Health Risk Awareness Scale of Food Shoppers on Digital Shopping Platforms: A Validation Study

Health Risk Awareness Scale of Food Shoppers on Digital Shopping Platforms: A Validation Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Health Risk Awareness Scale of Food Shoppers on Digital Shopping Platforms: A Validation Study Yusuf Çelik, Mehmet Aziz Çakmak, Haşim Çapar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6959514/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: Food shopping, especially through digital media, has changed consumer behaviour; however, this change has unintentionally supported sedentary lifestyles and posed significant health risks. The aim of this research is to develop a valid and reliable measurement tool that can measure the health risk awareness of e-food shoppers. Method: This study was conducted as a methodological study between January 1-30, 2025 in Türkiye with 198 participants. An item pool was created as part of the scale development. Then, these items were sent to experts. The items corrected according to expert opinions were sent to a group of 50 people for a pilot study. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were performed within the scope of validity after the pilot application. For reliability, Cronbach's alpha values, test-retest reliability and corrected item-total correlations of the scale were reported. T-test and ANOVA analyses were conducted for the psychometric measurements of the scale. Results: As a result of EFA conducted within the scope of validity, six sub-dimensions with 24 items were obtained. This structure was confirmed with CFA. Because chi-square value (x²/df=2.77; p<0.001) was found to be significant. RMSEA=0.06 and SRMR=0.07 values ​​belonging to CFA were found to be below the critical value of 0.08. Goodness of fit values ​​such as GFI=0.91, CFI=0.96 and IFI=0.96 belonging to the scale were also found to be above the critical value of 0.90. Cronbach alpha value reported for reliability was found to be 0.79, and test-retest reliability was found to be 0.798. Corrected item total correlation results were found to be above the critical value of 0.3. According to the results of hypothesis tests conducted to reveal the psychometric properties of the scale, health risk awareness of e-food shoppers did not show any significant difference in terms of age, living alone, family type, chronic disease, exercise, food sensitivity, body mass index, general health and life satisfaction (p>0.05). However, age, e-food recommendation, reason to e-food and frequency of e-food shopping showed statistically significant differences. Conclusion: Our scale provides a robust tool for identifying gaps in health literacy and consumer awareness in this rapidly evolving digital environment. These insights have direct implications for public health policy, digital platform regulation, and consumer education initiatives. Health Risk Awareness Digital Shopping Scale Validation Reliability Figures Figure 1 Figure 2 Introduction Inadequate physical activity is closely associated with many non-communicable diseases (NCDs) such as obesity, cardiovascular diseases and diabetes (Saqib et al, 2020 ; Katzmarzyk et al, 2022 ). Therefore, movement is of great importance in protecting human health. Physical inactivity not only negatively affects individual health, but also imposes a serious economic burden on health systems (Ding et al, 2016 ). According to the results of a study conducted by Shilton et al, ( 2024 ), inactivity costs health systems approximately 67,5 billion dollars each year and it is estimated that this figure may exceed 300 billion dollars in NCD-related expenditures by 2030. Regular physical activity can reduce the risk of cardiovascular disease by up to 35 per cent (Shilton et al., 2024 ). In addition, exercise has positive effects on mental health and reduces symptoms of anxiety and depression (Ströhle, 2009 ; Mikkelsen et al., 2017 ; Suhail, 2021 ). Active individuals generally report higher quality of life and better overall health outcomes (Suhail, 2021 ). However, inactivity is now cited as the leading cause of 500 million new cases of NCDs worldwide by 2030 (Shilton et al., 2024 ). The economic consequences are also profound. Increased health expenditures and productivity losses seriously affect the economies of countries (Torjesen, 2016 ). The widespread use of digital shopping with the effect of technological developments has caused people to remain inactive for longer periods of time. Research shows that individuals exhibit sedentary behaviour for an average of 7 hours 52 minutes a day (Fernate et al., 2024 ). Food shopping, especially through digital media, has changed consumer behaviour; however, this change has unintentionally supported sedentary lifestyles and posed significant health risks. However, although increased digital access seems to be a positive development, it may create inequalities in access to health services, especially for vulnerable groups (Jakobsen, 2024 ). Overall, lifestyle changes brought about by the digital age have profound effects on health. Therefore, developing holistic and inclusive strategies against increasing inactivity is an urgent need both to improve the quality of life of individuals and to ease the burden on health systems (Kumanyika et al, 2008 ; Roubal, 2015 ). The aim of this research is to develop a valid and reliable measurement tool that can measure the health risk awareness of e-food shoppers. By achieving this aim, the knowledge and awareness levels of individuals who shop for food via digital platforms regarding health risks will be revealed. Methods Study Type The current study is a methodological study type because it creates the items of a new measurement tool for the first time, performs item simplification, and follows the validity and reliability processes related to the measurement tool, such as Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). Study Setting and Time This methodological study was conducted online in Türkiye between January 1, 2025 and January 30, 2025 with individuals who voluntarily accepted to participate in the study, could shop for food online in digital environments, and were digitally literate. Study Population, Sampling and Sample Size The universe of the study is individuals who live in Türkiye, speak Turkish, are 18 years old and above, have enough knowledge to shop from digital platforms and are technologically literate. Since it is difficult to reach the entire polulation of the study, a non-probability sampling method, convenience sampling, was used, 206 people were reached with the online survey method conducted with convenience sampling. After the data review and elimination, it was determined that 8 of the collected surveys were missing or answered incorrectly. Therefore, it was thought that the sample size of the study could be conducted with 198 participants, considering that at least 5 participants per item is sufficient for measurement tool development studies (Costello and Osborne, 2005). Initial Item Developments and Expert Opinions In order to develop the measurement tool, an item pool was first created. Since there were no previous studies on the subject, each item was carefully prepared based entirely on expert opinion and the knowledge and experience of the researchers. Each item planned to be included in the scale was created based on the information available and sent to experts in the field to be evaluated. The 27-item draft scale created for the beginning was sent to five experts who are academicians in the nutrition and dietetics departments. The experts made suggestions to change some words and concepts in their evaluations. However, since no expert suggested removing questions, no questions were removed at this stage. Since it was not suggested to remove items in the early stages of the study, this rule was not violated (Clark and Watson, 1995), Pilot Study After expert opinions, the simplified 27-item draft scale was sent to 50 participants by random convenience sampling method for content and understanding. The purpose, scope and relevant ethical documents of the research were presented to the participants. Written consent was obtained from the participants. Participants who volunteered to participate in the research were asked to provide feedback for each item of the scale. After the data collected from the participants were examined, necessary corrections were made within the scope of the feedback. After the pilot study, the scale items were finalized and a 27-item measurement tool ready for the main study was obtained. Measurement Tools and Measurement Procedures A survey form was used in this study. This survey form consists of four sections: 1 . Demographic Questions: This section consists of nine questions regarding height, weight, age, gender, living alone, household income level, family structure, recommending digital food shopping and why to reveal the participant profile. 2 . General Health Questions: This section consists of seven questions regarding general health status assessment, level of satisfaction with life, presence of chronic disease and disability status to reveal the general health status of the participants. 3 . Digital Shopping Questions: This section consists of five questions regarding e-food shopping status, reason for e-food shopping, frequency of e-food shopping, and sensitivity to any food to reveal the participants' digital shopping status. 4 . Health Risk Awareness Scale of E-Food Shoppers: This scale, developed by researchers, is a 27-item measurement tool prepared in a 5-point Likert type as 1=Strongly disagree, 2=Disagree, 3=Undecided, 4=Agree, 5=Strongly agree. This measurement tool aims to measure the health risk awareness of e-food shoppers. Accordingly, high scores obtained from the scale indicate high risk awareness, while low scores indicate low risk awareness. The "R" next to the items in the measurement tool indicates reverse items. The study was conducted with an online survey (Scale Supplamentary). Participants were informed about the research in the introduction part of the survey. Participants were also informed that documents and approvals related to ethics were received. Then, in light of this information, participants were asked to participate in the survey voluntarily and give their consent. Participants who completed all necessary procedures were also included in the main study. Statistical Analysis Within the scope of the research, validity, reliability and psychometric analyses were conducted to develop the new measurement tool. A 5% error margin was accepted for these analyses. Discrete data were reported with percentage and frequency values, and continuous data were reported with mean and standard deviation values. For the assumption of normality, skewness and kurtosis values for the variables were reported, and those between -1.5 and 1.5 were considered normal (Adawi et al., 2018). Analyses were performed with Jamovi Version 2.4 (R Core Team, 2022; The Jamovi Project, 2023). Within the scope of validity of the scale, content validity, structural validity (EFA and CFA), discriminant and criterion validity analyses were conducted, while within the scope of reliability analyses, internal consistency and test-retest analyses were conducted. Within the scope of psychometric analyses, independent sample t-test and ANOVA tests were conducted. Ethical Statement Before starting the research, ethics committee approval was obtained from Dicle University (ERB number: 827677, Date: 12,12,2024). After starting the research, participants were given research information on the first page of the survey form (Scale Supplamentary). Written consent was then obtained for voluntary participation. The Declaration of Helsinki was followed throughout the research (World Medical Association, 2013). Results Participant Information Results Table 1 . Demographic Information and Attitudes Towards E-Food Shopping Variables n % Gender Female 121 61.1 Male 77 38.9 Age Under 31 106 53.5 31 and above 92 46.5 Living Alone Yes 39 19.7 No 159 80.3 Family Type Nuclear family 140 70.7 Extended family 58 29.3 E-Food Recommendation Yes 111 56.1 No 87 43.9 Chronic Disorder Yes 41 20.7 No 157 79.3 Income status My income is equal to my expenses 71 35.9 My income is higher than my expenses 49 24.7 My income is lower than my expenses 78 39.4 Exercise Yes 123 62.1 No 75 37.9 Reason to E-Food Obligation 145 73.2 Preference 53 26.8 Food Sensitivity Yes 45 22.7 No 153 77.3 Body Mass Index Healthy 102 51.5 Unhealthy 96 48.5 General Health Bad 88 44.4 Average 42 21.2 Good 68 34.3 Satisfaction with Life Low 86 43.4 Average 66 33.3 High 46 23.2 Frequency of E-Food Shopping Daily 52 26.3 Weekly 115 58.1 Monthl 31 15.7 When Table 1 is examined, it was determined that the majority of the participants were women, under the average age of 31, did not live alone, lived in a nuclear family, recommended e-food to others, did not have a chronic disease, had lower income than expenses, did exercise, preferred e-food out of necessity, did not have food sensitivities, were healthy according to body mass index, had poor general health, had low satisfaction with life, and frequently shopped for e-food once a week (Table 1). Validity Analysis Content Validity After the items were created, they were sent to five academicians who are experts in their fields for content review. The experts were asked to choose between "appropriate", "needs correction" and "not appropriate" for each item. The answers given by the experts were used for the content validity index (CVI). The answers from the experts formed the content validity index (CVI), CVI is a score consisting of two measurements, the content validity index of the items (ICVI) and the average S-CVI (S-CVI/Ave) (Lynn, 1986). The item content validity index (I-CVI) and average S-CVI (S-CVI/AVE) were calculated based on feedback from experts. Accordingly, I-CVI values were between 0.879-0.893. In addition, the calculated S-CVI/AVE value was found to be 0.91, which is an acceptable value. In light of this information, it was determined that the E-Food Shopper's Health Risk Awareness scale had good content validity (Shi et al., 2012). Construct Validity Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were performed for construct validity. First of all, Bartlett's sphericity test and Kaiser Meyer Olkin (KMO) value, which are necessary for conducting EFA, were examined. Accordingly, the KMO value was found to be 0.764, which was higher than the criterion of 0.60 (Kaiser, 1970). Additionally, Bartlett's test of sphericity was found to be statistically significant (p<0,05) (Barlett, 1954). Since the necessary assumptions were met, EFA analysis was performed with a data of 100 people using the Varimax rotation method and Principal Component Analysis (PCA) to determine the structure of the scale and the factors on which the items were loaded and the factor loadings. Eigenvalue was used for factor determination in the EFA analysis. Accordingly, while the eigenvalue of the factors was accepted as over one, a value of 0.40 was accepted for the factor loading of each item (Iacobucci et al., 2022). Both scree plot and total variance were reported to display the factors. Since three items were loaded on more than one factor at the same time in the EFA analysis, these three items were deleted. As a result of the deleted items, a six-factor structure with 24 items emerged. This structure explained 55.9% of the total variance (Table 2, Figure 1). Table 2 . Factors, Factor Loadings, Items, EFA Assumptions and Explained Variance Item No Items Factor Loadings Factor 1: Hygiene and Food Safety Q15-R The foods I buy online are prepared under healthy conditions 0.742 Q16-R Hygiene conditions are taken into consideration when packaging the foods I buy online 0.882 Q17-R The people responsible for the preparation and transportation of the food I buy online comply with the hygiene conditions and rules 0.684 Q23-R In e-food shopping, situations that may harm health are reported clearly and visibly 0.566 Factor 2: Psychological Well-being Q10-R E-food shopping has a positive effect on my psychological well-being 0.526 Q11-R E-food shopping makes me as happy as physical shopping 0.883 Q12-R E-food shopping satisfies me emotionally as much as physical shopping 0.906 Factor 3: Weight Control and Quick Access Q2-R Thanks to e-food shopping, I can easily adjust meal portions 0.800 Q3-R Thanks to e-food shopping, I can easily adjust the amount of meals I want 0.883 Q4-R E-food platforms offer options for vegetarians 0.479 Q5-R E-food platforms facilitate access to healthy food/nutrients 0.483 Q6-R E-food shopping provides variety on the table by offering different food options for families 0.485 Factor 4: Economical and Time Saving Q24 E-food shopping reduces expenses such as transportation costs 0.578 Q25 E-food shopping allows me to spend more time with myself and my family 0.845 Q26 E-food shopping allows me to spend more time on my personal affairs 0.904 Q27 E-food shopping is more profitable than traditional shopping 0.540 Factor 5: General Health and Social Well-being Q1 E-food shopping makes it harder to control my weight 0.410 Q8 E-food shopping reduces my physical activity 0.708 Q9 E-food shopping negatively affects my overall health in the long run 0.779 Q13 E-food shopping negatively affects my social well-being 0.409 Q14 Platforms that provide e-food shopping psychologically force people to shop 0.551 Q18 Platforms that provide digital food shopping do not take people's health into consideration 0.405 Factor 6: Label Information and Control Q20 I check whether the necessary warnings are included in the products marketed through e-food shopping platforms 0.857 Q21 I check whether the product labels marketed through e-food shopping channels contain correct and necessary information 0.835 KMO=0 . 764 Barlett’s Test of Sphericity=(X 2 153=9840; p<0 . 001) Explained Variance=55 . 9% In order to verify the structure that emerged after EFA within the scope of construct validity, CFA analysis was conducted with a data of 98 people. This analysis confirmed the 24-item six-factor scale structure with good goodness of fit values (Epskamp, 2022; Fox and Weisberg, 2020; Gallucci, 2021; Gallucci and Jentschke, 2021; Jorgensen et al., 2022; Revelle, 2023; Rosseel, 2012). Goodness of fit values obtained from the CFA analysis were reported (Hair et al., 2009). Accordingly, the Chi-square (χ2)/degree of freedom (sd) (χ2/df) value reported for the validity of the model was found to be 2.77. This value is smaller than the threshold value of three. Accordingly, it can be said that the goodness of fit values are valid. From the reported goodness of fit values, Root Mean Square of Approximation (RMSEA) = 0.06 and Standardized Root Mean Square of Residual (SRMR) = 0.07 were found. These values are below the critical value of 0.08 (Edwards, 2015). Some other reported goodness of fit values are reported as Goodness of Fit Index (GFI) = 0.91, Comparative Fit Index (CFI) = 0.96 and Bollen's Incremental Fit Index (IFI) = 0.96. Since all values exceed the critical value of 0.90, it shows that the relevant scale is in good fit (Hu and Bentler, 1951) (Figure 2). The lowest score that can be obtained from the Health Risk Awareness of E-Food Shopper scale, which emerged as a 24-item, six-factor structure as a result of CFA, is 24, while the highest score is 120. High scores indicate high health risk awareness. Discriminant Validity Another validity examined within the scope of construct validity is discriminant validity. The mean score of the scale was found and then a two-choice variable was assigned for below (0) and above (1) the mean. Whether the mean score of the scale changed according to this two-choice variable was examined with an independent sample t-test. According to the t-test result, a difference was found between the two groups (p<0,05). This showed that the scale had discriminant validity. Detailed information is reported in Table 3. Table 3 . Results of Independent Sample T-Test for Discriminant Validity Items Low group (n=106) Mean(sd) High group (n=92 Mean(sd) t p Q1 2.60(0.91) 3.26(1.37) -4.026 < .001 Q2-R 3.06(0.89) 3.87(1.03) -5.952 < .001 Q3-R 2.90(0.92) 3.88(1.03) -7.138 < .001 Q4-R 2.41(1.03) 3.50(1.23) -0.588 < .001 Q5-R 2.78(0.87) 3.70(1.21) -6.137 < .001 Q6-R 2.42(1.07) 3.09(0.90) -4.318 < .001 Q8 3.51(1.16) 4.23(1.07) -4.515 < .001 Q9 3.02(1.00) 4.12(0.97) -7.812 < .001 Q10-R 2.68(0.90) 3.59(1.11) -6.348 <.001 Q11-R 2.81(0.90) 4.00(1.11) -8.295 < .001 Q12-R 2.76(0.91) 4.01(1.03) -9.028 < .001 Q13 2.62(0.85) 3.16(1.23) -3.634 < .001 Q14 2.97(1.10) 3.75(1.23) -4.705 < .001 Q15-R 3.02(0.72) 3.99(0.91) -8.395 < .001 Q16-R 2.97(0.71) 3.87(0.90) -7.817 < .001 Q17-R 3.05(0.68) 3.88(0.85) -7.653 < .001 Q18 2.71(0.72) 3.39(1.10) -5.248 < .001 Q20 3.40(1.00) 3.85(1.24) 0.919 < .001 Q21 2.92(0.87) 2.40(1.05) 3.753 < .001 Q23-R 3.13(1.07) 3.46(1.25) -1.966 < .001 Q24 3.40(1.00) 3.75(1.24) -1.087 < .001 Q25 3.13(1.05) 3.92(1.32) -1.825 < .001 Q26 3.28(1.06) 3.69(1.29) 2.969 < .001 Q27 2.93(0.96) 2.48(1.20) 3.753 < .001 Reliability Analysis Internal Consistency Reliability The Cronbach alpha value reported for internal consistency reliability was found to be 0.79 for the entire scale. The sub-factors were found to be 0.85 for factor 1, 0.87 for factor 2, 0.77 for factor 3, 0.78 for factor 4, 0.72 for factor 5, and 0.87 for factor 6. All of these values were found to be higher than the critical value of 0.70 (Cronbach, 1951). These findings indicated high internal consistency. The corrected item-total correlation, which shows the correlation of each item of the scale with the total of the other items, was found to be higher than the critical value of 0.3 (Ferketich, 1991). Construct validity can be tested by using convergent and divergent validity tests based on average variance extracted (AVE) values. Composite reliability (CR) value can be considered as an alternative to Cronbach's Alpha value. Essentially, the CR value, which is stronger than Cronbach's Alpha, should be above 0.70. while AVE value is expected to greater than 0.50 (Naktiyok&Zengin, 2021; Keskin et al, 2023). The findings are reported in Table 4. Table 4 . Scale, Factors and Items Reliability Results Factor/Items Mean SD Item-rest correlation If item dropped Cronbach's α Composite Reliability (CR) Average Variance Extracted (AVE) Scale 70.76 9.75 - 0.79 Factor 1 13.62 3.09 - 0.85 0.81 0.53 Factor 2 9.81 3.03 - 0.87 0.83 0.63 Factor 3 15.18 3.99 - 0.77 0.83 0.42 Factor 4 12.65 3.58 - 0.78 0.82 0.54 Factor 5 19.52 4.39 - 0.72 0.72 0.32 Factor 6 6.84 2.06 - 0.87 0.83 0.72 Q1 2.91 1.19 0.18 0.74 - - Q2 3.43 1.04 0.43 0.72 - - Q3 3.35 1.08 0.51 0.72 - - Q4 2.45 1.12 0.10 0.74 - - Q5 3.21 1.14 0.43 0.72 - - Q6 2.73 1.12 0.32 0.73 - - Q8 3.84 1.17 0.31 0.73 - - Q9 3.53 1.13 0.49 0.72 - - Q10 3.10 1.10 0.39 0.72 - - Q11 3.36 1.17 0.46 0.72 - - Q12 3.34 1.15 0.49 0.72 - - Q13 2.87 1.08 0.31 0.73 - - Q14 3.33 1.22 0.35 0.73 - - Q15 3.47 0.94 0.53 0.72 - - Q16 3.39 0.92 0.51 0.72 - - Q18 3.43 0.87 0.51 0.72 - - Q19 3.03 0.97 0.40 0.72 - - Q20 3.33 1.12 0.03 0.75 - - Q21 3.51 1.08 0.14 0.74 - - Q23 3.32 0.99 0.17 0.74 - - Q24 3.28 1.17 0.14 0.74 - - Q25 3.22 1.18 0.02 0.75 - - Q26 3.42 1.18 0.04 0.75 - - Q27 2.72 1.10 0.30 0.77 - - Test-Retest Reliability Another test conducted within the scope of reliability is the test-retest analysis. A second test was conducted with 30 participants whose information was obtained in the main application, with two 15-day intervals. The correlation values between both tests were reported. The Pearson correlation coefficient was found to be 0.798. This value was found to be higher than the critical value of 0.70. In this case, it can be stated that the scale has a good test-retest reliability (Devon et al., 2007). Psychometric Analysis Before the psychometric tests, normality assumptions were examined. Accordingly, it was understood that the skewness and kurtosis values of the scale and its sub-dimensions showed a normal distribution since they were between -1.5 and +1.5 (Adawi et al., 2018) (Table 5). Table 5. Descriptive and Normality Results Statistics Total Scale Hygiene and Food Safety Psychological Well-being Weight Control and Quick Access Economical and Time Saving General Health and Social Well-being Label Information and Control Mean 70.8 13.6 9.81 15.2 12.6 19.5 6.84 Sd 9.75 3.09 3.03 3.99 3.58 4.39 2.06 Skewness 0.69 0.45 0.02 0.43 -0.30 -0.31 -0.38 Kurtosis 1.06 0.01 -0.57 0.10 -0.03 0.62 -0.17 Table 6 shows the psychometric test results of the scale. The findings showed that the health risk awareness of e-food shoppers showed statistically significant differences according to age, e-food recommendation, e-food reason and e-food frequency (p<0.05). Detailed information is given in Table 6. Table 6. Psychometric Analysis Results of the Scale Variables Mean Sd t/F p Differences Gender Female 52.3 7.53 0.319 0.750 Male 51.9 7.84 Age Under 31 50.1 6.64 3.94 <0.001*** 31 and above 54.3 8.25 Living Alone Yes 52.0 7.41 0.050 0.960 No 52.1 7.79 Family Type Nuclear family 52.0 7.78 0.035 0.972 Extended family 52.1 7.57 E-Food Recommendation Yes 50.9 7.01 3.56 0.001*** No 55.2 8.66 Chronic Disorder Yes 51.3 7.49 0.710 0.478 No 52.3 7.77 Income status My income is equal to my expenses 53.1 8.01 2.24 0.111 My income is higher than my expenses 52.8 7.94 My income is lower than my expenses 50.7 7.13 Exercise Yes 52.3 7.88 0.667 0.505 No 51.6 7.43 Reason to E-Food Obligation 49.6 6.65 5.36 <0.001*** Preference 55.2 7.87 Food Sensitivity Yes 53.0 6.89 0.970 0.333 No 51.8 7.92 Body Mass Index Healthy 51.4 7.84 1.32 0.190 Unhealthy 52.8 7.52 General Health Bad 51.6 7.73 Average 50.9 8.31 1.74 0.180 Good 53.4 7.19 Satisfaction with Life Low 52.0 7.49 Average 52.1 7.56 0.005 0.995 High 52.0 8.42 Frequency of E-Food Shopping Daily 55.7 9.12 Weekly 51.3 6.88 8.91 (2) (3) Monthl 48.8 5.65 Note: * p < .05, ** p < . 01, *** p < .001; t= Independent Sample t-test, F=One-Way ANOVA, Sd=Standard Deviation Discussion The present study aimed to develop and validate a scale to assess health risk awareness among individuals who shop for food via digital platforms. The resulting six-factor structure comprises: (1) Hygiene and Food Safety, (2) Psychological Well-being, (3) Weight Control and Quick Access, (4) Economical and Time Saving, (5) General Health and Social Well-being, and (6) Label Information and Control. These dimensions reflect the complex interplay between cognitive, behavioral, and environmental variables that shape consumer awareness and perception of health-related risks in e-food contexts. The dimension of hygiene and food safety is among the most frequently emphasized concerns in consumer behavior literature. Studies across cultures report that perceptions of food hygiene strongly affect consumer trust, purchase decisions, and satisfaction with food delivery services. For instance, Ungku Fatimah et al. (2011) found that hygiene standards in foodservice environments are critical predictors of customer retention and satisfaction. Similarly, Galati et al. (2019) highlighted how risk perception tied to hygienic preparation influences consumer acceptance of novel or digitally mediated food offerings. These findings are consistent with our results and affirm the validity of including hygiene and food safety as a key construct. Psychological well-being dimension indicates the relationship between emotional state and food choice, which is another well-documented phenomenon. Several studies in the reviewed literature support the notion that stress, anxiety, and psychological discomfort can drive food-related decisions in digital environments. For example, Marx et al. (2021) conducted a scoping review showing how emotional states modulate food consumption, particularly under conditions like the COVID-19 lockdown. This supports the inclusion of psychological well-being in our model and illustrates how e-food platforms may serve both functional and emotional needs. Weight control and quick access is the third factor and relates to the increasing role of personalization and speed in digital food environments. Studies such as Coffino et al. (2020) demonstrate that consumers actively seek nutrition-focused filters and diet-friendly options when shopping online. This reflects the dual motivation of health monitoring and convenience, which our scale captures under a unified construct. These behaviors were particularly emphasized during the pandemic, where digital food services had to balance health consciousness with rapid delivery. Economical and time saving factor is related with economic considerations that remain a strong determinant in food choice, particularly for lower-income or time-pressed consumers. Salis et al. (2015) showed that perceptions of affordability and time-efficiency significantly drive customer loyalty and frequency of use in online food services. Our findings align with these results, reinforcing the relevance of this factor in capturing pragmatic motivations linked to perceived risk trade-offs. General health and social well-being factor is supported by evidence from studies addressing the broader lifestyle implications of e-food consumption. For instance, Algheshairy et al. (2022) demonstrated that frequent use of food delivery applications correlates with decreased physical activity and a decline in general health indicators among users. This dimension thus adds depth to our scale by incorporating long-term health perspectives beyond immediate food safety concerns. Label information and control is the last factor in our model. The importance of label transparency and the ability to control nutritional intake through informed choices is underscored in several studies. Uggioni and Salay (2014) found that consumers who understand and trust food labeling systems are more likely to make health-conscious decisions. This validates the inclusion of label awareness and information control as a distinct and essential component of health risk awareness in e-food shopping. The findings also revealed that awareness levels regarding health risks such as physical inactivity, poor diet, and long-term non-communicable disease (NCD) consequences remain limited among frequent digital food shoppers. This is particularly concerning given the increasing shift toward digitalized lifestyles and the accompanying reduction in physical activity. Our results align with previous findings indicating that digital convenience often contributes to unhealthy behaviors. Mertens et al. (2021) found that during COVID-19 lockdowns, individuals displayed increased sedentary behavior and less favorable dietary choices-especially students and young adults—supporting our observation that digital shopping may indirectly promote lifestyle-related health risks. Similarly, Lyzwinski et al. (2018) demonstrated a clear association between stress and maladaptive eating behaviors among university students, emphasizing how digital consumption under stress can deteriorate food choices and physical activity. Our results resonate with prior research by Algheshairy et al. (2022), who reported that increased usage of food delivery applications during the COVID-19 pandemic led to negative dietary behaviors, particularly among adult Saudi females. Moreover, Lupton (2021) highlighted how food delivery apps promote narratives of ease and satisfaction, while downplaying potential long-term health consequences. Our findings suggest that users often fail to perceive these latent risks—indicating the need for transparent communication mechanisms within these platforms. This finding is directly aligned with our participants' reported lack of risk awareness. Poelman et al. (2018) also found that low food literacy correlates with impulsivity and poor dietary outcomes, which reinforces the necessity of integrating food literacy principles into digital consumption awareness models. Saqib et al. (2020) and Katzmarzyk et al. (2022) both stressed that physical inactivity remains a top modifiable risk factor for chronic disease, affirming the critical role of movement awareness—a key dimension of our scale. Complementing this, Ströhle (2009) reviewed that physical activity reduces depression and anxiety symptoms, offering mental health benefits often overlooked in digital food culture. Similarly, Barcın-Güzeldere and Devrim-Lanpir (2022) found a significant relationship between emotional eating and higher BMI during partial quarantine, supporting the notion that digital lifestyles exacerbate stress-related and unhealthy eating behaviors. Aucoin et al. (2021) emphasized the relationship between dietary quality and mental health, reporting that unhealthy dietary patterns are significantly associated with increased anxiety and poor psychological well-being. This indirectly supports our findings by highlighting the broader implications of poor dietary awareness and behavior, which may extend into mental health domains, further reinforcing the need for integrated lifestyle awareness campaigns. Consistent with the literature, our scale reveals a correlation between digital food purchasing and low awareness regarding hygiene and food safety practices. This is particularly important considering findings from Grimes et al. (2017), who reported insufficient public understanding of salt intake guidelines despite widespread information campaigns. Likewise, Galati et al. (2019) emphasized that consumer risk perception significantly influences the acceptance of food technologies-suggesting that digital consumers may lack the literacy to assess food safety indicators critically. The current study's outcomes also resonate with Poelman et al. (2018), who found that individuals with higher food literacy exhibit healthier dietary patterns, greater self-control, and less impulsiveness. The relatively low scores observed in our awareness scale, especially in dimensions related to general health and psychological well-being, likely stem from insufficient food literacy-a conclusion that aligns with prior theoretical models of health behavior change. Wyse et al. (2021) demonstrated that behavioral nudges embedded in school canteen ordering systems significantly reduced energy and saturated fat intake. This again reinforces the necessity of embedding health-oriented awareness tools directly into the user interfaces of digital food platforms. This opens the possibility of using our validated scale to guide health-based modifications on digital food platforms. However, contrasting perspectives do exist. For instance, Flanagan and Soon-Sinclair (2025) found that online consumers can become more hygiene-aware when digital platforms clearly communicate food safety standards. This discrepancy suggests that the impact of digital engagement on health awareness may be context-dependent-shaped by platform design, user demographics, and regulatory visibility. Moreover, Alotaibi et al. (2021) demonstrated that interactive digital educational tools such as social media and workshops effectively enhance healthy lifestyle awareness among health sciences students during the pandemic. Erfanian et al. (2024) also emphasize that consumer awareness and preferences for plant-based alternatives, driven by health and environmental concerns, can reshape food systems toward sustainability when properly informed. However, such transformative potential requires deliberate consumer education, which our findings indicate is often lacking. This suggests a possible intervention path: the very digital platforms that contribute to sedentary lifestyles and poor dietary habits could also serve as tools for disseminating corrective health education, especially if tailored to target risk perceptions and health literacy. Other contextual factors also influence health behaviors. Gerards et al. (2016) illustrated that family nutrition climate and general parenting styles significantly correlate with children’s BMI and dietary outcomes, underscoring the importance of psychosocial variables when analyzing food-related behaviors in digital contexts. Finally, while the digital environment may encourage unhealthy consumption behaviors, it also holds potential for health promotion. Wyse et al. (2021) demonstrated that behavioral nudges embedded in online food ordering platforms can significantly reduce caloric intake and saturated fat levels. These findings point toward the viability of using our validated scale as a feedback tool to inform real-time health interventions on digital shopping platforms. The scale also may serve as a valuable instrument in both consumer education and public health strategy development. Conclusion This study presents a newly developed and psychometrically validated scale designed to measure health risk awareness among digital food shoppers. Findings indicate that while consumers increasingly rely on digital food platforms for convenience, they often underestimate the accompanying health risks-such as reduced physical activity, emotional eating, and food safety concerns. Our scale provides a robust tool for identifying gaps in health literacy and consumer awareness in this rapidly evolving digital environment. These insights have direct implications for public health policy, digital platform regulation, and consumer education initiatives. To mitigate the unintended consequences of digital food purchasing, interventions should target behavioral change through integrated digital health education. App-based nudges, clear food labeling, and awareness prompts could be embedded within shopping interfaces to guide healthier consumer choices. Moreover, public health strategies must extend beyond individual behavior and include collaborative efforts with digital platform designers and regulatory authorities. Future research should focus on longitudinal validation of the scale, cross-cultural adaptations, and the development of scalable digital interventions informed by its findings. Ultimately, aligning technological convenience with informed, health-conscious behavior is not only possible but essential for sustaining well-being in the digital age. Abbreviations None Declarations Ethics approval and consent to participate Ethical approval was obtained with the approval of Dicle University Social and Human Sciences Ethics Committee dated 12,12,2024 and numbered 827677. All study participants gave written, informed consent prior to taking part. All processes were carried out in accordance with the relevant guidelines and regulations of the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are not publicly available to protect anonymity, but are available on request from the corresponding author (Mehmet Aziz ÇAKMAK). Competing interests The authors declare no competing interests. Funding None Authors’ contributions Conceptualization: MAÇ, YÇ. Methodology: HÇ, YÇ. Formal analysis and investigation: HÇ., MAÇ. Writing—original draft preparation: MAÇ, HÇ. Writing—review and editing: YÇ, HÇ,MAÇ. Resources: MAÇ. Supervision: YÇ, HÇ. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6959514","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490214930,"identity":"7427f706-ddff-4c3a-8268-9faec9bcf46e","order_by":0,"name":"Yusuf Çelik","email":"","orcid":"","institution":"Acıbadem University","correspondingAuthor":false,"prefix":"","firstName":"Yusuf","middleName":"","lastName":"Çelik","suffix":""},{"id":490214931,"identity":"41d742a3-1ecb-48b4-a7b2-cf8b5b4102b1","order_by":1,"name":"Mehmet Aziz Çakmak","email":"data:image/png;base64,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","orcid":"","institution":"Mardin Artuklu University","correspondingAuthor":true,"prefix":"","firstName":"Mehmet","middleName":"Aziz","lastName":"Çakmak","suffix":""},{"id":490214932,"identity":"113ebcc7-8f99-4663-b75e-a9abd13ab661","order_by":2,"name":"Haşim Çapar","email":"","orcid":"","institution":"Dicle University","correspondingAuthor":false,"prefix":"","firstName":"Haşim","middleName":"","lastName":"Çapar","suffix":""}],"badges":[],"createdAt":"2025-06-23 19:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6959514/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6959514/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87708101,"identity":"744a018b-b5a9-4d05-9461-45b38ded04bf","added_by":"auto","created_at":"2025-07-28 08:13:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":56323,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScree Plot of Exploratory Factor Analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6959514/v1/acc530a7843d158cc7aa6152.png"},{"id":87708104,"identity":"7215b2bb-1b44-4f41-956e-849b6754b313","added_by":"auto","created_at":"2025-07-28 08:13:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":159563,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePath Diagram of Confirmatory Factor Analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6959514/v1/10a5deae300ebc676b0a125d.png"},{"id":87709950,"identity":"6173b120-4366-44ba-b61a-9725b3f07d28","added_by":"auto","created_at":"2025-07-28 08:29:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1770593,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6959514/v1/87497c83-25a4-48b9-ad62-3f74cfe27b26.pdf"},{"id":87708573,"identity":"76d89b52-8b7d-4db1-82e1-02f6cf974a34","added_by":"auto","created_at":"2025-07-28 08:21:58","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":22530,"visible":true,"origin":"","legend":"","description":"","filename":"scalesupplamentary.docx","url":"https://assets-eu.researchsquare.com/files/rs-6959514/v1/ca822759213515ffdc834052.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Health Risk Awareness Scale of Food Shoppers on Digital Shopping Platforms: A Validation Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInadequate physical activity is closely associated with many non-communicable diseases (NCDs) such as obesity, cardiovascular diseases and diabetes (Saqib et al, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Katzmarzyk et al, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, movement is of great importance in protecting human health. Physical inactivity not only negatively affects individual health, but also imposes a serious economic burden on health systems (Ding et al, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). According to the results of a study conducted by Shilton et al, (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), inactivity costs health systems approximately 67,5\u0026nbsp;billion dollars each year and it is estimated that this figure may exceed 300\u0026nbsp;billion dollars in NCD-related expenditures by 2030.\u003c/p\u003e\u003cp\u003eRegular physical activity can reduce the risk of cardiovascular disease by up to 35 per cent (Shilton et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In addition, exercise has positive effects on mental health and reduces symptoms of anxiety and depression (Str\u0026ouml;hle, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Mikkelsen et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Suhail, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Active individuals generally report higher quality of life and better overall health outcomes (Suhail, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, inactivity is now cited as the leading cause of 500\u0026nbsp;million new cases of NCDs worldwide by 2030 (Shilton et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The economic consequences are also profound. Increased health expenditures and productivity losses seriously affect the economies of countries (Torjesen, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe widespread use of digital shopping with the effect of technological developments has caused people to remain inactive for longer periods of time. Research shows that individuals exhibit sedentary behaviour for an average of 7 hours 52 minutes a day (Fernate et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Food shopping, especially through digital media, has changed consumer behaviour; however, this change has unintentionally supported sedentary lifestyles and posed significant health risks. However, although increased digital access seems to be a positive development, it may create inequalities in access to health services, especially for vulnerable groups (Jakobsen, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Overall, lifestyle changes brought about by the digital age have profound effects on health. Therefore, developing holistic and inclusive strategies against increasing inactivity is an urgent need both to improve the quality of life of individuals and to ease the burden on health systems (Kumanyika et al, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Roubal, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe aim of this research is to develop a valid and reliable measurement tool that can measure the health risk awareness of e-food shoppers. By achieving this aim, the knowledge and awareness levels of individuals who shop for food via digital platforms regarding health risks will be revealed.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy Type\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe current study is a methodological study type because it creates the items of a new measurement tool for the first time, performs item simplification, and follows the validity and reliability processes related to the measurement tool, such as Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Setting and Time\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis methodological study was conducted online in Türkiye between January 1, 2025 and January 30, 2025 with individuals who voluntarily accepted to participate in the study, could shop for food online in digital environments, and were digitally literate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy Population, Sampling and Sample Size\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe universe of the study is individuals who live in Türkiye, speak Turkish, are 18 years old and above, have enough knowledge to shop from digital platforms and are technologically literate. Since it is difficult to reach the entire polulation of the study, a non-probability sampling method, convenience sampling, was used, 206 people were reached with the online survey method conducted with convenience sampling. After the data review and elimination, it was determined that 8 of the collected surveys were missing or answered incorrectly. Therefore, it was thought that the sample size of the study could be conducted with 198 participants, considering that at least 5 participants per item is sufficient for measurement tool development studies (Costello and Osborne, 2005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInitial Item Developments and Expert Opinions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to develop the measurement tool, an item pool was first created. Since there were no previous studies on the subject, each item was carefully prepared based entirely on expert opinion and the knowledge and experience of the researchers. Each item planned to be included in the scale was created based on the information available and sent to experts in the field to be evaluated.\u003c/p\u003e\n\u003cp\u003eThe 27-item draft scale created for the beginning was sent to five experts who are academicians in the nutrition and dietetics departments. The experts made suggestions to change some words and concepts in their evaluations. However, since no expert suggested removing questions, no questions were removed at this stage. Since it was not suggested to remove items in the early stages of the study, this rule was not violated (Clark and Watson, 1995),\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePilot Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter expert opinions, the simplified 27-item draft scale was sent to 50 participants by random convenience sampling method for content and understanding. The purpose, scope and relevant ethical documents of the research were presented to the participants. Written consent was obtained from the participants. Participants who volunteered to participate in the research were asked to provide feedback for each item of the scale. After the data collected from the participants were examined, necessary corrections were made within the scope of the feedback. After the pilot study, the scale items were finalized and a 27-item measurement tool ready for the main study was obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement Tools and Measurement Procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA survey form was used in this study. This survey form consists of four sections:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e. Demographic Questions:\u0026nbsp;\u003c/strong\u003eThis section consists of nine questions regarding height, weight, age, gender, living alone, household income level, family structure, recommending digital food shopping and why to reveal the participant profile.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e. General Health Questions:\u0026nbsp;\u003c/strong\u003eThis section consists of seven questions regarding general health status assessment, level of satisfaction with life, presence of chronic disease and disability status to reveal the general health status of the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003cstrong\u003e. Digital Shopping Questions:\u0026nbsp;\u003c/strong\u003eThis section consists of five questions regarding e-food shopping status, reason for e-food shopping, frequency of e-food shopping, and sensitivity to any food to reveal the participants' digital shopping status.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e. Health Risk Awareness Scale of E-Food Shoppers:\u0026nbsp;\u003c/strong\u003eThis scale, developed by researchers, is a 27-item measurement tool prepared in a 5-point Likert type as 1=Strongly disagree, 2=Disagree, 3=Undecided, 4=Agree, 5=Strongly agree. This measurement tool aims to measure the health risk awareness of e-food shoppers. Accordingly, high scores obtained from the scale indicate high risk awareness, while low scores indicate low risk awareness. The \"R\" next to the items in the measurement tool indicates reverse items. The study was conducted with an online survey (Scale Supplamentary). Participants were informed about the research in the introduction part of the survey. Participants were also informed that documents and approvals related to ethics were received. Then, in light of this information, participants were asked to participate in the survey voluntarily and give their consent. Participants who completed all necessary procedures were also included in the main study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWithin the scope of the research, validity, reliability and psychometric analyses were conducted to develop the new measurement tool. A 5% error margin was accepted for these analyses. Discrete data were reported with percentage and frequency values, and continuous data were reported with mean and standard deviation values. For the assumption of normality, skewness and kurtosis values for the variables were reported, and those between -1.5 and 1.5 were considered normal (Adawi et al., 2018). Analyses were performed with Jamovi Version 2.4 (R Core Team, 2022; The Jamovi Project, 2023). Within the scope of validity of the scale, content validity, structural validity (EFA and CFA), discriminant and criterion validity analyses were conducted, while within the scope of reliability analyses, internal consistency and test-retest analyses were conducted. Within the scope of psychometric analyses, independent sample t-test and ANOVA tests were conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore starting the research, ethics committee approval was obtained from Dicle University (ERB number: 827677, Date: 12,12,2024). After starting the research, participants were given research information on the first page of the survey form (Scale Supplamentary). Written consent was then obtained for voluntary participation. The Declaration of Helsinki was followed throughout the research (World Medical Association, 2013).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant Information Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cstrong\u003e. Demographic Information and Attitudes Towards E-Food Shopping\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnder 31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46.5\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eLiving Alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e80.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eFamily Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNuclear family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e70.7\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExtended family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e29.3\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eE-Food Recommendation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e56.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eChronic Disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e20.7\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79.3\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eIncome status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMy income is equal to my expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e35.9\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMy income is higher than my expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e24.7\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMy income is lower than my expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e39.4\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eExercise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62.1\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37.9\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eReason to E-Food\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObligation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73.2\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePreference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.8\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eFood Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.7\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e77.3\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnhealthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGeneral Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e44.4\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.2\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSatisfaction with Life\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e43.4\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e33.3\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.2\u0026thinsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFrequency of E-Food Shopping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDaily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWeekly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e58.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMonthl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWhen Table 1 is examined, it was determined that the majority of the participants were women, under the average age of 31, did not live alone, lived in a nuclear family, recommended e-food to others, did not have a chronic disease, had lower income than expenses, did exercise, preferred e-food out of necessity, did not have food sensitivities, were healthy according to body mass index, had poor general health, had low satisfaction with life, and frequently shopped for e-food once a week (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidity Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eContent Validity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter the items were created, they were sent to five academicians who are experts in their fields for content review. The experts were asked to choose between \u0026quot;appropriate\u0026quot;, \u0026quot;needs correction\u0026quot; and \u0026quot;not appropriate\u0026quot; for each item. The answers given by the experts were used for the content validity index (CVI). The answers from the experts formed the content validity index (CVI), CVI is a score consisting of two measurements, the content validity index of the items (ICVI) and the average S-CVI (S-CVI/Ave) (Lynn, 1986). The item content validity index (I-CVI) and average S-CVI (S-CVI/AVE) were calculated based on feedback from experts. Accordingly, I-CVI values were between 0.879-0.893. In addition, the calculated S-CVI/AVE value was found to be 0.91, which is an acceptable value. In light of this information, it was determined that the E-Food Shopper\u0026apos;s Health Risk Awareness scale had good content validity (Shi et al., 2012).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConstruct Validity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were performed for construct validity. First of all, Bartlett\u0026apos;s sphericity test and Kaiser Meyer Olkin (KMO) value, which are necessary for conducting EFA, were examined. Accordingly, the KMO value was found to be 0.764, which was higher than the criterion of 0.60 (Kaiser, 1970). Additionally, Bartlett\u0026apos;s test of sphericity was found to be statistically significant (p\u0026lt;0,05) (Barlett, 1954). Since the necessary assumptions were met, EFA analysis was performed with a data of 100 people using the Varimax rotation method and Principal Component Analysis (PCA) to determine the structure of the scale and the factors on which the items were loaded and the factor loadings. Eigenvalue was used for factor determination in the EFA analysis. Accordingly, while the eigenvalue of the factors was accepted as over one, a value of 0.40 was accepted for the factor loading of each item (Iacobucci et al., 2022). Both scree plot and total variance were reported to display the factors. Since three items were loaded on more than one factor at the same time in the EFA analysis, these three items were deleted. As a result of the deleted items, a six-factor structure with 24 items emerged. This structure explained 55.9% of the total variance (Table 2, Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cstrong\u003e. Factors, Factor Loadings, Items, EFA Assumptions and Explained Variance\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"601\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItem No\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eItems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eFactor Loadings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFactor 1: Hygiene and Food Safety\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ15-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe foods I buy online are prepared under healthy conditions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ16-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHygiene conditions are taken into consideration when packaging the foods I buy online\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.882\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ17-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThe people responsible for the preparation and transportation of the food I buy online comply with the hygiene conditions and rules\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ23-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIn e-food shopping, situations that may harm health are reported clearly and visibly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFactor 2: Psychological Well-being\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ10-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE-food shopping has a positive effect on my psychological well-being\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.526\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ11-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE-food shopping makes me as happy as physical shopping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ12-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE-food shopping satisfies me emotionally as much as physical shopping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFactor 3: Weight Control and Quick Access\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ2-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThanks to e-food shopping, I can easily adjust meal portions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ3-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThanks to e-food shopping, I can easily adjust the amount of meals I want\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ4-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE-food platforms offer options for vegetarians\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ5-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE-food platforms facilitate access to healthy food/nutrients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ6-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE-food shopping provides variety on the table by offering different food options for families\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFactor 4: Economical and Time Saving\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE-food shopping reduces expenses such as transportation costs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE-food shopping allows me to spend more time with myself and my family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.845\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE-food shopping allows me to spend more time on my personal affairs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE-food shopping is more profitable than traditional shopping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFactor 5: General Health and Social Well-being\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE-food shopping makes it harder to control my weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE-food shopping reduces my physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE-food shopping negatively affects my overall health in the long run\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE-food shopping negatively affects my social well-being\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlatforms that provide e-food shopping psychologically force people to shop\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.551\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePlatforms that provide digital food shopping do not take people\u0026apos;s health into consideration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eFactor 6: Label Information and Control\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI check whether the necessary warnings are included in the products marketed through e-food shopping platforms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eQ21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eI check whether the product labels marketed through e-food shopping channels contain correct and necessary information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003eKMO=0\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e764\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003eBarlett\u0026rsquo;s Test of Sphericity=(X\u003csup\u003e2\u003c/sup\u003e153=9840; p\u0026lt;0\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e001)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003eExplained Variance=55\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e9%\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn order to verify the structure that emerged after EFA within the scope of construct validity, CFA analysis was conducted with a data of 98 people. This analysis confirmed the 24-item six-factor scale structure with good goodness of fit values (Epskamp, 2022; Fox and Weisberg, 2020; Gallucci, 2021; Gallucci and Jentschke, 2021; Jorgensen et al., 2022; Revelle, 2023; Rosseel, 2012). Goodness of fit values obtained from the CFA analysis were reported (Hair et al., 2009). Accordingly, the Chi-square (\u0026chi;2)/degree of freedom (sd) (\u0026chi;2/df) value reported for the validity of the model was found to be 2.77. This value is smaller than the threshold value of three. Accordingly, it can be said that the goodness of fit values are valid. From the reported goodness of fit values, Root Mean Square of Approximation (RMSEA) = 0.06 and Standardized Root Mean Square of Residual (SRMR) = 0.07 were found. These values are below the critical value of 0.08 (Edwards, 2015). Some other reported goodness of fit values are reported as Goodness of Fit Index (GFI) = 0.91, Comparative Fit Index (CFI) = 0.96 and Bollen\u0026apos;s Incremental Fit Index (IFI) = 0.96. Since all values exceed the critical value of 0.90, it shows that the relevant scale is in good fit (Hu and Bentler, 1951) (Figure 2). The lowest score that can be obtained from the Health Risk Awareness of E-Food Shopper scale, which emerged as a 24-item, six-factor structure as a result of CFA, is 24, while the highest score is 120. High scores indicate high health risk awareness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDiscriminant Validity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnother validity examined within the scope of construct validity is discriminant validity. The mean score of the scale was found and then a two-choice variable was assigned for below (0) and above (1) the mean. Whether the mean score of the scale changed according to this two-choice variable was examined with an independent sample t-test. According to the t-test result, a difference was found between the two groups (p\u0026lt;0,05). This showed that the scale had discriminant validity. Detailed information is reported in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003cstrong\u003e. Results of Independent Sample T-Test for Discriminant Validity\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eItems\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow group (n=106)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean(sd)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh group (n=92\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMean(sd)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.60(0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.26(1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-4.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ2-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.06(0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.87(1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ3-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.90(0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.88(1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-7.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ4-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.41(1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.50(1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ5-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.78(0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.70(1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-6.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ6-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.42(1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.09(0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-4.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.51(1.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.23(1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-4.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.02(1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.12(0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-7.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ10-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.68(0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.59(1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-6.348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ11-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.81(0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.00(1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-8.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ12-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.76(0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.01(1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-9.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.62(0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.16(1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-3.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.97(1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.75(1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-4.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ15-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.02(0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.99(0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-8.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ16-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.97(0.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.87(0.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-7.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ17-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.05(0.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.88(0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-7.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.71(0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.39(1.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-5.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.40(1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.85(1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.92(0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.40(1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ23-R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.13(1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.46(1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.40(1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.75(1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.13(1.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.92(1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.28(1.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.69(1.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eQ27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.93(0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.48(1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eReliability Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eInternal Consistency Reliability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Cronbach alpha value reported for internal consistency reliability was found to be 0.79 for the entire scale. The sub-factors were found to be 0.85 for factor 1, 0.87 for factor 2, 0.77 for factor 3, 0.78 for factor 4, 0.72 for factor 5, and 0.87 for factor 6. All of these values were found to be higher than the critical value of 0.70 (Cronbach, 1951). These findings indicated high internal consistency.\u0026nbsp;The corrected item-total correlation, which shows the correlation of each item of the scale with the total of the other items, was found to be higher than the critical value of 0.3 (Ferketich, 1991). Construct validity can be tested by using convergent and divergent validity tests based on average variance extracted (AVE) values. Composite reliability (CR) value can be considered as an alternative to Cronbach\u0026apos;s Alpha value. Essentially, the CR value, which is stronger than Cronbach\u0026apos;s Alpha, should be above 0.70. while AVE value is expected to greater than 0.50 (Naktiyok\u0026amp;Zengin, 2021; Keskin et al, 2023).\u0026nbsp;The findings are reported in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e\u003cstrong\u003e. Scale, Factors and Items Reliability Results\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"627\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor/Items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eItem-rest correlation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIf item dropped Cronbach\u0026apos;s \u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eComposite Reliability (CR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAverage Variance Extracted (AVE)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eScale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e70.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFactor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e13.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFactor 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e9.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFactor 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e15.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFactor 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e12.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFactor 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e19.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eFactor 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e6.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003eQ27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTest-Retest Reliability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnother test conducted within the scope of reliability is the test-retest analysis. A second test was conducted with 30 participants whose information was obtained in the main application, with two 15-day intervals. The correlation values between both tests were reported. The Pearson correlation coefficient was found to be 0.798. This value was found to be higher than the critical value of 0.70. In this case, it can be stated that the scale has a good test-retest reliability (Devon et al., 2007).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePsychometric Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore the psychometric tests, normality assumptions were examined. Accordingly, it was understood that the skewness and kurtosis values of the scale and its sub-dimensions showed a normal distribution since they were between -1.5 and +1.5 (Adawi et al., 2018) (Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Descriptive and Normality Results\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.4932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStatistics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.0174%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Scale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.7195%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHygiene and Food Safety\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7369%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePsychological Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4874%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight Control and Quick Access\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.7311%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEconomical and Time Saving\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.648%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral Health and Social Well-being\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 2.4557%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLabel Information and Control\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.4932%;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.0174%;\"\u003e\n \u003cp\u003e70.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.7195%;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7369%;\"\u003e\n \u003cp\u003e9.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4874%;\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.7311%;\"\u003e\n \u003cp\u003e12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.648%;\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.5822%;\"\u003e\n \u003cp\u003e6.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.4932%;\"\u003e\n \u003cp\u003eSd\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.0174%;\"\u003e\n \u003cp\u003e9.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.7195%;\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7369%;\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4874%;\"\u003e\n \u003cp\u003e3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.7311%;\"\u003e\n \u003cp\u003e3.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.648%;\"\u003e\n \u003cp\u003e4.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.5822%;\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.4932%;\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.0174%;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.7195%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7369%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4874%;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.7311%;\"\u003e\n \u003cp\u003e-0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.648%;\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.5822%;\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 5.4932%;\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 3.0174%;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.7195%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.7369%;\"\u003e\n \u003cp\u003e-0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 4.4874%;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 6.7311%;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 5.648%;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.5822%;\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\" style=\"width: 46.499%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 6 shows the psychometric test results of the scale. The findings showed that the health risk awareness of e-food shoppers showed statistically significant differences according to age, e-food recommendation, e-food reason and e-food frequency (p\u0026lt;0.05). Detailed information is given in Table 6.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Psychometric Analysis Results of the Scale\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSd\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003et/F\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifferences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnder 31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e54.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eLiving Alone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eFamily Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNuclear family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eExtended family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eE-Food Recommendation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eChronic Disorder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eIncome status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMy income is equal to my expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMy income is higher than my expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMy income is lower than my expenses\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eExercise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eReason to E-Food\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eObligation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e5.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePreference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eFood Sensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHealthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnhealthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGeneral Health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e50.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSatisfaction with Life\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e52.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFrequency of E-Food Shopping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDaily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e55.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWeekly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e8.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(1)\u0026gt;(2) (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMonthl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e48.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;* p \u0026lt; .05, ** p \u0026lt;\u0026nbsp;\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003cem\u003e01, *** p \u0026lt; .001; t= Independent Sample t-test, F=One-Way ANOVA, Sd=Standard Deviation\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study aimed to develop and validate a scale to assess health risk awareness among individuals who shop for food via digital platforms. The resulting six-factor structure comprises: (1) Hygiene and Food Safety, (2) Psychological Well-being, (3) Weight Control and Quick Access, (4) Economical and Time Saving, (5) General Health and Social Well-being, and (6) Label Information and Control. These dimensions reflect the complex interplay between cognitive, behavioral, and environmental variables that shape consumer awareness and perception of health-related risks in e-food contexts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe dimension of hygiene and food safety is among the most frequently emphasized concerns in consumer behavior literature. Studies across cultures report that perceptions of food hygiene strongly affect consumer trust, purchase decisions, and satisfaction with food delivery services. For instance, Ungku Fatimah et al. (2011) found that hygiene standards in foodservice environments are critical predictors of customer retention and satisfaction. Similarly, Galati et al. (2019) highlighted how risk perception tied to hygienic preparation influences consumer acceptance of novel or digitally mediated food offerings. These findings are consistent with our results and affirm the validity of including hygiene and food safety as a key construct.\u003c/p\u003e\n\u003cp\u003ePsychological well-being dimension indicates the relationship between emotional state and food choice, which is another well-documented phenomenon. Several studies in the reviewed literature support the notion that stress, anxiety, and psychological discomfort can drive food-related decisions in digital environments. For example, Marx et al. (2021) conducted a scoping review showing how emotional states modulate food consumption, particularly under conditions like the COVID-19 lockdown. This supports the inclusion of psychological well-being in our model and illustrates how e-food platforms may serve both functional and emotional needs.\u003c/p\u003e\n\u003cp\u003eWeight control and quick access is the third factor and relates to the increasing role of personalization and speed in digital food environments. Studies such as Coffino et al. (2020) demonstrate that consumers actively seek nutrition-focused filters and diet-friendly options when shopping online. This reflects the dual motivation of health monitoring and convenience, which our scale captures under a unified construct. These behaviors were particularly emphasized during the pandemic, where digital food services had to balance health consciousness with rapid delivery.\u003c/p\u003e\n\u003cp\u003eEconomical and time saving factor is related with economic considerations that remain a strong determinant in food choice, particularly for lower-income or time-pressed consumers. Salis et al. (2015) showed that perceptions of affordability and time-efficiency significantly drive customer loyalty and frequency of use in online food services. Our findings align with these results, reinforcing the relevance of this factor in capturing pragmatic motivations linked to perceived risk trade-offs.\u003c/p\u003e\n\u003cp\u003eGeneral health and social well-being factor is supported by evidence from studies addressing the broader lifestyle implications of e-food consumption. For instance, Algheshairy et al. (2022) demonstrated that frequent use of food delivery applications correlates with decreased physical activity and a decline in general health indicators among users. This dimension thus adds depth to our scale by incorporating long-term health perspectives beyond immediate food safety concerns.\u003c/p\u003e\n\u003cp\u003eLabel information and control is the last factor in our model. The importance of label transparency and the ability to control nutritional intake through informed choices is underscored in several studies. Uggioni and Salay (2014) found that consumers who understand and trust food labeling systems are more likely to make health-conscious decisions. This validates the inclusion of label awareness and information control as a distinct and essential component of health risk awareness in e-food shopping.\u003c/p\u003e\n\u003cp\u003eThe findings also revealed that awareness levels regarding health risks such as physical inactivity, poor diet, and long-term non-communicable disease (NCD) consequences remain limited among frequent digital food shoppers. This is particularly concerning given the increasing shift toward digitalized lifestyles and the accompanying reduction in physical activity.\u003c/p\u003e\n\u003cp\u003eOur results align with previous findings indicating that digital convenience often contributes to unhealthy behaviors. Mertens et al. (2021) found that during COVID-19 lockdowns, individuals displayed increased sedentary behavior and less favorable dietary choices-especially students and young adults—supporting our observation that digital shopping may indirectly promote lifestyle-related health risks. Similarly, Lyzwinski et al. (2018) demonstrated a clear association between stress and maladaptive eating behaviors among university students, emphasizing how digital consumption under stress can deteriorate food choices and physical activity.\u003c/p\u003e\n\u003cp\u003eOur results resonate with prior research by Algheshairy et al. (2022), who reported that increased usage of food delivery applications during the COVID-19 pandemic led to negative dietary behaviors, particularly among adult Saudi females. Moreover, Lupton (2021) highlighted how food delivery apps promote narratives of ease and satisfaction, while downplaying potential long-term health consequences. Our findings suggest that users often fail to perceive these latent risks—indicating the need for transparent communication mechanisms within these platforms. This finding is directly aligned with our participants' reported lack of risk awareness. Poelman et al. (2018) also found that low food literacy correlates with impulsivity and poor dietary outcomes, which reinforces the necessity of integrating food literacy principles into digital consumption awareness models.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSaqib et al. (2020) and Katzmarzyk et al. (2022) both stressed that physical inactivity remains a top modifiable risk factor for chronic disease, affirming the critical role of movement awareness—a key dimension of our scale. Complementing this, Ströhle (2009) reviewed that physical activity reduces depression and anxiety symptoms, offering mental health benefits often overlooked in digital food culture. Similarly, Barcın-Güzeldere and Devrim-Lanpir (2022) found a significant relationship between emotional eating and higher BMI during partial quarantine, supporting the notion that digital lifestyles exacerbate stress-related and unhealthy eating behaviors. Aucoin et al. (2021) emphasized the relationship between dietary quality and mental health, reporting that unhealthy dietary patterns are significantly associated with increased anxiety and poor psychological well-being. This indirectly supports our findings by highlighting the broader implications of poor dietary awareness and behavior, which may extend into mental health domains, further reinforcing the need for integrated lifestyle awareness campaigns.\u003c/p\u003e\n\u003cp\u003eConsistent with the literature, our scale reveals a correlation between digital food purchasing and low awareness regarding hygiene and food safety practices. This is particularly important considering findings from Grimes et al. (2017), who reported insufficient public understanding of salt intake guidelines despite widespread information campaigns. Likewise, Galati et al. (2019) emphasized that consumer risk perception significantly influences the acceptance of food technologies-suggesting that digital consumers may lack the literacy to assess food safety indicators critically.\u003c/p\u003e\n\u003cp\u003eThe current study's outcomes also resonate with Poelman et al. (2018), who found that individuals with higher food literacy exhibit healthier dietary patterns, greater self-control, and less impulsiveness. The relatively low scores observed in our awareness scale, especially in dimensions related to general health and psychological well-being, likely stem from insufficient food literacy-a conclusion that aligns with prior theoretical models of health behavior change. Wyse et al. (2021) demonstrated that behavioral nudges embedded in school canteen ordering systems significantly reduced energy and saturated fat intake. This again reinforces the necessity of embedding health-oriented awareness tools directly into the user interfaces of digital food platforms. This opens the possibility of using our validated scale to guide health-based modifications on digital food platforms.\u003c/p\u003e\n\u003cp\u003eHowever, contrasting perspectives do exist. For instance, Flanagan and Soon-Sinclair (2025) found that online consumers can become more hygiene-aware when digital platforms clearly communicate food safety standards. This discrepancy suggests that the impact of digital engagement on health awareness may be context-dependent-shaped by platform design, user demographics, and regulatory visibility. Moreover, Alotaibi et al. (2021) demonstrated that interactive digital educational tools such as social media and workshops effectively enhance healthy lifestyle awareness among health sciences students during the pandemic. Erfanian et al. (2024) also emphasize that consumer awareness and preferences for plant-based alternatives, driven by health and environmental concerns, can reshape food systems toward sustainability when properly informed. However, such transformative potential requires deliberate consumer education, which our findings indicate is often lacking. This suggests a possible intervention path: the very digital platforms that contribute to sedentary lifestyles and poor dietary habits could also serve as tools for disseminating corrective health education, especially if tailored to target risk perceptions and health literacy.\u003c/p\u003e\n\u003cp\u003eOther contextual factors also influence health behaviors. Gerards et al. (2016) illustrated that family nutrition climate and general parenting styles significantly correlate with children’s BMI and dietary outcomes, underscoring the importance of psychosocial variables when analyzing food-related behaviors in digital contexts.\u003c/p\u003e\n\u003cp\u003eFinally, while the digital environment may encourage unhealthy consumption behaviors, it also holds potential for health promotion. Wyse et al. (2021) demonstrated that behavioral nudges embedded in online food ordering platforms can significantly reduce caloric intake and saturated fat levels. These findings point toward the viability of using our validated scale as a feedback tool to inform real-time health interventions on digital shopping platforms. The scale also may serve as a valuable instrument in both consumer education and public health strategy development.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study presents a newly developed and psychometrically validated scale designed to measure health risk awareness among digital food shoppers. Findings indicate that while consumers increasingly rely on digital food platforms for convenience, they often underestimate the accompanying health risks-such as reduced physical activity, emotional eating, and food safety concerns.\u003c/p\u003e\n\u003cp\u003eOur scale provides a robust tool for identifying gaps in health literacy and consumer awareness in this rapidly evolving digital environment. These insights have direct implications for public health policy, digital platform regulation, and consumer education initiatives.\u003c/p\u003e\n\u003cp\u003eTo mitigate the unintended consequences of digital food purchasing, interventions should target behavioral change through integrated digital health education. App-based nudges, clear food labeling, and awareness prompts could be embedded within shopping interfaces to guide healthier consumer choices. Moreover, public health strategies must extend beyond individual behavior and include collaborative efforts with digital platform designers and regulatory authorities.\u003c/p\u003e\n\u003cp\u003eFuture research should focus on longitudinal validation of the scale, cross-cultural adaptations, and the development of scalable digital interventions informed by its findings. Ultimately, aligning technological convenience with informed, health-conscious behavior is not only possible but essential for sustaining well-being in the digital age.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eNone\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained with the approval of Dicle University Social and Human Sciences Ethics Committee dated\u0026nbsp;12,12,2024 and numbered\u0026nbsp;827677.\u0026nbsp;All study participants gave written, informed consent prior to taking part. All processes were carried out in accordance with the relevant guidelines and regulations of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not publicly available to protect anonymity, but are available on request from the corresponding author (Mehmet Aziz \u0026Ccedil;AKMAK).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: MA\u0026Ccedil;, Y\u0026Ccedil;. Methodology: H\u0026Ccedil;, Y\u0026Ccedil;. Formal analysis and investigation: H\u0026Ccedil;., MA\u0026Ccedil;. Writing\u0026mdash;original draft preparation: MA\u0026Ccedil;, H\u0026Ccedil;. Writing\u0026mdash;review and editing: Y\u0026Ccedil;, H\u0026Ccedil;,MA\u0026Ccedil;. Resources: MA\u0026Ccedil;. Supervision: Y\u0026Ccedil;, H\u0026Ccedil;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdawi M, Bragazzi NL, Argumosa-Villar L, Boada-Grau J, Vigil-Colet A, Yildirim C et al (2018) Translation and validation of the Nomophobia Questionnaire in the Italian language: Exploratory factor analysis. 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J Med Internet Res, 23(9), e26054\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-digital-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Digital Health](https://bmcdigitalhealth.biomedcentral.com/)","snPcode":"44247","submissionUrl":"https://submission.nature.com/new-submission/44247/3","title":"BMC Digital Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Health Risk Awareness, Digital Shopping, Scale, Validation, Reliability","lastPublishedDoi":"10.21203/rs.3.rs-6959514/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6959514/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eFood shopping, especially through digital media, has changed consumer behaviour; however, this change has unintentionally supported sedentary lifestyles and posed significant health risks. The aim of this research is to develop a valid and reliable measurement tool that can measure the health risk awareness of e-food shoppers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eThis study was conducted as a methodological study between January 1-30, 2025 in Türkiye with 198 participants. An item pool was created as part of the scale development. Then, these items were sent to experts. The items corrected according to expert opinions were sent to a group of 50 people for a pilot study. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were performed within the scope of validity after the pilot application. For reliability, Cronbach's alpha values, test-retest reliability and corrected item-total correlations of the scale were reported. T-test and ANOVA analyses were conducted for the psychometric measurements of the scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAs a result of EFA conducted within the scope of validity, six sub-dimensions with 24 items were obtained. This structure was confirmed with CFA. Because chi-square value (x²/df=2.77; p\u0026lt;0.001) was found to be significant. RMSEA=0.06 and SRMR=0.07 values ​​belonging to CFA were found to be below the critical value of 0.08. Goodness of fit values ​​such as GFI=0.91, CFI=0.96 and IFI=0.96 belonging to the scale were also found to be above the critical value of 0.90. Cronbach alpha value reported for reliability was found to be 0.79, and test-retest reliability was found to be 0.798. Corrected item total correlation results were found to be above the critical value of 0.3. According to the results of hypothesis tests conducted to reveal the psychometric properties of the scale, health risk awareness of e-food shoppers did not show any significant difference in terms of age, living alone, family type, chronic disease, exercise, food sensitivity, body mass index, general health and life satisfaction (p\u0026gt;0.05). However, age, e-food recommendation, reason to e-food and frequency of e-food shopping showed statistically significant differences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eOur scale provides a robust tool for identifying gaps in health literacy and consumer awareness in this rapidly evolving digital environment. These insights have direct implications for public health policy, digital platform regulation, and consumer education initiatives.\u003c/p\u003e","manuscriptTitle":"Health Risk Awareness Scale of Food Shoppers on Digital Shopping Platforms: A Validation Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 08:13:53","doi":"10.21203/rs.3.rs-6959514/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-12T11:04:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-08T16:16:55+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-05T12:18:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255404134983451631689385415219390618448","date":"2025-07-24T09:07:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"212255352591413079169379298661097715596","date":"2025-07-23T08:27:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-23T06:56:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-18T16:16:16+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-30T09:40:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-26T10:16:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Digital Health","date":"2025-06-26T10:12:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-digital-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Digital Health](https://bmcdigitalhealth.biomedcentral.com/)","snPcode":"44247","submissionUrl":"https://submission.nature.com/new-submission/44247/3","title":"BMC Digital Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"37111475-a68b-4e56-ba81-65722fdd4095","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-14T11:53:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-28 08:13:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6959514","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6959514","identity":"rs-6959514","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}