A Framework for understanding & classifying Urban Data Business Models

Governments’ objective to transition to ‘Smart Cities’ heralds new possibilities for urban data business models to address pressing city challenges and digital transformation imperatives. Urban data business models are not well understood due to such factors as the maturity of the market and limited available research within this domain. Understanding the barriers and challenges in urban data business model development as well as the types of opportunities in the ecosystem is essential for incumbents and new entrants. Therefore, the aim of this paper is to develop a framework for understanding and classifying Urban Data Business Models (UDBM). This paper uses an embedded case study method to derive the framework by analyzing 40 publicly funded and supported business model experiments that address pressing city challenges under one initiative. This research contributes to the scholarly discourse on business model innovation in the context of smart cities

How To Cope With The Dynamics Of Urban Sustainability: Urban Experimentation Platforms As Tools For Adaptive Policy-Making

The growing challenges of urban population, congestion, consumption and pollution, prompt cities to respond with policies that progress towards Urban Sustainability. Increasingly, Urban Experimentation (UX) engaging diverse stakeholders for local innovations, is viewed an enabler of iterative progress. Yet, despite various ‘smart city’ initiatives, how to cope with the dynamics underlying local innovation processes for urban sustainability is unclear. In this paper, we consider Urban Experimentation Platforms (UXPs) as a tool for coping with such dynamics. Using case data from the UXP of ‘OrganiCity’, our research considers how this UXP interacts with the dynamics of urban experimentation. We present early insights from our problem analysis using System Dynamics and outline our next steps. We find UXPs as both a tool for policy implementation and for adaptive policymaking, with understanding and utilisation of this latter aspect low. We conclude by discussing how IS research on UXPs contributes towards realising the potential of digital infrastructures for societal good

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

Organicity: Lessons from an Experimentation as a Service Model for Digital Civic Innovation

In the paradigm of the Smart City, cities are embracing new digital technologies and data innovation to redefine their relationship to citizens and enterprise. Increasingly, cities are developing visions, strategies, and related digital masterplans and action groups with which to coordinate these efforts. The European Horizon 2020 funded OrganiCity project explored a new model for providing access to all citizens to collaboratively develop and test their ideas for managing and improving the urban environment using data. The people centred and data driven approach of the OrganiCity project developed an Experimentation as a Service (EaaS) model across 13 cities and with 43 experiments. In this paper, we describe the 4 key service pillars that emerged through designing a platform to enable experimentation and the associated engagement practices required to facilitate testing in a city. The service pillars are: systematic experimentation, co-creation, federated ethics & privacy, and management of liability & Intellectual Property Rights. The EaaS approach provided a low-risk service blueprint for city authorities to democratically source, test and support scaling-up innovative solutions to their city challenges. Analysis of experimenter’s projects highlighted the importance of shared infrastructure for reducing the barrier to entry for accessing the digital tools, but more importantly highlighted the investment required, and value of, the human resources required to facilitate the process of experimentation