Prior to industrialization, manufacturing physical products involved just a few stakeholders. Due to the simplistic nature of most products back then, these stakeholders were able to man- age all product related information along the life of the products without assistance (Terzi et al., 2010, p. 362). Nowadays, complex products, short innovation cycles and new product re- lated services require organizations to systematically manage product related data that is spread over multiple stakeholders along the individual product lifecycles (Kiritsis, 2011, p. 481). A product centric way to do so is the approach of Product Lifecycle Management (PLM) (Stark, 2018, p. 13). PLM is not a set of tools that are used store and analyze lifecycle related data (Terzi et al., 2010, p. 363). Rather, PLM is a strategic approach (Staisch et al., 2012, p. 1) to create, manage, maintain, and provide product related information (Myllärniemi et al., 2009, p. 9). Per definition, PLM covers the whole product lifecycle. Nevertheless, the amount of available product related information traditionally drops, after the product is sold to the customer (Holler et al., 2016b, p. 7). Therefore, focus of PLM in practice is on the design and the manufacturing phases, where the manufacturer has physical access to the product and can gather all data directly (Holler et al., 2016b, p. 7). As a result, product related tasks that require product information from the use phase often rely on hypothetical data and assumptions (Zhang et al., 2017, p. 230). By integrating sensors, processors and communication technolo- gies into physical products, products become able to provide this traditionally missing product- related information from the use phase (Bajyere et al., 2020, p. 565). By systematically ana- lyzing these data with the PLM approach, many benefits for manufacturers, customers, and third parties arise (Bilgeri et al., 2019, p. 191). Experts expect high benefits, especially for technological products such as cars that have plenty of sensors already embedded (Stocker et al., 2017, p. 126). These high expectations in the automotive industry led to forecasts that manufacturers worldwide will equip one-third of all manufactured vehicles in 2023 with in- ternet connectivity units (Cäsar et al., 2019, p. 4) and enable vehicles to participate in the internet of things (IoT). In this IoT, vehicles transfer detailed status data from embedded sen- sors to a product external information system (IS) along their individual lives.

Like the growing interest of practitioners in the IoT, researchers' interest rapidly increased in the last few years. Although there is such a high interest among practitioners and researchers in internet-connected vehicles, it remains a challenge for manufacturers to systematically gather, manage, store, and analyze these data from individual products along their use phase (Stocker et al., 2017, p. 126) (Holler et al., 2016a, p. 486–487) (Brandt and Ahlemann, 2020, p. 12). To overcome these challenges, guidelines for practitioners to integrate the vehicle status data from the use phase into PLM are needed (Zhang et al., 2017, p. 229). By then, vehicle manufacturers can design new or extend their existing IS to gather, manage, store, and analyze these new data from individual products (Stocker et al., 2017, p. 128) systematically and ho- listically. As the integration of IoT data from internet-connected products involves domains such as business, IT, social science, and technology, IS researchers are well prepared to make substantial contributions (Baiyere et al., 2020, p. 567). Furthermore, IS Researchers have a long history of developing conceptual models that guide others in the design of IS (Legner et al., 2020, p. 735-736). Nevertheless, IS researchers paid little attention to gathering, managing, storing, and analyzing status data from connected products during the use phase and the inte- gration into PLM in the past (Marabelli et al., 2017, p. 352).

I split this thesis into six essays to develop an artifact that guides vehicle manufacturers and other stakeholders to design such IS. In the first essay, i.e., this one, I motivate the research, introduce key concepts, and structure the following essays. The main emphasis is on essays two to five that document the design process of the central research artifact, i.e., the pattern language. In essays two and three, I derive the demand for and the requirements for a pattern language for vehicle IS in the era of the IoT from existing research and practice. I design and evaluate the research artifact in essay four and five. In the last essay of this dissertation, I discuss the results and limitations of the pattern language. In addition, I include an appendix to this dissertation that discusses the artifact type that this thesis develops, i.e., a pattern lan- guage. The essay in the appendix is not a part of this dissertation and is just added for faster access.

This introductory essay is structured as follows. After motivating the research of this thesis and the overall research question in this chapter, key concepts are explained in chapter 2. Sub- sequently, the overarching Design Science Research (DSR) process is outlined, and the re- search questions are stated in chapter 3. Last, chapter 4 summarizes the results of the research essays.