Different challenges and uncertainty arise from digital transformation as managers are forced to find new channels or make alternative investments decisions. Companies can use experiments as knowledge-generating resources to mitigate the uncertainty surrounding the adapted business model. The B2B Startup Experimentation Framework (B-SEF) was developed for startups in the B2B environment to discover and validate a business model’s desirability through business experiments. This study uses the criteria completeness, consistency, plausibility, accuracy, and feasibility to investigate to what extent the B-SEF can be adapted to conduct business experiments in a B2C environment. Based on the B-SEF approach, two experimentation rounds are conducted regarding multiple online advertising channels and their efficiency in generating new customers in a B2C cosmetic online shop. Findings show that the B-SEF’s generic structure is also suitable for conducting business experiments in the B2C environment when two adjustments are made regarding the sales funnel in the macro-level of the framework. First, the funnel levels required reordering to represent the customer journey better. Second, the new funnel level “awareness” needs to be added, as tracking awareness is relevant in the success of an e-commerce store. This research contributes by providing a guideline for entrepreneurs who want to conduct similar business experiments and extracting the company’s most and least efficient advertising channels to acquire new profitable customers. The study’s originality lies in assessing the B-SEF’s suitability in the B2C context and providing a tool for its application to conduct business experiments comprehensively and successfully, especially regarding documentation and data collection.

AbstractDeveloping digital platform business models, especially in business-to-business (B2B) markets, has a high potential for companies who successfully develop their products in generations. The model of SGE - System Generation Engineering describes the development of mechatronic systems on subsystem level. The authors investigate to what extent a comprehensive and unified methodology can be identified, connecting the research areas of product development and digital B2B platform business models. Therefore, this study conducted a bibliometric analysis of scientific data to identify a research gap and a qualitative literature review to affirm the relevance of future research in this research area. The results show a gap between the research areas of digital B2B platform business models and product development. Essentially, several renowned platform researchers suggested performing future research with a methodology that fulfils the following purposes: (1) improve the general understanding of digital platforms, (2) understand their success factors and development, and (3) deal with challenges (e.g., monetization) and loss of valued personal relations in B2B markets through digitization.

Adding internet technologies to mechatronic system solutions is the next step for Industry 4.0 and has a high potential for digital platform business models linked with cyber-physical system (CPS).The model of SGE -System Generation Engineering describes the development of systems on subsystem level.So far, no known research investigated a coherent definition explaining a digital platform business model in the model of SGE according to CPS.Within a fourphase methodology, a systematic literature analysis identifies and processes 55 relevant definitions and extracts a data set of 32 definitions after eliminating 23 duplicates.In a semantic analysis, crucial primary and externally linking terms, combined with five case study findings, reveal the artefact, an economic platform with five characteristics.After initially validating the artefact, a workshop and academic discussion result in an intermediate artefact, further validated in a subsequent expert workshop.Findings suggest the terms value added, infrastructure, exchange, interaction, and openness describe digital platform business models.Essential defining aspects must entail facilitating value added through a multisided market by integrating specific entities, providing an open and/or closed infrastructure for flexible and compatible networking components, integrating data between the CPS and the platform, creating trustworthy and economic value added through entities, and scaling by network effects.This paper shows how to extract a precise, coherent, valuable, and iteratively validated definition to create a common understanding within a research field.Practitioners can learn about the digital platform business model concept within the model of SGE, supporting knowledge creation, research, and product development of CPS in generations.The originality of this work lies in extracting a validated definition by applying sophisticated tools and methods to guarantee its robustness.

In the past decade, digital platform business models have gained significant worth as they differ in creating and capturing value compared to traditional linear business processes. Previous research developed the SPEC – Smart Platform Experiment Cycle, a process to validate digital platform business models to ensure their successful implementation. In this context, it is intriguing to investigate whether and how step (1) of SPEC can be expanded by other platform design tools. This study developed a Live-Lab, namely KaPIL – Karlsruher Platform Innovation Lab, to design digital platform business models and test related tools and methods. Applying the Design Research Methodology, the designed Live-Lab is created by implementing ProVIL – Product Development in a Virtual Idea Laboratory combined with the Smart Education Concept and digital platform business knowledge. KaPIL was applied with students from the Karlsruhe University of Applied Sciences in cooperation with the company STIHL to assess its efficacy, applicability, and validity. KaPIL can be used to design digital platforms and shows that the Platform Canvas, the Platform Business Model Canvas, and the Platform Design Canvas can expand step (1) of SPEC. In future research, more applications of KaPIL are required to validate its robustness and extend it to other digital platform methods and tools. Keywords: digital platform business model, live-lab, design research methodology, innovation process, validation environment

Universities lack a systematic transfer from research and innovation projects into practice. German Mittelstand firms have limited resources to pursue explorative innovation, which is required to realize business opportunities and remain competitive. Since current collaborations do not fully unlock innovation potential, this research aims to conceptualize a company builder to bring these two parties together in an entrepreneurial ecosystem. In line with this research’s exploratory nature, a multi-method analysis was followed, applying two approaches for data analysis. The company builder information was analyzed via a qualitative document analysis. Seven guided expert interviews were conducted with employees in innovation-related positions in German Mittelstand firms and the university. Findings suggest the company builder’s core activity is the venture creation process, ensuring systematic access to university-relevant research and innovation and facilitating valuable interactions among the ecosystem partners. The company builder’s value lies in connecting relevant ecosystem partners through a comprehensive company-building environment to exchange knowledge and expertise and meeting the partners’ needs equally regarding the new venture. Thereby, it is independent of bureaucratic university structures. Future research should validate current findings with higher sample size and focus on other relevant aspects, such as generating revenue and participation requirements for other potential ecosystem partners. Keywords: company builder, entrepreneurial ecosystem, the German Mittelstand, ambidextrous organization

In today's connected age, numerous companies that develop mechatronic systems in generations pursue a digital platform business model. Previous research created the SPDS – Smart Platform Design Sprint to provide product development processes with the necessary tool to build digital platform business models. The SPDS is a five-day method to discover and design digital platform business models. This research validates and further develops the SPDS to provide insights into the first practical application and evaluates the methodology's functionality by solving a real-world problem. More applications of the SPDS are needed to verify its robustness for improved generalization.

In recent years, the significance of digital platform business models has been increasing. This growth creates an increasing demand for tools that companies and startups can apply to find and develop sustainable platform business models. Today, various platform design tools are available to help companies and startups in the platform development. Previous research by Brecht et al. (2021) on the validation of platform business models has provided methods requiring, amongst others, a discovered and verified business model. However, there is a lack of research in establishing guidelines on how to reach this verified state. By applying the Google Sprint, a popular method to quickly generate insights into a variety of problems and enriching it with platform design tools, this research creates the Smart Platform Design Sprint (SPDS). The SPDS provides a solution to discover and obtain a verified business model. Its novelty lies in incurring the speed of the Google Sprint and incorporating the expertise of platform design tools. Through a series of expert interviews, the SPDS is improved, and its necessity verified. In future research, the SPDS awaits application in a practical setting showing its feasibility. Keywords: platform design tools, business model, exploration, google sprint, smart platform experiment cycle

Startups searching for a business model face uncertainty This research aims to demonstrates how B2B startups can use business experiments to discover and validate their business model's desirability quickly and cost-effectively The research study follows a design science approach by focusing on two main steps: build and evaluate We first created a B2B-Startup Experimentation Framework based on well-known earlier frameworks After that, we applied the framework to the case of the German startup heliopas ai The framework consists of four steps (1) implementation of a measurement system, (2) hypothesis development and prioritization, (3) discovery, and (4) validation Within its application, we conducted business experiments, including online and offline advertisements, as well as interviews This research contributes in several ways to the understanding of how B2B-startups can use business experiments to discover and validate their business models: First, the designed B2B-Startup Experimentation Framework can serve as a guideline for company founders Second, the results were used to improve the existing business model of the German B2B startup heliopas ai Finally, applying the framework allowed us to formulate design principles for creating business experiments The design principles used in the study can be further tested in future studies

Abstract Digital platform business models are disrupting traditional business processes and reveal a new way of creating value. Current validation processes for business models are designed to assess pipeline business models. They cannot grasp the logic of digital platforms, which increasingly integrate Artificial Intelligence (AI) to ensure success. This study developed a new validation process for early market validation of digital platform business models by following the Design Science Research methodology. The designed process, the Smart Platform Experiment Cycle (SPEC), is created by combining the Four-Step Iterative Cycle of business experiments, the Customer Development Process, and the Build-Measure-Learn feedback loop of the Lean Startup approach and enriching it with the knowledge of digital platforms. It consists of five iterative steps showing the startup how to design their platform business model and corresponding experiments and how to run, measure, analyze, and learn from the outcomes and results. To assess its efficacy, applicability, and validity, SPEC was applied in the German startup GassiAlarm, a service marketplace business model. The application of SPEC revealed shortcomings in the pricing strategy and highlighted to what extent their current business model would be successful. SPEC reduces the risk of building a product or service the market deems redundant and gives insights into its success rate. More applications of the SPEC are needed to validate its robustness further and to extend it to other types of digital platform business models for improved generalization.