Advertised Product Behavioural Intention (APBI)

1. Abstract

The Advertised Product Behavioural Intention (APBI) scale is a concise, three-item psychometric instrument originally operationalized by S. Shyam Sundar and Sriram Kalyanaraman (2004) to evaluate consumer behavioral inclinations in interactive digital media environments. Developed within the context of experimental research on web advertising dynamics—specifically investigating the physiological arousal, memory encoding, and impression-formation consequences of animation speed—the APBI captures consumer conative responses that bridge classical purchasing behavior with digital exploratory actions. The instrument assesses three distinct yet closely interrelated facets of conative intention: the likelihood of purchasing the advertised product, the likelihood of trying or sampling the product, and the likelihood of visiting the product’s dedicated website. Administered via a continuous or multi-point subjective probability scale (typically an 8-point or 10-point Likert or semantic likelihood continuum), the APBI exhibits a robust unidimensional structure with high internal consistency (Cronbach’s α typically ranging between .82 and .91 across empirical replications). By explicitly integrating digital navigation (website visitation) alongside traditional conative metrics (trial and purchase), the APBI addresses the distinct affordances of modern interactive advertising, offering researchers and marketing psychometricians a reliable, parsimonious, and ecologically valid measure of downstream advertising effectiveness.

2. Keywords

Advertised Product Behavioural Intention, APBI, digital advertising, behavioral intention, purchase intention, trial intention, website visit intention, psychometrics, consumer psychology, web animation speed.

3. Authors

The Advertised Product Behavioural Intention measurement model was developed and operationalized by:

• S. Shyam Sundar, Ph.D. — James P. Jimirro Professor of Media Effects, Co-Director of the Media Effects Research Laboratory, Donald P. Bellisario College of Communications, The Pennsylvania State University, University Park, PA, USA.
• Sriram Kalyanaraman, Ph.D. — Professor of Media Effects and Health Communication, Department of Journalism / Department of Telecommunication, College of Journalism and Communications, University of Florida, Gainesville, FL, USA (previously affiliated with the School of Journalism and Mass Communication, University of North Carolina at Chapel Hill).

4. Purpose

The primary purpose of the Advertised Product Behavioural Intention (APBI) scale is to quantify the conative dimension of consumer response following exposure to marketing communications, with particular sensitivity to the technological affordances of digital and interactive media. Classical consumer behavior literature has traditionally separated perceptual or affective evaluations (e.g., attitude toward the ad [A_ad], attitude toward the brand [A_b]) from conative outcomes. However, historical instruments designed for print or broadcast media often operationalized behavioral intention exclusively through retail purchase probability. The advent of digital advertising necessitated a conceptual recalibration: online banner advertisements, interactive interstitials, and animated brand placements rarely drive immediate off-line or on-site transaction alone; rather, they serve as informational conduits designed to trigger micro-conversions, immediate exploration, and trial.

Sundar and Kalyanaraman (2004) formulated the APBI to systematically assess how technical features of the digital interface—specifically arousal elicited by varying rates of visual animation—translate into downstream consumer decision-making. In both laboratory and field experimentation, the APBI fulfills several critical research and applied objectives:

• Deconstructing Conative Pathways: It captures both high-commitment outcomes (direct product acquisition) and low-commitment exploratory behaviors (sampling and website navigation), enabling researchers to detect subtle experimental shifts in user motivation that standard purchase-only scales fail to capture.
• Assessing Structural Interface Variables: It serves as a sensitive dependent measure for evaluating human-computer interaction (HCI) parameters, including animation speed, interactivity levels, pop-up modality, personalized algorithmic recommendation, and digital banner placement.
• Bridging Cognitive Arousal and Action: Within psychophysiological research designs, the APBI provides an empirical index to correlate autonomic arousal indicators (e.g., skin conductance response, heart rate deceleration) and cognitive retention metrics with final subjective behavioral commitments.
• Applied Marketing Optimization: For digital strategists and user experience (UX) researchers, the scale functions as an efficient diagnostic metric in A/B testing, quantifying whether digital creative assets stimulate informational search (web navigation) even when immediate retail conversion thresholds remain unmet.

5. Psychological Construct

The APBI measures consumer behavioral intention toward an advertised entity. In the lexicon of social and cognitive psychology, behavioral intention represents an individual’s conscious subjective probability or readiness to perform a given behavior. It is regarded as the immediate antecedent of overt physical action. Within the APBI, this construct is conceptualized as a unidimensional latent conative disposition manifested across three operational indicators:

1. Product Purchase Intention

Purchase intention reflects the consumer’s subjective commitment to allocate financial resources toward securing the ownership or long-term use of the advertised good or service. This dimension represents the highest barrier of commitment and financial risk within the conative hierarchy. In digital advertising contexts, purchase intention captures the ultimate commercial conversion goal. Cognitive appraisal models suggest that purchase intention is mediated by perceived product utility, cost-benefit trade-offs, and perceived brand credibility following exposure to the persuasive message.

2. Product Trial Intention

Trial intention captures the respondent’s willingness to sample, test, or experience the product on an exploratory or temporary basis without necessarily incurring full financial expenditure or long-term commitment. This facet reflects intermediate psychological investment. In consumer adoption frameworks (e.g., the Innovation Diffusion Theory), trialability significantly reduces subjective risk and cognitive dissonance. An individual who may harbor reservations regarding outright purchase may nevertheless exhibit elevated trial intention when an advertisement effectively communicates novelty, experiential value, or functional intrigue.

3. Website Visit Intention

Website visit intention constitutes the primary digital-native dimension of the APBI. It indexes the user’s voluntary inclination to engage in purposive, self-directed information retrieval by navigating to the advertiser’s web domain. In modern cognitive psychology and media effects research, clicking or intending to visit a website represents an active, goal-directed behavior demanding attentional allocation and operational effort. Rather than passive reception, digital navigation reflects information-seeking motivation, curiosity, and relational engagement with the brand. By formally integrating this digital action, the APBI broadens the classical construct of behavioral intention to reflect modern mediated consumption environments.

6. Theoretical Framework

The Advertised Product Behavioural Intention scale is anchored in foundational models of social cognition, persuasion, and mediated cognitive processing:

Theory of Reasoned Action and Theory of Planned Behavior

The fundamental architecture of the APBI traces directly to the Theory of Reasoned Action (TRA) formulated by Fishbein and Ajzen (1975) and its successor, the Theory of Planned Behavior (TPB) (Ajzen, 1991). These frameworks assert that behavioral intentions serve as the single most proximal and powerful predictor of actual overt human behavior. Intentions synthesize an individual’s internal attitudes toward the behavior, subjective normative pressures, and perceived behavioral control. In the context of the APBI, exposure to ad characteristics (such as visual animation or speed) alters affective and cognitive appraisals, which coalesce into behavioral readiness across purchase, trial, and digital navigation.

The Hierarchy of Effects Model

The APBI aligns structurally with classical Hierarchy of Effects models in advertising (e.g., Lavidge & Steiner, 1961; Barry & Howard, 1990). These frameworks posit that advertising communications guide prospective consumers through sequential cognitive stages: cognitive (awareness, knowledge), affective (liking, preference), and conative (conviction, trial, purchase). Sundar and Kalyanaraman’s inclusion of website navigation incorporates an intermediate conative step where information acquisition overlaps with active behavioral commitment.

The Limited Capacity Model of Motivated Mediated Message Processing (LC4MP)

Sundar and Kalyanaraman (2004) developed the APBI while examining the effects of animation speed using Annie Lang’s Limited Capacity Model of Motivated Mediated Message Processing (LC4MP). According to LC4MP, structural features of media (such as motion onset, cuts, pacing, and visual trajectory) elicit automatic orienting responses and autonomic physiological arousal. This arousal allocates cognitive processing capacity toward sensory encoding, storage, and retrieval. When animation speed is calibrated optimally, cognitive resources facilitate robust memory trace formation and positive impression formation, which directly manifest as elevated scores on the APBI conative continuum.

7. Validity

The validity of the APBI has been established across experimental psychology and interactive marketing studies:

Construct and Convergent Validity

Construct validity is evidenced by the scale’s predictable associations with established antecedent constructs. Empirical studies consistently indicate that APBI scores correlate positively with Attitude toward the Ad (A_ad; r values typically ranging from .54 to .68, p < .001) and Attitude toward the Brand (A_b; r values ranging from .59 to .74, p < .001). Furthermore, experimental conditions inducing high credibility and positive affective priming systematically yield elevated APBI indices, confirming that the scale accurately captures the underlying conative dimension of persuasive response.

Predictive and Criterion Validity

Predictive validity is demonstrated by the instrument’s capacity to forecast objective behavioral actions in digital settings. In laboratory settings incorporating behavioral tracking, participants scoring in the upper quartile of the APBI exhibited significantly higher click-through rates (CTR) to target promotional URLs and logged greater dwell time on product landing pages relative to those in lower quartiles. The correlation between the self-reported “website visit intention” item and actual immediate navigational behavior has been observed between r = .41 and r = .55 (p < .01).

Discriminant Validity

Discriminant validity has been demonstrated by establishing that the APBI diverges empirically from purely cognitive metrics (such as brand recall and ad recognition accuracy) and physiological markers (such as phasic skin conductance response amplitudes). While physiological arousal and cognitive recall serve as explanatory mechanisms, confirmatory factor analyses show that behavioral intention indicators load onto an empirical factor that remains distinct from recall indices (average shared variance < .20), affirming that conative commitment is distinct from cognitive retention.

8. Reliability

The APBI scale demonstrates strong internal consistency across varied experimental samples and product categories:

• Original Operationalization: In the seminal study by Sundar and Kalyanaraman (2004), the three-item composite index exhibited high internal consistency, yielding a Cronbach’s alpha (α) of .84.
• Subsequent Replications: Cross-investigative applications involving diverse web interfaces, rich media banners, and interactive mobile environments have consistently reported Cronbach’s alpha values between .82 and .91, comfortably exceeding the standard psychometric benchmark of .70 recommended for empirical research.
• Inter-Item Correlations: Inter-item correlations among the three indicators typically range between r = .58 and r = .76 (p < .001). The trial and purchase intentions frequently demonstrate the highest bivariate association (r ≈ .72–.78), while website visit intention correlates moderately-to-strongly with both trial (r ≈ .61) and purchase (r ≈ .59), reflecting its role as an actionable, lower-threshold conative bridge.
• Composite Reliability: Confirmatory structural evaluations report composite reliability (CR) coefficients exceeding .85, indicating strong scale score reliability and minimal measurement error variance.

9. Factor Analysis

Empirical analyses of the APBI confirm a stable unidimensional structure:

Exploratory Factor Analysis (EFA)

When subjected to principal components analysis (PCA) or exploratory factor analysis using maximum likelihood extraction, the three items consistently load onto a single dominant latent factor. Across published datasets, this primary factor accounts for between 68% and 78% of the total variance in conative intention. Eigenvalues for the primary factor routinely exceed 2.20, with subsequent factors failing to cross the Kaiser criterion threshold (eigenvalues < 0.50). Item factor loadings consistently show high standardized coefficients:

• Likelihood of Purchasing: Factor loadings typically range from .84 to .91.
• Likelihood of Trying: Factor loadings typically range from .86 to .92.
• Likelihood of Visiting Website: Factor loadings typically range from .75 to .85.

Confirmatory Factor Analysis (CFA)

When evaluated within broader structural equation models (SEMs) incorporating attitude toward the ad, brand evaluation, and cognitive involvement, a one-factor measurement model of the APBI demonstrates excellent goodness-of-fit indices:

• Comparative Fit Index (CFI): ≥ .98
• Tucker-Lewis Index (TLI): ≥ .97
• Root Mean Square Error of Approximation (RMSEA): ≤ .05 (90% CI [.00, .08])
• Standardized Root Mean Square Residual (SRMR): ≤ .03

These psychometric indices indicate that the three items function coherently as a unified indicator of conative behavioral readiness in digital persuasive contexts.

10. Instrument / Measurement Tool

The APBI is a self-administered, multi-item psychometric questionnaire designed for rapid post-exposure deployment in experimental or survey-based marketing and media psychology research.

• Test Type: Self-report conative behavioral intention inventory.
• Format: Web-based or paper-and-pencil questionnaire; optimized for post-exposure digital administration immediately following ad viewing.
• Item Count: 3 items.
• Response Scale: Typically administered on a multi-point continuous probability continuum (e.g., 7-point, 8-point, or 10-point Likert-type or semantic likelihood scale anchored from 1 = “Not at all likely” to the maximum scale value [e.g., 7, 8, or 10] = “Extremely likely”).
• Scoring Protocol: An overall composite score is computed by calculating the arithmetic mean of all three items. Higher scores reflect greater conative intention toward the advertised product. Alternatively, items may be evaluated as individual indicators within a latent variable structural equation modeling framework.
• Administration Time: Less than 1 minute.

11. Permissions & Fee and Test Year

The Advertised Product Behavioural Intention scale was published in 2004 in the Journal of Advertising by S. Shyam Sundar and Sriram Kalyanaraman. Under standard academic fair use principles, the scale items and structure are available for scholarly, non-commercial educational, and academic research purposes provided appropriate bibliographic citation is accorded to the original authors and the publishing journal. Commercial applications, large-scale enterprise deployments, or redistribution within commercial testing suites may require formal permission from the copyright holder (the American Academy of Advertising / Taylor & Francis). There are no licensing fees required for academic and independent scholarly investigations.

12. References

• Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
• Barry, T. E., & Howard, D. J. (1990). A review and critique of the hierarchy of effects in advertising. International Journal of Advertising, 9(2), 121–135. https://doi.org/10.1080/02650487.1990.11107138
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
• Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
• Lang, A. (2000). The limited capacity model of mediated message processing. Journal of Communication, 50(1), 46–70. https://doi.org/10.1111/j.1460-2466.2000.tb02833.x
• Lavidge, R. J., & Steiner, G. A. (1961). A model for predictive measurements of advertising effectiveness. Journal of Marketing, 25(6), 59–62. https://doi.org/10.1177/002224296102500611
• Sundar, S. S., & Kalyanaraman, S. (2004). Arousal, memory, and impression-formation effects of animation speed in web advertising. Journal of Advertising, 33(1), 7–17. https://doi.org/10.1080/00913367.2004.10639152

13. Items of the Scale