Snakes and Ladders: Unpacking the Personalisation-Privacy Paradox in the Context of AI-Enabled Personalisation in the Physical Retail Environment

Canhoto, Ana Isabel; Keegan, Brendan James; Ryzhikh, Maria Article — Published Version Snakes and Ladders: Unpacking the PersonalisationPrivacy Paradox in the Context of AI-Enabled Personalisation in the Physical Retail Environment Information Systems Frontiers Provided in Cooperation with: Springer Nature Suggested Citation: Canhoto, Ana Isabel; Keegan, Brendan James; Ryzhikh, Maria (2023) : Snakes and Ladders: Unpacking the Personalisation-Privacy Paradox in the Context of AI-Enabled Personalisation in the Physical Retail Environment, Information Systems Frontiers, ISSN 1572-9419, Springer US, New York, NY, Vol. 26, Iss. 3, pp. 1005-1024, https://doi.org/10.1007/s10796-023-10369-7 This Version is available at: https://hdl.handle.net/10419/318044 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by/4.0/ Vol.:(0123456789) 1 3 Information Systems Frontiers (2024) 26:1005–1024 https://doi.org/10.1007/s10796-023-10369-7 Snakes andLadders: Unpacking thePersonalisation‑Privacy Paradox intheContext ofAI‑Enabled Personalisation inthePhysical Retail Environment AnaIsabelCanhoto1 · BrendanJamesKeegan2· MariaRyzhikh3 Accepted: 4 January 2023 / Published online: 14 January 2023 © The Author(s) 2023 Abstract Artificial intelligence (AI) is expected to bring to the physical retail environment the kind of mass personalisation that is already common in online commerce, delivering offers that are targeted to each customer, and that adapt to changes in the customer’s context. However, factors related to the in-store environment, the small screen where the offer is delivered, and privacy concerns, create uncertainty regarding how customers might react to highly personalised offers that are delivered to their smartphones while they are in a store. To investigate how customers exposed to this type of AI-enabled, personalised offer, perceive it and respond to it, we use the personalisation-privacy paradox lens. Case study data focused on UK based, female, fashion retail shoppers exposed to such offers reveal that they seek discounts on desired items and improvement of the in-store experience; they resent interruptions and generic offers; express a strong desire for autonomy; and attempt to control access to private information and to improve the recommendations that they receive. Our analysis also exposes contradictions in customers’ expectations of personalisation that requires location tracking. We conclude by drawing an analogy to the popular Snakes and Ladders game, to illustrate the delicate balance between drivers and barriers to acceptance of AI-enabled, highly personalised offers delivered to customers’ smartphones while they are in-store. Keywords Artificial intelligence· Personalisation· Privacy· Personalisation-privacy paradox· Retail· Geo-location 1 Introduction Artificial intelligence (AI) is expected to transform business practice in in-store retailing (Davenport etal., 2020), by bringing to the physical retail environment the kind of mass personalisation that is already common in online commerce (Kumar et al., 2017). Personalisation benefits retailers because targeted messages get noticed amid the noise of other communications (Balan & Mathew, 2020), increase sales, and support customer intimacy, involvement with the brand (Gardino etal., 2021) and customer loyalty (Pappas etal., 2018). Moreover, campaign response can be monitored directly and corrective action can be taken promptly, thus improving conversion rate (Chou & Shao, 2021). In the physical retail environment, personalisation is typically provided by the salesperson, which has several limitations. On the supply side, sales staff have access to limited customer data in-store which constrains their ability to adapt their recommendations (van de Sanden etal., 2019). On the demand side, increasingly, customers do not want to interact with a salesperson, particularly in the wake of the Covid-19 pandemic (Mondada etal., 2020; Yoganathan etal., 2021). Where technology is used for in-store recommendations, but not drawing on AI, these are based on customer segmentation rather than individual behaviours. Moreover, such recommendations tend not to reflect real time changes in the context, such as the customer’s location, the store’s inventory levels or the level of crowding in specific area. AI can overcome these limitations of in-store personalisation, due to its ability to integrate multiple sources of information, and create data-driven offers (Kietzmann etal., 2018). Moreover, given that many retail customers use their * Ana Isabel Canhoto [email protected] Brendan James Keegan brendan.keeg[email protected] Maria Ryzhikh maria.r[email protected] 1 University ofSussex, Sussex, UK 2 Maynooth University, Maynooth, Ireland 3 Weber-Stephen Products EMEA GmbH, Berlin, Germany 1006 Information Systems Frontiers (2024) 26:1005–1024 1 3 mobile phones while shopping (Rippé etal., 2017), retailers can deliver the AI-created, targeted messages to customers’ mobile devices while they are in—or near – their store. We refer to this type of targeted offer, which has been personalised by artificial intelligence technology and is delivered to individual shoppers’ phones, in the physical retail environment as “artificial intelligence enabled personalisation” (hereafter referred to as AI-EP). While there is a rich body of work examining consumer experiences of personalisation in the online environment (see Boerman etal., 2017 for a review), this has not been replicated for physical retail (van de Sanden etal., 2019). However, attitudes towards personalisation vary significantly with the context in which it takes place (Aguirre etal., 2016). First, as consumers’ motivations vary for online vs instore retail (Haridasan & Fernando, 2018), their perceptions and evaluation of personalisation in the physical environment may differ from those identified in the extant literature on personalisation. Second, the interface through which the message is delivered influences the perception of the extent to which the message has been personalised, with high quality interfaces increasing the perception of personalisation (Ameen etal., 2022). The small screen of mobile phones may impact negatively on consumers’ involvement with the message (Grewal etal., 2016), offsetting their suitability as targeting devices. Third, privacy concerns negatively impact consumers’ evaluation of personalisation in online shopping environments (Li etal., 2017). However, paradoxically, this effect was not detected in Ameen etal (2022)’s study of consumer interactions with smart technologies in shopping malls. In summary, while, from a technical perspective, AI-EP may be similar to online personalisation, factors related to the context of message delivery (in-store), the format of message delivery (small screen) and the salience of privacy concerns in different media suggest that consumer acceptance of personalisation may vary significantly across the two environments. This uncertainty represents a limitation in the current conceptual understanding of consumer acceptance of personalisation and is also a key barrier to adoption AI by businesses (Bughin etal., 2017). That is why Ameen etal (2022), Riegger etal (2021) and van de Sanden etal (2019), among others, have called for empirical research examining consumers’ attitudes towards AI-EP. This paper aims to advance the conceptual understanding of AI-EP by investigating the following research question: “How do consumers experience and respond to AI-EP?”. To frame this investigation, we draw on the personalisation-privacy paradox, particularly Sutanto etal’s (2013) research on smartphone users. This lens allows us to go beyond understanding whether consumers accept or reject AI-EP, to identify the reasons for their behaviour, as well as how they manage any tensions that may arise while interacting with AI-EP, as urged by Riegger etal (2021). We investigate these dynamics empirically by focusing on a UK fashion retail personalisation app. We focused on one specific app in order to develop an holistic understanding of the usage climate of this technology, as recommended by Wang etal (2015). We chose fashion retail because this is a highly dynamic industry, which benefits from targeted, locationbased communication with customers (Kumar etal., 2017); and because this is one of the most promising sectors for AI applications (Davenport etal., 2020). Finally, we chose the UK because it is at the forefront of the digital retailing revolution (Ameen etal., 2022). Given that AI-EP is a relatively unexplored phenomenon (Riegger etal., 2021), and the paradoxical findings that are beginning to emerge (e.g., Ameen etal., 2022), we opted for an exploratory approach. Specifically, a qualitative case study which included in-depth interviews with 18 female, millennial fashion retail shoppers, who had been exposed to a personalised advert. The paper makes three contributions. First, we show that customers welcome this innovative way of interacting with them in the retail environment. However, their experiences with online personalisation create very high expectations of the extent of AI-EP, as well as additional services such as creation of wish lists or the ability to edit their preferences. These findings can guide practitioners’ investment in AI-EP. Second, we provide empirical evidence of how the impact of the context of message delivery, the format of message delivery and the salience of privacy concerns differs for AI-EP vs online personalisation. This can guide the application of findings from extant research, and guide future research efforts. Third, we identify the content and process gratifications derived from AI-EP, extending Sutanto etal (2013)’s work on the manifestation of the personalisation-privacy paradox among smartphone users. The paper is organized as follows. Section2 considers the emerging literature on the opportunities and challenges for AI use in physical retail. Section3 presents the theoretical background. Section4 articulates the approach to data collection and analysis. Section5 communicates the empirical findings. Section6 discusses the findings, and uses the motif of the Snakes and Ladders game to capture the factors that support or prevent acceptance of AI-EP, Finally, Sect.7 captures the contributions of this empirical investigation to the advancement of theory and practice of AI deployment for personalisation in physical retail environments. 2 Research Background 2.1 Prior Studies inAI inRetail AI studies have seen a significant amount of attention in recent years from many different disciplines, and applied 1007Information Systems Frontiers (2024) 26:1005–1024 1 3 to many different settings, including retail (Dwivedi etal., 2021). Several studies propose that AI can help retailers develop new and innovative applications from the various datasets available to them (e.g., Davenport etal., 2020), and in doing so, achieve competitive advantage. However, they tend to lack empirical evidence, and to overlook the customer perspective. There is also a growing a body of work focusing on the obstacles to effective use of AI (e.g., Boratto etal., 2018). Authors mention the risk of consumer backlash and of negative impact for firms. Though, the lack of customer focused research results in insufficient understanding of consumers’ perceptions of AI use in retail. In turn, the literature on digital personalisation (e.g., Boerman etal, 2017) suggests that AI-EP could enhance but also frustrate customers. Yet, except for Ameen etal (2022), these studies examine personalisation in controlled experiments rather than actual in-store experience. Finally, the effectiveness of personalisation efforts tends to be limited by customers’ privacy concerns (e.g., Aguirre etal, 2016). While some of these studies focus on smartphones (e.g., Sutanto etal., 2013), they provided limited insight into how customers manage the tensions arising. Table1 summarises the notable themes identified in the stream of literature related to AI and its use for personalisation. The right-hand column emphasises the research gaps. 2.2 Personalisation‑Privacy Paradox The review of the literature revealed a lack of customer focused, evidenced based understanding of how AI-EP benefits retail customers, and which factors may create resistance to acceptance of AI-EP or destroy value for customers. While personalisation can bring benefits to consumers, they may resist personalisation if they deem that the collection and use of personal data that underpin personalisation is too invasive (Moore etal., 2015). This tension has been termed the Personalisation-Privacy paradox.1 To unpack the conditions under which the personalisation-privacy paradox manifests in each context, it is necessary to identify the gratifications that users derive from interacting with the medium through which personalisation is delivered, as well as their desires and concerns about information privacy (Sutanto etal., 2013). 2.2.1 Gratifications fromPersonalisation Sutanto etal (2013) put forward two types of gratification arising from personalisation: content gratification, referring to the enjoyment derived from the personalised message itself; and process gratification, referring to the enjoyment derived from the medium in which the personalised offer is delivered. The personalisation literature identifies various content related gratifications such as receiving offers that reflect customers’ preferences (Krishnaraju etal., 2016; Pappas etal, 2017) and context (Xu etal., 2011), reducing the effort or time required to complete the purchase (Tam & Ho, 2006), and enabling cost savings and other financial gains (Schmidt etal., 2020). However, personalised messages can also stir negative emotions such as irritation (Haghirian etal., 2005) or anger (Pappas etal., 2018), thus rendering personalisation efforts ineffective (Demoulin & Willems, 2019). Customers are likely to resist offers that are seen as a threat to their freedom of choice (Brehm & Brehm, 2013). AI-EP may be perceived as restricting the options available to customers, which may result in customers rejecting the AI offer, in order to reaffirm their autonomy (André etal., 2018). In turn, process gratification arises from the ability to control how messages are received (Brusilovsky & Tasso, 2004), such as being able to filter out certain messages, or to control when and how they are displayed (Sutanto etal., 2013). Research has also shown that being able to control which information is collected and how it is used increases message effectiveness (Tucker, 2014), while lack of transparency from firms has the opposite effect (Aguirre etal., 2015). AI algorithms are, typically, opaque (Burrell, 2016), preventing customers to see – and influence – how they produced a specific recommendation, which may result in resistance to AI-EP. While Sutanto etal (2013) found, in the context of smartphones, that personalisation gives users process gratification but not content gratification, by and large, the personalisation literature focuses on the latter (Boerman etal., 2017). 2.2.2 Privacy Concerns The effectiveness of personalisation efforts may be offset by users’ concerns over the privacy of their personal information (Awad & Krishnan, 2006). For instance, online ads that closely match customers’ browsing history reduce purchase intentions, because they raise concerns over firms’ surveillance practices (Aguirre etal., 2016). Customers set boundaries – psychological or physical – around their personal data (Stanton & Stam, 2003), and attempts to cross those boundaries raise concerns, and are met with resistance (Xu etal., 2008). Customers manage information boundaries by selectively sharing or withholding information (Sutanto etal., 2013). In addition, they may purposefully provide false information, such as using a false name or birth date (Miltgen & Smith, 2019), when firms attempt to collect personal data that they deem private. 1 The term “personalisation-privacy paradox” is also, sometimes, used to refer to the disparity between users’ privacy protection intentions and their privacy protection behaviours (e.g., Norberg, Horne & Horne 2007). 1008 Information Systems Frontiers (2024) 26:1005–1024 1 3 Table 1 Selected recent studies on AI in Retail Theme Source Key Claims Gap Identified AI Potential in Retail Davenport etal., 2020; Huang & Rust, 2021; Kietzmann etal., 2018; Syam & Sharma, 2018 AI can offer impressive potential in terms of data processing power for innovations such as: improved market segmentation, predictive analytics, sales forecasting, and personalisation Back-end processes such as customer data management and sales basket analysis have been enhanced by AI Studies focus on the envisaged potential of AI in retail, without empirical evidence. Extant studies appear to deduce overtly positive outcomes from applications of AI to retailers, overlooking the customer perspective Challenges of AI in Retail Boratto etal., 2018; Castillo etal., 2020; Crick etal., 2019; Dwivedi etal., 2021; Gardino etal., 2021; Griva etal., 2021 Many challenges exist for deployment of AI to process data efficiently and effectively, such as poor data availability, lack of skills and leadership buy-in, cost of deployment and ethical and regulatory restrictions Seasonal trends make prediction difficult and unstable, and can be dramatically influenced by a broad range of factors, as witnessed during the Covid-19 pandemic The gap between the AI promise and reality could result in customer backlash and reputation tarnishing, which could have significant, and long lasting, negative impact for firms. Yet, not many studies focus on consumer perceptions Digital Personalisation Ameen etal., 2022; Boerman etal., 2017; Riegger etal., 2021; Sutanto etal., 2013; van de Sanden etal., 2019; Wirtz etal., 2018 Personalisation can impress as well as frustrate customers, who are seeking offers unique to them, as derived by AI Studies examine personalisation in controlled experiments, outside of the shopping environment, and have yet to examine the in-store experience Privacy Aguirre etal, 2016; Awad & Krishnan, 2006; Castelo etal., 2019; De Bruyn etal., 2020; Grewal etal., 2016; Riegger etal., 2021; Sutanto etal., 2013; Yoganathan etal., 2021 Customers feel uncomfortable about, or react negatively to, interacting with AI, as it demands customer information to be effective. Customers are willing to submit information to take advantage of offers, but are ultimately uneasy with the process Studies to date identify tensions related to AI-enabled personalised offers delivered to their smartphones, but have not gone to the extent of understanding how customers manage any tensions that may arise 1009Information Systems Frontiers (2024) 26:1005–1024 1 3 The literature indicates that customers may be comfortable disclosing information deemed to be relevant for the intended outcome (Xu etal., 2011), when access to the service is time critical (Hubert etal., 2017), and where the information is routinely requested in that context (Stanton & Stam, 2003). However, customers resist sharing information that is deemed sensitive, such as their health status (Sutanto etal., 2013); or which could be used for discrimination (Stanton & Stam, 2003). They also resist sharing information when they feel that they lack control over what data are collected, how data are used, and with whom they are shared (Liu etal., 2019; Schmidt etal., 2020). However, information boundaries vary across individuals and are dynamic. Namely, those customers that value information transparency are also most likely to resist the data collection that underpins personalisation (Awad & Krishnan, 2006). Customers also change whether they share information depending on the perceived gains or losses of each situation (Kar, 2020). The perception of being under surveillance is particularly prevalent in online interactions and in smart services (Bues etal., 2017). Therefore, in addition to providing privacy features (Awad & Krishnan, 2006), firms also need to identify which information customers are comfortable to share, and what trade-offs they are prepared to make in order not to break their personal information boundaries (Pentina etal., 2016). This is particularly relevant for AI-EP, given the need for large volumes of data to support the development of targeted offers (Davenport etal., 2020). 3 Research Design The aim of our study was to advance the conceptual understanding of AI-EP by investigating the following research question: “How do consumers experience and respond to AI-EP?”. Hence, a qualitative, exploratory case study methodology (Sarker etal., 2018) was adopted. The unit of analysis was shoppers’ interactions with an AI-enabled smartphone application, in the context of fashion retail. This methodology offered an opportunity to collect primary data from customers insitu experiencing the AI-enabled personalisation offer, guided by key studies in the field (e.g., Ameen etal., 2022; Riegger etal., 2021). It also offered the unique opportunity to collect rich and diverse perspectives from participants, as they reflected upon the hybrid digitalphysical experience of AI-EP, extending previous works in the area, particularly Sutanto etal. (2013). In doing so, the method adopted allowed us to understand and analyse a broad range of participant views, and to theorise and conceptualise (Eisenhardt, 1989), in line with other case studies that have examined the impact of technology upon personalisation (e.g., Griva etal., 2021). 3.1 The Selected App The mobile app selected as the focus for this case study was the Regent Street App. The app was first launched in 2012 to enhance the shopping experience of visitors to this famous shopping district, in London (UK). As shown in Fig.1, the app included the option to receive personalised offers while shopping in the area. To create and deliver these offers, the app combined “two technologies: geofencing beacons that use location aware to offer content to users within a specified proximity to the store and cloud-based artificial intelligence (AI) to ensure personal relevancy of offers” (Lemmon, 2017). Circa 80% of the stores in this shopping district joined the scheme, implementing the associated technology in their premises, such as beacons around the store and microchips in the items on sale (Scott, 2014), in addition to artificial intelligence programme to personalise the offers. Moreover, 98.6% of app users created a personal profile and signed up to receive personalised content (Lemmon, 2017). The AI-EP messages are delivered when app users are in the vicinity of the stores that signed-up to the app (Dempsey, 2015), resulting in a 7.4% increase in response rate for AI-EP vs. untargeted offers (Lemmon, 2017). 3.2 Data Collection To gather customer experiences, we used in-depth, semistructured interviews, to allow participants to articulate their actions and intentions towards the AI-EP, as well as implications for their personal data. In order to recruit participants, one of the authors (who conducted all the interviews) positioned themselves outside a specific fashion store in Regent Street, which was known to use the Regent Street App for the delivery of AI-enabled personalised offers. As shoppers walked past the store, the interviewer approached them, showed them the advert in Fig.2, and invited them to participate in an interview. This approach is in line with Kar (2020)’s recommendation that research on customer perceptions of digital technology should take place immediately after encounter with that technology. Some interviews took place outside the store, others at a nearby café. No financial incentives were offered to the interview participants. The interview protocol (Table2) reflected the key themes identified in the extant literature. The questions focused on perceptions of the message rather than the technology underpinning it, as customers don’t always understand the technology behind personalisation. This approach allowed us to move beyond a simplistic view of positive vs. negative attitudes, and to understand the black box of the customers’ response (Belk, 2017). As resistance to AI-EP may depend on customer characteristics (Yoganathan etal., 2021), we recruited an 1010 Information Systems Frontiers (2024) 26:1005–1024 1 3 homogeneous sample via purposive sampling (Bryman & Bell, 2015), to give direction to the data collected in support of the case study (Yin, 2012). We focused on female shoppers aged 18 to 30years old, because, in the UK, this demographic group cares the most about looking trendy (YouGov, 2020). Women in this age group are twice as likely than men to agree that they spend a lot on clothes and to value immediate access to fashion items; and they are also more likely than men and then older women to shop at multiple retailers (YouGov, 2020). Consequently, this demographic group are a key target for high street fashion retailers’ promotional efforts. This demographic group are also more open than others to sharing their personal data with firms, given that they grew up in a digital world (Liu etal., 2019). However, women may resist AI, especially when outcomes are consequential (Castelo etal., 2019). We conducted 18 audio-recorded interviews, each lasting between 30min and one hour. Each recording was transcribed with an average of 9,000 words, equating to just over 160,000 words in the final dataset. The data was checked for accuracy and prepared for analysis. 3.3 Data Analysis The interview data was analysed using NVIVO and following Krippendorff's (2004) systematic approach to thematic analysis. As is customary of exploratory case studies in the information systems discipline (see Sarker etal., 2018), the theory was used to guide the design of the study and to set the general direction of data analysis. In practice, this meant that a preliminary coding book was developed based on the themes identified in the literature, and this was used in stage 1 of data analysis to deductively code the transcripts into a) gratifications from personalisation, b) privacy concerns and c) reaction to AI-EP. Subsequently, in stage 2, for each of the themes Fig. 1 Case study App. Image source: http:// okosv aros. lechn erkoz pont. hu/ en/ node/ 558 1011Information Systems Frontiers (2024) 26:1005–1024 1 3 in the code book, the analysis of the data proceeded in an inductive fashion, with subsequent codes emerging from the data. The final set of codes is depicted in Table3. The findings emerging from this analytical process are presented in the next section, following a polyphonic account. This approach presents the range of perspectives offered by the research participants in order to develop a layered account of the phenomenon being investigated (Travers, 2001), as is customary of interpretive research. This is in contrast with identifying the dominant narrative or single shared reality typical of positivist approaches to data analysis (Sarker etal., 2018). Fig. 2 Interview prompt Table 2 Interview protocol Section Question Stimulus Participant receives targeted prompt. Upon opening the screen, the participant learns that the offer is exclusive to users of the Regent Street mobile app walking past that store, and who have bought in that store, previously 1. What do you think of this offer? AI-EP – Gratifications 2. This offer has been personalised based on your location and shopping preferences. Is this offer useful? 3. How does it enhance your shopping experience? 4. When the brand sends real-time, relevant offers to your mobile phone, are they mostly trying to sell more, or trying to build relationships with customers like you, by serving your specific needs? 5. Do you think that the company will always make the best offer specifically for you? 6. Why do you suppose that? AI-EP – Outcomes 7. Do personalised offers help you develop bonds with this brand? 8. Would receiving this type of offer discourage you from switching to another brand? Why? AI-EP – Privacy concerns 9. Which personal information would enhance your experience with this retailer? [Probe for location and behavioural data] 10. Are you willing to share that information with the company, so that they can develop offers specifically for you? 11. Where should the limit be? 12. What are the benefits of letting the company access your personal data? 13. What are the risks of letting the company access your personal data? 14. Through the app, the company can track your movements not only in-store but also in the proximity of stores on Regent Street? How does that make you feel? 1012 Information Systems Frontiers (2024) 26:1005–1024 1 3 Table 3 Coding structure Aggregation 1st order 2nd order Illustrative quotes Gratifications Content Relevancy of the offer that is better than humans I mean if you can choose what you like and then they will remember it that would be so much easier to go and shop there and maybe you would buy a bit even more Time saving attributes to the customer experience It’s really useful because you get to know what is there Financial benefits that are attractive to modern customer base through appetite for discounts I would value it a lot. There is nothing to lose for customers and it is not like we are committing to a sale of any sort or a purchase of any sort Other benefits But also the things like pretty macarons or lemonade Process Message Delivery If I would receive an offer from a store I really like and I already have a 10% offer, I would definitely go inside and check out the stuff they have Information collection process I would rather have a setting in application— right now I am shopping for my dad. Rather than registering it under me. Or buying gifts for him or for her rather but still that the information being given Information use processes enhancing value to the in-store experience It would definitely help because I can make a profile of things I like. It is an amazing tool definitely Privacy concerns Information boundaries Boundary management practices I only share information about fashion. Only information where I know it can create value for me Information—Willingness to share if somebody wants to track me down they can do it, they have (the data), anyway… but on the other hand, it does not really matter what they are going to do because they can have it anyway Information—Desire to protect I want to know if it’s going to be used for more than just trying to fulfil my needs within the shop Acceptance of AI-EP Perceptions Positive Sometimes I just want to have something which I already have, which is different from what I already have. So, personalizing is useful for me in terms of fashion Negative If the company has bad intentions, there may be some downside in sharing the information Behaviour Acceptance of AI-EP and Customers are willing to share information to receive personalisation offer Telling them about your style, so they would know what specific things to target to you, and maybe saying your age group and gender, because that might help them to target you towards particular things as well Rejection due to irritation from notifications, interruptions, lack of control If I am not shopping I would not want that sort of notifications or if I am doing something else I do not know. If you end up passing there every day it could be quite annoying 1019Information Systems Frontiers (2024) 26:1005–1024 1 3 data such as the customer’s whereabouts, or contextual data such as the weather or crowd levels (Verhoef etal., 2017). However, the emotionally charged descriptors used by some of our participants, indicate that customers intensely dislike extensive tracking in the physical environment. This presents a challenge for fashion retailers: one the one hand, location data enables them to take full advantage of AI’s capabilities for personalisation; on the other hand, customers may see this as an invasion of privacy (Xu etal., 2008), which may result in negative attitudes towards AI-EP and, ultimately, its rejection (Shankar etal., 2016). 5.2 Effectiveness ofAI‑EP Based on our findings, attempts to use AI-EP for customer acquisition may be ineffective (Demoulin & Willems, 2019), or even detrimental (Baek & Morimoto, 2012) for the brand. This finding was somehow surprising given that the app considered in this case study was provided by a trusted party which offered discounts to a variety of stores in a given shopping district. Trust has been shown to impact the perception of a personalised offer (Aguirre etal., 2016) and, as such, familiarity with the Regent Street app might lead customers to be receptive to AI-EP attempts from new brands (Chen & Dibb, 2010). Furthermore, we found that customers expressed a strong desire for autonomy and freedom of choice, as reported in the context of online personalisation (Balan & Mathew, 2020). Though, while previous research focused on choice and agency in relation to the content of the message, we witnessed a willingness to control message delivery, too. Granting this flexibility might return a sense of control to customers (Brehm & Brehm, 2013), but may increase the complexity of the app (e.g., in terms of navigation), which will negatively impact the user experience (Shankar etal., 2016). Moreover, it reduces the retailers’ ability to collect data and deliver targeted messages (Chou & Shao, 2021). While AI can integrate multiple sources of customer, contextual and transactional data, our study exposes limitations to the extent of in-store personalisation (Ameen etal., 2022; Boratto etal., 2018). Namely, in contrast with the online environment, where personalisation may influence the search and evaluation stages (Davenport etal., 2020), AI-EP was revealed to be most valued at point of purchase stage, albeit not for payment purposes. Furthermore, whilst algorithms underpinning AI-EP need to be rigorously tested (Sutanto etal., 2013), our findings indicate that fashion shoppers have low tolerance for such trial and error. As in the online environment, consumer trust and positive emotions are essential for successful personalisation (e.g., Pappas, 2018). As with personalisation in the online environment (e.g., Pappas etal, 2017), customers have high expectations of AI-EP. The inflated expectations and the low tolerance for mistakes, are likely to result in disappointment and app abandonment (Riegger etal., 2021; Shankar etal., 2016), represents a waste of resources, and inability to continue collecting data about customers. Figure3 presents an overarching view of how in-store AI-EP can enhance customer experiences, capturing both the enabling factors from content and process gratifications, and the detracting factors related to unmet process gratification expectations and from privacy concerns. We represent the AIEP journey consisting of opportunities and threats for retailers, as encapsulated in the well-known game of Snakes and Ladders. This model highlights the potential as well as the risk for brands about to embark upon such an endeavour. Moreover, from our review of personalisation in both retail and digital spheres, this is the first such conceptual framework of its kind representing the user-end perspective of such innovations in technology. The game begins from the moment a user/player is within proximity of the store. The player is then faced with two options, either an enabling force (indicated by a ladder) moving them higher up the personalisation journey, or a detractor (indicated by a snake) preventing progress on the board. Each factor is described with key attributes as generated from the findings of the study. We envisage that AI-EP is not a one shoe fits all experience for users, and that it may take a circuitous route. As retailers continue to innovate, the blank squares represent the stages of the journey not relevant to AI-EP. The final goal is where the AI-EP has delivered a positive in-store experience and created value for customers and retailers. 6 Conclusion The deployment of AI technology for personalisation promises to address some of the business challenges faced by high-street retailers (Kumar etal., 2017), such as increased competition, heightened price sensitivity or the emergence of the show-rooming phenomenon. AI-EP apps, such as the one analysed in this paper, enable the creation of offers that draw on individual behaviours and contextual information, as opposed to aggregate segment information (as in the case of non-AI, automated personalisation) or intuition (as in the case of sales staff personalisation). As a result, AI-EP offers can be more relevant, granular and timely than either of those alternatives. However, factors related to the context of message delivery, the format of message delivery, and the salience of privacy concerns may impact the relevance of extant research on technologyenabled personalisation—mostly performed in the online environment—to help us understand consumers’ acceptance of AI-EP. Therefore, we responded to calls by Ameen etal (2022), Riegger etal (2021) and van de Sanden etal (2019), 1020 Information Systems Frontiers (2024) 26:1005–1024 1 3 among others, for empirical research on how consumers experience and respond to AI-EP. The qualitative investigation of consumers’ interaction with AI-DP in a shopping district with London, UK, through the lens of the personalisation-privacy paradox enabled us to identify the perceived content and process benefits derived from AI-EP, as well as how privacy concerns undermine these benefits and inform the customers’ boundary management tactics. Together, these factors result in a carefully orchestrated process whereby customers either accept or reject the artificial intelligencederived personalisation offer, but with a high degree of control over their interaction with the offer, and in particular the use of their personal information. 6.1 Theoretical Contributions Our study makes the following three contributions. First, we showed that customers welcome this innovative way of interacting with them in the retail environment as posited by Davenport etal. (2020) and others, which should give confidence to practitioners considering adoption of AI (Bughin etal., 2017). However, we found that customers’ experiences with online personalisation create very high expectations of the extent of personalisation possible via AI-EP, addressing the gaps identified in Table1. Those high expectations may be difficult to meet, given not only the technological restrictions of AI-EP but also consumers’ discomfort with location tracking as well as the safeguarding of data which is essential for the efficacy of the offer, which can create customer backlashes and reputation damage (Castillo etal., 2020). Customers’ online experiences also shape their desire for additional services and functionalities, such as the creation of wish lists or the ability to edit their preferences. This desire presents unique challenges from the point of view of interface design which have not been reported, yet. We represented the range of factors impacting positively vs negatively on customers’ experiences with – and assessment of – AI-EP via the motif of Snakes and Ladders boardgame. Second, we provided empirical evidence of how the impact of the context of message delivery, the format of message delivery and the salience of privacy concerns differs for AI-EP vs online personalisation. Specifically, regarding the impact of the different motivations for online vs. in-store retail on customers’ perception and evaluation of personalisation efforts (Haridasan & Fernando, 2018), we found that customers may be in a particular physical location for reasons other than shopping, and that this may result in Fig. 3 The Snakes and Ladders of AI-Enabled Personalisation 1021Information Systems Frontiers (2024) 26:1005–1024 1 3 heightened irritation from app notifications. Moreover, customers seem more sensitive to evidence of tracking of past purchase behaviour in the physical environment than online, and more likely to resist the tracking of location and shelfbrowsing behaviour than online browsing. In terms of the impact of message delivery interface, our findings confirm that the small screen of mobile phones impact negatively on consumers’ involvement with the message (Grewal etal., 2016), and that there is a need for attention-grabbing subject lines to make shoppers want to check the message, immediately. Future research could test the effectiveness of the same message delivered online vs via AI-EP, to quantify the effect of delivery interface on the effectiveness of personalisation campaigns. Another factor that could limit the impact of AI-EP was the high number of notifications that mobile phone users typically receive on their devices, not just from direct messages from other users, but also from social media apps, calendar apps and others. Having said that, AI-EP could be more effective than e-mail offers, possibly because of the relative novelty of this form of personalisation, but also because of the volume of traffic that e-mail may attract (including spam content). Finally, regarding the impact of privacy concerns on consumers’ evaluation of AI-EP, our findings – like Ameen etal (2022)’s study of consumer interactions with smart technologies in shopping malls – seem to contradict Li etal. (2017). Unlike studies of personalisation in the online environment (e.g., Pappas, 2018), customers do not seem too concerned with the firm’s access to their personal information, in principle. This could be because the collection of such information is now seen as a condition for accessing services in the digital era. However, it could also be because of the particular type of app used in our case study. Like Ameen etal (2022)’s app, ours was valid for a shopping area, rather than a specific retailer. This fact may decrease the customers’ perception of surveillance, and increase their trust in the firm behind the AI-EP. Further research is needed to separate the effect of type of app (i.e., retailer vs location specific) from the overall privacy concerns with AI-EP. However, customers did express concerns over access to information which they did not deem essential for the task at hand, and access by unfamiliar retailers. Our findings thus assist in contextualising extant literature on AI-enabled personalisation online vs in-store. Third, we identified the specific content and process gratifications derived from AI-EP, and how they enhance or detract from the value of AI-EP for retail customers. Content gratifications included discounts, time savings and relevance of offers, with the first one seemingly dominating the others. Receiving notifications on the phone was a process gratification for some but detracted from the overall benefit for others. Likewise, opinions were divided on the process gratification derived from how this app collected and used information for AI-EP. Our findings, thus, extend Sutanto etal (2013)’s work on the manifestation of the personalisation-privacy paradox among smartphone users, in hybrid (physical-digital) environments. 6.2 Practical Contributions Collectively, these findings mean that the use of AI technology for personalisation in the physical environment can address some of the business challenges faced by high-street retailers as suggested in Davenport etal. (2020), but with significant differences vis a vis personalisation in the online environment. Specifically, our findings have the following managerial implications. First, AI-EP is more suitable for customer retention efforts, than for customer acquisition. This is both because of the type of dataset required to deliver on customer expectations of AI-EP and avoid the risk of customer backlash, and because of customers’ intense negative reaction to receiving personalised offers from brands that they usually do not buy from. A better way to acquire customers in this demographic group might be through the use of dynamic, entertaining adverts on social media; or by including their items in clothing subscription services (YouGov, 2020). Second, to attract customers, retailers should offer enticing discounts on desired items. This is because, contrary to the online environment and to what is suggested in the literature (e.g., Kietzmann etal., 2018), we found that customers weren’t driven by hedonic offers, and that there was limited scope for shopping basket expansion. Third, retailers should focus on providing information about items’ features, availability and other attributes that are important in the pre-purchase stage. This is because, while shoppers may interact with their smartphones across all stages of the purchase process (e.g., Syam & Sharma, 2018), they seemed most receptive to AI-EP offers in the lead-up to the purchase, rather than during the purchase (e.g., payment options) or afterwards (e.g., asking for feedback). Fourth, retailers need to test various aspects of offer delivery, in order to minimise the concerns and irritants detected in our study. These include the number of notifications, to address shoppers' concerns with battery depletion and the fact that customers may be in the store’s neighbourhood for different reasons; and the wording of the message, to assuage customers’ desire to understand why they got a specific offer. It is also important for retailers to unpack which personalised offers are rejected because customers want to reaffirm their autonomy vs the AI (André etal., 2018), rather than because the offer itself was not persuasive. Fifth, retailers need to approach data collection and use, carefully. Our study revealed that the use of location and social media data, which is accepted in the online context, caused intense negative reactions among some customers. 1022 Information Systems Frontiers (2024) 26:1005–1024 1 3 Conversely, the relative novelty of in-store AI-EP means that customers may be willing to participate in ad-hoc data collection initiatives, if they perceive a link between the information requested and improvements in their shopping experience. 6.3 Research Limitations andFurther Research It is important to recognise the limitations resulting from the focus and characteristics of our approach. The focus on fashion retail, on a multi-store app, and on the UK may limit the transferability of our findings to other research contexts. Research into other empirical settings is needed before claims can be made about consumer perceptions and experiences of AI-EP, generally. Likewise, young female consumers exhibit distinct attitudes to fashion shopping, sharing digital data and interacting with AI, meaning that our findings may not be directly applicable to older female shoppers, or to male shoppers of similar age. Findings from personalisation in the online environment indicate that perception of personalisation benefits is a key a factor in acceptance of personalisation (Pappas etal., 2017). Therefore, it is important to identify which messages most clearly communicate the desired content gratification valued by different types of customers and/or different contexts. Moreover, by adopting a qualitative approach, we were able to identify a range of issues relevant for fashion retail customers. However, we are not able to quantify their absolute or relative importance. Further research employing quantitative approaches, namely natural experiments (e.g., Tag etal, 2021), is needed before claims can be made about the salience of specific gratifications and privacy concerns, or about the magnitude of their impact on consumer acceptance of AI-EP. Likewise, the use of methodologies such as fuzzy-set qualitative comparative analysis (see Pappas, 2018) would enable the identification of how the different factors identified in this study combine to amplify – or not – purchase intention when exposed to AI-EP. Furthermore, our focus on consumers overlooks the retailers’ perspective of AI-EP, which is a worthy area of further study. In particular, an avenue of further study that would advance our findings, as well as the work of Yoganathan etal. (2021), is to examine the relationship between AI-EP and access to onsite retail staff, homing in on the digital-physical customer experience dynamic. Given the practical nature of such an investigation, and the need for close collaboration with the organisation deploying the AI-EP solution, it would be beneficial to adopt the clinical inquiry approach method (see Schein, 2008). In this methodological approach, academic researchers and practitioners work together to shape the project, with the explicit goal of improving practice. Clinical inquiry is particularly useful for instigating digital innovation from within the organisation, as demonstrated in Vassilakopoulou etal (2022)’s analysis of the potential for creating hybrid human/AI service teams. Declarations Conflicts of Interest The authors have no relevant financial or nonfinancial interests to disclose. 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Decision Support Systems, 51(1), 42–52. Yin, R. K. (2012). Case study methods. Yoganathan, V., Osburg, V.-S., H. Kunz, W., & Toporowski, W. (2021). Check-in at the Robo-desk: Effects of automated social presence on social cognition and service implications. Tourism Management, 85, 104309. https:// doi. org/ 10. 1016/j. tourm an. 2021. 104309. YouGov. (2020). The Fashion Industry in Great Britain. YouGov. https:// yougov. co. uk/ topics/ consu mer/ ar tic lesrepor ts/ 2020/ 02/ 25/ fashi onindus trygreatbrita in [Last accessed 22 July 2022]. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Ana Isabel Canhoto is Professor of Digital Business at the University of Sussex, UK. Her research focuses on the role of digital technology (including Artificial Intelligence Big Data and theMetaverse) in interactions between firms and their customers. She examines drivers of adoption, user experiences, consequences of adoption, and the role of context. She is also committedto the pedagogical use of digital technology, including using machine learning to support pupil performance, creating quasi-simulations for experiential learning, and training early careerresearchers to use social media to develop and disseminate their work. Brendan James Keegan is an Assistant Professor in Marketing at Maynooth University, Ireland. His current research activities are within the areas of business to business relationships,artificial intelligence and machine learning applications in digital marketing, and digital placemaking. His work is published in Information Systems Frontiers, Industrial Marketing Management,European Journal of Marketing, European Management Review. He is the Principal Investigator for the ongoing digital placemaking research within the H2020 funded GoGreenRoutes Project. Maria Ryzhikh is the E-Commerce Manager for the EMEA region at Weber-Stephen Products EMEA GmbH. She completed the MSc Marketing program at Oxford Brookes University in 2016 and then continued to pursue her career in digital marketing in various technologically-driven companies in the UK and Germany. Her particular areas of interests lie in performance marketing,digital marketing analytics, online consumer behaviour and psychology, and conversion rate optimization.