Research on evaluation system of cross-border E-commerce platform based on the combined model

Lijun Lv

Detalles de publicación y publicación

Applied Mathematics and Nonlinear Sciences

Detalles de la revista

Formato: Revista
eISSN: 2444-8656
Primera edición: 01 Jan 2016
Calendario de la edición: 2 veces al año
Idiomas: Inglés

Against the continuous impact of the international financial crisis, the economy is in a stage of continuous differentiation, which presents a slow recovery and development trend, the trade pattern in the world is constantly being adjusted and changed, the global supply chain tends to be flat and China’s traditional foreign trade business has been facing a downturn in recent years. Especially, the competitiveness of small- and medium-sized enterprises (SMEs) is obviously weakened, the cost of enterprise resources and other factors keeps rising and there is a strong demand for a new way of foreign trade. Under such a condition, cross-border e-commerce (CBEC) came into being [1–3]. The main body of the CBEC market is made up of SMEs, which will help them to internationalise [4]. CBEC is characterised by low cost and low trade barriers so that it broadens the channels for SMEs to enter the overseas markets, and at the same time give them the opportunity to participate in the global economy [5, 6].

The business model of the CBEC is “Internet + Made in China + cross-border trade”, which is an innovation of the traditional foreign trade model and is characterised by competition, inclusiveness, freedom and openness. The development of Internet technology provides technical support for the development of CBEC, and online shopping has become an important consumption platform all over the world. Goods purchased through CBEC are not only cheap, but also rich in categories, and the ways of obtaining them are more convenient and diversified [7, 8].

CBEC is the inevitable trend in trade development. CBEC, which has the advantages of high efficiency, low cost and taking the Internet to realise transactions in foreign trade, is increasingly becoming the development bottleneck brought about by the traditional foreign trade mode, thus making companies to enter new market or global value chains. However, for traditional foreign trade, SMEs, for example, will be restricted by technology, capital, manpower, material resources and other conditions in their operation, and the cost of a self-built CBEC platform is high. Therefore, it is appropriate to use third-party CBEC platforms to transform the CBEC industry at the initial stage, which will be with relatively low risk and a high success rate [9–11]. The CBEC platform is one of the important channels for consumers to communicate with enterprises, and it is also a vital way for enterprises to directly connect with commodity inspection agencies, customs agencies and cross-border logistics [12]. For SMEs, the choice of relevant platforms is crucial, which will determine whether the CBEC industry of SMEs can develop smoothly in the future. Therefore, in the rational allocation of the CBEC platform, using data analysis to evaluate it objectively and accurately can provide a scientific decision for enterprises.

Selection of evaluation index in CBEC platform

2.1

Principle of index selection

To construct a comprehensive evaluation index system of the CBEC platform, it is necessary to combine the actual situation of enterprises with the characteristics of the platform, and establish scientific, comprehensive, objective and rigorous principles on the evaluation index. The details are as follows:

2.1.1

Comprehensiveness

The comprehensive evaluation index system should comprehensively analyse the comprehensive functional characteristics of platforms, such as logistics services, third-party payment services, customer groups and popularity.

2.1.2

Scientificity

Taking scientific principles as scientific evaluation indicators, the characteristics of the CBEC platform are highlighted by objective results. Based on the theory, combined with the practical application of enterprises, the scientific, reasonable, reliable and practical evaluation indexes at all levels are ensured.

2.1.3

Operability

The comparability here is also called unity, which emphasises horizontal comparison and refers to the comparability between different CBEC platforms. At the same time, the calculation measures of the evaluation index selection must be unified, and each index can not only reflect the characteristics of each platform but also facilitate for collection and operation.

2.1.4

Qualitative and quantitative

In order to reflect the characteristics of the platform scientifically and rigorously, the comprehensive evaluation index system can not only consider qualitative factors, but also deal with them quantitatively, so as to facilitate calculation and analysis, and make the index reflect the characteristics of the platform more scientifically and reasonably.

2.1.5

Typical principle

CBEC is global, real-time, paperless and low-cost, which is different from the traditional foreign trade and domestic e-commerce, so the evaluation index should be typical. The setting of a comprehensive evaluation index system should fully consider the quality of service and system of the CBEC platform.

2.1.6

Dynamic principle

It is necessary to consider the dynamic changes in the CBEC platform characteristics in different periods to ensure the long-term rationality of the evaluation indicators.

2.2

Classification of evaluation indicators

Based on the above principles and a large number of literature summaries, the dimensions of system function, cost, service quality and platform quality are finally determined as the first-level indicators, including 16 second-level indicators, as shown in Figure 1. The construction of a comprehensive evaluation index system, it will lay the foundation for the choice of CBEC for SMEs.

2.2.1

B1 systematic functions

B11 security and reliability: the security and reliability of the platform system means that the platform can protect and keep confidential the user’s account and private information, which provide a safer network environment for users and enhance their sense of security and trust.

B12 Data analysis function: The platform has the function of data analysis, and it does not need any third-party tools to provide the seller with the required operation and management data, assist them in analysing the target market and related business analysis, and at the same time provide the seller’s real transaction data to the buyer.

B13 ERP support: the platform cooperates with third-party managers to share information and data, thus simplifying the seller’s internal operation and management. Through the ERP system, not only can the complicated supply chain process inside the seller be simplified, but also the defect of data transmission that the platform system function cannot complete can be made up.

2.2.2

B2 service quality

B21 Logistics service: Consumers’ shopping experience will be affected by timeliness, logistics attitude and price. Parcel, overseas warehouse, dedicated-line logistics and international express delivery are all important components of CBEC logistics.

B22 Third-party payment service: the platform of the third-party payment is involved in CBEC transactions as service providers, and they can quickly process the transaction information of buyers and sellers by simplifying the tedious transmission between banks. This method of payment is safe and efficient, and is widely favoured by users.

B23 Overseas warehouse service: Platform overseas warehouse mainly refers to the CBEC platform and enterprises delivering goods to sales destinations individually or cooperatively, and managing goods, warehousing, picking, packaging and delivery in this process.

B24 Foreign exchange settlement service: it can realise the exchange and sharing of data and information among enterprises, customs and related administrative departments. The platform can provide declaration of agent customs, foreign exchange settlement and tax refund for merchants.

2.2.3

B3 Quality of platform

B31 Visits in platform: platform visits include Alex ranking, which refers to the frequency of visitors and traffic on the platform. The more the visits and traffic, the more helpful it is for the order conversion rate of the merchant store.

B32 Matching degree: Different platforms have different positions and ideas. The seller needs to judge the groups of customer and distribution of the platform, and choose the platform that matches his target market. Whether the functions, characteristics, attributes and uses of the products are within the category of hot-selling products in the platform should be considered.

B33 Marketing method: this mainly refers to the orientation of customer group and pricing method, etc.

B34 Operation and management mode: this mainly refers to the rigid requirements of the platform in operation and management, that is, whether inventory backlog is required.

2.2.4

B4 Cost

B41 Entry fee: platform entry fee, also known as member service fee, means that the platform provides a trading place for the seller, and the seller must be charged a certain percentage of management fee. After paying the member service fee, the seller can enjoy various services provided by the platform.

B42 Value-added service fee: After the buyers and sellers become members of the platform, the platform will charge the buyers and sellers a certain percentage of value-added service fee, which covers booth promotion, pay-per-click and bidding ranking, etc.

B43 Commission of transaction: merchants register for free, and only charge a certain commission according to different industries and different proportions after the successful transaction between buyers and sellers.

B44 Transaction fee: merchants register with the platform free of charge. After the successful transaction between the buyer and the seller, the platform will charge the seller a certain percentage of transaction fee for payment to the transaction provider.

B45 Marketing fee: Marketing and promotion fee is the operation and management fee charged by the platform to assist enterprises in product promotion.

Weight calculation of each index based on analytic hierarchy process (AHP)

AHP is a multi-criteria decision-making form combining quantitative and qualitative analysis. It is characterised in that on the basis of analysing the essence, the decision-making process of complex problems is presented mathematically with less quantitative information. Through data to solve complex decision-making problems with multi-objectives, multi-criteria or no structural characteristics, the purpose of simplifying the decision-making scheme is achieved [13, 14].

3.1

Model building

As shown in Figure 2, the general steps of AHP mainly include four steps. The first step is to build the hierarchical structure model; second, construct the judgement matrix; third, obtain hierarchical single sorting and its consistency check; and finally implement hierarchical general ranking and its consistency check.

General steps of AHP. AHP, analytic hierarchy process

3.1.1

Establishment of a hierarchical structure model

The decision to solve the problem is divided into three levels, namely, the target level, the decision criterion level and the decision scheme level. In the application of AHP, the problem to be solved is to calculate the relative weight of the bottom layer to the top layer, so as to sort the schemes and measures at the bottom layer and choose the best scheme [15, 16]. In this way, the objective of independent direction, variables to be thought of (choice models) and choice items are partitioned into the most significant level, the centre level and the least level as per their relationship.

3.1.2

Construction of a comparative judgement matrix

The construction of a judgement matrix is to determine the weight of the target layer by comparing each element with each other pairwise. In the AHP, the importance of each element in the judgement matrix is quantitatively displayed by introducing a scale of 1–9. A size of 1 demonstrates that two components are of equivalent significance, and a size of 9 shows that the previous is a higher priority than the last option. The proportional of scale demonstrates the significance of looking at the trade request of two elements.

The comparative judgement matrix of A is: 1 $A = {(a_{i j})}_{m \times n} = (\begin{matrix} a_{11} & a_{12} & \dots & a_{1 n} \\ a_{n 1} & \dots & \dots & \dots \end{matrix})$ where the elements in A should meet the following requirements: $a_{i j} > 0$ ; $a_{i j} = \frac{1}{a_{j i}}$ ; $a_{i i} = 1$ .

3.1.3

Hierarchical single sort

Hierarchical single sort refers to the evaluation of all elements in pairs for an element in the previous layer, and arrange the important order. The concrete calculation can be carried out according to the judgement matrix A, and the calculation can ensure that it can meet the characteristic root and characteristic vector conditions of AW = λ_max W, where the largest feature root of A is λ_max, and the normalised feature vector corresponding to λ_max is W, W_i is a component of W, which refers to the weight, and corresponds to the single ordering of its corresponding elements. Use the judgement matrix to calculate the weight of each factor a_ij to the target layer.

The calculation steps of the weight vector (W) and the maximum feature (λ_max) are as follows:

First of all, take the product of the row elements according to Eq. (2), and then raise it to the nth power: 2 ${\vec{W}}_{i} = \sqrt{\prod_{j = 1}^{n} a_{i j}} i, j = 1, 2, \dots, n$

Then, it is normalised into a ranking weight vector by formula (3), which is denoted as W, then W_i = (W₁,W₂, ⋯ ,W_n)² is the result of judging the hierarchical single ranking of the matrix.

3 $W_{i} = {\sum^{¯}}_{i = 1}^{n} {\bar{W}}_{i}$

Finally, determine the maximum characteristic root of the matrix by Formula (4): 4 $λ_{m a x} = \frac{1}{n} \sum_{i = 1}^{n} A W_{i}$

3.1.4

Consistency inspection

If the n-order judgement matrix is B, the maximum characteristic root λ_max can be obtained by the following methods: 5 $\begin{aligned} BW = λ W \end{aligned}$

The following consistency index (CI) is taken to test the CI of judgement: 6 $\begin{aligned} CI = \frac{λ_{max} - n}{n - 1} \end{aligned}$

CI = 0 means that the judgement matrix is completely consistent, and the larger the CI, the more serious the inconsistency of the judgement matrix.

3.1.5

Hierarchical general sorting

Assuming that A is the target layer, the weight coefficients of m total ranking of factor levels are as follows a₁, a₂, …, a_m; B is the middle layer, and the weight coefficients of n hierarchical single sort of factors are $b_{1}^{1} \dots b_{n}^{m}$ ; therefore, the total ranking of layer B is calculated according to formula (7): 7 $b_{i} = \sum_{j = 1}^{m} a_{j} b_{i j}$

Set the B layer as B₁, B₂ …, B_n On the upper layer (Alayer), the hierarchical ranking CI of factors A_j(j = 1,2, ⋯ ,m) is CI_j, the random CI is RI_j, and the consistency ratio (CR) of the hierarchical total sort is: 8 $C R = \frac{a_{1} C I_{1} + a_{2} C I_{2} + \dots + a_{m} C I_{m}}{a_{1} R I_{1} + a_{2} R I_{2} + \dots + a_{m} R I_{m}} = \frac{\sum a_{i} C I_{i}}{\sum a_{i} R I_{i}} = \frac{\sum a_{i} C I_{i}}{\sum a_{i} R I_{i}} = \frac{C I}{R I}$

When CR < 0.1, it is considered that the overall ranking of the hierarchy has passed the consistency test, otherwise, it is necessary to readjust the element values of the judgement matrix, and make the final decision according to the overall ranking of the decision-making level.

3.2

Results of weight calculation in evaluation index

3.2.1

Primary indicators

The maximum eigenvalue λ_max = 5.394271 and eigenvector W (0.369381, 0.461749, 0.122011, 0.069144) of the judgement matrix are calculated. The weight of the primary index is shown in Table 1:

Table 1

Weight calculation of primary index

	System function	Platform quality	Quality of service	Cost	Weight value
System function	1	3	4	7	0.369381
Platform quality	1/4	1/4	1	4	0.461749
Service quality	1/3	1/3	4	4	0.122011
Cost	1/7	1/7	1/4	1	0.069144

Where n = 4, CI = 0.098568, CR = 0.088007 < 0.1 (rounded off) can be calculated, and the weight can be used by the consistency test.

CT, consistency index; CR, consistency ratio.

3.2.2

Secondary indicators

Similarly, the weights of secondary indicators below the primary indicators can be calculated in turn, and the weights of secondary indicators are shown in Tables 2–5 in turn.

Table 2

Weight calculation of system function

	B11 Safety and reliability	B12 Data analysis function	B13 Support ERP	Weight value
B11 Safety and reliability	1	4	7	0.587033
B12 Data analysis function	1/4	1	4	0.287321
B13 Support ERP	1/7	1/4	1	0.125646

The maximum characteristic value λ_max = 5.441373, and the CRCR = 0.098521<0.1 (rounded). Through the consistency test, the weight value can be used.

CR, consistency ratio.

Table 3

Weight calculation of service quality

	B21 logistics service	B22 third-party payment service	B23 overseas warehouse service	B24 foreign exchange settlement service	Weight value
B21 logistics service	1	1/3	1/4	1/2	0.072149
B22 third-party payment service	3	1	1	4	0.218498
B23 overseas warehouse service	4	3	1	7	0.642498
B24 foreign exchange settlement service	2	1/7	1/4	1	0.069144

The maximum characteristic value = 4.230717 and the CR = 0.086411 <0.1 (rounded). Through the consistency test, the weight value can be used.

CR, consistency ratio.

Table 4

Weight calculation of platform quality

	B31 platform visits	B32 matching degree	B33 Marketing Mode	B34 operation management mode	Weight value
B31 Visits	1	1	2	1	0.252149
B32 Matching degree	1	1	1	2	0.338498
B33 Marketing Mode	1/4	3	1	2	0.162498
B34 Operation management mode	1	1/2	1/4	1	0.053144

The maximum characteristic value λ_max = 5.363058, and the CR = 0.081040 <0.1 (rounded). Through the consistency test, the weight value can be used.

CR, consistency ratio.

Table 5

Weight calculation of cost

	B41 platform entry fee	B42 Value-added service fee	B43 transaction commission	B44 transaction fee	B45 Marketing and promotion fee	Weight value
B41 Entry fee	1	1	5	7	3	0.399572
B42 Value-added service fee	1	1	4	2	3	0.294969
B43 Transaction commission	1/4	3	1	1	3	0.116201
B44 Transaction fee	1/3	1/2	1	1	2	0.111480
B45 Marketing and promotion fee	1/4	1/4	1/4	1/2	1	0.077777

The maximum characteristic value λ_max = 5.392050 and the CR = 0.087511 <0.1 (rounded). Through the consistency test, the weight value can be used.CR, consistency ratio.

Fuzzy comprehensive index evaluation

Take Amazon, a CBEC platform, as an example, to evaluate the fuzzy comprehensive index. 20 executives from the foreign trade department of X Company were investigated and interviewed, and the comment sets involved were arranged into five categories according to the order of priority, including “most consistent, very consistent, consistent, generally consistent and non-consistent”. Fuzzy evaluation method is used to process the questionnaire data, and finally the best option is selected.

4.1

Establishment of data set

Evaluation set: v = {1,2,0} = {most consistent, very consistent, consistent, generally consistent and non-consistent}, and the corresponding numerical sets are 90,75,60,45,30, respectively.

Weight set: the index weight set is established according to the above-mentioned secondary index weight.

4.2

Calculation of Index membership

According to the survey results, the subordinate subsets of the Amazon platform in system function evaluation are as follows: $\begin{aligned} R_{11} & =(0,0.3,0.3,,0) \\ R_{12} & =(0.3,0.5,0.1,,0) \\ R_{13} & =(0.1,0.7,0,,0) \\ R_{14} & =(0.1,0.4,0.4,,0) \end{aligned}$

Therefore, its fuzzy evaluation matrix is: $\begin{aligned} [\begin{matrix} 0, 0.3, 0.3, 0 \\ 0.3, 0.5, 0.1, 0 \\ 0.1, 0.7, 0, 0 \\ 0.1, 0.4, 0.4, 0 \end{matrix}] \end{aligned}$ $${\rm}\left[ {\matrix{ {0,\;0.3,\;0.3,\;0} \cr {0.3,0.5,0.1,0} \cr {0.1,\;0.7,\;0,\;0} \cr {0.1,\;0.4,\;0.4,0} \cr } } \right]{\rm}$$

After normalisation, the evaluation value of “system function” membership degree is as follows: $\begin{aligned} S_{1} = (0.202176, 0.573626, 0.149667, 0.07453, 0) \end{aligned}$

Similarly, the membership of service quality, platform quality and cost are as follows: $\begin{aligned} S_{2} = (0.363145, 0.268746, 0.247065, 0.121043, 0) \end{aligned}$ $\begin{aligned} S_{3} = (0.534502, 0.191682, 0.172305, 0.101512, 0) \end{aligned}$ $\begin{aligned} S_{4} = (0, 0.134479, 0.358858, 0.460336, 0.046326) \end{aligned}$

4.3

Result analysis

According to the above evaluation system, except for Amazon, the membership degrees of the four platforms of AliExpress, Wish and Lazada are evaluated, and the final evaluation scores of the four platforms are calculated. The results are shown in Table 6: most consistent, very consistent, consistent, generally consistent and non-consistent

Table 6

Evaluation scores of different CBEC platforms

	Most consistent	Very consistent	Consistent	Generally consistent	Non-consistent	Score	Rank
Amazon	0.238	0.420	0.249	0.083	0.0013	80.20	1
Lazada	0.020	0.521	0.359	0.09	0	74.36	2
AliExpress	0.086	0.248	0.523	0.124	0.019	62.68	4
Wish	0.078	0.314	0.441	0.139	0.020	66.85	3

CBEC, cross-border e-commerce.

According to the above scores, the Amazon platform has the highest evaluation score, which is consistent with the questionnaire results of X company executives. Digital e-commerce platform evaluation can provide suggestions for companies to match the CBEC platforms.

Conclusion

When making decisions, managers of cross-border enterprises must not only consider many intangible qualitative factors, but also make the best choices according to the judgement of quantitative factors. Therefore, in this paper, an evaluation index system of the CBEC platform was constructed by AHP and fuzzy evaluation method to quantify enterprise management. The results show that in this paper, the total ranking of the 16 evaluation indicators selected by this paper all passed the consistency test, and four e-commerce platforms, including Amazon, were evaluated by fuzzy analysis. Among them, the Amazon platform scored the highest, with 80.20 points, which was consistent with the comprehensive evaluation of enterprise executives, thus providing suggestions for companies to match CBEC platforms.

Figuras y tablas

Weight calculation of primary index

	System function	Platform quality	Quality of service	Cost	Weight value
System function	1	3	4	7	0.369381
Platform quality	1/4	1/4	1	4	0.461749
Service quality	1/3	1/3	4	4	0.122011
Cost	1/7	1/7	1/4	1	0.069144

Weight calculation of service quality

	B21 logistics service	B22 third-party payment service	B23 overseas warehouse service	B24 foreign exchange settlement service	Weight value
B21 logistics service	1	1/3	1/4	1/2	0.072149
B22 third-party payment service	3	1	1	4	0.218498
B23 overseas warehouse service	4	3	1	7	0.642498
B24 foreign exchange settlement service	2	1/7	1/4	1	0.069144

Weight calculation of cost

	B41 platform entry fee	B42 Value-added service fee	B43 transaction commission	B44 transaction fee	B45 Marketing and promotion fee	Weight value
B41 Entry fee	1	1	5	7	3	0.399572
B42 Value-added service fee	1	1	4	2	3	0.294969
B43 Transaction commission	1/4	3	1	1	3	0.116201
B44 Transaction fee	1/3	1/2	1	1	2	0.111480
B45 Marketing and promotion fee	1/4	1/4	1/4	1/2	1	0.077777

Weight calculation of system function

	B11 Safety and reliability	B12 Data analysis function	B13 Support ERP	Weight value
B11 Safety and reliability	1	4	7	0.587033
B12 Data analysis function	1/4	1	4	0.287321
B13 Support ERP	1/7	1/4	1	0.125646

Weight calculation of platform quality

	B31 platform visits	B32 matching degree	B33 Marketing Mode	B34 operation management mode	Weight value
B31 Visits	1	1	2	1	0.252149
B32 Matching degree	1	1	1	2	0.338498
B33 Marketing Mode	1/4	3	1	2	0.162498
B34 Operation management mode	1	1/2	1/4	1	0.053144

Evaluation scores of different CBEC platforms

	Most consistent	Very consistent	Consistent	Generally consistent	Non-consistent	Score	Rank
Amazon	0.238	0.420	0.249	0.083	0.0013	80.20	1
Lazada	0.020	0.521	0.359	0.09	0	74.36	2
AliExpress	0.086	0.248	0.523	0.124	0.019	62.68	4
Wish	0.078	0.314	0.441	0.139	0.020	66.85	3

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Applied Mathematics and Nonlinear Sciences

Research on evaluation system of cross-border E-commerce platform based on the combined model

Lijun Lv
Lijun Lv

Publicado en línea: 20 May 2022

Volumen & Edición: AHEAD OF PRINT

Páginas: -

Recibido: 25 Mar 2022

Aceptado: 10 Apr 2022

Detalles de publicación y publicación

Applied Mathematics and Nonlinear Sciences

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Fig. 1

Fig. 2

Figuras y tablas

Fig. 1

Fig. 2

Weight calculation of primary index

Weight calculation of service quality

Weight calculation of cost

Weight calculation of system function

Weight calculation of platform quality

Evaluation scores of different CBEC platforms

Referencias

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Applied Mathematics and Nonlinear Sciences

Research on evaluation system of cross-border E-commerce platform based on the combined model

Lijun LvLijun Lv

Publicado en línea: 20 May 2022

Volumen & Edición: AHEAD OF PRINT

Páginas: -

Recibido: 25 Mar 2022

Aceptado: 10 Apr 2022

Detalles de publicación y publicación

Applied Mathematics and Nonlinear Sciences

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Weight calculation of primary index

Weight calculation of service quality

Weight calculation of cost

Weight calculation of system function

Weight calculation of platform quality

Evaluation scores of different CBEC platforms

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