Food Sciences and Nutrition Department, College of Food and Agriculture Sciences, King Saud University, Riyadh, KSA
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
© 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/)
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.
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.
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 |
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.
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.
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.
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 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.
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 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.
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.
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.
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.
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.
The author declares that he has no conflicts of interest to disclose.
This work was solely authored by Mohammad Alshuniaber, with no other contributions to disclose.
Aithal, A. and Aithal, P., 2020. Development and validation of survey questionnaire & experimental data—A systematical review-based statistical approach. International Journal of Management, Technology, and Social Sciences. 5(2): 233–251. 10.5281/zenodo.4179499
Al Otaibi, N. and Yasmeen, K., 2014. Saudi consumer’s shopping behavior: Descriptive analysis. Journal of Sociological Research. 5(2): 75–94. 10.5296/jsr.v5i2.6641
Alissa, N., 2024. What are the perceptions of healthy food choices? A cross-sectional study from Saudi Arabia. Italian Journal of Food Science. 36(2): 195–204. 10.15586/ijfs.v36i2.2506
Almaghrabi, M., 2023. Assessing public interest, risk perceptions, and awareness of food safety in Saudi Arabia: A cross-sectional study. Food Control. 151: 109810. 10.1016/j.foodcont.2023.109810
Baig, M., Al-Zahrani, K., Schneider, F., Straquadine, G. and Mourad, M., 2019a. Food waste posing a serious threat to sustainability in the Kingdom of Saudi Arabia–A systematic review. Saudi Journal of Biological Sciences. 26(7): 1743–1752 10.1016/j.sjbs.2018.06.004
Baig, M., Gorski, I. and Neff, R., 2019b., Understanding and addressing waste of food in the Kingdom of Saudi Arabia. Saudi Journal of Biological Sciences. 26(6): 1633–1648. 10.1016/j.sjbs.2018.08.030
Baliwati, Y., Diana, R., Martianto, D., Sukandar, D. and Hendriadi, A., 2023. Development and validation of a social impact questionnaire for household food waste. MethodsX. 11: 102499. 10.1016/j.mex.2023.102499
Basri, N., Ahmad, R., Anuar, F. and Ismail, K., 2016. Effect of word of mouth communication on consumer purchase decision: Malay upscale restaurant. Procedia-Social and Behavioral Sciences. 222: 324–331. 10.1016/j.sbspro.2016.05.175
Bearth, A., Cousin, M. and Siegrist, M., 2014. The consumer’s perception of artificial food additives: Influences on acceptance, risk and benefit perceptions. Food Quality and Preference. 38: 14–23. 10.1016/j.foodqual.2014.05.008
Brečić, R., Mesić, Ž. and Cerjak, M., 2017. Importance of intrinsic and extrinsic quality food characteristics by different consumer segments. British Food Journal. 119(4): 845–862. 10.1108/bfj-06-2016-0284
Chamhuri, N. and Batt, P., 2015. Consumer perceptions of food quality in Malaysia. British Food Journal. 117(3): 1168–1187. 10.1108/BFJ-08-2013-0235
Cranage, D., Conklin, M. and Lambert, C., 2004. Effect of nutrition information in perceptions of food quality, consumption behavior and purchase intentions. Journal of Foodservice Business Research. 7(1): 43–61. 10.1300/J369v07n01_04
Espejel, J., Fandos, C. and Flavián, C., 2009. The influence of consumer involvement on quality signals perception. British Food Journal. 111(11): 1212–1236. 10.1108/00070700911001040
General Authority for Statistics. Annual statistical report: Chapter 11: Labor market and social protection—Average monthly wage (salary) of employed persons (15 years and above) by region, sex & nationality 2019 A.D. 2019. [cited 2024 Jun 15]. Available from: https://www.stats.gov.sa/ar/1017
General Authority for Statistics, 2018. Databases: Social classification of Saudi population. [cited 2024 Jun 15]. Available from: https://www.stats.gov.sa/ar/1039
General Authority for Statistics, 2023. SaudiCensus 2010–2022. [cited 2024 June 15]. Available from: https://portal.saudicensus.sa/portal/public/1/15/101462?type=TABLE
Hansen, T., 2005a. Understanding consumer perception of food quality: The cases of shrimps and cheese. British Food Journal. 107(7): 500–525. 10.1108/00070700510606909
Hansen, T., 2005b. Rethinking consumer perception of food quality. Journal of Food Products Marketing. 11(2): 75–93. 10.1300/J038v11n02_05
Hassig, A., Hartmann, C., Sanchez-Siles, L. and Siegrist, M., 2023. Perceived degree of food processing as a cue for perceived healthiness: The NOVA system mirrors consumers’ perceptions. Food Quality and Preference. (110): 104944. 10.1016/j.foodqual.2023.104944
Hsu, J., Sung, C. and Tseng, J., 2023. Willingness-to-pay for ready-to-eat clean label food products at convenient stores. Future Foods. (7): 100237. 10.1016/j.fufo.2023.100237
Ipsos, 2020. Food trend 2020: The changing food habits & attitudes of consumers in Saudi Arabia during the pandemic. [cited 2024 Aug 01]. Available from: https://www.ipsos.com/sites/default/files/ct/news/documents/2020-12/food_trends_2020_-_ksa.pdf
Kovacs, I. and Keresztes, E., 2022. Perceived consumer effectiveness and willingness to pay for credence product attributes of sustainable foods. Sustainability. 14: 4338. 10.3390/su14074338
Magnier, L., Schoormans, J. and Mugge, R., 2016. Judging a product by its cover: Packaging sustainability and perceptions of quality in food products. Food Quality and Preference. 53: 132–142. 10.1016/j.foodqual.2016.06.006
Mascarello, G., Pinto, A., Parise, N., Crovato, S. and Ravarotto, L., 2015. The perception of food quality. Profiling Italian consumers. Appetite. 89: 175–182. 10.1016/j.appet.2015.02.014
Palma, M., Collart, A. and Chammoun, C., 2015. Information asymmetry in consumer perceptions of quality-differentiated food products. Journal of Consumer Affairs. 49(3): 596–612. 10.1111/joca.12053
Röhr, A., Lüddecke, K., Drusch, S., Müller, M. and Alvensleben, R., 2005. Food quality and safety—Consumer perception and public health concern. Food Control. 16(8): 649–655. 10.1016/j.foodcont.2004.06.001
Roman, S., Sanchez-Siles, L. and Siegrist, M., 2017. The importance of food naturalness for consumers: Results of a systematic review. Trends in Food Science & Technology. 67: 44–57. 10.1016/j.tifs.2017.06.010
Rubio, N., Oubiña, J. and Villaseñor, N., 2014. Brand awareness–Brand quality inference and consumer’s risk perception in store brands of food products. Food Quality and Preference. 32: 289–298. 10.1016/j.foodqual.2013.09.006
Sabur, A., Alsharief, L. and Amer, S., 2022. Determinants of healthy food consumption and the effect of Saudi food related policies on the adult Saudi population, a National Descriptive Assessment 2019. Current Research in Nutrition and Food Science. 10(3): 1058–1076. 10.12944/CRNFSJ.10.3.21
Siegrist, M., Stampfli, N. and Kastenholz, H. 2008. Consumers’ willingness to buy functional foods. The influence of carrier, benefit and trust. Appetite. 51(3): 526–529. 10.1016/j.appet.2008.04.003
Silv, W., Udugama, J. and Mudalige, U., 2012. Consumer perceptions on quality attributes of liquid food products: An empirical analysis based on urban households. The Journal of Agricultural Sciences. 7(2): 85–96. 10.4038/jas.v7i2.4409
Statista, 2024. Food market revenue in Saudi Arabia from 2019 to 2029. [cited 2024 Aug 01]. Available from: https://www.statista.com/forecasts/1456808/saudi-arabia-food-market-revenue
Thøgersen, J., Pedersen, S., Paternoga, M., Schwendel, E. and Aschemann-Witzel, J., 2017. How important is country-of-origin for organic food consumers? A review of the literature and suggestions for future research. British Food Journal. 119(3): 542–557. 10.1108/bfj-09-2016-0406
Van, Rijswijk W. and Frewer, L., 2008. Consumer perceptions of food quality and safety and their relation to traceability. British Food Journal. 110(10): 1034–1046. 10.1108/00070700810906642
Wansink, B., Tal, A. and Brumberg, A., 2014. Ingredient-based food fears and avoidance: Antecedents and antidotes. Food Quality and Preference. 38: 40–48. 10.1016/j.foodqual.2014.05.015
Wansink, B., van Ittersum, K. and Painter, J., 2005. How descriptive food names bias sensory perceptions in restaurants. Food Quality and Preference. 16(5): 393–400. 10.1016/j.foodqual.2004.06.005
Wong, S., Hsu, C. and Chen, H., 2018. To buy or not to buy? Consumer attitudes and purchase intentions for suboptimal food. International Journal of Environmental Research and Public Health. 15(7): 1431. 10.3390/ijerph15071431
Zaibet, L., Bachta, M., Lajimi, A. and Abbassi, M., 2004. Consumers’ perception of food product quality in Tunisia. Journal of International Food & Agribusiness Marketing. 16(2): 165–178. 10.1300/J047v16n02_10