Multi-criteria evaluation of knowledge sharing level of university teachers based on improved AHP-CRITIC

Abstract

With the rapid development of knowledge economy, the importance of knowledge sharing in the field of higher education is becoming more and more prominent, and university teachers as an important subject, the evaluation of their knowledge sharing level can better understand the status quo and problems of knowledge sharing of university teachers and take timely measures to make greater contributions to the development of universities and social progress. This paper selects willingness of knowledge sharing, ability of knowledge sharing, atmosphere for knowledge sharing, content of knowledge sharing, and the effectiveness of knowledge sharing as indicators for improvement and integration of AHP, Critic, and fuzzy comprehensive evaluation. The weights obtained are coupled and assigned, and finally, the fuzzy comprehensive evaluation method is used to evaluate and rank the level of knowledge sharing among university teachers. Based on the weight of the five indicators, the article determines the impact of these indicators on the level of knowledge sharing among university teachers and provides corresponding suggestions, hoping to provide references for universities and relevant government departments.

Keywords

The level of knowledge sharing AHP method Critic method fuzzy comprehensive evaluation

1. Introduction

With the advent of the knowledge economy era, knowledge has become a key resource for promoting social development [1]. As an important base for knowledge innovation and talent cultivation, knowledge sharing among university teachers is of great significance for improving the overall teaching and research level of universities [2]. Secondly, there are phenomena such as knowledge monopoly and knowledge blockage in the current university teacher team, which not only hinder the academic growth of individual teachers but also restrict the overall development of universities.

Currently, the awareness of knowledge sharing among university teachers needs to be improved. Some teachers may be more inclined to keep their knowledge and experience to themselves, lacking the willingness to share [3]. Secondly, the channels for knowledge sharing are limited, mainly relying on academic exchanges, course training, teaching seminars, and other methods, which are restricted in time and space. In addition, the construction of knowledge sharing platforms is incomplete, lacking effective incentive mechanisms and knowledge management systems, resulting in low willingness among teachers to use them [4]. Finally, a good cultural atmosphere for knowledge sharing has not yet been formed in universities, lacking the motivation for mutual learning and communication. The knowledge sharing among university teachers requires policy support and incentives. However, currently, some university policy systems lack explicit support and rewards for knowledge sharing, resulting in a lack of motivation for teachers to participate in knowledge sharing.

Improving the level of knowledge sharing among university teachers can help enhance their teaching and educational skills. By sharing their respective teaching philosophies and methods, teachers can learn from and borrow ideas from each other, thereby improving their own teaching methods and enhancing teaching effectiveness [5]. At the same time, knowledge sharing also contributes to the formation of a good teaching atmosphere, promoting cooperation and mutual progress among teachers. Knowledge sharing can promote academic exchanges and cooperation [6]. Sharing academic research results and experiences among teachers can facilitate the in-depth development of academic research and enhance the overall research level of the academic community. In addition, knowledge sharing also facilitates interdisciplinary communication among teachers, promoting the intersection and integration of disciplines, and providing more possibilities for academic innovation. Through knowledge sharing, teachers can acquire more knowledge resources, continuously update their knowledge systems, and improve their personal teaching and research capabilities. At the same time, knowledge sharing also helps teachers discover their own shortcomings, enabling them to learn and improve with purpose. Through knowledge sharing, teachers can contribute more knowledge and technological achievements to society, promoting social progress and development. Additionally, knowledge sharing helps enhance society’s recognition and satisfaction with universities, strengthening their influence in society [7]. Therefore, it is crucial to evaluate the level of knowledge sharing among university teachers. By evaluating the level of knowledge sharing among university teachers, we can better understand the current situation and problems of knowledge sharing among university teachers, and take timely measures to make greater contributions to the development of universities and social progress.

Currently, the evaluation system for the level of knowledge sharing among university teachers is still not perfect. Therefore, this article improves and combines AHP, Critic, and fuzzy comprehensive evaluation methods. It couples and assigns weights obtained from AHP and Critic methods and finally evaluates and ranks the level of knowledge sharing among university teachers using the fuzzy comprehensive evaluation method. During the evaluation process, five indicators are introduced to assess the level of knowledge sharing among university teachers: willingness of knowledge sharing, ability of knowledge sharing, atmosphere for knowledge sharing, content of knowledge sharing, and the effectiveness of knowledge sharing.

The subsequent content structure of this study is as follows. Section 2 is the literature review. In this section, we briefly review the current research status of knowledge sharing and teachers’ knowledge sharing. Additionally, we present the research motivation and contributions of this study. Section 3 is the indicator system, which mainly introduces the evaluation indicators for the level of knowledge sharing among university teachers constructed in this article. Section 4 proposes the methodology of this study. Section 5 presents a research case to validate our proposed model method. Finally, Section 6 is the conclusion, which not only summarizes this study but also provides some relevant suggestions.

2. Literature review

2.1 Current research status of knowledge sharing

Current research on knowledge sharing mainly focuses on knowledge sharing behavior and willingness to share knowledge.

In the Knowledge Sharing Behaviour study, the knowledge sharing behaviors of students and firms were examined. Ngoc Hoi [8] found that the indirect effect of perceived Facebook pedagogical competence on cognitive engagement through knowledge-sharing behaviors was significant among students with moderate to high confidence in knowledge sharing, but not among students with low confidence. Gamlath and Wilson [9] drawing on a range of relevant literature, they categorized the various knowledge-sharing activities undertaken by students in the university environment along three dimensions: relevance to the curriculum, distance between students and level of formality. Rasheed et al. [10]. They found that students’ use of social media was associated with their creativity and engagement in postgraduate research training through knowledge-sharing behaviors. Ye et al. [11] extend research on the mediating role of the innovation environment and employee innovation behaviour. In addition, it deepens the understanding of the moderating role between knowledge sharing and innovative behaviour. Abou-Shouk et al. [12] found that positive personality traits significantly improved employees’ knowledge sharing behaviour and contributed to their innovative performance. Le and Nguyen [13] found that employees’ trust in their leaders positively mediated the relationship between ethical leadership and knowledge sharing behaviour. In particular, fair distribution significantly contributed to the effect of ethical leadership on both tacit and explicit knowledge sharing behaviors. Sung et al. [14] found that for employees with higher learning goals, upward social comparison had a significant positive indirect effect on proactive knowledge sharing through benign envy. In contrast, for employees with lower performance goal orientation, upward social comparison showed a significant negative indirect effect on reactive knowledge sharing through malignant jealousy. Xu et al. [15] found that leader attributional humour had positive direct and indirect effects on employee knowledge sharing through knowledge sharing, self-efficacy and team psychological safety, respectively.

Research on willingness to share knowledge has mainly focused on the study of factors influencing willingness to share knowledge. Wang et al. [16] constructed an evolutionary game model to investigate the dynamic process of knowledge sharing in online groups under different relationship strengths. The results show that relationship strength, value of shared knowledge, additive and synergistic benefits, and costs including speculative, direct and risk costs affect willingness to share knowledge in online groups. Zhao and Detlor [17] showed that costs have a negative impact on one’s willingness to share knowledge, while benefits have a positive impact on one’s willingness to share knowledge. Akram et al. [18] using validated factor analysis showed that the model fit well while structural equation modelling had a significant positive effect on employee innovative work behaviour and knowledge sharing.

In summary, current research on knowledge sharing mainly focuses on knowledge sharing behavior and influencing factors of knowledge sharing willingness, with no research on the level of knowledge sharing. Moreover, no scholars have established a particularly comprehensive evaluation system for the level of knowledge sharing. Therefore, this article combines the subject of knowledge representatives, namely university teachers, with the evaluation of the level of knowledge sharing, aiming to compensate for the deficiencies in existing research.

2.2 Current research status of teachers’ knowledge sharing

Current research in the area of teachers’ knowledge sharing has also focused on how to facilitate teachers’ knowledge sharing and the factors influencing it, Talebizadeh et al. [19] examined the relationship between learning-centred leadership and teachers’ professional learning in Iranian primary school principals, focusing on the mediating role of trust and knowledge sharing behaviors. role. Data were analyzed using validated factor analysis and structural equation modelling. The results proved that headmasters can enhance teacher learning by emphasizing teaching and learning in order to build trust among teachers and facilitate knowledge sharing. Al-Kurdi et al. [20] found that organizational leadership and trust were positively related to academics’ knowledge sharing behaviors. These findings suggest the need to consider organizational elements and their interactions in understanding and promoting knowledge sharing behaviors among academics in the context of higher education. Hove Langdal [21] found that knowledge sharing is a source of informal learning among teachers. This was achieved through collaboration in communities of practice. Experienced teachers (who had previously taught the programmes) were particularly valuable in sharing their insights and helping teachers prepare for teaching. This sharing of knowledge helps teachers in schools to improve their practice. Having a supportive social culture can encourage knowledge sharing among school teachers, but more time is needed to prioritize this collaboration among colleagues. Lin and Huang [22] found that the value contributed by team members was positively correlated with team trust and that each had a significant impact on knowledge sharing and team effectiveness.

Based on the analysis of the current research status of teacher knowledge sharing discussed above, it can be observed that the focus of research in this field is still on how to improve teacher knowledge sharing and its influencing factors. Few scholars have studied the evaluation of the level of teacher knowledge sharing. As important knowledge disseminators, university teachers need a comprehensive evaluation system to assess their level of knowledge sharing. Therefore, this article takes university teachers as the research subject to evaluate their level of knowledge sharing, hoping to provide valuable suggestions to universities and relevant government departments.

3. Indicator system

In this paper, the willingness of knowledge sharing, ability of knowledge sharing, atmosphere for knowledge sharing, content of knowledge sharing, and effectiveness of knowledge sharing are employed as indicators for evaluating the level of knowledge sharing among university teachers. Among them, the willingness to share knowledge reflects the internal motivation and attitude of university teachers towards knowledge exchange and cooperation. Only when university teachers have a willingness to share their knowledge will they actively participate in knowledge sharing activities, thereby promoting the flow and value addition of knowledge. This paper quantifies the willingness to share knowledge through the number of times university teachers participate in knowledge sharing activities. Hence, evaluating the willingness to share knowledge can, to some extent, assess the actual behavior of knowledge sharing among university teachers. The ability to share knowledge reflects how effectively university teachers can transfer their knowledge, experience, and skills to other teachers or students. This ability encompasses the capability to express, impart, and interpret complex concepts, ensuring that the information remains undistorted during transmission and can be accurately comprehended and applied by the receiver. This paper quantifies the ability to share knowledge through the number of papers published by university teachers and the number of national social science projects. The atmosphere for knowledge sharing reflects whether universities and society encourage and support knowledge exchange and cooperation among teachers. A positive atmosphere for knowledge sharing can stimulate teachers’ willingness to share knowledge, promoting frequent interaction and deep discussion among them. The atmosphere of knowledge sharing is quantified by the frequency of discussions between team members, the number of collaborative projects, and the activity of the knowledge sharing platform. These data can reflect the actual level and atmosphere of knowledge sharing. The content of knowledge sharing directly reflects the value and quality of the knowledge shared by university teachers. High-quality content can provide valuable information and insights to other teachers, promoting the deepening and expansion of academic research. However, low-quality or irrelevant content may not interest others, leading to poor effects of knowledge sharing. Quantify the content of knowledge sharing based on patents, theses, or other published results generated from shared knowledge content. The effectiveness of knowledge sharing directly reflects the actual impact of knowledge sharing activities on individuals, teams, and organizations. By objectively measuring the actual effects of knowledge sharing, we can determine the success of knowledge sharing activities. Quantify the knowledge sharing effect by the number of times a paper is cited by other scholars. Therefore, these five indicators are selected to evaluate the level of knowledge sharing among university teachers. The specific content is shown in Table 1.

Table 1
Indicator system table

Indicator	Content	References
The willingness of knowledge sharing	The willingness of knowledge sharing among university teachers refers to a psychological state where university teachers are willing to share the knowledge they possess with other teachers or organizations.	Le and Lei [24]
The ability of knowledge sharing	The ability of knowledge sharing among university teachers refers to the qualities and skills possessed by university teachers in the process of knowledge sharing, including knowledge organization, knowledge expression, knowledge transmission, knowledge absorption, and other aspects. These abilities directly affect the effectiveness and degree of teachers’ knowledge sharing.	Mahdi et al. [25]
The atmosphere for knowledge sharing	The atmosphere for knowledge sharing among university teachers refers to the environment and conditions within universities and society that promote knowledge sharing among teachers. A positive atmosphere for knowledge sharing can stimulate teachers’ willingness to participate in knowledge sharing, enhancing the effectiveness and degree of knowledge sharing.	Liu et al. [26]
The content of knowledge sharing	The content of knowledge sharing among university teachers refers to the types and scope of knowledge involved in the process of knowledge sharing among teachers. Teachers’ knowledge sharing not only includes the sharing of explicit knowledge but also the sharing of implicit knowledge.	Azeem et al. [27]
The effectiveness of knowledge sharing	The effectiveness of knowledge sharing among university teachers refers to the actual achievements and impacts obtained by university teachers in the process of knowledge sharing. The effectiveness of knowledge sharing is an important criterion for evaluating the success of knowledge sharing activities.	Wang and Hu [28]

4. Method

4.1 Analytic hierarchy process

Analytic Hierarchy Process (AHP) is a decision analysis method that combines qualitative and quantitative approaches, commonly used to address complex multi-objective problems. It decomposes the decision-making problem into different hierarchical structures according to the overall objective, various sub-objectives, evaluation criteria, and specific alternative solutions. By utilizing the method of solving the eigenvector of the judgment matrix, it determines the priority weights of each element in each level relative to a specific element in the previous level. Finally, through weighted summation, it hierarchically combines the final weights of each alternative solution relative to the overall objective. The alternative with the highest final weight is considered the optimal solution. The specific steps are as follows:

Step 1.
Firstly, experts will score the selected indicators, and based on the rules in Table 2, a judgment matrix will be formed.

$\displaystyle\left[\begin{array}[]{cccc}x_{11}&x_{12}&\ldots&x_{1m}\\ x_{21}&x_{22}&\ldots&x_{2m}\\ \vdots&\vdots&&\vdots\\ x_{n1}&x_{n2}&\ldots&x_{nm}\\ \end{array}\right]$

It should be satisfied here: $x_{ij}>0$ ; $x_{ij}=\frac{1}{x_{ji}}$ ; $x_{ii}=1$ .

Table 2
Analytic hierarchy process (AHP) Importance table

Scale Meaning of “ $i$ is more important than $j$ ”

1 Indicates that two factors have equal importance in comparison to each other.

3 Indicates that one factor is slightly more important than the other in comparison.

5 Indicates that one factor is significantly more important than the other in comparison.

7 Indicates that one factor is strongly more important than the other in comparison.

9 Indicates that one factor is extremely more important than the other in comparison.

2, 4, 6, 8 The median value between the above two adjacent judgments

Reciprocal “If the scale for comparison between A and B is 3, then the scale for comparison between B and A is 1/3.”

Step 2.
Calculate the maximum eigenvalue and its corresponding eigenvector.
Step 3.
Perform consistency check:

$\displaystyle\textit{CR}=\frac{\textit{CI}}{\textit{RI}}$ (1)

Here:

$\displaystyle\textit{CI}=\frac{\lambda_{\max}-n}{n-1}$ (2)

Here $\lambda_{\max}$ represents the maximum eigenvalue and RI represents the stochastic consistency index. This is a key parameter for judgement matrix consistency test, obtained by Saaty through simulation 1000 times. In the calculation process of hierarchical analysis method, when it is necessary to judge the consistency of the matrix, RI is an important reference index.The corresponding values for RI are shown in Table 3.

Table 3
RI

$n$ 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

RI 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46 1.49 1.52 1.54 1.56 1.58 1.59

If CR $<$ 0.1, the consistency of the judgment matrix can be considered acceptable; otherwise, corrections need to be made to the judgment matrix.
Step 4.
The eigenvector corresponding to the maximum eigenvalue that passes the test will be used as the weight vector for the indicators.

4.2 Improved Critic method

Scale	Meaning of “ $i$ is more important than $j$ ”
1	Indicates that two factors have equal importance in comparison to each other.
3	Indicates that one factor is slightly more important than the other in comparison.
5	Indicates that one factor is significantly more important than the other in comparison.
7	Indicates that one factor is strongly more important than the other in comparison.
9	Indicates that one factor is extremely more important than the other in comparison.
2, 4, 6, 8	The median value between the above two adjacent judgments
Reciprocal	“If the scale for comparison between A and B is 3, then the scale for comparison between B and A is 1/3.”

$n$	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15
RI	0	0	0.52	0.89	1.12	1.26	1.36	1.41	1.46	1.49	1.52	1.54	1.56	1.58	1.59

The Critic method is a comprehensive approach to objectively measure the weights of indicators based on the comparison intensity of evaluation criteria and the conflict between indicators. It considers both the degree of variation in indicators and their correlations, rather than assuming that a higher numerical value necessarily indicates greater importance. This method scientifically evaluates indicators using their objective attributes. In the original CRITIC method, conflictivity was measured by calculating the correlation coefficient between indicators. However, this method does not take into account the different meanings represented by the positive and negative signs of the correlation coefficients. A positive correlation coefficient indicates a positive correlation between indicators, while a negative correlation coefficient indicates a negative correlation between indicators. In order to reflect more accurately the conflicting nature of the indicators, we can regard the negative correlation coefficient as a strong conflict, i.e., the larger its absolute value, the stronger the conflict. Therefore, this paper proposes improvements to both the conflict calculation and the formula for calculating information carrying capacity. The specific steps are as follows:

Step 1.
Standardize the data using the formula as follows:

$\displaystyle x_{ij}^{\prime}=\left\{{\begin{array}[]{ll}\frac{x_{ij}-\min(x_{% j})}{\max(x_{j})-\min(x_{j})},&\textit{If the indicator is a positive indicator}\\ \frac{\max(x_{j})-x_{ij}}{\max(x_{j})-\min(x_{j})},&\textit{When the indicator is negative}\\ \end{array}}\right.$ (3)
Step 2.
Calculate the contrast intensity using the following formula:

$\displaystyle\sigma_{j}=\sqrt{\frac{\sum\limits_{i=1}^{m}{(x_{ij}^{\prime}-% \overline{x_{j}^{\prime}})^{2}}}{n-1}}$ (4)
Step 3.
Calculate the conflict using the following formula:

$\displaystyle A_{j}=\sum\limits_{i=1}^{n}{(1-|R_{ij}|)}$ (5)

Here:

$\displaystyle R_{ij}=\frac{\sum_{j,k=1}^{n}(x_{ij}^{\prime}-\overline{x_{j}^{% \prime}})(x_{ik}^{\prime}-\overline{x_{k}})}{\sqrt{\sum\limits_{j=1}^{n}{(x_{% ij}^{\prime}-\overline{x_{j}^{\prime}})^{2}\sum_{k=1}^{n}{(x_{ik}^{\prime}-% \overline{x_{k}^{\prime}})^{2}}}}}$ (6)

Here, $R_{ij}$ also represents the Person correlation coefficient.
Step 4.
Calculate the improved information carrying capacity using the following formula:

$\displaystyle C_{j}^{\prime}=(\sigma_{j}\times A_{j})^{\frac{1}{\partial}}$ (7)

Among them, $\partial=\frac{\max w_{j}}{\min w_{j}}$ , $w_{j}$ represents the weight of the indicator obtained from the unimproved Critic method. The calculation formula is as follows:

$\displaystyle w_{j}=\frac{C_{j}}{\sum\limits_{j=1}^{m}{C_{j}}}$ (8)

Here, $C_{j}=\sigma_{j}\times A_{j}$ , As the amount of information before improvement.
Step 5.
Calculate the improved weight of the indicators using the following formula:

$\displaystyle w_{j}^{\prime}=\frac{C_{j}^{\prime}}{\sum\limits_{j=1}^{m}{C_{j}% ^{\prime}}}$ (9)

4.3 Coupling weighting method

The basic theoretical core of the coupled assignment method lies in minimising the deviation between the resulting combined weights and the respective basic weights. This theoretical framework is constructed to ensure that the weights of different attributes can be considered in a comprehensive manner during the decision-making process, while minimising the differences between these weights and the original basic weights, so as to obtain a combined weight that both meets the practical situation and satisfies the decision-making requirements [23]. After obtaining the indicator weights through the AHP method and the Critic method, the coupling weighting method is applied to combine the weights from both methods. The steps are as follows:

Step 1.
Randomly combine the weights obtained from the AHP and improved Critic methods using the following method:

$\displaystyle W=\alpha_{1}W_{1}^{T}+\alpha_{2}W_{2}^{T}$

$W$ represents the combination vector of all possible weight data, $\alpha_{1}$ , $\alpha_{2}$ are linear combination coefficients, $W_{1}$ is the indicator weight obtained from the AHP method, and $W_{2}$ is the indicator weight obtained from the improved Critic method.
Step 2.
Based on the properties of matrix differentiation, the first-order derivative condition matrix for optimization is obtained as follows:

$\displaystyle\tilde{\varepsilon}=\min\left\|\alpha_{1}W_{1}^{T}+\alpha_{2}W_{2% }^{T}-W_{1}^{T}+W_{2}^{T}\right\|_{2}$ $\displaystyle\left[\begin{array}[]{cc}W_{1}W_{1}^{T}&W_{1}W_{2}^{T}\\ W_{2}W_{1}^{T}&W_{2}W_{2}^{T}\end{array}\right]\left[\begin{array}[]{c}\alpha_% {1}\\ \alpha_{2}\end{array}\right]=\left[\begin{array}[]{c}W_{1}W_{1}^{T}\\ W_{2}W_{2}^{T}\end{array}\right]$
Step 3.
Standardize and improve $\alpha_{1}$ , $\alpha_{2}$ , and then obtain the optimized weight coefficients $\alpha_{1}^{\ast}$ , $\alpha_{2}^{\ast}$ . The standardization formula is as follows:

$\displaystyle\alpha_{1}^{\ast}=\frac{\alpha_{1}}{\alpha_{1}+\alpha_{2}}$ (10) $\displaystyle\alpha_{2}^{\ast}=\frac{\alpha_{2}}{\alpha_{1}+\alpha_{2}}$ (11)
Step 4.
After obtaining the optimized weight coefficients, establish the optimized weight coupling model, and the calculation formula is as follows:

$\displaystyle W^{\ast}=\alpha_{1}^{\ast}W_{1}^{T}+\alpha_{2}^{\ast}W_{2}^{T}$ (12)

4.4 Fuzzy comprehensive evaluation method

After obtaining the weights of each indicator through coupling weighting, the fuzzy comprehensive evaluation method is used for evaluation and ranking. The specific steps are as follows:

Step 1.
Determine the factor set $U$
Step 2.
Determine the evaluation set $V$
Step 3.
Determine the weights of each factor. Let $A$ represent the weights of the factors, where $a_{i}\in A$ represents the weight of the $i$ factor, and $\sum_{i=1}^{n}a_{i}=1$ . Here, the weights obtained in Section 4.3 are used.
Step 4.
Determine the fuzzy comprehensive judgment matrix $R_{i}=\left[{r_{i1},r_{i2},r_{i3},\ldots,r_{im}}\right]$ , In the fuzzy comprehensive judgment matrix, $r_{i1}$ represents the degree of membership of $u_{i}$ to evaluation 1, $r_{im}$ represents the degree of membership of $u_{i}$ to evaluation m, and so on.

Obtain the fuzzy comprehensive judgment matrix by calculating several $R_{i}$ values:

$\displaystyle R=\left[{{\begin{array}[]{ccc}{r_{11}}&\ldots&{r_{1m}}\\ \vdots&\ddots&\vdots\\ {r_{n1}}&\cdots&{r_{nm}}\\ \end{array}}}\right]=\left({{\begin{array}[]{c}{R_{1}}\\ {R_{2}}\\ \vdots\\ {R_{n}}\\ \end{array}}}\right)$
Step 5.
Fuzzy comprehensive evaluation. Perform matrix composition operations to obtain the comprehensive evaluation results: $B=A*R$ .

The combined evaluation result is denoted as $B=[b_{1}$ , $b_{2}$ , $\ldots b_{n}$ ].

The meaning of $b_{i}$ is the degree of membership of the object to be evaluated to the evaluation $i$ .
Step 6.
The objects are ranked based on the combined evaluation results. The higher the value of $b_{i}$ , the higher the ranking.

5. Case analysis

Currently, in a teaching team of a well-known university, five teachers have attracted much attention due to their excellent teaching and research abilities. To further enhance the team’s knowledge sharing efficiency and innovation capabilities, and allow other teachers to learn from the experiences of these five teachers, the university has invited experts in the field of education to evaluate the knowledge sharing level of the five teachers based on five indicators: willingness of knowledge sharing, ability of knowledge sharing, atmosphere for knowledge sharing, content of knowledge sharing, and the effectiveness of knowledge sharing

5.1 Analytic hierarchy process

Invite experts in the field of education to evaluate the importance of the five indicators: willingness to share knowledge, ability to share knowledge, atmosphere for knowledge sharing, content of knowledge sharing, and effectiveness of knowledge sharing. Then, form a judgment matrix as follows:

$\displaystyle\left[{{\begin{array}[]{ccccc}1&{1/2}&2&3&3\\ 2&1&4&3&2\\ {1/2}&{1/4}&1&{1/2}&{1/3}\\ {1/3}&{1/3}&2&1&2\\ {1/3}&{1/2}&3&{1/2}&1\\ \end{array}}}\right]$

By calculating the matrix, the maximum eigenvalue of the judgment matrix can be obtained as $\lambda_{\max}=5.3447$ , and the weight vector (eigenvector) is $w=(0.2738,0.3636,0.0798,0.1478,0.1350)$ .

$\displaystyle\textit{CI}=\frac{5.3447-5}{5-1}=0.086175,\ \textit{CR}=\frac{% \textit{CI}}{\textit{RI}}=\frac{0.086175}{1.12}=0.0769<0.1,$

pass the consistency check. The weight obtained from the AHP method is $W_{1}=$ (0.2738, 0.3637, 0.0798, 0.1478, 0.1350).

5.2 Improved critic method

In this section, experts were asked to rate the five teachers on a 100-point scale based on five indicators: willingness of knowledge sharing, ability of knowledge sharing, atmosphere for knowledge sharing, content of knowledge sharing, and the effectiveness of knowledge sharing. The specific results are presented in Table 4.

Table 4
Scores of the five university teachers on the indicators

Teacher/ Indicator	The willingness of knowledge sharing	The ability of knowledge sharing	The atmosphere for knowledge sharing	The content of knowledge sharing	The effectiveness of knowledge sharing
Teacher 1	90	93	89	92	91
Teacher 2	94	90	91	90	89
Teacher 3	93	92	92	87	90
Teacher 4	90	91	88	96	92
Teacher 5	88	94	90	93	93

Standardize the data because all the indicators are positive indicators, so the following method is used:

$\displaystyle\frac{x_{ij}-\min(x_{j})}{\max(x_{j})-\min(x_{j})}$

The data after standardization is presented in Table 5.

Table 5

Table of standardized data

Teacher/ Indicator	The willingness of knowledge sharing	The ability of knowledge sharing	The atmosphere for knowledge sharing	The content of knowledge sharing	The effectiveness of knowledge sharing
Teacher1	0.333333333	0.75	0.25	0.555555556	0.50
Teacher2	1	0	0.75	0.333333333	0
Teacher3	0.833333333	0.50	1	0	0.25
Teacher4	0.333333333	0.25	0	1	0.75
Teacher5	0	1	0.50	0.666666667	1

Then calculate the Person correlation coefficients for each indicator, as shown in Table 6.

Table 6

Table of person correlation coefficients

Teacher/ Indicator	The willingness of knowledge sharing	The ability of knowledge sharing	The atmosphere for knowledge sharing	The content of knowledge sharing	The effectiveness of knowledge sharing
Teacher1	1	$-$ 0.775	0.645	$-$ 0.698	$-$ 0.968
Teacher2	$-$ 0.775	1	$-$ 0.1	0.094	0.7
Teacher3	0.645	$-$ 0.1	1	$-$ 0.941	$-$ 0.6
Teacher4	$-$ 0.698	0.094	$-$ 0.941	1	0.706
Teacher5	$-$ 0.968	0.7	$-$ 0.6	0.706	1

Calculate the contrast intensity $\sigma_{j}$ , and the specific values are presented in Table 7.

Table 7

Table of contrast intensity

The willingness of knowledge sharing	The ability of knowledge sharing	The atmosphere for knowledge sharing	The content of knowledge sharing	The effectiveness of knowledge sharing
0.40824829	0.395284708	0.395284708	0.373505251	0.395284708

Calculate the conflict intensity $A_{j}$ , and the specific values are presented in Table 8.

Table 8

Value of $A_{j}$

The willingness of knowledge sharing	The ability of knowledge sharing	The atmosphere for knowledge sharing	The content of knowledge sharing	The effectiveness of knowledge sharing
0.914	2.331	1.714	1.561	1.026

Calculate the improved weights $w_{j}$ , and the specific values are presented in Table 9.

Table 9

Table of original weights

The willingness of knowledge sharing	The ability of knowledge sharing	The atmosphere for knowledge sharing	The content of knowledge sharing	The effectiveness of knowledge sharing
0.12603195	0.311216327	0.228839462	0.196929012	0.136983248

Therefore, it can be calculated that $\partial=$ 2.469344688.

Calculate the improved information carrying capacity $C_{j}$ , as shown in Table 10.

Table 10

Value of $C_{j}$

The willingness of knowledge sharing	The ability of knowledge sharing	The atmosphere for knowledge sharing	The content of knowledge sharing	The effectiveness of knowledge sharing
0.670845095	0.967396237	0.854139348	0.80374146	0.693867689

Table 11

Value of $w_{j}$

The willingness of knowledge sharing	The ability of knowledge sharing	The atmosphere for knowledge sharing	The content of knowledge sharing	The effectiveness of knowledge sharing
0.1681	0.2425	0.2141	0.2014	0.1739

After the improvement calculation, the final weights $w_{j}$ are presented in Table 11.

$\displaystyle W_{2}=(0.1681,0.2425,0.2141,0.2014,0.1739)$

5.3 Coupling weighting method

Substitute the weight vector $W_{1}=(0.2738,0.3636,0.0798,0.1478,0.1350)$ obtained from the AHP method and the weight vector $W_{2}=(0.1681,0.2425,0.2141,0.2014,0.1739)$ obtained from the Critic method into the coupling weight model to obtain:

$\displaystyle\alpha_{1}^{\ast}=\frac{\left|{\alpha_{1}}\right|}{\left|{\alpha_% {1}+\alpha_{2}}\right|}=1.0219，\alpha_{2}^{\ast}=\frac{\left|{\alpha_{2}}% \right|}{\left|{\alpha_{1}+\alpha_{2}}\right|}=-0.0219$

Therefore, the final indicator weight obtained through coupled weighting is

$\displaystyle W^{\ast}=(0.2761,0.3663,0.0769,0.1466,0.1341).$

5.4 Fuzzy comprehensive evaluation method

After obtaining the final weight $W^{\ast}=(0.2761,0.3663,0.0769,0.1466,0.1341)$ through coupled weighting, a final fuzzy comprehensive evaluation is conducted on the knowledge sharing levels of the five university teachers.

Here, the factor set is represented by five indicators, which correspond to:

$\displaystyle U=\{u1,u2,u3,u4,u5\}$

The corresponding weights of the indicators are:

$\displaystyle W^{\ast}=(0.2761,0.3663,0.0769,0.1466,0.1341)$

All five indicators used in this paper are the higher the better, known as extremely large indicators. Based on the consensus among experts, a score below 85 is considered low, while a score above 95 is considered high. The trapezoidal function is used as the membership function, which is defined as follows:

$\displaystyle u_{A}=\left\{{\begin{array}[]{l}0,x<85\\ \frac{x-85}{95-85},85<x<95\\ 1,x>95\\ \end{array}}\right.$

Obtain the fuzzy comprehensive evaluation matrix based on the membership function:

$\displaystyle R=\left[{{\begin{array}[]{ccccc}{0.5}&{0.8}&{0.4}&{0.7}&{0.6}\\ {0.9}&{0.5}&{0.6}&{0.5}&{0.4}\\ {0.8}&{0.7}&{0.7}&{0.2}&{0.5}\\ {0.5}&{0.6}&{0.3}&1&{0.7}\\ {0.3}&{0.9}&{0.5}&{0.8}&{0.8}\\ \end{array}}}\right]$

Finally, conduct a fuzzy comprehensive evaluation:

$\displaystyle B=W\ast R=[0.6428,0.6665,0.4951,0.6457,0.5605]$

Therefore, the ranking of the knowledge sharing levels of the five university teachers is as follows: Teacher 2 $>$ Teacher 4 $>$ Teacher 1 $>$ Teacher 5 $>$ Teacher 3. This ranking reflects the comprehensive performance of the five teachers in the process of knowledge sharing. Among them, Teacher 2 exhibits the highest level of knowledge sharing, while Teacher 3 has a relatively lower level. Teacher 3 should seek advice from Teacher 2 to improve their own knowledge sharing level. Meanwhile, it can be seen that the weights of willingness to share knowledge, ability, atmosphere, content, and effect are 0.2761, 0.3663, 0.0769, 0.1466, and 0.1341, respectively. Willingness to share knowledge is the starting point of knowledge-sharing activities, and it represents the degree to which teachers are willing to share knowledge and experience. A strong willingness to share can push teachers to participate more actively in knowledge sharing activities, thus enhancing the overall level of knowledge sharing. Therefore, it is not difficult to understand that willingness occupies the highest weight in the evaluation. Knowledge sharing effects, on the other hand, can only be fed back after knowledge sharing behaviors have been implemented, and therefore have the lowest weighting.

6. Conclusion

From the final weights obtained through coupled weighting, it can be seen that the weight of university teachers’ knowledge sharing ability is the largest, having the greatest impact on their level of knowledge sharing. Followed by their willingness to share knowledge, the content of knowledge sharing, the effectiveness of knowledge sharing, and finally, the atmosphere for knowledge sharing. Therefore, improvements can be made to the current status of knowledge sharing among university teachers by addressing these five aspects based on their respective impact levels.

This paper provides valuable guidance for university administrators and government education departments, helping them better understand and optimize the mechanism of teacher knowledge sharing. By focusing on knowledge sharing ability, willingness, content, effectiveness, and atmosphere, universities can build an efficient and productive knowledge sharing environment, thereby promoting cooperation and innovation among teachers and enhancing the overall level of teaching and research.

Although this study contributes to the assessment of the level of knowledge sharing among university teachers, there are some limitations in the research. This article only selected first-level indicators without further subdividing the indicator content, making the evaluation relatively broad. In future studies, it can be considered to subdivide the five indicators of this article into different dimensions and then aggregate the weights of the subdivided indicators to form the weights of the first-level indicators. Further, it is that when the number of objects being evaluated becomes very large, the calculations in this paper are particularly large and difficult to deal with, and can be reduced with the help of programming software.

References

Singh

Gupta

Busso

Kamboj

. Top management knowledge value, knowledge sharing practices, open innovation and organizational performance. J Bus Res. 2021; 128: 788-98. doi: 10.1016/j.jbusres.2019.04.040.

Ahmed

Ahmad

Zakaria

. Social media for knowledge-sharing: A systematic literature review. Telematics Inform. 2019; 37: 72-112. doi: 10.1016/j.tele.2018.01.015.

Barnes

. Information management research and practice in the post-COVID-19 world. Int J Inf Manage. 2020; 55: 102175. doi: 10.1016/j.ijinfomgt.2020.102175.

Hofer

Nistor

Scheibenzuber

. Online teaching and learning in higher education: Lessons learned in crisis situations. Comput Hum Behav. 2021; 121: 106789. doi: 10.1016/j.chb.2021.106789.

Jackson

. Managing for competency with innovation change in higher education: Examining the pitfalls and pivots of digital transformation. Bus Horiz. 2019; 62(6): 761-72. doi: 10.1016/j.bushor.2019.08.002.

Lacka

Wong

Haddoud

. Can digital technologies improve students’ efficiency? Exploring the role of Virtual Learning Environment and Social Media use in Higher Education. Comput Educ. 2021; 163: 104099. doi: 10.1016/j.compedu.2020.104099.

Nisar

Prabhakar

Strakova

. Social media information benefits, knowledge management and smart organizations. J Bus Res. 2019; 94: 264-72. doi: 10.1016/j.jbusres.2018.05.005.

Ngoc Hoi

. Augmenting student engagement through the use of social media: The role of knowledge sharing behaviour and knowledge sharing self-efficacy. Interactive Learning Environ. 2023; 31(7): 4021-33. doi: 10.1080/10494820.2021.1948871.

Gamlath

Wilson

. Dimensions of student-to-student knowledge sharing in universities. Knowledge Manage Res Pract. 2022; 20(4): 542-56. doi: 10.1080/14778238.2020.1838961.

10.

Rasheed

Malik

Pitafi

Iqbal

Anser

Abbas

. Usage of social media, student engagement, and creativity: The role of knowledge sharing behavior and cyberbullying. Comput Educ. 2020; 159: 104002. doi: 10.1016/j.compedu.2020.104002.

11.

Liu

Tan

. Creative leadership, innovation climate and innovation behaviour: the moderating role of knowledge sharing in management. Eur J Innov Manage. 2022; 25(4): 1092-114. doi: 10.1108/EJIM-05-2020-0199.

12.

Abou-Shouk

Zoair

Aburumman

Abdel-Jalil

. The effect of personality traits and knowledge-sharing on employees’ innovative performance: A comparative study of Egypt and Jordan. Tourism Manage Perspect. 2022; 44: 101024. doi: 10.1016/j.tmp.2022.101024.

13.

Nguyen

DTN

. Stimulating knowledge-sharing behaviours through ethical leadership and employee trust in leadership: the moderating role of distributive justice. J Knowl Manage. 2023; 27(3): 820-41. doi: 10.1108/JKM-06-2021-0462.

14.

Sung

Choi

. Upward social comparison toward proactive and reactive knowledge sharing: The roles of envy and goal orientations. J Bus Res. 2024; 170: 114314. doi: 10.1016/j.jbusres.2023.114314.

15.

Gong

Sun

. Using leader affiliative humor to encourage employee knowledge sharing: The multilevel role of knowledge sharing self-efficacy and team psychological safety. J Innov Knowl. 2023; 8(3): 100408. doi: 10.1016/j.jik.2023.100408.

16.

Wang

Zhang

Qiu

Zhang

Liu

. What factors influence users’ willingness to share knowledge in online groups and how? J Knowl Econ. 2022; 1-18. doi: 10.1007/s13132-022-01082-y.

17.

Zhao

Detlor

. Towards a contingency model of knowledge sharing: interaction between social capital and social exchange theories. Knowl Manag Res Pract. 2023; 21(1): 197-209. doi: 10.1080/14778238.2020.1866444.

18.

Akram

Lei

Haider

Hussain

. The impact of organizational justice on employee innovative work behavior: Mediating role of knowledge sharing. J Innov Knowl. 2020; 5(2): 117-129. doi: 10.1016/j.jik.2019.10.001.

19.

Talebizadeh

Hosseingholizadeh

Bellibaş

MŞ

. Analyzing the relationship between principals’ learning-centered leadership and teacher professional learning: The mediation role of trust and knowledge sharing behavior. Stud Educ Eval. 2021; 68: 100970. doi: 10.1016/j.stueduc.2020.100970.

20.

Al-Kurdi

El-Haddadeh

Eldabi

. The role of organisational climate in managing knowledge sharing among academics in higher education. Int J Inf Manag. 2020; 50: 217-227. doi: 10.1016/j.ijinfomgt.2019.05.018.

21.

Hove Langdal

. Schoolteachers’ experiences of knowledge sharing when teaching new education programs. J Workplace Learn. 2023; 35(7): 585-598. doi: 10.1108/JWL-10-2022-0135.

22.

Lin

Huang

. Understanding the antecedents of knowledge sharing behaviour and its relationship to team effectiveness and individual learning. Australas J Educ Technol. 2020; 36(2): 89-104. doi: 10.14742/ajet.4549.

23.

Wang

Song

. Optimal investment selection of industrial and commercial rooftop distributed PV project based on combination weights and cloud-TODIM model from SMEs’ perspectives. J Clean Prod. 2019; 234: 534-548. doi: 10.1016/j.jclepro.2019.06.249.

24.

Lei

. Determinants of innovation capability: the roles of transformational leadership, knowledge sharing and perceived organizational support. J Knowl Manag. 2019; 23(3): 527-547. doi: 10.1108/JKM-09-2018-0568.

25.

Mahdi

Nassar

Almsafir

. Knowledge management processes and sustainable competitive advantage: An empirical examination in private universities. J Bus Res. 2019; 94: 320-334. doi: 10.1016/j.jbusres.2018.02.013.

26.

Liu

Wang

. Why knowledge sharing in scientific research teams is difficult to sustain: An interpretation from the interactive perspective of knowledge hiding behavior. Front Psychol. 2020; 11: 537833. doi: 10.3389/fpsyg.2020.537833.

27.

Azeem

Ahmed

Haider

Sajjad

. Expanding competitive advantage through organizational culture, knowledge sharing and organizational innovation. Technol Soc. 2021; 66: 101635. doi: 10.1016/j.techsoc.2021.101635.

28.

Wang

. Knowledge sharing in supply chain networks: Effects of collaborative innovation activities and capability on innovation performance. Technovation. 2020; 94: 102010. doi: 10.1016/j.technovation.2017.12.002.

Multi-criteria evaluation of knowledge sharing level of university teachers based on improved AHP-CRITIC

Abstract

Keywords

1. Introduction

2. Literature review

2.1 Current research status of knowledge sharing

2.2 Current research status of teachers’ knowledge sharing

3. Indicator system

Table 1 Indicator system table

4.1 Analytic hierarchy process

5.1 Analytic hierarchy process

5.2 Improved critic method

Table 4 Scores of the five university teachers on the indicators

5.4 Fuzzy comprehensive evaluation method

6. Conclusion

References

Table 1
Indicator system table

Table 4
Scores of the five university teachers on the indicators