Rural E-Commerce Entrepreneurship Education in Higher Education Institutions

[Full title: Rural E-Commerce Entrepreneurship Education in Higher Education Institutions: Model Construction via Empirical Analysis]

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Citation: 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/su 141710854 Academic Editor: Linda Hagedorn Received: 14 July 2022 Accepted: 26 August 2022 Published: 31 August 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations Copyright: © 2022 by the authors Licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/) sustainability Article Rural E-Commerce Entrepreneurship Education in Higher Education Institutions: Model Construction via Empirical Analysis Minling Zeng 1 , Yanling Zheng 2, * , Yu Tian 3, * and Abdelhamid Jebbouri 4 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 * Correspondence: zyl@guat.edu.cn (Y.Z.); mnsty@mail.sysu.edu.cn (Y.T.) 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 Keywords: entrepreneurship education; higher education institutions; fuzzy comprehensive evaluation method; rural e-commerce 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 ecommerce 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% Sustainability 2022 , 14 , 10854. https://doi.org/10.3390/su 141710854 https://www.mdpi.com/journal/sustainability

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Sustainability 2022 , 14 , 10854 2 of 18 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 – 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: a Develop a ‘student-centered’ model for evaluating EE and services in HEIs b 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

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Sustainability 2022 , 14 , 10854 3 of 18 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 1990 s, 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

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Sustainability 2022 , 14 , 10854 4 of 18 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 . Sustainability 2022 , 14 , x FOR PEER REVIEW 4 of 18 academic integration theory [40], the change assessment model [41] and the seven princi ‐ ples 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 learna ‐ bility 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 stu ‐ dent 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 Fig ‐ ure 1 Learning Input Student Behavior  Learning Habits  Peer Input  Teacher ‐ Student Interaction  Learning Motivation  Learning time  Other Pre ‐ college experience  Entrance Choice  Willingness to enroll  Academic Preparation  Family background  Race, Gender  Other Campus Conditions  First Year Experience  Academic Support  Campus Environment  Service Programs  Teaching Methods  Others Academic Achievement Learning Gains Output Figure 1. Diagram of a learning input theory model 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 out ‐ comes, 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 ‐ cen ‐ tered” evaluation concept was challenging and could not correctly reflect the actual con ‐ dition 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 entrepre ‐ neurship behaviors and outcomes On the other hand, students interested in receiving rural e ‐ commerce entrepreneurship services focus on this paper’s education and services Figure 1. Diagram of a learning input theory model 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 “studentcentered” 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

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Sustainability 2022 , 14 , 10854 5 of 18 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 firstand 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

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Sustainability 2022 , 14 , 10854 6 of 18 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 . Sustainability 2022 , 14 , x FOR PEER REVIEW 6 of 18 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 innova ‐ tion 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 entrepreneur ‐ ship papers and results, this paper evaluates four aspects: teaching tracking, feedback de ‐ mand 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. Establishing a set of evaluation factors Determine the weights of each indicator Determine the set of evaluation subject comments Build the evaluation object index set affiliation matrix Perform multi ‐ level fuzzy processing Determine the final evaluation level Hierarchical total ranking and consistency test Hierarchical list Sorting and consistency test Construction of judgment matrix Build hierarchical model Fuzzy Comprehensive Evaluation Method Analytic Hierarchy Process Figure 2. Schemes follow the same formatting Implementation steps of Analytic Hierarchy Process combined with Fuzzy Comprehensive Evaluation Method The composite evaluation method is chosen for three reasons: a It is not enough to rely on qualitative analysis when evaluating the process of stu ‐ dents’ awareness, behavior, and competence enhancement in EE Scholars such as Mimovi ć P and Krsti ć [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 b 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 stu ‐ dents are asked to evaluate the teaching ability of teachers, the feedback may be Figure 2. Schemes follow the same formatting. Implementation steps of Analytic Hierarchy Process combined with Fuzzy Comprehensive Evaluation Method The composite evaluation method is chosen for three reasons: a 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 Mimovi´c P. and Krsti´c [ 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 b 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 c 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

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Sustainability 2022 , 14 , 10854 7 of 18 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 u 1 , u 2 , u 3 , u 4 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 = { u 1 , u 2 , u 3 , u 4 }. Using uij to represent the indicator layer corresponding to each criterion layer, for example, u 11 , u 12 , u 13 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 Sustainability 2022 , 14 , x FOR PEER REVIEW 7 of 18 “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 c 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 fac ‐ tors 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 u 1 , u 2 , u 3 , u 4 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 = { u 1 , u 2 , u 3 , u 4 } Using uij to represent the indicator layer corresponding to each criterion layer, for example, u 11 , u 12 , u 13 are used to represent the three secondary indicators of learning motivation, learning habits, and en ‐ gagement time under the primary indicator of learning engagement Similarly, the hier ‐ archical structure of the index model for evaluating the quality of rural e ‐ commerce EE of students in HEIs in Figure 3 can be obtained Evaluation Index System of Rural E ‐ Commerce Entrepreneurship Education for Students in HEIs U Learning Input u 1 Educational support u 2 Educational process u 3 Feedback effectiveness u 4 learning motivation u 11 learning habits u 12 software and hardware facilities u 21 basic service facilities u 22 innovation and entrepreneurship atmosphere u 23 policy support u 24 teaching tracking u 41 feedback demand channels u 42 innovation and entrepreneurship knowledge u 43 entrepreneurial employment skills u 44 educational teachers u 31 teacher ‐ student interaction u 32 course teaching u 33 practical teaching u 34 assessment methods u 35 time commitment u 13 Figure 3. Evaluation index hierarchy chart Figure 3. Evaluation index hierarchy chart 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 u 11 , u 12 , u 13 , u 21 , u 22 , u 23 , u 24 , u 31 , u 32 , u 33 , u 34 , u 35 , u 41 , u 42 , u 43 , u 44 at respective criterion levels, as well as the four fundamental indicators of u 1 , u 2 , u 3 , u 4 1 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 Tables 1 – 5 .

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Sustainability 2022 , 14 , 10854 8 of 18 Table 1. Judgment matrix A of the second layer to the first layer U U u 1 u 2 u 3 u 4 u 1 1 3 2 1/3 u 2 1/3 1 1/3 1/5 u 3 1/2 3 1 1/2 u 4 3 5 2 1 Table 2. Judgment matrix B 1 of the third layer to the second layer u 1 U u 11 u 12 u 13 u 11 1 1/2 1/3 u 12 2 1 1/2 u 13 3 2 1 Table 3. Judgment matrix B 2 of the third layer to the second layer u 2 u 2 u 21 u 22 u 23 u 24 u 21 1 2 1/2 1/2 u 22 1/2 1 1/3 1/2 u 23 2 3 1 2 u 24 2 2 1/2 1 Table 4. Judgment matrix B 3 of the third layer to the second layer u 3 u 3 u 31 u 32 u 33 u 34 u 35 u 31 1 2 3 1/2 3 u 32 1/2 1 2 1/3 2 u 33 1/3 1/2 1 1/2 2 u 34 2 3 2 1 3 u 35 1/3 1/2 1/2 1/3 1 Table 5. Judgment matrix B 4 of the third layer to the second layer u 4 u 4 u 41 u 42 u 43 u 44 u 41 1 2 1/2 1/2 u 42 1/2 1 1/3 1/2 u 43 2 3 1 2 u 44 2 2 1/2 1 2 Calculation of eigenvectors and eigenvalues We calculate the above judgment matrix eigenvectors W i and use W 0 , W 1 , W 2 , W 3 , W 4 to denote the eigenvectors of judgment matrices A, B 1 , B 2 , B 3 , B 4 , respectively. After calculation, the results are as follows: W 0 = (0.832, 0.392, 1.150, 1.625) T W 1 = (0.491, 0.892, 1.617) T W 2 = (0.771, 0.484, 1.667, 1.078) T W 3 = (1.339, 0.805, 0.638, 1.792, 0.426) T W 4 = (0.698, 0.496, 1.389, 1.417) T After finding the eigenvectors of each matrix, its maximum eigenvalue roots λ max can be found accordingly. Using λ 0 , λ 1 , λ 2 , λ 3 , λ 4 to denote the maximum eigenvalue roots of the judgment matrices A, B 1 , B 2 , B 3 , B 4 , respectively, the following is obtained λ 0 = 4.122, λ 1 = 3.009, λ 2 = 4.071, λ 3 = 5.191, λ 4 = 4.103 3 Hierarchical single ranking and consistency tests

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Sustainability 2022 , 14 , 10854 9 of 18 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 λ max 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 = ( λ max − n )/( n − 1) (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 (2) The RI values for orders 1–10 are shown in Table 6 [ 57 ]. Table 6. 1–10 th order RI coefficients Order 1 2 3 4 5 6 7 8 9 10 RI 0.00 0.00 0.52 0.89 1.12 1.24 1.32 1.41 1.45 1.49 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 Table 7. Test on Judgment matrix consistency index CI RI CR Test Results Judgment Matrix A 0.041 0.890 0.046 Less than 0.1, pass the test Judgment Matrix B 1 0.005 0.520 0.010 Less than 0.1, pass the test Judgment Matrix B 2 0.024 0.890 0.027 Less than 0.1, pass the test Judgment Matrix B 3 0.048 1.120 0.043 Less than 0.1, pass the test Judgment Matrix B 4 0.034 0.890 0.038 Less than 0.1, pass the test 4 Hierarchical total ranking and consistency test The calculation of the hierarchical total ranking weights is shown in Equation (3) [ 57 ]. n ∑ j = 1 m ∑ i = 1 a i b i j = 1 (3) 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 (4) CI T = m ∑ i = 1 a i CI i (5) RI T = m ∑ i = 1 a i RI i (6)

Sustainability 2022 , 14 , 10854 10 of 18 When CR T < 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 u 1 , u 2 , u 3 , u 4 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 B 1 , the weights corresponding to u 11 , u 12 , u 13 are 0.1683, 0.2973, 0.5390, respectively. For the judgment matrix B 2 , the weights corresponding to u 21 , u 22 , u 23 , u 24 are 0.1928, 0.1209, 0.4168, 0.2695. For the judgment matrix B 3 , the weights of u 31 , u 32 , u 33 , u 34 , u 35 are 0.2678, 0.1610, 0.1277, 0.3583, 0.0852, respectively. For the judgment matrix B 4 , the weights of u 41 , u 42 , u 43 , u 44 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 n ∑ j = 1 m ∑ i = 1 a i b i j = 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 ecommerce EE in HEIs Table 8. Evaluation model of rural e-commerce entrepreneurship education in HEIs Indicator Model Criteria Level Indicators and Weighting Indicator Level Indicators and Weighting Comprehensive Weighting Evaluation model of Rural E-Commerce Entrepreneurship Education for Students in HEIs U Learning Input u 1 (0.2081) Learning motivation u 11 (0.1637) 0.0341 Learning habits u 12 (0.2973) 0.0619 Time commitment u 13 (0.5390) 0.1121 Educational support u 2 (0.0981) Software and hardware facilities u 21 (0.1928) 0.0189 Basic service facilities u 22 (0.1209) 0.0119 Entrepreneurship atmosphere u 23 (0.4168) 0.0409 Policy support u 24 (0.2695) 0.0264 Educational process u 3 (0.2875) Educational teachers u 31 (0.2678) 0.0770 Teacher-student interaction u 32 (0.1610) 0.0463 Course teaching u 33 (0.1277) 0.0367 Practical teaching u 34 (0.3583) 0.1030 Assessment methods u 35 (0.0852) 0.0245 Feedback effectiveness u 4 (0.4063) Teaching tracking u 41 (0.1745) 0.0709 Feedback demand channels u 42 (0.1240) 0.0504 Entrepreneurship knowledge u 43 (0.3471) 0.1410 Entrepreneurial employment skills u 44 (0.3544) 0.1440

[Find the meaning and references behind the names: Daily, Class, Active, Perfect, Pay, Else, Ideas, Area, Severe, Willing, Rich]

Sustainability 2022 , 14 , 10854 11 of 18 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 V 1 , V 2 , V 3 , V 4 , the rubric set was recorded V = { V 1 , V 2 , V 3 , V 4 }, 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 Table 9. Evaluation criteria of rural e-commerce EE in HEIs Indicators Evaluation Level Excellent Good Pass Failure Learning motivation Supported by consistent and stable internal motivation Can be motivated by external motivation Nt interested in learning No active motivation to learn Learning habits High enthusiasm and initiative in learning Willing to learn actively, but not consistently General enthusiasm and initiative in learning No active learning ideas Time commitment Average 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 students Hardware and software facilities are relatively complete Hardware and software facilities are perfect Weak awareness of the construction of software and hardware educational facilities Basic service facilities Well-established basic service facilities with comprehensive coverage Basic service facilities are relatively complete Basic service facilities are complete Basic service facilities are not well developed Entrepreneurship atmosphere The atmosphere of “mass entrepreneurship and innovation” is powerful The atmosphere of “mass entrepreneurship and innovation” is relatively strong School leaders, teachers, and students understand the situation of entrepreneurship School leaders, teachers, and students ignore entrepreneurship Policy support Support in various aspects such as materials Material and other support can be provided Limited support in a single area Nothing else Educational teachers Teachers have the rich practical experience and theoretical teaching skills related to rural e-commerce entrepreneurship Teachers are profound in lesson preparation, rich in knowledge, and have theoretical experience related to rural e-commerce entrepreneurship Teachers are in-class severe preparation and rich in knowledge Teachers’ class content is seriously disconnected from reality Teacher-student interaction Teachers are very focused on student-teacher interaction Teachers pay more attention to student-teacher interaction Teacher-student interaction is not obvious Little to no teacher-student interaction

[Find the meaning and references behind the names: Cont, Channel, Size, Get, Progress]

Sustainability 2022 , 14 , 10854 12 of 18 Table 9. Cont Indicators Evaluation Level Excellent Good Pass Failure Course teaching The curriculum is scientific and reasonable, with solid practicability The curriculum is reasonable and practical The practicality of the curriculum is general The curriculum is out of touch with reality Practical teaching Practical teaching accounts for a large proportion, and the model of collaborative education with enterprises is perfect Practical teaching accounts for a large proportion, and the model of collaborative education with enterprises is relatively complete The proportion of practical teaching is medium, and the practical effect of the model of educating people in collaboration with enterprises is average The proportion of practical teaching is small, and the model of collaborative education with enterprises is not perfect Assessment methods There are various assessment methods and can be converted into credits and included in academic performance and comprehensive assessment There are various assessment methods, and those who are particularly outstanding can be included in the student’s comprehensive assessment for extra points There are various assessment methods for students to participate in entrepreneurship courses and practice The assessment method is single, mainly based on course examinations Teaching tracking Track students’ teaching situation throughout the process and provide answers to questions Track student teaching and provide regular Q&A Only provide Q&A regularly No teaching situation tracking Feedback demand channels Feedback channels are open, and students’ opinions are taken seriously and closely interconnected with the HEIs, industry, and government Feedback channels are relatively open, and students’ opinions and suggestions are adopted to a certain extent Feedback channels are available, but the follow-up progress is unclear No feedback channel Entrepreneurship knowledge The entrepreneurship knowledge level is particularly significant Moderately significant improvement in knowledge of entrepreneurship The improvement of knowledge of entrepreneurship is generally significant No improvement in knowledge of entrepreneurship Entrepreneurial employment skills Students’ entrepreneurial and employment skills level has improved particularly significantly Students’ entrepreneurial and employment skills have improved more significantly The improvement of students’ entrepreneurial and employment skills is generally significant Students’ entrepreneurial and employment skills did not improve 4.2.4. Fuzzy Comprehensive Evaluation In the range of each factor subset U k (k = 1, 2, . . . , s), the fuzzy factor vector is determined according to the size of each factor A k = (a k 1 , a k 2 , . . . , a kn ), and the fuzzy operation is performed with the single-factor evaluation matrix R k , wherein the singlefactor evaluation matrix R k is composed of r kij (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 ) (7) 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, A 1 , A 2 , A 3 , A 4, respectively, based on the weights of each indicator determined using AHP above A = (0.2081, 0.0981, 0.2875, 0.4063) A 1 = (0.1683, 0.2972, 0.5390) A 2 = (0.1928, 0.1209, 0.4168, 0.2695) A 3 = (0.2678, 0.1610, 0.1277, 0.3583, 0.0852)

[Find the meaning and references behind the names: Ask, Works, Part, Author]

Sustainability 2022 , 14 , 10854 13 of 18 A 4 = (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 r kij of each factor can be evaluated, and the single-factor evaluation matrix R k 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 . Table 10. Evaluation table of learning input factors (unit: number of people) Criteria Level Indicators Indicator Level Indicators Evaluation Level Excellent Good Pass Failure Learning input u 1 Learning motivation u 11 188 107 87 2 Learning habits u 12 185 137 40 22 Time commitment u 13 107 199 78 0 Calculating the affiliation of the learning input factors and creating the learning input factor evaluation matrix R 1 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 R k is fuzzy-operated to obtain B k . The learning input factor is used as an example, whereby the questionnaire data determine the learning input factor evaluation matrix R 1 . The single-level evaluation result B 1 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, B 2 = (0.4322, 0.3697, 0.1815, 0.0166) B 3 = (0.3711, 0.4705, 0.1428, 0.0156) B 4 = (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 R k , the total evaluation matrix R of U is obtained as: R =    b 11 ◦ b 1 m . ◦ . b s 1 ◦ b sm    (8)

[Find the meaning and references behind the names: Amaral, Tool, Rosa, Simple, Still, Self, Lower]

Sustainability 2022 , 14 , 10854 14 of 18 Then the total composite judgment result is: B = A ◦ R =    A 1 ◦ R 1 . ◦ . A s ◦ R s    (9) According to Equation (8) for the single-factor evaluation matrix R k 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 ’studentcentered’ 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

[Find the meaning and references behind the names: Carry, Park, Broad, Path, Hire, Forward, Believe, Sample, Loan, Able]

Sustainability 2022 , 14 , 10854 15 of 18 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 ecommerce 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

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Sustainability 2022 , 14 , 10854 16 of 18 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 JYJG 202103 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. [ 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. [ 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. [ 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. [ CrossRef ] 6 Michaels Institute 2018 China Undergraduate Employment Report ; Social Science Literature Press: Beijing, China, 2018; pp. 35–50 7 Kuratko, D.F. The emergence of entrepreneurship education: Development, trends, and challenges Entrep. Theory Pract 2005 , 29 , 577–597. [ CrossRef ] 8 Solomon, G. An examination of entrepreneurship education in the United States J. Small Bus. Enterp. Dev 2007 , 14 , 168–182 [ 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. [ CrossRef ] 10 B é chard, J.P.; Gr é goire, D. Entrepreneurship education research revisited: The case of higher education Acad. Manag. Learn. Educ 2005 , 4 , 22–43. [ CrossRef ] 11 Levie, J Entrepreneurship Education in Higher Education in England: A Survey ; Department for Education and Employment: London, UK, 1999 12 Nabi, G.; Liñ á n, 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 13 Boldureanu, G.; Ionescu, A.M.; Bercu, A.M.; Bedrule-Grigorut , ă , M.V.; Boldureanu, D. Entrepreneurship education through successful entrepreneurial models in higher education institutions Sustainability 2020 , 12 , 1267. [ CrossRef ] 14 Ghina, A. Effectiveness of entrepreneurship education in higher education institutions Procedia-Soc. Behav. Sci 2014 , 115 , 332–345 [ CrossRef ] 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. [ 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.

[Find the meaning and references behind the names: Modern, Tinto, Oyebola, San, Dong, Buckley, Bridges, Bass, Europe, Bryk, Rose, Francisco, Forhad, Nwosu, Austria, Paris, Pascarella, Germany, Venture, Pers, Omarov, Hei, Pace, Mong, Kanu, Karine, Arpa, Alam, Smith, Luo, Coll, Jalali, Ayoola, Frankfurt, Cross, Akimov, Ness, Ind, Ling, Clinton, Zhou, Wien, France, Ahsan, Grow, River, Boston, Igwe, Okolie, Wilson, Addis, Oxford, Akwa, Heidi, Hayek, Washington]

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