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Based on the background of system intelligence in the Internet of things era, this paper applied the design field of interaction design and user experience in the early days, and conducted further in-depth investigation through a large number of case studies and the use of quantitative and qualitative investigation methods. Based on this, the theories and strategies of the interaction design between enterprise members and intelligent machines were put forward and tested by actual design. At present, air pollution, energy shortage, and other issues are becoming more and more prominent, and calls for energy conservation, emission reduction, strengthening corporate social responsibility, and reducing the impact of economic development on the environment and society are growing. Therefore, companies must rethink their strategies and adapt their supply chains. Based on limited resources, enterprise machines have traditionally acted as a tool or a communication tool for a person. Yet, at the same time as the economy develops, the direct interaction between human and machine gradually emerges, and the economic development of an enterprise is bound to contradict environmental protection and social responsibility. Therefore, for enterprises, in different periods, different priority strategies will be adopted for the three dimensions of economy, environment, and society. The results showed that the economic benefit has increased by about 30% or more, and the ecological pollution has been reduced by about 40% on the original basis. Under the action of a sustainable supply chain, consumer satisfaction tends to be full and can be maintained at about 97%. In this context, the comparative analysis of the strategic optimization of enterprises in the supply chain is the focus of this thesis. [ABSTRACT FROM AUTHOR]

With the increasingly fierce competition in the product economy, there are more and more constraints on the development of enterprises, especially from the requirements of customers. Enterprises should be committed to development and meet customer needs first. However, the existing marketing plan and quality management also take customer satisfaction into account, so this paper aims to design the enterprise precision marketing strategy and quality management mobile information system based on the customer satisfaction model. For the precise marketing strategy of enterprises, this paper proposes three indicators of product quality, product delivery, and product service based on the customer satisfaction model and uses the hesitant fuzzy set to quantify the indicator model and apply it in the information system. For the quality management system, this paper uses PDCA cycle indicators to upgrade and optimize the quality management system. The test results show that the system has achieved a customer retention rate of 95% in terms of precise marketing strategies; in terms of quality management, it has improved the quality of enterprise products by about 20%. In the overall test of the system, the communication delay and reliability of the system are obviously optimized. This proves that the system can adjust the marketing strategy in real time according to the opinions of customers, achieve the purpose of precise marketing, and improve the quality management to a new height in line with customer satisfaction which shows that the information system designed in this paper can meet the purpose of precise marketing strategy and quality management of enterprises. [ABSTRACT FROM AUTHOR]

At present, China's tourism market is huge, and traditional hotel accommodation is not popular with young people, and homestays are just in line with young people's yearning. And most of the current push methods are determined by consumption on large platforms such as Ctrip, which not conforms to the recommendation method of homestays, so it aims at personalized marketing research for homestays. In this paper, the multi-information fusion sensor network is used to push B&B hotels in tourist areas to achieve B&B hotel marketing and maximize profits. Using the multi-information fusion sensor network to carry out personalized platform design, platform users can easily and quickly search for the homestay they want and can realize the automatic push function to achieve the rationality of marketing. The experimental results in this paper found that the average relative speedup increased from 1.9 to 17.7 for the execution of the scoring-oriented algorithm in the algorithm compared to a single computer. Performing the sort-oriented algorithm in the algorithm, the average relative speedup increased from 1.9 to 18.8. It can illustrate the effectiveness of the design system in this paper, and the software can carry out personalized marketing for homestays. [ABSTRACT FROM AUTHOR]

With the increase in the number of online shopping users, customer loyalty is directly related to product sales. This research mainly explores the statistical modeling and simulation of online shopping customer loyalty based on machine learning and big data analysis. This research mainly uses machine learning clustering algorithm to simulate customer loyalty. Call the k-means interactive mining algorithm based on the Hash structure to perform data mining on the multidimensional hierarchical tree of corporate credit risk, continuously adjust the support thresholds for different levels of data mining according to specific requirements and select effective association rules until satisfactory results are obtained. After conducting credit risk assessment and early warning modeling for the enterprise, the initial preselected model is obtained. The information to be collected is first obtained by the web crawler from the target website to the temporary web page database, where it will go through a series of preprocessing steps such as completion, deduplication, analysis, and extraction to ensure that the crawled web page is correctly analyzed, to avoid incorrect data due to network errors during the crawling process. The correctly parsed data will be stored for the next step of data cleaning or data analysis. For writing a Java program to parse HTML documents, first set the subject keyword and URL and parse the HTML from the obtained file or string by analyzing the structure of the website. Secondly, use the CSS selector to find the web page list information, retrieve the data, and store it in Elements. In the overall fit test of the model, the root mean square error approximation (RMSEA) value is 0.053, between 0.05 and 0.08. The results show that the model designed in this study achieves a relatively good fitting effect and strengthens customers' perception of shopping websites, and relationship trust plays a greater role in maintaining customer loyalty. [ABSTRACT FROM AUTHOR]

In recent years, China's e-commerce industry has developed at a high speed, and the scale of various industries has continued to expand. Service-oriented enterprises such as e-commerce transactions and information technology came into being. This paper analyzes the shortcomings and challenges of traditional online shopping behavior prediction methods, and proposes an online shopping behavior analysis and prediction system. The paper chooses linear model logistic regression and decision tree based XGBoost model. After optimizing the model, it is found that the nonlinear model can make better use of these features and get better prediction results. In this paper, we first combine the single model, and then use the model fusion algorithm to fuse the prediction results of the single model. The purpose is to avoid the accuracy of the linear model easy to fit and the decision tree model over-fitting. The results show that the model constructed by the article has further improvement than the single model. Finally, through two sets of contrast experiments, it is proved that the algorithm selected in this paper can effectively filter the features, which simplifies the complexity of the model to a certain extent and improves the classification accuracy of machine learning. The XGBoost hybrid model based on p/n samples is simpler than a single model. Machine learning models are not easily over-fitting and therefore more robust. [ABSTRACT FROM AUTHOR]

Purpose The basic concept of transaction cost theory is that firms like to conduct transactions in a channel with lower transaction costs. Therefore, the purpose of this paper is to use the transaction cost perspective to identify which conditions cause companies to choose between outbound open innovation (hierarchy governance) and inbound open innovation (market governance).Design/methodology/approach Accordingly, transaction cost economics was used to relate the choice and implementation of open innovation using a sample of 250 electronics and information start-ups in China. Structural equation modeling was used to conduct confirmatory factor analysis to evaluate measurement model, while logistic regression analysis was used to test the hypotheses.Findings As expected, the dedicated asset specificity, human asset specificity, behavioral uncertainty, transaction frequency, and small number exchange were positively associated with outbound open innovation.Originality/value The contribution of this paper lies in explaining the role played by transaction cost economics in the process of open innovation for start-ups through empirical analysis. [ABSTRACT FROM AUTHOR]