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The traditional idea of the customer as a passive bystander while the company creates value for them has been challenged (Vargo & Lusch, 2004). Due to the high levels of technological innovation and globalisation in recent decades, customers have become more aware, informed and connected (Cho, Fu, & Wu, 2017; Prahalad & Ramaswamy, 2004). This has led to a shift away from a product-dominant logic (PD-L) to a service-dominant logic (SD-L), where the customer becomes active participants in creating value for themselves. Customers construct value phenomenologically by fitting services into their lives the way they require them (Vargo & Lusch, 2004).
A key ingredient for successful co-creation are interactions (Bolton & Saxena-Iyer, 2009; Grönroos, 2006, 2008; Vargo & Lusch, 2004). These interactions provide information and insight into value-in-use, so that the customer has a better understanding of what the product will do for them (Ballantyne & Varey, 2006; Vargo & Lusch, 2008). In this paper, we use the term ‘interactions’ rather than simply ‘information’ because customers need these interactions, where they are free to experience the product or its features, so they can create their own value rather than them being told what to feel as happens when they are only given information. This is a central tenet of co-creation theory – companies can facilitate the value creation process by providing infrastructure and platforms that encourage interactivity through dialogue, information and visualisation (Varadarajan et al., 2010).
We propose Augmented Reality (AR) as an emerging technology that can aid this facilitation of value creation. AR is the overlay of digital elements on the physical world (Vaughan-Nichols, 2009). It is anticipated that interactions created through this technology will lead to higher understanding, collection and analysis of information by the customer thus allowing them to make a better informed decisions than without the technology and therefore aid in the co-creation of value. By pointing a device fitted with a camera e.g. phone or wearable headset, users of AR can directly see and interact with the organisation, without having to revert to a website or mobile application. Figure 1 below shows how information (in this case GPS enabled information) can be overlaid on a smart device:
Figure 1. Tourist Location enhanced by AR (sourced from tech.co)
Purchase Decision Making
To understand how customers purchase products and services, we need to examine the ‘purchase intent’ literature. Purchase intent is commonly attributed to the likelihood that a customer will make a purchase decision (Morwitz, Steckel, & Gupta, 2007). One theory is that customers are more likely to purchase products that are perceived as low risk (Bauer, 1960). One of the reasons why risk exists is the possibility that the customer will purchase the wrong product (Bauer, 1960). Perceived risk is the likelihood that negative consequences will result from purchase of this uncertain product (Dowling & Staelin, 1994; Taylor, 1974). Thus, the higher the risk, the less likely that a customer will engage in the purchase decision (Wood & Scheer, 1996).
Another reason perceived risk affects purchase intent is related to customer trust. When a customer’s risk is reduced, they tend to have higher trust in the brand, product or company. Equally, when a customer already has high trust in a brand or company, their perceived risk of purchasing from this seller is comparatively lower than that of a customer with low trust. Although the relationship does not need to be causal, there is often a negative relationship between risk and trust (Corritore, Kracher, & Wiedenbeck, 2003; Eid, 2011; Harridge-March, 2006; Hsin & Wen, 2008; Warrington, Abgrab, & Caldwell, 2000). Increased trust will ultimately increase purchase intent (Harris & Goode, 2010; Schlosser, White, & Lloyd, 2006; Van-der-Heijden, Verhagen, & Creemers, 2003).