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

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^{2}

^{3}

^{4}

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## Abstract

**:**

## 1. Introduction

- Develop a ‘student-centered’ model for evaluating EE and services in HEIs.
- Provide practical guidance for evaluated HEIs.

## 2. Literature Review

#### 2.1. Rural E-Commerce and Entrepreneurship Education

#### 2.2. Evaluation of Entrepreneurship Education in Higher Education Institutions

## 3. Model Construction

#### 3.1. Constructing Objectives

#### 3.2. Construction Principles

#### 3.2.1. Systematic and Comprehensive

#### 3.2.2. Developmental and Dynamic

#### 3.2.3. Hierarchy and Scientificity

#### 3.3. Evaluation Index Construction

#### 3.3.1. Learning Input

#### 3.3.2. Educational Support

#### 3.3.3. Educational Process

#### 3.3.4. Feedback Effectiveness

## 4. Research Methodology and Empirical Analysis

#### 4.1. Research Methodology and Principle

- 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.

#### 4.2. Empirical Analysis

#### 4.2.1. Establishing the Evaluation Factor Set

_{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.

#### 4.2.2. Determining the Weights of Each Index

_{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}.

- Construction of judgment matrix

- 2.
- Calculation of eigenvectors and eigenvalues

_{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:

_{0}= (0.832, 0.392, 1.150, 1.625)

^{T}

_{1}= (0.491, 0.892, 1.617)

^{T}

_{2}= (0.771, 0.484, 1.667, 1.078)

^{T}

_{3}= (1.339, 0.805, 0.638, 1.792, 0.426)

^{T}

_{4}= (0.698, 0.496, 1.389, 1.417)

^{T}

_{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.

_{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

_{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.

_{max}− n)/(n − 1)

- 4.
- Hierarchical total ranking and consistency test

_{T}< 0.1 the analysis results can be used for decision-making, otherwise, readjustment is required [57].

_{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.

- 5.
- Index weights summarization

#### 4.2.3. Determine the Evaluation Object Rubric Set

_{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.

#### 4.2.4. Fuzzy Comprehensive Evaluation

_{k}(k = 1, 2, …, s), the fuzzy factor vector is determined according to the size of each factor A

_{k}= (a

_{k1}, a

_{k2}, …, a

_{kn}), and the fuzzy operation is performed with the single-factor evaluation matrix R

_{k}, wherein the single-factor evaluation matrix R

_{k}is composed of r

_{kij}(i = 1, 2, …, n; j = 1, 2, …, m), we can get:

_{1}, A

_{2}, A

_{3}, A

_{4,}respectively, based on the weights of each indicator determined using AHP above.

_{1}= (0.1683, 0.2972, 0.5390)

_{2}= (0.1928, 0.1209, 0.4168, 0.2695)

_{3}= (0.2678, 0.1610, 0.1277, 0.3583, 0.0852)

_{4}= (0.1745, 0.1240, 0.3471, 0.3544)

_{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.

_{1}yields.

_{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.

_{2}= (0.4322, 0.3697, 0.1815, 0.0166)

_{3}= (0.3711, 0.4705, 0.1428, 0.0156)

_{4}= (0.4153, 0.4061, 0.1698, 0.0088)

_{k}, the total evaluation matrix R of U is obtained as:

_{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.

## 5. Discussion

#### 5.1. Theoretical Implications

#### 5.2. Practical Implications

## 6. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**Schemes follow the same formatting. Implementation steps of Analytic Hierarchy Process combined with Fuzzy Comprehensive Evaluation Method.

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 |

U | u_{1}_{1} | u_{12} | u_{13} |
---|---|---|---|

u_{1}_{1} | 1 | 1/2 | 1/3 |

u_{12} | 2 | 1 | 1/2 |

u_{13} | 3 | 2 | 1 |

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 |

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 |

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 |

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 |

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 |

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 |

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 |

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 |

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 |

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## 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