurement models, including internal consistency reliability tests (Cronbach’s alpha and composite reliability), convergent validity (average variance extracted), discriminant validity (HTMT ratio), and multicollinearity (VIF values). These diagnostics ensure that the measurement model is robust.

It is necessary to test the collinearity of the independent variables in regression analysis [78]. The presence of multicollinearity makes the model estimation distorted or difficult to estimate accurately [76]. Multicollinearity can be evaluated by the variance inflation factor (VIF) and tolerance—when the variance expansion factor is greater than 5, the model has severe multicollinearity [77].

Table 2. Collinearity statistics (VIF values).

	Monetary Motivations	Non-Monetary Motivations	Financial Performance	Non-Financial Performance
VIF	1.41	1.756	1.00	1.00

shows that the VIF values of most variables are 1 and less than 2, indicating that there is no obvious multicollinearity problem between the variables in the model.

This study assesses the reliability of each construct using Cronbach’s alpha (α). A value below 0.7 indicates poor internal consistency requiring scale revision, values above 0.7 denote good consistency, and values exceeding 0.9 represent excellent reliability [79]. As shown in

Table 3. Reliability test.

Dimension	Cronbach’s Alpha	No. of Items
Basic salary	0.875	5
Benefits	0.885	5
Bonus	0.879	5
Fund management	0.886	5
Generate income	0.875	5
Increase income	0.878	5
Job service quality	0.88	5
Organization management	0.87	5
Social influence	0.894	5
Workplace environment	0.88	5
Performance management	0.88	5
Promotion	0.884	5
Satisfaction	0.877	5
Training	0.875	5

, all variables have α values above 0.87, confirming that the scales possess high internal consistency.

This study employed confirmatory factor analysis to evaluate the measurement model, focusing on establishing convergent validity by examining the factor loadings, average variance extracted (AVE), and composite reliability. Chen [80] explained that AVE reflects how well the items represent the latent variable, with values closer to 1 indicating a better fit and values above 0.5 generally considered acceptable. Additionally, a correctly specified model should have standardized factor loadings greater than 0.7 and composite reliability of at least 0.7 [81], with values near or above 0.9 indicating high reliability [82].

Table 4. Convergent validity.

Constructs Outer	Items	Loadings	AVE	Composite Reliability
	BS1	0.812
	BS2	0.823
Basic salary	BS3	0.809	0.668	0.909
	BS4	0.823
	BS5	0.818
	BE1	0.838
	BE2	0.856
Benefits	BE3	0.863	0.685	0.916
	BE4	0.789
	BE5	0.791
	BO1	0.790
	BO2	0.786
Bonus	BO3	0.851	0.674	0.912
	BO4	0.832
	BO5	0.842
	FM1	0.827
	FM2	0.831
Fund management	FM3	0.828	0.687	0.917
	FM4	0.815
	FM5	0.843
	GI1	0.826
	GI2	0.813
Generate income	GI3	0.802	0.665	0.909
	GI4	0.814
	GI5	0.825
	LI1	0.839
	LI2	0.824
Increase income	LI3	0.785	0.673	0.911
	LI4	0.813
	LI5	0.839
	JS1	0.833
	JS2	0.817
Job service quality	JS3	0.821	0.677	0.913
	JS4	0.798
	JS5	0.843
	OM1	0.838
	OM2	0.807
Organization management	OM3	0.803	0.657	0.906
	OM4	0.799
	OM5	0.806
	SI1	0.848
	SI2	0.822
Social influence	SI3	0.845	0.703	0.922
	SI4	0.824
	SI5	0.851
	WE1	0.837
	WE2	0.823
Workplace environment	WE3	0.807	0.675	0.912
	WE4	0.822
	WE5	0.819
Performance management	PM1	0.833	0.676	0.912
	PM2	0.789
	PM3	0.812
	PM4	0.841
	PM5	0.834
	PR1	0.828
	PR2	0.827
Promotion	PR3	0.838	0.683	0.915
	PR4	0.803
	PR5	0.836
	SA1	0.830
	SA2	0.817
Satisfaction	SA3	0.818	0.67	0.91
	SA4	0.831
	SA5	0.796
	TR1	0.837
	TR2	0.828
Training	TR3	0.795	0.668	0.909
	TR4	0.835
	TR5	0.789

shows that every item has a factor loading above 0.7, and all variables have AVE values exceeding 0.65, with social influence reaching the highest at 0.703; this demonstrates that the items effectively capture their latent variables. Although the composite reliability values are slightly below 0.9, they all surpass 0.87, confirming that the scales are highly reliable.

Table 5. Correlation analysis between variables.

	Monetary Motivations	Non-Monetary Motivations	Financial Performance	Non-Financial Performance
Monetary Motivations Non-monetary Motivations	1 0.699 **	1
Financial Performance Non-financial Performance	0.638 ** 0.677 **	0.641 ** 0.639 **	1 0.608 **	1

Note: ** p < 0.01 (highly significant).

reveals a high positive correlation between monetary and non-monetary motivations (r = 0.699, p < 0.05), suggesting that increases in one incentive type are often accompanied by enhancements in the other. Monetary motivations are significantly related to financial performance (r = 0.638, p < 0.05), indicating that bonuses and salary increases effectively boost financial outcomes for non-academic staff, while non-monetary motivations similarly impact financial performance (r = 0.641, p < 0.05) through factors such as training, promotion opportunities, and work autonomy. Furthermore, monetary motivations show a significant positive relationship with non-financial performance (r = 0.677, p < 0.05), and non-monetary motivations do as well (r = 0.639, p < 0.05), highlighting their roles in enhancing employee satisfaction, teamwork, and innovation. Finally, the significant positive correlation between financial and non-financial performance (r = 0.608, p < 0.05) indicates that improvements in one area are often associated with enhancements in the other.

Next, this study compares the square root of AVE for each construct and its correlation constructs with all other constructs based on the discriminant validity analysis results of the Fornell–Lacker criterion [83].

According to the values shown in

Table 6. Discriminant validity (Fornell–Lacker criterion).

	Financial Performance	Monetary Motivations	Non-Monetary Motivations	Non-Financial Performance
Financial Performance	0.620
Monetary Motivations	0.641	0.607
Non-monetary Motivations	0.642	0.699	0.612
Non-financial Performance	0.608	0.680	0.64	0.632

, the AVE values of these constructs all exceed the correlation coefficients between them and other variables, indicating that these variables are statistically well differentiated; that is, they each represent different concepts, and these concepts are relatively independent in measurement. To further validate the data, this study applied the HTMT (Heterotrait–Monotrait) criterion to assess discriminant validity between constructs, where acceptable HTMT values are below 0.90 or 0.85 [84].

Table 7. Discriminant validity (HTMT ratios).

	Monetary Motivations	Non-Monetary Motivations	Financial Performance	Non-Financial Performance
Monetary Motivations
Non-monetary Motivations	0.768
Financial Performance	0.711	0.714
Non-financial Performance	0.683	0.751	0.708

shows that all HTMT values between the variable pairs are below 0.90, with the highest value of 0.711 occurring between non-monetary motivations and monetary motivations. According to the criteria for Heterotrait–Monotrait (HTMT) discriminant validity, an acceptable HTMT value is less than 0.90 or 0.85 [84]. It can be seen from the data shown in Table 8 that the HTMT value between monetary motivations and financial performance is 0.711, and between non-monetary motivations and financial performance it is 0.714, indicating that there is a certain correlation between them. However, it is generally believed that when the HTMT value is less than 0.85, the differentiation between variables is acceptable. Similarly, the HTMT values between non-financial performance and the other three variables (0.683, 0.751, and 0.708, respectively) are also within the acceptable range. None of the HTMT values between the variables exceeded the threshold of 0.85, meaning that the variables were statistically different enough to be considered distinct constructs. This result supports the validity of using these variables as independent constructs in this study and provides a strong basis for subsequent analysis.

4. Results

4.1. Descriptive Statistics

This study employs structural equation modeling (SEM-PLS) using Smart-PLS4 to investigate the causal relationship between job motivation and performance among administrative staff. Data from 356 questionnaire responses were analyzed, with the survey divided into two parts: demographic items (covering gender, marital status, age, location, education level, position, and years of work experience) and constructs measuring monetary motivations, non-monetary motivations, financial performance, and non-financial performance. The analysis includes descriptive statistics, assessment of the structural model, and hypothesis testing with both direct and moderating effects, with a focus on demographic distributions such as gender ratio, age ratio, and educational background.

According to Table 8 the research sample includes slightly more male respondents (183; 51.4%) than female respondents (173; 48.6%), which aligns with Ministry of Education data indicating that women comprise 52.0% of Chinese university faculty and staff, suggesting a balanced gender distribution. Additionally, 64.8% of respondents are aged between 20 and 35 (37.6% in the 20–30 age group and 27.2% in the 30–35 group), with only 35.2% aged 36 and above and 7.9% over 46, indicating that the sample is predominantly young to middle-aged; a trend consistent with [85]. Furthermore, the educational background is varied, with 49.2% holding master’s degrees, 40.7% bachelor’s degrees, and 10.1% PhDs, reflecting a highly educated group, in line with [80] findings on the recruitment trends in Chinese university administrative teams.

Table 8. Demographic variables of the non-academic staff.

Demographic Variables	Frequency (N = 356)	Percent %
Gender
Male	183	51.4
Female	173	48.6
Age group
26–30	134	37.6
31–35	97	27.2
36–40	64	18.0
41–45	33	9.3
46–50	17	4.8
>51	11	3.1
Education
Bachelor’s	145	40.7
Master’s	175	49.2
PhD	36	10.1

Table 8. Demographic variables of the non-academic staff.

Demographic Variables	Frequency (N = 356)	Percent %
Gender
Male	183	51.4
Female	173	48.6
Age group
26–30	134	37.6
31–35	97	27.2
36–40	64	18.0
41–45	33	9.3
46–50	17	4.8
>51	11	3.1
Education
Bachelor’s	145	40.7
Master’s	175	49.2
PhD	36	10.1

4.2. Structural Model

The structural equation model is a path model that connects independent variables to dependent variables, allowing researchers to distinguish which independent variables can predict dependent variables [86]. This study uses a structural model for evaluation, mainly involving four variables: monetary motivations, non-monetary motivations, financial performance, and non-financial performance.

4.3. Discussion of the Results

4.3.1. Hypothesis 1

Table 9. Summary of path coefficients and hypotheses testing.

Relationship	β	p Values	T-Value	Decision	f2	Effect Size
Basic salary -> Fund	−0.008	0.883	0.147	Rejected	0
Basic salary -> Generate	0.076	0.171	1.369	Rejected	0.006	Small
income
Basic salary -> Increase income	0.025	0.652	0.451	Rejected	0.001
Basic salary -> Job service	0.118	0.023	2.267	Accepted	0.016	Medium
quality
Basic salary -> Social influence	0.034	0.54	0.613	Rejected	0.001	Small
Basic salary -> Satisfaction	0.05	0.369	0.898	Rejected	0.002	Small
Benefits -> Fund management	0.06	0.275	1.091	Rejected	0.003	Small
Benefits -> Generate income	0.016	0.775	0.286	Rejected	0
Benefits -> Increase income	0.12	0.035	2.109	Accepted	0.013	Medium
Benefits -> Job service quality	0.128	0.016	2.402	Accepted	0.018	Medium
management
Benefits -> Social influence	0.112	0.049	1.972	Accepted	0.012	Medium
Benefits -> Satisfaction	0.172	0.002	3.034	Accepted	0.027	Large
Bonus -> Fund management	0.143	0.012	2.505	Accepted	0.019	Medium
Bonus -> Generate income	0.12	0.029	2.188	Accepted	0.013	Medium
Bonus -> Increase income	0.084	0.128	1.523	Rejected	0.006	Small
Bonus -> Job service quality	0.16	0.005	2.795	Accepted	0.027	Large
Bonus -> Social influence	0.105	0.062	1.866	Rejected	0.01	Medium
Bonus -> Satisfaction	0.125	0.034	2.121	Accepted	0.014	Medium
Organization management ->Fund management	0.046	0.424	0.8	Rejected	0.002	Small
Organization management -> Generate income	0.136	0.013	2.498	Accepted	0.018	Medium
Organization management -> Increase income	0.132	0.016	2.402	Accepted	0.016	Medium
Organization management -> Job service quality	0.019	0.715	0.366	Rejected	0
Organization management -> Social influence	0.155	0.006	2.748	Accepted	0.023	Large
Organization management -> Satisfaction	0.062	0.285	1.068	Rejected	0.004	Small
Workplace environment -> Fund management	0.095	0.095	1.669	Rejected	0.008	Small
Workplace environment -> Generate income	0.151	0.01	2.582	Accepted	0.02	Small
Workplace environment -> Increase income	0.086	0.148	1.449	Rejected	0.006	Small
Workplace environment -> Job service quality	0.03	0.579	0.554	Rejected	0.001	Small
Workplace environment -> Social influence	−0.04	0.495	0.683	Rejected	0.001	Small
Workplace environment -> Satisfaction	0.054	0.382	0.874	Rejected	0.002	Small
Performance management -> Fund management	0.175	0.002	3.051	Accepted	0.028	Large
Performance management -> Generate income	0.15	0.01	2.581	Accepted	0.021	Large
Performance management -> Increase income	0.157	0.007	2.688	Accepted	0.022	Large
Performance management -> Job service quality	0.165	0.002	3.034	Accepted	0.029	Large
Performance management -> Social influence	0.134	0.016	2.41	Accepted	0.016	Medium
Performance management -> Satisfaction	0.111	0.062	1.864	Rejected	0.011	Medium
Promotion -> Fund management	0.138	0.012	2.527	Accepted	0.018	Medium
Promotion -> Generate income	0.053	0.316	1.003	Rejected	0.003	Small
Promotion -> Increase income	0.04	0.466	0.729	Rejected	0.001	Small
Promotion -> Job service quality	0.104	0.054	1.93	Rejected	0.012	Medium
Promotion -> Social influence	0.152	0.008	2.664	Accepted	0.021	Large
Promotion -> Satisfaction	0.131	0.015	2.424	Accepted	0.015	Medium
Training -> Fund management	0.122	0.034	2.126	Accepted	0.015	Medium
Training -> Generate income	0.072	0.199	1.285	Rejected	0.005	Small
Training -> Increase income	0.099	0.082	1.739	Rejected	0.009	Small
Training -> Job service quality	0.18	0	3.511	Accepted	0.037	Large
Training -> Social influence	0.115	0.038	2.081	Accepted	0.013	Medium
Training -> Satisfaction	0.029	0.596	0.53	Rejected	0.001	Small

shows that monetary motivations significantly improve financial performance, with basic salary having no effect (p > 0.05, f² < 0.01), while bonuses, benefits, and performance management yield moderate-to-large positive impacts. Specifically, performance management exhibits strong effects on fund management (β = 0.175, p = 0.002, f² = 0.028), income generation (β = 0.15, p = 0.01, f² = 0.021), and income increase (β = 0.157, p = 0.007, f² = 0.022). These results confirm that monetary incentives, particularly performance management, play a crucial role in enhancing the financial performance of non-academic staff, aligning with findings by [87] and suggesting that regional and cultural differences may explain deviations from [88].

This study found that there is a significant positive relationship between monetary motivations and financial performance of non-academic staff in Chinese universities. This study supports the theory that monetary motivations can meet the physiological and material needs of non-academic staff, which is consistent with the view that “basic needs are the basis of motivation” in the hierarchy of needs theory [7]. This study regards monetary motivation as an extrinsic motivation factor, which is consistent with the view that “compensation is a hygiene factor” in the two-factor theory. However, the two-factor theory also points out that extrinsic motivation can only prevent dissatisfaction but cannot directly improve performance. This study supports the view that extrinsic motivation (such as money) can improve performance under certain conditions, which is consistent with the self-determination theory [17].

This study supports the positive impact of monetary incentives on the financial performance of non-academic staff, which is consistent with the research results of [89] on the relationship between monetary incentives and work performance. Their research results indicate that strengthening monetary incentives for non-academic staff can improve financial performance. According to the path coefficient analysis results in Figure 2, monetary incentives have the most significant and greatest impact on the financial performance of non-academic staff. The results imply the nature of the work of non-academic staff, which focuses more on task completion and efficiency rather than creativity or autonomy; therefore, monetary incentives may be more effective than non-monetary incentives [90].

4.3.2. Hypothesis 2

Hypothesis 2 (H2) examines the impact of non-monetary motivations on the financial performance of non-academic staff. Table 8 shows that while the workplace environment has a weak yet significant positive effect on income generation (β = 0.151, p = 0.01, f² = 0.02), it does not significantly influence other financial performance indicators. In contrast, organizational management, promotion, and training have moderate positive impacts: organizational management significantly enhances income generation (β = 0.136, p = 0.013, f² = 0.018) and income increase (β = 0.132, p = 0.016, f² = 0.016); promotion significantly improves fund management (β = 0.138, p = 0.012, f² = 0.018); and training also significantly boosts fund management (β = 0.122, p = 0.034, f² = 0.015). These findings confirm that non-monetary motivations play a significant and moderate role in improving the financial performance of non-academic staff, suggesting that universities can enhance performance by investing in strategies such as improved organizational management, promotion opportunities, and training programs, consistent with Zhang [91].

This study found that non-monetary motivations are significantly positively correlated with the financial performance of non-academic staff at Chinese universities. Ming [92] suggested the demand for self-development of non-academic staff has gradually increased, which also means that the use of non-monetary motivation incentives by universities can promote an improvement in the financial performance of non-academic staff. Specifically, this means that universities can improve the financial performance of non-academic staff by providing non-monetary incentives. This result is also consistent with classic motivation theories such as self-determination theory. Deci and Ryan [17] showed that non-monetary incentives can enhance the intrinsic motivation of non-academic staff, thereby improving their work performance.

This finding is consistent with the findings of [93], who found that providing non-monetary incentives to non-academic staff can also promote non-academic staff to improve their financial performance. By providing non-monetary incentives such as training, promotion, improving the working environment, and improving the level of organizational management, even when the level of monetary motivation is low, the enthusiasm and initiative of non-academic staff can be mobilized to have a positive effect on the institutional operational job performance.

The results of this study are also consistent with the results of [94], who believe that although the form of non-monetary incentives can improve the institutional operational job performance of non-academic staff to a certain extent, after meeting the needs of non-academic staff. However, compared with non-monetary incentives, universities can better meet the needs of non-academic staff by using monetary incentives, which can greatly improve the institutional operational job performance of non-academic staff.

However, this finding is different from [95] on the impact of monetary and non-monetary motivations on work performance. They found that providing non-monetary incentives to non-academic staff can also have a positive effect on financial performance, and non-monetary incentives can usually have a more sustained and effective incentive for the institutional operational job performance of non-academic staff. According to [96], the imbalance of incentive mechanisms will affect the impact of monetary incentives and non-monetary incentives on employees’ financial performance. Yang believes that in many organizations, monetary incentives may be overemphasized, while the design and implementation of non-monetary incentives may not be perfect. This imbalance may lead to a more significant effect of monetary incentives.

If non-academic staff are satisfied with their work environment, career development opportunities, and organizational support, they may be more willing to work hard, thereby improving financial performance. According to the hierarchy of needs theory, non-monetary incentives (such as career development and job recognition) usually act on higher-level needs. The satisfaction of these needs can improve employees’ job satisfaction, thereby enhancing the effect of non-monetary incentives. If the higher-level needs of non-academic staff (such as self-realization and sense of belonging) are met, they may be more satisfied with their work and thus be more actively engaged in their work. In the context of this study, the higher-level needs of non-academic staff may not be fully met, thereby weakening the role of non-monetary incentives. This study found that there is a significant positive relationship between non-monetary motivation and the financial performance of non-academic staff in Chinese universities, although the impact strength is moderate.

Miao and Xu [97] suggested non-monetary incentives are considered to have a more significant impact on employee performance and have a long-term effect in improving employee performance. Bao et al. [98] showed that in developing countries or economically underdeveloped regions, due to the limitations of economic conditions, monetary incentives still dominate, the salary level of non-academic staff is limited, and employees may have higher expectations for salary, which makes the role of monetary incentives more significant.

4.3.3. Hypothesis 3

Hypothesis 3 (H3) posits that monetary motivation positively affects non-financial performance. Table 8 shows that components such as basic salary, bonuses, benefits, and performance management significantly enhance aspects of non-financial performance—namely job service quality, social influence, and satisfaction. Basic salary only impacts job service quality (β = 0.118, p = 0.023, T = 2.267, f² = 0.016) moderately, while benefits significantly improve job service quality (β = 0.128, p = 0.016, T = 2.402, f² = 0.018), social influence (β = 0.112, p = 0.049, T = 1.972, f² = 0.012), and satisfaction (β = 0.172, p = 0.002, T = 3.034, f² = 0.027) with a particularly strong effect on satisfaction. Bonuses also positively affect job service quality (β = 0.16, p = 0.005, T = 2.795, f² = 0.027) and satisfaction (β = 0.125, p = 0.034, T = 2.121, f² = 0.014), though their impact on social influence is not significant. Performance management shows strong positive effects on job service quality (β = 0.165, p = 0.002, T = 3.034, f² = 0.029) and moderate effects on social influence (β = 0.134, p = 0.016, T = 2.41, f² = 0.016) but does not significantly affect satisfaction. Overall, these results confirm that monetary motivation, especially through benefits, bonuses, and performance management, has a significant and positive impact on the non-financial performance of non-academic staff, aligning with the findings of [99].

This study found that there is a significant positive relationship between monetary motivation and non-financial performance of non-academic staff in Chinese universities. The finding that monetary incentives have a significant impact on institutional service job performance in this study is consistent with incentives, which can greatly promote the non-financial performance of non-academic staff. In this context, monetary incentives may be regarded as recognition of employee contributions rather than the satisfaction of basic needs. Therefore, the impact of monetary incentives on non-financial performance may be more related to psychological factors (such as being recognized and valued). In other words, the role of monetary incentives is more reflected in meeting employees’ basic economic needs, thereby directly improving their work motivation and performance.

The work of non-academic staff usually includes administration, technical support, logistics, etc., which may focus more on teamwork, service awareness, and organizational identity rather than direct economic output. These characteristics may make the impact and degree of monetary incentives on non-financial performance in the education industry different from those in other industries (such as enterprises and manufacturing). This study shows that monetary motivation not only has a significant impact on financial performance but also has a strong promoting effect on non-financial performance. This finding is consistent with the research results of [100] on the impact of monetary motivation and non-monetary motivation on work performance.

4.3.4. Hypothesis 4

Hypothesis 4 (H4) examines the impact of non-monetary motivation on non-financial performance. Table 8 shows that non-monetary factors, specifically organizational management, promotion, and training, significantly enhance non-financial outcomes such as job service quality, social influence, and satisfaction, whereas the workplace environment has no significant effect. Organizational management notably improves social influence (β = 0.155, p = 0.006, T = 2.748, f² = 0.023); promotion significantly boosts social influence (β = 0.152, p = 0.008, T = 2.664, f² = 0.021) and satisfaction (β = 0.131, p = 0.015, T = 2.424, f² = 0.015), though it does not significantly affect job service quality; and training substantially enhances job service quality (β = 0.18, p = 0.000, T = 3.511, f² = 0.037) and social influence (β = 0.115, p = 0.038, T = 2.081, f² = 0.013) but not satisfaction. Overall, these findings confirm that non-monetary motivation significantly improves non-financial performance, comprising satisfaction, service quality, and social influence, which is critical for the long-term development and competitiveness of universities, consistent with Kuai et al. [101].

According to the self-determination theory, intrinsic motivation has a significant impact on employee performance. The role of non-monetary motivation in this study can be partly attributed to its stimulation of employees’ intrinsic motivation, especially the provision of career development and learning opportunities, which enhances employees’ sense of competence and autonomy. This finding is consistent with the findings of previous works [102,103], who believe that when universities implement non-monetary incentives, they can significantly improve the institutional service job performance of non-academic staff, including improving the satisfaction of non-academic staff, allowing non-academic staff to actively improve the quality of work services, and enhancing the social influence of non-academic staff and universities. The non-monetary incentives can enhance the understanding and recognition of universities by non-academic staff, enhance the sense of accomplishment and satisfaction, and help the staff realize their self-worth, thereby achieving the improvement in non-financial performance.

Among the non-monetary motivation variables, career development has the strongest relationship with non-financial performance of non-academic staff, followed by learning opportunities, and finally work environment. This result may be due to the combined effect of multiple factors such as organizational culture, personal job satisfaction, and economic environment, which can regulate the relationship between motivation and performance. Zhao [104] shows that organizational culture plays an important role in the selection and effect of incentive methods. In universities, the roles of non-academic staff are mostly supportive work, and their work results are often difficult to quantify. Therefore, organizational culture may be more inclined to recognize their contributions through monetary incentives.

Second, personal job satisfaction is another key factor. Xu [105] believes that in the context of a developed economy with relatively limited salaries, non-academic staff may have high expectations for salary levels and working conditions, and monetary incentives are more likely to directly meet their needs, thereby improving job satisfaction. However, if non-academic staff are dissatisfied with the meaning of the work itself or career development opportunities, even if non-monetary incentives are provided, their effects may be greatly reduced. Therefore, job satisfaction plays an important role in moderating the relationship between non-monetary motivations and the institutional operational job performance.

Finally, the role characteristics of non-academic staff may also affect the effectiveness of incentives. The results of Hu’s work [106] show that in the education industry, non-academic staff work mostly in supporting roles, and their work results are often difficult to quantify, which makes monetary incentives the main way to recognize their contributions.

The results of the research by Liu [107] show that the non-financial performance of non-academic staff in universities usually includes service quality, work responsibility, teamwork, etc. These indicators pay more attention to long-term and stability so the impacts of non-monetary motivations are more significant. Xia [108] pointed out that enterprises or other organizations take profit as their main goal, and the work performance of employees is usually directly linked to economic benefits. The nature of employees’ work focuses more on short-term performance and result orientation.

Li [109] believes that in developed countries or economically developed regions, the basic material needs of non-academic staff are usually fully met so they pay more attention to non-monetary incentives (such as career development, work environment, work recognition, etc.). In these countries or regions, the impact of non-monetary motivations on institutional service job performance has emerged. These non-monetary motivations have a significant positive impact on institutional service job performance but have not yet reached a dominant position. This may be because the implementation of these non-monetary motivation measures may not be systematic and in-depth enough, and monetary motivations still play a role in some scenarios. This is consistent with the findings of this study, which shows that in developed countries or economically developed regions, although non-monetary incentives have a significant impact on institutional service job performance, their impact has not yet completely replaced monetary incentives, and the implementation of non-monetary incentives still needs to be further systematized and deepened. However, the results of Zheng’s research [110] showed that management practices in developed countries or economically developed regions are more mature, and non-monetary incentives have been systematized and institutionalized so the impact of non-monetary motivation on institutional service job performance may be stronger. This is somewhat different from the findings of this study.

The findings are also consistent with the research results of Chen’s [111] on the impact of motivation on work performance. They found that although it is not as good as non-monetary motivations, in addition to having a significant incentive effect on the institutional operational job performance, providing non-monetary incentives to non-academic staff will also have a positive effect on improving their institutional service job performance. This shows that when designing incentive mechanisms, universities should not only focus on monetary motivation measures but also pay attention to the role of non-monetary motivations. Non-monetary motivations can effectively improve employees’ job satisfaction and loyalty, thereby promoting the overall performance of universities. When formulating incentive policies, universities should comprehensively consider the balance between monetary and non-monetary incentives to achieve a comprehensive improvement in their institutional operational- and service job-performance.

5. Conclusions

This study employs a survey research method to assess the impact of motivation on the work performance of non-academic staff in Chinese universities, classifying motivation factors into monetary and non-monetary types and job performance changes into financial and non-financial outcomes. Using Smart-PLS4, this study tests and analyzes the relationships among these constructs, evaluating the proposed hypotheses. A key contribution of this research is its focus on the influence of non-monetary motivations on non-financial performance, a gap in previous studies that largely concentrated on financial motivation, demonstrating that non-monetary incentives have a significant direct effect on both financial and non-financial performance. These findings underscore the importance of universities developing balanced and effective incentive measures for non-academic staff that incorporate both monetary and non-monetary components.

This study offers practical contributions by providing evidence on the link between motivation factors and job performance. Hence, the university management teams could formulate cost-effective incentive mechanisms that enhance institutional efficiency and staff engagement. Improved motivation among non-academic staff also contributes to better support services for students and academic personnel, thus enriching the overall campus environment. For government and education authorities, this study offers implications for refining human resource policies in the higher education sector, promoting equity and performance among support personnel.

Although this study significantly advances our understanding of how motivation affects the job performance of non-academic staff in Chinese universities, it faces several limitations. First, the scope does not consider individual factors such as age, education level, and location that may influence the effectiveness of incentive measures. Second, this study relies on a one-time questionnaire, capturing only current preferences and limiting insight into the long-term impact of these motivators. Third, the sample is confined to provinces in East and South China, which may affect the generalizability and objectivity of the findings. Future research should incorporate additional individual factors, explore other dimensions of both monetary and non-monetary motivations (e.g., sense of accomplishment, work interest, and self-presentation), and include a broader, more diverse sample—such as academic staff from other regions—to enable a more comprehensive comparison.