Next Article in Journal
Family Farming and Social and Solidarity Economy Enterprises in the Amazon: Opportunities for Sustainable Development
Previous Article in Journal
Sustaining Formal and Informal English Language Learning through Social Networking Sites (SNS): A Systematic Review (2018–2022)
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Rural E-Commerce Entrepreneurship Education in Higher Education Institutions: Model Construction via Empirical Analysis

1
Department of Finance and Economics, Software Engineering Institute of Guangzhou, Guangzhou 510990, China
2
School of Management, Guilin University of Aerospace Technology, Guilin 541000, China
3
School of Business, Sun Yat-sen University, Guangzhou 510275, China
4
School of Tourism, Guangzhou University, Guangzhou 510006, China
*
Authors to whom correspondence should be addressed.
Sustainability 2022, 14(17), 10854; https://doi.org/10.3390/su141710854
Submission received: 14 July 2022 / Revised: 21 August 2022 / Accepted: 26 August 2022 / Published: 31 August 2022
(This article belongs to the Section Sustainable Education and Approaches)

Abstract

:
Rural e-commerce entrepreneurship education (EE) in Higher Education Institutions (HEIs) can effectively enhance the development of the rural e-commerce industry and improve the motivation of students to start or be employed in rural e-commerce, but how to conduct effective evaluation is an issue that remains to be clarified. The research objectives of this paper are as follows: to establish a “student-centered” evaluation model for EE in HEIs, to integrate rural e-commerce professional education with EE, and to provide practical guidance for the evaluated HEIs. This paper constructs an evaluation model of rural e-commerce EE in HEIs. The research method combines Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation Method. The questionnaire method was used to obtain 384 valid data for the empirical analysis of the education of the Software Engineering Institute of Guangzhou. The study’s results found that the final evaluation result of the school’s rural e-commerce EE grade was good. The indicators at the level of educational support and feedback effectiveness scored relatively high, but those at the level of learning input and educational process scored low. Based on the findings, recommendations were made in terms of developing more open feedback channels, providing a full range of services, and social flexibility of the training program.

1. Introduction

Chinese rural online retail sales will reach 2.05 trillion yuan in 2021, an increase of 11.3% year-over-year [1]. As an expanding style of economic activity, rural e-commerce is also an efficient subject needing practitioners with exceptional practical skills [2,3]. In recent years, in the context of “mass entrepreneurship and innovation”, the state has prioritized fostering Entrepreneurship Education (EE) in rural e-commerce for students. It has enacted a number of significant legislations to promote this initiative. The contradiction between the difficulty of student employment and the dearth of talent and skills among rural e-commerce teams must be resolved as soon as possible. Higher Instruction Institutions (HEIs) typically provide students with e-commerce education in rural areas. However, the practical abilities of many undergraduates majoring in e-commerce fall far short of the complex abilities that businesses require. Currently, a large number of students are dissatisfied with the EE services provided by their alma mater, and there is no common approach to evaluate the educational outcomes of HEIs. In EE courses for college e-commerce majors, theory is prioritized above practice. Some professional textbooks and instructional materials are severely lacking in depth [4]. Students majoring in e-commerce and related courses have limited options to enhance their professional abilities, inventiveness, and entrepreneurialism [5]. According to the China Undergraduate Employment Report, 56% of 2017 undergraduates say their alma mater lacks entrepreneurial practice opportunities. In contrast, 45% say there is a shortage of EE courses [6].
EE programs in HEIs have undergone tremendous expansion globally since the first entrepreneurship course was offered at Harvard Business School in 1947 [7,8]. In recent decades, scholars at home and abroad have explored and researched EE in higher education from multiple perspectives [9,10]. Levie [11] and Nabi et al. [12] consider EE as a series of courses on the topic of entrepreneurship, new business management or starting a new business. Moreover, they emphasized that EE focuses on new business activities rather than existing ones. Rural e-commerce, as an emerging industrial activity, can open up new markets for agricultural products and provide new directions for employment and entrepreneurship for university graduates. In this way, combining EE in HEIs with the emerging rural e-commerce industry is only logical.
Academics generally agree that EE in HEIs can have a significant positive effect, whether on students’ entrepreneurial attitudes and intentions [13,14], graduates’ adaptability to employment and entrepreneurship [7,15], business start-up and development [16,17,18], or the development of regional economies [19,20].
While EE is flourishing in HEIs, there are essential questions that have yet to be answered or clarified. EE programs for the rural e-commerce sector can undoubtedly positively impact students, but how can students’ entrepreneurial learning outcomes be judged? What indicators and research methods should be used? Furthermore, what is the applicability of the evaluation model proposed in the paper?
Based on these questions, the purpose of this study is as follows:
  • Develop a ‘student-centered’ model for evaluating EE and services in HEIs.
  • Provide practical guidance for evaluated HEIs.
This paper develops an evaluation index system for rural e-commerce EE based on George Kuh’s learning input theory. The input theory consists of learning input, educational support, educational process, and feedback effectiveness as the primary indicators. In selecting the evaluation method, considering the “fuzzy” nature of the objectives and the practical experience of scholars, a combination of the Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation Method was used.
The authors chose Software Engineering Institute of Guangzhou, where they work, to conduct an empirical study to verify the applicability of the indicator system. The empirical analysis concludes that the HEIs’ rural e-commerce EE is evaluated as good and suggests actionable guidelines. The paper’s contribution aims to explore the whole process of the EE evaluation model, enrich the education evaluation index system for student subjects, and make practical suggestions for the schools in the empirical analysis.
There are two innovations in this study. On the one hand, it expands the attempt to assess EE for new business activities. As a dynamic and far-reaching new business activity, rural e-commerce has attracted substantial attention and progressive EE implementation in many HEIs. However, there is no widely used model for assessing rural e-commerce EE in HEIs for reference. On the other hand, learning input theory has an extended theoretical and practical application. It is a new attempt to reflect on EE efforts in HEIs by using students’ learning experiences and judgments as an essential basis for model building and empirical research.
The remainder of the paper is divided into five sections. The literature review is discussed in Section 2. The theoretical model is built in Section 3, and the research hypotheses are presented. The research methodology and empirical findings are presented in Section 4. Section 5 examines our research’s theoretical and practical ramifications and offers some suggestions. Finally, Section 6 summarizes the study’s main findings and addresses the study’s shortcomings.

2. Literature Review

The most prevalent research findings are those of industrialized nations, such as the United States, the United Kingdom, and Japan, which conducted EE research earlier. In contrast, emerging nations such as India and Nigeria have steadily prioritized research on EE in higher education institutions (HEIs) to improve the entrepreneurial environment.

2.1. Rural E-Commerce and Entrepreneurship Education

Kshetri was one of the first scholars to examine rural e-commerce in the early 1990s, followed by Ryuhei, the father of Japanese marketing. He also pioneered the study of rural e-commerce in Asia [21]. Rural e-commerce, they concluded, is a type of networking that connects numerous resources and, in the end, benefits rural commerce. Rural e-commerce, according to Li [22], is a result of the deep integration of agriculture and e-commerce, and its purpose is to bring agriculture and the market closer together. In reality, combining rural e-commerce with new technologies such as big data and cloud computing has evolved into a digital business model for the agricultural industry, with a continually changing and updating service model [22]. Many BRICS countries, including China, India, Russia, and Iran, attach particular importance to rural e-commerce’s role in poverty eradication [23,24].
According to UNESCO, EE includes a variety of experiences and orientations that provide students with competence and perspective [25]. There is a consensus among academics that EE is an excellent method for fostering entrepreneurial attitudes and behaviors [26,27]. However, experts such as as Braun and Diensberg [28] and Hytti and Kuopusjarv [29] have argued that prior EE has not placed enough focus on establishing specialized entrepreneurial competencies. In recent years, some academics have conducted theoretical research on the confluence of rural e-commerce and entrepreneurship. For instance, Zhu [30] investigated the demand for inventive and entrepreneurial talent in the rural e-commerce sector. According to scholars such as Jiang [31] and Ye et al. [32], the effect of incorporating EE into professional e-commerce education can be realized by establishing and executing a curriculum framework for e-commerce students.

2.2. Evaluation of Entrepreneurship Education in Higher Education Institutions

With the increasing significance of entrepreneurship as a driver of economic growth. EE has been encouraged and integrated into school curricula in many countries [7,15,33] to compensate for the curriculum’s deficiencies in addressing employment issues. Boldureanu et al. [13] and Ekpoh and Edet [34] found a favorable link between EE and students’ career intentions in higher education institutions. According to Enu [35], Entrepreneurship programs in HEIs should be adaptable enough to overcome the perceived flaws in the current educational system. This places new demands on the innovativeness of schools’ EE programs in addressing students’ present and future needs and issues. Although the government and higher education institutions have developed numerous entrepreneurship programs and curricula to assist entrepreneurial activities, little is known about the efficacy of entrepreneurship program implementation [13].
The most influential HEIs evaluation system for EE is the Seven Elements of EE Program Evaluation, proposed by Richard Luecke [36], which uses factors such as courses offered, papers and publications published, impact on society, achievements of graduating alumni, innovation in the program itself, creation of new businesses by graduating alumni, and external academic connections. However, it was observed that the assessment of EE is often dominated by ex-post assessment designs such as the time-on-task theory [37], the quality of effort theory [38], the student engagement theory [39], the social and academic integration theory [40], the change assessment model [41] and the seven principles of effective teaching and learning at the undergraduate level [42], among six other classical theories. These HEIs are often assessed with a lack of acceptance of the EE process [43,44], which is in line with the observations of scholars such as Fauyolle [45] and Kailer [46].
Based on previous theoretical research, George Kuh developed a theory for assessing the effectiveness of the educational process [47]. George Kuh defines the theory of learnability as “a measure of the amount of time and experience students devote to effective educational activities and how they perceive the level of support provided by the school for their learning, which is essentially the result of the interaction between individual student behavior and it is essentially the result of the interaction between individual student behavior and the environment [47]”. Moreover, its theoretical model is illustrated in Figure 1.
Nonetheless, as researchers such as Garavan and Barra [43] point out, there is a lack of study on the effects of these programs in the field of EE today. In assessment practice, outputs of entrepreneurial results, such as the conversion rate of entrepreneurship outcomes, student awards, and other external indicators, are frequently used as evaluation criteria at the government, school, and societal levels. The assessment, however, does not take into account the enhancement of students’ consciousness, behavior, and abilities as a result of receiving EE.

3. Model Construction

3.1. Constructing Objectives

In the past, identifying indicators of outcome output type to reflect the “student-centered” evaluation concept was challenging and could not correctly reflect the actual condition of EE. On the one hand, the effectiveness of EE may be hampered by a time lag effect, i.e., the time between getting EE and establishing a firm is long [48]. It is too early to assess the success of HEIs that solely provide rural e-commerce EE regarding entrepreneurship behaviors and outcomes. On the other hand, students interested in receiving rural e-commerce entrepreneurship services focus on this paper’s education and services. After all, students who compete in entrepreneurship competitions and win awards are a small minority that cannot fully reflect the high quality of this rural e-commerce EE.
This paper’s evaluation model aims to create a “student-centered” education evaluated entrepreneurship index model. This model would examine and track the training objectives and effects of students receiving rural e-commerce EE from universities so that HEIs can improve their education and service programs over time.

3.2. Construction Principles

The following principles of the evaluation model were established based on the general principles of objectivity, comprehensiveness, and a combination of qualitative and quantitative analysis in education evaluation, as well as taking into account the motivation of student subjects in the educational process.

3.2.1. Systematic and Comprehensive

The selection of indicators and the construction of models are not isolated. However, they should have a holistic view, considering all dimensions and organically linking them to cover indicators from all perspectives of the student’s education.

3.2.2. Developmental and Dynamic

The evaluation constructed in this chapter is conducted in rural e-commerce EE. These belong to the development of dynamic process evaluation, so the selection of indicators should also follow the developmental and dynamic nature so that the evaluation can reflect the actual situation of students in the learning process.

3.2.3. Hierarchy and Scientificity

Students’ evaluation is closely related to hardware and software construction, theoretical and practical curriculum, teaching faculty, etc. In constructing model indexes, attention should be paid to the hierarchy of index selection to avoid the loss of scientificity due to the repetition of first- and second-level indexes.

3.3. Evaluation Index Construction

Based on the student’s perspective, we combine the implementation of EE in HEIs while following the purpose and principles of evaluation index model construction. Based on learning input theory [47], this paper refers to the relevant index settings of the China College Student Survey (CCSS) [49], as well as the literature on education evaluation at home and abroad. Under the advice and guidance of the project expert group, we developed education evaluation indexes. The four dimensions of learning input, educational support, educational process, and feedback effectiveness comprised 16 evaluation indicators.

3.3.1. Learning Input

Referring to Professor George Kuh’s principles of learning engagement theory [47], students were examined in terms of learning motivation, learning habits, and time commitment. Learning motivation is the intrinsic support to support students’ acceptance of EE. In contrast, learning habits and time commitment reveal students’ motivation and initiative to accept rural e-commerce EE.

3.3.2. Educational Support

Educational support is essential for rural e-commerce EE for students. Therefore, the educational support of the department mainly measures the software and hardware facilities, basic service facilities, entrepreneurship atmosphere, and policy support. The above factors are independent and intrinsically related, forming the evaluation index of the education guidance environment.

3.3.3. Educational Process

The previous education centered on teachers and teaching materials is not adapted to the characteristics of rural e-commerce EE and the development needs of students. However, the indispensable role of education faculty in cultivating students with innovation consciousness and entrepreneurial skills cannot be denied. The educational process evaluation consists of teachers, teacher–student interaction, course teaching, practical teaching, and assessment methods.

3.3.4. Feedback Effectiveness

As the object receiving education, students’ feedback can directly show the effect of rural e-commerce EE. However, unlike the traditional output indicators of entrepreneurship papers and results, this paper evaluates four aspects: teaching tracking, feedback demand channels, entrepreneurship knowledge, and entrepreneurial employment skills.

4. Research Methodology and Empirical Analysis

4.1. Research Methodology and Principle

This paper evaluates the rural e-commerce EE of students of Software Engineering Institute of Guangzhou, using a mix of Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation Method, with the implementation phases shown in Figure 2.
The composite evaluation method is chosen for three reasons:
  • It is not enough to rely on qualitative analysis when evaluating the process of students’ awareness, behavior, and competence enhancement in EE. Scholars such as Mimović P. and Krstić [50] and Zareinejad M. et al. [51] have also encountered such problems when evaluating in HEIs. When judging, some criteria are qualitative, and some criteria are quantitative. The AHP has been shown to be effective in combining qualitative and quantitative factors to make appropriate judgments.
  • The goal of the construction of the evaluation model is to evaluate the improvement of students’ awareness, behavior, and ability in the process of receiving innovative education. It can be seen that the goal itself has the characteristics of fuzziness, which is challenging to be described by specific mathematical tools. For example, when students are asked to evaluate the teaching ability of teachers, the feedback may be “good” or “very good”, with the line between the two being blurred. For this fuzzy phenomenon, fuzzy evaluation can be carried out using the theory and methods of fuzzy mathematics. Biswas [52] proposed two applications of fuzzy sets to student evaluation. Further, Chen and Lee [53] innovated the application of fuzzy evaluation.
  • The composite research approach is not the first of its kind by the authors; scholars such as Chen et al. [54], Chen [55], and Hu [56] have used this composite research approach to evaluate educational performance in practice and have achieved better feedback. However, we should also note that the use of this research method may have the following limitations: on the one hand, the system of indicators used in the AHP method needs to be supported by an expert system, and if the indicators given are not reasonable, the results obtained will not be accurate. On the other hand, when there are more elements, the consistency test may not pass.
The paper uses Analytic Hierarchy Process (AHP) to determine the weights of the evaluation indicators before constructing the index-set affiliation matrix of the Fuzzy Comprehensive Evaluation Method. Such a method can better solve the problems of factors that cannot be dealt with quantitatively in education evaluation and the unscientific formulation of evaluation index weights to produce quantitative evaluation results and improve the accuracy of evaluation.

4.2. Empirical Analysis

4.2.1. Establishing the Evaluation Factor Set

The ‘U’ evaluation factor is set up in an index evaluation model. The model shows that the total target layer is the evaluation of rural e-commerce EE for HEIs students. We then use u1, u2, u3, u4 to represent the four dimensions of learning input, education support, educational process, and feedback effectiveness. These dimensions are then included in the criterion layer, respectively. Whereby U = {u1, u2, u3, u4}. Using uij to represent the indicator layer corresponding to each criterion layer, for example, u11, u12, u13 are used to represent the three secondary indicators of learning motivation, learning habits, and engagement time under the primary indicator of learning engagement. Similarly, the hierarchical structure of the index model for evaluating the quality of rural e-commerce EE of students in HEIs in Figure 3 can be obtained.

4.2.2. Determining the Weights of Each Index

We employ AHP in this study to solve for the weights of 16 secondary indicators of u11, u12, u13, u21, u22, u23, u24, u31, u32, u33, u34, u35, u41, u42, u43, u44 at respective criterion levels, as well as the four fundamental indicators of u1, u2, u3, u4.
  • Construction of judgment matrix
According to the expert group’s comments, a two-by-two comparison of the evaluation factors was conducted, using the 1–9 scale method proposed by Professor Saaty as a reference [57]. The judgment matrix of the indicators are shown in Table 1, Table 2, Table 3, Table 4 and Table 5.
2.
Calculation of eigenvectors and eigenvalues
We calculate the above judgment matrix eigenvectors Wi and use W0, W1, W2, W3, W4 to denote the eigenvectors of judgment matrices A, B1, B2, B3, B4, respectively. After calculation, the results are as follows:
W0 = (0.832, 0.392, 1.150, 1.625) T
W1 = (0.491, 0.892, 1.617) T
W2 = (0.771, 0.484, 1.667, 1.078) T
W3 = (1.339, 0.805, 0.638, 1.792, 0.426) T
W4 = (0.698, 0.496, 1.389, 1.417) T
After finding the eigenvectors of each matrix, its maximum eigenvalue roots λmax can be found accordingly. Using λ0, λ1, λ2, λ3, λ4 to denote the maximum eigenvalue roots of the judgment matrices A, B1, B2, B3, B4, respectively, the following is obtained.
λ0 = 4.122, λ1 = 3.009, λ2 = 4.071, λ3 = 5.191, λ4 = 4.103
3.
Hierarchical single ranking and consistency tests
Since the judgment matrix was created artificially, a matrix consistency test is required to assess the matrix’s reliability. As indicated in Equation (1) [57], the ratio of the difference between the maximum eigenvalue root λmax and the order m of the judgment matrix to n − 1 is introduced as a measure of the judgment matrix’s divergence from consistency.
CI = (λmaxn)/(n − 1)
The smaller the CI value, the higher the degree of consistency of the matrix. When CI = 0, the judgment matrix is perfectly consistent. To measure whether the judgment matrices of different orders are satisfactorily consistent, Equation (2), which is the ratio CR of CI and the average random consistency index RI of the same order, is introduced to determine the random consistency ratio of the matrix [57].
CR = CI/RI
The RI values for orders 1–10 are shown in Table 6 [57].
When CR < 0.1, the judgment matrix is considered to have satisfactory consistency; otherwise, the judgment matrix needs to be readjusted [57]. The above judgment matrix’s index and random consistency ratio were obtained according to the formula shown in Table 7 below, and the listed judgment matrices passed the consistency test.
4.
Hierarchical total ranking and consistency test
The calculation of the hierarchical total ranking weights is shown in Equation (3) [57].
j = 1 n i = 1 m a i b j i = 1
The formula is the weight of the criterion level and the scheme level, and the hierarchical total ranking remains the normalized regular vector. Finally, there is a consistency test for the total ranking, as shown in Equations (4)–(6) [57].
CR T = CI T RI T
CI T = i = 1 m a i C I i
RI T = i = 1 m a i R I i
When CRT < 0.1 the analysis results can be used for decision-making, otherwise, readjustment is required [57].
After calculation, the weights of each indicator can be derived in the criterion layer and the indicator layer. For the judgment matrix A, the weights of u1, u2, u3, u4 are 0.2081, 0.0981, 0.2875, 0.4063, respectively, representing the weight assignments of the indicators in the criterion layer. For the judgment matrix B1, the weights corresponding to u11, u12, u13 are 0.1683, 0.2973, 0.5390, respectively. For the judgment matrix B2, the weights corresponding to u21, u22, u23, u24 are 0.1928, 0.1209, 0.4168, 0.2695. For the judgment matrix B3, the weights of u31, u32, u33, u34, u35 are 0.2678, 0.1610, 0.1277, 0.3583, 0.0852, respectively. For the judgment matrix B4, the weights of u41, u42, u43, u44 are 0.1745, 0.1240, 0.3471, and 0.3544, respectively, representing the weight assignments of the index layer. After obtaining the weights of each indicator, the total hierarchical ranking weights can be calculated according to Equation (3), and the total hierarchical ranking is a normalized regular vector.
j = 1 n i = 1 m a i b j i = 0.2081 × 0.1683 + 0.2081 × 0.0981 + 0.2081 × 0.4063 + 0.0981 × 0.1928 + 0.0981 × 0.1209 + 0.0981 × 0.4168 + 0.0981 × 0.2695 + 0.2875 × 0.2678 + 0.2875 × 0.1610 + 0.2875 × 0.1277 + 0.2875 × 0.3583 + 0.2875 × 0.0852 + 0.4063 × 0.1745 + 0.4063 × 0.1240 + 0.4063 × 0.3471 + 0.4063 × 0.3544 = 1
According to Equation (4), the total ranking has a calculated value of the consistency test is 0.0091. Its test result is much less than 0.1, which has a satisfactory consistency, indicating that this paper is reliable in dividing the weight assignments of each tier within the evaluation model of rural e-commerce EE in HEIs.
CR T = 0.2081 × 0.005 + 0.0981 × 0.024 + 0.2875 × 0.048 + 0.4063 × 0.034 0.2081 × 0.520 + 0.0981 × 0.890 + 0.2875 × 1.120 + 0.4063 × 0.890 = 0.0091
5.
Index weights summarization
This paper collates the weight assignments of the above indicators and obtains the total weights of each indicator. These weights were collapsed to obtain a model for evaluating rural e-commerce EE in HEIs, as shown in Table 8 below. The larger the weight assignment, the greater the relative importance of the indicator in evaluating the quality of rural e-commerce EE in HEIs.
From the assignment of indicator weights in the criterion layer, the most crucial evaluation is feedback effectiveness, followed by the educational process, learning input, and educational support. Under the feedback effectiveness criterion layer, the entrepreneurial employment skills significantly impact education evaluation. On the one hand, the educational process criterion layer on the practical teaching indicators significantly impacts education evaluation. While, on the other hand, the learning input criterion layer based on the time commitment indicators significantly impacts education evaluation. Subsequently, the educational support criterion layer resulted in the entrepreneurship atmosphere indicators having a more significant impact on evaluating education.
From the total ranking results of the indicator layer, the four indicators of entrepreneurial skills, entrepreneurship knowledge, investment time, and practical teaching are more than 0.10, which are more critical in evaluating education than other indicators of the indicator layer.

4.2.3. Determine the Evaluation Object Rubric Set

Rubric set V is established, and the following four rubrics and scores were determined for each evaluation index in the evaluation model of rural e-commerce EE in HEIs: excellent, good, pass and failure, which were expressed by V1, V2, V3, V4, the rubric set was recorded V= {V1, V2, V3, V4}, and the specific evaluation criteria of each index were shown in Table 9. In order to improve the accuracy of the evaluation, this paper describes the specific evaluation criteria for each evaluation index of “excellent, good, pass, and failure” in the design education model.

4.2.4. Fuzzy Comprehensive Evaluation

In the range of each factor subset Uk (k = 1, 2, …, s), the fuzzy factor vector is determined according to the size of each factor Ak = (ak1, ak2, …, akn), and the fuzzy operation is performed with the single-factor evaluation matrix Rk, wherein the single-factor evaluation matrix Rk is composed of rkij (i = 1, 2, …, n; j = 1, 2, …, m), we can get:
A k R k = B k = ( b k 1 , b k 2 , , b km ) ( k = 1 , 2 , , s )
The weight vectors of each indicator under the learning input criterion layer, educational support criterion layer, educational process criterion layer, and feedback effectiveness criterion layer are denoted by A, A1, A2, A3, A4, respectively, based on the weights of each indicator determined using AHP above.
A = (0.2081, 0.0981, 0.2875, 0.4063)
A1 = (0.1683, 0.2972, 0.5390)
A2 = (0.1928, 0.1209, 0.4168, 0.2695)
A3 = (0.2678, 0.1610, 0.1277, 0.3583, 0.0852)
A4 = (0.1745, 0.1240, 0.3471, 0.3544)
After the evaluation model has been constructed, the next part of this section describes how the empirical analysis was conducted to test the model’s applicability better. To better obtain the relevant data, it was chosen to be carried out in Software Engineering Institute of Guangzhou, where the author works. This paper used a questionnaire to ask students of Software Engineering Institute of Guangzhou to rate the rural e-commerce EE provided by the school. A total of 400 questionnaires were distributed to students, from freshmen to seniors, in the Department of Finance and Economics, and 384 valid data were obtained after excluding questionnaires that were not fully scored and those with inconsistent answers. The evaluation questionnaire was based on Table 9. Evaluation criteria of rural e-commerce EE in HEIs: students were asked to rate each indicator as “excellent, good, pass, fail”. Based on the aggregation of the collected evaluation results, the affiliation degree rkij of each factor can be evaluated, and the single-factor evaluation matrix Rk of the set of evaluation indicators can be established. Software Engineering Institute of Guangzhou students’ judgments on learning input factors is shown in Table 10.
Calculating the affiliation of the learning input factors and creating the learning input factor evaluation matrix R1 yields.
R 1 = [ 0.4896 0.2786 0.2266 0.0052 0.4818 0.3568 0.1042 0.0573 0.2786 0.5182 0.2031 0 ]
According to Equation (7), the single-factor evaluation matrix Rk is fuzzy-operated to obtain Bk. The learning input factor is used as an example, whereby the questionnaire data determine the learning input factor evaluation matrix R1. The single-level evaluation result B1 of the learning input factor can be obtained by fuzzy calculation.
B 1 = A 1 R 1 = ( 0.1637 , 0.2973 , 0.5390 ) [ 0.4896 0.2786 0.2266 0.0052 0.4818 0.3568 0.1042 0.0573 0.2786 0.5182 0.2031 0 ] =   ( 0.3736 ,   0.4310 ,   0.1775 ,   0.0179 ) .
According to the principle of full membership, the single-level evaluation result of the school’s learning input factor is good.
Similarly,
B2 = (0.4322, 0.3697, 0.1815, 0.0166)
B3 = (0.3711, 0.4705, 0.1428, 0.0156)
B4 = (0.4153, 0.4061, 0.1698, 0.0088)
Then the single-level evaluation results of the system performance, educational process, and feedback effectiveness factors are excellent, sound, and superior, respectively.
For the single-factor evaluation matrix Rk, the total evaluation matrix R of U is obtained as:
R = [ b 11 b 1 m b s 1 b s m ]
Then the total composite judgment result is:
B = A R = [ A 1 R 1 A s R s ]
According to Equation (8) for the single-factor evaluation matrix Rk to obtain the total evaluation matrix R about U. Finally, according to Equation (9), the total evaluation matrix R is fuzzily synthesized with the indicator weight vector A of each criterion layer under the total target layer to obtain the final evaluation result B.
B = A R = ( 0.2081 , 0.0981 , 0.2875 , 0.4063 ) [ 0.3736 0.4310 0.1775 0.0179 0.4322 0.3697 0.1815 0.0166 0.3711 0.4705 0.1428 0.0156 0.4153 0.4061 0.1698 0.0088 ] = ( 0.3956 , 0.4262 , 0.1684 , 0.0134 )
The final evaluation result of the scoring of rural e-commerce EE for students of Software Engineering Institute of Guangzhou can be obtained as good, according to the principle of maximum affiliation and the established evaluation criteria.
According to the criterion layer’s single-level evaluation score, Software Engineering Institute of Guangzhou’s rural e-commerce EE has a relatively higher system performance and feedback effectiveness but a lower score in terms of learning input and educational process. The results would indicate the capability of Software Engineering Institute of Guangzhou to nurture students as it can be seen that students have a higher level of recognition for the school’s rural e-commerce EE and services compared to other areas. They are more satisfied with the overall quality of service and improved knowledge and skills. Despite these, the self-awareness of their learning investment is still lacking.
In the learning input criterion layer specifically, the indicator of time invested has a low index layer affiliation score. This lower score indicates that students invest less time in rural e-commerce entrepreneurship. In the educational support criterion tier, the indicator tier affiliation score for school support was low, indicating that the current support provided by the school is more limited than the later support system. As for the educational process criterion layer, a lower index stratum membership score of practical teaching and assessment methods indicated that a proportion of the school’s practical teaching needs to be improved. In the feedback effectiveness criterion layer, the subordinate score of the teaching situation of the tracking indicator layer is low. The low scoring indicated that the feedback channel is relatively simple; thus, it would be suggested that the degree of emphasis on adopting students’ opinions is low.

5. Discussion

This paper accomplishes the objectives of the study, which are to develop a ’student-centered’ model for evaluating rural e-commerce EE in HEIs and to test the model’s applicability in practice. The evaluation results suggest that the college’s rural e-commerce EE has a solid overall score, with good, excellent, good, and excellent scores in learning input, educational support, educational process, and feedback effectiveness. We propose the following theoretical and practical implications based on the findings.

5.1. Theoretical Implications

In the course of our study, we found that many previous studies would prefer to evaluate the results obtained from education. However, our study emphasizes the evaluation of the whole process of education, which is in line with the studies of scholars such as Fauyolle [45] and Kailer [46]. Regarding the choice of subjects for educational evaluation, Rosa and Amaral [58] propose a Self-assessment Tool for Higher Education Institutions (HE Innovate), which takes HEIs as the subject of evaluation. Ruskovaara et al. [59] propose the Measurement Tool for Entrepreneurship Education (MTEE), which uses teachers as the subject of evaluation. This paper is based on the learning input theory [47] and places more emphasis on the role played by the educated subject in EE. Therefore, our study further expands the role of the whole process and student-centered EE evaluation models. We also found that EE encompasses a broader content range, and HEIs are less likely to integrate it with some professional education. This may make EE less relevant. We attempted to focus EE in HEIs on the field of rural e-commerce.

5.2. Practical Implications

The practical implications of this paper are to evaluate the education of Software Engineering Institute of Guangzhou and to suggest appropriate solutions for it. It also serves as a reference for the evaluation of more HEIs conducting rural e-commerce EE.
Firstly, it is suggested that HEIs such as Software Engineering Institute of Guangzhou should open up to a broader range of opinions. Then, they will be able to develop a more open feedback path for students based on the opinionated surveys. Henceforth, students interested in rural e-commerce entrepreneurship can give timely feedback on information related to the course. Such examples of the information would include innovation and entrepreneurship, feedback on teachers’ performance, courses, and resources on campus. All this feedback would continue to improve the campus’s incredible entrepreneurship atmosphere. Although the results indicate that the quality level of rural e-commerce EE in Software Engineering Institute of Guangzhou is promising, further construction can be strengthened. This strengthening is suggested around the indicators with low affiliation scores to improve the level of rural e-commerce entrepreneurship among students.
Secondly, to provide a full range of services for suitable projects interested in rural e-commerce entrepreneurship, the college should increase its investment in rural e-commerce EE. It should also provide entrepreneurial guidance, project incubation, business consulting, technology research and development, financing, and loan support based on the on-campus business park.
Finally, undergraduate training programs’ social flexibility needs to be enhanced to address the current societal demand for skilled individuals with a broad understanding of rural e-commerce. Further development in rural e-commerce EE would be required. The development phases required are scale and efficiency, quantity and quality of training, and employment. Hence, we believe that the education contents should be optimized. Moreover, rural e-commerce employers should be invited to participate in developing training programs and hire off-campus business mentors.

6. Conclusions

Focusing on the evaluation of rural e-commerce EE in HEIs, this study constructs a model of educational evaluation indicators. Three questions are discussed, including how to evaluate students’ EE learning outcomes, which indicators and research methods should be used for such evaluation, and how applicable the evaluation model is. Drawing on George Kuh’s learning engagement theory, this study follows the principles of systematic and comprehensive, developmental and dynamic, hierarchical and scientific. It mainly involves the four dimensions of learning input, educational support, educational process, and feedback effectiveness, comprising 16 evaluation indicators. An evaluation method combining AHP and Fuzzy Comprehensive Evaluation Method is used to combine qualitative and quantitative analysis, determine the weights of each indicator and the opinion sets of evaluation subjects, and carry out empirical analysis on the rural e-commerce EE practices of Software Engineering Institute of Guangzhou, finally putting forward corresponding improvement suggestions.
The limitations of this paper are as follows: Firstly, there are some limitations in the evaluation method, and it is a more complex problem to determine whether the indicator weights given by the expert system are reasonable. The generalizability of the evaluation model needs to be further tested. Secondly, due to the limited survey sample in the empirical analysis, the findings cannot be generalized to all schools in Guangzhou. Thirdly, the evaluation and interpretation of the results represent the author’s own views and experiences and should therefore be viewed with caution. In future research, the applicability of the evaluation indicators will also be adjusted according to the current state of development of rural e-commerce EE, and the scope of application of the empirical analysis will be further expanded.

Author Contributions

Conceptualization, M.Z. and Y.Z.; methodology, M.Z.; software, M.Z.; validation, M.Z.; formal analysis, Y.Z.; investigation, M.Z.; resources, Y.Z. and M.Z.; data curation, M.Z.; writing—original draft preparation, M.Z.; writing—review and editing, M.Z., Y.Z., Y.T. and A.J.; visualization, M.Z.; supervision, Y.Z.; project administration, M.Z. and Y.Z.; funding acquisition, M.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Teaching Reform Project of General Category of Teaching Steering Committee of E-Commerce in Guangdong Universities, grant number 202009; and Quality Engineering Construction Project in Software Engineering Institute of Guangzhou, grant number JYJG202103.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/MinlingZeng/mlz.git (accessed on 25 August 2022).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Ministry of Commerce of the People’s Republic of China. The Ministry of Commerce Reports on Chinese Online Retail Market and Service Outsourcing in 2021 and Answers Questions on the Historic Breakthrough in Sino-Russian Trade in 2021. Available online: http://www.gov.cn/xinwen/2022-01/27/content_5670877.htm (accessed on 4 April 2022).
  2. Huang, L.; Xie, G.; Huang, R.; Li, G.; Cai, W.; Apostolidis, C. Electronic commerce for sustainable rural development: Exploring the factors influencing BoPs’ entrepreneurial intention. Sustainability 2021, 13, 10604. [Google Scholar] [CrossRef]
  3. Xie, G.; Huang, L.; Bin, H.; Apostolidis, C.; Jiang, Y.; Li, G.; Cai, W. Sustainable entrepreneurship in rural E-commerce: Identifying entrepreneurs in practitioners by using deep neural networks approach. Front. Environ. Sci. 2022, 370, 840479. [Google Scholar] [CrossRef]
  4. Cai, W.; Song, Y.; Duan, H.; Song, Y.; Xia, Z. Multi-feature fusion-guided multiscale bidirectional attention networks for logistics pallet segmentation. CMES-Comput. Modeling Eng. Sci. 2022, 131, 1539–1555. [Google Scholar] [CrossRef]
  5. Cai, W.; Song, Y.; Wei, Z. Multimodal data guided spatial feature fusion and grouping strategy for E-commerce commodity demand forecasting. Mob. Inf. Syst. 2021, 2021, 5568208. [Google Scholar] [CrossRef]
  6. Michaels Institute. 2018 China Undergraduate Employment Report; Social Science Literature Press: Beijing, China, 2018; pp. 35–50. [Google Scholar]
  7. Kuratko, D.F. The emergence of entrepreneurship education: Development, trends, and challenges. Entrep. Theory Pract. 2005, 29, 577–597. [Google Scholar] [CrossRef]
  8. Solomon, G. An examination of entrepreneurship education in the United States. J. Small Bus. Enterp. Dev. 2007, 14, 168–182. [Google Scholar] [CrossRef]
  9. Hannon, P.D. Philosophies of enterprise and entrepreneurship education and challenges for higher education in the UK. Int. J. Entrep. Innov. 2005, 6, 105–114. [Google Scholar] [CrossRef]
  10. Béchard, J.P.; Grégoire, D. Entrepreneurship education research revisited: The case of higher education. Acad. Manag. Learn. Educ. 2005, 4, 22–43. [Google Scholar] [CrossRef]
  11. Levie, J. Entrepreneurship Education in Higher Education in England: A Survey; Department for Education and Employment: London, UK, 1999. [Google Scholar]
  12. Nabi, G.; Liñán, F.; Fayolle, A.; Krueger, N.; Walmsley, A. The impact of entrepreneurship education in higher education: A systematic review and research agenda. Acad. Manag. Learn. Educ. 2017, 16, 277–299. [Google Scholar]
  13. Boldureanu, G.; Ionescu, A.M.; Bercu, A.M.; Bedrule-Grigoruță, M.V.; Boldureanu, D. Entrepreneurship education through successful entrepreneurial models in higher education institutions. Sustainability 2020, 12, 1267. [Google Scholar] [CrossRef]
  14. Ghina, A. Effectiveness of entrepreneurship education in higher education institutions. Procedia-Soc. Behav. Sci. 2014, 115, 332–345. [Google Scholar] [CrossRef] [Green Version]
  15. Jones, P.; Pickernell, D.; Fisher, R.; Netana, C. A tale of two universities: Graduates perceived value of entrepreneurship education. Educ. Train. 2017, 59, 689–705. [Google Scholar] [CrossRef]
  16. Charney, A.; Libecap, G.D. The Impact of Entrepreneurship Education: An Evaluation of the Berger Entrepreneurship Program at the University of Arizona, 1985–1999; University of Arizona—Department of Economics: Tucson, AZ, USA, 2008. [Google Scholar]
  17. Forhad, M.A.R.; Ahsan, M.R.; Alam, G.M. Impact of introducing a customer services component during secondary school education to improve small and medium-sized enterprises: A case study in Bangladesh. Soc. Bus. Rev. 2020, 17, 337–353. [Google Scholar] [CrossRef]
  18. Akimov, O.O.; Karpa, M.I.; Parkhomenko-Kutsevil, O.; Kupriichuk, V.; Omarov, A.; Аkimov, О.О.; Каrpа, М.І.; Parhomenko-Kucevil, О.І.; Оmarov, A.; Купріїчук, B. Entrepreneurship education of the formation of the e-commerce managers professional qualities. Int. J. Entrep. 2021, 25, 1–8. [Google Scholar]
  19. Entrepreneurship, Innovation, and Economic Development; Oxford University Press: Oxford, UK, 2011.
  20. Matlay, H.; Addis, M. Adoption of ICT and e-commerce in small businesses: An HEI-based consultancy perspective. J. Small Bus. Enterp. Dev. 2003, 10, 321–335. [Google Scholar] [CrossRef]
  21. Kshetri, N. Barriers to E-Commerce and Competitive Business Models in Developing Countries: A Case Study. Electron. Commer. Res. Appl. 2007, 4, 443–452. [Google Scholar] [CrossRef]
  22. Li, J. Analysis on the development path of intelligent agriculture based on rural e-commerce. J. Commer. Econ. 2020, 7, 140–142. [Google Scholar]
  23. Karine, H. E-commerce development in rural and remote areas of BRICS countries. J. Integr. Agric. 2021, 20, 979–997. [Google Scholar]
  24. Jalali, A.A.; Okhovvat, M.R.; Okhovvat, M. A new applicable model of Iran rural e-commerce development. Procedia Comput. Sci. 2011, 3, 1157–1163. [Google Scholar] [CrossRef]
  25. Oyebola, M.; Irefin, S.; Olaposi, T. Evaluation of Entrepreneurship Education in Selected Nigerian Universitites. J. Entrep. Innov. Manag. 2015, 4, 49–75. [Google Scholar]
  26. Stampfl, C.; Hytti, U. Entrepreneurship als Herausforderung an das Bildungswesen: Ansätze in Österreich und europäischer Vergleich; Ergebnisse des Projekts ENTREDU; IBW-Inst. für Bildungsforschung d. Wirtschaft: Wien, Austria, 2002. [Google Scholar]
  27. Venesaar, U.; Ling, H.; Voolaid, K. Evaluation of the entrepreneurship education programme in university: A new approach. Amfiteatru Econ. 2011, 13, 377–391. [Google Scholar]
  28. Braun, G.; Diensberg, C. Evaluation und Erfolgsbewertung Internationaler Entrepreneurship-Trainings; Entrepreneurship in Forschung und Lehre: Frankfurt, Germany, 2003; pp. 205–221. [Google Scholar]
  29. Hytti, U.; Kuopusjärvi, P. Evaluating and Measuring Entrepreneurship and Enterprise Education: Methods, Tools and Practices; Small Business Institute: Clinton, MS, USA, 2004. [Google Scholar]
  30. Zhu, X.F. Analysis of the demand for innovative and entrepreneurial talents in the e-commerce industry from the perspective of the development of rural modern service industry. Agric. Econ. 2019, 10, 88–90. [Google Scholar]
  31. Jiang, J.H. Exploration and Practice in the Field of Innovation and Venture Education Integrating into EC’s professional education. Hebei Vocat. Educ. 2013, 9, 36–37. [Google Scholar]
  32. Ye, Y.; Zhou, C.J.; Li, M. Cultivation of innovative and entrepreneurial talents based on rural e-commerce. China J. Commer. 2018, 2, 180–182. [Google Scholar]
  33. Wilson, K.E. Chapter 5: Entrepreneurship Education in Europe. In Entrepreneurship and Higher Education; OECD: Paris, France, 2008. [Google Scholar]
  34. Ekpoh, U.; Edet, A. Entrepreneurship education and career intentions of tertiary education students in Akwa Ibom and Cross River States, Nigeria. Int. Educ. Stud. 2011, 4, 172–178. [Google Scholar] [CrossRef]
  35. Enu, D. Enhancing the Entrepreneurship Education in Nigeria. Am. J. Soc. Issues Humanit. 2012, 2, 232–239. [Google Scholar]
  36. Luecke, R. Entrepreneur’s Toolkit: Tools and Techniques to Launch and Grow Your New Business; Harvard Business Press: Boston, MA, USA, 2005; pp. 208–390. [Google Scholar]
  37. Lee, V.E.; Bryk, A.S.; Smith, J.B. Chapter 5: The Organization of Effective Secondary Schools. Rev. Res. Educ. 1993, 19, 171–267. [Google Scholar] [CrossRef]
  38. Pace, C.R. Measuring the Quality of Student Effort. Curr. Issues High. Educ. 1980, 2, 10–16. [Google Scholar]
  39. Astin, A.W. Student Involvement: A Developmental Theory for Higher Education. J. Coll. Stud. Pers. 1984, 25, 297–308. [Google Scholar]
  40. Tinto, V. Leaving College: Rethinking the Causes and Cures of Student Attrition; University of Chicago Press: Chicago, IL, USA, 1987; pp. 35–36. [Google Scholar]
  41. Pascarella, E.T.; Terenzini, P.T. How College Afects Students: Findings and Insights from Twenty Years of Research; Jossey-Bass Inc.: San Francisco, CA, USA, 1991; pp. 25–28. [Google Scholar]
  42. Chickering, A.W.; Gamson, Z.F. Seven Principles for Good Practice in Undergraduate Education. AAHE Bull. 1987, 3, 7–10. [Google Scholar]
  43. Garavan, T.N.; Barra, O. Entrepreneurship Education and Training Programmes: A Review and Evaluation—Part 1. J. Eur. Ind. Train. 1994, 18, 3–12. [Google Scholar] [CrossRef]
  44. Okolie, U.C.; Igwe, P.A.; Ayoola, A.A.; Nwosu, H.E.; Kanu, C.; Mong, I.K. Entrepreneurial competencies of undergraduate students: The case of universities in Nigeria. Int. J. Manag. Educ. 2021, 19, 100452. [Google Scholar] [CrossRef]
  45. Fayolle, A. Evaluation of entrepreneurship education: Behaviour performing or intention increasing? Int. J. Entrep. Small Bus. 2005, 2, 89–98. [Google Scholar] [CrossRef]
  46. Kailer, N. Evaluation of entrepreneurship education at universities. Ibw-Mitt. 2005, 3, 1–11. [Google Scholar]
  47. Kuh, G.D.; Kinzie, J.L.; Buckley, J.A.; Bridges, B.K.; Hayek, J.C. What Matters to Student Success: A Review of the Literature; National Postsecondary Education Cooperative: Washington, DC, USA, 2006; pp. 24–27. [Google Scholar]
  48. Dong, D.B. Construction of evaluation index system of innovation and entrepreneurship education based on “AHP analytic hierarchy process”. Educ. Rev. 2019, 237, 72–75. [Google Scholar]
  49. Luo, Y.; Heidi Rose Cen, Y.H. Higher Education Measurement in the Context of Globalization—The Development of NESS-China: Cultural adaptation, Reliability and Validity. Fudan Educ. Forum 2009, 7, 12–18. [Google Scholar]
  50. Mimović, P.; Krstić, A. The integrated application of the AHP and the DEA methods in evaluating the performances of higher education institutions in the Republic of Serbia. Ekon. Horiz. 2016, 18, 71–85. [Google Scholar] [CrossRef]
  51. Zareinejad, M.; Kaviani, M.; Esfahani, M.; Masoule, F.T. Performance evaluation of services quality in higher education institutions using modified SERVQUAL approach with grey analytic hierarchy process (G-AHP) and multilevel grey evaluation. Decis. Sci. Lett. 2014, 3, 143–156. [Google Scholar] [CrossRef]
  52. Biswas, R. An Application of Fuzzy Sets in Students’ Evaluation. Fuzzy Sets Syst. 1995, 74, 187–194. [Google Scholar] [CrossRef]
  53. Chen, S.M.; Lee, C.H. New Methods for Students’ Evaluation Using Fuzzy Sets. Fuzzy Sets Syst. 1999, 104, 209–218. [Google Scholar] [CrossRef]
  54. Chen, J.F.; Hsieh, H.N.; Do, Q.H. Evaluating teaching performance based on fuzzy AHP and comprehensive evaluation approach. Appl. Soft Comput. 2015, 28, 100–108. [Google Scholar] [CrossRef]
  55. Chen, S.X. Integrating hierarchical analysis and fuzzy mathematical methods to evaluate China’s postgraduate training model. High. Educ. Explor. 2005, 3, 91–93. [Google Scholar]
  56. Hu, Q.Y. A Multi-Level Fuzzy Evaluation of Students’ Comprehensive Qualities Based on the AHP; North China Electric Power University: Beijing, China, 2010. [Google Scholar]
  57. Saaty, T.L. Mathematical Models for Decision Support. In What is the Analytic Hierarchy Process? Springer: Berlin/Heidelberg, Germany, 1988; pp. 109–121. [Google Scholar]
  58. Rosa, M.J.; Amaral, A. Amaral, A. A Self-Assessment of Higher Education Institutions from the Perspective of the EFQM Excellence Model. In Quality Assurance in Higher Education; Springer: Dordrecht, The Netherlands, 2007; pp. 181–207. [Google Scholar]
  59. Ruskovaara, E.; Hämäläinen, M.; Pihkala, T. HEAD Teachers Managing Entrepreneurship Education—Empirical Evidence from General Education. Teach. Teach. Educ. 2016, 55, 155–164. [Google Scholar] [CrossRef]
Figure 1. Diagram of a learning input theory model.
Figure 1. Diagram of a learning input theory model.
Sustainability 14 10854 g001
Figure 2. Schemes follow the same formatting. Implementation steps of Analytic Hierarchy Process combined with Fuzzy Comprehensive Evaluation Method.
Figure 2. Schemes follow the same formatting. Implementation steps of Analytic Hierarchy Process combined with Fuzzy Comprehensive Evaluation Method.
Sustainability 14 10854 g002
Figure 3. Evaluation index hierarchy chart.
Figure 3. Evaluation index hierarchy chart.
Sustainability 14 10854 g003
Table 1. Judgment matrix A of the second layer to the first layer U.
Table 1. Judgment matrix A of the second layer to the first layer U.
Uu1u2u3u4
u11321/3
u21/311/31/5
u31/2311/2
u43521
Table 2. Judgment matrix B1 of the third layer to the second layer u1.
Table 2. Judgment matrix B1 of the third layer to the second layer u1.
Uu11u12u13
u1111/21/3
u12211/2
u13321
Table 3. Judgment matrix B2 of the third layer to the second layer u2.
Table 3. Judgment matrix B2 of the third layer to the second layer u2.
u2u21u22u23u24
u21121/21/2
u221/211/31/2
u232312
u24221/21
Table 4. Judgment matrix B3 of the third layer to the second layer u3.
Table 4. Judgment matrix B3 of the third layer to the second layer u3.
u3u31u32u33u34u35
u311231/23
u321/2121/32
u331/31/211/22
u3423213
u351/31/21/21/31
Table 5. Judgment matrix B4 of the third layer to the second layer u4.
Table 5. Judgment matrix B4 of the third layer to the second layer u4.
u4u41u42u43u44
u41121/21/2
u421/211/31/2
u432312
u44221/21
Table 6. 1–10 th order RI coefficients.
Table 6. 1–10 th order RI coefficients.
Order12345678910
RI0.000.000.520.891.121.241.321.411.451.49
Table 7. Test on Judgment matrix consistency index.
Table 7. Test on Judgment matrix consistency index.
CIRICRTest Results
Judgment Matrix A0.0410.8900.046Less than 0.1, pass the test
Judgment Matrix B10.0050.5200.010Less than 0.1, pass the test
Judgment Matrix B20.0240.8900.027Less than 0.1, pass the test
Judgment Matrix B30.0481.1200.043Less than 0.1, pass the test
Judgment Matrix B40.0340.8900.038Less than 0.1, pass the test
Table 8. Evaluation model of rural e-commerce entrepreneurship education in HEIs.
Table 8. Evaluation model of rural e-commerce entrepreneurship education in HEIs.
Indicator ModelCriteria Level Indicators and WeightingIndicator Level Indicators and WeightingComprehensive Weighting
Evaluation model of Rural E-Commerce Entrepreneurship Education for Students in HEIs
U
Learning Input u1
(0.2081)
Learning motivation u11 (0.1637)0.0341
Learning habits u12 (0.2973)0.0619
Time commitment u13 (0.5390)0.1121
Educational support u2
(0.0981)
Software and hardware facilities u21 (0.1928)0.0189
Basic service facilities u22 (0.1209)0.0119
Entrepreneurship atmosphere u23 (0.4168)0.0409
Policy support u24 (0.2695)0.0264
Educational process u3
(0.2875)
Educational teachers u31 (0.2678)0.0770
Teacher-student interaction u32 (0.1610)0.0463
Course teaching u33 (0.1277)0.0367
Practical teaching u34 (0.3583)0.1030
Assessment methods u35 (0.0852)0.0245
Feedback effectiveness u4
(0.4063)
Teaching tracking u41 (0.1745)0.0709
Feedback demand channels u42 (0.1240)0.0504
Entrepreneurship knowledge u43 (0.3471)0.1410
Entrepreneurial employment skills u44 (0.3544)0.1440
Table 9. Evaluation criteria of rural e-commerce EE in HEIs.
Table 9. Evaluation criteria of rural e-commerce EE in HEIs.
IndicatorsEvaluation Level
ExcellentGoodPassFailure
Learning motivationSupported by consistent and stable internal motivationCan be motivated by external motivationNt interested in learningNo active motivation to learn
Learning habitsHigh enthusiasm and initiative in learningWilling to learn actively, but not consistentlyGeneral enthusiasm and initiative in learningNo active learning ideas
Time commitmentAverage daily input time greater than 2 h Average daily input time greater than 1 h Average daily input time greater than 0.5 h The average daily input time is less than 0.5 h
Software and hardware facilities The hardware and software facilities are complete and actively open to studentsHardware and software facilities are relatively completeHardware and software facilities are perfectWeak awareness of the construction of software and hardware educational facilities
Basic service facilitiesWell-established basic service facilities with comprehensive coverageBasic service facilities are relatively completeBasic service facilities are completeBasic service facilities are not well developed
Entrepreneurship atmosphereThe atmosphere of “mass entrepreneurship and innovation” is powerfulThe atmosphere of “mass entrepreneurship and innovation” is relatively strongSchool leaders, teachers, and students understand the situation of entrepreneurshipSchool leaders, teachers, and students ignore entrepreneurship
Policy supportSupport in various aspects such as materialsMaterial and other support can be providedLimited support in a single areaNothing else
Educational teachersTeachers have the rich practical experience and theoretical teaching skills related to rural e-commerce entrepreneurshipTeachers are profound in lesson preparation, rich in knowledge, and have theoretical experience related to rural e-commerce entrepreneurshipTeachers are in-class severe preparation and rich in knowledgeTeachers’ class content is seriously disconnected from reality
Teacher-student interactionTeachers are very focused on student-teacher interactionTeachers pay more attention to student-teacher interactionTeacher-student interaction is not obviousLittle to no teacher-student interaction
Course teachingThe curriculum is scientific and reasonable, with solid practicabilityThe curriculum is reasonable and practicalThe practicality of the curriculum is generalThe curriculum is out of touch with reality
Practical teachingPractical teaching accounts for a large proportion, and the model of collaborative education with enterprises is perfectPractical teaching accounts for a large proportion, and the model of collaborative education with enterprises is relatively completeThe proportion of practical teaching is medium, and the practical effect of the model of educating people in collaboration with enterprises is averageThe proportion of practical teaching is small, and the model of collaborative education with enterprises is not perfect
Assessment methodsThere are various assessment methods and can be converted into credits and included in academic performance and comprehensive assessmentThere are various assessment methods, and those who are particularly outstanding can be included in the student’s comprehensive assessment for extra pointsThere are various assessment methods for students to participate in entrepreneurship courses and practiceThe assessment method is single, mainly based on course examinations
Teaching trackingTrack students’ teaching situation throughout the process and provide answers to questionsTrack student teaching and provide regular Q&AOnly provide Q&A regularlyNo teaching situation tracking
Feedback demand channels Feedback channels are open, and students’ opinions are taken seriously and closely interconnected with the HEIs, industry, and governmentFeedback channels are relatively open, and students’ opinions and suggestions are adopted to a certain extentFeedback channels are available, but the follow-up progress is unclearNo feedback channel
Entrepreneurship knowledge The entrepreneurship knowledge level is particularly significantModerately significant improvement in knowledge of entrepreneurshipThe improvement of knowledge of entrepreneurship is generally significantNo improvement in knowledge of entrepreneurship
Entrepreneurial employment skillsStudents’ entrepreneurial and employment skills level has improved particularly significantlyStudents’ entrepreneurial and employment skills have improved more significantlyThe improvement of students’ entrepreneurial and employment skills is generally significantStudents’ entrepreneurial and employment skills did not improve
Table 10. Evaluation table of learning input factors (unit: number of people).
Table 10. Evaluation table of learning input factors (unit: number of people).
Criteria Level IndicatorsIndicator Level IndicatorsEvaluation Level
ExcellentGoodPassFailure
Learning input u1Learning motivation u11188107872
Learning habits u121851374022
Time commitment u13107199780
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Zeng, M.; Zheng, Y.; Tian, Y.; Jebbouri, A. Rural E-Commerce Entrepreneurship Education in Higher Education Institutions: Model Construction via Empirical Analysis. Sustainability 2022, 14, 10854. https://doi.org/10.3390/su141710854

AMA Style

Zeng M, Zheng Y, Tian Y, Jebbouri A. Rural E-Commerce Entrepreneurship Education in Higher Education Institutions: Model Construction via Empirical Analysis. Sustainability. 2022; 14(17):10854. https://doi.org/10.3390/su141710854

Chicago/Turabian Style

Zeng, Minling, Yanling Zheng, Yu Tian, and Abdelhamid Jebbouri. 2022. "Rural E-Commerce Entrepreneurship Education in Higher Education Institutions: Model Construction via Empirical Analysis" Sustainability 14, no. 17: 10854. https://doi.org/10.3390/su141710854

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop