Smart service systems: An interdisciplinary perspective

Smart service systems are upon us. Fuelled by unprecedented advances in connectivity, sensors, data storage, and computation (Beverungen, Mueller, Matzner, Mendling, & vom Brocke, 2019), smart service systems are valuecocreating configurations of people, technologies, organisations, and information, which are capable of independent learning, adaptation, and decision making (National Science Foundation, 2014, p. 5). The smart service that such systems are capable of rendering is preemptive in its behaviour, adaptive to customer needs and contexts, thereby exceeding traditional offerings with respect to both perceived customer value and provider efficiency (Allmendinger & Lombreglia, 2005). Smart service systems have emerged in contexts as diverse as manufacturing, logistics, mobility, healthcare, and private living. For example, digitally connected aircraft engines report status data in real time, enabling predictive maintenance and pay-per-use business models. Cars analyse driving behaviour based on sensor data, schedule workshop appointments, and provide optimised eco-feedback to drivers. Public trash bins equipped with sensors track the volume and kinds of garbage to help calculate the type and number of collection vehicles to be dispatched and the time of the collections, thus, increasing efficiencies of operation and cost savings. Wearable systems monitor people's health status and support their personalised treatment. Smart service systems are a prime example to illustrate the growing convergence and reinforcement of two key developments of our time: digital transformation (Matt, Hess, Benlian, & Wiesboeck, 2016) and servitisation (Baines, Lightfoot, Benedettini, & Kay, 2009; Vandermerwe & Rada, 1988). Service industries have for a long time served as a key application area for the use of, and innovation with, digital technology (Breidbach & Maglio, 2015). Consequently, calls for more research linking information systems (IS) research with services have emerged (Rai & Sambamurthy, 2006). Following the same trajectory, the intersection of big data analytics and service innovation has also emerged as a key research priority for service research (Ostrom, Parasuraman, Bowen, Patrício, & Voss, 2015), with contributions investigating topics ranging from the applications of machine learning to the integration of service innovations and design (Antons & Breidbach, 2018) and the ethical implications of data analytics (Someh, Davern, Breidbach, & Shanks, 2019). Similarly, digital technology is a key enabler of new value propositions that underpin the transition of manufacturing firms as they become service and solution providers (Lerch & Gotsch, 2015). Bringing both trends together, Sklyar, Kowalkowski, Tronvoll, and Soerhammar (2019) have even introduced the notion of digital servitisation. Both IS and service (marketing) research have begun to explore the critical intersection of digital technology and service more generally, which has led to some important special issues in both marketing (Huang & Rust, 2013) and IS (Barrett, Davidson, Prabhu, & Vargo, 2015) journals. However, the academic research community appears to be struggling in its attempts to overcome the current fragmentation (e.g., between IS and service marketing). To remedy this challenge, the idea of an interdisciplinary science of service (or service science) was introduced some time ago (Maglio & Spohrer, 2008). As a boundary-spanning discipline, service science aims to design and analyse configurations of value-cocreating actors focusing on the service system as the basic unit of analysis (Maglio & Breidbach, 2014). DOI: 10.1111/isj.12275