Smart Service Systems: Analytic, Cognition and Innovation
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ItemPeople’s Interactions with Cognitive Assistants for Enhanced Performances( 2018-01-03)When cognitive computing enabled smart computers are growing in our daily lives, there are not many studies that explain how people interact and utilize these solutions, and the impact of these smart machines to people’s performance to do things. In this paper, a theoretical framework for boosting people’s performance using cognitive assistants (CAs) was developed and explained using the data analysis from 15 interviews. The results show that people interaction with CAs enhance their levels of cognition and intelligence that help them to enhance their capabilities. Enhanced capabilities help people to enhance their performance.
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ItemService Dominant Architecture: Conceptualizing the Foundation for Execution of Digital Strategies based on S-D logic( 2018-01-03)Service Innovations are an opportune strategy for companies to compete in the digital age and to transform their business models taking a service perspective on their value creation. Digital business models require unique value propositions that incorporate digital technologies. Companies are required to build new digital capabilities to design and implement digital strategies. The paper takes a visionary perspective and motivates to view value creation through a service lens to respond to current challenges of digital transformation. We apply Service Dominant Architecture (SDA) to translate requirements of business initiatives into sustainable new IT infrastructure capabilities.
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ItemService-Oriented Cognitive Analytics for Smart Service Systems: A Research Agenda( 2018-01-03)The development of analytical solutions for smart services systems relies on data. Typically, this data is distributed across various entities of the system. Cognitive learning allows to find patterns and to make predictions across these distributed data sources, yet its potential is not fully explored. Challenges that impede a cross-entity data analysis concern organizational challenges (e.g., confidentiality), algorithmic challenges (e.g., robustness) as well as technical challenges (e.g., data processing). So far, there is no comprehensive approach to build cognitive analytics solutions, if data is distributed across different entities of a smart service system. This work proposes a research agenda for the development of a service-oriented cognitive analytics framework. The analytics framework uses a centralized cognitive aggregation model to combine predictions being made by each entity of the service system. Based on this research agenda, we plan to develop and evaluate the cognitive analytics framework in future research.
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