Discussion on the business performance of life insurance industries in Taiwan and China's Mainland Application of Metafrontier model

When using the Data Development Analysis method for analyzing the efficiency of different firms, it is common to put all similar DMU together for measurement in order to figure out the efficiency values of various DMU. However, such an analysis may easily neglect the source property of an individual DMU, meaning that the differences among various DMUs derive from different environmental backgrounds, e.g. environment variables such as economic civilization, laws and regulations, and political backgrounds. Applying the Metafrontier model can overcome the barriers resulting from the environment variables, and it can analyze and measure the differences among various DMUs which have different source properties. It can also be used for measuring the difference between each group of DMU and all DMUs. Therefore, this study adopts the DEA method, assuming variable returns to scale to evaluate and comparatively analyze the business performance of life insurance industries in Taiwan and China's Mainland based on ＂BBC input orientation＂. When evaluating the business performance, the operating management echelon is affected by uncontrollable external environment variables. Thus, this study applies the Four-Stage Data Envelopment Analysis to discuss the impact of environment variables on business performance and re-measures the business efficiency of life insurance industries in Taiwan and China's Mainland after adjusting the input variables. The demonstration period adopted by this study is from 2003 to 2005, and the research subject comprises 43 companies in Taiwan and China's Mainland, among which, there are 19 companies in Taiwan and 24 companies in China's Mainland, and there are 129 sets of sample data. It is assumed that the discount rate is ？ （）, is set as 3% in this paper）, and figured out the change of each life insurance company in technical efficiency in the inter-period accumulative years from 2003 to 2005.

Dynamic Pricing Strategies Using Artificial Intelligence Algorithm

Pricing strategies can have a huge impact on a company’s success. This paper focuses on the advantages and disadvantages of using artificial intelligence in dynamic pricing strategies. A good understanding of the possible benefits and challenges will help companies to understand the impact of their chosen pricing strategies. AI-driven Dynamic pricing has great opportunities to increase a firm’s profits. Firms can benefit from personalized pricing based on personal behavior and characteristics, as well as cost reduction by increasing efficiency and reducing the need to use manual work and automation. However, AI-driven dynamic rewarding can have a negative impact on customers’ perception of trust, fairness and transparency. Since price discrimination is used, ethical issues such as privacy and equity may arise. Understanding the businesses and customers that determine pricing strategy is so important that one cannot exist without the other. It will provide a comprehensive overview of the main advantages and disadvantages of AI-assisted dynamic pricing strategy. The main objective of this research is to uncover the most notable advantages and disadvantages of implementing AI-enabled dynamic pricing strategies. Future research can extend the understanding of algorithmic pricing through case studies. In this way, new, practical implications can be developed in the future. It is important to investigate how issues related to customers’ trust and feelings of unfairness can be mitigated, for example by price framing.

Co-Design and Trust in Social Network’s Websites

This research is focused on internet shoppers co-design, individual tendency of trust, trust belief, and purchase intention. An online questionnaire was set out in survey form on Google Docs in or-der to collect data. The survey was conducted of participants in Mongolia. 236 usable responses were obtained from Mongolian potential customers in order to examine constructs. The findings suggest that co-design and individual tendency of trust have significant positive effect on the trust levels throughout intention to purchase.