Towards design elements to represent business models for cyber physical systems

Cyber-physical systems turn products into connected devices that enable interaction among individu-als, organizations, and other objects. They find application in areas such as healthcare and automo-tive, enabling new value propositions created by multiple players for a shared customer. Despite the perceived business potential, practitioners in primarily physical industries struggle to analyze and design value creation mechanisms for cyber-physical systems. The prevailing business model concep-tualizations follow a mono-organizational logic and are unable to express hybrid and interactive val-ue creation. To close this gap, we apply a design science research approach to develop and evaluate a taxonomy of design elements to represent business models for cyber-physical systems. Through an analysis of 21 use cases of value creation mechanisms in the automotive industry, we identify the de-sign elements adopted in practice; we then validate the identified design elements via 13 interviews and a workshop with our target users, obtaining a final taxonomy comprising 23 design elements. We improve the expressive power of business model conceptualizations by identifying specific roles, con-trol points, and value exchanges in a network of players, representing hybrid and interactive value creation

DESIGN, IMPLEMENT, REPEAT: ESSAYS ON BUSINESS MODEL MANAGEMENT IN OFFLINE-BORN ORGANIZATIONS

It is commonly acknowledged that business model innovation carries enormous opportunities for incumbent organizations, especially when driven by digital transformation. New revenue models, highly efficient value creation mechanisms, and unprecedented interaction with the customer are only few of the numerous benefits that managers expect to see. However, less is known and discussed about the challenges of organizations that were established before the diffusion of the Internet - these organizations are sometimes known as “offline -born” - which attempt to tackle business model innovation. Lack of digital expertise, a conservative mindset, resource constraints, and fear of cannibalization of long-established business models are hurdles that can prevent incumbents from embracing this journey of change. In this context, we contribute to the business model domain with two research streams having a common denominator: offline-born organizations performing business model innovation. The first research stream addresses the process of business model management, analyzing phases that go beyond business model design. Specifically, we shed light on how incumbents analyze, design, evaluate, implement, and control their business models. We observe this process in practice, complementing the predominantly conceptual literature. Our main contributions include the activities performed in each process phase and two approaches to business model management: on the one hand, a deterministic and waterfall approach, characterized by a high level of certainty and confidence by the management team and, on the other hand, a discovery-driven approach, in which numerous design and evaluation iterations are performed before business model implementation. The second research stream studies the design of business models for connected products. Phenomena like internet of things and smart cities require a complex network of actors in which organizations, individuals, and objects exchange value. Existing business model representations are not fully capable of describing such networks, having rather generic elements and components. Therefore, we take a first step towards new means of representation, proposing a taxonomy of design elements to represent business models for cyber-physical systems, the combination of physical and computational processes atthefoundationofconnectedproductsT. hemaincontributionofthisresearchisaspecificsetofactors’ roles, the value they exchange and perceive, as well as their dominance in the network

Behind the Blackbox of Digital Business Models

In recent years global competition has undergone significant change as a result of the increasing digitalization or digital transformation of business and society. New forms of value creation have emerged, particularly through the emergence of companies such as Google and Facebook, and other digital disruptors. As a result of these developments, established companies face the challenge of aligning their business model with the new circumstances. One way of doing this is digitalization of the existing business model or designing a completely new digital business model. In this context, however, there is no commonly accepted definition and understanding of the nature of digital business models representing the context of Industry 4.0, cyber-physical systems, and the Internet of Things. So, this work is based on a literature review to help understand the concept of a digital business model. With this contribution, we enable scientific added value for business model research and at the same time help business practice to understand exactly what a digital business model is and which levers of transformation need to be considered

Supporting Data-Driven Business Model Innovations: A structured literature review on tools and methods

Purpose: This paper synthesizes existing research on tools and methods that support data-driven business model innovation, and maps out relevant directions for future research. Design/methodology/approach: We have carried out a structured literature review and collected and analysed a respectable but not excessively large number of 33 publications, due to the comparatively emergent nature of the field. Findings: Current literature on supporting data-driven business model innovation differs in the types of contribution (taxonomies, patterns, visual tools, methods, IT tool and processes), the types of thinking supported (divergent and convergent) and the elements of the business models that are addressed by the research (value creation, value capturing and value proposition). Research limitations/implications: Our review highlights the following as relevant directions for future research. Firstly, most research focusses on supporting divergent thinking, i.e. ideation. However, convergent thinking, i.e. evaluating, prioritizing, and deciding, is also necessary. Secondly, the complete procedure of developing data-driven business models and also the development on chains of tools related to this have been under-investigated. Thirdly, scarcely any IT tools specifically support the development of data-driven business models. These avenues also highlight the necessity to integrate between research on specifics of data in business model innovation, on innovation management, information systems and business analytics. Originality/value: This paper is the first to synthesize the literature on how to identify and develop data-driven business models, and to map out (interdisciplinary) research directions for the community. Keywords: Business model innovation, data-driven business models, research agenda.   Article classification: Literature revie

Design Principles for Creating a Pay-per-Part Value Proposition in Data Ecosystems

In practice and research, pay-per-part business models are becoming increasingly popular. Amongst others mechanical engineering companies, banks, insurances, and IT companies are working on these new business models. There is increasing evidence that the enabler for pay-per-part approaches is the cooperative use of data across company boundaries, being discussed in literature under the term data ecosystem. Along two case studies, a total of eleven companies were accompanied from the definition of the cooperative pay-per-part value proposition to the implementation of a proof of concept. Based on these case studies, eleven design principles could be derived. These design principles provide companies a guidance when designing a cooperative value proposition within an ecosystem. The identified design principles were mapped to different stakeholder groups that are involved in the design of a cooperative value proposition. The generated design principles were evaluated and implications for practitioners and research given

Modeling Business Models: A cross-disciplinary Analysis of Business Model Modeling Languages and Directions for Future Research

Modeling languages for business models are a powerful and flexible means of representing and communicating knowledge related to business models. More than fifteen years after Osterwalder et al. (2005) clarified the ontology for the business model concept in this journal, we offer a systematic and cross-disciplinary assessment of the literature on business model modeling languages (BMMLs) that facilitate the visualization of this concept. In so doing, we synthesize and organize the knowledge dispersed across different disciplines in which BMMLs have originated and highlight the potential weaknesses in this literature to offer solid insights for future research. Our analysis reveals the existence of 17 BMMLs that have originated in traditional domains such as strategy and information systems, but also emerging domains such as sustainability. We contrast and compare these BMMLs along three dimensions: semantics, syntax, and pragmatics. We also analyze research that has made use of these BMMLs, differentiating between research that is conducted with a given BMML and research that is conducted about a given BMML. We conclude by offering a research agenda in which we illustrate the main challenges associated with the lack of well-accepted semantic, syntactic, and pragmatic foundations of BMMLs and outline opportunities for future research