Towards a Taxonomy of API Services in Logistics

Data are a valuable asset for companies in the logistics sector to optimize internally and develop new business models. They can be like a magnifying glass and make previously opaque logistical processes transparent and find previously hidden potentials for optimization. Typical applications are tracking of the transport status, route optimization, or monitoring of pharmaceutical products, or monitoring shocks for fragile cargo along the trade lanes. One way to use data is to tap into publicly or commercially available Application Programming Interfaces. Hereby, logistics service providers can get or provide data automatically via a machine-to-machine interface. However, the landscape of API service providers is vast, unstructured, and intransparent in terms of potential data that companies can leverage. Given their high potential for the logistics industry, the paper proposes a taxonomy of API services in logistics based on the inductive analysis of three API databases

The Digitization of Investment Management – An Analysis of Robo-Advisor Business Models

The emergence of so-called Robo-Advisors (RAs) is disrupting the financial services industry. RAs are algorithm-based systems that digitize and automate the investment advisory process including portfolio recommendation, risk diversification, portfolio rebalancing, and portfolio monitoring. Scientific research in this field is still in its infancy and lacks a comprehensive understanding of the underlying business model (BM) of RAs to comprehensively understand the RA business and to further identify their potential to disrupt the financial services industry. Therefore, in this article, we conduct a multiple case study across the fifteen biggest US-based RAs to explain the basic characteristics and special features of RA BMs. Thereby, we distinguish between pure algorithm-based RAs and hybrid RAs with dedicated human oversight. Through an in-depth analysis of publicly available qualitative data, we contribute to the existing research by unleashing significant elements that underline the power of RAs to disrupt the financial services industry

Federal Platform Ecosystems to Counter Monopolists: A Value-Based Vision for the Logistics Industry

Today, we observe the platformization of many industries. Although most platforms fail, a few monopolists have the potential to completely transform industries and their competitive dynamics. In contrast to this, European values, e.g., democracy and freedom, aim to protect today’s variety of companies and offers. This paper’s goal is to protect European values and companies’ competitiveness against potential monopolists. We suggest founding and governing federal platform ecosystems following the “swarm intelligence” principle where many small(er) organizations collaboratively fight off monopolists. While this is currently still a new and untested concept, we selected a use case to make it more tangible and adaptable. The government-funded project aims at a vision for the Logistics Broker (LB) – which is envisioned to become the center-piece of the German logistics industry’s federal platform ecosystem. To analyze the context, role, and stakeholders we conducted a workshop study and propose an agenda for future research

UNDERSTANDING DATA TRUSTS

Finding ways to share data while upholding data sovereignty is a key issue to succeed in building the digital economy. One way to achieve this is to install data intermediaries – so-called data trusts – that facilitate this process. Their role is to orchestrate data sharing for organizations and individuals by ensuring that data sovereignty (i.e., the right to decide how data can be used) remains with the data provider. However, while the concept is promising, research on it is still in its infancy. The paper tackles exactly that issue as we collect data on data trusts (interviews and publically available information) and construct a general solution space for designing data trusts. With our research, we provide an overview as well as options for designing data trusts

Show me the Money: How to monetize data in data-driven business models?

Increasing digitization and the associated tremendous usage of technology have led to data of unprecedented quantity, variety, and speed, which is generated, processed, and required in almost all areas of industry and life. The value creation and capturing from data presents companies with numerous challenges, as they must create or adapt appropriate structures and processes. As a link between corporate strategy and business processes, business models are a suitable instrument for meeting these challenges. However, few research has been conducted focusing on data-based monetization in the context of data-driven business models so far. Based on a systematic literature review the paper identifies five key components and 23 characteristics of data-driven business models having crucial influence on data-based value creation and value capturing and thus on monetization. The components represent key factors for achieving commercial benefits from data and serve as guidance for exploring and designing suitable data-driven business models

Transformer(s) of the Logistics Industry - Enabling Logistics Companies to Excel with Digital Platforms

Platformization is a prevailing trend that changes industries at their core. The rise and dominance of platform-based companies require incumbent companies and start-ups to rethink how they approach that novel challenge and leverage its full potential. To successfully steer and initiate this digitally enabled industry transformation, even in traditional industries like logistics, the incumbent companies require IT and specific platform design support. However, designing a digital platform is a complex task riddled with design options, potential pitfalls, and complex underlying mechanisms. Consequently, research and practice require tools to leverage past design knowledge and generate digital platforms in a goal-oriented fashion. This paper addresses precisely that issue as we report on a design science research study that developed a visual inquiry tool for digital platform design. Ultimately, the visual inquiry tool provides researchers and practitioners with the means to develop digital platforms more efficiently and strategically

The Interplay of Data-Driven Organizations and Data Spaces: Unlocking Capabilities for Transforming Organizations in the Era of Data Spaces

This research paper highlights the relationship between data-driven organizations and data spaces and focuses on unlocking capabilities that can be used to transform organizations and to remain competitive in the era of data spaces. The increasing availability and diversity of data, as well as advances in technology, have led to the emergence of data spaces. However, to fully leverage these opportunities, organizations must be able to effectively access, process and utilize data from these data spaces. Through an in-depth examination of current literature, this paper explores the capabilities required for organizations to participate in data space activities. The TOE framework was used to structure the derived capabilities. The findings of this research provide insights into the capabilities that organizations and data spaces must consider when looking to co-innovate and realize new business cases. We anticipate that our paper will have significant implications for both practitioners and researchers

How to Share Data Online (fast) – A Taxonomy of Data Sharing Business Models

Data is an integral part of almost every business. Sharing data enables new opportunities to generate value or enrich the existing data repository, opening up new potentials for optimization and business models. However, these opportunities are still untapped, as sharing data comes with many challenges. First and foremost, aspects such as trust in partners, transparency, and the desire for security are issues that need to be addressed. Only then can data sharing be used efficiently in business models. The paper addresses this issue and generates guidance for the data-sharing business model (DSBM) design in the form of a taxonomy. The taxonomy is built on the empirical analysis of 80 DSBMs. With this, the primary contributions are structuring the field of an emerging phenomenon and outlining design options for these types of business models