Energy utility companies hold a great potential to improve sustainable practices, as they have access to large amounts of energy consumption data that can be translated into actionable insights toward individual consumers. We investigate the role of energy informatics based artifacts for utility companies to (i) profile households and to individually address customers that have a large potential to elevate their energy efficiency and (ii) at the same time create revenue by purchasing energy efficient goods and related services. Furthermore, we demonstrate how the predicted attributes of millions of households can, in turn, contribute to the economic, environmental, and societal value creation for energy suppliers, utility customers, and the community as a whole. For our empirical study on the detection of households with old heating systems, we cooperated with a large energy supplier in Belgium. The results are used for targeted positioning of new heating systems in a cross-selling campaign and may serve as a blueprint for related data analytics artifacts in this field.
Utility companies generally have an extensive customer base, yet their knowledge about individual households is small. This adversely affects both the development of innovative, household specific services and the utilities’ key performance indicators such as customer loyalty and profitability. With the goal to overcome this knowledge deficit, persuasive systems in the form of customer self-service applications and efficiency coaching portals are becoming the getaway of data exchange between utility and user. While improved customer interaction and the collection of customer data within respective information systems is an important step towards a service-oriented company, the immediate value generated from the collected data is still limited, mostly due to the small fraction of customers actually using such systems. We show how to utilize the knowledge gained from the sparse number of active web users in order to provide low-cost and large-scale insights to potentially all residential utility customers. We do so using machine-learning-based Green IT artifacts that allow for improving decision-making, effectiveness of energy audits, and conservation campaigns, thus ultimately increasing the customer value and adoption of related services. Moreover, we show that data from the publically available geographic information systems can considerably improve the decision quality.
The research data landscape of the arts and humanities is characterized by a high degree of heterogeneity. To improve interoperability, recent initiatives and research infrastructures are encouraging the use of standards and best practices. However, custom data models are often considered necessary to exactly reflect the requirements of a particular collection or research project. To address the needs of scholars in the arts and humanities for a composition of research data irrespective of the degree of structuredness and standardization, we propose a concept on the basis of formal languages, which facilitates declarative data modeling by respective domain experts. By identifying and defining grammatical patterns and deriving transformation functions, the structure of data is generated or extended in accordance with the particular context and needs of the domain.
Although still in the early stages of diffusion, smartwatches represent the most popular type of wearable devices. Yet, little is known why some people are more likely to adopt smartwatches than others. To deepen the understanding of underlying factors prompting adoption behavior, the authors develop a theoretical model grounded in technology acceptance and social psychology literature. Empirical results reveal perceived usefulness and visibility as important factors that drive intention. The magnitude of these antecedents is influenced by an individual’s perception of viewing smartwatches as a technology and/or as a fashion accessory. Theoretical and managerial implications are discussed.