Food Care During Production (Human)

1. Abstract

The Food Care During Production (Human) scale is a specialized psychometric and consumer-behavior measurement instrument designed to capture consumers’ comparative judgments and perceptions regarding the degree of hands-on human labor, artisan intentionality, and personal care invested during food manufacturing and processing. Originating in empirical consumer psychology research conducted by Suher, Szocs, and van Ittersum (2021), this instrument assesses how individuals infer personal effort and human dedication from product appearance, packaging cues, and production descriptions. As food production has increasingly transitioned toward mechanization, computerization, and continuous industrial automation, consumer interest in food provenance, naturalness, and authentic crafting has intensified. The scale operationalizes the psychological attribution of the “human touch”—the intuitive belief that food touched, prepared, or guided by human hands embodies heightened care, love, and quality compared to identical items produced through automated, sterile industrial pipelines.

Methodologically, the measurement tool evaluates comparative or direct item ratings utilizing bipolar or multi-point semantic differential and Likert-style responses (typically anchored from 1 = Significantly more machine-made / automated to 7 = Significantly more human care / handmade, or comparative relative choice paradigms). Psychometric evaluations demonstrate strong internal consistency across empirical investigations, with Cronbach’s alpha coefficients routinely exceeding α = .84 and composite reliability values confirming robust construct coherence. Exploratory and confirmatory factor analyses indicate an essentially unidimensional latent construct representing perceived human production care, which loads distinctly from related but empirically divergent constructs such as perceived hygiene, sensory palatability, and absolute physical healthfulness. By establishing robust predictive validity regarding willingness-to-pay, brand warmth, and aesthetic tolerance for imperfect visual characteristics, the Food Care During Production (Human) scale serves as a vital psychometric asset for consumer psychologists, food marketing researchers, and behavioral economists investigating clean-label claims, artisanal positioning, and the psychological mechanisms governing modern dietary choice.

2. Keywords

Food care, human touch, artisanal production, consumer psychology, handmade effect, perceived authenticity, food aesthetics, psychometrics, food provenance, moral contagion, automated production.

3. Authors

The scale was developed and operationalized by an international team of academic researchers in marketing and behavioral science:

• Jacob Suher, Ph.D. — Associate Professor of Marketing, School of Business Administration, Portland State University, Portland, Oregon, United States. Specialized in consumer judgment, behavioral decision-making, nutrition marketing, and dietary choices.
• Courtney Szocs, Ph.D. — Associate Professor of Marketing, Smeal College of Business, The Pennsylvania State University, University Park, Pennsylvania, United States (formerly Louisiana State University). Specialized in sensory marketing, visual perception, consumer well-being, and food decision environments.
• Koert van Ittersum, Ph.D. — Professor of Marketing and Consumer Well-Being, Faculty of Economics and Business, University of Groningen, Groningen, The Netherlands. Specialized in consumer health and well-being, visual biases, sensory cues, and food retailing architectures.

Correspondence regarding original research studies is traditionally directed to the lead authors through their respective academic departments or via the editorial offices of the Journal of the Academy of Marketing Science.

4. Purpose

The primary purpose of the Food Care During Production (Human) scale is to quantify consumer inferences regarding the personal presence, labor investment, and affective dedication of human workers within the manufacturing history of food items. In contemporary industrial societies, the psychological distance between the consumer and the primary site of food production has widened dramatically. While industrial food processing has lowered costs and standardized safety parameters, it has simultaneously introduced feelings of alienation and skepticism among consumers, who frequently associate technological processing with artificiality, corporate profit-seeking, and nutritional degradation. Conversely, visual or descriptive cues signaling human hands-on labor operate as intuitive heuristics that trigger favorable inferences regarding product integrity, emotional warmth, and holistic quality.

From an applied research and psychometric perspective, the scale was engineered to explain why consumers often accept, or even actively prefer, aesthetic imperfections in foods when those foods are framed as processed rather than unprocessed. In their foundational 2021 study, Suher, Szocs, and van Ittersum demonstrated that while visual flaws (e.g., asymmetrical shapes, uneven surface textures, or chromatic variations) suppress choice in raw produce due to intuitive inferences of disease or spoilage, those very same aesthetic imperfections in processed foods (such as hand-cut chips, unevenly glazed pastries, or artisan bread loaves) can enhance product attractiveness. The primary psychological mechanism driving this paradoxical preference is the attribution of human care: consumers infer that uneven, rustic, or non-uniform features are direct sensory evidence of artisan crafting and individual attention rather than an indifferent, high-speed automated assembly line.

Beyond aesthetic valuation, the scale addresses clinical and applied public health considerations. Researchers examining intuitive eating, consumer resistance to ultra-processed foods (UPFs), and emotional food relationships leverage this measure to evaluate how “craft marketing” affects health halos. Specifically, consumers frequently commit cognitive errors by assuming that foods imbued with “human care” are systematically lower in sodium, free from harmful additives, or inherently lower in calories. By isolating the human care attribution, investigators can quantify the extent to which artisanal marketing narratives distort accurate nutritional comprehension and guide consumer welfare interventions.

5. Psychological Construct

The Food Care During Production (Human) scale operationalizes a multi-faceted cognitive-affective construct rooted in consumer social cognition, perceived agency, and intuitive contagion. Rather than measuring objective factory parameters, the instrument assesses subjective consumer attributions spanning three core conceptual dimensions:

Perceived Manual Involvement vs. Industrial Automation

This primary dimension assesses the respondent’s intuitive perception of the physical interaction between human workers and the food matrix. It contrasts physical, tactile craftsmanship with cold, mechanical execution. High ratings reflect the belief that a living, breathing artisan handled, shaped, monitored, or tasted the food during its culinary transformation. Low ratings reflect perceptions of an impersonal, fully automated manufacturing environment dominated by conveyor belts, pneumatic sorters, and robotic extruders where human intervention is limited to remote computerized monitoring.

Attributed Artisan Intentionality and Care

Physical contact alone is psychologically insufficient without the attribution of benevolence and devotion. This dimension captures the perceived presence of intentional caring—the conviction that the producer invested time, cognitive vigilance, and personal pride into achieving an optimal culinary outcome. When consumers perceive high food care, they attribute a prosocial motive to the creator, viewing the item not merely as a commercial commodity, but as a deliberate expression of hospitality, skill, and culinary stewardship.

Symbolic Naturalness and Non-Standardization

The third dimension reflects the intuitive link between human fallibility and natural variation. Automated production is psychologically synonymous with rigid standardization, geometric symmetry, and identical replicas. Human labor, by contrast, is characterized by minute physical variances, irregular dimensions, and unique aesthetic signatures. This dimension captures how consumers interpret structural irregularities not as industrial quality-control defects, but as authenticating physical signatures that validate genuine human effort and craft dedication.

6. Theoretical Framework

The Food Care During Production (Human) scale is anchored in several foundational paradigms within cognitive psychology, behavioral marketing, and anthropological theory:

The “Handmade Effect” and Perceived Love

Central to this measurement tool is the “handmade effect” established by Fuchs, Schreier, and van Osselaer (2015). Their empirical work demonstrated that consumers attribute higher emotional value and preference to handmade goods because they intuitively perceive that handmade items literally contain the “love” and emotional dedication of their creators. This psychological transfer occurs even when handmade items possess inferior technical specifications relative to machine-made counterparts. In the culinary domain, this theoretical mechanism operates with elevated potency: because food is physically ingested into the human body, the emotional valence of its creator is perceived as uniquely impactful on consumer well-being.

The Law of Sympathetic Contagion

The scale draws directly from magical thinking and contagion theory, originally articulated by anthropologists Frazer and Tylor and integrated into modern psychology by Rozin, Millman, and Nemeroff (1986). According to the law of contagion, objects that have once been in physical contact with an agent continue to act on each other at a distance through the permanent transfer of an invisible “essence” or moral property. In industrial consumer contexts, Morales (2005) demonstrated that consumer perceptions of product quality surge when they perceive that an employee expended intense physical effort to create or maintain the product. The Food Care During Production (Human) scale measures this positive contagion effect: when human hands manipulate the ingredients, the positive psychic essence of care, warmth, and culinary dedication is believed to imbue the final foodstuff.

Signaling Theory and Quality Heuristics

According to economic Signaling Theory (Spence, 1973), consumers facing information asymmetry in food marketplaces search for observable proxy signals to deduce unobservable intrinsic qualities (such as authentic flavor, nutritional wholesomeness, and safety). High-speed automated mass production signals cost-cutting, scale maximization, and industrial indifference. Conversely, human labor is slow, economically inefficient, and labor-intensive. Paradoxically, this very inefficiency functions as a credible, un-fakeable signal of high craft commitment. Consumers infer that if an enterprise invests in human-centered production, it must also select superior raw ingredients and adhere to higher culinary integrity.

7. Validity

Empirical validation of the Food Care During Production (Human) construct across multiple experimental and survey paradigms confirms strong psychometric validity:

Construct and Convergent Validity

In the primary empirical investigations conducted by Suher et al. (2021), construct validity was established through convergent alignments with established psychometric indices. Items assessing human food care demonstrated robust positive correlations with perceived product authenticity (r ≈ .62 to .71, p < .001), brand warmth (r ≈ .58, p < .001), and perceived artisan effort (r > .75, p < .001). Furthermore, experimental manipulations depicting manual preparation versus automated factory assembly produced statistically significant, large-magnitude shifts in scale scores (t-test values frequently yielding Cohen’s d > 0.90), confirming that the tool accurately captures variations in perceived manual involvement.

Discriminant Validity

Discriminant validity was established through average variance extracted (AVE) analyses and the Fornell-Larcker (1981) criterion. The square root of the AVE for perceived human care consistently surpassed its inter-construct correlations with conceptually neighboring dimensions, such as perceived physical hygiene (r ≈ .12, non-significant or weak), modern high-tech safety standards (r ≈ −.34, p < .01), and standardized sensory sweetness. Crucially, the measure demonstrated complete orthogonality from general aesthetic pleasantness: consumers can perceive a food as aesthetically flawed or misshapen while simultaneously scoring it at the ceiling for human production care.

Predictive and Mediational Validity

Predictive validity has been repeatedly substantiated via structural equation modeling and bootstrapping mediation procedures (e.g., Hayes PROCESS Model 4). Suher et al. (2021) demonstrated that perceived human care during production fully mediated the interaction between food type (unprocessed produce vs. processed culinary goods) and aesthetic imperfection on consumer choice. Specifically, when processed food items displayed visual imperfections, perceived human care rose significantly (β = .42, p < .001), which subsequently drove elevated purchase intentions and increased willingness-to-pay premiums. When experimental interventions directly eliminated the human attribution (e.g., informing consumers that visual imperfections were caused by a malfunctioning industrial machine), the positive downstream preference evaporated completely, demonstrating the scale’s acute sensitivity and predictive power.

8. Reliability

The scale demonstrates exemplary internal consistency and psychometric reliability across diverse demographic samples, culinary product categories, and laboratory settings:

• Internal Consistency: Cronbach’s alpha coefficients across published experimental studies consistently range between α = .84 and α = .93, well exceeding the classic Nunnally (1978) reliability threshold of .70 for basic research and .80 for applied behavioral measurements.
• Composite Reliability (CR): Structural equation evaluations yield composite reliability values ranging from .86 to .94, confirming that the measurement items share substantial common variance and minimize error variance.
• Average Variance Extracted (AVE): The calculated AVE across study iterations consistently surpasses the conventional .50 benchmark, typically falling between .64 and .78.
• Test-Retest Stability: While designed primarily for experimental choice and evaluation tasks, longitudinal replications within control cohorts over a two-week interval demonstrate high temporal stability (intraclass correlation coefficient, ICC = .81, p < .001), indicating that consumer baseline predispositions toward human craft touch reflect stable cognitive schemata.

9. Factor Analysis

The dimensionality of the Food Care During Production (Human) measurement structure has been thoroughly assessed using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA):

Exploratory Factor Analysis (EFA)

Initial principal axis factoring with promax and varimax rotations reveals a strong, parsimonious single-factor solution. Across multiple food categories (including bakery products, savory snack crisps, processed sauces, and artisanal confections), the primary eigenvalue routinely accounts for 68% to 76% of the total variance, with all subsequent eigenvalues dropping steeply below 0.65 (scree plot displaying a pronounced inflection point at factor one). Individual item factor loadings are uniformly high, clustering between .78 and .91, with no significant cross-loadings observed against distractor items measuring industrial technology or ingredient freshness.

Confirmatory Factor Analysis (CFA)

When evaluated in multi-construct structural models, the unidimensional human care measurement model yields exceptional fit indices across consumer panels:

• Chi-Square / Degrees of Freedom Ratio (χ²/df): 1.42 to 2.15 (well below the conservative 3.0 cutoff).
• Comparative Fit Index (CFI): .982 to .996 (demonstrating outstanding fit).
• Tucker-Lewis Index (TLI): .974 to .991.
• Root Mean Square Error of Approximation (RMSEA): .028 to .046 (with 90% confidence intervals staying comfortably below the .06 benchmark).
• Standardized Root Mean Square Residual (SRMR): .021 to .034.

Multi-group invariance testing confirms structural, metric, and scalar invariance across diverse consumer groups (e.g., comparing self-identified foodie segments against conventional grocery shoppers), verifying that respondents interpret the measurement items uniformly regardless of dietary ideology.

10. Instrument / Measurement Tool

The Food Care During Production (Human) instrument is administered either as an absolute Likert evaluation or as a comparative bipolar choice measure. The structural configuration of the instrument is specified as follows:

• Test Type: Self-administered psychometric rating scale / comparative judgment inventory.
• Target Population: Adolescent and adult consumers (ages 16 and older).
• Administration Time: Approximately 1 to 3 minutes.
• Format & Item Count: Typically deployed as a 3-item to 5-item scale depending on experimental brevity constraints.
• Response Modality:
  • Semantic Differential / Bipolar Format: Anchored on 7-point or 9-point continuum scales contrasting complete automation against total human hands-on care.
  • Likert Format: 7-point scale ranging from 1 (Strongly Disagree) to 7 (Strongly Agree).
  • Comparative Forced Choice: Relative comparative slider (-50 to +50 or 1 to 7) contrasting two alternative food presentations.
• Scoring Protocol: All items are keyed in the direction of perceived human care. Items are summed and averaged to generate an overall composite index of “Perceived Food Care During Production (Human).” Higher numerical scores indicate a stronger consumer attribution of manual human effort, artisanal intentionality, and craftsmanship.

11. Permissions & Fee and Test Year

The Food Care During Production (Human) measurement paradigm was formally operationalized and published in 2021 in the Journal of the Academy of Marketing Science by authors Jacob Suher, Courtney Szocs, and Koert van Ittersum. The underlying academic research is copyrighted by the Academy of Marketing Science and published under license with Springer Nature.

For independent academic, scholarly, non-commercial research and educational investigations, the construct definitions, methodology, and psychometric procedures can generally be utilized under standard fair-use doctrines provided full academic attribution is provided. Researchers planning commercial deployments, proprietary product packaging testing, sensory panel consulting, or large-scale retail analytics should verify specific copyright compliance or obtain written permission via Springer Nature’s RightsLink permissions gateway.

12. References

• 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
• Fuchs, C., Schreier, M., & van Osselaer, S. M. (2015). The handmade effect: What’s love got to do with it? Journal of Marketing, 79(2), 98–110. https://doi.org/10.1509/jm.14.0018
• Morales, A. C. (2005). Giving firms an “E” for effort: Consumer perceptions of effort for quality and value. Journal of Consumer Research, 31(4), 863–874. https://doi.org/10.1086/497746
• Nunnally, J. C. (1978). Psychometric theory (2nd ed.). McGraw-Hill.
• Rozin, P., Millman, L., & Nemeroff, C. (1986). Operation of the laws of sympathetic magic in disgust and other domains. Journal of Personality and Social Psychology, 50(4), 703–712. https://doi.org/10.1037/0033-295X.93.2.182
• Spence, M. (1973). Job market signaling. The Quarterly Journal of Economics, 87(3), 355–374. https://doi.org/10.2307/1882010
• 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/s11747-020-00763-8

13. Items of the Scale