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The diffusion and customer adoption of electric vehicles (EVs) are hampered by the absence of a well-developed publicly accessible charging infrastructure. We introduce an IT-enabled peer-to-peer (P2P) sharing and collaborative consumption (SCC) transportation service for private charging infrastructure as a means to alleviate this challenge. P2P SCC services facilitate private persons’ joint access to privately owned physical resources such as cars (Uber), apartments (Airbnb), or charging stations (focus of this article) via Internet-based sharing platforms. We apply New Service Development (NSD) as a guiding process towards developing a P2P SCC service and suggest provider assessment as an extension to existing NSD procedures. We show a specific application of the provider assessment for our proposed P2P SCC service by reporting the results from a survey assessing potential peer-providers. Thus, we demonstrate how extending NSD for the provider assessment aided us in developing an IT-enabled transportation service that is based on the joint access to privately owned resources.

Airbnb and Uber enable private individuals to share physical resources via their Internet platforms, and both have become popular subjects of information systems and e-business research. In the top dogs’ slipstream resides a variety of less-known platforms that allow to share different resources or to share resources in different ways. Researchers who study these platforms and the underlying consumer behaviors face the problem of situating their findings precisely. Practitioners lack an overview of the diversity of these platforms and their characteristics too. This article suggests a taxonomy that helps to better characterize platforms for Peer-to-Peer (P2P) Sharing and Collaborative Consumption (SCC). We used the taxonomy to describe the evolution of 522 P2P SCC platforms over the period of 35 months and to demonstrate and partly evaluate its application. All descriptions taken together constitute a comprehensive data source to study P2P SCC platforms. When researchers use the taxonomy, they can describe and situate insights precisely so that stakeholders can access them more easily. Furthermore, researchers and practitioners can use our database as well as the analyses we made based on the data for their purpose. Because we deliver the computer scripts that we used in our analysis, our study can easily be reproduced in the future to investigate the dynamics of the P2P SCC market.

The rise of e-commerce and lately m-commerce, and the ongoing digitization have a significant impact on traditional high street retailers, who often are not able to match the prices, variety of products, and services provided online. To prevent the looming decline of high streets, retailers need to catch up and start to engage the new digital customer. This article illuminates the interplay of diverse, digital interfaces, resources and actors in the co-creation of value in high street retail. Alliances and a digital platform ecosystem are presented as forms of cooperation, where retailers join forces to provide digital touchpoints and boundary-spanning service to the customer, and also actively use customer-generated data to improve their service and strengthen the overall attractiveness of their high streets. We tell a tale of two cities—Burnsley and Beckinsdale—set in the not so distant future, where retailers have taken very different approaches to respond to the digital age.

Poor availability of a nationwide, publicly accessible charging infrastructure in Europe is seen as a major impediment for a stronger market penetration of electric vehicles (EVs). Making privately owned charging points for electric vehicles accessible to the public in a Peer-to-Peer (P2P) charging network may present a remedy to this problem. Developing such a P2P service, among others, requires knowledge on the customers' willingness to pay for EV charging. We survey customers of a utility company in a mid-sized European city to assess their willingness to pay for EV charging. Using choice-based conjoint analysis, we obtain customers' willingness to pay differentiated by charging point's characteristics and its location. The estimates are subsequently implemented in form of price recommendations into an actual web-based P2P platform that enables private individuals to provide and access EV charging services. This research contributes to the knowledge on the design of payment models in general and on the design of EV charging infrastructure services in particular. Poor availability of a nationwide, publicly accessible charging infrastructure in Europe is seen as a major impediment for a stronger market penetration of electric vehicles (EVs). Making privately owned charging points for electric vehicles accessible to the public in a Peer-to-Peer (P2P) charging network may present a remedy to this problem. Developing such a P2P service, among others, requires knowledge on the customers' willingness to pay for EV charging. We survey customers of a utility company in a mid-sized European city to assess their willingness to pay for EV charging. Using choice-based conjoint analysis, we obtain customers' willingness to pay differentiated by charging point's characteristics and its location. The estimates are subsequently implemented in form of price recommendations into an actual web-based P2P platform that enables private individuals to provide and access EV charging services. This research contributes to the knowledge on the design of payment models in general and on the design of EV charging infrastructure services in particular.

(a) Situation faced: An inadequate number of publicly available charging points is among the main reasons that consumers do not buy electric vehicles (EVs). To address this problem, we suggest a peer-to-peer (P2P) sharing approach for private charging infrastructures. We formed a joint consortium between academia and industry to design and implement a web platform and an underlying business model for an infrastructure of individually owned EV-charging stations for public use. Currently, there are no standardized processes for EV charging, so we had to look elsewhere for processes that could be adapted or partly adopted as a foundation for the proposed web platform. (b) Action taken: We interviewed representatives of seven organizations that are already operating in the domain of EV charging about the relevant business processes. Applying the BPM lifecycle (Dumas et al., Fundamentals of business process management. Springer, 2013), we analyzed the resulting as-is processes for best practices and redesigned them for the scenario of a P2P platform for EV charging. (c) Results achieved: Sixteen to-be processes that comprised registration, authentication, charging, billing, and administration were modeled in BPMN and implemented in a software prototype. The prototype and associated processes are currently being evaluated to ensure their validity and effectiveness in the target environment while the partnering utility company prepares the solution’s staged roll-out to operate their own charging stations and then open the system to other providers. (d) Lessons learned: Analyzing and then designing business processes to reach a common goal has been a unifying factor in our joint research project, where partners from industry and academia have differing backgrounds, expectations, and individual goals. BPM practices enabled the project team to create an innovative business model and corresponding business processes that will have an impact in practice.

The microblogging service Twitter is a lively social media channel where people report on almost everything imaginable. Using tweets to analyze consumers' opinions about individual products and services is a valuable source of information used by brands and companies to learn about their public perception. We present an aspect-based sentiment analysis approach for extracting positive, negative, and neutral aspects from tweets by leveraging part-of-speech tagging and dependency parsing from natural language processing. The approach manifests in the design of a software toolkit that facilitates the sentiment analysis, starting from the extraction of tweets, filtering, over the automated analysis, up to the interactive display of the results. By applying dependency patterns between nouns, adjectives, adverbs, and negotiations to the individual sentences, the toolkit identifies aspects and sentiment expressions, for which the polarities are determined subsequently. We demonstrate the developed solution by looking at the case of Airbnb, where people can rent out their homes to others over a Web platform. It is important to note that Airbnb only provides the platform and has little influence on the actual lodging service. Thus, looking at word-of-mouth regarding individual service experiences yields evidence of praise and criticism, which can help to seize improvement potential.

Recent success stories such as those of Airbnb or Uber have proved that the Sharing Economy has evolved from a niche phenomenon to an independent economic sector. Against this backdrop, it is widely recognized that business models—although a research field still based on a highly-fragmented body of knowledge—constitute one of the most important factors to create such success stories. By leveraging the extant literature, a framework for business model generation is proposed, which is specifically tailored to the domain of the Sharing Economy. Hence, this work's contribution is multifaceted: Literature is synthesized to cope with the current fragmentation and used to study state-of-the-art business model (generation) approaches. Furthermore, shortcomings in the application of current business model (generation) frameworks with regard to the Sharing Economy are pointed out. The merit of this research is the presentation of a consolidated framework for business model conceptualization and generation in the Sharing Economy.

The amount of data within the Linking Open Data (LOD) Cloud is steadily increasing and resembles a rich source of information. Since Context-aware Services (CAS) are based on the correlation of heterogeneous data sources for deriving the contextual situation of a target, it makes sense to leverage that enormous amount of data already present in the LOD Cloud to enhance the quality of these services. Within this work, the applicability of the LOD Cloud as a context provider for enriching CAS is investigated. For this purpose, a deep analysis according to the discoverability and availability of datasets is performed. Furthermore, in order to ease the process of finding a dataset that matches the information needs of a CAS developer, techniques for retrieving contents of LOD datasets are discussed and different approaches to condense the dataset to its most important concepts are shown. Finally, a Context Data Lookup Service is introduced that enables context data discovery within the LOD Cloud and its applicability is highlighted based on an example.

The Sharing Economy with the central theme of “access over ownership” gains popularity and is receiving a lot of coverage by the media. Companies like UBER, AIRBNB and BLABLACAR are valued in the billions and enable individuals to act as private taxi drivers, rent out their apartments or participate in ridesharing. Besides those prominent companies, there are platforms that facilitate the sharing of tools, miscellaneous types of equipment, 3D printers and even pets. A peer searching for a specific resource to borrow needs to at first identify relevant websites and then look through various offers to find an appropriate one. This laborious process can be eased by making use of Semantic Web technologies. Using the Design Science Research Methodology (DSRM), we synthesize the characteristics of various exemplary Sharing Economy websites with regard to the description of resources and services that can be shared including the transaction details and other relevant components. Following up, core concepts serving as a foundation for the conceptualization and the design of a description language in the form of an ontology are derived. The description language is based on Semantic Web technologies to enable efficient semantic search and matchmaking using established standards like RDF(S), OWL, and SPARQL. The underlying idea is to abstract from proprietary implementations in order to provide a common ground for service description in the domain of the Sharing Economy. Thus, a search against multiple websites could be facilitated to improve the discoverability and comparability of individual offers on different websites.

Acceptance of electric mobility is hampered by an insufficient charging infrastructure as investors refrain from investing into additional charging stations while potential buyers of electric vehicles wait for a more advanced charging infrastructure to be established. The optimal siting and sizing of charging infrastructure is a major issue of electric mobility. In this context, the analysis of charging transaction data is a promising means to identify regions with lacking charging infrastructure as well as to learn about utilization and patterns in the charging behavior. Additionally, charging transaction data yields potential to learn about battery degradation when combined with state-of-health readings. To exploit the full potential of analytical assessment of charging transaction data, the enrichment of the data to integrate background as well as environmental information from various sources, e.g., weather and location data, is discussed. Different perspectives for analytics of charging transactions are motivated and preliminary findings are presented that motivate further research in this area.

Batteries in Electric Vehicles (EV) are subject to a degradation process that has many, yet not fully understood, influential factors. Typically, the battery is continuously monitored by a proprietary Battery Management System (BMS) which records and analyzes various key figures of the battery. Because the BMS is proprietary, the data collected throughout the lifetime of an EV and its battery cannot simply be looked into by the owner but only by the EV manufacturer and licensed service providers. Hence, the EV owner is dependent on the manufacturer to retrieve accurate data regarding the State of Health (SOH) of the battery, e.g. when selling the vehicle or when the battery needs replacement. An in-depth understanding of charging behavior and the degradation process of an EV's battery requires a vast amount of data, which is a crucial factor limiting current research. This paper proposes an information system that blends into the EV charging infrastructure and utilizes a crowdsourcing approach to collect charging transaction data. In order to identify concealed dependencies with regards to battery degradation and to identify patterns in the charging behavior, an enrichment of the raw transaction data is motivated and different information providers are discussed. This augmentation integrates environmental information from various sources such as weather and location data. On a macroscopic view, analyses could point out the correlation between weather, public events, location, and charging demand. On an individual basis, the effect of environmental impacts, charging behavior, and driving profile on battery degradation can be investigated and compared.

The general public and politics discuss electric vehicles (EVs) as promising means for achieving clean, carbon-free, and sustainable individual transportation. However, an insufficient charging infrastructure hampers a rapid diffusion of EVs. At the same time, investors have refrained from developing an EV-charging infrastructure on a large scale because of the limited demand for EVs. Against this backdrop, peer-to-peer (P2P) sharing and collaborative consumption (SCC) is a promising strategy with which to address this problem. This article describes the concept of an IT-based P2P SCC service and the research activities needed for its design. We do so by introducing a novel application of the sharing economy. Our primary contribution is to take a step toward finding a solution for a problem in the EV domain that is relevant for society. A second contribution lies in the introduction and discussion of predominantly infrastructure-creating (PIC) P2P SCC services and their characteristics.

A vast number of peer-to-peer (P2P) sharing and collaborative consumption (SCC) platforms has emerged over the last decade. These platforms allow private individuals to share their physical resources, such as vehicles, spaces, food, clothing, gear and even pets, with other private persons. The large number and diversity of the platforms make it difficult for potential peer-consumers and peer-providers to find an appropriate platform for their demands or offers. This research therefore designs an ontology-based web directory which facilitates an effective discovery of P2P SCC platforms. The paper includes three major contributions. First, we formalize concepts identified in the SCC literature by means of a description language. Second, we develop an ontology that provides a means to purposefully describe the domain of SCC platforms. Third, we describe the design of a web directory that uses the ontology to make discovering P2P SCC platforms more approachable.

The amount of data within the Linking Open Data (LOD) cloud is steadily increasing and resembles a rich source of information. Since Context-aware Services (CAS) can highly benefit from background information, e.g., about the environment of a user, it makes sense to leverage that enormous amount of data already present in the LOD cloud to enhance the quality of these services. Within this work, the applicability of the LOD cloud as provider for contextual information to enrich CAS is investigated. For this purpose, non-functional criteria of discoverability and availability are analyzed, followed by a presentation of an overview of the different domains covered by the LOD cloud. In order to ease the process of finding a dataset that matches the information needs of a developer of a CAS, techniques for retrieving contents of LOD datasets are discussed and different approaches to condense the dataset to its most important concepts are shown.