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ORIGINAL ARTICLE

Perception of food quality among Saudi consumers

Mohammad Abdulrahman Alshuniaber*

Food Sciences and Nutrition Department, College of Food and Agriculture Sciences, King Saud University, Riyadh, KSA

Abstract

Consumers’ perceptions of quality of food products increase their purchasing likelihood. This study provides a framework for exploring consumers’ perceptions of food quality for food product development and food marketing strategies by providing vital information related to consumer behavior and food choice. It establishes a method for measuring food quality perceptions through a validated questionnaire. It was found that adult consumers in Riyadh City, Saudi Arabia, perceive intrinsic food characteristics higher than of extrinsic food characteristics. Characteristics related to product safety, nutritional value, and sensory attributes were the most highly valued aspects. Sociodemographic factors such as age, marital status, education, occupation, income, and work/study in the food field were found to affect Saudi consumers’ perceptions of food quality. Furthermore, marital status, education, occupation, and income were the major classification factors for perception trends. Food identity and processing, food health prosperity, food safety and presentation, and food sensory attributes were the major qualities perceived by Saudi consumers.

Key words: consumer preference, extrinsic characteristic, intrinsic characteristics, food quality, food quality dimension, quality perception, willingness to pay

Corresponding Author: Mohammad A. Alshuniaber, Food Sciences and Nutrition Department, College of Food and Agriculture Sciences, King Saud University, Riyadh, Saudi Arabia. Email: [email protected]

Received: 22 June 2024; Accepted: 27 August 2024; Published: 29 October 2024

DOI: 10.15586/ijfs.v36i4.2662

© 2024 Codon Publications
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). License (http://creativecommons.org/licenses/by-nc-sa/4.0/)

Introduction

Food is a complex commodity with numerous measurable and/or perceivable characteristics or attributes. Food quality is determined by both consumers and producers interchangeably. Producers are responsible for translating consumer requirements into product criteria through product design and development (Hansen, 2005a). Ethnicity, local taste, and previous experience—among others—are motivations for consumers to purchase and/or repurchase food (Thogerson et al., 2017). Several studies on different regions have established an association between food characteristics and consumer purchasing decisions (Basri et al., 2016; Wong et al., 2018). Notably, food prices may significantly affect consumers’ expected eating quality and expected naturalness of food products. When other quality stimuli are absent, food prices strongly affect consumers’ choices (Hansen, 2005b). Therefore, it is important to understand consumers’ perceptions of food characteristics for the production of suitable food products. Such results should be considered in food manufacturing, product development, and food trade.

The intrinsic and extrinsic characteristics of foods are known to affect consumer preferences and their purchasing and dining experiences. Intrinsic and extrinsic quality attributes can be positively perceived by consumers; as a result, consumers’ loyalty levels would be higher (Espejel et al., 2009). Another study examined the perceptions of urban consumers of several food quality attributes, including price, safety, packaging, and labeling of four liquid products; the research argued that price was the most significant attribute affecting consumer choice, followed by food safety. The heterogeneity of consumer judgment is correlated with the socioeconomic status of consumers (Silv et al., 2012). Another study categorized food consumers—based on subjects’ responses—into two groups: organoleptic criteria-driven and production, place-, and method-driven consumers. The sociodemographic and behavioral differences between the two groups of consumers have affected their perceptions of quality and appear to affect their purchase choices and dietary patterns (Mascarello et al., 2015).

It was found that freshness was the most cited food attribute by respondents when they surveyed the quality of fresh meat, fruits, and vegetables. The respondents perceived several quality attributes, including cleanliness, safety, nutritional value, and Halal (credence cue). However, implicit cues (i.e., food safety) were the most important cue based on the data analysis (Chamhuri and Batt, 2015). Other attributes, such as being “native,” may affect consumer preferences. Consumers’ preferences and Willingness to Pay (WTP) were found to be greater for food claimed to be “native” than for other food varieties (Palma et al., 2015).

Consumers interlink food quality and safety aspects and believe that both food quality and safety are important attributes. Consumers also link traceability to both quality and safety; they also pay more attention to food quality during food shopping (van Rijswijk and Frewer, 2008). Research has shown that, among other quality attributes, food hygiene is the most significant attribute for consumers, and they tend to consume more food at home for hygienic and taste reasons (Zaibet et al., 2004). Another study revealed that the majority of consumers are willing to pay more for safe approved food products, especially animal products. Food safety was communicated to the subject by demonstrating “intensified inspection,” which increases consumers’ WTP (Rohr et al., 2005).

As food labels are a major feature of food packaging, providing information to consumers on the label or menu would empower their food selection and purchasing decisions. Consumers may be motivated to buy functional foods that carry appropriate health claims. Another study illustrated that using evocative descriptions in menu names created more positive comments about the food. Food items with descriptive names were rated more appealing, caloric, and tasty than their regularly named counterparts (Wansink et al., 2005). Research has shown that when nutrition information is displayed to customers, it leads to higher food quality ratings (satisfaction) and significantly greater intentions to repurchase than when food is displayed without nutrition information (Cranage et al., 2004).

The health claims of food products with a positive health image are positively rated. Older consumers and consumers who trust the food industry are more likely to buy functional foods than younger consumers and those who do not trust the food industry (Siegrist et al., 2008). On the other hand, quality-conscious consumers were found to negatively perceive functional risk for Store Brand (SB) food products. However, retailers may need to ensure that functional risk from their products is dismissed to improve consumers’ perceptions and make their products more appealing to quality-conscious consumers (Rubio et al., 2014).

Some food ingredients, such as chemical food additives, hydrogenated fat, and high-fructose corn syrup, could be perceived as a risk and influence consumer acceptance and/or preference. Consumer risk and benefit perceptions of food additives significantly influence consumer acceptance. Consumers and risk perceptions are influenced by consumers’ preferences for natural food products and their trust in and knowledge of regulations (Bearth et al., 2014). Furthermore, consumers who fear specific ingredient(s) may exaggerate risk perception; thus, the presence of such ingredients in a food would negatively affect their rating. However, manufacturers should ensure effective communication of ingredients’ background and history, which may reduce food fears (Wansink et al., 2014).

Food naturalness is a crucial food attribute for the majority of consumers worldwide. However, consumers define and measure naturalness differently. Food naturalness can be classified into three categories: origin (plantation and breeding), processing (technology and ingredients), and final product properties (Roman et al., 2017). A study assessed the effect of clean-label food (fresh natural food with minimum additives and processing) sold at convenience stores on consumers’ WTP. The influence of knowledge factors and involvement factors on WTP were considered. However, a clear food label stating ingredients was deemed important for consumers to identify healthy food choices. The study classified respondents into two clusters: high WTP (22.77%) and low WTP (77.23%). It was found that those under the high WTP cluster were willing to pay up to 14.06% more for clean-label food, while low WTP cluster were willing to pay up to 4.35% more (Hsu et al., 2023).

One study investigated consumers’ perception of the degree of processing based on the “NOVA” food processing classification system and their perception of healthiness based on the “Nutri-Score” label, which shows nutritional value. They studied 27 different foods and found that consumers have a negative association with the degree of food processing, and they use the degree of processing as a cue in their evaluation of the healthiness of food products. There was a negative association between the degree of processing and healthiness (Hassig et al., 2023).

Food packaged in sustainable packaging has a more positive quality perception than food packaged in conventional packaging (Magnier et al., 2016). A study on 1204 adults revealed that there is a significant relationship between sustainability-related perceived consumer effectiveness (PCE) and WTP for sustainable food. The study illustrated that freshness, healthiness, price, and ease of obtaining food were important influencing attributes that ranked “very important” or “rather important” for 95.3, 80.3, 79.9, and 78.1% of the respondents, respectively. Moreover, locality was an important factor and was perceived to be significantly greater in the 35–39 age group (70% important or very important) (Kovacs and Keresztes, 2022).

According to the Saudi General Authority for Statistics (GAS), in 2021, the population of Saudi Arabia was estimated to be 30.78 million. The population in the Riyadh area exceeded 8.17 million per capita, which represented approximately 26.5% of the Kingdom’s population. The Saudi population in the Riyadh area is estimated to be approximately 4.34 million (approximately 53%) (GAS, 2023). The average age at first marriage in Riyadh was 25.3 years for males and 20.4 years for females (GAS, 2018). The average monthly salary of Saudis in Riyadh city in 2019 was estimated to be 7030 SR (~1875 USD) (GAS, 2019).

Saudi Arabia is the largest food market in the Middle East and North Africa (MENA region) with an average food volume per capita of around 443.3 kg in 2023. The revenue of the food market in Saudi Arabia was estimated at around 57.83 billion USD, while only 2.7 billion USD from out-of-home revenue. Vegetables, bread and cereals, and dairy products were the most consumed food products in Saudi Arabia (Statista, 2024). Market research was conducted on 1000 adult Saudis nationwide to measure the effect of the COVID-19 pandemic on Saudi food habits and attitudes. The results illustrate that 53% of Saudis have become more conscious about healthy eating after the COVID-19 pandemic, while 48% of Saudis are eating a healthy diet. Cooking at home became more attractive as 67% of Saudis stated that they are eating more at home, while 69% stated they will continue to rely on home-cooked meals. It was found that 50% of consumers are buying a larger variety of food per trip, while 51% stated they are buying different food brands than what they are used to (Ipsos, 2020).

This study aimed to assess which food quality characteristics are most important for Saudi consumers living in Riyadh City, Saudi Arabia. It seeks to understand how Saudi consumers perceive food quality by measuring their perceptions of various food quality characteristics. It investigates the influences of sociodemographic factors on food quality perceptions. Such information is deemed crucial for identifying and understanding consumers’ behavior, particularly regarding food choices. The results can be utilized for informing regulatory, quality control, product development, and/or marketing strategies. However, to achieve this objective, the study implements a repeatable procedure using a validated informative questionnaire that comprises food quality characteristics that ordinary consumers can perceive and express.

Materials and Methods

Population of interest and sociodemographic characteristics

The targeted population of this study was Saudi adults residing in Riyadh City, Saudi Arabia. However, to ensure that only relevant responses were collected, the questionnaire’s sociodemographic section included two filtering questions about city of residence and nationality.

Six common sociodemographic factors (gender, age, education, marital status, occupation, and income) were examined to assess their impact on consumers’ perceptions of food quality. Additionally, it was assumed that perception might be influenced by consumers’ roles in household food preparation or shopping and their study or work in the food industry. Thus, a total of eight sociodemographic factors were considered.

Preparing questions list

Considering that consumers’ knowledge of food chemistry and processing is often limited, this questionnaire employs general food quality characteristics that are considered perceivable and expressible by ordinary consumers. Therefore, the questionnaire was generally developed using relevant questions from the literature to ensure its validity, clarity, and comparability with findings from other studies. Other questions were added to enhance the questionnaire’s completeness and ability to achieve the study objectives. The resulting list comprised 20 questions, each highlighting one major food quality characteristic. The list was then reviewed, and all questions were consistently rephrased to better align with the study’s purpose, promote clarity, facilitate data analysis, and minimize acquiescence response bias. All questions were presented as informative and affirmative sentences formulated to highlight food quality characteristics in a positive tone.

The questions were categorized based on their relationship to food composition into intrinsic and extrinsic characteristics. Each group was further divided into two subgroups: product-related and process-related characteristics. Additionally, characteristics were categorized into six groups based on the product’s safety, usability, sensory attributes, nutritional value, processing, and presentation. Table 1 illustrates the questions as they appeared in the end-user questionnaire.

Table 1. Questionnaire questions.

Intrinsic Extrinsic
Product-related questions Process-related questions Product-related questions Process-related questions
High-quality food should have an appealing aroma and appearance High-quality food should be free from natural and manufacturing faults High-quality food should have a higher price than its counterparts High-quality food should be handled in a sanitary environment
High-quality food should be tasty High-quality food should be organic High-quality food should have a reputable brand and appear in commercials High-quality food should be produced by certified institution
High-quality food should contain important nutrients High-quality food should be natural High-quality food should be convenient for storage and preparation High-quality food should be processed
High-quality food should be healthy High-quality food should be fortified High-quality food should have a longer shelf-life than its counterparts High-quality food should always be available in the market
High-quality food should be fresh and in season High-quality food should be free from residues and contaminants High-quality food should be produced or manufactured locally High-quality food should be packaged attractively with food label on it

Experimental design and questionnaire preparation

The study is a cross-sectional one-sample random test with a statistical significance level of 5% (α = 0.05) and a standard error (SE ± 2). Based on the categorization of food quality characteristics, the questionnaire was designed to be structured, encompassing four sections (Table 1). However, to minimize subject bias, all questions were presented without sectioning during questionnaire administration. Subsequently, the questionnaire was translated into Arabic.

Participants were asked to express their agreement with the statements using a five-point Likert scale: (1) “strongly disagree”; (2) “disagree”; (3) “neutral”; (4) “agree”; and (5) “strongly agree.” The study calculates the average score (AS) for each characteristic and each group of characteristics to quantify the intensity of perception. Since the AS is based on a five-point Likert scale, perception intensity could be classified into five categories. High perceived characteristics (4.2 < AS ≤ 5) significantly affect consumer choice; moderate perceived characteristics (3.4 < AS ≤ 4.2) considerably affect consumer choice; low perceived characteristics (2.6 < AS ≤ 3.4) have a minor/negligible effect on consumer choice; and nonperceived characteristics (1 ≤ AS ≤ 1.8) are assumed to have no effect on consumer choice.

Questionnaire validation

The questionnaire underwent several validation processes, including expert validation, face validation, test/retest, and other statistical validation (Aithal and Aithal, 2020; Baliwati et al., 2023). The experts’ opinions were sought to confirm the questionnaire’s content validity, including its relevance, fitness for purpose, clarity, and ability to achieve the study objectives. Twelve expert reviewers were planned to conduct a revision. The reviewers were instructed to identify unsuitable, irrelevant, unclear, or redundant questions.

Subsequently, a pilot study (Face Validation) was conducted on 20 randomly selected potential participants to assess language clarity, question comprehension, and the time required to complete the questionnaire. Furthermore, a test–retest procedure was conducted with 18 participants in the pilot study. Pearson’s correlation test was applied to the scores obtained from the first and second tests. The Pearson’s correlation coefficient was 0.597, indicating a strong positive correlation between the two tests and supporting the questionnaire’s reliability.

Spearman’s rho test assessed the correlation between food quality characteristics (i.e., dependent variables). At a significance level (α) of 0.01 and a sample size (N) of 20, all 20 characteristics exhibited statistically significant positive correlations. Additionally, Cronbach’s alpha test was performed to evaluate the internal consistency (i.e., reliability) of the food quality characteristics. The alpha coefficient for the 20 questions was 0.845, indicating a high level of internal consistency among the 20 questions. This result demonstrates the suitability and reliability of the questionnaire.

The analysis of variance (ANOVA) examined the variation within and between groups’ means. The results revealed statistically significant differences within-group means (P = 0.000) for all four quality characteristics and statistically significant differences between groups’ means (P = 0.000). This finding implies that each food characteristic contributes to overall variation and can be perceived differently and independently based on sociodemographic factors. Therefore, sociodemographic characteristics exert a significant influence on quality perception.

Survey administration and data analysis

The final questionnaire was designed for user-friendly, self-administered, electronic completion using Google Forms® (Alphabet Inc. “Google Inc.,” Mountain View, California, USA), adhering to online survey best practices. To minimize nonresponse bias due to incomplete questionnaires, all questions were set as mandatory, preventing participants from skipping or leaving questions blank. The survey was conducted from March 25 to April 10, 2023, targeting ordinary food consumers in Riyadh, Saudi Arabia. A link to the electronic questionnaire was distributed via social media platforms such as emails, Twitter®, Facebook®, and WhatsApp® to reach potential participants.

The resulting data were exported from Google Forms to Microsoft Excel® 360 (Microsoft, Redmond, WA, USA) to facilitate data transfer to analytical platforms, data quality checks, data table creation, and descriptive analysis. Descriptive analysis was employed to statistically describe the respondents’ sociodemographic characteristics (independent variables) and the distribution of sample responses (dependent variables). Subsequently, the data were exported to SPSS® software Version 22 (SPSS Inc., Chicago, Illinois, USA) for further analysis.

Results and Discussion

Respondent analysis

While the exact number of questionnaire recipients cannot be determined, 1582 responses were received. After filtering responses and conducting data quality checks, 889 responses were deemed valid for analysis. A descriptive analysis of the respondents revealed their sociodemographic characteristics. Table 2 indicates that male consumers are predominantly responsible for food purchasing, while female consumers are primarily responsible for food preparation. This finding aligns with previous research suggesting that prevailing societal norms restrict men’s involvement in food preparation at home, indicating that food preparation roles remain mainly related to women (Al Otaibi et al., 2014; Baig et al., 2019a, 2019b).

Table 2. Respondents’ sociodemographic analysis (n = 889).

Sociodemographic characteristic No. (%) Sociodemographic characteristic No. (%)
Gender Occupation
  Male 647 (72.8)   Student 67 (7.5)
  Female 242 (27.2)   Private sector employee 136 (15.3)
Age   Public sector employee 478 (53.8)
  < 18 8 (0.9)   Freelancer 64 (7.2)
  18–25 78 (8.8)   Unemployed 144 (16.2)
  26–35 197 (22.2) Monthly Income (SR)
  36–45 287 (32.3)   Less than 3000 110 (12.4)
  46– 5 199 (22.3)   3001–6000 54 (6.1)
  56–65 103 (11.6)   6001–11,000 140 (15.7)
  > 65 17 (1.9)   11,001–16,000 229 (25.8)
Marital Status   16,001–28,000 244 (27.4)
  Single 131 (14.7)   More than 28,000 112 (12.6)
  Married no kids 64 (7.2) Responsibility for food at home
  Married with kids 670 (75.4)   Buy and prepare food 145 (16.3)a
  Widowed/divorced no kids 10 (1.1)   Buy food 403 (45.3)b
  Widowed/divorced with kids 14 (1.6)   Prepare food 46 (5.2)c
Education   Sometimes help buy or prepare 223 (25.1)d
  Doctoral 89 (10.1)   Not involved at all 72 (8.1)e
  Master 158 (17.8) Do you work/study in the food field
  Bachelor 461 (51.7)   Yes 152 (17.1)f
  High school 167 (18.8)   No 737 (82.9)g
  Intermediate school or less 14 (1.6)

a42 (29%) male + 103 (71%) female

b388 (96.3%) male + 15 (3.7%) female

c1 (1.2%) male + 45 (97.8%) female

d153 (86.6%) male + 70 (13.4%) female

e63 (87.5%) male + 9 (12.5%) female [contribute 3.7% out of total female subjects]

f122 (80.3%) male + 30 (19.7%) female

g525 (71.2%) male + 212 (28.8%) female

Perception intensity of food quality characteristics

Perception intensity can be deduced from the average score (AS) for each food quality characteristic. The standard deviation (SD) reflects the extent of variation in perception among consumer groups. The results show that the overall mean AS for all 20 characteristics was 3.82 (SD ± 1.0), indicating a moderate perception of food quality among Saudi adults. Table 3 illustrates that all characteristics were perceived by Saudi adults, with eight characteristics (40%) being highly perceived. Only three characteristics were at the “perception threshold.” Logically, no quality characteristic was “not perceived” because all the characteristics were derived from the literature and validated before they were considered in this study.

Table 3. Average scores (AS) of quality characteristics.

Product-related characteristics AS
(Mean ± SD)
Process-related characteristics AS
(Mean ± SD)
Intrinsic Appealing (aroma and appearance)3 4.09 ± 1.14b Free from natural/manufacturing faults2 4.71 ± 0.7a
Tasty3 4.08 ± 1.22b Organic5 4.07 ± 1.12b
Nutrients’ availability4 4.72 ± 0.68a Natural5 4.36 ± 0.97a
Healthiness4 4.55 ± 0.84a Fortification (vitamin and minerals)4 3.48 ± 1.24b
Freshness and seasonality3 4.55 ± 0.77a Free from residues and contaminants1 4.89 ± 0.42a
GROUP OVERALL 4.4 ± 0.57a GROUP OVERALL 4.3 ± 0.59a
Extrinsic Price (higher than others)6 2.54 ± 1.32d Sanitation (in processing and handling)1 4.84 ± 0.5a
Reputation (branding and advertising)6 2.71 ± 1.41c Certification (competence of producer)1 4.28 ± 1.04a
Convenience (storage and preparation)2 2.59± 1.33d Processed (manufactured food)5 3.10 ± 1.42c
Shelf-life (longer shelf-life)2 2.52 ± 1.34d Availability in market6 3.37 ± 1.37c
Origin (produced/manufactured locally)5 3.05 ± 1.39c Packaging design and labeling6 3.91 ± 1.28b
GROUP OVERALL 2.68 ± 0.99c GROUP OVERALL 3.9 ± 0.77b

aHigh perception [4.2 < AS ≤ 5]

bModerate perception [3.4 < AS ≤ 4.2]

cLow perception [2.6 < AS ≤ 3.4]

dPerception threshold [1.8 < AS ≤ 2.6]

eNot perceived [1 ≤ AS ≤ 1.8]

1Product safety characteristics (AS = 4.67)

2Product usability characteristics (AS = 3.27)

3Product sensory attributes (AS = 4.24)

4Product nutritional prosperity characteristics (AS = 4.25)

5Product processing characteristics (AS = 3.65)

6Product presentation characteristics (AS = 3.13)

Moreover, intrinsic food quality characteristics were more important for Saudi consumers. The AS for intrinsic food quality characteristics, encompassing both product-related and process-related aspects, was 4.35, while the AS for extrinsic food quality characteristics was 3.29. Similarly, process-related food quality characteristics, both intrinsic and extrinsic, were found to be more important for Saudi consumers. The AS for process-related characteristics was 4.1, while the AS for product-related characteristics was 3.54.

Furthermore, characteristics related to product safety (superscripted by number 1) were found to be the most highly perceived (total AS = 4.67), indicating the awareness of Saudi consumers regarding food safety and nutritional quality. This result agrees with the findings of Almagrabi (2023) who reported that 43.9% of participants considered food safety when making food choices, while 67.3% were willing to pay more for approved safe food products (Almagrabi, 2023).

Nutritional prosperity characteristics (superscripted by number 3) were the second most important factor for Saudi consumers (total AS = 4.25), suggesting that Saudi consumers appreciate food nutritional quality. This result is supported by the findings of Alissa (2024), who revealed that Saudi participants performed above average (with an average of 1.5 points out of 3) in their knowledge, attitude, and practice of healthy food choices. The composite variable (average score) was 2.40, which is significantly greater than the average, indicating increased perceptions of healthy food choices. The study illustrated that the participants had adequate knowledge and positive attitudes about healthy food choices; however, practicing healthy food choices was lower (Alissa, 2024). Notably, the results of this study, along with those of Alissa (2024), may explain the findings of Sabur et al. (2019). Their study illustrated that only 1.53% of participants (out of 590 participants) met the recommendation of the Saudi Ministry of Health given on its national dietary guidelines. However, 34.7% of participants stated that they preferred healthy food, 18.8% preferred unhealthy food, and 46.5% preferred both types of food (Sabur et al., 2019).

Sensory attributes (superscripted by number 2) were found to be the third most important factor for Saudi consumers (total AS = 4.24). Recent research on international consumers also revealed the same results, indicating that food sensory attributes substantially affect consumers’ decisions regarding purchasing (Mascarello et al., 2015; Brecic et al., 2017). On the other hand, characteristics related to product processing were moderately perceived by Saudi adults, with AS = 3.65. Finally, characteristics related to product usability and presentation were found to have low perception with AS = 3.27 and 3.13, respectively.

Effect of sociodemographic characteristics

Nonparametric variance analysis tests (Kruskal‒Wallis and Mann‒Whitney) were conducted to assess the significance of differences in independent variables by mean rank. Tables 4 and 5 demonstrate that Saudi consumers perceive food quality characteristics differently based on their sociodemographic factors. However, “gender” (P = 0.077) and “food-related role at home” (P = 0.223) were found to have no significant impact on food quality perception. These results imply that Saudi males and females perceive food quality characteristics similarly and that food-related roles within the household have no significant influence on consumers’ perception of food quality characteristics. Additionally, these findings were confirmed by parametric one-way ANOVA between and within groups for each sociodemographic characteristic, and the results corroborated the findings of the nonparametric analysis.

Table 4. Kruskal-Wallis test.

Chi-square df Asymp. Sig.
Age 17.653 6 0.007
Marital status 14.461 4 0.006
Education 45.090 4 0.000
Occupation 16.682 4 0.002
Income 15.556 5 0.008
Food-related role at home 5.691 4 0.223

Table 5. Mann-Whitney test.

Mann-Whitney U Wilcoxon W Z Asymp. Sig. (2-tailed)
Gender 72,266.5 281,894.5 −1.767 0.077
Work/study in food field 41,990.5 53,618.5 −4.866 0.000

From Table 5, specialization in the food field (study or work) was found to have a significant impact on Saudi consumers’ perceptions of food quality characteristics. Interestingly, consumers who do not work or study in the food field exhibited a greater perception of food quality (total AS = 4.13) than those who do (total AS = 3.89). One possible explanation for this finding is that the amount of food-related knowledge available to people specializing in food could make them more critical in evaluating food quality.

Furthermore, a post hoc test was conducted as an exploratory multicomparison test to examine significant differences among mean ranks using the least significant difference (LSD) method. This test helps identify specific sociodemographic groups with significantly different perceptions of food quality from other groups. The LSD analysis revealed that the perceived quality characteristics of the 46–55 age group were greater than those of the other age groups (total AS = 3.94) but differed significantly from the four age groups (Table 6).

Table 6. LSD post hoc test for age.

Mean (I) Mean (J) Mean difference (I-J) Sig.
46–55 < 18 0.16534 0.012
18–25 0.18332 0.015
26–35 0.21351 0.000
36–45 0.14840 0.004

Similarly, when the effect of marital status was explored, “Single” consumers were found to have the least perception (total AS = 3.68) of food quality, their perception was significantly different from individuals with children (regardless of whether they were married/divorced/widowed) (Table 7). This finding provides evidence that parenthood significantly affects consumers’ perceptions.

Table 7. LSD post hoc test for marital status.

Mean (I) Mean (J) Mean difference (I-J) Sig.
Married with kids Single 0.15508* 0.004
Divorced/widowed with kids Single 0.33822* 0.032

Education was found to play a significant role in food quality perception. Consumers with education levels “less than high school” (total AS = 4.18) and “high school” (total AS = 4.02) were found to have different perceptions of food quality characteristics. There was also a significant difference in perception between consumers with a “Bachelor” degree (total AS = 3.82) and consumers with a “Master” degree (total AS = 3.63) (Table 8). This interesting finding suggests that education may play a complex role in how people evaluate and perceive food quality. While consumers with lower education levels may have a greater perception of food quality, consumers with higher education levels may be more critical of food quality. One possible explanation for this finding is that consumers with less education may be more likely to rely on traditional cues of food quality, such as appearance, taste, and smell. On the other hand, consumers with higher education levels are more likely to be exposed to information about the potential negative impacts of food production and processing, which could make them more critical of food quality.

Table 8. LSD post hoc test for education.

Mean (I) Mean (J) Mean difference (I-J) Sig.
Less than high school Bachelors 0.35883* 0.016
Masters 0.54471* 0.000
Doctorate 0.45273* 0.004
High school Bachelors 0.20242* 0.000
Masters 0.38829* 0.000
Doctorate 0.29631* 0.000
Bachelor Masters 0.18588* 0.000

The occupation was found to significantly impact consumer perception of food quality. Compared with those in other consumer groups, consumers in the “unemployed” group (total AS = 3.96) were found to have a significantly greater perception of food quality characteristics (Table 9). One possible explanation for this interesting finding could be the extra time that “unemployed” people have to spend on food shopping and/or dining. Moreover, the “students” (total AS = 3.68) had the lowest perception of food quality, but their perception was not significantly different from that of employed consumers in either the “government sector” or “private sector.” The “student” group also had a significant difference in quality perception compared with the “freelancer” group.

Table 9. LSD post hoc test for occupation.

Mean (I) Mean (J) Mean difference (I-J) Sig.
Unemployed Student 0.27347* 0.001
Private sector 0.15556* 0.020
Government sector 0.16288* 0.002
Freelancer Student 0.23744* 0.015

Income was found to significantly affect consumers’ perception of food quality. Consumers with a national average income of “6001–11,000 SR” had the highest perception (total AS = 3.96); however, this value was not significantly different from that of the consumer group with an income of “11,001–16,000 SR” (Table 10). High-income consumers in the “16,001–28,000 SR” and “> 28,000 SR” groups were found to have the lowest food quality perceptions.

Table 10. LSD post hoc test for income.

Mean (I) Mean (J) Mean difference (I-J) Sig.
6001–11,000 Less than 3000 0.16922* 0.018
16,001–28,000 0.21421* 0.000
More than 28,000 0.20696* 0.004
11,001–16,000 16,001–28,000 0.12295* 0.017

Cluster analysis

Cluster analysis was conducted to partition participants based on their responses to evaluate different perception trends. The nonhierarchical cluster analysis (k-means) was performed based on 20 questions and resulted in two clusters: cluster 1 contained 478 cases (53.77%), while cluster 2 contained 411 cases (46.23%). The Euclidean Distance was 4.686 (distance between final cluster centers), indicating significant dissimilarity between the two clusters. The contingency of sociodemographic characteristics between the two clusters was examined using a chi-square test to compare cluster characteristics. The two clusters differed significantly from each other in terms of sociodemography, except for “gender” (P = 0.46) and “food-related role at home” (P = 0.23), which were not significantly different between the clusters.

Furthermore, binary logistic regression was performed for each cluster to characterize the clusters and identify their significant sociodemographic factors. This analysis enabled the assessment of the extent to which the sociodemographic variables influenced the likelihood of belonging to Clusters 1 or 2. Overall, it was found that five major sociodemographic characteristics can be used to predict a participant’s affiliation with Cluster 1 or 2 (Table 11). These characteristics can be considered the primary factors that significantly impact Saudi consumers’ perceptions of food quality.

Table 11. Binary logistic regression for clusters.

Variable Wald P B
Cluster 1
B
Cluster 2
Marital status “Married with kids” 4.619 0.03 −0.845 4.691
Education “High school” 5.896 0.015 1.704 5.896
Education “Intermediate or less” 3.983 0.046 1.435 3.983
Occupation “Unemployed” 6.457 0.011 −1.184 6.457
Income “6001 – 11,000 SR” 3.837 0.05 −0.670 3.837

Additionally, to understand how food quality characteristics influence previously identified consumer groups (i.e., clusters), a regression F-test was performed. This test helps assess the impact of each characteristic in forming clusters. A higher F-value indicates a stronger influence of a characteristic on creating the clusters, while a larger cluster mean square indicates greater variation between clusters’ means (Table 12).

Table 12. Regression F-test based on dependent variables.

Final cluster centers Cluster Error F Sig. *
Cluster 1 Cluster 2 Mean Square df Mean Square df
Appealing (aroma & appearance) 3.7 4.6 165.107 1 1.127 887 146.495 0.000
Tasty 3.6 4.6 237.667 1 1.222 887 194.535 0.000
Nutrients availability 4.6 4.8 5.556 1 0.461 887 12.063 0.001
Healthiness 4.4 4.7 19.990 1 0.690 887 28.976 0.000
Freshness and seasonality 4.4 4.7 28.082 1 0.562 887 49.984 0.000
Free from natural/manufacturing faults 4.6 4.8 4.470 1 0.488 887 9.157 0.003
Organic 3.8 4.4 72.139 1 1.179 887 61.184 0.000
Natural 4.2 4.6 29.732 1 0.905 887 32.839 0.000
Fortification (vitamin and minerals) 3.0 4.1 248.210 1 1.269 887 195.560 0.000
Free from residues and contaminants 4.9 4.9 0.926 1 0.174 887 5.326 0.021
Price (higher than others) 2.0 3.1 271.649 1 1.429 887 190.125 0.000
Reputation (branding & advertising) 1.9 3.6 627.143 1 1.299 887 482.886 0.000
Convenience (storage & preparation) 1.8 3.5 616.843 1 1.081 887 570.843 0.000
Shelf-life (longer shelf-life) 1.8 3.4 540.355 1 1.185 887 455.875 0.000
Origin (produced/manufactured locally) 2.4 3.8 442.782 1 1.446 887 306.206 0.000
Sanitation (in processing & handling) 4.8 4.9 2.783 1 0.246 887 11.335 0.001
Certification (competence of producer) 4.0 4.6 89.322 1 0.987 887 90.522 0.000
Processed (manufactured food) 2.3 4.0 633.774 1 1.317 887 481.337 0.000
Availability at Market 2.6 4.2 583.362 1 1.223 887 476.947 0.000
Packaging Design & labeling 3.4 4.5 231.888 1 1.393 887 166.498 0.000

(*) F tests should be used only for descriptive purposes because the clusters have been chosen to maximize the differences among cases in different clusters. The observed significance levels are not corrected for this and thus cannot be interpreted as tests of the hypothesis that the cluster means are equal.

Principals Component Analysis (PCA)

The Kaiser–Meyer–Olkin (KMO) test was conducted to assess the data’s suitability for factor analysis. The KMO value of 0.856 indicates that the sample size is adequate for factor analysis. Bartlett’s test of sphericity was also performed to evaluate the suitability of the data matrix for factor analysis. The significance of the test (P = 0.000) indicates the presence of sufficient correlation among variables, which is essential for the effectiveness of factor analysis. After confirming the data’s suitability for factor analysis, PCA was conducted using the “Varimax with Kaiser normalization” rotation method.

PCA was performed to identify and quantify the sources of variation and to reduce variability from 20 variables to four factors that explained a total of 51.1% of the variance (Table 13). These components represent four major quality factors based on consumer perspectives, each encompassing a group of empirically and mutually correlated quality characteristics. Factor 1, accounting for 20% of the variance, comprised 8 quality characteristics related to “food identity and processing.” Factor 2, accounting for 14.3% of the variance, comprised 6 quality characteristics related to “food healthiness and prosperity.” Factor 3, accounting for 8.7% of the variance, comprised 4 quality characteristics related to “food safety and presentation.” Factor 4, accounting for 8.1% of the variance, comprised 2 quality characteristics related to “food sensory attributes.” These four factors can be considered the major criteria used by Saudi consumers to assess food quality characteristics.

Table 13. Principal component analysis: rotated component matrix*.

Component
1 2 3 4
Appealing (aroma & appearance)3 0.838
Tasty3 0.815
Nutrients availability4 0.626
Healthiness4 0.686
Freshness and seasonality3 0.522
Free from natural/manufacturing faults2 0.497
Organic5 0.697
Natural5 0.738
Fortification (vitamin and minerals)4 0.494
Free from residues and contaminants1 0.548
Price (higher than others)6 0.599
Reputation (branding & advertising)6 0.724
Convenience (storage & preparation)2 0.741
Shelf-life (longer shelf-life)2 0.704
Origin (produced/manufactured locally)5 0.587
Sanitation (in processing & handling)1 0.695
Certification (competence of producer)1 0.557
Processed (manufactured food)5 0.672
Availability in the market6 0.657
Packaging design & labeling6 .510
Total Variance 20% 14.3% 8.7% 8.1%

*Extraction method: Principal Component Analysis; *Rotation method: Varimax with Kaiser Normalization.

*Rotation converged in 9 iterations.

Conclusion

This study aimed to establish a repeatable and informative procedure for measuring consumers’ food quality perception to infer and understand consumers’ behavior and food choice. The study measured the food quality perception of adult Saudi consumers residing in Riyadh City, Saudi Arabia. It revealed that product safety, nutritional prosperity, and sensory attributes were the most perceived among Saudi consumers, with AS of 4.67, 4.25, and 4.24, respectively. Product processing was moderately perceived, with AS = 3.65; while product usability and presentation were found to have low perception with AS of 3.27 and 3.13, respectively.

Among the eight sociodemographic factors studied, only gender and food-related roles at home were found to have no significant effect on food quality perception. Moreover, there were two major trends in food quality perception among Saudis. Marital status, education, occupation, and income were key classification factors for food quality perception trends. Additionally, “food identity and processing,” “food healthiness prosperity,” “food safety and presentation,” and “food sensory attributes” were the four major aspects of food quality based on Saudi consumers’ point of view.

Acknowledgment

The author extends his sincere appreciation to the Department of Food Sciences and Nutrition, College of Food and Agriculture Sciences, King Saud University for support.

AI Declaration

I acknowledge the use of Google Gemini (https://gemini.google.com/) to improve grammar and clarity of the original text. The output was then further modified to better represent my writing style and to ensure context.

Conflict of Interest Disclosure

The author declares that he has no conflicts of interest to disclose.

Authors’ Contribution

This work was solely authored by Mohammad Alshuniaber, with no other contributions to disclose.

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