In general, supervised Machine Learning approaches using labeled training data currently promise the best classification results with respect to classification accuracy. Therefore, data annotation is a key component of most Machine Learning projects implemented. However, creating labels for a training data set is often an elaborate project involving arduous and repetitive work, which is why data scientists often try to minimize the effort for data annotation by automating the data annotation process itself. In this paper, we present a case study of two data annotation projects on the same data set of support tickets and compare these: one using human annotators and the other using algorithmic Learning Functions in a combination of Active Learning and Weak Supervision. Here, we achieved a weighted confidence score of >94% for the human-created labels, while also achieving up to 92% agreement between the labels of our automated project and the labels created by human annotators, with the need for only 10% of human annotation as the starting input of the automated approach. Additionally, we were able to reproduce the value of 85% for initial human classification accuracy in support ticket distribution from previous papers. We close with a reflection about the worth of business understanding in data annotation projects and the problem and proposed solutions to ticket ambiguity.

Innovation platforms enable companies to create broad and dynamic portfolios of applications, which can be developed internally and externally. An increasing number of established companies from traditional industries have begun to explore the potential of innovation platforms. These companies are often organised as multi-business organisations (MBOs). When introducing innovation platforms, they face tensions between the corporate centre that runs such platforms and business units that develop applications on the platform. Established approaches to information technology (IT) governance in MBOs are ineffective in addressing these tensions because they focus on cross-business-unit synergies, rather than on innovation from business units. To explore this issue further, we conducted a longitudinal case study of an automotive manufacturer that introduced an innovation platform in its infotainment system. The company faced four tensions: rigidity, alteration, mistrust, and competition. To address these tensions, the company evolved its IT governance, allowing for more flexibility for business units, supporting their development efforts, and prioritising them over external complementors. The role of the corporate centre evolved from a gatekeeper to an enabler of innovation. Our findings link the literature on IT governance in MBOs with that of platform governance, providing insights into how MBOs benefit from innovation platforms.

IT carve-out projects are complex and cost-intensive components of M&A transactions. Existing research sheds little light on the determinants of IT carve-out project complexity and/or its effects on divestor performance. Instead, research has focused on the post-acquisition IT integration project and acquirer performance. This paper presents the first divestor-centric model of IT transactions from the divestor to the acquirer when a Business Unit in a Multi-Business Organization (MBO) is carved out and integrated into another MBO. The model explains how divestor business and IT alignment pre-conditions contribute to increased IT carve-out project complexity. Such complexity increases IT carve-out project time to physical IT separation and creates IT stranded assets, which decrease post-divestment business, IT alignment and divestor performance. The current recommended strategy of adopting transitional service agreements (TSAs) to handle IT carve-out complexity is compared with two new proactive strategies derived from the model. TSA-based strategies restrict the divestor from both decommissioning IT stranded assets and reconfiguring its IT assets to support its new post-divestment business strategy. The two new strategies address IT carve-out complexity without incurring the negative effects from adopting TSAs.

Digital business model innovation (BMI) is critical to achieving and sustaining competitiveness in technology-driven environments. In those environments, firms must not only sense changes to identify opportunities but also effectively seize them in BMI. Therefore, sensing and seizing cannot be considered as isolated dynamic capabilities, but must be combined for successful BMI. However, research on sensing and seizing does not offer compelling suggestions for firms that struggle with connecting both while pursuing digital BMI. We use qualitative configurational analysis (QCA) to analyze a sample of 49 case studies on digital BMI to identify the antecedents that firms sense before seizing these changes with digital BMI. Based on ten configurations of sensing (represented by six antecedents) and seizing (represented by four BMI types), we explain the relationship between sensed antecedents and seized digital BMI. In addition, we derived four variables that explain “what” and “how” firms connect sensing and seizing. Based on the sensing-seizing connection, we introduce consolidating BMI as a new type of BMI unique to the digital context. This novel type enables firms to exploit and explore new BMs and subsequent digital BMIs through the means of digital infrastructure. This study extends the understanding of how different business models emerge and how firms create digital BMIs.

COVID-19 has created many constraint-related challenges for humans in general and organisations in particular. Specifically, businesses that require physical contact, such as mobility providers, have been severely impacted by the crisis. This paper reveals how mobility platforms and their ecosystem of actors have adapted faster than their non-platform competitors to become resilient. Whereas current research on resilience explicitly deals with the concept of organisational resilience, community resilience, or IT resilience, socio-technical characteristics of digital platforms have not been investigated. We build on a case survey approach, including heterogeneous qualitative evidence of 266 actions of 171 analysed mobility platforms. The results show five archetypes of how mobility platforms leverage their platform-based nature and the ecosystem to build resilience. Based on this, we develop the concept of platform ecosystem resilience as leveraging socio-technical factors of digital platforms and ecosystems frugally to design, deploy and use situation-specific responses to prepare for, endure and adapt by capturing new opportunities and engaging in transformative activities to cope with exogenous shocks and become resilient for future disruptions. Our results emphasise the importance of platform ecosystems for practitioners and policy planners to develop the “new normal” rather than resuming existing practices.

Global crises, such as the COVID-19 pandemic, change the context for research and bring with them many professional challenges for IS researchers – not the least of which is disrupting carefully thought-out data collection efforts. In this confessional tale, we describe how moving from an “open research ecosystem” to a “socially distanced research ecosystem” has affected a long-planned data collection effort. While government orders to socially distance and physically isolate may have made the world “stand still” for some, we found that these orders had dynamic and consequential effects for our in-process research. Against the backdrop of significant threats posed by the contextual change to our data collection, we explain how the crisis also opened up opportunities to invigorate our understanding of how the environment affects how we conduct research. We conclude our tale with guidelines for how to successfully respond when your research is interrupted by a change of context.

A top priority of organisations around the globe is to achieve IT-business alignment at all levels of the organisation. This paper addresses operational alignment within IT functions. Traditionally, IT functions are divided into highly independent subunits. In the face of pressure to adapt to rapidly changing customer demands and to manage increasingly complex IT architectures, many organisations have begun implementing joint, cross-functional DevOps teams, which integrate tasks, knowledge and skills pertaining to planning, building, and running software product activities. In this study, we examine eight cases of DevOps implementation. We apply grounded theory to identify three mechanisms comprising a tripartite model of intra-IT alignment: individual componentization, integrated responsibility, and multidisciplinary knowledge. Our model provides insights into how alignment between development and operations can be achieved in DevOps teams within the IT function.

Information Systems Research (“Wirtschaftsinformatik”) basically follows two research approaches: the behavioristic approach and the design-oriented approach. In this memorandum, 10 authors propose principles of design-oriented information systems research. Moreover, the memorandum is supported by 111 full professors from the German-speaking scientific community, who with their signature advocate the principles specified therein.

Exchanging and analyzing ideas across different software tools and repositories is needed to implement the concepts of open innovation and holistic innovation management. However, a precise and formal definition for the concept of an idea is hard to obtain. In this paper, the authors introduce an ontology to represent ideas. This ontology provides a common language to foster interoperability between tools and to support the idea life cycle. Through the use of an ontology, additional benefits like semantic reasoning and automatic analysis become available. Our proposed ontology captures both a core idea concept that covers the ‘heart of the idea’ and further concepts to support collaborative idea development, including rating, discussing, tagging, and grouping ideas. This modular approach allows the idea ontology to be complemented by additional concepts like customized evaluation methods. The authors present a case study that demonstrates how the ontology can be used to achieve interoperability between innovation tools and to answer questions relevant for innovation managers that demonstrate the advantages of semantic reasoning.