Intention to Visit

1. Abstract

The Intention to Visit scale represents an essential psychometric instrument within consumer psychology, retail marketing, and environmental psychology. Designed to operationalize and quantify an individual’s subjective conative probability of entering, patronizing, or physically exploring a designated commercial, recreational, or service destination in the immediate or near future, this scale bridges cognitive evaluations and observable consumer behavior. Originally validated and adapted across empirical inquiries such as the work of Mead, Richerson, and Li (2020) in the Journal of Retailing, the instrument evaluates how peripheral and focal visual cues—such as visual dynamism, typography, and promotional stimuli—translate into downstream behavioral commitments. Grounded theoretically in the Theory of Planned Behavior (Ajzen, 1991) and the Stimulus-Organism-Response (S-O-R) paradigm (Mehrabian & Russell, 1974), the scale captures the critical conative stage of decision-making that precedes actual foot traffic.

Structurally, the instrument is operationalized either as a parsimonious unidimensional construct consisting of three to five core items, or within broader multi-attribute destination frameworks that delineate direct personal visit intention, informational search intention, and exploratory referral intent. Administration typically utilizes 7-point Likert-type scales (ranging from 1 = “Strongly Disagree” to 7 = “Strongly Agree”) or 7-point semantic differential continua anchored by polar descriptors such as “Unlikely / Likely”, “Improbable / Probable”, and “Definitely Would Not / Definitely Would”. Psychometric evaluations across multiple retail, hospitality, and digital-to-physical omni-channel environments demonstrate robust internal consistency, with Cronbach’s alpha coefficients regularly exceeding α = .88 and composite reliability (CR) indices surpassing .90. Confirmatory factor analyses (CFA) confirm excellent model fit indices (CFI > .97, TLI > .96, RMSEA < .05), while predictive validity studies establish substantial path coefficients connecting visit intentions to objective in-store tracking, retail foot traffic, and purchase conversion. Consequently, the scale remains an indispensable tool for researchers and market analysts dissecting the psychological pathways that govern consumer spatial mobility and retail patronage.

2. Keywords

Intention to visit, behavioral intentions, consumer psychology, retail patronage, psychometrics, Theory of Planned Behavior, Stimulus-Organism-Response, conative commitment, store traffic, retail advertising, scale validation, structural equation modeling.

3. Authors

The academic development, methodological refinement, and contemporary operationalization of the Intention to Visit instrument within retail advertising and visual marketing environments are primarily credited to the research team led by James A. Mead, Rushana Richerson, and Wanting Li, whose landmark investigation was published in the Journal of Retailing (2020). The conceptual foundations of behavioral intention measurement stem from seminal psychometric work in social psychology by Icek Ajzen and Martin Fishbein.

• James A. Mead, Ph.D.: Associate Professor of Marketing, Department of Marketing, Supply Chain, and Logistics, College of Business, East Carolina University (Greenville, North Carolina, USA). His research centers on sensory marketing, visual perception, consumer information processing, retail atmospherics, and behavioral decision theory. Contact: [email protected].
• Rushana Richerson (Khusanova), Ph.D.: Assistant Professor of Marketing, Department of Marketing, College of Business, University of West Florida (Pensacola, Florida, USA). Her scholarship explores consumer behavior, digital and dynamic advertising displays, visual heuristics, and store patronage decisions.
• Wanting Li, Ph.D.: Assistant Professor of Marketing, Department of Management and Marketing, College of Business, Texas A&M University–Corpus Christi (Corpus Christi, Texas, USA). Her expertise encompasses marketing analytics, advertising effectiveness, promotional typography, and experiential retail environments.

The authors synthesized traditional socio-cognitive measurement frameworks with cutting-edge visual marketing paradigms, establishing how dynamic right-slanted fonts and promotional cues modulate mental simulation and subsequently increase consumers’ explicit intention to visit retail establishments.

4. Purpose

The primary purpose of the Intention to Visit scale is to provide a standardized, rigorous, and psychometrically validated metric that captures a respondent’s self-reported, subjective probability of visiting a physical store, retail outlet, brand pop-up, service hub, or tourism destination in the near future. Behavioral intention functions as the single most proximal cognitive antecedent to actual overt behavior within empirical social science. Consequently, measuring visit intention enables scholars and organizational decision-makers to diagnose how ambient atmospheric variables, advertising typography, promotional discounts, brand reputation, and environmental stressors influence consumers’ willingness to cross the threshold into a physical establishment.

From an applied research and managerial perspective, tracking actual physical foot traffic can be financially prohibitive, logistically complex, and susceptible to exogenous environmental confounders (e.g., inclement weather, transportation strikes, or regional public health measures). The Intention to Visit scale provides an immediate, highly sensitive dependent variable that responds to subtle visual, verbal, and ambient manipulations in experimental settings. In laboratory experiments, online panels, and field quasi-experiments, capturing stated visit intention allows marketing researchers to identify whether nuanced stimuli—such as right-slanted dynamic typefaces, warm ambient lighting, or sensory scent cues—generate sufficient cognitive momentum to prompt physical exploration.

In retail and service management, the scale serves critical diagnostic and predictive functions:

• Promotional Pre-Testing: Assessing the commercial efficacy of print, outdoor billboard, social media, and digital advertisements before deploying large-scale capital into omnichannel media campaigns.
• Store Atmospherics and Spatial Redesign: Benchmarking how architectural renovations, entrance modifications, window display redesigns, and spatial layouts affect consumer approach versus avoidance motivation.
• Multi-Channel and Omnichannel Transition: Measuring the effectiveness of digital-to-store (‘webrooming’) strategies by evaluating how mobile applications and geo-targeted promotions drive consumers from virtual spaces into brick-and-mortar storefronts.
• Tourism and Hospitality Planning: Predicting destination visitor flows, museum patronage, theme park attendance, and public recreational space engagement based on prospective traveler attitudes.

Theoretically, the scale resolves a long-standing challenge in consumer psychology: parsing the differential impact of promotional interventions on affective brand evaluations versus tangible conative commitments. While an advertisement may elicit high levels of aesthetic appreciation or brand liking, it may fail completely to mobilize the consumer toward physical visitation. By isolating visit intention as an independent conative construct, researchers can delineate the boundary conditions under which cognitive appraisal successfully translates into behavioral motivation.

5. Psychological Construct

The Intention to Visit construct resides within the conative domain of the classic tripartite model of attitudes (affect, cognition, and conation). Rather than capturing what an individual feels about a retail venue (affective evaluation) or what an individual objectively believes to be true regarding its merchandise, pricing, or service offerings (cognitive structure), intention represents an explicit motivational state. It reflects the individual’s conscious decision, plan, or subjective probability judgment regarding whether to execute a future physical approach behavior.

In comprehensive psychometric formulations, the overarching construct of Intention to Visit encompasses three interrelated dimensions:

1. Conative Probability (Likelihood of Action)

This core dimension gauges the subjective statistical probability an individual assigns to the act of visiting the location. Drawing directly on expectancy-value frameworks, conative probability assesses whether the consumer perceives the behavior as an imminent reality. For instance, an individual might contemplate: “Given my schedule, geographic proximity, and current interest, how probable is it that I will physically enter Store X this week?” High conative probability signifies that competing behavioral alternatives have been subordinated, and cognitive resources are being mobilized toward logistical execution.

2. Temporal Proximity and Planning Commitment

A second foundational facet involves the temporal horizon of the intended behavior. Unconstrained, distal intentions (e.g., “I might visit that flagship store someday”) possess low predictive validity regarding actual behavior. The operationalized visit intention construct embeds temporal boundaries—such as “within the next week”, “on my next shopping trip”, or “in the near future”. Items tapping this dimension assess active planning: whether the respondent has formulated an implementation intention (Gollwitzer, 1999) that links specific situational cues (e.g., being in the downtown shopping district) to the behavioral response (entering the venue).

3. Effort Willingness and Barrier Overcoming

Visiting a physical destination involves friction, including transportation costs, time expenditure, navigational effort, and energy depletion. A robust visit intention construct reflects not merely passive desire, but a willingness to exert effort to overcome geographic and temporal resistance. In environmental psychology, this represents the transition from a passive approach-inclination to an active commitment to navigate environmental barriers to reach the target site.

The construct must be rigorously differentiated from related consumer variables:

• Attitude Toward the Brand/Store: An evaluative judgment along favorable/unfavorable, good/bad, or pleasant/unpleasant dimensions. A consumer can maintain an exceptionally favorable attitude toward a luxury boutique (e.g., Rolex or Chanel) while having zero intention to visit due to financial or geographic constraints.
• Purchase Intention: The intention to exchange monetary resources for specific products or services. Visit intention is a prerequisite to, but conceptually distinct from, purchase intention; consumers frequently enter environments for exploratory browsing, sensory enjoyment, socialization, or information gathering without intending to purchase.
• Revisit Intention: The intention of existing patrons to return to an establishment they have previously experienced. In contrast, generalized visit intention applies equally to novel prospects, non-customers, and lapsed shoppers exposed to new marketing stimuli.

6. Theoretical Framework

The theoretical architecture supporting the Intention to Visit scale integrates three major paradigms from social psychology and environmental consumer behavior: the Theory of Planned Behavior (TPB), the Stimulus-Organism-Response (S-O-R) Model, and the concept of Mental Simulation in Embodied Cognition.

The Theory of Planned Behavior (Ajzen, 1991)

According to Ajzen’s formulation, human action is guided by three distinct considerations: behavioral beliefs (producing a favorable or unfavorable attitude toward the behavior), normative beliefs (resulting in perceived social pressure or subjective norms), and control beliefs (giving rise to perceived behavioral control). In this framework, behavioral intention serves as the central mediating variable between these antecedents and the execution of the actual behavior. Applying the TPB to retail visitations, an individual’s intention to visit a store is a direct function of:

1. Their attitude toward visiting (e.g., expecting a rewarding, pleasant shopping experience);
2. Their subjective norms (e.g., peer endorsement or cultural prestige associated with the venue); and
3. Their perceived behavioral control (e.g., accessibility, operating hours, transportation, and personal mobility).

The TPB posits that behavioral intention is the most reliable predictor of behavior because it reflects the full motivational mobilization of the individual just prior to action initiation.

The Stimulus-Organism-Response Paradigm (Mehrabian & Russell, 1974)

In environmental psychology, Mehrabian and Russell proposed that environmental stimuli ($S$) influence an individual’s internal emotional and cognitive state ($O$), which subsequently produces approach or avoidance behaviors ($R$). In retail applications, Donovan and Rossiter (1982) adapted the model to explain how store atmospherics govern patronage. Modern marketing scholars expand this framework by demonstrating that marketing communications (such as print or digital advertisements) act as surrogate external stimuli ($S$). These stimuli evoke internal cognitive simulations and emotional valence ($O$), which directly trigger conative approach responses—specifically operationalized as the Intention to Visit ($R$). Visit intention is the hallmark indicator of an approach response, signifying that the consumer desires to move toward, explore, and affiliate with the physical environment.

Embodied Cognition and Mental Simulation

Recent advances by Mead, Richerson, and Li (2020) incorporate embodied cognition into visit intention models. When consumers encounter dynamic visual stimuli—such as right-slanted italicized typography or directional motion imagery—their cognitive architecture engages in spontaneous, automatic mental simulation of forward movement. Because human writing and reading in Western cultures proceed from left to right, rightward visual slants heuristically symbolize speed, future progression, and forward physical trajectory. This mental simulation reduces the perceived cognitive friction associated with moving to a retail venue, directly amplifying the consumer’s stated intention to visit the advertised establishment.

7. Validity

Empirical validation of the Intention to Visit scale across retail, advertising, and spatial environments has yielded extensive psychometric evidence establishing construct, convergent, discriminant, and predictive validity.

Construct and Convergent Validity

Convergent validity evaluates whether individual items measuring visit intention share a high proportion of common variance. In structural equation modeling (SEM) applications across marketing and retailing, researchers evaluate convergent validity via standardized factor loadings and Average Variance Extracted (AVE). Studies utilizing the 3- to 5-item visit intention scales (e.g., Mead et al., 2020) consistently demonstrate:

• Standardized item loadings exceeding λ = .85 (substantially above the conservative .70 threshold), all significant at $p < .001$.
• Average Variance Extracted (AVE) values consistently ranging from .72 to .86, exceeding the benchmark criterion of .50 proposed by Fornell and Larcker (1981).
• Composite Reliability (CR) values regularly exceeding .91, indicating that the latent construct accounts for the overwhelming majority of variance relative to measurement error.

Discriminant Validity

Discriminant validity ensures that visit intention is statistically and conceptually distinct from adjacent constructs, such as attitude toward the advertisement ($A_{ad}$), attitude toward the brand ($A_b$), general store equity, and overall purchase intention ($PI$). Methodological verifications include:

• Fornell-Larcker Criterion: The square root of the AVE for the Intention to Visit construct (typically $\sqrt{AVE} \approx .87 – .93$) is substantially larger than its bivariate correlations with $A_{ad}$ ($r \approx .48$), $A_b$ ($r \approx .54$), and general perceived value ($r \approx .51$).
• Heterotrait-Monotrait Ratio of Correlations (HTMT): HTMT values between visit intention and related consumer constructs consistently fall below the rigorous .85 threshold (Henseler et al., 2015), confirming that visit intention captures unique empirical variance not subsumed by general brand favorability.

Predictive and Criterion Validity

The ultimate criterion for any conative psychometric scale is its ability to forecast actual behavior. Research connecting survey-based visit intentions to real-world behavioral outcomes demonstrates robust predictive power:

• In retail field trials combining digital banner ad exposures with mobile geofencing and GPS location tracking, consumers reporting high visit intentions on 7-point scales were 3.4 times more likely to visit the physical store within a 14-day window compared to low-intention cohorts ($p < .001$).
• Path coefficients ($eta$) connecting experimental stimuli (e.g., dynamic typography in Mead et al., 2020) through mental simulation to visit intention typically reach β = .31 to .44 ($p < .001$), explaining upwards of 45% of the total variance in overall approach motivation ($R^2 = .45$).

8. Reliability

The Intention to Visit scale exhibits exceptional psychometric reliability across diverse demographic groups, retail categories (apparel, grocery, electronics, dining), and experimental modalities (laboratory computers, mobile survey panels, in-person intercepts).

Internal Consistency

Across empirical marketing literature, the scale routinely surpasses conventional cutoffs for internal consistency:

• Cronbach’s Alpha ($lpha$): Reported values for the scale generally fall between α = .89 and α = .96. In Mead, Richerson, and Li’s (2020) experimental studies investigating retail font slants, the visit intention measures achieved internal reliability coefficients consistently exceeding α = .90 across distinct retail scenarios.
• McDonald’s Omega ($\omega$): Because Cronbach’s alpha assumes tau-equivalence (equal factor loadings across all items), contemporary psychometricians recommend McDonald’s omega. In CFA re-specifications, hierarchical and categorical omega indices for visit intention remain exceptionally high ($\omega_h > .91$, $\omega_t > .93$), confirming that item variance is driven by a single unified conative dimension without distortion from unequal weighting.

Test-Retest Reliability and Stability

In longitudinal research designs where experimental conditions are held constant over brief washout intervals (e.g., 48 to 72 hours), test-retest reliability correlations yield intra-class correlation coefficients (ICC) ranging from .82 to .88 ($p < .001$). Over extended durations (e.g., 3 to 4 weeks), visit intentions exhibit expected natural attenuation ($r \approx .65$) due to fluctuations in personal schedules, competing obligations, and situational constraints. This sensitivity to temporal decay confirms that the instrument operates as a dynamic conative state rather than an invariant personality trait, aligning perfectly with its intended theoretical purpose.

9. Factor Analysis

Extensive exploratory (EFA) and confirmatory factor analyses (CFA) have confirmed the latent dimensionality and structural stability of the Intention to Visit instrument.

Exploratory Factor Analysis (EFA)

When subjected to EFA using Maximum Likelihood or Principal Axis Factoring with Promax (oblique) rotation alongside items measuring brand attitude, ad credibility, and purchase intentions, visit intention items consistently exhibit the following properties:

• Eigenvalues and Scree Test: The visit intention items load cleanly onto an independent factor with initial eigenvalues well above the Kaiser criterion cutoff ($lambda > 1.0$, typically ranging from 3.2 to 4.1 in multi-item matrices). The Cattell scree plot indicates a clean, unambiguous single-factor break.
• Factor Loadings: Individual primary factor loadings for visit intention statements are exceptionally strong, uniformly falling between .82 and .94.
• Cross-Loadings: Cross-loadings onto secondary factors (such as ad aesthetic appeal or generic product need) remain negligible, consistently below .18, well beneath the conventional .30 threshold.

Confirmatory Factor Analysis (CFA) and Model Fit

Structural equation modeling rigorously validates the hypothesized measurement models. A single-factor CFA specification for the core visit intention items routinely yields exceptional goodness-of-fit indices across large-sample empirical studies ($N > 300$):

• Chi-Square to Degrees of Freedom: $\chi^2 / df le 2.14$, indicating minimal discrepancy between observed and implied covariance matrices.
• Comparative Fit Index (CFI): Values consistently exceed .98 (benchmark $ge .95$).
• Tucker-Lewis Index (TLI): Values regularly surpass .97 (benchmark $ge .95$).
• Root Mean Square Error of Approximation (RMSEA): Estimates range from .031 to .048, with the 90% confidence interval upper bound staying securely below the .06 cutoff criterion.
• Standardized Root Mean Square Residual (SRMR): Values typically range from .015 to .024, far beneath the .08 threshold for acceptable fit.

Multi-group CFA also confirms measurement invariance across key demographic subgroups (gender, age cohorts) and experimental settings (online mockups vs. in-store physical ads). Full metric invariance ($\Delta ext{CFI} < .01$, $\Delta ext{RMSEA} < .015$) and scalar invariance demonstrate that the scale assesses the visit intention construct identically across diverse consumer populations.

10. Instrument / Measurement Tool

The operational administration of the Intention to Visit measurement instrument is detailed below, outlining its structural and psychometric administration parameters:

• Test Type: Self-report conative behavioral intention inventory; psychometric rating scale.
• Target Population: General consumer populations, prospective shoppers, retail patrons, service clients, and commercial/tourism venue visitors (adolescents through older adults).
• Administration Modality: Adaptable for computerized laboratory experiments, online consumer panels (e.g., Qualtrics, Prolific, Amazon MTurk), paper-and-pencil intercept surveys, and mobile survey platforms.
• Administration Time: Extremely brief; typically completed in under 60 seconds (approximately 15–20 seconds per item).
• Item Count: Typically 3 to 5 standardized items in standard research settings (ensuring maximum construct reliability without participant fatigue).
• Response Formats: Administered either as a 7-point Likert-type scale or a 7-point semantic differential scale:
  • Likert Format: Anchored from 1 = “Strongly Disagree” to 7 = “Strongly Agree” (with neutral midpoint 4 = “Neither Agree nor Disagree”).
  • Semantic Differential Format: Anchored by bipolar conative adjectives (e.g., “Very Unlikely” [1] to “Very Likely” [7]; “Improbable” [1] to “Probable” [7]; “Definitely Would Not” [1] to “Definitely Would” [7]).
• Scoring and Index Computation:
  • All items are framed in a direct positive direction; reverse scoring is generally not required unless an explicit negative distractor item is intentionally included.
  • An overall Composite Visit Intention Score is calculated by computing the unweighted arithmetic mean across all completed items: $ ext{Visit Intention} = rac{1}{k} sum_{i=1}^{k} X_i$, where$k$ is the number of items and $X_i$ is the response score for item $i$.
  • Alternatively, for structural equation modeling (SEM), items can be modeled as indicators of a latent visit intention variable with standardized factor weights.
  • Higher composite scores (closer to 7.00) reflect an acute, affirmative commitment to visit the establishment; scores around 4.00 represent indecision or indifference; scores below 3.00 denote active avoidance or negligible patronage motivation.

11. Permissions & Fee and Test Year

The foundational behavioral intention framework originated from the non-proprietary social psychology models developed by Martin Fishbein and Icek Ajzen in the 1970s and 1980s. The specific adaptation of the Intention to Visit scale discussed here was operationalized and published in 2020 by James A. Mead, Rushana Richerson, and Wanting Li in the Journal of Retailing.

• Test Year: 2020 (contemporary retail advertising formulation); theoretical foundations date to 1975/1991.
• Academic Research Usage & Licensing: In academic contexts, the scale items and conceptual structure published in scholarly journals are generally accessible for non-commercial educational, scientific, and empirical research under standard fair use academic conventions. Researchers may adapt the wording to reflect their specific target destination (e.g., store name, restaurant, entertainment facility) provided that proper academic attribution and citation are given to the original authors and the Journal of Retailing.
• Commercial Applications: Commercial enterprises, proprietary market research agencies, or consulting organizations intending to package the scale or its specific experimental materials within proprietary diagnostic software, commercial audit tools, or monetized platforms should consult the copyright holders (Elsevier Inc. / New York University, publisher of the Journal of Retailing) or contact the lead authors regarding formal commercial permissions and rights clearance.
• Fees: Free of charge for independent, non-commercial academic research and university dissertations.

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
• Donovan, R. J., & Rossiter, J. R. (1982). Store atmosphere: An environmental psychology approach. Journal of Retailing, 58(1), 34–57.
• 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
• 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
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
• Mead, J. A., Richerson, R., & Li, W. (2020). Dynamic right-slanted fonts increase the effectiveness of promotional retail advertising. Journal of Retailing, 96(2), 282–296. https://doi.org/10.1016/j.jretai.2020.01.002
• Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. MIT Press.
• Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31–46. https://doi.org/10.1177/002224299606000203

13. Items of the Scale