Examination of Higher Education Teachers’ Self-Perception of Digital Competence, Self-Efficacy, and Facilitating Conditions: An Empirical Study in the Context of China

Abstract

In the digital age, it is necessary for higher education teachers to improve their digital competence to face the challenges of the advancement of technology. Because there are few studies on the digital competence of higher education teachers in the context of Asia, the aim of this study was to describe Chinese higher education teachers’ self-perceptions of digital competence and investigate the effects of self-efficacy and facilitating conditions and how they predict teachers’ digital competence. For this purpose, a quantitative methodology was used. A final sample of 525 in-service higher education teachers from China completed the questionnaire. The data were analyzed using descriptive analysis, inferential analysis, and latent variable path analysis. The results indicate, first, that teachers’ general perception of digital competence was at a high level, and of the seven competence areas, values, ethics, and security, digital resources, and continuing professional development areas ranked among the top four, respectively, based on their means. Significant differences were observed in the digital competence of teachers with regard to different levels of education, disciplines, and institutional categories. Second, according to the results of structural equation modeling, facilitating conditions were positively and significantly correlated with teachers’ self-efficacy and digital competence. In addition, self-efficacy exerted a significantly positive impact on teachers’ digital competence. Finally, self-efficacy was found to mediate the relationship between facilitating conditions and teachers’ digital competence. Implications are suggested for developing teachers’ digital competence according to the findings of this study.

1. Introduction

In the internet era characterized by digitization, informatization, and networking, the rapid development of new technologies has had an enormous impact on people’s daily lives, ways of living, and learning [1]. Considering the responsibility and crucial role that higher education plays in reshaping education for sustainability [2,3], as well as the new challenges that universities face, such as training young people to enter a more digital labor market [4], it is essential for higher education teachers and students to learn how to use digital technology [5]. Higher education teachers, as an important part of teaching, must develop digital competence, reflect on their teaching practices, and discover, explore, and solve problems in educational practice with digital thinking and models [6].

With regard to the continuing professional development of higher education teachers, it is necessary for teachers to keep up with the rapid development and change of the information age and to update their knowledge and skills to realize the development of digital competence. In a digital society, information is distributed across a network of connected digital technologies that allow access anywhere, anytime, wherever such connections are possible [7]. Therefore, teachers and students can not only develop their own information networks to obtain learning resources but also conduct personalized learning practices. Higher education teachers must develop their own and their students’ digital competence to better adapt to the future digital society.

Over the past few years, several studies have analyzed the digital competence of higher education teachers [6,8,9,10]. Some of them have discussed the current status of the digital competence of teachers [8,9], while others have discussed possible personal and institutional factors, including gender, generation, academic, and institutional factors [6,10]. However, the samples in these studies were mostly teachers in Europe, such as Spain and Portugal, and the United States [10,11,12,13].

China’s higher education has shifted to a stage of high-quality development [14]. In 2015, the strategic plan of the “double world-class project”, that is, to establish first-class universities and disciplines around the world to enhance the development of higher education, was created. In the digital age, the “double world-class project” has been endowed with new significance. It is not only a key part of the implementation of Education informatization 2.0, China’s education modernization 2035, and the National Education Digitalization Strategic Action, but also a response to the urgent requirements of higher education’s high-quality development in China [14].

However, the digital transformation of higher education institutions in China, whether institutions are included in the “double world-class project” or not, is facing challenges such as an imperfect incentive mechanism, insufficient financial support, or the training of teachers’ digital competence [15]. Although some studies have explored the relationship between gender, teaching experience, effort expectancy, and work engagement and the digital competence of higher education teachers in China [16,17], in the post-pandemic era after the large-scale adaptation of digital technology, the level of Chinese higher education teachers’ self-perception of their digital competence and the factors that affect their digital competence are issues worthy of study. Therefore, this study fills the gap in existing research on the digital competence of higher education teachers in the context of China.

The first aim of this study is to examine the self-perception of digital competence of Chinese in-service higher education teachers, including differences in the level of education, discipline, and institutional category. Moreover, the second aim of this study is to investigate the relationship between teachers’ self-efficacy, facilitating conditions, and digital competence.

2. Literature Review

2.1. Digital Competence of Teachers

Since the 1990s, the infiltration of the concept of sustainable development into higher education has become increasingly important. New factors such as production automation and intangible value creation introduced by the third and fourth industrial revolutions have caused tremendous changes in the way people work, resulting in a challenge for the higher education system to meet the need for cultivating high-quality future citizens and labor [18]. Therefore, facing the challenges of the advancement of technology and the need for high-quality education in the 21st century, several countries and organizations, mainly represented by the European Union, Norway, and Britain, have developed digital competence frameworks or standards for teachers, including higher education teachers, to face the challenges of the advancement of technology [19,20,21].

The European Commission developed the European Framework for the Digital Competence of Educators (DigCompEdu) in 2017, which includes six competence areas: professional engagement, digital resources, teaching and learning, assessment, empowering learners, and facilitating learners’ digital competence [19]. The Norwegian Centre for ICT in Education published the Professional Digital Competence Framework for Teachers (PDCT) in the same year, which divided teachers’ digital competence into seven dimensions: subjects and basic skills, school in society, ethics, pedagogy and subject didactics, leadership of learning processes, interaction and communication, and change and development [20]. Additionally, the National Institute of Educational Technologies and Teacher Training in Spain published the Common Digital Competence Framework for Teachers (CDCFT) in 2017, which identified five areas: information and data literacy, communication and collaboration, digital content creation, security, and problem solving [22]. In 2019, the Joint Information Systems Committee (JISC) in the UK published the Teacher Profile (higher education), which divided higher education teachers’ digital competence into five areas: (1) ICT proficiency; (2) digital creation, problem solving, and innovation; (3) digital communication, collaboration, and participation; (4) digital learning and development; and (5) information, data, and media literacy [21].

In China, the National Educational Technology Guides for Teachers in Higher Education (CETG) were released by the Chinese Educational Technology Association in Higher Education (CETA) in 2010. According to this document, higher education teachers should develop the following competence areas: (1) values and ethics; (2) knowledge and skills; (3) scientific research and innovation; (4) assessment; and (5) planning and implementation. This document provides a descriptive reference for higher education teachers in China that can be used for training purposes [23]. However, this document mainly targeted the educational technology abilities of teachers rather than the comprehensive competencies needed in a digital society. In 2022, with the modernization of education, the Ministry of Education launched a document named the Digital Literacy of Teachers, which divided teachers’ digital literacy into five dimensions: digital values, digital technology knowledge and skills, digital utilization, digital social responsibility, and professional development. This document promoted strategic action for national education digitization, the improvement of the educational informatization standard system, and the enhancement of teachers’ awareness, ability, and responsibility to use digital technology to optimize, innovate, and change teaching activities [24].

With regard to existing research, in terms of quantity, studies of teachers’ digital competence standards or frameworks in the West are more abundant than studies in China. With regard to content, studies in the West are focused on teachers’ digital teaching competence [25], while studies in China emphasize teachers’ digital teaching and scientific research competence [23,26]. Overall, China’s education informatization is still at an initial stage compared with developed countries.

Taking into account the main publications in the world and the two major tasks of Chinese higher education teachers, teaching and scientific research, the digital competence of higher education teachers in this study is defined as the set of knowledge, skills, and attitudes toward digital technology that enable teachers to function effectively within the higher education contexts that new digital technologies generate. It consists of seven areas: (1) values (V), referring to teachers’ awareness of the important role of digital technology in the development of higher education, scientific research, self-development and learner development [23]; (2) ethics and security (ES), emphasizing teachers’ awareness of the moral ethics, laws and regulations in the digital environment as well as their security consciousness [27]; (3) digital resources (DR), referring to teachers’ ability to effectively integrate digital resources and technologies into their teaching planning [19]; (4) teaching and learning (TL), referring to teachers’ ability to effectively use digital technologies to improve the teaching-learning process and assessment [26]; (5) scientific research (SR), referring to teachers’ ability to use digital technologies to improve the quality of their independent research, to effectively manage personal research achievements and to share and cooperate with others to produce innovative research results [23,28]; (6) continuing professional development (CPD), referring to teachers’ ability to use digital technologies to realize self-learning, professional communication and collaboration as well as to critically reflect on digital teaching and research practices to achieve their continuing professional development [29]; and (7) facilitating learners’ digital competence (FLD), referring to teachers’ ability to facilitate learners’ general digital competence and the professional digital competence needed to survive and achieve life-long learning and future professional development in the digital society [19].

2.2. Self-Efficacy

According to social cognitive theory [30], self-efficacy (SE) involves “beliefs in one’s capabilities to organize and execute the courses of action required to produce given attainments” [31]. The assumption of self-efficacy is that people are more willing to focus on their tasks and persist in them when they are confident about being able to complete them [32,33]. Thus, the perception of self-efficacy for performing a task is often related to behavioral outcomes and has a direct effect on behaviors [34]. For teachers, self-efficacy can be seen as a powerful predictor of the teaching activity of cultivating students as well as activities related to education and the development of other skills.

Self-efficacy is a fairly powerful predictor of the teaching practices that teachers use to foster student learning as well as activities related to the development of educational and other processing skills [35,36,37]. Moreover, self-efficacy is a direct and indirect predictor of teachers’ willingness to use ICT and its actual use [38]. Teachers with higher self-efficacy are more likely to have a higher level of persistence and provide a better teaching environment for students [39]. Hatlevik found that teachers’ basic ICT self-efficacy can effectively predict their digital competence [40].

2.3. Facilitating Conditions

According to the Unified Theory of Acceptance and Use of Technology (UTAUT), facilitating conditions (FC) refer to the enablers and barriers of particular behavioral use [41]. In the context of higher education, facilitating conditions can be defined as the degree to which teachers perceive support for their use of digital technologies provided by their government or universities in terms of technology or funding [42]. It includes, on the one hand, incentives or constraints on teachers’ behavior, such as digital policies and reward and punishment systems related to digital teaching and learning [43], and, on the other hand, objective conditions such as hardware facilities, software, digital resources, professional technical guidance, and training programs [42,44]. In other words, a more complete institutional system and more adequate resources provided by the government and university will benefit the inspiration of teachers’ digital technology use and the development of their digital competence [45].

2.4. Relationships among Variables and Research Hypotheses

Previous studies have emphasized the importance of sustainable development in higher education [18]. In the digital age, the rapid updating of digital technologies and the sustainable development of higher education underline the necessity of developing higher education teachers’ digital competence and exploring its possible factors. Although many countries and organizations have taken initiatives to improve teachers’ digital competence, such as publishing standards and frameworks [19,20,21,22,23,24], calls for more attention to teachers’ digital competence development still exist [46]. Research on higher education teachers’ digital competence in the context of different regions also indicates that there is no consensus yet on whether teachers’ digital competence differs in variables such as level of education, discipline, and institutional category [10,47]. Thus, further exploration of it in the context of China is of great significance.

In terms of possible factors, digital technology self-efficacy may be positively correlated with an individual’s digital competence. Research shows that self-efficacy can explain the differences in teachers’ use of digital technology, and high self-efficacy is conducive to more critical and frequent use of digital technology [40]. Studies on management support indicate that top managers who establish formal rules and policies can function as institutional factors and influence employees’ system-related beliefs and usage behaviors [48,49,50]. Similar results are found in educational research. Teachers’ emotions, including beliefs and identity, are shaped by their perception or appraisal of school staff [51] and the school environment [52,53].

In addition, improving facilitating conditions can enhance support for digital technology use, reduce obstacles, and help improve people’s use of digital technology. In the UTAUT model, facilitating conditions are the direct predictors of digital technology use. Bergeron, Rivard, and Serre noted that as technology users find multiple avenues for help and support throughout the organization, barriers to continuous use are removed, which can be expected to increase the effect of continuous use [54]. Existing research on teachers’ digital competence also shows that teacher training, limited facilities, school organization, and education policies can affect teachers’ digital competence [43,45].

Drawing on previous studies, the following hypotheses are proposed:

Hypothesis 1: 

There are significant differences in teachers’ digital competence with regard to different levels of education, disciplines, and institutional categories.

Hypothesis 2: 

There is a positive and significant relationship between self-efficacy and teachers’ digital competence.

Hypothesis 3: 

There is a positive and significant relationship between facilitating conditions and teachers’ digital competence.

Hypothesis 4: 

There is a positive and significant relationship between facilitating conditions and teachers’ self-efficacy.

Hypothesis 5: 

Self-efficacy plays a mediating role in the relationship between facilitating conditions and teachers’ digital competence.

A hypothetical model for this study is illustrated in Figure 1.

According to Figure 1, in this study, both teachers’ self-efficacy and facilitating conditions affect their digital competence, and facilitating conditions forecast teachers’ self-efficacy. In addition, self-efficacy mediates the relationship between facilitating conditions and digital competence.

3. Method

3.1. Research Design

The methodological approach chosen for the production of this paper is quantitative. The descriptive and inferential analyses were used in this study to examine the self-perception of digital competence of Chinese in-service higher education teachers, including differences in the level of education, discipline, and institutional category. The structural equation model was used to investigate the relationship between teachers’ self-efficacy, facilitating conditions, and digital competence.

3.2. Participants

The participants included 525 in-service higher education teachers from different provinces, regions, and institutions with various levels and funding models in China. Teachers were recruited for this study using convenience sampling and snowball sampling methods. A total of 54.47% (n = 286) of the sample was female, and 45.52% (n = 239) was male. The characteristics of the sample are presented in

Table 1. Characteristics of the sample (n = 525).

Profile	Category	Frequency	Percentage (%)
Level of Degree	PhD degree	208	39.62
	Master’s degree	249	47.43
	Bachelor’s degree	68	12.95
Discipline	Humanities	72	13.71
	Social Science	217	41.33
	Science	218	41.52
	Interdisciplinary	18	3.43
Institutional Category	Public institutions included in the “double world-class project”	182	34.67
	Public institutions not included in the “double world-class project”	251	47.81
	Private institutions	92	17.52

.

3.3. Measures

The questionnaire was divided into two parts: (1) identification (data to identify the respondents), including gender, age, and institutional category, and (2) teachers’ self-perception of digital competence, self-efficacy, and facilitating conditions.

3.3.1. Digital Competence

The digital competence questionnaire was adapted from two works. The first is a study conducted by the European Union with a self-refection tool for teachers called “DigCompEdu Check-In”, which provides a way to realize teachers’ self-assessment of their strengths and weaknesses in the use of digital technologies in education [55]. The Chinese adaptation, validity, and reliability of this scale were performed by Sang, Wang, Li, and Yang [17]. The second is research conducted by the National Educational Technology Guides for Teachers in Higher Education (CETG), which was originally written in Chinese [23].

The digital competence scale consists of seven subscales, including values (4 items), ethics and security (4 items), digital resources (4 items), teaching and learning (12 items), scientific research (6 items), continuing professional development (5 items), and facilitating learners’ digital competence (5 items). Each item on these scales is rated on a 5-point Likert-type scale ranging from Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), to Strongly Agree (5). Scores can range from 4 to 20 for the subscales including values, ethics, security, and digital resources; 12 to 60 for the teaching and learning subscale; 6 to 30 for the scientific research scale; 5 to 25 for the continuing professional development subscale; and 5 to 25 for the facilitating learners’ digital competence subscale. Higher scores reflect better self-perception of digital competence in each area.

A sample item from the values subscale is “I think the effective use of digital technology is of great significance in improving the quality of teaching and cultivating innovative talent.” A sample item from the ethics and security subscale is “I know and can take the initiative to protect myself and my students in the digital environment.” A sample item from the digital resources subscale is “I use different internet sites and search strategies to find and select a range of different digital resources.” A sample item from the teaching and learning subscale is “When learners work in groups, they use digital technologies to help them learn and effectively accomplish course tasks.” A sample item from the scientific research subscale is “I create my own researcher account in which I can add and review my research achievements.” A sample item from the continuing professional development subscale is “I use different digital channels to communicate with learners and colleagues whenever appropriate.” A sample item from the facilitating learners’ digital competence subscale is “I encourage students to use digital technologies creatively to solve concrete problems, e.g., to overcome obstacles or challenges emerging in the learning process.” All items are shown in Appendix A.

3.3.2. Self-Efficacy

The Generalized Self-Efficacy Scale (GSES), developed by Schwarzer and Jerusalem [56], was employed to measure teachers’ self-efficacy. It was originally developed in Germany and has been translated into Chinese [57]. The Chinese translation, validity, and reliability of it were carried out by several researchers [57,58]. In this study, the GSES scale was further adjusted to better reflect teachers’ self-efficacy in their use of digital technology. The self-efficacy scale in this study consists of 5 items. Each item is rated on a 5-point Likert-type scale ranging from Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4), to Strongly Agree (5). Scores can range from 5 to 25 on the self-efficacy scale. Higher scores reflect a greater level of self-efficacy. A sample item from the self-efficacy scale is “No matter what happens, I can easily respond to various digital technology use situations”, and all items are shown in Appendix B.

3.3.3. Facilitating Conditions

The facilitating conditions scale referred to the study of Venkatesh, Morris, Davis, and Davis [41], and its Chinese adaptation, validity, and reliability were carried out in the study by Zhang, Liu, Huang, and Wu [59]. In this study, the facilitating conditions scale consists of 8 items. Each item is rated on a 5-point Likert-type scale ranging from (1), Disagree (2), Neutral (3), Agree (4), to Strongly Agree (5). Scores can range from 8 to 40 on the facilitating conditions scale. Higher scores indicate better facilitating conditions for teachers’ digital technology use and their digital competence development. A sample item from the facilitating conditions scale is “There is a specific department or personnel in my university to ensure that teachers can get timely help”. All items are shown in Appendix C.

3.4. Procedure

The data were collected from 25 February 2023 to 23 March 2023. The questionnaires were shared with teachers working at different higher educational institutions across China through the internet. A total of 625 in-service higher education teachers completed the online survey. Preliminary data screening was conducted to eliminate invalid responses (e.g., straightlining responses, excessively fast responses, inconsistent responses). This process resulted in the removal of 100 cases. Therefore, a total of 525 samples were ultimately confirmed. All participants were informed that the anonymity of the data would be ensured, the data would only be used for research purposes, and the results would not influence their careers. Participants completed the questionnaires in approximately 15 min.

3.5. Statistical Analyses

All main statistical analyses were carried out using Statistical Package for the Social Sciences (SPSS V.26) and AMOS 26.0. Confirmatory factor analysis (CFA) was conducted to examine the validity of the proposed model. To figure out teachers’ self-perception of digital competence and the differences in the level of education, discipline, and institutional category, descriptive and inferential statistical techniques were applied, such as one-way nonparametric analysis of variance (ANOVA) and least significant difference (LSD) tests, using IBM SPSS Statistic for Windows v.26. A latent variable path analysis was conducted to investigate the relationship between teachers’ digital competence, self-efficacy, and facilitating conditions using the maximum likelihood estimation in AMOS 26.0. All statistical tests were evaluated at the p < 0.05 significance level and constituted two-tailed tests.

4. Results

4.1. Evaluation of the Measurement Model

The reliability and validity of the constructs were checked. A composite reliability (CR) value of 0.60 or more indicates good reliability [60]. As shown in

Table 2. Results for the Measurement Model.

Construct	Item	Factor Loading	Cronbach’s Alpha	CR	AVE
Digital competence			0.95	0.98	0.62
Values	V1	0.82	0.92	0.92	0.74
	V2	0.87
	V3	0.90
	V4	0.85
Ethics and Security	ES1	0.59	0.80	0.82	0.53
	ES2	0.82
	ES3	0.80
	ES4	0.67
Digital Resources	DR1	0.78	0.88	0.88	0.65
	DR2	0.83
	DR3	0.84
	DR4	0.79
Teaching and Learning	TL1	0.63	0.95	0.95	0.60
	TL2	0.79
	TL3	0.73
	TL4	0.78
	TL5	0.66
	TL6	0.82
	TL7	0.80
	TL8	0.68
	TL9	0.79
	TL10	0.89
	TL11	0.89
	TL12	0.80
Scientific Research	SR1	0.89	0.83	0.91	0.62
	SR2	0.76
	SR3	0.83
	SR4	0.84
	SR5	0.67
	SR6	0.73
Continuing Professional Development	CPD1	0.65	0.91	0.89	0.62
	CPD2	0.63
	CPD3	0.77
	CPD4	0.75
	CPD5	0.85
Facilitating Learners’ Digital Competence	FLD1	0.87	0.93	0.66	0.91
	FLD2	0.88
	FLD3	0.87
	FLD4	0.75
	FLD5	0.68
Self-Efficacy			0.89	0.87	0.58
	SE1	0.75
	SE2	0.76
	SE3	0.77
	SE4	0.86
	SE5	0.66
Facilitating Conditions			0.92	0.92	0.59
	FC1	0.73
	FC2	0.80
	FC3	0.79
	FC4	0.77
	FC5	0.77
	FC6	0.81
	FC7	0.79
	FC8	0.69

, all CRs were above 0.60. In addition, the internal consistency of all constructs as measured by Cronbach’s alpha coefficients was greater than the cutoff value of 0.70 [61]. Cronbach’s alpha internal consistency coefficients calculated for the digital competence scale were 0.95, 0.92, 0.80, 0.88, 0.95, 0.83, 0.91, and 0.93 for the subscales of values, ethics, and security, digital resources, teaching and learning, scientific research, continuing professional development, and facilitating learners’ digital competence, respectively, indicating adequate internal consistency. Cronbach’s alpha internal consistency coefficients calculated for self-efficacy and facilitating conditions were 0.89. and 0.92, respectively.

Content validity and construct validity were used for the validity examination. In terms of content validity, the rational analysis was conducted by a panel of judges consisting of seven professors and one director of information at a university in China. The questionnaire was sent to the judges, and they were asked to review and develop items in terms of relevance, appropriateness, comprehensiveness, and acceptability. All written and verbal comments were welcome. The results indicated that judges agreed with the questionnaire items. Following the advice of the judges, some of the items were modified to improve their clarity.

Convergent validity and discriminant validity were used for the construct validity examination. Convergent validity was checked by confirmatory factor analysis (CFA) and average variance extracted (AVE). The following criteria were used in this study to indicate goodness of fit: Chi-square to its degree of freedom (x²/df) 5 or lower, Root Mean Square Error of Approximation (RMSEA) 0.08 or lower, Comparative Fit Index (CFI) 0.90 and higher, Goodness of Fit Index (GFI) 0.80 and higher, and Tucker–Lewis Index (TLI) 0.90 and higher [34,62,63,64]. The CFA results for digital competence (χ²/df = 3.05; RMSEA = 0.07; CFI = 0.91; GFI = 0.82; TLI = 0.90), self-efficacy (χ²/df = 1.37; RMSEA = 0.03; CFI = 0.99; GFI = 0.99; TLI = 0.99), and facilitating conditions (χ²/df = 4.11; RMSEA = 0.07; CFI = 0.98; GFI = 0.97; TLI = 0.97) revealed a good model fit.

As shown in Table 2, the standardized estimates for the factor loading values of all constructs were higher than the recommended value of 0.30 [60], indicating acceptable convergent validity.

The results of the check for discriminant validity are presented in

Table 3. Discriminant validity.

	Digital Competence	Self-Efficacy	Facilitating Conditions
Digital Competence	(0.78)
Self-Efficacy	0.73 ***	(0.77)
Facilitating Conditions	0.65 ***	0.55 ***	(0.77)

Note. *** p < 0.001; the number in parentheses on diagonal is the square root of AVE.

. The square roots of AVE were greater than the correlation between each construct and all others, indicating good discriminant validity. In conclusion, all results demonstrated acceptable reliability and validity.

4.2. Chinese In-Service Higher Education Teachers’ Self-Perception of Digital Competence

The mean value of the digital competence of Chinese in-service higher education teachers was 4.16. As

Table 4. Statistical analysis results in seven areas.

Area	M	SD
Values	4.43	0.71
Ethics and Security	4.20	0.73
Digital Resources	4.17	0.73
Teaching and Learning	4.02	0.76
Scientific Research	3.87	0.87
Continuing Professional Development	4.24	0.79
Facilitating Learners’ Digital Competence	4.02	0.80

shows, the mean values of their digital competence in seven areas from high to low were values (4.43), continuing professional development (4.24), ethics and security (4.20), digital resources (4.17), facilitating learners’ digital competence (4.02), teaching and learning (4.02), and scientific research (3.87).

Table 5. One-way nonparametric ANOVA with the LSD post hoc test with regard to the level of degree variable.

	PhD Degree (M ± SD)	Master Degree (M ± SD)	Bachelor Degree (M ± SD)	F	p	LSD
Digital Competence	4.23 ± 0.68	4.17 ± 0.67	3.95 ± 0.72	4.36	0.013 *	1 > 2 > 3
Values	4.37 ± 0.77	4.51 ± 0.64	4.36 ± 0.74	2.49	0.084
Ethics and Security	4.13 ± 0.76	4.28 ± 0.69	4.10 ± 0.78	2.49	0.084
Digital Resources	4.14 ± 0.75	4.21 ± 0.70	4.11 ± 0.76	0.74	0.476
Teaching and Learning	3.94 ± 0.75	4.10 ± 0.76	3.96 ± 0.80	2.99	0.051
Scientific Research	4.26 ± 0.74	3.66 ± 0.82	3.46 ± 0.96	42.28	0.000 ***	1 > 2 > 3
Continuing Professional Development	4.44 ± 0.69	4.16 ± 0.80	3.95 ± 0.87	13.71	0.000 ***	1 > 2 > 3
Facilitating Learners’ Digital Competence	4.12 ± 0.77	3.99 ± 0.80	3.83 ± 0.84	3.56	0.029 *	1 > 2 > 3

Note. * p < 0.05, *** p < 0.001; 1 = teachers with a doctoral degree; 2 = teachers with a master’s degree; 3 = teachers with a bachelor’s degree.

presents the results of the comparison of mean values according to the variables of level of education. From the perspective of mean scores, higher education teachers with a doctoral degree had the highest self-perception of their digital competence (M = 4.23), followed by teachers with a master’s degree (M = 4.17) and teachers with a bachelor’s degree (M = 3.95).

The statistical results of the one-way nonparametric ANOVA show that there were significant differences in the areas of scientific research (p < 0.001), continuing professional development (p < 0.001), and facilitating learners’ digital competence (p < 0.05) among higher education teachers with different educational backgrounds. The post hoc analysis using the LSD test indicated that in all three areas, teachers with a doctoral degree rated themselves better than the other two groups. For instance, in the scientific research area, the mean score of teachers with a doctoral degree was 4.26, while teachers with a master’s degree and a bachelor’s degree were 3.66 and 3.46, respectively.

According to

Table 6. One-way nonparametric ANOVA with the LSD post hoc test with regard to the discipline variable.

	Humanities (M ± SD)	Social Science (M ± SD)	Science (M ± SD)	Interdisciplinary (M ± SD)	F	p	LSD
Digital Competence	3.95 ± 0.78	4.20 ± 0.65	4.21 ± 0.67	4.06 ± 0.73	3.02	0.030 *	3 > 2 > 4 > 1
Values	4.32 ± 0.86	4.51 ± 0.63	4.39 ± 0.74	4.47 ± 0.65	1.78	0.15
Ethics and Security	4.12 ± 0.88	4.24 ± 0.69	4.12 ± 0.70	4.19 ± 0.96	0.56	0.644
Digital Resources	3.98 ± 0.88	4.22 ± 0.70	4.21 ± 0.68	3.89 ± 0.85	3.11	0.026 *	2 > 3 > 1 > 4
Teaching and Learning	3.94 ± 0.84	4.10 ± 0.72	4.00 ± 0.73	3.58 ± 1.13	3.10	0.027 *	2 > 3 > 1 > 4
Scientific Research	3.53 ± 0.96	3.82 ± 0.80	4.08 ± 0.86	3.39 ± 0.83	10.62	0.000 ***	3 > 2 > 1 > 4
Continuing Professional Development	4.00 ± 0.93	4.22 ± 0.72	4.36 ± 0.79	4.14 ± 0.70	4.16	0.006 **	3 > 2 > 4 > 1
Facilitating Learners’ Digital Competence	3.82 ± 0.93	4.05 ± 0.72	4.08 ± 0.81	3.81 ± 0.86	2.48	0.061

Note. * p < 0.05, ** p < 0.01, *** p < 0.001; 1 = humanities teacher; 2 = social science teacher; 3 = science teacher; 4 = interdisciplinary teacher.

, science teachers had the highest self-perception of their digital competence (M = 4.21), followed by social science teachers (M = 4.20), interdisciplinary teachers (M = 4.06), and humanities teachers (M = 3.95). In terms of teachers’ perception of digital competence in seven areas, the results indicated that there were significant differences in digital resources (p < 0.05), teaching and learning (p < 0.05), scientific research (p < 0.001), and continuing professional development (p < 0.01).

Based on mean scores in the digital resources and teaching and learning areas, social science teachers’ self-perceptions of digital competence were the highest, followed by science teachers, humanities teachers, and interdisciplinary teachers. In the scientific research and continuing professional development area, science teachers had the highest mean score, followed by social sciences teachers. In general, science teachers who used digital technology, either educationally or not, rated their digital competence higher than other teachers.

In terms of the institutional category variable (see

Table 7. One-way nonparametric ANOVA with the LSD post hoc test with regard to the institutional category variable.

	Public Institutions Included in the “Double World-Class Project” (M ± SD)	Public Institutions That Were Not Included in the “Double World-Class Project” (M ± SD)	Private Higher Education Institutions (M ± SD)	F	p	LSD
Digital Competence	4.31 ± 0.63	4.10 ± 0.71	4.05 ± 0.68	6.83	0.001 **	1 > 2 > 3
Values	4.43 ± 0.73	4.45 ± 0.69	4.41 ± 0.73	0.11	0.900
Ethics and Security	4.22 ± 0.72	4.19 ± 0.74	4.18 ± 0.75	0.096	0.908
Digital Resources	4.25 ± 0.73	4.15 ± 0.76	4.09 ± 0.63	1.74	0.177
Teaching and Learning	4.05 ± 0.73	3.97 ± 0.84	4.09 ± 0.58	1.02	0.362
Scientific Research	4.23 ± 0.79	3.74 ± 0.89	3.52 ± 0.72	28.15	0.000 ***	1 > 2 > 3
Continuing Professional Development	4.53 ± 0.63	4.09 ± 0.85	4.10 ± 0.72	20.14	0.000 ***	1 > 3 > 2
Facilitating Learners’ Digital Competence	4.24 ± 0.71	3.91 ± 0.84	3.88 ± 0.74	11.40	0.000 ***	1 > 2 > 3

Note. ** p < 0.01, *** p < 0.001; 1 = teachers of public institutions included in the “double world-class project”; 2 = teachers of public institutions that were not included in the “double world-class project”; 3 = teachers of private higher education institutions.

), teachers at public institutions included in the “double world-class project” rated themselves highest in their digital competence (M = 4.31), followed by teachers at public institutions that were not included in the “double world-class project” (M = 4.10). Teachers at private higher education institutions had the lowest self-perception of their digital competence (M = 4.05). Significant differences could be seen in three areas, including scientific research (p < 0.001), continuing professional development (p < 0.001), and facilitating learners’ digital competence (p < 0.001), in which teachers of public institutions included in the “double world-class project” all had the highest mean score.

4.3. Testing the Structural Model and Hypotheses

The structural model demonstrated an unacceptable model fit initially with x²/df = 6.16, RMSEA = 0.09, CFI = 0.87, GFI = 0.82, and TLI = 0.87. Considering the sample size in this study, according to Hox and Bechger [65], it is advisable to see if the model can be improved in a meaningful way. Generally, one possible piece of advice is to apply modifications when there is a theoretical justification for them [65,66,67].

In this study, the modification indices suggested adding various covariances between error terms, and three of them were theoretically justifiable: the covariance between the residual errors for SE1 and SE2, SE3 and SE5, and FC1 and FC2. We decided to retain the modification for the following reasons: (1) these items referred to scales whose validity and reliability had been examined in previous studies [57,58,59]; (2) SE1, SE2, SE3, and SE5 were observed variables of the same factor, self-efficacy, measuring teachers’ beliefs in their ability to use digital technology. FC1 and FC2 were observed variables of the same factor facilitating conditions, measuring support by the organizations for teachers’ digital technology use. The covariances between these error terms are logical; (3) according to Wu’s opinion, a covariant relationship but not a path causation relationship between the error terms of observed variables could be added when applying modification indices [68]. Moreover, this modification was applied in previous study by Sang, Wang, Li, and Yang [17].

The resulting structural model demonstrated an acceptable model fit with χ²/df = 4.09, RMSEA = 0.07, GFI = 0.87, CFI = 0.92, and TLI = 0.91. As shown in Figure 2, there was a positive and significant relationship between teachers’ self-efficacy and digital competence (β = 0.54, p < 0.001), supporting the second research hypothesis (H2). The path between facilitating conditions and digital competence was also statistically significant, β = 0.35, p < 0.001. Therefore, the third research hypothesis (H3) was accepted. The direct path between facilitating conditions and self-efficacy was also statistically significant, β = 0.55, p < 0.001, suggesting that the greater the support for teachers’ use of digital technology, the more confident they will be in their digital competence.

The bootstrap resampling method was used to test the mediation effect of self-efficacy on the relationship between facilitating conditions and digital competence (see

Table 8. Structural Equation Modeling: Hypothesis Testing.

Hypotheses	Path	Standardized Path Coefficient	Conclusion
H2	Self-Efficacy→Digital Competence	0.54	Supported
H3	Facilitating Conditions→Digital Competence	0.35	Supported
H4	Facilitating Conditions→Self-Efficacy	0.55	Supported
H5	Facilitating Conditions→Self-Efficacy→Digital Competence	0.30	Supported

). Following the procedure proposed by Zhao, Lynch, and Chen [69], 5000 bootstrap resamples with 95% confidence intervals (CIs) were performed in Amos 26.0. The results showed that facilitating conditions had a significant indirect effect on digital competence, β = 0.30, p = 0.000, 95% CI [0.10, 0.20]. Overall, the results indicated that facilitating conditions had a direct effect on teachers’ digital competence and an indirect impact on digital competence by influencing self-efficacy.

5. Discussion

For the first research goal, the results of this study show that, in general, Chinese in-service higher education teachers’ self-perception of digital competence was positive. A similar favorable result was found in several studies that indicated that most higher education teachers in Spain and Portugal were at an intermediate level [10,47]. If we focus on the seven dimensions analyzed in this study, we find that the mean score of Chinese in-service higher education teachers’ self-perception of their digital competence in the four areas of values, continuing professional development, ethics and safety, and digital resources was above the overall mean score. However, their self-perception of digital competence in areas such as facilitating learners’ digital competence, teaching and learning, and scientific research was relatively low compared with the overall mean score. This indicates the positive effect on teachers’ digital competence brought by the experience of large-scale digital technology usage during COVID-19. Teachers’ good digital technology values and security awareness have been shaped, although their ability to integrate digital technologies into actual teaching and research practices still needs to be considered to facilitate students’ digital competence in the future.

For the first hypotheses, our finding indicates that there are differences in teachers’ digital competence with regard to different levels of education, disciplines, and institutional categories.

In terms of the level of education, teachers with a doctoral degree performed better than those with a master’s degree and a bachelor’s degree, especially in areas of scientific research, continuing professional development, and facilitating learners’ digital competence. In China, more professional learning and communication about knowledge and skills related to teaching and learning and scientific research are conducted during the graduate stage. Graduates may therefore be more familiar with these tools or technologies in their field, contributing to a higher self-perception of their digital competence after serving as teachers in higher education institutions. A similar result can be found in Santos, Pedro, and Mattar’s research showing that Portuguese teachers with master’s and doctoral degrees received higher scores on their digital competence than those without these degrees [10].

Based on discipline, a higher self-perception of the digital competence of science and social science teachers was observed. A study conducted by Cabero-Almenara, Guillén-Gámez, Ruiz-Palmero, and Palacios-Rodríguez suggested that male teaching staff in engineering-architecture and social-legal sciences had higher scores than teachers in other areas [47]. A possible explanation for this may be the type of knowledge they teach. As Rodriguez and Martinez said, although engineering teachers may not use technologies in a didactic way, they do use technology constantly [70].

Regarding the institutional category, public higher education institution teachers gave higher scores to their digital competence than private higher education institution teachers, with teachers of institutions included in the “double world-class project” performing best. A significant difference in the areas of scientific research, continuing professional development, and facilitating learners’ digital competence was observed concerning the institutional category variable. This result differs from the results of Santos, Pedro, and Mattar, who found that there was no statistically significant effect of the institutional funding sector (public or private) or administrative region on the level of professors’ digital competence in Portugal [10].

For the second research goal, our findings confirmed the second hypothesis: teachers’ self-efficacy had a positive and significant effect on their digital competence. Previous studies have also shown that teachers’ self-efficacy is positively correlated not only with their digital competence [40] but also with their belief in becoming an ICT teacher, the frequency of ICT use, and the use of ICT for teaching purposes [41,71,72,73]. Therefore, it is of great importance to improve higher education teachers’ self-efficacy, which is conducive not only to the development of their digital competence but also to their ability to provide young people with the education that is needed in a digital society [74].

The results indicated that there was a significant relationship between facilitating conditions and teachers’ digital competence, which supported the third research hypothesis. This finding is similar to many prior studies. Sánchez-Cruzado, Santiago Campión, and Sánchez-Compaña noted that in addition to teacher training, school organization, education policies, and the role of the publishing and technology industries influenced the improvement of digital competence [43]. Wigati and Fithriyah found that the availability of technology, limited facilities, and environmental influences were essential factors in encouraging teachers’ digital literacy [45].

Considering that the development of teachers’ digital competence requires significant assistance, it is necessary for the government and universities to immediately address the development of teachers’ digital capabilities and alleviate barriers that hinder the successful implementation of digital competence, such as the lack of technology and funding and the demands of teachers’ work [75].

Consistent with the third research hypothesis, facilitating conditions significantly predicted self-efficacy. According to Bandura, an enactive mastery experience is the most effective source for increasing self-efficacy [31]. The digital technology usage experience of higher education teachers largely comes from the training and projects provided by their organization, which indicates that the extensive experience teachers gain in these training and projects may contribute to their self-efficacy. Sarfo, Amankwah, and Konin found that teachers’ computer experience has a significant impact on their computer self-efficacy; that is, teachers with more experience perform better in terms of self-efficacy [76].

As hypothesized, self-efficacy mediated the relationship between facilitating conditions and teachers’ digital competence. Given the important role that self-efficacy plays, higher education teachers’ strong beliefs in the use of digital technology should be considered significant in promoting their digital competence. Therefore, when attempting to develop teachers’ digital competence, administrators should be aware of the positive role of top-down optimization of the digital technology use environment and acknowledge the importance of activities that can improve teachers’ self-efficacy. For example, when organizing the training of teachers’ digital competence, a workshop in which teachers can experience the technology itself individually or cooperatively rather than just watching videos about its operation could be better for reducing teachers’ prejudices and biased assumptions about the technology while enhancing their self-efficacy and the development of their digital competence [77].

Overall, self-efficacy has a direct effect on teachers’ digital competency and shows a greater total effect than facilitating conditions. In addition, facilitating conditions have a direct effect on teachers’ digital competence, and self-efficacy plays a mediating role in this relationship. These results indicate that, compared with facilitating conditions, self-efficacy is a more important driving force for the improvement of teachers’ digital competence. Shao, Li, Luo, and Benitez pointed out the significant positive effect of management support on employees’ digital performance improvement [78]. In fact, different measures have been taken by many colleges and universities to provide better support for teachers’ digital technology usage, such as infrastructure optimization and training or projects aimed at encouraging teachers’ ICT integration into teaching. However, these are external factors. Internal factors, such as teachers’ personalities and self-efficacy, are more important [38]. Thus, it is necessary for university administrators to optimize the environment and invest in psychological energy to reduce teachers’ technology anxiety, shape their attitudes toward technology, and enhance their confidence in using digital technology.

6. Limitations

First, the number of samples in this study was limited compared with the number of higher education teachers in China, and the questionnaire was submitted through an online platform, meaning that the participants may have had a high degree of acceptance or usage of digital technologies, which may have affected the results. Second, the methodology was limited. Throughout this study, we referred to higher education teachers’ self-perceptions rather than objective data about their level of digital competence, and the real state of teachers’ digital competence may not be reflected.

7. Practical Implications

Findings from this study have some practical implications for the development of teachers’ digital competence. First, this study highlights the lack of digital competence for Chinese higher education teachers in areas such as facilitating learners’ digital competence, teaching and learning, and scientific research, as well as the significant differences found in variables such as level of education, discipline, and institutional category. These results indicate the possible priority areas for Chinese higher education teachers’ digital competence training in the future and the necessity of considering the training needs of different groups of teachers.

Moreover, this study demonstrates the benefits of self-efficacy and facilitating conditions on digital competence and the mediator role that self-efficacy played in the relationship between facilitating conditions and digital competence, at least for Chinese higher education teachers. In this respect, for university administrators, teachers’ high self-efficacy of digital technology use in their work and their positive digital technology use environment are worthy of attention, especially the former one. For instance, facilities such as intelligent robots could be set up in public areas, or a 24 h online consultation platform could be built so that teachers can seek help when they encounter difficulties in using technology, enhancing their confidence in using digital technology. Alternatively, regular formal or informal meetings where teachers across departments can share their experiences and practices of technology use across the organization are encouraged. Managers can also demonstrate the importance of technology in achieving job success and inspire teachers to make better use of digital technologies.

8. Conclusions

Higher education teachers’ digital competence development is of great importance for the sustainability of digital education [2]. The findings of this study describe in-service higher education teachers’ self-perceptions of their digital competence in the context of China and differences in the level of education, discipline, and institutional category. Particular attention was given to the effects of self-efficacy and facilitating conditions and how they predict teachers’ digital competence. A combination of subjective investigation, objective investigation, and third-party investigation is suggested in future studies. For instance, a group of targeted interviewees could consist of higher education teachers with different genders, educational levels, and institutional categories. Moreover, the exploration of more factors that could affect teachers’ digital competence, such as performance expectations and social influence, could be interesting.