Towards a parsimonious model of faculty motivation,engagement,and work performance: A case study of a Chinese university

Abstract

BACKGROUND:

The domain of faculty internationalization is under-researched, especially in the context of China. Extant research does not lend itself to applied insight, despite offering theoretical and methodological precision.

OBJECTIVE:

Based on the motivational systems theory, this study developed a parsimonious measure of overall faculty motivation (including capacity beliefs, context beliefs, emotions, and goals) and investigated its effect on work performance through the mediating effect of engagement in internationalization among faculty in a Chinese research-centric university.

METHODS:

Data of 229 faculty members were analyzed using a structural equation modeling approach.

RESULTS:

Faculty responses indicated that: i) the second-order overall faculty motivation scale is superior to individual items in measuring the effect of overall faculty motivation on faculty engagement and work performance; ii) overall faculty motivation positively influences faculty engagement; iii) overall faculty motivation promotes work performance; iv) faculty engagement mediates the relationship between overall faculty motivation and work performance.

CONCLUSION:

Faculty responses indicated that the effect of the second-order overall faculty motivation construct is superior to individual items in terms of its effect sizes on faculty engagement and work performance. More so, the measurement model showed that overall faculty motivation exerts a direct and indirect (faculty engagement) positive effect on work performance. Chinese universities should align the goals, capacity belief, context belief, and emotions of faculty with national/institutional goals to yield superior engagement for better performance.

Keywords

Goals beliefs emotions work performance faculty

1 Introduction

As the primary source of an institution’s intellectual capital, faculty disseminate the institutional mission through teaching, researching, and service [1]. The globalization of economies has upsurged the need for international talent with global competency and multicultural skills. Faculty engagement is a crucial instrument of effective internationalization [2, 3], enabling all graduates to have the right skills to enter the global work-stream [4]. At the same time, faculty internationalization empowers institutions worldwide to promote world-level research and remain competitive [5]. Experts predict that measuring and assessing internationalization outcomes and impact will become crucial success indicators [6]. Therefore, governments and universities worldwide are actively engaging in internationalization strategies to enhance faculty international collaboration and capacity. China has also launched an internationalization program called the Education Action for The Belt and Road Initiative [7]. This program aims to increase people-to-people exchanges, strengthen cooperation in cultivating talent, and create a bright future for education among the Belt and Road economies. Other programs such as the “World-class Universities and First-class Disciplines Construction Overall Plan” [8] have lifted the status and international competitiveness of the higher education system in China.

The current literature suffers from several knowledge gaps on faculty engagement in internationalization (FEI). First, most past studies have predominantly focused on factors, including perceived organizational support [9], job satisfaction [10], expectations [11], work role and perceptions of climate [12], and pay [13]. Second, the antecedents, mediators, and outcomes of FEI in top-tier research-intensive Chinese universities remain widely under-researched [14]. Third, the current literature lacks empirical frameworks on Chinese faculty motivation and FEI, even though China is the world’s third-leading recipient of international students [15]. Indeed, Li and Tu established the first empirical link between faculty motivation (individual and environmental) and FEI in China [16]. The authors used data from general universities and built their model on the self-determination theory [17]. Fourth, prior studies have used partial measures of faculty motivation (rather than combining goals, emotions, and beliefs), even though the motivational system theory (MST) considers the four aspects as interdependent [18] (p. 3). To our knowledge, no empirical study has either proposed a measure of overall faculty motivation (antecedents) or linked it to work performance (WP) (outcome) and FEI (mediator). In this paper, internationalization is defined as “the intentional process of integrating an international, intercultural or global dimension into the purpose, functions, and delivery of post-secondary education, to enhance the quality of education and research for all students and staff and to make a meaningful contribution to society” [19]. In a qualitative study, Li, Khattak, and Jiang recently called for modeling motivation as an integrated construct of goals, beliefs, and emotions for future research [20].

Therefore, this study’s primary purpose is to address the above knowledge gaps by modeling overall faculty motivation (FMO), FEI, and WP using faculty responses in a Chinese research-centric university. For this purpose, the paper will first respond to previous assertions by developing the first empirically validated construct of FMO (MST theory) [18], comprising four interrelated dimensions. Second, the paper will measure the direct effect of overall faculty motivation on FEI and WP Third, the paper will examine the mediating role of FEI between overall faculty motivation and WP.

2 Theoretical background and hypotheses development

2.1 Motivational System Theory (MST): A brief overview

MST asserts the need to integrate different but generally compatible ideas into a systematic understanding of motivation and its operation [14]. This theory defines motivation “as the organized patterning of three psychological functions that direct, energize, and regulate goal-directed activity: personal goals, emotional arousal processes, and personal agency (comprising capacity and context) beliefs” [18] (p. 3). The theory “makes it possible to describe how motivational processes interact with biological, environmental, and nonmotivational psychological and behavioral processes to produce effective or ineffective functioning in the person as a whole” [18] (p. 9). Personal goals have two basic properties: content and process/direction. The content aspect refers to the desired (or undesired outcomes) to be achieved (or avoided). In contrast, the process or direction aspect guides efforts and strategies designed to produce desired results or prevent adverse outcomes [18] (p. 83). “Capability beliefs evaluate whether one has the personal skill needed to function effectively and context beliefs evaluate whether one has the responsive environment needed to support effective functioning” [18] (p. 123). Finally, emotional arousal processes the feelings that help one mobilize and deploy energy for engagement. Ford believes that emotion can regulate and energize “any event or behavior episode characteristic—especially those that involve the cognitive-evaluative components of the episode (e.g., personal agency beliefs) and the anchoring components of goals and contexts” [18] (p.136). Emotions can contribute to developing new goals around things, activities, and people.

2.2 Previous research on overall faculty motivation and faculty engagement in internationalization

As per the expectancy-value theories, behavior is a joint function of two things: a) people’s expectations of obtaining a particular outcome as a function of performing a behavior; b) the extent that they value those outcomes [21 –23]. These theories assume that people judge the likelihood of attaining various goals in given situations. An attractive goal and an attainable belief motivate people to act [24]. Huang et al. found a significant positive correlation between the goals, procedures, and effects of visiting abroad programs in Chinese faculty. Visiting abroad experience improves faculty foreign language ability, intention to explore advanced western knowledge further, other cultures, research output, and teaching reform [25]. Through interviews of 13 faculty with overseas visiting experience, Gong and Xu found multiple goals among faculty who visited abroad, e.g., promotion, knowledge, family care, social responsibility, and international contact [26]. Goals for junior faculty may include research output, resources, and reputation [27]. Chinese faculty aspired to improve teaching content and expand self and students’ abilities [20]. In China, research output and activities are among the main criteria used for faculty promotion in research universities [28, 29]. Faculty are expected to possess the necessary skills to conduct high-quality teaching and scientific research for better performance appraisals [30].

Individuals with solid capability beliefs are likely to pursue desired outcomes, whereas the inverse holds for individuals with weak capability beliefs. Wulandari [31] found that competence, along with knowledge, ideas, personal characteristics, self-concept, and skill indicators, significantly and positively affect employee commitment. As for teaching, skills to teach in cross-cultural environments are reasonably needed by faculty [32 –34]. Teaching approaches are tied to the faculty’s cultural background, life experience, educational philosophy, and discipline skills [35]. Regardless of institutional affiliation, academics in the physical sciences, biological sciences, mathematics, and engineering topped others in the number of publications in foreign professional journals and books and publications written in a foreign language [36]. In terms of research or service, foreign language ability to communicate or academic expression are vital for international activities. After studying more than 800 university faculty from Guangdong Province, Rao [37] noted that teachers’ capacity positively affects research output. Cao et al. [34] also demonstrated that objective capacity at the institutional level immensely influences faculty engagement. Foreign language ability and the ability to acquire international contacts and resources are essential factors for faculty to engage [38].

The perception of environment support is essential for faculty productivity [14 , 40–44]. Organizational climate has a positive and significant effect on employee performance [45]. Schwietz investigated the attitudes, beliefs, experiences, and involvement related to internationalization by faculty members at a group of universities in the United States [41]. Moreover, one aspect of campus climate emerged as a possibly significant indicator of faculty involvement. Under the guidance of the professional promotion evaluation policy, faculty form external utilitarian motivation by measuring their behavior preference, work content, normative belief, and control belief. After that, they rationally choose profit-seeking behavior [30]. The increasingly competitive environment in Chinese and US universities demonstrates a tournament-like system for faculty employment, evaluation, and promotion [46]. Fields’ study explored faculty attitudes and beliefs perceptions of the campus in Vermont (USA) [43]. The study indicated that a positive campus climate encouraging internationalization increased faculty engagement in international activities. Faculty members also reported the lack of financial resources as a salient barrier to faculty engagement [2, 46]. Sanyal and Hisam revealed a strong and significant link between the independent variables (e.g., teamwork, the climate of trust, leadership, structure, performance evaluation, and rewards) and the performance of the faculty members of Dhofar University in Sultanate of Oman [47]. As for Chinese faculty, Li et al. found that faculty appreciated the university and national support for international travel, funding, and research but criticized the complex travel procedures and teaching evaluations (context beliefs) [20].

Faculty members’ experience from previous teaching or research practices may also affect their current practices [34 , 49]. Luxon and Peelo found that though faculty may be discouraged by some comments, they will continue to engage in international teaching if they have the confidence and skills to continue [48]. Chang found that habitual patterns in teachers’ judgments may contribute significantly to teachers’ repeated experience of unpleasant emotions. Those emotions may eventually lead to burnout and adversely affect performance [50]. Cao et al.’s study also demonstrated that faculty’s subjective emotions shape their engagement readiness [34]. Li showed that faculty pursue happiness by keeping up with academic development, learning differences, gaining new ideas, and witnessing students’ growth [38]. Additionally, the faculty strongly felt the pressure to publish internationally and visit abroad for promotion.

2.3 Hypothesis development

Motivation is a dynamic process driven by the interaction of personal, sociopsychological, and contextual factors [51]. It is a joint function of internal psychological processes and environmental factors that can be adequately analyzed only if the characteristics of a person and the context within which the action happens are considered [52, 53]. Many studies have measured motivation by a group of factors. For instance, Dörnyei and Ushioda defined motivation as the direction and magnitude of human behavior [54]. In a qualitative study, Li, Khattak, and Jiang confirmed through interviews that overall faculty’s motivation comprised four interdependent aspects, including goals, context beliefs, capacity beliefs, and emotions [20]. Pautz and Vogel measured faculty public service motivation by the commitment to the public interest, social justice, civic duty, compassion, and self-sacrifice [55]. Richardson and Watt reviewed teacher motivation research, and they identified student outcomes, teachers’ psychological health, and well-being [56]. Richardson and McKenna [57] consider faculty motivations a complex and holistic mechanism driven by multiple goals, including adventure, learning, family, and change. Xu, Wang, and Zhu [27] also believe that faculty motivation is a comprehensive phenomenon encompassing engaging factors like increasing research output, acquiring academic resources, and establishing an academic reputation. These researchers also identified objective factors (e.g., work assignment, promotion policy, platform, and outside support) and subjective factors (e.g., positive attitude, interest, knowledge and skills, and academic network).

At prime facia, expert opinions are consistent with Ford’s belief that motivation is “the organized patterning of all three components (i.e., goals, beliefs, and emotions) functioning as an interdependent triumvirate” [18] (p. 24). It is the outcome of social environments, influencing behavior and personality [18] (p. 2). Later, Hendijani et al.’s [53] motivation framework contradicted the proponents of cognitive evaluation theory by suggesting that intrinsic motivation and performance-contingent external enhance performance and motivation among students while simultaneously supporting the advocates of behavioral psychology and economics. Thus, institutional efforts to increase motivation can benefit organizations by improving performance [58]. The faculty’s work performance is emotionally engaging and personally demanding. Engagement in international, intercultural, and global dimension researching and teaching activities requires faculty to put more work-related effort, including cognitive, emotional, and behavioral dimensions [59]. Therefore, it is safe to assume that FMO (as an integrated construct of goals, beliefs, and emotions) contributes to FEI and WP. In line with the above argument, the following hypotheses are:

Hypothesis 1: Modeling overall faculty motivation (FMO) with a second-order factor provides a better fitting model of how faculty motivation affects FEI and WP than a purely multidimensional representation.

Hypothesis 2: Overall faculty motivation (FMO) positively affects faculty engagement in internationalization (FEI).

Work performance appraisal of faculty has become a growing phenomenon worldwide [30]. Previous WP studies have mainly focused on external, organizational climate characteristics [39], salary [40], personal beliefs and expectations [60], integrated effects of individual and organizational factors [61 –64], technostress inhibitors (i.e., literacy facilitation, technical support provision, and involvement facilitation), and creators (techno-overload, techno-complexity, techno-insecurity, and techno-uncertainty) [65] and the faculty (affiliation) [66]. Also, studies suggest that personal characteristics (i.e., motivation) and stressors outside the workplace (i.e., lack of support) contribute significantly to faculty burnout, in turn, having consistent adverse consequences for performance [67]. Even if the institution supports internationalization goals, it does not mean faculty members feel a professional obligation to engage in international activities [68]. Tong and Zhang showed that faculty achievement motivation could effectively predict their WP [69]. Zhou found faculty achievement motivation positively affects their WP [70]. Based on the above, the following hypothesis is conjectured:

Hypothesis 3: Overall faculty motivation (FMO) positively affects work performance (WP).

FEI is a pro-internationalization behavior that can help translate positive perceptions, beliefs, and attitudes into numerous work-related individual and organizational outcomes [69]. Therefore, its potential role as a mediator or moderator between various antecedents on outcomes (e.g., WP) cannot be underestimated. Recognizing this potential, many researchers have asserted that more research on the outcomes of FEI will contribute to a new stance in the discourse on the internationalization of higher education [71]. Deardorff points to the advantage of distinguishing between identifying results on an institutional and individual level [72]. Some qualitative researchers have explored the relationship between FEI and WP at the faculty level. Past works corroborate that international experience (FEI) leads to personal and professional development (outcome) [73]. Faculty with overseas visiting experience has been reported to generate high research output, teaching reforms, and international collaborations [25, 75]. Ma and Wen [77] observed that training abroad benefited faculty in teaching, researching, and service. Through a survey, Huang, Yao, and Chen found that Chinese faculty participating in visiting scholar programs abroad demonstrated better performance in research output and management [25]. As Zhou found that a positive relationship between motivation and work performance is partially mediated by faculty’s job satisfaction and job involvement [69], it is rational to believe that FEI could be an essential factor linking FMO and WP.

Hypothesis 4: Faculty engagement in internationalization (FEI) mediates the relationship between overall faculty motivation (FMO) and work performance (WP).

3 Methods

3.1 Sample and procedure

Faculty carry out research activities instead of institutions [78]. This survey focuses on one of the top-tier universities in Fujian Province, China. As Fujian is the hometown of overseas Chinese, this university has international traditions, aiming to be international from its beginning. Another reason is that the internationalization activities of some regional institutes are not visible [16]. With the “Belt and Road Initiative,” this university has become the first university in China to open a branch campus abroad. In 2016, this university published its new international development plan, “Strategic planning for opening-up and cooperation 2016–2020,” to pursue “Double First-class” construction.

Email invitations were sent randomly to 900 academic faculty in different schools in the sample university with an online survey link in Survey Star. After deleting responses with excessive missing data, the sample size was 229. The entire faculty age range was well represented: 4% under 30 years old, 50% between 31 and 40 years old, 32% between 41 and 50 years old, 13% between 51 and 60 years old, and 1% over 60 years old. Regarding discipline, 22% of respondents majored in social sciences, 28% in humanities, and 50% in the natural, engineering, and health sciences. More males (69%) than females (31%). The ranking range: 25% assistant professor, 45% associate professor, and 30% professor (see Table 1).

Table 1
Demographics of the participants

Term Category % Term Category %

Age (year) <30 4 Discipline Social science 22

31–40 50 Humanity 28

41–50 32 Natural, engineering &health science 50

51–60 13

60+ 1

Gender Males 69 Academic ranking Assistant professor 25

Females 31 Associate professor 45

Professor 30

Term	Category	%	Term	Category	%
Age (year)	<30	4	Discipline	Social science	22
	31–40	50		Humanity	28
	41–50	32		Natural, engineering &health science	50
	51–60	13
	60+	1
Gender	Males	69	Academic ranking	Assistant professor	25
	Females	31		Associate professor	45
				Professor	30

3.2 Measures

3.2.1 Overall faculty motivation (FMO)

FMO comprised four sub-dimensions: goals, capacity beliefs, emotions, and context beliefs. It was modeled as a reflective second-order construct to capture complementarities from the four motivational dimensions, i.e., goals, context beliefs, capacity beliefs, and emotions. Data for these dimensions were measured using a 5-point scale, where “5”=strongly agree and “1”=strongly disagree.

For goals, the eight items scale was partially extracted from previous studies [79] and two from the interviews, measuring desired within-person and person-environment consequences. Sample statements included: “Learn more about the latest research and international trend” and “Gain more promotion opportunities.” The items for context beliefs, capacity beliefs, and emotions were developed using interviews conducted in earlier studies [20]. Context beliefs were measured through nine items with sample statements like “International expertise is part of the recruitment/selection of new academic staff; Administration and policies support academic staff to engage in internationalization”. Capacity beliefs were measured through seven items with example items like “I am a competent reviewer for international journals or grant programs” and “I communicate and interact well with foreign scholars.” Emotions were measured through seven items with sample statements like “I feel happy when I publish my research findings in international journals” and “I feel proud when international scholars recognize my work.”

3.2.2 Faculty engagement in internationalization (FEI)

There were 24 items measuring faculty’s frequency of engagement in three kinds of international activities for three years (2016 to 2018) by the number from 1 (0 times) to 5 (10 + times). Firstly, as for international teaching, five items were adopted from Doyle [49] and five from interviews. Example items include: “Units with international students” and “Units in which foreign books or material were used.” Secondly, four items were adopted from Fields [43] for international research activities and four from Proctor [80]. Example items include: “International conferences attended (overseas)” and “Articles in international journals (outside China).” Thirdly, six items were adopted from Proctor [80] for international service activities, and two came from interviews. Example items include: “Membership in international academic associations and journal editorial boards” and “Reviewer in international journals or international grant applications.”

3.2.3 Work performance

Successful faculty performance included nine items adapted from Sampson et al. [81]. Five items in the WP dimension were measured using a 5-point scale, ranging from 5 = strongly agree to 1 = strongly disagree. It includes the effectiveness of course delivery, the quality of course content, effectiveness in developing and managing instructions, quality of research, productivity in research, the sustainability of research, service to the institution, service to the profession, and serving society through consultation.

3.2.4 Analysis

First, CFA was conducted to examine the factor structures of motivation that included various alternative measurement models at the first-order and second-order levels. Then, structural equation modeling (SEM) was used to test the hypothesized model. During this stage, researchers first examined the direct effect of four dimensions of motivation on FEI and WP, then examined whether the FMO affected FEI and WP. Both the CFA and SEM were conducted using AMOS 25 software.

3.3 Ethical considerations

An institutional review board of the Institute of Education at Xiamen University approved this study in May 2020. All participants were informed of voluntary participation, anonymity, and confidentiality.

4 Results

This study investigated the direct and indirect relationship of FMO (independent variable) with FEI (mediator) and WP (dependent variable) using an SEM approach. Descriptive statistics, including means, standard deviations, Cronbach’s alpha, and Pearson’s correlations, are reported in Table 6. Cronbach’s alpha for each measure exceeded 0.9, indicating that the reliability was appropriate for research to construct motivation [82]. Consistent with the researchers’ expectations, all four motivational FMO factors significantly correlated with WP.

4.1 Test of the motivation model

According to the MST theory, FMO comprises four integrated dimensions, namely context beliefs (CO), capacity belief (CA), emotional experience (E), and goals (G). Generally, the reliability and validity of a construct are checked by comparing different models. In this context, the effect of a first-order model (4 factors) was compared with the impact of a second-order model, FMO. After grouping the four dimensions, confirmatory factor analysis (CFA) was conducted, and reliability and validity were assessed. As seen in Table 2, data analysis for the first-order model showed that the model goodness standard was acceptable. All scales’ loadings were above the standard threshold (i.e., 0.6, P > 0.001), indicating that all items had a relatively good measurement effect.

Table 2
Measurement model for first-order model

Construct Scale item Factor T-value Construct Scale item Factor T-value

Context beliefs (CO) CO1 0.823 11.834* Emotions (E) E1 0.754 15.12*

CO2 0.645 9.326* E2 0.752 15.166*

CO3 0.852 12.156* E3 0.771 15.734*

CO4 0.805 11.531* E4 0.621 11.01*

CO5 0.707 10.19* E5 0.826 18.188*

CO6 0.914 12.93* E6 0.741 14.522*

CO7 0.693 9.994* E7 0.933

CO8 0.727 10.403* Goals (G) G1 0.751 12.503*

CO9 0.692 G2 0.769 12.996*

Capacity beliefs (CA) CA1 0.726 10.821* G3 0.731 12.153*

CA2 0.822 12.444* G4 0.725 11.99*

CA3 0.848 12.755* G5 0.761 12.867*

CA4 0.89 13.458* G6 0.786 13.381*

CA5 0.673 10.042* G7 0.811

CA6 0.89 12.109* G8 0.75 11.276***

CA7 0.725

Construct	Scale item	Factor	T-value	Construct	Scale item	Factor	T-value
Context beliefs (CO)	CO1	0.823	11.834***	Emotions (E)	E1	0.754	15.12***
	CO2	0.645	9.326***		E2	0.752	15.166***
	CO3	0.852	12.156***		E3	0.771	15.734***
	CO4	0.805	11.531***		E4	0.621	11.01***
	CO5	0.707	10.19***		E5	0.826	18.188***
	CO6	0.914	12.93***		E6	0.741	14.522***
	CO7	0.693	9.994***		E7	0.933
	CO8	0.727	10.403***	Goals (G)	G1	0.751	12.503***
	CO9	0.692			G2	0.769	12.996***
Capacity beliefs (CA)	CA1	0.726	10.821***		G3	0.731	12.153***
	CA2	0.822	12.444***		G4	0.725	11.99***
	CA3	0.848	12.755***		G5	0.761	12.867***
	CA4	0.89	13.458***		G6	0.786	13.381***
	CA5	0.673	10.042***		G7	0.811
	CA6	0.89	12.109***		G8	0.75	11.276***
	CA7	0.725

Note: ***p < .001; Goodness of indicators for the first-order model: χ2/df = 1.02 (419.146/41); NFI = 0.92; RFI = 0.91; CFI = 0.998; GFI = 0.899; RMSEA = 0.009; PGFI = 0.745; PNFI = 0.813; AIC = 589.146.

As depicted in Table 3, the first-order model’s result showed that the correlation coefficient of variables CO, CA, E, and G ranged from 0.501–0.673, indicating a high-intensity correlation between the four dimensions. The goodness of fit of the first-order confirmatory model met the statistical standard. As the four factors were all affected by the higher-order potential variable FMO, it was feasible to construct a second-order model.

Table 3

Correlation analysis of FMO dimensions

Constructs	E	CA	G	CO
Emotions (E)
Capacity beliefs (CA)	0.501***
Goals (G)	0.561***	0.673***
Context beliefs (CO)	0.541***	0.639***	0.562***

Note: ^*** p < .001. Abbreviations: context beliefs = CO; capacity beliefs = CA; emotions = E; goals = G.

The results of the second-order model are reported in Table 4. The second-order confirmatory factor analysis model indicated good goodness of fit (see note for the goodness of fit). According to the measurement results of the FMO scale, the first-order potential factor loads of FMO ranged between 0.67–0.856, higher than 0.6, P < 0.001. The average variation extraction (AVE) was 0.585, higher than 0.5. The combination reliability (CR) was 0.848, higher than 0.7, supporting good convergence validity. More so, the factor loads of the first-order potential variables were all above 0.6, P < 0.001, AVE was above 0.5, and CR was above 0.7. These results showed that the convergence validity of each first-order possible factor was also good.

Table 4

Measurement model for second-order model

Construct	Scale item	Factor	T-value	AVE	CR
Overall faculty motivation (FMO)	E	0.67		0.585	0.848
	CA	0.856	8.058***
	G	0.784	8.248***
	C0	0.737	7.44***
Context beliefs (CO)	CO1	0.823	11.822***	0.588	0.927
	CO2	0.644	9.317***
	CO3	0.852	12.145***
	CO4	0.805	11.524***
	CO5	0.707	10.185***
	CO6	0.914	12.915***
	CO7	0.693	9.991***
	CO8	0.727	10.401***
	CO9	0.691
Capacity beliefs (CA)	CA1	0.726	10.822***	0.641	0.925
	CA2	0.822	12.439***
	CA3	0.848	12.754***
	CA4	0.89	13.457***
	CA5	0.673	10.043***
	CA6	0.89	12.109***
	CA7	0.725
Emotions (E)	E1	0.754	15.113***	0.602	0.913
	E2	0.752	15.178***
	E3	0.771	15.76***
	E4	0.621	11.012***
	E5	0.826	18.175***
	E6	0.741	14.511***
	E7	0.933
Goals (G)	G1	0.751	12.5***	0.579	0.917
	G2	0.769	12.998***
	G3	0.731	12.153***
	G4	0.725	11.983***
	G5	0.761	12.861***
	G6	0.786	13.373***
	G7	0.811
	G8	0.75	11.276***

Note: ^*** p < .001; Goodness of indicators for the first-order model: χ2/df = 1.017 (419.913/413); NFI = 0.92; RFI = 0.91; CFI = 0.999; GFI = 0.899; RMSEA = 0.009; PGFI = 0.748; PNFI = 0.817; AIC = 585.913; AVE = average variance extracted; CR = composite reliability.

4.2 Goodness of overall motivation model

Next, the goodness of the multiple factor model with the second-order model was compared (see Table 5). The single-factor model comprises four factors, the two-factor model composed of G, CO, and CA with E, and the three-factor model composed of G and CO with CA and E, respectively. G, CO, CA, and E were independent factors in the four-factor model. The second-order model was based on G, CO, CA, and E. Compared with the goodness of the first-order model, the four-factor model’s goodness was significantly higher than models of the single factor, two factors, and three factors. The four-factor scale had a well-differentiated validity. The goodness of fit of four-factor model: χ²/df = 1.017, NFI = 0.92, RFI = 0.91, CFI = 0.999, GFI = 0.899, RMSEA = 0.009, all met the acceptable statistical standards. The fitting results of the four-factor model in Table 4 showed that the item load of each factor was above 0.6 with P < 0.001. The above analysis proved that the first-order verification model of the FMO scale had a good validity; thereby, the multidimensional measurement structure of four dimensions was supported.

Table 5
Goodness of indicators for the different measurement models

Models χ²/df NFI RFI CFI GFI RMSEA

Single factor model 4.675 (1949.44/417) 0.629 0.586 0.68 0.535 0.127

Two-factor model 3.312 (1377.607/416) 0.738 0.707 0.799 0.62 0.101

Three-factor model 2.366 (979.64/414) 0.813 0.79 0.882 0.701 0.077

Four-factor model 1.02 (419.146/411) 0.92 0.91 0.998 0.899 0.009

Second order model 1.017 (419.913/413) 0.92 0.91 0.999 0.899 0.009

Models	χ²/df	NFI	RFI	CFI	GFI	RMSEA
Single factor model	4.675 (1949.44/417)	0.629	0.586	0.68	0.535	0.127
Two-factor model	3.312 (1377.607/416)	0.738	0.707	0.799	0.62	0.101
Three-factor model	2.366 (979.64/414)	0.813	0.79	0.882	0.701	0.077
Four-factor model	1.02 (419.146/411)	0.92	0.91	0.998	0.899	0.009
Second order model	1.017 (419.913/413)	0.92	0.91	0.999	0.899	0.009

Note: NFI = normed fit index; RFI = relative fit index; CFI = comparative fit index; GFI = goodness-of-fit index; RMSEA = residual mean square error of approximation

Table 6

Measurement model for FEI and WP

Construct	Scale item	Factor	T-value	AVE	CR
Faculty engagement in internationalization (FEI)	Research (R)	0.886	17.086***	0.721	0.886
	Service (S)	0.805	14.699***
	Teaching (T)	0.855
Work performance (WP)	WP 1	0.743	11.747***	0.614	0.934
	WP 2	0.888	9.413***
	WP 3	0.778	12.075***
	WP 4	0.937	11.513***
	WP 5	0.86	10.108***
	WP 6	0.664	12.870***
	WP 7	0.655	9.770***
	WP 8	0.684	10.148***
	WP 9	0.789

Note. *** p < .001; AVE = average variance extracted; CR = composite reliability.

According to the first-order verification model (see Table 5), G, CO, CA, and E had medium and high strength correlation, so the second-order verification model of FMO was established. The fitting of the second-order model was analyzed through three levels. Firstly, the goodness of fit of the two models was compared (see Table 4 for the comparison results). The values of the second-order model (χ²/df and CFI) were slightly higher than those of the four-factor model. Experts recommend a more concise model comprising fewer estimated parameters and higher degrees of freedom. The second-order model was accepted considering the above premise [83].

Secondly, in the second-order model, the factor loads of the measurement items of the first-order potential variables were all above 0.621, higher than 0.6, and P < 0.001. The AVEs of the first-order potential factors were all higher than 0.5. At the same time, the CR values were higher than 0.7, reflecting that the first-order potential factors had better measurement effectiveness in the second-order model.

Thirdly, in the second-order model, the factor loads of the first-order potential factor and the second-order potential factor were both higher than 0.6 (P < 0.001). The AVE (0.585) was higher than 0.5. The combination reliability CR was 0.848, higher than 0.8. The second-order potential factor could explain 58.5 percent of the information on the first-order potential factor.

Retrospectively, the above three data analyses provided sufficient evidence to support that the second-order factor model with FMO was a better choice. As the second-order factor FMO better explained the first-order potential factors G, CO, CA, and E, H1 was supported.

4.3 Direct and indirect effects

FEI was measured by teaching, research, and service factors, as depicted in Table 6. The factor’s loadings were between 0.805–0.886, p < 0.001, AVE was 0.721, and the composite reliability was 0.886. WP included items between WP1–WP9. The factor loadings of WP ranged from 0.655–0.937 (P < 0.001). AVE was 0.614, and the combination reliability was 0.934. The factor loadings were all above 0.6. The combination reliability was higher than 0.7. Moreover, the average variation extraction is higher than 0.5. Both the FEI and WP had good convergence validity and good measurement validity.

4.4 Description analysis, reliability analysis, correlation analysis, and differential validity test

In Table 7, the Cronbach’s α of each factor was above 0.9, showing excellent reliability. The mean square root of each factor was higher than that of other factors, so the differential validity of each factor was considered good. As for the correlation among the factors, G, CO, CA, and E had a positive correlation with the FEI (ranging between 0.502–640, P < 0.001) and WP (ranging between 0.509–0.670, P < 0.001). A positive correlation was found between FEI and WP (0.801, P < 0.001). In short, the results showed that all variables had a significant correlation.

Table 7
Means, standard deviations, reliabilities, and Pearson’s correlation coefficient

M SD α CO CA E G FEI WP

CO 3.834 0.625 0.924 0.765

CA 3.895 0.586 0.919 0.640^* 0.801

E 4.046 0.605 0.909 0.502^* 0.564^* 0.774

G 3.520 0.624 0.942 0.558^* 0.648^* 0.545^* 0.789

FEI 3.778 0.566 0.962 0.555^* 0.631^* 0.500^* 0.643^* 0.849

WP 3.841 0.620 0.933 0.574^* 0.67^* 0.509^* 0.551^* 0.801^*** 0.783

	M	SD	α	CO	CA	E	G	FEI	WP
CO	3.834	0.625	0.924	0.765
CA	3.895	0.586	0.919	0.640^***	0.801
E	4.046	0.605	0.909	0.502^***	0.564^***	0.774
G	3.520	0.624	0.942	0.558^***	0.648^***	0.545^***	0.789
FEI	3.778	0.566	0.962	0.555^***	0.631^***	0.500^***	0.643^***	0.849
WP	3.841	0.620	0.933	0.574^***	0.67^***	0.509^***	0.551^***	0.801^***	0.783

Note: *** p < .001; AVE = average variance extracted; AVE square root is along the diagonal. Abbreviations: CA = Capacity beliefs; G = Goals; E = Emotions; FEI = Faculty engagement in internationalization; WP = Work performance.

Table 8

Goodness of indicators for the two SEM models: Direct effect and parsimonious models

Model	χ2/df	NFI	RFI	CFI	GFI	RMSEA	PGFI	PNFI	AIC
Direct effect model (without second-order four factors)
Sub-model FMO ⟶ FEI	1.322 (772.05/584)	0.919	0.924	0.969	0.907	0.038	0.741	0.819	936.05
Sub-model FMO ⟶WP	1.378 (1114.425/809)	0.917	0.922	0.958	0.906	0.041	0.736	0.81	1302.425
Overall model	1.21 (1113.255/920)	0.922	0.926	0.975	0.909	0.03	0.738	0.812	1343.255
Parsimonious model (with second-order four factors)
Sub-model FMO ⟶ FEI	1.316 (775.366/589)	0.919	0.924	0.969	0.908	0.037	0.747	0.825	929.366
Sub-model FMO ⟶ WP	1.371 (1116.342/814)	0.917	0.922	0.958	0.906	0.04	0.74	0.814	1294.342
Overall model	1.21 (1122.812/928)	0.922	0.926	0.975	0.909	0.03	0.743	0.818	1336.812

Note: SEM = Structural equation model. Abbreviations: FEI = Faculty engagement in internationalization; WP = Work performance; FMO = Overall faculty motivation.

4.5 Structural equation modeling (SEM) analysis

AMOS25.0 was used to test the structural equation model. This study establishes two models for comparative analysis: i) direct effect model (without second-order potential factors); ii) indirect model (with second-order potential factors). Each group consists of two sub-models and a total model. Sub-model 1 was constructed to establish the direct impact model of FMO on FEI. Sub-model 2 aimed to assess the direct impact model of FMO on WP. The total model was formulated to establish the impact model of FMO on FEI and WP.

Table 8 compares the fitting index between the direct effect and indirect models. It shows that the three models, the direct effect and indirect models, all meet the acceptable standard. The PGFI and PNFI of the indirect model are 0.743 and 0.818, respectively, higher than the 0.738 and 0.812 of the direct effect models. The AIC value of the indirect model is 1336.812, which is lower than the 1343.255 direct effect models. Therefore, the goodness of fit of the indirect model (with second-order potential factors) was found better than the direct effect model, thus supporting H1.

Figure 1 shows the fitting results of the direct and indirect effect models. The results of the sub-model (FMO to FEI) showed that FMO had a significant positive impact on the FEI (β= 0.776, t = 8.65, P < 0.001), supporting H2.

Fig. 1

Hypothesized parsimonious model and its results, hypothesized direct effects model, and results. Abbreviations: CA = Capacity beliefs; G = Goals; E = Emotions; FEI = Faculty engagement in internationalization; WP = Work performance; FMO = Overall faculty motivation. Note ***p < .001.”–” indicates that the path is set as the reference path of the potential variable. It does not output its t-value and p < 0.001.

In Table 9, the results of sub-model (FEO to WP) showed that FMO positively affected WP (β= 0.766, t = 8.264, P < 0.001). Thus, H3 was accepted.

Table 9

Hypothesized parsimonious model and its results

Models	Paths	β	T-value
Sub-model: FMO ⟶ FEI	FMO⟶FEI	0.776	8.65***
Sub-model: FMO ⟶ WP	FMO⟶WP	0.766	8.264***
Overall model	FMO⟶FEI	0.768	8.656***
	FEI⟶WP	0.529	5.909***
	FMO⟶WP	0.363	3.956***
	FMO⟶CA	0.849	8.403***
	FMO⟶G	0.773	8.441***
	FMO⟶CO	0.746	7.656***
	FMO⟶E	0.676	–

Note: Abbreviations: FEI = Faculty engagement in internationalization; WP = Work performance; FMO = Overall faculty motivation. ***p < .001.

H4 represents the impact of FMO on WP mediated by the FEI. According to the reduction effect, the direct effect of FMO on WP is 0.363 (t = 3.956, P < 0.001), the total effect is 0.770, and the indirect effect is 0.406. According to the bootstrap result, if the ninety-five percent range of indirect effect ranges is [0.159, 0.619], then the intermediary (mediation) effect is acceptable. Therefore, H4 is supported. More so, the goodness of fit of the partial intermediary effect model was better than that of the complete intermediary effect model (Δ M2 = 16.455, Δ DF = 1, P < 0.001). In other words, the results proved the relationship between FMO and WP with a partial intermediary of FEI, validating H4.

5 Discussion

This paper is among the first studies evaluating FMO, FEI, and WP in a research-centric Chinese university using 229 faculty survey documents. As asserted by Ford [18], Li et al. [20], and Kanfer et al. [51], the current results confirmed that the four dimensions and consequences of motivation are intertwined. This outcome suggests that motivation does not occur in isolation but as a system of complementary events. This confirms Pautz and Vogel’s [55] view: the US faculty are motivated by different factors, e.g., desire to contribute to society, an interest in working with students and fulfillment, and an opportunity for intellectual stimulation and autonomy. However, the present findings reveal that Chinese faculty, in addition to the above, are also incited by goals. Unlike Xu, Wang, and Zhu’s [27] findings in a research-intensive university in Shanghai, the present findings reveal that motivational factors are linked and extend beyond goals, context beliefs, and capacity beliefs to emotions.

As demonstrated in Fig. 1, similar to the prior modeling approach and findings [83], examining the separate impact of specific independent motivational factors on WP could lead to misleading interpretations. If each factor were treated independently, the results would incorrectly suggest that only capacity beliefs and goals significantly impact FEI and WP. In that case, it would be hard to explain why four highly related dimensions have no apparent effect on FEI or WP. Although specific dimensions might exert particular effects, the effect of FMO supported the MST’s preposition by summarizing the influence of the individual dimensions. This finding, therefore, contributes to current research and theory in terms of introducing a more straightforward and precise scale for predicting action than a multidimensional approach to conceptualizing motivation. Another critical facet of this finding is the methodological strength of modeling motivation as a reflective second-order construct to capture complementarities from the four first-order constructs. This method helps overcome the shortcoming of multicollinearity resulting from highly correlated independent constructs. With less research on faculty motivation conducted to date [84], the empirically validated Ford’s MST framework could help leaders align faculty development, HR, and institutional strategies representing faculty goals, beliefs, and emotions to improve engagement and performance.

Second, the current model confirmed that FMO positively affects FEI, responding to the lack of empirical studies on second-order modeling of FMO and measuring its relationship with FEI in China [20]. In line with previous studies, faculty perceiving positive CO (e.g., more support from the institutions) [43 , 85] and CA (e.g., publication ability) [85] reported a high willingness to engage in internationalization. This concept resonates with prior theories that faculty beliefs about their capacity to perform in various internalization activities [34, 38], coupled with financial, psychological, and academic support (e.g., international research funding, international teaching opportunities, and promotion policy), encourage Chinese faculty to engage in international activities [20 , 87]. From a country-country perspective, the American faculty are motivated to engage in internationalization if it offers self-identity, satisfies curiosity for knowledge, provides opportunities to help students grow and gain international awareness and responsibility (G) [85], and increases the reputation of an academic program [14]. Instead, as per results, Chinese faculty motivational factors include visiting abroad (FEI), gaining global vision and experience (G), and promotion (CO) [76]. This finding uniquely extends motivation to capacity (CA) and emotions (E). This finding suggests that faculty simultaneously holistically reflect on their goals, beliefs, and emotions when considering engagement.

Third, the positive effect of FMO on WP reported in Fig. 1 corroborates the work of Tong and Zhang [69] and Zhou [70]. For instance, faculty who perceived superior foreign language ability (CA), more institutional support (CO), and positive experience with international students (E) incorporated more international and inter-culture content in their courses (WP). Such an international perspective facilitated positive dispositions about the quality of teaching, research, and consultation to the institutions and the society (WP). Jiang and Liu’s qualitative study found that FEI promotes Chinese faculty’s value changes, teaching and research practices, and international academic cooperation [75]. In contrast, the current model empirically confirmed that FEI (e.g., training abroad) resulted in superior work output in three dimensions instead of two areas, i.e., not only teaching and research but also service.

Fourth, past studies have shown that FEI favorably impacts WP [25 , 77]. Cao et al. [34] asserted that engagement in international teaching activities is conducive to teaching performance for the American faculty, e.g., improving students’ academic performance. Nevertheless, this study provides the first empirical evidence that FEI is a positive mediating influence between FMO and WP. This finding is novel as no other research has examined the FMO-FEI-WP nexus, especially in China. Nonetheless, this finding resonates with prior literature in behavioral psychology and economics [53, 58] that motivation is a significant determinant of performance. Simultaneously, it contradicts the view in cognitive evaluation theory that rewards linked to performance disrupt performance [86]. As observed herein, Chinese faculty views research funding and awards based on performance as favorable reinforcement.

Aiming to build a high-impact university, Chinese higher education institutes tend to support research internationalization and expect more output from the engagement [87]. In essence, faculty motivation and engagement in internationalization have become crucial for university management as Chinese universities seek to increase their global ranking. Faculty are expected to teach, research, and be involved in their institutions and professions’ service and administrative functions. Therefore, an invaluable contribution of this work is that it enhances the understanding of the positive effects of motivation, focusing on the mechanisms that link FMO with FEI and WP that remain widely under-researched [84].

5.1 Theoretical and practical implications

The current findings offer practical implications for enhancing faculty motivation, engagement in internationalization, and work performance. First, universities need to simultaneously consider the faculty’s ability, goals, perceived context support, and emotions. Although this study suggests an overall approach to motivation, it certainly does not advocate that universities ignore the individual dimension of faculty motivation. On the contrary, universities must implement all four dimensions of motivation simultaneously because faculty engagement in internationalization and work performance relies heavily on their coexistence and interdependence. Treating motivation as a single dimension is overly simplistic and may lead to poor implementation. Universities need to direct faculty to develop goals, create a supportive and inspiring environment for faculty to function well in different areas, including teaching, researching, and service [87], and provide better service so faculty could have more positive emotions. To ensure a high level of supportive context beliefs, universities tend to establish international collaboration relationships, provide development programs, and provide funding for an international trips and international projects. Faculty seems to require more support as they have a multidimensional role. To get enough feedback from the faculty, universities could carry out regular faculty surveys or organize faculty meetings about how the universities are progressing and the places to improve regarding faculty’s responsibilities. Those practices may strengthen their belief in context support and better understand their routine tasks and performance. In short, a specific motivational factor cannot be pursued in isolation from the others, and universities need to maximize the effects of overall motivation.

Second, universities can increase faculty members’ overall motivation for better engagement and performance. Faculty need to engage more in international activities as universities should prepare students to engage in a more globally interdependent world with skill, wisdom, and responsibility [89]. Besides, work performance improvement is the goal of all universities. Although university administrators may distinguish between various factors of faculty motivation, faculty engagement behavior is mainly driven by overall motivation. The findings support the widespread use of overall motivation as a motivational strategy. If faculty have some achievable goals, good support for international activities, reasonable beliefs of self-ability, and positive emotional experiences, they will engage in international activities, consequently improving their work performance. The importance of overall motivation highlights the need for universities to reconsider the management and services for faculty. Those services include university development targets, faculty workloads, promotion policy, faculty development programs, information systems, and funding supports.

Third, universities should promote internationalization on campus to achieve better faculty performance. It is well known that internationalization improves faculty’s teaching and research quality [89, 90]. This study also supports that engagement in internationalization ultimately influences faculty performance levels.

5.2 Limitations

The limitations of this study are as follows. First, the current findings on the link between FMO, FEI, and WP, are based on a small and geographically limited case study of a Chinese research-centric university. Researchers are encouraged to test the current model in general vs. research universities for fresh insight. Future studies can also compare the current findings using different educational institutes within continents, regions, and cultural settings. Secondly, the researchers did not measure FMO directly but modeled it as a reflective second-order construct. Future research may directly examine FMO with other potential influencing factors mentioned in earlier studies, including overall fairness [91], stress and self-efficacy [92], and burnouts [93] and stress [94].

Thirdly, further studies should incorporate other variables to understand the relationship between FMO and WP. As asserted in prior works, some potential mediators (e.g., satisfaction, distress, and position [84]) and moderator variables (e.g., peer example, career stages [87]) could assist in broadening the current understanding of FMO dynamics.

Fourthly, future studies in various disciplines are encouraged to modify and apply the parsimonious second-order construct and the model with different antecedents and outcomes (e.g., satisfaction, distress, and position) to assess its discriminant validity across different settings, e.g., education, health, post-COVID, and prevention. Some scholars argue that the changes in the working environment due to the COVID-19 pandemic have emotionally, psychologically, and physically affected teachers’ well-being, work, and motivation [95]. These changes include but are not limited to strict traveling restrictions and social distancing, campus lockdowns, safety and engagement protocols, and emergency remote teaching. Incorporating the effect of these factors in the current model is expected to render new insight [95].

Finally, investigating disparities among the parameters using various faculty levels could be an intriguing potential avenue of research. It is predicted that faculty with different beliefs and priorities in different countries may exhibit some differences vis-a-vis their overall institutional beliefs. Researchers should also focus on testing the sensitivity of faculty satisfaction and motivation models in Covid-related scenarios.

6 Conclusions

The key objective of this paper was built on Ford’s MST theory and past works. The paper tested two models (direct effect and parsimonious) to first establish overall faculty motivation (FMO) as an integrated construct comprising goals, beliefs, and emotions. After that, the study measured the direct and indirect effect of FMO (direct effect and parsimonious models) on WP with FEI as a mediator. First, the results provided the first empirically tested FMO (second-order) construct that outperformed individual motivational items in explaining the effect of FMO on FEI and WP, thereby extending MST statistically and theoretically. Second, the current estimates extend work and engagement literature by offering an initial case of the positive mediating role of the three-dimensional FEI (teaching, research, service) between FMO and WP. The findings suggest that world-class university development mandates a more holistic perspective on motivation and internationalization. Universities should align faculty goals, capacity belief, context belief, and emotions with national/institutional goals to yield superior engagement, thereby enabling better WP.

Chinese universities are swiftly building world-level universities through extensive internationalization in teaching, research, and service to align with the ambitious One Belt and One Road project. That said, it appears that Chinese higher education can only succeed in creating world-class universities if they adopt a holistic perspective when developing motivation and internationalization strategies, i.e., successfully align the goals, capacity belief, context belief, and emotions of faculty with national/institutional goals to yield superior engagement for better performance.

Ethical approval

Ethical approval for this study was obtained from the Institute of Education, Xiamen University, China.

Informed consent

Informed consent forms were completed by all participants prior to the survey. Participants were duly informed about their rights to voluntary participation and withdrawal.

Conflict of interest

None of the authors have any conflicts of interest to declare.

Footnotes

Acknowledgments

The authors would like to appreciate the anonymous reviewers and the Editors for their invaluable comments and feedback, which helped improve the overall quality of this research.

Funding

This study was supported by ‘Practical Research on Quality Culture of Huang Yanpei Vocational Education Ideology’, the key project of the First Huang Yanpei Vocational Education Thought Research from China Vocational Education Society, Grant no. ZJS2022Zd18.

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