Ads are watching me – a view from the interplay between anthropomorphism and customisation
Oregon State University
Consumers’ online and offline marketing activities are becoming more trackable and measurable with the embracement of new technologies. This rise in various types of technology platforms has led to an unprecedented increase in customisable content (Moreau & Herd 2010; Chu & Kim 2011; Muntinga et al. 2011; Franke et al. 2012; Thota et al. 2012) and personalised communications (Kwak 2012). For instance, in 2006, about 80% of marketers surveyed reported the use of customised communication in individual channels (Compton 2006). The essence of customisation lies in the tailoring of a firm’s marketing mix to the individual consumer (Arora et al. 2008). Customisation provides consumers with the benefit of receiving a variety of new product options, ranging from the capability to configure a final product by modifying its features to receiving recommendations of one or more products that closely match consumers’ individual preferences (Kramer et al. 2007; Franke et al. 2009). Researchers suggest that as the assortment of products available in the marketplace proliferates, consumers often rely on newer technologies, such as interactive decision aids and electronic recommendation agents, that can filter and condense a variety of options, thereby assisting them in product choice decisions (Cooke et al. 2002). Internet recommendation agents, such as Amazon.com, Pandora Radio, Netflix, Activebuyersguide.com and personal online advisers at financialengines.com, have been introduced to cope with consumers’ information overload. Earlier research regarding the effects of recommendation agents has been generally concordant in the fact that they increase customer satisfaction and retention rates (Kramer et al. 2007). However, later work has shown qualifying conditions related to the universally positive effects of recommendation agents. Specifically, research has focused more on individual features of recommendation agents, such as communication style, types of recommendations made for different product categories, as well as on consumer-level differences in the evaluations of these interactive sales assistants (Qiu & Benbasat 2009; Koehler et al. 2010). For example, Koehler et al. (2010) find that consumers react more favourably to a recommendation agent when it uses a style that communicates concrete product features for purchases made in the immediate vs distant future. These findings illustrate that the type of the interaction design of a recommendation agent might affect consumer attitudes towards it. In brief, it stands to reason that consumer evaluations of recommendation agents depend upon a variety of contextual factors.