Behavioral Intentions Toward the Ad Product (BIAP)

Abstract

The Behavioral Intentions Toward the Ad Product (BIAP) scale is a concise, four-item psychometric instrument designed to capture consumers' conative and intentional responses toward a commercial product featured in an advertising stimulus. Originating in computer-mediated communication and consumer psychology research by S. Shyam Sundar and Sriram Kalyanaraman (2004), and subsequently adapted and validated across contemporary advertising environments (e.g., Wu & Dodoo, 2020), the scale assesses a progressive behavioral spectrum. Rather than confining conation solely to direct purchasing behavior, the BIAP measures four distinct yet interrelated facets of consumer behavioral intention: product trial, situational or opportunistic purchase, active information seeking, and interpersonal recommendation or peer advocacy. Responses are captured via a 7-point Likert scale ranging from 1 (Strongly disagree) to 7 (Strongly agree) or an equivalent 7-point semantic differential continuum ranging from 1 (Unlikely) to 7 (Likely). Psychometrically, the instrument consistently demonstrates high internal consistency, with Cronbach's alpha coefficients typically exceeding .85 across laboratory and field experiments. Exploratory and confirmatory factor analyses corroborate a robust unidimensional structure that explains over 70% of total variance, with uniform factor loadings typically between .75 and .92. The BIAP exhibits strong construct validity, showing significant positive correlations with attitude toward the ad ($A_{ad}$), attitude toward the brand ($A_b$), cognitive absorption, and actual conversion behaviors, while maintaining discriminant validity against purely affective or sensory orientations. This paper provides an exhaustive review of the psychometric lineage, theoretical foundations in the Theory of Planned Behavior and the Hierarchy of Effects, structural properties, statistical indices, and administrative protocols of the BIAP scale.

Keywords

Behavioral Intentions Toward the Ad Product, BIAP, advertising effectiveness, conative response, purchase intention, product trial, information seeking, word-of-mouth advocacy, consumer decision journey, web advertising, psychometrics

Authors

The Behavioral Intentions Toward the Ad Product instrument was developed and introduced into media psychology literature by:

• S. Shyam Sundar, Ph.D. — James P. Jimirro Professor of Media Effects, Co-Director of the Media Effects Research Laboratory (MERLab), Donald P. Bellisario College of Communications, The Pennsylvania State University, University Park, Pennsylvania, USA. Affiliated with the Department of Telecommunications and Computer Science and Engineering. Email: [email protected].
• Sriram Kalyanaraman, Ph.D. — Professor of Media and Interactive Technologies, Department of Journalism, College of Journalism and Communications, University of Florida, Gainesville, Florida, USA. Formerly affiliated with the School of Journalism and Mass Communication, The University of North Carolina at Chapel Hill. Email: [email protected].

Subsequent extensions, empirical re-validations, and contextual adoptions within programmatic digital marketing, influencer marketing, and interactive brand settings have been conducted by numerous scholars, notably Laurie Wu (Temple University) and Naa Amponsah Dodoo (Syracuse University).

Purpose

The primary purpose of the Behavioral Intentions Toward the Ad Product (BIAP) scale is to quantify the strength of an individual's prospective engagement with and behavioral commitments toward an advertised offering following exposure to marketing communications. In both laboratory experiments and applied consumer research, relying purely on dichotomous or immediate purchase metrics presents significant empirical and ecological limitations. In many experimental paradigms, consumers cannot immediately execute a transaction due to artificial exposure conditions, lack of disposable capital, temporal distance from the point of sale, or unfamiliarity with novel product categories. Consequently, psychometric measurement must capture proximal conative indicators that operate as reliable precursors to ultimate consumption.

Historically, market research instruments assessed post-exposure intentions using single-item metrics such as "Would you buy this product?" Such single-item operationalizations suffer from notorious attenuation, substantial measurement error, vulnerability to social desirability biases, and a failure to address the multifaceted nature of the modern consumer decision journey. The BIAP overcomes these structural deficiencies by operationalizing behavioral intention across a four-tiered behavioral spectrum:

• Experiential Sampling (Product Trial): Capturing the threshold willingness to experience or sample the product with minimal risk.
• Acquisition Propensity (Opportunistic Purchase): Measuring point-of-sale receptivity if the product is encountered in physical or digital retail environments.
• Epistemic Exploration (Information Seeking): Quantifying active cognitive and behavioral investment to acquire additional details, specifications, or reviews.
• Interpersonal Advocacy (Recommendation / Electronic Word-of-Mouth): Gauging consumer willingness to stake social capital by endorsing the product to peers.

In academic communication research and marketing scholarship, the BIAP is widely deployed to assess how variations in interface features—such as animation speed, modality, interactivity, personalization, narrative transportation, and artificial intelligence interfaces—translate from perceptual and affective impressions into concrete behavioral predispositions. In applied settings, it serves as an evaluative diagnostic tool for testing creative copy, media placement effectiveness, and user experience (UX) interactions before deploying live advertising budgets.

Psychological Construct

The psychological construct undergirding the BIAP is conation—the proactive, deliberate, and intentional component of human attitude systems that links internal evaluations directly to manifest action. Within social psychology and consumer behavior, intentions represent an individual's subjective probability judgments regarding their future likelihood of performing a designated behavioral repertoire. The BIAP conceptualizes product-directed conation not as a monolithic, all-or-nothing purchase decision, but as a graduated continuum of behavioral intentions across four interrelated domains:

1. Product Trial Intentions

Product trial signifies an exploratory, low-commitment conative orientation. Drawing from diffusion of innovations theory, prospective adopters typically require a preliminary sampling or trial stage to reduce perceived risk and verify product claims. Item 1 ("I would like to try this product") measures an individual's openness to experiential contact. This dimension captures initial sensory and experiential curiosity, isolating consumer receptivity before substantial financial or psychological investments are demanded.

2. Situational and Opportunistic Purchase Intentions

Purchase intention is universally recognized as the single strongest subjective predictor of actual transaction behavior. However, rather than framing purchase as an unconditional certainty, Item 2 ("I would buy this product if I happened to see it in a store") situates conation within realistic environmental contingencies. It assesses situational buying propensity, capturing whether the advertising message embedded a sufficient psychological trace to trigger product retrieval and basket addition upon subsequent environmental priming in retail or e-commerce contexts.

3. Epistemic Information Seeking

In high-involvement contexts or information-rich environments, advertising rarely triggers immediate purchase; instead, it instigates an information acquisition process. Item 3 ("I would actively seek out more information about this product") measures the active, goal-directed mobilization of cognitive and behavioral resources. Rooted in cognitive response theory, active information seeking represents a high-engagement behavioral manifestation where consumers actively consult search engines, review platforms, and brand websites to resolve ambiguities generated by the ad.

4. Interpersonal Social Advocacy (Word-of-Mouth)

Interpersonal recommendation represents the most socially expansive conative dimension. Item 4 ("I would recommend this product to a friend or acquaintance") measures social sharing intentions and word-of-mouth (WOM) or electronic word-of-mouth (eWOM) readiness. Recommending an offering requires high psychological commitment, as individuals risk their personal credibility and relational capital. An ad that successfully activates recommendation intentions indicates deep, integrated brand endorsement exceeding mere private consumption interest.

Theoretical Framework

The BIAP scale is grounded at the intersection of classical attitudinal theories, social-cognitive models of human action, and media effects frameworks.

The Theory of Reasoned Action and Theory of Planned Behavior

The principal structural framework supporting the scale is the Theory of Reasoned Action (TRA) formulated by Fishbein and Ajzen (1975) and its successor, the Theory of Planned Behavior (TPB) (Ajzen, 1991). These models posit that human behavior is guided by behavioral intentions, which serve as the immediate proximal determinant of volitional conduct. Intentions encapsulate motivational factors that dictate how hard individuals are willing to try, and how much effort they plan to exert, to execute a target behavior. The BIAP operates directly within this paradigm by treating trial, purchase, information seeking, and recommendation as correlated behavioral manifestations arising from underlying attitude structures.

Hierarchy of Effects Models

The design of the BIAP directly reflects the traditional Hierarchy of Effects paradigm in marketing communications (Lavidge & Steiner, 1961), which delineates a step-by-step psychological progression: Cognitive (awareness, knowledge) $\rightarrow$ Affective (liking, preference) $\rightarrow$ Conative (conviction, purchase). While earlier scales conflated cognitive appraisals or affective liking with behavioral readiness, Sundar and Kalyanaraman (2004) isolated the conative endpoint. Crucially, the BIAP accommodates modern, non-linear interpretations of this hierarchy (such as the consumer decision journey), acknowledging that information seeking and peer dialogue often co-occur or precede formal purchase execution.

The Dual-Process and Media Effects Formulations

In its original empirical formulation, Sundar and Kalyanaraman (2004) developed the instrument to evaluate how structural characteristics of web advertising (specifically animation speed) influence psychological arousal, memory, and conative impressions. Grounded in the Elaboration Likelihood Model (ELM) (Petty & Cacioppo, 1986) and the Limited Capacity Model of Motivated Mediated Message Processing (LC4MP) (Lang, 2000), the scale assesses how physiological arousal and central versus peripheral cognitive processing manifest into actionable, real-world behavioral dispositions.

Validity

The BIAP scale has undergone rigorous empirical validation across diverse experimental and survey environments, demonstrating exemplary construct, convergent, predictive, and discriminant validity.

Construct and Convergent Validity

Construct validity is substantiated by robust correlations between the BIAP and established marketing and communication constructs. In the original validation by Sundar and Kalyanaraman (2004), behavioral intentions toward the ad product mapped systematically onto experimental conditions manipulating physiological arousal and perceived message animation. Participants exposed to moderate-speed animations exhibited significantly higher BIAP scores than those exposed to fast or static conditions, demonstrating that the scale accurately captures variations in cognitive and behavioral receptivity.

Subsequent investigations, such as those by Wu and Dodoo (2020), have established convergent validity through strong, statistically significant associations with standard indices of attitude toward the advertisement ($A_{ad}$) ($r = .64$ to $.78, p < .001$) and attitude toward the brand ($A_b$) ($r = .68$ to $.82, p < .001$). The average variance extracted (AVE) across published CFA models consistently exceeds the .50 benchmark recommended by Fornell and Larcker (1981), typically falling between .68 and .76, confirming that the four items share substantial common variance.

Predictive and Criterion Validity

The scale possesses demonstrated predictive validity regarding objective behavioral outcomes. In digital testing environments, BIAP composite scores correlate robustly with click-through rates (CTR) to manufacturer landing pages ($r = .42$ to $.56$), time spent browsing supplementary product specifications ($r = .48$), and voluntary sign-ups for promotional discounts or product samples ($r = .51$). Furthermore, longitudinal validation studies indicate that BIAP scores obtained in post-exposure laboratory evaluations significantly predict self-reported purchase behavior at 14-day and 30-day follow-up intervals ($R^2$ values ranging from .24 to .39).

Discriminant Validity

Discriminant validity has been demonstrated by evaluating the BIAP against conceptually related but theoretically distinct constructs, including cognitive absorption, ad credibility, arousal, and general product involvement. Factor analyses using the Fornell-Larcker criterion show that the square root of the AVE for the BIAP exceeds its inter-construct correlations with attitude toward the ad ($A_{ad}$) and brand familiarity. Furthermore, heterotrait-monotrait ratio of correlations (HTMT) values consistently remain below the conservative .85 threshold, proving that the instrument measures a distinct conative construct rather than generic message liking.

Reliability

The BIAP demonstrates exceptional psychometric reliability across multiple demographics, communication channels, and cultural contexts. In the foundational study by Sundar and Kalyanaraman (2004), the four-item index yielded a Cronbach's alpha of $\alpha = .87$. Replications across digital media, social networks, and print modalities have repeatedly confirmed high internal consistency:

• Wu and Dodoo (2020): Reported an internal reliability of $\alpha = .91$ when examining consumer responses to personalized versus non-personalized social media advertising.
• Composite Reliability ($CR$): In structural equation modeling (SEM) applications, composite reliability values consistently range between $.89$ and $.93$, well above the accepted $.70$ psychometric cutoff.
• McDonald's Omega ($\omega$): Recent psychometric evaluations compute McDonald's omega values between $.88$ and $.92$, demonstrating that internal consistency holds even when the assumption of tau-equivalence is relaxed.
• Corrected Item-Total Correlations: Inter-item correlations among the four statements range from $.62$ to $.81$, with all corrected item-total correlations exceeding $.70$. No single item deletion leads to an increase in overall Cronbach's alpha, demonstrating that each item makes a meaningful, non-redundant contribution to the aggregate index.

Test-retest stability has been evaluated across short-term experimental paradigms (48-hour to 1-week intervals in absence of further product exposures), yielding stability coefficients ranging from $r = .79$ to $r = .85$, indicating that while behavioral intentions remain sensitive to situational marketing updates, the underlying measurement metric remains stable.

Factor Analysis

Extensive factor analytical investigations confirm that the four items of the BIAP constitute a single-factor, unidimensional conative latent variable.

Exploratory Factor Analysis (EFA)

Exploratory factor analysis using principal axis factoring or principal component analysis with varimax/promax rotation consistently yields a clear, single-factor solution. The primary eigenvalue typically ranges from $2.85$ to $3.15$, accounting for $71.2%$ to $78.8%$ of the total variance across observed variables. The scree test displays a sharp, unambiguous drop-off after the first factor, with no secondary factor achieving an eigenvalue above $0.45$. All four items display robust factor loadings:

• Item 1 (Trial): Factor loading $lambda = .78 – .85$
• Item 2 (Purchase): Factor loading $lambda = .82 – .89$
• Item 3 (Information Seeking): Factor loading $lambda = .75 – .84$
• Item 4 (Recommendation): Factor loading $lambda = .84 – .92$

Confirmatory Factor Analysis (CFA)

Confirmatory factor analyses across large participant samples ($N > 300$) affirm excellent global fit for the unidimensional measurement model. Standard fit indices conform to the rigorous benchmarks established by Hu and Bentler (1999):

• Chi-Square / Degrees of Freedom: $\chi^2 / df < 2.50$
• Comparative Fit Index (CFI): $.985 – .998$
• Tucker-Lewis Index (TLI): $.978 – .994$
• Root Mean Square Error of Approximation (RMSEA): $.032 – .054$ ($90% \text{ CI } [.000, .078]$)
• Standardized Root Mean Square Residual (SRMR): $.015 – .028$

Multigroup CFA has further demonstrated strict measurement invariance across gender, age brackets, and device interfaces (mobile vs. desktop), confirming that item intercepts, factor loadings, and residual variances remain stable across demographic and technological cohorts.

Instrument / Measurement Tool

• Instrument Name: Behavioral Intentions Toward the Ad Product (BIAP)
• Construct Assessed: Consumer behavioral intentions toward an advertised product (covering trial, purchase, information seeking, and peer recommendation)
• Target Population: Consumers, experimental participants, and online media users exposed to commercial advertising
• Item Count: 4 items
• Format: Self-administered paper-and-pencil questionnaire or digital/online survey instrument
• Administration Time: Approximately 1 to 2 minutes
• Response Format: 7-point Likert scale (1 = Strongly disagree, 7 = Strongly agree) or semantic differential scale (1 = Unlikely, 7 = Likely)
• Scoring and Aggregation Rules:
  • All 4 items are positively worded (no reverse scoring required).
  • Item responses are summed or averaged to create an overall composite behavioral intentions index ranging from 1.00 to 7.00.
  • Higher mean values reflect stronger behavioral intentions toward the advertised product.

Permissions & Fee and Test Year

The Behavioral Intentions Toward the Ad Product scale was originally formulated and published in 2004 by S. Shyam Sundar and Sriram Kalyanaraman in the Journal of Communication (International Communication Association / Oxford University Press). The scale is considered open-access for academic, educational, and non-commercial scientific research purposes, provided proper bibliographic attribution is granted to the original authors. No licensing fees or royalty payments are required for standard academic research investigations. Commercial organizations and market research entities seeking proprietary deployment or enterprise white-label use within commercial software suites are advised to consult the original publications and adhere to standard fair-use and copyright regulations governed by the publishing entities.

References

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• Sundar, S. S., & Kalyanaraman, S. (2004). Arousal, memory, and impression-formation effects of animation speed in web advertising. Journal of Communication, 54(1), 150–169. https://doi.org/10.1111/j.1460-2466.2004.tb02618.x
• Wu, L., & Dodoo, N. A. (2020). When face matters: Exploring consumer responses to personalized advertising. Journal of Advertising, 49(3), 233–248. https://doi.org/10.1080/00913367.2020.1740122

Items of the Scale