The Relationships between Job Performance, Job Burnout, and Psychological Counselling: A Perspective on Sustainable Development Goals (SDGs)

Abstract

Job burnout (as an independent variable) and job performance (as a dependent variable) are often correlated, and one of the main arguments is that psychological counseling can mitigate job burnout, which in turn influences job performance. This article aims to introduce a new perspective on the subject by establishing a new paradigm in the field. It also explores the role of psychological counseling as a precautionary measure. Furthermore, it aligns with the United Nations Sustainable Development Goals (SDGs) 3 and 8, which seek to enhance public health, collective well-being, and decent work or at least equitable workplace conditions. Adopting a quantitative analysis method, this study used panel data from 2020 to 2023 and group regression to analyze these data. The instruments comprise job performance (KPI) and mental health records. This study revealed that job burnout as experienced by academics is regulated by their job performance (β = −0.013, p < 0.001. This study revealed that academics’ job performance remained unchanged despite the number of psychological counseling and interventions conducted, so consequently, psychological counseling and interventions do not affect performance directly. This research contributes to the literature by utilizing a novel measurement approach. It is concluded that competency is the key to having a decent work environment and staff experiencing collective well-being.

1. Introduction

In 2015, the United Nations introduced 17 Sustainable Development Goals (SDGs) aimed at addressing critical global challenges related to safeguarding the Earth, empowering individuals, and promoting prosperity and cooperation [1,2]. As part of the 2030 agenda, issues related to mental health, stress, work conditions, etc., are prominently featured in several SDGs and their specific targets. Specifically, SDG 3, “Good Health and Well-being”, had the objective of ensuring healthy lives and promoting well-being at all ages. With reference to staff health, target 3.4 aimed to increase the prevention, treatment, and promotion of mental health and well-being by 2030 [3]. Similarly, SDG 8, “Decent Work and Economic Growth”, and target 8.5, asserts the need to achieve full and productive employment and decent work for all [3]. Although numerous research studies have indicated that universities are pivotal platforms for achieving sustainable development [4,5], some scholars have argued that universities can ensure sustainable development through research and teaching. However, many studies suggest expanding the scope of sustainability research in universities to include staff members’ mental health so that a decent university work environment is maintained and ensures economic growth [6,7]. In recent years, analyses such as those by Sonetti et al. and Mohiuddin et al. [7,8] have discussed the contributions made by staff to the sustainability efforts of universities.

However, ongoing challenges posed by job burnout hinder international efforts to ensure the sustainability of the university work environment and the development of staff [9]. Job burnout undermines any investment in mental health services, and the process becomes inefficient. Theoretical and empirical evidence has confirmed that burnout negatively impacts health and personal well-being [10], posing obstacles to achieving SDG 3. The sustainability of universities can only be possible when the staff (human resources) are protected, and caution is taken when employing and utilizing them (staff) as natural resources. In general, internationalization and commodification in education through the adoption of market-responsive theory have compromised the well-being and mental health of many personnel as education institutions struggle to compete globally. The sustainability of higher education requires decent jobs and a motivated workforce [11]. Job burnout affects the mental health of staff and thereby reduces employee engagement and performance, leading to inefficiencies and increased costs closely linked to absenteeism and turnover [12].

For universities and other educational institutions, this means that burnout among staff can adversely affect the lives and well-being of the staff, stymies a decent work environment, and seriously undermines productive employment, thus limiting the implementation and achievement of SDG 8 [9]. Consequently, in order to promote the sustainability of staff and higher education, it is necessary to address the job burnout issues from both organizational and individual perspectives. Due to these circumstances, substantial studies have explored job burnout topics in higher education with the aim of ensuring sustainability and sustainable development. It is noted that the subject area has garnered greater attention since its inception in the early 2000s [13] and has subsequently sparked a wave of theoretical investigation into organizational administration and management practices, as well as staff welfare [14]. Pivotal to this is understanding specifically how psychological counseling from organizations and competency from individuals can reduce job burnout as a precautionary measure to achieve SDGs.

1.1. SDGs and Job Burnout in Higher Education

Research by Lei and Li et al. [9,15] indicates that universities are one of the epicenters of job burnout outbreaks. In recent decades, the landscape of higher education has undergone significant changes because of the increasing pressure to provide high-quality services and achieve sustainable development [16]. Subsequently, the issue of staff burnout in higher education has garnered significant attention. For example, Govindaras et al. and Usán Supervía and Salavera Bordás [17,18] have explained what they consider to be the main causes and reasons (for instance, more demanding requirements for top journal publications, maintaining ranking, achieving tenure success and high-quality teaching and supervision) for job burnout among academics in a sustainable environment. Meanwhile, Mijakoski et al. and Boamah et al. [19,20] warned of the drawbacks associated with job burnout in universities, for instance, ill health, staff turnover, and diminished work efficiency. They highlighted the negative impact of job burnout on the sustainable development of universities.

To address job burnout, promote staff health and well-being (SDG 3), and make sure that one’s work was, in fact, a decent job (SDG 8), Meng et al. and López-Núñez et al. [21,22] offered some solutions such as adequate resource allocation, the means to achieve personal accomplishment, organizational commitment, and achieving as work-life balance from the organizational and individual perspectives. However, with the emergence and increase in new cases in universities threatening employees’ health and well-being, turnover rates continue to rise, leaving the burnout crisis unresolved. Universities and staff typically address burnout reactively, failing to achieve SDG 3,8 comprehensively.

Leiter, Maslach, and Liu et al. [23,24] expanded on previous studies regarding job burnout by introducing incompetence as a critical factor that severely undermines employee performance and is a primary cause of burnout. Similarly, Fazey, Fazey, and Blaskova et al. [25,26] highlighted that the current human resources in higher education, including both staff and students, often lack the necessary skills, competence, knowledge, and motivation to effectively adapt and thrive in their environments where there is a lot of pressure to accomplish objectives. This deficiency contributes to and exacerbates the job burnout crisis.

Global data provide compelling evidence, showing that the establishment of large-scale higher education structures and systems in developing countries has led to a significant increase in the number of scholars, rising by nearly 9 million between 1980 and 2018 [27]. However, Watts and Robertson [28] argue that an increase in quantity does not always equate to an improvement in quality. In their work, Liu et al. [24] highlight a persistent burnout crisis in universities due to a lack of competence, which poses a significant risk to the sustainable development of these institutions.

Since 1999, the expansion of higher education in China has exemplified this trend. Between 2000 and 2021, the number of university institutions increased from 1000 to 2756 [29], while the number of academics rose from 0.46 million to 1.87 million. This change has positioned China as one of the countries with the largest educational populations globally. However, the issue of job burnout remains significant. Therefore, this study, set against the backdrop of China, explores job performance as a variable influencing job burnout. With this objective in mind, this study focuses on enhancing the sustainable development of universities. Thus, the paper examines the roles of competence and psychological counseling as preventive strategies to foster an academic environment free from burnout.

1.2. Job Performance and Job Burnout in Higher Education

In higher education, the concept of job performance refers to the sum of employee behaviors and roles that have some expected value, which can be either positive or negative. It refers to how employees fulfill their responsibilities and conduct themselves in the workplace [30]. For this reason, the functioning of a university relies largely on the collective performance of all its employees [31]. Universities assess job performance and reward positive behaviors and roles associated with excellent teaching and organizational policies [32,33].

Due to the limitations of the Annual Compensation Review (ACR) [34] and the Individual Work Performance Questionnaire (IWPQ) [35]—such as cronyism, bias, favoritism, and lack of specificity—the most commonly used method for measuring job performance currently is the Key Performance Indicator (KPI) model [36]. This model breaks down employee tasks into micro-units, each with specific goals and scores. Scores are assigned through quantitative and qualitative parameters involving numerous stakeholders and are allocated based on a hierarchical order [36]. Consequently, KPIs are used in most higher education institutions throughout the world [37], and China is no exception.

In recent decades, the academic literature on job burnout—whether such studies were qualitative, quantitative, or mixed methods—has predominantly treated job performance as a consequence of burnout [38,39,40]. One criticism or limitation of this research approach is its frequent usage in analyzing job burnout and job performance, prompting calls for new methodologies [38,41]. In their research, Leiter, Maslach, and Lei et al. [23,42] have expressed concerns that “job performance” may actually predict or influence “job burnout”. The relevant literature may have mistakenly documented poor performance as a symptom of burnout [43]. To address this issue and align with SDGs 3 and 8, this study treats “job burnout” as the dependent variable and investigates its relationship with “job performance” as the independent variable, aiming to establish a new paradigm in this field (see Figure 1).

Consequently, job burnout is the outcome of job performance. A comparison among various types of job performance groups will be employed to verify whether job performance can affect job burnout while controlling for job burnout factors. Based on this, in conjunction with SDGs 3 and 8, this study emphasizes the importance of promoting well-being and healthy living in a decent academic environment, ultimately contributing to the sustainable development of higher education institutions.

1.3. Research Aim, Questions, and Hypothesis Development

To promote SDGs 3 and 8 and reduce burnout, this study adopts a different approach based on the above discussion. It re-directs the analysis to propose that employee performance should determine the extent of burnout, aiming to provide applicable strategies for preventing and minimizing job burnout crises. This perspective has not yet been fully explored. Therefore, whether there is a significant relationship between the level of “job performance” and “job burnout” remains an open question. The following questions are designed to elucidate these objectives:

(1)

Does job performance influence job burnout among academic staff?

(2)

How can a mechanism be devised to address job burnout crises among academics?

(3)

Does reducing job burnout play an important role in supporting the achievement of SDGs?

The answers to these questions can lead to establishing results and conclusions that can help to enhance the sustainable development of higher education. Moreover, two hypotheses were formulated to guide this study, and they are explained below.

The current description of job burnout by the World Health Organization (WHO) classified it as an occupational disease that can result in life-threatening medical/health conditions, for instance, post-traumatic stress and depression [40,44]. Similarly, the WHO [45] recommended medical services and psychological counseling as the officially accepted procedures to treat job burnout in workplaces. According to the theory of health promotion and disease prevention [46], modern society supports the practice of health promotion and disease prevention and guides the identification, development, and implementation of intervention measures. In this study, based on SDG 3, job burnout is deemed to have a negative impact on the health of employees, but currently, what is only recognized is the post-measurement to reduce burnout, without comprehensive preventive measures to uphold the motto of “prevention is better than cure” [17,41]. Hence, this research hypothesizes that:

Ha1:

Academics’ “job performance” has a substantially detrimental impact on “job burnout” when all predictor variables are considered.

Ha2:

“Psychological counseling” has different moderating effects on academic performance and burnout when all predictor variables are considered.

After controlling for the factors associated with job burnout, the group’s academic performance was taken into account to establish the argument that having staff who are more competent means that they are resilient to job burnout. Hence, precautions should be taken during recruitment by ensuring competent staff is employed to help prevent later job burnout crises and relying on psychological counseling. Therefore, what will be investigated is whether taking pre- or post-cautionary steps significantly reduces “job burnout”. In this scenario, reducing job burnout can enhance the health and well-being of academics, the objective being to ensure productive employment, contributing to achieving SDGs 3 and 8.

2. Materials and Methods

2.1. Research Design and Participants

This quantitative study diverges from recent research by Lei et al. [42,47] by employing a different approach to data collection and analysis, using similar methodologies [42,47]. We compiled a panel dataset on job performance and job burnout among academics in Chinese universities, dissimilar to previous studies that relied on cross-sectional data. The use of a panel dataset provides both cross-sectional and time-series dimensions, enhancing the richness and diversity of these data. This approach enables us to track changes within the same group of academics over time and account for individual heterogeneity. By using a fixed effects model [48], we can control for unobserved individual and time-specific characteristics that might influence the results. As Hsiao [49] suggests, this method more accurately uncovers dynamic relationships between variables, revealing causal connections and long-term impacts.

This study was conducted from 3 January to 4 July 2024. To emphasize the multi-year career trajectories of the participants, only academics who were actively working between 2020 and 2023 were included. Those who did not participate in or missed any KPIs during this period were excluded, as were academics who had retired or were absent. This rigorous sample selection process ensures the research results’ reliability and validity.

2.2. Population and Sampling Techniques

All academics in the university serve as the population in this study. However, the multi-stage sampling approach serves to identify different strata, select target respondents within these strata, and then choose the sample from these respondents [50].

This study encompasses a nationwide sample, differing from previous research limited to a single province [42,47]. This broader sampling enhances the generalizability and reliability of the findings, further confirming the early results of published studies. According to documents from the National Bureau of Statistics [51], China is divided into four districts: the Western, Eastern, Central, and Northeastern districts. The Ministry of Education of China categorizes universities into double World-Class universities, vocational colleges, and regular universities [52]. To ensure triangulation, one university from each type in each region was selected, resulting in a total of 12 universities being incorporated into this study.

The number of academics amounted to 4648 from the Eastern district, 5882 from the Central district, 4803 from the Western district, and 4101 from the Northeastern district, making a total population of 19434 academics for this study. To calculate a representative sample size for each district, we use the following Cochran formula [53]. As a result, the sample size is 354 from the Eastern district, 360 from the Central district, 355 from the Western district, and 351 from the Northeastern district. Subsequently, the sample was allocated in a proportionate manner using the stratified sampling technique, which made it possible to select specific respondents from each university that was part of the sampling procedure.

The personnel departments of the sampled institutions assisted in randomly selecting academics for this study by picking every fourth name from the staff list. These academics contributed to both data collection and analysis. The randomly chosen samples were then divided into four groups based on their three-year performance records: non-performance, low, average, and high. This approach helped prevent sampling bias and ensured a random representation of the different job performance levels among academics, as shown in

Table 1. Sampling and observation grouping.

District	University Type	Total A.	Sample A.	High P.O.	Average P.O.	Low P.O.	Non-P.O.	Sample A.O.
Eastern	Double World-Class university	2156	164	83	171	81	157	492
	Regular university	1849	141	70	146	72	135	423
	Vocational college	643	49	27	45	32	43	147
Central	Double World-Class university	3512	215	101	198	105	241	645
	Regular university	1712	105	66	102	48	99	315
	Vocational college	658	40	19	47	21	33	120
Western	Double World-Class university	2683	198	91	201	97	205	594
	Regular university	1599	118	49	110	50	145	354
	Vocational college	521	39	21	39	20	37	117
Northeastern	Double World-Class university	2068	177	82	200	83	166	531
	Regular university	1532	131	57	144	69	123	393
	Vocational college	501	43	18	37	29	45	129
Total		19,434	1420	684	1440	707	1429	4260

Note: N = 1420, Total A. = Total academics, Sample A. = Sampled academics, High P.O. = High-performance observations, Average P.O. = Average performance observations, Low P.O. = Low-performance observations, Non-P.O. = Non-performance observations, Sample A.O. = Sampled academics observations.

.

Table 2. Samples’ demographic characteristics.

Control Variables	Number	Percentage %
Gender
Male	706	49.72
Female	714	50.28
Marital status
Married	878	61.83
Unmarried	542	38.17
Age
35 or below	312	21.97
36–45	373	26.27
46–55	360	25.35
56 or above	375	26.41
Majors
Social science	757	53.31
Natural science	663	46.69
Professional titles
Teaching assistant	305	21.48
Lecturer	399	28.1
Associate Professor	436	30.7
Professor	280	19.72
Years in service
10 or less	448	31.55
11–20	400	28.17
21–30	387	27.25
31 or above	185	13.03

Note: N = 1420.

summarizes the demographic characteristics.

2.3. Measures

In this study, secondary data were collected from the sampled universities with the assistance of the heads of personnel departments, providing greater reliability than questionnaire surveys. These data include demographic information, key performance indicator (KPI) results, records of burnout, and instances of seeking psychological counseling, all pertaining to the same respondents.

2.3.1. Control Variables

The control variables include individual characteristics (age, gender, and marital status) and work-related factors (years in service, majors, and professional titles), which have established associations with job burnout [54]. Other unmeasured time and individual characteristics would be controlled for using the fixed effect model.

2.3.2. Job Performance (KPI)

The academics’ KPI results were employed to determine how well they performed their work. Chinese colleges are mandated to apply specific KPIs to classify and rank staff members’ job performance. This method is based on a government policy known as the Guiding Opinions on Deepening the Reform of the Assessment and Evaluation System for Universities Teachers [55]. Regulations on the Assessment of Staff in Public Institutions [56], in general, divide academic achievement into four categories: non-performance (unqualified), low performance (basically qualified), average performance (qualified), and high performance (excellent).

2.3.3. Burnout Level

Burnout data are used to estimate the burnout level of academics. Data on burnout were acquired directly from the Mental Health Centers of the universities included in this study’s sample. This study applied a scale to score burnout levels into four categories (High burnout, Moderate burnout, Low burnout, and Non-burnout) to record the degree of academic burnout.

2.3.4. Psychological Counselling

The university’s Mental Health Centre provided more data on psychological counseling. According to the Guiding Opinions on Strengthening Psychological Health Care in [57], all universities are required to regularly offer staff members psychological assessments, health care based on psychology, and other related services. Based on the burnout results, this study categorizes psychological therapy into four types: Non-psychological counseling, Monthly Meetings, Bi-Weekly Meetings, and Weekly Meetings.

2.4. Ethical Considerations

The first author approached the relevant authorities at the selected universities to explain this study’s objectives and the type of information being sought. Before conducting these visits, the research team obtained ethical approval concerning safety aspects from one university (REFERENCE NO: JKEUPM-2023-676). Subsequently, authorization was secured from all the participating universities, which provided a reference number (YCTU20221017) for the researchers. The selected universities were informed that participation was voluntary and they could withdraw at any time.

To ensure participant confidentiality, we assigned numerical codes sequentially, starting with the first sample from our list. This coding system identified respondents as 1, 2, 3, 4, 5, 6, and so forth, keeping their personal information anonymous and undisclosed to this study’s researchers. Additionally, we assigned letters of the alphabet, beginning with A, to each university in the order they were listed. Respondents from the first university, designated as A, were coded as A1, A2, A3, A4, A5, A6, and so on, continuing until the last respondent. For the next university, we used B1, B2, B3, B4, B5, B6, and similar codes. Pseudonyms were applied to all respondents from the universities.

2.5. Data Analysis

A regression analysis was conducted on the first research question (RQ1) and Ha1, examining the relationship between job performance and burnout after accounting for time and individual effects using panel data. To first prevent bias from sample heterogeneity and, second, to further delineate the precise nature of the relationship between burnout and job performance, a group regression analysis will be employed to support the results of Ha1. Additionally, a figure will be created to compare burnout levels across different performance observation groups based on three years of data.

To test Ha2, hierarchical linear regression was first employed to examine whether psychological counseling exerts a significant impact on job burnout and whether it serves as a moderating factor between burnout and job performance. Subsequently, to further validate Ha2, group regression analysis was conducted to determine whether psychological counseling functions as a moderator across the four performance observation groups. Additionally, figures were utilized to discuss the impact of psychological counseling on job burnout over three years and to compare performance observation groups with different counseling frequencies.

Table 3. Analysis procedures based on the research questions.

Research Questions	Methodology	Hypotheses
Does job performance influence job burnout among academic staff? How can a mechanism be developed to address job burnout crises among academics? Does reducing job burnout play an important role in supporting the achievement of SDGs?	Quantitative Linear regression, frequency trends Results from RQ1 and RQ2	Ha1: Academics’ “job performance” has a substantially detrimental impact on “job burnout” when all predictor variables are considered. Ha2: “Psychological counseling” has different moderating effects on academic performance and burnout when all predictor variables are considered.

provides detailed information about the research questions, methods, and hypotheses involved in this study.

3. Results

3.1. Job Performance Predicts Job Burnout in Higher Education

To address Ha1 concerning the complex interplay between job performance and burnout, regression analysis was employed. The results of the panel data analysis over three years (

Table 4. Job performance’s influence on job burnout.

Variables	Overall
Gender	−0.043
	(0.030)
Marital status	−0.040
	(0.031)
Age	0.018
	(0.014)
Majors	−0.018
	(0.030)
Professional titles	0.049
	(0.044)
Years in service	0.001
	(0.008)
Job performance	−0.011 ***
	(0.001)
Constant	2.062 ***
	(0.101)
Observations	4260
R-squared	0.039
Individual fixed effect	YES
Time fixed effect	YES

Note: *** p < 0.001.

) strongly suggest that even after controlling for time and individual characteristics, a negative correlation is evident between academics’ job performance and their burnout levels (β = −0.011, p < 0.001). Thus, these findings support Ha1.

To substantiate the negative correlation between job performance and job burnout and to support the hypothesis that high-performing academics experience lower levels of burnout, further analysis was conducted. Group regression analysis was employed to examine four distinct performances, primarily to assess the impact of performance levels on job burnout after controlling for time and individual characteristics.

Table 5. Four distinct job performances’ influence on job burnout.

	High P.O.	Average P.O.	Low P.O.	Non-P.O.
Gender	−0.029	−0.044	−0.074	0.042
	(0.061)	(0.041)	(0.060)	(0.041)
Marital status	−0.114	0.014	0.021	0.032
	(0.063)	(0.042)	(0.063)	(0.043)
Age	0.044	0.020	0.016	0.008
	(0.028)	(0.019)	(0.027)	(0.019)
Majors	−0.103	0.011	−0.035	0.044
	(0.062)	(0.042)	(0.060)	(0.042)
Professional titles	0.088	0.029	0.074	−0.011
	(0.089)	(0.059)	(0.087)	(0.060)
Years in service	0.008	−0.005	−0.017	0.003
	(0.015)	(0.010)	(0.015)	(0.010)
Job performance	−0.214 ***	−0.110 ***	−0.230 ***	−0.108 ***
	(0.011)	(0.004)	(0.011)	(0.004)
Constant	21.477 ***	9.877 ***	16.102 ***	6.778 ***
	(0.999)	(0.298)	(0.708)	(0.212)
Observations	684	1440	707	1429
R-squared	0.404	0.408	0.423	0.399
Individual fixed effect	YES	YES	YES	YES
Time fixed effect	YES	YES	YES	YES

Note: *** p < 0.001; High P.O. = High-performance observations, Average P.O. = Average performance observations, Low P.O. = Low-performance observations, Non-P.O. = Non-performance observations.

provides a concise summary of the results, indicating a significant and clear negative correlation between job burnout and performance in high-performance observations (β = −0.214, p < 0.001), average performance observations (β = −0.110, p < 0.001), low-performance observations (β = −0.230, p < 0.001) and non-performance observations (β= −0.108, p < 0.001) over three years of data.

Regression analysis has demonstrated a negative correlation between job performance and job burnout, strongly indicating that higher academic performance is associated with lower levels of job burnout. As illustrated in Figure 2, the non-performance academics observation group consistently exhibits high levels of job burnout, whereas the observation group of high-performing academics shows low levels of job burnout.

3.2. Job Burnout: The Moderating Role of Psychological Counselling

To address Ha2, a hierarchical and group regression method was applied for inferential analysis. Among academics suffering from job burnout, after controlling the time and individual effects, no control factors have a substantial impact on burnout in the initial block. However, when the second block includes job performance, it then has a substantial negative impact on predicting burnout (β = −0.006, p < 0.001).

The effect of psychological counseling on “job burnout” is significantly negative, as observed in the third block (β = −0.577, p < 0.001), meaning that psychological counseling can significantly alleviate burnout issues. In Block 4, the interaction between psychological counseling and job performance is introduced, and it is found to significantly predict job burnout (β = −0.008, p < 0.001). Additionally, when evaluating the link between job performance academics and “job burnout”,

Table 6. Job performance’s influence and job burnout: the mediating role of psychological counseling.

	Block 1	Block 2	Block 3	Block 4
Gender	−0.060	−0.059	−0.046	−0.044
	(0.029)	(0.028)	(0.027)	(0.027)
Marital status	−0.037	−0.043	−0.027	−0.029
	(0.030)	(0.029)	(0.028)	(0.028)
Age	0.009	0.008	0.008	0.009
	(0.013)	(0.013)	(0.012)	(0.012)
Majors	−0.040	−0.048	−0.031	−0.037
	(0.029)	(0.029)	(0.027)	(0.027)
Professional titles	−0.001	−0.001	−0.007	−0.011
	(0.041)	(0.041)	(0.039)	(0.039)
Years in service	−0.007	−0.006	−0.007	−0.006
	(0.007)	(0.007)	(0.007)	(0.007)
Job performance		−0.006 ***		−0.004 ***
		(0.001)		(0.001)
Psychological counselling			−0.577 ***	−0.583 ***
			(0.029)	(0.029)
Job performance * psychological counseling				−0.008 ***
				(0.002)
Constant	1.768 ***	2.185 ***	2.146 ***	2.417 ***
	(0.075)	(0.093)	(0.073)	(0.089)
Observations	3248	3248	3248	3248
R-squared	0.008	0.025	0.118	0.133
Individual fixed effect	YES	YES	YES	YES
Time fixed effect	YES	YES	YES	YES

Note: *** p < 0.001.

clearly illustrates that psychological counseling significantly and negatively moderates the link.

Furthermore, when job performance and psychological counseling interacted in group regression, the analysis shows that psychological counseling moderates the low observation group (β = −0.065, p < 0.001) and non-performance (β = −0.030, p < 0.001) observation groups. This is after accounting for academics who missed or did not attend psychological in any of the three years (see details presented in

Table 7. Moderating role of psychological counseling on the influence of job performance and job burnout among observation groups.

	High P.O.	Average P.O.	Low P.O.	Non-P.O.
Gender	0.054	−0.077	−0.041	0.072
	(0.066)	(0.047)	(0.068)	(0.046)
Marital status	−0.146 *	−0.021	−0.009	−0.035
	(0.067)	(0.047)	(0.071)	(0.048)
Age	0.042	0.019	−0.014	0.015
	(0.030)	(0.021)	(0.031)	(0.020)
Majors	−0.160 *	0.032	0.071	0.066
	(0.067)	(0.047)	(0.068)	(0.046)
Professional titles	0.091	0.033	0.034	0.034
	(0.094)	(0.065)	(0.098)	(0.066)
Years in service	0.011	0.002	−0.027	0.015
	(0.017)	(0.012)	(0.017)	(0.011)
Job performance	−0.186 ***	−0.112 ***	−0.103 ***	−0.047 ***
	(0.031)	(0.011)	(0.028)	(0.010)
Psychological counselling	−0.155	−0.732	−0.077 **	−0.357 *
	(1.319)	(0.414)	(0.995)	(0.259)
Job performance * psychological counseling	0.002	0.009	−0.065 ***	−0.030 ***
	(0.014)	(0.005)	(0.015)	(0.005)
Constant	18.534 ***	10.079 ***	8.225 ***	3.683 ***
	(2.932)	(0.914)	(1.845)	(0.526)
Observations	460	944	433	925
R-squared	0.411	0.379	0.453	0.412
Individual fixed effect	YES	YES	YES	YES
Time fixed effect	YES	YES	YES	YES

Note: *** p < 0.001, ** p < 0.01, * p < 0.05; High P.O. = High-performance observations, Average P.O. = Average performance observations, Low P.O. = Low-performance observations, Non-P.O. = Non-performance observations.

). However, in the average (β = 0.009, p > 0.05) and high (β = −0.002, p > 0.05) performance observation groups, no moderating weight is evident. A result of this is that—when discussing the moderating effect of psychotherapy on job burnout—there are significant differences among non-performance, low, average, and high-performance groups. The outcomes validated Ha2.

To further understand the role of psychological counseling in the different performance observation groups, longitudinal data analysis was used on the job performance observation groups that received counseling for about three years. As shown in Figure 3, academics who received psychological counseling demonstrated lower levels of burnout compared with non-participants.

Similarly, Figure 4’s findings imply that academics’ job performance remained unchanged, independent of the number of psychological counseling and interventions that were conducted. These findings, therefore, suggest that although psychological therapy helps reduce academic job burnout, it has no effect on performance.

4. Discussion

4.1. Job-Performance vs. Job Burnout

This study’s results reveal that job performance is negatively related to job burnout even when accounting for time. Furthermore, individual effects, when employing panel data, are largely in line with the research conducted by Leiter and Maslach [23], Liu et al. [24], and Wang and Chen [54]. They have elucidated that highly motivated and efficient academics are inclined to make a concerted effort to overcome different sources of stress in their work environment and, in this way, prevent burnout. On the other hand, individuals who encounter difficulties in working effectively in their occupations will ultimately experience burnout as a result of their incapacity to handle the expectations placed upon them. As well, when academics strive to enhance their performance, they develop a greater level of expertise in their jobs and achievements. This can lead to individual rewards such as promotion in the organization or recognition of how well they perform their job. The results concur with Schaufeli and Bakker [14], and they confirm that the job resources (job performance) assimilated can accomplish things: reduce job burnout and increase the explanatory power of the job demands–resources model.

This study enhances the existing literature on the job burnout setting by explaining which academics are influenced by the correlation between job performance and job burnout, and this study also provides a valuable practical contribution to achieving SDGs 3 and 8. On the one hand, while much research readily concentrates on reducing employee job burnout to enhance their performance [43], this research introduces a novel methodological approach to examine the connection between job performance and burnout, making a substantial contribution to the field of job burnout research. On the other hand, the practical contributions made by this study provide a fresh viewpoint on how to enhance the sustainable development of universities from the perspective of human resource development, which can more effectively achieve the SDG goals.

In situations where academics lack the required competency, they may experience burnout and feel that their work lacks meaning and does not meet their expectations, which, based on SDG 8, cannot be considered “decent work” [58]. This also conflicts with SDG 3, as many scholars have confirmed that burnout inversely affects well-being and healthy living [22,38]. Therefore, persistent burnout can hinder the achievement of SDGs 3 and 8 and seriously affect the sustainable development of universities. Consequently, job performance plays a crucial role in reducing burnout and achieving SDGs 3 and 8.

4.2. Psychological Counselling to Alleviate Treating Burnout: Post-Cautionary Measurement vs. Pre-Cautionary Measurement

In line with the conclusions reached by Lei and Pijpker et al. [47,59], psychological counseling can be regarded as a post-measurement that mitigates job burnout in different contexts. According to WHO [45], psychological counseling is inherently linked to SDG 3—“Good Health and Well-being”. The chief result of this study not only supports the importance of psychological counseling in reducing burnout but also demonstrates its significance in implementing SDG 3. SDG 8 calls for higher education institutions to provide employees with a “safe and secure working environment” and to reduce “precarious employment” [58]. Providing reliable psychological counseling services to academics reduces burnout, thus contributing to them having work that is more decent.

Furthermore, this study’s results provide concrete evidence that psychological counseling can mediate the relationship between job performance and burnout in low and non-performance observation groups. This is based on findings related to the initial research question (RQ1), as underperforming academics are more likely to experience exhaustion and, therefore, are more likely to turn to counseling services for help. In this way, psychological counseling is crucial in low- and non-performance observation groups. In contrast, psychological counseling does not wield a moderating effect in the average and high-performance observation groups because those who perform well typically experience less burnout, reducing the need for counseling assistance. Although the role of psychological counseling varies across performance observation groups, its importance as a follow-up measure to alleviate job burnout remains significant.

Additionally, this study reveals the relationship between job performance, burnout, and psychological counseling. The findings suggest that while psychological counseling may help reduce burnout, it does not necessarily improve their performance. Consequently, as long as the academic staff’s job performance remains less than ideal, burnout is likely to recur, requiring ongoing reliance on psychological counseling for relief. In this context, job burnout issues may not be permanently resolved, and for this reason, the importance of pre-measurements requires thorough investigation.

In the context of this study, the concept of “prevention is better than cure” should be put into action [60]. However, in the contemporary process of sustainable development in higher education, research related to job burnout is highly debated and very controversial. Most universities focus only on addressing burnout after it occurs, while the proactive academic recruitment process, crucial for their viability, is often neglected, underestimated, or rarely considered [61]. Therefore, to reduce job burnout among academics, pre-measurement strategies that enhance their performance should be implemented to achieve SDGs 3 and 8. Higher education institutions are advised to develop proactive recruitment strategies as a preventive measure to avoid any job burnout crisis among academics. Essentially, if individuals hired are well-matched to their positions and possess strong work capabilities, it can help prevent job burnout to a certain extent.

To address the issue that psychological counseling can only reduce burnout but not improve job performance, it is essential to recognize that counseling alone cannot cultivate capable, energetic, and motivated employees. In the modern era, characterized by the huge growth in higher education, especially in developing countries, there is a common misconception that becoming an academic guarantees job stability with minimal dedication and no sense of professional responsibility [62]. This assumption and attitude should be removed before employment contracts commence. Before applicants enter higher education institutions, they must undergo a thorough assessment to evaluate their psychological suitability for academic positions. This evaluation should analyze their motivation, passion, and determination so that it can be determined if they possess the capabilities required to become an academic staff. This approach aims to make the profession a decent one, aligning with SDG 8.

Based on these findings, it is recommended that psychological counseling be incorporated earlier in the human resources recruitment process. A comprehensive assessment of all applicants’ abilities and psychological conditions should be conducted to ensure they are in good health and well-being, which is crucial for achieving SDG 3. At the same time, universities should provide relevant training courses for graduate students who intend to pursue a teaching career. This will not only help to enhance their work abilities as academics, but it will also improve their resilience and ability to self-evaluate their psychology while preparing them to ensure that their future job performance is not compromised. Once the appropriate individuals have been recruited, it is essential to establish plans for continual professional development, encompassing both academic and psychological aspects. This will provide them with the necessary skills to adapt to sustainable development in the higher education system. Furthermore, the university administration section should strengthen training programs aimed at improving the job performance and competence of staff members to ensure staff sustainability. This can be achieved by identifying areas where their abilities are missing and also by promoting their mental well-being.

4.3. Reducing Job Burnout for SDGs in Higher Education

Based on the previously discussed results, RQ 3 was explored. It is clear that job performance and psychological counseling can reduce burnout and play an important role in enhancing staff health and well-being, making work more decent, and promoting employee sustainability [10,63], thus aligning with SDGs 3 and 8. However, it is important to recognize the limitations of psychological counseling. If academic performance cannot be enhanced through psychological counseling, we can stress that psychological counseling alone as post-measurement cannot fundamentally address burnout issues or significantly improve staff health and well-being to create a truly decent workplace (sustainability). As a result, achieving SDGs 3 and 8 remains unattainable, and the sustainability of staff and sustainable development of universities cannot be effectively promoted.

Globally, the occurrence of job burnout is posing serious challenges to the attainment of SDGs 3 and 8 [9]. While numerous studies have suggested solutions to burnout to facilitate the attainment of SDGs 3 and 8, it still hinders the current sustainable development of universities [64]. Thus, in China, implementing pre-cautionary measures instead of post-cautionary ones to address burnout presents an innovative approach to promoting the attainment of the SDGs. Although job performance and psychological counseling cannot completely resolve burnout issues, they nonetheless play a significant role in advancing the sustainability of staff and sustainable development of universities (health and well-being of staff; decent work).

5. Limitations and Future Studies

Although this study utilized an innovative analytical approach and research pathway to examine the impact of job performance on burnout, it does have several limitations. On the one hand, while this study suggests incorporating psychological assessments into the recruitment process for academics, it does not specify the psychological criteria used or recommend assessment methods. Conversely, although quantitative analysis was utilized to sample different regions of China and control for time and individual characteristics, the results were based on data, making it difficult to gain other kinds of research on burnout in complex environments. Given this, future research may benefit from qualitative or mixed methods research to truly establish the universality of reported findings. Similarly, this study has not addressed whether the recruitment of professors privileges the levels of productivity in the profiles—instead of creativity or intelligence. Further research on this topic can also explore the rate of return of most productive academics in the midst of university expansion and internationalization, their profiles, and whether they exhibit lower rates of burnout.

6. Conclusions and Implications

The findings and discussions presented in this paper relate to SDGs, providing an additional perspective compared with the studies conducted by Lei et al. [42,47] in the context of China. Moreover, this study’s insights highlight the importance of preventive and supportive measures in academic institutions to promote staff sustainability and a sustainable work environment. These data revealed that performance is a contributing factor to job burnout in academic settings. Furthermore, the research found that psychological counseling wields a moderating effect on the association between job performance and job burnout. While the results validated the effectiveness of psychological counseling in addressing burnout, they questioned the ability of psychological counseling to transform underperforming or poorly performing groups into high-performing ones.

Based on the conclusions, job burnout can seriously undermine health and employee well-being [38]. This conflicts with SDG 3, which seeks to realize well-being and healthy living for all people of all ages. Subsequently, SDG 8 aims to create “decent work”, which is defined as an occupation where one’s duties or tasks are meaningful, income is reasonable, and the job meets the staff member’s expectations [59]. This paper concludes that employing competent, resilient workers involves staff who are able to finish their tasks within the designated time and are capable of executing challenging workloads without experiencing job burnout. In this way, SDG 8 is achieved.

In order to mitigate job burnout and achieve SDGs 3 and 8, institutions should improve their recruitment strategies, and they can do this by incorporating a group of psychological counselors to evaluate the cognitive, social, emotional, and psychological intelligence of candidates seeking academic jobs. This will be much better than solely relying on their formal qualifications, academic abilities, and medical examinations. Additionally, universities should provide relevant skills training for future academics (graduate students) and pre-test their abilities, resilience, and psychological fitness.

Moreover, ensuring less job burnout among staff fosters SDGs 3 and 8, which can also jointly achieve other SDGs, such as SDG 4 (Quality Education) in higher education. This comprehensive approach to recruitment and staff support underscores the interconnected nature of the SDGs and sustainability, emphasizing that promoting health and well-being (SDG 3) contributes to broader sustainable development goals.