Andre Hanelt

Discovering digital business models in traditional industries

Purpose The purpose of this paper is to provide managers from traditional industries with a blueprint to systematically analyze and discover digital business models and, thus, better cope with the digital transformation of their industrial businesses. Design/methodology/approach The proposed blueprint is built on state-of-the-art research on digital business model innovation and a rigorous taxonomy-building approach. The process is demonstrated through a simplified case study of a passenger transport company. Findings The process involves three steps: identifying existing products and services, deconstructing business models and discovering new configurations. The managers from the case company very positively evaluated the efficiency and effectiveness of the proposed procedure. Originality/value The proven methodology relates the generic components of digital business models to a specific firm’s context, listing the solution space for each relevant dimension. The resulting framework aids in better understanding the existing business models and serves as a tool for the systematic discovery of new models.

The Business Model Pattern Database — A Tool For Systematic Business Model Innovation

Companies are more frequently seen shifting their focus from technological innovation towards business model innovation. One efficient option for business model innovation is to learn from existing solutions, i.e., business model patterns. However, the various understandings of the business model pattern concept are often confusing and contradictory, with the available collections incomplete, overlapping, and inconsistently structured. Therefore, the rich body of literature on business model patterns has not yet reached its full potential for both practical application as well as theoretic advancement. To help remedy this, we conduct an exhaustive review, filter for duplicates, and structure the patterns along several dimensions by applying a rigorous taxonomy-building approach. The resulting business model pattern database allows for navigation to the relevant set of patterns for a specific impact on a company’s business model. It can be used for systematic business model innovation, which we illustrate via a simplified case study.

Driving business transformation toward sustainability: exploring the impact of supporting IS on the performance contribution of eco-innovations

Information systems (IS) can foster business transformation toward sustainability on a large scale by supporting green technologies, thereby creating hybrid physical–digital solutions that are able to fulfil organizational performance requirements and contribute to sustainable business practices. These supporting IS provide an alternative path to corporate sustainability rather than just contributing to the ‘greening’ of business practices by aiming to improve the organizational performance impact of environmental advantageous innovations, which in turn fosters their adoption. Employing a multiple case study comprising eight companies that have implemented such eco‐innovations (specifically electric vehicles) in their business processes, our findings indicate that the organizational performance contribution of eco‐innovations is improved when complemented by supporting IS. This effect is achieved by (1) enhancing the efficiency of the business processes in which the eco‐innovations are deployed, thus increasing eco‐efficiency, and (2) enabling new functionalities, processes and business models that help achieve organizational sustainability goals, thereby driving eco‐effectiveness. With these two aspects, we add two functional affordances – technological flexibility and digital eco‐innovation – to the existing knowledge base of Green IS and point to an elaborated role of IS in sustainability transformation.

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.

Sharing Yet Caring - Mitigating Moral Hazard in Access-Based Consumption through IS-Enabled Value Co-Capturing with Consumers

The quest for creating smart and sustainable cities entails various substantial challenges, such as environmental degradation and a shortage of space. To negotiate these hurdles, innovative approaches must be implemented. A key aspect in this regard is the shared use of resources via forms of access-based consumption. Owing to advances in the digitalization of contemporary societies, these concepts have recently attracted both consumer and scholarly interest. However, the digitally enabled separation of ownership and use brings along the risk of moral hazard by consumers using resources in careless or wasteful ways, which is detrimental to the sustainability of the overall system. In this study, the authors conceptualize and empirically investigate how these adverse effects can be mitigated by applying the potentials of connectivity and digital data to enable users to participate economically while acting favorably from a collective perspective. The results of the quasi-experimental research design, situated in a carsharing context and comprising data records of 2,983 bookings, indicate that this form of value co-capturing with consumers can significantly motivate users to alter their behavior. From these findings, the authors derive important implications for research on the sustainability of digital business eco-systems in the specific context of smart cities.

Design Options for Carsharing Business Models

Carsharing clubs that grant members temporary access to vehicles have existed for more than half a century. Only recently, however, have technological advances such as the mobile internet begun to foster new carsharing business models, thereby increasing the attractiveness of carsharing for both operators and users. So far, these new business models were typically classified as roundtrip, point to point, nonprofit/cooperative, and P2P carsharing. However, not all operators fit into these rather broad groups; for instance, the carsharing company CiteeCar combines elements of several archetypes. Furthermore, important differences among the various design options of operators belonging to the same type are not addressed by this distinction. For example, the largest P2P carsharing companies, Getaround and Turo, employ very different business models: Getaround provides automatic access kits while Turo does not. Thus they address different use cases and customers. To account for such differentiation, we complement these archetypes by a classification scheme for carsharing business models, i.e., a taxonomy, listing the most important dimensions and corresponding characteristics. We proceed in three major steps: First, we create a global database of carsharing companies. Second, we use the database to create a taxonomy of carsharing business models. Third, we empirically derive more fine grained archetypes from the taxonomy. Our resulting framework allows researchers and practitioners to more accurately analyze and describe existing carsharing systems. Furthermore, it can be applied for business model innovation by copying successful business model patterns or systemtaically discovering new configurations.

Towards Distributed Cognitive Expert Systems

The process of Datafication gives rise to ubiquitousness of data. Data-driven approaches may create meaningful insights from the vast volumes of data available to businesses. However, coping with the great volume and variety of data requires improved data analysis methods. Many such methods are dependent on a user’s subjective domain knowledge. This dependency leads to a barrier for the use of sophisticated statistical methods, because a user would have to invest a significant amount of labor into the customization of such methods in order to incorporate domain knowledge into them. We argue that machines may efficiently support researchers and analysts even with non-quantitative data once they are equipped with the ability to develop their own subjective domain knowledge in a way that the amount of manual customization is reduced. Our contribution is a design theory – called the Division-of-Labor Framework – for generating and using Experts that can develop domain knowledge.