Understanding how augmented reality’s immersive experience and privacy concerns impact consumer responses: a cognitive perspective

Highlights

• •We identify spatial presence and cognitive involvement as AR immersive components.
• •Cognitive involvement increases attitude certainty while spatial presence does not.
• •Spatial presence and cognitive involvement improve attitude toward the product.
• •Privacy concerns moderate the effect of cognitive involvement on purchase intention via attitude certainty.

Abstract

Augmented reality (AR) has emerged as a promising tool for its capacity to enable spatial presence, the sense that virtual products exist in the real world and can be interacted with. However, previous inconsistent results about the outcomes of spatial presence highlight the need to better understand the components of the learning experience in AR. Additionally, the effect of users‘ privacy concerns and how it influences consumers‘ learning in AR is largely underexplored. Thus, this study examines the impact of spatial presence and cognitive involvement on consumers‘ confidence and attitudes toward products. Across two branded AR-based virtual try-on apps, findings reveal that spatial presence and cognitive involvement enhance consumers‘ attitudes toward products, but only cognitive involvement increases attitude certainty. Meanwhile, privacy concerns degrade the effect of cognitive involvement on attitude certainty and purchase intentions. These findings contribute to the cognitive theory of multimedia learning (CTML) and privacy calculus theory, offering a nuanced perspective on how consumers process AR experiences. From a managerial standpoint, this study provides actionable insights for marketers on enhancing spatial presence while mitigating privacy-related cognitive load, ultimately optimizing AR’s effectiveness in driving positive consumer responses.

1. Introduction

Consumers increasingly enjoy using augmented reality (AR) applications to inform their purchase decisions (Nikhashemi et al., 2021; Sun et al., 2022). AR enhances consumer confidence in evaluating product quality for 56 % of shoppers, while 61 % preferred shopping with retailers offering AR experiences (NielsenIQ, 2019). These insights illustrate the potential of AR to increase sales and improve consumer engagement (Tan et al., 2022). While AR offers immersive experiences, many consumers struggle to see the need for the technology and feel uncomfortable using it (James, 2022; Hanson, 2024). This reluctance often stems from privacy concerns, which negatively impact consumer experience when using self-viewing AR technologies (Cowan et al., 2021; Poushneh and Vasquez-Parraga, 2017). These applications require sharing sensitive biometric information, raising concerns about the potential misuse of facial recognition technologies (Burden, 2023). In today’s digital landscape, the desire to preserve one’s privacy often conflicts with the need to share data for digital technologies. The level of privacy concerns varies among individuals based on their own beliefs about online services and the privacy risks associated with their usage (Li, 2012). Notably, nearly 80 % of Americans express some concerns about how companies utilize their personal data (Pew Research Center, 2019). If AR is to fulfill its potential in retail, addressing these privacy concerns is crucial to fostering consumer trust and encouraging adoption.

Despite the growing relevance of privacy concerns in AR, research on this topic remains scarce, leaving marketers with limited insights into how privacy perceptions shape consumer responses to products. Existing studies have primarily focused on usage intention, continuous use, and recommendation intention (Harborth and Pape, 2021; Qin et al., 2024). In response, retailers need more insights to better understand consumer experience with AR (Aslam and Davis, 2024). Thus, we examine the determinants of the AR experience, spatial presence, and cognitive involvement, to improve attitudes and confidence about products (i.e., attitude certainty) and purchase intentions. Spatial presence in AR refers to the consumer’s sense of “being here” in the virtual environment and the sense of possibility of action within the app (Hilken et al., 2017). In virtual environments, cognitive involvement refers to the active processing of virtual information (Schubert, 2009). Privacy concerns refer to the extent to which consumers are worried about their awareness of a firm’s privacy policy (Hilken et al., 2017).

This study contributes to AR literature by showing that spatial presence and cognitive involvement are key drivers of positive consumer responses toward products. We also clarify prior inconsistencies by highlighting that privacy concerns degrade consumer responses when they are involved in the experience. From a theoretical perspective, we build on the cognitive theory of multimedia learning (CTML) and privacy calculus theory to show that while spatial presence and cognitive involvement enhance consumer attitudes, only cognitive involvement enhances attitude certainty. The positive effect of cognitive involvement is reduced for consumers with concerns about their privacy.

From a managerial standpoint, these insights offer practical guidance for designing AR-driven marketing strategies. For instance, to maximize AR’s effectiveness, marketers should prioritize enhancing spatial presence to positively influence product evaluations while simultaneously mitigating privacy concerns with color-coded risk labels to reduce extraneous processing. By carefully balancing these elements, businesses can foster consumer trust, improve engagement, and ultimately drive sales through more effective AR experiences.

2. Literature review and theoretical background

2.1. Augmented reality and privacy concerns

Virtual try-on apps heighten concerns about data privacy, as they collect personal data of consumers interacting with products in their homes or applying them to their faces (Table 1). High privacy concerns increase perceived intrusiveness and degrade app attitudes, perceived usefulness, and flow (Feng and Xie, 2019; Cowan et al., 2021). AR enhances decision-making quality and purchase intentions however, these benefits diminish when privacy concerns are high (Sengupta and Cao, 2022). Similarly, although spatial presence typically enhances decision comfort, this effect is only observed at low and moderate levels of privacy awareness, disappearing entirely among individuals with high privacy concerns (Hilken et al., 2017). These findings underscore the complex relationship between AR and privacy concerns, demonstrating that while AR can offer immersive and informative experiences, heightened privacy awareness can diminish its perceived value.

Table 1. AR marketing use cases.

Use Case	Industry	Company	Key Benefit	Privacy concerns	Augmentation
Empty Cell	Empty Cell	Empty Cell	Empty Cell	Empty Cell	Self/Other	Environment	Product
Virtual Try-On	Retail	Gucci	Allows users to try on shoes virtually using their mobile app, enhancing customer engagement and reducing returns.	High	x
Home Decor	Home Furnishing	IKEA	Allows users to visualize furniture in their intended environment, enhancing choice confidence.	Medium/high		x
Tourism Guides	Travel & Tourism	Google Maps and Lens	Google’s AR walking directions enhance the tourist experience with real-time navigation and information overlays, enhancing visitor engagement and learning.	Medium		x
Navigation	Retail	Lowe’s	Lowe’s introduced AR navigation in stores through their app, improving the in-store experience and reducing shopping time.	Low		x
Event Marketing	Event Management	The New York Times	The New York Times used AR during events to create immersive storytelling experiences enhancing event engagement and attendee experience.	Low		x
Instruction Manuals	Consumer Electronics	Bosch	Uses AR to provide interactive manuals and guides for their products, improving customer support.	Low			x
Gamified Loyalty Programs	Retail	Starbucks	Starbucks introduced AR experiences in its stores as part of its loyalty program in China, increasing customer loyalty and engagement.	Low			x
Packaging	FMCG	Coca-Cola	Coca-Cola used AR in the FIFA World Cup for their packaging, boosting engagement with the brand.	Low			x
Advertisements	Advertising	Pepsi	Pepsi used AR to create interactive and surprising ads, enhancing ad recall.	Low			x
Customization	Automotive	Porsche	Their AR app allows users to customize cars and see real-time changes, delivering a personalized experience.	Low			x

Source: Authors own work.

To optimize AR’s effectiveness, it is crucial to address privacy concerns and clarify its role in shaping the consumer experience. This study examines three types of information that consumers exchange within the AR virtual try-on: product information, app information, and private information. In the current study, these dimensions are captured by spatial presence, cognitive involvement within the app, and privacy concerns, respectively. Existing research largely emphasizes spatial presence, while cognitive involvement and privacy concerns receive less attention (Table 2). By investigating these underexplored dimensions, we provide a more comprehensive understanding of how privacy considerations influence consumer responses to AR.

Table 2. Literature review on spatial presence, cognitive involvement and privacy concerns in AR.

Authors	Independent variable	Dependent variable	Moderator or mediator	Theory	Role of AR	Method	Main findings
Verhagen et al. (2014).	Pictures vs. 360-spin, virtual mirror	Purchase intentions	Med: Local presence, Product tangibility, product likability		Virtual mirror	Lab experiment	AR is better suited to create a local presence and results in higher product tangibility, product likability, and purchase intentions.
Huang and Hsu Liu (2014).	Perception presence, narrative, media richness	Aesthetics, playfulness, Consumer ROI, service excellence		Narrative theory, media richness theory	Virtual try-on	Survey	Narrative experience induces a higher experiential value than other simulative experiences.
Hilken et al. (2017).	Simulated physical control Environmental embedding	Purchase intentions Word-of-Mouth	Med: Spatial presence, Utilitarian & hedonic values, decision comfort, Mod: Privacy concerns, style-of-processing	Situated cognition theory	AR virtual mirror	Experiments	Positive effects of spatial presence on decision comfort at low and medium awareness of privacy practices levels, but not at high levels.
Feng & Xie (2019).	AR viewing (self vs other)	Attitude toward app, brand attitude, purchase intentions	Med: perceived intrusiveness Mod: privacy concerns	Psychological reactance theory, protection motivation theory	Virtual try-on	Experiments	For people with high levels of privacy concerns, self-viewing in AR tends to generate higher levels of perceived intrusiveness and more negative app attitudes. Giving users control over the privacy settings mitigates these negative outcomes.
Smink et al. (2020).	AR app vs. non-AR app	App responses, brand responses	Med: Spatial presence, perceived personalization, perceived intrusiveness	Reactance theory states	Virtual mirror, furniture app	Experiments	In the virtual mirror, perceived personalization enhanced purchase intentions, while perceived intrusiveness had negative persuasive consequences.
Cowan et al. (2021).	Privacy policy disclosure (concrete vs. abstract)	Intention to use Snapchat, Word of Mouth	Med: perceived usefulness, flow	Privacy calculus, construal level theory	AR face filter	Survey & experiment	Privacy concerns decrease the perceived usefulness, flow, and intentions to use AR.
Harborth & Pape (2021).	App popularity, app price, permission justification and sensitivity, AR label, usefulness	Download intention	Med: Trust in the app, privacy concerns	Contextual integrity, “antecedents – privacy concerns – outcomes”	Measurement AR app	Online experiment	Privacy concerns are predicted by permission sensitivity, trust in the AR app and non-contextual privacy-related constructs. Permission justification did not influence privacy concerns.
Sengupta & Cao (2022).	AR app	Purchase intention	Med: immersion, decision-making quality Mod: privacy concerns	Stimulus-organism-response	Interior design app	Survey	AR enhances immersion and the quality of decision-making and results in higher purchase intentions. Higher privacy concerns reduce the impact of decision-making quality on purchase intentions.
Ameen et al. (2022).	Interface design, trust, consumer peer interaction, relationship commitment	Smart shopping mall loyalty	Med: personalization Mod: privacy concerns	Trust-commitment theory, privacy calculus theory	Shopping techs, including AR	Survey	Interface design, trust, consumer peer interaction, and relationship commitment enhance personalization and in turn, smart shopping mall loyalty.
Lavoye et al. (2023).	Spatial presence, self-presence, social presence	Purchase intentions	Mod: Level of body modification	Social cognitive theory	Virtual try-on	Survey	Spatial presence enhances purchase intentions differently depending on the level of body modification of products.
Qin et al. (2024).	Interactivity, aesthetics, visual quality, service quality, technicality	Continuous use intention, recommendation intention	Med: Hedonic and utilitarian benefits, privacy concerns. Mod: AR app type	Uses and gratification theory	Translation app, AR game	Survey	Privacy concerns were negatively associated with satisfaction with the translation app but not with the video game app.
This study.	Spatial presence, cognitive involvement	Purchase intention	Mod: Privacy concerns	Cognitive theory of multimedia learning, privacy calculus theory	Virtual try-on	Surveys	Spatial presence improves product attitude while cognitive involvement improves product attitude and increases attitude certainty.Privacy concerns moderate the positive effect of cognitive involvement on attitude certainty.

Notes: Med: Mediator; Mod: Moderator, AR: Augmented reality.

Source: Authors‘ own work.

2.2. The cognitive theory of multimedia learning and privacy calculus theory

Active learning occurs when individuals select relevant information, organize it, and integrate it with their prior knowledge (Mayer, 2005). In the context of AR, consumers use virtual try-ons to actively learn about the fit and feel of fashion products. This study, therefore, focuses on three types of information encountered in AR shopping that dovetail with CTML’s three types of cognitive processing when people engage with immersive technologies.

First, essential processing refers to task-relevant processing that is required to make sense of the information (Mayer, 2024). Spatial presence enables users to overlay virtual filters directly onto their faces, select the sunglasses they wish to try on, and move their heads to evaluate the fit and feel (Hilken et al., 2017). It is crucial for understanding and mentally representing the core try-on experience.

Second, generative processing occurs when people engage with and actively interpret the information (Mayer, 2024). Cognitive involvement reflects the degree to which consumers actively engage with and process the information presented in AR (Schubert, 2009). This enables consumers to organize the AR experience into their knowledge of styles and fits and aligns with generative processing.

Lastly, extraneous processing does not contribute to the main goal but arises from poor instructions or distractions during the virtual experience (Mayer, 2024). In AR, privacy concerns do not support the try-on goal and instead shift attention toward the collection, storage, and future usage of personal data. This worry reflects extraneous cognitive processing, as emotions like anxiety and concern increase the mental effort involved in the task (Plass and Kalyuga, 2019). Given people’s limited cognitive capacity, this added burden can result in overload and degrade outcomes such as engagement or learning (Mayer, 2005). Prior research supports this by showing that privacy concerns hinder learning in AR environments (Feng and Xie, 2019; Hilken et al., 2017), suggesting they create extraneous cognitive load. According to privacy calculus theory, when the perceived risks outweigh the benefits, consumers may disengage or avoid the technology altogether (Dinev and Hart, 2006). Unlike traditional sources of extraneous load, like poor design, privacy concerns involve a cognitively and emotionally demanding cost-benefit analysis. Therefore, we suggest that privacy concerns in AR negatively impact cognitive involvement, whereas spatial presence, being more perceptual, remains unaffected.

3. Hypothesis development

3.1. The effect of spatial presence on product evaluation

Our theoretical development suggests that spatial presence enables the essential processing of the virtual try-on experience by virtually displaying how the product would look on individuals. The realistic placement of the augmented product in the real environment enables users to get more information and a better understanding of the product, thereby helping users form positive attitudes toward the product (Fan et al., 2020). AR enables users to feel a sense of spatial presence while using the app (Smink et al., 2020; Verhagen et al., 2014), which improves product likeability (Verhagen et al., 2014) and consumers’ attitudes toward products and brands (Lavoye et al., 2023; Heller et al., 2019).

H1a

Spatial presence improves attitude toward the product.

Active processing increases consumer certainty (Tormala and Rucker, 2018). Spatial presence results in higher decision comfort (Hilken et al., 2017) and lowers uncertainty about product quality and fit (Sun et al., 2022). Similarly, Poushneh (2018) suggests that AR increases certainty because consumers can evaluate the product in the real environment.

H1b

Spatial presence increases attitude certainty.

3.2. The effect of cognitive involvement on product evaluations

Our theoretical development suggests that cognitive involvement aligns with generative processing, which represents a deeper and more advanced processing of the experience (Mayer, 2024). When consumers‘ motivation for processing information is matched by the level of resources that are required to understand the information, consumers form positive attitudes toward the product (Meyers-Levy and Peracchio, 1995). Thus, when consumers allocate more cognitive effort to the experience, it leads to more favorable attitudes toward the products. Similarly, more cognitive involvement in AR enhances consumer attitudes (Recalde et al., 2024).

H2a

Cognitive involvement improves attitude toward the product.

Individuals assess the quality and reliability of the reasoning underlying their attitudes (Tormala and Rucker, 2007). When consumers perceive their attitudes as the result of careful and deliberate consideration, they are more confident in those attitudes (Rucker et al., 2008). Consumers feel more certain about their attitudes and their choices when they use AR to try on clothes virtually (Sun et al., 2022). Therefore, attitude certainty in AR is strongly influenced by the information acquired during the AR experience and the perception of thorough cognitive processing.

H2b

Cognitive involvement increases attitude certainty.

3.3. The role of product attitude, attitude certainty

Extensive research suggests that attitudes strongly predict intentions (Ajzen and Cote, 2008). Similarly, AR enhances product attitudes and purchase intentions (Park and Yoo, 2020; Phua and Kim, 2018). Additionally, AR also stimulates utilitarian and hedonic benefits that lead to higher purchase intentions (McLean and Wilson, 2019; Plotkina and Saurel, 2019).

H3

Product attitude increases purchase intentions.

Among attitudes, strong attitudes are the most influential in predicting subsequent intentions and behaviors (Howe and Krosnick, 2017) and they are the hardest to change (Howe and Krosnick, 2017). For instance, in the context of travel websites, technology features such as interactivity increase attitude certainty and attitude strength (Lee and Gretzel, 2012). AR reduces uncertainty about product fit and quality and improves product attitude (Sun et al., 2022). Thus, attitude certainty simplifies the purchase decision thus, we propose that.

H4

Attitude certainty increases purchase intentions.

3.4. The moderating role of privacy concerns

Based on our development, cognitive involvement typically increases attitude certainty (Rucker et al., 2008; Sun et al., 2022). CTML proposes that inappropriate implementation of technology would increase cognitive load and lower learning levels (Mayer, 2005). Based on this, privacy concerns increase the cognitive load and may deplete cognitive resources to think about the product. Thus, when privacy concerns are low, cognitive involvement enhances attitude certainty; however, this effect will be depleted as the privacy concerns increase. Formally.

H5

The interaction effect of privacy concerns and cognitive involvement decreases attitude certainty so that:

• (a)Cognitive involvement more strongly increases attitude certainty for people at low levels of privacy concerns.
• (b)Cognitive involvement increases attitude certainty less strongly for those at medium levels of privacy concerns.
• (c)Cognitive involvement does not significantly increase attitude certainty for those at high levels of privacy concerns.

Attitude certainty is a strong predictor of intentions (Howe and Krosnick, 2017). Therefore, a negative effect on attitude certainty is likely to result in reduced purchase intentions. Perceived risk in e-commerce degrades purchase intentions, while privacy risk did not appear to have a significant direct effect (Phamthi et al., 2024). However, prior research suggests that in AR for furniture, a high level of processing quality and privacy concerns decrease purchase intention (Sengupta and Cao, 2022). Based on this, we suggest that consumers seeing their face and body in AR will make privacy concerns a relevant factor that degrades purchase intentions because it decreases attitude certainty. Thus:

H6

The interaction effect of privacy concerns and cognitive involvement decreases purchase intentions through attitude certainty, so that:

• a)Cognitive involvement more strongly increases their purchase intentions via attitude certainty for people at low levels of privacy concerns.
• b)Cognitive involvement increases purchase intentions via attitude certainty less strongly for those at medium levels of privacy concerns.
• c)Cognitive involvement does not significantly increase purchase intentions via attitude certainty for those at high levels of privacy concerns.

Therefore, this study investigates the following theoretical framework (see Fig. 1).

4. Methodology

4.1. Overview of studies

We ran two separate surveys on Qualtrics, and participants were selected to be representative of the U.S. population in terms of age groups, which is coherent with the actual age groups that use AR for shopping (Statista, 2024). The constructs are well-identified and show good commonalities, thus, in keeping with Hair et al. (2010), our sample sizes can be considered sufficient for both studies. Participants accessed the survey with their mobile devices and were selected for one of the studies. Upon accessing the questionnaire, participants in Study 1 received instructions on how to use the Ray-Ban virtual try-on feature to evaluate two or three pairs of sunglasses, while participants in Study 2 were instructed to assess two or three lipsticks from MAC Cosmetics. They were also told that using the AR virtual try-on requires accepting to turn on their live camera on their device. They were directed to the retailers’ virtual try-on on their website, where they could wear the virtual products through AR. Compliance was verified by asking participants whether they tried the product directly on themselves or a model. Only participants who passed the attention check were selected for further analysis. After trying on the products, participants answered questions on their demographics and the focal variables. Participants spent approximately 5 min on average on the overall survey for both studies. Both studies use well-validated measures for all focal variables (Table A1 in the appendix). Specifically, to measure purchase intentions, participants responded to items adapted from Verhagen et al. (2014), such as “It is likely that I will soon buy sunglasses via this AR app”. Spatial presence was captured using items from Hilken et al. (2017) and Tussyadiah (2010), including the statement “It was as though the true location of the product had shifted into the real-world environment”. To evaluate cognitive involvement, we relied on measures from Vorderer et al. (2004), including the item “I thought most about things having to do with the app”. Privacy concerns were assessed with items adapted from Hilken et al. (2017), such as “It is very important to me that I am aware and knowledgeable about how my information and images will be used”. Attitude toward the product was measured using bipolar adjective pairs from Briñol et al. (2004), including “unfavorable/favorable,” while attitude certainty was evaluated through items like “I am completely confident of my attitude,” also based on Briñol et al. (2004), capturing the level of confidence participants had in their product evaluations.

Study 1 aims to show that spatial presence improves the attitude toward products and increases attitude certainty (H1a, b). Cognitive involvement improves product attitude and increases attitude certainty (H2a, b). In turn, product attitude (H3) and attitude certainty (H4) increase purchase intentions. Thus, showing the key role of cognitive involvement in the AR experience. Study 2 aims to show that when consumers are more concerned about their privacy, cognitive involvement decreases attitude certainty (H5). This effect also extends to purchase intentions so that when consumers are concerned about their privacy, cognitive involvement decreases attitude certainty and subsequently purchase intentions (H6).

4.2. Study 1

4.2.1. Sample

The study includes 254 U.S. individuals with an average age range between 25 and 34 years old. The sample included 104 females, 144 males, and six others. The sample also had varied education levels (High school: n = 123, bachelor’s degree: n = 66; master’s degree: n = 34, Ph.D.: n = 9, No degree: n = 22).

4.2.2. Measures

Table 3 presents all descriptive statistics and includes the skewness and kurtosis tests for the normality assumption. We use structural equation modeling (SEM)–confirmatory factor analysis (CFA) in R software, specifically utilizing the packages “lavaan” (Rosseel, 2012) for structural equation modeling and “psych” for psychometric analysis (Revelle, 2023). These tools facilitated the rigorous examination of the relationships between the variables and the validation of the constructs measured in the study. The CFA confirms that the measurement model is good (RMSEA = 0.04, χ² = 82.41, d.f. = 55, SRMR = 0.03, normed fit index (NFI) = 0.99, non-normed fit index (NNFI) = 0.98). We confirm convergent and discriminant validity (Table 4) because all values of composite reliability are above 0.87 and reach the threshold of 0.60 and all average variance extracted measures are above 0.69 and reach the threshold of 0.50 (Hair et al., 2010). We also verify that the square root of AVEs is above the correlations of the corresponding variables. Thereafter, we confirm that this data does not suffer from common method bias and specify a common factor that loads on all items in the measurement model so that if method bias is present, it should account for a significant proportion of the relationships between variables (Podsakoff et al., 2003).

Table 3. Descriptive statistics.

Construct	Items	Mean	S.D.	Item loading	Alpha	Skewness	Kurtosis
Spatial presence	Total	4.11	1.39		0.90	−0.26	−0.13
	SPP1			0.86
	SPP2			0.87
	SPP3			0.89
Involvement	Total	4.32	1.38		0.78	−0.38	0.07
	INV1			0.79
	INV2			0.81
Product attitude	Total	4.50	1.52		0.91	−0.42	0.01
	PA1			0.90
	PA2			0.92
	PA3			0.81
Attitude certainty	Total	4.84	1.32		0.88	−0.41	0.05
	AC1			0.92
	AC2			0.83
Purchase intentions	Total	4.05	1.58		0.91	−0.21	−0.58
	PI1			0.84
	PI2			0.91
	PI3			0.89

Note: S.D. = Standard deviation, Alpha = Cronbach’s alpha.

Source: Authors‘ own work.

Table 4. Measure properties.

Model	χ2 (d.f.)		Δχ2 (Δd.f.)		RMSEA	NNFI	CFI	SRMR
Measurement model	82.41 (55)				0.04	0.99	0.99	0.03
Method bias model	692.86 (65)		610.45 (10)a		0.19	0.70	0.75	0.09
Measures	1	2	3	4	5	Square root AVE
1. Spatial presence	1					0.87
2. Involvement	0.70	1				0.80
3. Product attitude	0.62	0.73	1			0.88
4. Attitude certainty	0.48	0.63	0.60	1		0.85
5. Purchase intention	0.78	0.77	0.74	0.54	1	0.88
Composite reliability	0.90	0.78	0.91	0.88	0.91
Average variance extracted	0.76	0.64	0.77	0.72	0.77

Notes: RMSEA = root means square error of approximation, CFI = comparative fit index, and NNFI = non-normed fit index.a

Relative to the measurement model, the method bias model shows a significant deterioration (increase) in chi-square at 5 %.

Source: Authors‘ own work.

4.2.3. Results

We then ran the structural equation model for hypothesis testing. The model displays an acceptable fit (RMSEA = 0.08, χ² = 161.85, d.f. = 58, SRMR = 0.07, NFI = 0.94, NNFI = 0.94). As shown in Fig. 2 and Table 5, Hypothesis 1(a) is supported because spatial presence significantly improves product attitudes (H1a, B = 0.21, SE = 0.13, t = 2.59, p = .01). However, Hypothesis 1(b) is not supported as spatial presence does not significantly influence attitude certainty (H1b, B = 0.08, SE = 0.13, t = 0.77, p = .44). We suggest that this effect is non-significant because spatial presence is processed with low effort and does not enable to form strong attitude such as attitude certainty. Hypothesis 2 is supported as cognitive involvement improves product attitude (H2a, B = 0.63, SE = 0.18, t = 5.74, p < .001) and increases attitude certainty (H2b, B = 0.59, SE = 0.15, t = 5.01, p < .001). Hypothesis 3 is supported because product attitudes increase purchase intentions (H3, B = 0.66, SE = 0.08, t = 8.10, p < .001). Hypothesis 4 is also supported because attitude certainty increases purchase intentions (H4, B = 0.20, SE = 0.07, t = 3.39, p < .001).

Table 5. Results of hypothesis testing.

Relationships	Std. Effect	Unstd. Effect	Std. Error	t-value	Hypothesis
Spatial presence → Product attitude	0.21∗∗	0.35	0.13	2.59	H1a: Supported
Spatial presence → Attitude certainty	0.08n.s.	0.10	0.13	0.77	H1b: Not Supported
Involvement → Product attitude	0.63∗∗∗	1.02	0.18	5.74	H2a: Supported
Involvement → Attitude certainty	0.59∗∗∗	0.77	0.15	5.01	H2b: Supported
Product attitude → PI	0.66∗∗∗	0.66	0.08	8.10	H3: Supported
Attitude certainty → PI	0.20∗∗∗	0.25	0.07	3.39	H4: Supported

Note: Critical t-value (one-tailed) = 1.645; Significance level: ∗∗∗p < .001, ∗∗p < .01, ∗p < .05. n.s.: non-significant. PI = purchase intentions.

Source: Authors‘ own work.

4.3. Study 2

4.3.1. Sample

A total of 236 participants were recruited on Qualtrics, and all satisfied the same quality requirements as in Study 1. Only women have been recruited because they are the typical target consumers for lipstick brands. Most participants had a high school level education (52 %). In terms of age, 26 participants were between 18 and 24, 45 participants were between 25 and 34, and 49 participants were between 35 and 44. 69 participants were between 45 and 54, and 47 participants were between 55 and 64. Most of the participants did not have prior knowledge of fashion try-on services (76 %).

4.3.2. Measures

Table 6 presents all descriptive statistics and includes the skewness and kurtosis tests for the normality assumption. The highest variance inflation factor (VIF) of focal variables on purchase intentions is 1.66; thus, we confirm that all VIF values are well below the threshold of 10 and the highest condition index is 12.36 thus below 30, and therefore these results provide evidence that multicollinearity is not an issue in this data. Specifically, cognitive involvement (VIF = 1.18, condition index = 8.79), privacy concerns (VIF = 1.50, condition index = 11.49), and attitude certainty (VIF = 1.65, condition index = 12.42).

Table 6. Descriptive statistics.

Construct	Items	Mean	S.D.	Item loading	Alpha	Skewness	Kurtosis
Cognitive involvement	Total	4.27	1.27		0.71	0.01	0.53
	INV1			0.71
	INV2			0.78
Privacy concerns	Total	5.10	1.36		0.90	−0.38	−0.28
	PRI1			0.83
	PRI2			0.90
	PRI3			0.87
Attitude certainty	Total	4.82	1.23		0.88	−0.26	0.36
	AC1			0.82
	AC2			0.88
	AC3			0.81
Purchase intentions	Total	4.21	1.52		0.94	−0.24	−0.30
	PI1			0.86
	PI2			0.94
	PI3			0.93

Note: S.D. = Standard deviation, Alpha = Cronbach’s alpha.

Source: Authors‘ own work.

We use LISREL 12 and find that the CFA measurement model displays good fit indices (RMSEA = 0.06, χ2 = 78.38, d.f. = 38, SRMR = 0.04, NFI = 0.94, NNFI = 0.95). The model shows a good internal consistency of our measures as all values of composite reliability are above 0.71 (Table 7). Convergent validity is confirmed because all average variance extracted measures are above 0.56 (Hair et al., 2010). All square root AVEs are greater than the corresponding correlations; thus, the results confirm discriminant validity. We also confirm that common method bias is not an issue in this study (Podsakoff et al., 2003).

Table 7. Measure properties.

Model	χ2 (d.f.)	Δχ2 (Δd.f.)	RMSEA	NNFI	CFI	SRMR
Measurement model	78.38 (38)		0.06	0.95	0.96	0.04
Method bias model	954.64 (44)	876.26 (6)a	0.30	0.33	0.46	0.18
Measures	1	2	3	4	Square root AVE
1. Cognitive involvement	1				0.75
2. Privacy concerns	0.14	1			0.87
3. Attitude certainty	0.32	0.63	1		0.84
4. Purchase intention	0.22	0.21	0.46	1	0.91
Composite reliability	0.71	0.90	0.88	0.94
Average variance extracted	0.56	0.75	0.71	0.83

Notes: RMSEA = root means square error of approximation, CFI = comparative fit index, and NNFI = non-normed fit index.a

Relative to the measurement model, the method bias model shows a significant deterioration (increase) in chi-square at 5 %.

Source: Authors‘ own work.

4.3.3. Hypothesis testing

We aim to show that the interaction of privacy concerns and cognitive involvement decreases attitude certainty and purchase intentions. We test the hypothesis of moderated mediation using SPSS with Hayes’ model 7 Process MACRO with 5000 bootstrap and 95 % confidence intervals (2018). Cognitive involvement (B = 0.81∗∗∗, S.E. = 0.17, t = 4.17, LLCI – ULCI: 0.47 to 1.15) and privacy concerns (B = 0.85∗∗∗, S.E. = 0.13, t = 6.51, LLCI – ULCI: 0.59 to 1.10) increase attitude certainty (see Table 8). As shown in Fig. 3, privacy concerns decrease the effect of cognitive involvement on attitude certainty (H5, B = – 0.10∗∗∗, S.E. = 0.03, t = – 3.26, LLCI – ULCI: −0.16 to −0.04). Cognitive involvement more strongly increases attitude certainty for people with low levels of privacy concern (H5a, B = 0.41, S.E. = 0.07, t = 6.04, LLCI – ULCI: 0.28 to 0.55). Meanwhile, cognitive involvement less strongly increases attitude certainty strongly for those at medium levels (H5b, B = 0.31, S.E. = 0.06, t = 5.54, LLCI – ULCI: 0.20 to 0.42) and the positive effect is non-significant for those at high levels of privacy concerns (H5c, B = 0.11, S.E. = 0.07, t = 1.47, LLCI – ULCI: −0.04 to 0.26). Thus, supporting hypotheses 5 (a, b, c).

Table 8. Result of moderation analysis.

Relationship/Effect	Unstd. Effect	Std. Error	t-value	LLCI – ULCI	Hypothesis
Direct Effects on Attitude Certainty (R2 = .42), F(4, 232) = 56.79, p < .001
Cognitive involvement → Attitude certainty	0.81∗∗∗	0.17	4.74	0.47 to 1.15
Privacy concerns → Attitude certainty	0.85∗∗∗	0.13	6.51	0.59 to 1.10
Interaction effect of privacy concerns on the relationship between cognitive involvement and attitude certainty	−0.10∗∗∗	0.03	−3.26	−0.16 to −0.04	H5: Supported
Conditional Indirect Effect of Cognitive Involvement on Attitude Certainty
Low privacy concerns	0.41	0.07		0.28 to 0.55	H5a: Supported
Medium privacy concerns	0.31	0.06		0.20 to 0.42	H5b: Supported
High privacy concerns	0.11	0.07		−0.04 to 0.26	H5c: Supported
Direct Effects on Purchase Intentions (R2 = .27), F(4, 233) = 42.51, p < .001
Cognitive involvement → Purchase intentions	0.45∗∗∗	0.09	5.44	0.29 to 0.51
Attitude certainty → Purchase intentions	0.36∗∗∗	0.08	4.72	0.21 to 0.51
Index of Moderated Mediation
Cognitive involvement → Attitude certainty → Purchase intentions	−0.04	0.01		−0.07 to −0.01	H6: Supported
Conditional Indirect Effect of Cognitive Involvement on Purchase Intentions
Low privacy concerns	0.15	0.05		0.06 to 0.25	H6a: Supported
Medium privacy concerns	0.11	0.04		0.04 to 0.19	H6b: Supported
High privacy concerns	0.04	0.04		−0.02 to 0.12	H6c: Supported

Bolded values were significant. ^n.s.: Non-significant. Significance level: ∗∗∗p < .001, ∗∗p < .01, ∗p < .05.

Source: Authors‘ own work.

Cognitive involvement (B = 0.45, S.E. = 0.09, t = 5.44, LLCI – ULCI: 0.29 to 0.51) and attitude certainty (B = 0.36, S.E. = 08, t = 4.72, LLCI – ULCI: 0.21 to 0.51) increase purchase intentions. Regarding the moderated mediation (Fig. 4), we find that the index of moderated mediation is negative and significant (H6, index: −0.04, BootS.E. = 0.01, BootLLCI – BootULCI: −0.07 to −0.01). Specifically, for people with a low level of concern about privacy policies, cognitive involvement more strongly increases purchase intentions through attitude certainty (H6a, B = 0.15, BootS.E. = 0.05, BootLLCI – BootULCI: 0.06 to 0.25). Meanwhile, the effect of cognitive involvement on purchase intentions through attitude certainty is weaker for those at medium levels of privacy concerns (H6b, B = 0.11, BootS.E.: 0.04, BootLLCI – BootULCI: 0.04 to 0.19) and non-significant for those at high levels of privacy concerns (H6c, B = 0.04, BootS.E.: 0.04, BootLLCI – BootULCI: −0.02 to 0.12). Thus, hypotheses 6 (a, b, c) are supported.

5. Key contributions

5.1. Theoretical contribution

Our study complements existing knowledge in CTML by conceptualizing spatial presence as a form of immersive realism that enhances product evaluations through essential processing, and cognitive involvement as a form of mental engagement that influences attitude certainty via generative processing. As a deeper form of processing, cognitive involvement increases attitude certainty, which is a key predictor of consumer decision confidence (Tormala and Rucker, 2007). Meanwhile, spatial presence does not enhance attitude certainty because it relies on simpler essential processing. Additionally, the study further relies on privacy calculus theory to conceptualize privacy concerns as a form of extraneous processing. Privacy concerns weakened or even eliminated the positive effect of cognitive involvement on consumer responses. Specifically, this is explained based on the CTML, when cognitive processing, which uses a generative and complex processing of information, is coupled with high extraneous processing for consumers with a high level of privacy concerns, it results in a cognitive overload that degrades consumer confidence and purchase intentions. This research adds nuance to prior research that found that when consumers are concerned about privacy policies, spatial presence degrades decision comfort (Hilken et al., 2017) and suggests that cognitive involvement is a better predictor of attitude certainty, which is closely linked to decision confidence.

Our study contributes to the AR presence literature (Rauschnabel et al., 2022) by unpicking two different facets of the AR experience and explicating their distinct functions and related consumer perceptions and evaluations of products. Previous research has shown that AR, compared to traditional 2D presentations, significantly enhances consumers‘ perceived product understanding and sales (Tarafdar et al., 2024). Cognitive involvement plays an important role in the AR experience, yet it is often overlooked and merely treated as a control variable (Hilken et al., 2017). Our study clarifies that privacy concerns act as a boundary condition, moderating the efficacy of cognitive processing within AR. This distinction contributes to the broader retail and consumer services literature by explaining the elements of AR that contribute to positive evaluations and confidence, two key drivers of conversion and brand loyalty.

Lastly, we contribute to the ongoing discussion on the impact of privacy concerns with AR. In line with privacy calculus theory, we observe that consumers weigh the risks and benefits of AR, which degrades the positive effect of cognitive involvement on attitude certainty and purchase intentions. Thus, we find that privacy calculus theory in this context predicts behavioral intentions beyond the intention to use AR apps (Harborth and Pape, 2021) and continuous use and recommendation intention (Qin et al., 2024). Additionally, the finding that privacy concerns degrade cognitive involvement reveals a new important dimension that was overlooked in the recent review of customer privacy concerns (Okazaki et al., 2020).

5.2. Practical contributions

First, while spatial presence and cognitive involvement are interrelated, they are different constructs. Previous research has shown that the relationship between spatial presence and decision comfort is reduced when consumers have a high level of privacy concerns (Hilken et al., 2017). The authors propose that consumers may begin to doubt the authenticity of the AR experience when they are overly focused on privacy risks, which attenuates the comforting effects of spatial presence for consumer decision-making (Hilken et al., 2017). However, our paper finds that spatial presence does not increase attitude certainty. Instead, the effect of cognitive involvement on attitude certainty is moderated by privacy concerns. This contributes to a more nuanced theoretical insight and shows that not all immersive effects are equally sensitive to privacy threats. Based on this, we encourage the development of more embedded and embodied AR apps without the risk of negative backlash from privacy concerns. To enhance spatial presence, designers should incorporate intuitive gestures, uncluttered interfaces, and simple product interaction features that maintain immersion while minimizing cognitive load. Regarding content strategy, we recommend limiting promotional content within the AR environment to avoid cognitive overload during product evaluation. Instead, task-relevant cues should be prioritized to support engagement and decision-making.

Second, this study contributes to prior research on the positive effect of AR to improve attitude toward products. Our study extends this finding and shows that both spatial presence and cognitive involvement contribute to more positive attitudes toward products (Söderström et al., 2024; Aslam and Davis, 2024). This insight further supports the large body of research showing that brands benefit from using AR to promote their products.

Lastly, this study suggests that unclear privacy policies negatively impact consumer decision-making by decreasing confidence in their attitudes. This aligns with previous research showing that privacy concerns reduce technology’s perceived value (Pizzi and Scarpi, 2020) and lower engagement (Cowan et al., 2021). While these studies focused on non-branded AR filters in social media contexts, our study extends this understanding to branded AR apps, revealing that a brand name alone is insufficient to foster trust. Privacy concerns, especially during deep cognitive involvement, can hinder information processing and reduce attitude certainty. To mitigate these effects, we recommend improving transparency in privacy policies and providing granular opt-in privacy settings for virtual try-ons. In practice, privacy policy communication should be seamlessly embedded within the app interface, using visual aids such as icons or color-coded risk levels to help users easily assess data-related risks. This approach becomes even more critical as AI-powered AR introduces complex privacy concerns related to consumer data used for personalized experiences, raising essential questions about data collection, consent, and storage.

6. Limitations and future research directions

As is typical of research, the current study has certain limitations that may be addressed in future research. The data was collected from Qualtrics as a survey with cross-sectional data, thus involving the risk of common method bias (Podsakoff et al., 2012), which we considered before and after data collection. Before the data collection, we ensured that common method bias would not be an issue as recommended (MacKenzie and Podsakoff, 2012). After data collection, Harman’s criterion was used to further mitigate the risk of common method bias (MacKenzie and Podsakoff, 2012).

Cross-sectional data provides preliminary evidence for correlation but is not a strong test for causality. We acknowledge the possibility of self-selection and lack of randomization biases, as participants voluntarily engaged in the immersive shopping experience, potentially overrepresenting tech-savvy individuals. However, we do not think this bias has the potential to confound our effects because AR users are tech-savvy people. Future research should use longitudinal or experimental designs to track consumer attitudes over time. Cross-cultural studies might help to compare how privacy concerns affect AR engagement globally. Another interesting avenue would be to explore consumer cognitive and affective responses to AR across industries such as automobiles, healthcare, and hospitality. As the adoption of AR technology increases, future research should include less tech-savvy users and focus on wider audiences or conduct experiments in controlled environments to avoid bias. Moreover, personal factors such as digital literacy, technological familiarity, trust disposition, and privacy sensitivity might be explored to strengthen the existing model.

Lastly, we hope that this research will inspire future research on privacy concerns because they are a crucial element in consumer online safety and well-being. Notably, the research on privacy concerns would benefit from a qualitative investigation to enrich the understanding of consumer perception across gender, age, and cultural backgrounds.

CRediT authorship contribution statement

Virginie Lavoye: Writing – original draft, Visualization, Supervision, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Harish Kumar: Writing – review & editing, Writing – original draft, Investigation, Conceptualization.

Funding

This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sector.

Declaration of competing interest

The authors declare that there are no conflicts of interest regarding the publication of this manuscript.

Appendix A. 

Table A1. Items for questionnaires and instructions

Construct	Item wording
Introduced by the following statement: “Does this statement correspond to the task you completed?”
Attention check Study 1 & 2	I fitted products on fashion models” (correct answer: “No”).
Respondents answered on a seven-point Likert scale ranging from 1 = “strongly disagree” to 7 = “strongly agree”.
Purchase intentions ( Verhagen et al., 2014), Study 1 & 2	PI1: It is likely that I will soon buy sunglasses via this AR app. PI2: It is likely that I will purchase sunglasses from this AR app in the future. PI3: It is likely that I will return to this AR app.
Spatial presence (Hilken et al., 2017) , Study 1	SP1: It was as though the true location of the product had shifted into the real-world environment. SP2: I felt like I could move the product around in the real world. SP3: The product gave me the feeling I could do things with it.
Cognitive involvement(Vorderer et al., 2004), Study 1 & 2	INV1: I thought most about things having to do with the app. INV2: I thoroughly considered what the things in the app had to do with one another.
Privacy Concerns, (Hilken et al., 2017), Study 2	PRI1: Companies using this online try-on tool should disclose the way the personal information and images are collected, processed, and used PRI2: A good consumer online privacy policy should have a clear and conspicuous disclosure. PRI3: It is very important to me that I am aware and knowledgeable about how my information and images will be used.
Introduced by the following statement: “Which of the following adjectives best describes your attitude toward the products displayed in the try-on service?”
Attitude toward products(Briñol et al., 2004), Study 1	PA1: Unfavorable/favorable. PA2: Negative/positive. PA3: Bad/good.
Respondents answered on a seven-point Likert scale ranging from 1 = “strongly disagree” to 7 = “strongly agree”.
Attitude certainty (Briñol et al., 2004), Study 1 (2 items) & 2	AC1: I am absolutely certain of my attitude toward the product. AC2: I am convinced that my attitude toward the product is correct. AC3: I am completely confident of my attitude.

Source: Authors‘ own work.

Data availability

Data will be made available on request.

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