An interactive full-length mirror that allows you to browse through an endless collection ofclothing and see immediately whether something fits you, including when you turn around, and which also allows you to send a picture quickly to your family and friends to hear what they think. This mirror is a technological development that is already possible and which is being introduced in fashion stores here and there. But how probable is it that this technological innovation will become a permanent feature of our shopping experience? To answer this question we shall describe the expectations that exist about the developments in shopping over the coming years. We shall then examine to what extent these developments already play a role in shopping now, in 2014. In order to maintain an overview, we shall introduce a typology based on the STOF model. All of the innovations mentioned are ultimately aimed at offering added value for the consumer, but who is that consumer and what does he or she need? An inventory of how the shopping consumer is regarded makes it clear that new perspectives are required in order to do justice to the complexity of the retail behaviour and the retail experience. Finally, we will briefly examine specific cross-media aspects of shopping, such as the multichannel strategy of retail outlets and the role of the physical store in relation to the webshop. We end by offering a research framework for the 'service encounter' in the retail process based on the concept of Servicescapes. This framework allows to chart and answer a number of essential questions surrounding the probability of innovations more systematically.
While consumers have become increasingly aware of the need for sustainability in fashion, many do not translate their intention to purchase sustainable fashion into actual behavior. Insights can be gained from those who have successfully transitioned from intention to behavior (i.e., experienced sustainable fashion consumers). Despite a substantial body of literature exploring predictors of sustainable fashion purchasing, a comprehensive view on how predictors of sustainable fashion purchasing vary between consumers with and without sustainable fashion experience is lacking. This paper reports a systematic literature review, analyzing 100 empirical articles on predictors of sustainable fashion purchasing among consumer samples with and without purchasing experience, identified from the Web of Science and Scopus databases.
MULTIFILE
In this research, the experiences and behaviors of end-users in a smart grid project are explored. In PowerMatching City, the leading Dutch smart grid project, 40 households were equipped with various decentralized energy sources (PV and microCHP), hybrid heat pumps, smart appliances, smart meters and an in-home display. Stabilization and optimization of the network was realized by trading energy on the market. To reduce peak loads on the smart grid, several types of demand side management were tested. Households received feedback on their energy use either based on costs, or on the percentage of consumed energy that had been produced locally. Furthermore, devices could be controlled automatically, smartly or manually to optimize the energy use of the households. Results from quantitative and qualitative research showed that: (1) feedback on costs reduction is valued most; (2) end-users preferred to consume self-produced energy (this may even be the case when, from a cost or sustainability perspective, it is not the most efficient strategy to follow); (3) automatic and smart control are most popular, but manually controlling appliances is more rewarding; (4) experiences and behaviors of end-users depended on trust between community members, and on trust in both technology (ICT infrastructure and connected appliances) and the participating parties.
Despite the benefits of the widespread deployment of diverse Internet-enabled devices such as IP cameras and smart home appliances - the so-called Internet of Things (IoT) has amplified the attack surface that is being leveraged by cyber criminals. While manufacturers and vendors keep deploying new products, infected devices can be counted in the millions and spreading at an alarming rate all over consumer and business networks. The objective of this project is twofold: (i) to explain the causes behind these infections and the inherent insecurity of the IoT paradigm by exploring innovative data analytics as applied to raw cyber security data; and (ii) to promote effective remediation mechanisms that mitigate the threat of the currently vulnerable and infected IoT devices. By performing large-scale passive and active measurements, this project will allow the characterization and attribution of compromise IoT devices. Understanding the type of devices that are getting compromised and the reasons behind the attacker’s intention is essential to design effective countermeasures. This project will build on the state of the art in information theoretic data mining (e.g., using the minimum description length and maximum entropy principles), statistical pattern mining, and interactive data exploration and analytics to create a casual model that allows explaining the attacker’s tactics and techniques. The project will research formal correlation methods rooted in stochastic data assemblies between IoT-relevant measurements and IoT malware binaries as captured by an IoT-specific honeypot to aid in the attribution and thus the remediation objective. Research outcomes of this project will benefit society in addressing important IoT security problems before manufacturers saturate the market with ostensibly useful and innovative gadgets that lack sufficient security features, thus being vulnerable to attacks and malware infestations, which can turn them into rogue agents. However, the insights gained will not be limited to the attacker behavior and attribution, but also to the remediation of the infected devices. Based on a casual model and output of the correlation analyses, this project will follow an innovative approach to understand the remediation impact of malware notifications by conducting a longitudinal quasi-experimental analysis. The quasi-experimental analyses will examine remediation rates of infected/vulnerable IoT devices in order to make better inferences about the impact of the characteristics of the notification and infected user’s reaction. The research will provide new perspectives, information, insights, and approaches to vulnerability and malware notifications that differ from the previous reliance on models calibrated with cross-sectional analysis. This project will enable more robust use of longitudinal estimates based on documented remediation change. Project results and methods will enhance the capacity of Internet intermediaries (e.g., ISPs and hosting providers) to better handle abuse/vulnerability reporting which in turn will serve as a preemptive countermeasure. The data and methods will allow to investigate the behavior of infected individuals and firms at a microscopic scale and reveal the causal relations among infections, human factor and remediation.
