编号：时间：2021年x月x日书山有路勤为径，学海无涯苦作舟页码：第1页 共1页Mining customer knowledge for tourism new product development and customer relationship managementOriginal Research ArticleExpert Systems with ApplicationsIn recent years tourism has become one of the fastest growing sectors of the world economy and is widely recognized for its contribution to regional and national economic development. Tourism product design and development have become important activities in many areas/countries as a growing source of foreign and domestic earnings. On the other hand, customer relationship management is a competitive strategy that businesses need in order to stay focused on the needs of their customers and to integrate a customer-oriented approach throughout the organization. Thus, this paper uses the Apriori algorithm as a methodology for association rules and clustering analysis for data mining, which is implemented for mining customer knowledge from the case firm, Phoenix Tours International, in Taiwan. Knowledge extraction from data mining results is illustrated as knowledge patterns, rules, and knowledge maps in order to propose suggestions and solutions to the case firm for new product development and customer relationship management.Article Outline1. Introduction2. The case firm the Phoenix Tours International 2.1. Background of the case firm2.2. The new product development procedure of the case firm3. Methodology 3.1. Research framework3.2. Questionnaire design and data collection3.3. Relational database design3.4. Association rule Apriori algorithm3.5. Clustering analysis4. Research results 4.1. New product development 4.1.1. Travel area inbound travel (pattern A) 4.1.1.1. Inbound travel association analysis4.1.1.2. Inbound travel cluster analysis4.1.2. Travel area outbound travel Asia (pattern B) 4.1.2.1. Outbound travel association analysis4.1.2.2. Outbound travel cluster analysis Asia area4.2. Customer relationship management 4.2.1. Travel service 4.2.1.1. Travel service association analysis (pattern C)4.2.1.2. Travel service cluster analysis4.2.2. Direct marketing 4.2.2.1. Travel web site usage association analysis (pattern D)4.2.2.2. Direct marketing cluster analysis5. Discussion 5.1. In the regard of current market strategy5.2. In the regard of future market strategy5.3. In the regard of customer value and satisfaction5.4. In the regard of new business model6. ConclusionAcknowledgementsReferencesCustomer satisfaction driven quality improvement target planning for product development in automotive industryOriginal Research ArticleInternational Journal of Production EconomicsCustomer satisfaction targets for vehicle attributes are set at the corporate level with limited consideration of the engineering feasibility and interactions between different product features. This paper presents a comprehensive framework for target planning for customer satisfaction driven quality improvement efforts in the product development process. The proposed framework facilitates a link between corporate decision making and engineering decision making by integrating best practices and structuring technical activities. Potential vehicle attributes are classified and prioritized for further improvement using Kano model and quality function deployment. Customer satisfaction targets are established based on rigorous business analysis and trade-off studies. These targets are converted into objective engineering metrics using regression models. Transfer function equations are developed to provide a link between higher-level product characteristics and lower-level design variables. The mathematical models are formulated as optimization problems to cascade down top-level targets to lower-level elements within given constraints. A case example is presented to demonstrate the proposed methodology.Article Outline1. Introduction2. Target planning process3. Methodology 3.1. Identify and prioritize improvement opportunities 3.1.1. Customer requirements3.1.2. Corporate and regulatory requirements3.1.3. Classification of vehicle attributes3.1.4. Prioritization of improvement opportunities3.2. Set attribute-level CS targets3.3. Establish attribute-level objective metric (measurable) targets3.4. Target cascading process 3.4.1. Identify critical characteristics3.4.2. Develop transfer function model3.4.3. Target cascading 3.4.3.1. Mathematical model3.4.3.2. Vehicle-level target cascading3.4.3.3. System-level target cascading3.4.3.4. Sub-system-level target cascading3.5. Component-level design optimization4. Example 4.1. Vehicle-level target cascading model4.2. System-level target cascading model4.3. Sub-system-level target cascading model5. ConclusionAcknowledgementsReferencesManaging the trade-off between relationships and value networks. Towards a value-based approach of customer relat