Development and Validation of a Questionnaire to Assess Perceptions and Acceptance of Micronutrient-Fortified Bouillon Cubes in Northern Ghana

Development and Validation of a Questionnaire to Assess Perceptions and Acceptance of Micronutrient-Fortified Bouillon Cubes in Northern Ghana | 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 Development and Validation of a Questionnaire to Assess Perceptions and Acceptance of Micronutrient-Fortified Bouillon Cubes in Northern Ghana Felix Kwaku Kyereh, Agartha N. Ohemeng, Reina Engle-Stone, K Ryan Wessells, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6975434/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2026 Read the published version in BMC Public Health → Version 1 posted 12 You are reading this latest preprint version Abstract Background Micronutrient deficiencies remain a major public health issue in West Africa, contributing to anaemia, impaired cognitive development, and increased infection risk. Bouillon cubes are widely consumed in the region and offer a culturally appropriate vehicle for micronutrient delivery. However, no validated instrument exists to assess the psychosocial and behavioural factors influencing household acceptance of fortified bouillon cubes. This study aimed to develop and validate a questionnaire to assess perceptions and acceptance of micronutrient-fortified bouillon cubes among non-index household members (NIHMs) in northern Ghana. Methods A 29-item questionnaire was developed based on the Theory of Planned Behaviour and the Health Belief Model. Development involved literature review, expert consultation, and pretesting with 18 adults. The instrument included 26 five-point Likert-scale items and 3 categorical items. One NIHM (aged ≥ 15 years) was randomly selected per household using the Kish method. The questionnaire was administered to 731 NIHMs within 1–2 months of intervention initiation as part of a double-blind randomised controlled trial comparing multiple micronutrient-fortified bouillon cubes to iodine-only cubes in Kumbungu and Tolon districts. The dataset was split for exploratory (n = 292) and confirmatory (n = 439) factor analyses. Psychometric properties were evaluated using Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE). Results Participants had a mean age of 40.8 ± 17.4 years; 58.8% were female. Exploratory factor analysis identified two latent constructs comprising Perception (8 items) and Acceptance (10 items) explaining 56% of total variance. Confirmatory factor analysis showed good model fit (chi-square/df = 1.91, root mean square error of approximation [RMSEA] = 0.04, comparative fit index [CFI] = 0.98, Tucker–Lewis’s index [TLI] = 0.98, and standardised root mean square residual [SRMR] = 0.06). Internal consistency was acceptable (Cronbach’s alpha: 0.71 for Perception, 0.72 for Acceptance; CR: 0.86 and 0.82, respectively). AVE was 0.52 for Perception and 0.46 for Acceptance. Discriminant validity was supported. Conclusion This validated 18-item questionnaire demonstrates strong psychometric properties and provides a standardised tool for assessing household-level perception and acceptance of fortified foods. It is suitable for use in programme design, evaluation, and behavioural monitoring in settings where bouillon is commonly consumed. Micronutrient deficiencies fortified bouillon cubes perception and acceptance questionnaire validation public health nutrition Ghana 1.0 Background Micronutrient deficiencies (MNDs), commonly referred to as hidden hunger, remain a widespread public health challenge, particularly affecting women of reproductive age and preschool children in West Africa ( 1 , 2 ). These deficiencies contribute significantly to anaemia, impaired cognitive development, and increased vulnerability to infections ( 3 , 4 ). In the northern regions of Ghana, dietary reliance on nutrient-poor staples, compounded by socioeconomic constraints, continues to sustain high rates of MNDs ( 5 – 7 ). Large-scale food fortification is a proven strategy to mitigate MNDs and has demonstrated population-level benefits in low- and middle-income countries ( 8 – 10 ). However, in Ghana, the implementation of fortification initiatives has been constrained by regulatory challenges and limited access to adequately fortified products ( 5 , 11 ). The issue is not that households reject fortified versions, but rather that the food vehicles they routinely consume such as oil and wheat flour are often not adequately fortified. According to the Ghana Micronutrient Survey (2017), only 56 percent of oil samples nationally, 36% in the Northern Belt, and less than 6% of wheat flour samples met fortification standards ( 12 ). Bouillon cubes, consumed regularly by nearly all households across different socioeconomic groups, represent a culturally familiar and feasible vehicle for micronutrient delivery ( 13 – 17 ). They offer the advantage of requiring minimal behavioural change in food preparation practices ( 18 ). Building on this premise, the Condiment Micronutrient Innovation Trial (CoMIT) conducted a community-based, double-blind, randomised controlled trial in the Kumbungu and Tolon districts of northern Ghana to assess the impact of multiple micronutrient-fortified bouillon cubes on nutritional status among women and children ( 19 ). Within households participating in this trial, key decisions regarding food preparation, including the use of study-supplied bouillon cubes, are often influenced by non-index household members (NIHMs) such as household heads and primary cooks. Their perceptions and acceptance of fortified bouillon cubes are key determinants of the reliability and effectiveness of fortification interventions at the household level. Despite the documented physiological benefits of food fortification ( 20 – 24 ), there is a notable gap in research on the psychosocial and behavioural determinants that influence household-level adoption and sustained use of fortified foods ( 25 – 27 ). Existing studies in Ghana have largely explored bouillon cubes as flavouring agents rather than examining factors such as taste preferences, cultural beliefs, and economic accessibility that shape consumer acceptance of fortified versions ( 14 , 28 , 29 ). Moreover, no validated instrument currently exists to systematically assess the household perceptions and acceptance of micronutrient-fortified foods in this context. This study aimed to develop and validate a quantitative questionnaire to assess household perceptions and acceptance of micronutrient-fortified bouillon cubes, using a theory-driven approach grounded in the Theory of Planned Behaviour and the Health Belief Model ( 30 , 31 ). The validated tool is intended to support the evaluation and design of fortification interventions and inform public health strategies targeting MNDs in low-resource settings. 2.0 Methods 2.1 Study design and setting This questionnaire validation was conducted as a sub-study within the Condiment Micronutrient Innovation Trial (CoMIT). Briefly, CoMIT was a double-blind, community-based randomised controlled trial conducted at 16 sites in the Kumbungu and Tolon districts of the Northern Region of Ghana (trial registration: ClinicalTrials.gov NCT05178407; Pan‑African Clinical Trial Registry PACTR202206868437931) and detailed in the published protocol ( 19 ). The trial assessed the impact of household use of bouillon cubes fortified with multiple micronutrients (iodine, vitamin A, folic acid, vitamin B12, iron, and zinc) compared to control cubes fortified with iodine alone. Outcomes included haemoglobin levels and selected biomarkers among three groups of index participants: non-pregnant, non-lactating women of reproductive age; lactating women 4–18 months postpartum; and children aged 2–5 years. Households were randomised using a computer-generated block design, and blinding was maintained through identical packaging. Bouillon cubes were distributed biweekly, based on household size, over a nine-month period. 2.2 Study participants and recruitment Participants in this sub-study were non-index household members (NIHMs) aged 15 years or older who regularly shared meals with the index participant, were not enrolled in the main trial, and written informed consent. Following household consent and recruitment of the index participants, one NIHM was randomly selected per household using the Kish method, based on the household roster compiled during the recruitment of the index participant. 2.3 Theoretical Framework The questionnaire was developed using the Theory of Planned Behaviour (TPB) and the Health Belief Model (HBM). TPB posits that behavioural intentions are influenced by attitudes, subjective norms, and perceived behavioural control ( 30 ). In this study, attitudes were measured through evaluations of sensory attributes (e.g., “You like the smell of the study bouillon cubes”) and overall satisfaction with meals prepared using the product ( 32 , 33 ). Subjective norms were assessed with items such as “You do not think your neighbours or friends would like the study bouillon cubes” and “You have good things to say about the study bouillon cubes.” Perceived behavioural control was evaluated using statements like “The study bouillon cubes can be used in your household every day during the week” and “The study bouillon cubes can be used in your household any number of times in a day.” Complementing TPB, the Health Belief Model (HBM) emphasises health-related beliefs, including perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy ( 31 ). Perceived benefits were captured with items such as “You want your household to use the study bouillon cubes in the future if they are available,” while perceived barriers were measured with items like “In the past month, you think your household should have purchased non-study bouillon from the market.” Cues to action and self-efficacy were addressed through statements such as “You would be interested in buying the study bouillon cubes if they are sold in the future” and “Your household members have not observed any problems with using the study bouillon cubes.” The use of this dual-framework approach enabled a comprehensive assessment of cognitive, social, and health-related factors influencing the acceptance and use of fortified bouillon cubes ( 34 – 37 ). 2.4 Questionnaire development and pretesting process A literature review was conducted to identify key constructs from the TPB and the HBM relevant to the adoption of micronutrient-fortified foods. The review focused on fortified products such as condiments, seasonings, rice, and complementary foods, examining factors including flavour preferences, compatibility with traditional diets, cultural attitudes, and perceived health benefit ( 28 , 38 – 41 ). Based on these domains, an initial pool of 33 items was generated. An interdisciplinary panel from the CoMIT research team, comprising experts in nutrition, public health, consumer behaviour, and statistics, evaluated the items for clarity and relevance. This process reduced the pool to 29 items. The instrument was forward-translated into Dagbani and back-translated into English, following standard cross-cultural adaptation protocols ( 42 ). Translational validity was further assessed by pretesting the questionnaire with 18 adult household members (cooks, household heads, and formal workers aged 20 to 70 years) from two sites excluded from the main data collection to prevent contamination. During the pretesting, participants evaluated the clarity and cultural appropriateness of the questionnaires. Feedback from this process prompted iterative refinements, including rewording several items for brevity, adjusting response options, and clarifying scale definitions before finalising the questionnaire. The final questionnaire consisted of 26 items on a five-point Likert scale (1 = completely disagree to 5 = completely agree, with an additional non-applicable option), two categorical items, and one binary item [see Additional file 1 ]. 2.5 Data collection procedure. Trained fieldworkers, instructed in study-specific ethical procedures, including the avoidance of leading questions, cultural sensitivity, and the maintenance of participant confidentiality, administered the questionnaire through face-to-face interviews using tablet-based data entry (SurveyCTO). Participant background information, including age, sex, household cooking role, education, and occupation, was also recorded. Supervisors reviewed datasets daily to ensure data quality and adherence to the study protocol. Data collection occurred between March and September 2023, and for each household, the questionnaire was administered within the first two months of receiving the bouillon intervention. 2.6 Data Processing and Analysis Data were collected from 742 participants, and after excluding 11 incomplete or inconsistent responses, 731 valid responses remained for analysis. Prior to analysis, negatively framed items (Q13, Q14, Q15, Q23, Q25, Q27) were reverse scored to ensure consistent interpretation. Three categorical items were recoded to match the five-point Likert scale structure, enabling their inclusion in the factor analysis alongside the Likert-scale items. Five items (Q8, Q9, Q15, Q21, Q22) were removed prior to factor analysis due to a lack of response variance, with over 95% of participants selecting the same response. Items lacking variance cannot differentiate between respondents or contribute meaningfully to factor structure, and were therefore excluded ( 43 , 44 ). Univariate outliers were identified using Z-scores, with values exceeding ± 3 considered extreme; this process identified 218 univariate outliers. Multivariate outliers were detected using Mahalanobis distance, resulting in 98 multivariate outliers. Extreme values were managed through Winsorisation applied across the dataset, with scores capped at ± 3 ( 45 – 47 ). The remaining 24 items were standardised using Z-score transformation (mean = 0, standard deviation = 1) to ensure comparability and reduce scale-related bias ( 48 , 49 ). Multivariate normality was assessed using Mardia’s test, which indicated significant deviations (skewness b1p = 921.9, p ≤ 0.001; kurtosis b2p = 1774.76, p ≤ 0.001), justifying the application of robust estimation methods ( 50 , 51 ). For psychometric evaluation, the processed dataset (n = 731) was randomly split into two subsets: approximately 40% (n = 292) for exploratory factor analysis (EFA) and 60% (n = 439) for confirmatory factor analysis (CFA). Randomisation was performed using a fixed seed to ensure reproducibility ( 49 ). The EFA sample size exceeded the recommended criterion of 5–10 respondents per item ( 52 ), and the CFA sample exceeded the minimum of 200–300 participants required for stable model estimation ( 44 , 53 ). Background characteristics of participants were summarised using descriptive statistics. All analyses were conducted using R software (version 4.3.1) and the lavaan package in RStudio ( 54 ). 2.6.1 Exploratory Factor Analysis (EFA) Exploratory factor analysis was conducted to examine the underlying structure of the 24-item questionnaire and to explore whether the data supported a two-factor structure consistent with the theorised constructs of Perception and Acceptance ( 55 ). Sampling adequacy was assessed using the Kaiser–Meyer–Olkin (KMO) measure, with values ≥ 0.80 considered meritorious for factor analysis ( 56 ). Bartlett’s test of sphericity was also performed to assess whether the correlation matrix was suitable for factor analysis, with significant test at p < 0.05 indicating that the inter-item correlations were sufficient for factor extraction ( 57 , 58 ). Fixed two-factor solution was specified, based on the theoretical expectation of two latent dimensions: Perception and Acceptance. Principal Axis Factoring was used as the extraction method, and Direct Oblimin rotation was applied to allow for correlation between the two factors ( 58 , 59 ). Minimum factor loading threshold of 0.40 was used to retain items with meaningful contributions to the identified factors ( 60 ). 2.6.2 Confirmatory factor analysis (CFA) and model validation CFA was conducted to validate the two-construct model identified through EFA, assessing the relationships between observed items and their corresponding latent constructs (Perception and Acceptance) ( 51 , 58 ). Due to the ordinal nature of the data and significant deviations observed from multivariate normality, the Weighted Least Squares Mean and Variance adjusted (WLSMV) estimator was used ( 61 , 62 ). A scaling correction factor of 1.104 was applied to the chi-square statistic to ensure accurate interpretation of robust model fit indices ( 51 , 63 ). Model fit was evaluated using multiple standard indices. The Root Mean Square Error of Approximation (RMSEA) ≤ 0.06 indicated good fit, with values between 0.06 and 0.08 considered acceptable ( 64 , 65 ). Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) values ≥ 0.95 reflected excellent fit, while values between 0.90 and 0.95 were deemed acceptable. A Standardised Root Mean Square Residual (SRMR) ≤ 0.08 indicated adequate fit ( 66 , 67 ). The normed chi-square (χ²/df) was also reported, with values < 3 indicating acceptable model parsimony ( 68 ). Beyond overall model fit, construct validity was evaluated to determine how well the observed items represented the intended theoretical constructs ( 69 , 70 ). Convergent validity, the extent to which items within a factor are correlated, was assessed using Average Variance Extracted (AVE), with values > 0.50 indicating adequate convergence ( 71 ). Discriminant validity was assessed using the Fornell–Larcker criterion, whereby the square root of the AVE for each construct exceeded the inter-construct correlation ( 72 ). Internal consistency and construct reliability were evaluated using Cronbach’s alpha and composite reliability (CR), with values ≥ 0.70 considered acceptable ( 73 , 74 ). To further assess convergent validity, factor loadings were examined to evaluate the strength of the relationships between observed items and their respective latent constructs. Standardised factor loadings were examined, with items loading ≥ 0.40 retained. Loadings ≥ 0.50 were considered acceptable, while values ≥ 0.70 indicated strong associations ( 60 , 73 ). Model refinement was conducted following standard CFA procedures to improve overall fit. Correlation between the two constructs was permitted based on theoretical expectations. One item (Q7) from the Acceptance construct was removed due to a low standardised factor loading (0.35), which persisted despite allowing for correlated error terms. Its exclusion was justified by its weak association with the latent construct ( 71 , 75 ). 3.0 Results 3.1 Background characteristics Table 1 present the results of background characteristics. A total of 731 participants were included in the study. The mean age was 40.8 years (SD = 17.4), and 58.8% were female. Participants’ relationships to the household head were as follows: 27.9% were household heads, 38.6% were spouses, 9.4% were parents of household heads, 13.8% were sons or daughters, and 10.3% were siblings or mothers-in-law. In terms of household cooking roles, 40.0% reported never cooking, 38.4% were the primary cooks, and 21.6% cooked occasionally. Educational attainment was low, with 74.8% having no formal education, 13.0% having completed basic education, and 12.2% having completed senior high school or higher. Most participants identified as Dagomba (98.9%) and were Muslim (98.4%). Regarding occupation, 46.8% were engaged in agriculture or farming, 16.7% were homemakers, 28.9% operated small businesses, and 7.7% were employed in government or private sector jobs. Table 1 Background characteristics of the study participants in the Northern region of Ghana1 (Total n = 731) Variables/characteristics Frequencies (%) Age, years (Mean ± S.D) 40.8 ± 17.4 Sex Female 437 (58.8) Relationship with household head (HH) Household head 204 (27.9) Wife of HH 282 (38.6) Father/mother of HH 69 (9.4) Sons/daughters of HH 101 (13.8) Siblings/mother in-law of HH 75 (10.3) Position in household cooking Never cook 292 (40.0) Primary cook 281 (38.4) Occasional/rarely cook 158 (21.6) Educational level completed No Formal Education 547 (74.8) Basic Education 95 (13.0) Senior High School or Higher 89 (12.2) Ethnicity Dagomba 723(98.9) Gonjas/Mosi 8 (1.1) Religion Islam 719 (98.4) Christianity 12 (1.6) Occupation Agriculture/Farming 342 (46.8) Homemaker 122 (16.7) Small Business Owner 211 (28.9) Government/ Private Employee 56 (7.7) Abbreviations: HH, Household Head. 1 Values are reported as means and standard deviation for continuous variable and count and percentages for categorical variables 3.2 EFA The KMO measure of sampling adequacy was 0.85, indicating that the sample was suitable for factor analysis. Bartlett’s test of sphericity was significant, χ²(276) = 2156.05, p < .001, confirming that the inter-item correlations were sufficient for factor extraction. Exploratory factor analysis (EFA) revealed a two-factor structure, comprising Factor 1 (Perception) and Factor 2 (Acceptance), as shown in Table 2 . For the Perception factor, loadings ranged from 0.47 (Q6) to 0.76 (Q27), with eight items retained: Q1, Q3, Q4, Q6, Q23, Q24, Q26, and Q27. For the Acceptance factor, loadings ranged from 0.43 (Q10) to 0.67 (Q20), with eleven items retained: Q2, Q5, Q7, Q10, Q16, Q17, Q18, Q20, Q25, Q28, and Q29. No items exhibited cross-loadings ≥ 0.30 on both factors. Together, the two factors explained 56% of the total variance, with Acceptance accounting for 31% and Perception for 25%. Internal consistency was acceptable, with Cronbach’s alpha of 0.71 for Perception and 0.72 for Acceptance. Table 2 Factor loadings and communalities from the exploratory factor analysis (n = 292) Question (Item) Item description Factor 1 (Perception) Factor 2 (Acceptance) Communality Q1 views unchanged since receiving study bouillon 0.70* -0.04 0.498 Q2 Okay to use bouillon for all household members -0.02 0.57* 0.331 Q3 Study bouillon smells same as regular bouillon 0.72* -0.03 0.527 Q4 Study bouillon taste same as regular bouillon 0.74* 0.02 0.546 Q5 Study Bouillon can be used daily -0.04 0.50* 0.255 Q6 Study Bouillon to be used only on some days 0.47* -0.14 0.251 Q7 Study Bouillon can be used multiple times daily 0.03 0.46* 0.213 Q10 Agree with those who think it's good 0.03 0.43* 0.184 Q11 Agree with those who think it's neutral 0.33 0.35 0.201 Q12 Agree with those who think it's bad 0.18 0.35 0.136 Q13 Cooking frequency changed since receiving bouillon -0.09 0.16 0.037 Q14 Frequency of study bouillon use during cooking 0.01 0.02 0.000 Q16 Likes the smell of study bouillon -0.12 0.44* 0.224 Q17 Likes the taste of study bouillon -0.08 0.59* 0.371 Q18 Happy household receives study bouillon 0.00 0.55* 0.305 Q19 Study bouillon quantity received is sufficient 0.28 0.10 0.081 Q20 Enjoy foods prepared with study bouillon 0.06 0.67* 0.445 Q23 No personal problems observed with bouillon 0.61* 0.11 0.367 Q24 No problems observed by household members 0.61* 0.12 0.363 Q25 Positive views about study bouillon 0.09 0.57* 0.317 Q26 Does not want household to continue using bouillon 0.74* -0.05 0.551 Q27 Thinks neighbours/friends would not like bouillon 0.76* -0.05 0.584 Q28 Wants household to use bouillon in future -0.08 0.59* 0.365 Q29 Would buy bouillon if sold in future -0.05 0.66* 0.446 Note : Factor loadings ≥ 0.40 are considered acceptable. Items marked with an asterisk (*) indicate those retained for their respective factors. Communality represents the proportion of variance in each item explained by the extracted factors. 3.3 CFA 3.3.1 Final standardised factor loadings Table 3 presents the standardised factor loadings from the confirmatory factor analysis model. All retained items exceeded the pre-established threshold of ≥ 0.40, supporting their inclusion and contribution to their respective latent constructs. For the Perception construct, standardised loadings ranged from 0.464 (Q24) to 0.770 (Q1). The intermediate loadings included Q3 (0.756), Q4 (0.719), Q6 (0.483), Q23 (0.520), Q26 (0.743), and Q27 (0.756). For the Acceptance construct, standardised loadings ranged from 0.476 (Q5) to 0.692 (Q29). The remaining items had loadings as follows: Q2 (0.525), Q10 (0.486), Q16 (0.538), Q17 (0.606), Q18 (0.578), Q20 (0.607), Q25 (0.553), and Q28 (0.574). The final validated questionnaire measuring Perception and Acceptance is provided as supplementary material [see Additional file 2 ]. Table 3 Standardised factor loadings for items retained in the final CFA model (n = 439) Perception construct Loadings Acceptance construct Loadings Q1- Views unchanged since receiving study bouillon 0.770 Q2 – Okay to use bouillon for everyone 0.525 Q3 – Study bouillon smells same as regular bouillon 0.756 Q5 – Study Bouillon can be used daily 0.476 Q4 – Study bouillon taste same as regular bouillon 0.719 Q10 – Agree with those who think it's good 0.486 Q6 – Study bouillon to be used only on some days 0.483 Q16 – Likes the smell of study bouillon 0.538 Q23 – No personal problems observed with bouillon 0.520 Q17 – Likes the taste of study bouillon 0.606 Q24 – No problems observed by household members 0.464 Q18 – Happy household receives study bouillon 0.578 Q26 – Does not want household to continue using bouillon 0.743 Q20 – Enjoy foods prepared with study bouillon 0.607 Q27 – Thinks neighbours/friends would not like bouillon 0.756 Q25 – Positive views about study bouillon 0.553 Q28 – Wants household to use bouillon in future 0.574 Q29 – Would buy bouillon if sold in future 0.692 Note : All items exceeded the acceptable threshold for retention (standardised factor loading ≥ 0.40). 3.3.2 Model indices and validation results Table 4 presents the model fit indices and construct validation results from the CFA. The CFA indicated acceptable to excellent model fit based on multiple criteria: χ²(134) = 255.94, p < 0.001; χ²/df = 1.91; RMSEA = 0.043 (90% CI: 0.035–0.052); CFI = 0.981; TLI = 0.977; and SRMR = 0.061. These values meet or exceed conventional thresholds for good model fit. Composite reliability (CR) was 0.858 for the Perception construct and 0.824 for Acceptance, exceeding the recommended minimum of 0.70, thereby indicating strong internal consistency. The average variance extracted (AVE) was 0.52 for Perception and 0.46 for Acceptance. Although the AVE for Acceptance was slightly below the conventional 0.50 threshold, its CR remained robust, supporting convergent validity. Discriminant validity was also supported: the square root of the AVE was 0.721 for Perception and 0.678 for Acceptance, both exceeding the inter-construct correlation of 0.45, consistent with Fornell–Larcker criterion. Table 4 CFA model fit indices and construct validity results Index Value Acceptable Value χ² (df = 134) 255.94 Non-significant; sensitive to sample size p-value 0.05 (not strict criterion due to sample size) Chi-Square (χ²)/df 1.91 < 3.00 RMSEA (90% CI) 0.043 (0.035–0.052) < 0.06 (good); < 0.08 (acceptable) CFI 0.981 ≥ 0.95 (good); ≥ 0.90 (acceptable) TLI 0.977 ≥ 0.95 (good); ≥ 0.90 (acceptable) SRMR 0.061 Inter-construct correlation (0.45) Square root of AVE (Acceptance) 0.678 >Inter-construct correlation (0.45) Inter-construct correlation 0.450 < √AVE for each construct Note : CR = Composite Reliability; AVE = Average Variance Extracted; RMSEA = Root Mean Square Error of Approximation; CFI = Comparative Fit Index; TLI = Tucker-Lewis Index; SRMR = Standardised Root Mean Square Residual. CR > 0.70 and Square root of AVE > inter-construct correlation indicates good convergent and discriminant validity ( 72 ). 4.0 Discussion This study developed and validated an 18-item questionnaire to assess ‘Perceptions’ (8 items) and ‘Acceptance’ (10 items) of micronutrient-fortified bouillon cubes among non-index household members in northern Ghana. Exploratory and confirmatory factor analyses supported a two-factor structure, Perception and Acceptance, that accounted for 56% of the total variance. The model demonstrated good fit (χ²/df = 1.91; RMSEA = 0.043; CFI = 0.981; TLI = 0.977; SRMR = 0.061), with all standardised factor loadings exceeding 0.40. Internal consistency was acceptable (Cronbach’s alpha: 0.71 for Perception, 0.72 for Acceptance; composite reliability: 0.86 and 0.82, respectively). Convergent validity was confirmed for Perception (AVE = 0.52) and marginal for Acceptance (AVE = 0.46), while discriminant validity was confirmed via the Fornell–Larcker criterion. A key strength was that the questionnaire was theory-driven, drawing on constructs from the Theory of Planned Behaviour and the Health Belief Model ( 30 , 31 ). The questionnaire was reviewed by experts, pretested, and field-tested within a randomised controlled trial context ( 69 ). The use of both exploratory and confirmatory factor analysis on independent samples and the application of robust estimation methods (WLSMV) addressed the ordinal nature of the data and non-normality, enhancing the validity of the findings ( 51 , 63 ). While internal consistency and structural validity were supported, the AVE for the Acceptance construct (0.46) was slightly below the recommended threshold (0.50), which may reflect conceptual overlap or measurement limitations ( 71 ). However, the high composite reliability (0.82) indicates that the construct maintained acceptable internal consistency despite the marginal AVE ( 72 , 73 ). Additionally, the study’s cross-sectional design and single-region setting may limit generalisability to other populations with different cultural or dietary practices ( 76 ). Criterion validity was not assessed in this study, as no direct behavioural indicators (e.g., cube disappearance or household inventory) were available. Future research should consider validating the instrument against objective usage data ( 43 , 69 ). Further work is underway to explore how perception and acceptance scores relate to nutritional outcomes such as haemoglobin and iodine status. The two-factor structure “Perception and Acceptance” reflects distinct but related behavioural dimensions, consistent with the Theory of Planned Behaviour, which distinguishes between beliefs and behavioural intentions ( 30 ). The moderate correlation (r = 0.45) supports this differentiation and aligns with findings from similar behavioural instrument validations ( 69 , 77 ). Validated tools for assessing household perceptions and acceptance of fortified condiments are scarce in sub-Saharan Africa ( 78 ). This instrument provides a reliable, context-specific measure that can support programme monitoring and design, especially in regions where bouillon cubes are widely used ( 13 , 14 ). Its use in a trial setting enhances its relevance for evaluating behavioural responses to fortified food interventions. The validated questionnaire can be applied in both research and public health programme contexts to assess household acceptance and perception of fortified condiments. These constructs are relevant for informing communication strategies and intervention design ( 79 ). In programme settings, such as fortification initiatives implemented by ministries of health, non-governmental organisations, or other implementing partners, the tool can support planning and adaptation of interventions. It may be used to identify specific concerns through item-level responses or to generate composite scores for monitoring and evaluation purposes. The moderate association observed between perception and acceptance indicates that improving perceptions alone may not be sufficient to ensure adoption. Interventions should also address practical barriers, including taste preferences, cost, and cooking practices, to support sustained use ( 80 ). Additionally, because fortified condiments are used at the household level, it is important to consider the views of all household members, not only the primary beneficiaries, in both design and implementation. Although the tool was developed for fortified bouillon cubes, the approach may be adapted for other fortified foods that are consumed at the household level. However, documented applications of similar tools in other food fortification contexts are currently limited. Future research should assess this tool’s generalisability in different regions and cultural settings. Longitudinal studies can help evaluate changes over time in response to behaviour change interventions or market factors ( 81 , 82 ). Further validation, including predictive and criterion-related validity, is recommended to explore associations with consumption behaviours and health outcomes ( 44 , 83 ). Item refinement, particularly within the Acceptance construct, may also enhance measurement precision. This study could not evaluate measurement invariance across key demographic groups such as sex, education, or household cooking roles due to sample homogeneity. Future validation efforts should ensure more diverse subgroup representation to examine whether the factor structure holds consistently across these populations. This study addressed a key methodological gap by providing a culturally relevant, validated questionnaire to assess perceptions and acceptance of fortified bouillon cubes. The tool can support intervention design, behavioural monitoring, and contribute to context-specific strategies for addressing micronutrient deficiencies. Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the Ghana Health Service Ethical Review Committee (GHSERC ID: 024/11/21), the Institutional Review Board at the University of California, Davis (IRB ID: 1837253), and the Ghana Food and Drugs Authority (Certificate No. FDA/CT/2213[1]). The trial was registered at ClinicalTrials.gov (NCT05178407) and the Pan-African Clinical Trials Registry (PACTR202206868437931). Written informed consent was obtained from all participants prior to their involvement in the study. For participants under 18 years of age, assent was obtained in addition to parental or caregiver consent. All procedures were conducted in accordance with relevant guidelines and regulations. Consent for publication Not applicable. Availability of data and materials The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. The final version of the questionnaire is included as supplementary material (Additional file 1). Competing interests The authors declare that they have no competing interests. Funding This work was supported by a grant from Helen Keller International (66504-UCD-01; RES and SAV), through support from the Bill & Melinda Gates Foundation (INV-007916) to the University of California, Davis. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors' contributions FKK, RES, and SAA conceptualised the study. FKK led data collection, data analysis, and manuscript preparation. CDA and ANO contributed to study design, instrument development, and critical manuscript revision. KRW, XT and SAV reviewed and refined the questionnaire. SMK coordinated and supervised field data collection. JND, ERB, ADF, and KWN supported field coordination and translation processes. All authors read and approved the final manuscript. Acknowledgements We thank the participants and field staff of the CoMIT Trial in the Kumbungu and Tolon districts. We also acknowledge the support of the Department of Nutrition and Food Science, University of Ghana, and the Institute for Global Nutrition at the University of California, Davis. Authors’ information Author affiliations are listed on the title page. ORCID iDs Felix Kwaku Kyereh: 0009-0005-4364-330X Agartha N. Ohemeng: 0000-0002-7986-1350 Charles D Arnold: 0000-0001-6510-3172 Reina Engle-Stone: 0000-0003-2446-6166 References Stevens GA, Paciorek CJ, Flores-Urrutia MC, Borghi E, Namaste S, Wirth JP, et al. National, regional, and global estimates of anaemia by severity in women and children for 2000–19: a pooled analysis of population-representative data. The Lancet Global Health. 2022;10(5):e627-e39. WHO. Anaemia in women and children: WHO Global Anaemia estimates, 2021 Edition. 2024 [Available from: https://www.who.int/data/gho/data/themes/topics/anaemia_in_women_and_children. WHO. Vitamin A deficiencyhttps://www.who.int/data/nutrition/nlis/info/vitamin-a-deficiency 2024 [ Mao C, Shen Z, Long D, Liu M, Xu X, Gao X, et al. Epidemiological study of pediatric nutritional deficiencies: an analysis from the global burden of disease study 2019. Nutrition Journal. 2024;23(1):44. UNICEF. Ghana micronutrient survey 2017. UNICEF: Accra, Ghana. 2017. 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Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research. 1981;18(1):39-50. Hair Jr JF, Howard MC, Nitzl C. Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of business research. 2020;109:101-10. Sujati H, Akhyar M. Testing the construct validity and reliability of curiosity scale using confirmatory factor analysis. Journal of Educational and Social Research. 2020;20(4). Rönkkö M, Cho E. An updated guideline for assessing discriminant validity. Organizational Research Methods. 2022;25(1):6-14. Alavi M, Visentin DC, Thapa DK, Hunt GE, Watson R, Cleary M. Chi-square for model fit in confirmatory factor analysis. Journal of advanced nursing. 2020;76(9):2209-11. Dunn TJ, Baguley T, Brunsden V. From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. 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De Vet HC, Terwee CB, Mokkink LB, Knol DL. Measurement in medicine: a practical guide: Cambridge university press; 2011. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1Originalfullquestionnairebeforeitemreduction.docx Additionalfile2ValidatedQuestionnairePerceptionAcceptance.docx Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2026 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 07 Aug, 2025 Reviews received at journal 06 Aug, 2025 Reviews received at journal 06 Aug, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviews received at journal 15 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers agreed at journal 10 Jul, 2025 Reviewers invited by journal 10 Jul, 2025 Editor invited by journal 27 Jun, 2025 Editor assigned by journal 26 Jun, 2025 Submission checks completed at journal 26 Jun, 2025 First submitted to journal 25 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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These deficiencies contribute significantly to anaemia, impaired cognitive development, and increased vulnerability to infections (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In the northern regions of Ghana, dietary reliance on nutrient-poor staples, compounded by socioeconomic constraints, continues to sustain high rates of MNDs (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLarge-scale food fortification is a proven strategy to mitigate MNDs and has demonstrated population-level benefits in low- and middle-income countries (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, in Ghana, the implementation of fortification initiatives has been constrained by regulatory challenges and limited access to adequately fortified products (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The issue is not that households reject fortified versions, but rather that the food vehicles they routinely consume such as oil and wheat flour are often not adequately fortified. According to the Ghana Micronutrient Survey (2017), only 56 percent of oil samples nationally, 36% in the Northern Belt, and less than 6% of wheat flour samples met fortification standards (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Bouillon cubes, consumed regularly by nearly all households across different socioeconomic groups, represent a culturally familiar and feasible vehicle for micronutrient delivery (\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). They offer the advantage of requiring minimal behavioural change in food preparation practices (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBuilding on this premise, the Condiment Micronutrient Innovation Trial (CoMIT) conducted a community-based, double-blind, randomised controlled trial in the Kumbungu and Tolon districts of northern Ghana to assess the impact of multiple micronutrient-fortified bouillon cubes on nutritional status among women and children (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Within households participating in this trial, key decisions regarding food preparation, including the use of study-supplied bouillon cubes, are often influenced by non-index household members (NIHMs) such as household heads and primary cooks. Their perceptions and acceptance of fortified bouillon cubes are key determinants of the reliability and effectiveness of fortification interventions at the household level.\u003c/p\u003e\u003cp\u003eDespite the documented physiological benefits of food fortification (\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), there is a notable gap in research on the psychosocial and behavioural determinants that influence household-level adoption and sustained use of fortified foods (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Existing studies in Ghana have largely explored bouillon cubes as flavouring agents rather than examining factors such as taste preferences, cultural beliefs, and economic accessibility that shape consumer acceptance of fortified versions (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Moreover, no validated instrument currently exists to systematically assess the household perceptions and acceptance of micronutrient-fortified foods in this context.\u003c/p\u003e\u003cp\u003eThis study aimed to develop and validate a quantitative questionnaire to assess household perceptions and acceptance of micronutrient-fortified bouillon cubes, using a theory-driven approach grounded in the Theory of Planned Behaviour and the Health Belief Model (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The validated tool is intended to support the evaluation and design of fortification interventions and inform public health strategies targeting MNDs in low-resource settings.\u003c/p\u003e"},{"header":"2.0 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study design and setting\u003c/h2\u003e\u003cp\u003eThis questionnaire validation was conducted as a sub-study within the Condiment Micronutrient Innovation Trial (CoMIT). Briefly, CoMIT was a double-blind, community-based randomised controlled trial conducted at 16 sites in the Kumbungu and Tolon districts of the Northern Region of Ghana (trial registration: ClinicalTrials.gov NCT05178407; Pan‑African Clinical Trial Registry PACTR202206868437931) and detailed in the published protocol (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The trial assessed the impact of household use of bouillon cubes fortified with multiple micronutrients (iodine, vitamin A, folic acid, vitamin B12, iron, and zinc) compared to control cubes fortified with iodine alone. Outcomes included haemoglobin levels and selected biomarkers among three groups of index participants: non-pregnant, non-lactating women of reproductive age; lactating women 4\u0026ndash;18 months postpartum; and children aged 2\u0026ndash;5 years.\u003c/p\u003e\u003cp\u003eHouseholds were randomised using a computer-generated block design, and blinding was maintained through identical packaging. Bouillon cubes were distributed biweekly, based on household size, over a nine-month period.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Study participants and recruitment\u003c/h2\u003e\u003cp\u003eParticipants in this sub-study were non-index household members (NIHMs) aged 15 years or older who regularly shared meals with the index participant, were not enrolled in the main trial, and written informed consent. Following household consent and recruitment of the index participants, one NIHM was randomly selected per household using the Kish method, based on the household roster compiled during the recruitment of the index participant.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Theoretical Framework\u003c/h2\u003e\u003cp\u003eThe questionnaire was developed using the Theory of Planned Behaviour (TPB) and the Health Belief Model (HBM). TPB posits that behavioural intentions are influenced by attitudes, subjective norms, and perceived behavioural control (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In this study, attitudes were measured through evaluations of sensory attributes (e.g., \u0026ldquo;You like the smell of the study bouillon cubes\u0026rdquo;) and overall satisfaction with meals prepared using the product (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Subjective norms were assessed with items such as \u0026ldquo;You do not think your neighbours or friends would like the study bouillon cubes\u0026rdquo; and \u0026ldquo;You have good things to say about the study bouillon cubes.\u0026rdquo; Perceived behavioural control was evaluated using statements like \u0026ldquo;The study bouillon cubes can be used in your household every day during the week\u0026rdquo; and \u0026ldquo;The study bouillon cubes can be used in your household any number of times in a day.\u0026rdquo;\u003c/p\u003e\u003cp\u003eComplementing TPB, the Health Belief Model (HBM) emphasises health-related beliefs, including perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Perceived benefits were captured with items such as \u0026ldquo;You want your household to use the study bouillon cubes in the future if they are available,\u0026rdquo; while perceived barriers were measured with items like \u0026ldquo;In the past month, you think your household should have purchased non-study bouillon from the market.\u0026rdquo; Cues to action and self-efficacy were addressed through statements such as \u0026ldquo;You would be interested in buying the study bouillon cubes if they are sold in the future\u0026rdquo; and \u0026ldquo;Your household members have not observed any problems with using the study bouillon cubes.\u0026rdquo;\u003c/p\u003e\u003cp\u003eThe use of this dual-framework approach enabled a comprehensive assessment of cognitive, social, and health-related factors influencing the acceptance and use of fortified bouillon cubes (\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Questionnaire development and pretesting process\u003c/h2\u003e\u003cp\u003e A literature review was conducted to identify key constructs from the TPB and the HBM relevant to the adoption of micronutrient-fortified foods. The review focused on fortified products such as condiments, seasonings, rice, and complementary foods, examining factors including flavour preferences, compatibility with traditional diets, cultural attitudes, and perceived health benefit (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan additionalcitationids=\"CR39 CR40\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Based on these domains, an initial pool of 33 items was generated. An interdisciplinary panel from the CoMIT research team, comprising experts in nutrition, public health, consumer behaviour, and statistics, evaluated the items for clarity and relevance. This process reduced the pool to 29 items. The instrument was forward-translated into Dagbani and back-translated into English, following standard cross-cultural adaptation protocols (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTranslational validity was further assessed by pretesting the questionnaire with 18 adult household members (cooks, household heads, and formal workers aged 20 to 70 years) from two sites excluded from the main data collection to prevent contamination. During the pretesting, participants evaluated the clarity and cultural appropriateness of the questionnaires. Feedback from this process prompted iterative refinements, including rewording several items for brevity, adjusting response options, and clarifying scale definitions before finalising the questionnaire.\u003c/p\u003e\u003cp\u003eThe final questionnaire consisted of 26 items on a five-point Likert scale (1\u0026thinsp;=\u0026thinsp;completely disagree to 5\u0026thinsp;=\u0026thinsp;completely agree, with an additional non-applicable option), two categorical items, and one binary item [see \u003cb\u003eAdditional file 1\u003c/b\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Data collection procedure.\u003c/h2\u003e\u003cp\u003eTrained fieldworkers, instructed in study-specific ethical procedures, including the avoidance of leading questions, cultural sensitivity, and the maintenance of participant confidentiality, administered the questionnaire through face-to-face interviews using tablet-based data entry (SurveyCTO). Participant background information, including age, sex, household cooking role, education, and occupation, was also recorded. Supervisors reviewed datasets daily to ensure data quality and adherence to the study protocol. Data collection occurred between March and September 2023, and for each household, the questionnaire was administered within the first two months of receiving the bouillon intervention.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Data Processing and Analysis\u003c/h2\u003e\u003cp\u003eData were collected from 742 participants, and after excluding 11 incomplete or inconsistent responses, 731 valid responses remained for analysis. Prior to analysis, negatively framed items (Q13, Q14, Q15, Q23, Q25, Q27) were reverse scored to ensure consistent interpretation. Three categorical items were recoded to match the five-point Likert scale structure, enabling their inclusion in the factor analysis alongside the Likert-scale items.\u003c/p\u003e\u003cp\u003eFive items (Q8, Q9, Q15, Q21, Q22) were removed prior to factor analysis due to a lack of response variance, with over 95% of participants selecting the same response. Items lacking variance cannot differentiate between respondents or contribute meaningfully to factor structure, and were therefore excluded (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnivariate outliers were identified using Z-scores, with values exceeding\u0026thinsp;\u0026plusmn;\u0026thinsp;3 considered extreme; this process identified 218 univariate outliers. Multivariate outliers were detected using Mahalanobis distance, resulting in 98 multivariate outliers. Extreme values were managed through Winsorisation applied across the dataset, with scores capped at \u0026plusmn;\u0026thinsp;3 (\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe remaining 24 items were standardised using Z-score transformation (mean\u0026thinsp;=\u0026thinsp;0, standard deviation\u0026thinsp;=\u0026thinsp;1) to ensure comparability and reduce scale-related bias (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Multivariate normality was assessed using Mardia\u0026rsquo;s test, which indicated significant deviations (skewness b1p\u0026thinsp;=\u0026thinsp;921.9, p\u0026thinsp;\u0026le;\u0026thinsp;0.001; kurtosis b2p\u0026thinsp;=\u0026thinsp;1774.76, p\u0026thinsp;\u0026le;\u0026thinsp;0.001), justifying the application of robust estimation methods (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor psychometric evaluation, the processed dataset (n\u0026thinsp;=\u0026thinsp;731) was randomly split into two subsets: approximately 40% (n\u0026thinsp;=\u0026thinsp;292) for exploratory factor analysis (EFA) and 60% (n\u0026thinsp;=\u0026thinsp;439) for confirmatory factor analysis (CFA). Randomisation was performed using a fixed seed to ensure reproducibility (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). The EFA sample size exceeded the recommended criterion of 5\u0026ndash;10 respondents per item (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e), and the CFA sample exceeded the minimum of 200\u0026ndash;300 participants required for stable model estimation (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Background characteristics of participants were summarised using descriptive statistics. All analyses were conducted using R software (version 4.3.1) and the \u003cem\u003elavaan\u003c/em\u003e package in RStudio (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.6.1 Exploratory Factor Analysis (EFA)\u003c/h2\u003e\u003cp\u003eExploratory factor analysis was conducted to examine the underlying structure of the 24-item questionnaire and to explore whether the data supported a two-factor structure consistent with the theorised constructs of Perception and Acceptance (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Sampling adequacy was assessed using the Kaiser\u0026ndash;Meyer\u0026ndash;Olkin (KMO) measure, with values\u0026thinsp;\u0026ge;\u0026thinsp;0.80 considered meritorious for factor analysis (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Bartlett\u0026rsquo;s test of sphericity was also performed to assess whether the correlation matrix was suitable for factor analysis, with significant test at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating that the inter-item correlations were sufficient for factor extraction (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFixed two-factor solution was specified, based on the theoretical expectation of two latent dimensions: Perception and Acceptance. Principal Axis Factoring was used as the extraction method, and Direct Oblimin rotation was applied to allow for correlation between the two factors (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Minimum factor loading threshold of 0.40 was used to retain items with meaningful contributions to the identified factors (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.6.2 Confirmatory factor analysis (CFA) and model validation\u003c/h2\u003e\u003cp\u003eCFA was conducted to validate the two-construct model identified through EFA, assessing the relationships between observed items and their corresponding latent constructs (Perception and Acceptance) (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). Due to the ordinal nature of the data and significant deviations observed from multivariate normality, the Weighted Least Squares Mean and Variance adjusted (WLSMV) estimator was used (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). A scaling correction factor of 1.104 was applied to the chi-square statistic to ensure accurate interpretation of robust model fit indices (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eModel fit was evaluated using multiple standard indices. The Root Mean Square Error of Approximation (RMSEA)\u0026thinsp;\u0026le;\u0026thinsp;0.06 indicated good fit, with values between 0.06 and 0.08 considered acceptable (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Comparative Fit Index (CFI) and Tucker-Lewis Index (TLI) values\u0026thinsp;\u0026ge;\u0026thinsp;0.95 reflected excellent fit, while values between 0.90 and 0.95 were deemed acceptable. A Standardised Root Mean Square Residual (SRMR)\u0026thinsp;\u0026le;\u0026thinsp;0.08 indicated adequate fit (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). The normed chi-square (χ\u0026sup2;/df) was also reported, with values\u0026thinsp;\u0026lt;\u0026thinsp;3 indicating acceptable model parsimony (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond overall model fit, construct validity was evaluated to determine how well the observed items represented the intended theoretical constructs (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). Convergent validity, the extent to which items within a factor are correlated, was assessed using Average Variance Extracted (AVE), with values\u0026thinsp;\u0026gt;\u0026thinsp;0.50 indicating adequate convergence (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). Discriminant validity was assessed using the Fornell\u0026ndash;Larcker criterion, whereby the square root of the AVE for each construct exceeded the inter-construct correlation (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e). Internal consistency and construct reliability were evaluated using Cronbach\u0026rsquo;s alpha and composite reliability (CR), with values\u0026thinsp;\u0026ge;\u0026thinsp;0.70 considered acceptable (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). To further assess convergent validity, factor loadings were examined to evaluate the strength of the relationships between observed items and their respective latent constructs. Standardised factor loadings were examined, with items loading\u0026thinsp;\u0026ge;\u0026thinsp;0.40 retained. Loadings\u0026thinsp;\u0026ge;\u0026thinsp;0.50 were considered acceptable, while values\u0026thinsp;\u0026ge;\u0026thinsp;0.70 indicated strong associations (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eModel refinement was conducted following standard CFA procedures to improve overall fit. Correlation between the two constructs was permitted based on theoretical expectations. One item (Q7) from the Acceptance construct was removed due to a low standardised factor loading (0.35), which persisted despite allowing for correlated error terms. Its exclusion was justified by its weak association with the latent construct (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Background characteristics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e present the results of background characteristics. A total of 731 participants were included in the study. The mean age was 40.8 years (SD\u0026thinsp;=\u0026thinsp;17.4), and 58.8% were female. Participants\u0026rsquo; relationships to the household head were as follows: 27.9% were household heads, 38.6% were spouses, 9.4% were parents of household heads, 13.8% were sons or daughters, and 10.3% were siblings or mothers-in-law. In terms of household cooking roles, 40.0% reported never cooking, 38.4% were the primary cooks, and 21.6% cooked occasionally. Educational attainment was low, with 74.8% having no formal education, 13.0% having completed basic education, and 12.2% having completed senior high school or higher. Most participants identified as Dagomba (98.9%) and were Muslim (98.4%). Regarding occupation, 46.8% were engaged in agriculture or farming, 16.7% were homemakers, 28.9% operated small businesses, and 7.7% were employed in government or private sector jobs.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBackground characteristics of the study participants in the Northern region of Ghana1 (Total n\u0026thinsp;=\u0026thinsp;731)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables/characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequencies (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, \u003cem\u003eyears (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e437 (58.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRelationship with household head (HH)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold head\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e204 (27.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWife of HH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e282 (38.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFather/mother of HH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69 (9.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSons/daughters of HH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101 (13.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSiblings/mother in-law of HH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75 (10.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePosition in household cooking\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever cook\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e292 (40.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary cook\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e281 (38.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccasional/rarely cook\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e158 (21.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducational level completed\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo Formal Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e547 (74.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBasic Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e95 (13.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSenior High School or Higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89 (12.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDagomba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e723(98.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGonjas/Mosi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (1.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIslam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e719 (98.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChristianity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (1.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgriculture/Farming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e342 (46.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHomemaker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122 (16.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmall Business Owner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e211 (28.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGovernment/ Private Employee\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56 (7.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: HH, Household Head.\u003c/p\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eValues are reported as means and standard deviation for continuous variable and count and percentages for categorical variables\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.2 EFA\u003c/h2\u003e\u003cp\u003eThe KMO measure of sampling adequacy was 0.85, indicating that the sample was suitable for factor analysis. Bartlett\u0026rsquo;s test of sphericity was significant, χ\u0026sup2;(276)\u0026thinsp;=\u0026thinsp;2156.05, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, confirming that the inter-item correlations were sufficient for factor extraction.\u003c/p\u003e\u003cp\u003eExploratory factor analysis (EFA) revealed a two-factor structure, comprising Factor 1 (Perception) and Factor 2 (Acceptance), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eFor the Perception factor, loadings ranged from 0.47 (Q6) to 0.76 (Q27), with eight items retained: Q1, Q3, Q4, Q6, Q23, Q24, Q26, and Q27. For the Acceptance factor, loadings ranged from 0.43 (Q10) to 0.67 (Q20), with eleven items retained: Q2, Q5, Q7, Q10, Q16, Q17, Q18, Q20, Q25, Q28, and Q29. No items exhibited cross-loadings\u0026thinsp;\u0026ge;\u0026thinsp;0.30 on both factors.\u003c/p\u003e\u003cp\u003eTogether, the two factors explained 56% of the total variance, with Acceptance accounting for 31% and Perception for 25%. Internal consistency was acceptable, with Cronbach\u0026rsquo;s alpha of 0.71 for Perception and 0.72 for Acceptance.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFactor loadings and communalities from the exploratory factor analysis (n\u0026thinsp;=\u0026thinsp;292)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuestion\u003c/p\u003e\u003cp\u003e(Item)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eItem description\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFactor 1 (Perception)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFactor 2 (Acceptance)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCommunality\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eviews unchanged since receiving study bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.70*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.498\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOkay to use bouillon for all household members\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.57*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.331\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudy bouillon smells same as regular bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.72*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.527\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudy bouillon taste same as regular bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.74*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.546\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudy Bouillon can be used daily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.50*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.255\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudy Bouillon to be used only on some days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.47*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.251\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudy Bouillon can be used multiple times daily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.46*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree with those who think it's good\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.43*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree with those who think it's neutral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAgree with those who think it's bad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCooking frequency changed since receiving bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.037\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency of study bouillon use during cooking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLikes the smell of study bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.44*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLikes the taste of study bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.371\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHappy household receives study bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.55*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStudy bouillon quantity received is sufficient\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEnjoy foods prepared with study bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.67*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.445\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo personal problems observed with bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.61*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.367\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo problems observed by household members\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.61*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePositive views about study bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.57*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.317\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDoes not want household to continue using bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.74*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.551\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThinks neighbours/friends would not like bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.76*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.584\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWants household to use bouillon in future\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.365\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWould buy bouillon if sold in future\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.66*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.446\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNote\u003c/b\u003e: Factor loadings\u0026thinsp;\u0026ge;\u0026thinsp;0.40 are considered acceptable. Items marked with an asterisk (*) indicate those retained for their respective factors. Communality represents the proportion of variance in each item explained by the extracted factors.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.3 CFA\u003c/h2\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 Final standardised factor loadings\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the standardised factor loadings from the confirmatory factor analysis model. All retained items exceeded the pre-established threshold of \u0026ge;\u0026thinsp;0.40, supporting their inclusion and contribution to their respective latent constructs.\u003c/p\u003e\u003cp\u003eFor the Perception construct, standardised loadings ranged from 0.464 (Q24) to 0.770 (Q1). The intermediate loadings included Q3 (0.756), Q4 (0.719), Q6 (0.483), Q23 (0.520), Q26 (0.743), and Q27 (0.756).\u003c/p\u003e\u003cp\u003eFor the Acceptance construct, standardised loadings ranged from 0.476 (Q5) to 0.692 (Q29). The remaining items had loadings as follows: Q2 (0.525), Q10 (0.486), Q16 (0.538), Q17 (0.606), Q18 (0.578), Q20 (0.607), Q25 (0.553), and Q28 (0.574).\u003c/p\u003e\u003cp\u003eThe final validated questionnaire measuring Perception and Acceptance is provided as supplementary material [see \u003cb\u003eAdditional file 2\u003c/b\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eStandardised factor loadings for items retained in the final CFA model (n\u0026thinsp;=\u0026thinsp;439)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePerception construct\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLoadings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAcceptance construct\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLoadings\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1- Views unchanged since receiving study bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ2 \u0026ndash; Okay to use bouillon for everyone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.525\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3 \u0026ndash; Study bouillon smells same as regular bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ5 \u0026ndash; Study Bouillon can be used daily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.476\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4 \u0026ndash; Study bouillon taste same as regular bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ10 \u0026ndash; Agree with those who think it's good\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.486\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ6 \u0026ndash; Study bouillon to be used only on some days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.483\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ16 \u0026ndash; Likes the smell of study bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.538\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ23 \u0026ndash; No personal problems observed with bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ17 \u0026ndash; Likes the taste of study bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.606\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ24 \u0026ndash; No problems observed by household members\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ18 \u0026ndash; Happy household receives study bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ26 \u0026ndash; Does not want household to continue using bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ20 \u0026ndash; Enjoy foods prepared with study bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.607\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ27 \u0026ndash; Thinks neighbours/friends would not like bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.756\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ25 \u0026ndash; Positive views about study bouillon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.553\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ28 \u0026ndash; Wants household to use bouillon in future\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.574\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ29 \u0026ndash; Would buy bouillon if sold in future\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.692\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNote\u003c/b\u003e: All items exceeded the acceptable threshold for retention (standardised factor loading\u0026thinsp;\u0026ge;\u0026thinsp;0.40).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2 Model indices and validation results\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the model fit indices and construct validation results from the CFA. The CFA indicated acceptable to excellent model fit based on multiple criteria: χ\u0026sup2;(134)\u0026thinsp;=\u0026thinsp;255.94, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;1.91; RMSEA\u0026thinsp;=\u0026thinsp;0.043 (90% CI: 0.035\u0026ndash;0.052); CFI\u0026thinsp;=\u0026thinsp;0.981; TLI\u0026thinsp;=\u0026thinsp;0.977; and SRMR\u0026thinsp;=\u0026thinsp;0.061. These values meet or exceed conventional thresholds for good model fit.\u003c/p\u003e\u003cp\u003eComposite reliability (CR) was 0.858 for the Perception construct and 0.824 for Acceptance, exceeding the recommended minimum of 0.70, thereby indicating strong internal consistency. The average variance extracted (AVE) was 0.52 for Perception and 0.46 for Acceptance. Although the AVE for Acceptance was slightly below the conventional 0.50 threshold, its CR remained robust, supporting convergent validity.\u003c/p\u003e\u003cp\u003eDiscriminant validity was also supported: the square root of the AVE was 0.721 for Perception and 0.678 for Acceptance, both exceeding the inter-construct correlation of 0.45, consistent with Fornell\u0026ndash;Larcker criterion.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCFA model fit indices and construct validity results\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAcceptable Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eχ\u0026sup2; (df\u0026thinsp;=\u0026thinsp;134)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e255.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-significant; sensitive to sample size\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;0.05 (not strict criterion due to sample size)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChi-Square (χ\u0026sup2;)/df\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRMSEA (90% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.043 (0.035\u0026ndash;0.052)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.06 (good); \u0026lt; 0.08 (acceptable)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCFI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;0.95 (good); \u0026ge; 0.90 (acceptable)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;0.95 (good); \u0026ge; 0.90 (acceptable)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSRMR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR \u0026ndash; Perception\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCR \u0026ndash; Acceptance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.824\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAVE \u0026ndash; Perception\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;0.50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAVE \u0026ndash; Acceptance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;0.50 \u003cem\u003e(marginal)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSquare root of AVE (Perception)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.721\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;Inter-construct correlation (0.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSquare root of AVE (Acceptance)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;Inter-construct correlation (0.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInter-construct correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt; \u0026radic;AVE for each construct\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNote\u003c/b\u003e: CR\u0026thinsp;=\u0026thinsp;Composite Reliability; AVE\u0026thinsp;=\u0026thinsp;Average Variance Extracted; RMSEA\u0026thinsp;=\u0026thinsp;Root Mean Square Error of Approximation; CFI\u0026thinsp;=\u0026thinsp;Comparative Fit Index; TLI\u0026thinsp;=\u0026thinsp;Tucker-Lewis Index; SRMR\u0026thinsp;=\u0026thinsp;Standardised Root Mean Square Residual. CR\u0026thinsp;\u0026gt;\u0026thinsp;0.70 and Square root of AVE\u0026thinsp;\u0026gt;\u0026thinsp;inter-construct correlation indicates good convergent and discriminant validity (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eThis study developed and validated an 18-item questionnaire to assess \u0026lsquo;Perceptions\u0026rsquo; (8 items) and \u0026lsquo;Acceptance\u0026rsquo; (10 items) of micronutrient-fortified bouillon cubes among non-index household members in northern Ghana. Exploratory and confirmatory factor analyses supported a two-factor structure, Perception and Acceptance, that accounted for 56% of the total variance. The model demonstrated good fit (χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;1.91; RMSEA\u0026thinsp;=\u0026thinsp;0.043; CFI\u0026thinsp;=\u0026thinsp;0.981; TLI\u0026thinsp;=\u0026thinsp;0.977; SRMR\u0026thinsp;=\u0026thinsp;0.061), with all standardised factor loadings exceeding 0.40. Internal consistency was acceptable (Cronbach\u0026rsquo;s alpha: 0.71 for Perception, 0.72 for Acceptance; composite reliability: 0.86 and 0.82, respectively). Convergent validity was confirmed for Perception (AVE\u0026thinsp;=\u0026thinsp;0.52) and marginal for Acceptance (AVE\u0026thinsp;=\u0026thinsp;0.46), while discriminant validity was confirmed via the Fornell\u0026ndash;Larcker criterion.\u003c/p\u003e\u003cp\u003eA key strength was that the questionnaire was theory-driven, drawing on constructs from the Theory of Planned Behaviour and the Health Belief Model (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The questionnaire was reviewed by experts, pretested, and field-tested within a randomised controlled trial context (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). The use of both exploratory and confirmatory factor analysis on independent samples and the application of robust estimation methods (WLSMV) addressed the ordinal nature of the data and non-normality, enhancing the validity of the findings (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile internal consistency and structural validity were supported, the AVE for the Acceptance construct (0.46) was slightly below the recommended threshold (0.50), which may reflect conceptual overlap or measurement limitations (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). However, the high composite reliability (0.82) indicates that the construct maintained acceptable internal consistency despite the marginal AVE (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e). Additionally, the study\u0026rsquo;s cross-sectional design and single-region setting may limit generalisability to other populations with different cultural or dietary practices (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e). Criterion validity was not assessed in this study, as no direct behavioural indicators (e.g., cube disappearance or household inventory) were available. Future research should consider validating the instrument against objective usage data (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). Further work is underway to explore how perception and acceptance scores relate to nutritional outcomes such as haemoglobin and iodine status.\u003c/p\u003e\u003cp\u003eThe two-factor structure \u0026ldquo;Perception and Acceptance\u0026rdquo; reflects distinct but related behavioural dimensions, consistent with the Theory of Planned Behaviour, which distinguishes between beliefs and behavioural intentions (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The moderate correlation (r\u0026thinsp;=\u0026thinsp;0.45) supports this differentiation and aligns with findings from similar behavioural instrument validations (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eValidated tools for assessing household perceptions and acceptance of fortified condiments are scarce in sub-Saharan Africa (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). This instrument provides a reliable, context-specific measure that can support programme monitoring and design, especially in regions where bouillon cubes are widely used (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Its use in a trial setting enhances its relevance for evaluating behavioural responses to fortified food interventions.\u003c/p\u003e\u003cp\u003eThe validated questionnaire can be applied in both research and public health programme contexts to assess household acceptance and perception of fortified condiments. These constructs are relevant for informing communication strategies and intervention design (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e). In programme settings, such as fortification initiatives implemented by ministries of health, non-governmental organisations, or other implementing partners, the tool can support planning and adaptation of interventions. It may be used to identify specific concerns through item-level responses or to generate composite scores for monitoring and evaluation purposes. The moderate association observed between perception and acceptance indicates that improving perceptions alone may not be sufficient to ensure adoption. Interventions should also address practical barriers, including taste preferences, cost, and cooking practices, to support sustained use (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e). Additionally, because fortified condiments are used at the household level, it is important to consider the views of all household members, not only the primary beneficiaries, in both design and implementation. Although the tool was developed for fortified bouillon cubes, the approach may be adapted for other fortified foods that are consumed at the household level. However, documented applications of similar tools in other food fortification contexts are currently limited.\u003c/p\u003e\u003cp\u003eFuture research should assess this tool\u0026rsquo;s generalisability in different regions and cultural settings. Longitudinal studies can help evaluate changes over time in response to behaviour change interventions or market factors (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e). Further validation, including predictive and criterion-related validity, is recommended to explore associations with consumption behaviours and health outcomes (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e). Item refinement, particularly within the Acceptance construct, may also enhance measurement precision. This study could not evaluate measurement invariance across key demographic groups such as sex, education, or household cooking roles due to sample homogeneity. Future validation efforts should ensure more diverse subgroup representation to examine whether the factor structure holds consistently across these populations.\u003c/p\u003e\u003cp\u003eThis study addressed a key methodological gap by providing a culturally relevant, validated questionnaire to assess perceptions and acceptance of fortified bouillon cubes. The tool can support intervention design, behavioural monitoring, and contribute to context-specific strategies for addressing micronutrient deficiencies.\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 for this study was obtained from the Ghana Health Service Ethical Review Committee (GHSERC ID: 024/11/21), the Institutional Review Board at the University of California, Davis (IRB ID: 1837253), and the Ghana Food and Drugs Authority (Certificate No. FDA/CT/2213[1]). The trial was registered at ClinicalTrials.gov (NCT05178407) and the Pan-African Clinical Trials Registry (PACTR202206868437931). Written informed consent was obtained from all participants prior to their involvement in the study. For participants under 18 years of age, assent was obtained in addition to parental or caregiver consent. All procedures were conducted in accordance with relevant guidelines and regulations.\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 datasets generated and analysed during the current study are available from the corresponding author on reasonable request. The final version of the questionnaire is included as supplementary material (Additional file 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from Helen Keller International (66504-UCD-01; RES and SAV), through support from the Bill \u0026amp; Melinda Gates Foundation (INV-007916) to the University of California, Davis. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFKK, RES, and SAA conceptualised the study. FKK led data collection, data analysis, and manuscript preparation. CDA and ANO contributed to study design, instrument development, and critical manuscript revision. KRW, XT and SAV reviewed and refined the questionnaire. SMK coordinated and supervised field data collection. JND, ERB, ADF, and KWN supported field coordination and translation processes. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the participants and field staff of the CoMIT Trial in the Kumbungu and Tolon districts. We also acknowledge the support of the Department of Nutrition and Food Science, University of Ghana, and the Institute for Global Nutrition at the University of California, Davis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthor affiliations are listed on the title page.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID iDs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFelix Kwaku Kyereh: 0009-0005-4364-330X\u003cbr\u003eAgartha N. Ohemeng: 0000-0002-7986-1350\u003c/p\u003e\n\u003cp\u003eCharles D Arnold: 0000-0001-6510-3172\u003c/p\u003e\n\u003cp\u003eReina Engle-Stone: 0000-0003-2446-6166\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStevens GA, Paciorek CJ, Flores-Urrutia MC, Borghi E, Namaste S, Wirth JP, et al. 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Measurement in medicine: a practical guide: Cambridge university press; 2011.\u003c/li\u003e\n\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-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Micronutrient deficiencies, fortified bouillon cubes, perception and acceptance, questionnaire validation, public health nutrition, Ghana","lastPublishedDoi":"10.21203/rs.3.rs-6975434/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6975434/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eMicronutrient deficiencies remain a major public health issue in West Africa, contributing to anaemia, impaired cognitive development, and increased infection risk. Bouillon cubes are widely consumed in the region and offer a culturally appropriate vehicle for micronutrient delivery. However, no validated instrument exists to assess the psychosocial and behavioural factors influencing household acceptance of fortified bouillon cubes. This study aimed to develop and validate a questionnaire to assess perceptions and acceptance of micronutrient-fortified bouillon cubes among non-index household members (NIHMs) in northern Ghana.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA 29-item questionnaire was developed based on the Theory of Planned Behaviour and the Health Belief Model. Development involved literature review, expert consultation, and pretesting with 18 adults. The instrument included 26 five-point Likert-scale items and 3 categorical items. One NIHM (aged\u0026thinsp;\u0026ge;\u0026thinsp;15 years) was randomly selected per household using the Kish method. The questionnaire was administered to 731 NIHMs within 1\u0026ndash;2 months of intervention initiation as part of a double-blind randomised controlled trial comparing multiple micronutrient-fortified bouillon cubes to iodine-only cubes in Kumbungu and Tolon districts. The dataset was split for exploratory (n\u0026thinsp;=\u0026thinsp;292) and confirmatory (n\u0026thinsp;=\u0026thinsp;439) factor analyses. Psychometric properties were evaluated using Cronbach\u0026rsquo;s alpha, composite reliability (CR), and average variance extracted (AVE).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eParticipants had a mean age of 40.8\u0026thinsp;\u0026plusmn;\u0026thinsp;17.4 years; 58.8% were female. Exploratory factor analysis identified two latent constructs comprising Perception (8 items) and Acceptance (10 items) explaining 56% of total variance. Confirmatory factor analysis showed good model fit (chi-square/df\u0026thinsp;=\u0026thinsp;1.91, root mean square error of approximation [RMSEA]\u0026thinsp;=\u0026thinsp;0.04, comparative fit index [CFI]\u0026thinsp;=\u0026thinsp;0.98, Tucker\u0026ndash;Lewis\u0026rsquo;s index [TLI]\u0026thinsp;=\u0026thinsp;0.98, and standardised root mean square residual [SRMR]\u0026thinsp;=\u0026thinsp;0.06). Internal consistency was acceptable (Cronbach\u0026rsquo;s alpha: 0.71 for Perception, 0.72 for Acceptance; CR: 0.86 and 0.82, respectively). AVE was 0.52 for Perception and 0.46 for Acceptance. Discriminant validity was supported.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis validated 18-item questionnaire demonstrates strong psychometric properties and provides a standardised tool for assessing household-level perception and acceptance of fortified foods. It is suitable for use in programme design, evaluation, and behavioural monitoring in settings where bouillon is commonly consumed.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Questionnaire to Assess Perceptions and Acceptance of Micronutrient-Fortified Bouillon Cubes in Northern Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 09:58:43","doi":"10.21203/rs.3.rs-6975434/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-07T04:53:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-06T19:00:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-06T09:55:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"260999600008119376693023849406026519860","date":"2025-07-16T07:40:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-15T04:38:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123487029534577230797278956111937301570","date":"2025-07-10T17:14:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110521702209885412050424506659879029297","date":"2025-07-10T13:53:57+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-10T13:45:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-27T07:31:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-26T04:59:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-26T04:58:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-06-25T13:39:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2fc5a494-8a17-4a94-808f-365f51ca1e26","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-12T16:00:45+00:00","versionOfRecord":{"articleIdentity":"rs-6975434","link":"https://doi.org/10.1186/s12889-025-26144-z","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2026-01-07 15:57:20","publishedOnDateReadable":"January 7th, 2026"},"versionCreatedAt":"2025-07-14 09:58:43","video":"","vorDoi":"10.1186/s12889-025-26144-z","vorDoiUrl":"https://doi.org/10.1186/s12889-025-26144-z","workflowStages":[]},"version":"v1","identity":"rs-6975434","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6975434","identity":"rs-6975434","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}