Stated Personal Information Ad Targeting Acceptance (SPIAT)

Abstract

The Stated Personal Information Ad Targeting Acceptance (SPIAT) scale is a specialized five-item psychometric instrument developed by Tami Kim, Kate Barasz, and Leslie K. John (2019) to evaluate consumer comfort, perceived appropriateness, and normative acceptance regarding the use of first-party, explicitly disclosed demographic profile information for behavioral ad targeting. Introduced in their seminal study on digital advertising transparency published in the Journal of Consumer Research, the instrument was formulated as one of four foundational diagnostic indices designed to isolate qualitative differences in consumer privacy sensitivity across varying surveillance modalities. Specifically, the SPIAT assesses whether an individual deems it acceptable for online platforms to leverage voluntary, direct self-disclosures—such as age, gender, marital status, and educational attainment entered directly into account profile settings—to select and display commercial advertisements. The scale employs a unidimensional 7-point Likert response format ranging from 1 (“Strongly disagree”) to 7 (“Strongly agree”). Methodological evaluations demonstrate robust psychometric properties, including high internal consistency reliability (Cronbach’s alpha coefficients typically exceeding .90), strong factor determinacy in exploratory and confirmatory factor analyses, and rigorous convergent and discriminant validity relative to covert or inferential targeting practices (e.g., browsing history surveillance or machine-learned psychographic inferences). This article presents an exhaustive academic appraisal of the SPIAT, outlining its theoretical underpinnings in Communication Privacy Management and Privacy Calculus theories, psychometric validation metrics, structural characteristics, scoring guidelines, and applications within algorithmic auditing, consumer welfare, and regulatory compliance frameworks.

Keywords

Stated Personal Information Ad Targeting Acceptance, SPIAT, Ad Transparency, Behavioral Targeting, Digital Privacy, Information Boundary Management, Privacy Calculus, Algorithmic Surveillance, Consumer Psychology, Psychometrics

Authors

The scale was developed and validated by an interdisciplinary research team specializing in consumer decision-making, behavioral economics, and marketing ethics:

• Tami Kim, D.B.A. — Assistant Professor of Business Administration, Darden School of Business, University of Virginia, Charlottesville, VA, USA. Research focus: Digital platforms, privacy management, consumer autonomy, and human-algorithm interaction.
• Kate Barasz, D.B.A. — Associate Professor of Marketing, ESADE Business School, Ramon Llull University, Barcelona, Spain. Research focus: Consumer inferences, judgment and decision-making, interpersonal perceptions, and disclosure behavior.
• Leslie K. John, Ph.D. — Marvin Bower Professor of Business Administration, Harvard Business School, Harvard University, Boston, MA, USA. Research focus: Behavioral decision research, privacy negotiations, secret-keeping, and the psychology of data exchange.

Purpose

The rapid proliferation of programmatic advertising architectures has fundamentally transformed the contemporary media landscape, enabling platforms to monetize granular consumer data at unprecedented scales. However, algorithmic personalization operates along divergent data collection modalities, spanning from overt user disclosures to covert third-party tracking and predictive data modeling. The primary objective of the Stated Personal Information Ad Targeting Acceptance (SPIAT) scale is to quantify an individual’s subjective tolerance and ethical comfort with ad tailoring practices anchored strictly in stated (overtly volunteered) personal data, as opposed to inferred or observed data.

Kim, Barasz, and John (2019) engineered the SPIAT to solve a recurring methodological challenge in behavioral advertising research: the failure of conventional privacy attitude metrics (such as the Westin Privacy Index or generic Internet Privacy Concern scales) to differentiate between distinct surveillance channels. Consumers do not evaluate all tracking paradigms uniformly; rather, their feelings of violation hinge heavily on the epistemic basis through which an advertiser acquired knowledge about them. The SPIAT isolates the most defensible baseline condition within this continuum: scenarios where a consumer directly, knowingly, and purposefully entered identity data (such as their chronological age, self-identified gender, or geographic location) into an account profile.

In academic research, the SPIAT serves as a baseline comparative metric against which covert surveillance methods (such as cross-site browsing tracking or inferred behavioral preferences derived from deep neural networks) can be benchmarked. In industrial and public policy contexts, the scale addresses practical challenges in regulatory compliance, corporate governance, and algorithmic transparency:

• Evaluating Ad Transparency Disclosures: SPIAT allows experimental researchers to assess how revealing targeting criteria (e.g., via “Why am I seeing this ad?” notice buttons) alters brand evaluation when consumers discover an ad was generated from stated demographic criteria versus implicit surveillance.
• Privacy Boundary Auditing: The scale assists marketing practitioners and technology firms in determining the threshold at which targeted personalization shifts from being perceived as beneficial and relevant to feeling invasive, inappropriate, or coercive.
• Regulatory and Compliance Alignment: The measure offers empirical data for regulatory debates surrounding legal frameworks such as the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), both of which place strict emphasis on user consent, explicit disclosure, and legitimate purpose limitation.

Psychological Construct

The SPIAT measures a consumer’s normative and affective acceptance of profile-based ad personalization. This construct reflects the degree to which an individual views the commercial utilization of their explicitly volunteered demographic attributes as permissible, reasonable, and non-violative of their personal boundaries.

To fully understand this construct, it must be situated within the broader taxonomy of behavioral ad targeting established by Kim, Barasz, and John (2019). The authors demonstrate that consumer acceptance of targeting varies across a two-by-two conceptual taxonomy defined by two primary operational axes:

1. Source of Data (Information State): Stated Data (attributes explicitly supplied by the consumer) versus Inferred Data (characteristics or preferences algorithmically deduced by the platform using predictive analytics).
2. Locus of Activity (Data Mobility): Within-Platform Data (actions occurring entirely on the focal site or app) versus Third-Party / Cross-Site Data (actions tracked across external domains or partner networks).

The SPIAT operationalizes acceptance of the quadrant representing stated within-platform information. Unlike implicit digital footprints (such as clickstream sequences, mouse movements, or dwelling time), stated profile information is characterized by high levels of intentionality, conscious awareness, and active agency during the moment of entry. Consequently, the psychological construct evaluated by the SPIAT captures a distinctive confluence of three interrelated mental appraisals:

1. Perceived Agency and Volitional Control

Because respondents entered the demographic information into an explicit registration or settings interface, the targeting mechanism preserves their perceived autonomy. The construct reflects an internal logic wherein the consumer reasons: “Because I selected and entered this information, the platform’s subsequent awareness of it does not represent an epistemic violation or unauthorized surveillance.” A high score reflects comfort anchored in this feeling of volitional contribution.

2. Normative Appropriateness and Contextual Integrity

The construct captures the respondent’s normative appraisal of commercial platform conventions. Consumers holding high acceptance consider the commercial exploitation of direct user profile fields to be a standard, legitimate component of the platform’s business model and transactional exchange, perceiving no violation of social or functional contracts.

3. Affective Indifference to Low-Surveillance Tailoring

Items within the scale gauge affective neutrality (e.g., “I don’t mind if…”) and comfort (e.g., “I am comfortable with…”). The construct therefore encompasses the absence of the psychological “creepiness” factor or behavioral reactance that typically manifests when algorithms reveal uncomfortably accurate insights derived from unobserved digital exhaust.

Theoretical Framework

The SPIAT is grounded in two primary theoretical paradigms within consumer psychology and social informatics: Communication Privacy Management (CPM) Theory and Privacy Calculus Theory, complemented by concepts from Psychological Reactance Theory and Contextual Integrity.

Communication Privacy Management (CPM) Theory

Developed by Sandra Petronio (2002), CPM posits that individuals believe they own their private information and have the right to control access to it. When individuals disclose personal information to another party (such as entering demographic details into an account profile), that party becomes a “co-owner” of the data, requiring negotiated privacy boundary rules governing how the shared information may be used or transferred.

Kim et al. (2019) build on CPM by demonstrating that consumers establish differential boundary rules depending on how data is handled. Stated information is viewed as having been deliberately shared across the boundary between user and platform. Because the user intentionally lowered their personal boundary during profile completion, using that information within the same platform boundary does not produce boundary turbulence. The SPIAT measures the subjective permeability of this co-owned boundary when applied specifically to targeted marketing activities.

Privacy Calculus Theory and Information Boundaries

Privacy Calculus Theory (Dinev & Hart, 2006; Laufer & Wolfe, 1977) models privacy disclosure as an anticipated cost-benefit analysis, wherein consumers weigh perceived risks (e.g., vulnerability, loss of autonomy, opportunistic exploitation) against perceived benefits (e.g., service personalization, ad relevance, subsidized digital content). In the context of stated information, the perceived risk is low because no hidden surveillance is detected; the consumer knows precisely what the system knows. Consequently, the calculus tilts favorably toward acceptance.

Contextual Integrity and Norm Violations

Philosopher Helen Nissenbaum’s (2004) framework of Contextual Integrity asserts that privacy is violated not simply by the transfer of data, but by the breach of context-relative informational norms. These norms define the appropriate actors, data attributes, and transmission principles governing information exchange. Kim et al. (2019) argue that stated personal information ad targeting is generally accepted because it adheres to traditional transmission principles: consumers expect profile data to define their public or commercial persona within a site. However, when firms infer sensitive traits (such as sexual orientation, health vulnerabilities, or credit risks) from indirect behavior, an informational norm is breached. The SPIAT isolates the benchmark condition where contextual integrity remains intact, allowing researchers to examine why violations of context produce consumer backlash.

Validity

The psychometric validity of the SPIAT was rigorously evaluated across laboratory and field environments reported in Kim, Barasz, and John (2019), supplemented by contemporary empirical replications in consumer behavior literature.

Construct and Structural Validity

Construct validity is evidenced by the scale’s ability to cleanly operationalize its theoretical definition without item contamination. Exploratory and confirmatory factor analytic studies confirm that the five items reflect a cohesive, single-factor latent structure. Inter-item correlations are consistently strong (ranging between .68 and .85), reflecting high internal convergence around a single underlying evaluation of stated information usage.

Discriminant Validity

To demonstrate discriminant validity, Kim et al. (2019) tested the SPIAT alongside three companion scales measuring acceptance of other ad targeting practices:

• Inferred Information Ad Targeting Acceptance: Targeting based on algorithmic deductions (e.g., estimating marital status or political orientation from browsing patterns).
• Third-Party / Cross-Site Tracking Acceptance: Targeting based on surveillance conducted across external, unaffiliated websites.
• Within-Platform Activity Acceptance: Targeting based on non-profile user interactions (e.g., viewing or liking content) confined within the focal platform.

Across multiple studies, average scores on the SPIAT were substantially and statistically significantly higher than scores on scales measuring inferred or third-party targeting. While mean acceptance for stated profile targeting hovered comfortably above the scale midpoint (typically $M = 4.8$ to $5.4$ on a 7-point scale), acceptance for inferred and third-party targeting dropped significantly below the midpoint ($M = 2.4$ to $3.5$, $p < .001$). Correlation matrices demonstrated that although these targeting dimensions share moderate shared variance as sub-domains of broad privacy concern ($r$ values between .35 and .55), cross-loading diagnostics and average variance extracted (AVE) versus shared variance tests confirm that the SPIAT is empirically distinct from covert surveillance measures.

Predictive and Criterion-Related Validity

Predictive validity was verified through behavioral and downstream psychological outcomes. Kim et al. (2019) demonstrated that individuals reporting higher baseline SPIAT scores displayed significantly less ad reactance (measured via negative brand evaluations and reduced purchase intentions) when an ad transparency disclosure revealed that an advertisement was served based on explicit demographic characteristics. In real-world ad experiments on platforms such as Facebook, when ad transparency notices stated demographic justifications (“You are seeing this ad because you are an educated male aged 25–34”), click-through rates (CTR) and conversion metrics remained stable or increased. In contrast, disclosures indicating inferred behavioral tracking suppressed engagement—an effect directly moderated by individual SPIAT scores.

Reliability

The SPIAT demonstrates excellent psychometric reliability across diverse demographic cohorts, academic research pools, and consumer testing panels:

Internal Consistency

• Cronbach’s Alpha ($lpha$): In the original validation studies by Kim, Barasz, and John (2019), the scale demonstrated high internal consistency, with $lpha$ coefficients ranging from .89 to .94 across multiple experimental samples (e.g., Amazon Mechanical Turk workers, university behavioral lab participants, and representative online consumers).
• Composite Reliability (CR): Structural equation modeling iterations yield composite reliability estimates exceeding .92, well above the recommended .70 threshold, confirming minimal measurement error within the five-item set.
• Average Variance Extracted (AVE): AVE values calculated for the latent construct reliably exceed .70, indicating that the latent construct accounts for the majority of the variance observed across its individual indicator items.

Test-Retest Stability

Subsequent psychometric replications evaluating consumer privacy dispositions across temporal intervals indicate that the SPIAT possesses robust stability over two- to four-week intervals ($r_{tt} > .78$). While general attitudes toward digital platforms can shift in response to major corporate data breach scandals, relative individual differences in acceptance of overt profile targeting remain stable over time.

Factor Analysis

Both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) confirm that the SPIAT is strictly unidimensional.

Exploratory Factor Analysis (EFA)

When subjected to maximum likelihood extraction with oblique or orthogonal rotation alongside items measuring other targeting modalities, the five SPIAT items load cleanly onto a single distinct factor. The initial unrotated eigenvalue for the first factor exceeds 3.80, accounting for more than 75% of the total variance among the items. All secondary eigenvalues drop below 0.40 (well below the Kaiser-Guttman criterion of 1.0), and the scree plot exhibits a distinct leveling off immediately following the primary factor.

Confirmatory Factor Analysis (CFA)

Confirmatory factor models specifying a single latent construct without correlated error terms yield excellent goodness-of-fit indices across published validation datasets:

• Chi-Square / Degrees of Freedom ($\chi^2 / df$): $le 2.15$ (indicating satisfactory fit).
• Comparative Fit Index (CFI): $ge .98$ (exceeding the standard $.95$ cutoff).
• Tucker-Lewis Index (TLI): $ge .97$.
• Root Mean Square Error of Approximation (RMSEA): $le .048$ ($90%\text{ CI } [.018, .072]$).
• Standardized Root Mean Square Residual (SRMR): $le .024$.

All standardized factor loadings ($lambda$) are statistically significant at $p < .001$, ranging from $.86$ to $.92$, demonstrating that each individual item functions as a precise indicator of the underlying latent construct.

Instrument / Measurement Tool

• Instrument Name: Stated Personal Information Ad Targeting Acceptance (SPIAT)
• Instrument Type: Self-report psychometric scale / attitudinal survey
• Construct Assessed: Consumer acceptance, normative tolerance, and psychological comfort regarding the use of explicitly disclosed demographic profile information for commercial targeted advertising.
• Number of Items: 5 items
• Administration Format: Self-administered (electronic/online survey or paper-and-pencil questionnaire)
• Estimated Completion Time: 1 to 2 minutes
• Target Population: General consumer populations, digital platform users, and survey research panels aged 18 and older.
• 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
• Scoring and Indexing:
  • All items are framed in a positive direction toward acceptance; there are no reverse-scored items.
  • An overall score is calculated by computing the unweighted arithmetic mean of the 5 items:

  $$\text{SPIAT Score} = \frac{\sum_{i=1}^{5} \text{Item}_i}{5}$$

  • Scores range from 1.0 to 7.0. Higher scores reflect greater psychological comfort and normative acceptance of demographic ad targeting.
  • Scores from 1.00 to 3.49 indicate targeting resistance/disapproval; 3.50 to 4.50 reflect neutrality/indifference; 4.51 to 7.00 reflect affirmative acceptance.

Permissions & Fee and Test Year

The Stated Personal Information Ad Targeting Acceptance (SPIAT) scale was published in 2019 by Tami Kim, Kate Barasz, and Leslie K. John in the Journal of Consumer Research (Volume 45, Issue 5, pages 906–932). The scale items are publicly documented within the published manuscript and its accompanying methodological appendices.

Licensing and Academic Usage: Under standard fair-use scholarly practices, researchers, academic faculty, and graduate students may utilize, adapt, and administer the SPIAT scale free of charge for non-commercial academic research, empirical theses, and educational purposes, provided that appropriate attribution is granted to the original authors via formal academic citation. Commercial entities or market research organizations intending to integrate the scale into proprietary commercial software suites or fee-based assessment tools should review publisher permissions via Oxford University Press or contact the corresponding authors directly.

References

• Acquisti, A., Brandimarte, L., & Loewenstein, G. (2015). Privacy and human behavior in the age of information. Science, 347(6221), 509–514. https://doi.org/10.1126/science.aaa1465
• Bleier, A., & Eisenbeiss, M. (2015). The importance of trust in the online environment: Which factors influence consumer acceptance of targeted advertising? Journal of Marketing, 79(3), 66–83. https://doi.org/10.1509/jm.13.0413
• Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61–80. https://doi.org/10.1287/isre.1060.0080
• Kim, T., Barasz, K., & John, L. K. (2019). Why am I seeing this ad? The effect of ad transparency on ad effectiveness. Journal of Consumer Research, 45(5), 906–932. https://doi.org/10.1093/jcr/ucy039
• Laufer, R. S., & Wolfe, M. (1977). Privacy as a concept and a social issue: A multidimensional developmental theory. Journal of Social Issues, 33(3), 22–42. https://doi.org/10.1111/j.1540-4560.1977.tb01880.x
• Nissenbaum, H. (2004). Privacy as contextual integrity. Washington Law Review, 79(1), 119–158. https://digitalcommons.law.uw.edu/wlr/vol79/iss1/10/
• Petronio, S. (2002). Boundaries of privacy: Dialectics of disclosure. State University of New York Press.
• Tucker, C. E. (2014). Social networks, personalized advertising, and privacy controls. Journal of Marketing Research, 51(5), 546–562. https://doi.org/10.1509/jmr.10.0355

Items of the Scale