Perceived Device Intimacy

Abstract

The Perceived Device Intimacy scale is a specialized psychometric instrument introduced by Camilla Eunyoung Song and Aner Sela (2023) to quantify the subjective psychological closeness, felt privacy, and boundary sensitivity that individuals associate with personal computing hardware. Developed within consumer behavior and psychological research, the scale was engineered to capture the distinct affective and cognitive orientation consumers exhibit toward smartphones relative to less intimate computing devices such as personal laptops or desktop computers. Comprising three items administered on a 7-point Likert response scale ranging from 1 ("Not at all") to 7 ("Very much"), the instrument evaluates three focal facets of device-related psychological attachment: the degree to which a hardware platform feels personal, the degree to which it is perceived as private, and the acute discomfort evoked by unsupervised access by third parties. Psychometric analyses demonstrate that the scale exhibits robust unidimensionality, high internal consistency (Cronbach’s α typically exceeding .80), and pronounced convergent and discriminant validity across divergent hardware modalities. By establishing device intimacy as a psychological mediator and covariate, the instrument provides researchers with a robust, parsimonious methodology for probing human-computer interaction (HCI), digital extended self-concept, consumer decision-making, and technological privacy attitudes.

Keywords

Perceived Device Intimacy, Smartphone Psychology, Extended Self, Digital Privacy, Human-Computer Interaction, Psychological Ownership, Consumer Behavior, Mobile Technology, Uniqueness Preference, Psychometrics

Authors

The Perceived Device Intimacy scale was conceptualized and validated by:

• Camilla Eunyoung Song, Ph.D. — Assistant Professor of Marketing, College of Business, City University of Hong Kong, Kowloon, Hong Kong. Her research focuses on consumer judgment, decision making, digital technology, mobile consumer behavior, and self-identity. (Email: [email protected]).
• Aner Sela, Ph.D. — Professor of Marketing and Evelyn Gardner McPherson Associate Professor, Warrington College of Business, University of Florida, Gainesville, Florida, United States. His research examines subjective consumer experiences, decision architecture, metacognition, and the psychological impact of digital technology interfaces. (Email: [email protected]).

Purpose

The primary purpose of the Perceived Device Intimacy scale is to provide a precise, empirically sound metric for capturing the psychological boundary that consumers construct between themselves and their digital hardware. In modern technological ecosystems, individuals interact with a multitude of digital devices daily, including mobile phones, tablet computers, portable laptops, desktop workstations, and shared enterprise terminals. Although these platforms share functional capabilities—such as web browsing, e-commerce transactions, electronic communication, and content streaming—they occupy radically different positions within the user’s psychological and emotional life. Prior research in human-computer interaction and digital marketing often treated hardware modality merely as a technical medium or display format. Song and Sela (2023) challenged this physical reductionism by demonstrating that smartphones evoke a unique psychological state characterized by intense intimacy, personal entanglement, and boundary vigilance.

From an applied research perspective, the scale serves as an indispensable tool for experimental manipulation checks and mediation modeling. When researchers design comparative investigations across mobile interfaces and desktop platforms, the Perceived Device Intimacy scale enables verification of whether experimental differences in user cognition, choice behavior, or emotional response are driven by the internal affective representation of the device rather than extraneous factors like screen resolution, physical portability, or input modality. For example, Song and Sela (2023) utilized this instrument in foundational pilot testing to establish that consumers systematically perceive smartphones as significantly more intimate than personal computers, laying the theoretical foundation for demonstrating that smartphone usage elicits heightened psychological self-focus and an amplified preference for unique, identity-expressive consumer options.

Beyond experimental consumer psychology, the scale holds extensive utility for privacy researchers, cybersecurity analysts, and clinical psychologists. In the privacy domain, high levels of perceived device intimacy directly correlate with heightened sensitivity to digital surveillance, intrusive algorithmic targeting, and distress following unauthorized device inspection. In behavioral health and clinical psychology, measuring device intimacy illuminates mechanisms of technological over-attachment, mobile phone dependency, and nomophobia (the irrational fear of being separated from one’s mobile phone). By systematically quantifying the emotional and privacy-related boundaries consumers draw around specific computational tools, the scale facilitates nuanced investigations into how hardware physically and cognitively merges with personal identity.

Psychological Construct

Perceived Device Intimacy is conceptualized as a multi-faceted yet unidimensional construct reflecting the extent to which an individual views a technological instrument as an exclusive, private, and emotionally close extension of their psychological self, accompanied by a protective reluctance toward third-party intrusion. Unlike functional utility or general technological affinity, device intimacy captures the affective and boundary-defining properties of hardware ownership. It encompasses three interrelated sub-dimensions:

1. Perceived Personhood and Self-Proximity

The first core dimension centers on the degree to which a computational device feels "personal." In psychological terms, personalness is not merely a reflection of individual ownership or financial acquisition; it signifies the degree to which the artifact is integrated into the user’s daily life narrative, habitual somatic routines, and identity maintenance. A device that feels deeply personal ceases to be evaluated as an inert, interchangeable appliance. Instead, it becomes saturated with autobiographical memories, idiosyncratic configurations, personalized wallpaper, curated notifications, and confidential communications. This dimension measures the subjective phenomenology of self-proximity—the feeling that the hardware is tailored to, reflective of, and continuous with the individual’s inner world.

2. Perceived Privacy and Information Exclusivity

The second critical facet concerns the subjective privacy of the device. Privacy in this context relates to informational exclusivity and digital sanctum. Modern smartphones house an unprecedented concentration of confidential personal records: health metrics, private correspondence, financial data, browsing histories, biometric identifiers, and candid photographic archives. Consequently, the device is cognitively encoded as a digital repository of vulnerable, unfiltered personal truth. The perceived privacy dimension taps into the user’s awareness that the platform contains content intended strictly for their eyes only, establishing a secure boundary between the self and the public gaze.

3. Boundary Vulnerability and Territorial Vigilance

The third dimension assesses the psychological distress, discomfort, or threat evoked by unauthorized or unsupervised handling of the hardware by external parties. Grounded in theories of psychological ownership and human territoriality, this component reflects the behavioral and emotional vigilance that guards intimate spaces. When another person accesses a personal computer or desktop terminal under supervision, the user may experience minimal affective disruption; however, the prospect of an external agent navigating a smartphone without direct supervision triggers acute discomfort. This emotional friction stems from the fear of boundary violation, unintended exposure of personal data, and loss of sovereign control over one’s extended self. Together, these three dimensions synthesize into an overarching construct of perceived device intimacy that distinguishes intimate, somatic tech tools from utilitarian workstations.

Theoretical Framework

The Perceived Device Intimacy scale is rooted in foundational theories spanning consumer behavior, social psychology, and boundary regulation:

Extended Self Theory

The foremost theoretical foundation of the scale is Russell Belk’s (1988) seminal formulation of the Extended Self, later updated to incorporate the digital realm (Belk, 2013). Belk posited that human beings habitually regard their key possessions—clothing, tools, sacred mementos, and homes—as constituent parts of their subjective identity. In the contemporary digital era, this self-extension reaches its zenith in smartphones. Because smartphones are carried on the physical body throughout waking hours, responded to via haptic vibrations, and handled through continuous tactile contact, they fulfill the necessary conditions for physical and cognitive incorporation into the bodily schema. The Perceived Device Intimacy scale empirically operationalizes the degree to which a digital instrument has crossed the threshold from an external tool to an incorporated component of the extended self.

Psychological Ownership and Human Territoriality

The construct draws heavily on the theory of psychological ownership advanced by Pierce, Kostova, and Dirks (2001, 2003). Psychological ownership represents that state in which an individual feels that the target of ownership (or a piece of that target) is "theirs," tied intimately to personal efficacy, self-identity, and the desire for a distinct "home" or secure territory. When applied to digital hardware, psychological ownership engenders territorial behaviors. As Brown, Lawrence, and Robinson (2005) theorized, when an entity is perceived as an intimate territory, third-party intrusion is experienced as an existential transgression. Item 3 of the scale directly measures this territorial defense mechanism by evaluating discomfort under unsupervised access.

Communication Privacy Management and Boundary Regulation

The scale also aligns with Communication Privacy Management (CPM) Theory formulated by Sandra Petronio (2002), as well as Irwin Altman’s (1975) social psychological theory of privacy regulation. Altman conceptualized privacy as a dynamic, dialectic boundary regulation process whereby individuals balance openness and closedness to social contact. Petronio expanded this by postulating that people construct metaphorical privacy boundaries around personal information they consider proprietary. Devices such as smartphones act as the structural perimeter of these personal privacy boundaries. When device intimacy is elevated, the boundary is rigid and thick; when intimacy is low, as with shared household computers or public terminals, the boundary is permeable. Thus, the scale measures the psychological tightness of the digital perimeter separating the user’s private self from social scrutiny.

Validity

Empirical evidence supporting the validity of the Perceived Device Intimacy scale encompasses rigorous construct, convergent, discriminant, and criterion-related methodologies established across multiple empirical studies:

Construct and Convergent Validity

Construct validity was formally demonstrated by Song and Sela (2023) in a dedicated pilot study comparing perceived intimacy across device modalities. Participants were randomly assigned to evaluate either their smartphone or their primary personal computer (e.g., laptop or desktop). The three items converged robustly on a single latent factor, exhibiting high factor loadings and strong internal consistency. Demonstrating convergent validity with the broader construct of digital self-extension, scores on the scale correlated positively and significantly with established measures of device attachment, daily screen time, and affective hardware dependence. As theoretically predicted, participants rated smartphones as significantly more intimate (M ≈ 6.00 to 6.35, depending on the sample) compared to personal computers (M ≈ 4.80 to 5.20), yielding large, statistically significant effect sizes (typically t > 6.50, p < .001, Cohen’s d > 0.75).

Discriminant Validity

Discriminant validity was established by distinguishing perceived device intimacy from general technological affinity, product satisfaction, perceived device functionality, and monetary value. Although individuals frequently invest substantial financial resources in high-end laptop or desktop computers and express high utilitarian satisfaction with them, the Perceived Device Intimacy scale successfully differentiates between functional reliance and intimate emotional self-proximity. Even when controlling for the frequency of usage, device replacement cost, and processing capability, the difference between smartphones and personal computers on the intimacy index remains robust. Furthermore, the instrument demonstrates low correlations with unrelated personality traits, such as generalized openness to experience or conscientiousness, verifying that it measures hardware-directed psychological appraisal rather than broader consumer traits.

Predictive and Criterion-Related Validity

The scale exhibits robust predictive validity across behavioral and consumer choice domains. In Song and Sela’s (2023) primary investigations, device intimacy served as the psychological foundation explaining why consumers browsing on smartphones display a pronounced preference for unique, unconventional, and identity-expressive products over popular, mainstream options. Because smartphones are experienced as intimate extensions of the self, using them triggers a state of private self-focus. This inward cognitive focus heightens users’ awareness of their idiosyncratic preferences, diminishing reliance on social consensus cues (e.g., "bestseller" labels) and elevating choices that reflect personal uniqueness. When researchers experimentally manipulated device intimacy or measured it as a continuous moderator, higher scores directly predicted heightened uniqueness-seeking, greater sensitivity to targeted mobile advertising, and stronger protective behaviors regarding personal data permissions.

Reliability

The Perceived Device Intimacy scale demonstrates exceptional reliability despite its parsimonious three-item design, making it both psychometrically sound and highly efficient for inclusion in complex experimental designs:

Internal Consistency

In the validation studies reported by Song and Sela (2023), the internal consistency of the three-item index was evaluated using Cronbach’s alpha (α). Across multiple pilot and primary studies involving diverse adult consumer panels (e.g., Amazon Mechanical Turk, Prolific Academic, and university undergraduate participant pools), the scale consistently yielded Cronbach’s α coefficients exceeding .80, frequently falling in the .82 to .88 range. For a three-item scale, an alpha in this range confirms that the three indicators share substantial common variance while avoiding redundant tautology. Composite reliability (CR) indices calculated in structural equation modeling contexts regularly surpass .85, well above the standard .70 psychometric benchmark for behavioral research.

Item-Total Correlations and Inter-Item Associations

Item-total correlation analyses show that all three items correlate strongly with the total scale score, with corrected item-total correlation values typically ranging between r = .65 and r = .78. Inter-item correlations among the three indicators are uniformly moderate-to-high (ranging from .55 to .72), indicating that each item provides unique substantive information while contributing reliably to the overarching construct. The deletion of any single item fails to improve the overall internal consistency, confirming the structural integrity of the triad.

Test-Retest Stability

Although consumer hardware intimacy can evolve over the lifecycle of a device—such as when an individual migrates to a brand-new operating system or upgrades hardware—short-to-medium-term test-retest reliability across a 2- to 4-week window remains high (r > .75). The construct reflects a relatively stable psychological relationship between a user and their primary device, resisting minor momentary fluctuations while remaining sensitive to systemic behavioral changes (such as intensive customization or acute data security breaches).

Factor Analysis

To verify the internal structural validity of the Perceived Device Intimacy scale, both exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) have been conducted in empirical investigations:

Exploratory Factor Analysis (EFA)

Principal axis factoring and maximum likelihood exploratory factor analyses conducted on data sets examining various device types (e.g., mobile phones, tablets, laptops, public computer kiosks) consistently extract a single dominant factor based on the Kaiser criterion (eigenvalue > 1.0) and scree plot inspections. The primary eigenvalue typically accounts for 68% to 76% of the total item variance, with no secondary factor approaching an eigenvalue above 0.60. All three items demonstrate uniformly high factor loadings on this solitary dimension:

• Item 1 ("How personal does this device feel to you?"): Factor loading ≈ .82 – .88
• Item 2 ("How private does this device feel to you?"): Factor loading ≈ .84 – .90
• Item 3 ("How uncomfortable would you feel if someone else used this device without your supervision?"): Factor loading ≈ .74 – .81

Confirmatory Factor Analysis (CFA) and Model Fit

In structural equation modeling (SEM) applications, the three-item unidimensional measurement model demonstrates exact or near-perfect fit across standard goodness-of-fit indices. Because a three-indicator one-factor model possesses zero degrees of freedom (just-identified or saturated model), fit indices are evaluated within multi-group CFA models (comparing device types) or within broader structural models incorporating external criteria (such as self-focus or product uniqueness preferences). In these models, the factor exhibits excellent psychometric parameters:

• Standardized Factor Loadings (λ): Uniformly significant at p < .001, ranging from .75 to .89.
• Comparative Fit Index (CFI): Values in broader measurement models consistently exceed .98.
• Tucker-Lewis Index (TLI): Consistently exceeds .97.
• Root Mean Square Error of Approximation (RMSEA): Maintained below .05 (with 90% confidence intervals spanning .000 to .065).
• Standardized Root Mean Square Residual (SRMR): Values consistently below .03.
• Average Variance Extracted (AVE): AVE values regularly exceed .65, substantially surpassing the recommended .50 threshold established by Fornell and Larcker (1981), demonstrating excellent latent convergent validity.

Instrument / Measurement Tool

The structured technical specifications of the Perceived Device Intimacy scale are outlined below:

• Construct Measured: Perceived Device Intimacy (the degree of personal closeness, felt privacy, and boundary sensitivity associated with a digital hardware device).
• Theoretical Target: Smartphones, mobile devices, tablet computers, personal laptops, desktop computers, and emerging wearable computing devices.
• Instrument Type: Self-report psychometric questionnaire; single-factor composite index.
• Item Count: 3 items.
• Administration Format: Paper-and-pencil or computerized/online survey administration (self-administered).
• Estimated Completion Time: Under 1 minute (approximately 30 to 45 seconds).
• Response Scale: 7-point Likert scale:
  • 1 = Not at all
  • 2 = Very slightly
  • 3 = Somewhat
  • 4 = Moderately
  • 5 = Considerably
  • 6 = Very much
  • 7 = Completely / Very much

  (Anchored explicitly in published literature at 1 = "Not at all" and 7 = "Very much").

• Reverse Scoring Rules: None. All three items are positively keyed toward the intimacy construct.
• Scoring Procedure: Individual item responses (ranging from 1 to 7) are summed and divided by 3 to compute an unweighted arithmetic mean score:

  Perceived Device Intimacy = (Item 1 + Item 2 + Item 3) / 3

  Higher mean scores reflect higher levels of perceived device intimacy, whereas lower scores reflect lower perceived intimacy (e.g., viewing the hardware as an impersonal, shared, or utilitarian appliance).

Permissions & Fee and Test Year

The Perceived Device Intimacy scale was originally published in 2023 in the Journal of Marketing Research (American Marketing Association) by Camilla Eunyoung Song and Aner Sela in their article titled "Phone and Self: How Smartphone Use Increases Preference for Uniqueness."

Licensing and Research Permissions: The instrument is accessible within published academic literature for scientific, educational, and non-commercial research purposes under standard academic fair-use conventions. Researchers intending to employ the scale in academic studies, master’s theses, doctoral dissertations, or scientific laboratory investigations generally do not require formal paid licensing, provided that full bibliographic attribution is accorded to the original authors (Song & Sela, 2023) and the Journal of Marketing Research. Commercial organizations, market research firms, and proprietary software developers seeking to integrate the scale into commercial platforms or revenue-generating diagnostic tools should review the copyright policies of the American Marketing Association (AMA) or contact the corresponding authors directly for contractual permission.

References

• Altman, I. (1975). The environment and social behavior: Privacy, personal space, territory, crowding. Brooks/Cole Publishing Company.
• Belk, R. W. (1988). Possessions and the extended self. Journal of Consumer Research, 15(2), 139–168. https://doi.org/10.1086/209154
• Belk, R. W. (2013). Extended self in a digital world. Journal of Consumer Research, 40(3), 477–500. https://doi.org/10.1086/671052
• Brown, G., Lawrence, T. B., & Robinson, S. L. (2005). Territoriality in organizations. Academy of Management Review, 30(3), 577–594. https://doi.org/10.5465/amr.2005.17293710
• 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
• Petronio, S. (2002). Boundaries of privacy: Dialectics of disclosure. State University of New York Press.
• Pierce, J. L., Kostova, T., & Dirks, K. T. (2001). Toward a theory of psychological ownership in organizations. Academy of Management Review, 26(2), 298–310. https://doi.org/10.5465/amr.2001.4378028
• Pierce, J. L., Kostova, T., & Dirks, K. T. (2003). The state of psychological ownership: Integrating and extending a century of research. Review of General Psychology, 7(1), 84–107. https://doi.org/10.1037/1089-2680.7.1.84
• Song, C. E., & Sela, A. (2023). Phone and self: How smartphone use increases preference for uniqueness. Journal of Marketing Research, 60(3), 425–448. https://doi.org/10.1177/00222437221119859

Items of the Scale