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

中文翻译：

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智能服务系统：跨学科视角

智能服务系统就在我们身边。在连接、传感器、数据存储和计算方面前所未有的进步（Beverungen、Mueller、Matzner、Mendling 和 vom Brocke，2019 年）的推动下，智能服务系统是人、技术、组织和信息的价值共创配置，能够独立学习、适应和决策（美国国家科学基金会，2014 年，第 5 页）。此类系统能够提供的智能服务在其行为方面具有先发性，适应客户需求和环境，从而在感知客户价值和提供商效率方面超越传统产品（Allmendinger & Lombreglia，2005 年）。智能服务系统出现在制造、物流、移动、医疗保健和私人生活等多种多样的环境中。例如，数字连接的飞机发动机实时报告状态数据，支持预测性维护和按使用付费的商业模式。汽车根据传感器数据分析驾驶行为，安排车间预约，并为驾驶员提供优化的生态反馈。配备传感器的公共垃圾桶可追踪垃圾的数量和种类，帮助计算出将派送的收集车辆的类型和数量以及收集时间，从而提高运营效率并节省成本。可穿戴系统监控人们的健康状况并支持他们的个性化治疗。智能服务系统是一个很好的例子，可以说明我们这个时代两个关键发展的日益融合和强化：数字化转型（Matt、Hess、Benlian 和 Wiesboeck，2016 年）和服务化（Baines、Lightfoot，贝内德蒂尼和凯，2009 年；Vandermerwe 和拉达，1988 年）。长期以来，服务行业一直是数字技术使用和创新的关键应用领域（Breidbach & Maglio，2015 年）。因此，出现了将信息系统 (IS) 研究与服务联系起来的更多研究的呼声（Rai 和 Sambamurthy，2006 年）。沿着同样的轨迹，大数据分析和服务创新的交叉点也成为服务研究的一个关键研究优先事项（Ostrom、Parasuraman、Bowen、Patricio 和 Voss，2015 年），其贡献调查的主题包括机器应用程序学习服务创新和设计的整合（Antons & Breidbach，2018 年）以及数据分析的道德影响（Someh、Davern、Breidbach 和 Shanks，2019 年）。相似地，数字技术是新价值主张的关键推动者，这些价值主张支撑着制造企业转型为服务和解决方案提供商（Lerch & Gotsch，2015 年）。将这两种趋势结合在一起，Sklyar、Kowalkowski、Tronvoll 和 Soerhammar（2019 年）甚至引入了数字服务化的概念。IS 和服务（营销）研究都开始更广泛地探索数字技术和服务的关键交叉点，这导致了营销 (Huang & Rust, 2013) 和 IS (Barrett, Davidson, Prabhu, & Vargo, 2015) 期刊。然而，学术研究界似乎正在​​努力克服当前的分裂（例如，在 IS 和服务营销之间）。为了应对这一挑战，前段时间引入了跨学科服务科学（或服务科学）的想法（Maglio & Spohrer，2008 年）。作为一门跨界学科，​​服务科学旨在设计和分析价值共创参与者的配置，以服务系统为基本分析单元（Maglio & Breidbach，2014）。DOI：10.1111/isj.12275