Where to Start with AI?—Identifying and Prioritizing Use Cases for Health Insurance

Artificial Intelligence (AI) arguably represents a key technology for the digitalization of health care. Specifically, health insurers can benefit from AI as they typically have access to vast amounts of data. However, practitioners struggle to adopt AI in productive use, and extant research lacks an overview of use cases for AI in health insurance as well as prioritization criteria that can guide their implementation. To address this gap, we conduct explorative interviews in the context of the German statutory health insurance system. We identify AI use cases in the areas of predictive health, individualized service, anomaly detection, and operations enhancement. We find that health insurers are likely to prioritize these use cases according to implementation complexity and business orientation, whereas focusing on simple use cases that target cost savings is recommended by experts. Our study advances the understanding of AI adoption in health insurance and supports practitioners in guiding future AI initiatives

The Good, the Bad, and the Dynamic: Changes to Retail Business Models During COVID-19

Crises, such as the COVID-19 pandemic, challenge the economy and require firms to become resilient to external change. During COVID-19, the retail industry faced doubleedged consequences. While brick and mortar business models (BMs) were discontinued, online retail thrived. Extant BM research has investigated several crises; however, it still lacks an explanation of how BM change increases resilience to cope with crises. We analyze the BMs of 45 European retailers and the BM changes implemented during the COVID-19 pandemic and their influence on the retailers\u27 revenue. We identify three types of retailers implementing different strategies to cope with the crises: the »good,« the »bad,« and the »dynamic.« These represent resilient BMs, un-resilient BMs, and BMs becoming resilient enabled by digital technology. We show how BM change creates resilience and performance benefits. For practice, we show how retailers adapted their BM to a crisis leveraging digital technology

Business Capability Mining - Opportunities and Challenges

Business capability models are widely used in enterprise architecture management to generate an abstract overview of an organization’s business activities to reach its business objectives. The creation and maintenance of these models are associated with a huge manual workload. Research provides insights into opportunities for automated modeling of enterprise architecture models. However, most models address the application and technology layer and leave the business layer largely unexplored. Particularly, no research has been conducted on the automated generation of business capability models. This research paper uses 19 semi-structured expert interviews to identify possible automated modeling opportunities of business capabilities and related challenges and to jointly develop a business capability mining approach. This research benefit both, practice and research, by describing a situation-based business capability mining approach and identifying appropriate implementation scenarios

Balancing on the Triple-Bottom-Line: Tensions in the Success Factors of Digital Business Models for Sustainability

We need innovations that enable a sustainable economy and sustainable private consumption to meet the grand challenges of the UN Sustainable Development Goals. As an essential source of innovation, startups play a crucial role in improving sustainability by creating innovative and sustainable products and services as part of their business models (BMs). Since BMs are at a firm's core, BMs are a decisive factor that influences whether startups fail or thrive; we analyze the success factors of sustainable BMs. We interviewed 16 experts from 15 startups implementing sustainable BMs based on digital technologies and one incubator specializing in sustainability. We identify six success factors representing tensions in digital BM design that entrepreneurs need to address. Our analysis shows how the design of sustainable digital BMs differs from regular digital BMs and how the tensions affect the success of startups. For established firms, the results guide BM design and technology use

Leveraging Customer-integration Experience: A Review of Influencing Factors and Implications

Organizations have increasingly begun to co-create innovations, conduct idea competitions, or conduct crowdsourcing initiatives with customers in online communities. Yet, many customer-integration methods fail to attract sufficient customer participation and engagement. We draw on previous research to identify customers’ experience as an important determinant of whether customer-integration initiatives succeed. However, research has rarely applied the notion of experience in the context of customer integration. We conduct a cross-disciplinary literature review to identify the factors that constitute a positive customer-integration experience and the implications of the customer-integration experience. Based on 141 papers from marketing, technology and innovation management, information systems, human-computer interaction, and psychology research, we derive a framework for customer-integration experience that integrates 22 conceptually different influencing factors, 15 implications, and their interrelatedness based on motivation-hygiene theory. The framework sheds light on the current state of research on customer-integration experience and identifies possibilities for future research

The Influence of Digital Affordances and Generativity on Digital Platform Leadership

The increasing importance of digital platforms is undisputed. Digital platforms integrate and orchestrate an ecosystem of autonomous actors to co-create value instead of relying solely on internal innovation capabilities. To achieve this, the platform owner provides digital affordances through boundary resources that an ecosystem of complementors can use to create value-adding services. The platform combines internal innovation capabilities by providing digital affordances and utilizes external innovation capabilities between complementors that refer to the generativity of the ecosystem. However, it remains unclear how the provision of affordances and the interaction of complementors led to the tremendous success of digital platforms. To disentangle both internal and external innovation capabilities, we adhere to a fuzzy-set qualitative comparative analysis based on a set of 47 platforms. Preliminary results reveal four configurations of leading platforms that combine affordances of the platform and generativity in an ecosystem to point toward a fruitful area for future research

AI Startup Business Models

We currently observe the rapid emergence of startups that use Artificial Intelligence (AI) as part of their business model. While recent research suggests that AI startups employ novel or different business models, one could argue that AI technology has been used in business models for a long time already—questioning the novelty of those business models. Therefore, this study investigates how AI startup business models potentially differ from common IT-related business models. First, a business model taxonomy of AI startups is developed from a sample of 100 AI startups and four archetypal business model patterns are derived: AI-charged Product/Service Provider, AI Development Facilitator, Data Analytics Provider, and Deep Tech Researcher. Second, drawing on this descriptive analysis, three distinctive aspects of AI startup business models are discussed: (1) new value propositions through AI capabilities, (2) different roles of data for value creation, and (3) the impact of AI technology on the overall business logic. This study contributes to our fundamental understanding of AI startup business models by identifying their key characteristics, common instantiations, and distinctive aspects. Furthermore, this study proposes promising directions for future entrepreneurship research. For practice, the taxonomy and patterns serve as structured tools to support entrepreneurial action

A Taxonomy of Platform Envelopment: Revealing Patterns and Particularities

Platform envelopment describes a competitive move whereby a digital platform enters an adjacent market. On one hand, it might enable to dethrone an established platform. On the other hand, it might give rise to the creation of platform conglomerates, which increases the concentration of private power. Therefore, platform envelopment has recently attracted significant attention from regulators and scholars. However, the traditional view of platform envelopment does not consider recent platform envelopment practices observed in research and practice. In this study, we aim to determine and structure the complexity of platform envelopment. We investigated 20 cases and developed a taxonomy of platform envelopment. We further encoded these cases into the comprehensive taxonomy and derived platform envelopment patterns and particularities. Our work contributes to research by establishing a foundation for the conceptual understanding of platform envelopment. Regulators can use this taxonomy to classify platform envelopment cases and determine potentially anti-competitive conduct

Deploying AI Applications to Multiple Environments: Coping with Environmental, Data, and Predictive Variety

Deploying Artificial Intelligence (AI) proves to be challenging and resource-intensive in practice. To increase the economic value of AI deployments, organizations seek to deploy and reuse AI applications in multiple environments (e.g., different firm branches). This process involves generalizing an existing AI application to a new environment, which is typically not seamlessly possible. Despite its practical relevance, research lacks a thorough understanding of how organizations approach the deployment of AI applications to multiple environments. Therefore, we conduct an explorative multiple-case study with four computer vision projects as part of an ongoing research effort. Our preliminary findings suggest that new environments introduce variety, which is mirrored in the data produced in these environments and the required predictive capabilities. Organizations are found to cope with variety during AI deployment by 1) controlling variety in the environment, 2) capturing variety via data collection, and 3) adapting to variety by adjusting AI models

Value Drivers of Artificial Intelligence

Artificial intelligence (AI) holds great potential for firms to create new business models and gain competitive advantages. While some pioneers are effectively leveraging AI, most firms are struggling to capitalize on the opportunities for value creation. Previous research has highlighted the performance benefits, success factors, and challenges of adopting AI. However, the value drivers of AI, specifically regarding how AI creates value, remain unclear and need exploration so that firms can adapt their value creation to leverage the potential. To clarify how AI creates value, we conduct a case survey of 61 firms to identify six value drivers: efficiency, novelty, knowledge from data, ecosystem, personalization, and human resemblance. We discuss how these value drivers differ from other digital technologies. For practitioners, we provide valuable insights into the business value of AI and business model (BM) design opportunities to build on