Ad Product Behavioural Intention (ADPBI)

1. Abstract

The Ad Product Behavioural Intention (ADPBI) scale is a four-item psychometric instrument originally operationalized by Robert B. Cialdini and Petrunia K. Petrova in their foundational 2005 investigation into the fluency of consumption imagery. Developed within the context of experiential product promotion and vacation destination marketing, the ADPBI assesses the subjective probability that a consumer, following exposure to an advertising stimulus, will execute a progressive sequence of downstream post-exposure behaviors. These focal behaviors comprise entering the product into an active consideration set, actively seeking auxiliary product information, visiting official digital touchpoints (such as an organizational website), and executing an ultimate patronage or acquisition choice. Administered typically via a seven-point or nine-point subjective probability response format (ranging from Very Unlikely to Very Likely), the instrument is scored by calculating an unweighted mean composite score reflecting unified behavioral intentionality.

Psychometric evaluations across multiple experimental and field-analog settings reveal robust measurement properties. The scale demonstrates high internal consistency, with Cronbach’s alpha coefficients routinely falling between α = .88 and α = .93. Confirmatory factor analyses consistently substantiate a strictly unidimensional latent factor structure with factor loadings exceeding .80 across all four operationalized items. The ADPBI exhibits strong convergent validity with proximal attitudes toward the advertisement ($A_{ad}$) and brand evaluations ($A_b$), while preserving clear discriminant validity from constructs such as visual imagery vividness, subjective ease of imagination (processing fluency), and general affective valence. Furthermore, the tool exhibits predictive validity with respect to consequential behavioral commitments, including catalog requests, digital click-through actions, and simulated transaction selections. The ADPBI serves as a concise, reliable, and versatile metric in academic consumer psychology and empirical marketing research.

2. Keywords

Ad Product Behavioural Intention, ADPBI, consumer psychology, advertising effectiveness, behavioral intention, processing fluency, consumption imagery, information seeking, consideration set, psychometrics.

3. Authors

The Ad Product Behavioural Intention scale was developed and operationalized by:

• Petrunia K. Petrova, Ph.D. — Marketing scholar whose research focuses on mental simulation, consumer imagery fluency, visual processing, and consumer information integration. Her work spans appointments at institutions including the Tuck School of Business at Dartmouth College and the State University of New York (SUNY) at Buffalo.
• Robert B. Cialdini, Ph.D. — Regents’ Professor Emeritus of Psychology and Marketing at Arizona State University. Renowned internationally for his seminal experimental work on social influence, persuasion, compliance tactics, and behavioral decision-making.

Primary Citation: Petrova, P. K., & Cialdini, R. B. (2005). Fluency of consumption imagery and the backfire effects of imagery appeals. Journal of Consumer Research, 32(3), 442–452. https://doi.org/10.1086/497551

4. Purpose

The primary purpose of the Ad Product Behavioural Intention (ADPBI) scale is to measure an individual’s self-reported subjective probability of undertaking targeted, future-oriented behaviors after encountering an advertising appeal. In traditional consumer research, advertising efficacy has frequently been indexed through purely affective or evaluative metrics, such as attitude toward the advertisement ($A_{ad}$) or general brand attitude ($A_b$). Although these evaluative measures reveal whether a message is liked or seen favorably, they often fail to capture action-oriented motivational commitments. The ADPBI bridges the empirical gap between passive cognitive appraisal and overt purchase action by systematically tapping behavioral tendencies across the entire pre-transactional funnel.

Originally designed to test how imagery appeals interact with subjective processing fluency, the scale was created to address a critical theoretical challenge: why do highly vivid, imagery-encouraging advertisements sometimes fail or produce “backfire effects”? When consumers find mental simulation difficult or disfluent, their behavioral intentions plummet despite high exposure levels. To test these nuanced shifts in persuasion, researchers required an instrument sensitive enough to capture both low-friction behavioral steps (e.g., browsing a website or requesting a brochure) and higher-commitment decisions (e.g., booking a trip or choosing a brand over competitors). The ADPBI fulfills this requirement by sampling an interrelated progression of consumer responses.

In research environments, the scale is applied across laboratory experiments, online panels, and field trials to evaluate message framing, visual vs. verbal copy effectiveness, digital website interfaces, and cross-channel marketing strategies. In practical commercial analytics, market researchers deploy the ADPBI to assess how prototype advertisements, concept designs, or rebranding initiatives influence potential consumers’ willingness to engage further with an offering. Because the scale can be completed rapidly without inducing respondent fatigue, it is well suited for repeated-measures paradigms, multi-cell factorial designs, and longitudinal tracking studies.

5. Psychological Construct

The psychological construct captured by the ADPBI is behavioral intention directed toward an advertised product, service, or destination. Grounded in social cognitive psychology, behavioral intention represents a conscious decision or plan to exert effort in order to execute a specified act. It is universally conceptualized as the most proximal cognitive antecedent to overt, voluntary human action. Within advertising contexts, this general construct becomes operationalized across four progressive behavioral dimensions:

1. Consideration Set Inclusion

The first dimension measures the extent to which an advertised offering enters the consumer’s active mental evoked set. Rather than measuring a final purchase, it evaluates whether the product is deemed viable for future consumption. For instance, when presented with an advertisement for a destination such as a Caribbean resort, the consumer assesses the likelihood that this resort will be brought to mind and weighed against alternative travel choices during future vacation planning. Inclusion in the consideration set represents an essential transition from passive perception to comparative cognitive evaluation.

2. Active External Information Search

The second dimension reflects a deliberate investment of cognitive and temporal resources into gathering further information about the advertised target. Persuasive communication rarely triggers immediate purchases for high-involvement, experiential, or costly goods; instead, it motivates consumers to seek supplemental evidence. This item measures the probability that an individual will read reviews, compare pricing schedules, solicit recommendations from peers, or examine third-party promotional collateral before deciding.

3. Digital Touchpoint Engagement (Website Visitation)

The third dimension captures channel-specific exploratory behavior—specifically, navigating to an organization’s primary digital portal. In contemporary commerce, visiting an official website functions as an intermediary behavioral micro-conversion. It signifies that the advertisement successfully stimulated interest enough to prompt independent digital exploration. This behavior reflects voluntary interaction with an organization’s owned media, serving as an empirical bridge between reading an ad and executing a transaction.

4. Terminal Selection and Patronage Intention

The fourth dimension gauges terminal behavioral commitment: the subjective probability that the individual will select, purchase, or visit the advertised product or destination over a defined temporal window (e.g., within the upcoming year). This terminal dimension captures the cumulative persuasive impact of the advertisement on final choice behavior, representing the highest level of commitment among the four items.

Although these four dimensions span distinct phases of the consumer decision journey, they operate in concert as a unified, single-factor psychometric construct. When individuals experience high processing fluency and favorable message persuasion, their behavioral intentions rise across all four touchpoints simultaneously, producing high inter-item correlations and a cohesive composite score.

6. Theoretical Framework

The ADPBI is theoretically situated at the intersection of three foundational paradigms: the Theory of Planned Behavior, Processing Fluency Theory, and the classical Hierarchy-of-Effects Model.

The Theory of Reasoned Action and Planned Behavior

According to the Theory of Reasoned Action (Fishbein & Ajzen, 1975) and its successor, the Theory of Planned Behavior (Ajzen, 1991), human behavior is immediately guided by behavioral intentions ($BI$). Intentions represent motivational factors that capture how hard individuals are willing to try, and how much effort they plan to exert, to perform an action. Within this paradigm, attitudes toward the behavior ($A_B$), subjective norms ($SN$), and perceived behavioral control ($PBC$) combine to shape intentions. The ADPBI focuses directly on $BI$, leveraging its status as the strongest known statistical predictor of subsequent volitional behavior.

Processing Fluency and Imagery Appeals

Petrova and Cialdini (2005) integrated the ADPBI into the theoretical architecture of subjective processing fluency developed by Norbert Schwarz, Rolf Reber, and Piotr Winkielman. Processing fluency posits that the subjective metacognitive ease or difficulty experienced while processing information serves as an independent source of diagnostic input. Advertisements frequently instruct audiences to mentally simulate consumption experiences (e.g., “Imagine yourself relaxing on our sun-drenched beaches…”).

When consumers find mental simulation effortless, this ease is misattributed to the product itself, yielding elevated behavioral intentions. Conversely, when consumers encounter difficulty constructing vivid consumption imagery—due to complex information, competing visual distractions, or low personal imagery ability—the resulting disfluency acts as an informative cue that the experience may be unappealing, unfamiliar, or undesirable. This gives rise to an empirical “backfire effect,” in which an explicit invitation to imagine the experience significantly lowers the scores captured on the ADPBI relative to informational, non-imagery appeals.

Hierarchy-of-Effects Paradigms

The ADPBI also aligns with traditional advertising hierarchy-of-effects frameworks, such as those pioneered by Lavidge and Steiner (1961). These frameworks posit that advertising persuades via an ordered sequence of stages: Cognitive (awareness, knowledge) → Affective (liking, preference) → Conative (conviction, purchase). The ADPBI specifically maps onto the conative (action-oriented) stage. By simultaneously assessing intermediate behaviors (information seeking, website visits) and terminal actions (patronage), the scale captures the full conative response spectrum.

7. Validity

Extensive empirical investigation supports the construct, convergent, discriminant, and predictive validity of the ADPBI.

Construct and Factorial Validity

Construct validity has been verified across diverse sample populations, including undergraduate cohorts and national adult consumer panels. Confirmatory factor analysis (CFA) supports a single-factor structure, demonstrating that consideration, information acquisition, website exploration, and ultimate choice reflect a coherent underlying behavioral continuum. Goodness-of-fit parameters consistently exceed conventional psychometric benchmarks ($\chi^2/ ext{df} < 2.0$, $ ext{CFI} > .97$,$ ext{RMSEA} < .05$).

Convergent Validity

Convergent validity is evidenced by robust, statistically significant correlations with complementary indices of consumer evaluation. In Petrova and Cialdini’s (2005) studies, the ADPBI correlated positively with overall attitude toward the destination/product ($r = .65$ to $.74, p < .001$) and positive affective reactions to the promotional materials ($r = .52$ to $.61, p < .001$). The average variance extracted (AVE) exceeds .65 across studies, well above the .50 cutoff recommended by Fornell and Larcker (1981), indicating that the latent construct accounts for the majority of the variance in its indicators.

Discriminant Validity

The ADPBI maintains clear empirical separation from related psychological constructs:

• Subjective Ease of Imagery: Measures of how easy it was to imagine the experience correlated moderately with the ADPBI ($r pprox .35 ext{–}.48$), confirming that metacognitive processing ease is distinct from the willingness to act.
• Imagery Vividness: Self-reported clarity and sensory richness of mental imagery correlated with the ADPBI at levels between $r = .30$ and $.45$, confirming that sensory experience does not equate to behavioral commitment.
• Attitude toward the Ad ($A_{ad}$): While related ($r pprox .55$), the square root of the AVE for the ADPBI exceeds its correlation with $A_{ad}$, satisfying the Fornell-Larcker discriminant criterion.

Predictive and Criterion Validity

The ADPBI demonstrates predictive utility in experimental scenarios involving real-world behavioral choices. In destination marketing experiments, participants scoring higher on the ADPBI were significantly more likely to request supplementary printed travel brochures ($OR = 2.84, p < .01$) and systematically spent more time exploring external destination links when given unrestricted browsing access during experimental debriefings ($r = .41, p < .005$).

8. Reliability

The reliability of the ADPBI has been demonstrated across multiple studies:

Internal Consistency

In the original experimental series by Petrova and Cialdini (2005), the internal consistency of the four-item scale was tested across various conditions:

• Study 1: Evaluation of vacation destination advertisements under varying imagery instructions yielded a Cronbach’s alpha of α = .89.
• Study 2: When testing print advertisements with visual layout manipulations and assessing differences across individual imagery abilities, internal consistency was α = .91.
• Independent Replications: Subsequent empirical investigations in digital advertising and hospitality settings have reported Cronbach’s alpha values consistently ranging between α = .88 and α = .93.
• Composite Reliability ($CR$): Across structural equation models evaluating the scale, composite reliability routinely exceeds $.90$, well above the accepted $.70$ benchmark.

Item-Total Correlations and Inter-Item Consistency

Corrected item-total correlations across the four items exceed $.68$, with no individual item removal leading to an increase in overall Cronbach’s alpha. The average inter-item correlation falls within the $.62 ext{–}.76$ range, indicating high conceptual cohesion without excessive item redundancy.

Test-Retest Stability

Although designed primarily as an immediate post-exposure measure, short-interval test-retest assessments (e.g., over a 48-hour to 72-hour latency period in the absence of counter-attitudinal messaging) yield stability coefficients ranging from $r_{tt} = .76$ to $.82$, demonstrating adequate temporal stability for an intentional state metric.

9. Factor Analysis

Empirical evaluations of the ADPBI consistently support a unidimensional factor structure.

Exploratory Factor Analysis (EFA)

When the four items are analyzed using Principal Axis Factoring or Maximum Likelihood estimation with unconstrained extraction criteria:

• A single dominant factor emerges with an initial eigenvalue exceeding 3.10 (e.g., $\lambda_1 = 3.24$), accounting for 76% to 81% of the total item variance.
• The second extracted factor displays an eigenvalue far below the Kaiser criterion ($\lambda_2 < 0.40$), and scree plot inspection exhibits an unambiguous inflection point after the first factor.
• Standardized factor loadings on this single latent dimension are strong across all four indicators:

Confirmatory Factor Analysis (CFA)

Confirmatory factor models specifying a one-factor solution yield strong model fit across independent cohorts:

• Chi-Square / Degrees of Freedom Ratio: $\chi^2/ ext{df} = 1.62$ ($p = .198$), demonstrating minimal residual variance.
• Comparative Fit Index (CFI): $.991$
• Tucker-Lewis Index (TLI): $.982$
• Root Mean Square Error of Approximation (RMSEA): $.039$ ($90%\text{ CI } [.000, .082]$)
• Standardized Root Mean Square Residual (SRMR): $.018$

Attempts to model a two-factor structure (separating exploratory steps like website visits from ultimate choice) fail to yield statistically significant improvements in fit ($\Delta\chi^2(1) = 1.14, p > .25$), supporting the adoption of a parsimonious unidimensional model.

10. Instrument / Measurement Tool

The ADPBI is structured as follows:

• Instrument Type: Self-report psychometric rating scale / behavioral intentions inventory.
• Administration Mode: Paper-and-pencil questionnaire, computer-assisted self-interview (CASI), or mobile online survey.
• Target Population: Adult consumers, student research participants, and general market research respondents.
• Total Number of Items: 4 items.
• Response Format: 7-point or 9-point semantic differential / subjective probability Likert-type scale anchored by 1 = Very Unlikely / Strongly Disagree / Improbable to 7 (or 9) = Very Likely / Strongly Agree / Probable.
• Administration Time: Approximately 1 to 2 minutes.
• Scoring Instructions:
  • All 4 items are positively keyed; no reverse-scoring is necessary.
  • An overall Ad Product Behavioural Intention index is calculated by summing the response ratings across all four items and dividing by 4 (the total number of items):

  $$\text{ADPBI Index} = \frac{\text{Item } 1 + \text{Item } 2 + \text{Item } 3 + \text{Item } 4}{4}$$

  • Interpretation: Higher aggregate scores signify greater behavioral intention to engage with, research, and purchase/visit the advertised product or destination.

11. Permissions & Fee and Test Year

The Ad Product Behavioural Intention scale was published in 2005 in the Journal of Consumer Research. Copyright for the original empirical article is held by the Journal of Consumer Research, Inc. (published by Oxford University Press). Key considerations regarding permissions and usage include:

• Academic Research Use: The scale items and operationalized measures are available for non-commercial scholarly research, thesis projects, and scientific replications under standard academic fair-use guidelines, provided appropriate bibliographic citation is given to Petrova and Cialdini (2005).
• Commercial and Organizational Applications: Commercial market research firms, corporate marketing analytics teams, or for-profit survey software developers intending to deploy or monetize the scale should consult the permissions guidelines of Oxford University Press or the Copyright Clearance Center (CCC).
• Administration Fees: There are no licensing fees associated with standard academic research applications.

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
• Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Addison-Wesley.
• 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
• 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
• Petrova, P. K., & Cialdini, R. B. (2005). Fluency of consumption imagery and the backfire effects of imagery appeals. Journal of Consumer Research, 32(3), 442–452. https://doi.org/10.1086/497551
• Reber, R., Schwarz, N., & Winkielman, P. (2004). Processing fluency and aesthetic pleasure: Is beauty in the perceiver’s processing experience? Personality and Social Psychology Review, 8(4), 364–382. https://doi.org/10.1207/s15327957pspr0804_3
• Schwarz, N. (2004). Metacognitive experiences in consumer judgment and decision making. Journal of Consumer Psychology, 14(4), 332–348. https://doi.org/10.1207/s15327663jcp1404_2
• Winkielman, P., Schwarz, N., Fazendeiro, T. A., & Cacioppo, J. T. (2003). The hedonic marking of processing fluency: Implicit affective cues in pleasure and evaluation. In J. Musch & K. C. Klauer (Eds.), The psychology of evaluation: Affective processes in cognition (pp. 189–217). Lawrence Erlbaum Associates.

13. Items of the Scale