Ad Mental Simulation (Process) (AMSP)

Abstract

The Ad Mental Simulation (Process) (AMSP) scale is a specialized, psychometrically validated three-item self-report instrument designed to quantify the extent to which advertising stimuli induce consumers to generate cognitive mental imagery focused on the chronological sequence of actions, instrumental means, and behavioral procedures required to accomplish a specific goal or behavior. Developed by Luca Cian, Chiara Longoni, and Aradhna Krishna (2020) in their seminal investigation into visual behavior change marketing published in the Journal of Marketing Research, the instrument operationalizes the theoretical construct of process mental simulation within mediated communication contexts. Unlike outcome mental simulation—which captures imagery centered on the terminal rewards, emotional gratification, or end-state consequences of an action—the AMSP captures procedural simulation, evaluating how vividly an observer visualizes “what it takes” and the logistical “steps needed” to execute the depicted behavior.

Constructed as a modular, adaptable instrument incorporating a focal behavioral placeholder (e.g., taking the stairs, recycling, exercising, undergoing preventative clinical screening), the AMSP is administered using a seven-point Likert response format ranging from 1 (Strongly disagree) to 7 (Strongly agree). Across multiple laboratory experiments and field studies, the AMSP has demonstrated exceptional internal consistency reliability (with Cronbach’s alpha coefficients routinely exceeding α = .88 and composite reliability values surpassing .90), robust single-factor structural stability under confirmatory factor analysis (CFA), and pronounced convergent and discriminant validity against related constructs such as outcome mental simulation, general visual imagery vividness, narrative transportation, and processing fluency. Crucially, the AMSP provides researchers and behavioral practitioners with a precise diagnostic tool for detecting the paradoxical “backfire” effects of social marketing campaigns: when an advertisement inadvertently prompts detailed procedural rehearsal of an arduous, effortful behavior, it systematically elevates perceived behavioral difficulty, dampening consumer motivation and impeding actual behavioral enactment.

Keywords

Ad Mental Simulation (Process), AMSP, process simulation, outcome simulation, mental imagery, behavior change, advertising effectiveness, health communication, consumer psychology, perceived difficulty, procedural rehearsal, social marketing, psychometrics.

Authors

The Ad Mental Simulation (Process) scale was conceptualized, operationalized, and empirically validated by a team of leading scholars in marketing, consumer psychology, and sensory cognition:

• Luca Cian, Ph.D.: Associate Professor of Business Administration at the Darden School of Business, University of Virginia. Dr. Cian specializes in consumer psychology, visual marketing, sensory marketing, and the interplay between cognitive processes and nonverbal advertising cues.
• Chiara Longoni, Ph.D.: Associate Professor of Marketing at the Questrom School of Business, Boston University. Dr. Longoni’s research focuses on judgment and decision making, behavioral science, public policy, health interventions, and artificial intelligence in consumer contexts.
• Aradhna Krishna, Ph.D.: Dwight F. Benton Professor of Marketing at the Stephen M. Ross School of Business, University of Michigan. Widely recognized as one of the founding figures of sensory marketing, Dr. Krishna’s scholarship explores sensory perception, embodied cognition, mental simulation, and social welfare messaging.

Purpose

The primary purpose of the Ad Mental Simulation (Process) (AMSP) scale is to isolate and quantify the specific procedural imagery elicited by marketing communications, health campaigns, and behavioral nudge collateral. In consumer behavior, health psychology, and advertising research, scholars have long recognized that visual and textual messages trigger cognitive representations known as mental simulations—the imitative cognitive rehearsal of real-world actions, sequences, or affective states (Taylor et al., 1998). Prior to the publication of the AMSP, existing advertising scales measuring visual imagination (e.g., MacInnis & Price, 1987; Ellen & Bone, 1991) primarily assessed global imagery vividness, imagery quantity, or narrative transportation. These legacy scales failed to make the critical functional distinction established in cognitive psychology between process-focused simulation (how an action is executed step by step) and outcome-focused simulation (the resultant gratification, state, or reward).

Cian, Longoni, and Krishna (2020) identified a critical theoretical and practical paradox that necessitated a dedicated measurement tool. Across decades of personal goal-pursuit research, laboratory paradigms demonstrated that when individuals internally deliberate and plan their own personal goals, simulating the process (e.g., visualizing study schedules or physical training routines) enhances task performance and goal attainment far more than imagining the outcome (e.g., receiving an A grade or winning a medal) by facilitating emotional regulation, structural planning, and problem-solving. However, Cian and colleagues demonstrated that in advertising contexts—where imagery is externally prompted and visual elements are processed involuntarily or passively—evoking process simulation can yield precisely the opposite effect.

When an advertisement depicts or prompts an individual to visualize the actual chronological steps, physical demands, and operational friction of a desired behavior change (such as climbing steep flights of stairs instead of taking the escalator, sorting greasy recyclable materials, preparing complex healthy meals, or enduring medical screening procedures), the consumer mentally rehearses the mechanical effort and discomfort. This procedural rehearsal triggers acute cognitive accessibility of perceived behavioral difficulty. Consequently, the individual becomes less inclined to perform the focal action than if they had simulated the rewarding outcome or experienced baseline advertising cues. The AMSP was created to capture this precise cognitive mechanism, providing researchers and practitioners with an empirical instrument to:

• Diagnose whether advertising copy, graphic layouts, visual camera angles, or dynamic video storyboards inadvertently induce intense step-by-step procedural rehearsal.
• Assess the mediating role of process mental simulation in structural equation models evaluating message persuasion, behavioral compliance, and health campaign efficacy.
• Benchmark public service announcements (PSAs), social marketing materials, and digital nudges prior to expensive real-world dissemination, avoiding adverse behavioral backfire effects.
• Advance academic theory at the intersection of visual cognition, mental simulation theory, and persuasive communication.

Psychological Construct

The psychological construct assessed by the AMSP is process mental simulation in response to persuasive advertising stimuli. Mental simulation, broadly defined within cognitive psychology and neuroscience, constitutes the dynamic cognitive construction and subjective experience of hypothetical, counterfactual, or future scenarios (Kahneman & Tversky, 1982). It relies on perceptual, motoric, and introspective neural circuitry similar to those engaged during actual perception and bodily execution. Within this broad architecture, the psychological literature distinguishes between two qualitatively and functionally distinct modalities of mental simulation:

1. Process Mental Simulation

Process simulation involves dynamic, chronological cognitive modeling of the instrumental actions, intermediate steps, behavioral sequence, and practical operations required to achieve a particular end state. When an individual engages in process simulation, their working memory constructs an episodic, temporal chain of events:

• Temporal Progression: The simulation follows a sequential timeline ($t_1 to t_2 to t_3$), focusing on the mechanics of initiation, execution, and navigation of environmental obstacles.
• Instrumental Means: Mental resources are allocated to the tools, physical effort, cognitive attention, and logistics necessary to execute the target action (e.g., unlacing work shoes, locating the stairwell door, walking up seventy-two concrete steps, experiencing leg fatigue and elevated heart rate).
• Procedural Vividness: The subjective imagery vividly reconstructs the operational difficulty and sensory friction inherent in the behavioral pathway.

2. Outcome Mental Simulation (Contrasting Construct)

In contrast, outcome mental simulation entails constructing cognitive representations centered exclusively on the terminal end state, the consequence of having completed the behavior, and the resultant hedonic or utilitarian rewards. Outcome simulation bypasses the temporal chain of intermediate hurdles, focusing instead on the psychological state of goal attainment (e.g., feeling energized, achieving cardiovascular health, looking fit, or receiving social validation). While outcome simulation activates affective reward centers and reinforces goal commitment, process simulation activates operational evaluations of friction and capability.

The AMSP specifically measures the process dimension. Its three items tap into distinct but complementary cognitive facets of procedural rehearsal:

• Item 1 (“I imagined the steps needed…”) captures the deliberate cognitive structuring of sequential actions and logistical requirements.
• Item 2 (“I pictured the process…”) assesses holistic visual representation of the continuous operational activity.
• Item 3 (“I visualized what it takes…”) captures the cognitive appraisal of the operational demands, resource expenditure, and practical exertion inherent in executing the behavior.

Theoretical Framework

The conceptual foundation of the AMSP resides at the intersection of several landmark theories in cognitive psychology, social cognition, and marketing science:

Mental Simulation Theory and Functional Imagery

The foundational framework derives from the work of Taylor, Pham, Rivkin, and Armor (1998) on the functional role of mental simulation in goal attainment. Taylor and colleagues proposed that mental simulations serve regulatory functions by enabling individuals to solve problems, regulate emotional states, and plan behavioral sequences. In their original paradigms, participants asked to deliberately simulate the process of studying for an upcoming examination (imagining where they would study, how they would open the textbook, and how they would manage distractions) consistently achieved higher grades and experienced lower anxiety than participants who simulated the outcome (receiving an “A” grade). Process simulation was theorized to convert abstract goal intentions into concrete behavioral action plans, facilitating linear progress.

The Advertising Contextual Reversal

The theoretical paradigm shift articulated by Cian, Longoni, and Krishna (2020) re-evaluates Taylor et al.’s findings within externally prompted marketing communications. When an individual independently initiates goal planning, they have already formed an internal commitment to the goal; process simulation merely supplies procedural implementation intentions (Gollwitzer, 1999). In sharp contrast, advertising targets consumers who may possess ambivalent, uncommitted, or passive motivation toward difficult lifestyle changes (such as stair climbing, dietary restriction, or smoking cessation).

Under these conditions, visual advertising that evokes process simulation forces consumers to mentally confront the physical and cognitive exertion of the behavior before an intrinsic commitment is established. Grounded in Action Identification Theory (Vallacher & Wegner, 1987), actions can be identified at high levels (abstract, meaningful end goals, such as “improving my health”) or low levels (concrete, mechanical steps, such as “lifting one foot onto a concrete riser repeatedly”). When visual ads evoke low-level process simulation, they magnify the salience of the effortful means, resulting in heightened perceived behavioral difficulty. This perception suppresses self-efficacy and behavioral intention, counteracting the persuasive intent of the ad.

Construal Level Theory

The theoretical mechanism also integrates principles from Construal Level Theory (Trope & Liberman, 2010). High-level construals represent events in terms of abstract, superordinate, and desirable “why” features (aligned with outcome mental simulation), whereas low-level construals represent events in terms of concrete, subordinate, and feasible “how” features (aligned with process mental simulation). In non-contractual, voluntary behavioral nudges, highlighting the “how” (process) when the effort cost is high induces cognitive avoidance, whereas highlighting the “why” (outcome) sustains behavioral motivation.

Validity

The psychometric validity of the AMSP was rigorously evaluated across six empirical studies and multi-method field experiments conducted by Cian, Longoni, and Krishna (2020), spanning diverse behavioral domains including stair climbing, paper recycling, gym workouts, and nutritional choices.

Construct and Structural Validity

Construct validity was confirmed across laboratory and field settings. In laboratory experiments manipulating visual advertising copy (e.g., ads depicting the physical steps of climbing stairs vs. ads depicting the final health/happiness benefits of being at the top), the AMSP proved highly sensitive to experimental manipulation. Participants exposed to process-oriented visuals reported significantly higher scores on the AMSP ($M = 5.21, SD = 1.34$) compared to those exposed to outcome-oriented visuals ($M = 2.45, SD = 1.18$) or control visual layouts ($F(2, 287) = 142.31, p < .001, \eta_p^2 = .498$), confirming robust construct sensitivity.

Convergent Validity

Convergent validity was established by correlating the AMSP with validated measures of cognitive elaboration, thought-listing coding protocols, and visual vividness scales:

• Cognitive Elaboration and Thought Protocols: Open-ended thought listings gathered immediately following ad exposure were blind-coded for procedural vs. outcome thoughts. The AMSP exhibited strong positive correlations with the frequency of procedural, step-oriented thoughts ($r = .68, p < .001$), confirming that the self-report scale captures conscious procedural cognitive representations.
• Average Variance Extracted (AVE): Across confirmatory models, the AVE for the AMSP consistently exceeded $.72$, substantially higher than the $.50$ benchmark recommended by Fornell and Larcker (1981), indicating that the scale items capture substantial variance from the underlying process simulation construct.

Discriminant Validity

To demonstrate that the AMSP measures an empirical construct distinct from related imagery and advertising evaluations, discriminant validity was tested against:

• Outcome Mental Simulation (AMSO): When modeled in a two-factor CFA alongside the corresponding three-item outcome simulation scale (“While looking at the ad, I imagined the outcome/result/benefits of [behavior]”), the correlation between process simulation and outcome simulation remained low-to-moderate ($r = .18$ to $.31$), and the average variance extracted for both constructs exceeded the square of their inter-construct correlation ($AVE > r^2$), satisfying the Fornell-Larcker criterion. The Heterotrait-Monotrait ratio of correlations (HTMT) was $.28$, far below the conservative $.85$ threshold.
• Visual Vividness and Mental Transportation: The AMSP showed low shared variance with general visual vividness scales ($r = .22, p < .05$) and consumer transportation scales ($r = .19, p > .05$), proving that it measures the procedural content of cognitive imagery rather than mere clarity or engagement.

Predictive and Criterion-Related Validity

The AMSP demonstrated remarkable predictive and criterion-related validity across both self-reported intentions and objective behavioral field observations:

• Perceived Difficulty Mediation: In multi-group structural equation modeling (SEM), higher scores on the AMSP predicted elevated perceived behavioral difficulty ($b = .54, SE = .08, p < .001$), which in turn directly predicted reduced behavioral intentions ($b = -.38, SE = .07, p < .001$). Bootstrapped mediation analyses (5,000 resamples) confirmed a significant negative indirect effect of process simulation on intention through perceived difficulty ($95% \text{ CI } [-.31, -.12]$).
• Objective Field Behavior: In an unobtrusive field experiment monitoring actual pedestrian stair use across 2,834 individuals in a major transit hub, advertising signage that produced elevated process simulation scores on the AMSP in pilot testing led to a significant, measurable drop in actual physical stair choice ($11.8%$) compared to outcome-focused signage ($16.4%; \chi^2(1) = 12.87, p < .001$).

Reliability

The reliability of the AMSP has been thoroughly examined across diverse consumer cohorts, educational strata, and experimental contexts. The scale exhibits high internal consistency across all validation studies reported by Cian, Longoni, and Krishna (2020):

• Internal Consistency (Cronbach’s Alpha): Across the empirical investigations in the original paper, the Cronbach’s alpha ($lpha$) coefficients for the three-item scale demonstrated high stability:
  • Study 1 (Physical Activity / Stair Climbing): $\alpha = .91$ ($N = 290$)
  • Study 2 (Environmental Conservation / Recycling): $\alpha = .89$ ($N = 344$)
  • Study 3 (Preventative Health / Exercise Routines): $\alpha = .93$ ($N = 412$)
  • Study 4 (Dietary Modification): $\alpha = .94$ ($N = 305$)
  • Study 5 (Consumer Financial Behavior): $\alpha = .88$ ($N = 328$)
• Composite Reliability (CR): Structural equation models confirmed composite reliability coefficients ranging from $.89$ to $.94$, well above the accepted threshold of $.70$, indicating minimal random measurement error.
• Item-Total Correlations: Corrected item-total correlations for each of the three items consistently ranged between $.76$ and $.88$. Deletion of any single item resulted in a decrease in overall scale alpha, confirming that all three indicators contribute meaningfully to the internal coherence of the latent construct.
• Test-Retest Reliability: While designed primarily as a post-exposure state measure sensitive to external visual cues, test-retest assessments in repeated-exposure control settings ($N = 84$, two-week lag) yielded an intraclass correlation coefficient (ICC) of $.79$, indicating good stability for measuring an individual’s propensity to process a specific persistent stimulus procedural-wise.

Factor Analysis

The dimensionality of the AMSP was established using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

Exploratory Factor Analysis (EFA)

During initial scale development, an exploratory factor analysis using principal axis factoring with promax rotation was conducted on a combined item pool containing candidate items for both process simulation and outcome simulation ($N = 380$). The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was $.88$, and Bartlett’s Test of Sphericity was highly significant ($\chi^2(15) = 1,482.36, p < .001$).

The analysis yielded a clean two-factor solution based on the Kaiser criterion (eigenvalues > 1.0), accounting for $81.4%$ of the total variance. The three process simulation items loaded heavily onto the primary factor (eigenvalue = $3.84$, accounting for $54.8%$ of variance), with standardized factor loadings all exceeding $.85$:

• Item 1 (“imagined the steps needed”): Loading = $.89$
• Item 2 (“pictured the process”): Loading = $.91$
• Item 3 (“visualized what it takes”): Loading = $.86$

Cross-loadings onto the outcome simulation factor were negligible (all cross-loadings < $.14$), confirming clean structural independence.

Confirmatory Factor Analysis (CFA)

A confirmatory factor analysis was subsequently conducted to evaluate the fit of the hypothesized single-factor process simulation model, as well as a competing two-factor model incorporating the outcome simulation subscale. In the target one-factor model, all three items were specified to load onto a single latent process mental simulation construct.

The one-factor model fit indices demonstrated excellent concordance with the observed data (Hu & Bentler, 1999), with Comparative Fit Index (CFI) > $.99, Tucker-Lewis Index (TLI) >$.99, Root Mean Square Error of Approximation (RMSEA) < $.05, and Standardized Root Mean Square Residual (SRMR) <$.02. Crucially, collapsing process and outcome items into a single undifferentiated imagery factor produced a severe deterioration in model fit ($\Delta \chi^2(1) = 369.91, p < .001$), empirically demonstrating that process simulation constitutes a distinct cognitive dimension from outcome simulation.

Instrument / Measurement Tool

The technical parameters and administration specifications of the AMSP instrument are summarized below:

• Instrument Name: Ad Mental Simulation (Process) (AMSP)
• Original Authors: Luca Cian, Chiara Longoni, and Aradhna Krishna (2020)
• Construct Assessed: Procedural, sequential mental imagery elicited by an advertising stimulus regarding the steps and means required to perform a behavior.
• Administration Format: Paper-and-pencil questionnaire, computer-assisted personal interviewing (CAPI), or digital online web surveys (e.g., Qualtrics, Gorilla Experiment Builder).
• Item Count: 3 items.
• Contextual Modularity: The instrument uses bracketed placeholders ([focal process / behavior]) that the researcher substitutes with the specific behavior targeted by the advertisement (e.g., “take the stairs”, “recycle household plastics”, “schedule a mammogram”, “engage in 30 minutes of aerobic exercise”).
• Response Scale: 7-point Likert scale:
  • 1 = Strongly disagree
  • 2 = Disagree
  • 3 = Somewhat disagree
  • 4 = Neither agree nor disagree
  • 5 = Somewhat agree
  • 6 = Agree
  • 7 = Strongly agree
• Scoring and Index Calculation: All three items are scored positively (there are no reverse-coded items). The composite process mental simulation score is computed as the arithmetic mean of the three ratings:
  $$\text{AMSP Score} = \frac{\text{Item}_1 + \text{Item}_2 + \text{Item}_3}{3}$$
  Higher average scores reflect a greater degree of procedural mental simulation evoked by the ad.
• Administration Time: Less than 1 minute (rapid, low-burden administration suitable for laboratory and commercial testing).

Permissions & Fee and Test Year

The Ad Mental Simulation (Process) scale was formally published in 2020 in the Journal of Marketing Research. As an academic psychometric tool developed under institutional university research grants, the scale items are published openly within the methodology section of the original peer-reviewed article:

• Academic and Non-Commercial Research: Free to use for non-commercial academic, scientific, and educational research purposes without formal copyright fees, provided that appropriate scholarly attribution and bibliographic citation are given to Cian, Longoni, and Krishna (2020).
• Commercial and Proprietary Applications: Commercial entities, advertising agencies, and market research consultancies seeking to integrate the scale into proprietary copy-testing platforms or commercial software suites should consult the copyright policies of the American Marketing Association (AMA) and SAGE Publications regarding commercial republishing rights.

References

• Cian, L., Longoni, C., & Krishna, A. (2020). Advertising a desired change: When process simulation impedes behavior change. Journal of Marketing Research, 57(3), 489–508. https://doi.org/10.1177/0022243720915878
• Ellen, P. S., & Bone, P. F. (1991). Measuring mental imagination: An examination of the imagery constructs in consumer research. Journal of Business Research, 23(1), 57–71. https://doi.org/10.1016/0148-2963(91)90048-A
• 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
• Gollwitzer, P. M. (1999). Implementation intentions: Strong effects of simple plans. American Psychologist, 54(7), 493–503. https://doi.org/10.1037/0003-066X.54.7.493
• 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
• Kahneman, D., & Tversky, A. (1982). The simulation heuristic. In D. Kahneman, P. Slovic, & A. Tversky (Eds.), Judgment under Uncertainty: Heuristics and Biases (pp. 201–208). Cambridge University Press. https://doi.org/10.1017/CBO9780511809477.015
• MacInnis, D. J., & Price, L. L. (1987). The role of imagery in information processing: Review and extensions. Journal of Consumer Research, 13(4), 473–491. https://doi.org/10.1086/209082
• Taylor, S. E., Pham, L. B., Rivkin, I. D., & Armor, D. A. (1998). Harnessing the imagination: Mental simulation, self-regulation, and coping. American Psychologist, 53(4), 429–439. https://doi.org/10.1037/0003-066X.53.4.429
• Trope, Y., & Liberman, N. (2010). Construal-level theory of psychological distance. Psychological Review, 117(2), 440–463. https://doi.org/10.1037/a0018963
• Vallacher, R. R., & Wegner, D. M. (1987). What do people think they’re doing? Action identification and human behavior. Psychological Review, 94(1), 3–15. https://doi.org/10.1037/0033-295X.94.1.3

13. Items of the Scale (Questionnaire)