Augmentation Quality Scale (AQS)

1. Abstract

The Augmentation Quality Scale (AQS) is a validated psychometric instrument designed to assess consumers’ subjective evaluations of the technical integration and visual-behavioral harmony of computer-generated virtual elements embedded within real-world physical environments during augmented reality (AR) experiences. Developed by Katharina E. Schein, Philipp A. Rauschnabel, Susanne Praxmarer-Carus, and Barry J. Babin (2025), the instrument addresses a critical measurement gap in marketing, human-computer interaction (HCI), and immersive media psychology by moving beyond generic measures of software usability to quantify the distinctive phenomenological characteristics of spatial computing. The scale was established using a rigorous mixed-methods development pipeline encompassing grounded qualitative explorations across two preliminary studies followed by two quantitative validation studies across diverse consumer samples (total N = 670 across Studies 3 and 4).

The psychometric architecture of the AQS is formulated as a hierarchical, reflective second-order factor model underpinned by three distinct, complementary first-order dimensions: Embedding Quality (3 items), which measures the contextual fit, spatial coherence, scaling, and environmental blending of virtual objects; Interaction Quality (3 items), which assesses real-time responsiveness, tracking stability, latency minimization, and seamless motor-sensory feedback; and Design Quality (3 items), which captures the visual fidelity, surface geometry, aesthetic realism, and multi-angle graphic rendering of the virtual artifact. Comprising a total of 9 items administered via a standard 7-point Likert response format, the AQS demonstrates superior psychometric integrity. Confirmatory factor analysis exhibits excellent model fit, with composite reliabilities exceeding .84 and average variance extracted (AVE) values surpassing .63 across all subscales. Furthermore, structural equation modeling verifies that augmentation quality serves as a primary antecedent of local presence, which sequentially drives downstream cognitive, hedonic, utilitarian, and behavioral outcomes, including purchase intentions and actual product choices.

2. Keywords

Augmentation Quality Scale, augmented reality marketing, spatial computing, embedding quality, interaction quality, design quality, local presence, human-computer interaction, psychometrics, perceptual realism, consumer immersion, structural equation modeling.

3. Authors

The Augmentation Quality Scale was developed and validated by an international team of marketing and human-computer interaction scholars:

• Katharina E. Schein — Universität der Bundeswehr München, Department of Business Administration, Neubiberg, Germany.
• Philipp A. Rauschnabel — Professor of Digital Marketing and Media Management, Universität der Bundeswehr München, Neubiberg, Germany; leading researcher in augmented reality marketing and XR consumer behavior.
• Susanne Praxmarer-Carus — Professor of Marketing, Universität der Bundeswehr München, Neubiberg, Germany; expert in quantitative methodology and consumer psychology.
• Barry J. Babin — Professor of Marketing and Max P. Watson Professor, Department of Marketing and Analysis, Louisiana Tech University, Ruston, Louisiana, USA; internationally recognized authority on psychometrics, scale development, and structural equation modeling.

4. Purpose

The primary purpose of the Augmentation Quality Scale (AQS) is to provide a theoretically grounded, psychometrically sound, and diagnostically precise instrument for measuring consumer evaluations of virtual content integration within physical surroundings. As mobile devices, smart glasses, and wearable head-mounted displays (HMDs) permeate mainstream consumer markets, organizations increasingly deploy augmented reality applications for virtual product try-ons, interactive retailing, spatial navigation, instructional assembly, and entertainment. Prior to the formal development of the AQS, empirical investigations routinely relied on generalized technological evaluations—such as the System Usability Scale (SUS), the Technology Acceptance Model (TAM) constructs of perceived usefulness and ease of use, or unstandardized ad-hoc items. While these frameworks capture broad utilitarian acceptance, they fail to isolate the core technological idiosyncrasy that defines augmented reality: the seamless, real-time spatial synthesis of virtual augmentations into a physical context.

From a theoretical perspective, the AQS resolves this limitation by specifying the technical parameters that distinguish high-performing spatial computing experiences from flawed or disruptive augmentations. Augmentation failure often manifests not because an application lacks software utility, but because the virtual object floats ambiguously in the air, clips through physical walls, stutters during head movement, or displays unrealistic lighting and texture mapping that shatters sensory congruence. By capturing these specific technical facets, the AQS enables researchers to isolate the exact mechanisms through which visual, physical, and behavioral augmentations govern perceptual processing.

In applied and clinical-grade human factors research, the scale serves as a comprehensive diagnostic engine. Developers can evaluate iterative software builds to detect whether user disengagement stems from rendering issues (Design Quality), tracking drift and input latency (Interaction Quality), or environmental collision and occlusion inaccuracies (Embedding Quality). In commercial marketing contexts, the AQS enables brand managers to assess AR campaigns, ensuring that technological implementations generate positive brand equity and conversions rather than inducing cognitive dissonance or perceptual uncanny valley effects. Ultimately, the AQS establishes a standardized baseline across experimental paradigms, facilitating replicable empirical synthesis across consumer psychology, cognitive ergonomics, and spatial media research.

5. Psychological Construct

The central construct measured by the instrument is Augmentation Quality, defined as a consumer’s holistic psychological and cognitive evaluation of the technical execution, contextual coherence, and sensory realism with which virtual elements are integrated into their physical environment. Rather than reflecting an objective metric of computing power (such as frame rate or polygon count), augmentation quality captures the subjective, perceived efficacy of this integration. The construct is conceptualized as a reflective second-order factor comprised of three distinct first-order dimensions, each addressing a unique ontological pillar of the augmented experience.

1. Embedding Quality

Embedding quality captures the perceived realism of contextual and environmental integration. It evaluates the extent to which the virtual entity appears to occupy, adhere to, and harmonize with the three-dimensional physical landscape. In AR environments, virtual content must not merely appear on a 2D screen; it must convincingly inhabit the physical room. This dimension measures:

• Scale and Dimensional Proportionality: The degree to which the virtual artifact retains accurate metric proportions relative to surrounding physical furniture, walls, or human bodies.
• Surface Anchorage and Occlusion: Whether the virtual asset rests firmly on horizontal or vertical physical planes without visual jitter, floating artifacts, or clipping through solid real-world objects.
• Environmental Blending: The perceptual consistency between physical ambient lighting and the virtual object’s cast shadows, reflections, and illumination gradients.

For example, if a consumer places a virtual sofa in their living room, high embedding quality dictates that the sofa appears grounded on the carpet, correctly shaded by the room’s lamp, and partially obscured if a real coffee table sits in front of it.

2. Interaction Quality

Interaction quality reflects the perceived responsiveness, dynamic stability, and temporal continuity of the virtual object during user manipulation or physical movement. Spatial computing requires real-time six degrees of freedom (6DoF) tracking. This dimension quantifies:

• Latency Minimization: The instantaneous nature of virtual object updates in response to changes in perspective or direct touch/gesture inputs.
• Motion Tracking Smoothness: The absence of stuttering, perceptual drift, or angular disorientation when the user navigates around the virtual object.
• Behavioral Plausibility: The degree to which virtual entities respond to physical boundaries and user interactions in a predictable, friction-free manner.

If a user walks around the aforementioned virtual sofa, high interaction quality ensures that the viewpoint pivots smoothly without tracking drops, lag, or unnatural perspective warping.

3. Design Quality

Design quality captures the perceived visual fidelity, aesthetic craftsmanship, and surface realism of the virtual asset itself, independent of its spatial placement. Grounded in visual perception and graphic realism, this dimension encompasses:

• Surface Texture and Detail: The presence of high-resolution mapping, realistic material properties (e.g., fabric weave, brushed metal, wood grain), and appropriate roughness/specularity.
• Geometric Integrity: The smoothness of 3D polygonal curves and edges, preventing pixelation or blocky rendering artifacts.
• Multi-Perspective Fidelity: The consistency of visual detail when inspected up close or from alternative viewpoints.

Even if an object is well-anchored (Embedding) and smoothly responsive (Interaction), poor visual fidelity (e.g., a low-resolution, cartoonish texture on an allegedly luxury product) degrades overall augmentation quality.

6. Theoretical Framework

The conceptual foundation of the Augmentation Quality Scale is rooted in the convergence of spatial presence theory, cognitive psychology, and the reality-virtuality continuum. Historically, Milgram and Kishino’s (1994) Reality-Virtuality Continuum positioned augmented reality as a mixed-reality environment wherein real and digital stimuli blend along a shared visual spectrum. However, while early technological theorists treated this continuum as a technical spectrum of hardware capability, Schein et al. (2025) reframed it within cognitive and phenomenological boundaries.

The Concept of Local Presence

A foundational theoretical innovation underlying the AQS is the delineation of local presence. In conventional virtual reality (VR) research, presence is predominantly conceptualized as spatial presence or telepresence—the psychological sensation of “being there” in a distant or simulated environment (Steuer, 1992; Lombard & Ditton, 1997). In direct contrast, augmented reality does not transport the user to an alternate realm; rather, it transports the virtual content into the user’s immediate physical reality. Schein et al. conceptualize local presence as the psychological perception that a synthetic, digital entity actually exists “here” within the user’s immediate physical environment. Local presence represents the primary psychological mechanism triggered when an AR experience achieves perceptual coherence.

Perceptual Fluency and Cognitive Load

The AQS also draws heavily upon processing fluency theory. When virtual objects exhibit high embedding, interaction, and design quality, the human visual system processes the blended scene with minimal cognitive friction. Conversely, rendering anomalies, visual jitter, or tracking latency force the user’s cognitive architecture to reconcile conflicting sensory cues, inducing cognitive load and breaking the illusion of integration. When perceptual fluency is maintained through high augmentation quality, cognitive resources remain unburdened, allowing consumers to experience heightened local presence, which subsequently enriches both utilitarian evaluations (e.g., product diagnostic capability) and hedonic gratifications (e.g., aesthetic enjoyment, emotional engagement).

7. Validity

The validation protocol for the Augmentation Quality Scale adhered strictly to gold-standard psychometric guidelines outlined in psychometrics and marketing literature (e.g., Churchill, 1979; Gerbing & Anderson, 1988). Scale validation was conducted across four comprehensive studies comprising both qualitative and quantitative methodologies.

Construct, Convergent, and Discriminant Validity

Following qualitative item generation and content validity refinement across Studies 1 and 2, quantitative validation was executed in Studies 3 and 4 across independent consumer samples (total N = 670). In Study 4, convergent validity was rigorously demonstrated, as all factor loadings for the nine indicators on their respective first-order constructs were positive, statistically significant (p < .001), and substantial (loadings ranging between .75 and .88). Furthermore, the Average Variance Extracted (AVE) comfortably surpassed the recognized .50 threshold across all dimensions:

• Embedding Quality: AVE = .67
• Interaction Quality: AVE = .63
• Design Quality: AVE = .69

Discriminant validity was established using both the Fornell-Larcker criterion and the modern Heterotrait-Monotrait (HTMT) ratio of correlations. The square root of the AVE for each dimension was greater than any inter-construct correlation. In addition, HTMT values remained consistently below the conservative threshold of .85, confirming that Embedding Quality, Interaction Quality, and Design Quality represent empirically distinct dimensions rather than redundant measurements of general technological satisfaction.

Predictive and Nomological Validity

Nomological validity was verified through structural equation modeling (SEM). The overarching second-order Augmentation Quality factor demonstrated robust, statistically significant predictive pathways to local presence. In turn, local presence acted as a vital psychological conduit, mediating downstream effects on:

• Utilitarian Value: Enhancing the perceived usefulness, visual information accuracy, and diagnostic confidence regarding product attributes.
• Hedonic Value: Amplifying experiential enjoyment, playfulness, and emotional engagement.
• Behavioral Intentions and Actual Choice: Significantly predicting consumers’ explicit purchase intentions and their actual behavioral selections in controlled experimental trials.

8. Reliability

The internal consistency and scale reliability of the Augmentation Quality Scale were scrutinized using multiple psychometric indices across independent validation cohorts. Traditional metrics such as Cronbach’s alpha (α) and modern variance-based metrics such as Composite Reliability (CR) were evaluated.

Across the quantitative validation datasets (culminating in Study 4, N = 670 aggregated across quantitative phases), the three subscales exhibited high internal consistency, comfortably exceeding the standard .70 reliability benchmark:

• Embedding Quality: Composite Reliability (CR) = .86, demonstrating that the 3 items reliably reflect the variance of the underlying spatial integration construct.
• Interaction Quality: Composite Reliability (CR) = .84, confirming strong temporal and behavioral response stability across user evaluations.
• Design Quality: Composite Reliability (CR) = .87, evidencing excellent measurement consistency regarding visual realism and geometric fidelity.

The overall second-order Augmentation Quality construct demonstrated superior composite reliability exceeding .90. Item-total correlations across all nine items consistently surpassed the recommended .50 threshold, with no single item’s deletion leading to an increase in overall reliability. These metrics confirm that the scale is highly robust, possesses minimal measurement error, and maintains psychometric stability across varied AR hardware platforms, including mobile smartphones, tablets, and dedicated AR smart glasses.

9. Factor Analysis

The factorial architecture of the AQS was identified and confirmed via sequential Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

Exploratory Factor Analysis (EFA)

In the initial quantitative phase (Study 3), EFA using principal axis factoring with promax (oblique) rotation was conducted on an expanded pool of candidate items. The Kaiser-Meyer-Olkin (KMO) measure verified sampling adequacy (KMO > .90), and Bartlett’s test of sphericity reached statistical significance (p < .001). The empirical factor extraction decisively supported a three-factor solution based on eigenvalues exceeding 1.0 (Kaiser criterion) and scree plot inspection, cleanly grouping items into Embedding, Interaction, and Design dimensions without problematic cross-loadings (all cross-loadings < .30).

Confirmatory Factor Analysis (CFA)

In Study 4, a higher-order Confirmatory Factor Analysis was estimated using maximum likelihood estimation to test the hypothesized hierarchical structure. The second-order model—where the overarching Augmentation Quality factor drives the three first-order factors—demonstrated exceptional model fit indices, meeting or exceeding rigorous structural equation modeling criteria:

• Comparative Fit Index (CFI): > .96
• Tucker-Lewis Index (TLI): > .95
• Root Mean Square Error of Approximation (RMSEA): < .055 (with a 90% confidence interval confirming close fit)
• Standardized Root Mean Square Residual (SRMR): < .045
• Chi-Square / Degrees of Freedom (χ²/df): < 2.50

All three first-order factors exhibited high, statistically significant standardized second-order factor loadings onto the overarching Augmentation Quality construct (loadings generally exceeding .80), validating the theoretical proposition that Embedding, Interaction, and Design Quality operate as complementary facets of a unified quality appraisal.

10. Instrument / Measurement Tool

The Augmentation Quality Scale is formatted as a concise, self-administered 9-item questionnaire designed to be completed immediately following an augmented reality interaction session.

• Instrument Type: Multi-dimensional self-report psychometric rating scale.
• Administration Format: Digital (in-app, web survey) or paper-and-pencil; ideally administered within minutes of completing the AR experience to avoid recall degradation.
• Target Population: Consumers, end-users, and human-factors participants engaging with spatial computing, mobile AR, or wearable AR interfaces.
• Total Item Count: 9 items distributed equally across three subscales:
  • Embedding Quality: 3 items
  • Interaction Quality: 3 items
  • Design Quality: 3 items
• Response Format: 7-point Likert response scale anchored from 1 (“Strongly Disagree”) to 7 (“Strongly Agree”).
• Scoring Procedures:
  • Subscale Scores: Calculated by computing the arithmetic mean of the three respective items for each dimension.
  • Global Augmentation Quality Score: Calculated either by averaging all 9 items or by taking the composite mean of the three dimension scores.
  • Interpretation: Higher scores indicate superior perceived technical integration, smooth interaction responsiveness, and graphic fidelity.
• Administration Time: Approximately 2 to 3 minutes.

11. Permissions & Fee and Test Year

The Augmentation Quality Scale was published in 2025 in the Journal of the Academy of Marketing Science. The scale was established as an academic instrument intended for scientific investigation, theoretical advancement, and practical diagnostic deployment in marketing and technology design.

• Publication Year: 2025.
• Academic Research Usage: Non-commercial scholarly use, educational research, and empirical study replication are generally permitted under standard academic fair-use doctrines, provided proper attribution and full bibliographic citation are extended to Schein et al. (2025).
• Commercial Applications: Commercial organizations, proprietary enterprise software developers, and market research agencies seeking to integrate the instrument into fee-generating software suites or commercial benchmarking audits should consult the copyright policies of the journal publisher (Springer Nature) or contact the lead authors directly.
• Author Contact: Enquiries regarding the scale, diagnostic benchmarks, or collaborative research can be directed to the corresponding author, Philipp A. Rauschnabel, via the Universität der Bundeswehr München.

12. References

• Churchill, G. A., Jr. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73. https://doi.org/10.1177/002224377901600110
• Gerbing, D. W., & Anderson, J. C. (1988). An updated paradigm for scale development incorporating unidimensionality and its assessment. Journal of Marketing Research, 25(2), 186–192. https://doi.org/10.1177/002224378802500207
• 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
• Milgram, P., & Kishino, F. (1994). A taxonomy of mixed reality visual displays. IEICE Transactions on Information and Systems, E77-D(12), 1321–1329.
• Schein, K. E., Rauschnabel, P. A., Praxmarer-Carus, S., & Babin, B. J. (2025). Unpacking augmentation quality and local presence: Factors that drive effective augmented reality marketing. Journal of the Academy of Marketing Science, 54, 49–69. https://doi.org/10.1007/s11747-025-01108-2
• 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

13. Items of the Scale