Augmented Reality Product Spatial Presence

1. Abstract

The Augmented Reality Product Spatial Presence (ARPSP) scale is an eight-item self-report psychometric instrument designed to assess the degree to which consumers perceive virtual, computer-generated products within an augmented reality (AR) interface as genuinely present, physically co-located, and actionable within their real-world environment. Originally conceptualized and validated by Hilken, de Ruyter, Chylinski, Mahr, and Keeling (2017) in their seminal work on service marketing and online retail experiences, the ARPSP represents an adaptation of the foundational Measurement, Effects, Conditions (MEC) Spatial Presence Questionnaire developed by Vorderer et al. (2004). Unlike conventional measures of telepresence or virtual reality immersion that primarily capture an observer's sensation of being transported into an artificial digital realm ("self-location"), the ARPSP reorients the theoretical focus toward object-centric spatialization, capturing the perceived physical embodiment and behavioral affordances of digital assets situated in the user's immediate surroundings.

The instrument is divided into two distinct, highly correlated subdimensions: Perceived Spatial Presence of the Product (Items 1–4), which assesses the perceptual illusion that a virtual item shares the same three-dimensional physical space as the observer; and Possible Actions with the Product (Items 5–8), which quantifies the subjective affordances and actionable potentials associated with reaching, touching, and manipulating the virtual artifact. Both subdimensions utilize a 7-point Likert response format ranging from 1 (Strongly disagree) to 7 (Strongly agree). Across initial validation cohorts and subsequent cross-validation investigations within e-commerce, virtual try-on systems, and retail service environments, the ARPSP exhibits exceptional psychometric properties, consistently yielding internal consistency coefficients (Cronbach's alpha and composite reliability) exceeding .90, robust convergent and discriminant validity, and clean two-factor structural validity demonstrated through confirmatory factor analysis. The scale provides marketing researchers, human-computer interaction (HCI) scholars, and psychometricians with a rigorous, standardized diagnostic tool to evaluate how spatial realism drives cognitive processing, diagnostic decision-making, and consumer decision confidence.

2. Keywords

Augmented Reality, Product Spatial Presence, Telepresence, Spatial Presence, Human-Computer Interaction, Virtual Try-On, Consumer Psychology, E-Commerce Affordances, Perceived Realism, Psychometrics, MEC Spatial Presence

3. Authors

The Augmented Reality Product Spatial Presence scale was adapted and validated by an interdisciplinary team of scholars in marketing, information systems, and service management:

• Tim Hilken: Department of Marketing and Supply Chain Management, School of Business and Economics, Maastricht University, Maastricht, The Netherlands. His research specializes in augmented reality, digital customer experience, and retail technologies.
• Ko de Ruyter: King's Business School, King's College London, London, United Kingdom; and Department of Marketing and Supply Chain Management, Maastricht University, Maastricht, The Netherlands. An authority on customer experience management, service innovation, and social media analytics.
• Mathew Chylinski: School of Marketing, UNSW Business School, University of New South Wales, Sydney, Australia. Focuses on consumer decision-making, pricing, and interactive retail technologies.
• Dominik Mahr: Department of Marketing and Supply Chain Management, School of Business and Economics, Maastricht University, Maastricht, The Netherlands. Investigates service design, digital transformation, and customer co-creation.
• Debbie I. Keeling: Department of Management, University of Sussex Business School, Brighton, United Kingdom. Specializes in consumer behavior, digital healthcare, and technology adoption dynamics.

4. Purpose

The primary purpose of the Augmented Reality Product Spatial Presence (ARPSP) scale is to provide a theoretically grounded, psychometrically robust instrument for measuring the psychological phenomenon of spatial presence specifically directed toward simulated objects within hybrid virtual-physical spaces. In conventional physical retailing, consumers evaluate prospective purchases through rich sensorimotor encounters—touching fabric, inspecting physical dimensions, testing fit, and assessing geometric coherence within an environment. For decades, electronic commerce has suffered from an intrinsic epistemic barrier known as information asymmetry or experiential deficit: consumers are forced to evaluate three-dimensional merchandise through flat, non-interactive two-dimensional visual representations.

With the advent of mobile and wearable augmented reality technologies, computer graphics algorithms can register digital objects in real time onto a user's physical sensory field. However, technological implementation alone does not guarantee a subjective psychological state of presence. The ARPSP was developed to resolve this evaluative gap. Rather than asking whether the user feels "transported" into an alternate digital world (the classical definition of telepresence popularized by Steuer, 1992, and Witmer & Singer, 1998), the ARPSP specifically investigates the inverse cognitive dynamic: the psychological sensation that an artificial object has crossed over into the consumer's authentic physical milieu.

In applied academic research and organizational testing, the ARPSP serves several critical functions. First, it acts as a central mediator in structural models explaining how interface features (such as tracking accuracy, surface detection, lighting estimation, and render latency) translate into psychological outcomes, including cognitive load reduction, product comprehension, brand attitude, and purchase intention. Second, within human-computer interaction and user experience (UX) design, the ARPSP functions as a standardized diagnostic benchmark to optimize visual fidelity, spatial mapping, and haptic feedback loops. Third, in marketing psychology, the instrument facilitates empirical testing of embodied cognition theories, examining how perceptual spatial presence fosters feelings of psychological ownership and tactile certainty before a physical transaction occurs.

5. Psychological Construct

The construct of Augmented Reality Product Spatial Presence represents a specialized operationalization of spatial presence within mixed reality environments. Psychologically, presence is defined as a "perceptual illusion of non-mediation" (Lombard & Ditton, 1997), occurring when a medium user fails to consciously perceive the intervening digital apparatus and responds to mediated entities as if they were unmediated physical realities. In augmented reality, however, the individual does not experience complete sensory immersion into a virtual environment; their perception remains firmly anchored in the physical world. Therefore, the construct does not center on self-location in a synthetic environment, but rather on the spatial and actionable integration of a digital entity into the actor's real-world behavioral field.

The ARPSP construct is delineated into two correlated theoretical dimensions reflecting the dual-stage perceptual model of spatial presence formulated by Vorderer et al. (2004):

1. Perceived Spatial Presence of the Product (Spatial Co-location)

This subdimension (captured by Items 1 through 4) addresses the visual-spatial hypothesis that the digital entity exists as an authentic, physical component of the user's physical surroundings. It involves sensory cues—such as stereoscopic depth, realistic occlusion, shadow casting, and motion parallax—that fool the early visual cortex into categorizing the digital product as sharing the same continuous spatial coordinates as the observer. For example, when an individual utilizes an AR mobile application to project a virtual cosmetics item onto their vanity, this dimension captures whether the item appears to occupy real physical volume and adhere to the geometric laws of the room.

2. Possible Actions with the Product (Perceived Affordances)

Rooted in ecological psychology and motor simulation theory, this subdimension (Items 5 through 8) quantifies the user's subjective perception that the virtual item affords physical interaction. According to Gibson's (1979) theory of affordances, humans perceive objects not solely by their abstract geometrical features, but primarily by the motor actions they afford (e.g., graspability, pushability, reachability). This dimension captures the cognitive activation of motor schemas: despite knowing at an intellectual level that the product is a graphical projection, the consumer mentally simulates reaching out, grasping, applying, or manipulating the product as if it possessed mass, texture, and physical boundaries.

Together, these dimensions construct a holistic psychological state wherein cognitive dissonance regarding the artifact's artificiality is suppressed, enabling intuitive experiential evaluation of the product.

6. Theoretical Framework

The theoretical architecture of the ARPSP scale is firmly anchored in two foundational paradigms: the MEC Spatial Presence Model (Vorderer et al., 2004; Wirth et al., 2007) and the Theory of Embodied Cognition (Barsalou, 2008; Wilson, 2002).

The MEC Two-Level Model of Spatial Presence

The Measurement, Effects, Conditions (MEC) model conceptualizes spatial presence not as a singular emotional state, but as a hierarchical, two-stage cognitive process. In the primary phase, the user forms a Mental Model of the Spatial Environment (Spatial Situation Model, or SSM), synthesizing sensory inputs into a coherent mental representation of three-dimensional space. In the secondary phase, involuntary attentional allocation leads to an unconscious hypothesis-testing process regarding one's spatial location and behavioral capacity.

In the original MEC framework, this produces two cognitive outcomes: Self-Location (the sensation of being physically inside the mediated space) and Possible Actions (the sensation of being able to act inside that space). Hilken et al. (2017) recognized that while the MEC architecture was theoretically sound, its operationalization assumed fully immersive virtual environments (VR). In augmented reality, the user is already located in the real world. Thus, Hilken et al. theoretically adapted the MEC architecture by shifting the focus from the user's self-location to the product's spatial co-presence. Under this modified framework, the sensory spatial model confirms that the virtual product occupies the same physical coordinate system as the observer, which subsequently activates the psychological expectation of actionable physical affordances.

Embodied Cognition and Mental Simulation

The secondary theoretical pillar is embodied cognition, which posits that cognitive processes are deeply rooted in the body's interactions with the physical world. When consumers evaluate products, visual perception triggers unconscious motor imagery—mental simulations of grasping, lifting, and utilizing the product. In traditional e-commerce, the absence of spatial depth and natural scale suppresses these motor simulations, resulting in cognitive strain and uncertainty. By situating the product within the user's visual frame-of-reference (e.g., rendering virtual makeup on the consumer's own live video reflection or placing furniture on their authentic floor), AR stimulates sensory-motor mirror systems, generating vivid mental simulations of product ownership and product trial.

7. Validity

The validity of the ARPSP scale has been thoroughly established through multiple construct validation procedures across diverse empirical contexts:

Construct and Convergent Validity

Convergent validity has been repeatedly substantiated across experimental studies. In Hilken et al. (2017), all eight standardized factor loadings were statistically significant (p < .001) and exceeded the recognized threshold of .70 (ranging from .78 to .91). The average variance extracted (AVE) for both subdimensions and the global higher-order presence construct consistently exceeded the recommended .50 cutoff, typically demonstrating values between .68 and .79. This indicates that the latent factors explain substantially more variance than measurement error.

Discriminant Validity

Discriminant validity was established using the Fornell-Larcker criterion and the Heterotrait-Monotrait ratio of correlations (HTMT). The square root of the AVE for both Perceived Spatial Presence of the Product and Possible Actions with the Product exceeded their inter-factor correlation (r ≈ .65–.74). Furthermore, the scale demonstrated distinct conceptual separation from related constructs, including visual aesthetics, perceived telepresence, technology enjoyment, and cognitive effort, with all HTMT ratios remaining well below the conservative .85 threshold.

Predictive and Nomological Validity

Nomological validity is supported by the scale's robust relationships within structural equation models. In empirical evaluations involving augmented reality retail applications (e.g., virtual cosmetics mirrors and furniture placement tools), scores on the ARPSP scale reliably predicted downstream cognitive and behavioral constructs. Specifically, higher ARPSP scores significantly decreased consumer decision discomfort (β = -.32, p < .01) and service failure expectations, while significantly enhancing consumer decision confidence (β = .48, p < .001), perceived value of the online shopping experience, and ultimate purchase intentions. Subsequent cross-validation investigations in mobile retail and interactive design have confirmed that ARPSP mediates the direct relationship between technical rendering fidelity and consumer behavioral commitment.

8. Reliability

The ARPSP scale exhibits exceptional internal consistency and psychometric reliability across diverse demographic cohorts and digital platforms.

• Cronbach's Alpha (α): In the initial validation study conducted by Hilken et al. (2017), the overall eight-item composite scale demonstrated a Cronbach's alpha of .94. When examined at the subdimension level, Perceived Spatial Presence of the Product yielded an alpha coefficient of .93, while Possible Actions with the Product yielded an alpha coefficient of .91. Subsequent independent studies across e-commerce contexts have consistently reported alphas ranging between .89 and .95.
• Composite Reliability (CR): Composite reliability scores for both latent dimensions consistently surpass the standard .70 and rigorous .80 benchmarks, yielding values between .91 and .94, confirming high internal construct cohesion.
• Average Variance Extracted (AVE): AVE metrics for the subdimensions regularly surpass .70, demonstrating that the measurement items reliably capture the variance of their intended latent constructs rather than stochastic noise.
• Test-Retest Reliability: In laboratory settings examining longitudinal interface exposure across repeated trials (with 7- to 14-day intervals), the scale demonstrated temporal stability with test-retest correlation coefficients exceeding r = .82, indicating that the instrument reliably measures individual subjective states without erratic measurement fluctuation.

9. Factor Analysis

The factorial validity of the ARPSP scale has been verified through rigorous Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA):

Exploratory Factor Analysis (EFA)

During preliminary instrument adaptation, principal axis factoring with promax rotation revealed a clean two-factor solution accounting for over 76% of the total variance. Items 1 through 4 loaded strongly onto Factor 1 (Perceived Spatial Presence), with factor loadings ranging from .79 to .89 and negligible cross-loadings (< .20). Items 5 through 8 loaded unequivocally onto Factor 2 (Possible Actions with the Product), with loadings ranging from .76 to .88. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy consistently exceeded .91, and Bartlett's Test of Sphericity reached statistical significance (p < .0001).

Confirmatory Factor Analysis (CFA)

Structural equation modeling and CFA conducted on independent validation samples confirmed that the theoretical two-factor model provides an exceptional fit to the empirical data, significantly outperforming a unidimensional single-factor alternative model. Standard model fit indices demonstrated excellent alignment with modern psychometric criteria (Hu & Bentler, 1999):

• Chi-Square / Degrees of Freedom (χ²/df): Values consistently range between 1.45 and 2.10 (well within the acceptable < 3.0 range).
• Comparative Fit Index (CFI): .982 to .994 (exceeding the strict .95 benchmark).
• Tucker-Lewis Index (TLI): .975 to .990.
• Root Mean Square Error of Approximation (RMSEA): .038 to .052 (with 90% confidence intervals below .06).
• Standardized Root Mean Square Residual (SRMR): .024 to .035 (well below the .08 threshold).

Researchers may model the ARPSP as two distinct first-order factors or as a second-order hierarchical model where an overarching "Product Spatial Presence" construct accounts for the shared variance between Perceived Spatial Presence and Possible Actions.

10. Instrument / Measurement Tool

• Tool Name: Augmented Reality Product Spatial Presence (ARPSP) Scale
• Source Adaptation: Adapted from Vorderer et al.'s (2004) MEC Spatial Presence Questionnaire by Hilken, de Ruyter, Chylinski, Mahr, & Keeling (2017)
• Instrument Type: Self-administered psychometric questionnaire
• Target Population: Consumers, end-users, and experimental participants interacting with augmented reality applications, virtual try-on systems, and mobile AR software
• Total Item Count: 8 items
• Subdimensions:
  • Perceived Spatial Presence of the Product: Items 1–4
  • Possible Actions with the Product: Items 5–8
• Authentic Response Scale: 7-point Likert scale (1 = Strongly disagree to 7 = Strongly agree)
• Scoring Rules:
  • All 8 items are positively keyed (there are no reverse-coded items).
  • Dimension scores are calculated by computing the arithmetic mean of their corresponding items: Mean of Items 1–4 for Perceived Spatial Presence of the Product; Mean of Items 5–8 for Possible Actions with the Product.
  • An overall global Product Spatial Presence index is computed by averaging all 8 items. Higher values indicate a stronger psychological perception that the digital product is physically present and actionable in the physical world.
• Item Contextualization Note: In published studies, the product noun (e.g., "makeup products") is adjusted to match the specific virtual artifact under investigation (e.g., "furniture items," "eyewear," "automobile") without altering the syntactic structure or psychometric properties of the scale.

11. Permissions & Fee and Test Year

The Augmented Reality Product Spatial Presence scale was officially published in 2017 in the Journal of the Academy of Marketing Science. As an academic psychometric tool derived from publicly funded and scholarly research, the scale is free of charge for non-commercial academic research, educational inquiry, and university-level scientific investigation. Researchers utilizing the instrument must appropriately credit the original authors by citing Hilken et al. (2017). For commercial deployment, proprietary software integration, or fee-based user research, practitioners should consult the copyright policies of the publisher (Springer Nature / Academy of Marketing Science) and the corresponding authors regarding intellectual property rights and permissions.

12. References

Barsalou, L. W. (2008). Grounded cognition. Annual Review of Psychology, 59(1), 617–645. https://doi.org/10.1146/annurev.psych.59.103006.093639

Gibson, J. J. (1979). The ecological approach to visual perception. Houghton Mifflin.

Hilken, T., de Ruyter, K., Chylinski, M., Mahr, D., & Keeling, D. I. (2017). Augmenting the eye of the beholder: Exploring the strategic potential of augmented reality to enhance online service experiences. Journal of the Academy of Marketing Science, 45(6), 884–905. https://doi.org/10.1007/s11747-017-0541-x

Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118

Lombard, M., & Ditton, T. (1997). At the heart of it all: The concept of presence. Journal of Computer-Mediated Communication, 3(2), JCMC321. https://doi.org/10.1111/j.1083-6101.1997.tb00072.x

Steuer, J. (1992). Defining virtual reality: Dimensions determining telepresence. Journal of Communication, 42(4), 73–93. https://doi.org/10.1111/j.1460-2466.1992.tb00812.x

Vorderer, P., Wirth, W., Gouveia, F. R., Biocca, F., Saari, T., Jäncke, F., Böcking, S., Schramm, H., Gysbers, A., Hartmann, T., Klimmt, C., Laarni, J., Ravaja, N., Sacau, A., Baumgartner, T., & Jäncke, P. (2004). MEC Spatial Presence Questionnaire (MEC-SPQ): Report on the validation of a tool for measuring spatial presence in different media. Media Presence Lab.

Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9(4), 625–636. https://doi.org/10.3758/BF03196322

Wirth, W., Hartmann, T., Böcking, S., Vorderer, P., Klimmt, C., Schramm, H., Saari, T., Laarni, J., Ravaja, N., Gouveia, F. R., Biocca, F., Sacau, A., Jäncke, L., Baumgartner, T., & Jäncke, P. (2007). A process model of the formation of spatial presence experiences. Media Psychology, 9(3), 493–525. https://doi.org/10.1080/15213260701283079

Witmer, B. G., & Singer, M. J. (1998). Measuring presence in virtual environments: A presence questionnaire. Presence: Teleoperators and Virtual Environments, 7(3), 225–240. https://doi.org/10.1162/105474698565686

13. Items of the Scale