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Article

AI-Driven Business Model: How AI-Powered Try-On Technology Is Refining the Luxury Shopping Experience and Customer Satisfaction

by
Xin Song
1,* and
Carole Bonanni
2
1
Facultad de Economía y Negocios, Universidad Anáhuac México, Huixquilucan 52786, Mexico
2
Department of Strategy and Innovation, Rennes School of Business, 35000 Rennes, France
*
Author to whom correspondence should be addressed.
J. Theor. Appl. Electron. Commer. Res. 2024, 19(4), 3067-3087; https://doi.org/10.3390/jtaer19040148
Submission received: 11 September 2024 / Revised: 28 October 2024 / Accepted: 30 October 2024 / Published: 5 November 2024
(This article belongs to the Topic Interactive Marketing in the Digital Era)
Browse Figure
Figure 1
<p>Conceptual Model.</p> ">
Versions Notes

Abstract

:
Artificial Intelligence (AI) has revolutionized interactive marketing, creating dynamic and personalized customer experiences. To the best of our knowledge, no studies have ventured into how firms in the luxury sector can leverage AI marketing activities to innovate their business model and boost the development of future digital marketing to enhance the luxury shopping experience (LSE). Building on the existing LSE literature and adopting a business model innovation (BMI) lens, we conducted an experimental study to identify how AI-powered try-on technology (ATT) can contribute to LSEs and create customer value proxied by customer satisfaction. In addition, we determined the specific dimensions of the LSE that are most affected by AI marketing efforts. Furthermore, our findings explored the role of AI in driving BMI and the interrelationship between enhanced customer satisfaction and BMI. This research contributes to understanding the crucial role of AI in shaping the future of interactive marketing in the luxury context.

1. Introduction

“People do not want to feel targeted; they want experiences that feel tailored to their needs.”—Jamie Brighton, Product & Industry Marketing EMEA, Adobe.
Artificial Intelligence (AI) has emerged as a defining technology in the digital era, presenting great opportunities for using sophisticated technologies such as algorithms, big data, machine learning, and robots. When integrated with other interactive technologies, such as augmented reality (AR) and virtual reality (VR), AI has become a major source of innovation and competitiveness in interactive marketing [1,2]. AI has powered upheavals in digital marketing, enabling unprecedented levels of customer interaction and engagement, thereby transforming the marketing landscape [3,4,5]. This study focuses on interactive elements of AI marketing activities and, more specifically, on how AI-powered try-on technologies (ATT), such as AR and VR, influence the luxury shopping experience (LSE) and potential business model innovation (BMI). ATT conveys an AR product experience, can detect the face, and overlay virtual makeup that looks as realistic as its physical copy. By interacting with a camera, consumers can try on beauty products and find precise color matches. Leveraging machine-learning algorithms, ATT further enhances customer experiences by analyzing individual features, such as skin tone, texture, and age, to provide personalized product recommendations [6]. AI-empowered technology has notable advantages in terms of speed and precision. The highly efficient algorithm rapidly identifies multiple skin concerns within seconds, thereby enhancing the user experience. We are focusing on the luxury sector as, according to a recent analysis by Boston Consulting Group [7], the global luxury market is projected to reach EUR 1.3 trillion (USD 1.5 trillion) by 2025, with experiential luxury growing at a faster rate than personal luxury. This growth highlights the increasing relevance of interactive and AI-driven technologies in creating differentiated customer experiences.
Previous studies adopted a fragmented approach to examine the relationship between interactive AI marketing activities and BMI. Some studies have shown the role of technology in improving customer engagement, whereas others have focused on demonstrating that BMI is a novel approach to customer engagement [8]. We aim to bridge this gap between the two approaches by providing a comprehensive view of the impact of interactive AI marketing activities on BMI. In addition, while prior studies have shown the positive effects of virtual shopping experiences on consumer engagement and purchase intentions [9], some have noted potential drawbacks, such as decreased purchase intention in certain contexts [10,11]. These mixed results underscore the need for continued investigation of the role of AI in interactive marketing activities [12,13]. Luxury brands must exercise caution when integrating AI-driven technologies into physical retail environments to maintain and enhance customer engagement [14]. Moreover, prior studies have not considered the luxury shopping experience (LSE) in their analysis of virtual shopping environments [8].
Researchers have shown that innovation in customer experience management is increasingly sourced from BMI. The importance of customer experience in interactive marketing cannot be overstated, as it plays a particularly important role in marketing activities and is likely to create long-term business value and innovative business models by enhancing or creating new customer experiences [2,15,16]. Novel technology and applications of AI are likely to reshape the creative and luxury industries, which have been apprehensive about digital disruption [17]. It is imperative to accord proper attention to exploring whether AI-empowered customer experiences can facilitate BMI in the luxury sector, particularly within interactive marketing frameworks [18].
Furthermore, this study addresses key empirical gaps in AI-empowered customer experiences in the context of luxury shopping. First, we conduct an experimental study to determine whether AI technologies enhance customer satisfaction in these settings. Our findings deepen the understanding of AI’s influence on the luxury customer journey. Second, we also identified specific dimensions of luxury shopping that are most affected by AI marketing efforts [12,19] and explored how AI marketing can catalyze BMI, impacting customer satisfaction. This study expands the interactive marketing literature and clarifies the relationship between AI-driven satisfaction and BMI in luxury retail. Third, unlike traditional retail environments, our findings suggest that the unique context of luxury shopping requires new theoretical approaches [16]. By blending traditional values with innovative marketing strategies, luxury firms can leverage AI to innovate their business models and enhance digital marketing efforts [18,20]. Ultimately, this study enriches our understanding of AI in refining customer satisfaction and driving the BMI in the luxury sector. Therefore, our research questions are as follows:
RQ1: How can interactive AI marketing activities (AMA) drive business model innovation (BMI) in the luxury sector by impacting the luxury shopping experience (LSE)?
RQ2: Whether and how interactive can AMA moderate the relationship between the LSE and customer satisfaction?
RQ3: Would customer satisfaction with the impacted LSE affect BMI?

2. Theoretical Background and Hypothesis Development

2.1. Luxury Shopping Experience

The luxury shopping experience is a specific segment of the customer experience. Lemon and Verhoef [21] (p. 74) conceptualized as “customer experience is a multidimensional construct focusing on a customer’s cognitive, emotional, behavioral, sensorial, and social responses to a firm’s offerings during the customer’s entire purchase journey”. In the luxury sector, each element plays an important role and has a specific definition. The luxury literature defines the LSE as “Deluxe” [22] or luxuriousness, a feeling that a product looks flashy, splendid, or extravagant [23], which is one of the major components of luxury’s personal experience and hedonistic features [24]. Hudders et al. [25] argue that luxury brands are not inherently luxurious but should be experienced as luxurious by an individual. A luxurious experience is a multifaceted expression of personal identity characterized by sophistication and the art of living [26,27]. Key elements include authenticity, uniqueness, superior quality, aesthetics, conspicuousness, signaling status, and hedonism [22,28,29,30]. Authenticity is central, rooted in perceptions of a brand’s heritage and tradition, making the experience feel genuine and connected to a rich history [31]. Uniqueness is marked by exclusivity and rarity, while superior quality is defined by exceptional craftsmanship and scarcity, reinforcing luxury status [22,28]. The aesthetic dimension emphasizes exquisiteness, extravagance, and refined taste, distinguishing luxury from the ordinary [28]. Conspicuousness serves as a sign of wealth and trendiness, reinforcing social identity and exclusivity through signaling status [32]. The hedonistic aspect, focused on sensory pleasure and excitement, elevates the luxury experience beyond mere products to a deeper, emotionally resonant encounter [33,34]. Lastly, Holmqvist et al. [30,35] have pointed out that the uniqueness of the LSE is rooted not only in exclusive, prestigious, and hedonic experiences, but also in escapism, which is becoming a key approach in the LSE. For example, customers may lose track of time or feel like a “Parisian” touring the brand store for a while [24,30]. These interconnected elements form the core of a luxurious experience, merging tradition, quality, and pleasure.

2.2. Luxury Shopping Experience (LSE) and Customer Satisfaction

Previous research has established the dominant role of customer satisfaction as a customer shopping experience evaluation metric for years [21]. The importance of delivering a superior LSE through products and services is widely recognized as a potential antecedent to customer satisfaction [23,34,36,37]. Luxury hotel managers find that customer satisfaction can be achieved when the perception of service quality matches customer expectations (i.e., the best value, premium price, and VIP services) [38]. Ko et al. [39] investigated luxury brand strategies by identifying critical hedonic and symbolic values conveyed through customer experiences that affect customer satisfaction. Chung et al. [36] also provided empirical evidence that the marketing efforts of AI chatbot e-service agents used in luxury brands would enhance customer satisfaction through higher perceived communication quality and further establish intimate relations with customers. Amatulli et al. [40] suggested that luxury firms gain customer satisfaction by providing unique personalized experiences and fulfilling customers by integrating products and services to create a fascinating, positive experience. Accordingly, we assume that:
H1. 
LSE has a positive effect on customer satisfaction.

2.3. Marketing Activities and Business Model Innovation (BMI)

A business model is the content, structure, and governance of transactions designed to create value by leveraging business opportunities [41]. In addition to articulating a value proposition, the business model implements a strategy by configuring these essential elements, allowing firms to create and capture value through integrated systems of activities [41]. Business model innovation (BMI) can be achieved by reconfiguring either its content (introducing new activities), its structure (rearranging the connections between activities), or governance (altering the individuals responsible for the activities) [41]. BMI helps firms maintain long-term growth and profit by focusing on discovering and seizing new opportunities [42]. Digital technologies have accelerated the development of BMI concepts [43,44,45,46]. Warner and Wäger [47] provided a process model of digitally driven BMI to explain how incumbents in traditional industries develop digital capabilities for digital transformation. For instance, Arnold et al. [48] argue that the IoT has a disruptive effect on business models in manufacturing industries, which leads to a radical BMI. Advanced digital technologies have a significant impact on BMI in a data-driven dynamic environment [49] and AI innovators can profit from BMI [50].

2.4. AI-Powered Try-On Technology (ATT), LSE, and Customer Satisfaction

AI marketing has become a mainstream subfield within digital marketing [21]. Prior research has defined AI as a prediction technology that enables products and services to perform tasks requiring intelligence and autonomous decision-making on behalf of humans [51,52]. In marketing, AI technologies have a profound impact on a variety of market practices, such as improving marketing functions, performing marketing tasks [4], solving marketing problems [53], better-predicting customer preferences, and managing customer experiences to achieve a long-term brand–customer relationship with enhanced customer trust and satisfaction [4,36,54,55]. AI-powered technologies profoundly transform customer interaction. For instance, AI-powered chatbots can conduct interviews and analyze data in real time [54]. ATT, specifically, falls under the role of AI in customer engagement and personalization, and leverages computer vision and machine learning to enable virtual try-ons, allowing consumers to visualize products on themselves before buying, increasing confidence [6,56]. Such systems integrate predictive models to suggest products based on personal traits and preferences, fitting into AI applications in creating tailored marketing experiences [2]. AI chatbots and voice assistants further optimize the customer journey by offering immediate, personalized assistance and enhancing interaction and satisfaction [57,58]. This integration of AI-driven personalization and real-time responsiveness positions AI try-on technology favorably within the broader context of interactive and immersive AI in marketing.
Previous research on the informative and hedonic dimensions of digital technology marketing activities focuses on retaining or enhancing the LSE without necessarily detracting from or replacing the value of traditional in-store shopping environments, such as luxury flagship stores and high-end boutiques [59]. For instance, Beuckels and Hudders [33] suggested that interactive images can positively enhance customers’ perception of luxury brands in a virtual shopping context, which includes exclusivity, conspicuousness, uniqueness, quality, hedonic value, and extended self. Chung et al. [36] examined the impact of AI chatbot marketing efforts on customer satisfaction, highlighting aspects such as interaction, entertainment, trendiness, customization, and problem-solving. In addition, Colella et al. [26] noted that consumer perceptions of luxury vary by digital platform, with social media interactions perceived as more luxurious than brand websites. Javornik et al. [37] investigated the use of AR in luxury branding activities, which influences brand experience through enhanced luxury attributes, including authenticity, prestige, exclusivity, hedonism, high quality, aesthetics, deep connection, and premium price. However, the luxury marketing literature is concerned that digital technologies may dilute the essence of luxury, challenging the preservation of luxury’s “sacral” heritage in retail settings [14,29]. The potential role of digital technologies in the LSE is far from fully understood, especially when luxury stores try to implement advanced technologies in existing marketing activities, and misalignment risks remain [35]. To further benefit from the potential value of digital technology innovation for luxury brands, this study focuses on the unique moderating effect of AI marketing activities (AMA) on LSE.
According to the extant literature on the impact of AI marketing on the shopping experience, we selected four dimensions that we believe are the most relevant for AI-powered try-on technology in the LSE. Higher levels of AMA in ATT—specifically, uniqueness, telepresence, delegation, and interactivity—create highly personalized, immersive, and interactive luxury shopping environments that enhance customer satisfaction. For instance, uniqueness enables tailored experiences through virtual fitting rooms and AI service robots, fostering exclusivity and meeting customers’ desires for distinct experiences [40,55]. Telepresence enhances immersion, as seen in virtual boutiques where customers can explore brand stories and architectures in detail, guarantee expectations of continuity or meet higher expectations, simulating ownership and intensifying luxury perception [33]. Delegation to AI applications for routine shopping tasks can facilitate convenience and satisfaction by minimizing customer reliance on sales associates [57] and expanding options in terms of delegating tasks for desirable performance design and governance [60]. Interactivity enables real-time engagement with luxury content, enhancing perceived enjoyment and exclusivity [1,20,35]. Collectively, AMA can influence how the LSE attributes impact satisfaction, thus we propose:
H2. 
Higher AMA (uniqueness, telepresence, delegation, interactivity) can moderate the relationship between the LSE and customer satisfaction.
Through AMA, digital interactions can significantly enhance the authenticity of the luxury experience. Advanced technologies serve to reflect the brand’s rich heritage and core values, fostering a meaningful alignment [37]. This synergy often contributes to a more genuine experience, ultimately enriching customer satisfaction. As a result, customers tend to view ATT-enabled experiences as extensions of the brand’s fundamental identity.
H2a. 
Higher AMA can moderate the relationship between authenticity in the LSE and customer satisfaction.
Examples include virtual boutiques such as Valentino’s 3D Virtual Store and the Dolce & Gabbana Virtual Boutique, where customers can immerse in the brand’s universe, discover and experience the majestic luxurious architecture, unique style, and the creativity imbued with the brand’s essentials, as well as the “AR wall” where customers are introduced to the story of the birth of the brand [7]. ATT marketing activities enable luxury brands to create an unparalleled level of uniqueness and individualization in the LSE. This tailored approach makes each customer’s experience distinct, enhancing satisfaction by fulfilling the desire for unique and highly individualized interactions [61].
H2b. 
Higher AMA can moderate the relationship between uniqueness in the LSE and customer satisfaction.
Through high-quality visuals, precise AI recommendations, and seamless service delivery, AMA strengthens the perception of superior quality in the LSE. Customers acknowledge the value of enhanced digital features, which effectively highlight the brand’s commitment to craftsmanship and excellence, ultimately leading to increased satisfaction [22,34].
H2c. 
Higher AMA can moderate the relationship between superior quality in the LSE and customer satisfaction.
AMA has the potential to enhance the aesthetic appeal of the LSE by creating an immersive environment that resonates with luxury brand standards. This alignment with sophisticated aesthetics can deepen customer perceptions of superiority in taste, ultimately contributing to a more satisfying shopping experience enriched by visual exquisiteness [30,62].
H2d. 
Higher AMA can moderate the relationship between aesthetic in the LSE and customer satisfaction.
The conspicuous elements of luxury are amplified by AMA, which enhances the visibility of luxury markers and makes the exclusivity of the experience apparent. AMA empowers customers to subtly showcase their high-status associations, ultimately enriching their satisfaction by showing off wealth, sophistication, and current trends through luxury consumption [32].
H2e. 
Higher AMA can moderate the relationship between conspicuousness in the LSE and customer satisfaction.
AMA enables customers to engage in status signaling through distinct luxury markers and digital representations of exclusivity. By supporting this social status signaling, AMA enhances satisfaction among elitist customers who derive value from displaying social identity association with high-status brands [40].
H2f. 
Higher AMA can moderate the relationship between signaling status in the LSE and customer satisfaction.
Through sensory engagement and interactive features, AMA enriches the hedonistic aspects of LSE, making the experience more enjoyable and fulfilling. Prioritizing pleasure within the LSE elevates customer satisfaction as it transforms shopping into a gratifying, luxurious experience [35].
H2g. 
Higher AMA can moderate the relationship between hedonism in the LSE and customer satisfaction.
With its immersive feature, AMA facilitates escapism in the LSE, enabling customers to momentarily disconnect from their daily routines, immersing customers in a brand’s carefully crafted reality. This escapist experience intensifies satisfaction by creating a transformative and memorable experience that aligns with the allure of luxury [30,35].
H2h. 
Higher AMA can moderate the relationship between escapism in the LSE and customer satisfaction.

2.5. AI-Augmented Marketing Activities (AMA), BMI, and Customer Satisfaction

Prior studies have illustrated that various digital technologies can drive BMI [63]. For instance, Arifiani and Arifiani [64] suggest that market orientation and technology orientation can drive BMI, and Garzella et al. [65] empirically demonstrate that digital technologies can positively affect BMI. Although such studies show no agreement on the theoretical underpinning, there is still a lack of empirical evidence for digital technologies as antecedents of BMI. The current research indicates that value creation in business models arises not only from producers but also from customers and other stakeholders, resulting in competitive advantages from multiple sources [45]. In addition, prior studies largely employ “digital transformation” to digitalize existing activities and optimize business processes, which in turn leads to BMI [66]. Within the marketing discipline, both scholars and practitioners have acknowledged the primary role of BMI and the antecedents of digital BMI [15,67]. Therefore, we propose the following hypothesis:
H3. 
Higher perceived AMA will drive BMI.
Furthermore, the literature also suggests that digital technologies can drive BMI by fulfilling new customer needs [46], monitoring complex customer experiences [8], and even adapting customer experience orientation [68]. Likewise, the digital enhancement of customer experience is an important dimension of digital transformation when firms implement digital technologies and innovate existing business models [66,67]. However, prior studies on BMI still lack sufficient empirical support to consider the relationship between digital technology enhanced customer experiences and BMI [8]. Therefore, we hypothesize the following:
H4. 
Customer Satisfaction is positively correlated with BMI.

3. Research Method

3.1. Conceptual Model

The conceptual model presented in Figure 1 illustrates the framework of this study. This model employs LSE measurements within the luxury retail environment to examine the influence of ATT’s marketing activities on the shopping experience. Additionally, it explores how the change in LSE may subsequently impact BMI.

3.2. Measurement and Procedure Design

In this study, we tested the effect of adding ATT to the luxury shopping context, which is not generally adopted in a luxury boutique. Thus, using vignettes can help focus on the participants and clarify study principles, even if they have no prior experience with the technology [69]. In line with recent studies [70], a video-based vignette experimental study was conducted because this technology is new within the context. Online experimental vignettes have been recognized for their effectiveness in revealing perceptions, attitudes, and behaviors, and for not forcing participants to have a solid insight into the research topic in question [69]. For instance, the obstacles posed by conducting field studies on real-life marketing decisions include the constraints of time and financial resources [71]. Well-crafted and relevant vignettes can offer valuable insights into appropriate managerial implications for addressing specific concerns [72].
They were then asked questions about ATT marketing activities, shopping experiences, and satisfaction. We expect ATT to strengthen the positive customer satisfaction of the LSE, including all LSE latent dimensions. Thus, considering the benefits of video vignettes, we conducted an online video vignette-based experiment using SoJump (www.sojump.com, accessed on 16 April 2024), a professional and free online database employed frequently for research in China, i.e., [73,74]. To ensure that all respondents had the same understanding of the two luxury shopping environments (with ATT vs. without ATT), the experiment included a link to a scripted video displaying ATT functions, such as AI-driven personalized beauty and skincare recommendations, including skin tone detection for foundation matching, personalized diagnostics, and tailored product suggestions. In addition, we added concise and eye-catching Chinese text explanations to the corresponding video vignettes to achieve better results. A pre-test of 17 participants (11 females, 6 males, α < 0.954) was conducted to be sure about how the manipulation material works. A total of 256 Chinese participants were randomly recruited online for a between-group design, but only 237 participants had prior shopping experience in luxury beauty stores. Thirty-seven responses were rejected as participants did not pass the attention checks. Finally, we had 200 participants randomly divided into two groups. Our treatment group (n = 100, 60 participants were female and 40 participants were male) was assigned an ATT shopping condition, and the control group (n = 100, 81 participants were women and 19 participants were male) was assigned the same shopping setting, but without ATT. Thus, most of our sample was female (Table 1).
We measured the following five constructs:
  • AI Marketing Activities (AMA): We gathered thirteen items (see Table A1) used in previous studies to measure AI marketing activities (AMA) according to uniqueness, telepresence, delegation, and interactivity [69,70,75,76,77,78,79]. These items were previously adopted to verify how AI technologies affect customer perception and behavior related to customer experiences.
  • Telepresence: To measure “Telepresence”, we selected three items from Nowak and Biocca [80] such as “Using this ATT makes me feel immersed in the environment you saw/heard”.
  • LSE: Prior research shows that customers’ perceptions of the LSE are highly differentiated and subjective [25]. Therefore, we employed twenty-three items used in previous studies to measure the LSE, measuring the following eight dimensions: authenticity, uniqueness, superior quality, aesthetic, conspicuousness, signaling status, hedonism, and escapism [30,31,33,34,76,79,81,82].
  • Customer satisfaction: We measured customer satisfaction by adopting three items from the works by Barhorst et al. [70].
  • BMI: We selected the nine items from Bhatti et al. [83] to measure BMI.
Each item is measured on a seven-point range Likert scale from 1 (strongly disagree) to 7 (strongly agree) (shown in Table A1).

3.3. Results and Analysis

First, our data analysis used SPSS AMOS v26, applying the following analysis to ensure reliable and unbiased results. Confirmatory factor analysis (CFA) was used to test the reliability of the measurement instrument in terms of accuracy and precision. The authenticity of the LSE was removed as its Cronbach’s alpha = 0.21, and one dimension of delegation was removed as its Cronbach’s alpha = 0.381. To reduce the dimensionality of the datasets, in our Principal Component Analysis (PCA) analysis, conspicuousness, hedonism, and escapism of the LSE were omitted due to the rise of the Cronbach’s alpha (AVE = 0.42, CR = 0.69), and only three dimensions of BMI remained as a rising value of average variance extracted (AVE) and composite reliability (CR) (AVE = 0.28, CR = 0.77). We kept all two groups’ items >0.5 (shown in Table 2), which indicated that the reliability of the survey items used in this research was verified. Then, we tested validity using the Kaiser–Meyer–Olkin (KMO) test and Bartlett’s Test of Sphericity. The KMO measure of sampling adequacy is more than 0.8 or above, denoting a good correlation; it was 0.882 (treatment group) and 0.859 (control group) in our data analysis. In addition, the Bartlett’s Test results were significant at both df = 45, p < 0.001 (treatment group) and df = 10, p < 0.001 (control group). Furthermore, we employed Pearson’s correlation analysis to observe the correlation coefficients between variables, as shown in Table A2 and Table A3. As shown in Table 3, the variance inflation factor (VIF) values for the independent variables ranged between 1.600 and 3.367, indicating that the data satisfied the criteria for the multicollinearity test.
The moderating effect of AMA between the LSE and customer satisfaction (H2b, H2c, H2d, H2f) is reported below in Table 4.
Second, to check the manipulation, the results of the one-sample t-test indicated that the respondents perceived the stimulus as more satisfying (M = 5.867, SD = 0.682, t (99) = 86.066, p < 0.001) than the control group’s satisfaction (M = 4.107, SD = 1.572, t (99) = 26.124, p < 0.001). Specifically, compared with those in the control condition (M = 4.625, SD = 1.572, t (99) = 25.921, p < 0.001), the respondents from the treatment group perceived more of the uniqueness dimension of the LSE (M = 6.050, SD = 1.604, t (99)= 87.573, p < 0.001) and more of the superior quality dimension of the LSE (M = 5.820, SD = 0.7369, t (99) = 78.979, p < 0.001) than the respondents from the control group (M = 4.430, SD = 1.931, t (99) = 22.941, p < 0.001); slightly more of the aesthetic dimension of the LSE (M = 5.603, SD = 0.868, t (99)= 64.550, p < 0.001) than the respondents from the control group (M = 5.578, SD = 0.927, t (99) = 60.201, p < 0.001); and more of the signaling status dimension of the LSE (M = 4.970, SD = 1.070, t (99)= 46.447, p < 0.001) than the respondents from the control group (M = 4.433, SD = 1.870, t (99) = 23.710, p < 0.001). Therefore, we can conclude that the manipulation check is valid. Table 5 summarizes the model results for both the treatment group (with ATT) and control group (without ATT) structural models. As shown in Table 5, H1 is supported by comparing the R2 values of the LSE’s impact on customer satisfaction with ATT (R2 = 0.592) and without ATT (R2 = 260).
Third, Table 4 shows our results, which reveal a significant negative moderating effect of AMA on the relationship between the LSE and satisfaction (b = −0.287, p < 0.001) [84]. Therefore, H2 is supported. Specifically, we investigated the moderating effect of all acceptable LSE latent dimensions on satisfaction and determined two of the four underlying dimensions of the LSE. The results indicate that AMAs strongly weaken the effect of the aesthetic of the LSE on satisfaction (b = −0.598, p < 0.001), while AMA can enhance the effect of the superior quality of the LSE on satisfaction (b = 0.145, p < 0.05). Thus, both H2c and H2d are supported with R2 = 0.812. Contrary to our expectations, as shown in Table 4, there was no significant moderating effect of the uniqueness of the LSE on satisfaction by the moderator AMA (p = 0.340 > 0.05). However, as predicted, AMAs did not moderate the effect of the LSE signaling status on satisfaction (p = 0.558 > 0.05); therefore, H2f is unsupported.
Fourth, H3 is supported, as shown in Table 5, showing that AMA drives BMI with coefficient b = 0.539 (p < 0.001) and R2 = 0.250. Moreover, there was conclusive evidence regarding the significance of the positive association between satisfaction and BMI (p < 0.001). Therefore, H4 is supported.

4. Discussion and Conclusion

4.1. Summary of Findings

Previous studies have mainly investigated AI applications [36,54,57] and offered insights into the marketing activities that influence customer experience and engagement [18]. To our knowledge, no in-depth analysis of the AI-enabled customer experience and its effects on BMI in the luxury industry has been performed. Our results demonstrate whether AI marketing activities moderate AI-enabled LSEs and customer satisfaction and further investigate which dimension(s) of LSE is (are) more influenced. It is imperative for both marketing and strategy scholars to understand better how AI marketing activities influence customer experiences and BMI. The findings corroborate the underlying hypotheses that (1) the LSE has a positive effect on customer satisfaction, (2) AI marketing activities play a moderating role in the relationship between the LSE and customer satisfaction, (3) the LSE can drive BMI, and (4) customer satisfaction is positively correlated with BMI. The findings of this study support marketing research that contends that digital technologies affect customer satisfaction by assessing new technology via shopping experiences [36,57] and strategy research that considers that AI and AI-enabled customer experience can drive BMI [8,85].
Interestingly, the study revealed that certain AI-driven technologies, particularly AR and somatosensory experiences, have a complex influence on the LSE. For instance, while AR can impact the sensory interactivity of customers [86], it may also detract from the traditional exclusivity associated with luxury consumption, leading to a nuanced impact on customer satisfaction. This contradicts earlier findings suggesting that AI-empowered technologies typically enhance digital marketing efforts [36,58,70]. The results suggest that AI marketing activities could replace traditional LSEs rather than enhance them, especially when the focus is on the aesthetics and sensory experience of the LSE, which luxury customers value highly. Furthermore, our dataset indicates that our measurements may not sufficiently capture the dimensions of authenticity, conspicuousness, hedonism, and escapism in the LSE for several reasons. First, authenticity, hedonism, and escapism are subjective constructs typically assessed through nuanced behavioral or in-depth qualitative data, which our dataset lacks. Additionally, measuring conspicuousness and signaling status alongside hedonism and satisfaction may confuse respondents. Without psychographic variables to explore these underlying motivations, evaluating these dimensions becomes challenging. Lastly, the immersive nature of the LSE complicates the accurate assessment of escapism and hedonism in our dataset.
Moreover, the study highlights that the demographic profile of luxury consumers, particularly younger consumers, is integral to the growth of luxury brands [7], and they may not always seek the same level of uniqueness or status signaling through the LSE, which could explain the mixed results. Additionally, Yi et al. [87] illustrate specific face-to-face shopping behaviors of customers in China, highlighting how these behaviors align with the social hierarchy of value propositions in BMI. To enhance their own appearance, Chinese customers may not seek uniqueness or feel inclined to signal status through the LSE. As luxury customers continue to prioritize traditional non-digital shopping environments for their exclusivity and greater status signaling [32,81], the challenge for marketers is to balance the integration of AI technologies with the preservation of these core dimensions of the LSE.

4.2. Theoretical Contribution

First, we contribute to the fragmented body of existing academic research and the limited evidence of the changes caused by AI technologies in interactive marketing [12,86]. We address the challenges and opportunities presented by AI technologies, which are increasingly utilized to enhance customer satisfaction and create novel shopping experiences [4]. For instance, recent studies in interactive marketing have explored various dimensions of AI’s impacts, such as the delightful somatosensory experiences in AR [86] and the role of AI-powered voice assistants in building brand loyalty [58]. These insights highlight the expanding role of AI in shaping customer experiences and interactions in digital environments [18]. Qu and Baek [13] examined tactics to increase trust in virtual influencers, whereas Zhu et al. [20] explored how virtual social cues in VR tourism can enhance user adoption. These studies underscore the importance of AI in creating immersive customer experiences and trustworthy consumer interactions, which our study further validates within the luxury market context, providing empirical evidence that AI marketing activities significantly influence customer experience and satisfaction [2,57].
Second, we contribute to bridging the gap between BMI [44,63] and the marketing literature [15,67]. However, research on aligning marketing activities and BMI remains scarce [15]. Thus, we argue that digital marketing activities are antecedents of BMI, which, to the best of our knowledge, is a pioneering study. However, research has increasingly investigated the significant impact of digital technologies on interactive marketing [4,53,55,78] and BMI [64,65,68,83]. In line with the recent literature on interactive marketing, the transformative role of AI in innovating business models and interactive marketing strategies should be considered. For instance, the conceptual frameworks proposed by Peltier et al. [12] suggest that AI in interactive marketing is not merely an operational tool but also a strategic enabler of BMI. Yim et al. [88] explored how AI-driven agents influence consumer attachment, further supporting the notion that AI can be a significant driver of BMI by deepening customer relationships and satisfaction. Thus, our research provides empirical evidence that BMI in the luxury context can be driven by AI marketing activities, which also positively correlates with customer satisfaction. This aligns with the broader discourse on the role of AI in interactive marketing [19,86].
Third, our study addresses the gap in the limited empirical luxury research by examining the negative moderating effect of AI marketing activities on the relationship between customer experience and satisfaction [10,11]. In contrast to the predominant literature [26,33,36,37,58], we propose that the luxury shopping experience (LSE) could be one of the most effective settings for evaluating satisfaction in a luxury shopping environment [23], while AI marketing activities may negatively moderate satisfaction within specific LSE dimensions such as aesthetics. This finding challenges the prevailing view that AI uniformly enhances customer satisfaction in luxury contexts, suggesting that AI’s impacts may vary depending on the specific dimensions of the LSE, potentially moderating satisfaction in certain cases. This insight is consistent with recent studies such as those by Qu and Baek [13] and Zhu et al. [20], which explore how AI can both positively and negatively moderate consumer trust and engagement. From both scholarly and managerial perspectives, this study sheds light on enhancing the knowledge of AI in interactive marketing and provides new perspectives for future challenges and trends in the luxury segment.

4.3. Managerial Implication

This study highlights the key implications of AI for luxury marketers and managers. Embracing new technologies is essential, but preserving the luxury shopping experience (LSE) without compromising brand heritage is crucial. AI should enhance brand identity and the uniqueness of the LSE by delivering superior quality experiences such as accurate product information and craftsmanship. Hence, this study suggests that luxury brands have the opportunity to cultivate capabilities that will enable them to integrate AI seamlessly into BMI, thereby elevating the customer experience.
Second, in line with recent discussions on the role of AI in interactive marketing [5,12,88], we suggest that luxury service managers use AI as an enabler for similar brands or products in their business model innovation (BMI). As AI technologies continue to evolve, it is crucial to educate customers on how they function to enhance service delivery. Therefore, luxury firms should adapt to current market trends and accelerate their adoption of digital technologies accordingly [19,86].
Third, in light of our findings, we challenge the dominant narrative highlighted in previous studies that promotes hedonic experiences as paramount in luxury marketing [34,37]. Our research underscores the need for luxury brands to be judicious regarding the extent of their digital engagement with consumers. Excessive digital interaction could undermine a brand’s intrinsic aesthetic appeal, along with its profound connection to luxury consumers [27,32]. As AI continues to influence consumer interactions, luxury brands must strike a careful balance between embracing technological advancements and safeguarding their foundational values and unique identities.

4.4. Limitation

This study examines ATT tailored for the luxury industry to enhance the shopping experience. Although AI-powered AR is a viable option, future research could benefit from exploring other digital technologies and luxury brands for a wider range of outcomes. Our study focuses on two key factors that may alleviate concerns in this sector: the unique nature of the luxury industry and customers’ trust in AI. For example, Baker et al. [89] suggested that Internet-based technologies might not fully capture the heritage of luxury, potentially leaving luxury experiential needs unmet. Holmqvist et al. [35] highlighted the challenges of integrating advanced digital technologies with luxury attributes, pointing out the risks of misalignment. Interestingly, Chinese consumers tend to have a higher level of trust in AI partly because of government support [90]. Research has shown that AI-driven personalization and customer service can significantly enhance customer trust [20]. However, the study’s focus on Chinese participants indicates the need for broader longitudinal research across various contexts.

Author Contributions

Conceptualization, X.S.; methodology, X.S.; software, X.S.; validation, X.S. and C.B.; formal analysis, X.S.; data curation, X.S.; writing—original draft preparation, X.S.; writing—review and editing, X.S. and C.B.; supervision, C.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical review and approval were waived for this study, as it did not involve any interventions or procedures that required ethical approval; the data was obtained from a third-party platform.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study is available on request from the corresponding author. The data is not publicly available due to restrictions.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Measurement.
Table A1. Measurement.
VariableItemsReference
AI Marketing Activities (AMA)1. Uniqueness:
1.1 Using this ATT makes me feel completely different from using other digital touchpoints.
1.2 Using this ATT is personalized for me.
1.3 Using this ATT makes me feel exclusive.		[78]
2. Telepresence:
2.1 Using this ATT makes me feel immersed in the environment I saw/heard.
2.2 Using this ATT makes me feel inside the environment I saw/heard.
2.3 Using this ATT makes me feel surrounded by the environment I saw/heard.		[80]
3. Delegation:
To what extent can the ATT delegate the luxury shopping task?
Using the ATT at the store made my shopping experience easy so that:
3.1 I can spend time and effort on activities that are more satisfactory and meaningful.
3.2 I can search for product information by myself rather than asking for help from salespeople.
3.3 I can search for product information without using my own mobile device.
3.4 I feel free to try things on rather than spending time in communication with shop assistants.		[75,77]
4. Interactivity:
4.1 I have control over what I wanted to see.
4.2 I have control over the pace of the interaction.
4.3 I was in control of my navigation through the ATT. 		[70,79]
Luxury Shopping Experience (LSE)5. Authenticity:
5.1 I consider this shopping experience is authentic.
5.2 This experience conveys luxury brand heritage.		[30,31]
6. Uniqueness:
6.1 This experience is unique to me.
6.2 I think this experience is unique.		[76]
7. Superior quality:
7.1 This shopping experience shows excellent quality.
7.2 This shopping experience shows sophistication.
7.3 This shopping experience shows luxuriousness.		[34]
8. Aesthetic:
8.1 This experience shows superiority in aesthetic taste.
8.2 This experience shows a luxury aesthetic.
8.3 This experience shows elegance.		[25,30]
9. Conspicuousness:
9.1 This experience is conspicuous.
9.2 This experience is symbolic.		[22,33]
10. Signaling status:
10.1 This experience signals an upper position in social hierarchies.
10.2 This experience makes me obtain greater prestige.
10.3 This experience crafts the image of my ideal self.		[81]
11. Hedonism:
11.1 This shopping experience is fun to use.
11.2 This shopping experience is entertaining.
11.3 This shopping experience is enjoyable.
11.4 This shopping experience is pleasing.		[79]
12. Escapist:
12.1 While shopping, I could imagine I was a different person.
12.2 While shopping, I could feel being in a different world.
12.3 While shopping, I could feel being in a different place and time.
12.4 While shopping, I could feel completely immersed in this experience.		[82]
Customer Satisfaction 13.1 I am satisfied with the experience.
13.2 This experience is exactly what I need.
13.3 This experience has not worked out as well as I thought it would (reversed scored)		[70]
Business Model Innovation (BMI)14.1 Priority should be given to enhancing EXISTING products and services or to creating entirely NEW ones.
14.2 The focus should either remain on serving CURRENT markets and customer segments or shift to identifying and targeting completely NEW ones.
14.3 Emphasis should be placed on enhancing CURRENT resources and capabilities (e.g., technology, personnel, IT systems) or on expanding by acquiring NEW ones.
14.4 Attention should be directed toward optimizing EXISTING core processes and activities (e.g., design, logistics, marketing) or toward establishing NEW ones.
14.5 The focus should be on enhancing relationships with CURRENT strategic business partners (e.g., suppliers, distributors, end users) or on forming connections with NEW strategic partners.
14.6 Efforts should be aimed at improving CURRENT tools for customer relationship management (e.g., personal service, memberships, bonus programs) or at creating NEW tools for this purpose.
14.7 Emphasis should be placed on selling products and services through EXISTING channels (e.g., own stores, partner stores, online) or on exploring NEW distribution channels.
14.8 The priority should be on reducing CURRENT operational costs or on making SIGNIFICANT CHANGES to the cost structure of the company.
14.9 The focus should be on increasing sales from EXISTING revenue sources (e.g., products, services, leasing, sponsorships) or on developing NEW methods of generating income.		[83]
Table A2. Discriminant validity (control group): Pearson correlation and AVE square root value.
Table A2. Discriminant validity (control group): Pearson correlation and AVE square root value.
Uniqueness_LSESuperior QualityAestheticSignaling StatusSatisfaction
Uniqueness_LSE0.966 a
Superior quality0.725 ***b0.949
Aesthetic0.763 ***0.726 ***0.965
Signaling status0.705 ***0.763 ***0.772 ***0.925
Satisfaction0.421 ***0.518 ***0.471 ***0.419 ***0.932
a: AVE square root value, b: Pearson correlation. *** p < 0.001.
Table A3. Discriminant validity (treatment group): Pearson correlation and AVE square root value.
Table A3. Discriminant validity (treatment group): Pearson correlation and AVE square root value.
UniquenessTelepresenceDelegationInteractivityUniqueness_LSESuperior QualityAestheticSignaling StatusSatisfactionBMI
Uniqueness0.714 a
Telepresence0.622 b***0.819
Delegation0.439 ***0.310 **0.707
Interactivity0.509 ***0.535 ***0.586 ***0.787
Uniqueness_LSE0.650 ***0.380 ***0.469 ***0.620 ***0.860
Superior quality0.334 ***0.236 *0.355 ***0.397 ***0.430 ***0.825
Aesthetic0.585 ***0.560 ***0.464 ***0.598 ***0.446 ***0.466 *** 0.812
Signaling status0.579 ***0.513 ***0.349 ***0.450 ***0.401 ***0.298 **0.675 ***0.812
Satisfaction0.750 ***0.579 ***0.559 ***0.665 ***0.622 ***0.441 ***0.703 ***0.601 *** 0.781
BMI0.454 ***0.405 ***0.308 **0.412 ***0.447 ***0.272 **0.407 ***0.376 ***0.383 ***0.709
a: AVE square root value, b: Pearson correlation. * p < 0.05, ** p < 0.01, *** p < 0.001.

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Jtaer 19 00148 g001
Figure 1. Conceptual Model.
Figure 1. Conceptual Model.
Jtaer 19 00148 g001
Table 1. Descriptive Analysis.
Table 1. Descriptive Analysis.
Treatment Group: With ATT
ItemsN of SamplesMinMaxMeanStd. DeviationMedian
Uniqueness1003.0007.0005.8100.6395.835
Telepresence1002.6707.0005.7070.7675.670
Delegation1003.0007.0005.9870.6676.000
Interactivity1003.3307.0005.6930.7715.670
Uniqueness_LSE1003.5007.0006.0500.6916.000
Superior quality1003.5007.0005.8200.7376.000
Aesthetic1001.6707.0005.6030.8685.670
Signaling status1001.0007.0004.9701.0705.000
Customer satisfaction1001.3307.0005.8670.6826.000
BMI1003.0007.0005.3930.9605.670
Perception Gender (1 Male, 2 Female)1001.0002.0001.6000.4922.000
Perception Age1001.0007.0003.3401.1913.000
Education Level1001.0003.0002.2400.4522.000
Monthly salary Level1001.0005.0002.9901.3823.000
Control Group: Without ATT
Authenticity1001.0007.0004.8651.6475.000
Uniqueness_LSE1001.0007.0004.9351.6015.000
Superior quality1001.0007.0004.6401.7265.000
Aesthetic1001.0007.0004.8631.7805.330
Signaling status1001.0007.0004.7461.6675.200
Satisfaction1001.0007.0004.6741.6935.000
Perception Gender (1 Male, 2 Female)1001.0002.0001.7100.3272.000
Perception Age1001.0007.0002.8801.4303.000
Education Level1001.0004.0001.8700.6462.000
Monthly salary Level1001.0004.0002.7000.9373.000
Table 2. Confirmatory Factor Analyses.
Table 2. Confirmatory Factor Analyses.
FactorsCronbach’s Alpha (Control Group)Average Variance Extracted
(Control Group)		Composite Reliability (Control Group)
AI Marketing Activities (AMA)0.800.630.87
Uniqueness:0.510.510.76
1. Using this ATT makes me feel completely different from using other digital touchpoints.
2. Using this ATT is personalized for me.
3. Using this ATT makes me feel exclusive.
Telepresence:0.750.670.86
1. Using this ATT makes me feel immersed in the environment I saw/heard
2. Using this ATT makes me feel inside the environment I saw/heard.
3. Using this ATT makes me feel surrounded by the environment I saw/heard.
Delegation:0.500.500.75
To what extent can the ATT delegate the luxury shopping task?
Using the ATT at the store made my shopping experience easy so that:
1. I can spend time and effort on activities that are more satisfactory and meaningful.
2. I can search for product information by myself rather than asking for help from salespeople.
3. I feel free to try things on rather than spending time in communication with shop assistants.
Interactivity:0.690.620.83
1. I have control over what I wanted to see.
2. I have control over the pace of the interaction.
3. I was in control of my navigation through the ATT.
Luxury Shopping Experience (LSE)0.8950.560.72
Uniqueness_LSE:0.64 (0.97)0.74 (0.97)0.85 (0.97)
1. This experience is unique to me.
2. I think this experience is unique.
Superior quality:0.53 (0.96)0.68 (0.93)0.81 (0.98)
1. This shopping experience shows excellent quality.
2. This shopping experience shows sophistication.
Aesthetic:0.74 (0.98)0.66 (0.95)0.86 (0.98)
1. This experience shows superiority in aesthetic taste.
2. This experience shows a luxury aesthetic.
3. This experience shows elegance.
Signaling status:0.74 (0.95)0.66 (0.90)0.85 (0.97)
1. This experience signals an upper position in social hierarchies.
2. This experience makes me obtain greater prestige.
3. This experience crafts the image of my ideal self.
Customer satisfaction:0.68 (0.95)0.61 (0.91)0.82 (0.97)
1. I am satisfied with the experience.
2. This experience is exactly what I need.
3. This experience has not worked out as well as I thought it would (reversed scored)
Business Model Innovation (BMI):0.500.500.75
3. Emphasis should be placed on enhancing EXISTING resources and capabilities (e.g., technology, personnel, IT systems) or on expanding by acquiring NEW ones.
4. Attention should be directed toward optimizing EXISTING core processes and activities (e.g., design, logistics, marketing) or toward establishing NEW ones.
9. The focus should be on increasing sales from EXISTING revenue sources (e.g., products, services, leasing, sponsorships) or on developing NEW methods of generating income.
Control group: χ2 = 84.417, df = 72 (χ2/df = 1.172, p = 0.150), IFI = 0.994, TLI = 0.992, CFI = 0.994, RMSEA = 0.042.
Treatment group: χ2 = 432.020, df = 338 (χ2/df = 1.451, p = 0.000), IFI = 0.855, TLI = 0.832, CFI = 0.850, RMSEA = 0.068.
Table 3. Treatment and Control Group Collinearity Statistics (VIF).
Table 3. Treatment and Control Group Collinearity Statistics (VIF).
Independent VariableDependent VariableVIF (T)VIF (C)
Uniqueness of the LSESatisfaction1.6002.856
Superior Quality of the LSE1.7372.952
Aesthetic of the LSE2.7703.327
Signaling Status of the LSE2.0253.198
Table 4. Moderation Parameter Estimates.
Table 4. Moderation Parameter Estimates.
Model 1Model 2Model 3
Constant5.867 ***
(94.805)		5.867 ***
(147.694)		5.917 ***
(157.908)
LSE0.378 ***
(4.720)		0.038
(0.645)		0.005
(0.101)
AMA 0.973 ***
(11.868)		0.810 **
(9.905)
LSE × AMA −0.240 ***
(−4.735)
n100100100
R20.1850.6680.731
Adj. R20.1770.6610.722
FF (1,98) = 22.274, p = 0.000F (2,97) = 97.449, p = 0.000F (3,96) = 86.786, p = 0.000
ΔR20.1850.4820.063
ΔFF (1,98) = 22.274, p = 0.000F (1,97) = 140.840, p = 0.000F (1,96) = 22.420, p = 0.000
Dependent Variable: Satisfaction
** p < 0.01, *** p < 0.001, t statistics in parentheses.
Table 5. SEM Model Results.
Table 5. SEM Model Results.
Treatment Group
HypothesesResultStandardized Estimate βt-ValueR2
H1: LSE → satisfactionSupported0.770 ***11.9310.592
H2: AMAs moderate the relationship between the LSE and satisfactionSupported−0.287 ***−5.1730.766
H2b: AMAs moderate the relationship between the uniqueness of the LSE and satisfactionUnsupported0.073 ns0.9590.812
H2c: AMAs moderate the relationship between the superior quality of the LSE and satisfactionSupported0.145 *2.3410.812
H2d: AMAs moderate the relationship between the aesthetic of the LSE and satisfactionSupported−0.598 ***−3.7900.812
H2f: AMAs moderate the relationship between the signaling status of the LSE and satisfactionUnsupported0.090 ns0.5880.812
H3: AMA → BMISupported0.539 ***3.6550.250
H4: Satisfaction has an impact on BMISupportedPearson Correlation
0.491 ***
Control Group Standardized Estimate βt-valueR2
LSE → Satisfaction 0.510 ***5.8710.260
*** p < 0.001, * p < 0.05, ns = not significant.
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MDPI and ACS Style

Song, X.; Bonanni, C. AI-Driven Business Model: How AI-Powered Try-On Technology Is Refining the Luxury Shopping Experience and Customer Satisfaction. J. Theor. Appl. Electron. Commer. Res. 2024, 19, 3067-3087. https://doi.org/10.3390/jtaer19040148

AMA Style

Song X, Bonanni C. AI-Driven Business Model: How AI-Powered Try-On Technology Is Refining the Luxury Shopping Experience and Customer Satisfaction. Journal of Theoretical and Applied Electronic Commerce Research. 2024; 19(4):3067-3087. https://doi.org/10.3390/jtaer19040148

Chicago/Turabian Style

Song, Xin, and Carole Bonanni. 2024. "AI-Driven Business Model: How AI-Powered Try-On Technology Is Refining the Luxury Shopping Experience and Customer Satisfaction" Journal of Theoretical and Applied Electronic Commerce Research 19, no. 4: 3067-3087. https://doi.org/10.3390/jtaer19040148

APA Style

Song, X., & Bonanni, C. (2024). AI-Driven Business Model: How AI-Powered Try-On Technology Is Refining the Luxury Shopping Experience and Customer Satisfaction. Journal of Theoretical and Applied Electronic Commerce Research, 19(4), 3067-3087. https://doi.org/10.3390/jtaer19040148

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