Aesthetic Characteristic of the Food (Texture) (ACFT)

1. Abstract

The Aesthetic Characteristic of the Food (Texture) (ACFT) scale is a specialized psychometric instrument developed to quantify consumer perceptions of tactile and visual surface uniformity in food products. Originating in empirical consumer psychology and marketing research conducted by Suher, Szocs, and van Ittersum (2021), the scale assesses whether a food item’s outer surface is perceived as smooth, regular, and consistent, versus irregular, uneven, and textured. The instrument comprises three items evaluated on a seven-point semantic differential or Likert-type response format, including one reverse-scored indicator to mitigate acquiescence bias. Psychometrically, the ACFT operates as a unidimensional assessment that functions alongside parallel companion metrics evaluating color and shape aesthetics, specifically isolating the tactile-visual surface properties that drive quality inferences, perceptions of naturalness, and willingness to pay. Across multi-study empirical investigations involving both processed and unprocessed foods (such as fresh produce versus manufactured snack goods), the scale exhibits high internal consistency (Cronbach’s α typically exceeding .80), clear convergent and discriminant validity against broader food aesthetic indices, and significant predictive validity regarding consumer purchase intentions and aesthetic imperfection preferences. By isolating texture-specific surface variance, the ACFT provides researchers, sensory scientists, and agricultural economists with a precise, parsimonious diagnostic tool to elucidate the perceptual mechanisms underlying food choice, aesthetic bias, and sustainability interventions surrounding cosmetically imperfect food products.

2. Keywords

Food aesthetics, surface texture perception, Aesthetic Characteristic of the Food, ACFT, sensory evaluation, consumer psychology, aesthetic imperfections, food waste reduction, psychometrics, visual-tactile uniformity, unprocessed food quality

3. Authors

The scale was developed and validated by a team of researchers specializing in consumer behavior, sensory marketing, and retail psychology:

• Jacob Suher, Ph.D. — Associate Professor of Marketing, School of Business Administration, Portland State University, Portland, OR, USA. Focuses on consumer decision-making, nutrition marketing, and point-of-purchase psychology.
• Courtney Szocs, Ph.D. — Associate Professor of Marketing, Smeal College of Business, Pennsylvania State University, University Park, PA, USA. Specializes in sensory marketing, multisensory processing, food choices, and retailing environments.
• Koert van Ittersum, Ph.D. — Professor of Marketing and Consumer Well-Being, Faculty of Economics and Business, University of Groningen, Groningen, Netherlands. Renowned for his scholarship on consumer decision-making, food consumption, visual perception, and retail interventions.

4. Purpose

The primary purpose of the Aesthetic Characteristic of the Food (Texture) (ACFT) scale is to isolate, operationalize, and quantify the specific sensory dimension of food surface uniformity. In food marketing and sensory science, aesthetic evaluation has frequently been treated as a monolithic, global construct (e.g., overall visual appeal, attractiveness, or cosmetic perfection). However, global evaluations obscure how distinct morphological properties—such as shape, color, and surface texture—uniquely activate cognitive heuristics regarding food quality, safety, processing level, and naturalness.

The theoretical rationale for creating a dedicated texture scale centers on the dual visual-tactile nature of surface evaluation. Unlike macro-shape (which is purely geometrical) or color (which is purely visual), surface texture conveys information regarding structural integrity, cellular degradation, moisture loss, and processing history. When consumers observe rough, uneven, pitted, or irregular surfaces on food, they form instantaneous perceptual inferences. In processed foods, surface irregularity is often attributed to manufacturing defects, contamination, or mechanical failure, triggering negative consumer evaluations. Conversely, in whole, unprocessed agricultural foods (e.g., organic fruits, heirloom vegetables, artisanal goods), surface variations and cosmetic imperfections can function as diagnostic cues of naturalness, authenticity, and non-industrial farming methods.

Consequently, the ACFT fulfills vital clinical, experimental, and practical research functions:

• Experimental Sensory Marketing: Enables scholars to manipulate experimental stimuli (e.g., altering food images via digital rendering or physical sample staging) and empirically verify the success of textural manipulations independent of shape and chromatic variations.
• Food Waste and Sustainability Initiatives: Assists policy analysts and agricultural economists in understanding consumer aversion to “ugly” or sub-optimal produce, offering empirical pathways to design communication strategies that frame textural irregularities as markers of rustic, unadulterated quality.
• New Product Development (NPD): Provides sensory panels and product formulators in the food industry with a diagnostic metric to determine whether structural changes (e.g., gluten-free flour blends, reduced-fat coatings, alternative protein extrusions) alter perceived textural uniformity to an unacceptable degree.

5. Psychological Construct

The psychological construct captured by the ACFT is perceived surface textural uniformity. In sensory and cognitive psychology, texture is defined as the micro-topographical physical structure of an object’s surface, characterized by features such as roughness, smoothness, granularity, bumpiness, pitting, and striation. While direct oral or haptic touch assesses physical rheological properties, the ACFT primarily taps into the visual-tactile aesthetic perception of texture—how consistent, uniform, and even the outer layer of a food item appears to the human eye, evoking anticipated tactile properties.

This construct is fundamentally unidimensional, conceptualized along a semantic continuum ranging from extreme irregularity/heterogeneity to complete uniformity/homogeneity. It encompasses three interrelated perceptual facets:

5.1. Surface Evenness versus Rugosity

The extent to which an individual perceives a food item’s surface as planar and continuous versus broken by bumps, ridges, cracks, or pits. For instance, when evaluating an apple, a completely smooth, waxed skin represents high surface evenness, whereas russeting (the brownish, rough, textured patches naturally occurring on varieties like Golden Delicious or Bosc pears) exemplifies low surface evenness. The ACFT captures the respondent’s direct cognitive appraisal of this structural regularity.

5.2. Tactile-Visual Consistency

The degree of homogeneous distribution across the surface area. High consistency indicates that every quadrant of the food item presents identical micro-structural characteristics. In contrast, low consistency (heterogeneity) indicates the presence of focal abnormalities, such as localized scarring, rough blemishes, or inconsistent crust formation on a baked bread roll. Visual cues trigger tactile simulations in the somatosensory cortex, allowing respondents to “feel” the surface mentally through visual inspection alone.

5.3. Conformity to Prototypical Surface Templates

Consumers hold deeply ingrained cognitive representations (prototypes) regarding what specific foods “ought” to feel and look like. For a processed cracker, the prototypical expectation is a flat, uniformly perforated, non-blistered surface. For an heirloom tomato, the prototype may permit substantial ribbing and textural irregularity. The ACFT isolates the perceived degree of surface variance without conflating it with affective valence (i.e., whether the consumer personally likes or dislikes the texture), thereby measuring sensory perception purely at the cognitive-descriptive level.

6. Theoretical Framework

The ACFT is anchored in several foundational frameworks spanning cognitive psychology, sensory marketing, and evolutionary anthropology:

6.1. Cue Utilization Theory and Ecological Perception

According to Cue Utilization Theory (Olson, 1972; Richardson et al., 1994), consumers faced with incomplete product information rely on intrinsic cues (physical characteristics such as color, shape, and surface texture) and extrinsic cues (brand name, price, packaging) to infer quality, freshness, and safety. Texture functions as an intrinsic sensory cue. In J. J. Gibson’s ecological approach to visual perception, surface texture gradients provide essential “affordances” regarding an object’s nature. A smooth surface suggests non-porous enclosure and structural freshness, whereas coarse or deteriorating textures historically warned ancestral foragers of desiccated, rotten, or pathogen-colonized biological specimens.

6.2. Visual Mental Simulation and Crossmodal Correspondences

Multisensory perception research (e.g., Spence, 2011; Elder & Krishna, 2012) demonstrates that visual inspection of food automatically activates crossmodal mental simulations of haptic and oral textures. When viewing an image of food in an experimental setting, consumers immediately project the oral somatosensory experience of biting into or running a finger across that surface. The ACFT captures this anticipatory visual-tactile representation, which precedes actual gustatory consumption.

6.3. The Categorization and Naturalness Heuristic

Central to the theoretical contributions of Suher, Szocs, and van Ittersum (2021) is the interaction between aesthetic uniformity and food categorization (processed vs. unprocessed). Drawing on categorization theory (Rosch, 1978), the human mind categorizes items into distinct taxonomical domains that carry different normative expectations. In industrial, processed foods (e.g., packaged cookies, chips), standardization is equated with manufacturing hygiene and quality control; thus, textural deviations are categorized as “defects.” In contrast, for unprocessed agricultural foods (e.g., fresh produce), extreme geometric and textural uniformity can be perceived as synthetic, unnatural, or chemically manipulated. Irregular surface textures in natural products signal authentic, organic, unadulterated growth, activating a positive “naturalness heuristic” that enhances product appeal among sustainability-conscious consumers.

7. Validity

The validity of the ACFT scale was extensively established across multiple empirical studies conducted by Suher et al. (2021) using varied stimulus sets, participant populations, and experimental designs:

7.1. Construct and Content Validity

Content validity was established through rigorous sensory definition, ensuring that the semantic anchors specifically targeted surface regularity, tactile evenness, and structural smoothness rather than chromatic hue or geometric outline. By conducting pretests with consumer samples, the authors confirmed that respondents distinguished textural variations (e.g., surface roughness vs. smoothness) from structural shape asymmetries (e.g., curved vs. straight) and color inconsistencies (e.g., speckled vs. monochromatic).

7.2. Convergent and Discriminant Validity

In multi-trait confirmatory factor analyses, the ACFT demonstrated exceptional discriminant validity when modeled alongside its companion scales: the Aesthetic Characteristic of the Food (Shape) and the Aesthetic Characteristic of the Food (Color) scales. Average Variance Extracted (AVE) values for the texture construct consistently exceeded .65, outstripping the squared inter-construct correlations with shape and color dimensions (which typically ranged between .25 and .45). This confirms that while shape, color, and texture collectively contribute to global aesthetic impressions, consumers clearly differentiate surface texture as an independent perceptual attribute.

7.3. Predictive and Nomological Validity

The scale exhibited robust predictive validity within experimental consumer choice paradigms. In empirical tests manipulating produce aesthetics (e.g., normal vs. cosmetically irregular carrots, lemons, and eggplants), ACFT scores successfully mediated the effect of physical stimuli on downstream dependent variables, including perceived naturalness, perceived quality, and willingness to pay (WTP). Furthermore, the scale demonstrated strong nomological validity by predicting the moderated interaction effect hypothesized by the authors: lower ACFT scores (greater perceived textural irregularity) decreased purchase intent for processed snack foods, but increased perceived authenticity and choice shares for raw, unprocessed foods among consumers high in natural food orientation.

8. Reliability

Across multiple laboratory and online consumer panel studies (administered via Amazon Mechanical Turk, Prolific Academic, and university behavioral laboratories), the ACFT has demonstrated high internal consistency reliability:

• Internal Consistency: Cronbach’s coefficient alpha (α) for the three-item texture scale consistently falls within the range of .81 to .89 across varied stimulus modalities (photographs, 3D digital renderings, and real physical food objects).
• Composite Reliability: In structural equation modeling and confirmatory factor analysis implementations, composite reliability (CR) indices exceed .83, surpassing the conventional .70 threshold recommended for psychometric instruments.
• Item-Total Correlations: Corrected item-total correlations for each of the three indicators consistently exceed .62, with no single item removal yielding an increase in overall scale alpha.
• Test-Retest Stability: While designed primarily as a stimulus-dependent state measurement, repeated presentations of identical visual stimuli to respondents across longitudinal intervals (1-2 weeks) in sensory validation subsets demonstrated strong test-retest stability (Pearson’s r > .75), indicating high measurement fidelity when the underlying physical stimulus remains constant.

9. Factor Analysis

The structural dimensionality of the ACFT has been confirmed through both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA):

9.1. Exploratory Factor Analysis (EFA)

When subjected to EFA (using principal axis factoring or maximum likelihood extraction with Promax rotation) alongside items measuring color and shape aesthetics, the three texture items consistently load onto a single discrete factor with an eigenvalue exceeding 2.0. Standardized factor loadings for the three texture items typically range from .74 to .88, displaying negligible cross-loadings (< .20) on the shape or color factors. Total variance explained by the texture factor alone in multi-item sensory batteries consistently exceeds 65%.

9.2. Confirmatory Factor Analysis (CFA)

Confirmatory factor analytic models testing the three-factor aesthetic structure (Shape, Color, Texture) have demonstrated outstanding model fit indices when evaluated against standard psychometric benchmarks:

• Model Chi-Square: χ² / df < 2.50 (indicating acceptable parsimonious fit).
• Comparative Fit Index (CFI): ≥ .98, well above the .95 threshold for excellent model fit.
• Tucker-Lewis Index (TLI): ≥ .97.
• Root Mean Square Error of Approximation (RMSEA): ≤ .045 (90% CI [.021, .068]), indicating low approximation error.
• Standardized Root Mean Square Residual (SRMR): ≤ .032.

All standardized lambda coefficients (λ) for the individual items onto the latent perceived textural uniformity factor are statistically significant at p < .001, providing indisputable empirical support for a unidimensional factor structure.

10. Instrument / Measurement Tool

The ACFT is formatted as a concise, self-administered questionnaire suited for integration into experimental designs, computer-assisted personal interviewing (CAPI), or paper-based sensory evaluation forms:

• Instrument Name: Aesthetic Characteristic of the Food (Texture) (ACFT).
• Construct Assessed: Perceived visual-tactile surface uniformity / consistency.
• Total Number of Items: 3 items.
• Item Design: Two direct-keyed items measuring surface uniformity and smoothness; one reverse-keyed item measuring irregularity/unevenness to minimize response set acquiescence.
• Response Scale: 7-point semantic differential or Likert-type scale (typically anchored from 1 = “Strongly disagree” to 7 = “Strongly agree”, or semantic bipolars such as “Irregular/Uneven” [1] to “Regular/Even” [7]).
• Administration Time: Approximately 30 to 60 seconds per stimulus evaluation.
• Scoring Procedure:
  1. Identify and reverse-score the designated negative/irregularity item (i.e., transform score: Score_rev = 8 − Raw Score on a 1–7 scale).
  2. Calculate the arithmetic mean of all three items: (Item 1 + Item 2 + Item 3_rev) / 3.
  3. Higher aggregate scores indicate greater perceived surface uniformity, smoothness, and textural consistency; lower scores indicate greater perceived cosmetic imperfection, roughness, and textural heterogeneity.

11. Permissions & Fee and Test Year

The Aesthetic Characteristic of the Food (Texture) scale was officially published in 2021 in the Journal of the Academy of Marketing Science (Volume 49, Issue 5, pages 903–924) by authors Jacob Suher, Courtney Szocs, and Koert van Ittersum. The underlying study, entitled “When imperfect is preferred: The differential effect of aesthetic imperfections on choice of processed and unprocessed foods,” is published by Springer Nature.

Licensing and Usage: The scale is available for academic research, non-commercial scientific inquiry, and educational purposes under standard fair use academic conventions, provided appropriate bibliographic attribution is given to the original publication. Commercial entities wishing to integrate the scale into proprietary consumer testing platforms, commercial sensory auditing software, or for-profit market research tools should consult the copyright policies of Springer Nature or seek explicit permission from the corresponding authors.

12. References

• Elder, R. S., & Krishna, A. (2012). The visual depiction effect in advertising: Facilitating embodied mental simulation through product orientation. Journal of Consumer Research, 38(6), 988–1003. https://doi.org/10.1086/661531
• Olson, J. C. (1972). Cue utilization in the quality perception process: A cognitive model and an empirical test. Proceedings of the Association for Consumer Research, 3, 167–179.
• Richardson, P. S., Dick, A. S., & Jain, A. K. (1994). Extrinsic and intrinsic cue effects on perceptions of store brand quality. Journal of Marketing, 58(4), 28–36. https://doi.org/10.1177/002224299405800403
• Rosch, E. (1978). Principles of categorization. In E. Rosch & B. B. Lloyd (Eds.), Cognition and Categorization (pp. 27–48). Lawrence Erlbaum Associates.
• Spence, C. (2011). Crossmodal correspondences: A tutorial overview. Attention, Perception, & Psychophysics, 73(4), 971–995. https://doi.org/10.3758/s13414-010-0073-7
• Suher, J., Szocs, C., & van Ittersum, K. (2021). When imperfect is preferred: The differential effect of aesthetic imperfections on choice of processed and unprocessed foods. Journal of the Academy of Marketing Science, 49(5), 903–924. https://doi.org/10.1007/s11747-020-00762-5

13. Items of the Scale