Evaluating Platform Openness in Logistics based on a Taxonomic Analysis

Digital platforms are becoming increasingly important in logistics to enhance business models and ensure competitiveness. As new players enter from the B2C sector, the need to innovate is intensifying for traditional firms. To compensate disadvantages, such as missing platform knowledge or a late entrance, open strategies, e.g., shared governance or open source, can spur platform development and establishment. The resulting open platform ecosystems are a promising approach in entering the platform business for struggling firms. As first initiatives aim to promote open logistics ecosystems, our research objective is to evaluate the current state of openness regarding logistics platforms. We use a taxonomy to identify relevant design elements from a business model’s perspective. Building on the taxonomic analysis, we evaluate relevant openness dimensions to display the current state of openness in logistics platform ecosystems. We conclude by giving an outlook on future research avenues by providing potential research questions

'License to VIT’ - A Design Taxonomy for Visual Inquiry Tools

Visual Inquiry Tools are valuable assets to work conjointly on an ill-structured or wicked problem and solve it creatively. With visual inquiry tools, designers can sketch the problem-space of an artifact-to-be-designed and generate solutions in a priori defined ontological elements. While there exists guidance in how visual inquiry tools should be designed content-wise, there is a lack of clarification on the design options available to design them. Subsequently, the paper proposes a taxonomy of visual inquiry tools outlining options for their design. We do this by incorporating a sample of 24 visual inquiry tools developed in the scientific literature corpus as well as 15 through empirical example

Discovering Business Models of Data Marketplaces

The modern economy relies heavily on data as a resource for advancement and growth. Data marketplaces have gained an increasing amount of attention since they provide possibilities to exchange, trade and access data across organizations. Due to the rapid development of the field, the research on business models of data marketplaces is fragmented. We aimed to address this issue in this article by identifying the dimensions and characteristics of data marketplaces from a business model perspective. Following a rigorous process for taxonomy building, we propose a business model taxonomy for data marketplaces. Using evidence collected from a final sample of twenty data marketplaces, we analyze the frequency of specific characteristics of data marketplaces. In addition, we identify four data marketplace business model archetypes. The findings reveal the impact of the structure of data marketplaces as well as the relevance of anonymity and encryption for identified data marketplace archetypes

ARCHETYPES OF DIGITAL BUSINESS MODELS IN LOGISTICS START-UPS

Our work develops an archetypical representation of current digital business models of Start-Ups in the logistics sector. In order to achieve our goal, we analyze the business models of 125 Start-Ups. We draw our sample from the Start-Up database AngelList and focus on platform-driven businesses. We chose Start-Ups as they often are at the forefront of innovation and thus have a high likelihood of operating digital business models. Following well-established methodological guidelines, we construct a taxonomy of digital business models in multiple iterations. We employ different algorithms for cluster analysis to find and generate clusters based on commonalities between the business models across the dimensions and characteristics of the taxonomy. Ultimately, we use the dominant features of the emerging patterns within the clusters to derive archetypes

What constitutes a machine-learning-driven business model? A taxonomy of B2B start-ups with machine learning at their core

Artificial intelligence, specifically machine learning (ML), technologies are powerfully driving business model innovation in organizations against the backdrop of increasing digitalization. The resulting novel business models are profoundly shaped by ML, a technology that brings about unique opportunities and challenges. However, to date, little research examines what exactly constitutes these business models that use ML at their core and how they can be distinguished. Therefore, this study aims to contribute to an increased understanding of the anatomy of ML-driven business models in the business-to-business segment. To this end, we develop a taxonomy that allows researchers and practitioners to differentiate these ML-driven business models according to their characteristics along ten dimensions. Additionally, we derive archetypes of ML-driven business models through a cluster analysis based on the characteristics of 102 start-ups from the database Crunchbase. Our results are cross-industry, providing fertile soil for expansion through future investigations

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

Unlashing the next Wave of Business Models in the Internet of Things Era: New Directions for a Research Agenda based on a Systematic Literature Review

Pervasive digitization of products and services open additional avenues for the next wave of business model opportunities. Most of firms are aware of the monetization potentials that the Internet of Things has to offer, however, they still struggle to create a compelling value propositions. Despite the attention of both research and practice onto business models and the IoT, only few concepts and research endeavors regarding their intersections exist. This paper tends to unleash the specificity of the business models within the IoT technologies, and motivate new, ecosystem, perspective for upcoming research. Following a rigorous methodology for a comprehensive and systematic literature review, we develop five literature clusters related to the IoT-driven business model research, evaluate and analyze the papers within clusters, and finally identify the gaps and propose directions for future research

Taxonomy of Digital Platforms: A Business Model Perspective

Digital platforms (DPs) – technical core artifacts augmented by peripheral third-party complementary resources – facilitate the interaction and collaboration of different actors through highly-efficient resource matching. As DPs differ significantly in their configurations and applications, it is important from both a descriptive and a design perspective to define classes of DPs. As an intentionally designed artifact, every classification pursues a certain purpose. In this research, the purpose is to classify DPs from a business model perspective, i.e. to identify DP clusters that each share a similar business model type. We follow Nickerson et al.’s (2013) method for taxonomy development. By validating the conceptually derived design dimensions with ten DP cases, we identify platform structure and platform participants as the major clustering constituent characteristics. Building on the proposed taxonomy, we derive four DP archetypes that follow distinct design configurations, namely business innovation platforms, consumer innovation platforms, business exchange platforms and consumer exchange platforms

Artificial Intelligence in Energy Demand Response: A Taxonomy of Input Data Requirements

The ongoing energy transition increases the share of renewable energy sources. To combat inherent intermittency of RES, increasing system flexibility forms a major opportunity. One way to provide flexibility is demand response (DR). Research already reflects several approaches of artificial intelligence (AI) for DR. However, these approaches often lack considerations concerning their applicability, i.e., necessary input data. To help putting these algorithms into practice, the objective of this paper is to analyze, how input data requirements of AI approaches in the field of DR can be systematized from a practice-oriented information systems perspective. Therefore, we develop a taxonomy consisting of eight dimensions encompassing 30 characteristics. Our taxonomy contributes to research by illustrating how future AI approaches in the field of DR should represent their input data requirements. For practitioners, our developed taxonomy adds value as a structuring tool, e.g., to verify applicability with respect to input data requirements

Uncovering the Role of IS in Business Model Innovation - A Taxonomy-driven Approach to Structure the Field

Business model innovations (BMIs) are one of the key activities organizations must undertake to survive and thrive. As information systems (IS) penetrate more and more aspects of life, they become an important factor affecting both the process and the outcome of business model innovations. The increased importance of IS in a growing number of industries has led various researchers to focus on examining the role of IS in innovation. However, these insights concentrate on process, product, and service innovations, while business model innovations encompass characteristics that are fundamentally different from these. Therefore, in this paper we use a rigorous taxonomy-building approach to uncover the distinct roles IS play in this important endeavor, employing a meta-perspective and drawing from documented empirical research on business model innovations. We found that IS act, first, as enablers of business model innovation, second, as capabilities in the business model innovation process, and third, as frames of reference for business model innovations. Our findings indicate that IS are thus both operand and operant resources in business model innovations. Hence, business managers must be aware of all of these roles, as they could have transformative impacts in every industry