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Design of IoT-Based and Data-Driven Mechanism to Drive Innovation in International Business and Finance Statistics.

  • Academic Journal
  • Yu, Zhichao1 (AUTHOR)
  • Mathematical Problems in Engineering. 3/10/2022, p1-7. 7p.
  • Article
  • The 43 national cross-border e-commerce policies promulgated by the Chinese government in recent years are selected as the research samples, and the PMC index model is applied to evaluate the mechanism design based on the IoT and data-driven innovation mechanism of international business finance statistics. On this basis, the implementation effects of specific policies on cross-border e-commerce in China are evaluated and analyzed, and the role of national macropolicies in guiding local policies is also discussed, with the aim of providing a reference basis for the Chinese government to formulate relevant policies, as well as providing new ideas and methods for the evaluation of cross-border e-commerce policies. [ABSTRACT FROM AUTHOR]
Full Text

AN0155699299;[1hiy]10mar.22;2022Mar14.06:54;v2.2.500

Design of IoT-Based and Data-Driven Mechanism to Drive Innovation in International Business and Finance Statistics 

1. Introduction

The 43 national cross-border e-commerce policies promulgated by the Chinese government in recent years are selected as the research samples, and the PMC index model is applied to evaluate the mechanism design based on the IoT and data-driven innovation mechanism of international business finance statistics. On this basis, the implementation effects of specific policies on cross-border e-commerce in China are evaluated and analyzed, and the role of national macropolicies in guiding local policies is also discussed, with the aim of providing a reference basis for the Chinese government to formulate relevant policies, as well as providing new ideas and methods for the evaluation of cross-border e-commerce policies.

The scale of enterprise groups is increasing, their business contents are becoming more and more complex, and their operation speed is becoming faster and faster. Under this scenario, the financial work of enterprise groups is facing a severe test. The large number of enterprise group members, the large span of hierarchy, and the spread of suborganizations in different parts of the world make the financial work of the group complicated and highly complex [[1]]. International Business Financial Statistics Center solves the problems of high cost, difficult control, slow response, and high financial and operational risks caused by the traditional financial work method of scattered work, redundant organizations, and layer-by-layer aggregation and greatly improves the operational efficiency of the enterprise group, which makes the enterprise group with heavy processes light and agile [[3]].

International business financial statistics means that the financial work of each enterprise group member is centralized and processed together, and the group members share one international business financial statistics center, which provides standardized, process-oriented, and high-efficiency financial services for all group members [[4]]. The International Business Financial Statistics Center concentrates the homogeneous and repetitive financial work of enterprise group members and adopts standardized and intensive processes to centralize these financial works, which can take advantage of the gathering of professional teams to form a scale effect, improve financial efficiency, and reduce operating costs [[5]]. Compared with the traditional group financial work, the essence of the international business financial statistics innovation is the transformation of working methods and business processes, which is the management practice of economy of scale and process reengineering ideas. Many studies have found that international business finance statistics has an important role in promoting the operation of enterprise groups and found that it has made outstanding contributions in controlling operating costs, improving the quality of financial work, enhancing the group's centralized control and decision-making ability, promoting the integration of business and finance, and improving risk control ability, etc. [[6]].

Data content diversity means that the dataset contains various forms of structured and unstructured data, data response speed requires more real-time data processing needs, and low data value density means that, in today's society, where large-scale data have become the norm, the massive amount of data must be analyzed to obtain valuable content [[8]]. Today, with the rapid development of the Internet and the Internet of Things, Big Data is not just a technology, but the core idea of Big Data in management is to regard data as an important asset, to discover knowledge and useful information from data, and to transform it into enterprise value. Applying Big Data technology to an enterprise group's financial sharing platform can significantly improve the system's data management capabilities, achieve high-speed response, and provide computing power for the use of tools such as artificial intelligence [[9]].

Artificial intelligence technology is the information technology that makes machines with human intelligence, thus replacing humans to perform certain tasks. At present, humans are still in the stage of weak artificial intelligence, but artificial intelligence technology in some fields has reached or exceeded the level of human ability. Artificial intelligence technologies mainly include machine learning, natural language processing, and computer vision [[11]]. Machine learning is the information technology that enables machines to simulate the human learning process so that they have certain learning and thinking ability. Machine learning is the core technology of artificial intelligence [[12]].

Machine learning technology can be used to automate the analysis of large-scale data, explore the laws of data, and discover its value; especially, for semistructured and unstructured data, machine learning is more efficient and effective than previous data mining techniques. Natural language processing is the study of technologies that use natural language to communicate with computers. Natural language processing enables computers to "understand" and use human language, thus replacing part of human work, such as document organization, translation, and question answering, through human-computer language communication [[13]]. Natural language processing currently consists of text analysis and speech recognition technologies. Text analysis can analyze and organize a large amount of unstructured documents and Web data, while speech recognition can assist in data collection and execution of voice commands, e.g., Shunfeng in China has used speech recognition technology to help field personnel quickly enter courier information, and Apple and Microsoft have developed their own voice assistants to assist in office work [[14]].

Computer vision is mainly image recognition technology, which has been widely used in many fields. Adobe and Hanvon Technology have very mature applications in text recognition, Skynet system based on image recognition and tracking has been applied in China's traffic control department on a large scale, and Deloitte Robotics has greatly improved the processing efficiency of financial bills by using image recognition technology [[15]]. The application of artificial intelligence technology will enhance the processing capability of the international business financial statistics platform for unstructured data, as well as its ability to automate the analysis of large-scale data, which is the key to support the international business financial statistics to provide new data products and services and deeply support the integration of industry and finance [[16]].

How to make reasonable policy optimization and adjustment on the basis of scientific evaluation of existing policies, so as to achieve the synergistic development of industry evolution and policy formulation [[17]], is the focus of attention of government departments and the majority of cross-border e-commerce participants. In view of this, the author applies the Policy Modeling Consistency (PMC) and text mining methods to construct a cross-border e-commerce policy evaluation index system and conducts an empirical study using the national cross-border e-commerce policies promulgated by the Chinese government between 2014 and 2017 as a sample, in order to provide theoretical support and decision-making basis for the new round of policy adjustment, formulation, and implementation [[18]].

2. Related Work

With the rapid advancement of global economic integration, cross-border e-commerce has become a research hotspot in the field of international trade and e-commerce. For example, Igwe et al. [[19]] applied the data envelopment analysis to analyze the policies related to the manufacturing industry in the United States and proposed improvement suggestions for the changes of the manufacturing environment; Wen et al. [[20]] focused on the policies of cultural and creative industries at home and abroad, introduced the gray correlation model to evaluate the effectiveness of the existing cultural industry policies in China, and provided feasible suggestions for the adjustment and formulation of related policies; Tseng et al. [[1]] applied the hierarchical analysis method to construct the evaluation indexes of the pension insurance system reform policy and evaluated the rationality of related policies. These research studies provide a basis for the evaluation of cross-border e-commerce policies. These studies provide useful references for the evaluation of cross-border e-commerce policies. In the existing research results of cross-border e-commerce policy evaluation, scholars mainly adopt qualitative evaluation methods such as content analysis and literature tracking. For example, Bahoo et al. [[2]] evaluated the effects of cross-border e-commerce policies in the areas of finance, taxation, privacy, logistics, etc., through empirical studies on the formulation and implementation of cross-border e-commerce policies driven by the network environment and information technology; Paul and Criado [[3]] conducted a questionnaire survey on cross-border e-commerce consumers and found that the development of cross-border e-commerce in the EU has problems such as rising costs of cross-language transactions and inefficient online payment systems. On this basis, the effectiveness of relevant policies is evaluated, and feasible suggestions are put forward for further improvement of cross-border e-commerce policies in the EU; Dluhopolskyi et al. [[14]] analyzed the current situation of cross-border e-commerce policy development in China in terms of the number of policies, topics involved, and enacting agencies, evaluated the policy implementation effect by the proposed policy evaluation criteria, and put forward countermeasure suggestions for the specific problems in the process of policy formulation and implementation.

The PMC index model is proposed to provide an effective method for single-policy evaluation. In view of this, the author starts from single-policy evaluation, selects representative hotspot policies as research samples, and adopts the PMC index model to evaluate and analyze the effects of these policies, so as to discover the key problems and constraints in policy formulation and provide decision support for the government to improve and adjust policies.

3. Interactive Cloud Model

3.1. Introduction to Interactive Cloud Organization Model

In this study, an interactive cloud organization model for large-scale personalization is proposed, as shown in Figure 1. The basic idea is as follows. In order to continuously improve the needs of business users and business user experience, and carry out large-scale business personalized customization on the interactive cloud manufacturing platform, it is necessary to deeply analyze the needs of business users and reasonably adjust and modify their respective needs, so as to meet the real product requirements of customized enterprises. In this platform, each enterprise user can participate in the production service of the whole life cycle of customized products and use the Internet of Things technology, cloud computing technology, and physical information fusion technology [[9]] to adjust and modify the product structure and parameter requirements at any node, which realizes the all-round customization of customized products by enterprise users and enables enterprises to produce customized products that meet the needs of enterprise users in an efficient, fast, and low-cost manner so that the satisfaction of enterprise users is maximized.

Graph: Figure 1 Interactive cloud organization model for large-scale personalization.

3.2. Intelligent Interaction in Cloud Environments

In order to improve the validity, real rationality, and customized business user experience of enterprise user requirements, the intelligent interaction process of large-scale personalization in cloud manufacturing environment is designed, as shown in Figure 2, and the specific process is as follows.

Graph: Figure 2 Intelligent interaction process of MP in cloud manufacturing environment.

The text of enterprise user requirements is loaded into the cloud platform, and the enterprise user requirements are analyzed and processed, and the prediction and evaluation system, based on the enterprise user behavior, personality preference, scenario, and other information, converts the prediction and evaluation system. The enterprise user behavior, personality preferences, scenarios, and other information will convert the implicit user requirements into expected product functional requirements through the prediction and evaluation system and selective evaluation and rationalization preprocessing using natural language processing technology [[14]]. The enterprise user accesses the stored resource information and uses the personalized recommendation system to modify the expected product functional requirements appropriately and uses the personalized module system to retrieve the product manufacturing resources in the cloud pool based on appearance, functionality, performance, economy, and extensibility as the standard intelligence to retrieve the manufacturing resources that meet the product functional requirements.

Business users participate in the personalized recommendation system to select the appropriate custom product parameters and store the demand information. If the manufacturing resources that do not fully match the product functional requirements and parameters are not available, then the feasible demand information will be stored after improvement and adjustment of the product functional requirements and parameters through interactive negotiation among business users, core customization enterprises, and technical expert groups using the interactive system. If it does not meet the requirements, we will conduct interactive negotiation again until we get the product requirement information that satisfies the enterprise users and meets the manufacturing resources.

Adopt the third-party online data model library and establish the product structure model with 3D software to convert the product demand into product structure demand, while at the core custom enterprise side, the product structure and reference demand, i.e., custom product target, are selected modularly using the product service system to determine the custom product category and plan together with the custom product demand of other enterprise users.

Using the demand interaction conversion process model in the large-scale personalized interactive cloud platform, the customized product demand stored on the platform is expressed into the manufacturing demand of each batch of parts through the monitoring service system, demand configuration system, interaction system, and expert system and is assigned to the corresponding manufacturing group and service provider for the production and processing of customized products in accordance with the CCMS.

3.3. Demand Interaction Transfer

Since the customized parts are the core production parts of the business users' requirements, some parts parameters and standard parts specifications have an optional range, so each business user can repeatedly interact and negotiate through the platform's interactive system to adjust and modify the features (i.e., parts structure and parameters) of the parts in the total manufacturing requirements matrix T until all features <msub>Aij</msub> meet the business users' requirements and output the parts features. The features of each part of the customized product are represented as follows:

(1)<msub>Pi</msub>=<msub>Ai1</msub>,<msub>Ai2</msub>,...,<msub>Aiq</msub>,

where <msub>Aiq</msub> is the characteristics of the customized product and q denotes the customized product is decomposed into q parts of the manufacturing requirements.

Number of customized products:

(2)N=<msub>P1</msub>,<msub>P2</msub>,...,<msub>Pn</msub>.

Total matrix of component manufacturing requirements consolidated into custom products:

(3)T=<msub>A11</msub><msub>A12</msub>⋯<msub>A1m</msub><msub>A21</msub><msub>A22</msub>⋯<msub>A2m</msub>⋮⋮⋮<msub>An1</msub><msub>An2</msub>⋯<msub>Anm</msub>T,

where n is the number of customized products, m is the demand item containing differentiated parameters such as size and performance, and <msub>Anm</msub> is the mth customized feature and specification requirement of the nth customized product.

Then, the requirements are reorganized, and each part is decomposed and categorized into batches of parts (which contain a large number of standard parts and a small number of customized parts), and the standard parts can be simply categorized into the same batch according to the model number, while a small number of customized parts are subjected to the metric analysis of similarity clustering algorithm [[16]] and then categorized into batches, and the steps of this similarity clustering algorithm are as follows.

Calculate the similarity of custom parts:

(4)<msub>d12</msub>=<mstyle displaystyle="true"><munderover>∑k=1n</munderover><msub>X1k</msub>−<msub>Xkmin</msub><msub>Xkmax</msub>−<msub>Xkmin</msub>−<msub>X2k</msub>−<msub>Xkmin</msub><msub>Xkmax</msub>−<msub>Xkmin</msub>2</mstyle>1/2.

The interval mean is added to the "Euclidean distance method," and <msub>Xkmax</msub> and <msub>Xkmin</msub> represent the maximum and minimum values of the kth column of parameters, respectively. The result of equation (4) is expressed as a similarity matrix:

(5)D=000⋯0<msub>d21</msub>00⋯0<msub>d31</msub><msub>d32</msub>0⋯0⋮⋮⋮⋮<msub>dn1</msub><msub>dn2</msub><msub>dn3</msub>⋯0.

The closer the value of <msub>dij</msub> is to 0, the higher the similarity of i and j customization requirements.

Based on the similarity of the customized parts, each similar customized part is aggregated into a production batch. First, a similarity threshold <msub>dmax</msub> is set, and the similarity threshold <msub>dmax</msub> is taken by using an expert system to analyze the parameters of customized parts by technical experts of registered enterprise service providers in the cloud platform and set based on relevant industry experience, and if <msub>dij</msub> < <msub>dmax</msub> exists, all <msub>dij</msub> that meet the conditions are included in the same batch.

4. Cross-Border e-Commerce Policy Evaluation

Statistical analysis and text mining methods are applied to extract specific evaluation indexes from the policy text and set the index parameters. On this basis, the input-output table of cross-border e-commerce policy evaluation is constructed to provide a standard basis for the calculation of PMC indexes and surface plotting of individual policies.

5Since the measures of the four level indicators of policy tendency, policy content, action area, and action target cannot be obtained directly from the information of the sample attributes, using the naming entity identification technology, the names of industries in the policy text are used as the second-level evaluation indicators for the policy "action area" and the names of institutions and professions are used as the second-level indicators for the "action target." Thus, a total of 47 secondary evaluation indicators were established [[21]].

The PMC index can reflect the overall effect of policy formulation, and its calculation process follows the following four steps: first, establish the correlation between the primary and secondary evaluation indicators through the cross-border e-commerce policy evaluation table; second, code the secondary evaluation indicators to determine the specific value of each secondary indicator, and finally, calculate the PMC index of each policy by equations (6)–(9):

(6)X∼N0,1,(7)X=XR:0∼1,(8)<msub>Xi</msub><mstyle displaystyle="true"><munderover>∑j=1n</munderover><msub>Xij</msub>T<msub>Xij</msub></mstyle>,(9)PMC=<mstyle displaystyle="true"><munderover>∑i=19</munderover><msub>Xi</msub><mstyle displaystyle="true"><munderover>∑j=1n</munderover><msub>Xij</msub>T<msub>Xij</msub></mstyle></mstyle>.

In equations (7) and (9), i is a primary variable, i = 1, 2, 3, ..., m, and j is a secondary variable, j = 1, 2, ..., n.

5. Experimental Results

Based on the results of PMC index calculation, I selected one policy (P4 and P8) from the acceptable level and one from the excellent level for analysis and drew the corresponding PMC surface (as shown in Figure 3). The PMC surface is created by identifying the coordinates of each indicator in a 3 × 3 matrix. Different color blocks represent different values of index scores, and the convex part of the surface indicates that the policy scores higher on the corresponding evaluation index, while the depressed part indicates that it scores lower on the corresponding evaluation index.

Graph: Figure 3 Policy PMC surface plan.

We plot the PMC indexes of the eight cross-border e-commerce policies in chronological order. It can be seen that the scores of the above eight policies fluctuate greatly, but generally show an upward trend. As shown in Figure 3, different regulatory schemes are formulated for both, which build a good market environment for cross-border e-commerce enterprises and consumers. This policy is a temporary policy during the transition period, and the PMC index values of both policies are low.

With the rapid development of China's cross-border e-commerce, relevant government departments have introduced a series of national policies to actively guide the standardized operation of industry players, with strong guidance and influence, as shown in Figure 4.

Graph: Figure 4 Different innovation capabilities.

The application of artificial intelligence tools requires big data systems to provide data base and computing power for them, so the improvement of the international business financial statistics platform requires close cooperation between the two. Combining the existing technical capabilities and the development experience of the international business financial statistics platform, the following improvement plan is proposed in this study. The improved international business financial statistics platform consists of three levels of systems, data collection system, big data management system, and data application system, as shown in Figure 5.

Graph: Figure 5 The effect of the platform under different policies.

6. Conclusions

Based on the design of IoT and data-driven innovation mechanism of international business and financial statistics, the PMC index model evaluation method is applied. The functional upgrade of the center will prompt big data analysis ability to become an important professional skill for economic industry, and the role of finance personnel, especially management accountants, will move closer to that of data scientists from being mere accounting accountants. This trend is already visible in the economic industry and is expected to spread to the accounting industry in the near future.

Data Availability

The data underlying the results presented in the study are available within the article.

Disclosure

The author confirms that the content of the manuscript has not been published or submitted for publication elsewhere.

Conflicts of Interest

The author declares no potential conflicts of interest.

Authors' Contributions

The author has seen the manuscript and approved to submit to the journal.

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By Zhichao Yu

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1School of Economics and Management, Huaibei Normal University, Huaibei, Anhui 235000, China
4017
1024-123X
10.1155/2022/6792561
155699299

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