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PurposeTrade in counterfeit goods is perceived as a substantial threat to various industries. No longer is the emergence of imitation products confined to branded luxury goods and final markets. Counterfeit articles are increasingly finding their way into other sectors, including the fast‐moving consumer goods, pharmaceutical, and automotive industries – with, in part, severe negative consequences for consumers, licit manufacturers, and brand owners alike. This paper seeks to shed light on the economic principles of counterfeit trade and the underlying illicit supply chains.Design/methodology/approachAn extensive literature review was conducted that comprised contributions from different strands of management research.FindingsThough governments as well as management have clearly identified the problem, very little is known – both in practice and theory – about the mechanisms and structure of the illicit market, the tactics of counterfeit producers, consumer behavior with respect to imitation products and the financial impact on individual companies. The diversity of the counterfeit phenomenon underlines the need for further research in this area and the development of company‐specific measures for fighting product piracy.Research limitations/implicationsThe clandestine nature of the counterfeit market limits direct accessibility to the phenomenon. Consequently, the existing body of literature does not necessarily cover all aspects of counterfeit activities. The review helps to highlight existing research gaps but may not be able to identify additional aspects of the phenomenon that, thus far, have not been deemed relevant.Originality/valueThe paper critically reviews the current state of research across different management‐related disciplines. From an academic perspective it may serve as a starting point for a future research agenda that addresses the current knowledge gaps. From a practitioner's perspective it is helpful for understanding the relevant influence factors and for developing appropriate, state‐of‐the‐art counterstrategies.

Non-intrusive load monitoring (NILM) is a popular approach to estimate appliance-level electricity consumption from aggregate consumption data of households. Assessing the suitability of NILM algorithms to be used in real scenarios is however still cumbersome, mainly because there exists no standardized evaluation procedure for NILM algorithms and the availability of comprehensive electricity consumption data sets on which to run such a procedure is still limited. This paper contributes to the solution of this problem by: (1) outlining the key dimensions of the design space of NILM algorithms; (2) presenting a novel, comprehensive data set to evaluate the performance of NILM algorithms; (3) describing the design and implementation of a framework that significantly eases the evaluation of NILM algorithms using different data sets and parameter configurations; (4) demonstrating the use of the presented framework and data set through an extensive performance evaluation of four selected NILM algorithms. Both the presented data set and the evaluation framework are made publicly available.

Inattention and imperfect information bias behavior toward the salient and immediately visible. This distortion creates costs for individuals, the organizations in which they work, and society at large. We show that an effective way to overcome this bias is by making the implications of one's behavior salient in real time, while individuals can directly adapt. In a large-scale field experiment, we gave participants real-time feedback on the resource consumption of a daily, energy-intensive activity (showering). We find that real-time feedback reduced resource consumption for the target behavior by 22%. At the household level, this led to much larger conservation gains in absolute terms than conventional policy interventions that provide aggregate feedback on resource use. High baseline users displayed a larger conservation effect, in line with the notion that realtime feedback helps eliminate "slack" in resource use. The approach is cost effective, is technically applicable to the vast majority of households, and generated savings of 1.2 kWh per day and household, which exceeds the average energy use for lighting. The intervention also shows how digitalization in our everyday lives makes information available that can help individuals overcome salience bias and act more in line with their preferences.

Utilities are currently deploying smart electricity meters in millions of households worldwide to collect fine-grained electricity consumption data. We present an approach to automatically analyzing this data to enable personalized and scalable energy efficiency programs for private households. In particular, we develop and evaluate a system that uses supervised machine learning techniques to automatically estimate specific "characteristics" of a household from its electricity consumption. The characteristics are related to a household's socio-economic status, its dwelling, or its appliance stock. We evaluate our approach by analyzing smart meter data collected from 4,232 households in Ireland at a 30-minute granularity over a period of 1.5 years. Our analysis shows that revealing characteristics from smart meter data is feasible, as our method achieves an accuracy of more than 70% over all households for many of the characteristics and even exceeds 80% for some of the characteristics. The findings are applicable to all smart metering systems without making changes to the measurement infrastructure. The inferred knowledge paves the way for targeted energy efficiency programs and other services that benefit from improved customer insights. On the basis of these promising results, the paper discusses the potential for utilities as well as policy and privacy implications.