Abstract
Smart mobility is a central issue in the recent discourse about urban development policy towards smart cities. The design of innovative and sustainable mobility infrastructures as well as public policies require cooperation and innovations between various stakeholders—businesses as well as policy makers—of the business ecosystems that emerge around smart city initiatives. This poses a challenge for deploying instruments and approaches for the proactive management of such business ecosystems. In this article, we report on findings from a smart city initiative we have used as a case study to inform the development, implementation, and prototypical deployment of a visual analytic system (VAS). As results of our design science research we present an agile framework to collaboratively collect, aggregate and map data about the ecosystem. The VAS and the agile framework are intended to inform and stimulate knowledge flows between ecosystem stakeholders in order to reflect on viable business and policy strategies. Agile processes and roles to collaboratively manage and adapt business ecosystem models and visualizations are defined. We further introduce basic categories for identifying, assessing and selecting Internet data sources that provide the data for ecosystem models and we detail the ecosystem data and view models developed in our case study. Our model represents a first explication of categories for visualizing business ecosystem models in a smart city mobility context.
Highlights
The digital transformation—and its accompanying changes—have long reached cities including their outskirts and rural satellites, and are expected to provoke “(fundamental) changes to traditional local economic structures” [1]
For our initial data collection, the identified Internet sources were evaluated using the following criteria: (a) data access—we focused on openly available Internet data sources; (b) platform focus—the data source should at least contain data that is relevant for a mobility business ecosystem within the scope of the smart city project; (c) geographic focus—the content of the source should contain data that is relevant for the local mobility ecosystem; (d) data scope—defining what kind of data is covered regarding (d1) entities—with attributes such as name, legal type, headquarter, CEO, description, and (d2) relations—with attributes such as type of relation, involved partners, date; (e) data extraction—how easy can the relevant data be extracted from the source, and (f) data validity—can the source be trusted
We report on findings from the development, implementation and prototypical deployment of a Visual Analytic System (VAS) in the case study context of a smart city initiative
Summary
The digital transformation—and its accompanying changes—have long reached cities including their outskirts and rural satellites, and are expected to provoke “(fundamental) changes to traditional local economic structures” [1]. Smart cities are a recent vision in urban development policy of novel technology-based infrastructures to improve all facets of urban life [4]. It is often considered as a possible solution to challenges cities are confronted with, such as urbanization, migration, pollution, as well as changes in the demographic structure of societies, and climate change, which parallel the societal task to develop sustainable and humane technologies and lifestyles [5,6,7]. The facets of a smart city are diverse, as digital technologies are used to include citizens in governmental processes and decisions (“smart governance”), to measure air quality or noise level (“smart environment”) or to enhance digital services in vehicles, traffic systems, and infrastructure (“smart mobility”) [11], to name just a few. Thereby, smart mobility is often recognized as the most common indicator of smart cities [12]
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