An interactive inventory of retail technologies

This webpage provides an interactive inventory of retail technologies. Filter the set of technologies by choosing the value type In line with previous studies on retail technologies from a consumer's point of view (cf. e.g., Renko and Druzijanic, 2014), technology is able to provide consumers value in terms of three broad types, namely (1) COST: saving costs (e.g., price comparison apps; smart shopping trolleys to navigate efficiently through the store and as such save time and effort), as well as in terms of (2) FUNCTION: offering utilitarian benefits (e.g., in-store information kiosks to compare products and optimize one's choice, smart fitting rooms with RFID-enabled touch screens that recommend suited accessories to the dress the consumer may be fitting), and (3) FUN: providing hedonic benefits (e.g., digital screens providing diner inspiration in a supermarket, apps to share pictures of a potential purchase to get social feedback in the process). , the stage of the shopping cycle In general, a full shopping cycle consists of 5 stages; (1) the need recognition stage, (2) the information search stage, (3) the stage where the customer evaluates alternatives in his/her consideration set, (4) the actual purchase stage, and (5) the post-purchase stage (Hoyer, MacInnis, and Pieters, 2012). Advanced retail technologies can address one or more of these stages (Pantano and Naccarato, 2010). For instance, in the need recognition stage, a technology '[...] can inform consumers about the new arrivals in the stores, and suggest them the products capable to stimulate the emerging of new needs' (Pantano and Naccarato, 2010, p.203), whereas in the information search stage, '[...] technologies become a useful tool for consumers to achieve fast and detailed information about products in the store' (Pantano and Naccarato, 2010, p.203). the technology needs to be applied for, and/or a descriptive cluster We used an affinity diagramming approach to find appropriate clusters of technologies and further improve the organization of the in-store technologies. there are five clusters of technologies that relate more closely to the customer and technology-provided user benefits in the shopper's personal decision making process, namely (1) a cluster of 'context-aware data pool technologies' like the Digital Grocery Shopping List (Heinrichs, Schreiber, and Schöning, 2011), (2) the cluster of 'product finding technologies' (e.g., SoloFind; Wiethoff and Broll, 2011), (3) 'personal product assistants' such as the IRL SmartCart and the Mobile Productlens (IRL, 2014), (4) 'product decision support systems', comprising technologies such as the Ecofriends app (Tholander et al., 2012) or the Digital sommelier (IRL, 2014), and finally (5) price comparison technologies (e.g., Bargain Finder app; Karpischek, Geron, and Michahelles, 2011). of technologies you are interested in. It is possible to select multiple values in a column.

An in-depth discussion of retail technologies is available in the following article: Kim Willems, Annelien Smolders, Malaika Brengman, Kris Luyten, Johannes Schöning, The path-to-purchase is paved with digital opportunities: An inventory of shopper-oriented retail technologies, Technological Forecasting and Social Change, Available online 21 November 2016, ISSN 0040-1625, http://dx.doi.org/10.1016/j.techfore.2016.10.066.

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This work has been done by Kim Willems (VUB, Belgium),Annelien Smolders (VUB, Belgium), Johannes Schöning (University of Bremen, Germany), Kris Luyten (UHasselt - imec, Belgium) and Malaika Brengman (VUB, Belgium).
This website makes use of Exhibit.
Contact paper, framework and approach: Kim Willems - Contact dataset and website: Kris Luyten