RETRACTED: Modeling the mediating roles of self-directed learning and knowledge management processes between emotional intelligence and learning outcomes in higher education

Abstract

Objective: Drawing on the knowledge-based view and ability-based theories of emotional intelligence (EI), in this study, we investigate the effects of EI on learning outcomes related to academics and administrators in Chinese research universities and we also test the direct association between learning outcomes and creative performance. In addition, we examine the mediating role of self-directed learning (SDL) and knowledge management processes (KMPs) on the relationship between EI and learning outcomes. Methods: The sample, for this study, consisted of 547 academic and administrative personnel at Chinese higher educational institutions (HEIs), and the hypothesized associations were examined through partial least squares structural equation modeling. Results: Our results indicated that EI has no significant influence on learning outcomes. However, an indirect relationship between EI and learning outcomes is established through SDL and KMPs. Conclusions: This study strengthens the professional understanding of EI and supports that the personnel at HEIs should value SDL and KMPs, which in turn enhances their learning outcomes. Although EI has received increased importance in higher education institutions, there are few studies that have investigated the relationship of EI, SDL, KMP, and learning outcomes. This is one of the initial studies that has empirically examined the interface of EI and learning outcomes in HEIs and also provides timely insights into the understanding of the mediating role of SDL and KMP.

1. Introduction

Higher education produces theoretical and practical knowledge through teamwork that implies the importance of learning and knowledge management in the educational sector [1]. Furthermore, higher educational institutions (HEIs) advocate knowledge perseverance, critical reasoning, creativity, social and emotional skills, self-directed learning, and learning outcomes aligned with current social demands [2, 3]; therefore, they are meant to create, acquire, store, share, and use knowledge [4, 5]. In addition, EI is channeled in HEIs in pursuit of increased knowledge, enrichment of self-confidence, autonomy of personnel, and societal change; therefore, there is a need for ongoing research and developmental activities related to EI in Chinese HEIs [6]. In addition to the abovementioned points, the following reasons support our motivation for this study.

First, there are limited strategies for adopting EI in Chinese higher education [7]. In addition, Fall, Kelly [8] highlighted the need to explain the importance of EI in the educational sector. EI is an individual’s awareness of their emotions and their ability to control their emotions to strengthen social relationships [9]. Therefore, EI is directly associated with the educational process in terms of societal augmentation, communication, and alliance for a better tomorrow [2], corroborating the ability-based theory of EI [10] that emphasizes management of personal emotions in the face of challenges to embrace learning with appropriate navigation of social settings [11]. Furthermore, knowledge and learning are significantly influenced by emotions. A university environment, for instance, with an emotionally intelligent staff strengthens the institute’s overall excellence, and therefore attracts the best talent to the university [12]. Furthermore, Gelaidan, Al-Swidi [13] argued that EI is at the core of HEIs, addressing the issues of academics such as intense competition between universities to be relevant, and continuously adapting teaching and learning using rapidly advancing technologies, as well as administrative issues such as meeting the needs of the rapidly increasing number of students in HEIs, funding challenges, and intense competition to maintain institutional standings.

Secondly, Zhoc, Chung [3] investigated the relationship between EI and learning outcomes of HEI students, and suggested different operational tiers to authenticate the generalizability; therefore, in this study, we intend to explore this relationship and focus on academics and administration in Chinese HEIs. In response to the rapid pace of change in modern society, learning outcomes (social, cognitive, self-growth outcomes, and satisfaction with university experience) are deemed to be an important educational aim in many countries including China [3]. An institution’s learning outcomes, therefore, are considered to be the foundation to their individuals’ successes, including beyond university life [14, 15]. Learning outcomes, therefore may be addressed as “encompassing a wide range of attributes and abilities, from cognitive to affective, which are a measure of how their college experiences have supported their development as individuals, including the acquisition of specific knowledge and skills, as in a major; what do individuals know that they didn’t know before to fit for future endeavors” [16]. Emotions, nevertheless, have an important effect on the learning process. Learners differ in how they manage their emotions, which can either motivate them to learn or discourage the learning process; therefore, EI can certainly affect the learning outcomes [17]. The emotional contagion theory states that learners are heavily impacted by the characteristics and emotional expressions of mentors [18]. Therefore, mentors who practice EI can motivate learners to maximize their learning outcomes. Furthermore, the incremental theory of intelligence, i.e., intelligence can be changed and increased, promotes positive emotions that help staff to foster their learning outcomes, in contrast to the entity theory of intelligence, i.e., intelligence is fixed and stable, that promotes negativity and ultimately discourages learning [19].

Third, “self-directed learning” has been reported to be a facilitative factor between EI and learning outcomes in Chinese HEIs [3]. Knowles [20] defined self-directed learning as “a process in which individuals take the initiative, with or without the help of others, in diagnosing their learning needs, formulating learning goals, identifying human and material resources for learning, choosing and implementing appropriate learning strategies and evaluating learning outcomes”. Therefore, the self-determination theory, a combination of intrinsic motivation and internalization of extrinsic motivation, supports personal choice, optimal challenge, informational feedback, interpersonal involvement, and acknowledgment of feelings [21]. Self-directed learning as an antecedent of self-determination theory, therefore, has revolutionized higher education because of its emphasis on personal autonomy, responsibility, as well as growth and learning [22]. Zhoc, Chung [3] explained the effects of EI on self-directed learning, while Greene [23] investigated self-directed learning to assess the vital learning outcomes in HEIs. Goleman [24], stated that EI plays a more important role in the success of individuals than IQ, as personnel who favor learning through their own planned learning improved learning for others. Goleman [24], also claimed that self-confidence, flexibility, teamwork, perseverance for self-development, which are not measures of IQ, were also importance in the evaluation of learning outcomes.

Fourth, [25] stated that knowledge management (KM) is at an early stage in Chinese higher education as compared with sectors such as IT, business, and non-governmental organizations of developed countries like USA and Europe, because developed countries have acknowledged the importance of KM in HEIs, and hence have demonstrated unconditional support for KM [26]. Furthermore, Iran is an additional region that has realized the importance of KM and is using it in the service sector [27]. KMPs are well defined associations with knowledge creation, acquisition, storage, sharing, and utilization that enhance the overall learning outcomes [28] and may act as facilitative factors for learning outcomes in HEIs [6]. Therefore, with the importance of the knowledge-based view (KBV) theory, in the information age, organizations have devoted much attention to the area of knowledge management as an important strategic resource for survival and success [29], especially in case of innovative KM systems usage [30, 31]. Considering [32]’s model of EI, Karkoulian, Harake [33] found a positive link between EI and KMP (knowledge sharing) and, subsequently, Goh and Lim [34] endorsed [33]’s study by investigating EI and knowledge sharing, moreover, their studies [33, 34] were conducted in the commercial sector, while, in our study, we investigating HEIs that are basically knowledge intensive organizations. Moreover, the success of knowledge management depends on teachers being emotionally intelligent [35] and KMPs are essential to teachers. In an educational setting, a teacher seeks emotionally intelligent peers to develop KMPs and to be perceived as a teacher with a fair degree of EI [34]. Although EI and KMPs have not yet been examined directly, EI has been shown to have a direct positive relationship with knowledge sharing and knowledge transfer, however, a study by [34] suggested investigating KMPs in educational organizations. Ramachandran, Chong [36], investigated HEIs and established a positive link between KMPs and learning outcomes. Furthermore, Lin [37] and Leal-Rodríguez, Roldán [38] investigated the relationship between KMPs and learning outcomes in the commercial sector. Lin [37] investigated knowledge sharing, storage, and acquisition, while Leal-Rodríguez, Roldán [38] explored KMPs in terms of tacit–explicit and internal–external resources in business organizations and the healthcare sector, respectively. Our study, to the best of our knowledge, is the first investigate KMPs and learning outcomes in HEIs.

Fifth, Greene, Freed [39] highlighted the need for future studies on learning outcomes in HEIs and their effect on creative performance. Creative performance is a social process, involving interpersonal interactions between a person and relationship partners including their immediate environment [40]. Furthermore, creative self-efficacy and leadership/supervisor support serve as antecedents of creative performance [41, 42]; hence, both are taken as the predictor of creative performance on the recommendations of Thundiyil, Chiaburu [43] and Mathisen [44], respectively. Therefore, in this study, creative self-efficacy is important for research academics while leadership/supervisor support encourages administrative personnel to excel in their field. Additionally, HEIs also need to channelize creative performance in their immediate individuals i.e., students, academicians and administrative professionals [39, 42]. Enacting and maintaining creative performance is essential to HEIs, to hold a competitive advantage among institutions [5]. The componential theory of creativity, moreover, describes the psychological and social components necessary for an individual to produce creative work, emphasizing the key role of intrinsic motivation and the impact of the organizational context on this type of motivation [45], particularly, supported by compensations and benefits practices [46]. It is therefore, reiterated that reciprocal psychological and social assistance in HEIs may be shared as norm that in turn, will coax individuals to perform creatively irrespective of their potential financial benefits [42]. Furthermore, Shalley, Gilson [47], argued that dynamic work assignments necessitate more creative problem solving, therefore, personnel need self-directed adaptation and learning to maintain and improve their intellect.

Studies on Chinese HEIs are worthwhile, since research activities in this area are at the beginning stage [6]. Over the last five years, China has experienced increased development in higher education and enhanced research activities [48]. As a result of this expansion of higher education, Chinese HEIs are accessible to the general population, thus presenting serious challenges for Chinese government HEIs. Chinese HEI personnel are under constant stress due to enhanced workloads, attributed to teaching, research development with paper publications, and growth of professionalism [6]. Ongoing encouragement through EI, therefore, helps improve these challenges [17]. Higher education, certainly, is instrumental for the research, economic and societal development of emerging nations such as China [49]. Consequently, in this study, we aim at answering the following research questions (RQ):

• RQ1. Is there a direct impact of EI on learning outcomes (social, cognitive, self-growth outcomes, and satisfaction with university experience) in Chinese universities?

• RQ2. Is there a direct impact of learning outcomes on creative performance (creative self-efficacy and leadership/supervisor support) in Chinese universities?

• RQ3. Do self-directed learning (individuals’ autonomy, responsibility, and growth) and KMPs (knowledge creation, acquisition, storage, sharing, and utilization) mediate the relationship between EI and learning outcomes in Chinese universities?

2. Hypotheses development and conceptual framework

2.1. Theoretical background

This study is based on the ability-based theory of EI and the KM capability model. Next, we explain the theoretical background and application. EI represents the perception, appraisal, and channelization of one’s own and others’ emotions for the sake of learning and countering problems [50]. The ability-based theory of EI, therefore, is a blend of four vital emotional competences, developed by Mayer [10]. The first competency is the practice of social adaptation through accurately perceiving and comprehending the emotions of oneself and others. The second competency advocates goal attainment through controlling and adjusting the emotional situations of oneself and others. The third competency involves problem solving through proper utilization of self-emotions. The fourth competency requires self-regulation of emotions over time to sustain emotional and intellectual development. This approach to EI has been well accepted throughout the years in research and education [3, 11].

Furthermore, the KM capability model, by [51], comprises knowledge infrastructure capability and knowledge process capability. Knowledge infrastructure capability includes knowledge and emotional leadership that motivates individuals to be autonomous, responsible, and creative; whereas, knowledge process capability represents knowledge creation, acquisition, storage, sharing, and utilization. Gold’s model explains that knowledge infrastructure capability requires knowledge-oriented leadership to enable the KMPs, thus, inspiring autonomy, responsibility, growth, learning, and creativity of individuals at the organizational level. This model further delineates that knowledge infrastructure capability convinces individuals and organizations to operate efficiently which, in turn, initializes the effective functionality of KMPs, i.e., knowledge creation, acquisition, storage, sharing, and utilization. Knowledge infrastructure capability thus acts as an enabler for the knowledge process capability. This study, moreover, supports the understanding that knowledge infrastructure capability (i.e., KBV) assists the expansion of KMPs that results in enhanced learning outcomes for individuals in HEIs, and therefore eventually improves the creative performance of HEIs’ individuals. Furthermore, the Gold’s model is also frequently cited in the literature [5, 25, 52].

2.2. Emotional intelligence, self-directed learning, and learning outcomes

Self-directed learning entails three vital elements, i.e., autonomy, responsibility, and growth [3]. Boyatzis [53], argued that emotional commitment plays an important role to garner the said elements of self-directed learning. Furthermore, personnel in HEIs with self-set goals adopt certain required emotional techniques, otherwise, they soon become demotivated and even stop learning [54]. Moreover, in HEIs, self-directed learning habits are developed within a context of social and emotional interactions encompassing varying bounds, for example, in the context of safe and supportive relationships between subordinates and a boss, subordinates and peers, and subordinates with guardians [55]. Hence, emotion is the foundation of learning, including self-directed learning [56]. Emotion, however, is a double-edged sword, which can serve as a motivator to enhance learning, but can also prevent one from learning effectively [17]. Furthermore, EI, which involves the ability to manage emotions, can make a critical difference to the learning and performance of HEIs personnel [3]. More specifically, the relationship between EI and self-directed learning could be unraveled from McCombs and Whisler [57] analysis regarding the role of affective variables in self-directed learning. They revealed the significance of the following two elements in driving self-directed learning: (i) self-regulation and control of affect; and (ii) generation of positive affect and motivation, which are both core components of EI.

Educating self-directed practices in personnel across various fields involves more than a focus on cognitive strategies for acquiring new knowledge and skills [58]. There is accumulating research evidence on the positive relationships between self-directed learning and various learning outcomes. For example, Lounsbury, Levy [59] reported that self-directed learning was significantly correlated with a number of cognitive abilities of HEIs’ personnel, including oral, statistical, theoretical, and overall intellectual rationale. Indeed, the experience and opportunity to engage in self-directed learning activities were found to enhance confidence, intrinsic motivation to learn, critical thinking, and quality of understanding, as well as retention and recall of HEI personnel [60]. Development of critical thinking and motivation for academic cum administrative achievements in new learning management models lead to satisfaction with advanced learning results and have further continuation in building relations with customers and internal stakeholders [42, 61]. Learning outcomes, therefore are ascertained and matured with the help of emotional stability and self-directed learning in HEIs [3].

Studies have described self-directed learning as a mediator [3], lending support for the opted mediation. One recent study described self-directed learning as a mediator between EI and learning outcomes in higher education [3]. Lunyk-Child, Crooks [60] discussed expected differing behaviors of self-directed learner, either discouraging or encouraging, which, in turn, led the scholars to look for some facilitative factors such as EI [62]. Therefore, we propose the following hypotheses based on the provided arguments:

• Hypothesis 1. There is a significant direct influence of EI on learning outcomes.

• Hypothesis 2a. There is a significant positive influence of EI on self-directed learning.

• Hypothesis 2b. There is a significant positive influence of self-directed learning on learning outcomes.

• Hypothesis 2c. Self-directed learning mediates between EI and learning outcomes.

2.3. Emotional intelligence, knowledge management processes, and learning outcomes

Iqbal, Latif [5], focused on KM research at the personnel level of HEIs because an employee is considered to be responsible for the success or failure of the KMPs, since the individual employee, not the organization, creates and transfers knowledge. Emotions cannot be separated from the development and sharing of knowledge. Moreover, recently, the impact of emotions on individual’s attitudes towards knowledge sharing was examined [63] with positive results. Thus, it is reasonable to claim that emotions are interwoven with knowledge sharing. An emotionally intelligent personnel, therefore, self-regulates emotions and also manages those of others, which consequently expedite knowledge sharing with increased communications [33]. Essentially, one’s contribution towards improvement of job performance greatly depends upon the ability to utilize motivation, i.e., emotions for achieving goals [64]. To exemplify, awareness of emotions in others helps employees to realize each other’s needs, and thus they are more likely to engage in knowledge sharing in problem solving [65]. In addition to improving performance, individuals that exercise appropriate emotions tend to influence the behavior of others through the message they deliver, which may potentially reinforce knowledge sharing that involves a reciprocal relationship [66].

The contribution of KMPs to an individual or organization can be identified by analyzing their learning outcomes [67]. KMPs (sharing, storage, and acquisition) invite further investigation while identifying growth and learning prospective in commercial sector [37]. Studies, further have confirmed that KMPs (acquisition, creation, transfer and utilization) are vital for learning strategies and outcomes irrespective of their nature, i.e., tangible or intangible [68]. KMPs are examined from the perspective of a balanced scorecard in an organization that generates intangible outcomes of employees in terms of learning and growth [67].

Studies have identified KMPs as mediators [69]. Moreover, KMPs plays a vital role in effective coordination among knowledge employees to achieve learning outcomes [69]. Furthermore, Sahibzada, Jianfeng [6] asked the future researchers to probe whether knowledge management is there to facilitate employee outcomes in HEIs. Therefore, we can deduce the following hypotheses based on the provided arguments:

• Hypothesis 3a. There is a significant influence of EI on KMPs.

• Hypothesis 3b. There is a significant influence of KMPs on learning outcomes.

• Hypothesis 3c. KMPs mediate between EI and learning outcomes.

2.4. Learning outcomes and creative performance

Learning outcomes can also be divided into social outcomes (communication skills, leadership, and teamwork), cognitive outcomes (critical and analytical thinking and problem solving), self-growth outcomes (time management and critical self-reflection), and satisfaction with university experience [3]. HEIs are focused on creativity, critical thinking, problem solving, and decision making as vital learning outcomes in the 21st century [70]. Moreover, knowledge and problem solving efforts with a touch of situational factors enhance the creative performance of HEIs’ personnel [71]. Furthermore, studies on HEIs have described a positive relationship between the stated learning outcomes and creative performance of incumbents [72]. Therefore, we can deduce the following hypothesis based on the provided arguments:

• Hypothesis 4. There is a significant direct influence of learning outcomes on creative performance.

3. Research methodology

3.1. Research universities

The Ministry of Education of the People Republic of China provides a comprehensive set of educational guidelines for universities [73]. Irrespective of the Ministry of Education’s assertions, there are limited research activities and measures on learning outcomes in Chinese universities [6], therefore, an efficient application of EI is needed to address the contemporary challenges in HEIs [74]. Hence, the objective of this study is to investigate the abovementioned research questions (Section 1) and try to infer whether research activities and measures taken for learning outcomes at HEIs on behalf of academic and administrative personnel are satisfactory.

3.2. Population, sample, and data collection procedure

It was written consent that was ascertained during the process of data collection. The written consent further, is attached with the questionnaire at the bottom of the manuscript. Moreover, Northwestern Polytechnical University Research Ethics Review Committee’s approval attached with the manuscript, restrained the researchers to involve the individuals in research process who only are willing once after they are explained the research purpose and confidentiality of gathered research data. For this study, we selected public Chinese HEIs from Xi’an (297 i.e., 54.3%), Sichuan (137 i.e., 25.1%), and Beijing (113 i.e., 20.6%), i.e., eight universities from Xi’an and four each from Sichuan and Beijing, targeting the academic and administrative personnel, involving multidisciplinary educational tiers with a focus on research-oriented departments. The three selected provinces retain the major ratio of gross enrolment rate for higher education and have better facilities [75]. Additionally, we established that EI had been used by the sample of teachers and administrative personnel in HEIs [2, 76]. It is worthwhile to note that the combined sample of HEI academic and administrative personnel was undertaken on the suggestion of Zhoc, Chung [3], who recommended that studies should include diverse operational tiers of HEIs to ensure the generalizability of EI and learning outcomes. Furthermore, academicians and administrative individuals are investigated in Chinese HEIs on more than one occasion [6, 7]. A survey was used for data collection and distributed to a large population, improving its reliability and generalizability [5]. A total 1280 (100%) surveys, through convenience sampling technique, were addressed to the administration and faculties and sent by mail or hand delivered; 631 (49.3%) surveys were collected, 84 (6.6%) surveys were discarded and therefore 547 (42.7%) surveys were considered to be valid responses for statistical analysis. It is vital to mention that questionnaires’ distribution was ascertained through e-groups (123 i.e., 22.5% useful for analysis) and paper form (424 i.e., 77.5% useful for analysis). Further, the voluntary participation was hailed throughout the process. Gender statistics were taken into account as males (308 I.e., 56.3%) and females (239 i.e., 43.7%) accumulated the figure as 547. Education statistics, similarly were ascertained as PhDs accounted as 378 i.e., 69.1% and masters as 169 i.e., 30.9%. Likewise, statistics in relation with profession were determined as academicians accounted as 410 i.e., 74.9% and administrative personnel as 137 i.e., 25.1%. Further, the statistics related to designation were accounted as professors 89 i.e., 16.3%, associate professors 133 i.e., 24.3%, assistant professors 156 i.e., 28.5%, lecturers 32 i.e., 5.8% and administrative personnel 137 i.e., 25.1%. Moreover, statistics in relation with service tenure were ascertained in different time ranges as between 0–5 were accounted as 169 i.e., 30.9%, 6–10 were accounted as 128 i.e., 23.4%, 11–15 were accounted as 97 i.e., 17.7%, 16–20 accounted as 64 i.e., 11.7%, 21–25 were accounted as 57 i.e., 10.4% and 26–30 were accounted as 32 i.e., 5.8%.

The time span for data collection for the considered study was March to June 2019. Moreover, the sample size was enough for the application of the Structural Equation Model (SEM) to examine the complex path model [6].

3.3. Measures of the concepts

The survey comprised 85 measurement items developed based on the existing literature with modifications of terminology relevant in the context of HEIs [5, 49]. The survey items were constructed on a five-point Likert scale with responses from 1 (strongly disagree) to 5 (strongly agree).

3.4. Details for measurement selection

A validated instrument should be applied for the measurements in studies with a central variable in order to eliminate the effect of response error and maximize the validity of the data [77]. In this study, EI is a central variable, therefore, the 33-item Emotional Intelligence Scale (EIS) [78] was applied for the data exploration, with an internal consistency ranging from 0.85 to 0.93, as reported by Zhoc, Li [79].

For the follow-up survey, we used a 15-item Learning Outcomes Scale (LOS) that was designed to help HEI personnel to self-evaluate their achievement of the learning outcomes. The scale was devised with reference to the learning outcomes for the sampled universities, which could be categorized broadly into cognitive, social, and self-growth outcomes of HEIs personnel. The learning outcome’s construct was according to Zhoc, Chung [3].

Self-directed learning was measured using the Self-Directed Learning Scale (SDLS). The SDLS consists of 10 items and is a one-dimensional scale developed by Lounsbury and Gibson [80]; the framework for the scale is based directly on the theoretical concept by Brockett [81] that an individual engages in the learning process by taking responsibility in an autonomous, self-reliant manner with or without guidance from a supervisor or colleagues. Our SDLS was found to be an internally consistent measure which related positively to the satisfaction of personnel in HEIs and negatively to withdrawal and turnover intentions from HEIs [82].

Twenty items for KMPs (creation, acquisition, storage, sharing, and utilization) were taken from the existing literature. The KMPs items are listed in Table 1.

Table 1. Source of measurement instruments.

Variable	Dimensions	No. of Items	Source
Emotional Intelligence		33	[78]
Self-directed Learning		10	[3]
Knowledge Management	Creation	6	[101]
	Acquisition	3	[51, 102]
	Storage	4	[103]
	Sharing	4	[101]
	Utilization	3	[104]
Learning Outcomes	Cognitive Outcomes	5	[3]
	Social Outcomes	5	[3]
	Self-growth Outcomes	5	[3]
	Satisfaction with University Experience	1	[3]
Creative Performance		6	[83]

Six items obtained from Wang and Netemeyer [83] were used to test creative performance and the validity of this specific construct has recently been confirmed [84].

4. Data analysis

4.1. Data analysis techniques

Quantitative methods were followed for reporting the results from this cross-sectional study using SPSS 24 and Smart PLS 3.2.7 software; partial least square structural equation modeling (PLS-SEM) was utilized to assess the quantitative data. PLS-SEM is an emergent data analysis method that is, nowadays, commonly used in business and social sciences investigations for effective assessment of sample size and non-normal data and it is more feasible for investigating the current theories with multidimensional structural models [85]. The method involves a two-step analysis, i.e., assessment of the measurement model and structural model assessment. Assessment of the measurement model ensures the constructs have good indicator loading, convergent validity, composite reliability (CR), and discriminant validity for the second phase, i.e., structural model assessment. Structural model assessment identifies the path coefficients and analyzes their respective significance.

4.1.1. Assessment of the measurement model

The assessment of the measurement model ascertains the reliability and validity of opted constructs with their respective dimensions [5]. The assessed factor loading was analyzed as greater than or equal to the approved threshold of 0.60; hence, the incorporated 85 indicators were carried forward for further measurement model analysis. The results for factor loading are represented in Table 2. The AVE and CR of the applied constructs were also found to be greater than or equal to the approved values of 0.50 and 0.70, respectively; thus, convergent validity and reliability were confirmed. The established discriminant validity as per the suggested criterion by Fornell and Larcker [86] is shown in Table 3. Therefore, the extracted results from the confirmatory analysis authenticated the assessment of the measurement model for the assessment of the structural model.

Table 2. Item loadings, reliability and convergent validity.

		Λ	α	CR	AVE
Emotional Intelligence			0.979	0.980	0.599
EI1		0.750
EI2		0.741
EI3		0.855
EI4		0.721
EI5		0.756
EI6		0.740
EI7		0.796
EI8		0.678
EI9		0.684
EI10		0.821
EI11		0.745
EI12		0.757
EI13		0.738
EI14		0.769
EI15		0.836
EI16		0.838
EI17		0.793
EI18		0.735
EI19		0.825
EI20		0.834
EI21		0.758
EI22		0.824
EI23		0.829
EI24		0.726
EI25		0.815
EI26		0.846
EI27		0.772
EI28		0.822
EI29		0.718
EI30		0.714
EI31		0.737
EI32		0.769
EI33		0.748
Learning Outcomes
Cognitive Outcomes			0.937	0.952	0.799
CO1		0.854
CO2		0.887
CO3		0.838
CO4		0.887
CO5		0.855
Social Outcomes			0.927	0.945	0.774
SO1		0.838
SO2		0.857
SO3		0.822
SO4		O.856
SO5		0.866
Self-growth Outcomes			0.906	0.930	0.728
SGO1		0.728
SGO2		0.805
SGO3		0.891
SGO4		0.829
SGO5		0.837
Satisfaction with university experience			1.0	1.0	1.0
SUE1		0.830
Self-directed/Lifelong Learning			0.963	0.967	0.748
SDL1		0.865
SDL2		0.889
SDL3		0.862
SDL4		0.878
SDL5		0.857
SDL6		0.878
SDL7		0.852
SDL8		0.903
SDL9		0.838
SDL10		0.827
Knowledge Management Processes
Knowledge Creation			0.913	0.933	0.700
KC1		0.843
CT2		0.719
CT3		0.790
CT4		0.873
CT5		0.820
CT6		0.762
Knowledge Acquisition			0.848	0.908	0.767
AQ1		0.841
AQ2		0.731
AQ3		0.813
Knowledge Storage			0.884	0.920	0.742
ST1		0.778
ST2		0.794
ST3		0.864
ST4		0.849
Knowledge Sharing			0.864	0.908	0.712
SH1		0.738
SH2		0.868
SH3		0.730
SH4		0.793
Knowledge Utilization			0.819	0.892	0.735
UT1		0.779
UT2		0.839
UT3		0.676
Creative Performance			0.953	0.962	0.809
CP1		0.893
CP2		0.904
CP3		0.910
CP4		0.886
CP5		0.923
P6		0.881

Table 3. Discriminant validity (Fornell and Larcker criterion).

9	A	C	CO	CP	EI	SDL	SGO	SH	SO	ST	SUE	UT
A	0.876
C	0.849	0.836
CO	0.834	0.909	0.894
CP	0.860	0.921	0.913	0.899
EI	0.880	0.921	0.904	0.919	0.774
SDL	0.856	0.922	0.926	0.938	0.922	0.865
SGO	0.825	0.987	0.895	0.905	0.906	0.911	0.853
SH	0.820	0.842	0.867	0.885	0.872	0.882	0.830	0.844
SO	0.826	0.913	0.901	0.918	0.887	0.921	0.894	0.850	0.880
ST	0.832	0.902	0.904	0.912	0.908	0.943	0.897	0.863	0.895	0.862
SUE	0.889	0.797	0.785	0.790	0.814	0.775	0.775	0.764	0.767	0.780	1.000
UT	0.772	0.816	0.823	0.846	0.841	0.825	0.814	0.818	0.857	0.827	0.701	0.857

Note: The Data on the diagonal (in bold) is the square root of AVE of the construct while the other values are the correlations with other constructs.

4.1.2. Structural model assessment

After the required assessment of the measurement model, the assessment of the structural model was confirmed. Then, the hypotheses were analyzed following a number of sequential steps. First, the direct effect of EI on self-directed learning, KMPs, and learning outcomes were identified, and then analyzed. Secondly, the direct effects of learning outcomes were investigated in relation to creative performance. In addition, the bootstrap resampling method with 5000 resamples [87] was applied to access the significance of direct paths and for the standard error estimation. The results for the hypotheses predicting direct relationships are represented in Table 4. Finally, the effects of EI on learning outcomes through self-directed learning and KMPs were investigated, and the results are shown in Table 5.

Table 4. Results of structural model path coefficient (direct relationships).

Hypotheses	Relationship	B	SD	t-value	P Values	Decision
H1	EI -> LO	0.030	0.041	0.70	0.483	Rejected
H2a	EI -> SDL	0.918	0.017	55.25	0.000	Supported
H2b	SDL -> LO	0.210	0.057	3.80	0.000	Supported
H3a	EI -> KMP	0.950	0.010	90.89	0.000	Supported
H3b	KMP -> LO	0.748	0.063	11.88	0.000	Supported
H4	LO -> CP	0.944	0.012	81.84	0.000	Supported

Note: EI = Emotional Intelligence; LO = Learning Outcomes; SDL = Self-directed Learning; KMP’s = Knowledge Management Processes; CP = Creative Performance.

Table 5. Summary of mediation results.

	Indirect Path
Hypothesis	Path	β	Path	β	Mediation Effect β	t-value	Decision
H2c	EI → SDL	0.91	SDL→LO	0.21	0.19	3.84*	Supported
H3c	EI → KMP	0.95	KMP →LO	0.74	0.71	11.8*	Supported

Note: Bootstrapping (n = 500).

*P<0.001.

As shown in Table 5, there is no significant relationship between EI and learning outcomes (β = 0.03, p > 0.001); hence, Hypothesis 1 is rejected. However, the direct effects of EI on self-directed learning (β = 0.91, p < 0.001) and KMPs (β = 0.95, p < 0.001) were found to be significant and positive; therefore, Hypothesis 2a and Hypothesis 3a were supported. Similarly, the direct effects of self-directed learning (β = 0.21, p < 0.001) and KMPs (β = 0.74, p < 0.001) on learning outcomes were established significant and positive; hence, Hypothesis 2b and Hypothesis 3b were supported. Additionally, the direct effects of learning outcomes on creative performance (β = 0.94, p < 0.001) were found to be significant and positive; therefore, Hypothesis 4 was accepted.

4.1.3. Mediation analysis

Finally, we investigated Hypotheses 2c and 3c that considered self-directed learning and KMPs as mediators between EI and learning outcomes. The mediating variables were analyzed following the Preacher and Hayes [88] procedures that are rigorous and suitable to use with PLS-SEM and bootstrapping with 500 resamples [89]. The results of the mediation analysis maintained a significant indirect effect of EI on learning outcomes through self-directed learning and KMPs, substantiating that the effect of EI on learning outcomes is partially due to self-directed learning and KMPs. The results of the mediation analysis are presented in Table 5.

5. Discussion

The main objective of this study was to investigate the role of EI related to learning outcomes (i.e., social, cognitive, self-growth outcomes, and satisfaction with university experience) and the effect of learning outcomes on creative performance, with respect to academic and administrative personnel in Chinese HEIs. Furthermore, this empirical study developed and tested a framework that included two mediators, i.e., self-directed learning and KMPs.

The findings based on hypothesized relationships should be highlighted for a number of reasons. First, this study supports the role of EI for facilitating learning outcomes (i.e., social, cognitive, self-growth outcomes, and satisfaction with university experience) in Chinese HEIs. The results of the study did not show a significant relationship between EI and learning outcomes, contradicting the findings of Zhoc, Chung [3]; however, they were in agreement with other previous studies [90, 91]. This contradictory note is effectively explained by Goleman [24] who argued that if one cannot self-regulate their emotions or emotional situations and does not have empathy and effective relationships, then all efforts to learn are negated, no matter how smart one is. Similarly, Wolfe [92] explained that educators should be emotionally intelligent otherwise they can interfere with learning outcomes. Moreover, Kollontai [93] argued that learning outcomes in HEIs are negatively affected with unmanaged emotions which may result in dysfunctional human behaviors that can impact on social relationships. EI can only be developed in supporting relationships [94], otherwise there are negative effects on learning outcomes [95, 96]. Moreover, negative emotional expressions impede learning outcomes [97], therefore, one must challenge their own practices to be a positive role model and emotional coach for improving learning outcomes of others [98].

Secondly, our findings provide significant empirical understanding of the indirect relationship of EI on learning outcomes by means of mediators, i.e., self-directed learning and KMPs (knowledge creation, acquisition, storage, sharing, and utilization); our findings show that EI significantly and positively affects self-directed learning that, consequently, enhances learning outcomes. These findings are in agreement with the most recent studies by Zhoc, Chung [3] and Greene [23] therefore confirm that the ability-based theory of EI and self-determination theory are positive contributors in the realm of higher education. Similarly, our results show that EI has a significantly positive and indirect effect on learning outcomes through KMPs, supporting the KBV theory. KMPs emerged as true facilitators between EI and learning outcomes [33].

Thirdly, our results support that learning outcomes (social, cognitive, self-growth outcomes, and satisfaction with university experience) are significantly instrumental to creative performance (creative self-efficacy and leadership support) of academic and administrative personnel [72]. Tan and Md. Noor [99], highlighted the belief that sustained management can enhance the individual and institutional learning outcomes. Hence, professionals are encouraged to continuously share communications that support personnel looking up to their HEI and their actual affective and cognitive bearing regarding their colleagues, peers, and jobs [6]. Moreover, continuous interactive communications help to motivate employees and therefore enhance their skills and efforts for accomplishing the overall learning outcomes [100].

6. Conclusions and practical implications

Our results reinforce the theoretical understanding of KM capability model and advocate exactly how teachers and administration of HEIs can value EI, self-directed learning, and KMPs, which, in turn, enhances their learning outcomes, urging them to strive for creative performance. Furthermore, HEI personnel with self-scheduled goals are motivated to learn and to be creative with the help of KMPs, therefore, strengthening the utilization of KBV theory in the context of HEIs.

Our findings may influence HEIs to change their policies and guidelines for teachers and administrative personnel in public education. These findings, furthermore, demand HEIs to align their activities with the EI of personnel, self-directed learning, KMPs, and learning outcomes to remodel their spontaneous reactions in impenetrable circumstances for furtherance of their creative performance. Moreover, these findings may force policymakers at HEIs to consider policies to endure challenges calmly with well-trained groups of professionals. Similarly, teachers and administrative personnel in HEIs, as the vital components of institutions, should be offered opportunities to excel using contemporary approaches. However, there seems to be few training opportunities and trained EI personnel, especially among teachers and administrative personnel in HEIs [74]; hence, extensive communications and training on EI for HEI personnel would ultimately strengthen the commitment to better learning outcomes [3]. Therefore, these steps could assist teachers and administrative personnel at HEIs to manage challenging circumstances with determination, EI, and better learning, inducing them to perform better.

7. Future research directions

In future studies, a larger sample size with a random sampling of public and private HEIs could improve the generalizability of our results. Similarly, a multigroup conduction across public and private HEIs might get exciting to equate EI usefulness and may explain certain theoretical and practical implications. Furthermore, we recommend reproducing the present study in other regions and countries even reproduction as a cross-cultural study, especially in/with developing countries like Pakistan, to validate the findings established in this study. Researchers, moreover, may consider other variables such as internal marketing and even learning orientation as mediators between learning outcomes and creative performance in HEIs. Future research could use internal marketing as an enabler of EI, EI with sustainable competitive advantage, and EI with transformational and ethical leadership aiming at learning outcomes in HEIs.

Supporting information

S1 Dataset

(SAV)

S1 Questionnaire

(DOCX)

S1 File

(PDF)

S2 File

(PDF)

Data Availability

The data underlying the results presented in the study are uploaded in supporting information as zip file.

Funding Statement

Funding: The project was funded under the program of the Minister of Science and Higher Education titled “Regional Initiative of Excellence” in 2019–2022, project number 018/RID/2018/19, the amount of funding PLN 10 788 423,16.