Digital Service: Technological Agency in Service Systems

This paper defines digital service in the context of technologically enhanced value co-creation between service system entities. Progress in digitalization and Artificial Intelligence (AI) is increasing the relative share of technologically enhanced value co-creation between service system entities (e.g., people, companies, nations). Highly automated technical systems increasingly act as autonomous agents, on behalf of service providers, in value co-creation interactions with the system users. Sufficient conceptualization, abstractions and modeling paradigms for research and development of this type of value co-creation are absent from the literature and introduced in this paper. The main contribution of the paper is introduction and definition of digital service and digital service membrane as fundamental concepts in service science and service systems, with directions for future research on the topic

Introduction to the Minitrack on Smart Service Systems: Analytic, Cognition and Innovation

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Introduction to Business Intelligence, Analytics and Cognitive: Case Studies and Applications (COGS) Minitrack

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Rebuilding Evolution: A Service Science Perspective

This paper explores a simple idea and asks a simple question: What determines the speed limit of evolutionary processes, and might there be ways to speed up those processes for certain types of systems under certain conditions? Or even more simply, how rapidly can complex systems be rebuilt? To begin with, the universe can be viewed as an evolving ecology of entities. Entities correspond to types of systems - from atoms in stars to organisms on Earth to ideas in the heads of people. Service science is the study of the evolving ecology of service system entities, complex socio-technical systems with rights and responsibilities – such as people, businesses, and nations. We can only scratch the surface in this paper, but our explorations suggest this is an important research question and direction, especially as we enter the cognitive era of smart and wise service systems. For example, it takes a child multiple years of experience to learn language and basic social interactions skills, but could machine learning algorithms with the proper data sets learn those capabilities in a fraction of the time

People’s Interactions with Cognitive Assistants for Enhanced Performances

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

Innovations with Smart Service Systems: Analytics, Big Data, Cognitive Assistance, and the Internet of Everything

Service innovations, enabled by the confluence of big data, mobile solutions, cloud, social, and cognitive computing, and the Internet of Things, have gained a lot of attention among many enterprises in the past few years because they represent promising ways for companies to effectively and rapidly deliver new services. But one of today\u27s most pervasive and bedeviling challenges is how to start this journey and stay on course. In this paper, we review some of the important developments in this area and reports the views voiced by five industry leaders from IBM, Cisco, HP, and ISSIP at a panel session at the 24th Annual Compete through Service Symposium in 2013. Panelists provided an extensive list of recommendations to academicians and professionals. The biggest conclusion is that all of the information and communications technology (ICT)-enabled service innovations need to be human-centered and focused on co-creating value

From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues

We typically think of artificial intelligence (AI) as focusing on empowering machines with human capabilities so that they can function on their own, but, in truth, much of AI focuses on intelligence augmentation (IA), which is to augment human capabilities. We propose a framework for designing intelligent augmentation (IA) systems and it addresses six central questions about IA: why, what, who/whom, how, when, and where. To address the how aspect, we introduce four guiding principles: simplification, interpretability, human-centeredness, and ethics. The what aspect includes an IA architecture that goes beyond the direct interactions between humans and machines by introducing their indirect relationships through data and domain. The architecture also points to the directions for operationalizing the IA design simplification principle. We further identify some potential risks and emerging issues in IA design and development to suggest new questions for future IA research and to foster its positive impact on humanity

Intelligence Augmentation: Towards Building Human-Machine Symbiotic Relationship

Artificial intelligence, which people originally modeled after human intelligence, has made significant advances in recent years. These advances have caused many to fear that machines will surpass human intelligence and dominate humans. Intelligence augmentation (IA) has the potential to turn the tension between the two intelligence types into a symbiotic one. Although IA has not gained momentum until recent years, the idea that machines can amplify human abilities has existed for many decades. Expanded from a panel discussion on Intelligence Augmentation at the 2020 International Conference of Information Systems (ICIS), we define IA in light of its history and evolution and classify IA based on its capabilities, roles, and responsibilities. Based on reviewing the IA literature in terms of research themes, enabling technology, and applications, we identify key research issues, challenges, and future opportunities