Enhancing brick-and-mortar store shopping experience with an augmented reality shopping assistant application using personalized recommendations and explainable artificial intelligence

Robert Zimmermann (Digital Business Institut, University of Applied Sciences Upper Austria, Steyr, Austria)
Daniel Mora (Artificial Intelligence Lab, AWS-Institute for Digitized Products and Processes, Saarbrücken, Germany)
Douglas Cirqueira (School of Computing, Dublin City University, Dublin, Ireland) (School of Business, Lero – the Science Foundation Ireland Research Centre for Software, Dublin, Ireland)
Markus Helfert (School of Business, Lero – the Science Foundation Ireland Research Centre for Software, Dublin, Ireland) (Innovation Value Institute, Maynooth University, Maynooth, Ireland)
Marija Bezbradica (School of Computing, Dublin City University, Dublin, Ireland) (School of Business, Lero – the Science Foundation Ireland Research Centre for Software, Dublin, Ireland)
Dirk Werth (Artificial Intelligence Lab, AWS-Institute for Digitized Products and Processes, Saarbrücken, Germany)
Wolfgang Jonas Weitzl (Digital Business Institut, University of Applied Sciences Upper Austria, Steyr, Austria)
René Riedl (Institute of Business Informatics – Information Engineering, Johannes Kepler University Linz, Linz, Austria)
Andreas Auinger (Digital Business Institut, University of Applied Sciences Upper Austria, Steyr, Austria)

Journal of Research in Interactive Marketing

ISSN: 2040-7122

Article publication date: 12 April 2022

Issue publication date: 21 March 2023




The transition to omnichannel retail is the recognized future of retail, which uses digital technologies (e.g. augmented reality shopping assistants) to enhance the customer shopping experience. However, retailers struggle with the implementation of such technologies in brick-and-mortar stores. Against this background, the present study investigates the impact of a smartphone-based augmented reality shopping assistant application, which uses personalized recommendations and explainable artificial intelligence features on customer shopping experiences.


The authors follow a design science research approach to develop a shopping assistant application artifact, evaluated by means of an online experiment (n = 252), providing both qualitative and quantitative data.


Results indicate a positive impact of the augmented reality shopping assistant application on customers' perception of brick-and-mortar shopping experiences. Based on the empirical insights this study also identifies possible improvements of the artifact.

Research limitations/implications

This study's assessment is limited to an online evaluation approach. Therefore, future studies should test actual usage of the technology in brick-and-mortar stores. Contrary to the suggestions of established theories (i.e. technology acceptance model, uses and gratification theory), this study shows that an increase of shopping experience does not always convert into an increase in the intention to purchase or to visit a brick-and-mortar store. Additionally, this study provides novel design principles and ideas for crafting augmented reality shopping assistant applications that can be used by future researchers to create advanced versions of such applications.

Practical implications

This paper demonstrates that a shopping assistant artifact provides a good opportunity to enhance users' shopping experience on their path-to-purchase, as it can support customers by providing rich information (e.g. explainable recommendations) for decision-making along the customer shopping journey.


This paper shows that smartphone-based augmented reality shopping assistant applications have the potential to increase the competitive power of brick-and-mortar retailers.



Zimmermann, R., Mora, D., Cirqueira, D., Helfert, M., Bezbradica, M., Werth, D., Weitzl, W.J., Riedl, R. and Auinger, A. (2023), "Enhancing brick-and-mortar store shopping experience with an augmented reality shopping assistant application using personalized recommendations and explainable artificial intelligence", Journal of Research in Interactive Marketing, Vol. 17 No. 2, pp. 273-298. https://doi.org/10.1108/JRIM-09-2021-0237



Emerald Publishing Limited

Copyright © 2022, Robert Zimmermann, Daniel Mora, Douglas Cirqueira, Markus Helfert, Marija Bezbradica, Dirk Werth, Wolfgang Jonas Weitzl, René Riedl and Andreas Auinger


Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

With department store revenue declining since 2001 (Bureau, 2018; ICSC, 2018; Wolf, 2018) and e-commerce revenue continually growing (Cirqueira et al., 2020a; Statista, 2018), today's retailers face a significant challenge in staying competitive. In particular, traditional brick-and-mortar retail faces significant challenges (Berman, 2018). In the US alone, 9,879 stores declared bankruptcy in 2019 (Loeb, 2020). The COVID-19 pandemic has aggravated this problematic trend (Nicola et al., 2020).

In these troublesome times, it is even more important for retailers to understand how they can enhance customers' offline shopping experiences (von Briel, 2018). As a response to these challenges, retailers are engaging in a new form of retail referred to as “digital retail”, which uses information and communication technologies (e.g. smartphones) to engage customers, drive sales and offer unique shopping experiences that are superior to pure online shopping (Lemon and Verhoef, 2016). At the same time, customer expectations have been rising continuously (Parise et al., 2016) including the preference for an omnichannel experience (Briedis et al., 2020). In this regard, personalization and interactivity are key elements for creating a positive experience, leading to positive changes in customers' behaviors and intentions toward retail (Parise et al., 2016). Therefore, traditional retailers need to develop new systems that can provide a more customized and immersive shopping experience for customers in brick-and-mortar stores. Particularly since such a shopping experience may positively affect purchase intention and actual sales (Arora et al., 2021; Zimmermann et al., 2019).

Contemporary marketing strategies integrate new technologies to create meaningful interactions with customers (Wang, 2021). It is argued that the use of smartphones can provide an augmented and personalized customer shopping experience, driving sales in brick-and-mortar stores (Eriksson et al., 2018; Juaneda-Ayensa et al., 2016; Parise et al., 2016; Zimmermann and Auinger, 2020). A particular use case of such smartphone-based technology is an augmented reality shopping assistant application (hereafter ARSAA), which uses augmented reality (AR) to display content (e.g. tailor-made offers, product comparison and recommendations) by leveraging machine learning techniques (e.g. recommender systems) and explainable artificial intelligence (XAI). This technology enables a personalized and online-like shopping experience for customers in brick-and-mortar stores. Importantly, the usage of ARSAA in brick-and-mortar stores enables retailers to blur customers' perception of online and offline channels (Carroll and Guzman, 2013). Furthermore, it provides higher levels of interactivity, which is a primary area of future research in interactive marketing (Wang, 2021).

However, since the introduction of such technologies is expensive (Berman, 2018), retailers need to know if an ARSAA is a useful tool to deliver an augmented and personalized shopping experience. Unfortunately, current marketing literature does neither provide guidance on how to develop AR applications which provides personalized shopping experiences nor does it provide evaluation studies which empirically examined the impact of such applications on shopping experience.

Against the background of the presented research deficit and considering the need of corresponding research results for informed decision-making in practice (e.g. by retail managers and interactive marketing practitioners), the present study addresses the following research questions:


How does a customer's shopping experience with an ARSAA differ from a shopping experience without an ARSAA in brick-and-mortar stores?


How can the presented ARSAA prototype be improved to further enhance the shopping experience of brick-and-mortar store customers?

To answer these questions, we follow a design science research methodology. Here, an ARSAA is developed and evaluated by means of an online experiment. Thus, the present study addresses both phases of a design science research project: build and evaluate (Hevner et al., 2004; March and Smith, 1995).

The paper is structured as follows: Section 2 presents the background of our study by providing a literature review, the conceptual background of our study and a conceptual framework. Section 3 outlines the research methodology. Section 4 provides the study results. Section 5 discusses the results and main findings and outlines the theoretical contributions, practical implications, limitations and future research opportunities. Section 6 presents our conclusion.

2. Literature review and conceptual background

2.1 Literature review

A systematic literature review was conducted to illuminate the state of research in the field of personalized recommendations provided by AR applications. In accordance with vom Brooke et al. (2009), our literature review is extensive as we reviewed all literature found on this topic. To cover an extensive field of literature we used the following search string in the Scopus database:

(TITLE-ABS-KEY (“brick and mortar” OR “brick-and-mortar”) OR TITLE-ABS-KEY (“retail”)) AND (TITLE-ABS-KEY (“augmented reality” OR “AR”) AND TITLE-ABS-KEY (“personalization” OR “personalisation” OR “recommendations” OR “recommender system”))

The literature search resulted in 23 papers from the Scopus database that were screened for relevant information about recommender systems, which provide personalization in AR applications. A total of 16 papers did not contain such information and were excluded from the review. The remaining seven papers' contributions to literature in the field of personalized recommendations in AR are summarized in Table 1. Interestingly, no publication investigated the impact of personalized recommendations on shopping experience, although an explicit call for corresponding research exits (Lavoye et al., 2021).

Thus, based on the previous approaches in deploying recommender systems in physical stores, we identify the use of a smartphone-based ARSAA as a particularly promising, yet under researched tool to provide personalized recommendations in brick-and-mortar stores.

2.2 Conceptual background

To conceptualize the impact of ARSAA on shopping experience we also reviewed the trends in retailing, providing details on the emerging field of digital retail and its potential influence on shopping experience in brick-and-mortar stores, the use of recommender systems in brick-and-mortar stores and their impact on purchase intention, and the potential to increase trust in recommender systems by using XAI.

2.2.1 Digital retail shopping experience in brick-and-mortar stores

Digital retail makes use of state-of-the-art technologies that allow retailers to use a variety of different customer touchpoints such as smartphones and social media to offer unique shopping experiences. Consequently, retailers have started targeting customers across different channels, giving birth to multi-channel retailing. This approach is morphing into omnichannel retailing today, which merges all channels and touchpoints into a single seamless shopping experience (Verhoef et al., 2015).

Putting shopping experience at the center of their business model and deploying digital technologies to enhance this experience (Parise et al., 2016; Rigby, 2011) retailers can make use of personalized advertising and promotion via increasingly ubiquitous devices (MacKenzie et al., 2013). Smartphones represent the archetype of these devices (Pimenidis et al., 2018) as they get continuously updated with technologies that are predicted to revolutionize the retail sector, such as AI and AR (von Briel, 2018).

Considering these developments, and in line with Lavoye et al. (2021), ARSAA is assumed to positively influence customers' perception of shopping experience in brick-and-mortar stores by boosting hedonic (e.g. enjoyment) and utilitarian value (e.g. usefulness, informativeness). However, it must be noted that the use of AR can create media irritation, potentially diminishing this effect (Yim and Park, 2019). Hence, for assessing the overall quality of shopping experiences, this research includes the concepts of “Usefulness”, “Entertainment”, “Informativeness” (Pantano et al., 2017) and “Irritation” to capture the specifics of AR shopping experience.

“Perceived Usefulness” (PU) and “Perceived Ease of Use” (PEOU) are the key component of the technology acceptance model (TAM) (Davis, 1989). PU describes the degree to which a user believes that using a specific technology enhances the user's performance. PEOU describes how easy to use a system or application is. As PEOU was found to have a much weaker effect on behavioral intentions compared to PU (Venkatesh and Morris, 2000), this study focuses on PU only. In this regard, previous studies have shown (Chen and Wells, 1999; Song and Zinkhan, 2003) that, for websites, specific design features (e.g. menus, icons, colors and graphics) make websites fun, attractive and appealing thus increasing PU. As ARSAA uses similar design features to present information to users (e.g. interactive buttons, icons and graphics; see also Chapter 3.2), we hypothesize:


ARSAA assisted brick-and-mortar shopping is perceived as more useful than unassisted brick-and-mortar shopping.

The perceptual constructs “Entertainment”, “Informativeness”, and “Irritation” are the most robust constructs in uses and gratifications theory (UGT) (Katz et al., 1973; Luo, 2002). UGT has been shown to be a promising theory for explaining user behavior and perception of new media, especially in the area of interactive information and communication technologies (Baabdullah et al., 2022; Lee et al., 2020; McLean and Osei-Frimpong, 2019; Pelletier et al., 2020; Qin, 2020).

“Entertainment” describes the extent to which a media is fun and entertaining for users by fulfilling the need for escapism, diversion, esthetic enjoyment or emotional release (Ducoffe, 1996; Luo, 2002). A higher entertainment value motivates users to use specific media more often than others. This, for example, stimulates their purchase intention and information seeking behavior in e-commerce (Huang, 2008). “Informativeness” describes the extent to which media provide resourceful and helpful information to users (Chen and Wells, 1999; Ducoffe, 1995). Gathering information for goods and services is one of the most prominent reasons for consumers to use the Internet (Capgemini, 2020; Central Statistics Office, 2020; Statistisches Bundesamt, 2020).

As the ARSAA designed for this study uses multiple design elements to increase “Entertainment” (e.g. live interaction with a physical product on the smartphone) and “Informativeness” (e.g. displaying product information, giving recommendations), we hypothesize:


ARSAA assisted brick-and-mortar shopping is perceived as more entertaining than unassisted brick-and-mortar shopping.


ARSAA assisted brick-and-mortar shopping is perceived as more informative than unassisted brick-and-mortar shopping.

“Irritation” describes the extent to which using media bothers users because of annoying, offensive, confusing, distracting or messy design choices (Chen and Wells, 1999; Ducoffe, 1996). Previous studies show that disorganized websites can generate negative advertising value and diminish the intention to return (Hausman and Siekpe, 2009). Additionally, Ducoffe (1996) notes that irritating banner ads may increase user anxiety, distract customers and thus dilute customer shopping experiences. Although ARSAA, as presented in this study, does not use banner ads, it still uses personalized recommendations to provide additional information to its users that might be perceived as similarly irritating. It is thus crucial to investigate the amount of irritation caused by ARSAA. Accordingly, we hypothesize:


ARSAA assisted brick-and-mortar shopping is perceived as more irritating than unassisted brick-and-mortar shopping.

2.2.2 Recommender systems in brick-and-mortar stores

In the past decades, recommender systems have helped e-commerce to provide customers with more personalized experiences, creating a positive impact on sales and customer retention (Amatriain and Basilico, 2015; MacKenzie et al., 2013). Modern recommender systems work as an information filter based on machine learning techniques to determine user preferences. These preferences are the foundation for the generation of a ranked list of relevant products, typically based on a customer's past behavior, similarities to other customers and patterns in item information (Mora et al., 2020a). These systems allow companies to understand how they can target customers throughout the customer journey (Mora et al., 2020a). Recommender systems provide utilitarian value for users by improving their efficiency during information search and product comparison (Pimenidis et al., 2018).

For in-store recommendations, applications on smartphones are currently the most popular approach (Mora et al., 2020a; Piotrowicz and Cuthbertson, 2014). Their main advantages are the access to data inputs, personal user information (e.g. historical transactions associated with an app account), and integrated sensors. An early example of this approach was a smartphone-based recommender system for shopping malls that implicitly captured customer's preferences for stores by tracking their positions (Fang et al., 2012). Compared to conventional recommender systems, which do not consider a customer's context; this approach resulted in higher recommendation accuracy and customer satisfaction. Additionally, the system was perceived as more useful and easier to use. Recommender systems are also used in omnichannel contexts. Here, Carnein et al. (2019) developed a system that gathers and integrates data from different online channels, but without in-store context awareness—an important requirement in a brick-and-mortar application (Parise et al., 2016).

Additionally, one should note that ubiquitous devices other than smartphones have been hardly used in brick-and-mortar retailing for enhancing shopping experience. This might be a result of the limited availability of the used devices (e.g. Microsoft HoloLens). In comparison, the smartphone is the ideal device for an ARSAA, which can use recommender systems to ease the customers' path-to purchase. In particular, recommender systems in ARSAA can stimulate multiple motivators of mobile shopping (Huang and Zhou, 2018). As such, it can increase convenience, lead to money saving and bargain hunting and can offer a greater product variety, by giving access to additional information based on interactive product recommendations acknowledging customer preferences. Additionally, ARSAA can display product reviews made by others, thus fostering social interaction, which has been shown to have a significant impact on customer behavior (Dennis et al., 2009). Also, using ARSAA and exploring its product recommendations can motivate users to buy additional products (Ganesh et al., 2010). We therefore hypothesize:


ARSAA assisted brick-and-mortar shopping leads to a higher purchase intention than unassisted brick-and-mortar shopping.

2.2.3 Explainable artificial intelligence in recommender systems

Recommender systems using AI and machine learning models are trained to understand shoppers' behavior for providing useful recommendations (Fernández-García et al., 2019). However, with the ever-increasing amount of transaction data and complexity of AI, these recommender systems have often turned into black boxes for their users (Adadi and Berrada, 2018; Omar et al., 2018). This affects users overall trust in services using recommender systems (Fu et al., 2020). When shoppers receive recommendations, they typically do not know the reasons for these product suggestions, likely affecting consumers' reactions to such proposals. Therefore, XAI research now focuses on making AI predictions more understandable by developing transparent AI models and explanation methods (Adadi and Berrada, 2018). Indeed, explanations have the potential to support user decision-making, enhance customer shopping experience, increase trust, acceptance and adoption of AI-based technologies (Cirqueira et al., 2020b).

Explanations in recommender systems aim to enhance shopping experience through high-quality, interactive and intuitive suggestions, while keeping the recommendations easy to understand for consumers (Wang et al., 2018). In particular, explainable recommendations give the user reasons for the given recommendation (Wang and Benbasat, 2008). Here, evidence indicates that explanations can significantly impact customers' purchase intentions (Chen et al., 2019). This corresponds to Gefen et al. (2003) who showed that trust (in the context of TAM) influences customers' purchase intention during online shopping. Based on a meta-analysis, Kim and Peterson (2017) also demonstrated that trust is a robust indicator for purchase intention. However, although existing literature provides valuable insights into recommender systems and corresponding use cases (e.g. Cheng et al., 2019; He et al., 2015; Huang et al., 2019), a combination of explainable recommendations with AR has not yet been explored in a physical retail context. This motivates this research to hypothesize:


ARSAA assisted shopping using explainable recommendations is perceived as more trustworthy compared to ARSAA assisted shopping not using explainable recommendations.


ARSAA assisted shopping using explainable recommendations is superior in terms of perceived shopping experience and purchase intention compared to ARSAA assisted shopping not using explainable recommendations.

2.3 Conceptual framework

In this research, we compare the effects of different brick-and-mortar shopping scenarios on perceived shopping experience, purchase intention and trust in technology of ARSAA users. Three shopping scenarios are investigated: a regular shopping scenario (RSS) that does not feature the use of an ARSAA in a brick-and-mortar store, an AR shopping scenario (ARSS), in which the user is supported by an ARSAA in a brick-and-mortar store, and an ARSS in which the user is supported by an ARSAA in a brick-and-mortar store, but that additionally uses explainable recommendations (XARSS).

Figure 1 summarizes the research hypotheses in a conceptual framework. In detail, Hypotheses 1-4 and 7 investigate the differences in user perception of shopping experience and Hypotheses 5 and 6 evaluate the differences in user intention and technology evaluation. In brief, Hypotheses 1-5 address the differences between all shopping scenarios, and Hypotheses 6 and 7 specifically assess the differences between ARSS and XARSS.

3. Methodology

This chapter describes how the study was guided by the design science methodology (3.1), how our ARSAA prototype is designed (3.2) and how it was evaluated (3.3).

3.1 Design science research

This research follows a design science methodology originating from information systems research (Gregor and Hevner, 2013). This approach focuses on the development of an artifact that “solves identified organizational problems” (Hevner et al., 2004, p. 77). This methodology allows us to analyze a problem space, extract requirements for a desired problem-solving artifact and match the requirements to design an instantiated prototype. The design science research framework (Hevner et al., 2004; Prat et al., 2014) involves six steps (see Figure 2).

3.1.1 Problem identification and motivation

We started by investigating the problem environment, based on the current state of research, and discussions with practitioners within the PERFORM network, which is a European project and a consortium composed of retailers and universities (Perform-Network.eu, 2022). We clarified the problem as the lack of understanding as to whether ARSAA, with recommender systems and explainable recommendations, can enhance customer shopping experience in brick-and-mortar stores. We found that this lack of understanding creates a barrier for retailers to invest in such technologies in their physical stores.

3.1.2 Definition of the objectives for a solution

The research objective was focused on developing an ARSAA as an artifact and assessing its influence on customer brick-and-mortar store shopping experience. The scope of the research centers on personalized recommendations that retailers can provide to their customers by deploying an in-store based recommender system leveraged by a smartphone-based application. These tailored suggestions build around a particular product with which the customer is interacting, in the form of product recommendations, comparisons and offers. From our literature review and discussions with practitioners, we extracted and categorized the requirements of the ARSAA (see Table 2).

3.1.3 Design and development

This research adopted the framework of Ge and Helfert (2014) to mitigate threats to the validity of the study regarding artifact development, experiment and data analysis. Therefore, to guarantee its validity, we first had to establish the kernel theory, which governs the development of the artifact. We investigated extant taxonomies of explanation methods to select explanation types for AI recommendations to customers (Arrieta et al., 2020; Arya et al., 2019; Mueller et al., 2019; Sokol and Flach, 2020). Here, the focus was on local explanations to clarify the reasons for a particular recommendation.

3.1.4 Demonstration

The ARSAA and recommendations were implemented by following the artifact design by Mora et al. (2020b), which represents a former iteration of this design science project. The authors developed a shopping assistant application and identified the user requirements for such an in-store assistant. The application also focused on the design of recommender systems as the underlying system to provide personalized recommendations and presents product comparisons based on items with which the customer is interacting. This research was inspired by the artifact design of Mora et al. (2020a) to develop the mobile-based ARSAA artifact and user interface mockups.

3.1.5 Evaluation

The evaluation of our artifact aimed at testing the formulated hypothesis and hence contributes to solving the identified problem. We followed the framework of Prat et al. (2014) to conduct the evaluation of the ARSAA artifact. Specifically, a user experiment within three scenarios and an online survey were conducted.

3.1.6 Communication

This study further clarified the evaluation design and results obtained. This demonstrated the varying impact of an ARSAA artifact on the shopping experience of customers in-store. We presented practical and theoretical implications of our study.

3.2 Augmented reality shopping assistant application

The proposed artifact was developed as an application running on an android-based smartphone (R1). In the scenarios, the app was deployed by the retailer and the device was owned by the customer. Hence, it had access to personal information that was required to provide tailored recommendations (e.g. social media, historical purchase data). We conceptualized the artifact to provide augmented content. Anchored around the product of interest, the application displayed recommendations, offers, a comparison of items and a buy button on the smartphone. The artifact provided support to the customer through the shopping journey. As an example, the shopping assistant application could identify the product with which the customer was interacting (see Figure 3) and provide tailored content (R2).

During this stage of the prototype development, the ARSAA used a smartphone camera to detect a customer's object of interest (product). By doing so, the application could monitor the camera's field of view, to determine which product the user was examining at each point in time and to track the item in the physical space while the customer interacts with the product (R3). The involved object recognition was performed using software development kits, e.g. Vuforia (He et al., 2015). When the application recognized the product, it displayed multiple digital buttons anchored around the product that can be triggered by the customer to display relevant content using AR.

The user interface provided support to the customer through the shopping journey by identifying the product with which the customer interacted to provide tailored suggestions. The user interface provided easy navigation and intuitive visualization (see Figure 4).

To provide explainable recommendations (R4), we adopted Zhang and Chen's (2020) classification of explanation methods in the context of recommender systems: (1) user or item-based; (2) feature-level; (3) textual; (4) visual; and (5) social. The impact of XAI types on the ARSAA interface is summarized in Table 3.

Following the taxonomy of Ge and Helfert (2014), we implemented the five explanation methods into our ARSAA as the kernel theory governing the design of the artifact. Each method provided users with an explanation type to support them during the brick-and-mortar shopping journey.

3.3 Evaluation design

The evaluation design of our study was based on an online experiment. In this phase of the project, the artifact has not been evaluated in the real context of the problem or environment (i.e. in-store) because of the early stage of the artifact. Therefore, study participants interacted with an online version and mockup of the ARSAA. This was followed by an evaluation which included an online survey with a quantitative and qualitative assessment of the shopping experience, purchase intention and trust (R5).

3.3.1 Participants

Participants of the within-subject experiment were recruited using the crowdsourcing provider Clickworkercom (2020a). This provider was chosen as it ensured a high level of qualification of the study participants by requiring the use of real personal data, testing of writing and language qualifications and a constant evaluation of their members' response patterns (Clickworkercom, 2020b).

In total, 315 participants from the German-speaking area (Germany, Austria and Switzerland) were recruited. Participants who took less than seven minutes to complete the survey, used the same IP address to answer the survey multiple times, or entered only one word or random letters in the open questions were excluded to ensure data quality. Thus, the final sample size was 252 participants. A demographic overview of the participants is presented in Table 4. No abnormalities of our sample and resulting biases could be identified.

3.3.2 Survey

Our artifact evaluation was conducted by using an online survey, which took place in August 2020. In the survey, participants were introduced to the concept of ARSAA with the help of pictures and videos (the introductory videos provided in the survey can be accessed via the following links: https://www.youtube.com/watch?v=YhJ9QHVN1rs; https://www.youtube.com/watch?v=LCj4Y18O9z4).

Afterward, participants were presented with three different shopping scenarios. In each scenario, products on a shelf (e.g. groceries, luxury chocolate, shoes and books) were shown. The first experimental condition was a RSS, displaying no additional information (see upper-left image of Figure 4). The second condition was an ARSS, displaying augmented recommendations and product comparisons (see top-right and bottom-left image of Figure 4). The third condition was an ARSS with explainable AI features (XARSS), which additionally showed explanations why the recommendations and product comparisons were shown (see bottom-right of Figure 4). Following each condition, participants had to answer questions about how they perceived the shopping experience, how it influenced their overall purchase intention, and, for the second and third condition, how trustworthy they perceived the ARSAA.

For this assessment, the questionnaire included a series of validated constructs by Hausman and Siekpe (2009). Consequently, we measured the perception of the scenarios with the constructs “Usefulness” (4 items), “Entertainment” (3 items), “Informativeness” (3 items), “Irritation” (3 items) and “Purchase Intention” (4 items). Additionally, we measured trust towards ARSS and XARSS by adopting an established scale from Hoffman et al. (2018) (6 items). All items were measured using a 5-point Likert-type scale ranging from “Completely Disagree” to “Completely Agree”. The sequence of the scenarios and the sequence of questions in each scenario were randomly shuffled to avoid order bias. We also asked participants six open-ended questions to get their general opinion about the presented ARSAA. The questionnaire also included standard demographics (six items). The questionnaire was implemented with the survey software SurveyGizmo.com (2020). The original survey is provided in the Supplementary Material S1.

3.3.3 Validity and reliability

To assess validity and reliability of the constructs used, a confirmatory factor analysis was used to test the measurement models in each shopping scenario. It became apparent that in all scenarios one “Usefulness” item showed a misspecified error covariance, indicating a systematic measurement error for this item. Therefore, following Byrne (2010), we excluded the item. Additionally, in the ARSS and XARSS scenarios one trust item did not load on the “Trust” construct and two additional trust items were cross-loading on the factor “Usefulness”. Consequently, we also excluded these items from the measurement models (Byrne, 2010). All three final models showed an appropriate fit:

RSS: X2 (94) = 174.833, RMSEA = 0.059, CFI = 0.969, SRMR = 0.045; ARSS: X2 (137) = 175.585, RMSEA = 0.033, CFI = 0.990, SRMR = 0.028; XARSS: X2 (137) = 168.461, RMSEA = 0.030, CFI = 0.993, SRMR = 0.022.

All items displayed sufficient item to construct loadings (Hair et al., 2008) ranging from 0.609 to 0.926 (see Table 5). Establishing reliability, all constructs showed good Cronbach's α coefficients in all scenarios (Konting et al., 2009), ranging from 0.773 to 0.934 (see Table 6). Verifying convergent validity, composite reliability (>0.7) and average variance extracted (>0.5) exceeded the desired thresholds (Fornell and Larcker, 1981) in all scenarios (see Table 6). As the square root of the average variance extracted for each construct was greater than its highest correlation with any other construct (Hair et al., 2008), discriminant validity was established in the RSS and ARSS scenario. In the XARSS scenario, a violation (difference = 0.06) of discriminant validity could be detected between the construct “Irritation” and “Entertainment”. However, because the violation is minimal, these constructs have been validated by other researchers (e.g. Hausman and Siekpe, 2009; Hoffman et al., 2018), and the RSS and ARSS scenarios did not indicate any violation, we consider discriminant validity as established.

3.3.4 Analysis

As the survey contained more than 30 participants, according to the central limit theorem (Bortz and Schuster, 2010; Kähler, 2004; Tavakoli, 2013), this was sufficient to assume normal distribution, allowing parametric testing. Therefore, we analyzed participants' perceptions of the conditions (“Usefulness”, “Entertainment”, “Informativeness”, “Irritation”) and their “Purchase Intention” using a repeated measurement analysis of variance analysis (rmANOVA). The differences in trust towards ARSS and XARSS were analyzed using a paired sample t-test. When statistically significant differences were identified, we used Bonferroni adjusted post-hoc tests to highlight the differences between the scenarios. Subsequently, we tested the effect size of the discovered differences using Cohen's d. The software SPSS 26 (IBM.com, 2020) was used to analyze the survey data except for the open questions, which were manually reviewed and coded.

4. Results

We present, the quantitative results based on rmANOVA and paired sample t-test (see Table 7) together with the Bonferroni-adjusted post-hoc analyzes and effect sizes (see Table 8). Also, we show the participants' qualitative responses by screening the open questions for observable patterns and general sentiments (see Table 9).

4.1 Quantitative results

Based on the rmANOVA, and the paired sample t-test (see Table 7), significant differences in the perceptions of the three shopping scenarios were identified: “Usefulness” (p < 0.001), “Entertainment” (p < 0.001), “Informativeness” (p < 0.001), “Irritation” (p < 0.001). In contrast, no differences could be found with respect to “Purchase Intention” (p = 0.240), and “Trust” (p = 0.228).

The Bonferroni-adjusted post-hoc analyses (see Table 8) showed that participants rated “Usefulness”, “Entertainment”, “Informativeness” and “Irritation” significantly higher in ARSS and XARSS compared to RSS. Additionally, “Usefulness” and “Informativeness” were rated significantly higher in XARSS compared to ARSS. Concerning “Entertainment” and “Irritation” no statistically significant difference between ARSS and XARSS could be identified. Except for the construct “Usefulness”, the effect sizes for all significant differences ranged from d = −0.277 to d = −0.470, which, according to Cohen (1992), represents a small to medium effect. Regarding the construct “Usefulness” the significant difference between RSS and ARSS signaled a small to medium effect (d = −0.445), the difference between RSS and XARSS showed a medium to large effect (d = −0.555) and the difference between ARSS and XARSS suggested a small effect (d = −0.126).

Regarding H1, H2 and H3, we conclude that ARSAA had a positive influence on perceived shopping experience, as the constructs “Usefulness”, “Entertainment” and “Informativeness” were all significantly higher in the ARSAA assisted shopping conditions (ARSS and XARSS) than in the unassisted condition (RSS). However, this positive effect might indeed be diminished as the ARSAA assisted shopping scenarios also showed a significantly higher level of consumer “Irritation” supporting H4.

H5 was not supported as no significant differences were found between participants' “Purchase Intention” across the different scenarios. Similarly, H6 was not supported. Here the data did not reveal a significant difference in the “Trust”-level for the two ARSAA assisted shopping scenarios.

H7 was partially supported as “Usefulness” and “Informativeness” were rated significantly higher in ARSAA assisted shopping scenarios using explainable recommendations than in ARSAA assisted shopping scenarios not using explainable recommendations. However, for the constructs “Entertainment” and “Irritation” no significant influence of explainable recommendation could be observed.

4.2 Qualitative results

Answers from the qualitative questions were coded by patterns (i.e. recurring sentiments) given in the answers entered in the free text field. Results from qualitative data analysis are summarized in Table 9. It presents the general sentiment, most frequent patterns (defined as more than five text passages with a specific sentiment) and an example statement for each pattern.

5. Discussion

Our study revealed significant differences between the evaluated shopping scenarios (R5). In particular, participants evaluate RSS significantly lower in terms of “Usefulness”, “Entertainment”, “Information” and “Irritation” when compared to ARSS and XARSS. These results demonstrate that ARSAA is indeed able to positively influence the customer shopping experience. The observed effect sizes range from small to medium. This should be considered when interpreting the results. However, the difference between RSS and XARSS showed a large effect regarding “Usefulness”, which demonstrates that using ARSAA can indeed strongly support customers during shopping. Considering that PU is the key determinant of technology acceptance decisions (see the results of meta-analyses and reviews, e.g. King and He, 2006; Lee et al., 2003), this finding is of paramount importance.

As a complement to the quantitative data, our qualitative data provide evidence for the positive effects of ARSAA on shopping experience. Drawing from the results of Q6, most participants could imagine using ARSAA during one of their shopping scenarios and only a minority had no interest at all. Additionally, we observed that nearly half the participants of our survey would be motivated by ARSAA to visit a brick-and-mortar store (Q4). The main reasons being the availability of additional information, the fun of using the interactive application and the increase in shopping speed. Furthermore, most participants stated that ARSAA could make their shopping trip more secure (Q3) as it would require touching fewer things, interacting with fewer people and possibly speeding up their shopping trip. Q1 and Q2 demonstrate that the presented ARSAA can be improved by design and functionality changes, and by including features like in-depth product information, price comparison and personalization options which could further increase effectiveness.

In contrast to previous research (Hilken et al., 2017; Javornik, 2016; Poushneh and Vasquez-Parraga, 2017; Yim et al., 2017), our study could not detect a statistically significant difference in “Purchase Intention” when comparing RSS, ARSS and XARSS. However, we argue that the cumulative effect of increased usefulness, entertainment and information, added to the positive sentiment expressed in the open questions Q3 and Q4 and the demonstrated confidence of using ARSAA in various shopping scenarios in Q6, provides notable evidence that ARSAA can be used to influence in-store shopping experience positively. Additionally, we argue that the strength of these effects could even be improved by future versions of ARSAA, if the participants' feedback (see Q1 and Q2) is taken into consideration.

Analyzing the impact of explainable recommendations on “Trust”, in contrast to previous studies (Chen et al., 2019; Cirqueira et al., 2020b), no significant difference between XARSS and ARSS could be observed. However, partly in line with Cirqueira et al. (2020b), our study found further evidence that explainable recommendations can increase shopping experience as XARSS rated significantly higher in terms of “Usefulness” and “Entertainment”. This is also supported by the sentiment expressed in Q5, which shows that the explainable recommendations were perceived as helpful by the majority of participants.

Summarizing, in response to RQ1, it is evident that ARSAA can influence a customer's shopping experience by significantly influencing perceived “Usefulness”, “Entertainment”, “Information” and “Irritation”. In response to RQ2, it became apparent that improving the design and functionality, while including features like in-depth product information, price comparison and personalization options, would improve the overall satisfaction with ARSAA and, therefore, enhance its influence on shopping experience.

5.1 Theoretical contributions

This study offers theoretical contributions to advance the state of research in the field of personalized recommendations provided by an AR application in brick-and-mortar stores. Our literature review shows that no previous studies have evaluated the impact of such an application on in-store shopping experience. Hence, our results widen this understanding by providing experimental results. Therefore, this study demonstrates that ARSAA assisted shopping is perceived as more useful, entertaining and informative than unassisted shopping. Contrary to TAM studies (e.g. Gefen et al., 2003; Hausman and Siekpe, 2009) and UGT studies (e.g. E. Huang, 2008; Luo, 2002), our examination shows that this increase in customer shopping experience neither converted into an increase of purchase intention nor into intention to visit the store. Interestingly, although an increase of “Irritation” could be observed, its impact on shopping experience was not strong enough to cause an overall decrease in shopping experience, as suggested by previous studies (Chen and Wells, 1999; Ducoffe, 1996). Thus, this study provides evidence that, in the domain of “augmented reality applications used in digital retail”, classical theories such as TAM and UGT do not sufficiently account for the specific circumstances (e.g. connection of online and offline customer journey through smartphone applications) typical for digital retail to adequately describe the influence of the investigated perceptual constructs on customer shopping experience and purchase intention. This is line with recent research (for a recent review, see Sindermann et al., 2020), which suggests that, e.g. user personality has a significant impact on technology acceptance particularly in the more modern retailing contexts such as online shopping and omnichannel shopping (Hermes et al., 2022; Hermes and Riedl, 2021). Therefore, our study contributes to the further development of TAM and UGT by highlighting short comings of these theories, specifically concerning the interaction of customer experience, purchase intention and customer irritation in the omnichannel domain.

From a methodological perspective, the study illustrates the benefits of using design science methodology for solving real-world problems by designing an artifact with justified requirements that were extracted from an extensive literature review and discussions with retail experts. Hence, this research provides design principles and practices for developing an ARSAA. Scholars and practitioners, especially from the field of interactive marketing, can use this artifact to create advanced versions of ARSAA that can further increase shopping experience. Additionally, the study facilitates the need for user-centric experiments, which evaluate the impact of ARSAA artifacts in real world scenarios.

5.2 Practical implications

ARSAA provides an opportunity for retailers to enhance their customers' shopping experience, as it provides a more useful, entertaining and informative experience. Additionally, this study shows that ARSAA using explainable recommendations (XARSS) can increase the customers perceived “Usefulness” and “Informativeness” even further. Therefore, implementing explainable recommendations into AR applications is recommended.

Interestingly, explainable recommendations did not increase “Trust” in ARSAA. Consequently, we cannot advise retailers to implement explainable recommendations if their only goal is to increase trust in their AR application. Furthermore, in line with (Yim and Park, 2019), the results also show that an ARSAA can increase “Irritation”, which must be considered by retailers who want to implement such technology. To decrease irritation, retailers should focus on their customers' needs, requirements and capabilities when developing ARSAA. Therefore, they should put emphasis on optimizing the features and functionalities of ARSAA to ensure the creation of a smooth and easy-to-use application (Apple, 2022; Google, 2022).

Additionally, our study could not detect a significant, direct impact of ARSAA on purchase intention. However, as ARSAA positively influences shopping experience, an important part of the path to purchase, we still recommend that retailers who seek to increase their sales implement this technology.

Strategically, we recommend digital retailers to provide ARSAA to their brick-and-mortar customers as it has the potential to increase competitive power against online pure players. In particular, digital retailers could use explainable recommendations to initiate cross-selling or up-selling directly at the point of sale by giving customers the chance to access the entire product portfolio in a useful, entertaining, informative and interactive way. In this regard, the marketing department of a company must deliver the right interactive experience by presenting the correct product recommendations on customers' smartphones.

5.3 Limitations and future research

As with all research, our study has limitations, which provide avenues for future research. We restricted our assessment to an online evaluation approach. Therefore, future studies should test actual usage of the technology in a real brick-and-mortar store thereby increasing external validity and allowing the evaluation of additional constructs that require participants to use the technology in a real-life setting (e.g. flow). Importantly, “flow” has shown to have a strong influence on purchase intention (Hausman and Siekpe, 2009; Huang and Liao, 2017; Javornik, 2016). Thus, we call for future studies assessing the impact on important outcome variables (e.g. purchase intention) eventually complemented by further downstream variables such as actual purchase behavior. Such a future endeavor should also incorporate the knowledge gained from this study. The insights from Q1 and Q2 in particular can be used to improve the design of the ARSAA. Additionally, the fact that XARSS is perceived as much more useful than RSS should inspire future research to investigate the benefits of providing XARSS in brick-and-mortar stores. In this regard, it should also be noted that, according to the results of this study, theories like TAM and UGT might not give a complete picture of the impact of the investigated constructs on purchase intention or customer experience in the context of AR applications in the digital retail domain. Thus, future research might advance these theories to make them applicable in the emerging field of AR. In this regard, future research should especially consider personality as a direct determinant, or as moderator variable, in studies on ARSAA in brick-and-mortar stores as it has shown to have a significant influence technology acceptance (Hermes et al., 2022; Hermes and Riedl, 2021).

Additionally, we did not measure the impact of privacy concerns on shopping experience or actual usage behavior. However, especially for European retailers who must comply with the General Data Protection Regulations, this is an important area to be investigated by future research.

6. Conclusion

In this study, we followed a design science research methodology and tested the influence of an ARSAA artifact on consumers' in-store shopping experience. The results illustrate that using an ARSAA that uses personalized recommendations and XAI features can indeed increase the shopping experience by providing higher levels of interactivity, making it an important avenue of future research in the field of interactive marketing.

To conclude, as the retail sector moves forward and most brick-and-mortar retailers face challenges to remain competitive, this study can serve as a foundation when assessing the influence of an ARSAA on shopping experience and the design of such an application. It will be rewarding to see what insights future design science initiatives will reveal.


Conceptual framework

Figure 1

Conceptual framework

Design science research methodology in this study

Figure 2

Design science research methodology in this study

Demonstration of the application (left) and a prototype interface screenshot (right)

Figure 3

Demonstration of the application (left) and a prototype interface screenshot (right)

Screenshot of the interface of the functional prototype. In this example, book suggestions are provided with a social explanation based on friends' reviews

Figure 4

Screenshot of the interface of the functional prototype. In this example, book suggestions are provided with a social explanation based on friends' reviews

Literature review summary

SourceType of studyContribution to literature*
Löchtefeld et al. (2013)Empirical – ExperimentEvaluated the effectiveness of personalized recommendations in augmented reality applications in the context of increasing paper leaflet views
Hiranandani et al. (2017), Tanmay and Ayush (2019)Empirical – ExperimentImproved visual representation of personalized recommendations in augmented reality applications
Cruz et al. (2019)Empirical – ExperimentTested the general applicability of personalized recommendations in augmented reality applications
Márquez and Ziegler (2020)Empirical – ExperimentEvaluated the acceptance of personalized recommendations in augmented reality applications when shopping for home appliances
Joerβ et al. (2021)Empirical – ExperimentEvaluated the effectiveness of personalized recommendations in augmented reality applications shifting customer product choice to sustainable products
Lavoye et al. (2021)Conceptual – Literature ReviewHighlighted lack of studies addressing personalized recommendations in augmented reality applications

Note(s): *Regarding personalized recommendations in augmented reality applications

Identified requirements of the artifact (the ARSAA)

RequirementsDescriptionSolution in section
R1DeviceThe retailer needs to select the device to be used to enable the shopping assistant application3.2
R2Personalized experienceThe app is required to provide recommendations of products to the users based on authorized personal information3.2
R3Brick-and-Mortar focusThe shopping assistant application should be context-aware to enable recommendations for shoppers in physical stores3.2
R4Explainable recommendationsThe application should provide real-time explanations of the underlying decision-making for the items recommended3.2
R5Artifact evaluationThe shopping assistant application should be evaluated based on potential customers5

Explanation methods for recommendations and their impact on the ARSAA interface

XAI typesImpact on ARSAA interface
User or Item-BasedExplains that other customers frequently buy certain products together or that a set of items are similar to each other
Feature-LevelRelevant features of an item are displayed, e.g. a nutritional table from a cookie
TextualA box appears in the interface next to the recommendations with an explanation in text
VisualThe customer perceives important features on the representation of a product highlighted
SocialThe explanations are visualized by a friend's or social media comment or by an aggregated rating


N% N%
Age (Mage = 37.38 years; σ = 12.06)Country of Origin (GER 229; AUT 17; SUI 6)
Gender Education
Male11846.8%High School166.3%
Female13252.4%Graduated High School6927.4%
Diverse20.8%College or University9738.5%
Master or Doctorate7027.8%
Net household income (per month in EUR)
<1,0006325.0%Shopping frequency (last 30 days)
1,000 and < 20008533.7%Not at all frequently259.9%
2000 and < 3,0005321.0%Slightly frequently6325.0%
3,000 and < 4,0002911.5%Moderately frequently11545.6%
4,000 and < 5,000156.0%Very Frequently4216.7%
5,000 and above72.8%Extremely Frequently72.8%
Total252100% 252100%

Item to construct loadings

ScaleItemFactor loading
UsefulnessThis scenario can improve my shopping performance0.8420.8850.920
This scenario can increase my shopping productivity0.8660.8920.882
This scenario can increase my shopping effectiveness0.8600.8860.895
EntertainmentThe shown scenario is enjoyable0.8390.8850.897
The shown scenario is pleasing0.8200.8390.884
This scenario is entertaining0.7620.8170.756
InformativenessThe shown scenario offers a good source of product information0.8630.8650.854
This scenario supplies relevant information0.7860.8880.862
This scenario is informative concerning the shown products0.8800.8170.834
IrritationThe shown scenario is annoying0.6500.7760.759
The shown scenario is frustrating0.8960.8700.841
This scenario is irritating0.6090.8570.864
Purchase IntentionI would definitely buy products in this scenario0.7740.8460.821
I would intend to purchase products in this scenario in the near future0.8300.8970.876
If it would exist today, it is likely that I would purchase products in this scenario in the near future0.8990.9130.926
I would expect to purchase products in this scenario in the near future if it would exist today0.8680.8730.901
TrustI am confident in the application. I feel that it works wellN/A0.8070.855
The application seems very reliableN/A0.7880.840
I feel safe that when I rely on the application, I get the right informationN/A0.7740.835

Note(s): RSS (Regular Shopping Scenario), ARSS (Augmented Reality Shopping Scenario), XARSS (Augmented Reality Shopping Scenario with Explainable Artificial Intelligence)

Construct reliability and validity

1. Purchase Intention0.9070.9080.7120.844
2. Usefulness0.8910.8920.7330.6230.856
3. Entertainment0.8440.8490.6520.6510.7920.808
4. Informativeness0.8810.8810.7120.5740.7450.6790.844
5. Irritation0.7730.7680.532−0.453−0.434−0.505−0.4490.729
1. Purchase Intention0.9340.9340.7790.883
2. Usefulness0.9170.9180.7880.7680.888
3. Entertainment0.8840.8840.7180.7190.7940.847
4. Informativeness0.8910.8920.7350.6400.7430.6950.857
5. Irritation0.8730.8740.698−0.617−0.681−0.674−0.5880.835
6. Trust0.8330.8330.6240.7410.7580.7860.759−0.6700.790
1. Purchase Intention0.9330.9330.7780.882
2. Usefulness0.9260.9270.8080.8560.899
3. Entertainment0.8830.8840.7190.8110.8370.848
4. Informativeness0.8860.8870.7230.7720.8300.7630.850
5. Irritation0.8630.8620.677−0.697−0.742−0.829−0.6920.823
6. Trust0.8810.8810.7110.7780.7930.7930.771−0.6710.843

Note(s): α (Cronbach's alpha), CR (Composite reliability), AVE (Average Variance Extracted), RSS (Regular Shopping Scenario), ARSS (Augmented Reality Shopping Scenario), XARSS (Augmented Reality Shopping Scenario with Explainable Artificial Intelligence). Figures on the diagonal represent the square root of the average variance extracted of the corresponding factor

rmANOVA and paired sample t-test

ConstructScenarioMeanStd. Dev.df/errorF*/t**Sig
Purchase Intention*RSS3.16370.867891.448/363.4421.4310.240

Note(s): rmAnova with Greenhouse-Geisser correction, significance level is 0.05; *(rmANOVA), **(paired sample t-test), RSS (Regular Shopping Scenario), ARSS (Augmented Reality Shopping Scenario), XARSS (Augmented Reality Shopping Scenario with Explainable Artificial Intelligence)

Bonferroni-adjusted post-hoc tests and effect sizes

ConstructScenarioMDSESigCohen's d
UsefulnessRSS – ARSS−0.4050.077<0.001−0.445
RSS – XARSS−0.5320.083<0.001−0.555
ARSS – XARSS−0.1270.0450.015−0.126
EntertainmentRSS – ARSS−0.2790.063<0.001−0.321
RSS – XARSS−0.3510.068<0.001−0.395
ARSS – XARSS−0.0710.0480.424N/A
InformativenessRSS – ARSS−0.4300.071<0.001−0.470
RSS – XARSS−0.7450.075<0.001−0.389
ARSS – XARSS−0.3150.053<0.001−0.358
IrritationRSS – ARSS−0.2570.0730.002−0.277
RSS – XARSS−0.2330.0760.007−0.246
ARSS – XARSS0.0240.0501.000N/A

Note(s): Significance level is 0.05, significance values adjusted for multiple comparisons (Bonferroni); MD (Mean Difference), SE (Standard Error) RSS (Regular Shopping Scenario), ARSS (Augmented Reality Shopping Scenario), XARSS (Augmented Reality Shopping Scenario with explainable Artificial Intelligence)

Qualitative results (open question review)

QSNo.MCodeNo.MExample statement
Q1Yes109Product Information50“Yes, like the detailed nutritional information, calories, etc. ….”
Price Comparison32“Comparison with other stores for price”
Personalization14“Customizability of the app, change appearance, sizes of the UI elements”
No131All fine126“No, I don't miss any features”
Overloaded8“No, it's already quite overloaded with information.”
Q2Yes141App Design63“The design of the tiles that pop up around the product could be improved”
Missing Features31“Calculator for food waste reduction”
App Functionality26“Internet connection in some malls is very bad”
Q3Yes124Less need to touch49“Yes, because you don´t need to touch everything”
Less need to interact37“Yes, because I do not need to talk to the shop assistant”
Effective Shopping25“Yes, because then my shopping would be faster”
No106No Benefit73“No, because I still have to go to the store”
Time Waste16“No, even worse because I would stay in the shop for longer”
Q4Yes95Information44“Yes, because I can get more Information easily”
Fun19“Yes, fun to use”
Faster8“Yes, it makes shopping faster”
No117Don't need this34“No because in-store I get enough information”
Online Shopper21“No, Online shopping would still be easier”
Time Waste16“No, it would take too much time while using the application”
Q5Yes191Get Information40“Yes, because it shows what I want to know about the product”
Ease of Use29“Yes, they were clear and easy to understand”
Recommendations10“I find the explanations helpful because they make the recommendations lucid for me”
No38Unnecessary10“I do not really want to see what my friends think about a specific product”
Overloaded7“It's too much information”
Q6N/AElectronics108“Probably electronics because it's not always easy to find the relevant information for these products”
Groceries72“Grocery. I like to check the ingredients. Sometime I have to look up E-numbers ….”
Clothing38“I guess I would most likely use that app for shopping clothing (shoes) in order to check for the sizes and measurements”
Would not use it31“I would not use this application”
All Scenarios18“In all retail shopping situations”
Luxury13“I would use it for more expensive products and for products which I buy not very often”
Books6“Books, seeing my friends and other peoples recommendations”

Note(s): Q (Question), S (Sentiment), No. M (Number of Mentions); Q1. Looking at the presented application, are there features you are missing?; Q2. Do you see any issues or room for improvement when using this app? If yes, could you give examples?; Q3. Would this application help to make your shopping trip more secure during COVID19? If yes, why and if no, why not?; Q4. Would this application motivate you to shop in-store? If yes, why and if no, why not?; Q5. Did you find the explanations given by the application helpful? If yes, why and if no, why not?; Q6. In which shopping scenario (grocery/electronics/luxury/clothing/etc.) would you most likely use such an application?

Supplementary Materials

The supplementary file is available online for this article.


Adadi, A. and Berrada, M. (2018), “Peeking inside the black-box: a survey on explainable artificial intelligence (XAI)”, IEEE Access, Vol. 6, pp. 52138-52160, doi: 10.1109/ACCESS.2018.2870052.

Amatriain, X. and Basilico, J. (2015), “Recommender systems in industry: a Netflix case study”, in Recommender Systems Handbook, 2nd ed., Springer, Boston, doi: 10.1007/978-1-4899-7637-6.

Apple (2022), “Human interface guidelines - design - apple developer”, available at: https://developer.apple.com/design/human-interface-guidelines/.

Arora, N., Liu, W.W., Robinson, K., Stein, E., Ensslen, D., Fiedler, L. and Schüler, G. (2021), “The value of getting personalization right or wrong is multiplying”, McKinsey & Company, available at: https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/the-value-of-getting-personalization-right-or-wrong-is-multiplying.

Arrieta, A.B., Díaz-Rodríguez, N., Ser, J.D., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R. and Herrera, F. (2020), “Explainable Artificial Intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI”, Information Fusion, Vol. 58 No. 1, pp. 82-115.

Arya, V., Bellamy, R.K.E., Chen, P.Y., Dhurandhar, A., Hind, M., Hoffman, S.C., Houde, S., Vera Liao, Q., Luss, R., Mojsilović, A., Mourad, S., Pedemonte, P., Raghavendra, R., Richards, J., Sattigeri, P., Shanmugam, K., Singh, M., Varshney, K.R., Wei, D. and Zhang, Y. (2019), One Explanation Does Not Fit All: A Toolkit and Taxonomy of Ai Explainability Techniques, IBM Research - ArXiv, available at: https://arxiv.org/abs/1909.03012.

Baabdullah, A.M., Alsulaimani, A.A., Allamnakhrah, A., Alalwan, A.A., Dwivedi, Y.K. and Rana, N.P. (2022), “Usage of augmented reality (AR) and development of e-learning outcomes: an empirical evaluation of students' e-learning experience”, Computers and Education, Vol. 177, p. 104383, doi: 10.1016/j.compedu.2021.104383.

Berman, B. (2018), “Flatlined: combatting the death of retail stores”, Business Horizons, Vol. 62 No. 1, doi: 10.1016/j.bushor.2018.08.006.

Bortz, J. and Schuster, C. (2010), Statistik für Human- und Sozialwissenschaftler, Springer Berlin Heidelberg, doi: 10.1007/978-3-642-12770-0.

Briedis, H., Kronschnabl, A., Rodriguez, A. and Ungerman, K. (2020), Adapting to the Next Normal in Retail: the Customer Experience Imperative, McKinsey & Company, available at: http://dln.jaipuria.ac.in:8080/jspui/bitstream/123456789/1510/1/Adapting-to-the-next-normal-in-retail-the-customer-experience-imperative.pdf.

Byrne, B. (2010), Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming, 2nd ed., pp. 114-118, available at: https://www.amazon.com/Structural-Equation-Modeling-Barbara-Byrne/dp/B01CNWB3N0.

Bureau, U.S.C. (2018), “Department store sales (excluding leased departments) in the United States from 1992 to 2016”, in billion U.S. dollars, available at: https://www.statista.com/statistics/197712/annual-department-store-sales-in-the-us-since-1992/.

Capgemini (2020), “Share of global online population who say they are comfortable doing select activities online as of February 2020”, Statista, available at: https://www.statista.com/statistics/1132228/share-offline-population-worldwide-comfort-using-internet/.

Carnein, M., Homann, L., Trautmann, H. and Vossen, G. (2019), “A recommender system based on Omni-channel customer data”, 2019 IEEE 21st Conference on Business Informatics (CBI), pp. 65-74, doi: 10.1109/CBI.2019.00015.

Carroll, D. and Guzman, I. (2013), “The new omni-channel approach to serving customers”, Accenture, pp. 1-16, available at: https://scholar.google.com/scholar?cluster=18038375230090677065&hl=de&as_sdt=0,5.

Central Statistics Office (2020), “Type of internet activities - CSO - Central Statistics Office”, CSO, available at: https://www.cso.ie/en/releasesandpublications/ep/p-isshh/informationsocietystatistics-households2020/typeofinternetactivities/.

Chen, Q. and Wells, W.D. (1999), “Attitude toward the site”, Journal of Advertising Research, Vol. 39 No. 5, pp. 27-38, available at: https://go.gale.com/ps/i.do?id=GALE%7CA111508662&sid=googleScholar&v=2.1&it=r&linkaccess=abs&issn=00218499&p=AONE&sw=w&userGroupName=anon∼26a3659a.

Chen, X., Zhang, Y. and Qin, Z. (2019), “Dynamic explainable recommendation based on neural attentive models”, Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, pp. 53-60, doi: 10.1609/aaai.v33i01.330153.

Cheng, Z., Chang, X., Zhu, L., Kanjirathinkal, R.C. and Kankanhalli, M. (2019), “MMalfM: explainable recommendation by leveraging reviews and images”, ACM Transactions on Information Systems, Vol. 37 No. 2, doi: 10.1145/3291060.

Cirqueira, D., Hofer, M., Nedbal, D., Helfert, M. and Bezbradica, M. (2020a), “Customer purchase behavior prediction in e-commerce: a conceptual framework and research agenda”, in Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11948 LNAI, pp. 119-136, doi: 10.1007/978-3-030-48861-1_8.

Cirqueira, D., Nedbal, D., Helfert, M. and Bezbradica, M. (2020b), “Scenario-based requirements elicitation for user-centric explainable AI”, Lecture Notes in Computer Science, Vol. 12279, pp. 321-341, doi: 10.1007/978-3-030-57321-8_18.

Clickworker.com (2020a), “AI training data and other data management services”, available at: https://www.clickworker.com/.

Clickworker.com (2020b), “Our crowd - the clickworkers”, available at: https://www.clickworker.com/clickworker-crowd/.

Cohen, J. (1992), “A power primer”, Psychological Bulletin, Vol. 112 No. 1, pp. 155-159, doi: 10.1037/0033-2909.112.1.155.

Cruz, E., Orts-Escolano, S., Gomez-Donoso, F., Rizo, C., Rangel, J.C., Mora, H. and Cazorla, M. (2019), “An augmented reality application for improving shopping experience in large retail stores”, Virtual Reality, Vol. 23 No. 3, pp. 281-291, doi: 10.1007/s10055-018-0338-3.

Davis, F.D. (1989), “Perceived usefulness, perceived ease of use, and user acceptance of information technology”, MIS Quarterly, Vol. 13 No. 3, p. 319, doi: 10.2307/249008.

Dennis, C., Merrilees, B., Jayawardhena, C. and Tiu Wright, L. (2009), “E-consumer behaviour”, European Journal of Marketing, Vol. 43 Nos 9/10, pp. 1121-1139, doi: 10.1108/03090560910976393.

Ducoffe, R.H. (1995), “How consumers assess the value of advertising”, Journal of Current Issues and Research in Advertising, Vol. 17 No. 1, pp. 1-18, doi: 10.1080/10641734.1995.10505022.

Ducoffe, R.H. (1996), “Advertising value and advertising on the web”, Journal of Advertising Research, Vol. 36 No. 5, pp. 21-32, available at: https://slidelegend.com/advertising-value-and-advertising-on-the-web-blog-management_5a85ffde1723dd53ce733391.html.

Eriksson, N., Rosenbröijer, C.-J. and Fagerstrøm, A. (2018), “Smartphones as decision support in retail stores – the role of product category and gender”, Procedia Computer Science, Vol. 138, pp. 508-515, doi: 10.1016/j.procs.2018.10.070.

Fang, B., Liao, S., Xu, K., Cheng, H., Zhu, C. and Chen, H. (2012), “A novel mobile recommender system for indoor shopping”, Expert Systems with Applications, Vol. 39 No. 15, pp. 11992-12000, doi: 10.1016/j.eswa.2012.03.038.

Fernández-García, A.J., Iribarne, L., Corral, A., Criado, J. and Wang, J.Z. (2019), “A recommender system for component-based applications using machine learning techniques”, Knowledge-Based Systems, Vol. 164, pp. 68-84, doi: 10.1016/j.knosys.2018.10.019.

Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, p. 39, doi: 10.2307/3151312.

Fu, Z., Xian, Y., Gao, R., Zhao, J., Huang, Q., Ge, Y., Xu, S., Geng, S., Shah, C., Zhang, Y. and de Melo, G. (2020), Fairness-Aware Explainable Recommendation over Knowledge Graphs, pp. 69-78, ArXiv.

Ganesh, J., Reynolds, K.E., Luckett, M. and Pomirleanu, N. (2010), “Online shopper motivations, and e-store attributes: an examination of online patronage behavior and shopper typologies”, Journal of Retailing, Vol. 86 No. 1, pp. 106-115, doi: 10.1016/j.jretai.2010.01.003.

Ge, M. and Helfert, M. (2014), “A design science oriented framework for experimental research in information quality”, pp. 145-154, doi: 10.1007/978-3-642-55355-4_15.

Gefen, D., Krahanna, E. and Straub, D.W. (2003), “Trust and TAM in online shopping: an integrated model”, MIS Quarterly, Vol. 27 No. 1, p. 51, doi: 10.2307/30036519.

Google (2022), “Design for android | android developers”, available at: https://developer.android.com/design.

Gregor, S. and Hevner, A.R. (2013), “Positioning and presenting design science research for maximum impact”, MIS Quarterly, Vol. 37 No. 2, pp. 337-355.

Hair, J.F., Black, W.C., Babin, B.J. and Anderson, R.E. (2008), in Multivariate Data Analysis: Global Edition, 7th ed., Pearson Education, Upper Saddle River, available at: https://lib.ugent.be/catalog/rug01:001321386.

Hausman, A.V. and Siekpe, J.S. (2009), “The effect of web interface features on consumer online purchase intentions”, Journal of Business Research, Vol. 62 No. 1, pp. 5-13, doi: 10.1016/j.jbusres.2008.01.018.

He, X., Chen, T., Kan, M.Y. and Chen, X. (2015), “TriRank: review-aware explainable recommendation by modeling aspects”, International Conference on Information and Knowledge Management, Proceedings, Vol. 19-23, pp. 1661-1670, Oct-2015, doi: 10.1145/2806416.2806504.

Hermes, A. and Riedl, R. (2021), “Influence of personality traits on choice of retail purchasing channel: literature review and research agenda”, Journal of Theoretical and Applied Electronic Commerce Research, Vol. 16 No. 7, pp. 3299-3320, doi: 10.3390/jtaer16070179.

Hermes, A., Sindermann, C., Montag, C. and Riedl, R. (2022), “Exploring online and in-store purchase willingness: associations with the big five personality traits, trust, and need for touch”, Frontiers in Psychology. doi: 10.3389/fpsyg.2022.808500 (in press).

Hevner, A.R., Esearch, S.Y.R., March, S.T., Park, J. and Ram, S. (2004), “Design science in information systems research”, MIS Quarterly, Vol. 28 No. 1, pp. 75-105.

Hilken, T., de Ruyter, K., Chylinski, M., Mahr, D. and Keeling, D.I. (2017), “Augmenting the eye of the beholder: exploring the strategic potential of augmented reality to enhance online service experiences”, Journal of the Academy of Marketing Science, Vol. 45 No. 6, pp. 884-905, doi: 10.1007/s11747-017-0541-x.

Hiranandani, G., Ayush, K., Varsha, C., Sinha, A., Maneriker, P. and Maram, S.V.R. (2017), “[POSTER] enhanced personalized targeting using augmented reality”, 2017 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct), Vol. 69–74, doi: 10.1109/ISMAR-Adjunct.2017.34.

Hoffman, R.R., Mueller, S.T., Klein, G. and Litman, J. (2018), “Metrics for explainable AI: challenges and prospects”, available at: http://arxiv.org/abs/1812.04608.

Huang, E. (2008), “Use and gratification in e-consumers”, Internet Research, Vol. 18 No. 4, pp. 405-426, doi: 10.1108/10662240810897817.

Huang, T.-L. and Liao, S.-L. (2017), “Creating e-shopping multisensory flow experience through augmented-reality interactive technology”, Internet Research, Vol. 27 No. 2, pp. 449-475, doi: 10.1108/IntR-11-2015-0321.

Huang, J. and Zhou, L. (2018), “Timing of web personalization in mobile shopping: a perspective from Uses and Gratifications Theory”, Computers in Human Behavior, Vol. 88, pp. 103-113, doi: 10.1016/j.chb.2018.06.035.

Huang, X., Fang, Q., Qian, S., Sang, J., Li, Y. and Xu, C. (2019), “Explainable interaction-driven user modeling over knowledge graph for sequential recommendation”, MM 2019 - Proceedings of the 27th ACM International Conference on Multimedia, pp. 548-556, doi: 10.1145/3343031.3350893.

IBM.com (2020), “IBM SPSS Statistics 26”, available at: https://www.ibm.com/support/pages/downloading-ibm-spss-statistics-26.

ICSC (2018), “Sales growth of department stores in Western Europe from 2012 to 2017, by country”, available at: https://www.statista.com/statistics/911107/department-store-sales-growth-western-europe/.

Javornik, A. (2016), “It's an illusion, but it looks real!' Consumer affective, cognitive and behavioural responses to augmented reality applications”, Journal of Marketing Management, Vol. 32 Nos 9–10, pp. 987-1011, doi: 10.1080/0267257X.2016.1174726.

Joerß, T., Hoffmann, S., Mai, R. and Akbar, P. (2021), “Digitalization as solution to environmental problems? When users rely on augmented reality-recommendation agents”, Journal of Business Research, Vol. 128, pp. 510-523, doi: 10.1016/j.jbusres.2021.02.019.

Juaneda-Ayensa, E., Mosquera, A. and Murillo, Y.S. (2016), “Omnichannel customer behavior: key drivers of technology acceptance and use and their effects on purchase intention”, Frontiers in Psychology, Vol. 7, pp. 1-11, doi: 10.3389/fpsyg.2016.01117.

Kähler, W.-M. (2004), Statistische Datenanalyse, Vieweg+Teubner Verlag, Wiesbaden, doi: 10.1007/978-3-663-11498-7.

Katz, E., Blumler, J.G. and Gurevitch, M. (1973), “Uses and gratifications research”, Public Opinion Quarterly, Vol. 37 No. 4, p. 509, doi: 10.1086/268109.

Kim, Y. and Peterson, R.A. (2017), “A meta-analysis of online trust relationships in E-commerce”, Journal of Interactive Marketing, Vol. 38, pp. 44-54, doi: 10.1016/j.intmar.2017.01.001.

King, W.R. and He, J. (2006), “A meta-analysis of the technology acceptance model”, Information and Management, Vol. 43 No. 6, pp. 740-755, doi: 10.1016/j.im.2006.05.003.

Konting, M.M., Kamaruddin, N. and Man, N.A. (2009), “Quality assurance in higher education institutions: exist survey among Universiti Putra Malaysia graduating students”, International Education Studies, Vol. 2 No. 1, doi: 10.5539/ies.v2n1p25.

Lavoye, V., Mero, J. and Tarkiainen, A. (2021), “Consumer behavior with augmented reality in retail: a review and research agenda”, The International Review of Retail, Distribution and Consumer Research, Vol. 31 No. 3, pp. 299-329, doi: 10.1080/09593969.2021.1901765.

Lee, Y., Kozar, K.A. and Larsen, K.R.T. (2003), “The technology acceptance model: past, present, and future”, Communications of the Association for Information Systems, Vol. 12, doi: 10.17705/1cais.01250.

Lee, H., Jung, T.H., tom Dieck, M.C. and Chung, N. (2020), “Experiencing immersive virtual reality in museums”, Information and Management, Vol. 57 No. 5, p. 103229, doi: 10.1016/j.im.2019.103229.

Lemon, K.N. and Verhoef, P.C. (2016), “Understanding customer experience throughout the customer journey”, Journal of Marketing, Vol. 80 No. 6, pp. 69-96, available at: https://citavistorageredirector.azurewebsites.net/containerRedirect/14d7500d-73db-499d-bc8e-cf69d65ce21d?projectKey=gjyc1syvgnq5g62mey5wp4d8tyozfd4o80i62x7hsw&containerName=gjyc1syvgnq5g62mey5wp4d8tyozfd4o80i62x7hsw&permissions=9&expiry=2020-08-21T23:57:2.

Löchtefeld, M., Böhmer, M., Daiber, F. and Gehring, S. (2013), “Augmented reality-based advertising strategies for paper leaflets”, Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, pp. 1015-1022, doi: 10.1145/2494091.2496010.

Loeb, W. (2020), “More than 13,800 stores are closing in 2020 so far—a number that will surely rise”, Forbes, available at: https://www.forbes.com/sites/walterloeb/2020/07/06/9274-stores-are-closing-in-2020--its-the-pandemic-and-high-debt--more-will-close/.

Luo, X. (2002), “Uses and gratifications theory and E-consumer behaviors”, Journal of Interactive Advertising, Vol. 2 No. 2, pp. 34-41, doi: 10.1080/15252019.2002.10722060.

Márquez, Á.J.O. and Ziegler, J. (2020), “Store augmented reality-enabled product comparison and recommendation”, Fourteenth ACM Conference on Recommender Systems, pp. 180-189, doi: 10.1145/3383313.3412266.

MacKenzie, I., Meyer, C. and Noble, S. (2013), How Retailers Can Keep up with Consumers, McKinsey & Company, available at: https://files.mtstatic.com/site_3770/472/0?Expires=1649004406&Signature=uhuaW7PscwRq1LCBhrP7AZT210mKdtI7kt∼CAKWP5Y00-PQvHsLO901NOkcIRkw4TwGAkBF2dPXwxoyZ∼z0Aa7f8V5FBFV3vEwx9PQ-RN0zb0xVb4Q7ZZcmlqaihZfMhnN4mlQfTdbohSMbj2K6∼zO2QVwAz1lgQ4hIZ6E4vYes_&Key-Pair-Id=APKAJ5Y6AV4GI7A555NA.

March, S.T. and Smith, G.F. (1995), “Design and natural science research on information technology”, Decision Support Systems, Vol. 15, pp. 251-266.

McLean, G. and Osei-Frimpong, K. (2019), “Hey Alexa … examine the variables influencing the use of artificial intelligent in-home voice assistants”, Computers in Human Behavior, Vol. 99, pp. 28-37, doi: 10.1016/j.chb.2019.05.009.

Mora, D., Jain, S., Nalbach, O. and Werth, D. (2020a), “Holographic recommendations in brick-and-mortar stores”, 26th Americas Conference on Information Systems, AMCIS 2020, pp. 1-10.

Mora, D., Zimmermann, R., Cirqueira, D., Bezbradica, M., Helfert, M., Auinger, A. and Werth, D. (2020b), “Who wants to use an augmented reality shopping assistant application?”, Proceedings of the 4th International Conference on Computer-Human Interaction Research and Applications - WUDESHI-DR, doi: 10.5220/0010214503090318.

Mueller, S.T., Hoffman, R.R., Clancey, W., Emrey, A. and Klein Macrocognition, G. (2019), Explanation in Human-AI Systems: A Literature Meta-Review Synopsis of Key Ideas and Publications and Bibliography for Explainable AI, ArXiv Preprint ArXiv:1902.01876, February 2019.

Nicola, M., Alsafi, Z., Sohrabi, C., Kerwan, A., Al-Jabir, A., Iosifidis, C., Agha, M. and Agha, R. (2020), “The socio-economic implications of the coronavirus pandemic (COVID-19): a review”, International Journal of Surgery, Elsevier, Vol. 78, pp. 185-193, doi: 10.1016/j.ijsu.2020.04.018.

Omar, J., Márquez, Á. and Ziegler, J. (2018), “Augmented reality based recommending in the physical world”, Mensch und Computer 2018-Workshopband, September 2018, doi: 10.18420/muc2018-ws07-0461.

Pantano, E., Rese, A. and Baier, D. (2017), “Enhancing the online decision-making process by using augmented reality: a two country comparison of youth markets”, Journal of Retailing and Consumer Services, Vol. 38, pp. 81-95, doi: 10.1016/j.jretconser.2017.05.011.

Parise, S., Guinan, P.J. and Kafka, R. (2016), “Solving the Crisis of Immediacy: how digital technology can transform the customer experience”, Business Horizons, Vol. 59 No. 4, pp. 411-420, doi: 10.1016/j.bushor.2016.03.004.

Pelletier, M.J., Krallman, A., Adams, F.G. and Hancock, T. (2020), “One size doesn't fit all: a uses and gratifications analysis of social media platforms”, Journal of Research in Interactive Marketing, Vol. 14 No. 2, pp. 269-284, doi: 10.1108/JRIM-10-2019-0159.

Perform-Network.eu (2022), “PERFORM consortium”, available at: https://www.perform-network.eu/consortium/.

Pimenidis, E., Polatidis, N. and Mouratidis, H. (2018), “Mobile recommender systems: identifying the major concepts”, Journal of Information Science, Vol. 45 No. 3, doi: 10.1177/0165551518792213.

Piotrowicz, W. and Cuthbertson, R. (2014), “Introduction to the special issue information technology in retail: toward omnichannel retailing”, International Journal of Electronic Commerce, Vol. 18 No. 4, pp. 5-16, doi: 10.2753/jec1086-4415180400.

Poushneh, A. and Vasquez-Parraga, A.Z. (2017), “Discernible impact of augmented reality on retail customer's experience, satisfaction and willingness to buy”, Journal of Retailing and Consumer Services, Vol. 34, pp. 229-234, doi: 10.1016/j.jretconser.2016.10.005.

Prat, N., Comyn-Wattiau, I. and Akoka, J. (2014), “Artifact evaluation in information systems design-science research-a holistic view”, Proceedings PACIS, Vol. 23.

Qin, Y.S. (2020), “Fostering brand–consumer interactions in social media: the role of social media uses and gratifications”, Journal of Research in Interactive Marketing, Vol. 14 No. 3, pp. 337-354, doi: 10.1108/JRIM-08-2019-0138.

Rigby, D.K. (2011), “The future of shopping”, Harvard Business Review, Vol. 89 No. 12, pp. 64-75, available at: https://hbr.org/2011/12/the-future-of-shopping.

Sindermann, C., Riedl, R. and Montag, C. (2020), “Investigating the relationship between personality and technology acceptance with a focus on the smartphone from a gender perspective: results of an exploratory survey study”, Future Internet, Vol. 12 No. 7, p. 110, doi: 10.3390/fi12070110.

Sokol, K. and Flach, P. (2020), “Explainability fact sheets”, Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, pp. 56-67, doi: 10.1145/3351095.3372870.

Song, J.H. and Zinkhan, G.M. (2003), “Features of web site design, perceptions of web site quality, and patronage behavior”, ACME 2003 Proceedings, Vol. 106–114, available at: http://www.acme-fbd.org/Proceedings/ACME_2003_Proceedings.pdf#page=114.

Statista (2018), “eCommerce - worldwide 2020 | Statista market forecast”, available at: https://www.statista.com/outlook/243/100/ecommerce/worldwide?currency=eur.

Statistisches Bundesamt (2020), “Share of internet users going online for the following activities in Germany in 2020”, Statista, available at: https://www.statista.com/statistics/1188488/internet-activities-germany/.

SurveyGizmo.com (2020), Enterprise Online Survey Software and Tools - SurveyGizmo.

Tanmay, K. and Ayush, K. (2019), “Augmented reality based recommendations based on perceptual shape style compatibility with objects in the viewpoint and color compatibility with the background”, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 3361-3367, doi: 10.1109/ICCVW.2019.00418.

Tavakoli, H. (2013), A Dictionary of Research Methodology and Statistics in Applied Linguistics, Rahnamā, Tehran, available at: https://books.google.at/books?hl=de&lr=&id=-UcbDQAAQBAJ&oi=fnd&pg=PP1&dq=A+dictionary+of+research+methodology+and+statistics+in+applied+linguistics&ots=2dIcwiyklz&sig=jzU9mMrFN_-_YJGJEfNgcEb30tI&redir_esc=y#v=onepage&q=A%20dictionary%20of%20research%20methodology%20and%20statistics%20in%20applied%20linguistics&f=false.

Venkatesh, V. and Morris, M.G. (2000), “Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior”, MIS Quarterly, Vol. 24 No. 1, p. 115, doi: 10.2307/3250981.

Verhoef, P.C., Kannan, P.K. and Inman, J.J. (2015), “From multi-channel retailing to omni-channel retailing. Introduction to the special issue on multi-channel retailing”, Journal of Retailing, Vol. 91 No. 2, pp. 174-181, doi: 10.1016/j.jretai.2015.02.005.

Vom Brocke, J., Simons, A., Niehaves, B., Niehaves, B., Reimer, K., Brocke, J., Vom, Simons, A., Niehaves, B., Niehaves, B., Reimer, K., Plattfaut, R. and Cleven, A. (2009), “Reconstructing the giant: on the importance of rigour in documenting the literature search PROCESS”, ECIS 2009 Proceedings, Vol. 161, available at: https://aisel.aisnet.org/ecis2009/161.

von Briel, F. (2018), “The future of omnichannel retail: a four-stage Delphi study”, Technological Forecasting and Social Change, Vol. 132, pp. 217-229, doi: 10.1016/j.techfore.2018.02.004.

Wang, C.L. (2021), “New frontiers and future directions in interactive marketing: inaugural Editorial”, Journal of Research in Interactive Marketing, Vol. 15 No. 1, pp. 1-9, doi: 10.1108/JRIM-03-2021-270.

Wang, W. and Benbasat, I. (2008), “Attributions of trust in decision support technologies: a study of recommendation agents for e-commerce”, Journal of Management Information Systems, Vol. 24 No. 4, pp. 249-273, doi: 10.2753/MIS0742-1222240410.

Wang, N., Wang, H., Jia, Y. and Yin, Y. (2018), “Explainable recommendation via multi-task learning in opinionated text data”, 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018, doi: 10.1145/3209978.3210010, pp. 165-174.

Wolf, R. (2018), “Here's which brick-and-mortar retailers are getting hit the hardest”, available at: https://www.businessinsider.com/brick-and-mortar-retailers-getting-hit-the-hardest-2018-5?IR=T.

Yim, M.Y.C. and Park, S.Y. (2019), “I am not satisfied with my body, so I like augmented reality (AR)”, Journal of Business Research, Vol. 100, pp. 581-589, doi: 10.1016/j.jbusres.2018.10.041.

Yim, M.Y.C., Chu, S.C. and Sauer, P.L. (2017), “Is augmented reality technology an effective tool for E-commerce? An interactivity and vividness perspective”, Journal of Interactive Marketing, Vol. 39, pp. 89-103, doi: 10.1016/j.intmar.2017.04.001.

Zhang, Y. and Chen, X. (2020), “Explainable recommendation: a survey and new perspectives”, in Foundations and Trends in Information Retrieval, Now Publishers, Vol. 14 No. 1, pp. 1-101, doi: 10.1561/1500000066.

Zimmermann, R. and Auinger, A. (2020), “Identifying brand-owned touchpoints along the digital retail customer journey – a practical approach”, in Gronau, N., Heine, M., Poustcchi, K. and Krasnova, H. (Eds), WI2020 Community Tracks, GITO Verlag, pp. 291-305, TS-CrossRef, doi: 10.30844/wi_2020_y2-zimmermann.

Zimmermann, R., Auinger, A. and Riedl, R. (2019), “Smartphones as an opportunity to increase sales in brick-and-mortar stores: identifying sales influencers based on a literature review”, in Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 82-98, 11588 LNCS, doi: 10.1007/978-3-030-22335-9_6.


This research is a part of the European Training Network project PERFORM that has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No [765395]; and supported, in part, by Science Foundation Ireland grant [13/RC/2094_P2]. This research reflects only the authors' view, the European Commission is not responsible for any use that may be made of the information it contains.

Data Availability: The qualitative and quantitative data used to support the findings of this study have been deposited in the Zenodo repository [DOI: 10.5281/zenodo.4723468].

Corresponding author

Robert Zimmermann can be contacted at: robert.zimmermann@fh-steyr.at

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