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|Other Titles: ||A Soa-Based Framework of Internet-Enabled Crm---Collective Intelligence, Customer Value, and E-Service Pricng|
|Issue Date: ||2010-04-15 15:48:34 (UTC+8)|
|Abstract: ||A Roadmap of SOA-Based Framework for Internet-Enabled CRM Due to the changes of service economy, service-oriented management (SOM) approach is adopted widely for contemporary enterprises. Service-oriented management is the operational management of service delivery within a service-oriented architecture. SOA allows e-service companies to design customized e-services and combine them dynamically based on various needs. In this paper, we propose a SOA-based concept called Internet-Enabled CRM, which is defined as “companies conduct CRM by utilizing devices which can deliver e-services through the Internet”. In this study, we identify the importance of Internet-Enabled CRM in terms of an e-service value cube and SOA-based CRM framework. The e-service value cube enfolds three dimensions of value: (1) business value, (2) customer perceived value, and (3) social value. The proposed SOA-based CRM framework provides also two perspectives: (1) customer perspective and (2) e-service provider perspective. In short, this work not only indicates the identical components for Internet-Enabled CRM but provides a roadmap for future e-service industry. A CBR-Based Delphi Model for Quality Group Decisions Decision making in groups can ease personal biases which enables the participants discuss, argue and coordinate the ideas of coalitions. The final outcome of a group decision making process reaches a consensus decision. However, using one particular method should not preclude the consideration of other models or other means of assessing group decision making. This study aims to provide a heuristic approach based on Delphi method for group decision making model from collective intelligence. Hence, this work (1) empowers the collective intelligence from social network, (2) leverages the decision effort of experts (i.e., each level has selected experts), (3) ensures the heterogeneity of experts, and (4) diminishes the domination from certain experts. A Mixture Model to Estimate Customer Value for E-Services In the past, companies changed their focus from product-oriented within marketing (1960s) to demand-oriented within quality improvement (1980s). Today, they emphasize customer service, customer loyalty, and customer profitability. The significance of customer-centric services has become critical and essential. However, certain research that investigates the effect of customer lifetime value (CLV) focuses on lifetime value only of existing customers. This study devises a novel customer value model (the CV model) to predict customers’ value in an Internet environment, utilizing the concept of finance in the current status to predict future value based on historical data. The proposed model makes three contributions: (1) it constructs an equation to measure customer value for Internet-based services, (2) it considers the customer and enterprise perspectives simultaneously, and (3) it observes changes in customer value for any specific Internet user. The simulated results reveal that, in a long-term simulation, customer value decreases as the predictive time moves away from now because of deviations in perception and expectation. Hence, the new CV model complements the existing CLV model by addressing customer value from a different perspective and provides clues to customer value for future e-service industries. Pricing e-service through customer perceived quality: A utility-based approach E-service pricing has become an important issue for the era of service economy. In this study, we propose a novel pricing approach for e-services which considers perceived e-service premium. Profit (from provide perspective) and utility (from customer perspective) are two major components in the model. Our method is an extended cost-plus pricing approach that includes variable cost, fixed cost, and profit goal. In addition, the e-service characteristics premium is estimated as the added value in pricing process. Conversely, this work utilizes multi-attribute utility theory to estimate customer utility based on perceived quality. The perceived quality is measured by a quantitative survey (E-S-QUAL and E-RecS-QUA) based on the work of Parasurman et al. (2005). The proposed pricing approach decreases pricing complexity by quantifying and converting perceived quality and perceived utility. This work makes three major contributions: (1) considering profits and utility simultaneously, (2) quantifying e-service premium characteristics, and (3) generating a personalized e-service price. In short, this research is a preliminary attempt for contributing e-service pricing approach and considers e-service characteristics premium in the pricing process.|
|Appears in Collections:||[企業管理學系暨研究所] 研究報告|
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