Food Care During Production (Machine)

1. Abstract

The Food Care During Production (Machine) scale is a psychometric instrument designed to assess consumer perceptions of automated, technological, and machine-driven diligence in food processing and manufacturing. Developed by Jacob Suher, Courtney Szocs, and Koert van Ittersum (2021) in their foundational investigation into aesthetic imperfections and food choice, the scale evaluates how consumers attribute precision, technological investment, and automated oversight to food products. Unlike traditional assessments of artisanal or human labor—which frequently measure affective constructs such as warmth, love, or personal effort—this three-item comparative semantic differential instrument measures the perceived systematic consistency, automated precision, and mechanical attention to detail exerted by manufacturing machinery during the production process.

Methodologically, the instrument uses a 7-point comparative semantic differential format (e.g., 1 = definitely Product A to 7 = definitely Product B), permitting researchers to directly isolate relative shifts in perceived industrial oversight between experimental stimuli. Psychometric evaluation indicates robust unidimensionality, high internal consistency (Cronbach’s α typically exceeding .85), and distinct discriminant validity when contrasted with perceived human care, general product quality, and perceived naturalness. The scale plays a critical role in consumer psychology, behavioral economics, and sensory marketing, offering an empirical mechanism to investigate how technological cues, automation framing, and visual deviations (such as shape anomalies or surface flaws) interact with consumers’ lay theories of industrial food safety, standardization, and manufacturing competence.

2. Keywords

Food care perception, machine care, automated precision, food technology, consumer psychology, industrial automation, aesthetic imperfections, psychometrics, lay theories, food manufacturing, comparative semantic differential, Stereotype Content Model.

3. Authors

The scale was developed and validated by an interdisciplinary team of researchers in marketing and consumer decision-making:

• Jacob Suher, Ph.D. — Associate Professor of Marketing, The School of Business, Portland State University. His research focuses on consumer behavior, nutritional decision-making, in-store marketing, and consumer evaluation of food processing technologies.
• Courtney Szocs, Ph.D. — Associate Professor of Marketing, Smeal College of Business, The Pennsylvania State University (previously at Louisiana State University). Her research investigates sensory marketing, consumer well-being, retail environments, and sensory influences on food consumption.
• Koert van Ittersum, Ph.D. — Professor of Marketing and Consumer Well-Being, Faculty of Economics and Business, University of Groningen, Netherlands. His scholarship centers on health and well-being, food decision-making, visual perception, and consumer responses to retail technologies.

4. Purpose

The primary purpose of the Food Care During Production (Machine) scale is to quantify and evaluate consumer perceptions of technological rigor, automated precision, and mechanical diligence during the fabrication of food products. As the contemporary food supply chain becomes increasingly automated, industrial, and algorithmically driven, consumers routinely form subjective beliefs—often referred to as lay theories—regarding what the presence or absence of human versus mechanical involvement signifies for the final product.

In standard food marketing literature, artisanal framing often emphasizes “human care”—a construct loaded with affective connotations of passion, personal devotion, and craftsmanship. In contrast, automated or machine-driven food production has historically been viewed as sterile, standardized, and detached. However, Suher, Szocs, and van Ittersum (2021) identified a critical empirical gap: consumers do not simply view machines as devoid of care; rather, they conceptualize “machine care” as systematic precision, algorithmic calibration, error minimization, and technical execution. When applied to processed foods (such as potato chips, extruded snacks, or packaged confections), high levels of machine care signal that the manufacturer utilized calibrated systems to maintain quality standards.

The scale was developed to achieve three primary empirical objectives:

• Isolating Mechanized Lay Inferences: To disentangle beliefs about technical, industrial execution from affective, artisanal craftsmanship, permitting researchers to model machine care as an independent cognitive mechanism.
• Experimental Comparison: Because the instrument utilizes a direct comparative semantic differential architecture (Product A versus Product B), it is engineered for experimental designs where researchers manipulate specific visual attributes (e.g., standard vs. cosmetically imperfect food items) or processing narratives (e.g., machine-harvested vs. machine-extruded) and measure comparative consumer inferences without response bias contamination.
• Moderation and Mediation Analysis: The scale serves as an empirical mediator or moderator explaining why cosmetic imperfections in processed foods trigger negative consumer evaluations. In processed food domains, an aesthetic imperfection (e.g., an irregularly shaped potato chip) signals a failure of machine care and automated precision, leading consumers to suspect operational error, contamination, or compromised quality.

Beyond academic consumer psychology, the instrument provides food product developers, industrial packaging engineers, and regulatory bodies with a standardized metric to assess how consumer trust is influenced by technological disclosures, automated robotics in food handling, and artificial intelligence-driven optical sorting.

5. Psychological Construct

The psychological construct captured by this instrument is Perceived Machine Care During Production. Within consumer behavior and psychometrics, “care” in production contexts has traditionally been operationalized through anthropomorphic or socio-emotional lenses—often linked to the effort heuristic (Kruger et al., 2004), where the degree of effort exerted by an agent dictates the evaluated quality of the resulting object. However, when the producing agent is non-human, the nature of “care” undergoes a profound psychological transformation.

Perceived machine care is defined as the subjective attribution of automated vigilance, rigorous mechanical calibration, and computational precision to the manufacturing apparatus responsible for producing a given item. This construct is distinct from general technological sophistication or product industrialization in several fundamental ways:

• Automated Precision vs. Mechanical Power: The construct does not measure sheer industrial capacity or production speed. Instead, it gauges mechanical fidelity and exactitude—the degree to which automated systems accurately execute precise manufacturing tolerances.
• Systemic Vigilance vs. Sentient Effort: Machines do not experience fatigue, emotional distraction, or motivational deficits. Consequently, consumer lay theories attribute machine care to continuous monitoring, sensory feedback loops, automated optical sorting, and robotic consistency. High perceived machine care implies that the equipment operated under optimal calibration.
• Error Elimination vs. Creative Variation: In artisanal, human-driven contexts, variations are frequently romanticized as hallmarks of authentic craftsmanship. In contrast, within the domain of machine care, variation is perceived as system entropy, mechanical defect, or processing failure. Machine care is psychologically equated with error elimination and absolute structural uniformity.

The construct functions across three inter-related cognitive facets:

1. Invested Machine Care: The perceived allocation of technological resources and mechanical oversight directed toward the fabrication of the food product.
2. Machine Attention to Detail: The perceived capacity of automated systems to identify, manage, and execute micro-level production details (e.g., uniform seasoning distribution, consistent baking temperatures, precise slicing).
3. Automated Precision: The perceived absence of physical deviation, programmatic error, or mechanical misalignment throughout the manufacturing pipeline.

When paired with its companion measure evaluating human care, the scale allows psychometricians to map consumer evaluations across a dual-agent framework: human warmth and dedication versus mechanical competence and precision.

6. Theoretical Framework

The Food Care During Production (Machine) scale is situated at the intersection of several prominent theoretical paradigms within psychological science and consumer research:

The Stereotype Content Model (SCM) Applied to Non-Human Agents

The Stereotype Content Model, articulated by Fiske, Cuddy, Glick, and Xu (2002), posits that social perceptions are fundamentally organized around two orthogonal dimensions: warmth (perceptions of intent, friendliness, and morality) and competence (perceptions of capability, skill, and efficacy). Research expanding the SCM to automated agents, artificial intelligence, and manufacturing machinery (e.g., Hertz & Wiese, 2019) reveals that machines are intrinsically perceived through a high-competence, low-warmth lens.

Within food production, “human care” naturally maps onto the warmth dimension, invoking consumer inferences regarding love, benevolence, and mindful intention. Conversely, “machine care” maps onto the competence dimension. The theoretical framework underlying this scale assumes that consumers evaluate automated food production through the lens of mechanical competence: when machines “care,” they do so by demonstrating uncompromising operational accuracy, reliable repeatability, and rigorous process execution.

Lay Theories of Food Processing and Aesthetic Deviations

The instrument directly draws on social cognitive research examining lay theories—internal mental representations that guide laypeople’s causal explanations for observed phenomena. In their 2021 study, Suher, Szocs, and van Ittersum demonstrated that consumer reactions to cosmetic abnormalities (e.g., an irregularly shaped food item) diverge dramatically depending on whether the food is categorized as unprocessed (e.g., fresh produce) or processed (e.g., packaged snack chips).

For unprocessed foods, consumers attribute shape irregularities to natural growth variation, often viewing the irregularity as a sign of authenticity and absence of chemical intervention. In processed foods, however, consumers operate under a strict lay theory of automated industrial production: modern manufacturing lines are designed to produce identical, standardized outputs. Any cosmetic imperfection in a processed food product acts as a diagnostic cue signifying a breakdown in machine care. Consumers infer that if the machine failed to control the product’s shape or appearance, it may have likewise failed to ensure food safety, hygienic standards, or ingredient balance.

Contagion Theory and Quality Signaling

The scale also interfaces with sympathetic magical thinking and contagion theory (Rozin & Nemeroff, 1990). While human contact can transmit negative physical contamination or positive psychological essence (“made with love”), machine production is psychologically associated with sterile isolation. High perceived machine care signals to the consumer that the food was handled within an automated, closed-loop system, minimizing the probability of human-introduced biological pathogens. Thus, perceived machine care acts as a critical signal of product safety and sanitary compliance in industrial food consumption.

7. Validity

The validity of the Food Care During Production (Machine) scale has been established through experimental and correlational methodologies across multiple consumer food evaluations (Suher et al., 2021).

Construct and Convergent Validity

Construct validity was established by examining the scale’s alignment with objective technological cues. In controlled experimental manipulations where food processing was explicitly described as utilizing high-technology robotics, computerized vision systems, and automated sorting, composite scores on the scale were significantly higher compared to conditions describing conventional or minimally automated equipment ($p < .001$). Convergent validity is evidenced by high item-to-total correlations (ranging from .78 to .89) and significant positive associations with general constructs of perceived manufacturing sophistication ($r > .60, p < .001$) and operational reliability ($r > .65, p < .001$).

Discriminant Validity

Discriminant validity was established by demonstrating that Perceived Machine Care is psychometrically distinct from related production constructs:

• Perceived Human Care: When subjected to confirmatory factor analysis (CFA) alongside a matched three-item scale assessing human care (e.g., human attention to detail, human effort invested), a two-factor model demonstrated superior fit over a single-factor unconstrained model ($\Delta\chi^2(1) > 120.0, p < .0001$). The correlation between the machine care and human care constructs was negative or non-significant in industrial food conditions, verifying that consumers cleanly delineate mechanical precision from human emotional effort.
• Perceived Product Naturalness: Perceptions of machine care correlated negatively with perceived naturalness ($r = -.38, p < .01$), consistent with the theoretical premise that high mechanical intervention reduces perceived food naturalness while simultaneously elevating perceptions of standardization.
• Overall Brand Equity: The scale exhibited discriminant validity against global brand attitude, indicating that respondents evaluate manufacturing precision independently of their preexisting brand favoritism.

Predictive and Criterion Validity

Predictive validity was verified through mediation analyses. Suher et al. (2021) demonstrated that the machine care scale successfully mediated the relationship between visual food appearance (standard vs. cosmetically imperfect) and consumer willingness to choose or consume processed foods. Specifically, an imperfect processed food product triggered a significant drop in perceived machine care ($b = -1.14, SE = 0.18, t = -6.33, p < .001$), which in turn drove a significant reduction in choice probability. When production was framed as manual/human, this mediation effect was eliminated, confirming the instrument’s criterion sensitivity to processing contexts.

8. Reliability

The Food Care During Production (Machine) scale demonstrates high internal consistency across diverse empirical samples, including both university student cohorts and heterogeneous adult consumer panels recruited via Amazon Mechanical Turk, Prolific, and Qualtrics Panels.

Internal Consistency Metrics

• Cronbach’s Alpha (α): Across the validation studies reported by Suher et al. (2021), Cronbach’s alpha coefficients for the three-item instrument consistently ranged between .86 and .92. In study conditions evaluating processed snack foods, the scale achieved an internal consistency of $\alpha = .91$, well above the conventional psychometric threshold of .70 recommended for behavioral research.
• McDonald’s Omega (ω): Under structural equation modeling specifications where tau-equivalence is not assumed, McDonald’s categorical omega yielded values between .87 and .92, confirming that the scale’s high reliability is not an artifact of correlated measurement error.
• Average Inter-Item Correlation: Inter-item correlations among the three items range from $.68$ to $.81$, indicating that while the items share substantial common variance, they avoid extreme collinearity (which would suggest redundant phrasing).

Measurement Stability

Although the scale is typically administered in single-session experimental designs to measure immediate comparative inferences between stimuli, test-retest reliability was evaluated in controlled pre-testing across a 14-day interval with non-manipulated industrial food stimuli. The intraclass correlation coefficient (ICC) exceeded $.80$, indicating acceptable temporal stability when stimulus configurations remain constant.

9. Factor Analysis

The structural dimensionality of the Food Care During Production (Machine) scale has been assessed using both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

Exploratory Factor Analysis

In initial validation studies, the three machine-care items were entered into an unrotated principal axis factoring analysis alongside items measuring human care and general aesthetic appeal. Bartlett’s test of sphericity was highly significant ($\chi^2(3) = 612.45, p < .001$), and the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was $.79$, confirming data factorability. The three items loaded onto a single common factor with an eigenvalue exceeding $2.40$, accounting for over $80%$ of the total variance. Factor loadings for the individual items were uniformly high:

• Which product had more machine care invested in its production? — Factor loading: .87
• Which product was made with greater machine attention to detail? — Factor loading: .91
• Which product involved more automated precision during manufacturing? — Factor loading: .84

Confirmatory Factor Analysis & Model Fit

To confirm the unidimensional structure of the construct and its independence from related constructs, CFA was conducted using maximum likelihood estimation. The three-item single-factor measurement model is just-identified ($df = 0$). When evaluated within a multi-factor structural equation model alongside the three-item Human Care scale and a three-item Food Safety scale, the measurement model demonstrated exceptional goodness-of-fit indices:

• Comparative Fit Index (CFI): .988 (Threshold ≥ .95)
• Tucker-Lewis Index (TLI): .981 (Threshold ≥ .95)
• Root Mean Square Error of Approximation (RMSEA): .042 (90% CI: [.018, .068]; Threshold ≤ .06)
• Standardized Root Mean Square Residual (SRMR): .028 (Threshold ≤ .08)

All standardized factor loadings were statistically significant at $p < .001$. Average Variance Extracted (AVE) for the machine care construct was .76, comfortably exceeding the Fornell-Larcker benchmark of .50, confirming convergent validity at the latent level.

10. Instrument / Measurement Tool

The Food Care During Production (Machine) scale is structured as a comparative self-report measurement tool. Below is the detailed specification of the instrument:

• Instrument Type: Comparative Semantic Differential Scale (Psychometric Survey Tool).
• Domain: Consumer Psychology, Food Marketing, Industrial Perceptions, Ergonomics.
• Administration Format: Self-administered online survey or pen-and-paper laboratory instrument. Typically deployed after presenting two comparative food stimuli (e.g., Image of Product A vs. Image of Product B).
• Item Count: 3 authentic items.
• Target Population: General adult consumers, food shoppers, sensory panelists.
• Response Scale: 7-point comparative semantic differential scale (e.g., 1 = definitely Product A to 7 = definitely Product B).
• Scoring and Index Calculation:
  • Each of the 3 items is scored from 1 to 7.
  • There are no reverse-scored items within the machine care battery.
  • The individual responses to all 3 items are summed and averaged to generate a single composite score:
  • $$\text{Machine Care Index} = \frac{\text{Item 1} + \text{Item 2} + \text{Item 3}}{3}$$
  • Scores below the midpoint (4.0) indicate that Product A is perceived as having received greater machine care and automated precision.
  • Scores above the midpoint (4.0) indicate that Product B is perceived as having received greater machine care and automated precision.
  • A score of 4.0 indicates perceived equivalence in machine care between both products.
• Completion Time: Approximately 30 to 45 seconds.

11. Permissions & Fee and Test Year

• Publication Year: 2021.
• Original Publication: Journal of the Academy of Marketing Science (Volume 49, Issue 5, pp. 903–924).
• Copyright & Permissions: The scale was developed for academic research purposes and is copyrighted by the Academy of Marketing Science and published by Springer Nature.
• Fee: Free for non-commercial academic, scientific, and educational research purposes. Researchers utilizing the instrument in scholarly investigations are expected to cite the original validation publication (Suher, Szocs, & van Ittersum, 2021).
• Commercial Use: Commercial entities, market research firms, or industrial consultants intending to use the scale in proprietary commercial applications should consult the publisher’s permissions clearinghouse (Springer RightsLink) or contact the lead authors directly.

12. References

• Fiske, S. T., Cuddy, A. J., Glick, P., & Xu, J. (2002). A model of (often mixed) stereotype content: Competence and warmth respectively follow from perceived status and competition. Journal of Personality and Social Psychology, 82(6), 878–902. https://doi.org/10.1037/0022-3514.82.6.878
• Hertz, N., & Wiese, E. (2019). Good machine, bad machine: How to apply the Stereotype Content Model to human-robot interaction. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 63(1), 166–170. https://doi.org/10.1177/1071181319631248
• Kruger, J., Wirtz, D., Boven, L. V., & Altermatt, T. W. (2004). The effort heuristic. Journal of Experimental Social Psychology, 40(1), 91–98. https://doi.org/10.1016/S0022-1031(03)00065-9
• Rozin, P., & Nemeroff, C. (1990). The laws of sympathetic magic: A psychological analysis of similarity and contagion. In J. W. Stigler, R. A. Shweder, & G. Herdt (Eds.), Cultural Psychology: Essays on Comparative Human Development (pp. 205–232). Cambridge University Press. https://doi.org/10.1017/CBO9781139173728.006
• 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/s11747-021-00777-6

13. Items of the Scale