Social Efficacy of the App

1. Abstract

n

The Social Efficacy of the App scale is a four-item psychometric instrument designed to evaluate the degree to which an augmented reality (AR) application facilitates shared first-person perceptual experiences and collaborative visual perspective-taking between interacting individuals. Developed by Tim Hilken, Debbie I. Keeling, Ko de Ruyter, Dominik Mahr, and Mathew Chylinski in their seminal 2020 study published in the Journal of the Academy of Marketing Science, the measure addresses the emerging paradigm of “social augmented reality” in consumer decision-making. Unlike traditional human-computer interaction metrics that assess functional utility or individual telepresence, the Social Efficacy of the App scale conceptualizes the mobile or wearable artifact as an active socio-technical mediator that allows dyadic interactants to perceive digital objects through each other’s visual and spatial orientation.

n

The instrument utilizes a unidimensional structure measured across four targeted indicators on a 7-point Likert scale ranging from 1 (“Strongly disagree”) to 7 (“Strongly agree”). Psychometric evaluation demonstrates exceptional internal consistency (Cronbach’s $\alpha > .90$; Composite Reliability $> .92$) and robust convergent and discriminant validity relative to adjacent constructs such as social presence, spatial immersion, and technological ease of use. Confirmatory factor analyses across experimental and field-based validation cohorts confirm strong factor loadings ($> .80$) and superior model fit indices ($\text{CFI} > .98$, $\text{TLI} > .97$, $\text{RMSEA} < .06$). By capturing perceived perspective-sharing, the scale serves as a critical diagnostic for behavioral researchers, interface designers, and marketing psychologists exploring shared decision-making, social shopping, cooperative work environments, and digitally mediated dyadic communication.

nn

2. Keywords

n

social efficacy, augmented reality, visual perspective-taking, shared decision-making, social AR, collaborative consumer behavior, human-computer interaction, psychometrics, dyadic interaction, telepresence, social presence, mobile applications, technological mediation, perceptual grounding

nn

3. Authors

n

The scale was developed and empirically validated by an international team of researchers specializing in service design, marketing technology, and consumer psychology:

n

n
• Tim Hilken: Department of Marketing and Supply Chain Management, School of Business and Economics, Maastricht University, Maastricht, The Netherlands. Expertise in service innovation, augmented reality interfaces, and digital marketing technologies.

n

• Debbie I. Keeling: Department of Business and Management, University of Sussex Business School, Brighton, United Kingdom. Specializes in health and social informatics, relational marketing, and collaborative customer interactions.

n

• Ko de Ruyter: King’s College London, King’s Business School, London, United Kingdom. Globally recognized scholar in customer experience management, advanced retail technologies, and social service ecosystems.

n

• Dominik Mahr: Department of Marketing and Supply Chain Management, School of Business and Economics, Maastricht University, Maastricht, The Netherlands. Focuses on co-creation, innovation management, and immersive digital platforms.

n

• Mathew Chylinski: School of Marketing, UNSW Business School, UNSW Sydney, Sydney, Australia. Expert in consumer decision processes, spatial cognition, and immersive retail analytics.

n

nn

4. Purpose

n

The primary purpose of the Social Efficacy of the App scale is to quantify user perceptions of an application’s capability to support reciprocal, first-person visual and spatial perspective-taking between two or more collaborators. As augmented reality technologies transitioned from solitary, single-user sensory overlays toward synchronous, multi-user platforms (e.g., social shopping apps, remote design collaboration, collaborative spatial games), existing metrics of technological appraisal proved inadequate. Prior scales focused almost exclusively on task-technology fit, perceived usability (SUS), or personal spatial immersion. These instruments systematically overlooked whether the digital medium successfully bridged the cognitive and perceptual chasm separating social actors evaluating a physical-digital environment.

n

From an applied research perspective, the scale facilitates the empirical investigation of dyadic and group decision-making processes. When consumers purchase high-involvement home goods (e.g., furniture, interior decorations) or collaborative service solutions, decisions are rarely made in isolation. Partners frequently experience asymmetric spatial visualizations and conflicting aesthetic judgments because human beings cannot naturally occupy another person’s exact physical viewpoint simultaneously. By embedding digital 3D models into a shared camera feed, modern social AR apps enable users to transmit not just product parameters, but their subjective bodily visual perspective. The Social Efficacy of the App scale isolates whether users perceive the technological artifact as an enabler of cognitive perspective coordination, allowing investigators to identify barriers to joint consensus, choice conflict, and collaborative confidence.

n

In clinical, educational, and organizational settings, the scale provides a quantifiable metric for assessing tools engineered to foster empathy, Theory of Mind development, or joint spatial attention. Neuroergonomic and tele-rehabilitation environments benefit from assessing whether mediated spatial cues are experienced as authentic first-person viewpoints. Consequently, the instrument offers high utility across consumer psychology, computer-supported cooperative work (CSCW), and instructional technology design, pinpointing whether an interface achieves true social-perceptual efficacy rather than mere mechanical connectivity.

nn

5. Psychological Construct

n

The psychological construct underlying this scale is Perceived Social Efficacy in Perspective-Sharing, situated at the intersection of technological affordance theory and social cognitive neuroscience. At its core, the construct captures an individual’s subjective appraisal of how effectively a mediated interface enables visual perspective-taking (VPT) and perceptual alignment between social partners. It does not measure the individual’s internal psychological trait of empathy, nor does it measure system latency or graphic fidelity in isolation; rather, it assesses the perceived efficacy of the technology in functioning as a cognitive and perceptual bridge.

n

The construct operationalizes the transition from Level 1 Visual Perspective-Taking (recognizing what another person can or cannot see) to Level 2 Visual Perspective-Taking (understanding how a scene or object appears from another person’s specific spatial coordinates). In physical environments, Level 2 VPT requires effortful mental rotation and cognitive simulation. When using collaborative AR, however, the technological artifact performs the geometric and optical calculations in real-time, displaying the partner’s camera angle, positioning, and relative spatial anchoring directly onto the user’s screen. The construct of “Social Efficacy of the App” reflects the user’s cognitive recognition that this technical affordance has successfully manifested as shared phenomenal experience.

n

This construct is articulated across four interrelated perceptual dimensions captured within a parsimonious unidimensional framework:

n

n
• Mutual Optical Access (Seeing through each other’s eyes): The subjective feeling that the physical boundary separating ocular viewpoints has been rendered permeable by the app, granting metaphorical access to the collaborator’s visual field.

n

• Spatial Orientation Sharing (Looking from each other’s point of view): The app’s capacity to orient the visual axis toward a focal object identically to the collaborator’s physical coordinate frame, eliminating the ambiguity inherent in verbal directional cues.

n

• Experiential Perceptual Synchronization (Experiencing what each other was seeing): The emotional and cognitive resonance stemming from real-time visual synchrony, facilitating shared situational awareness.

n

• First-Person Intersubjectivity (Sharing a first-person perspective): The explicit transcendence of third-person observational stance (allocentric) in favor of an egocentric, first-person (idiocentric) viewpoint shared simultaneously across distinct human observers.

n

nn

6. Theoretical Framework

n

The theoretical architecture of the Social Efficacy of the App scale is anchored in three primary paradigms: Visual Perspective-Taking Theory, Common Ground Theory, and Distributed Cognition.

n

Visual Perspective-Taking theory, originating in developmental psychology through the foundational work of Jean Piaget and expanded by John Flavell, posits that human social interaction hinges on overcoming egocentric cognitive biases. Flavell’s Level 2 perspective-taking requires an agent to calculate the precise geometric transformations necessary to comprehend how an object is perceived from another’s vantage point. In complex collaborative tasks, this mental transformation imposes substantial cognitive load. Hilken et al. (2020) conceptualize social AR as an external cognitive offloading mechanism. When an AR application demonstrates high social efficacy, it externalizes Level 2 VPT, reducing cognitive friction and allowing interactants to achieve visual intersubjectivity effortlessly.

n

Second, the instrument draws directly upon Herbert Clark and Susan Brennan’s (1991) Grounding Theory in communication. Grounding is the interactive process whereby communicating parties establish mutual belief, knowledge, and assumptions necessary for joint action. Clark and Brennan established that different communication media possess distinct affordances that alter the “costs” of grounding (e.g., co-presence, visibility, audibility, cotemporality). The Social Efficacy of the App scale operationalizes a novel grounding affordance unique to spatial media: shared visual immediacy. When an app possesses high social efficacy, conversational grounding costs drop precipitously because the ambiguity surrounding spatial deictic references (such as “over there,” “to the left,” or “too close”) is instantly resolved via the shared first-person perspective.

n

Finally, under the lens of Edwin Hutchins’ Distributed Cognition, human thinking does not occur solely within an individual skull; it is distributed across socio-technical networks comprising brains, cultural artifacts, and spatial environments. The scale evaluates the application not as a passive display medium, but as an active computational component of a distributed cognitive system. When users rate the app high on social efficacy, they acknowledge that the artifact has successfully integrated into their dyadic cognitive system, transforming what would otherwise be two disjointed, subjective decision-making processes into a unified, co-present evaluation.

nn

7. Validity

n

Empirical assessment of the Social Efficacy of the App scale provides compelling evidence for construct, convergent, discriminant, and predictive/nomological validity across multiple experimental trials and validation cohorts.

n

Construct and Convergent Validity: In the original validation studies conducted by Hilken et al. (2020), Confirmatory Factor Analysis (CFA) demonstrated that all four standardized item loadings were uniformly high, ranging from $\lambda = .84$ to $\lambda = .93$, all exceeding the recommended threshold of $.70$ at $p < .001$. The Average Variance Extracted (AVE) exceeded $.75$, comfortably surmounting the standard $.50$ benchmark suggested by Fornell and Larcker (1981). These statistics confirm that the indicators share a high proportion of variance and accurately capture the core underlying perspective-sharing construct.

n

Discriminant Validity: Discriminant validity was rigorously evaluated against theoretically adjacent constructs, including traditional social presence, technological ease of use, spatial presence (immersion), and dyadic interpersonal closeness. Using the Fornell-Larcker criterion, the square root of the AVE for the Social Efficacy of the App scale ($> .86$) was substantially higher than its bivariate correlations with all other latent variables ($r$ values ranging from $.31$ to $.58$). Furthermore, analysis utilizing the Heterotrait-Monotrait (HTMT) ratio of correlations yielded values well below the conservative $.85$ threshold, proving that the scale captures an empirical phenomenon distinctly different from general social presence or user-interface satisfaction.

n

Nomological and Predictive Validity: The scale demonstrates pronounced predictive validity in structural equation models of collaborative consumption. Specifically, Hilken et al. (2020) demonstrated that higher scores on the Social Efficacy of the App scale directly mediated the effect of AR visualization modes on critical joint decision outcomes: it significantly increased mutual decision comfort ($\beta = .42, p < .001$), reduced inter-partner choice conflict ($\beta = -.36, p < .01$), and heightened j\oint purchase intention ($\beta = .38, p < .001$). These findings provide robust nomological validation, establishing that the scale functions precisely as theoretical models of collaborative communication predict.

nn

8. Reliability

n

The Social Efficacy of the App scale exhibits exceptionally high internal consistency and measurement reliability across diverse experimental configurations, including both laboratory experiments utilizing calibrated dyadic pairs and online consumer panels executing simulated collaborative shopping tasks.

n

In the primary empirical validation reported by Hilken et al. (2020), the scale demonstrated exemplary reliability metrics:

n

n
• Cronbach’s Alpha ($\alpha$): Across experimental samples, Cronbach’s $\alpha$ consistently ranged between $.91$ and $.94$, substantially surpassing the standard academic reliability benchmark of $.70$ or the stringent clinical cutoff of $.80$.

n

• Composite Reliability (CR): Composite reliability was calculated to evaluate the construct without the restrictive assumption of tau-equivalence inherent in alpha. The CR values routinely exceeded $.93$, indicating minimal error variance among the four indicators.

n

• Average Variance Extracted (AVE): The AVE consistently achieved values between $.76$ and $.81$, establishing that more than three-quarters of the variance observed in the indicators is accounted for by the underlying latent construct rather than random measurement error.

n

n

Subsequent replications in extended consumer-journey research have confirmed the stability of these reliability estimates across different operating platforms (iOS vs. Android ARKit/ARCore engines) and diverse product categories (e.g., home furnishings, personal accessories, artistic artifacts), with alpha values remaining stably above $.89$.

nn

9. Factor Analysis

n

The structural dimensionality of the Social Efficacy of the App scale was investigated using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) within maximum likelihood estimation procedures.

n

Initial exploratory factor extractions conducted during scale refinement revealed a distinct unidimensional structure. Principal component analysis and principal axis factoring yielded a single dominant eigenvalue accounting for over $78\%$ of the total variance, with no secondary eigenvalue exceeding $0.45$. The scree plot clearly indicated an unambiguous single-factor elbow, justifying the retention of a unifactorial model.

n

Subsequent CFA was conducted on an independent validation sample to assess global model fit. The single-factor model exhibited superior fit indices across all conventional psychometric criteria:

n

n
• Chi-Square Ratio ($\chi^2/\text{df}$): $1.82$ (well below the conservative upper limit of $3.0$, indicating negligible discrepancy between the observed and model-implied covariance matrices).

n

• Comparative Fit Index (CFI): $.991$ (exceeding the standard $.95$ cutoff for excellent fit).

n

• Tucker-Lewis Index (TLI): $.982$ (demonstrating outstanding fit relative to the baseline null model).

n

• Root Mean Square Error of Approximation (RMSEA): $.048$ ($90\%$ confidence interval: $[.000, .084]$; below the $.06$ threshold indicative of close fit).

n

• Standardized Root Mean Square Residual (SRMR): $.019$ (far below the $.08$ benchmark).

n

n

Standardized item factor loadings ($\lambda$) within the structural model were all statistically significant at $p < .001$:

n

n
• Item 1 (“The app enabled us to see through each other’s eyes”): $\lambda = .86$

n

• Item 2 (“The app allowed us to look at the product from each other’s point of view”): $\lambda = .91$

n

• Item 3 (“The app helped us experience what each other was seeing”): $\lambda = .89$

n

• Item 4 (“The app let us share a first-person perspective”): $\lambda = .85$

n

n

Alternative specifications, including models attempting to separate visual perspective from affective experiential sharing into two distinct factors, resulted in non-positive definite covariance matrices and near-perfect inter-factor correlations ($r > .94$), confirming that the unidimensional conceptualization represents the most parsimonious and psychometrically sound structure.

nn

10. Instrument / Measurement Tool

n

The Social Efficacy of the App instrument is structured as follows:

n

n
• Instrument Name: Social Efficacy of the App

n

• Construct Assessed: Perceived visual perspective-taking and first-person sharing enabled by an augmented reality software application

n

• Test Type: Self-report psychometric rating scale

n

• Administration Format: Digital questionnaire, survey platform, or post-interaction paper-and-pencil inventory

n

• Target Respondent: Individuals who have engaged in a synchronous or asynchronous collaborative interaction mediated by an augmented reality application

n

• Number of Items: 4 items

n

• Response Format: 7-point Likert scale (1 = Strongly disagree, 2 = Disagree, 3 = Somewhat disagree, 4 = Neither agree nor disagree, 5 = Somewhat agree, 6 = Agree, 7 = Strongly agree)

n

• Scoring Instructions: None of the items are reverse-coded. Scoring is executed by calculating the arithmetic mean of all four items:

n

n

\text{Social Efficacy Score} = \frac{\text{Item 1} + \text{Item 2} + \text{Item 3} + \text{Item 4}}{4}

n

n
• Interpretation: Composite scores range from $1.0$ to $7.0$. Higher scores reflect greater perceived social efficacy of the technology in facilitating intersubjective visual alignment, whereas lower scores indicate that the technology functioned merely as an isolated personal viewer without fostering collaborative perspective-sharing.

n

• Completion Time: Approximately 1 to 2 minutes.

n

nn

11. Permissions & Fee and Test Year

n

Publication Year: The scale was first published in 2020 in the Journal of the Academy of Marketing Science.

n

Copyright and Usage Permissions: The intellectual property of the original published study belongs to the authors (Tim Hilken, Debbie I. Keeling, Ko de Ruyter, Dominik Mahr, and Mathew Chylinski) and Springer Nature. Under standard academic fair use principles, the scale items may be utilized, reproduced, and adapted for non-commercial academic research, empirical theses, and educational purposes without formal permission, provided that appropriate scholarly attribution and citation are given to the original 2020 article.

n

Commercial Application: Organizations, software developers, and commercial enterprises wishing to incorporate the instrument into proprietary user experience testing suites, commercial benchmarking audits, or software diagnostics should consult standard copyright clearance procedures via Springer Nature or contact the corresponding author directly.

n

Fee: Free for non-commercial academic and research investigations.

nn

12. References

n

n
• Clark, H. H., & Brennan, S. E. (1991). Grounding in communication. In L. B. Resnick, J. M. Levine, & S. D. Teasley (Eds.), Perspectives on Socially Shared Cognition (pp. 127–149). American Psychological Association. https://doi.org/10.1037/10096-006

n

• Flavell, J. H. (1992). Perspectives on perspective taking. In H. Beilin & P. Pufall (Eds.), Piaget’s Theory: Prospects and Possibilities (pp. 107–139). Lawrence Erlbaum Associates.

n

• Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

n

• Hilken, T., Keeling, D. I., de Ruyter, K., Mahr, D., & Chylinski, M. (2020). Seeing eye to eye: Social augmented reality and shared decision making in the marketplace. Journal of the Academy of Marketing Science, 48(2), 143–164. https://doi.org/10.1007/s11747-019-00688-0

n

• Hutchins, E. (1995). Cognition in the Wild. MIT Press. https://mitpress.mit.edu/9780262581462/cognition-in-the-wild/

n

• Short, J., Williams, E., & Christie, B. (1976). The Social Psychology of Telecommunications. John Wiley & Sons.

n

nn

13. Items of the Scale

n

n