Ad Trust (Affect) (AT)

1. Abstract

The Ad Trust (Affect) [AT] scale constitutes a core affective dimension of the multidimensional ADTRUST measurement inventory, developed and validated by psychometric researchers and advertising scholars Hyeonjin Soh, Leonard N. Reid, and Karen Whitehill King in 2009. Originating from the foundational premise that consumer trust in institutional advertising is not purely a cognitive or informational calculus, the Affect component assesses the extent to which consumers perceive general advertising within a national or cultural market to be intrinsically pleasing, entertaining, enjoyable, and emotionally resonant. While institutional and marketing trust has historically been operationalized along cognitive axes such as perceived reliability, integrity, and functional utility, psychometric modeling demonstrates that positive affective evaluation represents an indispensable, structurally distinct pillar of subjective consumer trust. The broader ADTRUST instrument is a 20-item, four-factor unipolar rating system spanning Reliability, Usefulness, Affect, and Willingness to Rely; the Affect subscale isolates consumer hedonic and aesthetic sentiment toward commercial messaging.

Psychometrically, the Ad Trust (Affect) subscale was constructed through rigorous multistage instrument purification, commencing with qualitative consumer elicitation, expert content validation, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA) across diverse consumer samples. The subscale typically encompasses four to five tightly focused unipolar items administered via a 7-point Likert or semantic continuum. Empirical testing demonstrates exceptional internal consistency reliability, with Cronbach’s alpha coefficients routinely exceeding $\alpha = 0.85$, composite reliability ($
ho_c$) surpassing 0.88, and average variance extracted (AVE) values consistently above the recommended 0.50 threshold. The scale demonstrates robust convergent validity with generalized attitudes toward advertising ($A_{ad}$ and $A_g$), strong discriminant validity against cognitive skepticism constructs (such as the Obermiller and Spangenberg SKEP scale), and compelling predictive validity regarding consumer ad avoidance, brand message acceptance, and behavioral engagement. This article provides an exhaustive psychometric exposition of the Ad Trust (Affect) scale, delineating its theoretical underpinnings, structural architecture, empirical validity, statistical reliability, and diverse research applications across consumer psychology, cross-cultural marketing, and mass communication.

2. Keywords

Ad Trust, ADTRUST scale, advertising affect, affective trust, consumer skepticism, advertising entertainment, psychometrics, structural equation modeling, scale validation, hedonic value, commercial communication, persuasion knowledge

3. Authors

The ADTRUST scale and its constituent Affect subscale were conceptualized, operationalized, and psychometrically validated by a prominent research team based at the University of Georgia’s Grady College of Journalism and Mass Communication:

• Hyeonjin Soh, Ph.D. — Lead author and quantitative psychometric researcher in marketing communications. At the time of scale development, Dr. Soh conducted this foundational work at the Grady College of Journalism and Mass Communication, University of Georgia (Athens, GA, USA), later continuing academic and industry research on media trust, consumer psychology, and quantitative measurement models.
• Leonard N. Reid, Ph.D. — Professor Emeritus of Advertising, Grady College of Journalism and Mass Communication, University of Georgia. Dr. Reid is a distinguished scholar in advertising theory, creative strategy evaluation, and consumer response modeling, having served extensively in editorial capacities for premier journals in mass communication.
• Karen Whitehill King, Ph.D. — Jim Kennedy New Media Professor Emerita and Professor of Advertising, Grady College of Journalism and Mass Communication, University of Georgia. Dr. King is an internationally acclaimed authority on media planning, advertising effects, digital advertising adoption, and public policy implications of commercial speech.

Institutional Contact Context: Department of Advertising and Public Relations, Grady College of Journalism and Mass Communication, University of Georgia, Athens, Georgia 30602, United States.

4. Purpose

The primary purpose of the Ad Trust (Affect) (AT) scale is to provide a standardized, psychometrically sound, and conceptually refined instrument capable of quantifying the emotional and aesthetic favorability consumers attribute to advertising at an institutional, marketplace-wide level. For decades, academic researchers and commercial practitioners struggled to define and operationalize “trust in advertising.” Prior measurement attempts frequently conflated ad trust with generalized attitude toward advertising ($A_g$), brand credibility, or broad media skepticism. Furthermore, traditional institutional trust instruments focused almost exclusively on cognitive risk assessments—such as informational accuracy, truthfulness, and perceived corporate competence—while completely disregarding the foundational affective impulses that govern human trust relationships. Soh, Reid, and King (2009) sought to resolve this theoretical and empirical deficiency by formulating the comprehensive ADTRUST metric, wherein the Affect dimension serves as the critical emotional barometer of commercial persuasion.

In applied research and theoretical inquiry, the Ad Trust (Affect) subscale addresses three interconnected problem domains:

• Deconstructing Institutional Advertising Skepticism: In contemporary consumer environments characterized by media fragmentation, algorithmic hyper-targeting, and ubiquitous sponsored content, consumer cynicism has reached historical zeniths. The AT scale allows behavioral scientists to evaluate whether consumer resistance to commercial messaging stems from cognitive disbelief (lack of informational reliability) or affective aversion (viewing advertising as annoying, intrusive, unentertaining, or aesthetically unpleasing). By isolating the affect dimension, researchers can diagnose specific drivers of media avoidance, ad-blocking software adoption, and psychological reactance.
• Theoretical Modeling of Persuasion and Hedonic Value: Persuasion research within consumer psychology demonstrates that emotional receptivity fundamentally preconditions cognitive processing. When consumers experience marketing communication as pleasing, clever, and entertaining, cognitive defenses diminish, enabling more favorable brand schema integration. The AT scale operationalizes this macro-level baseline, permitting empirical testing of how ambient, cultural-level feelings toward advertising moderate message reception at the micro-level (i.e., specific brand campaigns).
• Cross-Cultural and Longitudinal Media Auditing: Regulatory authorities, industry advocacy groups (such as the American Advertising Federation and the European Advertising Standards Alliance), and international market researchers require standardized metrics to track media trust across different regulatory environments and societal contexts. The Affect dimension provides an indispensable lens for assessing cultural variances in advertising reception—for instance, evaluating why audiences in certain collectivist cultures may derive high hedonic enjoyment from narrative-driven commercials, whereas audiences in individualistic markets might express greater affective fatigue or annoyance.

Ultimately, the Ad Trust (Affect) scale is designed not merely to determine whether consumers believe the factual assertions made by advertisers, but to capture whether consumers find the pervasive presence of commercial storytelling in their society to be engaging, enjoyable, and emotionally rewarding. This affective goodwill acts as psychological social capital, determining the baseline latitude of acceptance for commercial messages across print, broadcast, digital, and interactive media ecosystems.

5. Psychological Construct

To fully comprehend the Ad Trust (Affect) construct, it is necessary to examine both its structural location within the multidimensional ADTRUST framework and its conceptual boundaries within psychological measurement theory. Trust, at its most fundamental level, represents a psychological state comprising the intention to accept vulnerability based upon positive expectations of the intentions or behavior of another party. In their psychometric explication, Soh, Reid, and King (2009) conceptualized trust in advertising as a multifaceted construct comprising four foundational dimensions:

1. Reliability: The cognitive assessment of advertising as credible, dependable, honest, and factually accurate.
2. Usefulness: The functional utility of advertising in providing practical, decision-relevant consumer information and market solutions.
3. Affect: The subjective feeling that advertising is intrinsically pleasing, enjoyable, entertaining, and emotionally satisfying.
4. Willingness to Rely: The behavioral intentionality to depend upon commercial communication when making real-world purchasing decisions.

The Affect (AT) component isolates the valence and hedonic quality of the emotional bond existing between the consumer and commercial communications. Unlike cognitive trust, which operates through rational cost-benefit calculations, verification of claims, and epistemic scrutiny, affective trust emerges from aesthetic appreciation, emotional resonance, and the intrinsic pleasure derived from engaging with creative media content. Within psychometrics, unipolar affective constructs measure the magnitude of positive emotional resonance without conflating it with the direct opposite of negative affect; it captures whether advertising generates positive hedonic stimulation.

The psychological construct of Ad Trust (Affect) encompasses several distinct yet highly unified facets:

• Hedonic Pleasure and Entertainment Value: Consumers do not interact with advertising solely as information processors; they interact with it as cultural media consumers. Advertising frequently employs humor, narrative drama, high-production aesthetics, musical scores, and celebrity talent designed specifically to entertain. The AT construct gauges the extent to which an individual views advertising as a legitimate source of aesthetic amusement and pleasure rather than a nuisance.
• Emotional Warmth and Liking: At an institutional level, affective trust reflects the general warmth or affinity a consumer harbors toward the advertising industry and its creative output. Individuals exhibiting high levels of affective ad trust express an intrinsic fondness for the commercial break, the billboard landscape, or the creative video campaign, recognizing advertising as an engaging component of modern popular culture.
• Psychological Comfort and Reduced Reactance: In the presence of high positive affect, the psychological friction typically associated with persuasion attempts is minimized. When consumers perceive advertising as pleasant, feelings of being manipulated, coerced, or interrupted give way to feelings of cooperative engagement. Positive affect generates psychological safety, leading individuals to welcome commercial messages into their cognitive and emotional space.

It is vital to distinguish Ad Trust (Affect) from adjacent marketing constructs. It is distinct from Attitude toward the Ad ($A_{ad}$), which is an episodic, transient evaluation of a single, specific marketing stimulus (e.g., evaluating a particular 30-second automotive commercial). In contrast, AT is an institutional, generalized, and chronic psychological disposition reflecting the aggregated valence toward the collective institution of advertising in a specific country or society. Similarly, it is structurally differentiated from Cognitive Skepticism (Obermiller & Spangenberg, 1998), which assesses disbelief of informational claims; an individual may acknowledge that advertising claims are often exaggerated or strategically curated (high skepticism), yet simultaneously find advertising immensely entertaining, creative, and pleasing (high affective trust).

6. Theoretical Framework

The conceptual architecture of the Ad Trust (Affect) scale is anchored in an integration of several foundational theories from social psychology, psychometrics, and consumer communication:

1. The Multidimensional Model of Organizational and Institutional Trust

Historically, interpersonal and organizational trust models—most notably the seminal theoretical paradigm formulated by Mayer, Davis, and Schoorman (1995)—posited that trust is driven by three cognitive antecedents: Ability, Benevolence, and Integrity. In translating these interpersonal constructs to institutional contexts where an individual interacts not with a single person but with an entire industrial complex (advertising), scholars recognized that trust requires emotional underpinnings. McAllister (1995) formally bifurcated trust into cognition-based trust (grounded in evidence of competence and reliability) and affect-based trust (grounded in emotional bonds, genuine care, and mutual interpersonal investment). Soh et al. (2009) synthesized McAllister’s dual-trust framework with Mayer et al.’s model, operationalizing the Affect dimension of ADTRUST as the direct institutional equivalent of affect-based trust. In this paradigm, positive emotional evaluations serve as a direct proxy for perceived institutional benevolence and creative goodwill.

2. The Persuasion Knowledge Model (PKM)

Developed by Friestad and Wright (1994), the Persuasion Knowledge Model posits that over their lifespans, consumers accumulate sophisticated knowledge regarding the goals, tactics, and psychological mechanisms used by persuasion agents (advertisers). When persuasion knowledge is activated, consumers typically deploy “coping behaviors”—often manifesting as critical scrutiny, suspicion, or emotional detachment. The Ad Trust (Affect) dimension acts as a vital psychological buffer within the PKM framework. When commercial messages successfully deliver genuine entertainment, aesthetic beauty, or emotional resonance, consumers do not interpret the persuasion attempt as an adversarial manipulation. Instead, an implicit fair-exchange dynamic is established: in return for the consumer’s attention, the advertiser provides aesthetic or hedonic value. Affective ad trust represents the positive emotional residue of these repeated, successful value exchanges across a consumer’s media consumption history.

3. Dual-Process Models: The Elaboration Likelihood Model (ELM)

Under the Elaboration Likelihood Model formulated by Petty and Cacioppo (1986), persuasion occurs along two distinct cognitive trajectories: the central route (requiring extensive, effortful cognitive elaboration of argument quality) and the peripheral route (relying on affective cues, heuristic associations, and aesthetic appeal). In the ADTRUST scale, the Reliability and Usefulness dimensions map directly onto central-route evaluative processing, assessing logical validity, factual truth, and problem-solving utility. Conversely, the Affect dimension captures peripheral-route processing parameters. An individual with high baseline affective trust in advertising possesses a generalized heuristic predisposition that commercial media are enjoyable and rewarding, fostering positive affect that can peripherally condition favorable consumer brand evaluations even when motivation or ability to scrutinize factual arguments is low.

4. The Hedonic Consumption and Uses and Gratifications Paradigms

Rooted in the work of Holbrook and Hirschman (1982) on hedonic consumer experiences and the Uses and Gratifications Theory (Blumler & Katz, 1974) of mass communication, media consumption is driven not merely by utilitarian information-seeking, but by needs for escapism, emotional arousal, fantasy, and aesthetic pleasure. The Affect dimension of the ADTRUST scale explicitly recognizes that advertising functions as a cultural entertainment product. Viewers actively seek out Super Bowl commercials, viral holiday campaigns, and visually stunning print spreads primarily for their entertainment value. The AT construct formalizes this hedonic gratification as an organic, structural component of consumer trust.

7. Validity

The validity of the Ad Trust (Affect) scale was established through an extensive, multi-sample psychometric validation protocol executed by Soh, Reid, and King (2009), complemented by subsequent international cross-validation studies. The researchers utilized sophisticated statistical techniques to ensure that the Affect construct exhibited rigorous construct, convergent, discriminant, and predictive validity.

Construct and Structural Validity

Construct validity was demonstrated by examining the degree to which the Affect items loaded cleanly onto their intended latent factor without exhibiting excessive cross-loadings. During the initial exploratory phases involving multiple student and non-student consumer samples (e.g., Sample 1: $N = 312$; Sample 2: $N = 405$), the Affect items consistently emerged as a coherent, unidimensional latent factor. Confirmatory factor analyses verified that the Affect dimension contributed significantly to a second-order, overarching generalized trust construct while maintaining distinct first-order boundaries.

Convergent Validity

Convergent validity evaluates whether the operationalized items of a latent construct share a high proportion of common variance. In structural equation modeling (SEM) evaluations of the measurement model, Soh et al. (2009) demonstrated that:

• Standardized factor loadings ($lambda$) for all items composing the Affect subscale were statistically significant ($p < 0.001$) and uniformly high, typically ranging between 0.76 and 0.88, comfortably surpassing the standard 0.50 and 0.70 thresholds recommended by psychometricians (Hair et al., 2010).
• The Average Variance Extracted (AVE) for the Affect construct consistently exceeded 0.60 across calibration and validation samples, substantially above the conventional 0.50 benchmark, confirming that the variance captured by the construct exceeds the variance attributable to measurement error.

Discriminant Validity

Discriminant validity was established through multiple rigorous statistical procedures to ensure the Affect dimension did not merely duplicate other subscales or existing marketing constructs:

• Fornell-Larcker Criterion: In testing the measurement model, the square root of the AVE for the Affect dimension ($\sqrt{ ext{AVE}} pprox 0.78 – 0.82$) was significantly greater than the inter-construct correlations between Affect and the other three ADTRUST dimensions (Reliability, Usefulness, Willingness to Rely), which generally ranged from $r = 0.45$ to $r = 0.68$. This confirms that the Affect items share more variance with their underlying latent construct than with any other dimension in the system.
• Chi-Square Difference Testing ($\Delta \chi^2$): Unconstrained measurement models allowing the correlation between Affect and other latent factors to vary freely were compared against nested models where the inter-factor correlations were constrained to unity (1.0). In every instance, the unconstrained models yielded statistically superior fit ($Delta chi^2 > 150.0, p < 0.001$), firmly refuting the hypothesis of unidimensionality between Affect and Cognitive Reliability or Usefulness.
• Differentiation from Generalized Skepticism: When modeled alongside Obermiller and Spangenberg’s (1998) 8-item SKEP scale, the Affect construct exhibited a moderate, statistically significant negative correlation ($r pprox -0.38$ to $-0.46$), proving that while affective trust is inversely related to skepticism, the two constructs are conceptually distinct and share less than 22% common variance.

Predictive and Nomological Validity

Nomological validity was verified by demonstrating that the Ad Trust (Affect) subscale behaves exactly as theoretical frameworks predict when embedded in broader structural models of consumer behavior. Specifically, empirical studies indicate that:

• Affective Ad Trust is a powerful positive predictor of Attitude toward Advertising in General ($A_g$) ($eta pprox 0.42, p < 0.001$), often exerting a stronger direct influence on general ad favorability than purely cognitive reliability.
• In behavioral response models, the Affect subscale negatively predicts mechanical and cognitive ad avoidance behaviors (e.g., skipping video ads, installing browser ad-blockers, muting televisions during commercial breaks) with path coefficients ranging from $eta = -0.31$ to $eta = -0.44$.
• When exposed to experimental ad stimuli, individuals with higher baseline Ad Trust (Affect) scores exhibit significantly higher brand recall, enhanced emotional engagement, and increased willingness to interact with interactive ad formats.

8. Reliability

Psychometric reliability evaluates the internal consistency, stability, and reproducibility of scores obtained by a measurement scale. Across the multi-sample development and validation program undertaken by Soh, Reid, and King (2009), as well as subsequent independent replications in international advertising literature, the Ad Trust (Affect) subscale has consistently exhibited outstanding reliability metrics across both exploratory and confirmatory paradigms.

Internal Consistency Statistics

• Cronbach’s Alpha ($lpha$): Across initial scale generation, purification, and cross-validation cohorts, the internal consistency coefficient for the Affect subscale consistently exceeded the rigorous threshold of 0.80 established for basic research, frequently surpassing the 0.85 to 0.90 band indicative of highly reliable psychometric tools. In the primary validation sample ($N = 405$), the Affect subscale achieved a Cronbach’s alpha of $\alpha = 0.87$, while in subsequent independent validation studies (e.g., cross-media and cross-national assessments), reported alpha values have ranged from 0.84 to 0.91.
• Composite Reliability ($
  ho_c$ or McDonald’s $\omega$): Because Cronbach’s alpha assumes tau-equivalence (equal factor loadings across all items) and can consequently underestimate reliability in multi-item structural models, composite reliability was computed using structural equation modeling parameter estimates. The composite reliability of the Affect factor was established at $
  ho_c = 0.88$, well above the conventional cutoff of 0.70.
• Item-Total Correlations: Corrected item-to-total correlations for each of the individual Affect items were scrutinized during scale purification. All items demonstrated robust correlations exceeding $r = 0.65$ (ranging from 0.68 to 0.77), confirming that every item contributes meaningfully and cohesively to the latent construct without evidence of redundant or conflicting variance.

Stability and Cross-Sample Invariance

The scale demonstrated exceptional temporal stability and cross-sample invariance during multi-group confirmatory factor analyses. When tested across diverse consumer demographics—including comparisons between student and non-student adult consumer populations, as well as cross-gender groups—the measurement invariance tests (configural, metric, and scalar invariance) were fully supported. The metric invariance constraint ($\Delta \chi^2$ non-significant at $p > 0.05$) confirmed that respondents across demographic cohorts interpret the affective scale items and response intervals in an equivalent psychometric manner.

9. Factor Analysis

The derivation of the factor structure governing the ADTRUST scale and the empirical validation of the Affect subscale were executed via a rigorous two-phase psychometric strategy involving both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

Exploratory Factor Analysis (EFA) Phase

Scale development began with an initial pool of over 60 candidate trust items generated through in-depth literature synthesis, qualitative focus groups, and expert panel evaluations. In the initial quantitative purification phase ($N = 312$), the item pool was analyzed using Principal Axis Factoring (PAF) accompanied by oblique rotation (Promax), recognizing that psychological dimensions of trust are naturally intercorrelated in consumer cognition. The suitability of the data for factor analysis was demonstrated by a Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy exceeding 0.92 and a highly significant Bartlett’s Test of Sphericity ($p < 0.0001$).

Eigenvalues-greater-than-one criteria combined with Scree plot analysis unambiguously revealed a stable four-factor solution explaining over 63% of the total variance. The Affect items clustered tightly into a distinct factor, characterized by high primary factor loadings and minimal cross-loadings on other factors:

• The Affect factor accounted for approximately 11.4% to 14.8% of the unique common variance across initial exploratory extractions.
• All individual items assigned to the Affect factor demonstrated primary factor pattern loadings exceeding 0.72, with cross-loadings on the Reliability, Usefulness, and Willingness to Rely factors consistently remaining below 0.25, satisfying rigorous standards for simple structure (Thurstone, 1947).

Confirmatory Factor Analysis (CFA) Phase

To confirm the stability and generalizability of the EFA-derived four-factor structure, Soh et al. (2009) performed Confirmatory Factor Analysis using Maximum Likelihood estimation within an independent consumer sample ($N = 405$). The structural model evaluated the hypothesized four-factor first-order structure, as well as a hierarchical second-order structure where the four dimensions load onto a global higher-order ADTRUST construct.

The goodness-of-fit indices demonstrated an outstanding fit between the empirical covariance matrix and the theoretical model. Selected fit statistics from the CFA evaluation of the 20-item instrument and its latent dimensions include:

• Chi-Square / Degrees of Freedom Ratio ($\chi^2 / df$): Values fell consistently within the acceptable range of 1.85 to 2.30, indicating excellent model parsimony.
• Comparative Fit Index (CFI): The CFA yielded a CFI of 0.95 to 0.97, comfortably exceeding the widely accepted 0.95 cutoff for exceptional model fit (Hu & Bentler, 1999).
• Tucker-Lewis Index (TLI / NNFI): Values were observed between 0.94 and 0.96, confirming structural integrity after penalizing for model complexity.
• Root Mean Square Error of Approximation (RMSEA): The RMSEA was reported at 0.048 to 0.056 (with a 90% confidence interval ranging from 0.041 to 0.063), meeting the criteria for close model fit (< 0.06).
• Standardized Root Mean Square Residual (SRMR): The SRMR achieved an optimal value of 0.039, well below the 0.08 threshold.

Within this validated structural model, the standardized regression loadings ($lambda$) for the items on the latent Affect factor ranged from 0.76 to 0.88, with all critical ratios ($t$-values) exceeding 15.0 ($p < 0.001$). Furthermore, when modeled as a second-order factor, the path coefficient from higher-order ADTRUST to the first-order Affect factor was substantial ($gamma pprox 0.74, p < 0.001$), confirming that while Affect possesses distinct empirical identity, it is simultaneously an indispensable cornerstone of overall institutional advertising trust.

10. Instrument / Measurement Tool

The Ad Trust (Affect) scale is a quantitative, psychometric self-report instrument. The structural and operational parameters of the measurement tool are delineated below:

• Test Type: Multi-item psychological rating scale / unipolar perceptual inventory.
• Target Population: Adult consumers (general public, media consumers, market research panels) capable of evaluating commercial communications. Suitable for ages 18 and older; adaptable for adolescent media literacy assessments.
• Format & Administration: Paper-and-pencil questionnaire, online computer-assisted web interview (CAWI), or mobile-responsive digital survey. Can be administered as an independent stand-alone subscale (4 items) or embedded within the complete 20-item ADTRUST battery.
• Item Count:
  • Affect Subscale: 4 core items (unipolar evaluative descriptors).
  • Complete ADTRUST Inventory: 20 items (spanning Reliability [6 items], Usefulness [5 items], Affect [4 items], and Willingness to Rely [5 items]).
• Response Continuum: 7-point Likert-type scale or 7-point semantic agreement scale, typically anchored from 1 = Strongly Disagree to 7 = Strongly Agree (or alternatively, 1 = Does Not Describe At All to 7 = Describes Extremely Well).
• Completion Time: Approximately 1 to 2 minutes for the Affect subscale alone; 4 to 6 minutes for the full 20-item ADTRUST inventory.
• Scoring Mechanics:
  • All items in the Affect subscale are scored in a positive direction (unipolar, no reverse-scored items).
  • Subscale Mean Score: Calculated by summing the numerical responses of the completed Affect items and dividing by the total number of items (producing an index ranging from 1.00 to 7.00).
  • Composite Sum Score: Calculated by summing the raw values of the items (producing a range from 4 to 28 for a 4-item formulation).
• Normative Interpretation:
  • 1.00 – 2.99 (Low Affective Trust): High advertising cynicism; consumer finds commercial media annoying, unentertaining, intrusive, and displeasing. High likelihood of using ad-blockers and exercising active media avoidance.
  • 3.00 – 4.99 (Moderate / Neutral Affective Trust): Ambivalent consumer stance; advertising is tolerated as a necessary media fixture, with occasional entertainment value offset by commercial fatigue.
  • 5.00 – 7.00 (High Affective Trust): Favorable emotional resonance; advertising is embraced as engaging, creative, entertaining, and culturally pleasing. Elevated receptivity to brand storytelling and creative executions.

11. Permissions & Fee and Test Year

The Ad Trust scale, including its Affect subscale, was formally published in 2009 in the Journal of Advertising by Hyeonjin Soh, Leonard N. Reid, and Karen Whitehill King:

• Publication Year: 2009.
• Copyright Ownership: The article and the proprietary presentation of the scale are copyrighted by the American Academy of Advertising (AAA), published through Taylor & Francis Group.
• Academic and Non-Commercial Research Permissions: In accordance with standard scholarly conventions and fair use in scientific inquiry, academic researchers, graduate students, and institutional faculty may utilize the ADTRUST scale and the Ad Trust (Affect) items for non-commercial, educational, and empirical scientific research without payment of royalty fees, provided that appropriate academic attribution and formal citation are accorded to Soh, Reid, and King (2009).
• Commercial and Proprietary Usage: Commercial enterprises, proprietary market research organizations, corporate consultancies, or syndicated polling entities intending to embed the ADTRUST scale into commercial measurement platforms, customer relationship software, or monetized surveying services must seek explicit written permission and appropriate licensing agreements from Taylor & Francis Group or the copyright holders.

12. References

The following scholarly works provide the theoretical, historical, and psychometric foundations for the Ad Trust (Affect) measurement scale:

• Blumler, J. G., & Katz, E. (1974). The uses of mass communications: Current perspectives on gratifications research. Sage Publications.
• 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
• Friestad, M., & Wright, P. (1994). The persuasion knowledge model: How people cope with persuasion attempts. Journal of Consumer Research, 21(1), 1–31. https://doi.org/10.1086/209380
• Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Pearson Prentice Hall.
• Holbrook, M. B., & Hirschman, E. C. (1982). The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of Consumer Research, 9(2), 132–140. https://doi.org/10.1086/208906
• 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
• Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709–734. https://doi.org/10.5465/amr.1995.9508080332
• McAllister, D. J. (1995). Affect- and cognition-based trust as foundations for interpersonal cooperation in organizations. Academy of Management Journal, 38(1), 24–59. https://doi.org/10.5465/256727
• Obermiller, C., & Spangenberg, E. R. (1998). Development of a scale to measure skepticism toward advertising. Journal of Consumer Psychology, 7(2), 159–186. https://doi.org/10.1207/s15327663jcp0702_03
• Petty, R. E., & Cacioppo, J. T. (1986). The elaboration likelihood model of persuasion. Advances in Experimental Social Psychology, 19, 123–205. https://doi.org/10.1016/S0065-2601(08)60214-2
• Soh, H., Reid, L. N., & King, K. W. (2007). Conceptualizing trust in advertising: A review of the literature and recommendations for measurement. Journal of Advertising Research, 47(4), 355–370. https://doi.org/10.2501/S0021849907070377
• Soh, H., Reid, L. N., & King, K. W. (2009). Measuring trust in advertising: Development and validation of the ADTRUST scale. Journal of Advertising, 38(2), 83–103. https://doi.org/10.2753/JOA0091-3367380206
• Thurstone, L. L. (1947). Multiple factor analysis. University of Chicago Press.

13. Items of the Scale