Emphasizing a Service Phase Perspective for Machine Manufacturers Seeking Digital Servitization - a Taxonomy for Industrial Service Phases

The ongoing shift to solution-oriented business models and growing digitalization lead to an increasing importance of services in manufacturing industry. Machine manufacturers in particular struggle to grasp the extent of transformational impact enabled or required by service developments. This is due to a narrow perspective on specific service characteristics, but not on the entire service process. Therefore, a service-dominant perspective is essential in the value creation of manufacturers, placing relevant service phases in the foreground. However, the process-related character of services is rarely considered in the literature. For this purpose, this study provides a taxonomy that classifies services based on phases. In addition to a systematic literature analysis, this study builds on practical insights by conducting eight expert interviews. The applicability and usefulness of the taxonomy is then demonstrated through exemplary application based on a case study, enabling practitioners to adopt a phase-oriented perspective on digital servitization

Designing business model taxonomies – synthesis and guidance from information systems research

Classification is an essential approach in business model research. Empirical classifications, termed taxonomies, are widespread in and beyond Information Systems (IS) and enjoy high popularity as both stand-alone artifacts and the foundation for further application. In this article, we focus on the study of empirical business model taxonomies for two reasons. Firstly, as these taxonomies serve as a tool to store empirical data about business models, we investigate their coverage of different industries and technologies. Secondly, as they are emerging artifacts in IS research, we aim to strengthen rigor in their design by illustrating essential design dimensions and characteristics. In doing this, we contribute to research and practice by synthesizing the diffusion of business model taxonomies that helps to draw on the available body of empirical knowledge and providing artifact-specific guidance for building taxonomies in the context of business models

Barriers to the Development of Data-Driven Services: An ISM Approach for SMEs

Data is nowadays considered as a key resource and represents the most valuable asset of our technology-driven world. However, the ability to use this resource in a value-adding way requires a holistic perspective. Small- and medium-sized enterprises in particular face major challenges in the innovation and development process. Despite preliminary research in the area of data-driven services (DDS), there is a lack of methodological analysis of the key barriers for SMEs in the context of DDS development. To address this shortcoming, we have developed an interpretive structural model based on a two-stage mixed-method approach by combining a structured literature review with practice-oriented focus group interviews to identify key barriers and their interdependencies and interactions. Our paper strengthens the knowledge of DDS development through a methodological barrier analysis and provides a guide for practitioners to eliminate the most relevant barriers to DSS development

Hunting the Treasure: Modeling Data Ecosystem Value Co-Creation

Data ecosystems are an emerging theme in IS research. They represent the complex dynamics of inter-organizational value co-creation based on data sharing. Interestingly, empirical research on the value that the various actors can extract from participating in a data ecosystem is still sparse. We address this issue by analyzing 64 Gaia-X use cases, each representing a data ecosystem. From them, we derive roles relevant to data ecosystems and describe them according to typical ontological business model elements (value proposition, value creation, value delivery, and value capture). To visualize the value co-creation in data ecosystems, we use the modeling language e3-value. We illustrate this approach by modeling the specific Agri-Gaia use case. Our work contributes to understanding value co-creation in data ecosystems more in-depth as we extract roles, demonstrate how to model actors and their value co-creation, and discuss the implications of a service ecosystems perspective

Uncovering Research Streams in the Data Economy Using Text Mining Algorithms

Data-driven business models arise in different social and industrial sectors, while new sensors and devices are breaking down the barriers for disruptive ideas and digitally transforming established solutions. This paper aims at providing insights about emerging topics in the data economy that are related to companies' innovation potential. The paper uses text mining supported by systematic literature review to automatize the extraction and analysis of beneficial insights for both scientists and practitioners that would not be possible by a manual literature review. By doing so, we were able to analyze 860 scientific publications resulting in an overview of the research field of data economy and innovation. Nine clusters and their key topics are identified, analyzed as well as visualized, as we uncover research streams in the paper

SERVICE DOMINANT LOGIC PERSPECTIVE ON DATA ECOSYSTEMS - A CASE STUDY BASED MORPHOLOGY

The importance of data as the key resource in start-ups and traditional businesses rises steadily. Thus, traditional organizations need to adapt accordingly and develop adequate strategies to incorporate data into their value creation process. Creating value from data often requires collaboration between various actors with different requirements and individual objectives, in short, in an ecosystem. However, as of now, there are few studies investigating data ecosystems in-depth. Thus, we address that issue and report on a Design Science Research study developing a morphology of data ecosystems based both on a structured literature review and two explorative case studies conducted in traditional organizations. Therefore, we generate new knowledge through first-hand insights gained via use case studies over 18 months with practitioners and support these results with existing expertise in the scientific literature. We created a morphology to reveal key characteristics and elements of data ecosystems to develop a tool for companies and scientists alike to acquire a better understanding and perception of data ecosystems

Towards Design Principles for Data-Driven Services in Industrial Environments

The ever-growing amounts of data offer companies many opportunities to exploit them. Resulting data-driven services hold great potential for creating unique value for customers and the achievement of competitive advantages. Nevertheless, especially companies in the industrial environment struggle to implement successful data-driven service innovations. Surprisingly, there is a lack of scientific research addressing this issue. Thus, our research generates design principles for data-driven services to aid in their development. For this purpose, we present a qualitative interview study with experts in different lines of businesses among the industry sector, holding varying positions and roles in service systems. Through practical examples, we show which challenges exist in the development and use of data-driven services. On this basis, we derive design principles to help understanding data-driven services and to overcome difficulties identified in practice, notably, that allows practitioners to develop new services or re-design existing ones

A Taxonomy for Data-Driven Services in Manufacturing Industries

Data are a new resource to generate novel value for customers and to extend existing traditional services with a digital component. The manufacturing industry, usually, is characterized by analog service offerings built around product sales and therefore misses new market opportunities. One reason for that is the lack of assistance in innovating new data-driven services. Thus, to address that issue, this research paper generates a taxonomy of data-driven services in manufacturing industries. We use the established theoretical framework Service-Dominant Logic (SDL) to conceptualize the role and nature of data in new service offerings. The taxonomy is grounded both theoretically through its link to the existing knowledge base and empirically through the analysis of 100 data-driven services drawn from Crunchbase. It serves as a scientific instrument to profoundly and explicitly describe data-driven services in manufacturing and gives companies significant insight to incorporate value creation through goal-oriented data utilization and service design