The soaring cost of healthcare service demands alternative payment schemes to control expenditures while maintaining quality of service. The capitation payment scheme is a tentative substitute for the Fee-For-Service (FFS) scheme. A pilot capitation program study was conducted by the Bureau of National Health Insurance (BNHI) to evaluate the feasibility of a national rollout. Estimating the potential outcomes of a specific pilot program, the multi-agent simulation approach is used to model and simulate the service system under different settings. In this study, we chose the Zhishan Community Healthcare Group (CHG) service system as an example. The model was built and validated according to publicly available statistical and transactional data from Zhishan-CHG, and estimated the system's financial sustainability under different conditions. Five scenarios were explored in the experiment, simulating the capitation payment scheme over a period of three years. The results are summarized as follows: (1) medical cost expenditure per person can be reduced as more patients are enrolled in the capitation program; (2) the service model with a health promotion agent outperforms the model without one in an FFS cost control system; (3) higher quality and retention rate improve capitation income; (4) the financial risk of the CHG service system can be mitigated by excluding inpatient medical costs from the virtual points assigned by BNHI, and (5) simulation results show a positive financial outcome for Zhishan-CHG. These simulation results could be potentially used by the BNHI to revise its policy in terms of the population for one CHG and the coverage of medical services of a capitation payment system, such that the financial risk of the CHG could be reduced and the capitation policy sustained.

Based on information from preventive care and capitation pilot projects sponsored by Taiwan's Bureau of National Health Insurance (BNHI), this study modeled and analyzed the Zhishan community healthcare group (CHG) service system using a multi-agent simulation. The model was built and validated according to publicly available statistical and transactional data from Zhishan-CHG, and estimated the system's financial sustainability under different conditions. Five scenarios were explored in the experiment simulating the capitation payment scheme in over a period of three years. The results can be summarized as follows: (1) medical cost expenditure per person can be reduced as more patients are enrolled in the capitation system, (2) the service model with a health promotion agent outperforms the model without one in a fee-for-service (FFS) cost control system, (3) greater quality and retention rates improve capitation income, (4) the risk of the CHG service system can be mitigated by excluding inpatient medical costs from the virtual points assigned by NHI, and (5) simulation results show a positive financial outcome for Zhishan-CHG. These simulation results suggest that for primary care institutes forming CHGs in Taiwan, combining the capitation model with proper risk estimates is cost effective.

Tele-heatlh care services extending healthcare services from medical perspective of treatment and diagnosis to enhance holistic wellness of individuals, have been widely recognized by healthcare providers. This study explored the trust in the healthcare providers to generate the trust-based service value chain framework in tele-healthcare services. We report the results of survey from 1304 members of a district hospital in 7 villages in two townships of a county in the middle of Taiwan. Four motivations were identified from literature review in the study, which are the perception of tele-healthcare value, the perception of medical value, social influence, and effort expectancy. These motivators were used to predict the intention of using tele-healthcare services provided by the service provider. We employed trust as a mediator in this framework. From the statistical analysis, we obtain the following results: (1) the relationship between effort expectancy and intention to use is mediated by the trust in the service provider (2) Social influence insignificantly affects trust, however, it significantly affects the intention to use tele-healthcare services, (3) The perceived benefits and medical value are significantly mediated by calculation-based trust in the service provider. From these findings, we suggest that trust built is critical for service value co-creation stage in tele-healthcare services.

In this explorative research, we aim to find the most important service experience variables that determine customer purchasing decision and the clerks' influence on customers' purchases. This study was conducted as a case study of a children's apparel company, denoted Company L, which has 243 retail stores. Company L has implemented Point of Sale (POS) systems in its retail stores, and would like to know what functions could be added to induce storefront employees to deliver better customer service. We, therefore, focus on observing the services provided by storefront employees and their reflection on a customer's purchasing decision in a retail store. The study generated decision trees via Weka, a data mining open source software platform, to analyze multiple data sources to (1) understand what makes a good service experience for a customer, (2) get explicit knowledge from service encounter information, and (3) externalize the tacit knowledge of storefront service experiences. These findings can be used to improve Company L's POS system to guide storefront employees to learn from trained decision rules. Moreover, the company can internalize service experience knowledge by aggregating learned rules from the company's retail stores.

In this explorative research, we seek to find the most important service experience variables determining the customer purchase decision and the clerks' influence on customers' purchase. This study conducted a case study of a children's apparel company, denoted L, which has 243 retailing stores. Company L has implemented Point of Sale (POS) systems in its retailing stores, and the management wanted to know what functions can be added in facilitating storefront employees achieve better customer service experience. We, therefore, focus on observing the services provided by storefront employees and their reflection on customer's purchasing decision in a retailing store. The study generated decision trees via WEKA to analyze multiple data sources in order to (1) understand what makes good service experience in service encounter, (2) get explicit knowledge from service encounter information, and (3) learn about customers for clerks to internalize tacit knowledge. The findings can be used to add into company L's POS system in order to guide storefront employees to learn from trained decision rules. Moreover, the company can internalize service experience knowledge by aggregating learned rules from all retailing stores of the company.

A similarity relation with its partition tree has been applied in the performance evaluation area for obtaining an agglomerative hierarchical clustering. These fuzzy relation-based methods require a decision maker to perform pair-wise comparisons for the similarity among criteria as forming a fuzzy relation matrix. The approach is developed based on real membership values of fuzzy relations. However, interval-valued memberships may be better than real membership values to represent higher-order imprecision and vagueness for human perception. Thus, in this paper we would like to extend fuzzy relations to interval-valued fuzzy relations and then construct interval-valued similarity relations for performance evaluation. We first give some definitions for these interval-valued types of fuzzy relation, similarity relation and resolution form. We then construct an interval-valued fuzzy similarity relation into a hierarchical structure schema. It is shown that both of procedures and results for the partition tree derived from interval-valued and crisp-valued similarity relation matrices have some corresponding relationships and different merits. To demonstrate the usefulness of the proposed approach, performance evaluations for academic departments of higher education are considered by using actual engineering school data in Taiwan.

This study presents an additive scale model for the Analytic Hierarchy Process (AHP) that suits the decision problem using a linear preference comparison. This study discusses issues related to mathematical denotation, axiom, transitivity and numerical analysis for the additive scale model of AHP. The least squares method and correlation analysis are used to obtain the relative criteria weights and consistency index. Moreover, a fuzzy model is devel- oped to enhance the practical flexibility of the additive scale model of AHP in applications. Two examples are used to demonstrate that the criteria weights derived from the proposed approach are steady and effectively reflect the intensity of perception, and the consistency index is invariant to the scale multiplier employed.

In this paper, we present a new clustering method that involves data envelopment analysis (DEA). The proposed DEA-based clustering approach employs the piecewise production functions derived from the DEA method to cluster the data with input and output items. Thus, each evaluated decision-making unit (DMU) not only knows the cluster that it belongs to, but also checks the production function type that it confronts. It is important for managerial decision-making where decision-makers are interested in knowing the changes required in combining input resources so it can be classified into a desired cluster/class. In particular, we examine the fundamental CCR model to set up the DEA clustering approach. While this approach has been carried for the CCR model, the proposed approach can be easily extended to other DEA models without loss of generality. Two examples are given to explain the use and effectiveness of the proposed DEA-based clustering method.

The fuzzy weighted average is widely used to solve hierarchical evaluation problems, including fuzzy consideration for the operations of scoring, weighting and aggregating. Previous works considered the fuzziness of score and weight, and used the additive function to aggregate these weighted scores. This study considers the aggregation operator also as a fuzzy variable, and uses a generalized means function to fuzzify the aggregation operator within a fuzzy weighted average. In practice, the proposed model not only considers both the relative important of the criteria and its achieved performance, but also conveys the influence of the DM’s (Decision Maker’s) evaluation attitude. Thus the proposed model can flexibly reflect any DM’s evaluation attitude, such as open, neutral or rigorous. Thereby, the proposed model can make an objective evaluation that approaches a real decision making situation, and thus has the potential to be a useful management tool for improved resolution of fuzzy hierarchical evaluation problems.

The purpose of this study is to establish hierarchical structures for the performance evaluation of vague, humanistic complicated systems. To overcome the difficulties due to partial information and the vagueness of human knowledge and recognition, a fuzzy relation-based clustering method is proposed to model this evaluation. First, the effects of different max-t"i compositions on the formation of clusters are discussed. Then, an improved clustering algorithm is developed to produce several partition trees with different levels and clusters according to different t"i-norm compositions. To demonstrate the usefulness of the proposed algorithm, the academic departments of higher education were considered using actual engineering school data in Taiwan. Three performance evaluation structures are established by using max-t"1, max-t"2 and max-t"3 compositions. The results show that the proposed fuzzy hierarchical approach is useful and practical for performance evaluations of complicated humanistic systems.

The purpose of this paper is to propose a multiple structures analysis model for Analytical Hierarchy Process (AHP). The approach is developed owing to the fact that in the analysis of large-scale problem, a different viewpoint or a specific points emphasis will result in different hierarchy structures. Traditionally, the AHP process, based only on single evaluation hierarchy, presents the Decision Making (DM) with a problem of schema selection for the hierarchy structure. It is difficult to verify an appropriate structure. To avoid or reduce the effect of an inappropriate selection of an evaluation hierarchy structure is therefore critical to the success of the hierarchy evaluation process. According to statistics and group decision theories, a more objective evaluation will be obtained if we can compromise several estimates. This paper will, therefore, utilize the Hierarchies Consistency Analysis (HCA) theory to establish multiple structures evaluation model for the AHP. We will construct several different structure schemata for a problem to be analyzed by the AHP approach, and determine the weight of criteria for all levels of each structure. Furthermore, we will use the weight consistent property and compromise process of the HCA theory to integrate these AHP models to obtain more objective evaluation result that is as free as possible from structure bias.