Shaping the Boundaries of a Service Ecosystem: The Case of Udacity

Service-dominant logic highlights the ability of service ecosystems to ‘self-adjust’ as a reaction to systemic inefficiencies or external changes [1]–[3]. We contribute to the question on how focal actors shape the boundaries of service ecosystems through service innovation. This is a single case study on a digital ecosystem focused on a first mover in digital platforms for Massive Open Online Courses (MOOCs): Udacity. We found two mechanisms, where Udacity shaped the boundaries of its ecosystem: ‘user self-service integration’ and ‘gradual partner disintegration’. Throughout three phases between 2011 and 2015 they disintegrated services from higher education, namely offering courses online, designing courses, and accreditation due to lowly perceived adaptability of univer-sities and external pressures for finding a sustain-able business model. Additionally, they disinte-grated self-organized solutions of user needs and re-integrated them with new actors. This led to newly shaped boundaries of the service ecosystem

SHADOW IT SYSTEMS: DISCERNING THE GOOD AND THE EVIL

Shadow IT is becoming increasingly important as digital work practices make it easier than ever for business units crafting their own IT solutions. Prior research on shadow IT systems has often used fixed accounts of good or evil: They have been celebrated as powerful drivers of innovation or demonized as lacking central governance. We introduce a method to IT managers and architects enabling a more nuanced understanding of shadow IT systems with respect to their architectural embeddedness. Drawing on centrality measures from network analysis, the method portrays shadow IT systems as most critical if they hold a central position in a network of applications and information flows. We use enterprise architecture data from a recycling company to demonstrate and evaluate the method in a real project context. In the example, several critical and yet disregarded shadow IT systems have been identified and measures were taken to govern them decently

Educational Service Improvement Cycle

Die starke Verbreitung von Begriffen wie E-, M- oder Blended Learning zeigt bereits, dass die Dienstleistung Lehre zunehmend stärker durch Webtechnologien unterstützt wird. Ein Großteil der Nutzungsprozesse solcher Lernservices bleibt für die Lehrenden jedoch verborgen. Vor dem Hintergrund der Service- Dominant Logic fehlt damit ein wesentlicher Einblick in die gemeinsame Wertschöpfung zwischen Lehrenden und Lernenden. Die Learning Analytics könnte Methoden bereitstellen, welche eine kontinuierliche Entwicklung webbasierter Lernservices durch die Aufdeckung von Nutzungsdaten ermöglicht. Eine systematische Literaturrecherche legt jedoch dar, dass bislang kein geeignetes oder ausreichend konkretisiertes Vorgehen existiert, welches Lehrende beim Einsatz solcher Methoden unterstützt. Ziel dieser gestaltungsorientierten Arbeit ist daher die systematische Entwicklung eines Vorgehensmodells, dem "Educational Service Improvement Cycle (ESIC)". Dafür werden vier Gestaltungsparameter aus der Literaturrecherche abgeleite . Die iterative Entwicklung des Vorgehensmodells findet anhand zweier Lernszenarien aus der Entrepreneurship Education statt. Der ESIC besteht aus sechs Schritten, welche die systematische Analyse von Nutzungsprozessen ermöglichen. Das Vorgehen konkretisiert diese Schritte durch Empfehlungen von Methoden, einem Rollenkonzept und einer umfassenden Übersicht zu möglichen Indikatoren für die Analyse von Lernservices. Die Evaluation erfolgt ex ante durch die iterative Erstellung anhand der Fallstudien Net Economy und BWL@VetMed. In einer ex post Evaluation verwenden Studierende das Vorgehen zur Gestaltung eines Dashboards für die Weiterbildung von Gründern. Schließlich bestätigen auch Experteninterviews die wahrgenommene Nützlichkeit und Einfachheit des Vorgehens.Terms like e-, m- or Blended Learning show, currently many educational services are supported by web technologies. Within such services predominant parts of learner’s usage processes are hidden from the educator’s perception. In front of a service-dominant logic understanding usage processes is essential to comprehend the value-co-creation of educators and learners. Learning analytics may hold methods to enable a continual improvement process by collecting and analyzing usage data. A systematic literature review reveals that neither educational service nor learning analytics literature present a suitable or adequately specified procedural model for this purpose. Following a design science research approach this dissertation introduces a new procedural model to systematically improve educational services. It is called ’Educational Service Improvement Cycle (ESIC)’. Design parameters are derived from the literature review. As part of an iterative design process two learning scenarios from higher education are used to develop the procedural model. The ESIC consists of six activities, which enable a systematic analysis of usage processes. Recommended methods, a role concept and a broad overview on possible indicators are presented to clarify the ESIC. Besides their demonstrative purposes both learning scenarios are also part of an ex ante evaluation. The ex post evaluation contains another single case study, where students make use of the ESIC and create a learning analytics dashboard for advanced training of entrepreneurs. Additional interviews with experts of the field also indicate its perceived usefulness and ease-of-use

Introduction to the Minitrack on Addressing Diversity in Digitalization

International audienc

Shadow Systems, Risk, and Shifting Power Relations in Organizations

Drawing on notions of power and the social construction of risk, we build new theory to understand the persistence of shadow systems in organizations. From a single case study in a mid-sized savings bank, we derive two feedback cycles that concern shifting power relations between business units and central IT associated with shadow systems. A distant business-IT relationship and changing business needs can create repeated cost and time pressures that make business units draw on shadow systems. The perception of risk can trigger an opposing power shift back through the decommissioning and recentralization of shadow systems. However, empirical findings suggest that the weakening tendency of formal risk-management programs may not be sufficient to stop the shadow systems cycle spinning if they fail to address the underlying causes for the emergence of shadow systems. These findings highlight long-term dynamics associated with shadow systems and pose “risk” as a power-shifting construct

Scaling AI Ventures: How to Navigate Tensions between Automation and Augmentation

AI ventures promise to automate and augment ever more human tasks. This provides rich opportunities for growth. Yet, digital and human resources that involve AI are oftentimes task-specific and hard to scale. Furthermore, clients remain skeptical to be fully automated by external services. Thus, it remains unclear how AI ventures achieve growth. We adopt a grounded theory approach on an interview study with founders, product managers and investors to inquire how resources afford or constrain scaling in AI ventures. For this, we blend the notion of (non-)scale free resources with the layered architecture of digital technologies. Our study suggests that AI ventures scale by organizing digital and human resources for replicability in that they keep AI-specific resources distant from clients while simultaneously externalizing human-intensive tasks to their clients. As we inquire the roles of human and digital resources, our study suggests that ventures seek to quickly find an optimal degree on the continuum between augmentation and automation when bundling resources

Uncovering Inverse Generativity: An Exploratory Prompt Analysis in LLM Platforms

Extended generativity theory states that while generativity lead to more users, more users also affect product boundaries of a platform. We seek to uncover the complex relationship by turning to large language model (LLM) platforms, such as ChatGPT, Gemini or GPT4. LLM platforms are unique, because they draw from an unbounded supply of complements and are considered as ”generative technologies”. Given a nearly infinite amount of complements, how does a growing user base and increasing user engagement impact the scope of services handled by LLM platforms? We assign over 300,000 prompts to NAICS product categories, analyzing the introduction of new categories over time to perform a quantitative analysis on the data. Our findings indicate that product boundaries tend to stabilize, showing degressive growth of product categories. We also discover that engaged users, whom we term ”complementary explorers”, are the primary drivers of product boundary expansion

Competition between platform ecosystems: a longitudinal study of MOOC platforms

The last decade has seen a rise in software-based platforms that engender entirely new ecosystems. In newly emerging platform markets, platforms compete for partners and customers in a rapidly changing environment. Yet, extant research mostly studies platforms\u27 supply-side and demand-side strategies in relatively established platform markets. By combining a market-level and platform-level perspective, our research aims to develop a holistic understanding about the interdependencies between business model decisions, market evolution, and performance outcomes of platforms in emerging markets. We focus on the novel context of Massive Open Online Course (MOOC) platforms, analyzing longitudinal data for 35 MOOC platforms and their ecosystems. To account for the multi-level perspective, our research applies an innovative mixed-methods approach that combines qualitative methods with quan-titative measures and visualizations derived from network analysis. Our findings suggest that platforms in new markets converge towards common business models as market leaders imitate the business model innovations of its smaller competitors to manifest their market position. Based on these analyses, we derive four propositions on how the dynamics of a platform’s business model and ecosystem posi-tion affect each other and the platform’s market performance