Call for Papers—Service Science Special Issue on Service Mining: Market, Technology, and Application

Abstract

The service mind-set has increasingly gained importance in the 20th century. The shift in the focus of service from the 1980s to the 2000s has addressed that IT not only lower the cost of service but create avenues to enhance revenue through service. Enterprises currently expand revenue through IT-based services. For instance, e-service has several features: it is mobile, flexible, interactive, and interchangeable. Additionally, e-services have much to offer in overcoming obstacles faced by the traditional service industry. The concept of Service Science, proposed by IBM, combines several issues into traditional service management. Moreover, the paradigm of service was also transferred from the traditional service industry to an IT-based service industry. FedEx is an excellent example in that it was able to successfully transfer to the paradigm of e-service, including self-service, customization, search engine, flexibility, and automatic response. Google is another great example of an enterprise that provides IT-based services (e.g., e-services) in the new paradigm. Service-dominant (SD) logic can be considered a new direction for enterprises to obtain high competency in dynamic service contexts. Accordingly, SD logic-based service mining, which is novel, addresses several research areas from the viewpoints of technology, model, management, and application. SD logic-based service mining is defined as a systematical process that includes service discovery, service experience, service recovery, and service retention to discover unique patterns and exceptional values within the existing service pool. The goal of SD logic-based service mining is similar to data mining, text mining, or Web mining in that it aims to detect something new from the service pool. The major difference is the feature of service, which is quite distinct to mining targets such as data or text. In other words, service is a process of value co-creation and differs by various perception of customer. In the concept of SD logic-based service mining, the mining target is not only traditional services but also IT-based services. As previously mentioned, SD logic-based service mining includes the process of discovering patterns, such as service discovery, service experience, service recovery, and service retention. According to the four steps of SD logic-based service mining process, the concept ranges from service exploratory to service maintenance. Furthermore, SD logic-based service mining goes beyond the existing service management and is considered as a branch of Service Science. In addition, SD logic-based service mining covers four dimensions: technology, model, management, and application. This special issue is devoted to the exploration of the four dimensions across different disciplines with regard to the issue of mining “services” under Service Science. With the significant growth of revenue in the service industry around the world, SD logic-based service mining is worth investigating to help enterprises gain and create values with customers now and going forward. Hence, we seek high-quality, unpublished contributions on the following and other related topics: • Technology: Service value networks, service system complexity, service system scalability, service infrastructure • Model: Service computing, system configuration, service system reconfigurability • Management: Service cooperation, service branding, service pricing, service innovation, service recovery, service sustainability, service experience • Application: Social network services, Web services, e-services, traditional services The contributors to this special issue will be asked to explain their SD logic-based service mining positions, giving a tentative answer to the following questions: • What are the keys to building a quality mechanism of SD logic-based service mining? • What are useful service mining mechanisms to systematically explore the appropriate service? • How can stakeholders accurately deliver/require suitable services? • What are the core values by using mining approaches in services? • How can stakeholders effectively co-create values in services? • How can stakeholders apply and put SD logic-based service mining into practice? • What contributions will bring stakeholders to SD logic-based service mining? • What role can SD logic-based service mining play in Service Science?